diff --git a/.github/workflows/api-docs.yml b/.github/workflows/api-docs.yml index 62e7aafd..e78f53a5 100644 --- a/.github/workflows/api-docs.yml +++ b/.github/workflows/api-docs.yml @@ -9,7 +9,7 @@ on: type: boolean default: false image-tag: - default: latest + default: 10.6.0-b.0.0 description: Development Docker Image Tag pull_request: paths: @@ -39,7 +39,7 @@ jobs: # because the `env` context is only accessible at the step level; # hence, it is hard-coded image: |- - ghcr.io/khiopsml/khiops-python/khiopspydev-ubuntu22.04:${{ inputs.image-tag || 'latest' }} + ghcr.io/khiopsml/khiops-python/khiopspydev-ubuntu22.04:${{ inputs.image-tag || '10.6.0-b.0.0' }} # Use the 'runner' user (1001) from github so checkout actions work properly # https://github.com/actions/runner/issues/2033#issuecomment-1598547465 options: --user 1001 diff --git a/.github/workflows/conda.yml b/.github/workflows/conda.yml index 6fc955fe..65f77710 100644 --- a/.github/workflows/conda.yml +++ b/.github/workflows/conda.yml @@ -3,13 +3,13 @@ name: Conda Package env: # Note: The default Khiops version must never be an alpha release as they are # ephemeral. To test alpha versions run the workflow manually. - DEFAULT_KHIOPS_CORE_VERSION: 10.2.4 + DEFAULT_KHIOPS_CORE_VERSION: 10.6.0b.0 DEFAULT_SAMPLES_VERSION: 10.2.4 on: workflow_dispatch: inputs: khiops-core-version: - default: 10.2.4 + default: 10.6.0b.0 description: khiops-core version for testing khiops-samples-version: default: 10.2.4 diff --git a/.github/workflows/dev-docker.yml b/.github/workflows/dev-docker.yml index 8c3bb1ef..19a1ccb4 100644 --- a/.github/workflows/dev-docker.yml +++ b/.github/workflows/dev-docker.yml @@ -1,7 +1,7 @@ --- name: Dev Docker env: - DEFAULT_KHIOPS_REVISION: 10.2.4 + DEFAULT_KHIOPS_REVISION: 10.6.0-b.0 DEFAULT_IMAGE_INCREMENT: 0 DEFAULT_SERVER_REVISION: main DEFAULT_PYTHON_VERSIONS: 3.8 3.9 3.10 3.11 3.12 3.13 @@ -14,7 +14,7 @@ on: inputs: khiops-revision: type: string - default: 10.2.4 + default: 10.6.0-b.0 description: Khiops Revision image-increment: type: number diff --git a/.github/workflows/pip.yml b/.github/workflows/pip.yml index a079246e..8bbb333d 100644 --- a/.github/workflows/pip.yml +++ b/.github/workflows/pip.yml @@ -9,7 +9,7 @@ on: default: 10.2.4 description: khiops-samples repo revision image-tag: - default: latest + default: 10.6.0-b.0.0 description: Development Docker Image Tag pull_request: paths: @@ -64,7 +64,7 @@ jobs: # because the `env` context is only accessible at the step level; # hence, it is hard-coded image: |- - ghcr.io/khiopsml/khiops-python/khiopspydev-${{ matrix.container }}:${{ inputs.image-tag || 'latest' }} + ghcr.io/khiopsml/khiops-python/khiopspydev-${{ matrix.container }}:${{ inputs.image-tag || '10.6.0-b.0.0' }} steps: - name: Set parameters as env run: | diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml index 6e05850b..056d51a2 100644 --- a/.github/workflows/tests.yml +++ b/.github/workflows/tests.yml @@ -2,7 +2,7 @@ name: Tests env: DEFAULT_SAMPLES_REVISION: 10.2.4 - DEFAULT_KHIOPS_DESKTOP_REVISION: 10.2.4 + DEFAULT_KHIOPS_DESKTOP_REVISION: 10.6.0-b.0 on: workflow_dispatch: inputs: @@ -10,7 +10,7 @@ on: default: 10.2.4 description: Git Tag/Branch/Commit for the khiops-samples Repo image-tag: - default: latest + default: 10.6.0-b.0.0 description: Development Docker Image Tag khiops-desktop-revision: default: 10.2.4 @@ -43,7 +43,7 @@ jobs: # because the `env` context is only accessible at the step level; # hence, it is hard-coded image: |- - ghcr.io/khiopsml/khiops-python/khiopspydev-ubuntu22.04:${{ inputs.image-tag || 'latest' }} + ghcr.io/khiopsml/khiops-python/khiopspydev-ubuntu22.04:${{ inputs.image-tag || '10.6.0-b.0.0' }} credentials: username: ${{ github.actor }} password: ${{ secrets.GITHUB_TOKEN }} @@ -312,7 +312,7 @@ jobs: # because the `env` context is only accessible at the step level; # hence, it is hard-coded image: |- - ghcr.io/khiopsml/khiops-python/khiopspydev-${{ matrix.container }}:${{ inputs.image-tag || 'latest' }} + ghcr.io/khiopsml/khiops-python/khiopspydev-${{ matrix.container }}:${{ inputs.image-tag || '10.6.0-b.0.0' }} credentials: username: ${{ github.actor }} password: ${{ secrets.GITHUB_TOKEN }} diff --git a/doc/samples/samples.rst b/doc/samples/samples.rst index db67f39e..5823c987 100644 --- a/doc/samples/samples.rst +++ b/doc/samples/samples.rst @@ -66,12 +66,12 @@ Samples # Set the file paths data_table_path = os.path.join(kh.get_samples_dir(), "Adult", "Adult.txt") dictionary_file_path = os.path.join(kh.get_samples_dir(), "Adult", "Adult.kdic") - results_dir = os.path.join("kh_samples", "detect_data_table_format") - transformed_data_table_path = os.path.join(results_dir, "AdultWithAnotherFormat.txt") + output_dir = os.path.join("kh_samples", "detect_data_table_format") + transformed_data_table_path = os.path.join(output_dir, "AdultWithAnotherFormat.txt") # Create the output directory - if not os.path.isdir(results_dir): - os.mkdir(results_dir) + if not os.path.isdir(output_dir): + os.mkdir(output_dir) # Detect the format of the table format_spec = kh.detect_data_table_format(data_table_path) @@ -128,10 +128,10 @@ Samples # Set the file paths dictionary_file_path = os.path.join(kh.get_samples_dir(), "Adult", "Adult.kdic") - results_dir = os.path.join("kh_samples", "export_dictionary_file") - output_dictionary_file_path = os.path.join(results_dir, "ModifiedAdult.kdic") - output_dictionary_json_path = os.path.join(results_dir, "ModifiedAdult.kdicj") - alt_output_dictionary_json_path = os.path.join(results_dir, "AltModifiedAdult.kdicj") + output_dir = os.path.join("kh_samples", "export_dictionary_file") + output_dictionary_file_path = os.path.join(output_dir, "ModifiedAdult.kdic") + output_dictionary_json_path = os.path.join(output_dir, "ModifiedAdult.kdicj") + alt_output_dictionary_json_path = os.path.join(output_dir, "AltModifiedAdult.kdicj") # Load the dictionary domain from initial dictionary file # Then obtain the "Adult" dictionary within @@ -147,10 +147,10 @@ Samples # Create output directory if necessary if not os.path.exists("kh_samples"): os.mkdir("kh_samples") - os.mkdir(results_dir) + os.mkdir(output_dir) else: - if not os.path.exists(results_dir): - os.mkdir(results_dir) + if not os.path.exists(output_dir): + os.mkdir(output_dir) # Export to kdic domain.export_khiops_dictionary_file(output_dictionary_file_path) @@ -171,7 +171,9 @@ Samples # Set the file paths dictionary_file_path = os.path.join(kh.get_samples_dir(), "Adult", "Adult.kdic") data_table_path = os.path.join(kh.get_samples_dir(), "Adult", "Adult.txt") - results_dir = os.path.join("kh_samples", "train_predictor") + analysis_report_file_path = os.path.join( + "kh_samples", "train_predictor", "AnalysisReport.khj" + ) # Train the predictor kh.train_predictor( @@ -179,7 +181,7 @@ Samples "Adult", data_table_path, "class", - results_dir, + analysis_report_file_path, max_trees=0, ) .. autofunction:: train_predictor_file_paths @@ -192,15 +194,17 @@ Samples # Set the file paths dictionary_file_path = os.path.join(kh.get_samples_dir(), "Adult", "Adult.kdic") data_table_path = os.path.join(kh.get_samples_dir(), "Adult", "Adult.txt") - results_dir = os.path.join("kh_samples", "train_predictor_file_paths") + report_file_path = os.path.join( + "kh_samples", "train_predictor_file_paths", "AnalysisResults.khj" + ) # Train the predictor - report_file_path, modeling_dictionary_file_path = kh.train_predictor( + _, modeling_dictionary_file_path = kh.train_predictor( dictionary_file_path, "Adult", data_table_path, "class", - results_dir, + report_file_path, max_trees=0, ) print("Reports file available at " + report_file_path) @@ -218,9 +222,10 @@ Samples # Set the file paths with a nonexistent dictionary file dictionary_file_path = "NONEXISTENT_DICTIONARY_FILE.kdic" data_table_path = os.path.join(kh.get_samples_dir(), "Adult", "Adult.txt") - results_dir = os.path.join("kh_samples", "train_predictor_error_handling") - log_file_path = os.path.join(results_dir, "khiops.log") - scenario_path = os.path.join(results_dir, "scenario._kh") + output_dir = os.path.join("kh_samples", "train_predictor_error_handling") + report_file_path = os.path.join(output_dir, "AnalysisResults.khj") + log_file_path = os.path.join(output_dir, "khiops.log") + scenario_path = os.path.join(output_dir, "scenario._kh") # Train the predictor and handle the error try: @@ -229,7 +234,7 @@ Samples "Adult", data_table_path, "class", - results_dir, + report_file_path, trace=True, log_file_path=log_file_path, output_scenario_path=scenario_path, @@ -261,7 +266,9 @@ Samples dictionary_file_path = os.path.join(accidents_dir, "Accidents.kdic") accidents_table_path = os.path.join(accidents_dir, "Accidents.txt") vehicles_table_path = os.path.join(accidents_dir, "Vehicles.txt") - results_dir = os.path.join("kh_samples", "train_predictor_mt") + report_file_path = os.path.join( + "kh_samples", "train_predictor_mt", "AccidentsSummaryAnalysisResults.khj" + ) # Train the predictor. Besides the mandatory parameters, we specify: # - A python dictionary linking data paths to file paths for non-root tables @@ -272,8 +279,8 @@ Samples "Accident", accidents_table_path, "Gravity", - results_dir, - additional_data_tables={"Accident`Vehicles": vehicles_table_path}, + report_file_path, + additional_data_tables={"Vehicles": vehicles_table_path}, max_trees=0, ) .. autofunction:: train_predictor_mt_with_specific_rules @@ -288,7 +295,11 @@ Samples dictionary_file_path = os.path.join(accidents_dir, "Accidents.kdic") accidents_table_path = os.path.join(accidents_dir, "Accidents.txt") vehicles_table_path = os.path.join(accidents_dir, "Vehicles.txt") - results_dir = os.path.join("kh_samples", "train_predictor_mt_with_specific_rules") + report_file_path = os.path.join( + "kh_samples", + "train_predictor_mt_with_specific_rules", + "AccidentsSummaryAnalysisResults.khj", + ) # Train the predictor. Besides the mandatory parameters, it is specified: # - A python dictionary linking data paths to file paths for non-root tables @@ -300,8 +311,8 @@ Samples "Accident", accidents_table_path, "Gravity", - results_dir, - additional_data_tables={"Accident`Vehicles": vehicles_table_path}, + report_file_path, + additional_data_tables={"Vehicles": vehicles_table_path}, max_constructed_variables=1000, construction_rules=["TableMode", "TableSelection"], max_trees=0, @@ -320,7 +331,9 @@ Samples vehicles_table_path = os.path.join(accidents_dir, "Vehicles.txt") users_table_path = os.path.join(accidents_dir, "Users.txt") places_table_path = os.path.join(accidents_dir, "Places.txt") - results_dir = os.path.join("kh_samples", "train_predictor_mt_snowflake") + report_file_path = os.path.join( + "kh_samples", "train_predictor_mt_snowflake", "AccidentsAnalysisResults.khj" + ) # Train the predictor. Besides the mandatory parameters, we specify: # - A python dictionary linking data paths to file paths for non-root tables @@ -331,11 +344,11 @@ Samples "Accident", accidents_table_path, "Gravity", - results_dir, + report_file_path, additional_data_tables={ - "Accident`Vehicles": vehicles_table_path, - "Accident`Vehicles`Users": users_table_path, - "Accident`Place": places_table_path, + "Vehicles": vehicles_table_path, + "Vehicles/Users": users_table_path, + "Place": places_table_path, }, max_trees=0, ) @@ -349,7 +362,11 @@ Samples # Set the file paths dictionary_file_path = os.path.join(kh.get_samples_dir(), "Adult", "Adult.kdic") data_table_path = os.path.join(kh.get_samples_dir(), "Adult", "Adult.txt") - results_dir = os.path.join("kh_samples", "train_predictor_with_train_percentage") + report_file_path = os.path.join( + "kh_samples", + "train_predictor_with_train_percentage", + "P90_AdultAnalysisResults.khj", + ) # Train the predictor. Besides the mandatory parameters, it is specified: # - A 90% sampling rate for the training dataset @@ -360,11 +377,10 @@ Samples "Adult", data_table_path, "class", - results_dir, + report_file_path, sample_percentage=90, use_complement_as_test=True, max_trees=0, - results_prefix="P90_", ) .. autofunction:: train_predictor_with_trees .. code-block:: python @@ -376,7 +392,9 @@ Samples # Set the file paths dictionary_file_path = os.path.join(kh.get_samples_dir(), "Letter", "Letter.kdic") data_table_path = os.path.join(kh.get_samples_dir(), "Letter", "Letter.txt") - results_dir = os.path.join("kh_samples", "train_predictor_with_trees") + report_file_path = os.path.join( + "kh_samples", "train_predictor_with_trees", "P80_LetterAnalysisResults.khj" + ) # Train the predictor with at most 15 trees (default 10) kh.train_predictor( @@ -384,10 +402,9 @@ Samples "Letter", data_table_path, "lettr", - results_dir, + report_file_path, sample_percentage=80, use_complement_as_test=True, - results_prefix="P80_", max_trees=15, ) .. autofunction:: train_predictor_with_pairs @@ -400,7 +417,9 @@ Samples # Set the file paths dictionary_file_path = os.path.join(kh.get_samples_dir(), "Adult", "Adult.kdic") data_table_path = os.path.join(kh.get_samples_dir(), "Adult", "Adult.txt") - results_dir = os.path.join("kh_samples", "train_predictor_with_pairs") + report_file_path = os.path.join( + "kh_samples", "train_predictor_with_pairs", "AdultAnalysisResults.khj" + ) # Train the predictor with at most 10 pairs as follows: # - Include pairs age-race and capital_gain-capital_loss @@ -410,7 +429,7 @@ Samples "Adult", data_table_path, "class", - results_dir, + report_file_path, use_complement_as_test=True, max_trees=0, max_pairs=10, @@ -430,9 +449,10 @@ Samples # Set the file paths dictionary_file_path = os.path.join(kh.get_samples_dir(), "Adult", "Adult.kdic") data_table_path = os.path.join(kh.get_samples_dir(), "Adult", "Adult.txt") - results_dir = os.path.join("kh_samples", "train_predictor_with_multiple_parameters") - output_script_path = os.path.join(results_dir, "output_scenario._kh") - log_path = os.path.join(results_dir, "log.txt") + output_dir = os.path.join("kh_samples", "train_predictor_with_multiple_parameters") + report_file_path = os.path.join(output_dir, "AdultAnalysisResults.khj") + output_script_path = os.path.join(output_dir, "output_scenario._kh") + log_path = os.path.join(output_dir, "log.txt") # Train the predictor. Besides the mandatory parameters, we specify: # - The value "more" as main target value @@ -445,7 +465,7 @@ Samples "Adult", data_table_path, "class", - results_dir, + report_file_path, main_target_value="more", output_scenario_path=output_script_path, log_file_path=log_path, @@ -462,8 +482,9 @@ Samples # Set the file paths dictionary_file_path = os.path.join(kh.get_samples_dir(), "Iris", "Iris.kdic") data_table_path = os.path.join(kh.get_samples_dir(), "Iris", "Iris.txt") - results_dir = os.path.join("kh_samples", "train_predictor_detect_format") - transformed_data_table_path = os.path.join(results_dir, "TransformedIris.txt") + output_dir = os.path.join("kh_samples", "train_predictor_detect_format") + transformed_data_table_path = os.path.join(output_dir, "TransformedIris.txt") + report_file_path = os.path.join(output_dir, "IrisAnalysisResults.khj") # Transform the database format from header_line=True and field_separator=TAB # to header_line=False and field_separator="," @@ -484,7 +505,7 @@ Samples "Iris", transformed_data_table_path, "Class", - results_dir, + report_file_path, header_line=True, field_separator="", ) @@ -501,7 +522,7 @@ Samples "Iris", transformed_data_table_path, "Class", - results_dir, + report_file_path, ) .. autofunction:: train_predictor_with_cross_validation .. code-block:: python @@ -514,12 +535,12 @@ Samples # Set the file paths dictionary_file_path = os.path.join(kh.get_samples_dir(), "Adult", "Adult.kdic") data_table_path = os.path.join(kh.get_samples_dir(), "Adult", "Adult.txt") - results_dir = os.path.join("kh_samples", "train_predictor_with_cross_validation") - fold_dictionary_file_path = os.path.join(results_dir, "AdultWithFolding.kdic") + output_dir = os.path.join("kh_samples", "train_predictor_with_cross_validation") + fold_dictionary_file_path = os.path.join(output_dir, "AdultWithFolding.kdic") # Create the output directory - if not os.path.isdir(results_dir): - os.mkdir(results_dir) + if not os.path.isdir(output_dir): + os.mkdir(output_dir) # Load the learning dictionary object domain = kh.read_dictionary_file(dictionary_file_path) @@ -556,35 +577,39 @@ Samples train_aucs = [] test_aucs = [] for fold_index in range(1, fold_number + 1): + analysis_report_file_path = os.path.join( + output_dir, "Fold" + str(fold_index) + "AdultAnalysisResults.khj" + ) # Train a model from the sub-dataset where IsInTrainDataset is 1 - train_reports_path, modeling_dictionary_file_path = kh.train_predictor( + _, modeling_dictionary_file_path = kh.train_predictor( domain, "Adult", data_table_path, "class", - results_dir, + analysis_report_file_path, sample_percentage=100, selection_variable="IsInTrainDataset" + str(fold_index), selection_value=1, max_trees=0, - results_prefix="Fold" + str(fold_index), ) + evaluation_report_file_path = os.path.join( + output_dir, "Fold" + str(fold_index) + "AdultEvaluationResults.khj" + ) # Evaluate the resulting model in the subsets where IsInTrainDataset is 0 test_evaluation_report_path = kh.evaluate_predictor( modeling_dictionary_file_path, - "Adult", + "SNB_Adult", data_table_path, - results_dir, + evaluation_report_file_path, sample_percentage=100, selection_variable="IsInTrainDataset" + str(fold_index), selection_value=0, - results_prefix="Fold" + str(fold_index), ) # Obtain the train AUC from the train report and the test AUC from the # evaluation report and print them - train_results = kh.read_analysis_results_file(train_reports_path) + train_results = kh.read_analysis_results_file(analysis_report_file_path) test_evaluation_results = kh.read_analysis_results_file(test_evaluation_report_path) train_auc = train_results.train_evaluation_report.get_snb_performance().auc test_auc = test_evaluation_results.evaluation_report.get_snb_performance().auc @@ -606,6 +631,32 @@ Samples print("final auc") print("train auc: " + str(mean_train_auc) + " +- " + str(sd_train_auc)) print("test auc: " + str(mean_test_auc) + " +- " + str(sd_test_auc)) +.. autofunction:: interpret_predictor +.. code-block:: python + + # Imports + import os + from khiops import core as kh + + dictionary_file_path = os.path.join(kh.get_samples_dir(), "Adult", "Adult.kdic") + data_table_path = os.path.join(kh.get_samples_dir(), "Adult", "Adult.txt") + output_dir = os.path.join("kh_samples", "interpret_predictor") + analysis_report_file_path = os.path.join(output_dir, "AdultAnalysisResults.khj") + interpretor_file_path = os.path.join(output_dir, "AdultIntepretationModel.kdic") + + # Build prediction model + _, predictor_file_path = kh.train_predictor( + dictionary_file_path, + "Adult", + data_table_path, + "class", + analysis_report_file_path, + ) + + # Build interpretation model + kh.interpret_predictor(predictor_file_path, "SNB_Adult", interpretor_file_path) + + print(f"The intepretation model is '{interpretor_file_path}'") .. autofunction:: multiple_train_predictor .. code-block:: python @@ -632,7 +683,8 @@ Samples # Set the file paths dictionary_file_path = os.path.join(kh.get_samples_dir(), "Adult", "Adult.kdic") data_table_path = os.path.join(kh.get_samples_dir(), "Adult", "Adult.txt") - results_dir = os.path.join("kh_samples", "multiple_train_predictor") + output_dir = os.path.join("kh_samples", "multiple_train_predictor") + report_file_path = os.path.join(output_dir, "AdultAnalysisResults.khj") # Read the dictionary file to obtain an instance of class Dictionary dictionary_domain = kh.read_dictionary_file(dictionary_file_path) @@ -640,20 +692,20 @@ Samples # Train a SNB model using all the variables print("\t#vars\ttrain auc\ttest auc") - json_result_file_path, _ = kh.train_predictor( + kh.train_predictor( dictionary_file_path, "Adult", data_table_path, "class", - results_dir, + report_file_path, sample_percentage=70, use_complement_as_test=True, max_trees=0, ) - display_test_results(json_result_file_path) + display_test_results(report_file_path) # Read results to obtain the variables sorted by decreasing Level - analysis_results = kh.read_analysis_results_file(json_result_file_path) + analysis_results = kh.read_analysis_results_file(report_file_path) preparation_results = analysis_results.preparation_report # Train a sequence of models with a decreasing number of variables @@ -667,21 +719,22 @@ Samples dictionary.get_variable(variable.name).used = False # Train the model with this dictionary domain object - prefix = f"V{variable_number - 1 - i}_" - json_result_file_path, _ = kh.train_predictor( + report_file_path = os.path.join( + output_dir, f"V{variable_number - 1 - i}_AdultAnalysisResults.khj" + ) + kh.train_predictor( dictionary_domain, "Adult", data_table_path, "class", - results_dir, + report_file_path, sample_percentage=70, use_complement_as_test=True, - results_prefix=prefix, max_trees=0, ) # Show a preview of the results - display_test_results(json_result_file_path) + display_test_results(report_file_path) .. autofunction:: evaluate_predictor .. code-block:: python @@ -692,24 +745,29 @@ Samples # Set the file paths dictionary_file_path = os.path.join(kh.get_samples_dir(), "Adult", "Adult.kdic") data_table_path = os.path.join(kh.get_samples_dir(), "Adult", "Adult.txt") - results_dir = os.path.join("kh_samples", "evaluate_predictor") - model_dictionary_file_path = os.path.join(results_dir, "Modeling.kdic") + output_dir = os.path.join("kh_samples", "evaluate_predictor") + analysis_report_file_path = os.path.join(output_dir, "AdultAnalysisResults.khj") # Train the predictor - kh.train_predictor( + _, model_dictionary_file_path = kh.train_predictor( dictionary_file_path, "Adult", data_table_path, "class", - results_dir, + analysis_report_file_path, max_trees=0, ) + evaluation_report_file_path = os.path.join(output_dir, "AdultEvaluationResults.khj") + # Evaluate the predictor - report_file_path = kh.evaluate_predictor( - model_dictionary_file_path, "Adult", data_table_path, results_dir + kh.evaluate_predictor( + model_dictionary_file_path, + "SNB_Adult", + data_table_path, + evaluation_report_file_path, ) - print("Evaluation report available at " + report_file_path) + print("Evaluation report available at " + evaluation_report_file_path) .. autofunction:: access_predictor_evaluation_report .. code-block:: python @@ -720,8 +778,9 @@ Samples # Set the file paths dictionary_file_path = os.path.join(kh.get_samples_dir(), "Adult", "Adult.kdic") data_table_path = os.path.join(kh.get_samples_dir(), "Adult", "Adult.txt") - results_dir = os.path.join("kh_samples", "access_predictor_evaluation_report") - evaluation_report_path = os.path.join(results_dir, "AllReports.khj") + report_file_path = os.path.join( + "kh_samples", "access_predictor_evaluation_report", "AdultAnalysisReport.khj" + ) # Train the SNB predictor and some univariate predictors # Note: Evaluation in test is 30% by default @@ -730,13 +789,12 @@ Samples "Adult", data_table_path, "class", - results_dir, + report_file_path, max_trees=0, - univariate_predictor_number=4, ) # Obtain the evaluation results - results = kh.read_analysis_results_file(evaluation_report_path) + results = kh.read_analysis_results_file(report_file_path) evaluation_report = results.test_evaluation_report snb_performance = evaluation_report.get_snb_performance() @@ -776,9 +834,9 @@ Samples + str(random_lift_curve.values[i]) ) - # Print univariate metrics for an univariate predictor + # Print metrics for an SNB predictor predictor_performance = evaluation_report.get_predictor_performance( - "Univariate relationship" + "Selective Naive Bayes" ) print("\n\nperformance metrics for " + predictor_performance.name) for metric_name in predictor_performance.get_metric_names(): @@ -793,10 +851,14 @@ Samples # Set the file paths dictionary_file_path = os.path.join(kh.get_samples_dir(), "Adult", "Adult.kdic") data_table_path = os.path.join(kh.get_samples_dir(), "Adult", "Adult.txt") - results_dir = os.path.join("kh_samples", "train_recoder") + report_file_path = os.path.join( + "kh_samples", "train_recoder", "AdultAnalysisResults.khj" + ) # Train the recoder model - kh.train_recoder(dictionary_file_path, "Adult", data_table_path, "class", results_dir) + kh.train_recoder( + dictionary_file_path, "Adult", data_table_path, "class", report_file_path + ) .. autofunction:: train_recoder_with_multiple_parameters .. code-block:: python @@ -807,7 +869,11 @@ Samples # Set the file paths dictionary_file_path = os.path.join(kh.get_samples_dir(), "Adult", "Adult.kdic") data_table_path = os.path.join(kh.get_samples_dir(), "Adult", "Adult.txt") - results_dir = os.path.join("kh_samples", "train_recoder_with_multiple_parameters") + report_file_path = os.path.join( + "kh_samples", + "train_recoder_with_multiple_parameters", + "AdultAnalysisResults.khj", + ) # Train the recoder model kh.train_recoder( @@ -815,7 +881,7 @@ Samples "Adult", data_table_path, "class", - results_dir, + report_file_path, max_pairs=10, categorical_recoding_method="part label", numerical_recoding_method="part label", @@ -832,7 +898,9 @@ Samples dictionary_file_path = os.path.join(accidents_dir, "Accidents.kdic") accidents_table_path = os.path.join(accidents_dir, "Accidents.txt") vehicles_table_path = os.path.join(accidents_dir, "Vehicles.txt") - results_dir = os.path.join("kh_samples", "train_recoder_mt_flatten") + report_file_path = os.path.join( + "kh_samples", "train_recoder_mt_flatten", "AccidentsAnalysisResults.khj" + ) # Train the recoder. Besides the mandatory parameters, it is specified: # - A python dictionary linking data paths to file paths for non-root tables @@ -844,8 +912,8 @@ Samples "Accident", accidents_table_path, "Gravity", - results_dir, - additional_data_tables={"Accident`Vehicles": vehicles_table_path}, + report_file_path, + additional_data_tables={"Vehicles": vehicles_table_path}, max_constructed_variables=1000, informative_variables_only=False, categorical_recoding_method="none", @@ -863,17 +931,17 @@ Samples # Set the file paths dictionary_file_path = os.path.join(kh.get_samples_dir(), "Adult", "Adult.kdic") data_table_path = os.path.join(kh.get_samples_dir(), "Adult", "Adult.txt") - results_dir = os.path.join("kh_samples", "deploy_model") - model_dictionary_file_path = os.path.join(results_dir, "Modeling.kdic") - output_data_table_path = os.path.join(results_dir, "ScoresAdult.txt") + output_dir = os.path.join("kh_samples", "deploy_model") + report_file_path = os.path.join(output_dir, "AdultAnalysisResults.khj") + output_data_table_path = os.path.join(output_dir, "ScoresAdult.txt") # Train the predictor - kh.train_predictor( + _, model_dictionary_file_path = kh.train_predictor( dictionary_file_path, "Adult", data_table_path, "class", - results_dir, + report_file_path, max_trees=0, ) @@ -894,18 +962,18 @@ Samples dictionary_file_path = os.path.join(accidents_dir, "Accidents.kdic") accidents_table_path = os.path.join(accidents_dir, "Accidents.txt") vehicles_table_path = os.path.join(accidents_dir, "Vehicles.txt") - results_dir = os.path.join("kh_samples", "deploy_model_mt") - model_dictionary_file_path = os.path.join(results_dir, "Modeling.kdic") - output_data_table_path = os.path.join(results_dir, "TransferredAccidents.txt") + output_dir = os.path.join("kh_samples", "deploy_model_mt") + report_file_path = os.path.join(output_dir, "AccidentsSummaryAnalysisResults.khj") + output_data_table_path = os.path.join(output_dir, "TransferredAccidents.txt") # Train the predictor (see train_predictor_mt for details) - kh.train_predictor( + _, model_dictionary_file_path = kh.train_predictor( dictionary_file_path, "Accident", accidents_table_path, "Gravity", - results_dir, - additional_data_tables={"Accident`Vehicles": vehicles_table_path}, + report_file_path, + additional_data_tables={"Vehicles": vehicles_table_path}, max_trees=0, ) @@ -917,7 +985,58 @@ Samples "SNB_Accident", accidents_table_path, output_data_table_path, - additional_data_tables={"SNB_Accident`Vehicles": vehicles_table_path}, + additional_data_tables={"Vehicles": vehicles_table_path}, + ) +.. autofunction:: deploy_model_mt_with_interpretation +.. code-block:: python + + # Imports + import os + from khiops import core as kh + + # Set the file paths + accidents_dir = os.path.join(kh.get_samples_dir(), "AccidentsSummary") + dictionary_file_path = os.path.join(accidents_dir, "Accidents.kdic") + accidents_table_path = os.path.join(accidents_dir, "Accidents.txt") + vehicles_table_path = os.path.join(accidents_dir, "Vehicles.txt") + output_dir = os.path.join("kh_samples", "deploy_model_mt") + report_file_path = os.path.join(output_dir, "AccidentsSummaryAnalysisResults.khj") + interpretor_file_path = os.path.join( + output_dir, "AccidentsSummaryInterpretationModel.kdic" + ) + output_data_table_path = os.path.join(output_dir, "InterpretedAccidents.txt") + + # Train the predictor (see train_predictor_mt for details) + # Add max_evaluated_variables so that an interpretation model can be built + # (see https://github.com/KhiopsML/khiops/issues/577) + _, model_dictionary_file_path = kh.train_predictor( + dictionary_file_path, + "Accident", + accidents_table_path, + "Gravity", + report_file_path, + additional_data_tables={"Vehicles": vehicles_table_path}, + max_trees=0, + max_evaluated_variables=10, + ) + + # Interpret the predictor + kh.interpret_predictor( + model_dictionary_file_path, + "SNB_Accident", + interpretor_file_path, + reinforcement_target_value="NonLethal", + ) + + # Deploy the interpretation model on the database + # Besides the mandatory parameters, it is specified: + # - A python dictionary linking data paths to file paths for non-root tables + kh.deploy_model( + interpretor_file_path, + "Interpretation_SNB_Accident", + accidents_table_path, + output_data_table_path, + additional_data_tables={"Vehicles": vehicles_table_path}, ) .. autofunction:: deploy_model_mt_snowflake .. code-block:: python @@ -933,24 +1052,24 @@ Samples vehicles_table_path = os.path.join(accidents_dir, "Vehicles.txt") users_table_path = os.path.join(accidents_dir, "Users.txt") places_table_path = os.path.join(accidents_dir, "Places.txt") - results_dir = os.path.join("kh_samples", "deploy_model_mt_snowflake") - model_dictionary_file_path = os.path.join(results_dir, "Modeling.kdic") - output_data_table_path = os.path.join(results_dir, "TransferredAccidents.txt") + output_dir = os.path.join("kh_samples", "deploy_model_mt_snowflake") + report_file_path = os.path.join(output_dir, "AccidentsAnalysisResults.khj") + output_data_table_path = os.path.join(output_dir, "TransferredAccidents.txt") # Train the predictor. Besides the mandatory parameters, we specify: # - A python dictionary linking data paths to file paths for non-root tables # - To not construct any decision tree # The default number of automatic features is 100 - kh.train_predictor( + _, model_dictionary_file_path = kh.train_predictor( dictionary_file_path, "Accident", accidents_table_path, "Gravity", - results_dir, + report_file_path, additional_data_tables={ - "Accident`Vehicles": vehicles_table_path, - "Accident`Vehicles`Users": users_table_path, - "Accident`Place": places_table_path, + "Vehicles": vehicles_table_path, + "Vehicles/Users": users_table_path, + "Place": places_table_path, }, max_trees=0, ) @@ -964,9 +1083,9 @@ Samples accidents_table_path, output_data_table_path, additional_data_tables={ - "SNB_Accident`Vehicles": vehicles_table_path, - "SNB_Accident`Vehicles`Users": users_table_path, - "SNB_Accident`Place": places_table_path, + "Vehicles": vehicles_table_path, + "Vehicles/Users": users_table_path, + "Place": places_table_path, }, ) .. autofunction:: deploy_model_expert @@ -979,17 +1098,17 @@ Samples # Set the file paths dictionary_file_path = os.path.join(kh.get_samples_dir(), "Adult", "Adult.kdic") data_table_path = os.path.join(kh.get_samples_dir(), "Adult", "Adult.txt") - results_dir = os.path.join("kh_samples", "deploy_model_expert") - model_dictionary_file_path = os.path.join(results_dir, "Modeling.kdic") - output_data_table_path = os.path.join(results_dir, "ScoresAdult.txt") + output_dir = os.path.join("kh_samples", "deploy_model_expert") + report_file_path = os.path.join(output_dir, "AdultAnalysisResults.khj") + output_data_table_path = os.path.join(output_dir, "ScoresAdult.txt") # Train the predictor - kh.train_predictor( + _, model_dictionary_file_path = kh.train_predictor( dictionary_file_path, "Adult", data_table_path, "class", - results_dir, + report_file_path, max_trees=0, ) @@ -1020,8 +1139,9 @@ Samples # Set the file paths dictionary_file_path = os.path.join(kh.get_samples_dir(), "Adult", "Adult.kdic") data_table_path = os.path.join(kh.get_samples_dir(), "Adult", "Adult.txt") - results_dir = os.path.join("kh_samples", "deploy_classifier_for_metrics") - output_data_table_path = os.path.join(results_dir, "ScoresAdult.txt") + output_dir = os.path.join("kh_samples", "deploy_classifier_for_metrics") + report_file_path = os.path.join(output_dir, "AdultAnalysisResults.khj") + output_data_table_path = os.path.join(output_dir, "ScoresAdult.txt") # Train the classifier for the target "class" _, modeling_dictionary_file_path = kh.train_predictor( @@ -1029,10 +1149,9 @@ Samples "Adult", data_table_path, "class", - results_dir, + report_file_path, max_trees=0, ) - # Obtain the scores of the SNB on the test dataset to calculate the PR curve kh.deploy_predictor_for_metrics( modeling_dictionary_file_path, @@ -1087,8 +1206,9 @@ Samples # Set the file paths dictionary_file_path = os.path.join(kh.get_samples_dir(), "Adult", "Adult.kdic") data_table_path = os.path.join(kh.get_samples_dir(), "Adult", "Adult.txt") - results_dir = os.path.join("kh_samples", "deploy_regressor_for_metrics") - output_data_table_path = os.path.join(results_dir, "TrueAndPredictedAges.txt") + output_dir = os.path.join("kh_samples", "deploy_regressor_for_metrics") + report_file_path = os.path.join(output_dir, "AdultAnalysisResults.khj") + output_data_table_path = os.path.join(output_dir, "TrueAndPredictedAges.txt") # Train the regressor for the target "age" (with 20% train to be quick) _, modeling_dictionary_file_path = kh.train_predictor( @@ -1096,7 +1216,7 @@ Samples "Adult", data_table_path, "age", - results_dir, + report_file_path, sample_percentage=20, max_trees=0, ) @@ -1219,18 +1339,51 @@ Samples splice_dir = os.path.join(kh.get_samples_dir(), "SpliceJunction") dictionary_file_path = os.path.join(splice_dir, "SpliceJunction.kdic") data_table_path = os.path.join(splice_dir, "SpliceJunctionDNA.txt") - results_dir = os.path.join("kh_samples", "train_coclustering") + coclustering_report_path = os.path.join( + "kh_samples", "train_coclustering", "SpliceJunctionCoclusteringResults.khcj" + ) # Train a coclustering model for variables "SampleId" and "Char" - coclustering_report_path = kh.train_coclustering( + kh.train_coclustering( dictionary_file_path, "SpliceJunctionDNA", data_table_path, ["SampleId", "Char"], - results_dir, + coclustering_report_path, ) print(f"Coclustering report file available at {coclustering_report_path}") + # If you have Khiops Co-Visualization installed you may open the report as follows + # kh.visualize_report(coclustering_report_path) +.. autofunction:: train_instance_variable_coclustering +.. code-block:: python + + # Imports + import os + from khiops import core as kh + + # Set the file paths + iris_dir = os.path.join(kh.get_samples_dir(), "Iris") + dictionary_file_path = os.path.join(iris_dir, "Iris.kdic") + data_table_path = os.path.join(iris_dir, "Iris.txt") + coclustering_report_path = os.path.join( + "kh_samples", + "train_instance_variable_coclustering", + "IrisInstanceVariable_CoclusteringResults.khcj", + ) + + # Train a coclustering model for variables "SampleId" and "Char" + kh.train_instance_variable_coclustering( + dictionary_file_path, + "Iris", + data_table_path, + coclustering_report_path, + ) + print( + "Instance-variable coclustering report file available " + f"at {coclustering_report_path}" + ) + # If you have Khiops Co-Visualization installed you may open the report as follows # kh.visualize_report(coclustering_report_path) .. autofunction:: simplify_coclustering @@ -1244,9 +1397,11 @@ Samples splice_dir = os.path.join(kh.get_samples_dir(), "SpliceJunction") dictionary_file_path = os.path.join(splice_dir, "SpliceJunction.kdic") data_table_path = os.path.join(splice_dir, "SpliceJunctionDNA.txt") - results_dir = os.path.join("kh_samples", "simplify_coclustering") - coclustering_file_path = os.path.join(results_dir, "Coclustering.khc") - simplified_coclustering_file_name = "simplified_coclustering.khc" + output_dir = os.path.join("kh_samples", "simplify_coclustering") + coclustering_file_path = os.path.join(output_dir, "Coclustering.khcj") + simplified_coclustering_file_path = os.path.join( + output_dir, "simplified_coclustering.khcj" + ) # Train coclustering model for variables "SampleId" and "Char" kh.train_coclustering( @@ -1254,7 +1409,7 @@ Samples "SpliceJunctionDNA", data_table_path, ["SampleId", "Char"], - results_dir, + coclustering_file_path, ) # Simplify the trained coclustering with the constraints @@ -1262,8 +1417,7 @@ Samples # - maximum total parts number: 4 kh.simplify_coclustering( coclustering_file_path, - simplified_coclustering_file_name, - results_dir, + simplified_coclustering_file_path, max_preserved_information=80, max_total_parts=4, ) @@ -1274,9 +1428,9 @@ Samples splice_dir = os.path.join(kh.get_samples_dir(), "SpliceJunction") dictionary_file_path = os.path.join(splice_dir, "SpliceJunction.kdic") data_table_path = os.path.join(splice_dir, "SpliceJunctionDNA.txt") - results_dir = os.path.join("kh_samples", "extract_clusters") - coclustering_file_path = os.path.join(results_dir, "Coclustering.khc") - clusters_file_path = os.path.join(results_dir, "extracted_clusters.txt") + output_dir = os.path.join("kh_samples", "extract_clusters") + coclustering_file_path = os.path.join(output_dir, "Coclustering.khcj") + clusters_file_path = os.path.join(output_dir, "extracted_clusters.txt") # Train a coclustering model for variables "SampleId" and "Char" kh.train_coclustering( @@ -1284,7 +1438,7 @@ Samples "SpliceJunctionDNA", data_table_path, ["SampleId", "Char"], - results_dir, + coclustering_file_path, ) # Extract clusters @@ -1300,8 +1454,10 @@ Samples splice_dir = os.path.join(kh.get_samples_dir(), "SpliceJunction") data_table_path = os.path.join(splice_dir, "SpliceJunctionDNA.txt") dictionary_file_path = os.path.join(splice_dir, "SpliceJunction.kdic") - results_dir = os.path.join("kh_samples", "deploy_coclustering") - coclustering_file_path = os.path.join(results_dir, "Coclustering.khc") + output_dir = os.path.join("kh_samples", "deploy_coclustering") + coclustering_file_path = os.path.join(output_dir, "Coclustering.khcj") + coclustering_dictionary_file_path = os.path.join(output_dir, "Coclustering.kdic") + output_data_table_path = os.path.join(output_dir, "DeployedSpliceJunctionDNA.txt") # Train a coclustering model for variables "SampleId" and "Char" kh.train_coclustering( @@ -1309,7 +1465,7 @@ Samples "SpliceJunctionDNA", data_table_path, ["SampleId", "Char"], - results_dir, + coclustering_file_path, ) # Deploy "Char" clusters in the training database @@ -1320,7 +1476,8 @@ Samples coclustering_file_path, ["SampleId"], "Char", - results_dir, + coclustering_dictionary_file_path, + output_data_table_path, header_line=True, ) .. autofunction:: deploy_coclustering_expert @@ -1335,8 +1492,8 @@ Samples dictionary_file_path = os.path.join(splice_dir, "SpliceJunction.kdic") data_table_path = os.path.join(splice_dir, "SpliceJunction.txt") secondary_data_table_path = os.path.join(splice_dir, "SpliceJunctionDNA.txt") - results_dir = os.path.join("kh_samples", "deploy_coclustering_expert") - coclustering_file_path = os.path.join(results_dir, "Coclustering.khc") + output_dir = os.path.join("kh_samples", "deploy_coclustering_expert") + coclustering_file_path = os.path.join(output_dir, "Coclustering.khcj") # Train a coclustering model for variables "SampleId" and "Char" print("train coclustering on SpliceJunctionDNA") @@ -1345,39 +1502,38 @@ Samples "SpliceJunctionDNA", secondary_data_table_path, ["SampleId", "Char"], - results_dir, + coclustering_file_path, ) print("prepare_coclustering_deployment") # The input dictionary is extended with new coclustering based variables + augmented_dictionary_file_path = os.path.join(output_dir, "Coclustering.kdic") kh.prepare_coclustering_deployment( dictionary_file_path, "SpliceJunction", coclustering_file_path, "DNA", "SampleId", - results_dir, + augmented_dictionary_file_path, ) - augmented_dictionary_file_path = os.path.join(results_dir, "Coclustering.kdic") print("prepare_coclustering_deployment with at most two clusters") # Extend the already extended dictionary with the new variables from a simplified CC + reaugmented_dictionary_file_path = os.path.join( + output_dir, "ReaugmentedCoclustering.kdic" + ) kh.prepare_coclustering_deployment( augmented_dictionary_file_path, "SpliceJunction", coclustering_file_path, "DNA", "SampleId", - results_dir, - results_prefix="Reaugmented", + reaugmented_dictionary_file_path, variables_prefix="C2_", max_part_numbers={"SampleId": 2}, ) - reaugmented_dictionary_file_path = os.path.join( - results_dir, "ReaugmentedCoclustering.kdic" - ) - output_data_table_path = os.path.join(results_dir, "TransferredSpliceJunction.txt") + output_data_table_path = os.path.join(output_dir, "TransferredSpliceJunction.txt") # Deploy the coclustering with the extended dictionary print("deploy_model with the new coclustering based variables") @@ -1386,11 +1542,11 @@ Samples "SpliceJunction", data_table_path, output_data_table_path, - additional_data_tables={"SpliceJunction`DNA": secondary_data_table_path}, + additional_data_tables={"DNA": secondary_data_table_path}, ) deployed_dictionary_file_path = os.path.join( - results_dir, "Transferred_Coclustering.kdic" + output_dir, "Transferred_Coclustering.kdic" ) print("build_deployed_dictionary to get the new dictionary") kh.build_deployed_dictionary( @@ -1408,7 +1564,9 @@ Samples # Set the file paths dictionary_file_path = os.path.join(kh.get_samples_dir(), "Adult", "Adult.kdic") data_table_path = os.path.join(kh.get_samples_dir(), "Adult", "Adult.txt") - results_dir = os.path.join("kh_samples", "scenario_prologue") + report_file_path = os.path.join( + "kh_samples", "scenario_prologue", "AdultAnalysisResults.khj" + ) # Set the maximum memory "by hand" with an scenario prologue scenario_prologue = """ @@ -1422,7 +1580,7 @@ Samples "Adult", data_table_path, "class", - results_dir, + report_file_path, max_trees=0, scenario_prologue=scenario_prologue, ) @@ -1436,8 +1594,9 @@ Samples # Set the file paths dictionary_file_path = os.path.join(kh.get_samples_dir(), "Iris", "Iris.kdic") data_table_path = os.path.join(kh.get_samples_dir(), "Iris", "Iris.txt") - results_dir = os.path.join("kh_samples", "build_deployed_dictionary") - deployed_dictionary_file_path = os.path.join(results_dir, "SNB_Iris_deployed.kdic") + output_dir = os.path.join("kh_samples", "build_deployed_dictionary") + deployed_dictionary_file_path = os.path.join(output_dir, "SNB_Iris_deployed.kdic") + report_file_path = os.path.join(output_dir, "IrisAnalysisResults.khj") # Train the predictor _, modeling_dictionary_file_path = kh.train_predictor( @@ -1445,7 +1604,7 @@ Samples "Iris", data_table_path, "Class", - results_dir, + report_file_path, max_trees=0, ) diff --git a/doc/samples/samples_sklearn.rst b/doc/samples/samples_sklearn.rst index 8a57e838..e3fa6e25 100644 --- a/doc/samples/samples_sklearn.rst +++ b/doc/samples/samples_sklearn.rst @@ -803,156 +803,3 @@ Samples print("Predicted clusters (only three at most)") print(X_clusters) print("---") -.. autofunction:: khiops_classifier_multitable_list -.. code-block:: python - - # Imports - import os - import pandas as pd - from khiops import core as kh - from khiops.sklearn import KhiopsClassifier - from sklearn import metrics - from sklearn.model_selection import train_test_split - - # Load the root table of the dataset into a pandas dataframe - accidents_data_dir = os.path.join(kh.get_samples_dir(), "AccidentsSummary") - accidents_df = pd.read_csv( - os.path.join(accidents_data_dir, "Accidents.txt"), - sep="\t", - ) - X = accidents_df.drop("Gravity", axis=1) - y = accidents_df["Gravity"] - - # Split the dataset into train and test - X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1) - - # Load the secondary table of the dataset into a pandas dataframe - vehicles_df = pd.read_csv(os.path.join(accidents_data_dir, "Vehicles.txt"), sep="\t") - - # Split the secondary dataframe with the keys of the split root dataframe - X_train_ids = X_train["AccidentId"].to_frame() - X_test_ids = X_test["AccidentId"].to_frame() - X_train_secondary = X_train_ids.merge(vehicles_df, on="AccidentId") - X_test_secondary = X_test_ids.merge(vehicles_df, on="AccidentId") - - # Create the classifier specifying the key column name - khc = KhiopsClassifier(key="AccidentId") - - # Train the classifier - khc.fit([X_train, X_train_secondary], y_train) - - # Predict the class on the test dataset - y_test_pred = khc.predict([X_test, X_test_secondary]) - print("Predicted classes (first 10):") - print(y_test_pred[:10]) - print("---") - - # Predict the class probability on the test dataset - y_test_probas = khc.predict_proba([X_test, X_test_secondary]) - print("Predicted class probabilities (first 10):") - print(y_test_probas[:10]) - print("---") - - # Evaluate accuracy and auc metrics on the test dataset - test_accuracy = metrics.accuracy_score(y_test, y_test_pred) - test_auc = metrics.roc_auc_score(y_test, y_test_probas[:, 1]) - print(f"Test accuracy = {test_accuracy}") - print(f"Test auc = {test_auc}") -.. autofunction:: khiops_classifier_multitable_star_file -.. code-block:: python - - # Imports - import os - import pandas as pd - from khiops import core as kh - from khiops.sklearn import KhiopsClassifier - from sklearn import metrics - from sklearn.model_selection import train_test_split - - # Create output directory - results_dir = os.path.join("kh_samples", "khiops_classifier_multitable_star_file") - if not os.path.exists("kh_samples"): - os.mkdir("kh_samples") - os.mkdir(results_dir) - else: - if not os.path.exists(results_dir): - os.mkdir(results_dir) - - # Load the root table of the dataset into a pandas dataframe - accidents_dataset_path = os.path.join(kh.get_samples_dir(), "AccidentsSummary") - accidents_df = pd.read_csv( - os.path.join(accidents_dataset_path, "Accidents.txt"), - sep="\t", - ) - - # Split the root dataframe into train and test - X_train_main, X_test_main = train_test_split( - accidents_df, test_size=0.3, random_state=1 - ) - - # Load the secondary table of the dataset into a pandas dataframe - vehicles_df = pd.read_csv( - os.path.join(accidents_dataset_path, "Vehicles.txt"), sep="\t" - ) - - # Split the secondary dataframe with the keys of the split root dataframe - X_train_ids = X_train_main["AccidentId"].to_frame() - X_test_ids = X_test_main["AccidentId"].to_frame() - X_train_secondary = X_train_ids.merge(vehicles_df, on="AccidentId") - X_test_secondary = X_test_ids.merge(vehicles_df, on="AccidentId") - - # Write the train and test dataset sets to disk - # For the test file we remove the target column from the main table - X_train_main_path = os.path.join(results_dir, "X_train_main.txt") - X_train_main.to_csv(X_train_main_path, sep="\t", header=True, index=False) - X_train_secondary_path = os.path.join(results_dir, "X_train_secondary.txt") - X_train_secondary.to_csv(X_train_secondary_path, sep="\t", header=True, index=False) - X_test_main_path = os.path.join(results_dir, "X_test_main.txt") - y_test = X_test_main.sort_values("AccidentId")["Gravity"] - X_test_main.drop(columns="Gravity").to_csv( - X_test_main_path, sep="\t", header=True, index=False - ) - X_test_secondary_path = os.path.join(results_dir, "X_test_secondary.txt") - X_test_secondary.to_csv(X_test_secondary_path, sep="\t", header=True, index=False) - - # Define the dictionary of train - X_train = { - "main_table": "Accidents", - "tables": { - "Accidents": (X_train_main_path, "AccidentId"), - "Vehicles": (X_train_secondary_path, ["AccidentId", "VehicleId"]), - }, - "format": ("\t", True), - } - X_test = { - "main_table": "Accidents", - "tables": { - "Accidents": (X_test_main_path, "AccidentId"), - "Vehicles": (X_test_secondary_path, ["AccidentId", "VehicleId"]), - }, - "format": ("\t", True), - } - - # Create the classifier and fit it - khc = KhiopsClassifier(output_dir=results_dir) - khc.fit(X_train, y="Gravity") - - # Predict the class in addition to the class probabilities on the test dataset - y_test_pred_path = khc.predict(X_test) - y_test_pred = pd.read_csv(y_test_pred_path, sep="\t") - print("Predicted classes (first 10):") - print(y_test_pred["PredictedGravity"].head(10)) - print("---") - - y_test_probas_path = khc.predict_proba(X_test) - y_test_probas = pd.read_csv(y_test_probas_path, sep="\t") - proba_columns = [col for col in y_test_probas if col.startswith("Prob")] - print("Predicted class probabilities (first 10):") - print(y_test_probas[proba_columns].head(10)) - print("---") - - # Evaluate accuracy and auc metrics on the test dataset - test_accuracy = metrics.accuracy_score(y_test, y_test_pred["PredictedGravity"]) - test_auc = metrics.roc_auc_score(y_test, y_test_probas["ProbGravityLethal"]) - print(f"Test accuracy = {test_accuracy}") - print(f"Test auc = {test_auc}") diff --git a/khiops/core/api.py b/khiops/core/api.py index f6a6babe..9b6a794c 100644 --- a/khiops/core/api.py +++ b/khiops/core/api.py @@ -19,21 +19,17 @@ import warnings import khiops.core.internals.filesystems as fs -from khiops.core.dictionary import DictionaryDomain, read_dictionary_file +from khiops.core.dictionary import DictionaryDomain from khiops.core.exceptions import KhiopsRuntimeError -from khiops.core.helpers import build_multi_table_dictionary_domain from khiops.core.internals.common import ( CommandLineOptions, SystemSettings, - create_unambiguous_khiops_path, deprecation_message, is_string_like, type_error_message, ) -from khiops.core.internals.io import KhiopsOutputWriter from khiops.core.internals.runner import get_runner from khiops.core.internals.task import get_task_registry -from khiops.core.internals.version import KhiopsVersion # List of all available construction rules in the Khiops tool all_construction_rules = [ @@ -164,7 +160,6 @@ def _preprocess_arguments(args): # Create a command line options object command_line_options = CommandLineOptions( - batch_mode=args["batch_mode"] if "batch_mode" in args else True, log_file_path=(args["log_file_path"] if "log_file_path" in args else ""), output_scenario_path=( args["output_scenario_path"] if "output_scenario_path" in args else "" @@ -216,6 +211,36 @@ def _preprocess_task_arguments(task_args): bool ``True`` if the task was called with an input `.DictionaryDomain`. """ + # Process the output path + # if path is dir, then generate full report path according to GUI defaults + file_path_arg_names = { + "analysis_report_file_path": "AnalysisResults.khj", + "evaluation_report_file_path": "EvaluationReport.khj", + "coclustering_report_file_path": "Coclustering.khcj", + "coclustering_dictionary_file_path": "Coclustering.kdic", + } + for file_path_arg_name, default_file_name in file_path_arg_names.items(): + if file_path_arg_name in task_args: + file_path = task_args[file_path_arg_name] + + # If path ends with path separator, then consider + # it is dir and concatenate default report file name to it + if file_path.endswith(os.path.sep): + # Add deprecation warning + warnings.warn( + deprecation_message( + "'results_dir'", + "11.0.1", + replacement=file_path_arg_name, + quote=False, + ) + ) + # Update the path + full_file_path = fs.get_child_path( + os.path.normpath(file_path), default_file_name + ) + task_args[file_path_arg_name] = full_file_path + # Process the input dictionary domain if any # build_frequency_variables and detect_format are processed differently below task_called_with_domain = False @@ -227,54 +252,32 @@ def _preprocess_task_arguments(task_args): task_args["dictionary_file_path_or_domain"], task_args["trace"] ) - # Set the discretization/grouping default values - if "discretization_method" in task_args: - # Set the default values if the discretization method is not specified + # Set the default discretization method for unsupervised analysis + if "discretization_method" in task_args and not task_args.get("target_variable"): if task_args["discretization_method"] is None: - if task_args["target_variable"]: - task_args["discretization_method"] = "MODL" - else: - task_args["discretization_method"] = "EqualWidth" - # Otherwise raise an error if the values are not in the range to avoid a khiops - # segmentation fault. This won't be necessary from version 11 on. - else: - if not task_args["target_variable"] and task_args[ - "discretization_method" - ] not in ("EqualWidth", "EqualFrequency", "None"): - raise ValueError( - "'discretization_method' must be either " - "'EqualWidth', 'EqualFrequency' or 'None'." - ) + task_args["discretization_method"] = "MODL" + + # Remove discretization method if specified for supervised analysis: + # it is always MODL + if "discretization_method" in task_args and task_args.get("target_variable"): + del task_args["discretization_method"] - if "grouping_method" in task_args: - # Set the default values if the grouping method is not specified + # Set the default grouping method for unsupervised analysis + if "grouping_method" in task_args and not task_args.get("target_variable"): if task_args["grouping_method"] is None: - if task_args["target_variable"]: - task_args["grouping_method"] = "MODL" - else: - task_args["grouping_method"] = "BasicGrouping" - # Otherwise raise an error if the values are not in the range to avoid a khiops - # segmentation fault. This won't be necessary from version 11 on. - else: - if not task_args["target_variable"] and task_args[ - "grouping_method" - ] not in ("BasicGrouping", "None"): - raise ValueError( - "'grouping_method' must be either 'BasicGrouping' or 'None'." - ) + task_args["grouping_method"] = "MODL" + + # Remove grouping method if specified for supervised analysis: + # it is always MODL + if "grouping_method" in task_args and task_args.get("target_variable"): + del task_args["grouping_method"] # Transform the use_complement_as_test bool parameter to its string counterpart if "use_complement_as_test" in task_args: if task_args["use_complement_as_test"]: - if get_khiops_version() < KhiopsVersion("10.0.0"): - task_args["fill_test_database_settings"] = True - else: - task_args["test_database_mode"] = "Complementary" + task_args["test_database_mode"] = "Complementary" else: - if get_khiops_version() < KhiopsVersion("10"): - task_args["fill_test_database_settings"] = False - else: - task_args["test_database_mode"] = "None" + task_args["test_database_mode"] = "None" del task_args["use_complement_as_test"] # Preprocess the database format parameters @@ -302,43 +305,15 @@ def _preprocess_task_arguments(task_args): if isinstance(task_args["selection_value"], (int, float)): task_args["selection_value"] = str(task_args["selection_value"]) - # Warn the simple deprecations for Khiops 11 - simple_khiops_11_deprecations = [ - ("max_groups", "the upcoming 'max_parts' parameter", 0), - ("max_intervals", "the upcoming 'max_parts' parameter", 0), - ("min_group_frequency", None, 0), - ("min_interval_frequency", None, 0), - ("results_prefix", None, ""), - ("snb_predictor", None, True), - ("univariate_predictor_number", None, 0), - ] - for param, replacement_param, param_default_value in simple_khiops_11_deprecations: - if param in task_args and task_args[param] != param_default_value: - warnings.warn( - deprecation_message( - f"'{param}'", "11.0.0", replacement=replacement_param, quote=False - ) - ) - - # Warn the grouping/interval supervised method deprecation values for Khiops 11 - if "target_variable" in task_args and task_args["target_variable"] != "": - if "grouping_method" in task_args and task_args["grouping_method"] != "MODL": - warnings.warn( - deprecation_message( - "'grouping_method' on supervised learning", "11.0.0", quote=False - ) - ) - if ( - "discretization_method" in task_args - and task_args["discretization_method"] != "MODL" - ): + # Discard the max_variable_importances interpretation parameters + if "max_variable_importances" in task_args: + if task_args["max_variable_importances"] is not None: warnings.warn( - deprecation_message( - "'discretization_method' on supervised learning", - "11.0.0", - quote=False, - ) + "The 'max_variable_importances' parameter of the " + "'khiops.core.api.intepret_predictor' function is not supported " + " yet. All model variables' importances are computed." ) + del task_args["max_variable_importances"] # Flatten kwargs if "kwargs" in task_args: @@ -390,7 +365,6 @@ def _clean_task_args(task_args): """ # Remove non-task parameters command_line_arg_names = [ - "batch_mode", "log_file_path", "output_scenario_path", "task_file_path", @@ -456,7 +430,6 @@ def get_samples_dir(): def export_dictionary_as_json( dictionary_file_path_or_domain, json_dictionary_file_path, - batch_mode=True, log_file_path=None, output_scenario_path=None, task_file_path=None, @@ -497,7 +470,6 @@ def build_dictionary_from_data_table( detect_format=True, header_line=None, field_separator=None, - batch_mode=True, log_file_path=None, output_scenario_path=None, task_file_path=None, @@ -556,7 +528,6 @@ def check_database( selection_value="", additional_data_tables=None, max_messages=20, - batch_mode=True, log_file_path=None, output_scenario_path=None, task_file_path=None, @@ -628,7 +599,7 @@ def train_predictor( dictionary_name, data_table_path, target_variable, - results_dir, + analysis_report_file_path, detect_format=True, header_line=None, field_separator=None, @@ -638,26 +609,23 @@ def train_predictor( selection_variable="", selection_value="", additional_data_tables=None, + do_data_preparation_only=False, main_target_value="", - snb_predictor=True, - univariate_predictor_number=0, + keep_selected_variables_only=True, max_evaluated_variables=0, max_selected_variables=0, - max_constructed_variables=100, + max_constructed_variables=1000, construction_rules=None, + max_text_features=10000, max_trees=10, max_pairs=0, all_possible_pairs=True, specific_pairs=None, + text_features="words", group_target_value=False, discretization_method=None, - min_interval_frequency=0, - max_intervals=0, grouping_method=None, - min_group_frequency=0, - max_groups=0, - results_prefix="", - batch_mode=True, + max_parts=0, log_file_path=None, output_scenario_path=None, task_file_path=None, @@ -684,8 +652,10 @@ def train_predictor( Name of the target variable. If the specified variable is categorical it constructs a classifier and if it is numerical a regressor. If equal to "" it performs an unsupervised analysis. - results_dir : str - Path of the results directory. + analysis_report_file_path : str + Path to the analysis report file in the JSON format. An additional dictionary + file with the same name and extension `.model.kdic` is built, which contains + the trained models. detect_format : bool, default ``True`` If ``True`` detects automatically whether the data table file has a header and its field separator. It is set to ``False`` if ``header_line`` or @@ -709,9 +679,6 @@ def train_predictor( use_complement_as_test : bool, default ``True`` Uses the complement of the sampled database as test database for computing the model's performance metrics. - fill_test_database_settings : bool, default ``False`` - It creates a test database as the complement of the train database. - **Deprecated** will be removed in Khiops 11, use ``use_complement_as_test`` selection_variable : str, default "" It trains with only the records such that the value of ``selection_variable`` is equal to ``selection_value``. Ignored if equal to "". @@ -720,71 +687,63 @@ def train_predictor( additional_data_tables : dict, optional A dictionary containing the data paths and file paths for a multi-table dictionary file. For more details see :doc:`/multi_table_primer`. + do_data_preparation_only : bool, default ``False`` + If ``False``, it trains a Selective Naive Bayes predictor. Otherwise, only data + preparation via MODL preprocessing is done. main_target_value : str, default "" If this target value is specified then it guarantees the calculation of lift curves for it. - snb_predictor : bool, default ``True`` - If ``True`` it trains a Selective Naive Bayes predictor. **Deprecated** will be - removed in Khiops 11. - univariate_predictor_number : int, default 0 - Number of univariate predictors to train.**Deprecated** will be removed in - Khiops 11. - map_predictor : bool, default ``False`` - If ``True`` trains a Maximum a Posteriori Naive Bayes predictor. - **Deprecated** will be removed in Khiops Python 11. + keep_selected_variables_only : bool, default ``True`` + Keeps only predictor-selected variables in the supervised analysis report. max_evaluated_variables : int, default 0 Maximum number of variables to be evaluated in the SNB predictor training. If equal to 0 it evaluates all informative variables. max_selected_variables : int, default 0 Maximum number of variables to be selected in the SNB predictor. If equal to 0 it selects all the variables kept in the training. - max_constructed_variables : int, default 100 + max_constructed_variables : int, default 1000 Maximum number of variables to construct. construction_rules : list of str, optional Allowed rules for the automatic variable construction. If not set it uses all possible rules. + max_text_features : int, default 10000 + Maximum number of text features to construct. max_trees : int, default 10 - Maximum number of trees to construct. Not yet available in regression. + Maximum number of trees to construct. max_pairs : int, default 0 - Maximum number of variables pairs to construct. + Maximum number of variable pairs to construct. specific_pairs : list of tuple, optional User-specified pairs as a list of 2-tuples of feature names. If a given tuple contains only one non-empty feature name, then it generates all the pairs containing it (within the maximum limit ``max_pairs``). These pairs have top priority: they are constructed first. + text_features : str, default "words" + Type of the text features. Can be either one of: + - "words": sequences of non-space characters + - "ngrams": sequences of bytes + - "tokens": user-defined all_possible_pairs : bool, default ``True`` If ``True`` tries to create all possible pairs within the limit ``max_pairs``. Pairs specified with ``specific_pairs`` have top priority: they are constructed first. - only_pairs_with : str, default "" - Constructs only pairs with the specified variable name. If equal to the empty - string "" it considers all variables to make pairs. - **Deprecated** will be removed in Khiops Python 11, use ``specific_pairs``. group_target_value : bool, default ``False`` Allows grouping of the target variable values in classification. It can substantially increase the training time. discretization_method : str - Name of the discretization method. Its valid values depend on the task: - - Supervised: "MODL" (default), "EqualWidth" or "EqualFrequency" - - Unsupervised: "EqualWidth" (default), "EqualFrequency" or "None" - min_interval_frequency : int, default 0 - Minimum number of instances in an interval. If equal to 0 it is - automatically calculated. **Deprecated** will be removed in Khiops 11. - max_intervals : int, default 0 - Maximum number of intervals to construct. If equal to 0 it is automatically - calculated. **Deprecated** will be replaced by ``max_parts`` in Khiops 11. + Name of the discretization method, for unsupervised analysis only. + Its valid values are: "MODL" (default), "EqualWidth", "EqualFrequency" + or "None". Ignored for supervised analysis. grouping_method : str - Name of the grouping method. Its valid values depend on the task: - - Supervised: "MODL" (default) or "BasicGrouping" - - Unsupervised: "BasicGrouping" (default) or "None" - min_group_frequency : int, default 0 - Minimum number of instances for a group. **Deprecated** will be removed in - Khiops 11. - max_groups : int, default 0 - Maximum number of groups. If equal to 0 it is automatically calculated. - **Deprecated** will be replaced by ``max_parts`` in Khiops 11. - results_prefix : str, default "" - Prefix of the result files. **Deprecated** will be removed in Khiops 11. + Name of the grouping method, for unsupervised analysis only. + Its valid values are: "MODL" (default), "BasicGrouping" or "None". + Ignored for supervised analysis. + max_parts : int, default 0 + Maximum number of variable parts produced by preprocessing methods. If equal + to 0 it is automatically calculated. + Set to 10 intervals for unsupervised analysis if ``discretization_method`` + is set to "EqualWidth" or "EqualFrequency". + Set to 10 groups for unsupervised analysis if ``grouping_method`` + is set to "BasicGrouping". ... : See :ref:`core-api-common-params`. @@ -826,28 +785,75 @@ def train_predictor( _run_task("train_predictor", task_args) # Return the paths of the JSON report and modelling dictionary file - reports_file_name = results_prefix - if get_runner().khiops_version < KhiopsVersion("10.0.0"): - reports_file_name += "AllReports.json" - else: - reports_file_name += "AllReports.khj" - reports_file_path = fs.get_child_path(results_dir, reports_file_name) - if target_variable != "": + current_dir = os.path.dirname(analysis_report_file_path) + report_file_name, _ = os.path.splitext( + os.path.basename(analysis_report_file_path) + ) modeling_dictionary_file_path = fs.get_child_path( - results_dir, f"{results_prefix}Modeling.kdic" + current_dir, f"{report_file_name}.model.kdic" ) else: modeling_dictionary_file_path = None - return (reports_file_path, modeling_dictionary_file_path) + return (analysis_report_file_path, modeling_dictionary_file_path) + + +def interpret_predictor( + dictionary_file_path_or_domain, + predictor_dictionary_name, + interpretor_file_path, + max_variable_importances=None, + reinforcement_target_value="", + reinforcement_lever_variables=None, + log_file_path=None, + output_scenario_path=None, + task_file_path=None, + trace=False, + stdout_file_path="", + stderr_file_path="", + max_cores=None, + memory_limit_mb=None, + temp_dir="", + scenario_prologue="", + **kwargs, +): + r"""Builds an intepretation dictionary from a predictor + + Parameters + ---------- + dictionary_file_path_or_domain : str or `.DictionaryDomain` + Path of a Khiops dictionary file or a DictionaryDomain object. + predictor_dictionary_name : str + Name of the predictor dictionary used while building the interpretation model. + intepretor_file_path : str + Path to the intepretor dictionary file. + max_variable_importances : int, optional + Maximum number of variable importances to be selected in the intepretation + model. If not set, then all the variables in the prediction model are + considered. + ..note:: Not currently supported; not taken into account if set. + reinforcement_target_value : str, default "" + If this target value is specified, then its probability of occurrence is + tentatively increased. + reinforcement_lever_variables : list of str, optional + The names of variables to use as lever variables while building the + intepretation model. Min length: 0. Max length: the total number of variables + in the prediction model. If not specified, all variables are used. + """ + # Save the task arguments + # WARNING: Do not move this line, see the top of the "tasks" section for details + task_args = locals() + + # Run the task + _run_task("interpret_predictor", task_args) def evaluate_predictor( dictionary_file_path_or_domain, train_dictionary_name, data_table_path, - results_dir, + evaluation_report_file_path, detect_format=True, header_line=None, field_separator=None, @@ -857,8 +863,6 @@ def evaluate_predictor( selection_value="", additional_data_tables=None, main_target_value="", - results_prefix="", - batch_mode=True, log_file_path=None, output_scenario_path=None, task_file_path=None, @@ -881,8 +885,8 @@ def evaluate_predictor( Name of the main dictionary used while training the models. data_table_path : str Path of the evaluation data table file. - results_dir : str - Path of the results directory. + evaluation_report_file_path : str + Path to the evaluation report file, in the JSON format. detect_format : bool, default ``True`` If ``True`` detects automatically whether the data table file has a header and its field separator. It is set to ``False`` if ``header_line`` or @@ -916,8 +920,6 @@ def evaluate_predictor( main_target_value : str, default "" If this target value is specified then it guarantees the calculation of lift curves for it. - results_prefix : str, default "" - Prefix of the result files. **Deprecated** will be removed in Khiops 11. ... : See :ref:`core-api-common-params`. @@ -942,24 +944,11 @@ def evaluate_predictor( # WARNING: Do not move this line, see the top of the "tasks" section for details task_args = locals() - # Create the evaluation file path and remove the directory and prefix arguments - task_args["evaluation_report_path"] = fs.get_child_path( - task_args["results_dir"], f"{task_args['results_prefix']}EvaluationReport.xls" - ) - del task_args["results_dir"] - del task_args["results_prefix"] - # Run the task _run_task("evaluate_predictor", task_args) # Return the path of the JSON report - report_file_name = results_prefix - if get_runner().khiops_version < KhiopsVersion("10.0.0"): - report_file_name += "EvaluationReport.json" - else: - report_file_name += "EvaluationReport.khj" - - return fs.get_child_path(results_dir, report_file_name) + return evaluation_report_file_path def train_recoder( @@ -967,7 +956,7 @@ def train_recoder( dictionary_name, data_table_path, target_variable, - results_dir, + analysis_report_file_path, detect_format=True, header_line=None, field_separator=None, @@ -978,10 +967,12 @@ def train_recoder( additional_data_tables=None, max_constructed_variables=100, construction_rules=None, - max_trees=0, + max_text_features=10000, + max_trees=10, max_pairs=0, all_possible_pairs=True, specific_pairs=None, + text_features="words", informative_variables_only=True, max_variables=0, keep_initial_categorical_variables=False, @@ -991,13 +982,8 @@ def train_recoder( pairs_recoding_method="part Id", group_target_value=False, discretization_method=None, - min_interval_frequency=0, - max_intervals=0, grouping_method=None, - min_group_frequency=0, - max_groups=0, - results_prefix="", - batch_mode=True, + max_parts=0, log_file_path=None, output_scenario_path=None, task_file_path=None, @@ -1035,8 +1021,10 @@ def train_recoder( Path of the data table file. target_variable : str Name of the target variable. If equal to "" it trains an unsupervised recoder. - results_dir : str - Path of the results directory. + analysis_report_file_path : str + Path to the analysis report file in the JSON format. An additional dictionary + file with the same name and extension `.model.kdic` is built, which contains + the trained recoding model. detect_format : bool, default ``True`` If ``True`` detects automatically whether the data table file has a header and its field separator. It is set to ``False`` if ``header_line`` or @@ -1069,8 +1057,10 @@ def train_recoder( construction_rules : list of str, optional Allowed rules for the automatic variable construction. If not set it uses all possible rules. - max_trees : int, default 0 - Maximum number of trees to construct. Not yet available in regression. + max_text_features : int, default 10000 + Maximum number of text features to construct. + max_trees : int, default 10 + Maximum number of trees to construct. max_pairs : int, default 0 Maximum number of variables pairs to construct. specific_pairs : list of tuple, optional @@ -1078,27 +1068,18 @@ def train_recoder( contains only one non-empty feature name, then it generates all the pairs containing it (within the maximum limit ``max_pairs``). These pairs have top priority: they are constructed first. + text_features : str, default "words" + Type of the text features. Can be either one of: + - "words": sequences of non-space characters + - "ngrams": sequences of bytes + - "tokens": user-defined all_possible_pairs : bool, default ``True`` If ``True`` tries to create all possible pairs within the limit ``max_pairs``. Pairs specified with ``specific_pairs`` have top priority: they are constructed first. - only_pairs_with : str, default "" - Constructs only pairs with the specified variable name. If equal to the empty - string "" it considers all variables to make pairs. - **Deprecated** will be removed in Khiops Python 11, use ``specific_pairs``. group_target_value : bool, default ``False`` Allows grouping of the target variable values in classification. It can substantially increase the training time. - discretization_method : str - Name of the discretization method. Its valid values depend on the task: - - Supervised: "MODL" (default), "EqualWidth" or "EqualFrequency" - - Unsupervised: "EqualWidth" (default), "EqualFrequency" or "None" - min_interval_frequency : int, default 0 - Minimum number of instances in an interval. If equal to 0 it is - automatically calculated. **Deprecated** will be removed in Khiops 11. - max_intervals : int, default 0 - Maximum number of intervals to construct. If equal to 0 it is automatically - calculated. **Deprecated** will be replaced by ``max_parts`` in Khiops 11. informative_variables_only : bool, default ``True`` If ``True`` keeps only informative variables. max_variables : int, default 0 @@ -1132,18 +1113,21 @@ def train_recoder( - "0-1 binarization": A 0's and 1's coding the interval/group id - "conditional info": Conditional information of the interval/group - "none": Keeps the variable as-is + discretization_method : str + Name of the discretization method, for unsupervised analysis only. + Its valid values are: "MODL" (default), "EqualWidth", "EqualFrequency" + or "None". Ignored for supervised analysis. grouping_method : str - Name of the grouping method. Its valid values depend on the task: - - Supervised: "MODL" (default) or "BasicGrouping" - - Unsupervised: "BasicGrouping" (default) or "None" - min_group_frequency : int, default 0 - Minimum number of instances for a group. **Deprecated** will be removed in - Khiops 11. - max_groups : int, default 0 - Maximum number of groups. If equal to 0 it is automatically calculated. - **Deprecated** will be replaced by ``max_parts`` in Khiops 11. - results_prefix : str, default "" - Prefix of the result files. **Deprecated** will be removed in Khiops 11. + Name of the grouping method, for unsupervised analysis only. + Its valid values are: "MODL" (default), "BasicGrouping" or "None". + Ignored for supervised analysis. + max_parts : int, default 0 + Maximum number of variable parts produced by preprocessing methods. If equal + to 0 it is automatically calculated. + Set to 10 intervals for unsupervised analysis if ``discretization_method`` + is set to "EqualWidth" or "EqualFrequency". + Set to 10 groups for unsupervised analysis if ``grouping_method`` + is set to "BasicGrouping". ... : See :ref:`core-api-common-params`. @@ -1169,17 +1153,14 @@ def train_recoder( _run_task("train_recoder", task_args) # Return the paths of the JSON report and modelling dictionary file - reports_file_name = f"{results_prefix}AllReports" - if get_runner().khiops_version < KhiopsVersion("10.0.0"): - reports_file_name += ".json" - else: - reports_file_name += ".khj" - reports_file_path = fs.get_child_path(results_dir, reports_file_name) + current_dir = os.path.dirname(analysis_report_file_path) + report_file_name, _ = os.path.splitext(os.path.basename(analysis_report_file_path)) + modeling_dictionary_file_path = fs.get_child_path( - results_dir, f"{results_prefix}Modeling.kdic" + current_dir, f"{report_file_name}.model.kdic" ) - return (reports_file_path, modeling_dictionary_file_path) + return (analysis_report_file_path, modeling_dictionary_file_path) def deploy_model( @@ -1198,7 +1179,6 @@ def deploy_model( output_header_line=True, output_field_separator="\t", output_additional_data_tables=None, - batch_mode=True, log_file_path=None, output_scenario_path=None, task_file_path=None, @@ -1259,8 +1239,6 @@ def deploy_model( output_additional_data_tables : dict, optional A dictionary containing the output data paths and file paths for a multi-table dictionary file. For more details see :doc:`/multi_table_primer`. - results_prefix : str, default "" - Prefix of the result files. **Deprecated** will be removed in Khiops 11. ... : See :ref:`core-api-common-params`. @@ -1290,7 +1268,6 @@ def build_deployed_dictionary( dictionary_file_path_or_domain, dictionary_name, output_dictionary_file_path, - batch_mode=True, log_file_path=None, output_scenario_path=None, task_file_path=None, @@ -1345,7 +1322,6 @@ def sort_data_table( field_separator=None, output_header_line=True, output_field_separator="\t", - batch_mode=True, log_file_path=None, output_scenario_path=None, task_file_path=None, @@ -1421,7 +1397,6 @@ def extract_keys_from_data_table( field_separator=None, output_header_line=True, output_field_separator="\t", - batch_mode=True, log_file_path=None, output_scenario_path=None, task_file_path=None, @@ -1488,7 +1463,7 @@ def train_coclustering( dictionary_name, data_table_path, coclustering_variables, - results_dir, + coclustering_report_file_path, detect_format=True, header_line=None, field_separator=None, @@ -1499,8 +1474,6 @@ def train_coclustering( additional_data_tables=None, frequency_variable="", min_optimization_time=0, - results_prefix="", - batch_mode=True, log_file_path=None, output_scenario_path=None, task_file_path=None, @@ -1525,8 +1498,8 @@ def train_coclustering( Path of the data table file. coclustering_variables : list of str The names of variables to use in coclustering. Min length: 2. Max length: 10. - results_dir : str - Path of the results directory. + coclustering_report_file_path : str + Path to the coclustering report file in the JSON format. detect_format : bool, default ``True`` If ``True`` detects automatically whether the data table file has a header and its field separator. It is set to ``False`` if ``header_line`` or @@ -1558,8 +1531,6 @@ def train_coclustering( Name of frequency variable. min_optimization_time : int, default 0 Minimum optimization time in seconds. - results_prefix : str, default "" - Prefix of the result files. **Deprecated** will be removed in Khiops 11. ... : See :ref:`core-api-common-params`. @@ -1594,19 +1565,118 @@ def train_coclustering( _run_task("train_coclustering", task_args) # Return the path of the coclustering file - return fs.get_child_path(results_dir, results_prefix + "Coclustering.khcj") + return coclustering_report_file_path + + +def train_instance_variable_coclustering( + dictionary_file_path_or_domain, + dictionary_name, + data_table_path, + coclustering_report_file_path, + detect_format=True, + header_line=None, + field_separator=None, + sample_percentage=100.0, + sampling_mode="Include sample", + selection_variable="", + selection_value="", + additional_data_tables=None, + min_optimization_time=0, + log_file_path=None, + output_scenario_path=None, + task_file_path=None, + trace=False, + stdout_file_path="", + stderr_file_path="", + max_cores=None, + memory_limit_mb=None, + temp_dir="", + scenario_prologue="", + **kwargs, +): + r"""Trains a coclustering model from a data table + Note: if keys are available in the input dictionary, they are used as instance + identifiers. Otherwise, line numbers in the instance data table are used as + instance idenfitiers. + + Parameters + ---------- + dictionary_file_path_or_domain : str or `.DictionaryDomain` + Path of a Khiops dictionary file or a DictionaryDomain object. + dictionary_name : str + Name of the dictionary to be analyzed. + data_table_path : str + Path of the data table file. + coclustering_report_file_path : str + Path to the coclustering report file in the JSON format. + detect_format : bool, default ``True`` + If ``True`` detects automatically whether the data table file has a header and + its field separator. It is set to ``False`` if ``header_line`` or + ``field_separator`` are set. + header_line : bool, optional (default ``True``) + If ``True`` it uses the first line of the data as column names. Sets + ``detect_format`` to ``False`` if set. Ignored if ``detect_format`` + is ``True``. + field_separator : str, optional (default "\\t") + A field separator character. "" has the same effect as "\\t". Sets + ``detect_format`` to ``False`` if set. Ignored if ``detect_format`` + is ``True``. + sample_percentage : float, default 100.0 + See ``sampling_mode`` option below. + sampling_mode : "Include sample" or "Exclude sample" + If equal to "Include sample" it trains the coclustering estimator on + ``sample_percentage`` percent of the data. If equal to "Exclude sample" it + trains the coclustering estimator on the complement of the data selected with + "Include sample". See also :ref:`core-api-sampling-mode`. + selection_variable : str, default "" + It trains with only the records such that the value of ``selection_variable`` is + equal to ``selection_value``. Ignored if equal to "". + selection_value: str or int or float, default "" + See ``selection_variable`` option above. Ignored if equal to "". + additional_data_tables : dict, optional + A dictionary containing the data paths and file paths for a multi-table + dictionary file. For more details see :doc:`/multi_table_primer`. + min_optimization_time : int, default 0 + Minimum optimization time in seconds. + ... : + See :ref:`core-api-common-params`. + + Returns + ------- + str + The path of the of the resulting coclustering file. + + Raises + ------ + `ValueError` + Number of coclustering variables out of the range 2-10. + `TypeError` + Invalid type of an argument. + + Examples + -------- + See the following function of the ``samples.py`` documentation script: + - `samples.train_coclustering()` + """ + # Save the task arguments + # WARNING: Do not move this line, see the top of the "tasks" section for details + task_args = locals() + + # Run the task + _run_task("train_instance_variable_coclustering", task_args) + + # Return the path of the coclustering file + return coclustering_report_file_path def simplify_coclustering( coclustering_file_path, simplified_coclustering_file_path, - results_dir, + results_dir=None, max_preserved_information=0, max_cells=0, max_total_parts=0, max_part_numbers=None, - results_prefix="", - batch_mode=True, log_file_path=None, output_scenario_path=None, task_file_path=None, @@ -1627,8 +1697,6 @@ def simplify_coclustering( Path of the coclustering file (extension ``.khc``, or ``.khcj``). simplified_coclustering_file_path : str Path of the output coclustering file. - results_dir : str - Path of the results directory. max_preserved_information : int, default 0 Maximum information preserve in the simplified coclustering. If equal to 0 there is no limit. @@ -1641,8 +1709,6 @@ def simplify_coclustering( max_part_numbers : dict, optional Dictionary that associate variable names to their maximum number of parts to preserve in the simplified coclustering. If not set there is no limit. - results_prefix : str, default "" - Prefix of the result files. **Deprecated** will be removed in Khiops 11. ... : See :ref:`core-api-common-params`. @@ -1660,6 +1726,34 @@ def simplify_coclustering( # WARNING: Do not move this line, see the top of the "tasks" section for details task_args = locals() + # Special processing for: + # - simplified_coclustering_file_path which does not start with the path separator + # (i.e. is a file name or a relative path), and + # - results_dir which is not None + # * concatenate simplified_coclustering_file_path to results_dir + # * issue deprecation warning for results_dir + # * remove 'results_dir' from the task arguments + + if results_dir is not None and not simplified_coclustering_file_path.startswith( + os.path.sep + ): + warnings.warn( + deprecation_message( + f"'{results_dir}'", + "11.0.1", + replacement="simplified_coclustering_file_path", + quote=False, + ) + ) + task_args["simplified_coclustering_file_path"] = os.path.join( + results_dir, simplified_coclustering_file_path + ) + + # Remove results_dir from the task arguments in all cases + # Note: it is ignored if None or if simplified_coclustering_file_path is + # absolute + del task_args["results_dir"] + # Run the task _run_task("simplify_coclustering", task_args) @@ -1670,16 +1764,15 @@ def prepare_coclustering_deployment( coclustering_file_path, table_variable, deployed_variable_name, - results_dir, + coclustering_dictionary_file_path, max_preserved_information=0, max_cells=0, + max_total_parts=0, max_part_numbers=None, build_cluster_variable=True, build_distance_variables=False, build_frequency_variables=False, variables_prefix="", - results_prefix="", - batch_mode=True, log_file_path=None, output_scenario_path=None, task_file_path=None, @@ -1706,14 +1799,17 @@ def prepare_coclustering_deployment( Name of the table variable in the dictionary. deployed_variable_name : str Name of the coclustering variable to deploy. - results_dir : str - Path of the results directory. + coclustering_dictionary_file_path : str + Path of the coclustering dictionary file for deployment. max_preserved_information : int, default 0 Maximum information preserve in the simplified coclustering. If equal to 0 there is no limit. max_cells : int, default 0 Maximum number of cells in the simplified coclustering. If equal to 0 there is no limit. + max_total_parts : int, default 0 + Maximum number of parts totaled over all variables. If equal to 0 there is no + limit. max_part_numbers : dict, optional Dictionary associating variable names to their maximum number of parts to preserve in the simplified coclustering. For variables not present in @@ -1726,8 +1822,6 @@ def prepare_coclustering_deployment( If ``True`` includes the frequency variables in the deployment. variables_prefix : str, default "" Prefix for the variables in the deployment dictionary. - results_prefix : str, default "" - Prefix of the result files. **Deprecated** will be removed in Khiops 11. ... : See :ref:`core-api-common-params`. @@ -1755,7 +1849,8 @@ def extract_clusters( clusters_file_path, max_preserved_information=0, max_cells=0, - batch_mode=True, + max_total_parts=0, + max_part_numbers=None, log_file_path=None, output_scenario_path=None, task_file_path=None, @@ -1784,6 +1879,12 @@ def extract_clusters( max_cells : int, default 0 Maximum number of cells in the simplified coclustering. If equal to 0 there is no limit. + max_total_parts : int, default 0 + Maximum number of parts totaled over all variables. If equal to 0 there is no + limit. + max_part_numbers : dict, optional + Dictionary that associate variable names to their maximum number of parts to + preserve in the simplified coclustering. If not set there is no limit. ... : See :ref:`core-api-common-params`. @@ -1796,17 +1897,6 @@ def extract_clusters( # WARNING: Do not move this line, see the top of the "tasks" section for details task_args = locals() - # Obtain the directory and name of the clusters file - clusters_file_path = create_unambiguous_khiops_path(task_args["clusters_file_path"]) - clusters_file_name = os.path.basename(clusters_file_path) - clusters_file_dir_path = fs.get_parent_path(clusters_file_path) - clusters_file_dir = create_unambiguous_khiops_path(clusters_file_dir_path) - task_args["clusters_file_name"] = clusters_file_name - task_args["results_dir"] = clusters_file_dir - - # Delete the clusters_file_path argument - del task_args["clusters_file_path"] - # Run the task _run_task("extract_clusters", task_args) @@ -1815,6 +1905,9 @@ def detect_data_table_format( data_table_path, dictionary_file_path_or_domain=None, dictionary_name=None, + log_file_path=None, + output_scenario_path=None, + task_file_path=None, trace=False, stdout_file_path="", stderr_file_path="", @@ -1822,6 +1915,7 @@ def detect_data_table_format( memory_limit_mb=None, temp_dir="", scenario_prologue="", + **kwargs, ): """Detects the format of a data table @@ -1856,11 +1950,15 @@ def detect_data_table_format( # Save the task arguments # WARNING: Do not move this line, see the top of the "tasks" section for details task_args = locals() + # If not defined, create log file to save the detect format output - # Create log file to save the detect format output - log_file_path = get_runner().create_temp_file("_detect_data_table_format_", ".log") - task_args["log_file_path"] = log_file_path - + delete_log_file = False + if log_file_path is None: + delete_log_file = True + log_file_path = get_runner().create_temp_file( + "_detect_data_table_format_", ".log" + ) + task_args["log_file_path"] = log_file_path # Run the task without dictionary if dictionary_file_path_or_domain is None: if "dictionary_name" in task_args: @@ -1911,90 +2009,10 @@ def detect_data_table_format( # Clean up the log file if necessary if trace: print(f"detect_format log file: {log_file_path}") - else: + elif delete_log_file is True: fs.remove(log_file_path) return header_line, field_separator # pylint: enable=unused-argument - -######################## -# Deprecated functions # -######################## - - -# pylint: disable=unused-argument - - -def build_multi_table_dictionary( - dictionary_file_path_or_domain, - root_dictionary_name, - secondary_table_variable_name, - output_dictionary_file_path, - overwrite_dictionary_file=False, - batch_mode=True, - log_file_path=None, - output_scenario_path=None, - task_file_path=None, - trace=False, -): - """Builds a multi-table dictionary from a dictionary with a key - - .. warning:: - This method is *deprecated* since Khiops 10.1.3 and will be removed in Khiops - 11. Use the `.build_multi_table_dictionary_domain` helper function to - the same effect. - - Parameters - ---------- - dictionary_file_path_or_domain : str or `.DictionaryDomain` - Path of a Khiops dictionary file or a `.DictionaryDomain` object. - root_dictionary_name : str - Name for the new root dictionary - secondary_table_variable_name : str - Name, in the root dictionary, for the "table" variable of the secondary table. - output_dictionary_file_path : str - Path of the output dictionary path. - overwrite_dictionary_file : bool, default ``False`` - If ``True`` it will overwrite an input dictionary file. - ... : - See :ref:`core-api-common-params`. - - Raises - ------ - `ValueError` - Invalid values of an argument - """ - # Warn the user that this API function is deprecated and will be removed - warnings.warn(deprecation_message("build_multi_table_dictionary", "11.0.0")) - - # Create the execution dictionary domain if it is a file - _check_dictionary_file_path_or_domain(dictionary_file_path_or_domain) - if isinstance(dictionary_file_path_or_domain, str): - dictionary_domain = read_dictionary_file(dictionary_file_path_or_domain) - else: - dictionary_domain = dictionary_file_path_or_domain - - # Generate multi-table domain by using the eponymous helper function - # Honor exception API: - try: - multi_table_domain = build_multi_table_dictionary_domain( - dictionary_domain, root_dictionary_name, secondary_table_variable_name - ) - except TypeError as error: - raise ValueError from error - - # If overwrite_dictionary_file is set and the input is a path to a dictionary, - # then the output path is set to the input path - if overwrite_dictionary_file and isinstance(dictionary_file_path_or_domain, str): - output_dictionary_file_path = dictionary_file_path_or_domain - - # Write multi-table domain to file - with io.BytesIO() as stream: - writer = KhiopsOutputWriter(stream) - multi_table_domain.write(writer) - fs.write(output_dictionary_file_path, stream.getvalue()) - - -# pylint: enable=unused-argument diff --git a/khiops/core/coclustering_results.py b/khiops/core/coclustering_results.py index 77fe73f9..13df8723 100644 --- a/khiops/core/coclustering_results.py +++ b/khiops/core/coclustering_results.py @@ -506,6 +506,9 @@ class CoclusteringDimension: ---------- name : str Name of the variable associated to this dimension. + is_variable_part : boolean + True if the dimension is a part of a variable in an instance-variable + coclustering. type : "Numerical" or "Categorical" Dimension type. part_number : int @@ -536,6 +539,7 @@ def __init__(self): """See class docstring""" # Summary attributes self.name = "" + self.is_variable_part = False self.type = "" self.part_number = 0 self.initial_part_number = 0 @@ -587,6 +591,7 @@ def init_summary(self, json_data=None): # Initialize the summary fields self.name = json_data.get("name", "") + self.is_variable_part = json_data.get("isVarPart", False) self.type = json_data.get("type", "") self.part_number = json_data.get("parts", 0) self.initial_part_number = json_data.get("initialParts", 0) @@ -791,6 +796,7 @@ def write_dimension_header_line(self, writer): Output writer for the report file. """ writer.write("Name\t") + writer.write("Is variable part\t") writer.write("Type\t") writer.write("Parts\t") writer.write("Initial parts\t") @@ -807,6 +813,7 @@ def write_dimension_line(self, writer): Output writer for the report file. """ writer.write(f"{self.name}\t") + writer.write(f"{self.is_variable_part}\t") writer.write(f"{self.type}\t") writer.write(f"{self.part_number}\t") writer.write(f"{self.initial_part_number}\t") diff --git a/khiops/core/dictionary.py b/khiops/core/dictionary.py index 32af135d..c3c196f0 100644 --- a/khiops/core/dictionary.py +++ b/khiops/core/dictionary.py @@ -252,6 +252,8 @@ def _extract_data_paths( current_data_path.append(current_dictionary_alias) else: current_data_path.append(current_dictionary.name) + if source_dictionary.name in current_data_path: + current_data_path.remove(source_dictionary.name) data_paths.append(current_data_path) # Analyze variables to extract additional data paths @@ -280,6 +282,8 @@ def _extract_data_paths( if source_dictionary.name in entity_dictionary_names: entity_dictionary_names.remove(source_dictionary.name) + # Remove + # Extract the data paths recursively for the entity dictionaries found during # the first extraction # Recall that _extract_data_paths modifies the 'entity_dictionary_names' list; @@ -289,10 +293,9 @@ def _extract_data_paths( entity_dictionary_name = entity_dictionary_names[name_index] entity_dictionary = self.get_dictionary(entity_dictionary_name) name_index += 1 - _extract_data_paths(entity_dictionary, []) - + _extract_data_paths(entity_dictionary, [], f"/{entity_dictionary.name}") # Remove first data path (that of the source dictionary) before returning - return ["`".join(data_path) for data_path in data_paths[1:]] + return ["/".join(data_path) for data_path in data_paths[1:]] def get_dictionary_at_data_path(self, data_path): """Returns the dictionary name for the specified data path @@ -313,15 +316,30 @@ def get_dictionary_at_data_path(self, data_path): `ValueError` If the path is not found. """ - data_path_parts = data_path.split("`") - source_dictionary_name = data_path_parts[0] + data_path_parts = data_path.lstrip("/").split("/") + # Attempt to get the first dictionary from the data path: + # - either it is found as such, + # - or it is a Table or Entity variable whose table needs to be looked-up + first_table_variable_name = data_path_parts[0] try: - dictionary = self.get_dictionary(source_dictionary_name) + dictionary = self.get_dictionary(first_table_variable_name) except KeyError as error: - raise ValueError( - f"Source dictionary not found: '{source_dictionary_name}'" - ) from error + for a_dictionary in self.dictionaries: + try: + table_variable = a_dictionary.get_variable( + first_table_variable_name + ) + if table_variable.type not in ["Table", "Entity"]: + raise ValueError + dictionary = self.get_dictionary(table_variable.object_type) + break + except (KeyError, ValueError): + continue + else: + raise ValueError( + f"Dictionary not found in data path: '{data_path}'" + ) from error for table_variable_name in data_path_parts[1:]: try: diff --git a/khiops/core/helpers.py b/khiops/core/helpers.py index 2b9654e9..3df8f0f3 100644 --- a/khiops/core/helpers.py +++ b/khiops/core/helpers.py @@ -11,17 +11,14 @@ import khiops.core.internals.filesystems as fs from khiops.core import api +from khiops.core.coclustering_results import read_coclustering_results_file from khiops.core.dictionary import ( Dictionary, DictionaryDomain, Variable, read_dictionary_file, ) -from khiops.core.internals.common import ( - create_unambiguous_khiops_path, - is_list_like, - type_error_message, -) +from khiops.core.internals.common import is_list_like, type_error_message def build_multi_table_dictionary_domain( @@ -117,7 +114,8 @@ def deploy_coclustering( coclustering_file_path, key_variable_names, deployed_variable_name, - results_dir, + coclustering_dictionary_file_path, + output_data_table_path, detect_format=True, header_line=None, field_separator=None, @@ -125,26 +123,23 @@ def deploy_coclustering( output_field_separator="\t", max_preserved_information=0, max_cells=0, + max_total_parts=0, max_part_numbers=None, build_cluster_variable=True, build_distance_variables=False, build_frequency_variables=False, variables_prefix="", - results_prefix="", - batch_mode=True, log_file_path=None, output_scenario_path=None, task_file_path=None, trace=False, ): - r"""Deploys an *individual-variable* coclustering on a data table - - This procedure generates the following files in ``results_dir``: - - ``Coclustering.kdic``: A multi-table dictionary file for further deployments - of the coclustering with deploy_model - - ``Keys``: A data table file containing only the keys of - individual - - ``Deployed``: A data table file containing the deployed + r"""Deploys a coclustering on a data table + + This procedure generates the following files: + - ``coclustering_dictionary_file_path``: A multi-table dictionary file for + further deployments of the coclustering with deploy_model + - ``output_data_table_path``: A data table file containing the deployed coclustering model Parameters @@ -156,13 +151,16 @@ def deploy_coclustering( data_table_path : str Path of the data table file. coclustering_file_path : str - Path of the coclustering model file (extension ``.khc`` or ``.khcj``) + Path of the coclustering model file (extension ``.khc`` or ``.khcj``). + .. note:: Instance-variable coclustering is not currently supported. key_variable_names : list of str Names of the variables forming the unique keys of the individuals. deployed_variable_name : str Name of the coclustering variable to deploy. - results_dir : str - Path of the results directory. + coclustering_dictionary_file_path : str + Path of the coclustering dictionary file to deploy. + output_data_table_path : str + Path of the output data file. detect_format : bool, default ``True`` If True detects automatically whether the data table file has a header and its field separator. It's ignored if ``header_line`` or ``field_separator`` are set. @@ -182,6 +180,9 @@ def deploy_coclustering( max_cells : int, default 0 Maximum number of cells in the simplified coclustering. If equal to 0 there is no limit. + max_total_parts : int, default 0 + Maximum number of parts totaled over all variables. If equal to 0 there is no + limit. max_part_numbers : dict, optional Dictionary associating variable names to their maximum number of parts to preserve in the simplified coclustering. For variables not present in @@ -194,8 +195,6 @@ def deploy_coclustering( If True includes the frequency variables in the deployment. variables_prefix : str, default "" Prefix for the variables in the deployment dictionary. - results_prefix : str, default "" - Prefix of the result files. ... : Options of the `.KhiopsRunner.run` method from the class `.KhiopsRunner`. @@ -213,12 +212,26 @@ def deploy_coclustering( Invalid type ``dictionary_file_path_or_domain`` or ``key_variable_names`` `ValueError` If the type of the dictionary key variables is not equal to ``Categorical`` + `NotImplementedError` + If the coclustering to be deployed is of the instance-variable type Examples -------- See the following function of the ``samples.py`` documentation script: - `samples.deploy_coclustering()` """ + # Fail early for instance-variable coclustering, which is not supported + if any( + dimension.is_variable_part + for dimension in read_coclustering_results_file( + coclustering_file_path + ).coclustering_report.dimensions + ): + raise NotImplementedError( + "Deployment support for instance-variable coclustering is not yet " + "implemented." + ) + # Obtain the dictionary of the table where the coclustering variables are api._check_dictionary_file_path_or_domain(dictionary_file_path_or_domain) if isinstance(dictionary_file_path_or_domain, DictionaryDomain): @@ -226,9 +239,6 @@ def deploy_coclustering( else: domain = read_dictionary_file(dictionary_file_path_or_domain) - # Disambiguate the results directory path if necessary - results_dir = create_unambiguous_khiops_path(results_dir) - # Check the type of non basic keyword arguments specific to this function if not is_list_like(key_variable_names): raise TypeError( @@ -277,16 +287,15 @@ def deploy_coclustering( coclustering_file_path, table_variable_name, deployed_variable_name, - results_dir, + coclustering_dictionary_file_path, max_preserved_information=max_preserved_information, max_cells=max_cells, + max_total_parts=max_total_parts, max_part_numbers=max_part_numbers, build_cluster_variable=build_cluster_variable, build_distance_variables=build_distance_variables, build_frequency_variables=build_frequency_variables, variables_prefix=variables_prefix, - results_prefix=results_prefix, - batch_mode=batch_mode, log_file_path=log_file_path, output_scenario_path=output_scenario_path, task_file_path=task_file_path, @@ -295,7 +304,7 @@ def deploy_coclustering( # Extract the keys from the tables to a temporary file data_table_file_name = os.path.basename(data_table_path) - keys_table_file_path = fs.get_child_path(results_dir, f"Keys{data_table_file_name}") + keys_table_file_path = fs.get_child_path(os.getcwd(), f"Keys{data_table_file_name}") api.extract_keys_from_data_table( domain, dictionary_name, @@ -308,16 +317,7 @@ def deploy_coclustering( trace=trace, ) - # Deploy the coclustering model - coclustering_dictionary_file_path = fs.get_child_path( - results_dir, "Coclustering.kdic" - ) - output_data_table_path = fs.get_child_path( - results_dir, f"Deployed{data_table_file_name}" - ) - additional_data_tables = { - f"{root_dictionary_name}`{table_variable_name}": data_table_path - } + additional_data_tables = {table_variable_name: data_table_path} api.deploy_model( coclustering_dictionary_file_path, root_dictionary_name, @@ -330,6 +330,11 @@ def deploy_coclustering( additional_data_tables=additional_data_tables, trace=trace, ) + + # Delete auxiliary file, no longer useful + if fs.exists(keys_table_file_path): + fs.remove(keys_table_file_path) + return output_data_table_path, coclustering_dictionary_file_path @@ -381,7 +386,7 @@ def deploy_predictor_for_metrics( as "\\t"). sample_percentage : int, default 70 See ``sampling_mode`` option below. - sampling_mode : "Include sample" or "Exclude sample" + sampling_mode : "Include sample" or "Exclude sample", default "Include sample" If equal to "Include sample" deploys the predictor on ``sample_percentage`` percent of data and if equal to "Exclude sample" on the complementary ``100 - sample_percentage`` percent of data. diff --git a/khiops/core/internals/common.py b/khiops/core/internals/common.py index b39db441..95858e56 100644 --- a/khiops/core/internals/common.py +++ b/khiops/core/internals/common.py @@ -99,20 +99,15 @@ class CommandLineOptions: task_file_path : str, default "" Path of the task file for the Khiops process (command line option ``-p`` of the desktop app). If equal to "" then it writes no task file. - batch_mode : bool, default True - *Deprecated* Will be removed in Khiops 11. If ``True`` activates batch mode - (command line option ``-b`` of the app). """ def __init__( self, - batch_mode=True, log_file_path="", task_file_path="", output_scenario_path="", ): """See class docstring""" - self.batch_mode = batch_mode self.log_file_path = log_file_path self.task_file_path = task_file_path self.output_scenario_path = output_scenario_path @@ -127,9 +122,8 @@ def to_str(string): repr_str = string.decode("utf8", errors="replace") return repr_str - command_line_options = [] - if self.batch_mode: - command_line_options += ["-b"] + # Enable batch execution by default + command_line_options = ["-b"] if self.output_scenario_path: command_line_options += ["-o", to_str(self.output_scenario_path)] if self.log_file_path: @@ -140,9 +134,8 @@ def to_str(string): return "Khiops command line options: " + " ".join(command_line_options) def build_command_line_options(self, scenario_path): - command_line_options = [] - if self.batch_mode: - command_line_options += ["-b"] + # Enable batch execution + command_line_options = ["-b"] command_line_options += ["-i", scenario_path] if self.output_scenario_path: command_line_options += ["-o", self.output_scenario_path] @@ -161,8 +154,6 @@ def check(self): `TypeError` If any of the command line options does not have the proper type. """ - if not isinstance(self.batch_mode, bool): - raise TypeError(type_error_message("batch_mode", self.batch_mode, bool)) if self.output_scenario_path and not is_string_like(self.output_scenario_path): raise TypeError( type_error_message( @@ -280,21 +271,6 @@ def type_error_message(variable_name, variable, *target_types): ) -def removal_message(removed_feature, since, replacement=None): - """Formats a feature removal message""" - message = f"'{removed_feature}' removed since {since}. " - if replacement: - message += f"Use '{replacement}'." - else: - message += "There is no replacement." - return message - - -def renaming_message(renamed_feature, new_name, since): - """Formats a feature renaming message""" - return f"Ignoring '{renamed_feature}': renamed to '{new_name}' since {since}." - - def invalid_keys_message(kwargs): """Formats an invalid keyword parameter message""" return f"Ignoring invalid parameter(s): {','.join(kwargs.keys())}." diff --git a/khiops/core/internals/runner.py b/khiops/core/internals/runner.py index d2c1f4ea..a9c426d6 100644 --- a/khiops/core/internals/runner.py +++ b/khiops/core/internals/runner.py @@ -881,6 +881,9 @@ def _initialize_khiops_environment(self): else: os.environ[var_name] = var_value + # Set KHIOPS_API_MODE to `true` + os.environ["KHIOPS_API_MODE"] = "true" + # Check the tools exist and are executable self._check_tools() diff --git a/khiops/core/internals/tasks/__init__.py b/khiops/core/internals/tasks/__init__.py index 1f64f177..10d4888b 100644 --- a/khiops/core/internals/tasks/__init__.py +++ b/khiops/core/internals/tasks/__init__.py @@ -21,10 +21,12 @@ export_dictionary_as_json, extract_clusters, extract_keys_from_data_table, + interpret_predictor, prepare_coclustering_deployment, simplify_coclustering, sort_data_table, train_coclustering, + train_instance_variable_coclustering, train_predictor, train_recoder, ) @@ -42,10 +44,12 @@ export_dictionary_as_json, extract_clusters, extract_keys_from_data_table, + interpret_predictor, prepare_coclustering_deployment, simplify_coclustering, sort_data_table, train_coclustering, + train_instance_variable_coclustering, train_predictor, train_recoder, ] diff --git a/khiops/core/internals/tasks/build_dictionary_from_data_table.py b/khiops/core/internals/tasks/build_dictionary_from_data_table.py index b6243a23..204e4e44 100644 --- a/khiops/core/internals/tasks/build_dictionary_from_data_table.py +++ b/khiops/core/internals/tasks/build_dictionary_from_data_table.py @@ -14,7 +14,7 @@ tm.KhiopsTask( "build_dictionary_from_data_table", "khiops", - "10.0.0", + "10.6.0-b.0", [ ("data_table_path", StringLikeType), ("output_dictionary_name", StringLikeType), @@ -29,13 +29,14 @@ # fmt: off """ // Dictionary building settings - ClassManagement.BuildClassDefButton - SourceDataTable.DatabaseName __data_table_path__ - SourceDataTable.HeaderLineUsed __header_line__ - SourceDataTable.FieldSeparator __field_separator__ + ClassManagement.ManageClasses + BuildClassDefButton + SourceDataTable.DatabaseSpec.Data.DatabaseName __data_table_path__ + SourceDataTable.DatabaseSpec.Data.HeaderLineUsed __header_line__ + SourceDataTable.DatabaseSpec.Data.FieldSeparator __field_separator__ __OPT__ __detect_format__ - SourceDataTable.DatabaseFormatDetector.DetectFileFormat + SourceDataTable.DatabaseSpec.Data.DatabaseFormatDetector.DetectFileFormat __END_OPT__ BuildClassDef ClassName __output_dictionary_name__ @@ -45,6 +46,7 @@ // Save dictionary ClassFileName __output_dictionary_file_path__ OK + Exit """, # fmt: on ), diff --git a/khiops/core/internals/tasks/check_database.py b/khiops/core/internals/tasks/check_database.py index 1c13602c..2fbeb1a8 100644 --- a/khiops/core/internals/tasks/check_database.py +++ b/khiops/core/internals/tasks/check_database.py @@ -20,7 +20,7 @@ tm.KhiopsTask( "check_database", "khiops", - "10.0.0", + "10.6.0-b.0", [ ("dictionary_file_path", StringLikeType), ("dictionary_name", StringLikeType), @@ -44,26 +44,26 @@ ClassManagement.OpenFile ClassFileName __dictionary_file_path__ OK - ClassManagement.ClassName __dictionary_name__ // Train database settings - TrainDatabase.DatabaseFiles.List.Key __dictionary_name__ - TrainDatabase.DatabaseFiles.DataTableName __data_table_path__ + TrainDatabase.ClassName __dictionary_name__ + TrainDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key + TrainDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName __data_table_path__ __DICT__ __additional_data_tables__ - TrainDatabase.DatabaseFiles.List.Key - TrainDatabase.DatabaseFiles.DataTableName + TrainDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key + TrainDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName __END_DICT__ - TrainDatabase.HeaderLineUsed __header_line__ - TrainDatabase.FieldSeparator __field_separator__ + TrainDatabase.DatabaseSpec.Data.HeaderLineUsed __header_line__ + TrainDatabase.DatabaseSpec.Data.FieldSeparator __field_separator__ __OPT__ __detect_format__ - TrainDatabase.DatabaseFormatDetector.DetectFileFormat + TrainDatabase.DatabaseSpec.Data.DatabaseFormatDetector.DetectFileFormat __END_OPT__ - TrainDatabase.SampleNumberPercentage __sample_percentage__ - TrainDatabase.SamplingMode __sampling_mode__ - TrainDatabase.SelectionAttribute __selection_variable__ - TrainDatabase.SelectionValue __selection_value__ + TrainDatabase.DatabaseSpec.Sampling.SampleNumberPercentage __sample_percentage__ + TrainDatabase.DatabaseSpec.Sampling.SamplingMode __sampling_mode__ + TrainDatabase.DatabaseSpec.Selection.SelectionAttribute __selection_variable__ + TrainDatabase.DatabaseSpec.Selection.SelectionValue __selection_value__ // Log messages limit AnalysisSpec.SystemParameters.MaxErrorMessageNumberInLog __max_messages__ diff --git a/khiops/core/internals/tasks/deploy_model.py b/khiops/core/internals/tasks/deploy_model.py index 7feda78e..bad6cae1 100644 --- a/khiops/core/internals/tasks/deploy_model.py +++ b/khiops/core/internals/tasks/deploy_model.py @@ -14,7 +14,7 @@ tm.KhiopsTask( "deploy_model", "khiops", - "10.0.0", + "10.6.0-b.0", [ ("dictionary_file_path", StringLikeType), ("dictionary_name", StringLikeType), @@ -57,34 +57,34 @@ ClassName __dictionary_name__ // Input database settings - SourceDatabase.DatabaseFiles.List.Key __dictionary_name__ - SourceDatabase.DatabaseFiles.DataTableName __data_table_path__ + SourceDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key + SourceDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName __data_table_path__ __DICT__ __additional_data_tables__ - SourceDatabase.DatabaseFiles.List.Key - SourceDatabase.DatabaseFiles.DataTableName + SourceDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key + SourceDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName __END_DICT__ - SourceDatabase.HeaderLineUsed __header_line__ - SourceDatabase.FieldSeparator __field_separator__ + SourceDatabase.DatabaseSpec.Data.HeaderLineUsed __header_line__ + SourceDatabase.DatabaseSpec.Data.FieldSeparator __field_separator__ __OPT__ __detect_format__ - SourceDatabase.DatabaseFormatDetector.DetectFileFormat + SourceDatabase.DatabaseSpec.Data.DatabaseFormatDetector.DetectFileFormat __END_OPT__ - SourceDatabase.SampleNumberPercentage __sample_percentage__ - SourceDatabase.SamplingMode __sampling_mode__ - SourceDatabase.SelectionAttribute __selection_variable__ - SourceDatabase.SelectionValue __selection_value__ + SourceDatabase.DatabaseSpec.Sampling.SampleNumberPercentage __sample_percentage__ + SourceDatabase.DatabaseSpec.Sampling.SamplingMode __sampling_mode__ + SourceDatabase.DatabaseSpec.Selection.SelectionAttribute __selection_variable__ + SourceDatabase.DatabaseSpec.Selection.SelectionValue __selection_value__ // Output database settings - TargetDatabase.DatabaseFiles.List.Key __dictionary_name__ - TargetDatabase.DatabaseFiles.DataTableName __output_data_table_path__ + TargetDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key + TargetDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName __output_data_table_path__ __DICT__ __output_additional_data_tables__ - TargetDatabase.DatabaseFiles.List.Key - TargetDatabase.DatabaseFiles.DataTableName + TargetDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key + TargetDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName __END_DICT__ - TargetDatabase.HeaderLineUsed __output_header_line__ - TargetDatabase.FieldSeparator __output_field_separator__ + TargetDatabase.DatabaseSpec.Data.HeaderLineUsed __output_header_line__ + TargetDatabase.DatabaseSpec.Data.FieldSeparator __output_field_separator__ // Transfer TransferDatabase diff --git a/khiops/core/internals/tasks/detect_data_table_format.py b/khiops/core/internals/tasks/detect_data_table_format.py index f9874a7c..7e91d5a2 100644 --- a/khiops/core/internals/tasks/detect_data_table_format.py +++ b/khiops/core/internals/tasks/detect_data_table_format.py @@ -14,7 +14,7 @@ tm.KhiopsTask( "detect_data_table_format", "khiops", - "10.0.1", + "10.6.0-b.0", [ ("data_table_path", StringLikeType), ], @@ -23,10 +23,9 @@ # fmt: off """ // Detect format on the "Build Dictionary" window - ClassManagement.BuildClassDefButton - SourceDataTable.DatabaseName __data_table_path__ - SourceDataTable.DatabaseFormatDetector.DetectFileFormat - Exit + TrainDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key // List item selection + TrainDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName __data_table_path__ + TrainDatabase.DatabaseSpec.Data.DatabaseFormatDetector.DetectFileFormat """, # fmt: on ), diff --git a/khiops/core/internals/tasks/detect_data_table_format_with_dictionary.py b/khiops/core/internals/tasks/detect_data_table_format_with_dictionary.py index 0003ff11..ed12582a 100644 --- a/khiops/core/internals/tasks/detect_data_table_format_with_dictionary.py +++ b/khiops/core/internals/tasks/detect_data_table_format_with_dictionary.py @@ -14,7 +14,7 @@ tm.KhiopsTask( "detect_data_table_format_with_dictionary", "khiops", - "10.0.1", + "10.6.0-b.0", [ ("dictionary_file_path", StringLikeType), ("dictionary_name", StringLikeType), @@ -28,12 +28,13 @@ ClassManagement.OpenFile ClassFileName __dictionary_file_path__ OK - ClassManagement.ClassName __dictionary_name__ + + TrainDatabase.ClassName __dictionary_name__ // Detect format the data table format on the "Extract Keys" window LearningTools.ExtractKeysFromDataTable - SourceDataTable.DatabaseName __data_table_path__ - SourceDataTable.DatabaseFormatDetector.DetectFileFormat + SourceDataTable.DatabaseSpec.Data.DatabaseName __data_table_path__ + SourceDataTable.DatabaseSpec.Data.DatabaseFormatDetector.DetectFileFormat Exit """, # fmt: on diff --git a/khiops/core/internals/tasks/evaluate_predictor.py b/khiops/core/internals/tasks/evaluate_predictor.py index 2f3dfd0a..a1aa5990 100644 --- a/khiops/core/internals/tasks/evaluate_predictor.py +++ b/khiops/core/internals/tasks/evaluate_predictor.py @@ -14,12 +14,12 @@ tm.KhiopsTask( "evaluate_predictor", "khiops", - "10.0.0", + "10.6.0-b.0", [ ("dictionary_file_path", StringLikeType), ("train_dictionary_name", StringLikeType), ("data_table_path", StringLikeType), - ("evaluation_report_path", StringLikeType), + ("evaluation_report_file_path", StringLikeType), ], [ ("detect_format", BoolType, True), @@ -35,7 +35,7 @@ [ "dictionary_file_path", "data_table_path", - "evaluation_report_path", + "evaluation_report_file_path", "additional_data_tables", ], # fmt: off @@ -48,24 +48,26 @@ // Evaluate predictor settings LearningTools.EvaluatePredictors MainTargetModality __main_target_value__ - EvaluationDatabase.DatabaseFiles.List.Key __train_dictionary_name__ - EvaluationDatabase.DatabaseFiles.DataTableName __data_table_path__ + EvaluationDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key + EvaluationDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName __data_table_path__ __DICT__ __additional_data_tables__ - EvaluationDatabase.DatabaseFiles.List.Key - EvaluationDatabase.DatabaseFiles.DataTableName + EvaluationDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key + EvaluationDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName __END_DICT__ - EvaluationDatabase.HeaderLineUsed __header_line__ - EvaluationDatabase.FieldSeparator __field_separator__ + EvaluationDatabase.DatabaseSpec.Data.HeaderLineUsed __header_line__ + EvaluationDatabase.DatabaseSpec.Data.FieldSeparator __field_separator__ __OPT__ __detect_format__ - EvaluationDatabase.DatabaseFormatDetector.DetectFileFormat + EvaluationDatabase.DatabaseSpec.Data.DatabaseFormatDetector.DetectFileFormat __END_OPT__ - EvaluationDatabase.SampleNumberPercentage __sample_percentage__ - EvaluationDatabase.SamplingMode __sampling_mode__ - EvaluationDatabase.SelectionAttribute __selection_variable__ - EvaluationDatabase.SelectionValue __selection_value__ - EvaluationFileName __evaluation_report_path__ + EvaluationDatabase.DatabaseSpec.Sampling.SampleNumberPercentage __sample_percentage__ + EvaluationDatabase.DatabaseSpec.Sampling.SamplingMode __sampling_mode__ + EvaluatedPredictors.List.Key __train_dictionary_name__ + EvaluationDatabase.DatabaseSpec.Selection.SelectionAttribute __selection_variable__ + EvaluationDatabase.DatabaseSpec.Selection.SelectionValue __selection_value__ + ExportAsXls false + EvaluationFileName __evaluation_report_file_path__ // Evaluate predictor EvaluatePredictors diff --git a/khiops/core/internals/tasks/export_dictionary_as_json.py b/khiops/core/internals/tasks/export_dictionary_as_json.py index 95f1d642..61868d29 100644 --- a/khiops/core/internals/tasks/export_dictionary_as_json.py +++ b/khiops/core/internals/tasks/export_dictionary_as_json.py @@ -14,7 +14,7 @@ tm.KhiopsTask( "export_dictionary_as_json", "khiops", - "10.0.0", + "10.6.0-b.0", [ ("dictionary_file_path", StringLikeType), ("json_dictionary_file_path", StringLikeType), diff --git a/khiops/core/internals/tasks/extract_clusters.py b/khiops/core/internals/tasks/extract_clusters.py index 21551cd0..aae59a0e 100644 --- a/khiops/core/internals/tasks/extract_clusters.py +++ b/khiops/core/internals/tasks/extract_clusters.py @@ -6,7 +6,7 @@ ###################################################################################### """extract_clusters task family""" from khiops.core.internals import task as tm -from khiops.core.internals.types import IntType, StringLikeType +from khiops.core.internals.types import DictType, IntType, StringLikeType # Disable long lines to have readable scenarios # pylint: disable=line-too-long @@ -14,21 +14,19 @@ tm.KhiopsTask( "extract_clusters", "khiops_coclustering", - "10.0.0", + "10.6.0-b.0", [ ("coclustering_file_path", StringLikeType), ("cluster_variable", StringLikeType), - ("results_dir", StringLikeType), - ("clusters_file_name", StringLikeType), + ("clusters_file_path", StringLikeType), ], [ ("max_preserved_information", IntType, 0), ("max_cells", IntType, 0), + ("max_total_parts", IntType, 0), + ("max_part_numbers", DictType(StringLikeType, IntType), None), ], - [ - "coclustering_file_path", - "results_dir", - ], + ["coclustering_file_path", "clusters_file_path"], # fmt: off """ // Extract cluster settings @@ -36,13 +34,18 @@ SelectInputCoclustering InputCoclusteringFileName __coclustering_file_path__ OK - CoclusteringAttributeSpec.CoclusteringAttribute __cluster_variable__ + CoclusteringAttribute __cluster_variable__ PostProcessingSpec.MaxPreservedInformation __max_preserved_information__ PostProcessingSpec.MaxCellNumber __max_cells__ + PostProcessingSpec.MaxTotalPartNumber __max_total_parts__ + __DICT__ + __max_part_numbers__ + PostProcessingSpec.PostProcessedAttributes.List.Key + PostProcessingSpec.PostProcessedAttributes.MaxPartNumber + __END_DICT__ // Output settings - AnalysisResults.ResultFilesDirectory __results_dir__ - AnalysisResults.ClusterFileName __clusters_file_name__ + ClusterFileName __clusters_file_path__ // Extract clusters ExtractClusters diff --git a/khiops/core/internals/tasks/extract_keys_from_data_table.py b/khiops/core/internals/tasks/extract_keys_from_data_table.py index 95340fdc..414e8257 100644 --- a/khiops/core/internals/tasks/extract_keys_from_data_table.py +++ b/khiops/core/internals/tasks/extract_keys_from_data_table.py @@ -14,7 +14,7 @@ tm.KhiopsTask( "extract_keys_from_data_table", "khiops", - "10.0.0", + "10.6.0-b.0", [ ("dictionary_file_path", StringLikeType), ("dictionary_name", StringLikeType), @@ -43,16 +43,16 @@ // Extract keys settings LearningTools.ExtractKeysFromDataTable ClassName __dictionary_name__ - SourceDataTable.DatabaseName __data_table_path__ - SourceDataTable.HeaderLineUsed __header_line__ - SourceDataTable.FieldSeparator __field_separator__ + SourceDataTable.DatabaseSpec.Data.DatabaseName __data_table_path__ + SourceDataTable.DatabaseSpec.Data.HeaderLineUsed __header_line__ + SourceDataTable.DatabaseSpec.Data.FieldSeparator __field_separator__ __OPT__ __detect_format__ - SourceDataTable.DatabaseFormatDetector.DetectFileFormat + SourceDataTable.DatabaseSpec.Data.DatabaseFormatDetector.DetectFileFormat __END_OPT__ - TargetDataTable.DatabaseName __output_data_table_path__ - TargetDataTable.HeaderLineUsed __output_header_line__ - TargetDataTable.FieldSeparator __output_field_separator__ + TargetDataTable.DatabaseSpec.Data.DatabaseName __output_data_table_path__ + TargetDataTable.DatabaseSpec.Data.HeaderLineUsed __output_header_line__ + TargetDataTable.DatabaseSpec.Data.FieldSeparator __output_field_separator__ ExtractKeysFromDataTable Exit """, diff --git a/khiops/core/internals/tasks/interpret_predictor.py b/khiops/core/internals/tasks/interpret_predictor.py new file mode 100644 index 00000000..7a1eb958 --- /dev/null +++ b/khiops/core/internals/tasks/interpret_predictor.py @@ -0,0 +1,60 @@ +###################################################################################### +# Copyright (c) 2023-2025 Orange. All rights reserved. # +# This software is distributed under the BSD 3-Clause-clear License, the text of # +# which is available at https://spdx.org/licenses/BSD-3-Clause-Clear.html or # +# see the "LICENSE.md" file for more details. # +###################################################################################### +"""interpret_predictor task family""" +from khiops.core.internals import task as tm +from khiops.core.internals.types import ListType, StringLikeType + +# Disable long lines to have readable scenarios +# pylint: disable=line-too-long +TASKS = [ + tm.KhiopsTask( + "interpret_predictor", + "khiops", + "10.6.0-b.0", + [ + ("dictionary_file_path", StringLikeType), + ("predictor_dictionary_name", StringLikeType), + ("interpretor_file_path", StringLikeType), + ], + [ + ("reinforcement_target_value", StringLikeType, ""), + ("reinforcement_lever_variables", ListType(StringLikeType), None), + ], + ["dictionary_file_path", "interpretor_file_path"], + # pylint: disable=line-too-long + # fmt: off + """ + // Dictionary file and class settings + ClassManagement.OpenFile + ClassFileName __dictionary_file_path__ + OK + + // Intepretation settings + TrainDatabase.ClassName __predictor_dictionary_name__ + + // Interpret model + LearningTools.InterpretPredictor + HowParameter.HowClass __reinforcement_target_value__ + + __DICT__ + __reinforcement_lever_variables__ + HowParameter.leverVariablesSpecView.UnselectAll + HowParameter.leverVariablesSpecView.AttributeSpecs.List.Key + HowParameter.leverVariablesSpecView.AttributeSpecs.Used + __END_DICT__ + + // Build intepretation dictionary + BuildInterpretationClass + + // Output settings + ClassFileName __interpretor_file_path__ + OK + Exit + """, + # fmt: on + ), +] diff --git a/khiops/core/internals/tasks/prepare_coclustering_deployment.py b/khiops/core/internals/tasks/prepare_coclustering_deployment.py index 283fdd12..f72e46f5 100644 --- a/khiops/core/internals/tasks/prepare_coclustering_deployment.py +++ b/khiops/core/internals/tasks/prepare_coclustering_deployment.py @@ -14,29 +14,29 @@ tm.KhiopsTask( "prepare_coclustering_deployment", "khiops_coclustering", - "10.0.0", + "10.6.0-b.0", [ ("dictionary_file_path", StringLikeType), ("dictionary_name", StringLikeType), ("coclustering_file_path", StringLikeType), ("table_variable", StringLikeType), ("deployed_variable_name", StringLikeType), - ("results_dir", StringLikeType), + ("coclustering_dictionary_file_path", StringLikeType), ], [ ("max_preserved_information", IntType, 0), ("max_cells", IntType, 0), + ("max_total_parts", IntType, 0), ("max_part_numbers", DictType(StringLikeType, IntType), None), ("build_cluster_variable", BoolType, True), ("build_distance_variables", BoolType, False), ("build_frequency_variables", BoolType, False), ("variables_prefix", StringLikeType, ""), - ("results_prefix", StringLikeType, ""), ], [ "dictionary_file_path", "coclustering_file_path", - "results_dir", + "coclustering_dictionary_file_path", ], # fmt: off """ @@ -44,7 +44,6 @@ ClassManagement.OpenFile ClassFileName __dictionary_file_path__ OK - ClassManagement.ClassName __dictionary_name__ // Prepare deployment window LearningTools.PrepareDeployment @@ -57,6 +56,7 @@ // Simplification settings PostProcessingSpec.MaxPreservedInformation __max_preserved_information__ PostProcessingSpec.MaxCellNumber __max_cells__ + PostProcessingSpec.MaxTotalPartNumber __max_total_parts__ __DICT__ __max_part_numbers__ PostProcessingSpec.PostProcessedAttributes.List.Key @@ -64,6 +64,7 @@ __END_DICT__ // Deployment dictionary settings + DeploymentSpec.InputClassName __dictionary_name__ DeploymentSpec.InputObjectArrayAttributeName __table_variable__ DeploymentSpec.DeployedAttributeName __deployed_variable_name__ DeploymentSpec.BuildPredictedClusterAttribute __build_cluster_variable__ @@ -72,8 +73,7 @@ DeploymentSpec.OutputAttributesPrefix __variables_prefix__ // Output settings - AnalysisResults.ResultFilesDirectory __results_dir__ - AnalysisResults.ResultFilesPrefix __results_prefix__ + CoclusteringDictionaryFileName __coclustering_dictionary_file_path__ // Execute prepare deployment PrepareDeployment diff --git a/khiops/core/internals/tasks/simplify_coclustering.py b/khiops/core/internals/tasks/simplify_coclustering.py index 9925dc20..a81610fb 100644 --- a/khiops/core/internals/tasks/simplify_coclustering.py +++ b/khiops/core/internals/tasks/simplify_coclustering.py @@ -14,23 +14,20 @@ tm.KhiopsTask( "simplify_coclustering", "khiops_coclustering", - "10.1.0", + "10.6.0-b.0", [ ("coclustering_file_path", StringLikeType), ("simplified_coclustering_file_path", StringLikeType), - ("results_dir", StringLikeType), ], [ ("max_preserved_information", IntType, 0), ("max_cells", IntType, 0), ("max_total_parts", IntType, 0), ("max_part_numbers", DictType(StringLikeType, IntType), None), - ("results_prefix", StringLikeType, ""), ], [ "coclustering_file_path", "simplified_coclustering_file_path", - "results_dir", ], # fmt: off """ @@ -49,9 +46,7 @@ __END_DICT__ // Output settings - AnalysisResults.ResultFilesDirectory __results_dir__ - AnalysisResults.ResultFilesPrefix __results_prefix__ - AnalysisResults.PostProcessedCoclusteringFileName __simplified_coclustering_file_path__ + PostProcessedCoclusteringFileName __simplified_coclustering_file_path__ // Simplify Coclustering PostProcessCoclustering diff --git a/khiops/core/internals/tasks/sort_data_table.py b/khiops/core/internals/tasks/sort_data_table.py index 40ef54d1..f2b5d80d 100644 --- a/khiops/core/internals/tasks/sort_data_table.py +++ b/khiops/core/internals/tasks/sort_data_table.py @@ -14,7 +14,7 @@ tm.KhiopsTask( "sort_data_table", "khiops", - "10.0.0", + "10.6.0-b.0", [ ("dictionary_file_path", StringLikeType), ("dictionary_name", StringLikeType), @@ -40,10 +40,10 @@ ClassManagement.OpenFile ClassFileName __dictionary_file_path__ OK - ClassManagement.ClassName __dictionary_name__ // Sort table settings LearningTools.SortDataTableByKey + ClassName __dictionary_name__ SortAttributes.SelectDefaultKeyAttributes __LIST__ __sort_variables__ @@ -62,18 +62,18 @@ __END_LIST__ // Source table settings - SourceDataTable.DatabaseName __data_table_path__ - SourceDataTable.HeaderLineUsed __header_line__ - SourceDataTable.FieldSeparator __field_separator__ + SourceDataTable.DatabaseSpec.Data.DatabaseName __data_table_path__ + SourceDataTable.DatabaseSpec.Data.HeaderLineUsed __header_line__ + SourceDataTable.DatabaseSpec.Data.FieldSeparator __field_separator__ __OPT__ __detect_format__ - SourceDataTable.DatabaseFormatDetector.DetectFileFormat + SourceDataTable.DatabaseSpec.Data.DatabaseFormatDetector.DetectFileFormat __END_OPT__ // Target table settings - TargetDataTable.HeaderLineUsed __output_header_line__ - TargetDataTable.DatabaseName __output_data_table_path__ - TargetDataTable.FieldSeparator __output_field_separator__ + TargetDataTable.DatabaseSpec.Data.HeaderLineUsed __output_header_line__ + TargetDataTable.DatabaseSpec.Data.DatabaseName __output_data_table_path__ + TargetDataTable.DatabaseSpec.Data.FieldSeparator __output_field_separator__ // Sort table SortDataTableByKey diff --git a/khiops/core/internals/tasks/train_coclustering.py b/khiops/core/internals/tasks/train_coclustering.py index af9883fd..fa787f49 100644 --- a/khiops/core/internals/tasks/train_coclustering.py +++ b/khiops/core/internals/tasks/train_coclustering.py @@ -21,13 +21,13 @@ tm.KhiopsTask( "train_coclustering", "khiops_coclustering", - "10.0.0", + "10.6.0-b.0", [ ("dictionary_file_path", StringLikeType), ("dictionary_name", StringLikeType), ("data_table_path", StringLikeType), ("coclustering_variables", ListType(StringLikeType)), - ("results_dir", StringLikeType), + ("coclustering_report_file_path", StringLikeType), ], [ ("detect_format", BoolType, True), @@ -40,12 +40,11 @@ ("additional_data_tables", DictType(StringLikeType, StringLikeType), None), ("frequency_variable", StringLikeType, ""), ("min_optimization_time", IntType, 0), - ("results_prefix", StringLikeType, ""), ], [ "dictionary_file_path", "data_table_path", - "results_dir", + "coclustering_report_file_path", "additional_data_tables", ], # fmt: off @@ -54,26 +53,26 @@ ClassManagement.OpenFile ClassFileName __dictionary_file_path__ OK - ClassManagement.ClassName __dictionary_name__ // Train database settings - Database.DatabaseFiles.List.Key __dictionary_name__ - Database.DatabaseFiles.DataTableName __data_table_path__ + Database.ClassName __dictionary_name__ + Database.DatabaseSpec.Data.DatabaseFiles.List.Key + Database.DatabaseSpec.Data.DatabaseFiles.DataTableName __data_table_path__ __DICT__ __additional_data_tables__ - Database.DatabaseFiles.List.Key - Database.DatabaseFiles.DataTableName + Database.DatabaseSpec.Data.DatabaseFiles.List.Key + Database.DatabaseSpec.Data.DatabaseFiles.DataTableName __END_DICT__ - Database.HeaderLineUsed __header_line__ - Database.FieldSeparator __field_separator__ + Database.DatabaseSpec.Data.HeaderLineUsed __header_line__ + Database.DatabaseSpec.Data.FieldSeparator __field_separator__ __OPT__ __detect_format__ - Database.DatabaseFormatDetector.DetectFileFormat + Database.DatabaseSpec.Data.DatabaseFormatDetector.DetectFileFormat __END_OPT__ - Database.SampleNumberPercentage __sample_percentage__ - Database.SamplingMode __sampling_mode__ - Database.SelectionAttribute __selection_variable__ - Database.SelectionValue __selection_value__ + Database.DatabaseSpec.Sampling.SampleNumberPercentage __sample_percentage__ + Database.DatabaseSpec.Sampling.SamplingMode __sampling_mode__ + Database.DatabaseSpec.Selection.SelectionAttribute __selection_variable__ + Database.DatabaseSpec.Selection.SelectionValue __selection_value__ // Coclustering variables settings __LIST__ @@ -81,14 +80,13 @@ AnalysisSpec.CoclusteringParameters.Attributes.InsertItemAfter AnalysisSpec.CoclusteringParameters.Attributes.Name __END_LIST__ - AnalysisSpec.CoclusteringParameters.FrequencyAttribute __frequency_variable__ + AnalysisSpec.CoclusteringParameters.FrequencyAttributeName __frequency_variable__ // Minimum optimization time AnalysisSpec.SystemParameters.OptimizationTime __min_optimization_time__ // Output settings - AnalysisResults.ResultFilesDirectory __results_dir__ - AnalysisResults.ResultFilesPrefix __results_prefix__ + AnalysisResults.CoclusteringFileName __coclustering_report_file_path__ // Train BuildCoclustering diff --git a/khiops/core/internals/tasks/train_instance_variable_coclustering.py b/khiops/core/internals/tasks/train_instance_variable_coclustering.py new file mode 100644 index 00000000..15e8590e --- /dev/null +++ b/khiops/core/internals/tasks/train_instance_variable_coclustering.py @@ -0,0 +1,88 @@ +###################################################################################### +# Copyright (c) 2023-2025 Orange. All rights reserved. # +# This software is distributed under the BSD 3-Clause-clear License, the text of # +# which is available at https://spdx.org/licenses/BSD-3-Clause-Clear.html or # +# see the "LICENSE.md" file for more details. # +###################################################################################### +"""train_instance_variable_coclustering task family""" +from khiops.core.internals import task as tm +from khiops.core.internals.types import ( + BoolType, + DictType, + FloatType, + IntType, + StringLikeType, +) + +# Disable long lines to have readable scenarios +# pylint: disable=line-too-long +TASKS = [ + tm.KhiopsTask( + "train_instance_variable_coclustering", + "khiops_coclustering", + "10.6.0-b.0", + [ + ("dictionary_file_path", StringLikeType), + ("dictionary_name", StringLikeType), + ("data_table_path", StringLikeType), + ("coclustering_report_file_path", StringLikeType), + ], + [ + ("detect_format", BoolType, True), + ("header_line", BoolType, True), + ("field_separator", StringLikeType, ""), + ("sample_percentage", FloatType, 100.0), + ("sampling_mode", StringLikeType, "Include sample"), + ("selection_variable", StringLikeType, ""), + ("selection_value", StringLikeType, ""), + ("additional_data_tables", DictType(StringLikeType, StringLikeType), None), + ("min_optimization_time", IntType, 0), + ], + [ + "dictionary_file_path", + "data_table_path", + "coclustering_report_file_path", + "additional_data_tables", + ], + # fmt: off + """ + // Dictionary file and class settings + ClassManagement.OpenFile + ClassFileName __dictionary_file_path__ + OK + + // Train database settings + Database.ClassName __dictionary_name__ + Database.DatabaseSpec.Data.DatabaseFiles.List.Key + Database.DatabaseSpec.Data.DatabaseFiles.DataTableName __data_table_path__ + __DICT__ + __additional_data_tables__ + Database.DatabaseSpec.Data.DatabaseFiles.List.Key + Database.DatabaseSpec.Data.DatabaseFiles.DataTableName + __END_DICT__ + Database.DatabaseSpec.Data.HeaderLineUsed __header_line__ + Database.DatabaseSpec.Data.FieldSeparator __field_separator__ + __OPT__ + __detect_format__ + Database.DatabaseSpec.Data.DatabaseFormatDetector.DetectFileFormat + __END_OPT__ + Database.DatabaseSpec.Sampling.SampleNumberPercentage __sample_percentage__ + Database.DatabaseSpec.Sampling.SamplingMode __sampling_mode__ + Database.DatabaseSpec.Selection.SelectionAttribute __selection_variable__ + Database.DatabaseSpec.Selection.SelectionValue __selection_value__ + + // Enforce instance x variable coclustering + AnalysisSpec.CoclusteringType Instances x Variables coclustering + + // Minimum optimization time + AnalysisSpec.SystemParameters.OptimizationTime __min_optimization_time__ + + // Output settings + AnalysisResults.CoclusteringFileName __coclustering_report_file_path__ + + // Train + BuildCoclustering + """, + # fmt: on + ), +] diff --git a/khiops/core/internals/tasks/train_predictor.py b/khiops/core/internals/tasks/train_predictor.py index a62c5181..faaea43a 100644 --- a/khiops/core/internals/tasks/train_predictor.py +++ b/khiops/core/internals/tasks/train_predictor.py @@ -22,31 +22,32 @@ tm.KhiopsTask( "train_predictor", "khiops", - "10.0.0", + "10.6.0-b.0", [ ("dictionary_file_path", StringLikeType), ("dictionary_name", StringLikeType), ("data_table_path", StringLikeType), ("target_variable", StringLikeType), - ("results_dir", StringLikeType), + ("analysis_report_file_path", StringLikeType), ], [ ("detect_format", BoolType, True), ("header_line", BoolType, True), ("field_separator", StringLikeType, ""), - ("sample_percentage", FloatType, 100.0), + ("sample_percentage", FloatType, 70.0), ("sampling_mode", StringLikeType, "Include sample"), ("test_database_mode", StringLikeType, "Complementary"), ("selection_variable", StringLikeType, ""), ("selection_value", StringLikeType, ""), ("additional_data_tables", DictType(StringLikeType, StringLikeType), None), + ("do_data_preparation_only", BoolType, False), ("main_target_value", StringLikeType, ""), - ("snb_predictor", BoolType, True), - ("univariate_predictor_number", IntType, 0), + ("keep_selected_variables_only", BoolType, True), ("max_evaluated_variables", IntType, 0), ("max_selected_variables", IntType, 0), - ("max_constructed_variables", IntType, 0), + ("max_constructed_variables", IntType, 1000), ("construction_rules", ListType(StringLikeType), None), + ("max_text_features", IntType, 10000), ("max_trees", IntType, 10), ("max_pairs", IntType, 0), ("all_possible_pairs", BoolType, True), @@ -55,19 +56,15 @@ ListType(TupleType(StringLikeType, StringLikeType)), None, ), + ("text_features", StringLikeType, "words"), ("group_target_value", BoolType, False), ("discretization_method", StringLikeType, "MODL"), - ("min_interval_frequency", IntType, 0), - ("max_intervals", IntType, 0), ("grouping_method", StringLikeType, "MODL"), - ("min_group_frequency", IntType, 0), - ("max_groups", IntType, 0), - ("results_prefix", StringLikeType, ""), + ("max_parts", IntType, 0), ], [ "dictionary_file_path", "data_table_path", - "results_dir", "additional_data_tables", ], # pylint: disable=line-too-long @@ -77,43 +74,41 @@ ClassManagement.OpenFile ClassFileName __dictionary_file_path__ OK - ClassManagement.ClassName __dictionary_name__ // Train/test database settings - TrainDatabase.DatabaseFiles.List.Key __dictionary_name__ - TrainDatabase.DatabaseFiles.DataTableName __data_table_path__ + TrainDatabase.ClassName __dictionary_name__ + TrainDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key + TrainDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName __data_table_path__ __DICT__ __additional_data_tables__ - TrainDatabase.DatabaseFiles.List.Key - TrainDatabase.DatabaseFiles.DataTableName + TrainDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key + TrainDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName __END_DICT__ - TrainDatabase.HeaderLineUsed __header_line__ - TrainDatabase.FieldSeparator __field_separator__ + TrainDatabase.DatabaseSpec.Data.HeaderLineUsed __header_line__ + TrainDatabase.DatabaseSpec.Data.FieldSeparator __field_separator__ __OPT__ __detect_format__ - TrainDatabase.DatabaseFormatDetector.DetectFileFormat + TrainDatabase.DatabaseSpec.Data.DatabaseFormatDetector.DetectFileFormat __END_OPT__ - TrainDatabase.SampleNumberPercentage __sample_percentage__ - TrainDatabase.SamplingMode __sampling_mode__ - TrainDatabase.SelectionAttribute __selection_variable__ - TrainDatabase.SelectionValue __selection_value__ + TrainDatabase.DatabaseSpec.Sampling.SampleNumberPercentage __sample_percentage__ + TrainDatabase.DatabaseSpec.Sampling.SamplingMode __sampling_mode__ + TrainDatabase.DatabaseSpec.Selection.SelectionAttribute __selection_variable__ + TrainDatabase.DatabaseSpec.Selection.SelectionValue __selection_value__ TrainDatabase.TestDatabaseSpecificationMode __test_database_mode__ // Target variable AnalysisSpec.TargetAttributeName __target_variable__ AnalysisSpec.MainTargetModality __main_target_value__ - // Predictors to train - AnalysisSpec.PredictorsSpec.SelectiveNaiveBayesPredictor __snb_predictor__ - AnalysisSpec.PredictorsSpec.AdvancedSpec.UnivariatePredictorNumber __univariate_predictor_number__ + // Do data preparation only + AnalysisSpec.PredictorsSpec.AdvancedSpec.DataPreparationOnly __do_data_preparation_only__ // Selective Naive Bayes settings - AnalysisSpec.PredictorsSpec.AdvancedSpec.InspectSelectiveNaiveBayesParameters - TrainParameters.MaxEvaluatedAttributeNumber __max_evaluated_variables__ - SelectionParameters.MaxSelectedAttributeNumber __max_selected_variables__ - Exit + AnalysisSpec.PredictorsSpec.AdvancedSpec.SelectiveNaiveBayesParameters.TrainParameters.MaxEvaluatedAttributeNumber __max_evaluated_variables__ + AnalysisSpec.PredictorsSpec.AdvancedSpec.SelectiveNaiveBayesParameters.SelectionParameters.MaxSelectedAttributeNumber __max_selected_variables__ // Feature engineering + AnalysisSpec.PredictorsSpec.ConstructionSpec.MaxTextFeatureNumber __max_text_features__ AnalysisSpec.PredictorsSpec.ConstructionSpec.MaxTreeNumber __max_trees__ AnalysisSpec.PredictorsSpec.ConstructionSpec.MaxAttributePairNumber __max_pairs__ AnalysisSpec.PredictorsSpec.AdvancedSpec.InspectAttributePairsParameters @@ -125,6 +120,7 @@ SpecificAttributePairs.SecondName __END_LIST__ Exit + AnalysisSpec.PredictorsSpec.ConstructionSpec.KeepSelectedAttributesOnly __keep_selected_variables_only__ AnalysisSpec.PredictorsSpec.ConstructionSpec.MaxConstructedAttributeNumber __max_constructed_variables__ AnalysisSpec.PredictorsSpec.AdvancedSpec.InspectConstructionDomain __DICT__ @@ -134,21 +130,26 @@ ConstructionRules.Used __END_DICT__ Exit + + // Text feature parameters + AnalysisSpec.PredictorsSpec.AdvancedSpec.InspectTextFeaturesParameters + TextFeatures __text_features__ + Exit + // Data preparation (discretization & grouping) settings AnalysisSpec.PreprocessingSpec.TargetGrouped __group_target_value__ - AnalysisSpec.PreprocessingSpec.DiscretizerSpec.SupervisedMethodName __discretization_method__ - AnalysisSpec.PreprocessingSpec.DiscretizerSpec.UnsupervisedMethodName __discretization_method__ - AnalysisSpec.PreprocessingSpec.DiscretizerSpec.MinIntervalFrequency __min_interval_frequency__ - AnalysisSpec.PreprocessingSpec.DiscretizerSpec.MaxIntervalNumber __max_intervals__ - AnalysisSpec.PreprocessingSpec.GrouperSpec.SupervisedMethodName __grouping_method__ - AnalysisSpec.PreprocessingSpec.GrouperSpec.UnsupervisedMethodName __grouping_method__ - AnalysisSpec.PreprocessingSpec.GrouperSpec.MinGroupFrequency __min_group_frequency__ - AnalysisSpec.PreprocessingSpec.GrouperSpec.MaxGroupNumber __max_groups__ + + AnalysisSpec.PreprocessingSpec.InspectAdvancedParameters // Advanced parameters + DiscretizerUnsupervisedMethodName __discretization_method__ + GrouperUnsupervisedMethodName __grouping_method__ + Exit + + // Max parts + AnalysisSpec.PreprocessingSpec.MaxPartNumber __max_parts__ // Output settings - AnalysisResults.ResultFilesDirectory __results_dir__ - AnalysisResults.ResultFilesPrefix __results_prefix__ + AnalysisResults.ReportFileName __analysis_report_file_path__ // Build model ComputeStats diff --git a/khiops/core/internals/tasks/train_recoder.py b/khiops/core/internals/tasks/train_recoder.py index cbed74db..ac1c92d9 100644 --- a/khiops/core/internals/tasks/train_recoder.py +++ b/khiops/core/internals/tasks/train_recoder.py @@ -22,13 +22,13 @@ tm.KhiopsTask( "train_recoder", "khiops", - "10.0.0", + "10.6.0-b.0", [ ("dictionary_file_path", StringLikeType), ("dictionary_name", StringLikeType), ("data_table_path", StringLikeType), ("target_variable", StringLikeType), - ("results_dir", StringLikeType), + ("analysis_report_file_path", StringLikeType), ], [ ("detect_format", BoolType, True), @@ -39,8 +39,9 @@ ("selection_variable", StringLikeType, ""), ("selection_value", StringLikeType, ""), ("additional_data_tables", DictType(StringLikeType, StringLikeType), None), - ("max_constructed_variables", IntType, 0), + ("max_constructed_variables", IntType, 1000), ("construction_rules", ListType(StringLikeType), None), + ("max_text_features", IntType, 10000), ("max_trees", IntType, 10), ("max_pairs", IntType, 0), ("all_possible_pairs", BoolType, True), @@ -49,10 +50,9 @@ ListType(TupleType(StringLikeType, StringLikeType)), None, ), + ("text_features", StringLikeType, "words"), ("group_target_value", BoolType, False), ("discretization_method", StringLikeType, "MODL"), - ("min_interval_frequency", IntType, 0), - ("max_intervals", IntType, 0), ("informative_variables_only", BoolType, True), ("max_variables", IntType, 0), ("keep_initial_categorical_variables", BoolType, True), @@ -61,14 +61,11 @@ ("numerical_recoding_method", StringLikeType, "part Id"), ("pairs_recoding_method", StringLikeType, "part Id"), ("grouping_method", StringLikeType, "MODL"), - ("min_group_frequency", IntType, 0), - ("max_groups", IntType, 0), - ("results_prefix", StringLikeType, ""), + ("max_parts", IntType, 0), ], [ "dictionary_file_path", "data_table_path", - "results_dir", "additional_data_tables", ], # fmt: off @@ -77,36 +74,36 @@ ClassManagement.OpenFile ClassFileName __dictionary_file_path__ OK - ClassManagement.ClassName __dictionary_name__ + TrainDatabase.ClassName __dictionary_name__ // Train/test database settings - TrainDatabase.DatabaseFiles.List.Key __dictionary_name__ - TrainDatabase.DatabaseFiles.DataTableName __data_table_path__ + TrainDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key + TrainDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName __data_table_path__ __DICT__ __additional_data_tables__ - TrainDatabase.DatabaseFiles.List.Key - TrainDatabase.DatabaseFiles.DataTableName + TrainDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key + TrainDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName __END_DICT__ - TrainDatabase.HeaderLineUsed __header_line__ - TrainDatabase.FieldSeparator __field_separator__ + TrainDatabase.DatabaseSpec.Data.HeaderLineUsed __header_line__ + TrainDatabase.DatabaseSpec.Data.FieldSeparator __field_separator__ __OPT__ __detect_format__ - TrainDatabase.DatabaseFormatDetector.DetectFileFormat + TrainDatabase.DatabaseSpec.Data.DatabaseFormatDetector.DetectFileFormat __END_OPT__ - TrainDatabase.SampleNumberPercentage __sample_percentage__ - TrainDatabase.SamplingMode __sampling_mode__ - TrainDatabase.SelectionAttribute __selection_variable__ - TrainDatabase.SelectionValue __selection_value__ + TrainDatabase.DatabaseSpec.Sampling.SampleNumberPercentage __sample_percentage__ + TrainDatabase.DatabaseSpec.Sampling.SamplingMode __sampling_mode__ + TrainDatabase.DatabaseSpec.Selection.SelectionAttribute __selection_variable__ + TrainDatabase.DatabaseSpec.Selection.SelectionValue __selection_value__ // Target variable AnalysisSpec.TargetAttributeName __target_variable__ - // Disable predictors - AnalysisSpec.PredictorsSpec.SelectiveNaiveBayesPredictor false - AnalysisSpec.PredictorsSpec.NaiveBayesPredictor false - AnalysisSpec.PredictorsSpec.AdvancedSpec.UnivariatePredictorNumber 0 + // Disable predictors: do data preparation only + AnalysisSpec.PredictorsSpec.AdvancedSpec.DataPreparationOnly true + // Feature engineering + AnalysisSpec.PredictorsSpec.ConstructionSpec.MaxTextFeatureNumber __max_text_features__ AnalysisSpec.PredictorsSpec.ConstructionSpec.MaxTreeNumber __max_trees__ AnalysisSpec.PredictorsSpec.ConstructionSpec.MaxAttributePairNumber __max_pairs__ AnalysisSpec.PredictorsSpec.AdvancedSpec.InspectAttributePairsParameters @@ -128,16 +125,20 @@ __END_DICT__ Exit + // Text feature parameters + AnalysisSpec.PredictorsSpec.AdvancedSpec.InspectTextFeaturesParameters + TextFeatures __text_features__ + Exit + // Data preparation (discretization & grouping) settings AnalysisSpec.PreprocessingSpec.TargetGrouped __group_target_value__ - AnalysisSpec.PreprocessingSpec.DiscretizerSpec.SupervisedMethodName __discretization_method__ - AnalysisSpec.PreprocessingSpec.DiscretizerSpec.UnsupervisedMethodName __discretization_method__ - AnalysisSpec.PreprocessingSpec.DiscretizerSpec.MinIntervalFrequency __min_interval_frequency__ - AnalysisSpec.PreprocessingSpec.DiscretizerSpec.MaxIntervalNumber __max_intervals__ - AnalysisSpec.PreprocessingSpec.GrouperSpec.SupervisedMethodName __grouping_method__ - AnalysisSpec.PreprocessingSpec.GrouperSpec.UnsupervisedMethodName __grouping_method__ - AnalysisSpec.PreprocessingSpec.GrouperSpec.MinGroupFrequency __min_group_frequency__ - AnalysisSpec.PreprocessingSpec.GrouperSpec.MaxGroupNumber __max_groups__ + + AnalysisSpec.PreprocessingSpec.MaxPartNumber __max_parts__ + AnalysisSpec.PreprocessingSpec.InspectAdvancedParameters + DiscretizerUnsupervisedMethodName __discretization_method__ + GrouperUnsupervisedMethodName __grouping_method__ + Exit + // Recoder Settings AnalysisSpec.RecodersSpec.Recoder true @@ -150,8 +151,7 @@ AnalysisSpec.RecodersSpec.RecodingSpec.RecodeBivariateAttributes __pairs_recoding_method__ // Output settings - AnalysisResults.ResultFilesDirectory __results_dir__ - AnalysisResults.ResultFilesPrefix __results_prefix__ + AnalysisResults.ReportFileName __analysis_report_file_path__ // Train recoder ComputeStats diff --git a/khiops/extras/docker.py b/khiops/extras/docker.py index 0580a6a5..da8bebaa 100644 --- a/khiops/extras/docker.py +++ b/khiops/extras/docker.py @@ -105,11 +105,6 @@ def _run( trace, ): # Check arguments - if not command_line_options.batch_mode: - warnings.warn( - "Ignoring unsupported KhiopsDockerRunner parameter 'batch_mode'", - stacklevel=3, - ) if command_line_options.output_scenario_path: output_scenario_dir = fs.get_parent_path( command_line_options.output_scenario_path diff --git a/khiops/samples/samples.ipynb b/khiops/samples/samples.ipynb index 5cd9c434..77efec18 100644 --- a/khiops/samples/samples.ipynb +++ b/khiops/samples/samples.ipynb @@ -78,12 +78,12 @@ "# Set the file paths\n", "data_table_path = os.path.join(kh.get_samples_dir(), \"Adult\", \"Adult.txt\")\n", "dictionary_file_path = os.path.join(kh.get_samples_dir(), \"Adult\", \"Adult.kdic\")\n", - "results_dir = os.path.join(\"kh_samples\", \"detect_data_table_format\")\n", - "transformed_data_table_path = os.path.join(results_dir, \"AdultWithAnotherFormat.txt\")\n", + "output_dir = os.path.join(\"kh_samples\", \"detect_data_table_format\")\n", + "transformed_data_table_path = os.path.join(output_dir, \"AdultWithAnotherFormat.txt\")\n", "\n", "# Create the output directory\n", - "if not os.path.isdir(results_dir):\n", - " os.mkdir(results_dir)\n", + "if not os.path.isdir(output_dir):\n", + " os.mkdir(output_dir)\n", "\n", "# Detect the format of the table\n", "format_spec = kh.detect_data_table_format(data_table_path)\n", @@ -166,10 +166,10 @@ "\n", "# Set the file paths\n", "dictionary_file_path = os.path.join(kh.get_samples_dir(), \"Adult\", \"Adult.kdic\")\n", - "results_dir = os.path.join(\"kh_samples\", \"export_dictionary_file\")\n", - "output_dictionary_file_path = os.path.join(results_dir, \"ModifiedAdult.kdic\")\n", - "output_dictionary_json_path = os.path.join(results_dir, \"ModifiedAdult.kdicj\")\n", - "alt_output_dictionary_json_path = os.path.join(results_dir, \"AltModifiedAdult.kdicj\")\n", + "output_dir = os.path.join(\"kh_samples\", \"export_dictionary_file\")\n", + "output_dictionary_file_path = os.path.join(output_dir, \"ModifiedAdult.kdic\")\n", + "output_dictionary_json_path = os.path.join(output_dir, \"ModifiedAdult.kdicj\")\n", + "alt_output_dictionary_json_path = os.path.join(output_dir, \"AltModifiedAdult.kdicj\")\n", "\n", "# Load the dictionary domain from initial dictionary file\n", "# Then obtain the \"Adult\" dictionary within\n", @@ -185,10 +185,10 @@ "# Create output directory if necessary\n", "if not os.path.exists(\"kh_samples\"):\n", " os.mkdir(\"kh_samples\")\n", - " os.mkdir(results_dir)\n", + " os.mkdir(output_dir)\n", "else:\n", - " if not os.path.exists(results_dir):\n", - " os.mkdir(results_dir)\n", + " if not os.path.exists(output_dir):\n", + " os.mkdir(output_dir)\n", "\n", "# Export to kdic\n", "domain.export_khiops_dictionary_file(output_dictionary_file_path)\n", @@ -222,7 +222,9 @@ "# Set the file paths\n", "dictionary_file_path = os.path.join(kh.get_samples_dir(), \"Adult\", \"Adult.kdic\")\n", "data_table_path = os.path.join(kh.get_samples_dir(), \"Adult\", \"Adult.txt\")\n", - "results_dir = os.path.join(\"kh_samples\", \"train_predictor\")\n", + "analysis_report_file_path = os.path.join(\n", + " \"kh_samples\", \"train_predictor\", \"AnalysisReport.khj\"\n", + ")\n", "\n", "# Train the predictor\n", "kh.train_predictor(\n", @@ -230,7 +232,7 @@ " \"Adult\",\n", " data_table_path,\n", " \"class\",\n", - " results_dir,\n", + " analysis_report_file_path,\n", " max_trees=0,\n", ")" ] @@ -256,15 +258,17 @@ "# Set the file paths\n", "dictionary_file_path = os.path.join(kh.get_samples_dir(), \"Adult\", \"Adult.kdic\")\n", "data_table_path = os.path.join(kh.get_samples_dir(), \"Adult\", \"Adult.txt\")\n", - "results_dir = os.path.join(\"kh_samples\", \"train_predictor_file_paths\")\n", + "report_file_path = os.path.join(\n", + " \"kh_samples\", \"train_predictor_file_paths\", \"AnalysisResults.khj\"\n", + ")\n", "\n", "# Train the predictor\n", - "report_file_path, modeling_dictionary_file_path = kh.train_predictor(\n", + "_, modeling_dictionary_file_path = kh.train_predictor(\n", " dictionary_file_path,\n", " \"Adult\",\n", " data_table_path,\n", " \"class\",\n", - " results_dir,\n", + " report_file_path,\n", " max_trees=0,\n", ")\n", "print(\"Reports file available at \" + report_file_path)\n", @@ -295,9 +299,10 @@ "# Set the file paths with a nonexistent dictionary file\n", "dictionary_file_path = \"NONEXISTENT_DICTIONARY_FILE.kdic\"\n", "data_table_path = os.path.join(kh.get_samples_dir(), \"Adult\", \"Adult.txt\")\n", - "results_dir = os.path.join(\"kh_samples\", \"train_predictor_error_handling\")\n", - "log_file_path = os.path.join(results_dir, \"khiops.log\")\n", - "scenario_path = os.path.join(results_dir, \"scenario._kh\")\n", + "output_dir = os.path.join(\"kh_samples\", \"train_predictor_error_handling\")\n", + "report_file_path = os.path.join(output_dir, \"AnalysisResults.khj\")\n", + "log_file_path = os.path.join(output_dir, \"khiops.log\")\n", + "scenario_path = os.path.join(output_dir, \"scenario._kh\")\n", "\n", "# Train the predictor and handle the error\n", "try:\n", @@ -306,7 +311,7 @@ " \"Adult\",\n", " data_table_path,\n", " \"class\",\n", - " results_dir,\n", + " report_file_path,\n", " trace=True,\n", " log_file_path=log_file_path,\n", " output_scenario_path=scenario_path,\n", @@ -351,7 +356,9 @@ "dictionary_file_path = os.path.join(accidents_dir, \"Accidents.kdic\")\n", "accidents_table_path = os.path.join(accidents_dir, \"Accidents.txt\")\n", "vehicles_table_path = os.path.join(accidents_dir, \"Vehicles.txt\")\n", - "results_dir = os.path.join(\"kh_samples\", \"train_predictor_mt\")\n", + "report_file_path = os.path.join(\n", + " \"kh_samples\", \"train_predictor_mt\", \"AccidentsSummaryAnalysisResults.khj\"\n", + ")\n", "\n", "# Train the predictor. Besides the mandatory parameters, we specify:\n", "# - A python dictionary linking data paths to file paths for non-root tables\n", @@ -362,8 +369,8 @@ " \"Accident\",\n", " accidents_table_path,\n", " \"Gravity\",\n", - " results_dir,\n", - " additional_data_tables={\"Accident`Vehicles\": vehicles_table_path},\n", + " report_file_path,\n", + " additional_data_tables={\"Vehicles\": vehicles_table_path},\n", " max_trees=0,\n", ")" ] @@ -391,7 +398,11 @@ "dictionary_file_path = os.path.join(accidents_dir, \"Accidents.kdic\")\n", "accidents_table_path = os.path.join(accidents_dir, \"Accidents.txt\")\n", "vehicles_table_path = os.path.join(accidents_dir, \"Vehicles.txt\")\n", - "results_dir = os.path.join(\"kh_samples\", \"train_predictor_mt_with_specific_rules\")\n", + "report_file_path = os.path.join(\n", + " \"kh_samples\",\n", + " \"train_predictor_mt_with_specific_rules\",\n", + " \"AccidentsSummaryAnalysisResults.khj\",\n", + ")\n", "\n", "# Train the predictor. Besides the mandatory parameters, it is specified:\n", "# - A python dictionary linking data paths to file paths for non-root tables\n", @@ -403,8 +414,8 @@ " \"Accident\",\n", " accidents_table_path,\n", " \"Gravity\",\n", - " results_dir,\n", - " additional_data_tables={\"Accident`Vehicles\": vehicles_table_path},\n", + " report_file_path,\n", + " additional_data_tables={\"Vehicles\": vehicles_table_path},\n", " max_constructed_variables=1000,\n", " construction_rules=[\"TableMode\", \"TableSelection\"],\n", " max_trees=0,\n", @@ -436,7 +447,9 @@ "vehicles_table_path = os.path.join(accidents_dir, \"Vehicles.txt\")\n", "users_table_path = os.path.join(accidents_dir, \"Users.txt\")\n", "places_table_path = os.path.join(accidents_dir, \"Places.txt\")\n", - "results_dir = os.path.join(\"kh_samples\", \"train_predictor_mt_snowflake\")\n", + "report_file_path = os.path.join(\n", + " \"kh_samples\", \"train_predictor_mt_snowflake\", \"AccidentsAnalysisResults.khj\"\n", + ")\n", "\n", "# Train the predictor. Besides the mandatory parameters, we specify:\n", "# - A python dictionary linking data paths to file paths for non-root tables\n", @@ -447,11 +460,11 @@ " \"Accident\",\n", " accidents_table_path,\n", " \"Gravity\",\n", - " results_dir,\n", + " report_file_path,\n", " additional_data_tables={\n", - " \"Accident`Vehicles\": vehicles_table_path,\n", - " \"Accident`Vehicles`Users\": users_table_path,\n", - " \"Accident`Place\": places_table_path,\n", + " \"Vehicles\": vehicles_table_path,\n", + " \"Vehicles/Users\": users_table_path,\n", + " \"Place\": places_table_path,\n", " },\n", " max_trees=0,\n", ")" @@ -478,7 +491,11 @@ "# Set the file paths\n", "dictionary_file_path = os.path.join(kh.get_samples_dir(), \"Adult\", \"Adult.kdic\")\n", "data_table_path = os.path.join(kh.get_samples_dir(), \"Adult\", \"Adult.txt\")\n", - "results_dir = os.path.join(\"kh_samples\", \"train_predictor_with_train_percentage\")\n", + "report_file_path = os.path.join(\n", + " \"kh_samples\",\n", + " \"train_predictor_with_train_percentage\",\n", + " \"P90_AdultAnalysisResults.khj\",\n", + ")\n", "\n", "# Train the predictor. Besides the mandatory parameters, it is specified:\n", "# - A 90% sampling rate for the training dataset\n", @@ -489,11 +506,10 @@ " \"Adult\",\n", " data_table_path,\n", " \"class\",\n", - " results_dir,\n", + " report_file_path,\n", " sample_percentage=90,\n", " use_complement_as_test=True,\n", " max_trees=0,\n", - " results_prefix=\"P90_\",\n", ")" ] }, @@ -518,7 +534,9 @@ "# Set the file paths\n", "dictionary_file_path = os.path.join(kh.get_samples_dir(), \"Letter\", \"Letter.kdic\")\n", "data_table_path = os.path.join(kh.get_samples_dir(), \"Letter\", \"Letter.txt\")\n", - "results_dir = os.path.join(\"kh_samples\", \"train_predictor_with_trees\")\n", + "report_file_path = os.path.join(\n", + " \"kh_samples\", \"train_predictor_with_trees\", \"P80_LetterAnalysisResults.khj\"\n", + ")\n", "\n", "# Train the predictor with at most 15 trees (default 10)\n", "kh.train_predictor(\n", @@ -526,10 +544,9 @@ " \"Letter\",\n", " data_table_path,\n", " \"lettr\",\n", - " results_dir,\n", + " report_file_path,\n", " sample_percentage=80,\n", " use_complement_as_test=True,\n", - " results_prefix=\"P80_\",\n", " max_trees=15,\n", ")" ] @@ -555,7 +572,9 @@ "# Set the file paths\n", "dictionary_file_path = os.path.join(kh.get_samples_dir(), \"Adult\", \"Adult.kdic\")\n", "data_table_path = os.path.join(kh.get_samples_dir(), \"Adult\", \"Adult.txt\")\n", - "results_dir = os.path.join(\"kh_samples\", \"train_predictor_with_pairs\")\n", + "report_file_path = os.path.join(\n", + " \"kh_samples\", \"train_predictor_with_pairs\", \"AdultAnalysisResults.khj\"\n", + ")\n", "\n", "# Train the predictor with at most 10 pairs as follows:\n", "# - Include pairs age-race and capital_gain-capital_loss\n", @@ -565,7 +584,7 @@ " \"Adult\",\n", " data_table_path,\n", " \"class\",\n", - " results_dir,\n", + " report_file_path,\n", " use_complement_as_test=True,\n", " max_trees=0,\n", " max_pairs=10,\n", @@ -598,9 +617,10 @@ "# Set the file paths\n", "dictionary_file_path = os.path.join(kh.get_samples_dir(), \"Adult\", \"Adult.kdic\")\n", "data_table_path = os.path.join(kh.get_samples_dir(), \"Adult\", \"Adult.txt\")\n", - "results_dir = os.path.join(\"kh_samples\", \"train_predictor_with_multiple_parameters\")\n", - "output_script_path = os.path.join(results_dir, \"output_scenario._kh\")\n", - "log_path = os.path.join(results_dir, \"log.txt\")\n", + "output_dir = os.path.join(\"kh_samples\", \"train_predictor_with_multiple_parameters\")\n", + "report_file_path = os.path.join(output_dir, \"AdultAnalysisResults.khj\")\n", + "output_script_path = os.path.join(output_dir, \"output_scenario._kh\")\n", + "log_path = os.path.join(output_dir, \"log.txt\")\n", "\n", "# Train the predictor. Besides the mandatory parameters, we specify:\n", "# - The value \"more\" as main target value\n", @@ -613,7 +633,7 @@ " \"Adult\",\n", " data_table_path,\n", " \"class\",\n", - " results_dir,\n", + " report_file_path,\n", " main_target_value=\"more\",\n", " output_scenario_path=output_script_path,\n", " log_file_path=log_path,\n", @@ -643,8 +663,9 @@ "# Set the file paths\n", "dictionary_file_path = os.path.join(kh.get_samples_dir(), \"Iris\", \"Iris.kdic\")\n", "data_table_path = os.path.join(kh.get_samples_dir(), \"Iris\", \"Iris.txt\")\n", - "results_dir = os.path.join(\"kh_samples\", \"train_predictor_detect_format\")\n", - "transformed_data_table_path = os.path.join(results_dir, \"TransformedIris.txt\")\n", + "output_dir = os.path.join(\"kh_samples\", \"train_predictor_detect_format\")\n", + "transformed_data_table_path = os.path.join(output_dir, \"TransformedIris.txt\")\n", + "report_file_path = os.path.join(output_dir, \"IrisAnalysisResults.khj\")\n", "\n", "# Transform the database format from header_line=True and field_separator=TAB\n", "# to header_line=False and field_separator=\",\"\n", @@ -665,7 +686,7 @@ " \"Iris\",\n", " transformed_data_table_path,\n", " \"Class\",\n", - " results_dir,\n", + " report_file_path,\n", " header_line=True,\n", " field_separator=\"\",\n", " )\n", @@ -682,7 +703,7 @@ " \"Iris\",\n", " transformed_data_table_path,\n", " \"Class\",\n", - " results_dir,\n", + " report_file_path,\n", ")" ] }, @@ -708,12 +729,12 @@ "# Set the file paths\n", "dictionary_file_path = os.path.join(kh.get_samples_dir(), \"Adult\", \"Adult.kdic\")\n", "data_table_path = os.path.join(kh.get_samples_dir(), \"Adult\", \"Adult.txt\")\n", - "results_dir = os.path.join(\"kh_samples\", \"train_predictor_with_cross_validation\")\n", - "fold_dictionary_file_path = os.path.join(results_dir, \"AdultWithFolding.kdic\")\n", + "output_dir = os.path.join(\"kh_samples\", \"train_predictor_with_cross_validation\")\n", + "fold_dictionary_file_path = os.path.join(output_dir, \"AdultWithFolding.kdic\")\n", "\n", "# Create the output directory\n", - "if not os.path.isdir(results_dir):\n", - " os.mkdir(results_dir)\n", + "if not os.path.isdir(output_dir):\n", + " os.mkdir(output_dir)\n", "\n", "# Load the learning dictionary object\n", "domain = kh.read_dictionary_file(dictionary_file_path)\n", @@ -750,35 +771,39 @@ "train_aucs = []\n", "test_aucs = []\n", "for fold_index in range(1, fold_number + 1):\n", + " analysis_report_file_path = os.path.join(\n", + " output_dir, \"Fold\" + str(fold_index) + \"AdultAnalysisResults.khj\"\n", + " )\n", " # Train a model from the sub-dataset where IsInTrainDataset is 1\n", - " train_reports_path, modeling_dictionary_file_path = kh.train_predictor(\n", + " _, modeling_dictionary_file_path = kh.train_predictor(\n", " domain,\n", " \"Adult\",\n", " data_table_path,\n", " \"class\",\n", - " results_dir,\n", + " analysis_report_file_path,\n", " sample_percentage=100,\n", " selection_variable=\"IsInTrainDataset\" + str(fold_index),\n", " selection_value=1,\n", " max_trees=0,\n", - " results_prefix=\"Fold\" + str(fold_index),\n", " )\n", "\n", + " evaluation_report_file_path = os.path.join(\n", + " output_dir, \"Fold\" + str(fold_index) + \"AdultEvaluationResults.khj\"\n", + " )\n", " # Evaluate the resulting model in the subsets where IsInTrainDataset is 0\n", " test_evaluation_report_path = kh.evaluate_predictor(\n", " modeling_dictionary_file_path,\n", - " \"Adult\",\n", + " \"SNB_Adult\",\n", " data_table_path,\n", - " results_dir,\n", + " evaluation_report_file_path,\n", " sample_percentage=100,\n", " selection_variable=\"IsInTrainDataset\" + str(fold_index),\n", " selection_value=0,\n", - " results_prefix=\"Fold\" + str(fold_index),\n", " )\n", "\n", " # Obtain the train AUC from the train report and the test AUC from the\n", " # evaluation report and print them\n", - " train_results = kh.read_analysis_results_file(train_reports_path)\n", + " train_results = kh.read_analysis_results_file(analysis_report_file_path)\n", " test_evaluation_results = kh.read_analysis_results_file(test_evaluation_report_path)\n", " train_auc = train_results.train_evaluation_report.get_snb_performance().auc\n", " test_auc = test_evaluation_results.evaluation_report.get_snb_performance().auc\n", @@ -802,6 +827,45 @@ "print(\"test auc: \" + str(mean_test_auc) + \" +- \" + str(sd_test_auc))" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### `interpret_predictor()`\n\n", + "Builds intepretation model for existing predictor\n\n It calls `~.api.train_predictor` and `~.api.interpret_predictor` only with\n their mandatory parameters.\n \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Imports\n", + "import os\n", + "from khiops import core as kh\n", + "\n", + "dictionary_file_path = os.path.join(kh.get_samples_dir(), \"Adult\", \"Adult.kdic\")\n", + "data_table_path = os.path.join(kh.get_samples_dir(), \"Adult\", \"Adult.txt\")\n", + "output_dir = os.path.join(\"kh_samples\", \"interpret_predictor\")\n", + "analysis_report_file_path = os.path.join(output_dir, \"AdultAnalysisResults.khj\")\n", + "interpretor_file_path = os.path.join(output_dir, \"AdultIntepretationModel.kdic\")\n", + "\n", + "# Build prediction model\n", + "_, predictor_file_path = kh.train_predictor(\n", + " dictionary_file_path,\n", + " \"Adult\",\n", + " data_table_path,\n", + " \"class\",\n", + " analysis_report_file_path,\n", + ")\n", + "\n", + "# Build interpretation model\n", + "kh.interpret_predictor(predictor_file_path, \"SNB_Adult\", interpretor_file_path)\n", + "\n", + "print(f\"The intepretation model is '{interpretor_file_path}'\")" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -839,7 +903,8 @@ "# Set the file paths\n", "dictionary_file_path = os.path.join(kh.get_samples_dir(), \"Adult\", \"Adult.kdic\")\n", "data_table_path = os.path.join(kh.get_samples_dir(), \"Adult\", \"Adult.txt\")\n", - "results_dir = os.path.join(\"kh_samples\", \"multiple_train_predictor\")\n", + "output_dir = os.path.join(\"kh_samples\", \"multiple_train_predictor\")\n", + "report_file_path = os.path.join(output_dir, \"AdultAnalysisResults.khj\")\n", "\n", "# Read the dictionary file to obtain an instance of class Dictionary\n", "dictionary_domain = kh.read_dictionary_file(dictionary_file_path)\n", @@ -847,20 +912,20 @@ "\n", "# Train a SNB model using all the variables\n", "print(\"\\t#vars\\ttrain auc\\ttest auc\")\n", - "json_result_file_path, _ = kh.train_predictor(\n", + "kh.train_predictor(\n", " dictionary_file_path,\n", " \"Adult\",\n", " data_table_path,\n", " \"class\",\n", - " results_dir,\n", + " report_file_path,\n", " sample_percentage=70,\n", " use_complement_as_test=True,\n", " max_trees=0,\n", ")\n", - "display_test_results(json_result_file_path)\n", + "display_test_results(report_file_path)\n", "\n", "# Read results to obtain the variables sorted by decreasing Level\n", - "analysis_results = kh.read_analysis_results_file(json_result_file_path)\n", + "analysis_results = kh.read_analysis_results_file(report_file_path)\n", "preparation_results = analysis_results.preparation_report\n", "\n", "# Train a sequence of models with a decreasing number of variables\n", @@ -874,21 +939,22 @@ " dictionary.get_variable(variable.name).used = False\n", "\n", " # Train the model with this dictionary domain object\n", - " prefix = f\"V{variable_number - 1 - i}_\"\n", - " json_result_file_path, _ = kh.train_predictor(\n", + " report_file_path = os.path.join(\n", + " output_dir, f\"V{variable_number - 1 - i}_AdultAnalysisResults.khj\"\n", + " )\n", + " kh.train_predictor(\n", " dictionary_domain,\n", " \"Adult\",\n", " data_table_path,\n", " \"class\",\n", - " results_dir,\n", + " report_file_path,\n", " sample_percentage=70,\n", " use_complement_as_test=True,\n", - " results_prefix=prefix,\n", " max_trees=0,\n", " )\n", "\n", " # Show a preview of the results\n", - " display_test_results(json_result_file_path)" + " display_test_results(report_file_path)" ] }, { @@ -912,24 +978,29 @@ "# Set the file paths\n", "dictionary_file_path = os.path.join(kh.get_samples_dir(), \"Adult\", \"Adult.kdic\")\n", "data_table_path = os.path.join(kh.get_samples_dir(), \"Adult\", \"Adult.txt\")\n", - "results_dir = os.path.join(\"kh_samples\", \"evaluate_predictor\")\n", - "model_dictionary_file_path = os.path.join(results_dir, \"Modeling.kdic\")\n", + "output_dir = os.path.join(\"kh_samples\", \"evaluate_predictor\")\n", + "analysis_report_file_path = os.path.join(output_dir, \"AdultAnalysisResults.khj\")\n", "\n", "# Train the predictor\n", - "kh.train_predictor(\n", + "_, model_dictionary_file_path = kh.train_predictor(\n", " dictionary_file_path,\n", " \"Adult\",\n", " data_table_path,\n", " \"class\",\n", - " results_dir,\n", + " analysis_report_file_path,\n", " max_trees=0,\n", ")\n", "\n", + "evaluation_report_file_path = os.path.join(output_dir, \"AdultEvaluationResults.khj\")\n", + "\n", "# Evaluate the predictor\n", - "report_file_path = kh.evaluate_predictor(\n", - " model_dictionary_file_path, \"Adult\", data_table_path, results_dir\n", + "kh.evaluate_predictor(\n", + " model_dictionary_file_path,\n", + " \"SNB_Adult\",\n", + " data_table_path,\n", + " evaluation_report_file_path,\n", ")\n", - "print(\"Evaluation report available at \" + report_file_path)" + "print(\"Evaluation report available at \" + evaluation_report_file_path)" ] }, { @@ -953,8 +1024,9 @@ "# Set the file paths\n", "dictionary_file_path = os.path.join(kh.get_samples_dir(), \"Adult\", \"Adult.kdic\")\n", "data_table_path = os.path.join(kh.get_samples_dir(), \"Adult\", \"Adult.txt\")\n", - "results_dir = os.path.join(\"kh_samples\", \"access_predictor_evaluation_report\")\n", - "evaluation_report_path = os.path.join(results_dir, \"AllReports.khj\")\n", + "report_file_path = os.path.join(\n", + " \"kh_samples\", \"access_predictor_evaluation_report\", \"AdultAnalysisReport.khj\"\n", + ")\n", "\n", "# Train the SNB predictor and some univariate predictors\n", "# Note: Evaluation in test is 30% by default\n", @@ -963,13 +1035,12 @@ " \"Adult\",\n", " data_table_path,\n", " \"class\",\n", - " results_dir,\n", + " report_file_path,\n", " max_trees=0,\n", - " univariate_predictor_number=4,\n", ")\n", "\n", "# Obtain the evaluation results\n", - "results = kh.read_analysis_results_file(evaluation_report_path)\n", + "results = kh.read_analysis_results_file(report_file_path)\n", "evaluation_report = results.test_evaluation_report\n", "snb_performance = evaluation_report.get_snb_performance()\n", "\n", @@ -1009,9 +1080,9 @@ " + str(random_lift_curve.values[i])\n", " )\n", "\n", - "# Print univariate metrics for an univariate predictor\n", + "# Print metrics for an SNB predictor\n", "predictor_performance = evaluation_report.get_predictor_performance(\n", - " \"Univariate relationship\"\n", + " \"Selective Naive Bayes\"\n", ")\n", "print(\"\\n\\nperformance metrics for \" + predictor_performance.name)\n", "for metric_name in predictor_performance.get_metric_names():\n", @@ -1039,10 +1110,14 @@ "# Set the file paths\n", "dictionary_file_path = os.path.join(kh.get_samples_dir(), \"Adult\", \"Adult.kdic\")\n", "data_table_path = os.path.join(kh.get_samples_dir(), \"Adult\", \"Adult.txt\")\n", - "results_dir = os.path.join(\"kh_samples\", \"train_recoder\")\n", + "report_file_path = os.path.join(\n", + " \"kh_samples\", \"train_recoder\", \"AdultAnalysisResults.khj\"\n", + ")\n", "\n", "# Train the recoder model\n", - "kh.train_recoder(dictionary_file_path, \"Adult\", data_table_path, \"class\", results_dir)" + "kh.train_recoder(\n", + " dictionary_file_path, \"Adult\", data_table_path, \"class\", report_file_path\n", + ")" ] }, { @@ -1066,7 +1141,11 @@ "# Set the file paths\n", "dictionary_file_path = os.path.join(kh.get_samples_dir(), \"Adult\", \"Adult.kdic\")\n", "data_table_path = os.path.join(kh.get_samples_dir(), \"Adult\", \"Adult.txt\")\n", - "results_dir = os.path.join(\"kh_samples\", \"train_recoder_with_multiple_parameters\")\n", + "report_file_path = os.path.join(\n", + " \"kh_samples\",\n", + " \"train_recoder_with_multiple_parameters\",\n", + " \"AdultAnalysisResults.khj\",\n", + ")\n", "\n", "# Train the recoder model\n", "kh.train_recoder(\n", @@ -1074,7 +1153,7 @@ " \"Adult\",\n", " data_table_path,\n", " \"class\",\n", - " results_dir,\n", + " report_file_path,\n", " max_pairs=10,\n", " categorical_recoding_method=\"part label\",\n", " numerical_recoding_method=\"part label\",\n", @@ -1104,7 +1183,9 @@ "dictionary_file_path = os.path.join(accidents_dir, \"Accidents.kdic\")\n", "accidents_table_path = os.path.join(accidents_dir, \"Accidents.txt\")\n", "vehicles_table_path = os.path.join(accidents_dir, \"Vehicles.txt\")\n", - "results_dir = os.path.join(\"kh_samples\", \"train_recoder_mt_flatten\")\n", + "report_file_path = os.path.join(\n", + " \"kh_samples\", \"train_recoder_mt_flatten\", \"AccidentsAnalysisResults.khj\"\n", + ")\n", "\n", "# Train the recoder. Besides the mandatory parameters, it is specified:\n", "# - A python dictionary linking data paths to file paths for non-root tables\n", @@ -1116,8 +1197,8 @@ " \"Accident\",\n", " accidents_table_path,\n", " \"Gravity\",\n", - " results_dir,\n", - " additional_data_tables={\"Accident`Vehicles\": vehicles_table_path},\n", + " report_file_path,\n", + " additional_data_tables={\"Vehicles\": vehicles_table_path},\n", " max_constructed_variables=1000,\n", " informative_variables_only=False,\n", " categorical_recoding_method=\"none\",\n", @@ -1132,7 +1213,7 @@ "metadata": {}, "source": [ "### `deploy_model()`\n\n", - "Deploys a model in the simplest possible\n\n It is a call to `~.api.deploy_model` with its mandatory parameters.\n\n In this example, a Selective Naive Bayes (SNB) model is deployed by applying its\n associated dictionary to the input database. The model predictions are written to\n the output database.\n \n" + "Deploys a model in the simplest way possible\n\n It is a call to `~.api.deploy_model` with its mandatory parameters.\n\n In this example, a Selective Naive Bayes (SNB) model is deployed by applying its\n associated dictionary to the input database. The model predictions are written to\n the output database.\n \n" ] }, { @@ -1148,17 +1229,17 @@ "# Set the file paths\n", "dictionary_file_path = os.path.join(kh.get_samples_dir(), \"Adult\", \"Adult.kdic\")\n", "data_table_path = os.path.join(kh.get_samples_dir(), \"Adult\", \"Adult.txt\")\n", - "results_dir = os.path.join(\"kh_samples\", \"deploy_model\")\n", - "model_dictionary_file_path = os.path.join(results_dir, \"Modeling.kdic\")\n", - "output_data_table_path = os.path.join(results_dir, \"ScoresAdult.txt\")\n", + "output_dir = os.path.join(\"kh_samples\", \"deploy_model\")\n", + "report_file_path = os.path.join(output_dir, \"AdultAnalysisResults.khj\")\n", + "output_data_table_path = os.path.join(output_dir, \"ScoresAdult.txt\")\n", "\n", "# Train the predictor\n", - "kh.train_predictor(\n", + "_, model_dictionary_file_path = kh.train_predictor(\n", " dictionary_file_path,\n", " \"Adult\",\n", " data_table_path,\n", " \"class\",\n", - " results_dir,\n", + " report_file_path,\n", " max_trees=0,\n", ")\n", "\n", @@ -1192,18 +1273,18 @@ "dictionary_file_path = os.path.join(accidents_dir, \"Accidents.kdic\")\n", "accidents_table_path = os.path.join(accidents_dir, \"Accidents.txt\")\n", "vehicles_table_path = os.path.join(accidents_dir, \"Vehicles.txt\")\n", - "results_dir = os.path.join(\"kh_samples\", \"deploy_model_mt\")\n", - "model_dictionary_file_path = os.path.join(results_dir, \"Modeling.kdic\")\n", - "output_data_table_path = os.path.join(results_dir, \"TransferredAccidents.txt\")\n", + "output_dir = os.path.join(\"kh_samples\", \"deploy_model_mt\")\n", + "report_file_path = os.path.join(output_dir, \"AccidentsSummaryAnalysisResults.khj\")\n", + "output_data_table_path = os.path.join(output_dir, \"TransferredAccidents.txt\")\n", "\n", "# Train the predictor (see train_predictor_mt for details)\n", - "kh.train_predictor(\n", + "_, model_dictionary_file_path = kh.train_predictor(\n", " dictionary_file_path,\n", " \"Accident\",\n", " accidents_table_path,\n", " \"Gravity\",\n", - " results_dir,\n", - " additional_data_tables={\"Accident`Vehicles\": vehicles_table_path},\n", + " report_file_path,\n", + " additional_data_tables={\"Vehicles\": vehicles_table_path},\n", " max_trees=0,\n", ")\n", "\n", @@ -1215,7 +1296,71 @@ " \"SNB_Accident\",\n", " accidents_table_path,\n", " output_data_table_path,\n", - " additional_data_tables={\"SNB_Accident`Vehicles\": vehicles_table_path},\n", + " additional_data_tables={\"Vehicles\": vehicles_table_path},\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### `deploy_model_mt_with_interpretation()`\n\n", + "Deploys a multi-table interpretor in the simplest way possible\n\n It is a call to `~.api.deploy_model` with additional parameters to handle\n multi-table deployment.\n\n In this example, a Selective Naive Bayes (SNB) interpretation model is\n deployed by applying its associated dictionary to the input database.\n The model variable importances are written to the output data table.\n \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Imports\n", + "import os\n", + "from khiops import core as kh\n", + "\n", + "# Set the file paths\n", + "accidents_dir = os.path.join(kh.get_samples_dir(), \"AccidentsSummary\")\n", + "dictionary_file_path = os.path.join(accidents_dir, \"Accidents.kdic\")\n", + "accidents_table_path = os.path.join(accidents_dir, \"Accidents.txt\")\n", + "vehicles_table_path = os.path.join(accidents_dir, \"Vehicles.txt\")\n", + "output_dir = os.path.join(\"kh_samples\", \"deploy_model_mt\")\n", + "report_file_path = os.path.join(output_dir, \"AccidentsSummaryAnalysisResults.khj\")\n", + "interpretor_file_path = os.path.join(\n", + " output_dir, \"AccidentsSummaryInterpretationModel.kdic\"\n", + ")\n", + "output_data_table_path = os.path.join(output_dir, \"InterpretedAccidents.txt\")\n", + "\n", + "# Train the predictor (see train_predictor_mt for details)\n", + "# Add max_evaluated_variables so that an interpretation model can be built\n", + "# (see https://github.com/KhiopsML/khiops/issues/577)\n", + "_, model_dictionary_file_path = kh.train_predictor(\n", + " dictionary_file_path,\n", + " \"Accident\",\n", + " accidents_table_path,\n", + " \"Gravity\",\n", + " report_file_path,\n", + " additional_data_tables={\"Vehicles\": vehicles_table_path},\n", + " max_trees=0,\n", + " max_evaluated_variables=10,\n", + ")\n", + "\n", + "# Interpret the predictor\n", + "kh.interpret_predictor(\n", + " model_dictionary_file_path,\n", + " \"SNB_Accident\",\n", + " interpretor_file_path,\n", + " reinforcement_target_value=\"NonLethal\",\n", + ")\n", + "\n", + "# Deploy the interpretation model on the database\n", + "# Besides the mandatory parameters, it is specified:\n", + "# - A python dictionary linking data paths to file paths for non-root tables\n", + "kh.deploy_model(\n", + " interpretor_file_path,\n", + " \"Interpretation_SNB_Accident\",\n", + " accidents_table_path,\n", + " output_data_table_path,\n", + " additional_data_tables={\"Vehicles\": vehicles_table_path},\n", ")" ] }, @@ -1244,24 +1389,24 @@ "vehicles_table_path = os.path.join(accidents_dir, \"Vehicles.txt\")\n", "users_table_path = os.path.join(accidents_dir, \"Users.txt\")\n", "places_table_path = os.path.join(accidents_dir, \"Places.txt\")\n", - "results_dir = os.path.join(\"kh_samples\", \"deploy_model_mt_snowflake\")\n", - "model_dictionary_file_path = os.path.join(results_dir, \"Modeling.kdic\")\n", - "output_data_table_path = os.path.join(results_dir, \"TransferredAccidents.txt\")\n", + "output_dir = os.path.join(\"kh_samples\", \"deploy_model_mt_snowflake\")\n", + "report_file_path = os.path.join(output_dir, \"AccidentsAnalysisResults.khj\")\n", + "output_data_table_path = os.path.join(output_dir, \"TransferredAccidents.txt\")\n", "\n", "# Train the predictor. Besides the mandatory parameters, we specify:\n", "# - A python dictionary linking data paths to file paths for non-root tables\n", "# - To not construct any decision tree\n", "# The default number of automatic features is 100\n", - "kh.train_predictor(\n", + "_, model_dictionary_file_path = kh.train_predictor(\n", " dictionary_file_path,\n", " \"Accident\",\n", " accidents_table_path,\n", " \"Gravity\",\n", - " results_dir,\n", + " report_file_path,\n", " additional_data_tables={\n", - " \"Accident`Vehicles\": vehicles_table_path,\n", - " \"Accident`Vehicles`Users\": users_table_path,\n", - " \"Accident`Place\": places_table_path,\n", + " \"Vehicles\": vehicles_table_path,\n", + " \"Vehicles/Users\": users_table_path,\n", + " \"Place\": places_table_path,\n", " },\n", " max_trees=0,\n", ")\n", @@ -1275,9 +1420,9 @@ " accidents_table_path,\n", " output_data_table_path,\n", " additional_data_tables={\n", - " \"SNB_Accident`Vehicles\": vehicles_table_path,\n", - " \"SNB_Accident`Vehicles`Users\": users_table_path,\n", - " \"SNB_Accident`Place\": places_table_path,\n", + " \"Vehicles\": vehicles_table_path,\n", + " \"Vehicles/Users\": users_table_path,\n", + " \"Place\": places_table_path,\n", " },\n", ")" ] @@ -1303,17 +1448,17 @@ "# Set the file paths\n", "dictionary_file_path = os.path.join(kh.get_samples_dir(), \"Adult\", \"Adult.kdic\")\n", "data_table_path = os.path.join(kh.get_samples_dir(), \"Adult\", \"Adult.txt\")\n", - "results_dir = os.path.join(\"kh_samples\", \"deploy_model_expert\")\n", - "model_dictionary_file_path = os.path.join(results_dir, \"Modeling.kdic\")\n", - "output_data_table_path = os.path.join(results_dir, \"ScoresAdult.txt\")\n", + "output_dir = os.path.join(\"kh_samples\", \"deploy_model_expert\")\n", + "report_file_path = os.path.join(output_dir, \"AdultAnalysisResults.khj\")\n", + "output_data_table_path = os.path.join(output_dir, \"ScoresAdult.txt\")\n", "\n", "# Train the predictor\n", - "kh.train_predictor(\n", + "_, model_dictionary_file_path = kh.train_predictor(\n", " dictionary_file_path,\n", " \"Adult\",\n", " data_table_path,\n", " \"class\",\n", - " results_dir,\n", + " report_file_path,\n", " max_trees=0,\n", ")\n", "\n", @@ -1357,8 +1502,9 @@ "# Set the file paths\n", "dictionary_file_path = os.path.join(kh.get_samples_dir(), \"Adult\", \"Adult.kdic\")\n", "data_table_path = os.path.join(kh.get_samples_dir(), \"Adult\", \"Adult.txt\")\n", - "results_dir = os.path.join(\"kh_samples\", \"deploy_classifier_for_metrics\")\n", - "output_data_table_path = os.path.join(results_dir, \"ScoresAdult.txt\")\n", + "output_dir = os.path.join(\"kh_samples\", \"deploy_classifier_for_metrics\")\n", + "report_file_path = os.path.join(output_dir, \"AdultAnalysisResults.khj\")\n", + "output_data_table_path = os.path.join(output_dir, \"ScoresAdult.txt\")\n", "\n", "# Train the classifier for the target \"class\"\n", "_, modeling_dictionary_file_path = kh.train_predictor(\n", @@ -1366,10 +1512,9 @@ " \"Adult\",\n", " data_table_path,\n", " \"class\",\n", - " results_dir,\n", + " report_file_path,\n", " max_trees=0,\n", ")\n", - "\n", "# Obtain the scores of the SNB on the test dataset to calculate the PR curve\n", "kh.deploy_predictor_for_metrics(\n", " modeling_dictionary_file_path,\n", @@ -1437,8 +1582,9 @@ "# Set the file paths\n", "dictionary_file_path = os.path.join(kh.get_samples_dir(), \"Adult\", \"Adult.kdic\")\n", "data_table_path = os.path.join(kh.get_samples_dir(), \"Adult\", \"Adult.txt\")\n", - "results_dir = os.path.join(\"kh_samples\", \"deploy_regressor_for_metrics\")\n", - "output_data_table_path = os.path.join(results_dir, \"TrueAndPredictedAges.txt\")\n", + "output_dir = os.path.join(\"kh_samples\", \"deploy_regressor_for_metrics\")\n", + "report_file_path = os.path.join(output_dir, \"AdultAnalysisResults.khj\")\n", + "output_data_table_path = os.path.join(output_dir, \"TrueAndPredictedAges.txt\")\n", "\n", "# Train the regressor for the target \"age\" (with 20% train to be quick)\n", "_, modeling_dictionary_file_path = kh.train_predictor(\n", @@ -1446,7 +1592,7 @@ " \"Adult\",\n", " data_table_path,\n", " \"age\",\n", - " results_dir,\n", + " report_file_path,\n", " sample_percentage=20,\n", " max_trees=0,\n", ")\n", @@ -1621,15 +1767,17 @@ "splice_dir = os.path.join(kh.get_samples_dir(), \"SpliceJunction\")\n", "dictionary_file_path = os.path.join(splice_dir, \"SpliceJunction.kdic\")\n", "data_table_path = os.path.join(splice_dir, \"SpliceJunctionDNA.txt\")\n", - "results_dir = os.path.join(\"kh_samples\", \"train_coclustering\")\n", + "coclustering_report_path = os.path.join(\n", + " \"kh_samples\", \"train_coclustering\", \"SpliceJunctionCoclusteringResults.khcj\"\n", + ")\n", "\n", "# Train a coclustering model for variables \"SampleId\" and \"Char\"\n", - "coclustering_report_path = kh.train_coclustering(\n", + "kh.train_coclustering(\n", " dictionary_file_path,\n", " \"SpliceJunctionDNA\",\n", " data_table_path,\n", " [\"SampleId\", \"Char\"],\n", - " results_dir,\n", + " coclustering_report_path,\n", ")\n", "print(f\"Coclustering report file available at {coclustering_report_path}\")\n", "\n", @@ -1637,6 +1785,50 @@ "# kh.visualize_report(coclustering_report_path)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### `train_instance_variable_coclustering()`\n\n", + "Trains an instance-variable coclustering model in the simplest way possible\n\n It is a call to `~.api.train_instance_variable_coclustering` with only its mandatory\n parameters.\n \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Imports\n", + "import os\n", + "from khiops import core as kh\n", + "\n", + "# Set the file paths\n", + "iris_dir = os.path.join(kh.get_samples_dir(), \"Iris\")\n", + "dictionary_file_path = os.path.join(iris_dir, \"Iris.kdic\")\n", + "data_table_path = os.path.join(iris_dir, \"Iris.txt\")\n", + "coclustering_report_path = os.path.join(\n", + " \"kh_samples\",\n", + " \"train_instance_variable_coclustering\",\n", + " \"IrisInstanceVariable_CoclusteringResults.khcj\",\n", + ")\n", + "\n", + "# Train a coclustering model for variables \"SampleId\" and \"Char\"\n", + "kh.train_instance_variable_coclustering(\n", + " dictionary_file_path,\n", + " \"Iris\",\n", + " data_table_path,\n", + " coclustering_report_path,\n", + ")\n", + "print(\n", + " \"Instance-variable coclustering report file available \"\n", + " f\"at {coclustering_report_path}\"\n", + ")\n", + "\n", + "# If you have Khiops Co-Visualization installed you may open the report as follows\n", + "# kh.visualize_report(coclustering_report_path)" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -1659,9 +1851,11 @@ "splice_dir = os.path.join(kh.get_samples_dir(), \"SpliceJunction\")\n", "dictionary_file_path = os.path.join(splice_dir, \"SpliceJunction.kdic\")\n", "data_table_path = os.path.join(splice_dir, \"SpliceJunctionDNA.txt\")\n", - "results_dir = os.path.join(\"kh_samples\", \"simplify_coclustering\")\n", - "coclustering_file_path = os.path.join(results_dir, \"Coclustering.khc\")\n", - "simplified_coclustering_file_name = \"simplified_coclustering.khc\"\n", + "output_dir = os.path.join(\"kh_samples\", \"simplify_coclustering\")\n", + "coclustering_file_path = os.path.join(output_dir, \"Coclustering.khcj\")\n", + "simplified_coclustering_file_path = os.path.join(\n", + " output_dir, \"simplified_coclustering.khcj\"\n", + ")\n", "\n", "# Train coclustering model for variables \"SampleId\" and \"Char\"\n", "kh.train_coclustering(\n", @@ -1669,7 +1863,7 @@ " \"SpliceJunctionDNA\",\n", " data_table_path,\n", " [\"SampleId\", \"Char\"],\n", - " results_dir,\n", + " coclustering_file_path,\n", ")\n", "\n", "# Simplify the trained coclustering with the constraints\n", @@ -1677,8 +1871,7 @@ "# - maximum total parts number: 4\n", "kh.simplify_coclustering(\n", " coclustering_file_path,\n", - " simplified_coclustering_file_name,\n", - " results_dir,\n", + " simplified_coclustering_file_path,\n", " max_preserved_information=80,\n", " max_total_parts=4,\n", ")" @@ -1702,9 +1895,9 @@ "splice_dir = os.path.join(kh.get_samples_dir(), \"SpliceJunction\")\n", "dictionary_file_path = os.path.join(splice_dir, \"SpliceJunction.kdic\")\n", "data_table_path = os.path.join(splice_dir, \"SpliceJunctionDNA.txt\")\n", - "results_dir = os.path.join(\"kh_samples\", \"extract_clusters\")\n", - "coclustering_file_path = os.path.join(results_dir, \"Coclustering.khc\")\n", - "clusters_file_path = os.path.join(results_dir, \"extracted_clusters.txt\")\n", + "output_dir = os.path.join(\"kh_samples\", \"extract_clusters\")\n", + "coclustering_file_path = os.path.join(output_dir, \"Coclustering.khcj\")\n", + "clusters_file_path = os.path.join(output_dir, \"extracted_clusters.txt\")\n", "\n", "# Train a coclustering model for variables \"SampleId\" and \"Char\"\n", "kh.train_coclustering(\n", @@ -1712,7 +1905,7 @@ " \"SpliceJunctionDNA\",\n", " data_table_path,\n", " [\"SampleId\", \"Char\"],\n", - " results_dir,\n", + " coclustering_file_path,\n", ")\n", "\n", "# Extract clusters\n", @@ -1724,7 +1917,7 @@ "metadata": {}, "source": [ "### `deploy_coclustering()`\n\n", - "Deploys an \"individual-variable\" coclustering\n" + "Deploys a coclustering\n" ] }, { @@ -1741,8 +1934,10 @@ "splice_dir = os.path.join(kh.get_samples_dir(), \"SpliceJunction\")\n", "data_table_path = os.path.join(splice_dir, \"SpliceJunctionDNA.txt\")\n", "dictionary_file_path = os.path.join(splice_dir, \"SpliceJunction.kdic\")\n", - "results_dir = os.path.join(\"kh_samples\", \"deploy_coclustering\")\n", - "coclustering_file_path = os.path.join(results_dir, \"Coclustering.khc\")\n", + "output_dir = os.path.join(\"kh_samples\", \"deploy_coclustering\")\n", + "coclustering_file_path = os.path.join(output_dir, \"Coclustering.khcj\")\n", + "coclustering_dictionary_file_path = os.path.join(output_dir, \"Coclustering.kdic\")\n", + "output_data_table_path = os.path.join(output_dir, \"DeployedSpliceJunctionDNA.txt\")\n", "\n", "# Train a coclustering model for variables \"SampleId\" and \"Char\"\n", "kh.train_coclustering(\n", @@ -1750,7 +1945,7 @@ " \"SpliceJunctionDNA\",\n", " data_table_path,\n", " [\"SampleId\", \"Char\"],\n", - " results_dir,\n", + " coclustering_file_path,\n", ")\n", "\n", "# Deploy \"Char\" clusters in the training database\n", @@ -1761,7 +1956,8 @@ " coclustering_file_path,\n", " [\"SampleId\"],\n", " \"Char\",\n", - " results_dir,\n", + " coclustering_dictionary_file_path,\n", + " output_data_table_path,\n", " header_line=True,\n", ")" ] @@ -1771,7 +1967,7 @@ "metadata": {}, "source": [ "### `deploy_coclustering_expert()`\n\n", - "Deploys an \"individual-variable\" coclustering step-by-step\n\n The `.api.prepare_coclustering_deployment` method is called twice to prepare the\n deployment at two granularity levels. Then, the model is deployed and the respective\n deployment dictionary is built.\n\n This is one of the most complex workflows of the Khiops suite.\n \n" + "Deploys a coclustering step-by-step\n\n The `.api.prepare_coclustering_deployment` method is called twice to prepare the\n deployment at two granularity levels. Then, the model is deployed and the respective\n deployment dictionary is built.\n\n This is one of the most complex workflows of the Khiops suite.\n \n" ] }, { @@ -1789,8 +1985,8 @@ "dictionary_file_path = os.path.join(splice_dir, \"SpliceJunction.kdic\")\n", "data_table_path = os.path.join(splice_dir, \"SpliceJunction.txt\")\n", "secondary_data_table_path = os.path.join(splice_dir, \"SpliceJunctionDNA.txt\")\n", - "results_dir = os.path.join(\"kh_samples\", \"deploy_coclustering_expert\")\n", - "coclustering_file_path = os.path.join(results_dir, \"Coclustering.khc\")\n", + "output_dir = os.path.join(\"kh_samples\", \"deploy_coclustering_expert\")\n", + "coclustering_file_path = os.path.join(output_dir, \"Coclustering.khcj\")\n", "\n", "# Train a coclustering model for variables \"SampleId\" and \"Char\"\n", "print(\"train coclustering on SpliceJunctionDNA\")\n", @@ -1799,39 +1995,38 @@ " \"SpliceJunctionDNA\",\n", " secondary_data_table_path,\n", " [\"SampleId\", \"Char\"],\n", - " results_dir,\n", + " coclustering_file_path,\n", ")\n", "\n", "print(\"prepare_coclustering_deployment\")\n", "# The input dictionary is extended with new coclustering based variables\n", + "augmented_dictionary_file_path = os.path.join(output_dir, \"Coclustering.kdic\")\n", "kh.prepare_coclustering_deployment(\n", " dictionary_file_path,\n", " \"SpliceJunction\",\n", " coclustering_file_path,\n", " \"DNA\",\n", " \"SampleId\",\n", - " results_dir,\n", + " augmented_dictionary_file_path,\n", ")\n", - "augmented_dictionary_file_path = os.path.join(results_dir, \"Coclustering.kdic\")\n", "\n", "print(\"prepare_coclustering_deployment with at most two clusters\")\n", "# Extend the already extended dictionary with the new variables from a simplified CC\n", + "reaugmented_dictionary_file_path = os.path.join(\n", + " output_dir, \"ReaugmentedCoclustering.kdic\"\n", + ")\n", "kh.prepare_coclustering_deployment(\n", " augmented_dictionary_file_path,\n", " \"SpliceJunction\",\n", " coclustering_file_path,\n", " \"DNA\",\n", " \"SampleId\",\n", - " results_dir,\n", - " results_prefix=\"Reaugmented\",\n", + " reaugmented_dictionary_file_path,\n", " variables_prefix=\"C2_\",\n", " max_part_numbers={\"SampleId\": 2},\n", ")\n", "\n", - "reaugmented_dictionary_file_path = os.path.join(\n", - " results_dir, \"ReaugmentedCoclustering.kdic\"\n", - ")\n", - "output_data_table_path = os.path.join(results_dir, \"TransferredSpliceJunction.txt\")\n", + "output_data_table_path = os.path.join(output_dir, \"TransferredSpliceJunction.txt\")\n", "\n", "# Deploy the coclustering with the extended dictionary\n", "print(\"deploy_model with the new coclustering based variables\")\n", @@ -1840,11 +2035,11 @@ " \"SpliceJunction\",\n", " data_table_path,\n", " output_data_table_path,\n", - " additional_data_tables={\"SpliceJunction`DNA\": secondary_data_table_path},\n", + " additional_data_tables={\"DNA\": secondary_data_table_path},\n", ")\n", "\n", "deployed_dictionary_file_path = os.path.join(\n", - " results_dir, \"Transferred_Coclustering.kdic\"\n", + " output_dir, \"Transferred_Coclustering.kdic\"\n", ")\n", "print(\"build_deployed_dictionary to get the new dictionary\")\n", "kh.build_deployed_dictionary(\n", @@ -1875,7 +2070,9 @@ "# Set the file paths\n", "dictionary_file_path = os.path.join(kh.get_samples_dir(), \"Adult\", \"Adult.kdic\")\n", "data_table_path = os.path.join(kh.get_samples_dir(), \"Adult\", \"Adult.txt\")\n", - "results_dir = os.path.join(\"kh_samples\", \"scenario_prologue\")\n", + "report_file_path = os.path.join(\n", + " \"kh_samples\", \"scenario_prologue\", \"AdultAnalysisResults.khj\"\n", + ")\n", "\n", "# Set the maximum memory \"by hand\" with an scenario prologue\n", "scenario_prologue = \"\"\"\n", @@ -1889,7 +2086,7 @@ " \"Adult\",\n", " data_table_path,\n", " \"class\",\n", - " results_dir,\n", + " report_file_path,\n", " max_trees=0,\n", " scenario_prologue=scenario_prologue,\n", ")" @@ -1916,8 +2113,9 @@ "# Set the file paths\n", "dictionary_file_path = os.path.join(kh.get_samples_dir(), \"Iris\", \"Iris.kdic\")\n", "data_table_path = os.path.join(kh.get_samples_dir(), \"Iris\", \"Iris.txt\")\n", - "results_dir = os.path.join(\"kh_samples\", \"build_deployed_dictionary\")\n", - "deployed_dictionary_file_path = os.path.join(results_dir, \"SNB_Iris_deployed.kdic\")\n", + "output_dir = os.path.join(\"kh_samples\", \"build_deployed_dictionary\")\n", + "deployed_dictionary_file_path = os.path.join(output_dir, \"SNB_Iris_deployed.kdic\")\n", + "report_file_path = os.path.join(output_dir, \"IrisAnalysisResults.khj\")\n", "\n", "# Train the predictor\n", "_, modeling_dictionary_file_path = kh.train_predictor(\n", @@ -1925,7 +2123,7 @@ " \"Iris\",\n", " data_table_path,\n", " \"Class\",\n", - " results_dir,\n", + " report_file_path,\n", " max_trees=0,\n", ")\n", "\n", diff --git a/khiops/samples/samples.py b/khiops/samples/samples.py index f95a3d6c..6dfef376 100644 --- a/khiops/samples/samples.py +++ b/khiops/samples/samples.py @@ -78,14 +78,12 @@ def detect_data_table_format(): # Set the file paths data_table_path = os.path.join(kh.get_samples_dir(), "Adult", "Adult.txt") dictionary_file_path = os.path.join(kh.get_samples_dir(), "Adult", "Adult.kdic") - results_dir = os.path.join("kh_samples", "detect_data_table_format") - transformed_data_table_path = os.path.join( - results_dir, "AdultWithAnotherFormat.txt" - ) + output_dir = os.path.join("kh_samples", "detect_data_table_format") + transformed_data_table_path = os.path.join(output_dir, "AdultWithAnotherFormat.txt") # Create the output directory - if not os.path.isdir(results_dir): - os.mkdir(results_dir) + if not os.path.isdir(output_dir): + os.mkdir(output_dir) # Detect the format of the table format_spec = kh.detect_data_table_format(data_table_path) @@ -147,12 +145,10 @@ def export_dictionary_files(): # Set the file paths dictionary_file_path = os.path.join(kh.get_samples_dir(), "Adult", "Adult.kdic") - results_dir = os.path.join("kh_samples", "export_dictionary_file") - output_dictionary_file_path = os.path.join(results_dir, "ModifiedAdult.kdic") - output_dictionary_json_path = os.path.join(results_dir, "ModifiedAdult.kdicj") - alt_output_dictionary_json_path = os.path.join( - results_dir, "AltModifiedAdult.kdicj" - ) + output_dir = os.path.join("kh_samples", "export_dictionary_file") + output_dictionary_file_path = os.path.join(output_dir, "ModifiedAdult.kdic") + output_dictionary_json_path = os.path.join(output_dir, "ModifiedAdult.kdicj") + alt_output_dictionary_json_path = os.path.join(output_dir, "AltModifiedAdult.kdicj") # Load the dictionary domain from initial dictionary file # Then obtain the "Adult" dictionary within @@ -168,10 +164,10 @@ def export_dictionary_files(): # Create output directory if necessary if not os.path.exists("kh_samples"): os.mkdir("kh_samples") - os.mkdir(results_dir) + os.mkdir(output_dir) else: - if not os.path.exists(results_dir): - os.mkdir(results_dir) + if not os.path.exists(output_dir): + os.mkdir(output_dir) # Export to kdic domain.export_khiops_dictionary_file(output_dictionary_file_path) @@ -193,7 +189,9 @@ def train_predictor(): # Set the file paths dictionary_file_path = os.path.join(kh.get_samples_dir(), "Adult", "Adult.kdic") data_table_path = os.path.join(kh.get_samples_dir(), "Adult", "Adult.txt") - results_dir = os.path.join("kh_samples", "train_predictor") + analysis_report_file_path = os.path.join( + "kh_samples", "train_predictor", "AnalysisReport.khj" + ) # Train the predictor kh.train_predictor( @@ -201,7 +199,7 @@ def train_predictor(): "Adult", data_table_path, "class", - results_dir, + analysis_report_file_path, max_trees=0, ) @@ -215,15 +213,17 @@ def train_predictor_file_paths(): # Set the file paths dictionary_file_path = os.path.join(kh.get_samples_dir(), "Adult", "Adult.kdic") data_table_path = os.path.join(kh.get_samples_dir(), "Adult", "Adult.txt") - results_dir = os.path.join("kh_samples", "train_predictor_file_paths") + report_file_path = os.path.join( + "kh_samples", "train_predictor_file_paths", "AnalysisResults.khj" + ) # Train the predictor - report_file_path, modeling_dictionary_file_path = kh.train_predictor( + _, modeling_dictionary_file_path = kh.train_predictor( dictionary_file_path, "Adult", data_table_path, "class", - results_dir, + report_file_path, max_trees=0, ) print("Reports file available at " + report_file_path) @@ -254,9 +254,10 @@ def train_predictor_error_handling(): # Set the file paths with a nonexistent dictionary file dictionary_file_path = "NONEXISTENT_DICTIONARY_FILE.kdic" data_table_path = os.path.join(kh.get_samples_dir(), "Adult", "Adult.txt") - results_dir = os.path.join("kh_samples", "train_predictor_error_handling") - log_file_path = os.path.join(results_dir, "khiops.log") - scenario_path = os.path.join(results_dir, "scenario._kh") + output_dir = os.path.join("kh_samples", "train_predictor_error_handling") + report_file_path = os.path.join(output_dir, "AnalysisResults.khj") + log_file_path = os.path.join(output_dir, "khiops.log") + scenario_path = os.path.join(output_dir, "scenario._kh") # Train the predictor and handle the error try: @@ -265,7 +266,7 @@ def train_predictor_error_handling(): "Adult", data_table_path, "class", - results_dir, + report_file_path, trace=True, log_file_path=log_file_path, output_scenario_path=scenario_path, @@ -302,7 +303,9 @@ def train_predictor_mt(): dictionary_file_path = os.path.join(accidents_dir, "Accidents.kdic") accidents_table_path = os.path.join(accidents_dir, "Accidents.txt") vehicles_table_path = os.path.join(accidents_dir, "Vehicles.txt") - results_dir = os.path.join("kh_samples", "train_predictor_mt") + report_file_path = os.path.join( + "kh_samples", "train_predictor_mt", "AccidentsSummaryAnalysisResults.khj" + ) # Train the predictor. Besides the mandatory parameters, we specify: # - A python dictionary linking data paths to file paths for non-root tables @@ -313,8 +316,8 @@ def train_predictor_mt(): "Accident", accidents_table_path, "Gravity", - results_dir, - additional_data_tables={"Accident`Vehicles": vehicles_table_path}, + report_file_path, + additional_data_tables={"Vehicles": vehicles_table_path}, max_trees=0, ) @@ -335,7 +338,11 @@ def train_predictor_mt_with_specific_rules(): dictionary_file_path = os.path.join(accidents_dir, "Accidents.kdic") accidents_table_path = os.path.join(accidents_dir, "Accidents.txt") vehicles_table_path = os.path.join(accidents_dir, "Vehicles.txt") - results_dir = os.path.join("kh_samples", "train_predictor_mt_with_specific_rules") + report_file_path = os.path.join( + "kh_samples", + "train_predictor_mt_with_specific_rules", + "AccidentsSummaryAnalysisResults.khj", + ) # Train the predictor. Besides the mandatory parameters, it is specified: # - A python dictionary linking data paths to file paths for non-root tables @@ -347,8 +354,8 @@ def train_predictor_mt_with_specific_rules(): "Accident", accidents_table_path, "Gravity", - results_dir, - additional_data_tables={"Accident`Vehicles": vehicles_table_path}, + report_file_path, + additional_data_tables={"Vehicles": vehicles_table_path}, max_constructed_variables=1000, construction_rules=["TableMode", "TableSelection"], max_trees=0, @@ -368,7 +375,9 @@ def train_predictor_mt_snowflake(): vehicles_table_path = os.path.join(accidents_dir, "Vehicles.txt") users_table_path = os.path.join(accidents_dir, "Users.txt") places_table_path = os.path.join(accidents_dir, "Places.txt") - results_dir = os.path.join("kh_samples", "train_predictor_mt_snowflake") + report_file_path = os.path.join( + "kh_samples", "train_predictor_mt_snowflake", "AccidentsAnalysisResults.khj" + ) # Train the predictor. Besides the mandatory parameters, we specify: # - A python dictionary linking data paths to file paths for non-root tables @@ -379,11 +388,11 @@ def train_predictor_mt_snowflake(): "Accident", accidents_table_path, "Gravity", - results_dir, + report_file_path, additional_data_tables={ - "Accident`Vehicles": vehicles_table_path, - "Accident`Vehicles`Users": users_table_path, - "Accident`Place": places_table_path, + "Vehicles": vehicles_table_path, + "Vehicles/Users": users_table_path, + "Place": places_table_path, }, max_trees=0, ) @@ -401,7 +410,11 @@ def train_predictor_with_train_percentage(): # Set the file paths dictionary_file_path = os.path.join(kh.get_samples_dir(), "Adult", "Adult.kdic") data_table_path = os.path.join(kh.get_samples_dir(), "Adult", "Adult.txt") - results_dir = os.path.join("kh_samples", "train_predictor_with_train_percentage") + report_file_path = os.path.join( + "kh_samples", + "train_predictor_with_train_percentage", + "P90_AdultAnalysisResults.khj", + ) # Train the predictor. Besides the mandatory parameters, it is specified: # - A 90% sampling rate for the training dataset @@ -412,11 +425,10 @@ def train_predictor_with_train_percentage(): "Adult", data_table_path, "class", - results_dir, + report_file_path, sample_percentage=90, use_complement_as_test=True, max_trees=0, - results_prefix="P90_", ) @@ -429,7 +441,9 @@ def train_predictor_with_trees(): # Set the file paths dictionary_file_path = os.path.join(kh.get_samples_dir(), "Letter", "Letter.kdic") data_table_path = os.path.join(kh.get_samples_dir(), "Letter", "Letter.txt") - results_dir = os.path.join("kh_samples", "train_predictor_with_trees") + report_file_path = os.path.join( + "kh_samples", "train_predictor_with_trees", "P80_LetterAnalysisResults.khj" + ) # Train the predictor with at most 15 trees (default 10) kh.train_predictor( @@ -437,10 +451,9 @@ def train_predictor_with_trees(): "Letter", data_table_path, "lettr", - results_dir, + report_file_path, sample_percentage=80, use_complement_as_test=True, - results_prefix="P80_", max_trees=15, ) @@ -454,7 +467,9 @@ def train_predictor_with_pairs(): # Set the file paths dictionary_file_path = os.path.join(kh.get_samples_dir(), "Adult", "Adult.kdic") data_table_path = os.path.join(kh.get_samples_dir(), "Adult", "Adult.txt") - results_dir = os.path.join("kh_samples", "train_predictor_with_pairs") + report_file_path = os.path.join( + "kh_samples", "train_predictor_with_pairs", "AdultAnalysisResults.khj" + ) # Train the predictor with at most 10 pairs as follows: # - Include pairs age-race and capital_gain-capital_loss @@ -464,7 +479,7 @@ def train_predictor_with_pairs(): "Adult", data_table_path, "class", - results_dir, + report_file_path, use_complement_as_test=True, max_trees=0, max_pairs=10, @@ -499,9 +514,10 @@ def train_predictor_with_multiple_parameters(): # Set the file paths dictionary_file_path = os.path.join(kh.get_samples_dir(), "Adult", "Adult.kdic") data_table_path = os.path.join(kh.get_samples_dir(), "Adult", "Adult.txt") - results_dir = os.path.join("kh_samples", "train_predictor_with_multiple_parameters") - output_script_path = os.path.join(results_dir, "output_scenario._kh") - log_path = os.path.join(results_dir, "log.txt") + output_dir = os.path.join("kh_samples", "train_predictor_with_multiple_parameters") + report_file_path = os.path.join(output_dir, "AdultAnalysisResults.khj") + output_script_path = os.path.join(output_dir, "output_scenario._kh") + log_path = os.path.join(output_dir, "log.txt") # Train the predictor. Besides the mandatory parameters, we specify: # - The value "more" as main target value @@ -514,7 +530,7 @@ def train_predictor_with_multiple_parameters(): "Adult", data_table_path, "class", - results_dir, + report_file_path, main_target_value="more", output_scenario_path=output_script_path, log_file_path=log_path, @@ -532,8 +548,9 @@ def train_predictor_detect_format(): # Set the file paths dictionary_file_path = os.path.join(kh.get_samples_dir(), "Iris", "Iris.kdic") data_table_path = os.path.join(kh.get_samples_dir(), "Iris", "Iris.txt") - results_dir = os.path.join("kh_samples", "train_predictor_detect_format") - transformed_data_table_path = os.path.join(results_dir, "TransformedIris.txt") + output_dir = os.path.join("kh_samples", "train_predictor_detect_format") + transformed_data_table_path = os.path.join(output_dir, "TransformedIris.txt") + report_file_path = os.path.join(output_dir, "IrisAnalysisResults.khj") # Transform the database format from header_line=True and field_separator=TAB # to header_line=False and field_separator="," @@ -554,7 +571,7 @@ def train_predictor_detect_format(): "Iris", transformed_data_table_path, "Class", - results_dir, + report_file_path, header_line=True, field_separator="", ) @@ -571,7 +588,7 @@ def train_predictor_detect_format(): "Iris", transformed_data_table_path, "Class", - results_dir, + report_file_path, ) @@ -585,12 +602,12 @@ def train_predictor_with_cross_validation(): # Set the file paths dictionary_file_path = os.path.join(kh.get_samples_dir(), "Adult", "Adult.kdic") data_table_path = os.path.join(kh.get_samples_dir(), "Adult", "Adult.txt") - results_dir = os.path.join("kh_samples", "train_predictor_with_cross_validation") - fold_dictionary_file_path = os.path.join(results_dir, "AdultWithFolding.kdic") + output_dir = os.path.join("kh_samples", "train_predictor_with_cross_validation") + fold_dictionary_file_path = os.path.join(output_dir, "AdultWithFolding.kdic") # Create the output directory - if not os.path.isdir(results_dir): - os.mkdir(results_dir) + if not os.path.isdir(output_dir): + os.mkdir(output_dir) # Load the learning dictionary object domain = kh.read_dictionary_file(dictionary_file_path) @@ -627,35 +644,39 @@ def train_predictor_with_cross_validation(): train_aucs = [] test_aucs = [] for fold_index in range(1, fold_number + 1): + analysis_report_file_path = os.path.join( + output_dir, "Fold" + str(fold_index) + "AdultAnalysisResults.khj" + ) # Train a model from the sub-dataset where IsInTrainDataset is 1 - train_reports_path, modeling_dictionary_file_path = kh.train_predictor( + _, modeling_dictionary_file_path = kh.train_predictor( domain, "Adult", data_table_path, "class", - results_dir, + analysis_report_file_path, sample_percentage=100, selection_variable="IsInTrainDataset" + str(fold_index), selection_value=1, max_trees=0, - results_prefix="Fold" + str(fold_index), ) + evaluation_report_file_path = os.path.join( + output_dir, "Fold" + str(fold_index) + "AdultEvaluationResults.khj" + ) # Evaluate the resulting model in the subsets where IsInTrainDataset is 0 test_evaluation_report_path = kh.evaluate_predictor( modeling_dictionary_file_path, - "Adult", + "SNB_Adult", data_table_path, - results_dir, + evaluation_report_file_path, sample_percentage=100, selection_variable="IsInTrainDataset" + str(fold_index), selection_value=0, - results_prefix="Fold" + str(fold_index), ) # Obtain the train AUC from the train report and the test AUC from the # evaluation report and print them - train_results = kh.read_analysis_results_file(train_reports_path) + train_results = kh.read_analysis_results_file(analysis_report_file_path) test_evaluation_results = kh.read_analysis_results_file( test_evaluation_report_path ) @@ -709,7 +730,8 @@ def display_test_results(json_result_file_path): # Set the file paths dictionary_file_path = os.path.join(kh.get_samples_dir(), "Adult", "Adult.kdic") data_table_path = os.path.join(kh.get_samples_dir(), "Adult", "Adult.txt") - results_dir = os.path.join("kh_samples", "multiple_train_predictor") + output_dir = os.path.join("kh_samples", "multiple_train_predictor") + report_file_path = os.path.join(output_dir, "AdultAnalysisResults.khj") # Read the dictionary file to obtain an instance of class Dictionary dictionary_domain = kh.read_dictionary_file(dictionary_file_path) @@ -717,20 +739,20 @@ def display_test_results(json_result_file_path): # Train a SNB model using all the variables print("\t#vars\ttrain auc\ttest auc") - json_result_file_path, _ = kh.train_predictor( + kh.train_predictor( dictionary_file_path, "Adult", data_table_path, "class", - results_dir, + report_file_path, sample_percentage=70, use_complement_as_test=True, max_trees=0, ) - display_test_results(json_result_file_path) + display_test_results(report_file_path) # Read results to obtain the variables sorted by decreasing Level - analysis_results = kh.read_analysis_results_file(json_result_file_path) + analysis_results = kh.read_analysis_results_file(report_file_path) preparation_results = analysis_results.preparation_report # Train a sequence of models with a decreasing number of variables @@ -744,21 +766,53 @@ def display_test_results(json_result_file_path): dictionary.get_variable(variable.name).used = False # Train the model with this dictionary domain object - prefix = f"V{variable_number - 1 - i}_" - json_result_file_path, _ = kh.train_predictor( + report_file_path = os.path.join( + output_dir, f"V{variable_number - 1 - i}_AdultAnalysisResults.khj" + ) + kh.train_predictor( dictionary_domain, "Adult", data_table_path, "class", - results_dir, + report_file_path, sample_percentage=70, use_complement_as_test=True, - results_prefix=prefix, max_trees=0, ) # Show a preview of the results - display_test_results(json_result_file_path) + display_test_results(report_file_path) + + +def interpret_predictor(): + """Builds intepretation model for existing predictor + + It calls `~.api.train_predictor` and `~.api.interpret_predictor` only with + their mandatory parameters. + """ + # Imports + import os + from khiops import core as kh + + dictionary_file_path = os.path.join(kh.get_samples_dir(), "Adult", "Adult.kdic") + data_table_path = os.path.join(kh.get_samples_dir(), "Adult", "Adult.txt") + output_dir = os.path.join("kh_samples", "interpret_predictor") + analysis_report_file_path = os.path.join(output_dir, "AdultAnalysisResults.khj") + interpretor_file_path = os.path.join(output_dir, "AdultIntepretationModel.kdic") + + # Build prediction model + _, predictor_file_path = kh.train_predictor( + dictionary_file_path, + "Adult", + data_table_path, + "class", + analysis_report_file_path, + ) + + # Build interpretation model + kh.interpret_predictor(predictor_file_path, "SNB_Adult", interpretor_file_path) + + print(f"The intepretation model is '{interpretor_file_path}'") def evaluate_predictor(): @@ -773,24 +827,29 @@ def evaluate_predictor(): # Set the file paths dictionary_file_path = os.path.join(kh.get_samples_dir(), "Adult", "Adult.kdic") data_table_path = os.path.join(kh.get_samples_dir(), "Adult", "Adult.txt") - results_dir = os.path.join("kh_samples", "evaluate_predictor") - model_dictionary_file_path = os.path.join(results_dir, "Modeling.kdic") + output_dir = os.path.join("kh_samples", "evaluate_predictor") + analysis_report_file_path = os.path.join(output_dir, "AdultAnalysisResults.khj") # Train the predictor - kh.train_predictor( + _, model_dictionary_file_path = kh.train_predictor( dictionary_file_path, "Adult", data_table_path, "class", - results_dir, + analysis_report_file_path, max_trees=0, ) + evaluation_report_file_path = os.path.join(output_dir, "AdultEvaluationResults.khj") + # Evaluate the predictor - report_file_path = kh.evaluate_predictor( - model_dictionary_file_path, "Adult", data_table_path, results_dir + kh.evaluate_predictor( + model_dictionary_file_path, + "SNB_Adult", + data_table_path, + evaluation_report_file_path, ) - print("Evaluation report available at " + report_file_path) + print("Evaluation report available at " + evaluation_report_file_path) def access_predictor_evaluation_report(): @@ -806,8 +865,9 @@ def access_predictor_evaluation_report(): # Set the file paths dictionary_file_path = os.path.join(kh.get_samples_dir(), "Adult", "Adult.kdic") data_table_path = os.path.join(kh.get_samples_dir(), "Adult", "Adult.txt") - results_dir = os.path.join("kh_samples", "access_predictor_evaluation_report") - evaluation_report_path = os.path.join(results_dir, "AllReports.khj") + report_file_path = os.path.join( + "kh_samples", "access_predictor_evaluation_report", "AdultAnalysisReport.khj" + ) # Train the SNB predictor and some univariate predictors # Note: Evaluation in test is 30% by default @@ -816,13 +876,12 @@ def access_predictor_evaluation_report(): "Adult", data_table_path, "class", - results_dir, + report_file_path, max_trees=0, - univariate_predictor_number=4, ) # Obtain the evaluation results - results = kh.read_analysis_results_file(evaluation_report_path) + results = kh.read_analysis_results_file(report_file_path) evaluation_report = results.test_evaluation_report snb_performance = evaluation_report.get_snb_performance() @@ -862,9 +921,9 @@ def access_predictor_evaluation_report(): + str(random_lift_curve.values[i]) ) - # Print univariate metrics for an univariate predictor + # Print metrics for an SNB predictor predictor_performance = evaluation_report.get_predictor_performance( - "Univariate relationship" + "Selective Naive Bayes" ) print("\n\nperformance metrics for " + predictor_performance.name) for metric_name in predictor_performance.get_metric_names(): @@ -883,11 +942,13 @@ def train_recoder(): # Set the file paths dictionary_file_path = os.path.join(kh.get_samples_dir(), "Adult", "Adult.kdic") data_table_path = os.path.join(kh.get_samples_dir(), "Adult", "Adult.txt") - results_dir = os.path.join("kh_samples", "train_recoder") + report_file_path = os.path.join( + "kh_samples", "train_recoder", "AdultAnalysisResults.khj" + ) # Train the recoder model kh.train_recoder( - dictionary_file_path, "Adult", data_table_path, "class", results_dir + dictionary_file_path, "Adult", data_table_path, "class", report_file_path ) @@ -903,7 +964,11 @@ def train_recoder_with_multiple_parameters(): # Set the file paths dictionary_file_path = os.path.join(kh.get_samples_dir(), "Adult", "Adult.kdic") data_table_path = os.path.join(kh.get_samples_dir(), "Adult", "Adult.txt") - results_dir = os.path.join("kh_samples", "train_recoder_with_multiple_parameters") + report_file_path = os.path.join( + "kh_samples", + "train_recoder_with_multiple_parameters", + "AdultAnalysisResults.khj", + ) # Train the recoder model kh.train_recoder( @@ -911,7 +976,7 @@ def train_recoder_with_multiple_parameters(): "Adult", data_table_path, "class", - results_dir, + report_file_path, max_pairs=10, categorical_recoding_method="part label", numerical_recoding_method="part label", @@ -932,7 +997,9 @@ def train_recoder_mt_flatten(): dictionary_file_path = os.path.join(accidents_dir, "Accidents.kdic") accidents_table_path = os.path.join(accidents_dir, "Accidents.txt") vehicles_table_path = os.path.join(accidents_dir, "Vehicles.txt") - results_dir = os.path.join("kh_samples", "train_recoder_mt_flatten") + report_file_path = os.path.join( + "kh_samples", "train_recoder_mt_flatten", "AccidentsAnalysisResults.khj" + ) # Train the recoder. Besides the mandatory parameters, it is specified: # - A python dictionary linking data paths to file paths for non-root tables @@ -944,8 +1011,8 @@ def train_recoder_mt_flatten(): "Accident", accidents_table_path, "Gravity", - results_dir, - additional_data_tables={"Accident`Vehicles": vehicles_table_path}, + report_file_path, + additional_data_tables={"Vehicles": vehicles_table_path}, max_constructed_variables=1000, informative_variables_only=False, categorical_recoding_method="none", @@ -956,7 +1023,7 @@ def train_recoder_mt_flatten(): def deploy_model(): - """Deploys a model in the simplest possible + """Deploys a model in the simplest way possible It is a call to `~.api.deploy_model` with its mandatory parameters. @@ -971,17 +1038,17 @@ def deploy_model(): # Set the file paths dictionary_file_path = os.path.join(kh.get_samples_dir(), "Adult", "Adult.kdic") data_table_path = os.path.join(kh.get_samples_dir(), "Adult", "Adult.txt") - results_dir = os.path.join("kh_samples", "deploy_model") - model_dictionary_file_path = os.path.join(results_dir, "Modeling.kdic") - output_data_table_path = os.path.join(results_dir, "ScoresAdult.txt") + output_dir = os.path.join("kh_samples", "deploy_model") + report_file_path = os.path.join(output_dir, "AdultAnalysisResults.khj") + output_data_table_path = os.path.join(output_dir, "ScoresAdult.txt") # Train the predictor - kh.train_predictor( + _, model_dictionary_file_path = kh.train_predictor( dictionary_file_path, "Adult", data_table_path, "class", - results_dir, + report_file_path, max_trees=0, ) @@ -1011,18 +1078,18 @@ def deploy_model_mt(): dictionary_file_path = os.path.join(accidents_dir, "Accidents.kdic") accidents_table_path = os.path.join(accidents_dir, "Accidents.txt") vehicles_table_path = os.path.join(accidents_dir, "Vehicles.txt") - results_dir = os.path.join("kh_samples", "deploy_model_mt") - model_dictionary_file_path = os.path.join(results_dir, "Modeling.kdic") - output_data_table_path = os.path.join(results_dir, "TransferredAccidents.txt") + output_dir = os.path.join("kh_samples", "deploy_model_mt") + report_file_path = os.path.join(output_dir, "AccidentsSummaryAnalysisResults.khj") + output_data_table_path = os.path.join(output_dir, "TransferredAccidents.txt") # Train the predictor (see train_predictor_mt for details) - kh.train_predictor( + _, model_dictionary_file_path = kh.train_predictor( dictionary_file_path, "Accident", accidents_table_path, "Gravity", - results_dir, - additional_data_tables={"Accident`Vehicles": vehicles_table_path}, + report_file_path, + additional_data_tables={"Vehicles": vehicles_table_path}, max_trees=0, ) @@ -1034,7 +1101,67 @@ def deploy_model_mt(): "SNB_Accident", accidents_table_path, output_data_table_path, - additional_data_tables={"SNB_Accident`Vehicles": vehicles_table_path}, + additional_data_tables={"Vehicles": vehicles_table_path}, + ) + + +def deploy_model_mt_with_interpretation(): + """Deploys a multi-table interpretor in the simplest way possible + + It is a call to `~.api.deploy_model` with additional parameters to handle + multi-table deployment. + + In this example, a Selective Naive Bayes (SNB) interpretation model is + deployed by applying its associated dictionary to the input database. + The model variable importances are written to the output data table. + """ + # Imports + import os + from khiops import core as kh + + # Set the file paths + accidents_dir = os.path.join(kh.get_samples_dir(), "AccidentsSummary") + dictionary_file_path = os.path.join(accidents_dir, "Accidents.kdic") + accidents_table_path = os.path.join(accidents_dir, "Accidents.txt") + vehicles_table_path = os.path.join(accidents_dir, "Vehicles.txt") + output_dir = os.path.join("kh_samples", "deploy_model_mt") + report_file_path = os.path.join(output_dir, "AccidentsSummaryAnalysisResults.khj") + interpretor_file_path = os.path.join( + output_dir, "AccidentsSummaryInterpretationModel.kdic" + ) + output_data_table_path = os.path.join(output_dir, "InterpretedAccidents.txt") + + # Train the predictor (see train_predictor_mt for details) + # Add max_evaluated_variables so that an interpretation model can be built + # (see https://github.com/KhiopsML/khiops/issues/577) + _, model_dictionary_file_path = kh.train_predictor( + dictionary_file_path, + "Accident", + accidents_table_path, + "Gravity", + report_file_path, + additional_data_tables={"Vehicles": vehicles_table_path}, + max_trees=0, + max_evaluated_variables=10, + ) + + # Interpret the predictor + kh.interpret_predictor( + model_dictionary_file_path, + "SNB_Accident", + interpretor_file_path, + reinforcement_target_value="NonLethal", + ) + + # Deploy the interpretation model on the database + # Besides the mandatory parameters, it is specified: + # - A python dictionary linking data paths to file paths for non-root tables + kh.deploy_model( + interpretor_file_path, + "Interpretation_SNB_Accident", + accidents_table_path, + output_data_table_path, + additional_data_tables={"Vehicles": vehicles_table_path}, ) @@ -1051,24 +1178,24 @@ def deploy_model_mt_snowflake(): vehicles_table_path = os.path.join(accidents_dir, "Vehicles.txt") users_table_path = os.path.join(accidents_dir, "Users.txt") places_table_path = os.path.join(accidents_dir, "Places.txt") - results_dir = os.path.join("kh_samples", "deploy_model_mt_snowflake") - model_dictionary_file_path = os.path.join(results_dir, "Modeling.kdic") - output_data_table_path = os.path.join(results_dir, "TransferredAccidents.txt") + output_dir = os.path.join("kh_samples", "deploy_model_mt_snowflake") + report_file_path = os.path.join(output_dir, "AccidentsAnalysisResults.khj") + output_data_table_path = os.path.join(output_dir, "TransferredAccidents.txt") # Train the predictor. Besides the mandatory parameters, we specify: # - A python dictionary linking data paths to file paths for non-root tables # - To not construct any decision tree # The default number of automatic features is 100 - kh.train_predictor( + _, model_dictionary_file_path = kh.train_predictor( dictionary_file_path, "Accident", accidents_table_path, "Gravity", - results_dir, + report_file_path, additional_data_tables={ - "Accident`Vehicles": vehicles_table_path, - "Accident`Vehicles`Users": users_table_path, - "Accident`Place": places_table_path, + "Vehicles": vehicles_table_path, + "Vehicles/Users": users_table_path, + "Place": places_table_path, }, max_trees=0, ) @@ -1082,9 +1209,9 @@ def deploy_model_mt_snowflake(): accidents_table_path, output_data_table_path, additional_data_tables={ - "SNB_Accident`Vehicles": vehicles_table_path, - "SNB_Accident`Vehicles`Users": users_table_path, - "SNB_Accident`Place": places_table_path, + "Vehicles": vehicles_table_path, + "Vehicles/Users": users_table_path, + "Place": places_table_path, }, ) @@ -1108,17 +1235,17 @@ def deploy_model_expert(): # Set the file paths dictionary_file_path = os.path.join(kh.get_samples_dir(), "Adult", "Adult.kdic") data_table_path = os.path.join(kh.get_samples_dir(), "Adult", "Adult.txt") - results_dir = os.path.join("kh_samples", "deploy_model_expert") - model_dictionary_file_path = os.path.join(results_dir, "Modeling.kdic") - output_data_table_path = os.path.join(results_dir, "ScoresAdult.txt") + output_dir = os.path.join("kh_samples", "deploy_model_expert") + report_file_path = os.path.join(output_dir, "AdultAnalysisResults.khj") + output_data_table_path = os.path.join(output_dir, "ScoresAdult.txt") # Train the predictor - kh.train_predictor( + _, model_dictionary_file_path = kh.train_predictor( dictionary_file_path, "Adult", data_table_path, "class", - results_dir, + report_file_path, max_trees=0, ) @@ -1150,8 +1277,9 @@ def deploy_classifier_for_metrics(): # Set the file paths dictionary_file_path = os.path.join(kh.get_samples_dir(), "Adult", "Adult.kdic") data_table_path = os.path.join(kh.get_samples_dir(), "Adult", "Adult.txt") - results_dir = os.path.join("kh_samples", "deploy_classifier_for_metrics") - output_data_table_path = os.path.join(results_dir, "ScoresAdult.txt") + output_dir = os.path.join("kh_samples", "deploy_classifier_for_metrics") + report_file_path = os.path.join(output_dir, "AdultAnalysisResults.khj") + output_data_table_path = os.path.join(output_dir, "ScoresAdult.txt") # Train the classifier for the target "class" _, modeling_dictionary_file_path = kh.train_predictor( @@ -1159,10 +1287,9 @@ def deploy_classifier_for_metrics(): "Adult", data_table_path, "class", - results_dir, + report_file_path, max_trees=0, ) - # Obtain the scores of the SNB on the test dataset to calculate the PR curve kh.deploy_predictor_for_metrics( modeling_dictionary_file_path, @@ -1218,8 +1345,9 @@ def deploy_regressor_for_metrics(): # Set the file paths dictionary_file_path = os.path.join(kh.get_samples_dir(), "Adult", "Adult.kdic") data_table_path = os.path.join(kh.get_samples_dir(), "Adult", "Adult.txt") - results_dir = os.path.join("kh_samples", "deploy_regressor_for_metrics") - output_data_table_path = os.path.join(results_dir, "TrueAndPredictedAges.txt") + output_dir = os.path.join("kh_samples", "deploy_regressor_for_metrics") + report_file_path = os.path.join(output_dir, "AdultAnalysisResults.khj") + output_data_table_path = os.path.join(output_dir, "TrueAndPredictedAges.txt") # Train the regressor for the target "age" (with 20% train to be quick) _, modeling_dictionary_file_path = kh.train_predictor( @@ -1227,7 +1355,7 @@ def deploy_regressor_for_metrics(): "Adult", data_table_path, "age", - results_dir, + report_file_path, sample_percentage=20, max_trees=0, ) @@ -1372,15 +1500,17 @@ def train_coclustering(): splice_dir = os.path.join(kh.get_samples_dir(), "SpliceJunction") dictionary_file_path = os.path.join(splice_dir, "SpliceJunction.kdic") data_table_path = os.path.join(splice_dir, "SpliceJunctionDNA.txt") - results_dir = os.path.join("kh_samples", "train_coclustering") + coclustering_report_path = os.path.join( + "kh_samples", "train_coclustering", "SpliceJunctionCoclusteringResults.khcj" + ) # Train a coclustering model for variables "SampleId" and "Char" - coclustering_report_path = kh.train_coclustering( + kh.train_coclustering( dictionary_file_path, "SpliceJunctionDNA", data_table_path, ["SampleId", "Char"], - results_dir, + coclustering_report_path, ) print(f"Coclustering report file available at {coclustering_report_path}") @@ -1388,6 +1518,42 @@ def train_coclustering(): # kh.visualize_report(coclustering_report_path) +def train_instance_variable_coclustering(): + """Trains an instance-variable coclustering model in the simplest way possible + + It is a call to `~.api.train_instance_variable_coclustering` with only its mandatory + parameters. + """ + # Imports + import os + from khiops import core as kh + + # Set the file paths + iris_dir = os.path.join(kh.get_samples_dir(), "Iris") + dictionary_file_path = os.path.join(iris_dir, "Iris.kdic") + data_table_path = os.path.join(iris_dir, "Iris.txt") + coclustering_report_path = os.path.join( + "kh_samples", + "train_instance_variable_coclustering", + "IrisInstanceVariable_CoclusteringResults.khcj", + ) + + # Train a coclustering model for variables "SampleId" and "Char" + kh.train_instance_variable_coclustering( + dictionary_file_path, + "Iris", + data_table_path, + coclustering_report_path, + ) + print( + "Instance-variable coclustering report file available " + f"at {coclustering_report_path}" + ) + + # If you have Khiops Co-Visualization installed you may open the report as follows + # kh.visualize_report(coclustering_report_path) + + def simplify_coclustering(): """Simplifies a coclustering model while preserving 80% of its information""" # Imports @@ -1398,9 +1564,11 @@ def simplify_coclustering(): splice_dir = os.path.join(kh.get_samples_dir(), "SpliceJunction") dictionary_file_path = os.path.join(splice_dir, "SpliceJunction.kdic") data_table_path = os.path.join(splice_dir, "SpliceJunctionDNA.txt") - results_dir = os.path.join("kh_samples", "simplify_coclustering") - coclustering_file_path = os.path.join(results_dir, "Coclustering.khc") - simplified_coclustering_file_name = "simplified_coclustering.khc" + output_dir = os.path.join("kh_samples", "simplify_coclustering") + coclustering_file_path = os.path.join(output_dir, "Coclustering.khcj") + simplified_coclustering_file_path = os.path.join( + output_dir, "simplified_coclustering.khcj" + ) # Train coclustering model for variables "SampleId" and "Char" kh.train_coclustering( @@ -1408,7 +1576,7 @@ def simplify_coclustering(): "SpliceJunctionDNA", data_table_path, ["SampleId", "Char"], - results_dir, + coclustering_file_path, ) # Simplify the trained coclustering with the constraints @@ -1416,8 +1584,7 @@ def simplify_coclustering(): # - maximum total parts number: 4 kh.simplify_coclustering( coclustering_file_path, - simplified_coclustering_file_name, - results_dir, + simplified_coclustering_file_path, max_preserved_information=80, max_total_parts=4, ) @@ -1429,9 +1596,9 @@ def extract_clusters(): splice_dir = os.path.join(kh.get_samples_dir(), "SpliceJunction") dictionary_file_path = os.path.join(splice_dir, "SpliceJunction.kdic") data_table_path = os.path.join(splice_dir, "SpliceJunctionDNA.txt") - results_dir = os.path.join("kh_samples", "extract_clusters") - coclustering_file_path = os.path.join(results_dir, "Coclustering.khc") - clusters_file_path = os.path.join(results_dir, "extracted_clusters.txt") + output_dir = os.path.join("kh_samples", "extract_clusters") + coclustering_file_path = os.path.join(output_dir, "Coclustering.khcj") + clusters_file_path = os.path.join(output_dir, "extracted_clusters.txt") # Train a coclustering model for variables "SampleId" and "Char" kh.train_coclustering( @@ -1439,7 +1606,7 @@ def extract_clusters(): "SpliceJunctionDNA", data_table_path, ["SampleId", "Char"], - results_dir, + coclustering_file_path, ) # Extract clusters @@ -1447,7 +1614,7 @@ def extract_clusters(): def deploy_coclustering(): - """Deploys an "individual-variable" coclustering""" + """Deploys a coclustering""" # Imports import os from khiops import core as kh @@ -1456,8 +1623,10 @@ def deploy_coclustering(): splice_dir = os.path.join(kh.get_samples_dir(), "SpliceJunction") data_table_path = os.path.join(splice_dir, "SpliceJunctionDNA.txt") dictionary_file_path = os.path.join(splice_dir, "SpliceJunction.kdic") - results_dir = os.path.join("kh_samples", "deploy_coclustering") - coclustering_file_path = os.path.join(results_dir, "Coclustering.khc") + output_dir = os.path.join("kh_samples", "deploy_coclustering") + coclustering_file_path = os.path.join(output_dir, "Coclustering.khcj") + coclustering_dictionary_file_path = os.path.join(output_dir, "Coclustering.kdic") + output_data_table_path = os.path.join(output_dir, "DeployedSpliceJunctionDNA.txt") # Train a coclustering model for variables "SampleId" and "Char" kh.train_coclustering( @@ -1465,7 +1634,7 @@ def deploy_coclustering(): "SpliceJunctionDNA", data_table_path, ["SampleId", "Char"], - results_dir, + coclustering_file_path, ) # Deploy "Char" clusters in the training database @@ -1476,13 +1645,14 @@ def deploy_coclustering(): coclustering_file_path, ["SampleId"], "Char", - results_dir, + coclustering_dictionary_file_path, + output_data_table_path, header_line=True, ) def deploy_coclustering_expert(): - """Deploys an "individual-variable" coclustering step-by-step + """Deploys a coclustering step-by-step The `.api.prepare_coclustering_deployment` method is called twice to prepare the deployment at two granularity levels. Then, the model is deployed and the respective @@ -1499,8 +1669,8 @@ def deploy_coclustering_expert(): dictionary_file_path = os.path.join(splice_dir, "SpliceJunction.kdic") data_table_path = os.path.join(splice_dir, "SpliceJunction.txt") secondary_data_table_path = os.path.join(splice_dir, "SpliceJunctionDNA.txt") - results_dir = os.path.join("kh_samples", "deploy_coclustering_expert") - coclustering_file_path = os.path.join(results_dir, "Coclustering.khc") + output_dir = os.path.join("kh_samples", "deploy_coclustering_expert") + coclustering_file_path = os.path.join(output_dir, "Coclustering.khcj") # Train a coclustering model for variables "SampleId" and "Char" print("train coclustering on SpliceJunctionDNA") @@ -1509,39 +1679,38 @@ def deploy_coclustering_expert(): "SpliceJunctionDNA", secondary_data_table_path, ["SampleId", "Char"], - results_dir, + coclustering_file_path, ) print("prepare_coclustering_deployment") # The input dictionary is extended with new coclustering based variables + augmented_dictionary_file_path = os.path.join(output_dir, "Coclustering.kdic") kh.prepare_coclustering_deployment( dictionary_file_path, "SpliceJunction", coclustering_file_path, "DNA", "SampleId", - results_dir, + augmented_dictionary_file_path, ) - augmented_dictionary_file_path = os.path.join(results_dir, "Coclustering.kdic") print("prepare_coclustering_deployment with at most two clusters") # Extend the already extended dictionary with the new variables from a simplified CC + reaugmented_dictionary_file_path = os.path.join( + output_dir, "ReaugmentedCoclustering.kdic" + ) kh.prepare_coclustering_deployment( augmented_dictionary_file_path, "SpliceJunction", coclustering_file_path, "DNA", "SampleId", - results_dir, - results_prefix="Reaugmented", + reaugmented_dictionary_file_path, variables_prefix="C2_", max_part_numbers={"SampleId": 2}, ) - reaugmented_dictionary_file_path = os.path.join( - results_dir, "ReaugmentedCoclustering.kdic" - ) - output_data_table_path = os.path.join(results_dir, "TransferredSpliceJunction.txt") + output_data_table_path = os.path.join(output_dir, "TransferredSpliceJunction.txt") # Deploy the coclustering with the extended dictionary print("deploy_model with the new coclustering based variables") @@ -1550,11 +1719,11 @@ def deploy_coclustering_expert(): "SpliceJunction", data_table_path, output_data_table_path, - additional_data_tables={"SpliceJunction`DNA": secondary_data_table_path}, + additional_data_tables={"DNA": secondary_data_table_path}, ) deployed_dictionary_file_path = os.path.join( - results_dir, "Transferred_Coclustering.kdic" + output_dir, "Transferred_Coclustering.kdic" ) print("build_deployed_dictionary to get the new dictionary") kh.build_deployed_dictionary( @@ -1577,7 +1746,9 @@ def scenario_prologue(): # Set the file paths dictionary_file_path = os.path.join(kh.get_samples_dir(), "Adult", "Adult.kdic") data_table_path = os.path.join(kh.get_samples_dir(), "Adult", "Adult.txt") - results_dir = os.path.join("kh_samples", "scenario_prologue") + report_file_path = os.path.join( + "kh_samples", "scenario_prologue", "AdultAnalysisResults.khj" + ) # Set the maximum memory "by hand" with an scenario prologue scenario_prologue = """ @@ -1591,7 +1762,7 @@ def scenario_prologue(): "Adult", data_table_path, "class", - results_dir, + report_file_path, max_trees=0, scenario_prologue=scenario_prologue, ) @@ -1606,8 +1777,9 @@ def build_deployed_dictionary(): # Set the file paths dictionary_file_path = os.path.join(kh.get_samples_dir(), "Iris", "Iris.kdic") data_table_path = os.path.join(kh.get_samples_dir(), "Iris", "Iris.txt") - results_dir = os.path.join("kh_samples", "build_deployed_dictionary") - deployed_dictionary_file_path = os.path.join(results_dir, "SNB_Iris_deployed.kdic") + output_dir = os.path.join("kh_samples", "build_deployed_dictionary") + deployed_dictionary_file_path = os.path.join(output_dir, "SNB_Iris_deployed.kdic") + report_file_path = os.path.join(output_dir, "IrisAnalysisResults.khj") # Train the predictor _, modeling_dictionary_file_path = kh.train_predictor( @@ -1615,7 +1787,7 @@ def build_deployed_dictionary(): "Iris", data_table_path, "Class", - results_dir, + report_file_path, max_trees=0, ) @@ -1651,6 +1823,7 @@ def build_deployed_dictionary(): train_predictor_with_multiple_parameters, train_predictor_detect_format, train_predictor_with_cross_validation, + interpret_predictor, multiple_train_predictor, evaluate_predictor, access_predictor_evaluation_report, @@ -1659,6 +1832,7 @@ def build_deployed_dictionary(): train_recoder_mt_flatten, deploy_model, deploy_model_mt, + deploy_model_mt_with_interpretation, deploy_model_mt_snowflake, deploy_model_expert, deploy_classifier_for_metrics, @@ -1667,6 +1841,7 @@ def build_deployed_dictionary(): sort_data_table_expert, extract_keys_from_data_table, train_coclustering, + train_instance_variable_coclustering, simplify_coclustering, extract_clusters, deploy_coclustering, diff --git a/khiops/samples/samples_sklearn.ipynb b/khiops/samples/samples_sklearn.ipynb index e8bbe94b..6a15c898 100644 --- a/khiops/samples/samples_sklearn.ipynb +++ b/khiops/samples/samples_sklearn.ipynb @@ -972,185 +972,6 @@ "print(X_clusters)\n", "print(\"---\")" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### `khiops_classifier_multitable_list()`\n\n", - "Trains a KhiopsClassifier using a list dataset specification\n\n .. warning::\n This dataset input method is **Deprecated** and will be removed in Khiops 11.\n \n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Imports\n", - "import os\n", - "import pandas as pd\n", - "from khiops import core as kh\n", - "from khiops.sklearn import KhiopsClassifier\n", - "from sklearn import metrics\n", - "from sklearn.model_selection import train_test_split\n", - "\n", - "# Load the root table of the dataset into a pandas dataframe\n", - "accidents_data_dir = os.path.join(kh.get_samples_dir(), \"AccidentsSummary\")\n", - "accidents_df = pd.read_csv(\n", - " os.path.join(accidents_data_dir, \"Accidents.txt\"),\n", - " sep=\"\\t\",\n", - ")\n", - "X = accidents_df.drop(\"Gravity\", axis=1)\n", - "y = accidents_df[\"Gravity\"]\n", - "\n", - "# Split the dataset into train and test\n", - "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1)\n", - "\n", - "# Load the secondary table of the dataset into a pandas dataframe\n", - "vehicles_df = pd.read_csv(os.path.join(accidents_data_dir, \"Vehicles.txt\"), sep=\"\\t\")\n", - "\n", - "# Split the secondary dataframe with the keys of the split root dataframe\n", - "X_train_ids = X_train[\"AccidentId\"].to_frame()\n", - "X_test_ids = X_test[\"AccidentId\"].to_frame()\n", - "X_train_secondary = X_train_ids.merge(vehicles_df, on=\"AccidentId\")\n", - "X_test_secondary = X_test_ids.merge(vehicles_df, on=\"AccidentId\")\n", - "\n", - "# Create the classifier specifying the key column name\n", - "khc = KhiopsClassifier(key=\"AccidentId\")\n", - "\n", - "# Train the classifier\n", - "khc.fit([X_train, X_train_secondary], y_train)\n", - "\n", - "# Predict the class on the test dataset\n", - "y_test_pred = khc.predict([X_test, X_test_secondary])\n", - "print(\"Predicted classes (first 10):\")\n", - "print(y_test_pred[:10])\n", - "print(\"---\")\n", - "\n", - "# Predict the class probability on the test dataset\n", - "y_test_probas = khc.predict_proba([X_test, X_test_secondary])\n", - "print(\"Predicted class probabilities (first 10):\")\n", - "print(y_test_probas[:10])\n", - "print(\"---\")\n", - "\n", - "# Evaluate accuracy and auc metrics on the test dataset\n", - "test_accuracy = metrics.accuracy_score(y_test, y_test_pred)\n", - "test_auc = metrics.roc_auc_score(y_test, y_test_probas[:, 1])\n", - "print(f\"Test accuracy = {test_accuracy}\")\n", - "print(f\"Test auc = {test_auc}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### `khiops_classifier_multitable_star_file()`\n\n", - "Trains a `.KhiopsClassifier` with a file path based dataset\n\n .. warning::\n This dataset input method is **Deprecated** and will be removed in Khiops 11.\n If you need to handle large datasets that do not easily fit into memory then you\n may use the `~.khiops.core` API directly, which allows to specify file paths\n directly.\n \n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Imports\n", - "import os\n", - "import pandas as pd\n", - "from khiops import core as kh\n", - "from khiops.sklearn import KhiopsClassifier\n", - "from sklearn import metrics\n", - "from sklearn.model_selection import train_test_split\n", - "\n", - "# Create output directory\n", - "results_dir = os.path.join(\"kh_samples\", \"khiops_classifier_multitable_star_file\")\n", - "if not os.path.exists(\"kh_samples\"):\n", - " os.mkdir(\"kh_samples\")\n", - " os.mkdir(results_dir)\n", - "else:\n", - " if not os.path.exists(results_dir):\n", - " os.mkdir(results_dir)\n", - "\n", - "# Load the root table of the dataset into a pandas dataframe\n", - "accidents_dataset_path = os.path.join(kh.get_samples_dir(), \"AccidentsSummary\")\n", - "accidents_df = pd.read_csv(\n", - " os.path.join(accidents_dataset_path, \"Accidents.txt\"),\n", - " sep=\"\\t\",\n", - ")\n", - "\n", - "# Split the root dataframe into train and test\n", - "X_train_main, X_test_main = train_test_split(\n", - " accidents_df, test_size=0.3, random_state=1\n", - ")\n", - "\n", - "# Load the secondary table of the dataset into a pandas dataframe\n", - "vehicles_df = pd.read_csv(\n", - " os.path.join(accidents_dataset_path, \"Vehicles.txt\"), sep=\"\\t\"\n", - ")\n", - "\n", - "# Split the secondary dataframe with the keys of the split root dataframe\n", - "X_train_ids = X_train_main[\"AccidentId\"].to_frame()\n", - "X_test_ids = X_test_main[\"AccidentId\"].to_frame()\n", - "X_train_secondary = X_train_ids.merge(vehicles_df, on=\"AccidentId\")\n", - "X_test_secondary = X_test_ids.merge(vehicles_df, on=\"AccidentId\")\n", - "\n", - "# Write the train and test dataset sets to disk\n", - "# For the test file we remove the target column from the main table\n", - "X_train_main_path = os.path.join(results_dir, \"X_train_main.txt\")\n", - "X_train_main.to_csv(X_train_main_path, sep=\"\\t\", header=True, index=False)\n", - "X_train_secondary_path = os.path.join(results_dir, \"X_train_secondary.txt\")\n", - "X_train_secondary.to_csv(X_train_secondary_path, sep=\"\\t\", header=True, index=False)\n", - "X_test_main_path = os.path.join(results_dir, \"X_test_main.txt\")\n", - "y_test = X_test_main.sort_values(\"AccidentId\")[\"Gravity\"]\n", - "X_test_main.drop(columns=\"Gravity\").to_csv(\n", - " X_test_main_path, sep=\"\\t\", header=True, index=False\n", - ")\n", - "X_test_secondary_path = os.path.join(results_dir, \"X_test_secondary.txt\")\n", - "X_test_secondary.to_csv(X_test_secondary_path, sep=\"\\t\", header=True, index=False)\n", - "\n", - "# Define the dictionary of train\n", - "X_train = {\n", - " \"main_table\": \"Accidents\",\n", - " \"tables\": {\n", - " \"Accidents\": (X_train_main_path, \"AccidentId\"),\n", - " \"Vehicles\": (X_train_secondary_path, [\"AccidentId\", \"VehicleId\"]),\n", - " },\n", - " \"format\": (\"\\t\", True),\n", - "}\n", - "X_test = {\n", - " \"main_table\": \"Accidents\",\n", - " \"tables\": {\n", - " \"Accidents\": (X_test_main_path, \"AccidentId\"),\n", - " \"Vehicles\": (X_test_secondary_path, [\"AccidentId\", \"VehicleId\"]),\n", - " },\n", - " \"format\": (\"\\t\", True),\n", - "}\n", - "\n", - "# Create the classifier and fit it\n", - "khc = KhiopsClassifier(output_dir=results_dir)\n", - "khc.fit(X_train, y=\"Gravity\")\n", - "\n", - "# Predict the class in addition to the class probabilities on the test dataset\n", - "y_test_pred_path = khc.predict(X_test)\n", - "y_test_pred = pd.read_csv(y_test_pred_path, sep=\"\\t\")\n", - "print(\"Predicted classes (first 10):\")\n", - "print(y_test_pred[\"PredictedGravity\"].head(10))\n", - "print(\"---\")\n", - "\n", - "y_test_probas_path = khc.predict_proba(X_test)\n", - "y_test_probas = pd.read_csv(y_test_probas_path, sep=\"\\t\")\n", - "proba_columns = [col for col in y_test_probas if col.startswith(\"Prob\")]\n", - "print(\"Predicted class probabilities (first 10):\")\n", - "print(y_test_probas[proba_columns].head(10))\n", - "print(\"---\")\n", - "\n", - "# Evaluate accuracy and auc metrics on the test dataset\n", - "test_accuracy = metrics.accuracy_score(y_test, y_test_pred[\"PredictedGravity\"])\n", - "test_auc = metrics.roc_auc_score(y_test, y_test_probas[\"ProbGravityLethal\"])\n", - "print(f\"Test accuracy = {test_accuracy}\")\n", - "print(f\"Test auc = {test_auc}\")" - ] } ], "metadata": {}, diff --git a/khiops/samples/samples_sklearn.py b/khiops/samples/samples_sklearn.py index c656cfda..2b42eabb 100644 --- a/khiops/samples/samples_sklearn.py +++ b/khiops/samples/samples_sklearn.py @@ -848,181 +848,6 @@ def khiops_coclustering_simplify(): print("---") -###################### -# Deprecated samples # -###################### - - -def khiops_classifier_multitable_list(): - """Trains a KhiopsClassifier using a list dataset specification - - .. warning:: - This dataset input method is **Deprecated** and will be removed in Khiops 11. - """ - # Imports - import os - import pandas as pd - from khiops import core as kh - from khiops.sklearn import KhiopsClassifier - from sklearn import metrics - from sklearn.model_selection import train_test_split - - # Load the root table of the dataset into a pandas dataframe - accidents_data_dir = os.path.join(kh.get_samples_dir(), "AccidentsSummary") - accidents_df = pd.read_csv( - os.path.join(accidents_data_dir, "Accidents.txt"), - sep="\t", - ) - X = accidents_df.drop("Gravity", axis=1) - y = accidents_df["Gravity"] - - # Split the dataset into train and test - X_train, X_test, y_train, y_test = train_test_split( - X, y, test_size=0.3, random_state=1 - ) - - # Load the secondary table of the dataset into a pandas dataframe - vehicles_df = pd.read_csv( - os.path.join(accidents_data_dir, "Vehicles.txt"), sep="\t" - ) - - # Split the secondary dataframe with the keys of the split root dataframe - X_train_ids = X_train["AccidentId"].to_frame() - X_test_ids = X_test["AccidentId"].to_frame() - X_train_secondary = X_train_ids.merge(vehicles_df, on="AccidentId") - X_test_secondary = X_test_ids.merge(vehicles_df, on="AccidentId") - - # Create the classifier specifying the key column name - khc = KhiopsClassifier(key="AccidentId") - - # Train the classifier - khc.fit([X_train, X_train_secondary], y_train) - - # Predict the class on the test dataset - y_test_pred = khc.predict([X_test, X_test_secondary]) - print("Predicted classes (first 10):") - print(y_test_pred[:10]) - print("---") - - # Predict the class probability on the test dataset - y_test_probas = khc.predict_proba([X_test, X_test_secondary]) - print("Predicted class probabilities (first 10):") - print(y_test_probas[:10]) - print("---") - - # Evaluate accuracy and auc metrics on the test dataset - test_accuracy = metrics.accuracy_score(y_test, y_test_pred) - test_auc = metrics.roc_auc_score(y_test, y_test_probas[:, 1]) - print(f"Test accuracy = {test_accuracy}") - print(f"Test auc = {test_auc}") - - -def khiops_classifier_multitable_star_file(): - """Trains a `.KhiopsClassifier` with a file path based dataset - - .. warning:: - This dataset input method is **Deprecated** and will be removed in Khiops 11. - If you need to handle large datasets that do not easily fit into memory then you - may use the `~.khiops.core` API directly, which allows to specify file paths - directly. - """ - # Imports - import os - import pandas as pd - from khiops import core as kh - from khiops.sklearn import KhiopsClassifier - from sklearn import metrics - from sklearn.model_selection import train_test_split - - # Create output directory - results_dir = os.path.join("kh_samples", "khiops_classifier_multitable_star_file") - if not os.path.exists("kh_samples"): - os.mkdir("kh_samples") - os.mkdir(results_dir) - else: - if not os.path.exists(results_dir): - os.mkdir(results_dir) - - # Load the root table of the dataset into a pandas dataframe - accidents_dataset_path = os.path.join(kh.get_samples_dir(), "AccidentsSummary") - accidents_df = pd.read_csv( - os.path.join(accidents_dataset_path, "Accidents.txt"), - sep="\t", - ) - - # Split the root dataframe into train and test - X_train_main, X_test_main = train_test_split( - accidents_df, test_size=0.3, random_state=1 - ) - - # Load the secondary table of the dataset into a pandas dataframe - vehicles_df = pd.read_csv( - os.path.join(accidents_dataset_path, "Vehicles.txt"), sep="\t" - ) - - # Split the secondary dataframe with the keys of the split root dataframe - X_train_ids = X_train_main["AccidentId"].to_frame() - X_test_ids = X_test_main["AccidentId"].to_frame() - X_train_secondary = X_train_ids.merge(vehicles_df, on="AccidentId") - X_test_secondary = X_test_ids.merge(vehicles_df, on="AccidentId") - - # Write the train and test dataset sets to disk - # For the test file we remove the target column from the main table - X_train_main_path = os.path.join(results_dir, "X_train_main.txt") - X_train_main.to_csv(X_train_main_path, sep="\t", header=True, index=False) - X_train_secondary_path = os.path.join(results_dir, "X_train_secondary.txt") - X_train_secondary.to_csv(X_train_secondary_path, sep="\t", header=True, index=False) - X_test_main_path = os.path.join(results_dir, "X_test_main.txt") - y_test = X_test_main.sort_values("AccidentId")["Gravity"] - X_test_main.drop(columns="Gravity").to_csv( - X_test_main_path, sep="\t", header=True, index=False - ) - X_test_secondary_path = os.path.join(results_dir, "X_test_secondary.txt") - X_test_secondary.to_csv(X_test_secondary_path, sep="\t", header=True, index=False) - - # Define the dictionary of train - X_train = { - "main_table": "Accidents", - "tables": { - "Accidents": (X_train_main_path, "AccidentId"), - "Vehicles": (X_train_secondary_path, ["AccidentId", "VehicleId"]), - }, - "format": ("\t", True), - } - X_test = { - "main_table": "Accidents", - "tables": { - "Accidents": (X_test_main_path, "AccidentId"), - "Vehicles": (X_test_secondary_path, ["AccidentId", "VehicleId"]), - }, - "format": ("\t", True), - } - - # Create the classifier and fit it - khc = KhiopsClassifier(output_dir=results_dir) - khc.fit(X_train, y="Gravity") - - # Predict the class in addition to the class probabilities on the test dataset - y_test_pred_path = khc.predict(X_test) - y_test_pred = pd.read_csv(y_test_pred_path, sep="\t") - print("Predicted classes (first 10):") - print(y_test_pred["PredictedGravity"].head(10)) - print("---") - - y_test_probas_path = khc.predict_proba(X_test) - y_test_probas = pd.read_csv(y_test_probas_path, sep="\t") - proba_columns = [col for col in y_test_probas if col.startswith("Prob")] - print("Predicted class probabilities (first 10):") - print(y_test_probas[proba_columns].head(10)) - print("---") - - # Evaluate accuracy and auc metrics on the test dataset - test_accuracy = metrics.accuracy_score(y_test, y_test_pred["PredictedGravity"]) - test_auc = metrics.roc_auc_score(y_test, y_test_probas["ProbGravityLethal"]) - print(f"Test accuracy = {test_accuracy}") - print(f"Test auc = {test_auc}") - - exported_samples = [ khiops_classifier, khiops_classifier_multiclass, @@ -1039,8 +864,6 @@ def khiops_classifier_multitable_star_file(): khiops_encoder_with_hyperparameters, khiops_coclustering, khiops_coclustering_simplify, - khiops_classifier_multitable_list, - khiops_classifier_multitable_star_file, ] diff --git a/khiops/sklearn/dataset.py b/khiops/sklearn/dataset.py index cc50d66c..21807f8c 100644 --- a/khiops/sklearn/dataset.py +++ b/khiops/sklearn/dataset.py @@ -7,8 +7,6 @@ """Classes for handling diverse data tables""" import csv import io -import json -import warnings from abc import ABC, abstractmethod from collections.abc import Iterable, Mapping, Sequence @@ -21,13 +19,7 @@ import khiops.core as kh import khiops.core.internals.filesystems as fs from khiops.core.dictionary import VariableBlock -from khiops.core.exceptions import KhiopsRuntimeError -from khiops.core.internals.common import ( - deprecation_message, - is_dict_like, - is_list_like, - type_error_message, -) +from khiops.core.internals.common import is_dict_like, is_list_like, type_error_message # Disable PEP8 variable names because of scikit-learn X,y conventions # To capture invalid-names other than X,y run: @@ -435,21 +427,18 @@ class Dataset: Parameters ---------- - X : `pandas.DataFrame` or dict (**Deprecated types**: tuple and list) + X : `pandas.DataFrame` or dict Either: - A single dataframe - A ``dict`` dataset specification - y : `pandas.Series` or str, optional + y : `pandas.Series`, `pandas.DataFrame`, `numpy.ndarray` or str, optional The target column. categorical_target : bool, default True ``True`` if the vector ``y`` should be considered as a categorical variable. If ``False`` it is considered as numeric. Ignored if ``y`` is ``None``. - key : str - The name of the key column for all tables. - **Deprecated:** Will be removed in khiops-python 11. """ - def __init__(self, X, y=None, categorical_target=True, key=None): + def __init__(self, X, y=None, categorical_target=True): # Initialize members self.main_table = None self.secondary_tables = None @@ -489,57 +478,17 @@ def __init__(self, X, y=None, categorical_target=True, key=None): ), ): check_array(X, accept_sparse=False) - # A tuple spec - elif isinstance(X, tuple): - warnings.warn( - deprecation_message( - "Tuple dataset input", - "11.0.0", - replacement="dict dataset spec", - quote=False, - ), - stacklevel=3, - ) - # Check the input tuple - self._check_input_tuple(X) - - # Obtain path and separator - path, sep = X - - # Initialization - self.main_table = FileTable("main_table", path=path, sep=sep) - self.secondary_tables = [] - - # A dataset sequence spec - # We try first for compatible python arrays then the deprecated sequences spec - elif is_list_like(X): - # Try to transform to a numerical array with sklearn's check_array - # On failure we try the old deprecated sequence interface - # When the old list interface is eliminated this will considerably reduce - # this branch's code - try: - X_checked = check_array(X, ensure_2d=True, force_all_finite=False) - self.main_table = NumpyTable("main_table", X_checked) - self.secondary_tables = [] - except ValueError: - warnings.warn( - deprecation_message( - "List dataset input", - "11.0.0", - replacement="dict dataset spec", - quote=False, - ), - stacklevel=3, - ) - self._init_tables_from_sequence(X, key=key) # A a dataset dict spec elif is_dict_like(X): self._init_tables_from_mapping(X) + elif is_list_like(X): + # Transform to a numerical array with sklearn's check_array + X_checked = check_array(X, ensure_2d=True, force_all_finite=False) + self.main_table = NumpyTable("main_table", X_checked) + self.secondary_tables = [] # Fail if X is not recognized else: - raise TypeError( - type_error_message("X", X, "array-like", tuple, Sequence, Mapping) - ) + raise TypeError(type_error_message("X", X, "array-like", Mapping, Sequence)) # Initialization of the target column if any if y is not None: @@ -550,17 +499,6 @@ def __init__(self, X, y=None, categorical_target=True, key=None): table.name: table for table in [self.main_table] + self.secondary_tables } - # Deprecation warning for file-based datasets - if isinstance(self.main_table, FileTable): - warnings.warn( - deprecation_message( - "File-based dataset spec", - "11.0.0", - "dataframe-based dataset or khiops.core API", - quote=False, - ), - ) - # Post-conditions assert self.main_table is not None, "'main_table' is 'None' after init" assert isinstance( @@ -581,35 +519,6 @@ def _check_input_tuple(self, X): if not isinstance(X[1], str): raise TypeError(type_error_message("X[1]", X[1], str)) - def _init_tables_from_sequence(self, X, key=None): - """Initializes the spec from a list-like 'X'""" - assert is_list_like(X), "'X' must be a list-like" - - # Check the input sequence - self._check_input_sequence(X, key=key) - - # Initialize the tables - if isinstance(X[0], pd.DataFrame): - self.main_table = PandasTable("main_table", X[0], key=key) - self.secondary_tables = [] - for index, dataframe in enumerate(X[1:], start=1): - self.secondary_tables.append( - PandasTable(f"secondary_table_{index:02d}", dataframe, key=key) - ) - else: - self.main_table = FileTable("main_table", X[0], key=key) - self.secondary_tables = [] - for index, table_path in enumerate(X[1:], start=1): - self.secondary_tables.append( - FileTable(f"secondary_table_{index:02d}", table_path, key=key) - ) - - # Create a list of relations - main_table_name = self.main_table.name - self.relations = [ - (main_table_name, table.name, False) for table in self.secondary_tables - ] - def _check_input_sequence(self, X, key=None): # Check the first table if len(X) == 0: @@ -646,39 +555,8 @@ def _init_tables_from_mapping(self, X): main_table_name = X["main_table"] main_table_source, main_table_key = X["tables"][main_table_name] - # Initialize a file dataset - if isinstance(main_table_source, str): - # Obtain the file format parameters - if "format" in X: - self.sep, self.header = X["format"] - else: - self.sep = "\t" - self.header = True - - # Initialize the tables - self.main_table = FileTable( - main_table_name, - main_table_source, - key=main_table_key, - sep=self.sep, - header=self.header, - ) - self.secondary_tables = [] - for table_name, (table_source, table_key) in X["tables"].items(): - if isinstance(table_key, str): - table_key = [table_key] - if table_name != main_table_name: - self.secondary_tables.append( - FileTable( - table_name, - table_source, - key=table_key, - sep=self.sep, - header=self.header, - ) - ) # Initialize a Pandas dataset - elif isinstance(main_table_source, pd.DataFrame): + if isinstance(main_table_source, pd.DataFrame): self.main_table = PandasTable( main_table_name, main_table_source, @@ -699,7 +577,7 @@ def _init_tables_from_mapping(self, X): ) self.secondary_tables = [] # Initialize a numpyarray dataset (monotable) - else: + elif hasattr(main_table_source, "__array__"): self.main_table = NumpyTable( main_table_name, main_table_source, @@ -710,6 +588,12 @@ def _init_tables_from_mapping(self, X): "with pandas dataframe source tables" ) self.secondary_tables = [] + else: + raise TypeError( + type_error_message( + "X's main table", main_table_source, "array-like", Mapping + ) + ) # If the relations are not specified initialize to a star schema if "relations" not in X: @@ -737,6 +621,7 @@ def _init_target_column(self, y): # - warn=True in column_or_1d is necessary to pass sklearn checks if isinstance(y, str): y_checked = y + # pandas.Series, pandas.DataFrame or numpy.ndarray else: y_checked = column_or_1d(y, warn=True) @@ -754,13 +639,6 @@ def _init_target_column(self, y): type_error_message("y", y, "array-like") + f" (X's tables are of type {type_message})" ) - if isinstance(self.main_table.data_source, str) and not isinstance( - y_checked, str - ): - raise TypeError( - type_error_message("y", y, str) - + " (X's tables are of type str [file paths])" - ) # Initialize the members related to the target # Case when y is a memory array @@ -805,16 +683,6 @@ def _init_target_column(self, y): else: self.main_table.khiops_types[self.target_column_id] = "Numerical" - @property - def is_in_memory(self): - """bool : ``True`` if the dataset is in-memory - - A dataset is in-memory if it is constituted either of only pandas.DataFrame - tables, numpy.ndarray, or scipy.sparse.spmatrix tables. - """ - - return isinstance(self.main_table, (PandasTable, NumpyTable, SparseTable)) - @property def table_type(self): """type : The table type of this dataset's tables @@ -824,7 +692,6 @@ def table_type(self): - `PandasTable` - `NumpyTable` - `SparseTable` - - `FileTable` """ return type(self.main_table) @@ -847,8 +714,6 @@ def to_spec(self): if self.relations: ds_spec["relations"] = [] ds_spec["relations"].extend(self.relations) - if self.table_type == FileTable: - ds_spec["format"] = (self.sep, self.header) return ds_spec @@ -895,8 +760,8 @@ def create_khiops_dictionary_domain(self): main_dictionary = self.main_table.create_khiops_dictionary() dictionary_domain.add_dictionary(main_dictionary) - # For in-memory datasets: Add the target variable if available - if self.is_in_memory and self.target_column is not None: + # Add the target variable if available + if self.target_column is not None: variable = kh.Variable() variable.name = get_khiops_variable_name(self.target_column_id) if self.categorical_target: @@ -964,20 +829,13 @@ def create_table_files_for_khiops(self, output_dir, sort=True): self.is_multitable or self.main_table.key is not None ) - # In-memory dataset: Create the table files and add the target column - if self.is_in_memory: - main_table_path = self.main_table.create_table_file_for_khiops( - output_dir, - sort=sort_main_table, - target_column=self.target_column, - target_column_id=self.target_column_id, - ) - # File dataset: Create the table files (the target column is in the file) - else: - main_table_path = self.main_table.create_table_file_for_khiops( - output_dir, - sort=sort_main_table, - ) + # Create the table files and add the target column + main_table_path = self.main_table.create_table_file_for_khiops( + output_dir, + sort=sort_main_table, + target_column=self.target_column, + target_column_id=self.target_column_id, + ) # Create a copy of each secondary table secondary_table_paths = {} @@ -1426,120 +1284,3 @@ def create_table_file_for_khiops( ) return output_table_path - - -class FileTable(DatasetTable): - """DatasetTable encapsulating a delimited text data file - - Parameters - ---------- - name : str - Name for the table. - path : str - Path of the file containing the table. - key : list-like of str, optional - The names of the columns composing the key. - sep : str, optional - Field separator character. If not specified it will be inferred from the file. - header : bool, optional - Indicates if the table. - """ - - def __init__( - self, - name, - path, - key=None, - sep="\t", - header=True, - ): - # Initialize parameters - super().__init__(name=name, key=key) - - # Check the parameters specific to this sub-class - if not isinstance(path, str): - raise TypeError(type_error_message("path", path, str)) - if not fs.exists(path): - raise ValueError(f"Non-existent data table file: {path}") - - # Initialize members specific to this sub-class - self.data_source = path - self.sep = sep - self.header = header - - # Build a dictionary file from the input data table - # Note: We use export_dictionary_as_json instead of read_dictionary_file - # because it makes fail the sklearn mocked tests (this is technical debt) - try: - tmp_kdic_path = kh.get_runner().create_temp_file("file_table_", ".kdic") - tmp_kdicj_path = kh.get_runner().create_temp_file("file_table_", ".kdicj") - kh.build_dictionary_from_data_table( - self.data_source, - self.name, - tmp_kdic_path, - field_separator=self.sep, - header_line=header, - ) - kh.export_dictionary_as_json(tmp_kdic_path, tmp_kdicj_path) - json_domain = json.loads(fs.read(tmp_kdicj_path)) - finally: - fs.remove(tmp_kdic_path) - fs.remove(tmp_kdicj_path) - - # Alert the user if the parsing failed - if len(json_domain["dictionaries"]) == 0: - raise KhiopsRuntimeError( - f"Failed to build a dictionary " - f"from data table file: {self.data_source}" - ) - - # Set the column names and types - variables = json_domain["dictionaries"][0]["variables"] - self.column_ids = [var["name"] for var in variables] - self.khiops_types = {var["name"]: var["type"] for var in variables} - - # Check key integrity - self.check_key() - - def create_table_file_for_khiops(self, output_dir, sort=True): - assert not sort or self.key is not None, "key is 'None'" - - # Create the input and output file resources - if sort: - output_table_file_path = fs.get_child_path( - output_dir, f"sorted_{self.name}.txt" - ) - else: - output_table_file_path = fs.get_child_path( - output_dir, f"copy_{self.name}.txt" - ) - - # Fail if they have the same path - if output_table_file_path == self.data_source: - raise ValueError(f"Cannot overwrite this table's path: {self.data_source}") - - # Create a sorted copy if requested - if sort: - # Create the sorting dictionary domain - sort_dictionary_domain = kh.DictionaryDomain() - sort_dictionary_domain.add_dictionary(self.create_khiops_dictionary()) - - # Delegate the sorting and copy to khiops.core.sort_data_table - # We use the same input format of the original table - kh.sort_data_table( - sort_dictionary_domain, - self.name, - self.data_source, - output_table_file_path, - self.key, - field_separator=self.sep, - header_line=self.header, - output_field_separator=self.sep, - output_header_line=self.header, - ) - - # Otherwise copy the contents to the output file - else: - fs.write(output_table_file_path, fs.read(self.data_source)) - - return output_table_file_path diff --git a/khiops/sklearn/estimators.py b/khiops/sklearn/estimators.py index 470afef4..26539abe 100644 --- a/khiops/sklearn/estimators.py +++ b/khiops/sklearn/estimators.py @@ -45,15 +45,9 @@ import khiops.core.internals.filesystems as fs from khiops.core.dictionary import DictionaryDomain from khiops.core.helpers import build_multi_table_dictionary_domain -from khiops.core.internals.common import ( - deprecation_message, - is_dict_like, - is_list_like, - type_error_message, -) +from khiops.core.internals.common import is_dict_like, is_list_like, type_error_message from khiops.sklearn.dataset import ( Dataset, - FileTable, get_khiops_variable_name, read_internal_data_table, ) @@ -149,7 +143,7 @@ def _check_categorical_target_type(ds): if ds.target_column is None: raise ValueError("Target vector is not specified.") - if ds.is_in_memory and not ( + if not ( isinstance(ds.target_column.dtype, pd.CategoricalDtype) or pd.api.types.is_string_dtype(ds.target_column.dtype) or pd.api.types.is_integer_dtype(ds.target_column.dtype) @@ -160,15 +154,6 @@ def _check_categorical_target_type(ds): "Only string, integer, float and categorical types " "are accepted for the target." ) - elif ( - not ds.is_in_memory - and ds.main_table.khiops_types[ds.target_column_id] != "Categorical" - ): - raise ValueError( - "Target column has invalid type " - f"'{ds.main_table.khiops_types[ds.target_column_id]}'. " - "Only Categorical types are accepted for file datasets." - ) def _check_numerical_target_type(ds): @@ -176,26 +161,15 @@ def _check_numerical_target_type(ds): if ds.target_column is None: raise ValueError("Target vector is not specified.") - # If in-memory: Check that the column is numerical and that the values are finite + # Check that the column is numerical and that the values are finite # The latter is required by sklearn - if ds.is_in_memory: - if not pd.api.types.is_numeric_dtype(ds.target_column.dtype): - raise ValueError( - f"Unknown label type '{ds.target_column.dtype}'. " - "Expected a numerical type." - ) - if ds.target_column is not None: - assert_all_finite(ds.target_column) - # Otherwise: Check the the Khiops type - elif ( - not ds.is_in_memory - and ds.main_table.khiops_types[ds.target_column_id] != "Numerical" - ): + if not pd.api.types.is_numeric_dtype(ds.target_column.dtype): raise ValueError( - "Target column has invalid type " - f"'{ds.main_table.khiops_types[ds.target_column_id]}'. " - "Only Numerical types are accepted for file datasets." + f"Unknown label type '{ds.target_column.dtype}'. " + "Expected a numerical type." ) + if ds.target_column is not None: + assert_all_finite(ds.target_column) def _check_pair_parameters(estimator): @@ -259,46 +233,20 @@ class KhiopsEstimator(ABC, BaseEstimator): auto_sort : bool, default ``True`` *Advanced.*: See concrete estimator classes for information about this parameter. - key : str, optional - The name of the column to be used as key. - **Deprecated** will be removed in Khiops 11. - internal_sort : bool, optional - *Advanced*: See concrete estimator classes for information about this - parameter. - **Deprecated** will be removed in Khiops 11. Use the ``auto_sort`` - estimator parameter instead. """ def __init__( self, - key=None, verbose=False, output_dir=None, auto_sort=True, - internal_sort=None, ): # Set the estimator parameters and internal variables self._khiops_model_prefix = None - self.key = key self.output_dir = output_dir self.verbose = verbose - # Set auto_sort and show a deprecation message for internal_sort - if internal_sort is not None: - self.auto_sort = internal_sort - warnings.warn( - deprecation_message( - "the 'internal_sort' estimator parameter", - "11.0.0", - replacement="the 'auto_sort' estimator parameter", - quote=False, - ) - ) - else: - self.auto_sort = auto_sort - - # Make sklearn get_params happy - self.internal_sort = internal_sort + self.auto_sort = auto_sort def __sklearn_tags__(self): # We disable this because this import is only available for scikit-learn>=1.6 @@ -337,6 +285,19 @@ def _get_main_dictionary(self): self._assert_is_fitted() return self.model_.get_dictionary(self.model_main_dictionary_name_) + def _read_model_from_dictionary_file(self, model_dictionary_file_path): + """ + Only keep dictionaries that have _khiops_model_prefix in their name + Thus, baseline-model dictionaries (present in regression) are removed. + """ + model = kh.read_dictionary_file(model_dictionary_file_path) + if self._khiops_model_prefix is not None: + for dictionary in model.dictionaries[:]: + if not dictionary.name.startswith(self._khiops_model_prefix): + # delete dictionary from model + model.dictionaries.remove(dictionary) + return model + def export_report_file(self, report_file_path): """Exports the model report to a JSON file @@ -362,7 +323,7 @@ def export_dictionary_file(self, dictionary_file_path): def _import_model(self, kdic_path): """Sets model instance attribute by importing model from ``.kdic``""" - self.model_ = kh.read_dictionary_file(kdic_path) + self.model_ = self._read_model_from_dictionary_file(kdic_path) def _get_output_dir(self, fallback_dir): if self.output_dir: @@ -400,7 +361,7 @@ def fit(self, X, y=None, **kwargs): # Create the dataset, fit the model and reset in case of any failure try: categorical_target = kwargs.get("categorical_target", True) - dataset = Dataset(X, y, categorical_target=categorical_target, key=self.key) + dataset = Dataset(X, y, categorical_target=categorical_target) self._fit(dataset, computation_dir, **kwargs) # Undefine any attributes to pass to "not fitted" except: @@ -463,17 +424,8 @@ def _fit(self, ds, computation_dir, **kwargs): self.is_multitable_model_ = ds.is_multitable self.n_features_in_ = ds.main_table.n_features() - def _fit_check_params(self, ds, **_): + def _fit_check_params(self, _ds, **_): """Check the model parameters including those data dependent (in kwargs)""" - if ( - self.key is not None - and not is_list_like(self.key) - and not isinstance(self.key, str) - ): - raise TypeError(type_error_message("key", self.key, str, "list-like")) - - if not ds.is_in_memory and self.output_dir is None: - raise ValueError("'output_dir' is not set but dataset is file-based") def _fit_check_dataset(self, ds): """Checks the pre-conditions of the tables to build the model""" @@ -490,7 +442,6 @@ def _fit_train_model(self, ds, computation_dir, **kwargs): - self.model_ - self.model_report_ - - self.model_report_raw_ (deprecated) """ @abstractmethod @@ -531,33 +482,28 @@ def _transform( ) # Post-process to return the correct output type and order - if deployment_ds.is_in_memory: - # Load the table as a dataframe - with io.BytesIO(fs.read(output_table_path)) as output_table_stream: - output_table_df = read_internal_data_table( - output_table_stream, column_dtypes=internal_table_column_dtypes - ) + # Load the table as a dataframe + with io.BytesIO(fs.read(output_table_path)) as output_table_stream: + output_table_df = read_internal_data_table( + output_table_stream, column_dtypes=internal_table_column_dtypes + ) - # On multi-table: - # - Reorder the table to the original table order - # - Because transformed data table file is sorted by key - # - Drop the key columns if specified - if deployment_ds.is_multitable: - key_df = deployment_ds.main_table.data_source[ - deployment_ds.main_table.key - ] - output_table_df_or_path = key_df.merge( - output_table_df, on=deployment_ds.main_table.key + # On multi-table: + # - Reorder the table to the original table order + # - Because transformed data table file is sorted by key + # - Drop the key columns if specified + if deployment_ds.is_multitable: + key_df = deployment_ds.main_table.data_source[deployment_ds.main_table.key] + output_table_df_or_path = key_df.merge( + output_table_df, on=deployment_ds.main_table.key + ) + if drop_key: + output_table_df_or_path.drop( + deployment_ds.main_table.key, axis=1, inplace=True ) - if drop_key: - output_table_df_or_path.drop( - deployment_ds.main_table.key, axis=1, inplace=True - ) - # On mono-table: Return the read dataframe as-is - else: - output_table_df_or_path = output_table_df + # On mono-table: Return the read dataframe as-is else: - output_table_df_or_path = output_table_path + output_table_df_or_path = output_table_df return output_table_df_or_path @@ -587,13 +533,6 @@ def _transform_deploy_model( Training dataset. Either an :external:term:`array-like` or a ``dict`` specification for multi-table datasets (see :doc:`/multi_table_primer`). - **Deprecated input types** (will be removed in Khiops 11): - - - tuple: A pair (``path_to_file``, ``separator``). - - list: A sequence of dataframes or paths, or pairs path-separator. The - first element of the list is the main table and the following are - secondary ones joined to the main table using ``key`` estimator parameter - root_name : str Name of root table in trained Khiops model .kdic @@ -637,13 +576,9 @@ def _transform_deploy_model( log_file_path = fs.get_child_path(output_dir, "khiops.log") output_data_table_path = fs.get_child_path(output_dir, transformed_file_name) - # Set the format parameters depending on the type of dataset - if deployment_ds.is_in_memory: - field_separator = "\t" - header_line = True - else: - field_separator = deployment_ds.main_table.sep - header_line = deployment_ds.main_table.header + # Set the format parameters + field_separator = "\t" + header_line = True # Call to core function deploy_model kh.deploy_model( @@ -663,11 +598,6 @@ def _transform_deploy_model( return output_data_table_path - def _transform_check_dataset(self, ds): - """Checks the dataset before deploying a model on them""" - if ds.table_type == FileTable and self.output_dir is None: - raise ValueError("'output_dir' is not set but dataset is file-based") - def _create_computation_dir(self, method_name): """Creates a temporary computation directory""" return kh.get_runner().create_temp_dir( @@ -708,30 +638,6 @@ class KhiopsCoclustering(ClusterMixin, KhiopsEstimator): to speed up the processing. This affects the `predict` method. *Note* The sort by key is performed in a left-to-right, hierarchical, lexicographic manner. - max_part_numbers : dict, optional - Maximum number of clusters for each of the co-clustered column. Specifically, a - key-value pair of this dictionary represents the column name and its respective - maximum number of clusters. If not specified there is no maximum number of - clusters is imposed on any column. - **Deprecated** will be removed in Khiops 11. Use the ``max_part_number`` - parameter of the `fit` method. - variables : list of str, optional - A list of column names/indexes to use in the coclustering. - **Deprecated** will be removed in Khiops 11. Use the ``columns`` parameter of - the `fit` method. - key : str, optional - *Multi-table only* : The name of the column to be used as key. - **Deprecated** will be removed in Khiops 11. Use ``id_column`` parameter of - the `fit` method. - internal_sort : bool, optional - *Advanced.* Only for multi-table inputs: If ``True`` input tables are - automatically sorted by their key before executing Khiops. If the input - tables are already sorted by their keys set this parameter to ``False`` - to speed up the processing. This affects the `predict` method. - *Note* The sort by key is performed in a left-to-right, hierarchical, - lexicographic manner. - **Deprecated** will be removed in Khiops 11. Use the ``auto_sort`` - parameter of the estimator instead. Attributes ---------- @@ -744,10 +650,6 @@ class KhiopsCoclustering(ClusterMixin, KhiopsEstimator): The name of the main Khiops dictionary within the ``model_`` domain. model_report_ : `.CoclusteringResults` The Khiops report object. - model_report_raw_ : dict - JSON object of the Khiops report. - **Deprecated** will be removed in Khiops 11. Use the ``json_data`` - attribute of the ``model_report_`` estimator attribute instead. Examples -------- @@ -763,55 +665,18 @@ def __init__( build_name_var=True, build_distance_vars=False, build_frequency_vars=False, - max_part_numbers=None, - key=None, - variables=None, - internal_sort=None, ): super().__init__( - key=key, verbose=verbose, output_dir=output_dir, auto_sort=auto_sort, - internal_sort=internal_sort, ) self._khiops_model_prefix = "CC_" self.build_name_var = build_name_var self.build_distance_vars = build_distance_vars self.build_frequency_vars = build_frequency_vars - self.variables = variables - self.max_part_numbers = max_part_numbers self.model_id_column = None - # Deprecation message for 'key' and 'variables' constructor parameter - if key is not None: - warnings.warn( - deprecation_message( - "'key' estimator parameter", - "11.0.0", - replacement="'id_column' parameter of the 'fit' method", - quote=False, - ) - ) - if variables is not None: - warnings.warn( - deprecation_message( - "'variables' estimator parameter", - "11.0.0", - replacement="'columns' parameter of the 'fit' method", - quote=False, - ) - ) - if max_part_numbers is not None: - warnings.warn( - deprecation_message( - "'max_part_numbers' estimator parameter", - "11.0.0", - replacement="'max_part_numbers' parameter of the 'simplify' method", - quote=False, - ) - ) - def __sklearn_tags__(self): # If we don't implement this trivial method it's not found by the sklearn. This # is likely due to the complex resolution of the multiple inheritance. @@ -826,25 +691,10 @@ def fit(self, X, y=None, **kwargs): X : :external:term:`array-like` of shape (n_samples, n_features_in) or dict Training dataset. Either an :external:term:`array-like` or a ``dict`` specification for multi-table datasets (see :doc:`/multi_table_primer`). - - **Deprecated input types** (will be removed in Khiops 11): - - - tuple: A pair (``path_to_file``, ``separator``). - - list: A sequence of dataframes or paths, or pairs path-separator. The - first element of the list is the main table and the following are - secondary ones joined to the main table using ``key`` estimator parameter. - id_column : str The column that contains the id of the instance. columns : list, optional The columns to be co-clustered. If not specified it uses all columns. - max_part_numbers : dict, optional - Maximum number of clusters for each of the co-clustered column. - Specifically, a key-value pair of this dictionary represents the column name - and its respective maximum number of clusters. If not specified, then no - maximum number of clusters is imposed on any column. - **Deprecated** (will be removed in Khiops 11). Use the ``simplify`` - method instead. Returns ------- @@ -866,7 +716,7 @@ def _fit_check_params(self, ds, **kwargs): # If 'columns' specified check that: # - Is a sequence of string # - Is contained in the columns names of the main table - columns = kwargs.get("columns", self.variables) + columns = kwargs.get("columns") if columns is not None: if not is_list_like(columns): raise TypeError(type_error_message("columns", columns, "list-like")) @@ -883,7 +733,7 @@ def _fit_check_params(self, ds, **kwargs): # - Is specified # - Is a string # - Is contained in the columns names of the main table - id_column = kwargs.get("id_column", self.key) + id_column = kwargs.get("id_column") if id_column is None: raise ValueError("'id_column' is a mandatory parameter") if not isinstance(id_column, str): @@ -891,18 +741,6 @@ def _fit_check_params(self, ds, **kwargs): if id_column not in ds.main_table.column_ids: raise ValueError(f"id column '{id_column}' not found in X") - # Deprecate the 'max_part_numbers' parameter - max_part_numbers = kwargs.get("max_part_numbers", self.max_part_numbers) - if max_part_numbers is not None: - warnings.warn( - deprecation_message( - "'max_part_numbers' 'fit' parameter", - "11.0.0", - replacement="'max_part_numbers' parameter of the 'simplify' method", - quote=False, - ) - ) - def _fit_train_model(self, ds, computation_dir, **kwargs): assert not ds.is_multitable, "Coclustering not available in multitable" @@ -918,18 +756,20 @@ def _fit_train_model(self, ds, computation_dir, **kwargs): # Set the 'variables' parameter if "columns" in kwargs: variables = kwargs["columns"] - elif self.variables is not None: - variables = self.variables else: variables = list(ds.main_table.column_ids) + coclustering_file_path = fs.get_child_path( + output_dir, f"{ds.main_table.name}CoclusteringResults.khcj" + ) + # Train the coclustering model coclustering_file_path = kh.train_coclustering( ds.create_khiops_dictionary_domain(), ds.main_table.name, main_table_path, variables, - output_dir, + coclustering_file_path, log_file_path=train_log_file_path, trace=self.verbose, ) @@ -948,13 +788,10 @@ def _fit_train_model(self, ds, computation_dir, **kwargs): # Save the report file self.model_report_ = kh.read_coclustering_results_file(coclustering_file_path) - self.model_report_raw_ = self.model_report_.json_data # Save the id column if "id_column" in kwargs: self.model_id_column = kwargs["id_column"] - else: - self.model_id_column = self.key # Check that the id column was clustered try: @@ -985,21 +822,12 @@ def _fit_train_model(self, ds, computation_dir, **kwargs): # Update the `model_` attribute of the coclustering estimator to the # new coclustering model - self.model_ = kh.read_dictionary_file( - fs.get_child_path(output_dir, "Coclustering.kdic") + self.model_ = self._read_model_from_dictionary_file( + fs.get_child_path( + output_dir, f"{self.model_main_dictionary_name_}_deployed.kdic" + ) ) - # If the deprecated `max_part_numbers` is not None, then call `simplify` - max_part_numbers = kwargs.get("max_part_numbers", self.max_part_numbers) - if max_part_numbers is not None: - # Get simplified estimator - simplified_cc = self._simplify(max_part_numbers=max_part_numbers) - - # Update main estimator model and report according to the simplified model - self.model_ = simplified_cc.model_ - self.model_report_ = simplified_cc.model_report_ - self.model_report_raw_ = self.model_report_.json_data - def _fit_training_post_process(self, ds): assert ( len(self.model_.dictionaries) == 2 @@ -1045,13 +873,16 @@ def _create_coclustering_model_domain( # Create the model by adding the coclustering variables # to the multi-table dictionary created before prepare_log_file_path = fs.get_child_path(output_dir, "khiops_prepare_cc.log") + deployed_coclustering_dictionary_file_path = fs.get_child_path( + output_dir, f"{self.model_main_dictionary_name_}_deployed.kdic" + ) kh.prepare_coclustering_deployment( mt_domain, self.model_main_dictionary_name_, coclustering_file_path, self.model_secondary_table_variable_name, self.model_id_column, - output_dir, + deployed_coclustering_dictionary_file_path, build_cluster_variable=self.build_name_var, build_distance_variables=self.build_distance_vars, build_frequency_variables=self.build_frequency_vars, @@ -1138,6 +969,9 @@ def _simplify( full_coclustering_file_path = fs.get_child_path( output_dir, "FullCoclustering.khcj" ) + simplified_coclustering_file_path = fs.get_child_path( + output_dir, "Coclustering.khcj" + ) self.model_report_.write_khiops_json_file(full_coclustering_file_path) kh.get_runner().root_temp_dir = computation_dir try: @@ -1147,8 +981,7 @@ def _simplify( # attribute accordingly kh.simplify_coclustering( full_coclustering_file_path, - "Coclustering.khc", - output_dir, + simplified_coclustering_file_path, max_preserved_information=max_preserved_information, max_cells=max_cells, max_total_parts=max_total_parts, @@ -1194,7 +1027,6 @@ def _simplify( simplified_cc.model_report_ = kh.read_coclustering_results_file( simplified_coclustering_file_path ) - simplified_cc.model_report_raw_ = simplified_cc.model_report_.json_data # Build the individual-variable coclustering model self._create_coclustering_model_domain( @@ -1203,8 +1035,10 @@ def _simplify( # Set the `model_` attribute of the new coclustering estimator to # the new coclustering model - simplified_cc.model_ = kh.read_dictionary_file( - fs.get_child_path(output_dir, "Coclustering.kdic") + simplified_cc.model_ = self._read_model_from_dictionary_file( + fs.get_child_path( + output_dir, f"{self.model_main_dictionary_name_}_deployed.kdic" + ) ) finally: self._cleanup_computation_dir(computation_dir) @@ -1261,13 +1095,6 @@ def predict(self, X): Training dataset. Either an :external:term:`array-like` or a ``dict`` specification for multi-table datasets (see :doc:`/multi_table_primer`). - **Deprecated input types** (will be removed in Khiops 11): - - - tuple: A pair (``path_to_file``, ``separator``). - - list: A sequence of dataframes or paths, or pairs path-separator. The - first element of the list is the main table and the following are - secondary ones joined to the main table using ``key`` estimator parameter. - Returns ------- `ndarray ` @@ -1296,9 +1123,8 @@ def predict(self, X): self._cleanup_computation_dir(computation_dir) kh.get_runner().root_temp_dir = initial_runner_temp_dir - # Transform to numpy.array for in-memory inputs - if ds.is_in_memory: - y_pred = y_pred.to_numpy() + # Transform to numpy.array + y_pred = y_pred.to_numpy() return y_pred @@ -1308,9 +1134,6 @@ def _transform_check_dataset(self, ds): len(self.model_.dictionaries) == 2 ), "'model' does not have exactly 2 dictionaries" - # Call the parent method - super()._transform_check_dataset(ds) - # Coclustering models are special: # - They are mono-table only # - They are deployed with a multitable model whose main table contain @@ -1330,7 +1153,7 @@ def _transform_check_dataset(self, ds): self.__class__.__name__, ) - def _transform_create_deployment_dataset(self, ds, computation_dir): + def _transform_create_deployment_dataset(self, ds, _): assert not ds.is_multitable, "'dataset' is multitable" # Build the multitable deployment dataset @@ -1338,58 +1161,24 @@ def _transform_create_deployment_dataset(self, ds, computation_dir): deploy_dataset_spec = {} deploy_dataset_spec["main_table"] = keys_table_name deploy_dataset_spec["tables"] = {} - if ds.is_in_memory: - # Extract the keys from the main table - keys_table_dataframe = pd.DataFrame( - { - self.model_id_column: ds.main_table.data_source[ - self.model_id_column - ].unique() - } - ) - - # Create the dataset with the keys table as the main one - deploy_dataset_spec["tables"][keys_table_name] = ( - keys_table_dataframe, - self.model_id_column, - ) - deploy_dataset_spec["tables"][ds.main_table.name] = ( - ds.main_table.data_source, - self.model_id_column, - ) - else: - # Create the table to extract the keys (sorted) - keyed_dataset = ds.copy() - keyed_dataset.main_table.key = [self.model_id_column] - main_table_path = keyed_dataset.main_table.create_table_file_for_khiops( - computation_dir, sort=self.auto_sort - ) + # Extract the keys from the main table + keys_table_dataframe = pd.DataFrame( + { + self.model_id_column: ds.main_table.data_source[ + self.model_id_column + ].unique() + } + ) - # Create a table storing the main table keys - keys_table_name = f"keys_{ds.main_table.name}" - keys_table_file_path = fs.get_child_path( - computation_dir, f"raw_{keys_table_name}.txt" - ) - kh.extract_keys_from_data_table( - keyed_dataset.create_khiops_dictionary_domain(), - keyed_dataset.main_table.name, - main_table_path, - keys_table_file_path, - header_line=ds.header, - field_separator=ds.sep, - output_header_line=ds.header, - output_field_separator=ds.sep, - trace=self.verbose, - ) - deploy_dataset_spec["tables"][keys_table_name] = ( - keys_table_file_path, - self.model_id_column, - ) - deploy_dataset_spec["tables"][ds.main_table.name] = ( - ds.main_table.data_source, - self.model_id_column, - ) - deploy_dataset_spec["format"] = (ds.sep, ds.header) + # Create the dataset with the keys table as the main one + deploy_dataset_spec["tables"][keys_table_name] = ( + keys_table_dataframe, + self.model_id_column, + ) + deploy_dataset_spec["tables"][ds.main_table.name] = ( + ds.main_table.data_source, + self.model_id_column, + ) return Dataset(deploy_dataset_spec) @@ -1417,15 +1206,11 @@ def __init__( verbose=False, output_dir=None, auto_sort=True, - key=None, - internal_sort=None, ): super().__init__( - key=key, verbose=verbose, output_dir=output_dir, auto_sort=auto_sort, - internal_sort=internal_sort, ) self.n_features = n_features self.n_trees = n_trees @@ -1434,17 +1219,7 @@ def __init__( self.construction_rules = construction_rules self._original_target_dtype = None self._predicted_target_meta_data_tag = None - - # Deprecation message for 'key' constructor parameter - if key is not None: - warnings.warn( - deprecation_message( - "'key' estimator parameter", - "11.0.0", - replacement="dict dataset input", - quote=False, - ) - ) + self._khiops_baseline_model_prefix = None def __sklearn_tags__(self): # If we don't implement this trivial method it's not found by the sklearn. This @@ -1477,21 +1252,9 @@ def fit(self, X, y=None, **kwargs): Training dataset. Either an :external:term:`array-like` or a ``dict`` specification for multi-table datasets (see :doc:`/multi_table_primer`). - **Deprecated input types** (will be removed in Khiops 11): - - - tuple: A pair (``path_to_file``, ``separator``). - - list: A sequence of dataframes or paths, or pairs path-separator. The - first element of the list is the main table and the following are - secondary ones joined to the main table using ``key`` estimator parameter. - y : :external:term:`array-like` of shape (n_samples,) The target values. - **Deprecated input types** (will be removed in Khiops 11): - - - str: A path to a data table file for file-based ``dict`` dataset - specifications. - Returns ------- self : `KhiopsSupervisedEstimator` @@ -1547,9 +1310,8 @@ def _fit_train_model(self, ds, computation_dir, **kwargs): return # Save the model domain object and report - self.model_ = kh.read_dictionary_file(model_kdic_file_path) + self.model_ = self._read_model_from_dictionary_file(model_kdic_file_path) self.model_report_ = kh.read_analysis_results_file(report_file_path) - self.model_report_raw_ = self.model_report_.json_data @abstractmethod def _fit_core_training_function(self, *args, **kwargs): @@ -1558,6 +1320,9 @@ def _fit_core_training_function(self, *args, **kwargs): def _fit_prepare_training_function_inputs(self, ds, computation_dir): # Set output path files output_dir = self._get_output_dir(computation_dir) + report_file_path = fs.get_child_path( + output_dir, f"{ds.main_table.name}AnalysisResults.khj" + ) log_file_path = fs.get_child_path(output_dir, "khiops.log") main_table_path, secondary_table_paths = ds.create_table_files_for_khiops( @@ -1574,33 +1339,27 @@ def _fit_prepare_training_function_inputs(self, ds, computation_dir): # Build the mandatory arguments args = [ - ds.create_khiops_dictionary_domain(), + ds_domain, ds.main_table.name, main_table_path, get_khiops_variable_name(ds.target_column_id), - output_dir, + report_file_path, ] # Build the optional parameters from a copy of the estimator parameters kwargs = self.get_params() # Remove non core.api params - del kwargs["key"] del kwargs["output_dir"] del kwargs["auto_sort"] - del kwargs["internal_sort"] # Set the sampling percentage to a 100% kwargs["sample_percentage"] = 100 # Set the format parameters depending on the type of dataset kwargs["detect_format"] = False - if ds.is_in_memory: - kwargs["field_separator"] = "\t" - kwargs["header_line"] = True - else: - kwargs["field_separator"] = ds.main_table.sep - kwargs["header_line"] = ds.main_table.header + kwargs["field_separator"] = "\t" + kwargs["header_line"] = True # Rename parameters to be compatible with khiops.core kwargs["max_constructed_variables"] = kwargs.pop("n_features") @@ -1621,19 +1380,15 @@ def _fit_training_post_process(self, ds): super()._fit_training_post_process(ds) # Save the target and key column dtype's - if ds.is_in_memory: - if self._original_target_dtype is None: - self._original_target_dtype = ds.target_column.dtype - if ds.main_table.key is not None: - self._original_key_dtypes = {} - for column_id in ds.main_table.key: - self._original_key_dtypes[column_id] = ( - ds.main_table.get_column_dtype(column_id) - ) - else: - self._original_key_dtypes = None + if self._original_target_dtype is None: + self._original_target_dtype = ds.target_column.dtype + if ds.main_table.key is not None: + self._original_key_dtypes = {} + for column_id in ds.main_table.key: + self._original_key_dtypes[column_id] = ds.main_table.get_column_dtype( + column_id + ) else: - self._original_target_dtype = None self._original_key_dtypes = None # Set the target variable name @@ -1644,15 +1399,26 @@ def _fit_training_post_process(self, ds): self.model_main_dictionary_name_ = self.model_.dictionaries[0].name else: for dictionary in self.model_.dictionaries: - assert dictionary.name.startswith(self._khiops_model_prefix), ( + + # The baseline model is mandatory for regression; + # absent for classification and encoding + assert ( + dictionary.name.startswith(self._khiops_model_prefix) + or self._khiops_baseline_model_prefix is not None + and dictionary.name.startswith(self._khiops_baseline_model_prefix) + ), ( f"Dictionary '{dictionary.name}' " - f"does not have prefix '{self._khiops_model_prefix}'" - ) - initial_dictionary_name = dictionary.name.replace( - self._khiops_model_prefix, "", 1 + f"does not have prefix '{self._khiops_model_prefix}' " + f"or '{self._khiops_baseline_model_prefix}'." ) - if initial_dictionary_name == ds.main_table.name: - self.model_main_dictionary_name_ = dictionary.name + + # Skip baseline model + if dictionary.name.startswith(self._khiops_model_prefix): + initial_dictionary_name = dictionary.name.replace( + self._khiops_model_prefix, "", 1 + ) + if initial_dictionary_name == ds.main_table.name: + self.model_main_dictionary_name_ = dictionary.name if self.model_main_dictionary_name_ is None: raise ValueError("No model dictionary after Khiops call") @@ -1673,10 +1439,10 @@ def _fit_training_post_process(self, ds): else: pair_feature_evaluated_names_ = [] pair_feature_evaluated_levels_ = [] - if "treePreparationReport" in self.model_report_raw_: - tree_preparation_report = self.model_report_raw_["treePreparationReport"][ - "variablesStatistics" - ] + if "treePreparationReport" in self.model_report_.json_data: + tree_preparation_report = self.model_report_.json_data[ + "treePreparationReport" + ]["variablesStatistics"] tree_feature_evaluated_names_ = [ tree_preparation_report[i]["name"] for i in range(0, len(tree_preparation_report)) @@ -1716,9 +1482,6 @@ def _fit_training_post_process(self, ds): def _transform_check_dataset(self, ds): assert isinstance(ds, Dataset), "'ds' is not 'Dataset'" - # Call the parent method - super()._transform_check_dataset(ds) - # Check the coherence between the input table and the model if self.is_multitable_model_ and not ds.is_multitable: raise ValueError( @@ -1792,8 +1555,6 @@ def __init__( verbose=False, output_dir=None, auto_sort=True, - key=None, - internal_sort=None, ): super().__init__( n_features=n_features, @@ -1804,8 +1565,6 @@ def __init__( verbose=verbose, output_dir=output_dir, auto_sort=auto_sort, - key=key, - internal_sort=internal_sort, ) # Data to be specified by inherited classes self._predicted_target_meta_data_tag = None @@ -1831,7 +1590,7 @@ def predict(self, X): try: # Create the input dataset - ds = Dataset(X, key=self.key) + ds = Dataset(X) # Call the template transform method y_pred = super()._transform( @@ -1891,17 +1650,14 @@ def _transform_prepare_deployment_for_predict(self, ds): model_dictionary.remove_variable(self.model_target_variable_name_) # Create the output column dtype dict - if ds.is_in_memory: - predicted_target_column_name = ( - self._predicted_target_name_prefix + self.model_target_variable_name_ - ) - output_columns_dtype = { - predicted_target_column_name: self._original_target_dtype - } - if self.is_multitable_model_: - output_columns_dtype.update(self._original_key_dtypes) - else: - output_columns_dtype = None + predicted_target_column_name = ( + self._predicted_target_name_prefix + self.model_target_variable_name_ + ) + output_columns_dtype = { + predicted_target_column_name: self._original_target_dtype + } + if self.is_multitable_model_: + output_columns_dtype.update(self._original_key_dtypes) return model_copy, output_columns_dtype @@ -1999,19 +1755,6 @@ class KhiopsClassifier(ClassifierMixin, KhiopsPredictor): affects the `fit`, `predict` and `predict_proba` methods. *Note* The sort by key is performed in a left-to-right, hierarchical, lexicographic manner. - key : str, optional - *Multi-table only* : The name of the column to be used as key. - **Deprecated** will be removed in Khiops 11. Use ``dict`` dataset - specifications in ``fit``, ``fit_predict``, ``predict`` and ``predict_proba``. - internal_sort : bool, optional - *Advanced.* Only for multi-table inputs: If ``True`` input tables are pre-sorted - by their key before executing Khiops. If the input tables are already sorted by - their keys set this parameter to ``False`` to speed up the processing. This - affects the `fit`, `predict` and `predict_proba` methods. - *Note* The sort by key is performed in a left-to-right, hierarchical, - lexicographic manner. - **Deprecated** will be removed in Khiops 11. Use the ``auto_sort`` - estimator parameter instead. Attributes ---------- @@ -2052,10 +1795,6 @@ class KhiopsClassifier(ClassifierMixin, KhiopsPredictor): The name of the main Khiops dictionary within the ``model_`` domain. model_report_ : `.AnalysisResults` The Khiops report object. - model_report_raw_ : dict - JSON object of the Khiops report. - **Deprecated** will be removed in Khiops 11. Use the ``json_data`` - attribute of the ``model_report_`` estimator attribute instead. Examples -------- @@ -2083,8 +1822,6 @@ def __init__( verbose=False, output_dir=None, auto_sort=True, - key=None, - internal_sort=None, ): super().__init__( n_features=n_features, @@ -2095,8 +1832,6 @@ def __init__( verbose=verbose, output_dir=output_dir, auto_sort=auto_sort, - key=key, - internal_sort=internal_sort, ) self.n_pairs = n_pairs self.specific_pairs = specific_pairs @@ -2170,21 +1905,9 @@ def fit(self, X, y, **kwargs): Training dataset. Either an :external:term:`array-like` or a ``dict`` specification for multi-table datasets (see :doc:`/multi_table_primer`). - **Deprecated input types** (will be removed in Khiops 11): - - - tuple: A pair (``path_to_file``, ``separator``). - - list: A sequence of dataframes or paths, or pairs path-separator. The - first element of the list is the main table and the following are - secondary ones joined to the main table using ``key`` estimator parameter. - y : :external:term:`array-like` of shape (n_samples,) The target values. - **Deprecated input types** (will be removed in Khiops 11): - - - str: A path to a data table file for file-based ``dict`` dataset - specifications. - Returns ------- self : `KhiopsClassifier` @@ -2200,16 +1923,15 @@ def _fit_check_dataset(self, ds): # Call the parent method super()._fit_check_dataset(ds) - # Check that the target is for classification in in_memory_tables - if ds.is_in_memory: - current_type_of_target = type_of_target(ds.target_column) - if current_type_of_target not in ["binary", "multiclass"]: - raise ValueError( - f"Unknown label type: '{current_type_of_target}' " - "for classification. Maybe you passed a floating point target?" - ) + # Check that the target is for classification + current_type_of_target = type_of_target(ds.target_column) + if current_type_of_target not in ["binary", "multiclass"]: + raise ValueError( + f"Unknown label type: '{current_type_of_target}' " + "for classification. Maybe you passed a floating point target?" + ) # Check if the target has more than 1 class - if ds.is_in_memory and len(np.unique(ds.target_column)) == 1: + if len(np.unique(ds.target_column)) == 1: raise ValueError( f"{self.__class__.__name__} can't train when only one class is present." ) @@ -2227,7 +1949,7 @@ def _fit_training_post_process(self, ds): for key in variable.meta_data.keys: if key.startswith("TargetProb"): self.classes_.append(variable.meta_data.get_value(key)) - if ds.is_in_memory and self._is_real_target_dtype_integer(): + if self._is_real_target_dtype_integer(): self.classes_ = [int(class_value) for class_value in self.classes_] self.classes_.sort() self.classes_ = column_or_1d(self.classes_) @@ -2272,13 +1994,6 @@ def predict(self, X): Training dataset. Either an :external:term:`array-like` or a ``dict`` specification for multi-table datasets (see :doc:`/multi_table_primer`). - **Deprecated input types** (will be removed in Khiops 11): - - - tuple: A pair (``path_to_file``, ``separator``). - - list: A sequence of dataframes or paths, or pairs path-separator. The - first element of the list is the main table and the following are - secondary ones joined to the main table using ``key`` estimator parameter. - Returns ------- `ndarray ` @@ -2312,13 +2027,6 @@ def predict_proba(self, X): Training dataset. Either an :external:term:`array-like` or a ``dict`` specification for multi-table datasets (see :doc:`/multi_table_primer`). - **Deprecated input types** (will be removed in Khiops 11): - - - tuple: A pair (``path_to_file``, ``separator``). - - list: A sequence of dataframes or paths, or pairs path-separator. The - first element of the list is the main table and the following are - secondary ones joined to the main table using ``key`` estimator parameter. - Returns ------- `numpy.array` or str @@ -2328,8 +2036,6 @@ def predict_proba(self, X): - Dataframe or dataframe-based ``dict`` dataset specification: `numpy.array` - - File-based ``dict`` dataset specification: A CSV file (the method - returns its path). The key columns are added for multi-table tasks. """ @@ -2342,7 +2048,7 @@ def predict_proba(self, X): # Call the generic transform method try: - ds = Dataset(X, key=self.key) + ds = Dataset(X) y_probas = self._transform( ds, computation_dir, @@ -2355,16 +2061,14 @@ def predict_proba(self, X): self._cleanup_computation_dir(computation_dir) kh.get_runner().root_temp_dir = initial_runner_temp_dir - # For in-memory datasets: # - Reorder the columns to that of self.classes_ # - Transform to np.ndarray - if ds.is_in_memory: - assert isinstance( - y_probas, (pd.DataFrame, np.ndarray) - ), "y_probas is not a Pandas DataFrame nor Numpy array" - y_probas = y_probas.reindex( - self._sorted_prob_variable_names(), axis=1, copy=False - ).to_numpy(copy=False) + assert isinstance( + y_probas, (pd.DataFrame, np.ndarray) + ), "y_probas is not a Pandas DataFrame nor Numpy array" + y_probas = y_probas.reindex( + self._sorted_prob_variable_names(), axis=1, copy=False + ).to_numpy(copy=False) assert isinstance(y_probas, (str, np.ndarray)), "Expected str or np.ndarray" return y_probas @@ -2392,15 +2096,12 @@ def _transform_prepare_deployment_for_predict_proba(self, ds): if self.model_target_variable_name_ not in list(ds.main_table.column_ids): model_dictionary.remove_variable(self.model_target_variable_name_) - if ds.is_in_memory: - output_columns_dtype = {} - if self.is_multitable_model_: - output_columns_dtype.update(self._original_key_dtypes) - for variable in model_dictionary.variables: - if variable.used and variable.name not in model_dictionary.key: - output_columns_dtype[variable.name] = np.float64 - else: - output_columns_dtype = None + output_columns_dtype = {} + if self.is_multitable_model_: + output_columns_dtype.update(self._original_key_dtypes) + for variable in model_dictionary.variables: + if variable.used and variable.name not in model_dictionary.key: + output_columns_dtype[variable.name] = np.float64 return model_copy, output_columns_dtype @@ -2446,19 +2147,6 @@ class KhiopsRegressor(RegressorMixin, KhiopsPredictor): affects the `fit` and `predict` methods. *Note* The sort by key is performed in a left-to-right, hierarchical, lexicographic manner. - key : str, optional - *Multi-table only* : The name of the column to be used as key. - **Deprecated** will be removed in Khiops 11. Use ``dict`` dataset - specifications in ``fit``, ``fit_predict`` and ``predict``. - internal_sort : bool, optional - *Advanced.* Only for multi-table inputs: If ``True`` input tables are pre-sorted - by their key before executing Khiops. If the input tables are already sorted by - their keys set this parameter to ``False`` to speed up the processing. This - affects the `fit` and `predict` methods. - *Note* The sort by key is performed in a left-to-right, hierarchical, - lexicographic manner. - **Deprecated** will be removed in Khiops 11. Use the ``auto_sort`` - estimator parameter instead. Attributes ---------- @@ -2494,10 +2182,6 @@ class KhiopsRegressor(RegressorMixin, KhiopsPredictor): The name of the main Khiops dictionary within the ``model_`` domain. model_report_ : `.AnalysisResults` The Khiops report object. - model_report_raw_ : dict - JSON object of the Khiops report. - **Deprecated** will be removed in Khiops 11. Use the ``json_data`` - attribute of the ``model_report_`` estimator attribute instead. Examples -------- @@ -2516,8 +2200,6 @@ def __init__( verbose=False, output_dir=None, auto_sort=True, - key=None, - internal_sort=None, ): super().__init__( n_features=n_features, @@ -2528,10 +2210,9 @@ def __init__( verbose=verbose, output_dir=output_dir, auto_sort=auto_sort, - key=key, - internal_sort=internal_sort, ) self._khiops_model_prefix = "SNB_" + self._khiops_baseline_model_prefix = "B_" self._predicted_target_meta_data_tag = "Mean" self._predicted_target_name_prefix = "M" self._original_target_dtype = np.float64 @@ -2549,21 +2230,9 @@ def fit(self, X, y=None, **kwargs): Training dataset. Either an :external:term:`array-like` or a ``dict`` specification for multi-table datasets (see :doc:`/multi_table_primer`). - **Deprecated input types** (will be removed in Khiops 11): - - - tuple: A pair (``path_to_file``, ``separator``). - - list: A sequence of dataframes or paths, or pairs path-separator. The - first element of the list is the main table and the following are - secondary ones joined to the main table using ``key`` estimator parameter. - y : :external:term:`array-like` of shape (n_samples,) The target values. - **Deprecated input types** (will be removed in Khiops 11): - - - str: A path to a data table file for file-based ``dict`` dataset - specifications. - Returns ------- self : `KhiopsRegressor` @@ -2632,13 +2301,6 @@ def predict(self, X): Training dataset. Either an :external:term:`array-like` or a ``dict`` specification for multi-table datasets (see :doc:`/multi_table_primer`). - **Deprecated input types** (will be removed in Khiops 11): - - - tuple: A pair (``path_to_file``, ``separator``). - - list: A sequence of dataframes or paths, or pairs path-separator. The - first element of the list is the main table and the following are - secondary ones joined to the main table using ``key`` estimator parameter. - Returns ------- `numpy.ndarray` or str @@ -2655,7 +2317,7 @@ def predict(self, X): # Call the parent's method y_pred = super().predict(X) - # Transform to np.ndarray for in-memory datasets + # Transform to np.ndarray if isinstance(y_pred, pd.DataFrame): y_pred = y_pred.astype("float64", copy=False).to_numpy(copy=False).ravel() @@ -2747,19 +2409,6 @@ class KhiopsEncoder(TransformerMixin, KhiopsSupervisedEstimator): affects the `fit` and `transform` methods. *Note* The sort by key is performed in a left-to-right, hierarchical, lexicographic manner. - key : str, optional - *Multi-table only* : The name of the column to be used as key. - **Deprecated** will be removed in Khiops 11. Use ``dict`` dataset - specifications in ``fit`` and ``transform``. - internal_sort : bool, optional - *Advanced.* Only for multi-table inputs: If ``True`` input tables are pre-sorted - by their key before executing Khiops. If the input tables are already sorted by - their keys set this parameter to ``False`` to speed up the processing. This - affects the `fit` and `transform` methods. - *Note* The sort by key is performed in a left-to-right, hierarchical, - lexicographic manner. - **Deprecated** will be removed in Khiops 11. Use the ``auto_sort`` - estimator parameter instead. Attributes ---------- @@ -2779,10 +2428,6 @@ class KhiopsEncoder(TransformerMixin, KhiopsSupervisedEstimator): The name of the main Khiops dictionary within the ``model_`` domain. model_report_ : `.AnalysisResults` The Khiops report object. - model_report_raw_ : dict - JSON object of the Khiops report. - **Deprecated** will be removed in Khiops 11. Use the ``json_data`` - attribute of the ``model_report_`` estimator attribute instead. Examples -------- @@ -2810,8 +2455,6 @@ def __init__( verbose=False, output_dir=None, auto_sort=True, - key=None, - internal_sort=None, ): super().__init__( n_features=n_features, @@ -2820,8 +2463,6 @@ def __init__( verbose=verbose, output_dir=output_dir, auto_sort=auto_sort, - key=key, - internal_sort=internal_sort, ) self.n_pairs = n_pairs self.specific_pairs = specific_pairs @@ -2994,21 +2635,9 @@ def fit(self, X, y=None, **kwargs): Training dataset. Either an :external:term:`array-like` or a ``dict`` specification for multi-table datasets (see :doc:`/multi_table_primer`). - **Deprecated input types** (will be removed in Khiops 11): - - - tuple: A pair (``path_to_file``, ``separator``). - - list: A sequence of dataframes or paths, or pairs path-separator. The - first element of the list is the main table and the following are - secondary ones joined to the main table using ``key`` estimator parameter. - y : :external:term:`array-like` of shape (n_samples,) The target values. - **Deprecated input types** (will be removed in Khiops 11): - - - str: A path to a data table file for file-based ``dict`` dataset - specifications. - Returns ------- self : `KhiopsEncoder` @@ -3074,13 +2703,6 @@ def transform(self, X): Training dataset. Either an :external:term:`array-like` or a ``dict`` specification for multi-table datasets (see :doc:`/multi_table_primer`). - **Deprecated input types** (will be removed in Khiops 11): - - - tuple: A pair (``path_to_file``, ``separator``). - - list: A sequence of dataframes or paths, or pairs path-separator. The - first element of the list is the main table and the following are - secondary ones joined to the main table using ``key`` estimator parameter. - Returns ------- `ndarray ` @@ -3094,7 +2716,7 @@ def transform(self, X): # Create and transform the dataset try: - ds = Dataset(X, key=self.key) + ds = Dataset(X) X_transformed = super()._transform( ds, computation_dir, @@ -3106,9 +2728,7 @@ def transform(self, X): finally: self._cleanup_computation_dir(computation_dir) kh.get_runner().root_temp_dir = initial_runner_temp_dir - if ds.is_in_memory: - return X_transformed.to_numpy(copy=False) - return X_transformed + return X_transformed.to_numpy(copy=False) def _transform_prepare_deployment_for_transform(self, ds): assert hasattr( @@ -3137,22 +2757,9 @@ def fit_transform(self, X, y=None, **kwargs): Training dataset. Either an :external:term:`array-like` or a ``dict`` specification for multi-table datasets (see :doc:`/multi_table_primer`). - **Deprecated input types** (will be removed in Khiops 11): - - - tuple: A pair (``path_to_file``, ``separator``). - - list: A sequence of dataframes or paths, or pairs path-separator. The - first element of the list is the main table and the following are - secondary ones joined to the main table using ``key`` estimator - parameter. - y : :external:term:`array-like` of shape (n_samples,) The target values. - **Deprecated input types** (will be removed in Khiops 11): - - - str: A path to a data table file for file-based ``dict`` dataset - specifications. - Returns ------- self : `KhiopsEncoder` diff --git a/khiops/sklearn/helpers.py b/khiops/sklearn/helpers.py index 426293fa..301ba8f5 100644 --- a/khiops/sklearn/helpers.py +++ b/khiops/sklearn/helpers.py @@ -11,7 +11,7 @@ from sklearn.model_selection import train_test_split from khiops.core.internals.common import is_dict_like, type_error_message -from khiops.sklearn.dataset import Dataset, FileTable +from khiops.sklearn.dataset import Dataset # Note: We build the splits with lists and itertools.chain avoid pylint warning about # unbalanced-tuple-unpacking. See issue https://github.com/pylint-dev/pylint/issues/5671 @@ -56,12 +56,6 @@ def train_test_split_dataset(ds_spec, y=None, test_size=0.25, **kwargs): # Build the dataset for the feature table ds = Dataset(ds_spec) - # Check the table type of the dataset - if isinstance(ds.main_table, FileTable): - raise ValueError( - "Only Pandas, NumPy, SciPy sparse datasets may be used in this method." - ) - # Obtain the keys for the other test_train_split function sklearn_split_params = {} for param in ("train_size", "random_state", "shuffle", "stratify"): diff --git a/khiops/tools.py b/khiops/tools.py index 707bb568..f6de417a 100644 --- a/khiops/tools.py +++ b/khiops/tools.py @@ -21,7 +21,6 @@ import zipfile import khiops.core as kh -from khiops.core.internals.common import deprecation_message from khiops.samples import samples as samples_core # We deactivate the warnings to not show a deprecation warning from sklearn @@ -35,21 +34,6 @@ # Note: We dont include these tools in coverage -def pk_status_entry_point(): # pragma: no cover - """Entry point of the pk-status command - - **Deprecated** will be removed in Khiops 11, use `kh_status_entry_point` instead. - """ - warnings.warn( - deprecation_message( - deprecated_feature="pk-status", - replacement="kh-status", - deadline_version="11.0.0", - ) - ) - kh_status_entry_point() - - def kh_status_entry_point(): # pragma: no cover """Entry point of the kh-status command""" status_code = kh.get_runner().print_status() diff --git a/packaging/conda/meta.yaml b/packaging/conda/meta.yaml index 5007f5ba..b070a6b7 100644 --- a/packaging/conda/meta.yaml +++ b/packaging/conda/meta.yaml @@ -26,7 +26,7 @@ requirements: - python run: - python - - khiops-core >=10.2.4,<10.2.5 + - khiops-core =10.6.0b.0 - pandas >=0.25.3 - scikit-learn >=0.22.2 run_constrained: diff --git a/tests/resources/analysis_results/ref_json_reports/AdultLegacy.khj b/tests/resources/analysis_results/ref_json_reports/AdultLegacy.khj deleted file mode 100644 index 6eb5a692..00000000 --- a/tests/resources/analysis_results/ref_json_reports/AdultLegacy.khj +++ /dev/null @@ -1,1007 +0,0 @@ -{ - "tool": "Khiops", - "version": "9.0.1", - "modelingReport": { - "reportType": "Modeling", - "summary": { - "dictionary": "Adult", - "database": "C:\\Applications\\khiops9\\samples\\Adult\\Adult.txt", - "learningTask": "Classification analysis", - "targetVariable": "class" - }, - "trainedPredictors": [ - { - "rank": "R1", - "type": "Classifier", - "family": "Selective Naive Bayes", - "name": "Selective Naive Bayes", - "variables": 23 - } - ], - "trainedPredictorsDetails": { - "R1": { - "selectedVariables": [ - { - "preparedName": "Pcapital_gain", - "name": "capital_gain", - "level": 0.134873, - "weight": 0.74488, - "map": true - }, - { - "preparedName": "Pcapital_loss", - "name": "capital_loss", - "level": 0.0524885, - "weight": 0.608755, - "map": true - }, - { - "preparedName": "Pnative_country", - "name": "native_country", - "level": 0.00648434, - "weight": 0.364978, - "map": true - }, - { - "preparedName": "Phours_per_week", - "name": "hours_per_week", - "level": 0.0668647, - "weight": 0.331739 - }, - { - "preparedName": "PTree_4", - "name": "Tree_4", - "level": 0.404389, - "weight": 0.250555 - }, - { - "preparedName": "Page", - "name": "age", - "level": 0.118282, - "weight": 0.212148 - }, - { - "preparedName": "Poccupation", - "name": "occupation", - "level": 0.0881436, - "weight": 0.177041 - }, - { - "preparedName": "PTree_3", - "name": "Tree_3", - "level": 0.408349, - "weight": 0.175507 - }, - { - "preparedName": "PTree_1", - "name": "Tree_1", - "level": 0.387744, - "weight": 0.147389 - }, - { - "preparedName": "Peducation_num", - "name": "education_num", - "level": 0.113419, - "weight": 0.122321 - }, - { - "preparedName": "PTree_10", - "name": "Tree_10", - "level": 0.386972, - "weight": 0.11885 - }, - { - "preparedName": "PTree_8", - "name": "Tree_8", - "level": 0.368957, - "weight": 0.11793 - }, - { - "preparedName": "PTree_9", - "name": "Tree_9", - "level": 0.341639, - "weight": 0.11602 - }, - { - "preparedName": "PTree_7", - "name": "Tree_7", - "level": 0.339119, - "weight": 0.112606, - "map": true - }, - { - "preparedName": "Prelationship", - "name": "relationship", - "level": 0.207564, - "weight": 0.10602 - }, - { - "preparedName": "Peducation", - "name": "education", - "level": 0.113596, - "weight": 0.104748 - }, - { - "preparedName": "PTree_2", - "name": "Tree_2", - "level": 0.383517, - "weight": 0.0892111 - }, - { - "preparedName": "Pworkclass", - "name": "workclass", - "level": 0.020766, - "weight": 0.08013 - }, - { - "preparedName": "Prace", - "name": "race", - "level": 0.0107747, - "weight": 0.0686931 - }, - { - "preparedName": "PTree_5", - "name": "Tree_5", - "level": 0.385644, - "weight": 0.0633754 - }, - { - "preparedName": "Pmarital_status", - "name": "marital_status", - "level": 0.198034, - "weight": 0.0547483 - }, - { - "preparedName": "Psex", - "name": "sex", - "level": 0.0455065, - "weight": 0.0422378 - }, - { - "preparedName": "PTree_6", - "name": "Tree_6", - "level": 0.385471, - "weight": 0.0338381 - } - ] - } - } - }, - "trainEvaluationReport": { - "reportType": "Evaluation", - "evaluationType": "Train", - "summary": { - "dictionary": "Adult", - "database": "C:\\Applications\\khiops9\\samples\\Adult\\Adult.txt", - "instances": 34174, - "learningTask": "Classification analysis", - "targetVariable": "class" - }, - "predictorsPerformance": [ - { - "rank": "R1", - "type": "Classifier", - "family": "Selective Naive Bayes", - "name": "Selective Naive Bayes", - "accuracy": 0.870018, - "compression": 0.488339, - "auc": 0.92972 - } - ], - "predictorsDetailedPerformance": { - "R1": { - "confusionMatrix": { - "values": ["less","more"], - "matrix": [ - [24104,2518], - [1924,5628] - ] - } - } - }, - "liftCurves": [ - { - "targetValue": "less", - "curves": [ - { - "classifier": "Optimal", - "values": 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- } - ] - } - ] - }, - "preparationReport": { - "reportType": "Preparation", - "summary": { - "dictionary": "Iris", - "variables": { - "types": [ - "Categorical", - "Numerical" - ], - "numbers": [ - 1, - 4 - ] - }, - "database": "..\/..\/..\/datasets\/Iris\/Iris.txt", - "instances": 99, - "learningTask": "Classification analysis", - "targetVariable": "Class", - "targetDescriptiveStats": { - "values": 3, - "mode": "Iris-versicolor", - "modeFrequency": 37 - }, - "targetValues": { - "values": ["Iris-setosa","Iris-versicolor","Iris-virginica"], - "frequencies": [31,37,31] - }, - "evaluatedVariables": 4, - "informativeVariables": 4, - "discretization": "MODL", - "valueGrouping": "MODL", - "nullModel": { - "constructionCost": 0.693147, - "preparationCost": 8.52714, - "dataCost": 103.619 - } - }, - "variablesStatistics": [ - { - "rank": "R1", - "name": "PetalLength", - "type": "Numerical", - "level": 0.644632, - "parts": 3, - "values": 36, - "min": 1, - "max": 6.9, - "mean": 3.801010101, - "stdDev": 1.712137004, - "missingNumber": 0, - "constructionCost": 2.07944, - "preparationCost": 28.0635, - "dataCost": 9.95655 - }, - { - "rank": "R2", - "name": "PetalWidth", - "type": "Numerical", - "level": 0.610094, - "parts": 3, - "values": 20, - "min": 0.1, - "max": 2.5, - "mean": 1.218181818, - "stdDev": 0.749863777, - "missingNumber": 0, - "constructionCost": 2.07944, - "preparationCost": 31.9608, - "dataCost": 9.95655 - }, - { - "rank": "R3", - "name": "SepalLength", - "type": "Numerical", - "level": 0.290065, - "parts": 3, - "values": 30, - "min": 4.3, - "max": 7.7, - "mean": 5.848484848, - "stdDev": 0.8065844732, - "missingNumber": 0, - "constructionCost": 2.07944, - "preparationCost": 27.4942, - "dataCost": 50.535 - }, - { - "rank": "R4", - "name": "SepalWidth", - "type": "Numerical", - "level": 0.121532, - "parts": 2, - "values": 22, - "min": 2, - "max": 4.4, - "mean": 3.042424242, - "stdDev": 0.4422374035, - "missingNumber": 0, - "constructionCost": 2.07944, - "preparationCost": 16.7264, - "dataCost": 80.32 - } - ], - "variablesDetailedStatistics": { - "R1": { - "dataGrid": { - "isSupervised": true, - "dimensions": [ - { - "variable": "PetalLength", - "type": "Numerical", - "partitionType": "Intervals", - "partition": [ - [1,2.4], - [2.4,4.95], - [4.95,6.9] - ] - }, - { - "variable": "Class", - "type": "Categorical", - "partitionType": "Values", - "partition": ["Iris-setosa","Iris-versicolor","Iris-virginica"] - } - ], - "partTargetFrequencies": [ - [31,0,0], - [0,36,2], - [0,1,29] - ], - "partInterests": [0.374224,0.311045,0.314731] - } - }, - "R2": { - "dataGrid": { - "isSupervised": true, - "dimensions": [ - { - "variable": "PetalWidth", - "type": "Numerical", - "partitionType": "Intervals", - "partition": [ - [0.1,0.7], - [0.7,1.75], - [1.75,2.5] - ] - }, - { - "variable": "Class", - "type": "Categorical", - "partitionType": "Values", - "partition": ["Iris-setosa","Iris-versicolor","Iris-virginica"] - } - ], - "partTargetFrequencies": [ - [31,0,0], - [0,36,2], - [0,1,29] - ], - "partInterests": [0.374224,0.311045,0.314731] - } - }, - "R3": { - "dataGrid": { - "isSupervised": true, - "dimensions": [ - { - "variable": "SepalLength", - "type": "Numerical", - "partitionType": "Intervals", - "partition": [ - [4.3,5.45], - [5.45,5.75], - [5.75,7.7] - ] - }, - { - "variable": "Class", - "type": "Categorical", - "partitionType": "Values", - "partition": ["Iris-setosa","Iris-versicolor","Iris-virginica"] - } - ], - "partTargetFrequencies": [ - [28,3,0], - [3,12,1], - [0,22,30] - ], - "partInterests": [0.493633,0.100396,0.405971] - } - }, - "R4": { - "dataGrid": { - "isSupervised": true, - "dimensions": [ - { - "variable": "SepalWidth", - "type": "Numerical", - "partitionType": "Intervals", - "partition": [ - [2,3.35], - [3.35,4.4] - ] - }, - { - "variable": "Class", - "type": "Categorical", - "partitionType": "Values", - "partition": ["Iris-setosa","Iris-versicolor","Iris-virginica"] - } - ], - "partTargetFrequencies": [ - [11,36,29], - [20,1,2] - ], - "partInterests": [0.268778,0.731222] - } - } - } - } -} diff --git a/tests/resources/analysis_results/ref_reports/AdultLegacy.txt b/tests/resources/analysis_results/ref_reports/AdultLegacy.txt deleted file mode 100644 index d89fc496..00000000 --- a/tests/resources/analysis_results/ref_reports/AdultLegacy.txt +++ /dev/null @@ -1,2563 +0,0 @@ -Tool Khiops -Version 9.0.1 -Short description - - -Report Preparation - -Dictionary Adult -Variables - Categorical 10 - Numerical 6 - Total 16 -Database C:\Applications\khiops9\samples\Adult\Adult.txt -Instances 34174 -Learning task Classification analysis -Target variable class -Target descriptive stats - Values 2 - Mode less - Mode frequency 26028 -Target variable stats - less 26028 - more 8146 -Evaluated variables 15 -Informative variables 13 -Discretization MODL -Value grouping MODL -Null model - Construction cost 0.693147 - Preparation cost 10.4392 - Data cost 18762.8 - -Variable statistics -Rank Name Type Level Target parts Parts Values Min Max Mean StdDev Missing number Mode Mode frequency Construction cost Preparation cost Data cost Derivation rule -R01 relationship Categorical 0.207564 4 6 Husband 13781 0.693147 46.5087 14829.9 -R02 marital_status Categorical 0.198034 3 7 Married-civ-spouse 15653 0.693147 39.1965 15016.2 -R03 capital_gain Numerical 0.134873 20 120 0 99999 1077.573272 7458.589277 0 0.693147 246.057 15995.1 -R04 age Numerical 0.118282 10 74 17 90 38.65719553 13.7247169 0 0.693147 106.285 16446.3 -R05 education Categorical 0.113596 7 16 HS-grad 11045 0.693147 89.1656 16551.4 -R06 education_num Numerical 0.113419 7 16 1 16 10.07233569 2.570667893 0 0.693147 92.5027 16551.4 -R07 occupation Categorical 0.0881436 7 14 Prof-specialty 6288 0.693147 86.5284 17031.9 -R08 hours_per_week Numerical 0.0668647 6 95 1 99 40.41060455 12.43409782 0 0.693147 69.08 17448.8 -R09 capital_loss Numerical 0.0524885 16 96 0 4356 88.68692573 405.648105 0 0.693147 212.711 17575.1 -R10 sex Categorical 0.0455065 2 2 Male 22863 0.693147 20.8139 17898.1 -R11 workclass Categorical 0.020766 4 8 Private 25713 0.693147 48.8804 18334.5 -R12 race Categorical 0.0107747 2 5 White 29231 0.693147 26.0889 18544.9 -R13 native_country Categorical 0.00648434 3 41 United-States 31267 0.693147 74.6717 18576.8 -R14 Label Categorical 0 1 34174 1 1 0.693147 10.4392 18762.8 -R15 fnlwgt Numerical 0 1 22523 12285 1490400 189619.5541 105285.481 0 0.693147 10.4392 18762.8 - -Detailed variable statistics - -Rank R01 relationship Categorical - -Data grid Supervised -Dimensions -relationship Categorical Value groups - {Husband, Wife} Husband Wife - {Not-in-family} Not-in-family - {Own-child, Other-relative} Own-child Other-relative * - {Unmarried} Unmarried -class Categorical Values - less - more -Cells -Value group less more Interest -{Husband, Wife} 8487 6942 0.42026 -{Not-in-family} 7916 871 0.144028 -{Own-child, Other-relative} 6210 107 0.331951 -{Unmarried} 3415 226 0.103761 - -Input values - Husband 13781 - Not-in-family 8787 - Own-child 5289 - Unmarried 3641 - Wife 1648 - Other-relative 1028 - -Rank R02 marital_status Categorical - -Data grid Supervised -Dimensions -marital_status Categorical Value groups - {Married-civ-spouse, Married-AF-spouse} Married-civ-spouse Married-AF-spouse * - {Never-married, Separated} Never-married Separated - {Divorced, Widowed, Married-spouse-absent} Divorced Widowed Married-spouse-absent -class Categorical Values - less - more -Cells -Value group less more Interest -{Married-civ-spouse, Married-AF-spouse} 8699 6977 0.428554 -{Never-married, Separated} 11773 550 0.467191 -{Divorced, Widowed, Married-spouse-absent} 5556 619 0.104255 - -Input values - Married-civ-spouse 15653 - Never-married 11245 - Divorced 4676 - Separated 1078 - Widowed 1054 - Married-spouse-absent 445 - Married-AF-spouse 23 - -Rank R03 capital_gain Numerical - -Data grid Supervised -Dimensions -capital_gain Numerical Intervals - ]-inf;57] 0 57 - ]57;3040] 57 3040 - ]3040;3120] 3040 3120 - ]3120;4240] 3120 4240 - ]4240;4400] 4240 4400 - ]4400;4668] 4400 4668 - ]4668;4826] 4668 4826 - ]4826;4932.5] 4826 4932.5 - ]4932.5;4970] 4932.5 4970 - ]4970;5119] 4970 5119 - ]5119;5310] 5119 5310 - ]5310;6667] 5310 6667 - ]6667;7070] 6667 7070 - ]7070;7436] 7070 7436 - ]7436;7560] 7436 7560 - ]7560;10543] 7560 10543 - ]10543;10585] 10543 10585 - ]10585;30900] 10585 30900 - ]30900;70000] 30900 70000 - ]70000;+inf[ 70000 99999 -class Categorical Values - less - more -Cells -Interval less more Interest -]-inf;57] 24935 6425 0.036328 -]57;3040] 483 0 0.0476561 -]3040;3120] 3 103 0.0488677 -]3120;4240] 327 0 0.032264 -]4240;4400] 11 66 0.0239359 -]4400;4668] 76 0 0.00749868 -]4668;4826] 0 22 0.0114311 -]4826;4932.5] 17 0 0.00167734 -]4932.5;4970] 0 8 0.00415677 -]4970;5119] 87 0 0.00858401 -]5119;5310] 0 104 0.054038 -]5310;6667] 31 28 0.00281621 -]6667;7070] 37 0 0.00365067 -]7070;7436] 0 260 0.135095 -]7436;7560] 5 0 0.000493334 -]7560;10543] 2 395 0.200881 -]10543;10585] 5 0 0.000493334 -]10585;30900] 1 564 0.290493 -]30900;70000] 8 0 0.000789334 -]70000;+inf[ 0 171 0.088851 - -Rank R04 age Numerical - -Data grid Supervised -Dimensions -age Numerical Intervals - ]-inf;20.5] 17 20.5 - ]20.5;23.5] 20.5 23.5 - ]23.5;26.5] 23.5 26.5 - ]26.5;28.5] 26.5 28.5 - ]28.5;31.5] 28.5 31.5 - ]31.5;35.5] 31.5 35.5 - ]35.5;42.5] 35.5 42.5 - ]42.5;55.5] 42.5 55.5 - ]55.5;61.5] 55.5 61.5 - ]61.5;+inf[ 61.5 90 -class Categorical Values - less - more -Cells -Interval less more Interest -]-inf;20.5] 2523 1 0.297283 -]20.5;23.5] 2485 34 0.238516 -]23.5;26.5] 2341 136 0.133666 -]26.5;28.5] 1538 190 0.040517 -]28.5;31.5] 2222 526 0.00767092 -]31.5;35.5] 2776 898 0.000161185 -]35.5;42.5] 4024 2008 0.0601262 -]42.5;55.5] 4950 3162 0.200273 -]55.5;61.5] 1427 707 0.020585 -]61.5;+inf[ 1742 484 0.00120059 - -Rank R05 education Categorical - -Data grid Supervised -Dimensions -education Categorical Value groups - {HS-grad} HS-grad - {Some-college} Some-college - {Bachelors} Bachelors - {11th, 10th, 7th-8th, ...} 11th 10th 7th-8th 9th 12th 5th-6th 1st-4th Preschool * - {Assoc-voc, Assoc-acdm} Assoc-voc Assoc-acdm - {Masters} Masters - {Prof-school, Doctorate} Prof-school Doctorate -class Categorical Values - less - more -Cells -Value group less more Interest -{HS-grad} 9292 1753 0.096573 -{Some-college} 6226 1437 0.0263235 -{Bachelors} 3288 2273 0.179713 -{11th, 10th, 7th-8th, ...} 4224 250 0.248922 -{Assoc-voc, Assoc-acdm} 1909 671 0.00150022 -{Masters} 831 1023 0.191076 -{Prof-school, Doctorate} 258 739 0.255893 - -Input values - HS-grad 11045 - Some-college 7663 - Bachelors 5561 - Masters 1854 - Assoc-voc 1465 - 11th 1265 - Assoc-acdm 1115 - 10th 952 - 7th-8th 658 - Prof-school 577 - 9th 535 - 12th 458 - Doctorate 420 - 5th-6th 362 - 1st-4th 190 - Preschool 54 - -Rank R06 education_num Numerical - -Data grid Supervised -Dimensions -education_num Numerical Intervals - ]-inf;8.5] 1 8.5 - ]8.5;9.5] 8.5 9.5 - ]9.5;10.5] 9.5 10.5 - ]10.5;12.5] 10.5 12.5 - ]12.5;13.5] 12.5 13.5 - ]13.5;14.5] 13.5 14.5 - ]14.5;+inf[ 14.5 16 -class Categorical Values - less - more -Cells -Interval less more Interest -]-inf;8.5] 4224 250 0.248922 -]8.5;9.5] 9292 1753 0.096573 -]9.5;10.5] 6226 1437 0.0263235 -]10.5;12.5] 1909 671 0.00150022 -]12.5;13.5] 3288 2273 0.179713 -]13.5;14.5] 831 1023 0.191076 -]14.5;+inf[ 258 739 0.255893 - -Rank R07 occupation Categorical - -Data grid Supervised -Dimensions -occupation Categorical Value groups - {Adm-clerical, Machine-op-inspct, Farming-fishing} Adm-clerical Machine-op-inspct Farming-fishing - {Prof-specialty, Armed-Forces} Prof-specialty Armed-Forces * - {Craft-repair, Transport-moving} Craft-repair Transport-moving - {Sales, Tech-support, Protective-serv} Sales Tech-support Protective-serv - {Exec-managerial} Exec-managerial - {Other-service, Priv-house-serv} Other-service Priv-house-serv - {Handlers-cleaners} Handlers-cleaners -class Categorical Values - less - more -Cells -Value group less more Interest -{Adm-clerical, Machine-op-inspct, Farming-fishing} 6177 933 0.149983 -{Prof-specialty, Armed-Forces} 4161 2140 0.0960551 -{Craft-repair, Transport-moving} 4644 1309 0.00333952 -{Sales, Tech-support, Protective-serv} 4030 1509 0.010052 -{Exec-managerial} 2222 2018 0.330687 -{Other-service, Priv-house-serv} 3463 138 0.325246 -{Handlers-cleaners} 1331 99 0.084638 - -Input values - Prof-specialty 6288 - Craft-repair 4290 - Exec-managerial 4240 - Adm-clerical 3917 - Sales 3852 - Other-service 3430 - Machine-op-inspct 2140 - Transport-moving 1663 - Handlers-cleaners 1430 - Farming-fishing 1053 - Tech-support 1003 - Protective-serv 684 - Priv-house-serv 171 - Armed-Forces 13 - -Rank R08 hours_per_week Numerical - -Data grid Supervised -Dimensions -hours_per_week Numerical Intervals - ]-inf;34.5] 1 34.5 - ]34.5;39.5] 34.5 39.5 - ]39.5;40.5] 39.5 40.5 - ]40.5;49.5] 40.5 49.5 - ]49.5;64.5] 49.5 64.5 - ]64.5;+inf[ 64.5 99 -class Categorical Values - less - more -Cells -Interval less more Interest -]-inf;34.5] 5485 410 0.45826 -]34.5;39.5] 1923 359 0.0349894 -]39.5;40.5] 12550 3441 0.0187227 -]40.5;49.5] 2163 1118 0.0674108 -]49.5;64.5] 3154 2406 0.390525 -]64.5;+inf[ 753 412 0.030092 - -Rank R09 capital_loss Numerical - -Data grid Supervised -Dimensions -capital_loss Numerical Intervals - ]-inf;70] 0 70 - ]70;1457] 70 1457 - ]1457;1494] 1457 1494 - ]1494;1551] 1494 1551 - ]1551;1568.5] 1551 1568.5 - ]1568.5;1820.5] 1568.5 1820.5 - ]1820.5;1859] 1820.5 1859 - ]1859;1881] 1859 1881 - ]1881;1927] 1881 1927 - ]1927;1975.5] 1927 1975.5 - ]1975.5;1978.5] 1975.5 1978.5 - ]1978.5;2161] 1978.5 2161 - ]2161;2176.5] 2161 2176.5 - ]2176.5;2384] 2176.5 2384 - ]2384;2450] 2384 2450 - ]2450;+inf[ 2450 4356 -class Categorical Values - less - more -Cells -Interval less more Interest -]-inf;70] 25227 7329 0.0135746 -]70;1457] 92 2 0.0155286 -]1457;1494] 20 33 0.0150177 -]1494;1551] 23 0 0.00533179 -]1551;1568.5] 0 29 0.0354029 -]1568.5;1820.5] 353 3 0.0707515 -]1820.5;1859] 2 49 0.0530989 -]1859;1881] 45 0 0.0104318 -]1881;1927] 13 372 0.408768 -]1927;1975.5] 19 0 0.00440452 -]1975.5;1978.5] 0 183 0.223404 -]1978.5;2161] 131 0 0.030368 -]2161;2176.5] 0 9 0.0109871 -]2176.5;2384] 83 32 0.000414099 -]2384;2450] 0 71 0.086676 -]2450;+inf[ 20 34 0.0158399 - -Rank R10 sex Categorical - -Data grid Supervised -Dimensions -sex Categorical Value groups - {Male} Male - {Female} Female * -class Categorical Values - less - more -Cells -Value group less more Interest -{Male} 15950 6913 0.283758 -{Female} 10078 1233 0.716242 - -Input values - Male 22863 - Female 11311 - -Rank R11 workclass Categorical - -Data grid Supervised -Dimensions -workclass Categorical Value groups - {Private, Without-pay, Never-worked} Private Without-pay Never-worked * - {Self-emp-not-inc, Local-gov, State-gov} Self-emp-not-inc Local-gov State-gov - {Self-emp-inc} Self-emp-inc - {Federal-gov} Federal-gov -class Categorical Values - less - more -Cells -Value group less more Interest -{Private, Without-pay, Never-worked} 20389 5347 0.164421 -{Self-emp-not-inc, Local-gov, State-gov} 4485 1768 0.078268 -{Self-emp-inc} 532 656 0.64463 -{Federal-gov} 622 375 0.112681 - -Input values - Private 25713 - Self-emp-not-inc 2698 - Local-gov 2216 - State-gov 1339 - Self-emp-inc 1188 - Federal-gov 997 - Without-pay 15 - Never-worked 8 - -Rank R12 race Categorical - -Data grid Supervised -Dimensions -race Categorical Value groups - {White, Asian-Pac-Islander} White Asian-Pac-Islander - {Black, Amer-Indian-Eskimo, Other} Black Amer-Indian-Eskimo Other * -class Categorical Values - less - more -Cells -Value group less more Interest -{White, Asian-Pac-Islander} 22590 7703 0.0972591 -{Black, Amer-Indian-Eskimo, Other} 3438 443 0.902741 - -Input values - White 29231 - Black 3276 - Asian-Pac-Islander 1062 - Amer-Indian-Eskimo 335 - Other 270 - -Rank R13 native_country Categorical - -Data grid Supervised -Dimensions -native_country Categorical Value groups - {United-States, Philippines, Germany, ...} United-States Philippines Germany Cuba China South Poland Portugal Ireland Thailand Yugoslavia Scotland - {Mexico, Puerto-Rico, El-Salvador, ...} Mexico Puerto-Rico El-Salvador Dominican-Republic Jamaica Columbia Vietnam Guatemala Haiti Ecuador Peru Nicaragua Laos Outlying-US Trinadad&Tobago Honduras Holand-Netherlands * - {Canada, India, England, ...} Canada India England Italy Japan Taiwan Iran Greece France Cambodia Hong Hungary -class Categorical Values - less - more -Cells -Value group less more Interest -{United-States, Philippines, Germany, ...} 24257 7816 0.0138949 -{Mexico, Puerto-Rico, El-Salvador, ...} 1363 98 0.848593 -{Canada, India, England, ...} 408 232 0.137512 - -Input values - United-States 31267 - Mexico 672 - Philippines 203 - Germany 150 - Puerto-Rico 133 - Canada 121 - El-Salvador 116 - Cuba 100 - India 98 - China 89 - England 84 - South 82 - Italy 75 - Dominican-Republic 74 - Jamaica 69 - Columbia 64 - Japan 63 - Vietnam 63 - Guatemala 60 - Poland 58 - Haiti 52 - Portugal 48 - Taiwan 46 - Iran 38 - Greece 36 - Ecuador 33 - Peru 31 - Nicaragua 29 - France 26 - Ireland 24 - Thailand 23 - Cambodia 21 - Laos 20 - Outlying-US 18 - Hong 17 - Yugoslavia 16 - Hungary 15 - Trinadad&Tobago 15 - Scotland 13 - Honduras 11 - Holand-Netherlands 1 - - -Report Modeling - -Dictionary Adult -Database C:\Applications\khiops9\samples\Adult\Adult.txt -Learning task Classification analysis -Target variable class - -Trained predictors -Rank Type Family Name Variables -R1 Classifier Selective Naive Bayes Selective Naive Bayes 23 - -Detailed trained predictors - -Rank R1 - -Selected variables -Prepared name Name Level Weight MAP -Pcapital_gain capital_gain 0.134873 0.74488 1 -Pcapital_loss capital_loss 0.0524885 0.608755 1 -Pnative_country native_country 0.00648434 0.364978 1 -Phours_per_week hours_per_week 0.0668647 0.331739 -PTree_4 Tree_4 0.404389 0.250555 -Page age 0.118282 0.212148 -Poccupation occupation 0.0881436 0.177041 -PTree_3 Tree_3 0.408349 0.175507 -PTree_1 Tree_1 0.387744 0.147389 -Peducation_num education_num 0.113419 0.122321 -PTree_10 Tree_10 0.386972 0.11885 -PTree_8 Tree_8 0.368957 0.11793 -PTree_9 Tree_9 0.341639 0.11602 -PTree_7 Tree_7 0.339119 0.112606 1 -Prelationship relationship 0.207564 0.10602 -Peducation education 0.113596 0.104748 -PTree_2 Tree_2 0.383517 0.0892111 -Pworkclass workclass 0.020766 0.08013 -Prace race 0.0107747 0.0686931 -PTree_5 Tree_5 0.385644 0.0633754 -Pmarital_status marital_status 0.198034 0.0547483 -Psex sex 0.0455065 0.0422378 -PTree_6 Tree_6 0.385471 0.0338381 - - -Report Evaluation Train - -Dictionary Adult -Database C:\Applications\khiops9\samples\Adult\Adult.txt -Instances 34174 -Learning task Classification analysis -Target variable class - -Predictors performance -Rank Type Family Name Accuracy Compression AUC -R1 Classifier Selective Naive Bayes Selective Naive Bayes 0.870018 0.488339 0.92972 - -Predictors detailed performance - -Rank R1 - -Confusion matrix - less more -$less 24104 2518 -$more 1924 5628 - 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-55.1 55.1 100 98.1463 -55.2 55.2 100 98.1954 -55.3 55.3 100 98.1954 -55.4 55.4 100 98.2074 -55.5 55.5 100 98.2323 -55.6 55.6 100 98.2568 -55.7 55.7 100 98.2691 -55.8 55.8 100 98.2814 -55.9 55.9 100 98.2936 -56.0 56.0 100 98.3305 -56.1 56.1 100 98.3673 -56.2 56.2 100 98.3918 -56.3 56.3 100 98.441 -56.4 56.4 100 98.4655 -56.5 56.5 100 98.5023 -56.6 56.6 100 98.5514 -56.7 56.7 100 98.6251 -56.8 56.8 100 98.6251 -56.9 56.9 100 98.6496 -57.0 57.0 100 98.6619 -57.1 57.1 100 98.6619 -57.2 57.2 100 98.6742 -57.3 57.3 100 98.6742 -57.4 57.4 100 98.6987 -57.5 57.5 100 98.711 -57.6 57.6 100 98.7233 -57.7 57.7 100 98.7233 -57.8 57.8 100 98.7479 -57.9 57.9 100 98.7479 -58.0 58.0 100 98.7479 -58.1 58.1 100 98.7724 -58.2 58.2 100 98.7847 -58.3 58.3 100 98.7847 -58.4 58.4 100 98.797 -58.5 58.5 100 98.797 -58.6 58.6 100 98.8092 -58.7 58.7 100 98.8215 -58.8 58.8 100 98.8338 -58.9 58.9 100 98.8338 -59.0 59.0 100 98.8338 -59.1 59.1 100 98.8583 -59.2 59.2 100 98.8706 -59.3 59.3 100 98.8706 -59.4 59.4 100 98.8952 -59.5 59.5 100 98.9074 -59.6 59.6 100 98.9074 -59.7 59.7 100 98.9074 -59.8 59.8 100 98.9197 -59.9 59.9 100 98.9197 -60.0 60.0 100 98.932 -60.1 60.1 100 98.9421 -60.2 60.2 100 98.9443 -60.3 60.3 100 98.989 -60.4 60.4 100 98.9934 -60.5 60.5 100 99.0302 -60.6 60.6 100 99.0302 -60.7 60.7 100 99.0425 -60.8 60.8 100 99.0548 -60.9 60.9 100 99.0548 -61.0 61.0 100 99.067 -61.1 61.1 100 99.067 -61.2 61.2 100 99.067 -61.3 61.3 100 99.0916 -61.4 61.4 100 99.0916 -61.5 61.5 100 99.0916 -61.6 61.6 100 99.0916 -61.7 61.7 100 99.1161 -61.8 61.8 100 99.1161 -61.9 61.9 100 99.1284 -62.0 62.0 100 99.153 -62.1 62.1 100 99.1652 -62.2 62.2 100 99.1775 -62.3 62.3 100 99.1835 -62.4 62.4 100 99.2143 -62.5 62.5 100 99.2143 -62.6 62.6 100 99.2143 -62.7 62.7 100 99.2143 -62.8 62.8 100 99.2389 -62.9 62.9 100 99.2551 -63.0 63.0 100 99.2634 -63.1 63.1 100 99.2634 -63.2 63.2 100 99.2757 -63.3 63.3 100 99.288 -63.4 63.4 100 99.288 -63.5 63.5 100 99.288 -63.6 63.6 100 99.288 -63.7 63.7 100 99.288 -63.8 63.8 100 99.3003 -63.9 63.9 100 99.3003 -64.0 64.0 100 99.3125 -64.1 64.1 100 99.3125 -64.2 64.2 100 99.3125 -64.3 64.3 100 99.3125 -64.4 64.4 100 99.3248 -64.5 64.5 100 99.3248 -64.6 64.6 100 99.3248 -64.7 64.7 100 99.3371 -64.8 64.8 100 99.3371 -64.9 64.9 100 99.3494 -65.0 65.0 100 99.3494 -65.1 65.1 100 99.3527 -65.2 65.2 100 99.3616 -65.3 65.3 100 99.3616 -65.4 65.4 100 99.3739 -65.5 65.5 100 99.3739 -65.6 65.6 100 99.3943 -65.7 65.7 100 99.423 -65.8 65.8 100 99.4353 -65.9 65.9 100 99.4353 -66.0 66.0 100 99.4353 -66.1 66.1 100 99.4476 -66.2 66.2 100 99.4476 -66.3 66.3 100 99.457 -66.4 66.4 100 99.4721 -66.5 66.5 100 99.4721 -66.6 66.6 100 99.4721 -66.7 66.7 100 99.4844 -66.8 66.8 100 99.4967 -66.9 66.9 100 99.4967 -67.0 67.0 100 99.509 -67.1 67.1 100 99.5212 -67.2 67.2 100 99.5335 -67.3 67.3 100 99.5335 -67.4 67.4 100 99.5458 -67.5 67.5 100 99.5458 -67.6 67.6 100 99.5581 -67.7 67.7 100 99.5581 -67.8 67.8 100 99.5703 -67.9 67.9 100 99.5826 -68.0 68.0 100 99.5826 -68.1 68.1 100 99.6072 -68.2 68.2 100 99.6099 -68.3 68.3 100 99.6194 -68.4 68.4 100 99.644 -68.5 68.5 100 99.644 -68.6 68.6 100 99.644 -68.7 68.7 100 99.644 -68.8 68.8 100 99.644 -68.9 68.9 100 99.6685 -69.0 69.0 100 99.6685 -69.1 69.1 100 99.6685 -69.2 69.2 100 99.6685 -69.3 69.3 100 99.6808 -69.4 69.4 100 99.699 -69.5 69.5 100 99.7054 -69.6 69.6 100 99.7054 -69.7 69.7 100 99.7054 -69.8 69.8 100 99.7054 -69.9 69.9 100 99.7054 -70.0 70.0 100 99.7054 -70.1 70.1 100 99.7054 -70.2 70.2 100 99.7054 -70.3 70.3 100 99.7054 -70.4 70.4 100 99.7054 -70.5 70.5 100 99.7177 -70.6 70.6 100 99.7177 -70.7 70.7 100 99.7177 -70.8 70.8 100 99.7177 -70.9 70.9 100 99.7177 -71.0 71.0 100 99.7177 -71.1 71.1 100 99.7299 -71.2 71.2 100 99.7299 -71.3 71.3 100 99.7422 -71.4 71.4 100 99.7422 -71.5 71.5 100 99.7422 -71.6 71.6 100 99.7422 -71.7 71.7 100 99.7422 -71.8 71.8 100 99.7422 -71.9 71.9 100 99.7545 -72.0 72.0 100 99.7545 -72.1 72.1 100 99.7545 -72.2 72.2 100 99.7545 -72.3 72.3 100 99.7545 -72.4 72.4 100 99.7668 -72.5 72.5 100 99.7668 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100 99.8159 -77.0 77.0 100 99.8159 -77.1 77.1 100 99.8281 -77.2 77.2 100 99.8281 -77.3 77.3 100 99.8281 -77.4 77.4 100 99.8281 -77.5 77.5 100 99.8281 -77.6 77.6 100 99.8404 -77.7 77.7 100 99.8404 -77.8 77.8 100 99.8404 -77.9 77.9 100 99.8527 -78.0 78.0 100 99.8527 -78.1 78.1 100 99.8527 -78.2 78.2 100 99.8527 -78.3 78.3 100 99.8527 -78.4 78.4 100 99.8527 -78.5 78.5 100 99.8527 -78.6 78.6 100 99.8527 -78.7 78.7 100 99.865 -78.8 78.8 100 99.865 -78.9 78.9 100 99.865 -79.0 79.0 100 99.865 -79.1 79.1 100 99.865 -79.2 79.2 100 99.865 -79.3 79.3 100 99.865 -79.4 79.4 100 99.865 -79.5 79.5 100 99.865 -79.6 79.6 100 99.865 -79.7 79.7 100 99.865 -79.8 79.8 100 99.8745 -79.9 79.9 100 99.8772 -80.0 80.0 100 99.8772 -80.1 80.1 100 99.8772 -80.2 80.2 100 99.8772 -80.3 80.3 100 99.8895 -80.4 80.4 100 99.8895 -80.5 80.5 100 99.8895 -80.6 80.6 100 99.8895 -80.7 80.7 100 99.8895 -80.8 80.8 100 99.8895 -80.9 80.9 100 99.8895 -81.0 81.0 100 99.8895 -81.1 81.1 100 99.8895 -81.2 81.2 100 99.8895 -81.3 81.3 100 99.8895 -81.4 81.4 100 99.8895 -81.5 81.5 100 99.8895 -81.6 81.6 100 99.8895 -81.7 81.7 100 99.9018 -81.8 81.8 100 99.9141 -81.9 81.9 100 99.9141 -82.0 82.0 100 99.9141 -82.1 82.1 100 99.9141 -82.2 82.2 100 99.9141 -82.3 82.3 100 99.9141 -82.4 82.4 100 99.9141 -82.5 82.5 100 99.9141 -82.6 82.6 100 99.9263 -82.7 82.7 100 99.9263 -82.8 82.8 100 99.9263 -82.9 82.9 100 99.9263 -83.0 83.0 100 99.9263 -83.1 83.1 100 99.9263 -83.2 83.2 100 99.9263 -83.3 83.3 100 99.9263 -83.4 83.4 100 99.9263 -83.5 83.5 100 99.9263 -83.6 83.6 100 99.9263 -83.7 83.7 100 99.9263 -83.8 83.8 100 99.9263 -83.9 83.9 100 99.9263 -84.0 84.0 100 99.9263 -84.1 84.1 100 99.9263 -84.2 84.2 100 99.9263 -84.3 84.3 100 99.9263 -84.4 84.4 100 99.9263 -84.5 84.5 100 99.9263 -84.6 84.6 100 99.9263 -84.7 84.7 100 99.9263 -84.8 84.8 100 99.9263 -84.9 84.9 100 99.9263 -85.0 85.0 100 99.9263 -85.1 85.1 100 99.9263 -85.2 85.2 100 99.9263 -85.3 85.3 100 99.9322 -85.4 85.4 100 99.9386 -85.5 85.5 100 99.9386 -85.6 85.6 100 99.9386 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100 99.9632 -90.1 90.1 100 99.9632 -90.2 90.2 100 99.9632 -90.3 90.3 100 99.9632 -90.4 90.4 100 99.9632 -90.5 90.5 100 99.9632 -90.6 90.6 100 99.9754 -90.7 90.7 100 99.9754 -90.8 90.8 100 99.9754 -90.9 90.9 100 99.9754 -91.0 91.0 100 99.9754 -91.1 91.1 100 99.9754 -91.2 91.2 100 99.9754 -91.3 91.3 100 99.9754 -91.4 91.4 100 99.9754 -91.5 91.5 100 99.9754 -91.6 91.6 100 99.9754 -91.7 91.7 100 99.9754 -91.8 91.8 100 99.9877 -91.9 91.9 100 99.9877 -92.0 92.0 100 99.9877 -92.1 92.1 100 99.9877 -92.2 92.2 100 99.9877 -92.3 92.3 100 99.9877 -92.4 92.4 100 99.9877 -92.5 92.5 100 99.9877 -92.6 92.6 100 99.9877 -92.7 92.7 100 99.9877 -92.8 92.8 100 99.9877 -92.9 92.9 100 99.9877 -93.0 93.0 100 99.9877 -93.1 93.1 100 99.9877 -93.2 93.2 100 99.9877 -93.3 93.3 100 99.9877 -93.4 93.4 100 99.9877 -93.5 93.5 100 99.9877 -93.6 93.6 100 99.9877 -93.7 93.7 100 99.9877 -93.8 93.8 100 99.9877 -93.9 93.9 100 99.9877 -94.0 94.0 100 99.9877 -94.1 94.1 100 99.9877 -94.2 94.2 100 99.9877 -94.3 94.3 100 99.9877 -94.4 94.4 100 99.9877 -94.5 94.5 100 99.9877 -94.6 94.6 100 99.9877 -94.7 94.7 100 99.9877 -94.8 94.8 100 99.9877 -94.9 94.9 100 99.9877 -95.0 95.0 100 99.9877 -95.1 95.1 100 100 -95.2 95.2 100 100 -95.3 95.3 100 100 -95.4 95.4 100 100 -95.5 95.5 100 100 -95.6 95.6 100 100 -95.7 95.7 100 100 -95.8 95.8 100 100 -95.9 95.9 100 100 -96.0 96.0 100 100 -96.1 96.1 100 100 -96.2 96.2 100 100 -96.3 96.3 100 100 -96.4 96.4 100 100 -96.5 96.5 100 100 -96.6 96.6 100 100 -96.7 96.7 100 100 -96.8 96.8 100 100 -96.9 96.9 100 100 -97.0 97.0 100 100 -97.1 97.1 100 100 -97.2 97.2 100 100 -97.3 97.3 100 100 -97.4 97.4 100 100 -97.5 97.5 100 100 -97.6 97.6 100 100 -97.7 97.7 100 100 -97.8 97.8 100 100 -97.9 97.9 100 100 -98.0 98.0 100 100 -98.1 98.1 100 100 -98.2 98.2 100 100 -98.3 98.3 100 100 -98.4 98.4 100 100 -98.5 98.5 100 100 -98.6 98.6 100 100 -98.7 98.7 100 100 -98.8 98.8 100 100 -98.9 98.9 100 100 -99.0 99.0 100 100 -99.1 99.1 100 100 -99.2 99.2 100 100 -99.3 99.3 100 100 -99.4 99.4 100 100 -99.5 99.5 100 100 -99.6 99.6 100 100 -99.7 99.7 100 100 -99.8 99.8 100 100 -99.9 99.9 100 100 -100.0 100.0 100 100 diff --git a/tests/resources/analysis_results/ref_reports/AnyCharLegacy.txt b/tests/resources/analysis_results/ref_reports/AnyCharLegacy.txt deleted file mode 100644 index 205e92fb..00000000 --- a/tests/resources/analysis_results/ref_reports/AnyCharLegacy.txt +++ /dev/null @@ -1,830 +0,0 @@ -Tool Khiops -Version 9.5.4i -Short description -Logs -Data preparation -warning : Data table ./AnyChar.txt : Record 10 : Variable with value << >> : tabulation replaced by space char -warning : Data table ./AnyChar.txt : Record 26 : Variable with value << >> : Ctrl-Z (ascii 26) replaced by space char -warning : Data table ./AnyChar.txt : Record 264 : Variable with value << >> : tabulation replaced by space char -warning : Data table ./AnyChar.txt : Record 280 : Variable with value << >> : Ctrl-Z (ascii 26) replaced by space char -warning : Data table ./AnyChar.txt : Record 772 : Variable with value << >> : tabulation replaced by space char -warning : Data table ./AnyChar.txt : Record 788 : Variable with value << >> : Ctrl-Z (ascii 26) replaced by space char -warning : Data table ./AnyChar.txt : Record 1026 : Variable with value << >> : tabulation replaced by space char -warning : Data table ./AnyChar.txt : Record 1042 : Variable with value << >> : Ctrl-Z (ascii 26) replaced by space char -warning : Data table ./AnyChar.txt : Record 1296 : Variable with value << >> : Ctrl-Z (ascii 26) replaced by space char -warning : Data table ./AnyChar.txt : Record 1534 : Variable with value << >> : tabulation replaced by space char -warning : Data table ./AnyChar.txt : Record 1550 : Variable with value << >> : Ctrl-Z (ascii 26) replaced by space char -warning : Data table ./AnyChar.txt : Record 1788 : Variable with value << >> : tabulation replaced by space char -warning : Data table ./AnyChar.txt : Record 2042 : Variable with value << >> : tabulation replaced by space char -warning : Data table ./AnyChar.txt : Record 2058 : Variable with value << >> : Ctrl-Z (ascii 26) replaced by space char -warning : Data table ./AnyChar.txt : Record 2312 : Variable with value << >> : Ctrl-Z (ascii 26) replaced by space char -warning : Data table ./AnyChar.txt : Record 2550 : Variable with value << >> : tabulation replaced by space char -warning : Data table ./AnyChar.txt : Record 2566 : Variable with value << >> : Ctrl-Z (ascii 26) replaced by space char -warning : Data table ./AnyChar.txt : Record 2804 : Variable with value << >> : tabulation replaced by space char -warning : Data table ./AnyChar.txt : Record 2820 : Variable with value << >> : Ctrl-Z (ascii 26) replaced by space char -warning : Data table ./AnyChar.txt : Record 3058 : Variable with value << >> : tabulation replaced by space char -warning : Data table : ... - - -Report Preparation - -Dictionary AnyChar -Variables - Categorical 13 - Numerical 1 - Total 14 -Database ./AnyChar.txt -Sample percentage 70 -Sampling mode Include sample -Selection variable -Selection value -Instances 3530 -Learning task Classification analysis -Target variable isalnum -Main target value 1 -Target descriptive stats - Values 2 - Mode 0 - Mode frequency 2663 -Target variable stats - 0 2663 - 1 867 -Evaluated variables 13 -Informative variables 13 -Max number of constructed variables 0 -Max number of trees 0 -Max number of variable pairs 0 -Discretization MODL -Value grouping MODL -Null model - Construction cost 0.693147 - Preparation cost 8.16934 - Data cost 1963.67 - -Variable statistics -Rank Name Type Level Target parts Parts Values Min Max Mean StdDev Missing number Mode Mode frequency Construction cost Preparation cost Data cost Derivation rule -R01 Index Numerical 0.958314 7 254 1 255 128.2150142 73.50981088 0 3.2581 78.9695 0 -R02 Char Categorical 0.900678 2 249 92 3.2581 192.658 0 -R03 Categorical 0.899624 2 252 < > 47 3.2581 194.737 0 -R04 isgraph Categorical 0.573163 2 2 0 2231 3.2581 16.306 822.386 -R05 isprint Categorical 0.564336 2 2 0 2215 3.2581 16.3111 839.794 -R06 isascii Categorical 0.376102 2 2 0 1777 3.2581 16.3781 1211.02 -R07 isupper Categorical 0.301092 2 2 0 3154 3.2581 15.4142 1359.95 -R08 islower Categorical 0.281514 2 2 0 3175 3.2581 15.3636 1398.62 -R09 isxdigit Categorical 0.238119 2 2 0 3223 3.2581 15.2337 1484.34 -R10 isdigit Categorical 0.0966412 2 2 0 3394 3.2581 14.4753 1764.17 -R11 iscntrl Categorical 0.0625008 2 2 0 3092 3.2581 15.5466 1830.44 -R12 ispunct Categorical 0.0615072 2 2 0 3098 3.2581 15.5348 1832.42 -R13 isspace Categorical 0.00678074 2 2 0 3454 3.2581 13.9166 1941.98 - -Detailed variable statistics - -Rank R01 Index Numerical - -Data grid Supervised -Dimensions -Index Numerical Intervals - ]-inf;47.5] 1 47.5 - ]47.5;57.5] 47.5 57.5 - ]57.5;64.5] 57.5 64.5 - ]64.5;90.5] 64.5 90.5 - ]90.5;96.5] 90.5 96.5 - ]96.5;122.5] 96.5 122.5 - ]122.5;+inf[ 122.5 255 -isalnum Categorical Values - 0 - 1 -Cells -Interval 0 1 Interest -]-inf;47.5] 638 0 0.0913781 -]47.5;57.5] 0 136 0.0970336 -]57.5;64.5] 100 0 0.0143226 -]64.5;90.5] 0 376 0.268269 -]90.5;96.5] 75 0 0.0107419 -]96.5;122.5] 0 355 0.253286 -]122.5;+inf[ 1850 0 0.264968 - -Rank R02 Char Categorical - -Data grid Supervised -Dimensions -Char Categorical Value groups - {, , �, ...}  � � : � �  / � � � � � � � � ) * > ]  � � � � � � � � � � � � �     ; { ~ � � � � � � � � � � � � � � � � � � � � � � � � �             ' + - < = } � � � � � � � � � � � � � � � � � � � � �    ! " & , [ ^ _ | � � � � � � � � � � � � � � � � � � � � � � � � � � � �  $ % @ � � � � � � � � � � � � � � � � � �  # ( . ? � � � � �   \ ` � � � � � � * - {a, 5, E, ...} a 5 E G N R d h s A F J L M Z g u v x 7 P U W Y j p w 0 2 3 6 9 C I f k m 4 B D K O Q X n q r S T b e y 1 H V i t z 8 c l o -isalnum Categorical Values - 0 - 1 -Cells -Value group 0 1 Interest -{, , �, ...} 2663 0 0.381411 -{a, 5, E, ...} 0 867 0.618589 - -Input values - 92 -  19 - � 19 - � 19 - : 18 - a 18 - � 18 - � 18 -  17 - / 17 - 5 17 - E 17 - G 17 - N 17 - R 17 - d 17 - h 17 - s 17 - � 17 - � 17 - � 17 - � 17 - � 17 - � 17 - � 17 - � 17 - ) 16 - * 16 - > 16 - A 16 - F 16 - J 16 - L 16 - M 16 - Z 16 - ] 16 - g 16 - u 16 - v 16 - x 16 -  16 - � 16 - � 16 - � 16 - � 16 - � 16 - � 16 - � 16 - � 16 - � 16 - � 16 - � 16 - � 16 - � 16 -  15 -  15 -  15 -  15 - 7 15 - ; 15 - P 15 - U 15 - W 15 - Y 15 - j 15 - p 15 - w 15 - { 15 - ~ 15 - � 15 - � 15 - � 15 - � 15 - � 15 - � 15 - � 15 - � 15 - � 15 - � 15 - � 15 - � 15 - � 15 - � 15 - � 15 - � 15 - � 15 - � 15 - � 15 - � 15 - � 15 - � 15 - � 15 - � 15 - � 15 -  14 -  14 -  14 -  14 -  14 -  14 -  14 -  14 -  14 -  14 -  14 -  14 - ' 14 - + 14 - - 14 - 0 14 - 2 14 - 3 14 - 6 14 - 9 14 - < 14 - = 14 - C 14 - I 14 - f 14 - k 14 - m 14 - } 14 - � 14 - � 14 - � 14 - � 14 - � 14 - � 14 - � 14 - � 14 - � 14 - � 14 - � 14 - � 14 - � 14 - � 14 - � 14 - � 14 - � 14 - � 14 - � 14 - � 14 - � 14 -  13 -  13 -  13 - ! 13 - " 13 - & 13 - , 13 - 4 13 - B 13 - D 13 - K 13 - O 13 - Q 13 - X 13 - [ 13 - ^ 13 - _ 13 - n 13 - q 13 - r 13 - | 13 - � 13 - � 13 - � 13 - � 13 - � 13 - � 13 - � 13 - � 13 - � 13 - � 13 - � 13 - � 13 - � 13 - � 13 - � 13 - � 13 - � 13 - � 13 - � 13 - � 13 - � 13 - � 13 - � 13 - � 13 - � 13 - � 13 - � 13 - � 13 -  12 - $ 12 - % 12 - @ 12 - S 12 - T 12 - b 12 - e 12 - y 12 - � 12 - � 12 - � 12 - � 12 - � 12 - � 12 - � 12 - � 12 - � 12 - � 12 - � 12 - � 12 - � 12 - � 12 - � 12 - � 12 - � 12 - � 12 -  11 - # 11 - ( 11 - . 11 - 1 11 - ? 11 - H 11 - V 11 - i 11 - t 11 - z 11 - � 11 - � 11 - � 11 - � 11 - � 11 -  10 -  10 - 8 10 - \ 10 - ` 10 - � 10 - � 10 - c 9 - l 9 - o 9 - � 9 - � 9 - � 9 - � 8 - -Rank R03 Categorical - -Data grid Supervised -Dimensions - Categorical Value groups - {< >, <>, <�>, ...} < > <> <�> <�> <:> <�> <�> <> <�> <�> <�> <�> <�> <�> <�> <�> < > <)> <*> <>> <]> <> <�> <�> <�> <�> <�> <�> <�> <�> <�> <�> <�> <�> <�> <> <> <> < > <> <;> <{> <~> <�> <�> <�> <�> <�> <�> <�> <�> <�> <�> <�> <�> <�> <�> <�> <�> <�> <�> <�> <�> <�> <�> <�> <�> <�> <> <> <> <> < > <> <> <> <> <> <> <> <> <'> <+> <-> <<> <=> <}> <�> <�> <�> <�> <�> <�> <�> <�> <�> <�> <�> <�> <�> <�> <�> <�> <�> <�> <�> <�> <�> <> <> <> <"> <&> <,> <[> <^> <_> <|> <�> <�> <�> <�> <�> <�> <�> <�> <�> <�> <�> <�> <�> <�> <�> <�> <�> <�> <�> <�> <�> <�> <�> <�> <�> <�> <�> <�> <> <$> <%> <@> <�> <�> <�> <�> <�> <�> <�> <�> <�> <�> <�> <�> <�> <�> <�> <�> <�> <�> <> <#> <(> <.> <�> <�> <�> <�> <�> <> <> <\> <`> <�> <�> <�> <�> <�> <�> * - {, <5>, , ...} <5> <7>

<0> <2> <3> <6> <9> <4> <1> <8> -isalnum Categorical Values - 0 - 1 -Cells -Value group 0 1 Interest -{< >, <>, <�>, ...} 2663 0 0.381411 -{, <5>, , ...} 0 867 0.618589 - -Input values - < > 47 - <> 19 - <�> 19 - <�> 19 - <:> 18 - 18 - <�> 18 - <�> 18 - <> 17 - 17 - <5> 17 - 17 - 17 - 17 - 17 - 17 - 17 - 17 - <�> 17 - <�> 17 - <�> 17 - <�> 17 - <�> 17 - <�> 17 - <�> 17 - <�> 17 - < > 16 - <)> 16 - <*> 16 - <>> 16 - 16 - 16 - 16 - 16 - 16 - 16 - <]> 16 - 16 - 16 - 16 - 16 - <> 16 - <�> 16 - <�> 16 - <�> 16 - <�> 16 - <�> 16 - <�> 16 - <�> 16 - <�> 16 - <�> 16 - <�> 16 - <�> 16 - <�> 16 - <�> 16 - <> 15 - <> 15 - <> 15 - < > 15 - <> 15 - <7> 15 - <;> 15 -

15 - 15 - 15 - 15 - 15 -

15 - 15 - <{> 15 - <~> 15 - <�> 15 - <�> 15 - <�> 15 - <�> 15 - <�> 15 - <�> 15 - <�> 15 - <�> 15 - <�> 15 - <�> 15 - <�> 15 - <�> 15 - <�> 15 - <�> 15 - <�> 15 - <�> 15 - <�> 15 - <�> 15 - <�> 15 - <�> 15 - <�> 15 - <�> 15 - <�> 15 - <�> 15 - <�> 15 - <> 14 - <> 14 - <> 14 - <> 14 - < > 14 - <> 14 - <> 14 - <> 14 - <> 14 - <> 14 - <> 14 - <> 14 - <> 14 - <'> 14 - <+> 14 - <-> 14 - <0> 14 - <2> 14 - <3> 14 - <6> 14 - <9> 14 - <<> 14 - <=> 14 - 14 - 14 - 14 - 14 - 14 - <}> 14 - <�> 14 - <�> 14 - <�> 14 - <�> 14 - <�> 14 - <�> 14 - <�> 14 - <�> 14 - <�> 14 - <�> 14 - <�> 14 - <�> 14 - <�> 14 - <�> 14 - <�> 14 - <�> 14 - <�> 14 - <�> 14 - <�> 14 - <�> 14 - <�> 14 - <> 13 - <> 13 - <> 13 - 13 - <"> 13 - <&> 13 - <,> 13 - <4> 13 - 13 - 13 - 13 - 13 - 13 - 13 - <[> 13 - <^> 13 - <_> 13 - 13 - 13 - 13 - <|> 13 - <�> 13 - <�> 13 - <�> 13 - <�> 13 - <�> 13 - <�> 13 - <�> 13 - <�> 13 - <�> 13 - <�> 13 - <�> 13 - <�> 13 - <�> 13 - <�> 13 - <�> 13 - <�> 13 - <�> 13 - <�> 13 - <�> 13 - <�> 13 - <�> 13 - <�> 13 - <�> 13 - <�> 13 - <�> 13 - <�> 13 - <�> 13 - <�> 13 - <> 12 - <$> 12 - <%> 12 - <@> 12 - 12 - 12 - 12 - 12 - 12 - <�> 12 - <�> 12 - <�> 12 - <�> 12 - <�> 12 - <�> 12 - <�> 12 - <�> 12 - <�> 12 - <�> 12 - <�> 12 - <�> 12 - <�> 12 - <�> 12 - <�> 12 - <�> 12 - <�> 12 - <�> 12 - <> 11 - <#> 11 - <(> 11 - <.> 11 - <1> 11 - 11 - 11 - 11 - 11 - 11 - 11 - <�> 11 - <�> 11 - <�> 11 - <�> 11 - <�> 11 - <> 10 - <> 10 - <8> 10 - <\> 10 - <`> 10 - <�> 10 - <�> 10 - 9 - 9 - 9 - <�> 9 - <�> 9 - <�> 9 - <�> 8 - -Rank R04 isgraph Categorical - -Data grid Supervised -Dimensions -isgraph Categorical Value groups - {0} 0 - {1} 1 * -isalnum Categorical Values - 0 - 1 -Cells -Value group 0 1 Interest -{0} 2231 0 0.550756 -{1} 432 867 0.449244 - -Input values - 0 2231 - 1 1299 - -Rank R05 isprint Categorical - -Data grid Supervised -Dimensions -isprint Categorical Value groups - {0} 0 - {1} 1 * -isalnum Categorical Values - 0 - 1 -Cells -Value group 0 1 Interest -{0} 2215 0 0.555278 -{1} 448 867 0.444722 - -Input values - 0 2215 - 1 1315 - -Rank R06 isascii Categorical - -Data grid Supervised -Dimensions -isascii Categorical Value groups - {0} 0 - {1} 1 * -isalnum Categorical Values - 0 - 1 -Cells -Value group 0 1 Interest -{0} 1777 0 0.665257 -{1} 886 867 0.334743 - -Input values - 0 1777 - 1 1753 - -Rank R07 isupper Categorical - -Data grid Supervised -Dimensions -isupper Categorical Value groups - {0} 0 - {1} 1 * -isalnum Categorical Values - 0 - 1 -Cells -Value group 0 1 Interest -{0} 2663 491 0.12591 -{1} 0 376 0.87409 - -Input values - 0 3154 - 1 376 - -Rank R08 islower Categorical - -Data grid Supervised -Dimensions -islower Categorical Value groups - {0} 0 - {1} 1 * -isalnum Categorical Values - 0 - 1 -Cells -Value group 0 1 Interest -{0} 2663 512 0.118247 -{1} 0 355 0.881753 - -Input values - 0 3175 - 1 355 - -Rank R09 isxdigit Categorical - -Data grid Supervised -Dimensions -isxdigit Categorical Value groups - {0} 0 - {1} 1 * -isalnum Categorical Values - 0 - 1 -Cells -Value group 0 1 Interest -{0} 2663 560 0.101081 -{1} 0 307 0.898919 - -Input values - 0 3223 - 1 307 - -Rank R10 isdigit Categorical - -Data grid Supervised -Dimensions -isdigit Categorical Value groups - {0} 0 - {1} 1 * -isalnum Categorical Values - 0 - 1 -Cells -Value group 0 1 Interest -{0} 2663 731 0.0431871 -{1} 0 136 0.956813 - -Input values - 0 3394 - 1 136 - -Rank R11 iscntrl Categorical - -Data grid Supervised -Dimensions -iscntrl Categorical Value groups - {0} 0 - {1} 1 * -isalnum Categorical Values - 0 - 1 -Cells -Value group 0 1 Interest -{0} 2225 867 0.0735672 -{1} 438 0 0.926433 - -Input values - 0 3092 - 1 438 - -Rank R12 ispunct Categorical - -Data grid Supervised -Dimensions -ispunct Categorical Value groups - {0} 0 - {1} 1 * -isalnum Categorical Values - 0 - 1 -Cells -Value group 0 1 Interest -{0} 2231 867 0.0725316 -{1} 432 0 0.927468 - -Input values - 0 3098 - 1 432 - -Rank R13 isspace Categorical - -Data grid Supervised -Dimensions -isspace Categorical Value groups - {0} 0 - {1} 1 * -isalnum Categorical Values - 0 - 1 -Cells -Value group 0 1 Interest -{0} 2587 867 0.0124886 -{1} 76 0 0.987511 - -Input values - 0 3454 - 1 76 diff --git a/tests/resources/analysis_results/ref_reports/Iris2DLegacy.txt b/tests/resources/analysis_results/ref_reports/Iris2DLegacy.txt deleted file mode 100644 index f9f66460..00000000 --- a/tests/resources/analysis_results/ref_reports/Iris2DLegacy.txt +++ /dev/null @@ -1,6582 +0,0 @@ -Tool Khiops -Version 9.0.1 -Short description - - -Report Preparation - -Dictionary Iris -Variables - Categorical 4 - Numerical 8 - Total 12 -Database ../../../datasets/Iris/Iris.txt -Instances 99 -Learning task Classification analysis -Target variable Class -Target descriptive stats - Values 3 - Mode Iris-versicolor - Mode frequency 37 -Target variable stats - Iris-setosa 31 - Iris-versicolor 37 - Iris-virginica 31 -Evaluated variables 11 -Informative variables 9 -Discretization MODL -Value grouping MODL -Null model - Construction cost 0.693147 - Preparation cost 8.52714 - Data cost 103.619 - -Variable statistics -Rank Name Type Level Target parts Parts Values Min Max Mean StdDev Missing number Mode Mode frequency Construction cost Preparation cost Data cost Derivation rule -R01 SPetalLength Categorical 0.640816 3 5 4 32 3.09104 27.4826 9.95655 AsCategorical(Floor(PetalLength)) -R02 PetalLength Numerical 0.635667 3 36 1 6.9 3.801010101 1.712137004 0 3.09104 28.0635 9.95655 -R03 PetalWidth Numerical 0.601129 3 20 0.1 2.5 1.218181818 0.749863777 0 3.09104 31.9608 9.95655 -R04 Class2 Categorical 0.473857 2 2 62 3.09104 15.5969 40.6817 IfC(EQc(Class, "Iris-versicolor"), "versicolor", "") -R05 LowerPetalLength Numerical 0.447267 2 9 1 3 2.517171717 0.7226550938 0 3.09104 14.7454 44.5336 If(LE(PetalLength, 3), PetalLength, 3) -R06 Class1 Categorical 0.441125 2 2 68 3.09104 15.4385 44.5336 IfC(EQc(Class, "Iris-setosa"), "setosa", "") -R07 UpperPetalWidth Numerical 0.306898 2 11 1.5 2.5 1.681818182 0.2962266524 0 3.09104 14.8064 60.3118 If(GE(PetalWidth, 1.5), PetalWidth, 1.5) -R08 SepalLength Numerical 0.2811 3 30 4.3 7.7 5.848484848 0.8065844732 0 3.09104 27.4942 50.535 -R09 SepalWidth Numerical 0.112567 2 22 2 4.4 3.042424242 0.4422374035 0 3.09104 16.7264 80.32 -R10 Dummy1 Numerical 0 1 1 0 0 0 0 0 0.693147 8.52714 103.619 Copy(0) -R11 Dummy2 Numerical 0 1 99 0.01372010867 0.9853969761 0.5371015665 0.2836682962 0 0.693147 8.52714 103.619 Random() - -Detailed variable statistics - -Rank R01 SPetalLength Categorical - -Data grid Supervised -Dimensions -SPetalLength Categorical Value groups - {4, 3} 4 3 - {1} 1 - {5, 6} 5 6 * -Class Categorical Values - Iris-setosa - Iris-versicolor - Iris-virginica -Cells -Value group Iris-setosa Iris-versicolor Iris-virginica Interest -{4, 3} 0 36 2 0.311045 -{1} 31 0 0 0.374224 -{5, 6} 0 1 29 0.314731 - -Input values - 4 32 - 1 31 - 5 24 - 3 6 - 6 6 - -Rank R02 PetalLength Numerical - -Data grid Supervised -Dimensions -PetalLength Numerical Intervals - ]-inf;2.4] 1 2.4 - ]2.4;4.95] 2.4 4.95 - ]4.95;+inf[ 4.95 6.9 -Class Categorical Values - Iris-setosa - Iris-versicolor - Iris-virginica -Cells -Interval Iris-setosa Iris-versicolor Iris-virginica Interest -]-inf;2.4] 31 0 0 0.374224 -]2.4;4.95] 0 36 2 0.311045 -]4.95;+inf[ 0 1 29 0.314731 - -Rank R03 PetalWidth Numerical - -Data grid Supervised -Dimensions -PetalWidth Numerical Intervals - ]-inf;0.7] 0.1 0.7 - ]0.7;1.75] 0.7 1.75 - ]1.75;+inf[ 1.75 2.5 -Class Categorical Values - Iris-setosa - Iris-versicolor - Iris-virginica -Cells -Interval Iris-setosa Iris-versicolor Iris-virginica Interest -]-inf;0.7] 31 0 0 0.374224 -]0.7;1.75] 0 36 2 0.311045 -]1.75;+inf[ 0 1 29 0.314731 - -Rank R04 Class2 Categorical - -Data grid Supervised -Dimensions -Class2 Categorical Value groups - {} - {versicolor} versicolor * -Class Categorical Values - Iris-setosa - Iris-versicolor - Iris-virginica -Cells -Value group Iris-setosa Iris-versicolor Iris-virginica Interest -{} 31 0 31 0.443449 -{versicolor} 0 37 0 0.556551 - -Input values - 62 - versicolor 37 - -Rank R05 LowerPetalLength Numerical - -Data grid Supervised -Dimensions -LowerPetalLength Numerical Intervals - ]-inf;2.4] 1 2.4 - ]2.4;+inf[ 2.4 3 -Class Categorical Values - Iris-setosa - Iris-versicolor - Iris-virginica -Cells -Interval Iris-setosa Iris-versicolor Iris-virginica Interest -]-inf;2.4] 31 0 0 0.584937 -]2.4;+inf[ 0 37 31 0.415063 - -Rank R06 Class1 Categorical - -Data grid Supervised -Dimensions -Class1 Categorical Value groups - {} - {setosa} setosa * -Class Categorical Values - Iris-setosa - Iris-versicolor - Iris-virginica -Cells -Value group Iris-setosa Iris-versicolor Iris-virginica Interest -{} 0 37 31 0.415063 -{setosa} 31 0 0 0.584937 - -Input values - 68 - setosa 31 - -Rank R07 UpperPetalWidth Numerical - -Data grid Supervised -Dimensions -UpperPetalWidth Numerical Intervals - ]-inf;1.55] 1.5 1.55 - ]1.55;+inf[ 1.55 2.5 -Class Categorical Values - Iris-setosa - Iris-versicolor - Iris-virginica -Cells -Interval Iris-setosa Iris-versicolor Iris-virginica Interest -]-inf;1.55] 31 33 2 0.407767 -]1.55;+inf[ 0 4 29 0.592233 - -Rank R08 SepalLength Numerical - -Data grid Supervised -Dimensions -SepalLength Numerical Intervals - ]-inf;5.45] 4.3 5.45 - ]5.45;5.75] 5.45 5.75 - ]5.75;+inf[ 5.75 7.7 -Class Categorical Values - Iris-setosa - Iris-versicolor - Iris-virginica -Cells -Interval Iris-setosa Iris-versicolor Iris-virginica Interest -]-inf;5.45] 28 3 0 0.493633 -]5.45;5.75] 3 12 1 0.100396 -]5.75;+inf[ 0 22 30 0.405971 - -Rank R09 SepalWidth Numerical - -Data grid Supervised -Dimensions -SepalWidth Numerical Intervals - ]-inf;3.35] 2 3.35 - ]3.35;+inf[ 3.35 4.4 -Class Categorical Values - Iris-setosa - Iris-versicolor - Iris-virginica -Cells -Interval Iris-setosa Iris-versicolor Iris-virginica Interest -]-inf;3.35] 11 36 29 0.268778 -]3.35;+inf[ 20 1 2 0.731222 - - -Report Bivariate preparation - -Dictionary Iris -Variables - Categorical 4 - Numerical 8 - Total 12 -Database ../../../datasets/Iris/Iris.txt -Instances 99 -Learning task Classification analysis -Target variable Class -Target descriptive stats - Values 3 - Mode Iris-versicolor - Mode frequency 37 -Target variable stats - Iris-setosa 31 - Iris-versicolor 37 - Iris-virginica 31 - -Variable pair statistics -Rank Name 1 Name 2 DeltaLevel Level Level 1 Level 2 Variables Parts 1 Parts 2 Cells Construction cost Preparation cost Data cost -R01 Class2 UpperPetalWidth -0.0981782 0.682577 0.473857 0.306898 2 2 2 4 7.22838 22.3204 6.2691 -R02 LowerPetalLength UpperPetalWidth -0.13874 0.615425 0.447267 0.306898 2 2 2 3 7.22838 19.1589 17.0079 -R03 Class2 LowerPetalLength -0.161003 0.760122 0.473857 0.447267 2 2 2 3 7.22838 19.8393 0 -R04 Class1 Class2 -0.190925 0.724057 0.441125 0.473857 2 2 2 3 10.6048 20.5325 0 -R05 PetalWidth UpperPetalWidth -0.319062 0.588965 0.601129 0.306898 1 3 1 3 8.20688 28.2175 9.95655 -R06 SPetalLength UpperPetalWidth -0.322692 0.625022 0.640816 0.306898 1 3 1 3 8.20688 24.1489 9.95655 -R07 Class2 PetalWidth -0.350101 0.724885 0.473857 0.601129 2 2 2 4 7.22838 23.8154 0 -R08 PetalLength UpperPetalWidth -0.350569 0.591996 0.635667 0.306898 1 3 1 3 8.20688 29.1352 8.69684 -R09 Class2 PetalLength -0.384729 0.724796 0.473857 0.635667 2 2 2 4 7.22838 23.8255 0 -R10 Class2 SPetalLength -0.414426 0.700247 0.473857 0.640816 2 2 2 3 10.6048 23.2191 0 -R11 Class1 PetalLength -0.43887 0.637922 0.441125 0.635667 2 2 2 3 7.92152 21.4096 11.5255 -R12 Class1 PetalWidth -0.443965 0.598289 0.441125 0.601129 2 2 2 3 10.6048 24.7676 9.95655 -R13 Class1 LowerPetalLength -0.450453 0.437939 0.441125 0.447267 1 1 2 2 4.83048 14.0586 44.5336 -R14 LowerPetalLength PetalLength -0.453362 0.629573 0.447267 0.635667 2 2 2 3 7.92152 22.3517 11.5255 -R15 Class1 SPetalLength -0.456918 0.625022 0.441125 0.640816 1 1 3 3 8.20688 24.1489 9.95655 -R16 LowerPetalLength SPetalLength -0.463061 0.625022 0.447267 0.640816 1 1 3 3 8.20688 24.1489 9.95655 -R17 LowerPetalLength PetalWidth -0.467337 0.581059 0.447267 0.601129 1 1 3 3 8.20688 29.1096 9.95655 -R18 PetalWidth SPetalLength -0.616923 0.625022 0.601129 0.640816 1 1 3 3 8.20688 24.1489 9.95655 -R19 PetalLength PetalWidth -0.6448 0.591996 0.635667 0.601129 1 3 1 3 8.20688 29.1352 8.69684 -R20 PetalLength SPetalLength -0.648297 0.628186 0.635667 0.640816 2 2 2 3 7.92152 22.5082 11.5255 - -Detailed variable pair statistics - -Rank R01 - -Data grid Supervised -Dimensions -Class2 Categorical Value groups - {} - {versicolor} versicolor * -UpperPetalWidth Numerical Intervals - ]-inf;1.55] 1.5 1.55 - ]1.55;+inf[ 1.55 2.5 -Target Categorical Values - Iris-setosa - Iris-versicolor - Iris-virginica -Cells -Cell id Class2 UpperPetalWidth Iris-setosa Iris-versicolor Iris-virginica Interest -C1 {} ]-inf;1.55] 31 0 2 0.305099 -C2 {versicolor} ]-inf;1.55] 0 33 0 0.322015 -C3 {} ]1.55;+inf[ 0 0 29 0.333855 -C4 {versicolor} ]1.55;+inf[ 0 4 0 0.0390321 - -Rank R02 - -Data grid Supervised -Dimensions -LowerPetalLength Numerical Intervals - ]-inf;2.4] 1 2.4 - ]2.4;+inf[ 2.4 3 -UpperPetalWidth Numerical Intervals - ]-inf;1.55] 1.5 1.55 - ]1.55;+inf[ 1.55 2.5 -Target Categorical Values - Iris-setosa - Iris-versicolor - Iris-virginica -Cells -Cell id LowerPetalLength UpperPetalWidth Iris-setosa Iris-versicolor Iris-virginica Interest -C1 ]-inf;2.4] ]-inf;1.55] 31 0 0 0.406488 -C2 ]2.4;+inf[ ]-inf;1.55] 0 33 2 0.306429 -C4 ]2.4;+inf[ ]1.55;+inf[ 0 4 29 0.287083 - -Rank R03 - -Data grid Supervised -Dimensions -Class2 Categorical Value groups - {} - {versicolor} versicolor * -LowerPetalLength Numerical Intervals - ]-inf;2.4] 1 2.4 - ]2.4;+inf[ 2.4 3 -Target Categorical Values - Iris-setosa - Iris-versicolor - Iris-virginica -Cells -Cell id Class2 LowerPetalLength Iris-setosa Iris-versicolor Iris-virginica Interest -C1 {} ]-inf;2.4] 31 0 0 0.332041 -C3 {} ]2.4;+inf[ 0 0 31 0.332041 -C4 {versicolor} ]2.4;+inf[ 0 37 0 0.335918 - -Rank R04 - -Data grid Supervised -Dimensions -Class1 Categorical Value groups - {} - {setosa} setosa * -Class2 Categorical Value groups - {} - {versicolor} versicolor * -Target Categorical Values - Iris-setosa - Iris-versicolor - Iris-virginica -Cells -Cell id Class1 Class2 Iris-setosa Iris-versicolor Iris-virginica Interest -C1 {} {} 0 0 31 0.332041 -C2 {setosa} {} 31 0 0 0.332041 -C3 {} {versicolor} 0 37 0 0.335918 - -Rank R07 - -Data grid Supervised -Dimensions -Class2 Categorical Value groups - {} - {versicolor} versicolor * -PetalWidth Numerical Intervals - ]-inf;1.35] 0.1 1.35 - ]1.35;+inf[ 1.35 2.5 -Target Categorical Values - Iris-setosa - Iris-versicolor - Iris-virginica -Cells -Cell id Class2 PetalWidth Iris-setosa Iris-versicolor Iris-virginica Interest -C1 {} ]-inf;1.35] 31 0 0 0.332041 -C2 {versicolor} ]-inf;1.35] 0 20 0 0.181578 -C3 {} ]1.35;+inf[ 0 0 31 0.332041 -C4 {versicolor} ]1.35;+inf[ 0 17 0 0.154341 - -Rank R09 - -Data grid Supervised -Dimensions -Class2 Categorical Value groups - {} - {versicolor} versicolor * -PetalLength Numerical Intervals - ]-inf;4.35] 1 4.35 - ]4.35;+inf[ 4.35 6.9 -Target Categorical Values - Iris-setosa - Iris-versicolor - Iris-virginica -Cells -Cell id Class2 PetalLength Iris-setosa Iris-versicolor Iris-virginica Interest -C1 {} ]-inf;4.35] 31 0 0 0.332041 -C2 {versicolor} ]-inf;4.35] 0 18 0 0.16342 -C3 {} ]4.35;+inf[ 0 0 31 0.332041 -C4 {versicolor} ]4.35;+inf[ 0 19 0 0.172499 - -Rank R10 - -Data grid Supervised -Dimensions -Class2 Categorical Value groups - {} - {versicolor} versicolor * -SPetalLength Categorical Value groups - {4, 5, 3, ...} 4 5 3 6 * - {1} 1 -Target Categorical Values - Iris-setosa - Iris-versicolor - Iris-virginica -Cells -Cell id Class2 SPetalLength Iris-setosa Iris-versicolor Iris-virginica Interest -C1 {} {4, 5, 3, ...} 0 0 31 0.332041 -C2 {versicolor} {4, 5, 3, ...} 0 37 0 0.335918 -C3 {} {1} 31 0 0 0.332041 - -Rank R11 - -Data grid Supervised -Dimensions -Class1 Categorical Value groups - {} - {setosa} setosa * -PetalLength Numerical Intervals - ]-inf;5.05] 1 5.05 - ]5.05;+inf[ 5.05 6.9 -Target Categorical Values - Iris-setosa - Iris-versicolor - Iris-virginica -Cells -Cell id Class1 PetalLength Iris-setosa Iris-versicolor Iris-virginica Interest -C1 {} ]-inf;5.05] 0 37 4 0.293318 -C2 {setosa} ]-inf;5.05] 31 0 0 0.37771 -C3 {} ]5.05;+inf[ 0 0 27 0.328973 - -Rank R12 - -Data grid Supervised -Dimensions -Class1 Categorical Value groups - {} - {setosa} setosa * -PetalWidth Numerical Intervals - ]-inf;1.75] 0.1 1.75 - ]1.75;+inf[ 1.75 2.5 -Target Categorical Values - Iris-setosa - Iris-versicolor - Iris-virginica -Cells -Cell id Class1 PetalWidth Iris-setosa Iris-versicolor Iris-virginica Interest -C1 {} ]-inf;1.75] 0 36 2 0.311045 -C2 {setosa} ]-inf;1.75] 31 0 0 0.374224 -C3 {} ]1.75;+inf[ 0 1 29 0.314731 - -Rank R14 - -Data grid Supervised -Dimensions -LowerPetalLength Numerical Intervals - ]-inf;2.4] 1 2.4 - ]2.4;+inf[ 2.4 3 -PetalLength Numerical Intervals - ]-inf;5.05] 1 5.05 - ]5.05;+inf[ 5.05 6.9 -Target Categorical Values - Iris-setosa - Iris-versicolor - Iris-virginica -Cells -Cell id LowerPetalLength PetalLength Iris-setosa Iris-versicolor Iris-virginica Interest -C1 ]-inf;2.4] ]-inf;5.05] 31 0 0 0.37771 -C2 ]2.4;+inf[ ]-inf;5.05] 0 37 4 0.293318 -C4 ]2.4;+inf[ ]5.05;+inf[ 0 0 27 0.328973 - -Rank R20 - -Data grid Supervised -Dimensions -PetalLength Numerical Intervals - ]-inf;5.05] 1 5.05 - ]5.05;+inf[ 5.05 6.9 -SPetalLength Categorical Value groups - {4, 5} 4 5 * - {1} 1 -Target Categorical Values - Iris-setosa - Iris-versicolor - Iris-virginica -Cells -Cell id PetalLength SPetalLength Iris-setosa Iris-versicolor Iris-virginica Interest -C1 ]-inf;5.05] {4, 5} 0 37 4 0.293318 -C2 ]5.05;+inf[ {4, 5} 0 0 27 0.328973 -C3 ]-inf;5.05] {1} 31 0 0 0.37771 - - -Report Modeling - -Dictionary Iris -Database ../../../datasets/Iris/Iris.txt -Learning task Classification analysis -Target variable Class - -Trained predictors -Rank Type Family Name Variables -R1 Classifier Selective Naive Bayes Selective Naive Bayes 9 - -Detailed trained predictors - -Rank R1 - -Selected variables -Prepared name Name Level Weight MAP -PClass2 Class2 0.473857 0.707516 1 -PPetalWidth PetalWidth 0.601129 0.374223 -PSPetalLength SPetalLength 0.640816 0.302293 -PSepalLength SepalLength 0.2811 0.29237 -PPetalLength PetalLength 0.635667 0.240717 -PClass1 Class1 0.441125 0.214152 1 -PUpperPetalWidth UpperPetalWidth 0.306898 0.168699 -PSepalWidth SepalWidth 0.112567 0.164374 -PLowerPetalLength LowerPetalLength 0.447267 0.131959 - - -Report Evaluation Train - -Dictionary Iris -Database ../../../datasets/Iris/Iris.txt -Instances 99 -Learning task Classification analysis -Target variable Class - -Predictors performance -Rank Type Family Name Accuracy Compression AUC -R1 Classifier Selective Naive Bayes Selective Naive Bayes 1 0.996643 1 - -Predictors detailed performance - -Rank R1 - -Confusion matrix - Iris-setosa Iris-versicolor Iris-virginica -$Iris-setosa 31 0 0 -$Iris-versicolor 0 37 0 -$Iris-virginica 0 0 31 - -Lift curves Iris-setosa -Size Random Optimal Selective Naive Bayes -0.0 0.0 0 0 -0.1 0.1 0.319355 0.319355 -0.2 0.2 0.63871 0.63871 -0.3 0.3 0.958065 0.958065 -0.4 0.4 1.27742 1.27742 -0.5 0.5 1.59677 1.59677 -0.6 0.6 1.91613 1.91613 -0.7 0.7 2.23548 2.23548 -0.8 0.8 2.55484 2.55484 -0.9 0.9 2.87419 2.87419 -1.0 1.0 3.19355 3.19355 -1.1 1.1 3.5129 3.5129 -1.2 1.2 3.83226 3.83226 -1.3 1.3 4.15161 4.15161 -1.4 1.4 4.47097 4.47097 -1.5 1.5 4.79032 4.79032 -1.6 1.6 5.10968 5.10968 -1.7 1.7 5.42903 5.42903 -1.8 1.8 5.74839 5.74839 -1.9 1.9 6.06774 6.06774 -2.0 2.0 6.3871 6.3871 -2.1 2.1 6.70645 6.70645 -2.2 2.2 7.02581 7.02581 -2.3 2.3 7.34516 7.34516 -2.4 2.4 7.66452 7.66452 -2.5 2.5 7.98387 7.98387 -2.6 2.6 8.30323 8.30323 -2.7 2.7 8.62258 8.62258 -2.8 2.8 8.94194 8.94194 -2.9 2.9 9.26129 9.26129 -3.0 3.0 9.58065 9.58065 -3.1 3.1 9.9 9.9 -3.2 3.2 10.2194 10.2194 -3.3 3.3 10.5387 10.5387 -3.4 3.4 10.8581 10.8581 -3.5 3.5 11.1774 11.1774 -3.6 3.6 11.4968 11.4968 -3.7 3.7 11.8161 11.8161 -3.8 3.8 12.1355 12.1355 -3.9 3.9 12.4548 12.4548 -4.0 4.0 12.7742 12.7742 -4.1 4.1 13.0935 13.0935 -4.2 4.2 13.4129 13.4129 -4.3 4.3 13.7323 13.7323 -4.4 4.4 14.0516 14.0516 -4.5 4.5 14.371 14.371 -4.6 4.6 14.6903 14.6903 -4.7 4.7 15.0097 15.0097 -4.8 4.8 15.329 15.329 -4.9 4.9 15.6484 15.6484 -5.0 5.0 15.9677 15.9677 -5.1 5.1 16.2871 16.2871 -5.2 5.2 16.6065 16.6065 -5.3 5.3 16.9258 16.9258 -5.4 5.4 17.2452 17.2452 -5.5 5.5 17.5645 17.5645 -5.6 5.6 17.8839 17.8839 -5.7 5.7 18.2032 18.2032 -5.8 5.8 18.5226 18.5226 -5.9 5.9 18.8419 18.8419 -6.0 6.0 19.1613 19.1613 -6.1 6.1 19.4806 19.4806 -6.2 6.2 19.8 19.8 -6.3 6.3 20.1194 20.1194 -6.4 6.4 20.4387 20.4387 -6.5 6.5 20.7581 20.7581 -6.6 6.6 21.0774 21.0774 -6.7 6.7 21.3968 21.3968 -6.8 6.8 21.7161 21.7161 -6.9 6.9 22.0355 22.0355 -7.0 7.0 22.3548 22.3548 -7.1 7.1 22.6742 22.6742 -7.2 7.2 22.9935 22.9935 -7.3 7.3 23.3129 23.3129 -7.4 7.4 23.6323 23.6323 -7.5 7.5 23.9516 23.9516 -7.6 7.6 24.271 24.271 -7.7 7.7 24.5903 24.5903 -7.8 7.8 24.9097 24.9097 -7.9 7.9 25.229 25.229 -8.0 8.0 25.5484 25.5484 -8.1 8.1 25.8677 25.8677 -8.2 8.2 26.1871 26.1871 -8.3 8.3 26.5065 26.5065 -8.4 8.4 26.8258 26.8258 -8.5 8.5 27.1452 27.1452 -8.6 8.6 27.4645 27.4645 -8.7 8.7 27.7839 27.7839 -8.8 8.8 28.1032 28.1032 -8.9 8.9 28.4226 28.4226 -9.0 9.0 28.7419 28.7419 -9.1 9.1 29.0613 29.0613 -9.2 9.2 29.3806 29.3806 -9.3 9.3 29.7 29.7 -9.4 9.4 30.0194 30.0194 -9.5 9.5 30.3387 30.3387 -9.6 9.6 30.6581 30.6581 -9.7 9.7 30.9774 30.9774 -9.8 9.8 31.2968 31.2968 -9.9 9.9 31.6161 31.6161 -10.0 10.0 31.9355 31.9355 -10.1 10.1 32.2548 32.2548 -10.2 10.2 32.5742 32.5742 -10.3 10.3 32.8935 32.8935 -10.4 10.4 33.2129 33.2129 -10.5 10.5 33.5323 33.5323 -10.6 10.6 33.8516 33.8516 -10.7 10.7 34.171 34.171 -10.8 10.8 34.4903 34.4903 -10.9 10.9 34.8097 34.8097 -11.0 11.0 35.129 35.129 -11.1 11.1 35.4484 35.4484 -11.2 11.2 35.7677 35.7677 -11.3 11.3 36.0871 36.0871 -11.4 11.4 36.4065 36.4065 -11.5 11.5 36.7258 36.7258 -11.6 11.6 37.0452 37.0452 -11.7 11.7 37.3645 37.3645 -11.8 11.8 37.6839 37.6839 -11.9 11.9 38.0032 38.0032 -12.0 12.0 38.3226 38.3226 -12.1 12.1 38.6419 38.6419 -12.2 12.2 38.9613 38.9613 -12.3 12.3 39.2806 39.2806 -12.4 12.4 39.6 39.6 -12.5 12.5 39.9194 39.9194 -12.6 12.6 40.2387 40.2387 -12.7 12.7 40.5581 40.5581 -12.8 12.8 40.8774 40.8774 -12.9 12.9 41.1968 41.1968 -13.0 13.0 41.5161 41.5161 -13.1 13.1 41.8355 41.8355 -13.2 13.2 42.1548 42.1548 -13.3 13.3 42.4742 42.4742 -13.4 13.4 42.7935 42.7935 -13.5 13.5 43.1129 43.1129 -13.6 13.6 43.4323 43.4323 -13.7 13.7 43.7516 43.7516 -13.8 13.8 44.071 44.071 -13.9 13.9 44.3903 44.3903 -14.0 14.0 44.7097 44.7097 -14.1 14.1 45.029 45.029 -14.2 14.2 45.3484 45.3484 -14.3 14.3 45.6677 45.6677 -14.4 14.4 45.9871 45.9871 -14.5 14.5 46.3065 46.3065 -14.6 14.6 46.6258 46.6258 -14.7 14.7 46.9452 46.9452 -14.8 14.8 47.2645 47.2645 -14.9 14.9 47.5839 47.5839 -15.0 15.0 47.9032 47.9032 -15.1 15.1 48.2226 48.2226 -15.2 15.2 48.5419 48.5419 -15.3 15.3 48.8613 48.8613 -15.4 15.4 49.1806 49.1806 -15.5 15.5 49.5 49.5 -15.6 15.6 49.8194 49.8194 -15.7 15.7 50.1387 50.1387 -15.8 15.8 50.4581 50.4581 -15.9 15.9 50.7774 50.7774 -16.0 16.0 51.0968 51.0968 -16.1 16.1 51.4161 51.4161 -16.2 16.2 51.7355 51.7355 -16.3 16.3 52.0548 52.0548 -16.4 16.4 52.3742 52.3742 -16.5 16.5 52.6935 52.6935 -16.6 16.6 53.0129 53.0129 -16.7 16.7 53.3323 53.3323 -16.8 16.8 53.6516 53.6516 -16.9 16.9 53.971 53.971 -17.0 17.0 54.2903 54.2903 -17.1 17.1 54.6097 54.6097 -17.2 17.2 54.929 54.929 -17.3 17.3 55.2484 55.2484 -17.4 17.4 55.5677 55.5677 -17.5 17.5 55.8871 55.8871 -17.6 17.6 56.2065 56.2065 -17.7 17.7 56.5258 56.5258 -17.8 17.8 56.8452 56.8452 -17.9 17.9 57.1645 57.1645 -18.0 18.0 57.4839 57.4839 -18.1 18.1 57.8032 57.8032 -18.2 18.2 58.1226 58.1226 -18.3 18.3 58.4419 58.4419 -18.4 18.4 58.7613 58.7613 -18.5 18.5 59.0806 59.0806 -18.6 18.6 59.4 59.4 -18.7 18.7 59.7194 59.7194 -18.8 18.8 60.0387 60.0387 -18.9 18.9 60.3581 60.3581 -19.0 19.0 60.6774 60.6774 -19.1 19.1 60.9968 60.9968 -19.2 19.2 61.3161 61.3161 -19.3 19.3 61.6355 61.6355 -19.4 19.4 61.9548 61.9548 -19.5 19.5 62.2742 62.2742 -19.6 19.6 62.5935 62.5935 -19.7 19.7 62.9129 62.9129 -19.8 19.8 63.2323 63.2323 -19.9 19.9 63.5516 63.5516 -20.0 20.0 63.871 63.871 -20.1 20.1 64.1903 64.1903 -20.2 20.2 64.5097 64.5097 -20.3 20.3 64.829 64.829 -20.4 20.4 65.1484 65.1484 -20.5 20.5 65.4677 65.4677 -20.6 20.6 65.7871 65.7871 -20.7 20.7 66.1065 66.1065 -20.8 20.8 66.4258 66.4258 -20.9 20.9 66.7452 66.7452 -21.0 21.0 67.0645 67.0645 -21.1 21.1 67.3839 67.3839 -21.2 21.2 67.7032 67.7032 -21.3 21.3 68.0226 68.0226 -21.4 21.4 68.3419 68.3419 -21.5 21.5 68.6613 68.6613 -21.6 21.6 68.9806 68.9806 -21.7 21.7 69.3 69.3 -21.8 21.8 69.6194 69.6194 -21.9 21.9 69.9387 69.9387 -22.0 22.0 70.2581 70.2581 -22.1 22.1 70.5774 70.5774 -22.2 22.2 70.8968 70.8968 -22.3 22.3 71.2161 71.2161 -22.4 22.4 71.5355 71.5355 -22.5 22.5 71.8548 71.8548 -22.6 22.6 72.1742 72.1742 -22.7 22.7 72.4935 72.4935 -22.8 22.8 72.8129 72.8129 -22.9 22.9 73.1323 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100 100 -92.3 92.3 100 100 -92.4 92.4 100 100 -92.5 92.5 100 100 -92.6 92.6 100 100 -92.7 92.7 100 100 -92.8 92.8 100 100 -92.9 92.9 100 100 -93.0 93.0 100 100 -93.1 93.1 100 100 -93.2 93.2 100 100 -93.3 93.3 100 100 -93.4 93.4 100 100 -93.5 93.5 100 100 -93.6 93.6 100 100 -93.7 93.7 100 100 -93.8 93.8 100 100 -93.9 93.9 100 100 -94.0 94.0 100 100 -94.1 94.1 100 100 -94.2 94.2 100 100 -94.3 94.3 100 100 -94.4 94.4 100 100 -94.5 94.5 100 100 -94.6 94.6 100 100 -94.7 94.7 100 100 -94.8 94.8 100 100 -94.9 94.9 100 100 -95.0 95.0 100 100 -95.1 95.1 100 100 -95.2 95.2 100 100 -95.3 95.3 100 100 -95.4 95.4 100 100 -95.5 95.5 100 100 -95.6 95.6 100 100 -95.7 95.7 100 100 -95.8 95.8 100 100 -95.9 95.9 100 100 -96.0 96.0 100 100 -96.1 96.1 100 100 -96.2 96.2 100 100 -96.3 96.3 100 100 -96.4 96.4 100 100 -96.5 96.5 100 100 -96.6 96.6 100 100 -96.7 96.7 100 100 -96.8 96.8 100 100 -96.9 96.9 100 100 -97.0 97.0 100 100 -97.1 97.1 100 100 -97.2 97.2 100 100 -97.3 97.3 100 100 -97.4 97.4 100 100 -97.5 97.5 100 100 -97.6 97.6 100 100 -97.7 97.7 100 100 -97.8 97.8 100 100 -97.9 97.9 100 100 -98.0 98.0 100 100 -98.1 98.1 100 100 -98.2 98.2 100 100 -98.3 98.3 100 100 -98.4 98.4 100 100 -98.5 98.5 100 100 -98.6 98.6 100 100 -98.7 98.7 100 100 -98.8 98.8 100 100 -98.9 98.9 100 100 -99.0 99.0 100 100 -99.1 99.1 100 100 -99.2 99.2 100 100 -99.3 99.3 100 100 -99.4 99.4 100 100 -99.5 99.5 100 100 -99.6 99.6 100 100 -99.7 99.7 100 100 -99.8 99.8 100 100 -99.9 99.9 100 100 -100.0 100.0 100 100 - - -Report Evaluation Test - -Dictionary Iris -Database ../../../datasets/Iris/Iris.txt -Instances 51 -Learning task Classification analysis -Target variable Class - -Predictors performance -Rank Type Family Name Accuracy Compression AUC -R1 Classifier Selective Naive Bayes Selective Naive Bayes 1 0.993937 1 - -Predictors detailed performance - -Rank R1 - -Confusion matrix - Iris-setosa Iris-versicolor Iris-virginica -$Iris-setosa 19 0 0 -$Iris-versicolor 0 13 0 -$Iris-virginica 0 0 19 - -Lift curves Iris-setosa -Size Random Optimal Selective Naive Bayes -0.0 0.0 0 0 -0.1 0.1 0.268421 0.268421 -0.2 0.2 0.536842 0.536842 -0.3 0.3 0.805263 0.805263 -0.4 0.4 1.07368 1.07368 -0.5 0.5 1.34211 1.34211 -0.6 0.6 1.61053 1.61053 -0.7 0.7 1.87895 1.87895 -0.8 0.8 2.14737 2.14737 -0.9 0.9 2.41579 2.41579 -1.0 1.0 2.68421 2.68421 -1.1 1.1 2.95263 2.95263 -1.2 1.2 3.22105 3.22105 -1.3 1.3 3.48947 3.48947 -1.4 1.4 3.75789 3.75789 -1.5 1.5 4.02632 4.02632 -1.6 1.6 4.29474 4.29474 -1.7 1.7 4.56316 4.56316 -1.8 1.8 4.83158 4.83158 -1.9 1.9 5.1 5.1 -2.0 2.0 5.36842 5.36842 -2.1 2.1 5.63684 5.63684 -2.2 2.2 5.90526 5.90526 -2.3 2.3 6.17368 6.17368 -2.4 2.4 6.44211 6.44211 -2.5 2.5 6.71053 6.71053 -2.6 2.6 6.97895 6.97895 -2.7 2.7 7.24737 7.24737 -2.8 2.8 7.51579 7.51579 -2.9 2.9 7.78421 7.78421 -3.0 3.0 8.05263 8.05263 -3.1 3.1 8.32105 8.32105 -3.2 3.2 8.58947 8.58947 -3.3 3.3 8.85789 8.85789 -3.4 3.4 9.12632 9.12632 -3.5 3.5 9.39474 9.39474 -3.6 3.6 9.66316 9.66316 -3.7 3.7 9.93158 9.93158 -3.8 3.8 10.2 10.2 -3.9 3.9 10.4684 10.4684 -4.0 4.0 10.7368 10.7368 -4.1 4.1 11.0053 11.0053 -4.2 4.2 11.2737 11.2737 -4.3 4.3 11.5421 11.5421 -4.4 4.4 11.8105 11.8105 -4.5 4.5 12.0789 12.0789 -4.6 4.6 12.3474 12.3474 -4.7 4.7 12.6158 12.6158 -4.8 4.8 12.8842 12.8842 -4.9 4.9 13.1526 13.1526 -5.0 5.0 13.4211 13.4211 -5.1 5.1 13.6895 13.6895 -5.2 5.2 13.9579 13.9579 -5.3 5.3 14.2263 14.2263 -5.4 5.4 14.4947 14.4947 -5.5 5.5 14.7632 14.7632 -5.6 5.6 15.0316 15.0316 -5.7 5.7 15.3 15.3 -5.8 5.8 15.5684 15.5684 -5.9 5.9 15.8368 15.8368 -6.0 6.0 16.1053 16.1053 -6.1 6.1 16.3737 16.3737 -6.2 6.2 16.6421 16.6421 -6.3 6.3 16.9105 16.9105 -6.4 6.4 17.1789 17.1789 -6.5 6.5 17.4474 17.4474 -6.6 6.6 17.7158 17.7158 -6.7 6.7 17.9842 17.9842 -6.8 6.8 18.2526 18.2526 -6.9 6.9 18.5211 18.5211 -7.0 7.0 18.7895 18.7895 -7.1 7.1 19.0579 19.0579 -7.2 7.2 19.3263 19.3263 -7.3 7.3 19.5947 19.5947 -7.4 7.4 19.8632 19.8632 -7.5 7.5 20.1316 20.1316 -7.6 7.6 20.4 20.4 -7.7 7.7 20.6684 20.6684 -7.8 7.8 20.9368 20.9368 -7.9 7.9 21.2053 21.2053 -8.0 8.0 21.4737 21.4737 -8.1 8.1 21.7421 21.7421 -8.2 8.2 22.0105 22.0105 -8.3 8.3 22.2789 22.2789 -8.4 8.4 22.5474 22.5474 -8.5 8.5 22.8158 22.8158 -8.6 8.6 23.0842 23.0842 -8.7 8.7 23.3526 23.3526 -8.8 8.8 23.6211 23.6211 -8.9 8.9 23.8895 23.8895 -9.0 9.0 24.1579 24.1579 -9.1 9.1 24.4263 24.4263 -9.2 9.2 24.6947 24.6947 -9.3 9.3 24.9632 24.9632 -9.4 9.4 25.2316 25.2316 -9.5 9.5 25.5 25.5 -9.6 9.6 25.7684 25.7684 -9.7 9.7 26.0368 26.0368 -9.8 9.8 26.3053 26.3053 -9.9 9.9 26.5737 26.5737 -10.0 10.0 26.8421 26.8421 -10.1 10.1 27.1105 27.1105 -10.2 10.2 27.3789 27.3789 -10.3 10.3 27.6474 27.6474 -10.4 10.4 27.9158 27.9158 -10.5 10.5 28.1842 28.1842 -10.6 10.6 28.4526 28.4526 -10.7 10.7 28.7211 28.7211 -10.8 10.8 28.9895 28.9895 -10.9 10.9 29.2579 29.2579 -11.0 11.0 29.5263 29.5263 -11.1 11.1 29.7947 29.7947 -11.2 11.2 30.0632 30.0632 -11.3 11.3 30.3316 30.3316 -11.4 11.4 30.6 30.6 -11.5 11.5 30.8684 30.8684 -11.6 11.6 31.1368 31.1368 -11.7 11.7 31.4053 31.4053 -11.8 11.8 31.6737 31.6737 -11.9 11.9 31.9421 31.9421 -12.0 12.0 32.2105 32.2105 -12.1 12.1 32.4789 32.4789 -12.2 12.2 32.7474 32.7474 -12.3 12.3 33.0158 33.0158 -12.4 12.4 33.2842 33.2842 -12.5 12.5 33.5526 33.5526 -12.6 12.6 33.8211 33.8211 -12.7 12.7 34.0895 34.0895 -12.8 12.8 34.3579 34.3579 -12.9 12.9 34.6263 34.6263 -13.0 13.0 34.8947 34.8947 -13.1 13.1 35.1632 35.1632 -13.2 13.2 35.4316 35.4316 -13.3 13.3 35.7 35.7 -13.4 13.4 35.9684 35.9684 -13.5 13.5 36.2368 36.2368 -13.6 13.6 36.5053 36.5053 -13.7 13.7 36.7737 36.7737 -13.8 13.8 37.0421 37.0421 -13.9 13.9 37.3105 37.3105 -14.0 14.0 37.5789 37.5789 -14.1 14.1 37.8474 37.8474 -14.2 14.2 38.1158 38.1158 -14.3 14.3 38.3842 38.3842 -14.4 14.4 38.6526 38.6526 -14.5 14.5 38.9211 38.9211 -14.6 14.6 39.1895 39.1895 -14.7 14.7 39.4579 39.4579 -14.8 14.8 39.7263 39.7263 -14.9 14.9 39.9947 39.9947 -15.0 15.0 40.2632 40.2632 -15.1 15.1 40.5316 40.5316 -15.2 15.2 40.8 40.8 -15.3 15.3 41.0684 41.0684 -15.4 15.4 41.3368 41.3368 -15.5 15.5 41.6053 41.6053 -15.6 15.6 41.8737 41.8737 -15.7 15.7 42.1421 42.1421 -15.8 15.8 42.4105 42.4105 -15.9 15.9 42.6789 42.6789 -16.0 16.0 42.9474 42.9474 -16.1 16.1 43.2158 43.2158 -16.2 16.2 43.4842 43.4842 -16.3 16.3 43.7526 43.7526 -16.4 16.4 44.0211 44.0211 -16.5 16.5 44.2895 44.2895 -16.6 16.6 44.5579 44.5579 -16.7 16.7 44.8263 44.8263 -16.8 16.8 45.0947 45.0947 -16.9 16.9 45.3632 45.3632 -17.0 17.0 45.6316 45.6316 -17.1 17.1 45.9 45.9 -17.2 17.2 46.1684 46.1684 -17.3 17.3 46.4368 46.4368 -17.4 17.4 46.7053 46.7053 -17.5 17.5 46.9737 46.9737 -17.6 17.6 47.2421 47.2421 -17.7 17.7 47.5105 47.5105 -17.8 17.8 47.7789 47.7789 -17.9 17.9 48.0474 48.0474 -18.0 18.0 48.3158 48.3158 -18.1 18.1 48.5842 48.5842 -18.2 18.2 48.8526 48.8526 -18.3 18.3 49.1211 49.1211 -18.4 18.4 49.3895 49.3895 -18.5 18.5 49.6579 49.6579 -18.6 18.6 49.9263 49.9263 -18.7 18.7 50.1947 50.1947 -18.8 18.8 50.4632 50.4632 -18.9 18.9 50.7316 50.7316 -19.0 19.0 51 51 -19.1 19.1 51.2684 51.2684 -19.2 19.2 51.5368 51.5368 -19.3 19.3 51.8053 51.8053 -19.4 19.4 52.0737 52.0737 -19.5 19.5 52.3421 52.3421 -19.6 19.6 52.6105 52.6105 -19.7 19.7 52.8789 52.8789 -19.8 19.8 53.1474 53.1474 -19.9 19.9 53.4158 53.4158 -20.0 20.0 53.6842 53.6842 -20.1 20.1 53.9526 53.9526 -20.2 20.2 54.2211 54.2211 -20.3 20.3 54.4895 54.4895 -20.4 20.4 54.7579 54.7579 -20.5 20.5 55.0263 55.0263 -20.6 20.6 55.2947 55.2947 -20.7 20.7 55.5632 55.5632 -20.8 20.8 55.8316 55.8316 -20.9 20.9 56.1 56.1 -21.0 21.0 56.3684 56.3684 -21.1 21.1 56.6368 56.6368 -21.2 21.2 56.9053 56.9053 -21.3 21.3 57.1737 57.1737 -21.4 21.4 57.4421 57.4421 -21.5 21.5 57.7105 57.7105 -21.6 21.6 57.9789 57.9789 -21.7 21.7 58.2474 58.2474 -21.8 21.8 58.5158 58.5158 -21.9 21.9 58.7842 58.7842 -22.0 22.0 59.0526 59.0526 -22.1 22.1 59.3211 59.3211 -22.2 22.2 59.5895 59.5895 -22.3 22.3 59.8579 59.8579 -22.4 22.4 60.1263 60.1263 -22.5 22.5 60.3947 60.3947 -22.6 22.6 60.6632 60.6632 -22.7 22.7 60.9316 60.9316 -22.8 22.8 61.2 61.2 -22.9 22.9 61.4684 61.4684 -23.0 23.0 61.7368 61.7368 -23.1 23.1 62.0053 62.0053 -23.2 23.2 62.2737 62.2737 -23.3 23.3 62.5421 62.5421 -23.4 23.4 62.8105 62.8105 -23.5 23.5 63.0789 63.0789 -23.6 23.6 63.3474 63.3474 -23.7 23.7 63.6158 63.6158 -23.8 23.8 63.8842 63.8842 -23.9 23.9 64.1526 64.1526 -24.0 24.0 64.4211 64.4211 -24.1 24.1 64.6895 64.6895 -24.2 24.2 64.9579 64.9579 -24.3 24.3 65.2263 65.2263 -24.4 24.4 65.4947 65.4947 -24.5 24.5 65.7632 65.7632 -24.6 24.6 66.0316 66.0316 -24.7 24.7 66.3 66.3 -24.8 24.8 66.5684 66.5684 -24.9 24.9 66.8368 66.8368 -25.0 25.0 67.1053 67.1053 -25.1 25.1 67.3737 67.3737 -25.2 25.2 67.6421 67.6421 -25.3 25.3 67.9105 67.9105 -25.4 25.4 68.1789 68.1789 -25.5 25.5 68.4474 68.4474 -25.6 25.6 68.7158 68.7158 -25.7 25.7 68.9842 68.9842 -25.8 25.8 69.2526 69.2526 -25.9 25.9 69.5211 69.5211 -26.0 26.0 69.7895 69.7895 -26.1 26.1 70.0579 70.0579 -26.2 26.2 70.3263 70.3263 -26.3 26.3 70.5947 70.5947 -26.4 26.4 70.8632 70.8632 -26.5 26.5 71.1316 71.1316 -26.6 26.6 71.4 71.4 -26.7 26.7 71.6684 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100 100 -98.0 98.0 100 100 -98.1 98.1 100 100 -98.2 98.2 100 100 -98.3 98.3 100 100 -98.4 98.4 100 100 -98.5 98.5 100 100 -98.6 98.6 100 100 -98.7 98.7 100 100 -98.8 98.8 100 100 -98.9 98.9 100 100 -99.0 99.0 100 100 -99.1 99.1 100 100 -99.2 99.2 100 100 -99.3 99.3 100 100 -99.4 99.4 100 100 -99.5 99.5 100 100 -99.6 99.6 100 100 -99.7 99.7 100 100 -99.8 99.8 100 100 -99.9 99.9 100 100 -100.0 100.0 100 100 diff --git a/tests/resources/analysis_results/ref_reports/IrisMAPLegacy.txt b/tests/resources/analysis_results/ref_reports/IrisMAPLegacy.txt deleted file mode 100644 index 419fba3c..00000000 --- a/tests/resources/analysis_results/ref_reports/IrisMAPLegacy.txt +++ /dev/null @@ -1,6201 +0,0 @@ -Tool Khiops -Version 9.0.1 -Short description - - -Report Preparation - -Dictionary Iris -Variables - Categorical 1 - Numerical 4 - Total 5 -Database ../../../datasets/Iris/Iris.txt -Instances 99 -Learning task Classification analysis -Target variable Class -Target descriptive stats - Values 3 - Mode Iris-versicolor - Mode frequency 37 -Target variable stats - Iris-setosa 31 - Iris-versicolor 37 - Iris-virginica 31 -Evaluated variables 4 -Informative variables 4 -Discretization MODL -Value grouping MODL -Null model - Construction cost 0.693147 - Preparation cost 8.52714 - Data cost 103.619 - -Variable statistics -Rank Name Type Level Target parts Parts Values Min Max Mean StdDev Missing number Mode Mode frequency Construction cost Preparation cost Data cost Derivation rule -R1 PetalLength Numerical 0.644632 3 36 1 6.9 3.801010101 1.712137004 0 2.07944 28.0635 9.95655 -R2 PetalWidth Numerical 0.610094 3 20 0.1 2.5 1.218181818 0.749863777 0 2.07944 31.9608 9.95655 -R3 SepalLength Numerical 0.290065 3 30 4.3 7.7 5.848484848 0.8065844732 0 2.07944 27.4942 50.535 -R4 SepalWidth Numerical 0.121532 2 22 2 4.4 3.042424242 0.4422374035 0 2.07944 16.7264 80.32 - -Detailed variable statistics - -Rank R1 PetalLength Numerical - -Data grid Supervised -Dimensions -PetalLength Numerical Intervals - ]-inf;2.4] 1 2.4 - ]2.4;4.95] 2.4 4.95 - ]4.95;+inf[ 4.95 6.9 -Class Categorical Values - Iris-setosa - Iris-versicolor - Iris-virginica -Cells -Interval Iris-setosa Iris-versicolor Iris-virginica Interest -]-inf;2.4] 31 0 0 0.374224 -]2.4;4.95] 0 36 2 0.311045 -]4.95;+inf[ 0 1 29 0.314731 - -Rank R2 PetalWidth Numerical - -Data grid Supervised -Dimensions -PetalWidth Numerical Intervals - ]-inf;0.7] 0.1 0.7 - ]0.7;1.75] 0.7 1.75 - ]1.75;+inf[ 1.75 2.5 -Class Categorical Values - Iris-setosa - Iris-versicolor - Iris-virginica -Cells -Interval Iris-setosa Iris-versicolor Iris-virginica Interest -]-inf;0.7] 31 0 0 0.374224 -]0.7;1.75] 0 36 2 0.311045 -]1.75;+inf[ 0 1 29 0.314731 - -Rank R3 SepalLength Numerical - -Data grid Supervised -Dimensions -SepalLength Numerical Intervals - ]-inf;5.45] 4.3 5.45 - ]5.45;5.75] 5.45 5.75 - ]5.75;+inf[ 5.75 7.7 -Class Categorical Values - Iris-setosa - Iris-versicolor - Iris-virginica -Cells -Interval Iris-setosa Iris-versicolor Iris-virginica Interest -]-inf;5.45] 28 3 0 0.493633 -]5.45;5.75] 3 12 1 0.100396 -]5.75;+inf[ 0 22 30 0.405971 - -Rank R4 SepalWidth Numerical - -Data grid Supervised -Dimensions -SepalWidth Numerical Intervals - ]-inf;3.35] 2 3.35 - ]3.35;+inf[ 3.35 4.4 -Class Categorical Values - Iris-setosa - Iris-versicolor - Iris-virginica -Cells -Interval Iris-setosa Iris-versicolor Iris-virginica Interest -]-inf;3.35] 11 36 29 0.268778 -]3.35;+inf[ 20 1 2 0.731222 - - -Report Modeling - -Dictionary Iris -Database ../../../datasets/Iris/Iris.txt -Learning task Classification analysis -Target variable Class - -Trained predictors -Rank Type Family Name Variables -R1 Classifier MAP Naive Bayes MAP Naive Bayes 1 - -Detailed trained predictors - -Rank R1 - -Selected variables -Prepared name Name Level Weight -PPetalLength PetalLength 0.644632 None - - -Report Evaluation Train - -Dictionary Iris -Database ../../../datasets/Iris/Iris.txt -Instances 99 -Learning task Classification analysis -Target variable Class - -Predictors performance -Rank Type Family Name Accuracy Compression AUC -R1 Classifier MAP Naive Bayes MAP Naive Bayes 0.969697 0.884895 0.983648 - -Predictors detailed performance - -Rank R1 - -Confusion matrix - Iris-setosa Iris-versicolor Iris-virginica -$Iris-setosa 31 0 0 -$Iris-versicolor 0 36 2 -$Iris-virginica 0 1 29 - -Lift curves Iris-setosa -Size Random Optimal MAP Naive Bayes -0.0 0.0 0 0 -0.1 0.1 0.319355 0.319355 -0.2 0.2 0.63871 0.63871 -0.3 0.3 0.958065 0.958065 -0.4 0.4 1.27742 1.27742 -0.5 0.5 1.59677 1.59677 -0.6 0.6 1.91613 1.91613 -0.7 0.7 2.23548 2.23548 -0.8 0.8 2.55484 2.55484 -0.9 0.9 2.87419 2.87419 -1.0 1.0 3.19355 3.19355 -1.1 1.1 3.5129 3.5129 -1.2 1.2 3.83226 3.83226 -1.3 1.3 4.15161 4.15161 -1.4 1.4 4.47097 4.47097 -1.5 1.5 4.79032 4.79032 -1.6 1.6 5.10968 5.10968 -1.7 1.7 5.42903 5.42903 -1.8 1.8 5.74839 5.74839 -1.9 1.9 6.06774 6.06774 -2.0 2.0 6.3871 6.3871 -2.1 2.1 6.70645 6.70645 -2.2 2.2 7.02581 7.02581 -2.3 2.3 7.34516 7.34516 -2.4 2.4 7.66452 7.66452 -2.5 2.5 7.98387 7.98387 -2.6 2.6 8.30323 8.30323 -2.7 2.7 8.62258 8.62258 -2.8 2.8 8.94194 8.94194 -2.9 2.9 9.26129 9.26129 -3.0 3.0 9.58065 9.58065 -3.1 3.1 9.9 9.9 -3.2 3.2 10.2194 10.2194 -3.3 3.3 10.5387 10.5387 -3.4 3.4 10.8581 10.8581 -3.5 3.5 11.1774 11.1774 -3.6 3.6 11.4968 11.4968 -3.7 3.7 11.8161 11.8161 -3.8 3.8 12.1355 12.1355 -3.9 3.9 12.4548 12.4548 -4.0 4.0 12.7742 12.7742 -4.1 4.1 13.0935 13.0935 -4.2 4.2 13.4129 13.4129 -4.3 4.3 13.7323 13.7323 -4.4 4.4 14.0516 14.0516 -4.5 4.5 14.371 14.371 -4.6 4.6 14.6903 14.6903 -4.7 4.7 15.0097 15.0097 -4.8 4.8 15.329 15.329 -4.9 4.9 15.6484 15.6484 -5.0 5.0 15.9677 15.9677 -5.1 5.1 16.2871 16.2871 -5.2 5.2 16.6065 16.6065 -5.3 5.3 16.9258 16.9258 -5.4 5.4 17.2452 17.2452 -5.5 5.5 17.5645 17.5645 -5.6 5.6 17.8839 17.8839 -5.7 5.7 18.2032 18.2032 -5.8 5.8 18.5226 18.5226 -5.9 5.9 18.8419 18.8419 -6.0 6.0 19.1613 19.1613 -6.1 6.1 19.4806 19.4806 -6.2 6.2 19.8 19.8 -6.3 6.3 20.1194 20.1194 -6.4 6.4 20.4387 20.4387 -6.5 6.5 20.7581 20.7581 -6.6 6.6 21.0774 21.0774 -6.7 6.7 21.3968 21.3968 -6.8 6.8 21.7161 21.7161 -6.9 6.9 22.0355 22.0355 -7.0 7.0 22.3548 22.3548 -7.1 7.1 22.6742 22.6742 -7.2 7.2 22.9935 22.9935 -7.3 7.3 23.3129 23.3129 -7.4 7.4 23.6323 23.6323 -7.5 7.5 23.9516 23.9516 -7.6 7.6 24.271 24.271 -7.7 7.7 24.5903 24.5903 -7.8 7.8 24.9097 24.9097 -7.9 7.9 25.229 25.229 -8.0 8.0 25.5484 25.5484 -8.1 8.1 25.8677 25.8677 -8.2 8.2 26.1871 26.1871 -8.3 8.3 26.5065 26.5065 -8.4 8.4 26.8258 26.8258 -8.5 8.5 27.1452 27.1452 -8.6 8.6 27.4645 27.4645 -8.7 8.7 27.7839 27.7839 -8.8 8.8 28.1032 28.1032 -8.9 8.9 28.4226 28.4226 -9.0 9.0 28.7419 28.7419 -9.1 9.1 29.0613 29.0613 -9.2 9.2 29.3806 29.3806 -9.3 9.3 29.7 29.7 -9.4 9.4 30.0194 30.0194 -9.5 9.5 30.3387 30.3387 -9.6 9.6 30.6581 30.6581 -9.7 9.7 30.9774 30.9774 -9.8 9.8 31.2968 31.2968 -9.9 9.9 31.6161 31.6161 -10.0 10.0 31.9355 31.9355 -10.1 10.1 32.2548 32.2548 -10.2 10.2 32.5742 32.5742 -10.3 10.3 32.8935 32.8935 -10.4 10.4 33.2129 33.2129 -10.5 10.5 33.5323 33.5323 -10.6 10.6 33.8516 33.8516 -10.7 10.7 34.171 34.171 -10.8 10.8 34.4903 34.4903 -10.9 10.9 34.8097 34.8097 -11.0 11.0 35.129 35.129 -11.1 11.1 35.4484 35.4484 -11.2 11.2 35.7677 35.7677 -11.3 11.3 36.0871 36.0871 -11.4 11.4 36.4065 36.4065 -11.5 11.5 36.7258 36.7258 -11.6 11.6 37.0452 37.0452 -11.7 11.7 37.3645 37.3645 -11.8 11.8 37.6839 37.6839 -11.9 11.9 38.0032 38.0032 -12.0 12.0 38.3226 38.3226 -12.1 12.1 38.6419 38.6419 -12.2 12.2 38.9613 38.9613 -12.3 12.3 39.2806 39.2806 -12.4 12.4 39.6 39.6 -12.5 12.5 39.9194 39.9194 -12.6 12.6 40.2387 40.2387 -12.7 12.7 40.5581 40.5581 -12.8 12.8 40.8774 40.8774 -12.9 12.9 41.1968 41.1968 -13.0 13.0 41.5161 41.5161 -13.1 13.1 41.8355 41.8355 -13.2 13.2 42.1548 42.1548 -13.3 13.3 42.4742 42.4742 -13.4 13.4 42.7935 42.7935 -13.5 13.5 43.1129 43.1129 -13.6 13.6 43.4323 43.4323 -13.7 13.7 43.7516 43.7516 -13.8 13.8 44.071 44.071 -13.9 13.9 44.3903 44.3903 -14.0 14.0 44.7097 44.7097 -14.1 14.1 45.029 45.029 -14.2 14.2 45.3484 45.3484 -14.3 14.3 45.6677 45.6677 -14.4 14.4 45.9871 45.9871 -14.5 14.5 46.3065 46.3065 -14.6 14.6 46.6258 46.6258 -14.7 14.7 46.9452 46.9452 -14.8 14.8 47.2645 47.2645 -14.9 14.9 47.5839 47.5839 -15.0 15.0 47.9032 47.9032 -15.1 15.1 48.2226 48.2226 -15.2 15.2 48.5419 48.5419 -15.3 15.3 48.8613 48.8613 -15.4 15.4 49.1806 49.1806 -15.5 15.5 49.5 49.5 -15.6 15.6 49.8194 49.8194 -15.7 15.7 50.1387 50.1387 -15.8 15.8 50.4581 50.4581 -15.9 15.9 50.7774 50.7774 -16.0 16.0 51.0968 51.0968 -16.1 16.1 51.4161 51.4161 -16.2 16.2 51.7355 51.7355 -16.3 16.3 52.0548 52.0548 -16.4 16.4 52.3742 52.3742 -16.5 16.5 52.6935 52.6935 -16.6 16.6 53.0129 53.0129 -16.7 16.7 53.3323 53.3323 -16.8 16.8 53.6516 53.6516 -16.9 16.9 53.971 53.971 -17.0 17.0 54.2903 54.2903 -17.1 17.1 54.6097 54.6097 -17.2 17.2 54.929 54.929 -17.3 17.3 55.2484 55.2484 -17.4 17.4 55.5677 55.5677 -17.5 17.5 55.8871 55.8871 -17.6 17.6 56.2065 56.2065 -17.7 17.7 56.5258 56.5258 -17.8 17.8 56.8452 56.8452 -17.9 17.9 57.1645 57.1645 -18.0 18.0 57.4839 57.4839 -18.1 18.1 57.8032 57.8032 -18.2 18.2 58.1226 58.1226 -18.3 18.3 58.4419 58.4419 -18.4 18.4 58.7613 58.7613 -18.5 18.5 59.0806 59.0806 -18.6 18.6 59.4 59.4 -18.7 18.7 59.7194 59.7194 -18.8 18.8 60.0387 60.0387 -18.9 18.9 60.3581 60.3581 -19.0 19.0 60.6774 60.6774 -19.1 19.1 60.9968 60.9968 -19.2 19.2 61.3161 61.3161 -19.3 19.3 61.6355 61.6355 -19.4 19.4 61.9548 61.9548 -19.5 19.5 62.2742 62.2742 -19.6 19.6 62.5935 62.5935 -19.7 19.7 62.9129 62.9129 -19.8 19.8 63.2323 63.2323 -19.9 19.9 63.5516 63.5516 -20.0 20.0 63.871 63.871 -20.1 20.1 64.1903 64.1903 -20.2 20.2 64.5097 64.5097 -20.3 20.3 64.829 64.829 -20.4 20.4 65.1484 65.1484 -20.5 20.5 65.4677 65.4677 -20.6 20.6 65.7871 65.7871 -20.7 20.7 66.1065 66.1065 -20.8 20.8 66.4258 66.4258 -20.9 20.9 66.7452 66.7452 -21.0 21.0 67.0645 67.0645 -21.1 21.1 67.3839 67.3839 -21.2 21.2 67.7032 67.7032 -21.3 21.3 68.0226 68.0226 -21.4 21.4 68.3419 68.3419 -21.5 21.5 68.6613 68.6613 -21.6 21.6 68.9806 68.9806 -21.7 21.7 69.3 69.3 -21.8 21.8 69.6194 69.6194 -21.9 21.9 69.9387 69.9387 -22.0 22.0 70.2581 70.2581 -22.1 22.1 70.5774 70.5774 -22.2 22.2 70.8968 70.8968 -22.3 22.3 71.2161 71.2161 -22.4 22.4 71.5355 71.5355 -22.5 22.5 71.8548 71.8548 -22.6 22.6 72.1742 72.1742 -22.7 22.7 72.4935 72.4935 -22.8 22.8 72.8129 72.8129 -22.9 22.9 73.1323 73.1323 -23.0 23.0 73.4516 73.4516 -23.1 23.1 73.771 73.771 -23.2 23.2 74.0903 74.0903 -23.3 23.3 74.4097 74.4097 -23.4 23.4 74.729 74.729 -23.5 23.5 75.0484 75.0484 -23.6 23.6 75.3677 75.3677 -23.7 23.7 75.6871 75.6871 -23.8 23.8 76.0065 76.0065 -23.9 23.9 76.3258 76.3258 -24.0 24.0 76.6452 76.6452 -24.1 24.1 76.9645 76.9645 -24.2 24.2 77.2839 77.2839 -24.3 24.3 77.6032 77.6032 -24.4 24.4 77.9226 77.9226 -24.5 24.5 78.2419 78.2419 -24.6 24.6 78.5613 78.5613 -24.7 24.7 78.8806 78.8806 -24.8 24.8 79.2 79.2 -24.9 24.9 79.5194 79.5194 -25.0 25.0 79.8387 79.8387 -25.1 25.1 80.1581 80.1581 -25.2 25.2 80.4774 80.4774 -25.3 25.3 80.7968 80.7968 -25.4 25.4 81.1161 81.1161 -25.5 25.5 81.4355 81.4355 -25.6 25.6 81.7548 81.7548 -25.7 25.7 82.0742 82.0742 -25.8 25.8 82.3935 82.3935 -25.9 25.9 82.7129 82.7129 -26.0 26.0 83.0323 83.0323 -26.1 26.1 83.3516 83.3516 -26.2 26.2 83.671 83.671 -26.3 26.3 83.9903 83.9903 -26.4 26.4 84.3097 84.3097 -26.5 26.5 84.629 84.629 -26.6 26.6 84.9484 84.9484 -26.7 26.7 85.2677 85.2677 -26.8 26.8 85.5871 85.5871 -26.9 26.9 85.9065 85.9065 -27.0 27.0 86.2258 86.2258 -27.1 27.1 86.5452 86.5452 -27.2 27.2 86.8645 86.8645 -27.3 27.3 87.1839 87.1839 -27.4 27.4 87.5032 87.5032 -27.5 27.5 87.8226 87.8226 -27.6 27.6 88.1419 88.1419 -27.7 27.7 88.4613 88.4613 -27.8 27.8 88.7806 88.7806 -27.9 27.9 89.1 89.1 -28.0 28.0 89.4194 89.4194 -28.1 28.1 89.7387 89.7387 -28.2 28.2 90.0581 90.0581 -28.3 28.3 90.3774 90.3774 -28.4 28.4 90.6968 90.6968 -28.5 28.5 91.0161 91.0161 -28.6 28.6 91.3355 91.3355 -28.7 28.7 91.6548 91.6548 -28.8 28.8 91.9742 91.9742 -28.9 28.9 92.2935 92.2935 -29.0 29.0 92.6129 92.6129 -29.1 29.1 92.9323 92.9323 -29.2 29.2 93.2516 93.2516 -29.3 29.3 93.571 93.571 -29.4 29.4 93.8903 93.8903 -29.5 29.5 94.2097 94.2097 -29.6 29.6 94.529 94.529 -29.7 29.7 94.8484 94.8484 -29.8 29.8 95.1677 95.1677 -29.9 29.9 95.4871 95.4871 -30.0 30.0 95.8065 95.8065 -30.1 30.1 96.1258 96.1258 -30.2 30.2 96.4452 96.4452 -30.3 30.3 96.7645 96.7645 -30.4 30.4 97.0839 97.0839 -30.5 30.5 97.4032 97.4032 -30.6 30.6 97.7226 97.7226 -30.7 30.7 98.0419 98.0419 -30.8 30.8 98.3613 98.3613 -30.9 30.9 98.6806 98.6806 -31.0 31.0 99 99 -31.1 31.1 99.3194 99.3194 -31.2 31.2 99.6387 99.6387 -31.3 31.3 99.9581 99.9581 -31.4 31.4 100 100 -31.5 31.5 100 100 -31.6 31.6 100 100 -31.7 31.7 100 100 -31.8 31.8 100 100 -31.9 31.9 100 100 -32.0 32.0 100 100 -32.1 32.1 100 100 -32.2 32.2 100 100 -32.3 32.3 100 100 -32.4 32.4 100 100 -32.5 32.5 100 100 -32.6 32.6 100 100 -32.7 32.7 100 100 -32.8 32.8 100 100 -32.9 32.9 100 100 -33.0 33.0 100 100 -33.1 33.1 100 100 -33.2 33.2 100 100 -33.3 33.3 100 100 -33.4 33.4 100 100 -33.5 33.5 100 100 -33.6 33.6 100 100 -33.7 33.7 100 100 -33.8 33.8 100 100 -33.9 33.9 100 100 -34.0 34.0 100 100 -34.1 34.1 100 100 -34.2 34.2 100 100 -34.3 34.3 100 100 -34.4 34.4 100 100 -34.5 34.5 100 100 -34.6 34.6 100 100 -34.7 34.7 100 100 -34.8 34.8 100 100 -34.9 34.9 100 100 -35.0 35.0 100 100 -35.1 35.1 100 100 -35.2 35.2 100 100 -35.3 35.3 100 100 -35.4 35.4 100 100 -35.5 35.5 100 100 -35.6 35.6 100 100 -35.7 35.7 100 100 -35.8 35.8 100 100 -35.9 35.9 100 100 -36.0 36.0 100 100 -36.1 36.1 100 100 -36.2 36.2 100 100 -36.3 36.3 100 100 -36.4 36.4 100 100 -36.5 36.5 100 100 -36.6 36.6 100 100 -36.7 36.7 100 100 -36.8 36.8 100 100 -36.9 36.9 100 100 -37.0 37.0 100 100 -37.1 37.1 100 100 -37.2 37.2 100 100 -37.3 37.3 100 100 -37.4 37.4 100 100 -37.5 37.5 100 100 -37.6 37.6 100 100 -37.7 37.7 100 100 -37.8 37.8 100 100 -37.9 37.9 100 100 -38.0 38.0 100 100 -38.1 38.1 100 100 -38.2 38.2 100 100 -38.3 38.3 100 100 -38.4 38.4 100 100 -38.5 38.5 100 100 -38.6 38.6 100 100 -38.7 38.7 100 100 -38.8 38.8 100 100 -38.9 38.9 100 100 -39.0 39.0 100 100 -39.1 39.1 100 100 -39.2 39.2 100 100 -39.3 39.3 100 100 -39.4 39.4 100 100 -39.5 39.5 100 100 -39.6 39.6 100 100 -39.7 39.7 100 100 -39.8 39.8 100 100 -39.9 39.9 100 100 -40.0 40.0 100 100 -40.1 40.1 100 100 -40.2 40.2 100 100 -40.3 40.3 100 100 -40.4 40.4 100 100 -40.5 40.5 100 100 -40.6 40.6 100 100 -40.7 40.7 100 100 -40.8 40.8 100 100 -40.9 40.9 100 100 -41.0 41.0 100 100 -41.1 41.1 100 100 -41.2 41.2 100 100 -41.3 41.3 100 100 -41.4 41.4 100 100 -41.5 41.5 100 100 -41.6 41.6 100 100 -41.7 41.7 100 100 -41.8 41.8 100 100 -41.9 41.9 100 100 -42.0 42.0 100 100 -42.1 42.1 100 100 -42.2 42.2 100 100 -42.3 42.3 100 100 -42.4 42.4 100 100 -42.5 42.5 100 100 -42.6 42.6 100 100 -42.7 42.7 100 100 -42.8 42.8 100 100 -42.9 42.9 100 100 -43.0 43.0 100 100 -43.1 43.1 100 100 -43.2 43.2 100 100 -43.3 43.3 100 100 -43.4 43.4 100 100 -43.5 43.5 100 100 -43.6 43.6 100 100 -43.7 43.7 100 100 -43.8 43.8 100 100 -43.9 43.9 100 100 -44.0 44.0 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100 100 -97.4 97.4 100 100 -97.5 97.5 100 100 -97.6 97.6 100 100 -97.7 97.7 100 100 -97.8 97.8 100 100 -97.9 97.9 100 100 -98.0 98.0 100 100 -98.1 98.1 100 100 -98.2 98.2 100 100 -98.3 98.3 100 100 -98.4 98.4 100 100 -98.5 98.5 100 100 -98.6 98.6 100 100 -98.7 98.7 100 100 -98.8 98.8 100 100 -98.9 98.9 100 100 -99.0 99.0 100 100 -99.1 99.1 100 100 -99.2 99.2 100 100 -99.3 99.3 100 100 -99.4 99.4 100 100 -99.5 99.5 100 100 -99.6 99.6 100 100 -99.7 99.7 100 100 -99.8 99.8 100 100 -99.9 99.9 100 100 -100.0 100.0 100 100 - - -Report Evaluation Test - -Dictionary Iris -Database C:\Applications\src\khiops\LearningTest\datasets\Iris\Iris.txt -Instances 51 -Learning task Classification analysis -Target variable Class - -Predictors performance -Rank Type Family Name Accuracy Compression AUC -R1 Classifier MAP Naive Bayes MAP Naive Bayes 0.901961 0.704425 0.95993 - -Predictors detailed performance - -Rank R1 - -Confusion matrix - Iris-setosa Iris-versicolor Iris-virginica -$Iris-setosa 19 0 0 -$Iris-versicolor 0 12 4 -$Iris-virginica 0 1 15 - -Lift curves Iris-setosa -Size Random Optimal MAP Naive Bayes -0.0 0.0 0 0 -0.1 0.1 0.268421 0.268421 -0.2 0.2 0.536842 0.536842 -0.3 0.3 0.805263 0.805263 -0.4 0.4 1.07368 1.07368 -0.5 0.5 1.34211 1.34211 -0.6 0.6 1.61053 1.61053 -0.7 0.7 1.87895 1.87895 -0.8 0.8 2.14737 2.14737 -0.9 0.9 2.41579 2.41579 -1.0 1.0 2.68421 2.68421 -1.1 1.1 2.95263 2.95263 -1.2 1.2 3.22105 3.22105 -1.3 1.3 3.48947 3.48947 -1.4 1.4 3.75789 3.75789 -1.5 1.5 4.02632 4.02632 -1.6 1.6 4.29474 4.29474 -1.7 1.7 4.56316 4.56316 -1.8 1.8 4.83158 4.83158 -1.9 1.9 5.1 5.1 -2.0 2.0 5.36842 5.36842 -2.1 2.1 5.63684 5.63684 -2.2 2.2 5.90526 5.90526 -2.3 2.3 6.17368 6.17368 -2.4 2.4 6.44211 6.44211 -2.5 2.5 6.71053 6.71053 -2.6 2.6 6.97895 6.97895 -2.7 2.7 7.24737 7.24737 -2.8 2.8 7.51579 7.51579 -2.9 2.9 7.78421 7.78421 -3.0 3.0 8.05263 8.05263 -3.1 3.1 8.32105 8.32105 -3.2 3.2 8.58947 8.58947 -3.3 3.3 8.85789 8.85789 -3.4 3.4 9.12632 9.12632 -3.5 3.5 9.39474 9.39474 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-89.8 89.8 100 100 -89.9 89.9 100 100 -90.0 90.0 100 100 -90.1 90.1 100 100 -90.2 90.2 100 100 -90.3 90.3 100 100 -90.4 90.4 100 100 -90.5 90.5 100 100 -90.6 90.6 100 100 -90.7 90.7 100 100 -90.8 90.8 100 100 -90.9 90.9 100 100 -91.0 91.0 100 100 -91.1 91.1 100 100 -91.2 91.2 100 100 -91.3 91.3 100 100 -91.4 91.4 100 100 -91.5 91.5 100 100 -91.6 91.6 100 100 -91.7 91.7 100 100 -91.8 91.8 100 100 -91.9 91.9 100 100 -92.0 92.0 100 100 -92.1 92.1 100 100 -92.2 92.2 100 100 -92.3 92.3 100 100 -92.4 92.4 100 100 -92.5 92.5 100 100 -92.6 92.6 100 100 -92.7 92.7 100 100 -92.8 92.8 100 100 -92.9 92.9 100 100 -93.0 93.0 100 100 -93.1 93.1 100 100 -93.2 93.2 100 100 -93.3 93.3 100 100 -93.4 93.4 100 100 -93.5 93.5 100 100 -93.6 93.6 100 100 -93.7 93.7 100 100 -93.8 93.8 100 100 -93.9 93.9 100 100 -94.0 94.0 100 100 -94.1 94.1 100 100 -94.2 94.2 100 100 -94.3 94.3 100 100 -94.4 94.4 100 100 -94.5 94.5 100 100 -94.6 94.6 100 100 -94.7 94.7 100 100 -94.8 94.8 100 100 -94.9 94.9 100 100 -95.0 95.0 100 100 -95.1 95.1 100 100 -95.2 95.2 100 100 -95.3 95.3 100 100 -95.4 95.4 100 100 -95.5 95.5 100 100 -95.6 95.6 100 100 -95.7 95.7 100 100 -95.8 95.8 100 100 -95.9 95.9 100 100 -96.0 96.0 100 100 -96.1 96.1 100 100 -96.2 96.2 100 100 -96.3 96.3 100 100 -96.4 96.4 100 100 -96.5 96.5 100 100 -96.6 96.6 100 100 -96.7 96.7 100 100 -96.8 96.8 100 100 -96.9 96.9 100 100 -97.0 97.0 100 100 -97.1 97.1 100 100 -97.2 97.2 100 100 -97.3 97.3 100 100 -97.4 97.4 100 100 -97.5 97.5 100 100 -97.6 97.6 100 100 -97.7 97.7 100 100 -97.8 97.8 100 100 -97.9 97.9 100 100 -98.0 98.0 100 100 -98.1 98.1 100 100 -98.2 98.2 100 100 -98.3 98.3 100 100 -98.4 98.4 100 100 -98.5 98.5 100 100 -98.6 98.6 100 100 -98.7 98.7 100 100 -98.8 98.8 100 100 -98.9 98.9 100 100 -99.0 99.0 100 100 -99.1 99.1 100 100 -99.2 99.2 100 100 -99.3 99.3 100 100 -99.4 99.4 100 100 -99.5 99.5 100 100 -99.6 99.6 100 100 -99.7 99.7 100 100 -99.8 99.8 100 100 -99.9 99.9 100 100 -100.0 100.0 100 100 diff --git a/tests/resources/coclustering_results/ref_reports/Adult.khc b/tests/resources/coclustering_results/ref_reports/Adult.khc index 4c2f0007..8c4726bb 100644 --- a/tests/resources/coclustering_results/ref_reports/Adult.khc +++ b/tests/resources/coclustering_results/ref_reports/Adult.khc @@ -1,2350 +1,2350 @@ -#Khiops 10.0.0.3i -Short description -Dimensions 6 -Name Type Parts Initial parts Values Interest Description -age Numerical 6 6 48842 1 -occupation Categorical 7 7 14 1 -education_num Numerical 4 4 48842 1 -hours_per_week Numerical 3 3 48842 1 -marital_status Categorical 3 3 7 1 -sex Categorical 2 2 2 1 - -Coclustering stats -Instances 48842 -Cells 2218 -Null cost 1723780 -Cost 1689150 -Level 0.02008957059 -Initial dimensions 9 -Frequency variable -Dictionary Adult -Database ../../../datasets/Adult/Adult.txt -Sample percentage 100 -Sampling mode Include sample -Selection variable -Selection value - -Bounds -Name Min Max -age 17 90 -education_num 1 16 -hours_per_week 1 99 - -Hierarchy age -Cluster ParentCluster Frequency Interest HierarchicalLevel Rank HierarchicalRank -]-inf;18.5] ]-inf;23.5] 1457 0.347768 1 1 25 -]18.5;23.5] ]-inf;23.5] 5769 0.883906 1 3 25 -]23.5;29.5] ]-inf;29.5] 7289 1 1 5 25 -]29.5;40.5] ]29.5;59.5] 14116 0.871164 1 7 25 -]40.5;59.5] ]29.5;59.5] 16156 0.848966 1 9 25 -]59.5;+inf[ ]29.5;+inf[ 4055 0.441664 1 11 25 -]-inf;23.5] ]-inf;29.5] 7226 0.775803 0.974251 2 22 -]-inf;29.5] ]-inf;+inf[ 14515 0.888388 0.745479 4 14 -]-inf;+inf[ 48842 0.833282 -0.000287731 6 7 -]29.5;59.5] ]29.5;+inf[ 30272 0.859317 0.948553 8 20 -]29.5;+inf[ ]-inf;+inf[ 34327 0.80998 0.850791 10 16 - -Hierarchy occupation -Cluster ParentCluster Frequency Interest HierarchicalLevel Rank HierarchicalRank -{Craft-repair, Transport-moving, Farming-fishing, ...} B18 10955 1 1 1 25 -{Machine-op-inspct, Handlers-cleaners} B18 5094 0.695825 1 3 25 -{Prof-specialty} B12 8981 0.864201 1 5 25 -{Exec-managerial} B19 6086 0.705213 1 7 25 -{Sales} B19 5504 0.534419 1 9 25 -{Other-service, Priv-house-serv} B14 5165 0.723836 1 11 25 -{Adm-clerical, Tech-support} B14 7057 0.715347 1 13 25 -B18 B5 16049 0.903454 0.988438 2 23 -B5 48842 0.783774 0.383406 4 10 -B12 B7 20571 0.728927 0.889146 6 17 -B19 B12 11590 0.624104 0.994704 8 24 -B7 B5 32793 0.725202 0.583172 10 12 -B14 B7 12222 0.718934 0.93014 12 19 - -Hierarchy education_num -Cluster ParentCluster Frequency Interest HierarchicalLevel Rank HierarchicalRank -]-inf;8.5] ]-inf;9.5] 6408 0.330495 1 1 25 -]8.5;9.5] ]-inf;9.5] 15784 0.671555 1 3 25 -]9.5;12.5] ]-inf;12.5] 14540 1 1 5 25 -]12.5;+inf[ ]-inf;+inf[ 12110 0.65894 1 7 25 -]-inf;9.5] ]-inf;12.5] 22192 0.573073 0.964171 2 21 -]-inf;12.5] ]-inf;+inf[ 36732 0.742068 0.911382 4 18 -]-inf;+inf[ 48842 0.721457 0.474326 6 11 - -Hierarchy hours_per_week -Cluster ParentCluster Frequency Interest HierarchicalLevel Rank HierarchicalRank -]-inf;36.5] ]-inf;+inf[ 10668 0.604579 1 1 25 -]36.5;43.5] ]36.5;+inf[ 24446 1 1 3 25 -]43.5;+inf[ ]36.5;+inf[ 13728 0.395421 1 5 25 -]-inf;+inf[ 48842 0.743704 0.664957 2 13 -]36.5;+inf[ ]-inf;+inf[ 38174 0.782583 0.808675 4 15 - -Hierarchy marital_status -Cluster ParentCluster Frequency Interest HierarchicalLevel Rank HierarchicalRank -{Never-married, Married-AF-spouse} E1 16154 1 1 1 25 -{Married-civ-spouse} E4 22379 0.936208 1 3 25 -{Divorced, Widowed, Separated, ...} E4 10309 0.730455 1 5 25 -E1 48842 0.913879 0 2 6 -E4 E1 32688 0.871319 0.248722 4 9 - -Hierarchy sex -Cluster ParentCluster Frequency Interest HierarchicalLevel Rank HierarchicalRank -{Female} F3 16192 1 1 1 25 -{Male} F3 32650 1 1 3 25 -F3 48842 1 0.227766 2 8 - -Composition occupation -Cluster Value Frequency Typicality -{Craft-repair, Transport-moving, Farming-fishing, ...} Craft-repair 6112 1 -{Craft-repair, Transport-moving, Farming-fishing, ...} Transport-moving 2355 0.573309 -{Craft-repair, Transport-moving, Farming-fishing, ...} Farming-fishing 1490 0.29446 -{Craft-repair, Transport-moving, Farming-fishing, ...} Protective-serv 983 0.106369 -{Craft-repair, Transport-moving, Farming-fishing, ...} Armed-Forces 15 0.0033027 -{Machine-op-inspct, Handlers-cleaners} Machine-op-inspct 3022 1 -{Machine-op-inspct, Handlers-cleaners} Handlers-cleaners 2072 0.798568 -{Prof-specialty} Prof-specialty 8981 1 -{Exec-managerial} Exec-managerial 6086 1 -{Sales} Sales 5504 1 -{Other-service, Priv-house-serv} Other-service 4923 1 -{Other-service, Priv-house-serv} Priv-house-serv 242 0.111602 -{Adm-clerical, Tech-support} Adm-clerical 5611 1 -{Adm-clerical, Tech-support} Tech-support 1446 0.187174 -{Adm-clerical, Tech-support} * 0 0 - -Composition marital_status -Cluster Value Frequency Typicality -{Never-married, Married-AF-spouse} Never-married 16117 1 -{Never-married, Married-AF-spouse} Married-AF-spouse 37 0.00173382 -{Married-civ-spouse} Married-civ-spouse 22379 1 -{Divorced, Widowed, Separated, ...} Divorced 6633 1 -{Divorced, Widowed, Separated, ...} Widowed 1518 0.443082 -{Divorced, Widowed, Separated, ...} Separated 1530 0.265394 -{Divorced, Widowed, Separated, ...} Married-spouse-absent 628 0.0718638 -{Divorced, Widowed, Separated, ...} * 0 0 - -Composition sex -Cluster Value Frequency Typicality -{Female} Female 16192 1 -{Male} Male 32650 1 -{Male} * 0 0 - -Cells -age occupation education_num hours_per_week marital_status sex Frequency -]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Male} 741 -]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Male} 592 -]40.5;59.5] {Prof-specialty} ]12.5;+inf[ ]43.5;+inf[ {Married-civ-spouse} {Male} 569 -]40.5;59.5] {Exec-managerial} ]12.5;+inf[ ]43.5;+inf[ {Married-civ-spouse} {Male} 557 -]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 480 -]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Male} 457 -]40.5;59.5] {Prof-specialty} ]12.5;+inf[ ]36.5;43.5] {Married-civ-spouse} {Male} 447 -]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 437 -]29.5;40.5] {Prof-specialty} ]12.5;+inf[ ]43.5;+inf[ {Married-civ-spouse} {Male} 408 -]40.5;59.5] {Exec-managerial} ]12.5;+inf[ ]36.5;43.5] {Married-civ-spouse} {Male} 360 -]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Male} 359 -]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 358 -]29.5;40.5] {Exec-managerial} ]12.5;+inf[ ]43.5;+inf[ {Married-civ-spouse} {Male} 353 -]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]36.5;43.5] {Married-civ-spouse} {Male} 298 -]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 295 -]29.5;40.5] {Prof-specialty} ]12.5;+inf[ ]36.5;43.5] {Married-civ-spouse} {Male} 294 -]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Male} 280 -]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Male} 275 -]40.5;59.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 273 -]40.5;59.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 260 -]18.5;23.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 239 -]40.5;59.5] {Exec-managerial} ]9.5;12.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 233 -]18.5;23.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 232 -]40.5;59.5] {Sales} ]12.5;+inf[ ]43.5;+inf[ {Married-civ-spouse} {Male} 225 -]18.5;23.5] {Prof-specialty} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 216 -]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]36.5;43.5] {Married-civ-spouse} {Male} 207 -]23.5;29.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Male} 204 -]18.5;23.5] {Sales} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 197 -]40.5;59.5] {Sales} ]9.5;12.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 196 -]23.5;29.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 189 -]29.5;40.5] {Sales} ]12.5;+inf[ ]43.5;+inf[ {Married-civ-spouse} {Male} 184 -]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 184 -]29.5;40.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 181 -]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 179 -]40.5;59.5] {Exec-managerial} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Male} 179 -]40.5;59.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Male} 176 -]23.5;29.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 174 -]29.5;40.5] {Exec-managerial} ]12.5;+inf[ ]36.5;43.5] {Married-civ-spouse} {Male} 174 -]18.5;23.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 165 -]29.5;40.5] {Exec-managerial} ]9.5;12.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 164 -]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]36.5;43.5] {Married-civ-spouse} {Male} 164 -]-inf;18.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 162 -]40.5;59.5] {Sales} ]8.5;9.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 160 -]18.5;23.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 158 -]-inf;18.5] {Sales} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 151 -]23.5;29.5] {Prof-specialty} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 150 -]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 150 -]40.5;59.5] {Prof-specialty} ]12.5;+inf[ ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 150 -]29.5;40.5] {Sales} ]9.5;12.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 146 -]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 145 -]18.5;23.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 144 -]29.5;40.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 143 -]18.5;23.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 142 -]18.5;23.5] {Prof-specialty} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 141 -]40.5;59.5] {Exec-managerial} ]8.5;9.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 141 -]29.5;40.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Male} 140 -]-inf;18.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 138 -]59.5;+inf[ {Prof-specialty} ]12.5;+inf[ ]-inf;36.5] {Married-civ-spouse} {Male} 137 -]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]12.5;+inf[ ]43.5;+inf[ {Married-civ-spouse} {Male} 136 -]40.5;59.5] {Sales} ]12.5;+inf[ ]36.5;43.5] {Married-civ-spouse} {Male} 136 -]29.5;40.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 135 -]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Male} 128 -]40.5;59.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Male} 128 -]40.5;59.5] {Adm-clerical, Tech-support} ]12.5;+inf[ ]36.5;43.5] {Married-civ-spouse} {Male} 127 -]23.5;29.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 126 -]23.5;29.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 125 -]40.5;59.5] {Prof-specialty} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Male} 125 -]29.5;40.5] {Exec-managerial} ]8.5;9.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 123 -]29.5;40.5] {Prof-specialty} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 121 -]29.5;40.5] {Sales} ]8.5;9.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 119 -]18.5;23.5] {Prof-specialty} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 118 -]40.5;59.5] {Exec-managerial} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Male} 118 -]40.5;59.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 117 -]40.5;59.5] {Sales} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Male} 116 -]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 115 -]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 114 -]23.5;29.5] {Prof-specialty} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 111 -]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]36.5;43.5] {Married-civ-spouse} {Male} 111 -]23.5;29.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 110 -]59.5;+inf[ {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]36.5;43.5] {Married-civ-spouse} {Male} 110 -]59.5;+inf[ {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Male} 110 -]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 109 -]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 109 -]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 109 -]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]12.5;+inf[ ]36.5;43.5] {Married-civ-spouse} {Male} 107 -]18.5;23.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 106 -]23.5;29.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Male} 106 -]29.5;40.5] {Prof-specialty} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 106 -]29.5;40.5] {Prof-specialty} ]12.5;+inf[ ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 106 -]23.5;29.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Male} 105 -]18.5;23.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 104 -]23.5;29.5] {Adm-clerical, Tech-support} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 104 -]18.5;23.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 103 -]23.5;29.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 102 -]23.5;29.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 102 -]23.5;29.5] {Prof-specialty} ]12.5;+inf[ ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 102 -]40.5;59.5] {Prof-specialty} ]12.5;+inf[ ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 101 -]40.5;59.5] {Sales} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Male} 101 -]40.5;59.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 100 -]29.5;40.5] {Sales} ]12.5;+inf[ ]36.5;43.5] {Married-civ-spouse} {Male} 98 -]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 98 -]40.5;59.5] {Prof-specialty} ]12.5;+inf[ ]-inf;36.5] {Married-civ-spouse} {Male} 98 -]18.5;23.5] {Prof-specialty} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 97 -]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 97 -]29.5;40.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 94 -]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 94 -]59.5;+inf[ {Prof-specialty} ]12.5;+inf[ ]43.5;+inf[ {Married-civ-spouse} {Male} 93 -]-inf;18.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 92 -]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Male} 92 -]59.5;+inf[ {Prof-specialty} ]12.5;+inf[ ]36.5;43.5] {Married-civ-spouse} {Male} 92 -]18.5;23.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 90 -]29.5;40.5] {Adm-clerical, Tech-support} ]12.5;+inf[ ]36.5;43.5] {Married-civ-spouse} {Male} 90 -]29.5;40.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Male} 89 -]29.5;40.5] {Exec-managerial} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Male} 88 -]18.5;23.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 87 -]18.5;23.5] {Sales} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 87 -]40.5;59.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Male} 87 -]59.5;+inf[ {Prof-specialty} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Male} 87 -]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]12.5;+inf[ ]43.5;+inf[ {Married-civ-spouse} {Male} 86 -]40.5;59.5] {Prof-specialty} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 86 -]29.5;40.5] {Exec-managerial} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Male} 83 -]29.5;40.5] {Prof-specialty} ]12.5;+inf[ ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 82 -]40.5;59.5] {Exec-managerial} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 80 -]40.5;59.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 80 -]40.5;59.5] {Exec-managerial} ]12.5;+inf[ ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 79 -]59.5;+inf[ {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Male} 79 -]23.5;29.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 78 -]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 78 -]18.5;23.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 77 -]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 77 -]29.5;40.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 77 -]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 76 -]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]12.5;+inf[ ]36.5;43.5] {Married-civ-spouse} {Male} 75 -]29.5;40.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 74 -]29.5;40.5] {Prof-specialty} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Male} 74 -]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Male} 74 -]18.5;23.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 73 -]18.5;23.5] {Sales} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 73 -]29.5;40.5] {Sales} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Male} 73 -]40.5;59.5] {Prof-specialty} ]9.5;12.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 73 -]18.5;23.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 72 -]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 72 -]40.5;59.5] {Prof-specialty} ]12.5;+inf[ ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 72 -]40.5;59.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Female} 71 -]40.5;59.5] {Prof-specialty} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Male} 71 -]23.5;29.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 70 -]29.5;40.5] {Exec-managerial} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 70 -]-inf;18.5] {Prof-specialty} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 69 -]-inf;18.5] {Prof-specialty} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 69 -]23.5;29.5] {Prof-specialty} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 69 -]40.5;59.5] {Prof-specialty} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 69 -]23.5;29.5] {Prof-specialty} ]12.5;+inf[ ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 68 -]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Male} 68 -]29.5;40.5] {Prof-specialty} ]12.5;+inf[ ]-inf;36.5] {Married-civ-spouse} {Female} 68 -]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 68 -]40.5;59.5] {Exec-managerial} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 68 -]18.5;23.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 67 -]18.5;23.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 67 -]40.5;59.5] {Exec-managerial} ]12.5;+inf[ ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 67 -]23.5;29.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]36.5;43.5] {Married-civ-spouse} {Male} 66 -]23.5;29.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 66 -]23.5;29.5] {Adm-clerical, Tech-support} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 65 -]29.5;40.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Male} 65 -]18.5;23.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 64 -]23.5;29.5] {Exec-managerial} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 64 -]29.5;40.5] {Prof-specialty} ]12.5;+inf[ ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 64 -]59.5;+inf[ {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 64 -]23.5;29.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 63 -]40.5;59.5] {Prof-specialty} ]12.5;+inf[ ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 63 -]59.5;+inf[ {Prof-specialty} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 63 -]29.5;40.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 62 -]40.5;59.5] {Exec-managerial} ]12.5;+inf[ ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 62 -]40.5;59.5] {Sales} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 62 -]18.5;23.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 61 -]23.5;29.5] {Prof-specialty} ]12.5;+inf[ ]36.5;43.5] {Married-civ-spouse} {Male} 61 -]29.5;40.5] {Adm-clerical, Tech-support} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 61 -]29.5;40.5] {Sales} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Male} 61 -]40.5;59.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 61 -]40.5;59.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 61 -]59.5;+inf[ {Adm-clerical, Tech-support} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 61 -]59.5;+inf[ {Other-service, Priv-house-serv} ]-inf;8.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 61 -]59.5;+inf[ {Prof-specialty} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Male} 61 -]29.5;40.5] {Exec-managerial} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 60 -]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 60 -]40.5;59.5] {Prof-specialty} ]12.5;+inf[ ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 60 -]40.5;59.5] {Prof-specialty} ]12.5;+inf[ ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 60 -]59.5;+inf[ {Other-service, Priv-house-serv} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 60 -]59.5;+inf[ {Prof-specialty} ]-inf;8.5] ]-inf;36.5] {Married-civ-spouse} {Male} 60 -]59.5;+inf[ {Prof-specialty} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Male} 60 -]18.5;23.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 59 -]29.5;40.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 59 -]29.5;40.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 59 -]40.5;59.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 59 -]40.5;59.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 59 -]40.5;59.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]36.5;43.5] {Married-civ-spouse} {Male} 59 -]59.5;+inf[ {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]-inf;36.5] {Married-civ-spouse} {Male} 59 -]18.5;23.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 58 -]23.5;29.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 58 -]23.5;29.5] {Prof-specialty} ]12.5;+inf[ ]43.5;+inf[ {Married-civ-spouse} {Male} 58 -]29.5;40.5] {Exec-managerial} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 58 -]29.5;40.5] {Exec-managerial} ]12.5;+inf[ ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 58 -]40.5;59.5] {Prof-specialty} ]12.5;+inf[ ]36.5;43.5] {Married-civ-spouse} {Female} 58 -]23.5;29.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 57 -]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 57 -]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 57 -]59.5;+inf[ {Exec-managerial} ]12.5;+inf[ ]43.5;+inf[ {Married-civ-spouse} {Male} 57 -]23.5;29.5] {Sales} ]12.5;+inf[ ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 56 -]29.5;40.5] {Exec-managerial} ]12.5;+inf[ ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 56 -]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 56 -]40.5;59.5] {Exec-managerial} ]9.5;12.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 56 -]40.5;59.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 56 -]40.5;59.5] {Prof-specialty} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 56 -]59.5;+inf[ {Exec-managerial} ]12.5;+inf[ ]36.5;43.5] {Married-civ-spouse} {Male} 56 -]18.5;23.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 55 -]23.5;29.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 55 -]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 55 -]29.5;40.5] {Prof-specialty} ]12.5;+inf[ ]36.5;43.5] {Married-civ-spouse} {Female} 55 -]40.5;59.5] {Adm-clerical, Tech-support} ]12.5;+inf[ ]43.5;+inf[ {Married-civ-spouse} {Male} 55 -]23.5;29.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 54 -]29.5;40.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 54 -]29.5;40.5] {Adm-clerical, Tech-support} ]12.5;+inf[ ]43.5;+inf[ {Married-civ-spouse} {Male} 54 -]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 54 -]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 54 -]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 54 -]23.5;29.5] {Exec-managerial} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 53 -]23.5;29.5] {Prof-specialty} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 53 -]29.5;40.5] {Prof-specialty} ]9.5;12.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 53 -]40.5;59.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Female} 53 -]40.5;59.5] {Adm-clerical, Tech-support} ]12.5;+inf[ ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 53 -]59.5;+inf[ {Adm-clerical, Tech-support} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 53 -]-inf;18.5] {Sales} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 52 -]23.5;29.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]36.5;43.5] {Married-civ-spouse} {Male} 52 -]29.5;40.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Female} 52 -]18.5;23.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 51 -]18.5;23.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 51 -]23.5;29.5] {Sales} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 51 -]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 51 -]59.5;+inf[ {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 51 -]23.5;29.5] {Exec-managerial} ]12.5;+inf[ ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 50 -]23.5;29.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 50 -]29.5;40.5] {Prof-specialty} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 50 -]40.5;59.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 50 -]40.5;59.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 50 -]40.5;59.5] {Prof-specialty} ]12.5;+inf[ ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 50 -]23.5;29.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Male} 49 -]23.5;29.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 49 -]23.5;29.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Male} 49 -]29.5;40.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Female} 49 -]40.5;59.5] {Prof-specialty} ]12.5;+inf[ ]-inf;36.5] {Married-civ-spouse} {Female} 49 -]18.5;23.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 48 -]23.5;29.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 48 -]29.5;40.5] {Sales} ]12.5;+inf[ ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 48 -]40.5;59.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 48 -]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Male} 48 -]40.5;59.5] {Exec-managerial} ]12.5;+inf[ ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 48 -]40.5;59.5] {Prof-specialty} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 48 -]29.5;40.5] {Adm-clerical, Tech-support} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 47 -]29.5;40.5] {Prof-specialty} ]12.5;+inf[ ]43.5;+inf[ {Married-civ-spouse} {Female} 47 -]40.5;59.5] {Prof-specialty} ]12.5;+inf[ ]43.5;+inf[ {Married-civ-spouse} {Female} 47 -]18.5;23.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 46 -]23.5;29.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 46 -]29.5;40.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 46 -]29.5;40.5] {Prof-specialty} ]12.5;+inf[ ]-inf;36.5] {Married-civ-spouse} {Male} 46 -]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 46 -]23.5;29.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 45 -]40.5;59.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 45 -]18.5;23.5] {Sales} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 44 -]23.5;29.5] {Sales} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 44 -]29.5;40.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 44 -]40.5;59.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 44 -]-inf;18.5] {Adm-clerical, Tech-support} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 43 -]18.5;23.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 43 -]23.5;29.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 43 -]23.5;29.5] {Exec-managerial} ]12.5;+inf[ ]43.5;+inf[ {Married-civ-spouse} {Male} 43 -]23.5;29.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 43 -]29.5;40.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 43 -]29.5;40.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 43 -]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 43 -]29.5;40.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 43 -]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 43 -]-inf;18.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 42 -]29.5;40.5] {Prof-specialty} ]12.5;+inf[ ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 42 -]29.5;40.5] {Sales} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 42 -]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]-inf;36.5] {Married-civ-spouse} {Male} 42 -]18.5;23.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 41 -]18.5;23.5] {Sales} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 41 -]23.5;29.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 41 -]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 41 -]40.5;59.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 41 -]40.5;59.5] {Sales} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 41 -]59.5;+inf[ {Prof-specialty} ]12.5;+inf[ ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 41 -]18.5;23.5] {Sales} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 40 -]23.5;29.5] {Sales} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 40 -]23.5;29.5] {Sales} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 40 -]23.5;29.5] {Sales} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 40 -]40.5;59.5] {Sales} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 40 -]40.5;59.5] {Sales} ]9.5;12.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 40 -]40.5;59.5] {Sales} ]12.5;+inf[ ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 40 -]59.5;+inf[ {Adm-clerical, Tech-support} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 40 -]18.5;23.5] {Prof-specialty} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 39 -]23.5;29.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Female} 39 -]29.5;40.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Male} 39 -]40.5;59.5] {Exec-managerial} ]9.5;12.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 39 -]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 39 -]59.5;+inf[ {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Male} 39 -]18.5;23.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 38 -]18.5;23.5] {Prof-specialty} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 38 -]29.5;40.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 38 -]29.5;40.5] {Prof-specialty} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Male} 38 -]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 38 -]59.5;+inf[ {Prof-specialty} ]-inf;8.5] ]36.5;43.5] {Married-civ-spouse} {Male} 38 -]59.5;+inf[ {Prof-specialty} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 38 -]18.5;23.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 37 -]23.5;29.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 37 -]23.5;29.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 37 -]23.5;29.5] {Sales} ]12.5;+inf[ ]43.5;+inf[ {Married-civ-spouse} {Male} 37 -]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 37 -]29.5;40.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 37 -]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 37 -]40.5;59.5] {Prof-specialty} ]8.5;9.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 37 -]40.5;59.5] {Prof-specialty} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 37 -]18.5;23.5] {Adm-clerical, Tech-support} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 36 -]18.5;23.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Male} 36 -]18.5;23.5] {Prof-specialty} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 36 -]18.5;23.5] {Prof-specialty} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 36 -]23.5;29.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 36 -]23.5;29.5] {Exec-managerial} ]12.5;+inf[ ]36.5;43.5] {Married-civ-spouse} {Male} 36 -]23.5;29.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 36 -]23.5;29.5] {Prof-specialty} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 36 -]29.5;40.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]36.5;43.5] {Married-civ-spouse} {Male} 36 -]59.5;+inf[ {Exec-managerial} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Male} 36 -]18.5;23.5] {Prof-specialty} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 35 -]23.5;29.5] {Prof-specialty} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 35 -]29.5;40.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 35 -]29.5;40.5] {Exec-managerial} ]12.5;+inf[ ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 35 -]29.5;40.5] {Prof-specialty} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 35 -]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]12.5;+inf[ ]36.5;43.5] {Married-civ-spouse} {Male} 35 -]18.5;23.5] {Sales} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 34 -]23.5;29.5] {Exec-managerial} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 34 -]23.5;29.5] {Exec-managerial} ]12.5;+inf[ ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 34 -]23.5;29.5] {Sales} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 34 -]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 34 -]29.5;40.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 34 -]29.5;40.5] {Prof-specialty} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 34 -]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 34 -]59.5;+inf[ {Adm-clerical, Tech-support} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 34 -]59.5;+inf[ {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]36.5;43.5] {Married-civ-spouse} {Male} 34 -]59.5;+inf[ {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Male} 34 -]18.5;23.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 33 -]23.5;29.5] {Adm-clerical, Tech-support} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 33 -]23.5;29.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 33 -]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 33 -]29.5;40.5] {Exec-managerial} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 33 -]29.5;40.5] {Sales} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 33 -]40.5;59.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 33 -]40.5;59.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 33 -]40.5;59.5] {Exec-managerial} ]8.5;9.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 33 -]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 33 -]18.5;23.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 32 -]18.5;23.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 32 -]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]-inf;36.5] {Married-civ-spouse} {Male} 32 -]29.5;40.5] {Prof-specialty} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 32 -]40.5;59.5] {Adm-clerical, Tech-support} ]12.5;+inf[ ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 32 -]40.5;59.5] {Exec-managerial} ]12.5;+inf[ ]-inf;36.5] {Married-civ-spouse} {Male} 32 -]40.5;59.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 32 -]40.5;59.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Male} 32 -]59.5;+inf[ {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Male} 32 -]-inf;18.5] {Sales} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 31 -]23.5;29.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 31 -]23.5;29.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 31 -]23.5;29.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 31 -]23.5;29.5] {Sales} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 31 -]29.5;40.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Female} 31 -]29.5;40.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 31 -]29.5;40.5] {Exec-managerial} ]12.5;+inf[ ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 31 -]29.5;40.5] {Exec-managerial} ]12.5;+inf[ ]36.5;43.5] {Married-civ-spouse} {Female} 31 -]29.5;40.5] {Prof-specialty} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 31 -]29.5;40.5] {Sales} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 31 -]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 31 -]40.5;59.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 31 -]18.5;23.5] {Prof-specialty} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 30 -]23.5;29.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Male} 30 -]23.5;29.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 30 -]23.5;29.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 30 -]23.5;29.5] {Exec-managerial} ]9.5;12.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 30 -]23.5;29.5] {Sales} ]8.5;9.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 30 -]29.5;40.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 30 -]29.5;40.5] {Exec-managerial} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 30 -]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 30 -]29.5;40.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 30 -]29.5;40.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 30 -]29.5;40.5] {Prof-specialty} ]12.5;+inf[ ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 30 -]29.5;40.5] {Sales} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 30 -]40.5;59.5] {Exec-managerial} ]-inf;8.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 30 -]40.5;59.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Female} 30 -]59.5;+inf[ {Exec-managerial} ]12.5;+inf[ ]-inf;36.5] {Married-civ-spouse} {Male} 30 -]59.5;+inf[ {Other-service, Priv-house-serv} ]-inf;8.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 30 -]59.5;+inf[ {Sales} ]8.5;9.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 30 -]59.5;+inf[ {Sales} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Male} 30 -]-inf;18.5] {Prof-specialty} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 29 -]18.5;23.5] {Prof-specialty} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 29 -]23.5;29.5] {Sales} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Male} 29 -]23.5;29.5] {Sales} ]9.5;12.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 29 -]29.5;40.5] {Exec-managerial} ]9.5;12.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 29 -]29.5;40.5] {Prof-specialty} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Female} 29 -]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 29 -]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 29 -]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 29 -]40.5;59.5] {Prof-specialty} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Male} 29 -]40.5;59.5] {Sales} ]9.5;12.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 29 -]59.5;+inf[ {Exec-managerial} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Male} 29 -]59.5;+inf[ {Prof-specialty} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Male} 29 -]59.5;+inf[ {Sales} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 29 -]59.5;+inf[ {Sales} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Male} 29 -]59.5;+inf[ {Sales} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Male} 29 -]59.5;+inf[ {Sales} ]12.5;+inf[ ]-inf;36.5] {Married-civ-spouse} {Male} 29 -]-inf;18.5] {Sales} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 28 -]18.5;23.5] {Adm-clerical, Tech-support} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 28 -]18.5;23.5] {Sales} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 28 -]23.5;29.5] {Adm-clerical, Tech-support} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 28 -]29.5;40.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 28 -]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 28 -]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 28 -]29.5;40.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 28 -]29.5;40.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 28 -]40.5;59.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Female} 28 -]40.5;59.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 28 -]59.5;+inf[ {Prof-specialty} ]-inf;8.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 28 -]23.5;29.5] {Exec-managerial} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 27 -]29.5;40.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Female} 27 -]29.5;40.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 27 -]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 27 -]40.5;59.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Female} 27 -]40.5;59.5] {Prof-specialty} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Female} 27 -]59.5;+inf[ {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 27 -]59.5;+inf[ {Sales} ]12.5;+inf[ ]36.5;43.5] {Married-civ-spouse} {Male} 27 -]18.5;23.5] {Prof-specialty} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 26 -]23.5;29.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 26 -]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 26 -]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Male} 26 -]29.5;40.5] {Exec-managerial} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Female} 26 -]29.5;40.5] {Exec-managerial} ]12.5;+inf[ ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 26 -]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 26 -]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 26 -]29.5;40.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 26 -]29.5;40.5] {Prof-specialty} ]8.5;9.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 26 -]29.5;40.5] {Sales} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 26 -]29.5;40.5] {Sales} ]9.5;12.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 26 -]40.5;59.5] {Exec-managerial} ]12.5;+inf[ ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 26 -]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Female} 26 -]59.5;+inf[ {Exec-managerial} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Male} 26 -]59.5;+inf[ {Other-service, Priv-house-serv} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Male} 26 -]59.5;+inf[ {Prof-specialty} ]12.5;+inf[ ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 26 -]-inf;18.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 25 -]18.5;23.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 25 -]18.5;23.5] {Exec-managerial} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 25 -]23.5;29.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 25 -]23.5;29.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 25 -]23.5;29.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 25 -]23.5;29.5] {Sales} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Male} 25 -]23.5;29.5] {Sales} ]12.5;+inf[ ]36.5;43.5] {Married-civ-spouse} {Male} 25 -]29.5;40.5] {Prof-specialty} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Female} 25 -]40.5;59.5] {Exec-managerial} ]8.5;9.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 25 -]40.5;59.5] {Exec-managerial} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 25 -]40.5;59.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 25 -]40.5;59.5] {Prof-specialty} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Male} 25 -]40.5;59.5] {Sales} ]-inf;8.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 25 -]59.5;+inf[ {Adm-clerical, Tech-support} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Male} 25 -]59.5;+inf[ {Sales} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Male} 25 -]59.5;+inf[ {Sales} ]12.5;+inf[ ]43.5;+inf[ {Married-civ-spouse} {Male} 25 -]18.5;23.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 24 -]18.5;23.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Male} 24 -]18.5;23.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 24 -]23.5;29.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Female} 24 -]23.5;29.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 24 -]23.5;29.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 24 -]23.5;29.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 24 -]23.5;29.5] {Prof-specialty} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 24 -]23.5;29.5] {Sales} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 24 -]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 24 -]29.5;40.5] {Exec-managerial} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 24 -]29.5;40.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 24 -]29.5;40.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 24 -]29.5;40.5] {Prof-specialty} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 24 -]40.5;59.5] {Adm-clerical, Tech-support} ]-inf;8.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 24 -]40.5;59.5] {Adm-clerical, Tech-support} ]12.5;+inf[ ]36.5;43.5] {Married-civ-spouse} {Female} 24 -]40.5;59.5] {Exec-managerial} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 24 -]40.5;59.5] {Sales} ]8.5;9.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 24 -]59.5;+inf[ {Exec-managerial} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Male} 24 -]59.5;+inf[ {Other-service, Priv-house-serv} ]-inf;8.5] ]-inf;36.5] {Married-civ-spouse} {Male} 24 -]59.5;+inf[ {Other-service, Priv-house-serv} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Male} 24 -]18.5;23.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 23 -]23.5;29.5] {Adm-clerical, Tech-support} ]12.5;+inf[ ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 23 -]29.5;40.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 23 -]29.5;40.5] {Adm-clerical, Tech-support} ]12.5;+inf[ ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 23 -]29.5;40.5] {Exec-managerial} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 23 -]29.5;40.5] {Exec-managerial} ]9.5;12.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 23 -]29.5;40.5] {Prof-specialty} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 23 -]29.5;40.5] {Prof-specialty} ]12.5;+inf[ ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 23 -]29.5;40.5] {Prof-specialty} ]12.5;+inf[ ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 23 -]29.5;40.5] {Sales} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 23 -]29.5;40.5] {Sales} ]8.5;9.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 23 -]40.5;59.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 23 -]40.5;59.5] {Prof-specialty} ]12.5;+inf[ ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 23 -]40.5;59.5] {Sales} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Female} 23 -]40.5;59.5] {Sales} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 23 -]18.5;23.5] {Exec-managerial} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 22 -]18.5;23.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]36.5;43.5] {Married-civ-spouse} {Male} 22 -]23.5;29.5] {Exec-managerial} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 22 -]23.5;29.5] {Exec-managerial} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 22 -]23.5;29.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 22 -]23.5;29.5] {Prof-specialty} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 22 -]23.5;29.5] {Prof-specialty} ]12.5;+inf[ ]36.5;43.5] {Married-civ-spouse} {Female} 22 -]29.5;40.5] {Adm-clerical, Tech-support} ]12.5;+inf[ ]36.5;43.5] {Married-civ-spouse} {Female} 22 -]29.5;40.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Female} 22 -]29.5;40.5] {Prof-specialty} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 22 -]40.5;59.5] {Adm-clerical, Tech-support} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 22 -]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 22 -]40.5;59.5] {Exec-managerial} ]12.5;+inf[ ]36.5;43.5] {Married-civ-spouse} {Female} 22 -]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 22 -]40.5;59.5] {Sales} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Male} 22 -]40.5;59.5] {Sales} ]12.5;+inf[ ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 22 -]59.5;+inf[ {Craft-repair, Transport-moving, Farming-fishing, ...} ]12.5;+inf[ ]-inf;36.5] {Married-civ-spouse} {Male} 22 -]59.5;+inf[ {Exec-managerial} ]8.5;9.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 22 -]-inf;18.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 21 -]-inf;18.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 21 -]-inf;18.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 21 -]-inf;18.5] {Prof-specialty} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 21 -]18.5;23.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Male} 21 -]18.5;23.5] {Exec-managerial} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 21 -]18.5;23.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 21 -]18.5;23.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 21 -]18.5;23.5] {Sales} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 21 -]23.5;29.5] {Adm-clerical, Tech-support} ]12.5;+inf[ ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 21 -]23.5;29.5] {Exec-managerial} ]8.5;9.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 21 -]23.5;29.5] {Exec-managerial} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Male} 21 -]23.5;29.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Male} 21 -]23.5;29.5] {Prof-specialty} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Male} 21 -]23.5;29.5] {Sales} ]12.5;+inf[ ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 21 -]29.5;40.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 21 -]29.5;40.5] {Exec-managerial} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 21 -]29.5;40.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Female} 21 -]29.5;40.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 21 -]29.5;40.5] {Sales} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 21 -]29.5;40.5] {Sales} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 21 -]29.5;40.5] {Sales} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 21 -]40.5;59.5] {Adm-clerical, Tech-support} ]-inf;8.5] ]36.5;43.5] {Married-civ-spouse} {Male} 21 -]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]12.5;+inf[ ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 21 -]40.5;59.5] {Exec-managerial} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 21 -]40.5;59.5] {Exec-managerial} ]12.5;+inf[ ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 21 -]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 21 -]40.5;59.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 21 -]40.5;59.5] {Prof-specialty} ]-inf;8.5] ]36.5;43.5] {Married-civ-spouse} {Male} 21 -]40.5;59.5] {Prof-specialty} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 21 -]40.5;59.5] {Prof-specialty} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 21 -]40.5;59.5] {Prof-specialty} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 21 -]40.5;59.5] {Prof-specialty} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Female} 21 -]40.5;59.5] {Prof-specialty} ]9.5;12.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 21 -]59.5;+inf[ {Adm-clerical, Tech-support} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Male} 21 -]59.5;+inf[ {Other-service, Priv-house-serv} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 21 -]59.5;+inf[ {Prof-specialty} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 21 -]-inf;18.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 20 -]-inf;18.5] {Prof-specialty} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 20 -]18.5;23.5] {Exec-managerial} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 20 -]18.5;23.5] {Prof-specialty} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 20 -]23.5;29.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 20 -]23.5;29.5] {Prof-specialty} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 20 -]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 20 -]29.5;40.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 20 -]29.5;40.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Female} 20 -]29.5;40.5] {Sales} ]12.5;+inf[ ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 20 -]40.5;59.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 20 -]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]12.5;+inf[ ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 20 -]40.5;59.5] {Exec-managerial} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Male} 20 -]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 20 -]40.5;59.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Female} 20 -]40.5;59.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 20 -]59.5;+inf[ {Exec-managerial} ]9.5;12.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 20 -]-inf;18.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 19 -]18.5;23.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 19 -]18.5;23.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 19 -]18.5;23.5] {Sales} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 19 -]23.5;29.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 19 -]23.5;29.5] {Adm-clerical, Tech-support} ]12.5;+inf[ ]36.5;43.5] {Married-civ-spouse} {Male} 19 -]23.5;29.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 19 -]29.5;40.5] {Adm-clerical, Tech-support} ]12.5;+inf[ ]-inf;36.5] {Married-civ-spouse} {Female} 19 -]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 19 -]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]12.5;+inf[ ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 19 -]29.5;40.5] {Exec-managerial} ]9.5;12.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 19 -]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 19 -]29.5;40.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 19 -]29.5;40.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 19 -]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 19 -]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 19 -]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]12.5;+inf[ ]-inf;36.5] {Married-civ-spouse} {Male} 19 -]40.5;59.5] {Exec-managerial} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Female} 19 -]40.5;59.5] {Exec-managerial} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 19 -]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 19 -]40.5;59.5] {Prof-specialty} ]9.5;12.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 19 -]40.5;59.5] {Sales} ]12.5;+inf[ ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 19 -]59.5;+inf[ {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 19 -]59.5;+inf[ {Other-service, Priv-house-serv} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 19 -]59.5;+inf[ {Prof-specialty} ]-inf;8.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 19 -]18.5;23.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 18 -]18.5;23.5] {Prof-specialty} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 18 -]23.5;29.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 18 -]23.5;29.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 18 -]23.5;29.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]12.5;+inf[ ]43.5;+inf[ {Married-civ-spouse} {Male} 18 -]23.5;29.5] {Prof-specialty} ]12.5;+inf[ ]-inf;36.5] {Married-civ-spouse} {Female} 18 -]23.5;29.5] {Sales} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 18 -]29.5;40.5] {Adm-clerical, Tech-support} ]12.5;+inf[ ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 18 -]29.5;40.5] {Adm-clerical, Tech-support} ]12.5;+inf[ ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 18 -]29.5;40.5] {Exec-managerial} ]8.5;9.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 18 -]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Female} 18 -]29.5;40.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 18 -]29.5;40.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 18 -]29.5;40.5] {Sales} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 18 -]29.5;40.5] {Sales} ]12.5;+inf[ ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 18 -]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 18 -]40.5;59.5] {Exec-managerial} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 18 -]40.5;59.5] {Exec-managerial} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Female} 18 -]40.5;59.5] {Exec-managerial} ]12.5;+inf[ ]43.5;+inf[ {Married-civ-spouse} {Female} 18 -]40.5;59.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]-inf;36.5] {Married-civ-spouse} {Female} 18 -]40.5;59.5] {Prof-specialty} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 18 -]40.5;59.5] {Sales} ]-inf;8.5] ]36.5;43.5] {Married-civ-spouse} {Male} 18 -]40.5;59.5] {Sales} ]8.5;9.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 18 -]40.5;59.5] {Sales} ]12.5;+inf[ ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 18 -]59.5;+inf[ {Exec-managerial} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 18 -]59.5;+inf[ {Prof-specialty} ]8.5;9.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 18 -]59.5;+inf[ {Prof-specialty} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 18 -]59.5;+inf[ {Sales} ]9.5;12.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 18 -]-inf;18.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 17 -]18.5;23.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 17 -]18.5;23.5] {Exec-managerial} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 17 -]18.5;23.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 17 -]18.5;23.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 17 -]18.5;23.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 17 -]18.5;23.5] {Prof-specialty} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 17 -]18.5;23.5] {Sales} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 17 -]23.5;29.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 17 -]23.5;29.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 17 -]23.5;29.5] {Exec-managerial} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Male} 17 -]23.5;29.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 17 -]23.5;29.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 17 -]23.5;29.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 17 -]23.5;29.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 17 -]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 17 -]29.5;40.5] {Exec-managerial} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 17 -]29.5;40.5] {Exec-managerial} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 17 -]29.5;40.5] {Exec-managerial} ]12.5;+inf[ ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 17 -]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 17 -]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]12.5;+inf[ ]36.5;43.5] {Married-civ-spouse} {Male} 17 -]29.5;40.5] {Prof-specialty} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 17 -]29.5;40.5] {Prof-specialty} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 17 -]29.5;40.5] {Sales} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 17 -]40.5;59.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Male} 17 -]40.5;59.5] {Adm-clerical, Tech-support} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 17 -]40.5;59.5] {Exec-managerial} ]-inf;8.5] ]36.5;43.5] {Married-civ-spouse} {Male} 17 -]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Male} 17 -]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 17 -]40.5;59.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]-inf;36.5] {Married-civ-spouse} {Male} 17 -]40.5;59.5] {Prof-specialty} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 17 -]40.5;59.5] {Sales} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Male} 17 -]40.5;59.5] {Sales} ]12.5;+inf[ ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 17 -]59.5;+inf[ {Adm-clerical, Tech-support} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Male} 17 -]59.5;+inf[ {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 17 -]-inf;18.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 16 -]23.5;29.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]12.5;+inf[ ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 16 -]23.5;29.5] {Exec-managerial} ]12.5;+inf[ ]36.5;43.5] {Married-civ-spouse} {Female} 16 -]23.5;29.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 16 -]23.5;29.5] {Prof-specialty} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 16 -]23.5;29.5] {Prof-specialty} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 16 -]23.5;29.5] {Prof-specialty} ]12.5;+inf[ ]-inf;36.5] {Married-civ-spouse} {Male} 16 -]23.5;29.5] {Sales} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 16 -]29.5;40.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 16 -]29.5;40.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 16 -]29.5;40.5] {Exec-managerial} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 16 -]29.5;40.5] {Exec-managerial} ]12.5;+inf[ ]43.5;+inf[ {Married-civ-spouse} {Female} 16 -]29.5;40.5] {Prof-specialty} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 16 -]29.5;40.5] {Prof-specialty} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Male} 16 -]29.5;40.5] {Sales} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 16 -]29.5;40.5] {Sales} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 16 -]29.5;40.5] {Sales} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Female} 16 -]40.5;59.5] {Adm-clerical, Tech-support} ]12.5;+inf[ ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 16 -]40.5;59.5] {Adm-clerical, Tech-support} ]12.5;+inf[ ]-inf;36.5] {Married-civ-spouse} {Male} 16 -]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 16 -]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 16 -]40.5;59.5] {Exec-managerial} ]9.5;12.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 16 -]40.5;59.5] {Exec-managerial} ]12.5;+inf[ ]-inf;36.5] {Married-civ-spouse} {Female} 16 -]40.5;59.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 16 -]40.5;59.5] {Other-service, Priv-house-serv} ]12.5;+inf[ ]36.5;43.5] {Married-civ-spouse} {Male} 16 -]40.5;59.5] {Prof-specialty} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Female} 16 -]40.5;59.5] {Sales} ]12.5;+inf[ ]-inf;36.5] {Married-civ-spouse} {Male} 16 -]59.5;+inf[ {Prof-specialty} ]12.5;+inf[ ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 16 -]59.5;+inf[ {Sales} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 16 -]18.5;23.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]36.5;43.5] {Married-civ-spouse} {Male} 15 -]18.5;23.5] {Exec-managerial} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 15 -]18.5;23.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 15 -]23.5;29.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 15 -]23.5;29.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 15 -]23.5;29.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]-inf;36.5] {Married-civ-spouse} {Male} 15 -]23.5;29.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]12.5;+inf[ ]36.5;43.5] {Married-civ-spouse} {Male} 15 -]23.5;29.5] {Exec-managerial} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 15 -]23.5;29.5] {Exec-managerial} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 15 -]23.5;29.5] {Machine-op-inspct, Handlers-cleaners} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 15 -]23.5;29.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Male} 15 -]23.5;29.5] {Prof-specialty} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 15 -]23.5;29.5] {Prof-specialty} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Female} 15 -]23.5;29.5] {Sales} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 15 -]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 15 -]29.5;40.5] {Exec-managerial} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Female} 15 -]29.5;40.5] {Exec-managerial} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 15 -]29.5;40.5] {Exec-managerial} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 15 -]29.5;40.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 15 -]29.5;40.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 15 -]29.5;40.5] {Prof-specialty} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Male} 15 -]29.5;40.5] {Prof-specialty} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 15 -]29.5;40.5] {Sales} ]12.5;+inf[ ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 15 -]40.5;59.5] {Exec-managerial} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 15 -]40.5;59.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Male} 15 -]40.5;59.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 15 -]40.5;59.5] {Sales} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 15 -]59.5;+inf[ {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 15 -]59.5;+inf[ {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 15 -]59.5;+inf[ {Exec-managerial} ]-inf;8.5] ]-inf;36.5] {Married-civ-spouse} {Male} 15 -]59.5;+inf[ {Prof-specialty} ]-inf;8.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 15 -]59.5;+inf[ {Sales} ]-inf;8.5] ]-inf;36.5] {Married-civ-spouse} {Male} 15 -]-inf;18.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 14 -]18.5;23.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 14 -]18.5;23.5] {Exec-managerial} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 14 -]18.5;23.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Male} 14 -]18.5;23.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 14 -]23.5;29.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 14 -]23.5;29.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Male} 14 -]23.5;29.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 14 -]23.5;29.5] {Exec-managerial} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 14 -]23.5;29.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 14 -]23.5;29.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Female} 14 -]23.5;29.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 14 -]23.5;29.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Female} 14 -]23.5;29.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 14 -]23.5;29.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 14 -]23.5;29.5] {Other-service, Priv-house-serv} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 14 -]23.5;29.5] {Other-service, Priv-house-serv} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 14 -]23.5;29.5] {Prof-specialty} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 14 -]23.5;29.5] {Prof-specialty} ]12.5;+inf[ ]43.5;+inf[ {Married-civ-spouse} {Female} 14 -]23.5;29.5] {Sales} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 14 -]29.5;40.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Male} 14 -]29.5;40.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 14 -]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 14 -]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Female} 14 -]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 14 -]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 14 -]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 14 -]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 14 -]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 14 -]29.5;40.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Male} 14 -]29.5;40.5] {Other-service, Priv-house-serv} ]12.5;+inf[ ]36.5;43.5] {Married-civ-spouse} {Male} 14 -]29.5;40.5] {Other-service, Priv-house-serv} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 14 -]29.5;40.5] {Sales} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 14 -]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 14 -]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 14 -]40.5;59.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 14 -]40.5;59.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]36.5;43.5] {Married-civ-spouse} {Female} 14 -]40.5;59.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 14 -]40.5;59.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Male} 14 -]40.5;59.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 14 -]40.5;59.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 14 -]40.5;59.5] {Prof-specialty} ]-inf;8.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 14 -]40.5;59.5] {Sales} ]-inf;8.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 14 -]40.5;59.5] {Sales} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 14 -]59.5;+inf[ {Exec-managerial} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 14 -]59.5;+inf[ {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 14 -]59.5;+inf[ {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]-inf;36.5] {Married-civ-spouse} {Male} 14 -]59.5;+inf[ {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 14 -]59.5;+inf[ {Other-service, Priv-house-serv} ]-inf;8.5] ]36.5;43.5] {Married-civ-spouse} {Male} 14 -]59.5;+inf[ {Prof-specialty} ]-inf;8.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 14 -]59.5;+inf[ {Prof-specialty} ]-inf;8.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 14 -]-inf;18.5] {Prof-specialty} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 13 -]18.5;23.5] {Adm-clerical, Tech-support} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 13 -]18.5;23.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 13 -]18.5;23.5] {Prof-specialty} ]12.5;+inf[ ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 13 -]18.5;23.5] {Sales} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 13 -]18.5;23.5] {Sales} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 13 -]23.5;29.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 13 -]23.5;29.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 13 -]23.5;29.5] {Sales} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 13 -]23.5;29.5] {Sales} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 13 -]29.5;40.5] {Adm-clerical, Tech-support} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 13 -]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]12.5;+inf[ ]-inf;36.5] {Married-civ-spouse} {Male} 13 -]29.5;40.5] {Exec-managerial} ]8.5;9.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 13 -]29.5;40.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 13 -]29.5;40.5] {Prof-specialty} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Female} 13 -]29.5;40.5] {Sales} ]-inf;8.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 13 -]29.5;40.5] {Sales} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Female} 13 -]29.5;40.5] {Sales} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 13 -]40.5;59.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Male} 13 -]40.5;59.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 13 -]40.5;59.5] {Adm-clerical, Tech-support} ]12.5;+inf[ ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 13 -]40.5;59.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 13 -]40.5;59.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 13 -]40.5;59.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 13 -]40.5;59.5] {Prof-specialty} ]-inf;8.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 13 -]40.5;59.5] {Prof-specialty} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 13 -]40.5;59.5] {Sales} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Female} 13 -]59.5;+inf[ {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 13 -]59.5;+inf[ {Craft-repair, Transport-moving, Farming-fishing, ...} ]12.5;+inf[ ]43.5;+inf[ {Married-civ-spouse} {Male} 13 -]59.5;+inf[ {Prof-specialty} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 13 -]59.5;+inf[ {Prof-specialty} ]12.5;+inf[ ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 13 -]-inf;18.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 12 -]-inf;18.5] {Prof-specialty} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 12 -]18.5;23.5] {Exec-managerial} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 12 -]18.5;23.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 12 -]18.5;23.5] {Other-service, Priv-house-serv} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 12 -]18.5;23.5] {Prof-specialty} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 12 -]18.5;23.5] {Sales} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 12 -]18.5;23.5] {Sales} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 12 -]23.5;29.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Female} 12 -]23.5;29.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 12 -]23.5;29.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 12 -]23.5;29.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 12 -]23.5;29.5] {Exec-managerial} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 12 -]23.5;29.5] {Exec-managerial} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 12 -]23.5;29.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 12 -]23.5;29.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 12 -]23.5;29.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 12 -]23.5;29.5] {Prof-specialty} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Male} 12 -]23.5;29.5] {Prof-specialty} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 12 -]29.5;40.5] {Adm-clerical, Tech-support} ]12.5;+inf[ ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 12 -]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]12.5;+inf[ ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 12 -]29.5;40.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Female} 12 -]29.5;40.5] {Other-service, Priv-house-serv} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 12 -]29.5;40.5] {Prof-specialty} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 12 -]29.5;40.5] {Prof-specialty} ]9.5;12.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 12 -]29.5;40.5] {Sales} ]12.5;+inf[ ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 12 -]40.5;59.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 12 -]40.5;59.5] {Exec-managerial} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Male} 12 -]40.5;59.5] {Exec-managerial} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 12 -]40.5;59.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 12 -]40.5;59.5] {Other-service, Priv-house-serv} ]12.5;+inf[ ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 12 -]40.5;59.5] {Prof-specialty} ]-inf;8.5] ]-inf;36.5] {Married-civ-spouse} {Male} 12 -]40.5;59.5] {Prof-specialty} ]-inf;8.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 12 -]40.5;59.5] {Prof-specialty} ]8.5;9.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 12 -]40.5;59.5] {Sales} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 12 -]40.5;59.5] {Sales} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 12 -]40.5;59.5] {Sales} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 12 -]40.5;59.5] {Sales} ]12.5;+inf[ ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 12 -]59.5;+inf[ {Adm-clerical, Tech-support} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 12 -]59.5;+inf[ {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 12 -]59.5;+inf[ {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 12 -]59.5;+inf[ {Exec-managerial} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 12 -]59.5;+inf[ {Other-service, Priv-house-serv} ]8.5;9.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 12 -]59.5;+inf[ {Other-service, Priv-house-serv} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 12 -]59.5;+inf[ {Prof-specialty} ]12.5;+inf[ ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 12 -]59.5;+inf[ {Prof-specialty} ]12.5;+inf[ ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 12 -]59.5;+inf[ {Sales} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 12 -]-inf;18.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 11 -]-inf;18.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 11 -]-inf;18.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 11 -]-inf;18.5] {Prof-specialty} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 11 -]18.5;23.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 11 -]18.5;23.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 11 -]18.5;23.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 11 -]18.5;23.5] {Sales} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 11 -]18.5;23.5] {Sales} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Male} 11 -]23.5;29.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 11 -]23.5;29.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 11 -]23.5;29.5] {Exec-managerial} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 11 -]23.5;29.5] {Exec-managerial} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 11 -]23.5;29.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 11 -]23.5;29.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 11 -]23.5;29.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 11 -]23.5;29.5] {Other-service, Priv-house-serv} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 11 -]23.5;29.5] {Prof-specialty} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 11 -]23.5;29.5] {Prof-specialty} ]9.5;12.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 11 -]23.5;29.5] {Sales} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 11 -]29.5;40.5] {Adm-clerical, Tech-support} ]-inf;8.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 11 -]29.5;40.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 11 -]29.5;40.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 11 -]29.5;40.5] {Adm-clerical, Tech-support} ]12.5;+inf[ ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 11 -]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 11 -]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]12.5;+inf[ ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 11 -]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 11 -]29.5;40.5] {Prof-specialty} ]-inf;8.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 11 -]29.5;40.5] {Prof-specialty} ]9.5;12.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 11 -]29.5;40.5] {Sales} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Female} 11 -]40.5;59.5] {Adm-clerical, Tech-support} ]12.5;+inf[ ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 11 -]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Female} 11 -]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 11 -]40.5;59.5] {Exec-managerial} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 11 -]40.5;59.5] {Exec-managerial} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 11 -]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 11 -]40.5;59.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 11 -]40.5;59.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 11 -]40.5;59.5] {Prof-specialty} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 11 -]40.5;59.5] {Prof-specialty} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 11 -]59.5;+inf[ {Adm-clerical, Tech-support} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Male} 11 -]59.5;+inf[ {Adm-clerical, Tech-support} ]12.5;+inf[ ]36.5;43.5] {Married-civ-spouse} {Male} 11 -]59.5;+inf[ {Craft-repair, Transport-moving, Farming-fishing, ...} ]12.5;+inf[ ]36.5;43.5] {Married-civ-spouse} {Male} 11 -]59.5;+inf[ {Exec-managerial} ]-inf;8.5] ]36.5;43.5] {Married-civ-spouse} {Male} 11 -]59.5;+inf[ {Exec-managerial} ]12.5;+inf[ ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 11 -]59.5;+inf[ {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Male} 11 -]59.5;+inf[ {Prof-specialty} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Female} 11 -]59.5;+inf[ {Prof-specialty} ]9.5;12.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 11 -]59.5;+inf[ {Prof-specialty} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 11 -]-inf;18.5] {Adm-clerical, Tech-support} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 10 -]-inf;18.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 10 -]18.5;23.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 10 -]18.5;23.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 10 -]18.5;23.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Male} 10 -]18.5;23.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 10 -]18.5;23.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 10 -]18.5;23.5] {Exec-managerial} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 10 -]18.5;23.5] {Exec-managerial} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 10 -]18.5;23.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 10 -]18.5;23.5] {Prof-specialty} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Female} 10 -]18.5;23.5] {Prof-specialty} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 10 -]18.5;23.5] {Sales} ]12.5;+inf[ ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 10 -]23.5;29.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 10 -]23.5;29.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 10 -]23.5;29.5] {Prof-specialty} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 10 -]23.5;29.5] {Sales} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 10 -]23.5;29.5] {Sales} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 10 -]29.5;40.5] {Exec-managerial} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 10 -]29.5;40.5] {Exec-managerial} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Male} 10 -]29.5;40.5] {Exec-managerial} ]12.5;+inf[ ]-inf;36.5] {Married-civ-spouse} {Female} 10 -]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 10 -]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Male} 10 -]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 10 -]29.5;40.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]-inf;36.5] {Married-civ-spouse} {Female} 10 -]29.5;40.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 10 -]29.5;40.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]36.5;43.5] {Married-civ-spouse} {Female} 10 -]29.5;40.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Male} 10 -]29.5;40.5] {Prof-specialty} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 10 -]29.5;40.5] {Prof-specialty} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 10 -]29.5;40.5] {Prof-specialty} ]12.5;+inf[ ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 10 -]29.5;40.5] {Sales} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 10 -]29.5;40.5] {Sales} ]12.5;+inf[ ]-inf;36.5] {Married-civ-spouse} {Male} 10 -]40.5;59.5] {Adm-clerical, Tech-support} ]-inf;8.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 10 -]40.5;59.5] {Exec-managerial} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 10 -]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 10 -]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]36.5;43.5] {Married-civ-spouse} {Female} 10 -]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 10 -]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 10 -]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 10 -]40.5;59.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 10 -]40.5;59.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 10 -]40.5;59.5] {Other-service, Priv-house-serv} ]12.5;+inf[ ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 10 -]40.5;59.5] {Prof-specialty} ]-inf;8.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 10 -]40.5;59.5] {Prof-specialty} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Female} 10 -]40.5;59.5] {Sales} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Female} 10 -]59.5;+inf[ {Adm-clerical, Tech-support} ]-inf;8.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 10 -]59.5;+inf[ {Adm-clerical, Tech-support} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Female} 10 -]59.5;+inf[ {Exec-managerial} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 10 -]59.5;+inf[ {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 10 -]59.5;+inf[ {Prof-specialty} ]12.5;+inf[ ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 10 -]59.5;+inf[ {Sales} ]-inf;8.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 10 -]59.5;+inf[ {Sales} ]-inf;8.5] ]36.5;43.5] {Married-civ-spouse} {Male} 10 -]-inf;18.5] {Sales} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 9 -]-inf;18.5] {Sales} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 9 -]-inf;18.5] {Sales} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 9 -]18.5;23.5] {Adm-clerical, Tech-support} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 9 -]18.5;23.5] {Adm-clerical, Tech-support} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 9 -]18.5;23.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 9 -]18.5;23.5] {Exec-managerial} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 9 -]18.5;23.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 9 -]18.5;23.5] {Prof-specialty} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 9 -]23.5;29.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 9 -]23.5;29.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Female} 9 -]23.5;29.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 9 -]23.5;29.5] {Exec-managerial} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 9 -]23.5;29.5] {Exec-managerial} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 9 -]23.5;29.5] {Exec-managerial} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Female} 9 -]23.5;29.5] {Exec-managerial} ]12.5;+inf[ ]43.5;+inf[ {Married-civ-spouse} {Female} 9 -]23.5;29.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 9 -]23.5;29.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 9 -]23.5;29.5] {Prof-specialty} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 9 -]23.5;29.5] {Prof-specialty} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Female} 9 -]23.5;29.5] {Prof-specialty} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Female} 9 -]23.5;29.5] {Prof-specialty} ]12.5;+inf[ ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 9 -]23.5;29.5] {Sales} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 9 -]29.5;40.5] {Adm-clerical, Tech-support} ]-inf;8.5] ]36.5;43.5] {Married-civ-spouse} {Male} 9 -]29.5;40.5] {Adm-clerical, Tech-support} ]12.5;+inf[ ]-inf;36.5] {Married-civ-spouse} {Male} 9 -]29.5;40.5] {Exec-managerial} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Female} 9 -]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 9 -]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 9 -]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 9 -]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]12.5;+inf[ ]43.5;+inf[ {Married-civ-spouse} {Male} 9 -]29.5;40.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 9 -]29.5;40.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 9 -]29.5;40.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 9 -]29.5;40.5] {Prof-specialty} ]-inf;8.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 9 -]29.5;40.5] {Sales} ]8.5;9.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 9 -]29.5;40.5] {Sales} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Male} 9 -]29.5;40.5] {Sales} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 9 -]40.5;59.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 9 -]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 9 -]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 9 -]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]12.5;+inf[ ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 9 -]40.5;59.5] {Exec-managerial} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Female} 9 -]40.5;59.5] {Exec-managerial} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Female} 9 -]40.5;59.5] {Exec-managerial} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 9 -]40.5;59.5] {Exec-managerial} ]12.5;+inf[ ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 9 -]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 9 -]40.5;59.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 9 -]40.5;59.5] {Other-service, Priv-house-serv} ]12.5;+inf[ ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 9 -]40.5;59.5] {Other-service, Priv-house-serv} ]12.5;+inf[ ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 9 -]40.5;59.5] {Sales} ]-inf;8.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 9 -]40.5;59.5] {Sales} ]8.5;9.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 9 -]40.5;59.5] {Sales} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 9 -]59.5;+inf[ {Adm-clerical, Tech-support} ]12.5;+inf[ ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 9 -]59.5;+inf[ {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 9 -]59.5;+inf[ {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Male} 9 -]59.5;+inf[ {Other-service, Priv-house-serv} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Male} 9 -]59.5;+inf[ {Prof-specialty} ]12.5;+inf[ ]-inf;36.5] {Married-civ-spouse} {Female} 9 -]-inf;18.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 8 -]-inf;18.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 8 -]-inf;18.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 8 -]-inf;18.5] {Prof-specialty} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 8 -]-inf;18.5] {Prof-specialty} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 8 -]18.5;23.5] {Adm-clerical, Tech-support} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 8 -]18.5;23.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 8 -]18.5;23.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]36.5;43.5] {Married-civ-spouse} {Male} 8 -]18.5;23.5] {Sales} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 8 -]23.5;29.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 8 -]23.5;29.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 8 -]23.5;29.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 8 -]23.5;29.5] {Adm-clerical, Tech-support} ]12.5;+inf[ ]36.5;43.5] {Married-civ-spouse} {Female} 8 -]23.5;29.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 8 -]23.5;29.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]36.5;43.5] {Married-civ-spouse} {Male} 8 -]23.5;29.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 8 -]23.5;29.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Female} 8 -]23.5;29.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 8 -]23.5;29.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 8 -]23.5;29.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 8 -]23.5;29.5] {Prof-specialty} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Male} 8 -]23.5;29.5] {Prof-specialty} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 8 -]23.5;29.5] {Prof-specialty} ]12.5;+inf[ ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 8 -]23.5;29.5] {Prof-specialty} ]12.5;+inf[ ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 8 -]23.5;29.5] {Sales} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 8 -]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 8 -]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 8 -]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 8 -]29.5;40.5] {Exec-managerial} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 8 -]29.5;40.5] {Exec-managerial} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 8 -]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 8 -]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]36.5;43.5] {Married-civ-spouse} {Female} 8 -]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 8 -]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 8 -]29.5;40.5] {Other-service, Priv-house-serv} ]12.5;+inf[ ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 8 -]29.5;40.5] {Other-service, Priv-house-serv} ]12.5;+inf[ ]-inf;36.5] {Married-civ-spouse} {Male} 8 -]29.5;40.5] {Prof-specialty} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Female} 8 -]29.5;40.5] {Prof-specialty} ]9.5;12.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 8 -]29.5;40.5] {Sales} ]-inf;8.5] ]36.5;43.5] {Married-civ-spouse} {Male} 8 -]29.5;40.5] {Sales} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 8 -]29.5;40.5] {Sales} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 8 -]29.5;40.5] {Sales} ]9.5;12.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 8 -]29.5;40.5] {Sales} ]12.5;+inf[ ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 8 -]40.5;59.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 8 -]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 8 -]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 8 -]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 8 -]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]12.5;+inf[ ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 8 -]40.5;59.5] {Exec-managerial} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 8 -]40.5;59.5] {Exec-managerial} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 8 -]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]-inf;36.5] {Married-civ-spouse} {Male} 8 -]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 8 -]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 8 -]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]12.5;+inf[ ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 8 -]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]12.5;+inf[ ]43.5;+inf[ {Married-civ-spouse} {Male} 8 -]40.5;59.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Female} 8 -]40.5;59.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 8 -]40.5;59.5] {Prof-specialty} ]-inf;8.5] ]36.5;43.5] {Married-civ-spouse} {Female} 8 -]40.5;59.5] {Prof-specialty} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 8 -]59.5;+inf[ {Adm-clerical, Tech-support} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Female} 8 -]59.5;+inf[ {Adm-clerical, Tech-support} ]12.5;+inf[ ]-inf;36.5] {Married-civ-spouse} {Male} 8 -]59.5;+inf[ {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 8 -]59.5;+inf[ {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 8 -]59.5;+inf[ {Exec-managerial} ]-inf;8.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 8 -]59.5;+inf[ {Other-service, Priv-house-serv} ]-inf;8.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 8 -]59.5;+inf[ {Prof-specialty} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 8 -]59.5;+inf[ {Prof-specialty} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Female} 8 -]59.5;+inf[ {Sales} ]-inf;8.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 8 -]59.5;+inf[ {Sales} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 8 -]-inf;18.5] {Sales} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 7 -]18.5;23.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Male} 7 -]18.5;23.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 7 -]18.5;23.5] {Adm-clerical, Tech-support} ]12.5;+inf[ ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 7 -]18.5;23.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]-inf;36.5] {Married-civ-spouse} {Male} 7 -]18.5;23.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 7 -]18.5;23.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Male} 7 -]18.5;23.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 7 -]18.5;23.5] {Prof-specialty} ]-inf;8.5] ]36.5;43.5] {Married-civ-spouse} {Female} 7 -]18.5;23.5] {Sales} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 7 -]18.5;23.5] {Sales} ]8.5;9.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 7 -]18.5;23.5] {Sales} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Female} 7 -]18.5;23.5] {Sales} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 7 -]18.5;23.5] {Sales} ]12.5;+inf[ ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 7 -]23.5;29.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 7 -]23.5;29.5] {Adm-clerical, Tech-support} ]12.5;+inf[ ]43.5;+inf[ {Married-civ-spouse} {Male} 7 -]23.5;29.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 7 -]23.5;29.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Male} 7 -]23.5;29.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 7 -]23.5;29.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 7 -]23.5;29.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 7 -]23.5;29.5] {Machine-op-inspct, Handlers-cleaners} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 7 -]23.5;29.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]-inf;36.5] {Married-civ-spouse} {Female} 7 -]23.5;29.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]36.5;43.5] {Married-civ-spouse} {Female} 7 -]23.5;29.5] {Other-service, Priv-house-serv} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 7 -]23.5;29.5] {Prof-specialty} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Female} 7 -]23.5;29.5] {Sales} ]-inf;8.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 7 -]23.5;29.5] {Sales} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Male} 7 -]23.5;29.5] {Sales} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 7 -]23.5;29.5] {Sales} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Female} 7 -]23.5;29.5] {Sales} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 7 -]29.5;40.5] {Adm-clerical, Tech-support} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 7 -]29.5;40.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 7 -]29.5;40.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 7 -]29.5;40.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 7 -]29.5;40.5] {Exec-managerial} ]-inf;8.5] ]36.5;43.5] {Married-civ-spouse} {Male} 7 -]29.5;40.5] {Exec-managerial} ]12.5;+inf[ ]-inf;36.5] {Married-civ-spouse} {Male} 7 -]29.5;40.5] {Exec-managerial} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 7 -]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]-inf;36.5] {Married-civ-spouse} {Male} 7 -]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 7 -]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Male} 7 -]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 7 -]29.5;40.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 7 -]29.5;40.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 7 -]29.5;40.5] {Prof-specialty} ]-inf;8.5] ]36.5;43.5] {Married-civ-spouse} {Male} 7 -]29.5;40.5] {Prof-specialty} ]-inf;8.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 7 -]29.5;40.5] {Prof-specialty} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 7 -]29.5;40.5] {Prof-specialty} ]8.5;9.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 7 -]29.5;40.5] {Sales} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 7 -]40.5;59.5] {Adm-clerical, Tech-support} ]-inf;8.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 7 -]40.5;59.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 7 -]40.5;59.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 7 -]40.5;59.5] {Adm-clerical, Tech-support} ]12.5;+inf[ ]-inf;36.5] {Married-civ-spouse} {Female} 7 -]40.5;59.5] {Adm-clerical, Tech-support} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 7 -]40.5;59.5] {Adm-clerical, Tech-support} ]12.5;+inf[ ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 7 -]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Female} 7 -]40.5;59.5] {Exec-managerial} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 7 -]40.5;59.5] {Exec-managerial} ]12.5;+inf[ ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 7 -]40.5;59.5] {Exec-managerial} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 7 -]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 7 -]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 7 -]40.5;59.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Female} 7 -]40.5;59.5] {Prof-specialty} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 7 -]40.5;59.5] {Sales} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 7 -]40.5;59.5] {Sales} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 7 -]40.5;59.5] {Sales} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 7 -]59.5;+inf[ {Adm-clerical, Tech-support} ]-inf;8.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 7 -]59.5;+inf[ {Adm-clerical, Tech-support} ]-inf;8.5] ]36.5;43.5] {Married-civ-spouse} {Male} 7 -]59.5;+inf[ {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 7 -]59.5;+inf[ {Exec-managerial} ]9.5;12.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 7 -]59.5;+inf[ {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 7 -]59.5;+inf[ {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 7 -]59.5;+inf[ {Other-service, Priv-house-serv} ]12.5;+inf[ ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 7 -]59.5;+inf[ {Prof-specialty} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 7 -]59.5;+inf[ {Prof-specialty} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 7 -]59.5;+inf[ {Sales} ]-inf;8.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 7 -]59.5;+inf[ {Sales} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 7 -]59.5;+inf[ {Sales} ]12.5;+inf[ ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 7 -]59.5;+inf[ {Sales} ]12.5;+inf[ ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 7 -]-inf;18.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 6 -]-inf;18.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 6 -]-inf;18.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 6 -]18.5;23.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Female} 6 -]18.5;23.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Female} 6 -]18.5;23.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 6 -]18.5;23.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 6 -]18.5;23.5] {Exec-managerial} ]12.5;+inf[ ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 6 -]18.5;23.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 6 -]18.5;23.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Female} 6 -]18.5;23.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Male} 6 -]18.5;23.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 6 -]18.5;23.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 6 -]18.5;23.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Male} 6 -]18.5;23.5] {Prof-specialty} ]-inf;8.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 6 -]18.5;23.5] {Prof-specialty} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 6 -]18.5;23.5] {Prof-specialty} ]12.5;+inf[ ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 6 -]18.5;23.5] {Sales} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 6 -]18.5;23.5] {Sales} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Male} 6 -]23.5;29.5] {Adm-clerical, Tech-support} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 6 -]23.5;29.5] {Adm-clerical, Tech-support} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 6 -]23.5;29.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 6 -]23.5;29.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Male} 6 -]23.5;29.5] {Adm-clerical, Tech-support} ]12.5;+inf[ ]-inf;36.5] {Married-civ-spouse} {Male} 6 -]23.5;29.5] {Adm-clerical, Tech-support} ]12.5;+inf[ ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 6 -]23.5;29.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 6 -]23.5;29.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 6 -]23.5;29.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 6 -]23.5;29.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 6 -]23.5;29.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Female} 6 -]23.5;29.5] {Other-service, Priv-house-serv} ]12.5;+inf[ ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 6 -]23.5;29.5] {Prof-specialty} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 6 -]23.5;29.5] {Sales} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 6 -]23.5;29.5] {Sales} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Female} 6 -]29.5;40.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 6 -]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Female} 6 -]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 6 -]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 6 -]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 6 -]29.5;40.5] {Exec-managerial} ]-inf;8.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 6 -]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 6 -]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 6 -]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 6 -]29.5;40.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 6 -]29.5;40.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 6 -]29.5;40.5] {Other-service, Priv-house-serv} ]12.5;+inf[ ]36.5;43.5] {Married-civ-spouse} {Female} 6 -]29.5;40.5] {Prof-specialty} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 6 -]29.5;40.5] {Prof-specialty} ]-inf;8.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 6 -]29.5;40.5] {Prof-specialty} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 6 -]29.5;40.5] {Prof-specialty} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 6 -]29.5;40.5] {Sales} ]-inf;8.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 6 -]29.5;40.5] {Sales} ]-inf;8.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 6 -]29.5;40.5] {Sales} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Male} 6 -]29.5;40.5] {Sales} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 6 -]29.5;40.5] {Sales} ]12.5;+inf[ ]43.5;+inf[ {Married-civ-spouse} {Female} 6 -]40.5;59.5] {Adm-clerical, Tech-support} ]-inf;8.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 6 -]40.5;59.5] {Adm-clerical, Tech-support} ]12.5;+inf[ ]43.5;+inf[ {Married-civ-spouse} {Female} 6 -]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 6 -]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 6 -]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 6 -]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]12.5;+inf[ ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 6 -]40.5;59.5] {Exec-managerial} ]8.5;9.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 6 -]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 6 -]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 6 -]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Female} 6 -]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Male} 6 -]40.5;59.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 6 -]40.5;59.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 6 -]40.5;59.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 6 -]40.5;59.5] {Other-service, Priv-house-serv} ]12.5;+inf[ ]-inf;36.5] {Married-civ-spouse} {Male} 6 -]40.5;59.5] {Other-service, Priv-house-serv} ]12.5;+inf[ ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 6 -]40.5;59.5] {Prof-specialty} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 6 -]40.5;59.5] {Prof-specialty} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 6 -]40.5;59.5] {Sales} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 6 -]40.5;59.5] {Sales} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 6 -]40.5;59.5] {Sales} ]12.5;+inf[ ]36.5;43.5] {Married-civ-spouse} {Female} 6 -]59.5;+inf[ {Adm-clerical, Tech-support} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Female} 6 -]59.5;+inf[ {Adm-clerical, Tech-support} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 6 -]59.5;+inf[ {Adm-clerical, Tech-support} ]9.5;12.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 6 -]59.5;+inf[ {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 6 -]59.5;+inf[ {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 6 -]59.5;+inf[ {Exec-managerial} ]12.5;+inf[ ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 6 -]59.5;+inf[ {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 6 -]59.5;+inf[ {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Male} 6 -]59.5;+inf[ {Other-service, Priv-house-serv} ]-inf;8.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 6 -]59.5;+inf[ {Other-service, Priv-house-serv} ]-inf;8.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 6 -]59.5;+inf[ {Other-service, Priv-house-serv} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 6 -]59.5;+inf[ {Other-service, Priv-house-serv} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Male} 6 -]59.5;+inf[ {Prof-specialty} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 6 -]59.5;+inf[ {Prof-specialty} ]12.5;+inf[ ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 6 -]59.5;+inf[ {Sales} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Female} 6 -]59.5;+inf[ {Sales} ]9.5;12.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 6 -]59.5;+inf[ {Sales} ]12.5;+inf[ ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 6 -]-inf;18.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 5 -]-inf;18.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 5 -]-inf;18.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 5 -]-inf;18.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 5 -]-inf;18.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 5 -]-inf;18.5] {Prof-specialty} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 5 -]-inf;18.5] {Prof-specialty} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 5 -]18.5;23.5] {Adm-clerical, Tech-support} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 5 -]18.5;23.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Female} 5 -]18.5;23.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 5 -]18.5;23.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 5 -]18.5;23.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 5 -]18.5;23.5] {Exec-managerial} ]12.5;+inf[ ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 5 -]18.5;23.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]36.5;43.5] {Married-civ-spouse} {Female} 5 -]18.5;23.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 5 -]18.5;23.5] {Machine-op-inspct, Handlers-cleaners} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 5 -]18.5;23.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 5 -]18.5;23.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Female} 5 -]18.5;23.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Male} 5 -]18.5;23.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Male} 5 -]18.5;23.5] {Other-service, Priv-house-serv} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 5 -]18.5;23.5] {Prof-specialty} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 5 -]18.5;23.5] {Prof-specialty} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Male} 5 -]18.5;23.5] {Sales} ]-inf;8.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 5 -]18.5;23.5] {Sales} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 5 -]18.5;23.5] {Sales} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Female} 5 -]23.5;29.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 5 -]23.5;29.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 5 -]23.5;29.5] {Exec-managerial} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Female} 5 -]23.5;29.5] {Exec-managerial} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Female} 5 -]23.5;29.5] {Exec-managerial} ]12.5;+inf[ ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 5 -]23.5;29.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 5 -]23.5;29.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 5 -]23.5;29.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Male} 5 -]23.5;29.5] {Machine-op-inspct, Handlers-cleaners} ]12.5;+inf[ ]43.5;+inf[ {Married-civ-spouse} {Male} 5 -]23.5;29.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Male} 5 -]23.5;29.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Male} 5 -]23.5;29.5] {Other-service, Priv-house-serv} ]12.5;+inf[ ]36.5;43.5] {Married-civ-spouse} {Male} 5 -]23.5;29.5] {Prof-specialty} ]-inf;8.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 5 -]23.5;29.5] {Prof-specialty} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 5 -]23.5;29.5] {Prof-specialty} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 5 -]23.5;29.5] {Prof-specialty} ]8.5;9.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 5 -]23.5;29.5] {Prof-specialty} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 5 -]23.5;29.5] {Prof-specialty} ]12.5;+inf[ ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 5 -]23.5;29.5] {Sales} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 5 -]23.5;29.5] {Sales} ]-inf;8.5] ]36.5;43.5] {Married-civ-spouse} {Male} 5 -]23.5;29.5] {Sales} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Male} 5 -]29.5;40.5] {Adm-clerical, Tech-support} ]-inf;8.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 5 -]29.5;40.5] {Adm-clerical, Tech-support} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 5 -]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 5 -]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 5 -]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 5 -]29.5;40.5] {Exec-managerial} ]8.5;9.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 5 -]29.5;40.5] {Exec-managerial} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 5 -]29.5;40.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 5 -]29.5;40.5] {Other-service, Priv-house-serv} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 5 -]29.5;40.5] {Other-service, Priv-house-serv} ]12.5;+inf[ ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 5 -]29.5;40.5] {Other-service, Priv-house-serv} ]12.5;+inf[ ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 5 -]29.5;40.5] {Other-service, Priv-house-serv} ]12.5;+inf[ ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 5 -]29.5;40.5] {Prof-specialty} ]-inf;8.5] ]-inf;36.5] {Married-civ-spouse} {Female} 5 -]29.5;40.5] {Prof-specialty} ]-inf;8.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 5 -]29.5;40.5] {Prof-specialty} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 5 -]29.5;40.5] {Sales} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 5 -]29.5;40.5] {Sales} ]-inf;8.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 5 -]29.5;40.5] {Sales} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Female} 5 -]29.5;40.5] {Sales} ]12.5;+inf[ ]-inf;36.5] {Married-civ-spouse} {Female} 5 -]40.5;59.5] {Adm-clerical, Tech-support} ]-inf;8.5] ]36.5;43.5] {Married-civ-spouse} {Female} 5 -]40.5;59.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 5 -]40.5;59.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 5 -]40.5;59.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 5 -]40.5;59.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 5 -]40.5;59.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 5 -]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 5 -]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]36.5;43.5] {Married-civ-spouse} {Female} 5 -]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 5 -]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 5 -]40.5;59.5] {Exec-managerial} ]-inf;8.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 5 -]40.5;59.5] {Exec-managerial} ]-inf;8.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 5 -]40.5;59.5] {Exec-managerial} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 5 -]40.5;59.5] {Exec-managerial} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 5 -]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 5 -]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 5 -]40.5;59.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 5 -]40.5;59.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 5 -]40.5;59.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 5 -]40.5;59.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 5 -]40.5;59.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 5 -]40.5;59.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 5 -]40.5;59.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 5 -]40.5;59.5] {Other-service, Priv-house-serv} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 5 -]40.5;59.5] {Prof-specialty} ]-inf;8.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 5 -]40.5;59.5] {Prof-specialty} ]-inf;8.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 5 -]40.5;59.5] {Prof-specialty} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 5 -]40.5;59.5] {Prof-specialty} ]8.5;9.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 5 -]40.5;59.5] {Prof-specialty} ]9.5;12.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 5 -]40.5;59.5] {Prof-specialty} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 5 -]40.5;59.5] {Sales} ]-inf;8.5] ]-inf;36.5] {Married-civ-spouse} {Male} 5 -]40.5;59.5] {Sales} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 5 -]40.5;59.5] {Sales} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 5 -]40.5;59.5] {Sales} ]9.5;12.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 5 -]40.5;59.5] {Sales} ]12.5;+inf[ ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 5 -]40.5;59.5] {Sales} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 5 -]59.5;+inf[ {Adm-clerical, Tech-support} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 5 -]59.5;+inf[ {Adm-clerical, Tech-support} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Female} 5 -]59.5;+inf[ {Adm-clerical, Tech-support} ]12.5;+inf[ ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 5 -]59.5;+inf[ {Adm-clerical, Tech-support} ]12.5;+inf[ ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 5 -]59.5;+inf[ {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 5 -]59.5;+inf[ {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 5 -]59.5;+inf[ {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 5 -]59.5;+inf[ {Exec-managerial} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 5 -]59.5;+inf[ {Exec-managerial} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 5 -]59.5;+inf[ {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 5 -]59.5;+inf[ {Machine-op-inspct, Handlers-cleaners} ]12.5;+inf[ ]36.5;43.5] {Married-civ-spouse} {Male} 5 -]59.5;+inf[ {Other-service, Priv-house-serv} ]-inf;8.5] ]-inf;36.5] {Married-civ-spouse} {Female} 5 -]59.5;+inf[ {Other-service, Priv-house-serv} ]12.5;+inf[ ]-inf;36.5] {Married-civ-spouse} {Male} 5 -]59.5;+inf[ {Prof-specialty} ]8.5;9.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 5 -]59.5;+inf[ {Prof-specialty} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 5 -]59.5;+inf[ {Prof-specialty} ]12.5;+inf[ ]36.5;43.5] {Married-civ-spouse} {Female} 5 -]59.5;+inf[ {Sales} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 5 -]59.5;+inf[ {Sales} ]8.5;9.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 5 -]-inf;18.5] {Adm-clerical, Tech-support} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 4 -]-inf;18.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 4 -]-inf;18.5] {Exec-managerial} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 4 -]-inf;18.5] {Exec-managerial} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 4 -]-inf;18.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 4 -]-inf;18.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 4 -]-inf;18.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 4 -]18.5;23.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Female} 4 -]18.5;23.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 4 -]18.5;23.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 4 -]18.5;23.5] {Exec-managerial} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 4 -]18.5;23.5] {Exec-managerial} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 4 -]18.5;23.5] {Exec-managerial} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Female} 4 -]18.5;23.5] {Exec-managerial} ]9.5;12.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 4 -]18.5;23.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]-inf;36.5] {Married-civ-spouse} {Male} 4 -]18.5;23.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 4 -]18.5;23.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 4 -]18.5;23.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Male} 4 -]18.5;23.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 4 -]18.5;23.5] {Machine-op-inspct, Handlers-cleaners} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 4 -]18.5;23.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Female} 4 -]18.5;23.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 4 -]18.5;23.5] {Prof-specialty} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Female} 4 -]18.5;23.5] {Sales} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 4 -]18.5;23.5] {Sales} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Male} 4 -]18.5;23.5] {Sales} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 4 -]18.5;23.5] {Sales} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 4 -]18.5;23.5] {Sales} ]9.5;12.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 4 -]18.5;23.5] {Sales} ]12.5;+inf[ ]36.5;43.5] {Married-civ-spouse} {Male} 4 -]23.5;29.5] {Adm-clerical, Tech-support} ]-inf;8.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 4 -]23.5;29.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 4 -]23.5;29.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 4 -]23.5;29.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 4 -]23.5;29.5] {Adm-clerical, Tech-support} ]12.5;+inf[ ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 4 -]23.5;29.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 4 -]23.5;29.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]12.5;+inf[ ]-inf;36.5] {Married-civ-spouse} {Male} 4 -]23.5;29.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]12.5;+inf[ ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 4 -]23.5;29.5] {Exec-managerial} ]8.5;9.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 4 -]23.5;29.5] {Exec-managerial} ]12.5;+inf[ ]-inf;36.5] {Married-civ-spouse} {Female} 4 -]23.5;29.5] {Exec-managerial} ]12.5;+inf[ ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 4 -]23.5;29.5] {Exec-managerial} ]12.5;+inf[ ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 4 -]23.5;29.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]-inf;36.5] {Married-civ-spouse} {Male} 4 -]23.5;29.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 4 -]23.5;29.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]36.5;43.5] {Married-civ-spouse} {Female} 4 -]23.5;29.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 4 -]23.5;29.5] {Machine-op-inspct, Handlers-cleaners} ]12.5;+inf[ ]36.5;43.5] {Married-civ-spouse} {Male} 4 -]23.5;29.5] {Machine-op-inspct, Handlers-cleaners} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 4 -]23.5;29.5] {Machine-op-inspct, Handlers-cleaners} ]12.5;+inf[ ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 4 -]23.5;29.5] {Prof-specialty} ]-inf;8.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 4 -]23.5;29.5] {Prof-specialty} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 4 -]23.5;29.5] {Prof-specialty} ]12.5;+inf[ ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 4 -]23.5;29.5] {Sales} ]-inf;8.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 4 -]23.5;29.5] {Sales} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Female} 4 -]23.5;29.5] {Sales} ]9.5;12.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 4 -]23.5;29.5] {Sales} ]12.5;+inf[ ]-inf;36.5] {Married-civ-spouse} {Male} 4 -]23.5;29.5] {Sales} ]12.5;+inf[ ]43.5;+inf[ {Married-civ-spouse} {Female} 4 -]29.5;40.5] {Adm-clerical, Tech-support} ]-inf;8.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 4 -]29.5;40.5] {Adm-clerical, Tech-support} ]-inf;8.5] ]36.5;43.5] {Married-civ-spouse} {Female} 4 -]29.5;40.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Male} 4 -]29.5;40.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 4 -]29.5;40.5] {Adm-clerical, Tech-support} ]12.5;+inf[ ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 4 -]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 4 -]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 4 -]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Female} 4 -]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Female} 4 -]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]12.5;+inf[ ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 4 -]29.5;40.5] {Exec-managerial} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Female} 4 -]29.5;40.5] {Exec-managerial} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Male} 4 -]29.5;40.5] {Exec-managerial} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 4 -]29.5;40.5] {Exec-managerial} ]12.5;+inf[ ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 4 -]29.5;40.5] {Exec-managerial} ]12.5;+inf[ ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 4 -]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Female} 4 -]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 4 -]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 4 -]29.5;40.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 4 -]29.5;40.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]-inf;36.5] {Married-civ-spouse} {Male} 4 -]29.5;40.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 4 -]29.5;40.5] {Other-service, Priv-house-serv} ]12.5;+inf[ ]-inf;36.5] {Married-civ-spouse} {Female} 4 -]29.5;40.5] {Other-service, Priv-house-serv} ]12.5;+inf[ ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 4 -]29.5;40.5] {Other-service, Priv-house-serv} ]12.5;+inf[ ]43.5;+inf[ {Married-civ-spouse} {Male} 4 -]29.5;40.5] {Prof-specialty} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 4 -]29.5;40.5] {Sales} ]-inf;8.5] ]-inf;36.5] {Married-civ-spouse} {Male} 4 -]29.5;40.5] {Sales} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 4 -]29.5;40.5] {Sales} ]12.5;+inf[ ]36.5;43.5] {Married-civ-spouse} {Female} 4 -]40.5;59.5] {Adm-clerical, Tech-support} ]12.5;+inf[ ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 4 -]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 4 -]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Female} 4 -]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 4 -]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 4 -]40.5;59.5] {Exec-managerial} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 4 -]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]-inf;36.5] {Married-civ-spouse} {Female} 4 -]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Female} 4 -]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 4 -]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]12.5;+inf[ ]-inf;36.5] {Married-civ-spouse} {Male} 4 -]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]12.5;+inf[ ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 4 -]40.5;59.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 4 -]40.5;59.5] {Other-service, Priv-house-serv} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 4 -]40.5;59.5] {Other-service, Priv-house-serv} ]12.5;+inf[ ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 4 -]40.5;59.5] {Prof-specialty} ]-inf;8.5] ]-inf;36.5] {Married-civ-spouse} {Female} 4 -]40.5;59.5] {Prof-specialty} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 4 -]40.5;59.5] {Prof-specialty} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 4 -]40.5;59.5] {Prof-specialty} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 4 -]40.5;59.5] {Sales} ]-inf;8.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 4 -]40.5;59.5] {Sales} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Female} 4 -]40.5;59.5] {Sales} ]12.5;+inf[ ]-inf;36.5] {Married-civ-spouse} {Female} 4 -]59.5;+inf[ {Adm-clerical, Tech-support} ]8.5;9.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 4 -]59.5;+inf[ {Adm-clerical, Tech-support} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 4 -]59.5;+inf[ {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 4 -]59.5;+inf[ {Craft-repair, Transport-moving, Farming-fishing, ...} ]12.5;+inf[ ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 4 -]59.5;+inf[ {Exec-managerial} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 4 -]59.5;+inf[ {Exec-managerial} ]12.5;+inf[ ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 4 -]59.5;+inf[ {Exec-managerial} ]12.5;+inf[ ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 4 -]59.5;+inf[ {Exec-managerial} ]12.5;+inf[ ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 4 -]59.5;+inf[ {Exec-managerial} ]12.5;+inf[ ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 4 -]59.5;+inf[ {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]36.5;43.5] {Married-civ-spouse} {Female} 4 -]59.5;+inf[ {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 4 -]59.5;+inf[ {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 4 -]59.5;+inf[ {Other-service, Priv-house-serv} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 4 -]59.5;+inf[ {Other-service, Priv-house-serv} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 4 -]59.5;+inf[ {Other-service, Priv-house-serv} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 4 -]59.5;+inf[ {Prof-specialty} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 4 -]59.5;+inf[ {Prof-specialty} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 4 -]59.5;+inf[ {Prof-specialty} ]9.5;12.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 4 -]59.5;+inf[ {Prof-specialty} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 4 -]59.5;+inf[ {Prof-specialty} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 4 -]59.5;+inf[ {Sales} ]8.5;9.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 4 -]-inf;18.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 3 -]-inf;18.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 3 -]-inf;18.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 3 -]-inf;18.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 3 -]-inf;18.5] {Prof-specialty} ]-inf;8.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 3 -]-inf;18.5] {Prof-specialty} ]-inf;8.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 3 -]-inf;18.5] {Prof-specialty} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 3 -]18.5;23.5] {Adm-clerical, Tech-support} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 3 -]18.5;23.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 3 -]18.5;23.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 3 -]18.5;23.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Male} 3 -]18.5;23.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 3 -]18.5;23.5] {Exec-managerial} ]8.5;9.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 3 -]18.5;23.5] {Exec-managerial} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 3 -]18.5;23.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 3 -]18.5;23.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 3 -]18.5;23.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]-inf;36.5] {Married-civ-spouse} {Male} 3 -]18.5;23.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Male} 3 -]18.5;23.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 3 -]18.5;23.5] {Other-service, Priv-house-serv} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 3 -]18.5;23.5] {Prof-specialty} ]-inf;8.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 3 -]18.5;23.5] {Prof-specialty} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 3 -]18.5;23.5] {Prof-specialty} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 3 -]18.5;23.5] {Prof-specialty} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Male} 3 -]18.5;23.5] {Sales} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 3 -]18.5;23.5] {Sales} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Male} 3 -]23.5;29.5] {Adm-clerical, Tech-support} ]-inf;8.5] ]36.5;43.5] {Married-civ-spouse} {Female} 3 -]23.5;29.5] {Adm-clerical, Tech-support} ]-inf;8.5] ]36.5;43.5] {Married-civ-spouse} {Male} 3 -]23.5;29.5] {Adm-clerical, Tech-support} ]12.5;+inf[ ]-inf;36.5] {Married-civ-spouse} {Female} 3 -]23.5;29.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 3 -]23.5;29.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]36.5;43.5] {Married-civ-spouse} {Female} 3 -]23.5;29.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 3 -]23.5;29.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 3 -]23.5;29.5] {Exec-managerial} ]-inf;8.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 3 -]23.5;29.5] {Exec-managerial} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 3 -]23.5;29.5] {Exec-managerial} ]8.5;9.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 3 -]23.5;29.5] {Exec-managerial} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Male} 3 -]23.5;29.5] {Exec-managerial} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 3 -]23.5;29.5] {Exec-managerial} ]12.5;+inf[ ]-inf;36.5] {Married-civ-spouse} {Male} 3 -]23.5;29.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 3 -]23.5;29.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Female} 3 -]23.5;29.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Male} 3 -]23.5;29.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Female} 3 -]23.5;29.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 3 -]23.5;29.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 3 -]23.5;29.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 3 -]23.5;29.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 3 -]23.5;29.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 3 -]23.5;29.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Female} 3 -]23.5;29.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 3 -]23.5;29.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 3 -]23.5;29.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 3 -]23.5;29.5] {Prof-specialty} ]-inf;8.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 3 -]23.5;29.5] {Prof-specialty} ]-inf;8.5] ]36.5;43.5] {Married-civ-spouse} {Female} 3 -]23.5;29.5] {Prof-specialty} ]-inf;8.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 3 -]23.5;29.5] {Prof-specialty} ]-inf;8.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 3 -]23.5;29.5] {Prof-specialty} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Male} 3 -]23.5;29.5] {Prof-specialty} ]8.5;9.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 3 -]23.5;29.5] {Sales} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Female} 3 -]23.5;29.5] {Sales} ]8.5;9.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 3 -]23.5;29.5] {Sales} ]8.5;9.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 3 -]23.5;29.5] {Sales} ]9.5;12.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 3 -]23.5;29.5] {Sales} ]12.5;+inf[ ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 3 -]23.5;29.5] {Sales} ]12.5;+inf[ ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 3 -]23.5;29.5] {Sales} ]12.5;+inf[ ]36.5;43.5] {Married-civ-spouse} {Female} 3 -]29.5;40.5] {Adm-clerical, Tech-support} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 3 -]29.5;40.5] {Adm-clerical, Tech-support} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 3 -]29.5;40.5] {Adm-clerical, Tech-support} ]12.5;+inf[ ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 3 -]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 3 -]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 3 -]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 3 -]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 3 -]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 3 -]29.5;40.5] {Exec-managerial} ]-inf;8.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 3 -]29.5;40.5] {Exec-managerial} ]-inf;8.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 3 -]29.5;40.5] {Exec-managerial} ]-inf;8.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 3 -]29.5;40.5] {Exec-managerial} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 3 -]29.5;40.5] {Exec-managerial} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 3 -]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 3 -]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 3 -]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 3 -]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 3 -]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Female} 3 -]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 3 -]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]12.5;+inf[ ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 3 -]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 3 -]29.5;40.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 3 -]29.5;40.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 3 -]29.5;40.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 3 -]29.5;40.5] {Other-service, Priv-house-serv} ]12.5;+inf[ ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 3 -]29.5;40.5] {Other-service, Priv-house-serv} ]12.5;+inf[ ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 3 -]29.5;40.5] {Other-service, Priv-house-serv} ]12.5;+inf[ ]43.5;+inf[ {Married-civ-spouse} {Female} 3 -]29.5;40.5] {Prof-specialty} ]-inf;8.5] ]-inf;36.5] {Married-civ-spouse} {Male} 3 -]29.5;40.5] {Prof-specialty} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 3 -]29.5;40.5] {Prof-specialty} ]-inf;8.5] ]36.5;43.5] {Married-civ-spouse} {Female} 3 -]29.5;40.5] {Prof-specialty} ]-inf;8.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 3 -]29.5;40.5] {Prof-specialty} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 3 -]29.5;40.5] {Prof-specialty} ]8.5;9.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 3 -]29.5;40.5] {Prof-specialty} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 3 -]29.5;40.5] {Prof-specialty} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 3 -]29.5;40.5] {Sales} ]-inf;8.5] ]-inf;36.5] {Married-civ-spouse} {Female} 3 -]29.5;40.5] {Sales} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 3 -]29.5;40.5] {Sales} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 3 -]40.5;59.5] {Adm-clerical, Tech-support} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 3 -]40.5;59.5] {Adm-clerical, Tech-support} ]-inf;8.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 3 -]40.5;59.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 3 -]40.5;59.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 3 -]40.5;59.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 3 -]40.5;59.5] {Adm-clerical, Tech-support} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 3 -]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 3 -]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]12.5;+inf[ ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 3 -]40.5;59.5] {Exec-managerial} ]-inf;8.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 3 -]40.5;59.5] {Exec-managerial} ]-inf;8.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 3 -]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 3 -]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 3 -]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 3 -]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 3 -]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 3 -]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 3 -]40.5;59.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 3 -]40.5;59.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 3 -]40.5;59.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 3 -]40.5;59.5] {Other-service, Priv-house-serv} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 3 -]40.5;59.5] {Prof-specialty} ]-inf;8.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 3 -]40.5;59.5] {Prof-specialty} ]8.5;9.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 3 -]40.5;59.5] {Sales} ]-inf;8.5] ]-inf;36.5] {Married-civ-spouse} {Female} 3 -]40.5;59.5] {Sales} ]-inf;8.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 3 -]40.5;59.5] {Sales} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 3 -]40.5;59.5] {Sales} ]-inf;8.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 3 -]40.5;59.5] {Sales} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 3 -]40.5;59.5] {Sales} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 3 -]40.5;59.5] {Sales} ]12.5;+inf[ ]43.5;+inf[ {Married-civ-spouse} {Female} 3 -]40.5;59.5] {Sales} ]12.5;+inf[ ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 3 -]59.5;+inf[ {Adm-clerical, Tech-support} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 3 -]59.5;+inf[ {Adm-clerical, Tech-support} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 3 -]59.5;+inf[ {Adm-clerical, Tech-support} ]9.5;12.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 3 -]59.5;+inf[ {Adm-clerical, Tech-support} ]12.5;+inf[ ]36.5;43.5] {Married-civ-spouse} {Female} 3 -]59.5;+inf[ {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 3 -]59.5;+inf[ {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 3 -]59.5;+inf[ {Exec-managerial} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 3 -]59.5;+inf[ {Exec-managerial} ]9.5;12.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 3 -]59.5;+inf[ {Exec-managerial} ]12.5;+inf[ ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 3 -]59.5;+inf[ {Exec-managerial} ]12.5;+inf[ ]36.5;43.5] {Married-civ-spouse} {Female} 3 -]59.5;+inf[ {Exec-managerial} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 3 -]59.5;+inf[ {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 3 -]59.5;+inf[ {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 3 -]59.5;+inf[ {Machine-op-inspct, Handlers-cleaners} ]12.5;+inf[ ]-inf;36.5] {Married-civ-spouse} {Male} 3 -]59.5;+inf[ {Other-service, Priv-house-serv} ]-inf;8.5] ]36.5;43.5] {Married-civ-spouse} {Female} 3 -]59.5;+inf[ {Other-service, Priv-house-serv} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 3 -]59.5;+inf[ {Other-service, Priv-house-serv} ]-inf;8.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 3 -]59.5;+inf[ {Other-service, Priv-house-serv} ]-inf;8.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 3 -]59.5;+inf[ {Other-service, Priv-house-serv} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Female} 3 -]59.5;+inf[ {Other-service, Priv-house-serv} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Female} 3 -]59.5;+inf[ {Other-service, Priv-house-serv} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 3 -]59.5;+inf[ {Other-service, Priv-house-serv} ]9.5;12.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 3 -]59.5;+inf[ {Other-service, Priv-house-serv} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 3 -]59.5;+inf[ {Prof-specialty} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 3 -]59.5;+inf[ {Prof-specialty} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 3 -]59.5;+inf[ {Prof-specialty} ]12.5;+inf[ ]43.5;+inf[ {Married-civ-spouse} {Female} 3 -]59.5;+inf[ {Sales} ]-inf;8.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 3 -]59.5;+inf[ {Sales} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 3 -]59.5;+inf[ {Sales} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 3 -]59.5;+inf[ {Sales} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 3 -]59.5;+inf[ {Sales} ]12.5;+inf[ ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 3 -]-inf;18.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 2 -]-inf;18.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 2 -]-inf;18.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Female} 2 -]-inf;18.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 2 -]-inf;18.5] {Prof-specialty} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 2 -]-inf;18.5] {Sales} ]-inf;8.5] ]-inf;36.5] {Married-civ-spouse} {Female} 2 -]-inf;18.5] {Sales} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 2 -]-inf;18.5] {Sales} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 2 -]-inf;18.5] {Sales} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 2 -]18.5;23.5] {Adm-clerical, Tech-support} ]-inf;8.5] ]-inf;36.5] {Married-civ-spouse} {Female} 2 -]18.5;23.5] {Adm-clerical, Tech-support} ]-inf;8.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 2 -]18.5;23.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 2 -]18.5;23.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 2 -]18.5;23.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 2 -]18.5;23.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 2 -]18.5;23.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]12.5;+inf[ ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 2 -]18.5;23.5] {Exec-managerial} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 2 -]18.5;23.5] {Exec-managerial} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 2 -]18.5;23.5] {Exec-managerial} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 2 -]18.5;23.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]-inf;36.5] {Married-civ-spouse} {Female} 2 -]18.5;23.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 2 -]18.5;23.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 2 -]18.5;23.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 2 -]18.5;23.5] {Machine-op-inspct, Handlers-cleaners} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 2 -]18.5;23.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 2 -]18.5;23.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 2 -]18.5;23.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 2 -]18.5;23.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 2 -]18.5;23.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 2 -]18.5;23.5] {Other-service, Priv-house-serv} ]12.5;+inf[ ]-inf;36.5] {Married-civ-spouse} {Male} 2 -]18.5;23.5] {Prof-specialty} ]-inf;8.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 2 -]18.5;23.5] {Prof-specialty} ]-inf;8.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 2 -]18.5;23.5] {Prof-specialty} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 2 -]18.5;23.5] {Prof-specialty} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Female} 2 -]18.5;23.5] {Prof-specialty} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Male} 2 -]18.5;23.5] {Prof-specialty} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 2 -]18.5;23.5] {Prof-specialty} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 2 -]18.5;23.5] {Prof-specialty} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Female} 2 -]18.5;23.5] {Prof-specialty} ]12.5;+inf[ ]-inf;36.5] {Married-civ-spouse} {Male} 2 -]18.5;23.5] {Prof-specialty} ]12.5;+inf[ ]36.5;43.5] {Married-civ-spouse} {Female} 2 -]18.5;23.5] {Prof-specialty} ]12.5;+inf[ ]36.5;43.5] {Married-civ-spouse} {Male} 2 -]18.5;23.5] {Prof-specialty} ]12.5;+inf[ ]43.5;+inf[ {Married-civ-spouse} {Male} 2 -]18.5;23.5] {Sales} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Female} 2 -]18.5;23.5] {Sales} ]8.5;9.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 2 -]18.5;23.5] {Sales} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Female} 2 -]23.5;29.5] {Adm-clerical, Tech-support} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 2 -]23.5;29.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Male} 2 -]23.5;29.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 2 -]23.5;29.5] {Adm-clerical, Tech-support} ]12.5;+inf[ ]43.5;+inf[ {Married-civ-spouse} {Female} 2 -]23.5;29.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 2 -]23.5;29.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 2 -]23.5;29.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Female} 2 -]23.5;29.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 2 -]23.5;29.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 2 -]23.5;29.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]12.5;+inf[ ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 2 -]23.5;29.5] {Exec-managerial} ]-inf;8.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 2 -]23.5;29.5] {Exec-managerial} ]-inf;8.5] ]36.5;43.5] {Married-civ-spouse} {Male} 2 -]23.5;29.5] {Exec-managerial} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 2 -]23.5;29.5] {Exec-managerial} ]-inf;8.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 2 -]23.5;29.5] {Exec-managerial} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 2 -]23.5;29.5] {Exec-managerial} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 2 -]23.5;29.5] {Exec-managerial} ]8.5;9.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 2 -]23.5;29.5] {Exec-managerial} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 2 -]23.5;29.5] {Exec-managerial} ]9.5;12.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 2 -]23.5;29.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 2 -]23.5;29.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 2 -]23.5;29.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 2 -]23.5;29.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 2 -]23.5;29.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Female} 2 -]23.5;29.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 2 -]23.5;29.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 2 -]23.5;29.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 2 -]23.5;29.5] {Other-service, Priv-house-serv} ]12.5;+inf[ ]-inf;36.5] {Married-civ-spouse} {Female} 2 -]23.5;29.5] {Other-service, Priv-house-serv} ]12.5;+inf[ ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 2 -]23.5;29.5] {Other-service, Priv-house-serv} ]12.5;+inf[ ]36.5;43.5] {Married-civ-spouse} {Female} 2 -]23.5;29.5] {Other-service, Priv-house-serv} ]12.5;+inf[ ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 2 -]23.5;29.5] {Other-service, Priv-house-serv} ]12.5;+inf[ ]43.5;+inf[ {Married-civ-spouse} {Male} 2 -]23.5;29.5] {Other-service, Priv-house-serv} ]12.5;+inf[ ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 2 -]23.5;29.5] {Prof-specialty} ]-inf;8.5] ]-inf;36.5] {Married-civ-spouse} {Female} 2 -]23.5;29.5] {Prof-specialty} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 2 -]23.5;29.5] {Prof-specialty} ]-inf;8.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 2 -]23.5;29.5] {Prof-specialty} ]-inf;8.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 2 -]23.5;29.5] {Prof-specialty} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 2 -]23.5;29.5] {Prof-specialty} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 2 -]23.5;29.5] {Prof-specialty} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 2 -]23.5;29.5] {Prof-specialty} ]9.5;12.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 2 -]23.5;29.5] {Prof-specialty} ]9.5;12.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 2 -]23.5;29.5] {Prof-specialty} ]12.5;+inf[ ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 2 -]23.5;29.5] {Sales} ]-inf;8.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 2 -]23.5;29.5] {Sales} ]-inf;8.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 2 -]23.5;29.5] {Sales} ]-inf;8.5] ]-inf;36.5] {Married-civ-spouse} {Male} 2 -]23.5;29.5] {Sales} ]-inf;8.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 2 -]23.5;29.5] {Sales} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 2 -]23.5;29.5] {Sales} ]-inf;8.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 2 -]23.5;29.5] {Sales} ]-inf;8.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 2 -]23.5;29.5] {Sales} ]8.5;9.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 2 -]23.5;29.5] {Sales} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 2 -]23.5;29.5] {Sales} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 2 -]23.5;29.5] {Sales} ]9.5;12.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 2 -]23.5;29.5] {Sales} ]12.5;+inf[ ]-inf;36.5] {Married-civ-spouse} {Female} 2 -]23.5;29.5] {Sales} ]12.5;+inf[ ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 2 -]29.5;40.5] {Adm-clerical, Tech-support} ]-inf;8.5] ]-inf;36.5] {Married-civ-spouse} {Female} 2 -]29.5;40.5] {Adm-clerical, Tech-support} ]-inf;8.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 2 -]29.5;40.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 2 -]29.5;40.5] {Adm-clerical, Tech-support} ]12.5;+inf[ ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 2 -]29.5;40.5] {Adm-clerical, Tech-support} ]12.5;+inf[ ]43.5;+inf[ {Married-civ-spouse} {Female} 2 -]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 2 -]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 2 -]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]12.5;+inf[ ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 2 -]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 2 -]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]12.5;+inf[ ]36.5;43.5] {Married-civ-spouse} {Female} 2 -]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]12.5;+inf[ ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 2 -]29.5;40.5] {Exec-managerial} ]-inf;8.5] ]36.5;43.5] {Married-civ-spouse} {Female} 2 -]29.5;40.5] {Exec-managerial} ]-inf;8.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 2 -]29.5;40.5] {Exec-managerial} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 2 -]29.5;40.5] {Exec-managerial} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 2 -]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 2 -]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 2 -]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Female} 2 -]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 2 -]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]12.5;+inf[ ]-inf;36.5] {Married-civ-spouse} {Male} 2 -]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]12.5;+inf[ ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 2 -]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]12.5;+inf[ ]36.5;43.5] {Married-civ-spouse} {Female} 2 -]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]12.5;+inf[ ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 2 -]29.5;40.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 2 -]29.5;40.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 2 -]29.5;40.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 2 -]29.5;40.5] {Other-service, Priv-house-serv} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 2 -]29.5;40.5] {Prof-specialty} ]8.5;9.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 2 -]29.5;40.5] {Prof-specialty} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 2 -]29.5;40.5] {Sales} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 2 -]29.5;40.5] {Sales} ]-inf;8.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 2 -]29.5;40.5] {Sales} ]-inf;8.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 2 -]29.5;40.5] {Sales} ]8.5;9.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 2 -]29.5;40.5] {Sales} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 2 -]29.5;40.5] {Sales} ]12.5;+inf[ ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 2 -]40.5;59.5] {Adm-clerical, Tech-support} ]-inf;8.5] ]-inf;36.5] {Married-civ-spouse} {Female} 2 -]40.5;59.5] {Adm-clerical, Tech-support} ]-inf;8.5] ]-inf;36.5] {Married-civ-spouse} {Male} 2 -]40.5;59.5] {Adm-clerical, Tech-support} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 2 -]40.5;59.5] {Adm-clerical, Tech-support} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 2 -]40.5;59.5] {Adm-clerical, Tech-support} ]-inf;8.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 2 -]40.5;59.5] {Adm-clerical, Tech-support} ]12.5;+inf[ ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 2 -]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]-inf;36.5] {Married-civ-spouse} {Female} 2 -]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Female} 2 -]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 2 -]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 2 -]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 2 -]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]12.5;+inf[ ]-inf;36.5] {Married-civ-spouse} {Female} 2 -]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]12.5;+inf[ ]43.5;+inf[ {Married-civ-spouse} {Female} 2 -]40.5;59.5] {Exec-managerial} ]-inf;8.5] ]-inf;36.5] {Married-civ-spouse} {Female} 2 -]40.5;59.5] {Exec-managerial} ]-inf;8.5] ]-inf;36.5] {Married-civ-spouse} {Male} 2 -]40.5;59.5] {Exec-managerial} ]-inf;8.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 2 -]40.5;59.5] {Exec-managerial} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 2 -]40.5;59.5] {Exec-managerial} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 2 -]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 2 -]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 2 -]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]12.5;+inf[ ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 2 -]40.5;59.5] {Other-service, Priv-house-serv} ]12.5;+inf[ ]-inf;36.5] {Married-civ-spouse} {Female} 2 -]40.5;59.5] {Other-service, Priv-house-serv} ]12.5;+inf[ ]43.5;+inf[ {Married-civ-spouse} {Female} 2 -]40.5;59.5] {Other-service, Priv-house-serv} ]12.5;+inf[ ]43.5;+inf[ {Married-civ-spouse} {Male} 2 -]40.5;59.5] {Other-service, Priv-house-serv} ]12.5;+inf[ ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 2 -]40.5;59.5] {Prof-specialty} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 2 -]40.5;59.5] {Prof-specialty} ]-inf;8.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 2 -]40.5;59.5] {Prof-specialty} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 2 -]40.5;59.5] {Prof-specialty} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 2 -]40.5;59.5] {Sales} ]-inf;8.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 2 -]40.5;59.5] {Sales} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 2 -]40.5;59.5] {Sales} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 2 -]59.5;+inf[ {Adm-clerical, Tech-support} ]-inf;8.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 2 -]59.5;+inf[ {Adm-clerical, Tech-support} ]-inf;8.5] ]-inf;36.5] {Married-civ-spouse} {Male} 2 -]59.5;+inf[ {Adm-clerical, Tech-support} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 2 -]59.5;+inf[ {Adm-clerical, Tech-support} ]-inf;8.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 2 -]59.5;+inf[ {Adm-clerical, Tech-support} ]8.5;9.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 2 -]59.5;+inf[ {Adm-clerical, Tech-support} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 2 -]59.5;+inf[ {Adm-clerical, Tech-support} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 2 -]59.5;+inf[ {Adm-clerical, Tech-support} ]12.5;+inf[ ]-inf;36.5] {Married-civ-spouse} {Female} 2 -]59.5;+inf[ {Adm-clerical, Tech-support} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 2 -]59.5;+inf[ {Adm-clerical, Tech-support} ]12.5;+inf[ ]43.5;+inf[ {Married-civ-spouse} {Male} 2 -]59.5;+inf[ {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 2 -]59.5;+inf[ {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Female} 2 -]59.5;+inf[ {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 2 -]59.5;+inf[ {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Female} 2 -]59.5;+inf[ {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 2 -]59.5;+inf[ {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 2 -]59.5;+inf[ {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 2 -]59.5;+inf[ {Craft-repair, Transport-moving, Farming-fishing, ...} ]12.5;+inf[ ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 2 -]59.5;+inf[ {Craft-repair, Transport-moving, Farming-fishing, ...} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 2 -]59.5;+inf[ {Craft-repair, Transport-moving, Farming-fishing, ...} ]12.5;+inf[ ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 2 -]59.5;+inf[ {Exec-managerial} ]-inf;8.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 2 -]59.5;+inf[ {Exec-managerial} ]-inf;8.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 2 -]59.5;+inf[ {Exec-managerial} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Female} 2 -]59.5;+inf[ {Exec-managerial} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 2 -]59.5;+inf[ {Exec-managerial} ]8.5;9.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 2 -]59.5;+inf[ {Exec-managerial} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 2 -]59.5;+inf[ {Exec-managerial} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Female} 2 -]59.5;+inf[ {Exec-managerial} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Female} 2 -]59.5;+inf[ {Exec-managerial} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 2 -]59.5;+inf[ {Exec-managerial} ]9.5;12.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 2 -]59.5;+inf[ {Exec-managerial} ]12.5;+inf[ ]-inf;36.5] {Married-civ-spouse} {Female} 2 -]59.5;+inf[ {Exec-managerial} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 2 -]59.5;+inf[ {Exec-managerial} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 2 -]59.5;+inf[ {Exec-managerial} ]12.5;+inf[ ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 2 -]59.5;+inf[ {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 2 -]59.5;+inf[ {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]-inf;36.5] {Married-civ-spouse} {Female} 2 -]59.5;+inf[ {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 2 -]59.5;+inf[ {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 2 -]59.5;+inf[ {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 2 -]59.5;+inf[ {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Female} 2 -]59.5;+inf[ {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 2 -]59.5;+inf[ {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 2 -]59.5;+inf[ {Other-service, Priv-house-serv} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Female} 2 -]59.5;+inf[ {Other-service, Priv-house-serv} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 2 -]59.5;+inf[ {Other-service, Priv-house-serv} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 2 -]59.5;+inf[ {Other-service, Priv-house-serv} ]8.5;9.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 2 -]59.5;+inf[ {Other-service, Priv-house-serv} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 2 -]59.5;+inf[ {Other-service, Priv-house-serv} ]9.5;12.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 2 -]59.5;+inf[ {Other-service, Priv-house-serv} ]12.5;+inf[ ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 2 -]59.5;+inf[ {Prof-specialty} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 2 -]59.5;+inf[ {Prof-specialty} ]-inf;8.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 2 -]59.5;+inf[ {Prof-specialty} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 2 -]59.5;+inf[ {Prof-specialty} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Female} 2 -]59.5;+inf[ {Prof-specialty} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 2 -]59.5;+inf[ {Prof-specialty} ]9.5;12.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 2 -]59.5;+inf[ {Prof-specialty} ]9.5;12.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 2 -]59.5;+inf[ {Prof-specialty} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 2 -]59.5;+inf[ {Sales} ]-inf;8.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 2 -]59.5;+inf[ {Sales} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Female} 2 -]59.5;+inf[ {Sales} ]12.5;+inf[ ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 2 -]-inf;18.5] {Adm-clerical, Tech-support} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 1 -]-inf;18.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Female} 1 -]-inf;18.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 1 -]-inf;18.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 1 -]-inf;18.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]36.5;43.5] {Married-civ-spouse} {Male} 1 -]-inf;18.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 1 -]-inf;18.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Male} 1 -]-inf;18.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 1 -]-inf;18.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 1 -]-inf;18.5] {Exec-managerial} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 1 -]-inf;18.5] {Exec-managerial} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 1 -]-inf;18.5] {Exec-managerial} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Female} 1 -]-inf;18.5] {Exec-managerial} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 1 -]-inf;18.5] {Exec-managerial} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 1 -]-inf;18.5] {Exec-managerial} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 1 -]-inf;18.5] {Exec-managerial} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 1 -]-inf;18.5] {Exec-managerial} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 1 -]-inf;18.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]36.5;43.5] {Married-civ-spouse} {Male} 1 -]-inf;18.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 1 -]-inf;18.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 1 -]-inf;18.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Male} 1 -]-inf;18.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 1 -]-inf;18.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 1 -]-inf;18.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]-inf;36.5] {Married-civ-spouse} {Male} 1 -]-inf;18.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 1 -]-inf;18.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]36.5;43.5] {Married-civ-spouse} {Male} 1 -]-inf;18.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 1 -]-inf;18.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 1 -]-inf;18.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 1 -]-inf;18.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 1 -]-inf;18.5] {Prof-specialty} ]-inf;8.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 1 -]-inf;18.5] {Prof-specialty} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 1 -]-inf;18.5] {Prof-specialty} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 1 -]-inf;18.5] {Prof-specialty} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Male} 1 -]-inf;18.5] {Prof-specialty} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 1 -]-inf;18.5] {Prof-specialty} ]12.5;+inf[ ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 1 -]-inf;18.5] {Sales} ]-inf;8.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 1 -]-inf;18.5] {Sales} ]-inf;8.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 1 -]-inf;18.5] {Sales} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Female} 1 -]-inf;18.5] {Sales} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 1 -]-inf;18.5] {Sales} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 1 -]18.5;23.5] {Adm-clerical, Tech-support} ]-inf;8.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 1 -]18.5;23.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 1 -]18.5;23.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 1 -]18.5;23.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 1 -]18.5;23.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Male} 1 -]18.5;23.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 1 -]18.5;23.5] {Adm-clerical, Tech-support} ]12.5;+inf[ ]-inf;36.5] {Married-civ-spouse} {Female} 1 -]18.5;23.5] {Adm-clerical, Tech-support} ]12.5;+inf[ ]36.5;43.5] {Married-civ-spouse} {Female} 1 -]18.5;23.5] {Adm-clerical, Tech-support} ]12.5;+inf[ ]36.5;43.5] {Married-civ-spouse} {Male} 1 -]18.5;23.5] {Adm-clerical, Tech-support} ]12.5;+inf[ ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 1 -]18.5;23.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 1 -]18.5;23.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 1 -]18.5;23.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 1 -]18.5;23.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Female} 1 -]18.5;23.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 1 -]18.5;23.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 1 -]18.5;23.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 1 -]18.5;23.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 1 -]18.5;23.5] {Exec-managerial} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 1 -]18.5;23.5] {Exec-managerial} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 1 -]18.5;23.5] {Exec-managerial} ]-inf;8.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 1 -]18.5;23.5] {Exec-managerial} ]-inf;8.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 1 -]18.5;23.5] {Exec-managerial} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 1 -]18.5;23.5] {Exec-managerial} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Male} 1 -]18.5;23.5] {Exec-managerial} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 1 -]18.5;23.5] {Exec-managerial} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Male} 1 -]18.5;23.5] {Exec-managerial} ]8.5;9.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 1 -]18.5;23.5] {Exec-managerial} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 1 -]18.5;23.5] {Exec-managerial} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Female} 1 -]18.5;23.5] {Exec-managerial} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Male} 1 -]18.5;23.5] {Exec-managerial} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 1 -]18.5;23.5] {Exec-managerial} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 1 -]18.5;23.5] {Exec-managerial} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Male} 1 -]18.5;23.5] {Exec-managerial} ]9.5;12.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 1 -]18.5;23.5] {Exec-managerial} ]12.5;+inf[ ]-inf;36.5] {Married-civ-spouse} {Male} 1 -]18.5;23.5] {Exec-managerial} ]12.5;+inf[ ]36.5;43.5] {Married-civ-spouse} {Male} 1 -]18.5;23.5] {Exec-managerial} ]12.5;+inf[ ]43.5;+inf[ {Married-civ-spouse} {Female} 1 -]18.5;23.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 1 -]18.5;23.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 1 -]18.5;23.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Female} 1 -]18.5;23.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 1 -]18.5;23.5] {Machine-op-inspct, Handlers-cleaners} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 1 -]18.5;23.5] {Machine-op-inspct, Handlers-cleaners} ]12.5;+inf[ ]43.5;+inf[ {Married-civ-spouse} {Male} 1 -]18.5;23.5] {Machine-op-inspct, Handlers-cleaners} ]12.5;+inf[ ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 1 -]18.5;23.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 1 -]18.5;23.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 1 -]18.5;23.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 1 -]18.5;23.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 1 -]18.5;23.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Female} 1 -]18.5;23.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Female} 1 -]18.5;23.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 1 -]18.5;23.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 1 -]18.5;23.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 1 -]18.5;23.5] {Other-service, Priv-house-serv} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 1 -]18.5;23.5] {Other-service, Priv-house-serv} ]12.5;+inf[ ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 1 -]18.5;23.5] {Other-service, Priv-house-serv} ]12.5;+inf[ ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 1 -]18.5;23.5] {Prof-specialty} ]-inf;8.5] ]-inf;36.5] {Married-civ-spouse} {Female} 1 -]18.5;23.5] {Prof-specialty} ]-inf;8.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 1 -]18.5;23.5] {Prof-specialty} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 1 -]18.5;23.5] {Prof-specialty} ]8.5;9.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 1 -]18.5;23.5] {Prof-specialty} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 1 -]18.5;23.5] {Sales} ]-inf;8.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 1 -]18.5;23.5] {Sales} ]-inf;8.5] ]36.5;43.5] {Married-civ-spouse} {Female} 1 -]18.5;23.5] {Sales} ]-inf;8.5] ]36.5;43.5] {Married-civ-spouse} {Male} 1 -]18.5;23.5] {Sales} ]-inf;8.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 1 -]18.5;23.5] {Sales} ]-inf;8.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 1 -]18.5;23.5] {Sales} ]-inf;8.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 1 -]18.5;23.5] {Sales} ]-inf;8.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 1 -]18.5;23.5] {Sales} ]-inf;8.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 1 -]18.5;23.5] {Sales} ]8.5;9.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 1 -]18.5;23.5] {Sales} ]9.5;12.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 1 -]18.5;23.5] {Sales} ]9.5;12.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 1 -]18.5;23.5] {Sales} ]12.5;+inf[ ]-inf;36.5] {Married-civ-spouse} {Female} 1 -]18.5;23.5] {Sales} ]12.5;+inf[ ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 1 -]18.5;23.5] {Sales} ]12.5;+inf[ ]43.5;+inf[ {Married-civ-spouse} {Male} 1 -]23.5;29.5] {Adm-clerical, Tech-support} ]-inf;8.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 1 -]23.5;29.5] {Adm-clerical, Tech-support} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 1 -]23.5;29.5] {Adm-clerical, Tech-support} ]-inf;8.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 1 -]23.5;29.5] {Adm-clerical, Tech-support} ]-inf;8.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 1 -]23.5;29.5] {Adm-clerical, Tech-support} ]-inf;8.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 1 -]23.5;29.5] {Adm-clerical, Tech-support} ]12.5;+inf[ ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 1 -]23.5;29.5] {Adm-clerical, Tech-support} ]12.5;+inf[ ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 1 -]23.5;29.5] {Adm-clerical, Tech-support} ]12.5;+inf[ ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 1 -]23.5;29.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 1 -]23.5;29.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 1 -]23.5;29.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Female} 1 -]23.5;29.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 1 -]23.5;29.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Female} 1 -]23.5;29.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 1 -]23.5;29.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]12.5;+inf[ ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 1 -]23.5;29.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]12.5;+inf[ ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 1 -]23.5;29.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]12.5;+inf[ ]43.5;+inf[ {Married-civ-spouse} {Female} 1 -]23.5;29.5] {Exec-managerial} ]-inf;8.5] ]36.5;43.5] {Married-civ-spouse} {Female} 1 -]23.5;29.5] {Exec-managerial} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 1 -]23.5;29.5] {Exec-managerial} ]-inf;8.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 1 -]23.5;29.5] {Exec-managerial} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Male} 1 -]23.5;29.5] {Exec-managerial} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 1 -]23.5;29.5] {Exec-managerial} ]9.5;12.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 1 -]23.5;29.5] {Exec-managerial} ]9.5;12.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 1 -]23.5;29.5] {Exec-managerial} ]12.5;+inf[ ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 1 -]23.5;29.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]-inf;36.5] {Married-civ-spouse} {Female} 1 -]23.5;29.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 1 -]23.5;29.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 1 -]23.5;29.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 1 -]23.5;29.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 1 -]23.5;29.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 1 -]23.5;29.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 1 -]23.5;29.5] {Machine-op-inspct, Handlers-cleaners} ]12.5;+inf[ ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 1 -]23.5;29.5] {Machine-op-inspct, Handlers-cleaners} ]12.5;+inf[ ]-inf;36.5] {Married-civ-spouse} {Male} 1 -]23.5;29.5] {Machine-op-inspct, Handlers-cleaners} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 1 -]23.5;29.5] {Machine-op-inspct, Handlers-cleaners} ]12.5;+inf[ ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 1 -]23.5;29.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]-inf;36.5] {Married-civ-spouse} {Male} 1 -]23.5;29.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 1 -]23.5;29.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 1 -]23.5;29.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 1 -]23.5;29.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 1 -]23.5;29.5] {Other-service, Priv-house-serv} ]12.5;+inf[ ]-inf;36.5] {Married-civ-spouse} {Male} 1 -]23.5;29.5] {Other-service, Priv-house-serv} ]12.5;+inf[ ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 1 -]23.5;29.5] {Prof-specialty} ]-inf;8.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 1 -]23.5;29.5] {Prof-specialty} ]-inf;8.5] ]-inf;36.5] {Married-civ-spouse} {Male} 1 -]23.5;29.5] {Prof-specialty} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 1 -]23.5;29.5] {Prof-specialty} ]8.5;9.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 1 -]23.5;29.5] {Prof-specialty} ]9.5;12.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 1 -]23.5;29.5] {Sales} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 1 -]23.5;29.5] {Sales} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 1 -]23.5;29.5] {Sales} ]12.5;+inf[ ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 1 -]29.5;40.5] {Adm-clerical, Tech-support} ]-inf;8.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 1 -]29.5;40.5] {Adm-clerical, Tech-support} ]-inf;8.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 1 -]29.5;40.5] {Adm-clerical, Tech-support} ]-inf;8.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 1 -]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 1 -]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]36.5;43.5] {Married-civ-spouse} {Female} 1 -]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 1 -]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]12.5;+inf[ ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 1 -]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]12.5;+inf[ ]-inf;36.5] {Married-civ-spouse} {Female} 1 -]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]12.5;+inf[ ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 1 -]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]12.5;+inf[ ]43.5;+inf[ {Married-civ-spouse} {Female} 1 -]29.5;40.5] {Exec-managerial} ]-inf;8.5] ]-inf;36.5] {Married-civ-spouse} {Female} 1 -]29.5;40.5] {Exec-managerial} ]-inf;8.5] ]-inf;36.5] {Married-civ-spouse} {Male} 1 -]29.5;40.5] {Exec-managerial} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 1 -]29.5;40.5] {Exec-managerial} ]-inf;8.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 1 -]29.5;40.5] {Exec-managerial} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 1 -]29.5;40.5] {Exec-managerial} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 1 -]29.5;40.5] {Exec-managerial} ]-inf;8.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 1 -]29.5;40.5] {Exec-managerial} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 1 -]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]12.5;+inf[ ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 1 -]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]12.5;+inf[ ]-inf;36.5] {Married-civ-spouse} {Female} 1 -]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 1 -]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]12.5;+inf[ ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 1 -]29.5;40.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 1 -]29.5;40.5] {Other-service, Priv-house-serv} ]12.5;+inf[ ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 1 -]29.5;40.5] {Prof-specialty} ]-inf;8.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 1 -]29.5;40.5] {Prof-specialty} ]-inf;8.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 1 -]29.5;40.5] {Prof-specialty} ]-inf;8.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 1 -]29.5;40.5] {Sales} ]-inf;8.5] ]36.5;43.5] {Married-civ-spouse} {Female} 1 -]29.5;40.5] {Sales} ]-inf;8.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 1 -]29.5;40.5] {Sales} ]-inf;8.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 1 -]29.5;40.5] {Sales} ]-inf;8.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 1 -]29.5;40.5] {Sales} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 1 -]29.5;40.5] {Sales} ]9.5;12.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 1 -]40.5;59.5] {Adm-clerical, Tech-support} ]-inf;8.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 1 -]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 1 -]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 1 -]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 1 -]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 1 -]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]12.5;+inf[ ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 1 -]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 1 -]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]12.5;+inf[ ]36.5;43.5] {Married-civ-spouse} {Female} 1 -]40.5;59.5] {Exec-managerial} ]-inf;8.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 1 -]40.5;59.5] {Exec-managerial} ]-inf;8.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 1 -]40.5;59.5] {Exec-managerial} ]-inf;8.5] ]36.5;43.5] {Married-civ-spouse} {Female} 1 -]40.5;59.5] {Exec-managerial} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 1 -]40.5;59.5] {Exec-managerial} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 1 -]40.5;59.5] {Exec-managerial} ]-inf;8.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 1 -]40.5;59.5] {Exec-managerial} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 1 -]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 1 -]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 1 -]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 1 -]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 1 -]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]12.5;+inf[ ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 1 -]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]12.5;+inf[ ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 1 -]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 1 -]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 1 -]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]12.5;+inf[ ]36.5;43.5] {Married-civ-spouse} {Female} 1 -]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 1 -]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]12.5;+inf[ ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 1 -]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]12.5;+inf[ ]43.5;+inf[ {Married-civ-spouse} {Female} 1 -]40.5;59.5] {Other-service, Priv-house-serv} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 1 -]40.5;59.5] {Other-service, Priv-house-serv} ]12.5;+inf[ ]36.5;43.5] {Married-civ-spouse} {Female} 1 -]40.5;59.5] {Prof-specialty} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 1 -]40.5;59.5] {Prof-specialty} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 1 -]40.5;59.5] {Prof-specialty} ]-inf;8.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 1 -]40.5;59.5] {Prof-specialty} ]-inf;8.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 1 -]40.5;59.5] {Sales} ]-inf;8.5] ]36.5;43.5] {Married-civ-spouse} {Female} 1 -]40.5;59.5] {Sales} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 1 -]40.5;59.5] {Sales} ]-inf;8.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 1 -]40.5;59.5] {Sales} ]-inf;8.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 1 -]40.5;59.5] {Sales} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 1 -]40.5;59.5] {Sales} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 1 -]59.5;+inf[ {Adm-clerical, Tech-support} ]-inf;8.5] ]-inf;36.5] {Married-civ-spouse} {Female} 1 -]59.5;+inf[ {Adm-clerical, Tech-support} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 1 -]59.5;+inf[ {Adm-clerical, Tech-support} ]-inf;8.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 1 -]59.5;+inf[ {Adm-clerical, Tech-support} ]-inf;8.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 1 -]59.5;+inf[ {Adm-clerical, Tech-support} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 1 -]59.5;+inf[ {Adm-clerical, Tech-support} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 1 -]59.5;+inf[ {Adm-clerical, Tech-support} ]8.5;9.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 1 -]59.5;+inf[ {Adm-clerical, Tech-support} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 1 -]59.5;+inf[ {Adm-clerical, Tech-support} ]9.5;12.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 1 -]59.5;+inf[ {Adm-clerical, Tech-support} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 1 -]59.5;+inf[ {Adm-clerical, Tech-support} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 1 -]59.5;+inf[ {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]36.5;43.5] {Married-civ-spouse} {Female} 1 -]59.5;+inf[ {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 1 -]59.5;+inf[ {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 1 -]59.5;+inf[ {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 1 -]59.5;+inf[ {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Female} 1 -]59.5;+inf[ {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 1 -]59.5;+inf[ {Craft-repair, Transport-moving, Farming-fishing, ...} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 1 -]59.5;+inf[ {Craft-repair, Transport-moving, Farming-fishing, ...} ]12.5;+inf[ ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 1 -]59.5;+inf[ {Craft-repair, Transport-moving, Farming-fishing, ...} ]12.5;+inf[ ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 1 -]59.5;+inf[ {Exec-managerial} ]-inf;8.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 1 -]59.5;+inf[ {Exec-managerial} ]-inf;8.5] ]-inf;36.5] {Married-civ-spouse} {Female} 1 -]59.5;+inf[ {Exec-managerial} ]-inf;8.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 1 -]59.5;+inf[ {Exec-managerial} ]-inf;8.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 1 -]59.5;+inf[ {Exec-managerial} ]-inf;8.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 1 -]59.5;+inf[ {Exec-managerial} ]-inf;8.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 1 -]59.5;+inf[ {Exec-managerial} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Female} 1 -]59.5;+inf[ {Exec-managerial} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 1 -]59.5;+inf[ {Exec-managerial} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 1 -]59.5;+inf[ {Exec-managerial} ]8.5;9.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 1 -]59.5;+inf[ {Exec-managerial} ]8.5;9.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 1 -]59.5;+inf[ {Exec-managerial} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 1 -]59.5;+inf[ {Exec-managerial} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 1 -]59.5;+inf[ {Exec-managerial} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 1 -]59.5;+inf[ {Exec-managerial} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 1 -]59.5;+inf[ {Exec-managerial} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 1 -]59.5;+inf[ {Exec-managerial} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 1 -]59.5;+inf[ {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 1 -]59.5;+inf[ {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 1 -]59.5;+inf[ {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 1 -]59.5;+inf[ {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 1 -]59.5;+inf[ {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Female} 1 -]59.5;+inf[ {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 1 -]59.5;+inf[ {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 1 -]59.5;+inf[ {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 1 -]59.5;+inf[ {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 1 -]59.5;+inf[ {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 1 -]59.5;+inf[ {Machine-op-inspct, Handlers-cleaners} ]12.5;+inf[ ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 1 -]59.5;+inf[ {Machine-op-inspct, Handlers-cleaners} ]12.5;+inf[ ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 1 -]59.5;+inf[ {Machine-op-inspct, Handlers-cleaners} ]12.5;+inf[ ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 1 -]59.5;+inf[ {Machine-op-inspct, Handlers-cleaners} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 1 -]59.5;+inf[ {Machine-op-inspct, Handlers-cleaners} ]12.5;+inf[ ]43.5;+inf[ {Married-civ-spouse} {Male} 1 -]59.5;+inf[ {Other-service, Priv-house-serv} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 1 -]59.5;+inf[ {Other-service, Priv-house-serv} ]-inf;8.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 1 -]59.5;+inf[ {Other-service, Priv-house-serv} ]-inf;8.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 1 -]59.5;+inf[ {Other-service, Priv-house-serv} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 1 -]59.5;+inf[ {Other-service, Priv-house-serv} ]8.5;9.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 1 -]59.5;+inf[ {Other-service, Priv-house-serv} ]8.5;9.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 1 -]59.5;+inf[ {Other-service, Priv-house-serv} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 1 -]59.5;+inf[ {Other-service, Priv-house-serv} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Female} 1 -]59.5;+inf[ {Other-service, Priv-house-serv} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 1 -]59.5;+inf[ {Other-service, Priv-house-serv} ]9.5;12.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 1 -]59.5;+inf[ {Other-service, Priv-house-serv} ]9.5;12.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 1 -]59.5;+inf[ {Other-service, Priv-house-serv} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 1 -]59.5;+inf[ {Other-service, Priv-house-serv} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 1 -]59.5;+inf[ {Other-service, Priv-house-serv} ]12.5;+inf[ ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 1 -]59.5;+inf[ {Other-service, Priv-house-serv} ]12.5;+inf[ ]36.5;43.5] {Married-civ-spouse} {Male} 1 -]59.5;+inf[ {Other-service, Priv-house-serv} ]12.5;+inf[ ]43.5;+inf[ {Married-civ-spouse} {Male} 1 -]59.5;+inf[ {Prof-specialty} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 1 -]59.5;+inf[ {Prof-specialty} ]-inf;8.5] ]36.5;43.5] {Married-civ-spouse} {Female} 1 -]59.5;+inf[ {Prof-specialty} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 1 -]59.5;+inf[ {Prof-specialty} ]-inf;8.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 1 -]59.5;+inf[ {Prof-specialty} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 1 -]59.5;+inf[ {Prof-specialty} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Female} 1 -]59.5;+inf[ {Prof-specialty} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 1 -]59.5;+inf[ {Prof-specialty} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 1 -]59.5;+inf[ {Sales} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 1 -]59.5;+inf[ {Sales} ]-inf;8.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 1 -]59.5;+inf[ {Sales} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 1 -]59.5;+inf[ {Sales} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 1 -]59.5;+inf[ {Sales} ]8.5;9.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 1 -]59.5;+inf[ {Sales} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 1 -]59.5;+inf[ {Sales} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 1 -]59.5;+inf[ {Sales} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 1 -]59.5;+inf[ {Sales} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 1 - +#Khiops 10.0.0.3i +Short description +Dimensions 6 +Name Is variable part Type Parts Initial parts Values Interest Description +age False Numerical 6 6 48842 1 +occupation False Categorical 7 7 14 1 +education_num False Numerical 4 4 48842 1 +hours_per_week False Numerical 3 3 48842 1 +marital_status False Categorical 3 3 7 1 +sex False Categorical 2 2 2 1 + +Coclustering stats +Instances 48842 +Cells 2218 +Null cost 1723780 +Cost 1689150 +Level 0.02008957059 +Initial dimensions 9 +Frequency variable +Dictionary Adult +Database ../../../datasets/Adult/Adult.txt +Sample percentage 100 +Sampling mode Include sample +Selection variable +Selection value + +Bounds +Name Min Max +age 17 90 +education_num 1 16 +hours_per_week 1 99 + +Hierarchy age +Cluster ParentCluster Frequency Interest HierarchicalLevel Rank HierarchicalRank +]-inf;18.5] ]-inf;23.5] 1457 0.347768 1 1 25 +]18.5;23.5] ]-inf;23.5] 5769 0.883906 1 3 25 +]23.5;29.5] ]-inf;29.5] 7289 1 1 5 25 +]29.5;40.5] ]29.5;59.5] 14116 0.871164 1 7 25 +]40.5;59.5] ]29.5;59.5] 16156 0.848966 1 9 25 +]59.5;+inf[ ]29.5;+inf[ 4055 0.441664 1 11 25 +]-inf;23.5] ]-inf;29.5] 7226 0.775803 0.974251 2 22 +]-inf;29.5] ]-inf;+inf[ 14515 0.888388 0.745479 4 14 +]-inf;+inf[ 48842 0.833282 -0.000287731 6 7 +]29.5;59.5] ]29.5;+inf[ 30272 0.859317 0.948553 8 20 +]29.5;+inf[ ]-inf;+inf[ 34327 0.80998 0.850791 10 16 + +Hierarchy occupation +Cluster ParentCluster Frequency Interest HierarchicalLevel Rank HierarchicalRank +{Craft-repair, Transport-moving, Farming-fishing, ...} B18 10955 1 1 1 25 +{Machine-op-inspct, Handlers-cleaners} B18 5094 0.695825 1 3 25 +{Prof-specialty} B12 8981 0.864201 1 5 25 +{Exec-managerial} B19 6086 0.705213 1 7 25 +{Sales} B19 5504 0.534419 1 9 25 +{Other-service, Priv-house-serv} B14 5165 0.723836 1 11 25 +{Adm-clerical, Tech-support} B14 7057 0.715347 1 13 25 +B18 B5 16049 0.903454 0.988438 2 23 +B5 48842 0.783774 0.383406 4 10 +B12 B7 20571 0.728927 0.889146 6 17 +B19 B12 11590 0.624104 0.994704 8 24 +B7 B5 32793 0.725202 0.583172 10 12 +B14 B7 12222 0.718934 0.93014 12 19 + +Hierarchy education_num +Cluster ParentCluster Frequency Interest HierarchicalLevel Rank HierarchicalRank +]-inf;8.5] ]-inf;9.5] 6408 0.330495 1 1 25 +]8.5;9.5] ]-inf;9.5] 15784 0.671555 1 3 25 +]9.5;12.5] ]-inf;12.5] 14540 1 1 5 25 +]12.5;+inf[ ]-inf;+inf[ 12110 0.65894 1 7 25 +]-inf;9.5] ]-inf;12.5] 22192 0.573073 0.964171 2 21 +]-inf;12.5] ]-inf;+inf[ 36732 0.742068 0.911382 4 18 +]-inf;+inf[ 48842 0.721457 0.474326 6 11 + +Hierarchy hours_per_week +Cluster ParentCluster Frequency Interest HierarchicalLevel Rank HierarchicalRank +]-inf;36.5] ]-inf;+inf[ 10668 0.604579 1 1 25 +]36.5;43.5] ]36.5;+inf[ 24446 1 1 3 25 +]43.5;+inf[ ]36.5;+inf[ 13728 0.395421 1 5 25 +]-inf;+inf[ 48842 0.743704 0.664957 2 13 +]36.5;+inf[ ]-inf;+inf[ 38174 0.782583 0.808675 4 15 + +Hierarchy marital_status +Cluster ParentCluster Frequency Interest HierarchicalLevel Rank HierarchicalRank +{Never-married, Married-AF-spouse} E1 16154 1 1 1 25 +{Married-civ-spouse} E4 22379 0.936208 1 3 25 +{Divorced, Widowed, Separated, ...} E4 10309 0.730455 1 5 25 +E1 48842 0.913879 0 2 6 +E4 E1 32688 0.871319 0.248722 4 9 + +Hierarchy sex +Cluster ParentCluster Frequency Interest HierarchicalLevel Rank HierarchicalRank +{Female} F3 16192 1 1 1 25 +{Male} F3 32650 1 1 3 25 +F3 48842 1 0.227766 2 8 + +Composition occupation +Cluster Value Frequency Typicality +{Craft-repair, Transport-moving, Farming-fishing, ...} Craft-repair 6112 1 +{Craft-repair, Transport-moving, Farming-fishing, ...} Transport-moving 2355 0.573309 +{Craft-repair, Transport-moving, Farming-fishing, ...} Farming-fishing 1490 0.29446 +{Craft-repair, Transport-moving, Farming-fishing, ...} Protective-serv 983 0.106369 +{Craft-repair, Transport-moving, Farming-fishing, ...} Armed-Forces 15 0.0033027 +{Machine-op-inspct, Handlers-cleaners} Machine-op-inspct 3022 1 +{Machine-op-inspct, Handlers-cleaners} Handlers-cleaners 2072 0.798568 +{Prof-specialty} Prof-specialty 8981 1 +{Exec-managerial} Exec-managerial 6086 1 +{Sales} Sales 5504 1 +{Other-service, Priv-house-serv} Other-service 4923 1 +{Other-service, Priv-house-serv} Priv-house-serv 242 0.111602 +{Adm-clerical, Tech-support} Adm-clerical 5611 1 +{Adm-clerical, Tech-support} Tech-support 1446 0.187174 +{Adm-clerical, Tech-support} * 0 0 + +Composition marital_status +Cluster Value Frequency Typicality +{Never-married, Married-AF-spouse} Never-married 16117 1 +{Never-married, Married-AF-spouse} Married-AF-spouse 37 0.00173382 +{Married-civ-spouse} Married-civ-spouse 22379 1 +{Divorced, Widowed, Separated, ...} Divorced 6633 1 +{Divorced, Widowed, Separated, ...} Widowed 1518 0.443082 +{Divorced, Widowed, Separated, ...} Separated 1530 0.265394 +{Divorced, Widowed, Separated, ...} Married-spouse-absent 628 0.0718638 +{Divorced, Widowed, Separated, ...} * 0 0 + +Composition sex +Cluster Value Frequency Typicality +{Female} Female 16192 1 +{Male} Male 32650 1 +{Male} * 0 0 + +Cells +age occupation education_num hours_per_week marital_status sex Frequency +]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Male} 741 +]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Male} 592 +]40.5;59.5] {Prof-specialty} ]12.5;+inf[ ]43.5;+inf[ {Married-civ-spouse} {Male} 569 +]40.5;59.5] {Exec-managerial} ]12.5;+inf[ ]43.5;+inf[ {Married-civ-spouse} {Male} 557 +]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 480 +]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Male} 457 +]40.5;59.5] {Prof-specialty} ]12.5;+inf[ ]36.5;43.5] {Married-civ-spouse} {Male} 447 +]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 437 +]29.5;40.5] {Prof-specialty} ]12.5;+inf[ ]43.5;+inf[ {Married-civ-spouse} {Male} 408 +]40.5;59.5] {Exec-managerial} ]12.5;+inf[ ]36.5;43.5] {Married-civ-spouse} {Male} 360 +]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Male} 359 +]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 358 +]29.5;40.5] {Exec-managerial} ]12.5;+inf[ ]43.5;+inf[ {Married-civ-spouse} {Male} 353 +]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]36.5;43.5] {Married-civ-spouse} {Male} 298 +]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 295 +]29.5;40.5] {Prof-specialty} ]12.5;+inf[ ]36.5;43.5] {Married-civ-spouse} {Male} 294 +]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Male} 280 +]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Male} 275 +]40.5;59.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 273 +]40.5;59.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 260 +]18.5;23.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 239 +]40.5;59.5] {Exec-managerial} ]9.5;12.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 233 +]18.5;23.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 232 +]40.5;59.5] {Sales} ]12.5;+inf[ ]43.5;+inf[ {Married-civ-spouse} {Male} 225 +]18.5;23.5] {Prof-specialty} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 216 +]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]36.5;43.5] {Married-civ-spouse} {Male} 207 +]23.5;29.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Male} 204 +]18.5;23.5] {Sales} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 197 +]40.5;59.5] {Sales} ]9.5;12.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 196 +]23.5;29.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 189 +]29.5;40.5] {Sales} ]12.5;+inf[ ]43.5;+inf[ {Married-civ-spouse} {Male} 184 +]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 184 +]29.5;40.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 181 +]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 179 +]40.5;59.5] {Exec-managerial} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Male} 179 +]40.5;59.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Male} 176 +]23.5;29.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 174 +]29.5;40.5] {Exec-managerial} ]12.5;+inf[ ]36.5;43.5] {Married-civ-spouse} {Male} 174 +]18.5;23.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 165 +]29.5;40.5] {Exec-managerial} ]9.5;12.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 164 +]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]36.5;43.5] {Married-civ-spouse} {Male} 164 +]-inf;18.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 162 +]40.5;59.5] {Sales} ]8.5;9.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 160 +]18.5;23.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 158 +]-inf;18.5] {Sales} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 151 +]23.5;29.5] {Prof-specialty} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 150 +]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 150 +]40.5;59.5] {Prof-specialty} ]12.5;+inf[ ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 150 +]29.5;40.5] {Sales} ]9.5;12.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 146 +]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 145 +]18.5;23.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 144 +]29.5;40.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 143 +]18.5;23.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 142 +]18.5;23.5] {Prof-specialty} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 141 +]40.5;59.5] {Exec-managerial} ]8.5;9.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 141 +]29.5;40.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Male} 140 +]-inf;18.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 138 +]59.5;+inf[ {Prof-specialty} ]12.5;+inf[ ]-inf;36.5] {Married-civ-spouse} {Male} 137 +]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]12.5;+inf[ ]43.5;+inf[ {Married-civ-spouse} {Male} 136 +]40.5;59.5] {Sales} ]12.5;+inf[ ]36.5;43.5] {Married-civ-spouse} {Male} 136 +]29.5;40.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 135 +]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Male} 128 +]40.5;59.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Male} 128 +]40.5;59.5] {Adm-clerical, Tech-support} ]12.5;+inf[ ]36.5;43.5] {Married-civ-spouse} {Male} 127 +]23.5;29.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 126 +]23.5;29.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 125 +]40.5;59.5] {Prof-specialty} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Male} 125 +]29.5;40.5] {Exec-managerial} ]8.5;9.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 123 +]29.5;40.5] {Prof-specialty} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 121 +]29.5;40.5] {Sales} ]8.5;9.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 119 +]18.5;23.5] {Prof-specialty} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 118 +]40.5;59.5] {Exec-managerial} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Male} 118 +]40.5;59.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 117 +]40.5;59.5] {Sales} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Male} 116 +]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 115 +]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 114 +]23.5;29.5] {Prof-specialty} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 111 +]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]36.5;43.5] {Married-civ-spouse} {Male} 111 +]23.5;29.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 110 +]59.5;+inf[ {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]36.5;43.5] {Married-civ-spouse} {Male} 110 +]59.5;+inf[ {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Male} 110 +]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 109 +]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 109 +]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 109 +]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]12.5;+inf[ ]36.5;43.5] {Married-civ-spouse} {Male} 107 +]18.5;23.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 106 +]23.5;29.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Male} 106 +]29.5;40.5] {Prof-specialty} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 106 +]29.5;40.5] {Prof-specialty} ]12.5;+inf[ ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 106 +]23.5;29.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Male} 105 +]18.5;23.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 104 +]23.5;29.5] {Adm-clerical, Tech-support} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 104 +]18.5;23.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 103 +]23.5;29.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 102 +]23.5;29.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 102 +]23.5;29.5] {Prof-specialty} ]12.5;+inf[ ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 102 +]40.5;59.5] {Prof-specialty} ]12.5;+inf[ ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 101 +]40.5;59.5] {Sales} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Male} 101 +]40.5;59.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 100 +]29.5;40.5] {Sales} ]12.5;+inf[ ]36.5;43.5] {Married-civ-spouse} {Male} 98 +]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 98 +]40.5;59.5] {Prof-specialty} ]12.5;+inf[ ]-inf;36.5] {Married-civ-spouse} {Male} 98 +]18.5;23.5] {Prof-specialty} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 97 +]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 97 +]29.5;40.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 94 +]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 94 +]59.5;+inf[ {Prof-specialty} ]12.5;+inf[ ]43.5;+inf[ {Married-civ-spouse} {Male} 93 +]-inf;18.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 92 +]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Male} 92 +]59.5;+inf[ {Prof-specialty} ]12.5;+inf[ ]36.5;43.5] {Married-civ-spouse} {Male} 92 +]18.5;23.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 90 +]29.5;40.5] {Adm-clerical, Tech-support} ]12.5;+inf[ ]36.5;43.5] {Married-civ-spouse} {Male} 90 +]29.5;40.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Male} 89 +]29.5;40.5] {Exec-managerial} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Male} 88 +]18.5;23.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 87 +]18.5;23.5] {Sales} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 87 +]40.5;59.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Male} 87 +]59.5;+inf[ {Prof-specialty} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Male} 87 +]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]12.5;+inf[ ]43.5;+inf[ {Married-civ-spouse} {Male} 86 +]40.5;59.5] {Prof-specialty} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 86 +]29.5;40.5] {Exec-managerial} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Male} 83 +]29.5;40.5] {Prof-specialty} ]12.5;+inf[ ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 82 +]40.5;59.5] {Exec-managerial} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 80 +]40.5;59.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 80 +]40.5;59.5] {Exec-managerial} ]12.5;+inf[ ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 79 +]59.5;+inf[ {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Male} 79 +]23.5;29.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 78 +]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 78 +]18.5;23.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 77 +]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 77 +]29.5;40.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 77 +]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 76 +]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]12.5;+inf[ ]36.5;43.5] {Married-civ-spouse} {Male} 75 +]29.5;40.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 74 +]29.5;40.5] {Prof-specialty} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Male} 74 +]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Male} 74 +]18.5;23.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 73 +]18.5;23.5] {Sales} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 73 +]29.5;40.5] {Sales} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Male} 73 +]40.5;59.5] {Prof-specialty} ]9.5;12.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 73 +]18.5;23.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 72 +]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 72 +]40.5;59.5] {Prof-specialty} ]12.5;+inf[ ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 72 +]40.5;59.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Female} 71 +]40.5;59.5] {Prof-specialty} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Male} 71 +]23.5;29.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 70 +]29.5;40.5] {Exec-managerial} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 70 +]-inf;18.5] {Prof-specialty} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 69 +]-inf;18.5] {Prof-specialty} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 69 +]23.5;29.5] {Prof-specialty} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 69 +]40.5;59.5] {Prof-specialty} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 69 +]23.5;29.5] {Prof-specialty} ]12.5;+inf[ ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 68 +]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Male} 68 +]29.5;40.5] {Prof-specialty} ]12.5;+inf[ ]-inf;36.5] {Married-civ-spouse} {Female} 68 +]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 68 +]40.5;59.5] {Exec-managerial} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 68 +]18.5;23.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 67 +]18.5;23.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 67 +]40.5;59.5] {Exec-managerial} ]12.5;+inf[ ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 67 +]23.5;29.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]36.5;43.5] {Married-civ-spouse} {Male} 66 +]23.5;29.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 66 +]23.5;29.5] {Adm-clerical, Tech-support} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 65 +]29.5;40.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Male} 65 +]18.5;23.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 64 +]23.5;29.5] {Exec-managerial} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 64 +]29.5;40.5] {Prof-specialty} ]12.5;+inf[ ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 64 +]59.5;+inf[ {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 64 +]23.5;29.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 63 +]40.5;59.5] {Prof-specialty} ]12.5;+inf[ ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 63 +]59.5;+inf[ {Prof-specialty} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 63 +]29.5;40.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 62 +]40.5;59.5] {Exec-managerial} ]12.5;+inf[ ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 62 +]40.5;59.5] {Sales} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 62 +]18.5;23.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 61 +]23.5;29.5] {Prof-specialty} ]12.5;+inf[ ]36.5;43.5] {Married-civ-spouse} {Male} 61 +]29.5;40.5] {Adm-clerical, Tech-support} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 61 +]29.5;40.5] {Sales} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Male} 61 +]40.5;59.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 61 +]40.5;59.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 61 +]59.5;+inf[ {Adm-clerical, Tech-support} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 61 +]59.5;+inf[ {Other-service, Priv-house-serv} ]-inf;8.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 61 +]59.5;+inf[ {Prof-specialty} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Male} 61 +]29.5;40.5] {Exec-managerial} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 60 +]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 60 +]40.5;59.5] {Prof-specialty} ]12.5;+inf[ ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 60 +]40.5;59.5] {Prof-specialty} ]12.5;+inf[ ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 60 +]59.5;+inf[ {Other-service, Priv-house-serv} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 60 +]59.5;+inf[ {Prof-specialty} ]-inf;8.5] ]-inf;36.5] {Married-civ-spouse} {Male} 60 +]59.5;+inf[ {Prof-specialty} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Male} 60 +]18.5;23.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 59 +]29.5;40.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 59 +]29.5;40.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 59 +]40.5;59.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 59 +]40.5;59.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 59 +]40.5;59.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]36.5;43.5] {Married-civ-spouse} {Male} 59 +]59.5;+inf[ {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]-inf;36.5] {Married-civ-spouse} {Male} 59 +]18.5;23.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 58 +]23.5;29.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 58 +]23.5;29.5] {Prof-specialty} ]12.5;+inf[ ]43.5;+inf[ {Married-civ-spouse} {Male} 58 +]29.5;40.5] {Exec-managerial} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 58 +]29.5;40.5] {Exec-managerial} ]12.5;+inf[ ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 58 +]40.5;59.5] {Prof-specialty} ]12.5;+inf[ ]36.5;43.5] {Married-civ-spouse} {Female} 58 +]23.5;29.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 57 +]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 57 +]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 57 +]59.5;+inf[ {Exec-managerial} ]12.5;+inf[ ]43.5;+inf[ {Married-civ-spouse} {Male} 57 +]23.5;29.5] {Sales} ]12.5;+inf[ ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 56 +]29.5;40.5] {Exec-managerial} ]12.5;+inf[ ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 56 +]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 56 +]40.5;59.5] {Exec-managerial} ]9.5;12.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 56 +]40.5;59.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 56 +]40.5;59.5] {Prof-specialty} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 56 +]59.5;+inf[ {Exec-managerial} ]12.5;+inf[ ]36.5;43.5] {Married-civ-spouse} {Male} 56 +]18.5;23.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 55 +]23.5;29.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 55 +]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 55 +]29.5;40.5] {Prof-specialty} ]12.5;+inf[ ]36.5;43.5] {Married-civ-spouse} {Female} 55 +]40.5;59.5] {Adm-clerical, Tech-support} ]12.5;+inf[ ]43.5;+inf[ {Married-civ-spouse} {Male} 55 +]23.5;29.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 54 +]29.5;40.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 54 +]29.5;40.5] {Adm-clerical, Tech-support} ]12.5;+inf[ ]43.5;+inf[ {Married-civ-spouse} {Male} 54 +]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 54 +]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 54 +]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 54 +]23.5;29.5] {Exec-managerial} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 53 +]23.5;29.5] {Prof-specialty} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 53 +]29.5;40.5] {Prof-specialty} ]9.5;12.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 53 +]40.5;59.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Female} 53 +]40.5;59.5] {Adm-clerical, Tech-support} ]12.5;+inf[ ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 53 +]59.5;+inf[ {Adm-clerical, Tech-support} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 53 +]-inf;18.5] {Sales} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 52 +]23.5;29.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]36.5;43.5] {Married-civ-spouse} {Male} 52 +]29.5;40.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Female} 52 +]18.5;23.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 51 +]18.5;23.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 51 +]23.5;29.5] {Sales} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 51 +]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 51 +]59.5;+inf[ {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 51 +]23.5;29.5] {Exec-managerial} ]12.5;+inf[ ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 50 +]23.5;29.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 50 +]29.5;40.5] {Prof-specialty} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 50 +]40.5;59.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 50 +]40.5;59.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 50 +]40.5;59.5] {Prof-specialty} ]12.5;+inf[ ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 50 +]23.5;29.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Male} 49 +]23.5;29.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 49 +]23.5;29.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Male} 49 +]29.5;40.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Female} 49 +]40.5;59.5] {Prof-specialty} ]12.5;+inf[ ]-inf;36.5] {Married-civ-spouse} {Female} 49 +]18.5;23.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 48 +]23.5;29.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 48 +]29.5;40.5] {Sales} ]12.5;+inf[ ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 48 +]40.5;59.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 48 +]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Male} 48 +]40.5;59.5] {Exec-managerial} ]12.5;+inf[ ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 48 +]40.5;59.5] {Prof-specialty} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 48 +]29.5;40.5] {Adm-clerical, Tech-support} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 47 +]29.5;40.5] {Prof-specialty} ]12.5;+inf[ ]43.5;+inf[ {Married-civ-spouse} {Female} 47 +]40.5;59.5] {Prof-specialty} ]12.5;+inf[ ]43.5;+inf[ {Married-civ-spouse} {Female} 47 +]18.5;23.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 46 +]23.5;29.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 46 +]29.5;40.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 46 +]29.5;40.5] {Prof-specialty} ]12.5;+inf[ ]-inf;36.5] {Married-civ-spouse} {Male} 46 +]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 46 +]23.5;29.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 45 +]40.5;59.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 45 +]18.5;23.5] {Sales} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 44 +]23.5;29.5] {Sales} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 44 +]29.5;40.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 44 +]40.5;59.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 44 +]-inf;18.5] {Adm-clerical, Tech-support} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 43 +]18.5;23.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 43 +]23.5;29.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 43 +]23.5;29.5] {Exec-managerial} ]12.5;+inf[ ]43.5;+inf[ {Married-civ-spouse} {Male} 43 +]23.5;29.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 43 +]29.5;40.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 43 +]29.5;40.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 43 +]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 43 +]29.5;40.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 43 +]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 43 +]-inf;18.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 42 +]29.5;40.5] {Prof-specialty} ]12.5;+inf[ ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 42 +]29.5;40.5] {Sales} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 42 +]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]-inf;36.5] {Married-civ-spouse} {Male} 42 +]18.5;23.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 41 +]18.5;23.5] {Sales} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 41 +]23.5;29.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 41 +]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 41 +]40.5;59.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 41 +]40.5;59.5] {Sales} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 41 +]59.5;+inf[ {Prof-specialty} ]12.5;+inf[ ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 41 +]18.5;23.5] {Sales} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 40 +]23.5;29.5] {Sales} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 40 +]23.5;29.5] {Sales} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 40 +]23.5;29.5] {Sales} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 40 +]40.5;59.5] {Sales} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 40 +]40.5;59.5] {Sales} ]9.5;12.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 40 +]40.5;59.5] {Sales} ]12.5;+inf[ ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 40 +]59.5;+inf[ {Adm-clerical, Tech-support} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 40 +]18.5;23.5] {Prof-specialty} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 39 +]23.5;29.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Female} 39 +]29.5;40.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Male} 39 +]40.5;59.5] {Exec-managerial} ]9.5;12.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 39 +]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 39 +]59.5;+inf[ {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Male} 39 +]18.5;23.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 38 +]18.5;23.5] {Prof-specialty} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 38 +]29.5;40.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 38 +]29.5;40.5] {Prof-specialty} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Male} 38 +]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 38 +]59.5;+inf[ {Prof-specialty} ]-inf;8.5] ]36.5;43.5] {Married-civ-spouse} {Male} 38 +]59.5;+inf[ {Prof-specialty} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 38 +]18.5;23.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 37 +]23.5;29.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 37 +]23.5;29.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 37 +]23.5;29.5] {Sales} ]12.5;+inf[ ]43.5;+inf[ {Married-civ-spouse} {Male} 37 +]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 37 +]29.5;40.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 37 +]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 37 +]40.5;59.5] {Prof-specialty} ]8.5;9.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 37 +]40.5;59.5] {Prof-specialty} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 37 +]18.5;23.5] {Adm-clerical, Tech-support} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 36 +]18.5;23.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Male} 36 +]18.5;23.5] {Prof-specialty} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 36 +]18.5;23.5] {Prof-specialty} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 36 +]23.5;29.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 36 +]23.5;29.5] {Exec-managerial} ]12.5;+inf[ ]36.5;43.5] {Married-civ-spouse} {Male} 36 +]23.5;29.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 36 +]23.5;29.5] {Prof-specialty} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 36 +]29.5;40.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]36.5;43.5] {Married-civ-spouse} {Male} 36 +]59.5;+inf[ {Exec-managerial} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Male} 36 +]18.5;23.5] {Prof-specialty} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 35 +]23.5;29.5] {Prof-specialty} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 35 +]29.5;40.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 35 +]29.5;40.5] {Exec-managerial} ]12.5;+inf[ ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 35 +]29.5;40.5] {Prof-specialty} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 35 +]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]12.5;+inf[ ]36.5;43.5] {Married-civ-spouse} {Male} 35 +]18.5;23.5] {Sales} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 34 +]23.5;29.5] {Exec-managerial} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 34 +]23.5;29.5] {Exec-managerial} ]12.5;+inf[ ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 34 +]23.5;29.5] {Sales} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 34 +]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 34 +]29.5;40.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 34 +]29.5;40.5] {Prof-specialty} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 34 +]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 34 +]59.5;+inf[ {Adm-clerical, Tech-support} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 34 +]59.5;+inf[ {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]36.5;43.5] {Married-civ-spouse} {Male} 34 +]59.5;+inf[ {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Male} 34 +]18.5;23.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 33 +]23.5;29.5] {Adm-clerical, Tech-support} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 33 +]23.5;29.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 33 +]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 33 +]29.5;40.5] {Exec-managerial} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 33 +]29.5;40.5] {Sales} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 33 +]40.5;59.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 33 +]40.5;59.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 33 +]40.5;59.5] {Exec-managerial} ]8.5;9.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 33 +]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 33 +]18.5;23.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 32 +]18.5;23.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 32 +]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]-inf;36.5] {Married-civ-spouse} {Male} 32 +]29.5;40.5] {Prof-specialty} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 32 +]40.5;59.5] {Adm-clerical, Tech-support} ]12.5;+inf[ ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 32 +]40.5;59.5] {Exec-managerial} ]12.5;+inf[ ]-inf;36.5] {Married-civ-spouse} {Male} 32 +]40.5;59.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 32 +]40.5;59.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Male} 32 +]59.5;+inf[ {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Male} 32 +]-inf;18.5] {Sales} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 31 +]23.5;29.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 31 +]23.5;29.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 31 +]23.5;29.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 31 +]23.5;29.5] {Sales} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 31 +]29.5;40.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Female} 31 +]29.5;40.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 31 +]29.5;40.5] {Exec-managerial} ]12.5;+inf[ ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 31 +]29.5;40.5] {Exec-managerial} ]12.5;+inf[ ]36.5;43.5] {Married-civ-spouse} {Female} 31 +]29.5;40.5] {Prof-specialty} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 31 +]29.5;40.5] {Sales} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 31 +]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 31 +]40.5;59.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 31 +]18.5;23.5] {Prof-specialty} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 30 +]23.5;29.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Male} 30 +]23.5;29.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 30 +]23.5;29.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 30 +]23.5;29.5] {Exec-managerial} ]9.5;12.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 30 +]23.5;29.5] {Sales} ]8.5;9.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 30 +]29.5;40.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 30 +]29.5;40.5] {Exec-managerial} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 30 +]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 30 +]29.5;40.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 30 +]29.5;40.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 30 +]29.5;40.5] {Prof-specialty} ]12.5;+inf[ ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 30 +]29.5;40.5] {Sales} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 30 +]40.5;59.5] {Exec-managerial} ]-inf;8.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 30 +]40.5;59.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Female} 30 +]59.5;+inf[ {Exec-managerial} ]12.5;+inf[ ]-inf;36.5] {Married-civ-spouse} {Male} 30 +]59.5;+inf[ {Other-service, Priv-house-serv} ]-inf;8.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 30 +]59.5;+inf[ {Sales} ]8.5;9.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 30 +]59.5;+inf[ {Sales} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Male} 30 +]-inf;18.5] {Prof-specialty} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 29 +]18.5;23.5] {Prof-specialty} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 29 +]23.5;29.5] {Sales} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Male} 29 +]23.5;29.5] {Sales} ]9.5;12.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 29 +]29.5;40.5] {Exec-managerial} ]9.5;12.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 29 +]29.5;40.5] {Prof-specialty} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Female} 29 +]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 29 +]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 29 +]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 29 +]40.5;59.5] {Prof-specialty} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Male} 29 +]40.5;59.5] {Sales} ]9.5;12.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 29 +]59.5;+inf[ {Exec-managerial} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Male} 29 +]59.5;+inf[ {Prof-specialty} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Male} 29 +]59.5;+inf[ {Sales} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 29 +]59.5;+inf[ {Sales} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Male} 29 +]59.5;+inf[ {Sales} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Male} 29 +]59.5;+inf[ {Sales} ]12.5;+inf[ ]-inf;36.5] {Married-civ-spouse} {Male} 29 +]-inf;18.5] {Sales} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 28 +]18.5;23.5] {Adm-clerical, Tech-support} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 28 +]18.5;23.5] {Sales} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 28 +]23.5;29.5] {Adm-clerical, Tech-support} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 28 +]29.5;40.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 28 +]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 28 +]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 28 +]29.5;40.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 28 +]29.5;40.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 28 +]40.5;59.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Female} 28 +]40.5;59.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 28 +]59.5;+inf[ {Prof-specialty} ]-inf;8.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 28 +]23.5;29.5] {Exec-managerial} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 27 +]29.5;40.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Female} 27 +]29.5;40.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 27 +]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 27 +]40.5;59.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Female} 27 +]40.5;59.5] {Prof-specialty} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Female} 27 +]59.5;+inf[ {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 27 +]59.5;+inf[ {Sales} ]12.5;+inf[ ]36.5;43.5] {Married-civ-spouse} {Male} 27 +]18.5;23.5] {Prof-specialty} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 26 +]23.5;29.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 26 +]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 26 +]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Male} 26 +]29.5;40.5] {Exec-managerial} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Female} 26 +]29.5;40.5] {Exec-managerial} ]12.5;+inf[ ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 26 +]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 26 +]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 26 +]29.5;40.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 26 +]29.5;40.5] {Prof-specialty} ]8.5;9.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 26 +]29.5;40.5] {Sales} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 26 +]29.5;40.5] {Sales} ]9.5;12.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 26 +]40.5;59.5] {Exec-managerial} ]12.5;+inf[ ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 26 +]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Female} 26 +]59.5;+inf[ {Exec-managerial} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Male} 26 +]59.5;+inf[ {Other-service, Priv-house-serv} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Male} 26 +]59.5;+inf[ {Prof-specialty} ]12.5;+inf[ ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 26 +]-inf;18.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 25 +]18.5;23.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 25 +]18.5;23.5] {Exec-managerial} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 25 +]23.5;29.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 25 +]23.5;29.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 25 +]23.5;29.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 25 +]23.5;29.5] {Sales} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Male} 25 +]23.5;29.5] {Sales} ]12.5;+inf[ ]36.5;43.5] {Married-civ-spouse} {Male} 25 +]29.5;40.5] {Prof-specialty} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Female} 25 +]40.5;59.5] {Exec-managerial} ]8.5;9.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 25 +]40.5;59.5] {Exec-managerial} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 25 +]40.5;59.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 25 +]40.5;59.5] {Prof-specialty} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Male} 25 +]40.5;59.5] {Sales} ]-inf;8.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 25 +]59.5;+inf[ {Adm-clerical, Tech-support} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Male} 25 +]59.5;+inf[ {Sales} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Male} 25 +]59.5;+inf[ {Sales} ]12.5;+inf[ ]43.5;+inf[ {Married-civ-spouse} {Male} 25 +]18.5;23.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 24 +]18.5;23.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Male} 24 +]18.5;23.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 24 +]23.5;29.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Female} 24 +]23.5;29.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 24 +]23.5;29.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 24 +]23.5;29.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 24 +]23.5;29.5] {Prof-specialty} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 24 +]23.5;29.5] {Sales} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 24 +]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 24 +]29.5;40.5] {Exec-managerial} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 24 +]29.5;40.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 24 +]29.5;40.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 24 +]29.5;40.5] {Prof-specialty} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 24 +]40.5;59.5] {Adm-clerical, Tech-support} ]-inf;8.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 24 +]40.5;59.5] {Adm-clerical, Tech-support} ]12.5;+inf[ ]36.5;43.5] {Married-civ-spouse} {Female} 24 +]40.5;59.5] {Exec-managerial} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 24 +]40.5;59.5] {Sales} ]8.5;9.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 24 +]59.5;+inf[ {Exec-managerial} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Male} 24 +]59.5;+inf[ {Other-service, Priv-house-serv} ]-inf;8.5] ]-inf;36.5] {Married-civ-spouse} {Male} 24 +]59.5;+inf[ {Other-service, Priv-house-serv} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Male} 24 +]18.5;23.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 23 +]23.5;29.5] {Adm-clerical, Tech-support} ]12.5;+inf[ ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 23 +]29.5;40.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 23 +]29.5;40.5] {Adm-clerical, Tech-support} ]12.5;+inf[ ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 23 +]29.5;40.5] {Exec-managerial} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 23 +]29.5;40.5] {Exec-managerial} ]9.5;12.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 23 +]29.5;40.5] {Prof-specialty} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 23 +]29.5;40.5] {Prof-specialty} ]12.5;+inf[ ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 23 +]29.5;40.5] {Prof-specialty} ]12.5;+inf[ ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 23 +]29.5;40.5] {Sales} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 23 +]29.5;40.5] {Sales} ]8.5;9.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 23 +]40.5;59.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 23 +]40.5;59.5] {Prof-specialty} ]12.5;+inf[ ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 23 +]40.5;59.5] {Sales} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Female} 23 +]40.5;59.5] {Sales} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 23 +]18.5;23.5] {Exec-managerial} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 22 +]18.5;23.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]36.5;43.5] {Married-civ-spouse} {Male} 22 +]23.5;29.5] {Exec-managerial} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 22 +]23.5;29.5] {Exec-managerial} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 22 +]23.5;29.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 22 +]23.5;29.5] {Prof-specialty} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 22 +]23.5;29.5] {Prof-specialty} ]12.5;+inf[ ]36.5;43.5] {Married-civ-spouse} {Female} 22 +]29.5;40.5] {Adm-clerical, Tech-support} ]12.5;+inf[ ]36.5;43.5] {Married-civ-spouse} {Female} 22 +]29.5;40.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Female} 22 +]29.5;40.5] {Prof-specialty} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 22 +]40.5;59.5] {Adm-clerical, Tech-support} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 22 +]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 22 +]40.5;59.5] {Exec-managerial} ]12.5;+inf[ ]36.5;43.5] {Married-civ-spouse} {Female} 22 +]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 22 +]40.5;59.5] {Sales} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Male} 22 +]40.5;59.5] {Sales} ]12.5;+inf[ ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 22 +]59.5;+inf[ {Craft-repair, Transport-moving, Farming-fishing, ...} ]12.5;+inf[ ]-inf;36.5] {Married-civ-spouse} {Male} 22 +]59.5;+inf[ {Exec-managerial} ]8.5;9.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 22 +]-inf;18.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 21 +]-inf;18.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 21 +]-inf;18.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 21 +]-inf;18.5] {Prof-specialty} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 21 +]18.5;23.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Male} 21 +]18.5;23.5] {Exec-managerial} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 21 +]18.5;23.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 21 +]18.5;23.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 21 +]18.5;23.5] {Sales} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 21 +]23.5;29.5] {Adm-clerical, Tech-support} ]12.5;+inf[ ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 21 +]23.5;29.5] {Exec-managerial} ]8.5;9.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 21 +]23.5;29.5] {Exec-managerial} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Male} 21 +]23.5;29.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Male} 21 +]23.5;29.5] {Prof-specialty} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Male} 21 +]23.5;29.5] {Sales} ]12.5;+inf[ ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 21 +]29.5;40.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 21 +]29.5;40.5] {Exec-managerial} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 21 +]29.5;40.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Female} 21 +]29.5;40.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 21 +]29.5;40.5] {Sales} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 21 +]29.5;40.5] {Sales} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 21 +]29.5;40.5] {Sales} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 21 +]40.5;59.5] {Adm-clerical, Tech-support} ]-inf;8.5] ]36.5;43.5] {Married-civ-spouse} {Male} 21 +]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]12.5;+inf[ ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 21 +]40.5;59.5] {Exec-managerial} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 21 +]40.5;59.5] {Exec-managerial} ]12.5;+inf[ ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 21 +]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 21 +]40.5;59.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 21 +]40.5;59.5] {Prof-specialty} ]-inf;8.5] ]36.5;43.5] {Married-civ-spouse} {Male} 21 +]40.5;59.5] {Prof-specialty} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 21 +]40.5;59.5] {Prof-specialty} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 21 +]40.5;59.5] {Prof-specialty} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 21 +]40.5;59.5] {Prof-specialty} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Female} 21 +]40.5;59.5] {Prof-specialty} ]9.5;12.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 21 +]59.5;+inf[ {Adm-clerical, Tech-support} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Male} 21 +]59.5;+inf[ {Other-service, Priv-house-serv} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 21 +]59.5;+inf[ {Prof-specialty} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 21 +]-inf;18.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 20 +]-inf;18.5] {Prof-specialty} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 20 +]18.5;23.5] {Exec-managerial} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 20 +]18.5;23.5] {Prof-specialty} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 20 +]23.5;29.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 20 +]23.5;29.5] {Prof-specialty} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 20 +]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 20 +]29.5;40.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 20 +]29.5;40.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Female} 20 +]29.5;40.5] {Sales} ]12.5;+inf[ ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 20 +]40.5;59.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 20 +]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]12.5;+inf[ ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 20 +]40.5;59.5] {Exec-managerial} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Male} 20 +]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 20 +]40.5;59.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Female} 20 +]40.5;59.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 20 +]59.5;+inf[ {Exec-managerial} ]9.5;12.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 20 +]-inf;18.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 19 +]18.5;23.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 19 +]18.5;23.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 19 +]18.5;23.5] {Sales} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 19 +]23.5;29.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 19 +]23.5;29.5] {Adm-clerical, Tech-support} ]12.5;+inf[ ]36.5;43.5] {Married-civ-spouse} {Male} 19 +]23.5;29.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 19 +]29.5;40.5] {Adm-clerical, Tech-support} ]12.5;+inf[ ]-inf;36.5] {Married-civ-spouse} {Female} 19 +]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 19 +]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]12.5;+inf[ ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 19 +]29.5;40.5] {Exec-managerial} ]9.5;12.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 19 +]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 19 +]29.5;40.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 19 +]29.5;40.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 19 +]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 19 +]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 19 +]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]12.5;+inf[ ]-inf;36.5] {Married-civ-spouse} {Male} 19 +]40.5;59.5] {Exec-managerial} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Female} 19 +]40.5;59.5] {Exec-managerial} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 19 +]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 19 +]40.5;59.5] {Prof-specialty} ]9.5;12.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 19 +]40.5;59.5] {Sales} ]12.5;+inf[ ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 19 +]59.5;+inf[ {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 19 +]59.5;+inf[ {Other-service, Priv-house-serv} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 19 +]59.5;+inf[ {Prof-specialty} ]-inf;8.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 19 +]18.5;23.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 18 +]18.5;23.5] {Prof-specialty} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 18 +]23.5;29.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 18 +]23.5;29.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 18 +]23.5;29.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]12.5;+inf[ ]43.5;+inf[ {Married-civ-spouse} {Male} 18 +]23.5;29.5] {Prof-specialty} ]12.5;+inf[ ]-inf;36.5] {Married-civ-spouse} {Female} 18 +]23.5;29.5] {Sales} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 18 +]29.5;40.5] {Adm-clerical, Tech-support} ]12.5;+inf[ ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 18 +]29.5;40.5] {Adm-clerical, Tech-support} ]12.5;+inf[ ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 18 +]29.5;40.5] {Exec-managerial} ]8.5;9.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 18 +]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Female} 18 +]29.5;40.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 18 +]29.5;40.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 18 +]29.5;40.5] {Sales} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 18 +]29.5;40.5] {Sales} ]12.5;+inf[ ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 18 +]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 18 +]40.5;59.5] {Exec-managerial} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 18 +]40.5;59.5] {Exec-managerial} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Female} 18 +]40.5;59.5] {Exec-managerial} ]12.5;+inf[ ]43.5;+inf[ {Married-civ-spouse} {Female} 18 +]40.5;59.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]-inf;36.5] {Married-civ-spouse} {Female} 18 +]40.5;59.5] {Prof-specialty} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 18 +]40.5;59.5] {Sales} ]-inf;8.5] ]36.5;43.5] {Married-civ-spouse} {Male} 18 +]40.5;59.5] {Sales} ]8.5;9.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 18 +]40.5;59.5] {Sales} ]12.5;+inf[ ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 18 +]59.5;+inf[ {Exec-managerial} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 18 +]59.5;+inf[ {Prof-specialty} ]8.5;9.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 18 +]59.5;+inf[ {Prof-specialty} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 18 +]59.5;+inf[ {Sales} ]9.5;12.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 18 +]-inf;18.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 17 +]18.5;23.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 17 +]18.5;23.5] {Exec-managerial} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 17 +]18.5;23.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 17 +]18.5;23.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 17 +]18.5;23.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 17 +]18.5;23.5] {Prof-specialty} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 17 +]18.5;23.5] {Sales} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 17 +]23.5;29.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 17 +]23.5;29.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 17 +]23.5;29.5] {Exec-managerial} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Male} 17 +]23.5;29.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 17 +]23.5;29.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 17 +]23.5;29.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 17 +]23.5;29.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 17 +]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 17 +]29.5;40.5] {Exec-managerial} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 17 +]29.5;40.5] {Exec-managerial} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 17 +]29.5;40.5] {Exec-managerial} ]12.5;+inf[ ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 17 +]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 17 +]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]12.5;+inf[ ]36.5;43.5] {Married-civ-spouse} {Male} 17 +]29.5;40.5] {Prof-specialty} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 17 +]29.5;40.5] {Prof-specialty} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 17 +]29.5;40.5] {Sales} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 17 +]40.5;59.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Male} 17 +]40.5;59.5] {Adm-clerical, Tech-support} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 17 +]40.5;59.5] {Exec-managerial} ]-inf;8.5] ]36.5;43.5] {Married-civ-spouse} {Male} 17 +]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Male} 17 +]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 17 +]40.5;59.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]-inf;36.5] {Married-civ-spouse} {Male} 17 +]40.5;59.5] {Prof-specialty} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 17 +]40.5;59.5] {Sales} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Male} 17 +]40.5;59.5] {Sales} ]12.5;+inf[ ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 17 +]59.5;+inf[ {Adm-clerical, Tech-support} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Male} 17 +]59.5;+inf[ {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 17 +]-inf;18.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 16 +]23.5;29.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]12.5;+inf[ ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 16 +]23.5;29.5] {Exec-managerial} ]12.5;+inf[ ]36.5;43.5] {Married-civ-spouse} {Female} 16 +]23.5;29.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 16 +]23.5;29.5] {Prof-specialty} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 16 +]23.5;29.5] {Prof-specialty} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 16 +]23.5;29.5] {Prof-specialty} ]12.5;+inf[ ]-inf;36.5] {Married-civ-spouse} {Male} 16 +]23.5;29.5] {Sales} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 16 +]29.5;40.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 16 +]29.5;40.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 16 +]29.5;40.5] {Exec-managerial} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 16 +]29.5;40.5] {Exec-managerial} ]12.5;+inf[ ]43.5;+inf[ {Married-civ-spouse} {Female} 16 +]29.5;40.5] {Prof-specialty} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 16 +]29.5;40.5] {Prof-specialty} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Male} 16 +]29.5;40.5] {Sales} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 16 +]29.5;40.5] {Sales} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 16 +]29.5;40.5] {Sales} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Female} 16 +]40.5;59.5] {Adm-clerical, Tech-support} ]12.5;+inf[ ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 16 +]40.5;59.5] {Adm-clerical, Tech-support} ]12.5;+inf[ ]-inf;36.5] {Married-civ-spouse} {Male} 16 +]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 16 +]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 16 +]40.5;59.5] {Exec-managerial} ]9.5;12.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 16 +]40.5;59.5] {Exec-managerial} ]12.5;+inf[ ]-inf;36.5] {Married-civ-spouse} {Female} 16 +]40.5;59.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 16 +]40.5;59.5] {Other-service, Priv-house-serv} ]12.5;+inf[ ]36.5;43.5] {Married-civ-spouse} {Male} 16 +]40.5;59.5] {Prof-specialty} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Female} 16 +]40.5;59.5] {Sales} ]12.5;+inf[ ]-inf;36.5] {Married-civ-spouse} {Male} 16 +]59.5;+inf[ {Prof-specialty} ]12.5;+inf[ ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 16 +]59.5;+inf[ {Sales} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 16 +]18.5;23.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]36.5;43.5] {Married-civ-spouse} {Male} 15 +]18.5;23.5] {Exec-managerial} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 15 +]18.5;23.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 15 +]23.5;29.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 15 +]23.5;29.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 15 +]23.5;29.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]-inf;36.5] {Married-civ-spouse} {Male} 15 +]23.5;29.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]12.5;+inf[ ]36.5;43.5] {Married-civ-spouse} {Male} 15 +]23.5;29.5] {Exec-managerial} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 15 +]23.5;29.5] {Exec-managerial} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 15 +]23.5;29.5] {Machine-op-inspct, Handlers-cleaners} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 15 +]23.5;29.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Male} 15 +]23.5;29.5] {Prof-specialty} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 15 +]23.5;29.5] {Prof-specialty} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Female} 15 +]23.5;29.5] {Sales} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 15 +]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 15 +]29.5;40.5] {Exec-managerial} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Female} 15 +]29.5;40.5] {Exec-managerial} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 15 +]29.5;40.5] {Exec-managerial} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 15 +]29.5;40.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 15 +]29.5;40.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 15 +]29.5;40.5] {Prof-specialty} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Male} 15 +]29.5;40.5] {Prof-specialty} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 15 +]29.5;40.5] {Sales} ]12.5;+inf[ ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 15 +]40.5;59.5] {Exec-managerial} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 15 +]40.5;59.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Male} 15 +]40.5;59.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 15 +]40.5;59.5] {Sales} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 15 +]59.5;+inf[ {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 15 +]59.5;+inf[ {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 15 +]59.5;+inf[ {Exec-managerial} ]-inf;8.5] ]-inf;36.5] {Married-civ-spouse} {Male} 15 +]59.5;+inf[ {Prof-specialty} ]-inf;8.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 15 +]59.5;+inf[ {Sales} ]-inf;8.5] ]-inf;36.5] {Married-civ-spouse} {Male} 15 +]-inf;18.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 14 +]18.5;23.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 14 +]18.5;23.5] {Exec-managerial} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 14 +]18.5;23.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Male} 14 +]18.5;23.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 14 +]23.5;29.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 14 +]23.5;29.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Male} 14 +]23.5;29.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 14 +]23.5;29.5] {Exec-managerial} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 14 +]23.5;29.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 14 +]23.5;29.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Female} 14 +]23.5;29.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 14 +]23.5;29.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Female} 14 +]23.5;29.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 14 +]23.5;29.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 14 +]23.5;29.5] {Other-service, Priv-house-serv} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 14 +]23.5;29.5] {Other-service, Priv-house-serv} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 14 +]23.5;29.5] {Prof-specialty} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 14 +]23.5;29.5] {Prof-specialty} ]12.5;+inf[ ]43.5;+inf[ {Married-civ-spouse} {Female} 14 +]23.5;29.5] {Sales} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 14 +]29.5;40.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Male} 14 +]29.5;40.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 14 +]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 14 +]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Female} 14 +]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 14 +]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 14 +]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 14 +]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 14 +]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 14 +]29.5;40.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Male} 14 +]29.5;40.5] {Other-service, Priv-house-serv} ]12.5;+inf[ ]36.5;43.5] {Married-civ-spouse} {Male} 14 +]29.5;40.5] {Other-service, Priv-house-serv} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 14 +]29.5;40.5] {Sales} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 14 +]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 14 +]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 14 +]40.5;59.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 14 +]40.5;59.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]36.5;43.5] {Married-civ-spouse} {Female} 14 +]40.5;59.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 14 +]40.5;59.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Male} 14 +]40.5;59.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 14 +]40.5;59.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 14 +]40.5;59.5] {Prof-specialty} ]-inf;8.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 14 +]40.5;59.5] {Sales} ]-inf;8.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 14 +]40.5;59.5] {Sales} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 14 +]59.5;+inf[ {Exec-managerial} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 14 +]59.5;+inf[ {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 14 +]59.5;+inf[ {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]-inf;36.5] {Married-civ-spouse} {Male} 14 +]59.5;+inf[ {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 14 +]59.5;+inf[ {Other-service, Priv-house-serv} ]-inf;8.5] ]36.5;43.5] {Married-civ-spouse} {Male} 14 +]59.5;+inf[ {Prof-specialty} ]-inf;8.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 14 +]59.5;+inf[ {Prof-specialty} ]-inf;8.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 14 +]-inf;18.5] {Prof-specialty} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 13 +]18.5;23.5] {Adm-clerical, Tech-support} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 13 +]18.5;23.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 13 +]18.5;23.5] {Prof-specialty} ]12.5;+inf[ ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 13 +]18.5;23.5] {Sales} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 13 +]18.5;23.5] {Sales} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 13 +]23.5;29.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 13 +]23.5;29.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 13 +]23.5;29.5] {Sales} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 13 +]23.5;29.5] {Sales} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 13 +]29.5;40.5] {Adm-clerical, Tech-support} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 13 +]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]12.5;+inf[ ]-inf;36.5] {Married-civ-spouse} {Male} 13 +]29.5;40.5] {Exec-managerial} ]8.5;9.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 13 +]29.5;40.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 13 +]29.5;40.5] {Prof-specialty} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Female} 13 +]29.5;40.5] {Sales} ]-inf;8.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 13 +]29.5;40.5] {Sales} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Female} 13 +]29.5;40.5] {Sales} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 13 +]40.5;59.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Male} 13 +]40.5;59.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 13 +]40.5;59.5] {Adm-clerical, Tech-support} ]12.5;+inf[ ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 13 +]40.5;59.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 13 +]40.5;59.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 13 +]40.5;59.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 13 +]40.5;59.5] {Prof-specialty} ]-inf;8.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 13 +]40.5;59.5] {Prof-specialty} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 13 +]40.5;59.5] {Sales} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Female} 13 +]59.5;+inf[ {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 13 +]59.5;+inf[ {Craft-repair, Transport-moving, Farming-fishing, ...} ]12.5;+inf[ ]43.5;+inf[ {Married-civ-spouse} {Male} 13 +]59.5;+inf[ {Prof-specialty} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 13 +]59.5;+inf[ {Prof-specialty} ]12.5;+inf[ ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 13 +]-inf;18.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 12 +]-inf;18.5] {Prof-specialty} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 12 +]18.5;23.5] {Exec-managerial} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 12 +]18.5;23.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 12 +]18.5;23.5] {Other-service, Priv-house-serv} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 12 +]18.5;23.5] {Prof-specialty} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 12 +]18.5;23.5] {Sales} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 12 +]18.5;23.5] {Sales} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 12 +]23.5;29.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Female} 12 +]23.5;29.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 12 +]23.5;29.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 12 +]23.5;29.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 12 +]23.5;29.5] {Exec-managerial} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 12 +]23.5;29.5] {Exec-managerial} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 12 +]23.5;29.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 12 +]23.5;29.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 12 +]23.5;29.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 12 +]23.5;29.5] {Prof-specialty} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Male} 12 +]23.5;29.5] {Prof-specialty} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 12 +]29.5;40.5] {Adm-clerical, Tech-support} ]12.5;+inf[ ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 12 +]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]12.5;+inf[ ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 12 +]29.5;40.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Female} 12 +]29.5;40.5] {Other-service, Priv-house-serv} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 12 +]29.5;40.5] {Prof-specialty} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 12 +]29.5;40.5] {Prof-specialty} ]9.5;12.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 12 +]29.5;40.5] {Sales} ]12.5;+inf[ ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 12 +]40.5;59.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 12 +]40.5;59.5] {Exec-managerial} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Male} 12 +]40.5;59.5] {Exec-managerial} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 12 +]40.5;59.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 12 +]40.5;59.5] {Other-service, Priv-house-serv} ]12.5;+inf[ ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 12 +]40.5;59.5] {Prof-specialty} ]-inf;8.5] ]-inf;36.5] {Married-civ-spouse} {Male} 12 +]40.5;59.5] {Prof-specialty} ]-inf;8.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 12 +]40.5;59.5] {Prof-specialty} ]8.5;9.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 12 +]40.5;59.5] {Sales} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 12 +]40.5;59.5] {Sales} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 12 +]40.5;59.5] {Sales} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 12 +]40.5;59.5] {Sales} ]12.5;+inf[ ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 12 +]59.5;+inf[ {Adm-clerical, Tech-support} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 12 +]59.5;+inf[ {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 12 +]59.5;+inf[ {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 12 +]59.5;+inf[ {Exec-managerial} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 12 +]59.5;+inf[ {Other-service, Priv-house-serv} ]8.5;9.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 12 +]59.5;+inf[ {Other-service, Priv-house-serv} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 12 +]59.5;+inf[ {Prof-specialty} ]12.5;+inf[ ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 12 +]59.5;+inf[ {Prof-specialty} ]12.5;+inf[ ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 12 +]59.5;+inf[ {Sales} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 12 +]-inf;18.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 11 +]-inf;18.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 11 +]-inf;18.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 11 +]-inf;18.5] {Prof-specialty} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 11 +]18.5;23.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 11 +]18.5;23.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 11 +]18.5;23.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 11 +]18.5;23.5] {Sales} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 11 +]18.5;23.5] {Sales} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Male} 11 +]23.5;29.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 11 +]23.5;29.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 11 +]23.5;29.5] {Exec-managerial} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 11 +]23.5;29.5] {Exec-managerial} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 11 +]23.5;29.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 11 +]23.5;29.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 11 +]23.5;29.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 11 +]23.5;29.5] {Other-service, Priv-house-serv} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 11 +]23.5;29.5] {Prof-specialty} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 11 +]23.5;29.5] {Prof-specialty} ]9.5;12.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 11 +]23.5;29.5] {Sales} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 11 +]29.5;40.5] {Adm-clerical, Tech-support} ]-inf;8.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 11 +]29.5;40.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 11 +]29.5;40.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 11 +]29.5;40.5] {Adm-clerical, Tech-support} ]12.5;+inf[ ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 11 +]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 11 +]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]12.5;+inf[ ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 11 +]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 11 +]29.5;40.5] {Prof-specialty} ]-inf;8.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 11 +]29.5;40.5] {Prof-specialty} ]9.5;12.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 11 +]29.5;40.5] {Sales} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Female} 11 +]40.5;59.5] {Adm-clerical, Tech-support} ]12.5;+inf[ ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 11 +]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Female} 11 +]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 11 +]40.5;59.5] {Exec-managerial} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 11 +]40.5;59.5] {Exec-managerial} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 11 +]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 11 +]40.5;59.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 11 +]40.5;59.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 11 +]40.5;59.5] {Prof-specialty} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 11 +]40.5;59.5] {Prof-specialty} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 11 +]59.5;+inf[ {Adm-clerical, Tech-support} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Male} 11 +]59.5;+inf[ {Adm-clerical, Tech-support} ]12.5;+inf[ ]36.5;43.5] {Married-civ-spouse} {Male} 11 +]59.5;+inf[ {Craft-repair, Transport-moving, Farming-fishing, ...} ]12.5;+inf[ ]36.5;43.5] {Married-civ-spouse} {Male} 11 +]59.5;+inf[ {Exec-managerial} ]-inf;8.5] ]36.5;43.5] {Married-civ-spouse} {Male} 11 +]59.5;+inf[ {Exec-managerial} ]12.5;+inf[ ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 11 +]59.5;+inf[ {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Male} 11 +]59.5;+inf[ {Prof-specialty} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Female} 11 +]59.5;+inf[ {Prof-specialty} ]9.5;12.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 11 +]59.5;+inf[ {Prof-specialty} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 11 +]-inf;18.5] {Adm-clerical, Tech-support} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 10 +]-inf;18.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 10 +]18.5;23.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 10 +]18.5;23.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 10 +]18.5;23.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Male} 10 +]18.5;23.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 10 +]18.5;23.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 10 +]18.5;23.5] {Exec-managerial} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 10 +]18.5;23.5] {Exec-managerial} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 10 +]18.5;23.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 10 +]18.5;23.5] {Prof-specialty} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Female} 10 +]18.5;23.5] {Prof-specialty} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 10 +]18.5;23.5] {Sales} ]12.5;+inf[ ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 10 +]23.5;29.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 10 +]23.5;29.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 10 +]23.5;29.5] {Prof-specialty} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 10 +]23.5;29.5] {Sales} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 10 +]23.5;29.5] {Sales} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 10 +]29.5;40.5] {Exec-managerial} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 10 +]29.5;40.5] {Exec-managerial} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Male} 10 +]29.5;40.5] {Exec-managerial} ]12.5;+inf[ ]-inf;36.5] {Married-civ-spouse} {Female} 10 +]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 10 +]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Male} 10 +]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 10 +]29.5;40.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]-inf;36.5] {Married-civ-spouse} {Female} 10 +]29.5;40.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 10 +]29.5;40.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]36.5;43.5] {Married-civ-spouse} {Female} 10 +]29.5;40.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Male} 10 +]29.5;40.5] {Prof-specialty} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 10 +]29.5;40.5] {Prof-specialty} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 10 +]29.5;40.5] {Prof-specialty} ]12.5;+inf[ ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 10 +]29.5;40.5] {Sales} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 10 +]29.5;40.5] {Sales} ]12.5;+inf[ ]-inf;36.5] {Married-civ-spouse} {Male} 10 +]40.5;59.5] {Adm-clerical, Tech-support} ]-inf;8.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 10 +]40.5;59.5] {Exec-managerial} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 10 +]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 10 +]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]36.5;43.5] {Married-civ-spouse} {Female} 10 +]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 10 +]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 10 +]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 10 +]40.5;59.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 10 +]40.5;59.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 10 +]40.5;59.5] {Other-service, Priv-house-serv} ]12.5;+inf[ ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 10 +]40.5;59.5] {Prof-specialty} ]-inf;8.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 10 +]40.5;59.5] {Prof-specialty} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Female} 10 +]40.5;59.5] {Sales} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Female} 10 +]59.5;+inf[ {Adm-clerical, Tech-support} ]-inf;8.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 10 +]59.5;+inf[ {Adm-clerical, Tech-support} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Female} 10 +]59.5;+inf[ {Exec-managerial} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 10 +]59.5;+inf[ {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 10 +]59.5;+inf[ {Prof-specialty} ]12.5;+inf[ ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 10 +]59.5;+inf[ {Sales} ]-inf;8.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 10 +]59.5;+inf[ {Sales} ]-inf;8.5] ]36.5;43.5] {Married-civ-spouse} {Male} 10 +]-inf;18.5] {Sales} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 9 +]-inf;18.5] {Sales} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 9 +]-inf;18.5] {Sales} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 9 +]18.5;23.5] {Adm-clerical, Tech-support} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 9 +]18.5;23.5] {Adm-clerical, Tech-support} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 9 +]18.5;23.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 9 +]18.5;23.5] {Exec-managerial} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 9 +]18.5;23.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 9 +]18.5;23.5] {Prof-specialty} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 9 +]23.5;29.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 9 +]23.5;29.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Female} 9 +]23.5;29.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 9 +]23.5;29.5] {Exec-managerial} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 9 +]23.5;29.5] {Exec-managerial} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 9 +]23.5;29.5] {Exec-managerial} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Female} 9 +]23.5;29.5] {Exec-managerial} ]12.5;+inf[ ]43.5;+inf[ {Married-civ-spouse} {Female} 9 +]23.5;29.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 9 +]23.5;29.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 9 +]23.5;29.5] {Prof-specialty} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 9 +]23.5;29.5] {Prof-specialty} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Female} 9 +]23.5;29.5] {Prof-specialty} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Female} 9 +]23.5;29.5] {Prof-specialty} ]12.5;+inf[ ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 9 +]23.5;29.5] {Sales} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 9 +]29.5;40.5] {Adm-clerical, Tech-support} ]-inf;8.5] ]36.5;43.5] {Married-civ-spouse} {Male} 9 +]29.5;40.5] {Adm-clerical, Tech-support} ]12.5;+inf[ ]-inf;36.5] {Married-civ-spouse} {Male} 9 +]29.5;40.5] {Exec-managerial} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Female} 9 +]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 9 +]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 9 +]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 9 +]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]12.5;+inf[ ]43.5;+inf[ {Married-civ-spouse} {Male} 9 +]29.5;40.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 9 +]29.5;40.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 9 +]29.5;40.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 9 +]29.5;40.5] {Prof-specialty} ]-inf;8.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 9 +]29.5;40.5] {Sales} ]8.5;9.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 9 +]29.5;40.5] {Sales} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Male} 9 +]29.5;40.5] {Sales} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 9 +]40.5;59.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 9 +]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 9 +]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 9 +]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]12.5;+inf[ ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 9 +]40.5;59.5] {Exec-managerial} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Female} 9 +]40.5;59.5] {Exec-managerial} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Female} 9 +]40.5;59.5] {Exec-managerial} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 9 +]40.5;59.5] {Exec-managerial} ]12.5;+inf[ ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 9 +]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 9 +]40.5;59.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 9 +]40.5;59.5] {Other-service, Priv-house-serv} ]12.5;+inf[ ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 9 +]40.5;59.5] {Other-service, Priv-house-serv} ]12.5;+inf[ ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 9 +]40.5;59.5] {Sales} ]-inf;8.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 9 +]40.5;59.5] {Sales} ]8.5;9.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 9 +]40.5;59.5] {Sales} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 9 +]59.5;+inf[ {Adm-clerical, Tech-support} ]12.5;+inf[ ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 9 +]59.5;+inf[ {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 9 +]59.5;+inf[ {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Male} 9 +]59.5;+inf[ {Other-service, Priv-house-serv} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Male} 9 +]59.5;+inf[ {Prof-specialty} ]12.5;+inf[ ]-inf;36.5] {Married-civ-spouse} {Female} 9 +]-inf;18.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 8 +]-inf;18.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 8 +]-inf;18.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 8 +]-inf;18.5] {Prof-specialty} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 8 +]-inf;18.5] {Prof-specialty} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 8 +]18.5;23.5] {Adm-clerical, Tech-support} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 8 +]18.5;23.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 8 +]18.5;23.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]36.5;43.5] {Married-civ-spouse} {Male} 8 +]18.5;23.5] {Sales} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 8 +]23.5;29.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 8 +]23.5;29.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 8 +]23.5;29.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 8 +]23.5;29.5] {Adm-clerical, Tech-support} ]12.5;+inf[ ]36.5;43.5] {Married-civ-spouse} {Female} 8 +]23.5;29.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 8 +]23.5;29.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]36.5;43.5] {Married-civ-spouse} {Male} 8 +]23.5;29.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 8 +]23.5;29.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Female} 8 +]23.5;29.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 8 +]23.5;29.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 8 +]23.5;29.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 8 +]23.5;29.5] {Prof-specialty} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Male} 8 +]23.5;29.5] {Prof-specialty} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 8 +]23.5;29.5] {Prof-specialty} ]12.5;+inf[ ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 8 +]23.5;29.5] {Prof-specialty} ]12.5;+inf[ ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 8 +]23.5;29.5] {Sales} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 8 +]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 8 +]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 8 +]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 8 +]29.5;40.5] {Exec-managerial} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 8 +]29.5;40.5] {Exec-managerial} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 8 +]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 8 +]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]36.5;43.5] {Married-civ-spouse} {Female} 8 +]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 8 +]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 8 +]29.5;40.5] {Other-service, Priv-house-serv} ]12.5;+inf[ ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 8 +]29.5;40.5] {Other-service, Priv-house-serv} ]12.5;+inf[ ]-inf;36.5] {Married-civ-spouse} {Male} 8 +]29.5;40.5] {Prof-specialty} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Female} 8 +]29.5;40.5] {Prof-specialty} ]9.5;12.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 8 +]29.5;40.5] {Sales} ]-inf;8.5] ]36.5;43.5] {Married-civ-spouse} {Male} 8 +]29.5;40.5] {Sales} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 8 +]29.5;40.5] {Sales} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 8 +]29.5;40.5] {Sales} ]9.5;12.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 8 +]29.5;40.5] {Sales} ]12.5;+inf[ ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 8 +]40.5;59.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 8 +]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 8 +]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 8 +]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 8 +]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]12.5;+inf[ ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 8 +]40.5;59.5] {Exec-managerial} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 8 +]40.5;59.5] {Exec-managerial} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 8 +]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]-inf;36.5] {Married-civ-spouse} {Male} 8 +]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 8 +]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 8 +]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]12.5;+inf[ ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 8 +]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]12.5;+inf[ ]43.5;+inf[ {Married-civ-spouse} {Male} 8 +]40.5;59.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Female} 8 +]40.5;59.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 8 +]40.5;59.5] {Prof-specialty} ]-inf;8.5] ]36.5;43.5] {Married-civ-spouse} {Female} 8 +]40.5;59.5] {Prof-specialty} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 8 +]59.5;+inf[ {Adm-clerical, Tech-support} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Female} 8 +]59.5;+inf[ {Adm-clerical, Tech-support} ]12.5;+inf[ ]-inf;36.5] {Married-civ-spouse} {Male} 8 +]59.5;+inf[ {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 8 +]59.5;+inf[ {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 8 +]59.5;+inf[ {Exec-managerial} ]-inf;8.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 8 +]59.5;+inf[ {Other-service, Priv-house-serv} ]-inf;8.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 8 +]59.5;+inf[ {Prof-specialty} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 8 +]59.5;+inf[ {Prof-specialty} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Female} 8 +]59.5;+inf[ {Sales} ]-inf;8.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 8 +]59.5;+inf[ {Sales} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 8 +]-inf;18.5] {Sales} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 7 +]18.5;23.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Male} 7 +]18.5;23.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 7 +]18.5;23.5] {Adm-clerical, Tech-support} ]12.5;+inf[ ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 7 +]18.5;23.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]-inf;36.5] {Married-civ-spouse} {Male} 7 +]18.5;23.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 7 +]18.5;23.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Male} 7 +]18.5;23.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 7 +]18.5;23.5] {Prof-specialty} ]-inf;8.5] ]36.5;43.5] {Married-civ-spouse} {Female} 7 +]18.5;23.5] {Sales} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 7 +]18.5;23.5] {Sales} ]8.5;9.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 7 +]18.5;23.5] {Sales} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Female} 7 +]18.5;23.5] {Sales} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 7 +]18.5;23.5] {Sales} ]12.5;+inf[ ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 7 +]23.5;29.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 7 +]23.5;29.5] {Adm-clerical, Tech-support} ]12.5;+inf[ ]43.5;+inf[ {Married-civ-spouse} {Male} 7 +]23.5;29.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 7 +]23.5;29.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Male} 7 +]23.5;29.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 7 +]23.5;29.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 7 +]23.5;29.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 7 +]23.5;29.5] {Machine-op-inspct, Handlers-cleaners} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 7 +]23.5;29.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]-inf;36.5] {Married-civ-spouse} {Female} 7 +]23.5;29.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]36.5;43.5] {Married-civ-spouse} {Female} 7 +]23.5;29.5] {Other-service, Priv-house-serv} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 7 +]23.5;29.5] {Prof-specialty} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Female} 7 +]23.5;29.5] {Sales} ]-inf;8.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 7 +]23.5;29.5] {Sales} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Male} 7 +]23.5;29.5] {Sales} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 7 +]23.5;29.5] {Sales} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Female} 7 +]23.5;29.5] {Sales} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 7 +]29.5;40.5] {Adm-clerical, Tech-support} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 7 +]29.5;40.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 7 +]29.5;40.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 7 +]29.5;40.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 7 +]29.5;40.5] {Exec-managerial} ]-inf;8.5] ]36.5;43.5] {Married-civ-spouse} {Male} 7 +]29.5;40.5] {Exec-managerial} ]12.5;+inf[ ]-inf;36.5] {Married-civ-spouse} {Male} 7 +]29.5;40.5] {Exec-managerial} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 7 +]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]-inf;36.5] {Married-civ-spouse} {Male} 7 +]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 7 +]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Male} 7 +]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 7 +]29.5;40.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 7 +]29.5;40.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 7 +]29.5;40.5] {Prof-specialty} ]-inf;8.5] ]36.5;43.5] {Married-civ-spouse} {Male} 7 +]29.5;40.5] {Prof-specialty} ]-inf;8.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 7 +]29.5;40.5] {Prof-specialty} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 7 +]29.5;40.5] {Prof-specialty} ]8.5;9.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 7 +]29.5;40.5] {Sales} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 7 +]40.5;59.5] {Adm-clerical, Tech-support} ]-inf;8.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 7 +]40.5;59.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 7 +]40.5;59.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 7 +]40.5;59.5] {Adm-clerical, Tech-support} ]12.5;+inf[ ]-inf;36.5] {Married-civ-spouse} {Female} 7 +]40.5;59.5] {Adm-clerical, Tech-support} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 7 +]40.5;59.5] {Adm-clerical, Tech-support} ]12.5;+inf[ ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 7 +]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Female} 7 +]40.5;59.5] {Exec-managerial} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 7 +]40.5;59.5] {Exec-managerial} ]12.5;+inf[ ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 7 +]40.5;59.5] {Exec-managerial} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 7 +]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 7 +]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 7 +]40.5;59.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Female} 7 +]40.5;59.5] {Prof-specialty} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 7 +]40.5;59.5] {Sales} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 7 +]40.5;59.5] {Sales} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 7 +]40.5;59.5] {Sales} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 7 +]59.5;+inf[ {Adm-clerical, Tech-support} ]-inf;8.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 7 +]59.5;+inf[ {Adm-clerical, Tech-support} ]-inf;8.5] ]36.5;43.5] {Married-civ-spouse} {Male} 7 +]59.5;+inf[ {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 7 +]59.5;+inf[ {Exec-managerial} ]9.5;12.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 7 +]59.5;+inf[ {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 7 +]59.5;+inf[ {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 7 +]59.5;+inf[ {Other-service, Priv-house-serv} ]12.5;+inf[ ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 7 +]59.5;+inf[ {Prof-specialty} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 7 +]59.5;+inf[ {Prof-specialty} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 7 +]59.5;+inf[ {Sales} ]-inf;8.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 7 +]59.5;+inf[ {Sales} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 7 +]59.5;+inf[ {Sales} ]12.5;+inf[ ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 7 +]59.5;+inf[ {Sales} ]12.5;+inf[ ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 7 +]-inf;18.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 6 +]-inf;18.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 6 +]-inf;18.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 6 +]18.5;23.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Female} 6 +]18.5;23.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Female} 6 +]18.5;23.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 6 +]18.5;23.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 6 +]18.5;23.5] {Exec-managerial} ]12.5;+inf[ ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 6 +]18.5;23.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 6 +]18.5;23.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Female} 6 +]18.5;23.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Male} 6 +]18.5;23.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 6 +]18.5;23.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 6 +]18.5;23.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Male} 6 +]18.5;23.5] {Prof-specialty} ]-inf;8.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 6 +]18.5;23.5] {Prof-specialty} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 6 +]18.5;23.5] {Prof-specialty} ]12.5;+inf[ ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 6 +]18.5;23.5] {Sales} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 6 +]18.5;23.5] {Sales} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Male} 6 +]23.5;29.5] {Adm-clerical, Tech-support} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 6 +]23.5;29.5] {Adm-clerical, Tech-support} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 6 +]23.5;29.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 6 +]23.5;29.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Male} 6 +]23.5;29.5] {Adm-clerical, Tech-support} ]12.5;+inf[ ]-inf;36.5] {Married-civ-spouse} {Male} 6 +]23.5;29.5] {Adm-clerical, Tech-support} ]12.5;+inf[ ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 6 +]23.5;29.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 6 +]23.5;29.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 6 +]23.5;29.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 6 +]23.5;29.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 6 +]23.5;29.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Female} 6 +]23.5;29.5] {Other-service, Priv-house-serv} ]12.5;+inf[ ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 6 +]23.5;29.5] {Prof-specialty} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 6 +]23.5;29.5] {Sales} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 6 +]23.5;29.5] {Sales} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Female} 6 +]29.5;40.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 6 +]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Female} 6 +]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 6 +]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 6 +]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 6 +]29.5;40.5] {Exec-managerial} ]-inf;8.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 6 +]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 6 +]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 6 +]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 6 +]29.5;40.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 6 +]29.5;40.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 6 +]29.5;40.5] {Other-service, Priv-house-serv} ]12.5;+inf[ ]36.5;43.5] {Married-civ-spouse} {Female} 6 +]29.5;40.5] {Prof-specialty} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 6 +]29.5;40.5] {Prof-specialty} ]-inf;8.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 6 +]29.5;40.5] {Prof-specialty} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 6 +]29.5;40.5] {Prof-specialty} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 6 +]29.5;40.5] {Sales} ]-inf;8.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 6 +]29.5;40.5] {Sales} ]-inf;8.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 6 +]29.5;40.5] {Sales} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Male} 6 +]29.5;40.5] {Sales} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 6 +]29.5;40.5] {Sales} ]12.5;+inf[ ]43.5;+inf[ {Married-civ-spouse} {Female} 6 +]40.5;59.5] {Adm-clerical, Tech-support} ]-inf;8.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 6 +]40.5;59.5] {Adm-clerical, Tech-support} ]12.5;+inf[ ]43.5;+inf[ {Married-civ-spouse} {Female} 6 +]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 6 +]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 6 +]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 6 +]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]12.5;+inf[ ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 6 +]40.5;59.5] {Exec-managerial} ]8.5;9.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 6 +]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 6 +]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 6 +]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Female} 6 +]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Male} 6 +]40.5;59.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 6 +]40.5;59.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 6 +]40.5;59.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 6 +]40.5;59.5] {Other-service, Priv-house-serv} ]12.5;+inf[ ]-inf;36.5] {Married-civ-spouse} {Male} 6 +]40.5;59.5] {Other-service, Priv-house-serv} ]12.5;+inf[ ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 6 +]40.5;59.5] {Prof-specialty} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 6 +]40.5;59.5] {Prof-specialty} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 6 +]40.5;59.5] {Sales} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 6 +]40.5;59.5] {Sales} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 6 +]40.5;59.5] {Sales} ]12.5;+inf[ ]36.5;43.5] {Married-civ-spouse} {Female} 6 +]59.5;+inf[ {Adm-clerical, Tech-support} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Female} 6 +]59.5;+inf[ {Adm-clerical, Tech-support} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 6 +]59.5;+inf[ {Adm-clerical, Tech-support} ]9.5;12.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 6 +]59.5;+inf[ {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 6 +]59.5;+inf[ {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 6 +]59.5;+inf[ {Exec-managerial} ]12.5;+inf[ ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 6 +]59.5;+inf[ {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 6 +]59.5;+inf[ {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Male} 6 +]59.5;+inf[ {Other-service, Priv-house-serv} ]-inf;8.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 6 +]59.5;+inf[ {Other-service, Priv-house-serv} ]-inf;8.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 6 +]59.5;+inf[ {Other-service, Priv-house-serv} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 6 +]59.5;+inf[ {Other-service, Priv-house-serv} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Male} 6 +]59.5;+inf[ {Prof-specialty} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 6 +]59.5;+inf[ {Prof-specialty} ]12.5;+inf[ ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 6 +]59.5;+inf[ {Sales} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Female} 6 +]59.5;+inf[ {Sales} ]9.5;12.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 6 +]59.5;+inf[ {Sales} ]12.5;+inf[ ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 6 +]-inf;18.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 5 +]-inf;18.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 5 +]-inf;18.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 5 +]-inf;18.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 5 +]-inf;18.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 5 +]-inf;18.5] {Prof-specialty} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 5 +]-inf;18.5] {Prof-specialty} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 5 +]18.5;23.5] {Adm-clerical, Tech-support} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 5 +]18.5;23.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Female} 5 +]18.5;23.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 5 +]18.5;23.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 5 +]18.5;23.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 5 +]18.5;23.5] {Exec-managerial} ]12.5;+inf[ ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 5 +]18.5;23.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]36.5;43.5] {Married-civ-spouse} {Female} 5 +]18.5;23.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 5 +]18.5;23.5] {Machine-op-inspct, Handlers-cleaners} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 5 +]18.5;23.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 5 +]18.5;23.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Female} 5 +]18.5;23.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Male} 5 +]18.5;23.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Male} 5 +]18.5;23.5] {Other-service, Priv-house-serv} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 5 +]18.5;23.5] {Prof-specialty} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 5 +]18.5;23.5] {Prof-specialty} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Male} 5 +]18.5;23.5] {Sales} ]-inf;8.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 5 +]18.5;23.5] {Sales} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 5 +]18.5;23.5] {Sales} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Female} 5 +]23.5;29.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 5 +]23.5;29.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 5 +]23.5;29.5] {Exec-managerial} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Female} 5 +]23.5;29.5] {Exec-managerial} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Female} 5 +]23.5;29.5] {Exec-managerial} ]12.5;+inf[ ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 5 +]23.5;29.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 5 +]23.5;29.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 5 +]23.5;29.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Male} 5 +]23.5;29.5] {Machine-op-inspct, Handlers-cleaners} ]12.5;+inf[ ]43.5;+inf[ {Married-civ-spouse} {Male} 5 +]23.5;29.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Male} 5 +]23.5;29.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Male} 5 +]23.5;29.5] {Other-service, Priv-house-serv} ]12.5;+inf[ ]36.5;43.5] {Married-civ-spouse} {Male} 5 +]23.5;29.5] {Prof-specialty} ]-inf;8.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 5 +]23.5;29.5] {Prof-specialty} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 5 +]23.5;29.5] {Prof-specialty} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 5 +]23.5;29.5] {Prof-specialty} ]8.5;9.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 5 +]23.5;29.5] {Prof-specialty} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 5 +]23.5;29.5] {Prof-specialty} ]12.5;+inf[ ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 5 +]23.5;29.5] {Sales} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 5 +]23.5;29.5] {Sales} ]-inf;8.5] ]36.5;43.5] {Married-civ-spouse} {Male} 5 +]23.5;29.5] {Sales} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Male} 5 +]29.5;40.5] {Adm-clerical, Tech-support} ]-inf;8.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 5 +]29.5;40.5] {Adm-clerical, Tech-support} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 5 +]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 5 +]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 5 +]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 5 +]29.5;40.5] {Exec-managerial} ]8.5;9.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 5 +]29.5;40.5] {Exec-managerial} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 5 +]29.5;40.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 5 +]29.5;40.5] {Other-service, Priv-house-serv} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 5 +]29.5;40.5] {Other-service, Priv-house-serv} ]12.5;+inf[ ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 5 +]29.5;40.5] {Other-service, Priv-house-serv} ]12.5;+inf[ ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 5 +]29.5;40.5] {Other-service, Priv-house-serv} ]12.5;+inf[ ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 5 +]29.5;40.5] {Prof-specialty} ]-inf;8.5] ]-inf;36.5] {Married-civ-spouse} {Female} 5 +]29.5;40.5] {Prof-specialty} ]-inf;8.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 5 +]29.5;40.5] {Prof-specialty} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 5 +]29.5;40.5] {Sales} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 5 +]29.5;40.5] {Sales} ]-inf;8.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 5 +]29.5;40.5] {Sales} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Female} 5 +]29.5;40.5] {Sales} ]12.5;+inf[ ]-inf;36.5] {Married-civ-spouse} {Female} 5 +]40.5;59.5] {Adm-clerical, Tech-support} ]-inf;8.5] ]36.5;43.5] {Married-civ-spouse} {Female} 5 +]40.5;59.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 5 +]40.5;59.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 5 +]40.5;59.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 5 +]40.5;59.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 5 +]40.5;59.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 5 +]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 5 +]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]36.5;43.5] {Married-civ-spouse} {Female} 5 +]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 5 +]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 5 +]40.5;59.5] {Exec-managerial} ]-inf;8.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 5 +]40.5;59.5] {Exec-managerial} ]-inf;8.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 5 +]40.5;59.5] {Exec-managerial} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 5 +]40.5;59.5] {Exec-managerial} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 5 +]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 5 +]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 5 +]40.5;59.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 5 +]40.5;59.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 5 +]40.5;59.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 5 +]40.5;59.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 5 +]40.5;59.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 5 +]40.5;59.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 5 +]40.5;59.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 5 +]40.5;59.5] {Other-service, Priv-house-serv} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 5 +]40.5;59.5] {Prof-specialty} ]-inf;8.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 5 +]40.5;59.5] {Prof-specialty} ]-inf;8.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 5 +]40.5;59.5] {Prof-specialty} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 5 +]40.5;59.5] {Prof-specialty} ]8.5;9.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 5 +]40.5;59.5] {Prof-specialty} ]9.5;12.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 5 +]40.5;59.5] {Prof-specialty} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 5 +]40.5;59.5] {Sales} ]-inf;8.5] ]-inf;36.5] {Married-civ-spouse} {Male} 5 +]40.5;59.5] {Sales} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 5 +]40.5;59.5] {Sales} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 5 +]40.5;59.5] {Sales} ]9.5;12.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 5 +]40.5;59.5] {Sales} ]12.5;+inf[ ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 5 +]40.5;59.5] {Sales} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 5 +]59.5;+inf[ {Adm-clerical, Tech-support} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 5 +]59.5;+inf[ {Adm-clerical, Tech-support} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Female} 5 +]59.5;+inf[ {Adm-clerical, Tech-support} ]12.5;+inf[ ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 5 +]59.5;+inf[ {Adm-clerical, Tech-support} ]12.5;+inf[ ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 5 +]59.5;+inf[ {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 5 +]59.5;+inf[ {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 5 +]59.5;+inf[ {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 5 +]59.5;+inf[ {Exec-managerial} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 5 +]59.5;+inf[ {Exec-managerial} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 5 +]59.5;+inf[ {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 5 +]59.5;+inf[ {Machine-op-inspct, Handlers-cleaners} ]12.5;+inf[ ]36.5;43.5] {Married-civ-spouse} {Male} 5 +]59.5;+inf[ {Other-service, Priv-house-serv} ]-inf;8.5] ]-inf;36.5] {Married-civ-spouse} {Female} 5 +]59.5;+inf[ {Other-service, Priv-house-serv} ]12.5;+inf[ ]-inf;36.5] {Married-civ-spouse} {Male} 5 +]59.5;+inf[ {Prof-specialty} ]8.5;9.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 5 +]59.5;+inf[ {Prof-specialty} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 5 +]59.5;+inf[ {Prof-specialty} ]12.5;+inf[ ]36.5;43.5] {Married-civ-spouse} {Female} 5 +]59.5;+inf[ {Sales} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 5 +]59.5;+inf[ {Sales} ]8.5;9.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 5 +]-inf;18.5] {Adm-clerical, Tech-support} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 4 +]-inf;18.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 4 +]-inf;18.5] {Exec-managerial} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 4 +]-inf;18.5] {Exec-managerial} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 4 +]-inf;18.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 4 +]-inf;18.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 4 +]-inf;18.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 4 +]18.5;23.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Female} 4 +]18.5;23.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 4 +]18.5;23.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 4 +]18.5;23.5] {Exec-managerial} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 4 +]18.5;23.5] {Exec-managerial} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 4 +]18.5;23.5] {Exec-managerial} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Female} 4 +]18.5;23.5] {Exec-managerial} ]9.5;12.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 4 +]18.5;23.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]-inf;36.5] {Married-civ-spouse} {Male} 4 +]18.5;23.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 4 +]18.5;23.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 4 +]18.5;23.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Male} 4 +]18.5;23.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 4 +]18.5;23.5] {Machine-op-inspct, Handlers-cleaners} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 4 +]18.5;23.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Female} 4 +]18.5;23.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 4 +]18.5;23.5] {Prof-specialty} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Female} 4 +]18.5;23.5] {Sales} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 4 +]18.5;23.5] {Sales} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Male} 4 +]18.5;23.5] {Sales} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 4 +]18.5;23.5] {Sales} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 4 +]18.5;23.5] {Sales} ]9.5;12.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 4 +]18.5;23.5] {Sales} ]12.5;+inf[ ]36.5;43.5] {Married-civ-spouse} {Male} 4 +]23.5;29.5] {Adm-clerical, Tech-support} ]-inf;8.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 4 +]23.5;29.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 4 +]23.5;29.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 4 +]23.5;29.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 4 +]23.5;29.5] {Adm-clerical, Tech-support} ]12.5;+inf[ ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 4 +]23.5;29.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 4 +]23.5;29.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]12.5;+inf[ ]-inf;36.5] {Married-civ-spouse} {Male} 4 +]23.5;29.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]12.5;+inf[ ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 4 +]23.5;29.5] {Exec-managerial} ]8.5;9.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 4 +]23.5;29.5] {Exec-managerial} ]12.5;+inf[ ]-inf;36.5] {Married-civ-spouse} {Female} 4 +]23.5;29.5] {Exec-managerial} ]12.5;+inf[ ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 4 +]23.5;29.5] {Exec-managerial} ]12.5;+inf[ ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 4 +]23.5;29.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]-inf;36.5] {Married-civ-spouse} {Male} 4 +]23.5;29.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 4 +]23.5;29.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]36.5;43.5] {Married-civ-spouse} {Female} 4 +]23.5;29.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 4 +]23.5;29.5] {Machine-op-inspct, Handlers-cleaners} ]12.5;+inf[ ]36.5;43.5] {Married-civ-spouse} {Male} 4 +]23.5;29.5] {Machine-op-inspct, Handlers-cleaners} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 4 +]23.5;29.5] {Machine-op-inspct, Handlers-cleaners} ]12.5;+inf[ ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 4 +]23.5;29.5] {Prof-specialty} ]-inf;8.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 4 +]23.5;29.5] {Prof-specialty} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 4 +]23.5;29.5] {Prof-specialty} ]12.5;+inf[ ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 4 +]23.5;29.5] {Sales} ]-inf;8.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 4 +]23.5;29.5] {Sales} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Female} 4 +]23.5;29.5] {Sales} ]9.5;12.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 4 +]23.5;29.5] {Sales} ]12.5;+inf[ ]-inf;36.5] {Married-civ-spouse} {Male} 4 +]23.5;29.5] {Sales} ]12.5;+inf[ ]43.5;+inf[ {Married-civ-spouse} {Female} 4 +]29.5;40.5] {Adm-clerical, Tech-support} ]-inf;8.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 4 +]29.5;40.5] {Adm-clerical, Tech-support} ]-inf;8.5] ]36.5;43.5] {Married-civ-spouse} {Female} 4 +]29.5;40.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Male} 4 +]29.5;40.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 4 +]29.5;40.5] {Adm-clerical, Tech-support} ]12.5;+inf[ ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 4 +]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 4 +]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 4 +]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Female} 4 +]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Female} 4 +]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]12.5;+inf[ ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 4 +]29.5;40.5] {Exec-managerial} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Female} 4 +]29.5;40.5] {Exec-managerial} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Male} 4 +]29.5;40.5] {Exec-managerial} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 4 +]29.5;40.5] {Exec-managerial} ]12.5;+inf[ ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 4 +]29.5;40.5] {Exec-managerial} ]12.5;+inf[ ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 4 +]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Female} 4 +]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 4 +]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 4 +]29.5;40.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 4 +]29.5;40.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]-inf;36.5] {Married-civ-spouse} {Male} 4 +]29.5;40.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 4 +]29.5;40.5] {Other-service, Priv-house-serv} ]12.5;+inf[ ]-inf;36.5] {Married-civ-spouse} {Female} 4 +]29.5;40.5] {Other-service, Priv-house-serv} ]12.5;+inf[ ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 4 +]29.5;40.5] {Other-service, Priv-house-serv} ]12.5;+inf[ ]43.5;+inf[ {Married-civ-spouse} {Male} 4 +]29.5;40.5] {Prof-specialty} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 4 +]29.5;40.5] {Sales} ]-inf;8.5] ]-inf;36.5] {Married-civ-spouse} {Male} 4 +]29.5;40.5] {Sales} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 4 +]29.5;40.5] {Sales} ]12.5;+inf[ ]36.5;43.5] {Married-civ-spouse} {Female} 4 +]40.5;59.5] {Adm-clerical, Tech-support} ]12.5;+inf[ ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 4 +]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 4 +]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Female} 4 +]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 4 +]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 4 +]40.5;59.5] {Exec-managerial} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 4 +]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]-inf;36.5] {Married-civ-spouse} {Female} 4 +]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Female} 4 +]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 4 +]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]12.5;+inf[ ]-inf;36.5] {Married-civ-spouse} {Male} 4 +]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]12.5;+inf[ ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 4 +]40.5;59.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 4 +]40.5;59.5] {Other-service, Priv-house-serv} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 4 +]40.5;59.5] {Other-service, Priv-house-serv} ]12.5;+inf[ ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 4 +]40.5;59.5] {Prof-specialty} ]-inf;8.5] ]-inf;36.5] {Married-civ-spouse} {Female} 4 +]40.5;59.5] {Prof-specialty} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 4 +]40.5;59.5] {Prof-specialty} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 4 +]40.5;59.5] {Prof-specialty} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 4 +]40.5;59.5] {Sales} ]-inf;8.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 4 +]40.5;59.5] {Sales} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Female} 4 +]40.5;59.5] {Sales} ]12.5;+inf[ ]-inf;36.5] {Married-civ-spouse} {Female} 4 +]59.5;+inf[ {Adm-clerical, Tech-support} ]8.5;9.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 4 +]59.5;+inf[ {Adm-clerical, Tech-support} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 4 +]59.5;+inf[ {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 4 +]59.5;+inf[ {Craft-repair, Transport-moving, Farming-fishing, ...} ]12.5;+inf[ ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 4 +]59.5;+inf[ {Exec-managerial} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 4 +]59.5;+inf[ {Exec-managerial} ]12.5;+inf[ ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 4 +]59.5;+inf[ {Exec-managerial} ]12.5;+inf[ ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 4 +]59.5;+inf[ {Exec-managerial} ]12.5;+inf[ ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 4 +]59.5;+inf[ {Exec-managerial} ]12.5;+inf[ ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 4 +]59.5;+inf[ {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]36.5;43.5] {Married-civ-spouse} {Female} 4 +]59.5;+inf[ {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 4 +]59.5;+inf[ {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 4 +]59.5;+inf[ {Other-service, Priv-house-serv} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 4 +]59.5;+inf[ {Other-service, Priv-house-serv} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 4 +]59.5;+inf[ {Other-service, Priv-house-serv} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 4 +]59.5;+inf[ {Prof-specialty} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 4 +]59.5;+inf[ {Prof-specialty} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 4 +]59.5;+inf[ {Prof-specialty} ]9.5;12.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 4 +]59.5;+inf[ {Prof-specialty} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 4 +]59.5;+inf[ {Prof-specialty} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 4 +]59.5;+inf[ {Sales} ]8.5;9.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 4 +]-inf;18.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 3 +]-inf;18.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 3 +]-inf;18.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 3 +]-inf;18.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 3 +]-inf;18.5] {Prof-specialty} ]-inf;8.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 3 +]-inf;18.5] {Prof-specialty} ]-inf;8.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 3 +]-inf;18.5] {Prof-specialty} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 3 +]18.5;23.5] {Adm-clerical, Tech-support} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 3 +]18.5;23.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 3 +]18.5;23.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 3 +]18.5;23.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Male} 3 +]18.5;23.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 3 +]18.5;23.5] {Exec-managerial} ]8.5;9.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 3 +]18.5;23.5] {Exec-managerial} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 3 +]18.5;23.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 3 +]18.5;23.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 3 +]18.5;23.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]-inf;36.5] {Married-civ-spouse} {Male} 3 +]18.5;23.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Male} 3 +]18.5;23.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 3 +]18.5;23.5] {Other-service, Priv-house-serv} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 3 +]18.5;23.5] {Prof-specialty} ]-inf;8.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 3 +]18.5;23.5] {Prof-specialty} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 3 +]18.5;23.5] {Prof-specialty} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 3 +]18.5;23.5] {Prof-specialty} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Male} 3 +]18.5;23.5] {Sales} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 3 +]18.5;23.5] {Sales} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Male} 3 +]23.5;29.5] {Adm-clerical, Tech-support} ]-inf;8.5] ]36.5;43.5] {Married-civ-spouse} {Female} 3 +]23.5;29.5] {Adm-clerical, Tech-support} ]-inf;8.5] ]36.5;43.5] {Married-civ-spouse} {Male} 3 +]23.5;29.5] {Adm-clerical, Tech-support} ]12.5;+inf[ ]-inf;36.5] {Married-civ-spouse} {Female} 3 +]23.5;29.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 3 +]23.5;29.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]36.5;43.5] {Married-civ-spouse} {Female} 3 +]23.5;29.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 3 +]23.5;29.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 3 +]23.5;29.5] {Exec-managerial} ]-inf;8.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 3 +]23.5;29.5] {Exec-managerial} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 3 +]23.5;29.5] {Exec-managerial} ]8.5;9.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 3 +]23.5;29.5] {Exec-managerial} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Male} 3 +]23.5;29.5] {Exec-managerial} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 3 +]23.5;29.5] {Exec-managerial} ]12.5;+inf[ ]-inf;36.5] {Married-civ-spouse} {Male} 3 +]23.5;29.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 3 +]23.5;29.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Female} 3 +]23.5;29.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Male} 3 +]23.5;29.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Female} 3 +]23.5;29.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 3 +]23.5;29.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 3 +]23.5;29.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 3 +]23.5;29.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 3 +]23.5;29.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 3 +]23.5;29.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Female} 3 +]23.5;29.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 3 +]23.5;29.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 3 +]23.5;29.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 3 +]23.5;29.5] {Prof-specialty} ]-inf;8.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 3 +]23.5;29.5] {Prof-specialty} ]-inf;8.5] ]36.5;43.5] {Married-civ-spouse} {Female} 3 +]23.5;29.5] {Prof-specialty} ]-inf;8.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 3 +]23.5;29.5] {Prof-specialty} ]-inf;8.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 3 +]23.5;29.5] {Prof-specialty} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Male} 3 +]23.5;29.5] {Prof-specialty} ]8.5;9.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 3 +]23.5;29.5] {Sales} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Female} 3 +]23.5;29.5] {Sales} ]8.5;9.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 3 +]23.5;29.5] {Sales} ]8.5;9.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 3 +]23.5;29.5] {Sales} ]9.5;12.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 3 +]23.5;29.5] {Sales} ]12.5;+inf[ ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 3 +]23.5;29.5] {Sales} ]12.5;+inf[ ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 3 +]23.5;29.5] {Sales} ]12.5;+inf[ ]36.5;43.5] {Married-civ-spouse} {Female} 3 +]29.5;40.5] {Adm-clerical, Tech-support} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 3 +]29.5;40.5] {Adm-clerical, Tech-support} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 3 +]29.5;40.5] {Adm-clerical, Tech-support} ]12.5;+inf[ ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 3 +]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 3 +]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 3 +]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 3 +]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 3 +]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 3 +]29.5;40.5] {Exec-managerial} ]-inf;8.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 3 +]29.5;40.5] {Exec-managerial} ]-inf;8.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 3 +]29.5;40.5] {Exec-managerial} ]-inf;8.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 3 +]29.5;40.5] {Exec-managerial} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 3 +]29.5;40.5] {Exec-managerial} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 3 +]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 3 +]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 3 +]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 3 +]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 3 +]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Female} 3 +]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 3 +]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]12.5;+inf[ ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 3 +]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 3 +]29.5;40.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 3 +]29.5;40.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 3 +]29.5;40.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 3 +]29.5;40.5] {Other-service, Priv-house-serv} ]12.5;+inf[ ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 3 +]29.5;40.5] {Other-service, Priv-house-serv} ]12.5;+inf[ ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 3 +]29.5;40.5] {Other-service, Priv-house-serv} ]12.5;+inf[ ]43.5;+inf[ {Married-civ-spouse} {Female} 3 +]29.5;40.5] {Prof-specialty} ]-inf;8.5] ]-inf;36.5] {Married-civ-spouse} {Male} 3 +]29.5;40.5] {Prof-specialty} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 3 +]29.5;40.5] {Prof-specialty} ]-inf;8.5] ]36.5;43.5] {Married-civ-spouse} {Female} 3 +]29.5;40.5] {Prof-specialty} ]-inf;8.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 3 +]29.5;40.5] {Prof-specialty} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 3 +]29.5;40.5] {Prof-specialty} ]8.5;9.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 3 +]29.5;40.5] {Prof-specialty} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 3 +]29.5;40.5] {Prof-specialty} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 3 +]29.5;40.5] {Sales} ]-inf;8.5] ]-inf;36.5] {Married-civ-spouse} {Female} 3 +]29.5;40.5] {Sales} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 3 +]29.5;40.5] {Sales} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 3 +]40.5;59.5] {Adm-clerical, Tech-support} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 3 +]40.5;59.5] {Adm-clerical, Tech-support} ]-inf;8.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 3 +]40.5;59.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 3 +]40.5;59.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 3 +]40.5;59.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 3 +]40.5;59.5] {Adm-clerical, Tech-support} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 3 +]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 3 +]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]12.5;+inf[ ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 3 +]40.5;59.5] {Exec-managerial} ]-inf;8.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 3 +]40.5;59.5] {Exec-managerial} ]-inf;8.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 3 +]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 3 +]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 3 +]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 3 +]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 3 +]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 3 +]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 3 +]40.5;59.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 3 +]40.5;59.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 3 +]40.5;59.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 3 +]40.5;59.5] {Other-service, Priv-house-serv} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 3 +]40.5;59.5] {Prof-specialty} ]-inf;8.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 3 +]40.5;59.5] {Prof-specialty} ]8.5;9.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 3 +]40.5;59.5] {Sales} ]-inf;8.5] ]-inf;36.5] {Married-civ-spouse} {Female} 3 +]40.5;59.5] {Sales} ]-inf;8.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 3 +]40.5;59.5] {Sales} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 3 +]40.5;59.5] {Sales} ]-inf;8.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 3 +]40.5;59.5] {Sales} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 3 +]40.5;59.5] {Sales} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 3 +]40.5;59.5] {Sales} ]12.5;+inf[ ]43.5;+inf[ {Married-civ-spouse} {Female} 3 +]40.5;59.5] {Sales} ]12.5;+inf[ ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 3 +]59.5;+inf[ {Adm-clerical, Tech-support} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 3 +]59.5;+inf[ {Adm-clerical, Tech-support} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 3 +]59.5;+inf[ {Adm-clerical, Tech-support} ]9.5;12.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 3 +]59.5;+inf[ {Adm-clerical, Tech-support} ]12.5;+inf[ ]36.5;43.5] {Married-civ-spouse} {Female} 3 +]59.5;+inf[ {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 3 +]59.5;+inf[ {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 3 +]59.5;+inf[ {Exec-managerial} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 3 +]59.5;+inf[ {Exec-managerial} ]9.5;12.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 3 +]59.5;+inf[ {Exec-managerial} ]12.5;+inf[ ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 3 +]59.5;+inf[ {Exec-managerial} ]12.5;+inf[ ]36.5;43.5] {Married-civ-spouse} {Female} 3 +]59.5;+inf[ {Exec-managerial} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 3 +]59.5;+inf[ {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 3 +]59.5;+inf[ {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 3 +]59.5;+inf[ {Machine-op-inspct, Handlers-cleaners} ]12.5;+inf[ ]-inf;36.5] {Married-civ-spouse} {Male} 3 +]59.5;+inf[ {Other-service, Priv-house-serv} ]-inf;8.5] ]36.5;43.5] {Married-civ-spouse} {Female} 3 +]59.5;+inf[ {Other-service, Priv-house-serv} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 3 +]59.5;+inf[ {Other-service, Priv-house-serv} ]-inf;8.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 3 +]59.5;+inf[ {Other-service, Priv-house-serv} ]-inf;8.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 3 +]59.5;+inf[ {Other-service, Priv-house-serv} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Female} 3 +]59.5;+inf[ {Other-service, Priv-house-serv} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Female} 3 +]59.5;+inf[ {Other-service, Priv-house-serv} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 3 +]59.5;+inf[ {Other-service, Priv-house-serv} ]9.5;12.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 3 +]59.5;+inf[ {Other-service, Priv-house-serv} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 3 +]59.5;+inf[ {Prof-specialty} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 3 +]59.5;+inf[ {Prof-specialty} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 3 +]59.5;+inf[ {Prof-specialty} ]12.5;+inf[ ]43.5;+inf[ {Married-civ-spouse} {Female} 3 +]59.5;+inf[ {Sales} ]-inf;8.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 3 +]59.5;+inf[ {Sales} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 3 +]59.5;+inf[ {Sales} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 3 +]59.5;+inf[ {Sales} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 3 +]59.5;+inf[ {Sales} ]12.5;+inf[ ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 3 +]-inf;18.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 2 +]-inf;18.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 2 +]-inf;18.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Female} 2 +]-inf;18.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 2 +]-inf;18.5] {Prof-specialty} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 2 +]-inf;18.5] {Sales} ]-inf;8.5] ]-inf;36.5] {Married-civ-spouse} {Female} 2 +]-inf;18.5] {Sales} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 2 +]-inf;18.5] {Sales} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 2 +]-inf;18.5] {Sales} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 2 +]18.5;23.5] {Adm-clerical, Tech-support} ]-inf;8.5] ]-inf;36.5] {Married-civ-spouse} {Female} 2 +]18.5;23.5] {Adm-clerical, Tech-support} ]-inf;8.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 2 +]18.5;23.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 2 +]18.5;23.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 2 +]18.5;23.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 2 +]18.5;23.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 2 +]18.5;23.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]12.5;+inf[ ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 2 +]18.5;23.5] {Exec-managerial} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 2 +]18.5;23.5] {Exec-managerial} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 2 +]18.5;23.5] {Exec-managerial} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 2 +]18.5;23.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]-inf;36.5] {Married-civ-spouse} {Female} 2 +]18.5;23.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 2 +]18.5;23.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 2 +]18.5;23.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 2 +]18.5;23.5] {Machine-op-inspct, Handlers-cleaners} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 2 +]18.5;23.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 2 +]18.5;23.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 2 +]18.5;23.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 2 +]18.5;23.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 2 +]18.5;23.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 2 +]18.5;23.5] {Other-service, Priv-house-serv} ]12.5;+inf[ ]-inf;36.5] {Married-civ-spouse} {Male} 2 +]18.5;23.5] {Prof-specialty} ]-inf;8.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 2 +]18.5;23.5] {Prof-specialty} ]-inf;8.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 2 +]18.5;23.5] {Prof-specialty} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 2 +]18.5;23.5] {Prof-specialty} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Female} 2 +]18.5;23.5] {Prof-specialty} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Male} 2 +]18.5;23.5] {Prof-specialty} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 2 +]18.5;23.5] {Prof-specialty} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 2 +]18.5;23.5] {Prof-specialty} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Female} 2 +]18.5;23.5] {Prof-specialty} ]12.5;+inf[ ]-inf;36.5] {Married-civ-spouse} {Male} 2 +]18.5;23.5] {Prof-specialty} ]12.5;+inf[ ]36.5;43.5] {Married-civ-spouse} {Female} 2 +]18.5;23.5] {Prof-specialty} ]12.5;+inf[ ]36.5;43.5] {Married-civ-spouse} {Male} 2 +]18.5;23.5] {Prof-specialty} ]12.5;+inf[ ]43.5;+inf[ {Married-civ-spouse} {Male} 2 +]18.5;23.5] {Sales} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Female} 2 +]18.5;23.5] {Sales} ]8.5;9.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 2 +]18.5;23.5] {Sales} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Female} 2 +]23.5;29.5] {Adm-clerical, Tech-support} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 2 +]23.5;29.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Male} 2 +]23.5;29.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 2 +]23.5;29.5] {Adm-clerical, Tech-support} ]12.5;+inf[ ]43.5;+inf[ {Married-civ-spouse} {Female} 2 +]23.5;29.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 2 +]23.5;29.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 2 +]23.5;29.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Female} 2 +]23.5;29.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 2 +]23.5;29.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 2 +]23.5;29.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]12.5;+inf[ ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 2 +]23.5;29.5] {Exec-managerial} ]-inf;8.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 2 +]23.5;29.5] {Exec-managerial} ]-inf;8.5] ]36.5;43.5] {Married-civ-spouse} {Male} 2 +]23.5;29.5] {Exec-managerial} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 2 +]23.5;29.5] {Exec-managerial} ]-inf;8.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 2 +]23.5;29.5] {Exec-managerial} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 2 +]23.5;29.5] {Exec-managerial} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 2 +]23.5;29.5] {Exec-managerial} ]8.5;9.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 2 +]23.5;29.5] {Exec-managerial} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 2 +]23.5;29.5] {Exec-managerial} ]9.5;12.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 2 +]23.5;29.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 2 +]23.5;29.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 2 +]23.5;29.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 2 +]23.5;29.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 2 +]23.5;29.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Female} 2 +]23.5;29.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 2 +]23.5;29.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 2 +]23.5;29.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 2 +]23.5;29.5] {Other-service, Priv-house-serv} ]12.5;+inf[ ]-inf;36.5] {Married-civ-spouse} {Female} 2 +]23.5;29.5] {Other-service, Priv-house-serv} ]12.5;+inf[ ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 2 +]23.5;29.5] {Other-service, Priv-house-serv} ]12.5;+inf[ ]36.5;43.5] {Married-civ-spouse} {Female} 2 +]23.5;29.5] {Other-service, Priv-house-serv} ]12.5;+inf[ ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 2 +]23.5;29.5] {Other-service, Priv-house-serv} ]12.5;+inf[ ]43.5;+inf[ {Married-civ-spouse} {Male} 2 +]23.5;29.5] {Other-service, Priv-house-serv} ]12.5;+inf[ ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 2 +]23.5;29.5] {Prof-specialty} ]-inf;8.5] ]-inf;36.5] {Married-civ-spouse} {Female} 2 +]23.5;29.5] {Prof-specialty} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 2 +]23.5;29.5] {Prof-specialty} ]-inf;8.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 2 +]23.5;29.5] {Prof-specialty} ]-inf;8.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 2 +]23.5;29.5] {Prof-specialty} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 2 +]23.5;29.5] {Prof-specialty} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 2 +]23.5;29.5] {Prof-specialty} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 2 +]23.5;29.5] {Prof-specialty} ]9.5;12.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 2 +]23.5;29.5] {Prof-specialty} ]9.5;12.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 2 +]23.5;29.5] {Prof-specialty} ]12.5;+inf[ ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 2 +]23.5;29.5] {Sales} ]-inf;8.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 2 +]23.5;29.5] {Sales} ]-inf;8.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 2 +]23.5;29.5] {Sales} ]-inf;8.5] ]-inf;36.5] {Married-civ-spouse} {Male} 2 +]23.5;29.5] {Sales} ]-inf;8.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 2 +]23.5;29.5] {Sales} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 2 +]23.5;29.5] {Sales} ]-inf;8.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 2 +]23.5;29.5] {Sales} ]-inf;8.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 2 +]23.5;29.5] {Sales} ]8.5;9.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 2 +]23.5;29.5] {Sales} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 2 +]23.5;29.5] {Sales} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 2 +]23.5;29.5] {Sales} ]9.5;12.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 2 +]23.5;29.5] {Sales} ]12.5;+inf[ ]-inf;36.5] {Married-civ-spouse} {Female} 2 +]23.5;29.5] {Sales} ]12.5;+inf[ ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 2 +]29.5;40.5] {Adm-clerical, Tech-support} ]-inf;8.5] ]-inf;36.5] {Married-civ-spouse} {Female} 2 +]29.5;40.5] {Adm-clerical, Tech-support} ]-inf;8.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 2 +]29.5;40.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 2 +]29.5;40.5] {Adm-clerical, Tech-support} ]12.5;+inf[ ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 2 +]29.5;40.5] {Adm-clerical, Tech-support} ]12.5;+inf[ ]43.5;+inf[ {Married-civ-spouse} {Female} 2 +]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 2 +]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 2 +]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]12.5;+inf[ ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 2 +]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 2 +]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]12.5;+inf[ ]36.5;43.5] {Married-civ-spouse} {Female} 2 +]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]12.5;+inf[ ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 2 +]29.5;40.5] {Exec-managerial} ]-inf;8.5] ]36.5;43.5] {Married-civ-spouse} {Female} 2 +]29.5;40.5] {Exec-managerial} ]-inf;8.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 2 +]29.5;40.5] {Exec-managerial} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 2 +]29.5;40.5] {Exec-managerial} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 2 +]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 2 +]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 2 +]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Female} 2 +]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 2 +]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]12.5;+inf[ ]-inf;36.5] {Married-civ-spouse} {Male} 2 +]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]12.5;+inf[ ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 2 +]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]12.5;+inf[ ]36.5;43.5] {Married-civ-spouse} {Female} 2 +]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]12.5;+inf[ ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 2 +]29.5;40.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 2 +]29.5;40.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 2 +]29.5;40.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 2 +]29.5;40.5] {Other-service, Priv-house-serv} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 2 +]29.5;40.5] {Prof-specialty} ]8.5;9.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 2 +]29.5;40.5] {Prof-specialty} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 2 +]29.5;40.5] {Sales} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 2 +]29.5;40.5] {Sales} ]-inf;8.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 2 +]29.5;40.5] {Sales} ]-inf;8.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 2 +]29.5;40.5] {Sales} ]8.5;9.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 2 +]29.5;40.5] {Sales} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 2 +]29.5;40.5] {Sales} ]12.5;+inf[ ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 2 +]40.5;59.5] {Adm-clerical, Tech-support} ]-inf;8.5] ]-inf;36.5] {Married-civ-spouse} {Female} 2 +]40.5;59.5] {Adm-clerical, Tech-support} ]-inf;8.5] ]-inf;36.5] {Married-civ-spouse} {Male} 2 +]40.5;59.5] {Adm-clerical, Tech-support} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 2 +]40.5;59.5] {Adm-clerical, Tech-support} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 2 +]40.5;59.5] {Adm-clerical, Tech-support} ]-inf;8.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 2 +]40.5;59.5] {Adm-clerical, Tech-support} ]12.5;+inf[ ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 2 +]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]-inf;36.5] {Married-civ-spouse} {Female} 2 +]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Female} 2 +]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 2 +]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 2 +]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 2 +]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]12.5;+inf[ ]-inf;36.5] {Married-civ-spouse} {Female} 2 +]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]12.5;+inf[ ]43.5;+inf[ {Married-civ-spouse} {Female} 2 +]40.5;59.5] {Exec-managerial} ]-inf;8.5] ]-inf;36.5] {Married-civ-spouse} {Female} 2 +]40.5;59.5] {Exec-managerial} ]-inf;8.5] ]-inf;36.5] {Married-civ-spouse} {Male} 2 +]40.5;59.5] {Exec-managerial} ]-inf;8.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 2 +]40.5;59.5] {Exec-managerial} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 2 +]40.5;59.5] {Exec-managerial} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 2 +]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 2 +]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 2 +]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]12.5;+inf[ ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 2 +]40.5;59.5] {Other-service, Priv-house-serv} ]12.5;+inf[ ]-inf;36.5] {Married-civ-spouse} {Female} 2 +]40.5;59.5] {Other-service, Priv-house-serv} ]12.5;+inf[ ]43.5;+inf[ {Married-civ-spouse} {Female} 2 +]40.5;59.5] {Other-service, Priv-house-serv} ]12.5;+inf[ ]43.5;+inf[ {Married-civ-spouse} {Male} 2 +]40.5;59.5] {Other-service, Priv-house-serv} ]12.5;+inf[ ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 2 +]40.5;59.5] {Prof-specialty} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 2 +]40.5;59.5] {Prof-specialty} ]-inf;8.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 2 +]40.5;59.5] {Prof-specialty} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 2 +]40.5;59.5] {Prof-specialty} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 2 +]40.5;59.5] {Sales} ]-inf;8.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 2 +]40.5;59.5] {Sales} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 2 +]40.5;59.5] {Sales} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 2 +]59.5;+inf[ {Adm-clerical, Tech-support} ]-inf;8.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 2 +]59.5;+inf[ {Adm-clerical, Tech-support} ]-inf;8.5] ]-inf;36.5] {Married-civ-spouse} {Male} 2 +]59.5;+inf[ {Adm-clerical, Tech-support} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 2 +]59.5;+inf[ {Adm-clerical, Tech-support} ]-inf;8.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 2 +]59.5;+inf[ {Adm-clerical, Tech-support} ]8.5;9.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 2 +]59.5;+inf[ {Adm-clerical, Tech-support} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 2 +]59.5;+inf[ {Adm-clerical, Tech-support} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 2 +]59.5;+inf[ {Adm-clerical, Tech-support} ]12.5;+inf[ ]-inf;36.5] {Married-civ-spouse} {Female} 2 +]59.5;+inf[ {Adm-clerical, Tech-support} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 2 +]59.5;+inf[ {Adm-clerical, Tech-support} ]12.5;+inf[ ]43.5;+inf[ {Married-civ-spouse} {Male} 2 +]59.5;+inf[ {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 2 +]59.5;+inf[ {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Female} 2 +]59.5;+inf[ {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 2 +]59.5;+inf[ {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Female} 2 +]59.5;+inf[ {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 2 +]59.5;+inf[ {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 2 +]59.5;+inf[ {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 2 +]59.5;+inf[ {Craft-repair, Transport-moving, Farming-fishing, ...} ]12.5;+inf[ ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 2 +]59.5;+inf[ {Craft-repair, Transport-moving, Farming-fishing, ...} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 2 +]59.5;+inf[ {Craft-repair, Transport-moving, Farming-fishing, ...} ]12.5;+inf[ ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 2 +]59.5;+inf[ {Exec-managerial} ]-inf;8.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 2 +]59.5;+inf[ {Exec-managerial} ]-inf;8.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 2 +]59.5;+inf[ {Exec-managerial} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Female} 2 +]59.5;+inf[ {Exec-managerial} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 2 +]59.5;+inf[ {Exec-managerial} ]8.5;9.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 2 +]59.5;+inf[ {Exec-managerial} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 2 +]59.5;+inf[ {Exec-managerial} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Female} 2 +]59.5;+inf[ {Exec-managerial} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Female} 2 +]59.5;+inf[ {Exec-managerial} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 2 +]59.5;+inf[ {Exec-managerial} ]9.5;12.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 2 +]59.5;+inf[ {Exec-managerial} ]12.5;+inf[ ]-inf;36.5] {Married-civ-spouse} {Female} 2 +]59.5;+inf[ {Exec-managerial} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 2 +]59.5;+inf[ {Exec-managerial} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 2 +]59.5;+inf[ {Exec-managerial} ]12.5;+inf[ ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 2 +]59.5;+inf[ {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 2 +]59.5;+inf[ {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]-inf;36.5] {Married-civ-spouse} {Female} 2 +]59.5;+inf[ {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 2 +]59.5;+inf[ {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 2 +]59.5;+inf[ {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 2 +]59.5;+inf[ {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Female} 2 +]59.5;+inf[ {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 2 +]59.5;+inf[ {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 2 +]59.5;+inf[ {Other-service, Priv-house-serv} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Female} 2 +]59.5;+inf[ {Other-service, Priv-house-serv} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 2 +]59.5;+inf[ {Other-service, Priv-house-serv} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 2 +]59.5;+inf[ {Other-service, Priv-house-serv} ]8.5;9.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 2 +]59.5;+inf[ {Other-service, Priv-house-serv} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 2 +]59.5;+inf[ {Other-service, Priv-house-serv} ]9.5;12.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 2 +]59.5;+inf[ {Other-service, Priv-house-serv} ]12.5;+inf[ ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 2 +]59.5;+inf[ {Prof-specialty} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 2 +]59.5;+inf[ {Prof-specialty} ]-inf;8.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 2 +]59.5;+inf[ {Prof-specialty} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 2 +]59.5;+inf[ {Prof-specialty} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Female} 2 +]59.5;+inf[ {Prof-specialty} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 2 +]59.5;+inf[ {Prof-specialty} ]9.5;12.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 2 +]59.5;+inf[ {Prof-specialty} ]9.5;12.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 2 +]59.5;+inf[ {Prof-specialty} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 2 +]59.5;+inf[ {Sales} ]-inf;8.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 2 +]59.5;+inf[ {Sales} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Female} 2 +]59.5;+inf[ {Sales} ]12.5;+inf[ ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 2 +]-inf;18.5] {Adm-clerical, Tech-support} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 1 +]-inf;18.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Female} 1 +]-inf;18.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 1 +]-inf;18.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 1 +]-inf;18.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]36.5;43.5] {Married-civ-spouse} {Male} 1 +]-inf;18.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 1 +]-inf;18.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Male} 1 +]-inf;18.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 1 +]-inf;18.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 1 +]-inf;18.5] {Exec-managerial} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 1 +]-inf;18.5] {Exec-managerial} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 1 +]-inf;18.5] {Exec-managerial} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Female} 1 +]-inf;18.5] {Exec-managerial} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 1 +]-inf;18.5] {Exec-managerial} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 1 +]-inf;18.5] {Exec-managerial} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 1 +]-inf;18.5] {Exec-managerial} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 1 +]-inf;18.5] {Exec-managerial} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 1 +]-inf;18.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]36.5;43.5] {Married-civ-spouse} {Male} 1 +]-inf;18.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 1 +]-inf;18.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 1 +]-inf;18.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Male} 1 +]-inf;18.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 1 +]-inf;18.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 1 +]-inf;18.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]-inf;36.5] {Married-civ-spouse} {Male} 1 +]-inf;18.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 1 +]-inf;18.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]36.5;43.5] {Married-civ-spouse} {Male} 1 +]-inf;18.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 1 +]-inf;18.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 1 +]-inf;18.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 1 +]-inf;18.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 1 +]-inf;18.5] {Prof-specialty} ]-inf;8.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 1 +]-inf;18.5] {Prof-specialty} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 1 +]-inf;18.5] {Prof-specialty} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 1 +]-inf;18.5] {Prof-specialty} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Male} 1 +]-inf;18.5] {Prof-specialty} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 1 +]-inf;18.5] {Prof-specialty} ]12.5;+inf[ ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 1 +]-inf;18.5] {Sales} ]-inf;8.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 1 +]-inf;18.5] {Sales} ]-inf;8.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 1 +]-inf;18.5] {Sales} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Female} 1 +]-inf;18.5] {Sales} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 1 +]-inf;18.5] {Sales} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 1 +]18.5;23.5] {Adm-clerical, Tech-support} ]-inf;8.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 1 +]18.5;23.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 1 +]18.5;23.5] {Adm-clerical, Tech-support} ]8.5;9.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 1 +]18.5;23.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 1 +]18.5;23.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Male} 1 +]18.5;23.5] {Adm-clerical, Tech-support} ]9.5;12.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 1 +]18.5;23.5] {Adm-clerical, Tech-support} ]12.5;+inf[ ]-inf;36.5] {Married-civ-spouse} {Female} 1 +]18.5;23.5] {Adm-clerical, Tech-support} ]12.5;+inf[ ]36.5;43.5] {Married-civ-spouse} {Female} 1 +]18.5;23.5] {Adm-clerical, Tech-support} ]12.5;+inf[ ]36.5;43.5] {Married-civ-spouse} {Male} 1 +]18.5;23.5] {Adm-clerical, Tech-support} ]12.5;+inf[ ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 1 +]18.5;23.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 1 +]18.5;23.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 1 +]18.5;23.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 1 +]18.5;23.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Female} 1 +]18.5;23.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 1 +]18.5;23.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 1 +]18.5;23.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 1 +]18.5;23.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 1 +]18.5;23.5] {Exec-managerial} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 1 +]18.5;23.5] {Exec-managerial} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 1 +]18.5;23.5] {Exec-managerial} ]-inf;8.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 1 +]18.5;23.5] {Exec-managerial} ]-inf;8.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 1 +]18.5;23.5] {Exec-managerial} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 1 +]18.5;23.5] {Exec-managerial} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Male} 1 +]18.5;23.5] {Exec-managerial} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 1 +]18.5;23.5] {Exec-managerial} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Male} 1 +]18.5;23.5] {Exec-managerial} ]8.5;9.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 1 +]18.5;23.5] {Exec-managerial} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 1 +]18.5;23.5] {Exec-managerial} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Female} 1 +]18.5;23.5] {Exec-managerial} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Male} 1 +]18.5;23.5] {Exec-managerial} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 1 +]18.5;23.5] {Exec-managerial} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 1 +]18.5;23.5] {Exec-managerial} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Male} 1 +]18.5;23.5] {Exec-managerial} ]9.5;12.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 1 +]18.5;23.5] {Exec-managerial} ]12.5;+inf[ ]-inf;36.5] {Married-civ-spouse} {Male} 1 +]18.5;23.5] {Exec-managerial} ]12.5;+inf[ ]36.5;43.5] {Married-civ-spouse} {Male} 1 +]18.5;23.5] {Exec-managerial} ]12.5;+inf[ ]43.5;+inf[ {Married-civ-spouse} {Female} 1 +]18.5;23.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 1 +]18.5;23.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 1 +]18.5;23.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Female} 1 +]18.5;23.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 1 +]18.5;23.5] {Machine-op-inspct, Handlers-cleaners} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 1 +]18.5;23.5] {Machine-op-inspct, Handlers-cleaners} ]12.5;+inf[ ]43.5;+inf[ {Married-civ-spouse} {Male} 1 +]18.5;23.5] {Machine-op-inspct, Handlers-cleaners} ]12.5;+inf[ ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 1 +]18.5;23.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 1 +]18.5;23.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 1 +]18.5;23.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 1 +]18.5;23.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 1 +]18.5;23.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Female} 1 +]18.5;23.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Female} 1 +]18.5;23.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 1 +]18.5;23.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 1 +]18.5;23.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 1 +]18.5;23.5] {Other-service, Priv-house-serv} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 1 +]18.5;23.5] {Other-service, Priv-house-serv} ]12.5;+inf[ ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 1 +]18.5;23.5] {Other-service, Priv-house-serv} ]12.5;+inf[ ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 1 +]18.5;23.5] {Prof-specialty} ]-inf;8.5] ]-inf;36.5] {Married-civ-spouse} {Female} 1 +]18.5;23.5] {Prof-specialty} ]-inf;8.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 1 +]18.5;23.5] {Prof-specialty} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 1 +]18.5;23.5] {Prof-specialty} ]8.5;9.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 1 +]18.5;23.5] {Prof-specialty} ]9.5;12.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 1 +]18.5;23.5] {Sales} ]-inf;8.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 1 +]18.5;23.5] {Sales} ]-inf;8.5] ]36.5;43.5] {Married-civ-spouse} {Female} 1 +]18.5;23.5] {Sales} ]-inf;8.5] ]36.5;43.5] {Married-civ-spouse} {Male} 1 +]18.5;23.5] {Sales} ]-inf;8.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 1 +]18.5;23.5] {Sales} ]-inf;8.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 1 +]18.5;23.5] {Sales} ]-inf;8.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 1 +]18.5;23.5] {Sales} ]-inf;8.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 1 +]18.5;23.5] {Sales} ]-inf;8.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 1 +]18.5;23.5] {Sales} ]8.5;9.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 1 +]18.5;23.5] {Sales} ]9.5;12.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 1 +]18.5;23.5] {Sales} ]9.5;12.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 1 +]18.5;23.5] {Sales} ]12.5;+inf[ ]-inf;36.5] {Married-civ-spouse} {Female} 1 +]18.5;23.5] {Sales} ]12.5;+inf[ ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 1 +]18.5;23.5] {Sales} ]12.5;+inf[ ]43.5;+inf[ {Married-civ-spouse} {Male} 1 +]23.5;29.5] {Adm-clerical, Tech-support} ]-inf;8.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 1 +]23.5;29.5] {Adm-clerical, Tech-support} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 1 +]23.5;29.5] {Adm-clerical, Tech-support} ]-inf;8.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 1 +]23.5;29.5] {Adm-clerical, Tech-support} ]-inf;8.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 1 +]23.5;29.5] {Adm-clerical, Tech-support} ]-inf;8.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 1 +]23.5;29.5] {Adm-clerical, Tech-support} ]12.5;+inf[ ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 1 +]23.5;29.5] {Adm-clerical, Tech-support} ]12.5;+inf[ ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 1 +]23.5;29.5] {Adm-clerical, Tech-support} ]12.5;+inf[ ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 1 +]23.5;29.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 1 +]23.5;29.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 1 +]23.5;29.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Female} 1 +]23.5;29.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 1 +]23.5;29.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Female} 1 +]23.5;29.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 1 +]23.5;29.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]12.5;+inf[ ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 1 +]23.5;29.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]12.5;+inf[ ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 1 +]23.5;29.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]12.5;+inf[ ]43.5;+inf[ {Married-civ-spouse} {Female} 1 +]23.5;29.5] {Exec-managerial} ]-inf;8.5] ]36.5;43.5] {Married-civ-spouse} {Female} 1 +]23.5;29.5] {Exec-managerial} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 1 +]23.5;29.5] {Exec-managerial} ]-inf;8.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 1 +]23.5;29.5] {Exec-managerial} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Male} 1 +]23.5;29.5] {Exec-managerial} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 1 +]23.5;29.5] {Exec-managerial} ]9.5;12.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 1 +]23.5;29.5] {Exec-managerial} ]9.5;12.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 1 +]23.5;29.5] {Exec-managerial} ]12.5;+inf[ ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 1 +]23.5;29.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]-inf;36.5] {Married-civ-spouse} {Female} 1 +]23.5;29.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 1 +]23.5;29.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 1 +]23.5;29.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 1 +]23.5;29.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 1 +]23.5;29.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 1 +]23.5;29.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 1 +]23.5;29.5] {Machine-op-inspct, Handlers-cleaners} ]12.5;+inf[ ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 1 +]23.5;29.5] {Machine-op-inspct, Handlers-cleaners} ]12.5;+inf[ ]-inf;36.5] {Married-civ-spouse} {Male} 1 +]23.5;29.5] {Machine-op-inspct, Handlers-cleaners} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 1 +]23.5;29.5] {Machine-op-inspct, Handlers-cleaners} ]12.5;+inf[ ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 1 +]23.5;29.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]-inf;36.5] {Married-civ-spouse} {Male} 1 +]23.5;29.5] {Other-service, Priv-house-serv} ]-inf;8.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 1 +]23.5;29.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 1 +]23.5;29.5] {Other-service, Priv-house-serv} ]8.5;9.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 1 +]23.5;29.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]43.5;+inf[ {Married-civ-spouse} {Male} 1 +]23.5;29.5] {Other-service, Priv-house-serv} ]12.5;+inf[ ]-inf;36.5] {Married-civ-spouse} {Male} 1 +]23.5;29.5] {Other-service, Priv-house-serv} ]12.5;+inf[ ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 1 +]23.5;29.5] {Prof-specialty} ]-inf;8.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 1 +]23.5;29.5] {Prof-specialty} ]-inf;8.5] ]-inf;36.5] {Married-civ-spouse} {Male} 1 +]23.5;29.5] {Prof-specialty} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 1 +]23.5;29.5] {Prof-specialty} ]8.5;9.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 1 +]23.5;29.5] {Prof-specialty} ]9.5;12.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 1 +]23.5;29.5] {Sales} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 1 +]23.5;29.5] {Sales} ]8.5;9.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 1 +]23.5;29.5] {Sales} ]12.5;+inf[ ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 1 +]29.5;40.5] {Adm-clerical, Tech-support} ]-inf;8.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 1 +]29.5;40.5] {Adm-clerical, Tech-support} ]-inf;8.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 1 +]29.5;40.5] {Adm-clerical, Tech-support} ]-inf;8.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 1 +]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 1 +]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]36.5;43.5] {Married-civ-spouse} {Female} 1 +]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 1 +]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]12.5;+inf[ ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 1 +]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]12.5;+inf[ ]-inf;36.5] {Married-civ-spouse} {Female} 1 +]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]12.5;+inf[ ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 1 +]29.5;40.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]12.5;+inf[ ]43.5;+inf[ {Married-civ-spouse} {Female} 1 +]29.5;40.5] {Exec-managerial} ]-inf;8.5] ]-inf;36.5] {Married-civ-spouse} {Female} 1 +]29.5;40.5] {Exec-managerial} ]-inf;8.5] ]-inf;36.5] {Married-civ-spouse} {Male} 1 +]29.5;40.5] {Exec-managerial} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 1 +]29.5;40.5] {Exec-managerial} ]-inf;8.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 1 +]29.5;40.5] {Exec-managerial} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 1 +]29.5;40.5] {Exec-managerial} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 1 +]29.5;40.5] {Exec-managerial} ]-inf;8.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 1 +]29.5;40.5] {Exec-managerial} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 1 +]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]12.5;+inf[ ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 1 +]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]12.5;+inf[ ]-inf;36.5] {Married-civ-spouse} {Female} 1 +]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 1 +]29.5;40.5] {Machine-op-inspct, Handlers-cleaners} ]12.5;+inf[ ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 1 +]29.5;40.5] {Other-service, Priv-house-serv} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 1 +]29.5;40.5] {Other-service, Priv-house-serv} ]12.5;+inf[ ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 1 +]29.5;40.5] {Prof-specialty} ]-inf;8.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 1 +]29.5;40.5] {Prof-specialty} ]-inf;8.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 1 +]29.5;40.5] {Prof-specialty} ]-inf;8.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 1 +]29.5;40.5] {Sales} ]-inf;8.5] ]36.5;43.5] {Married-civ-spouse} {Female} 1 +]29.5;40.5] {Sales} ]-inf;8.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 1 +]29.5;40.5] {Sales} ]-inf;8.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 1 +]29.5;40.5] {Sales} ]-inf;8.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 1 +]29.5;40.5] {Sales} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 1 +]29.5;40.5] {Sales} ]9.5;12.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 1 +]40.5;59.5] {Adm-clerical, Tech-support} ]-inf;8.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 1 +]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 1 +]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 1 +]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 1 +]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 1 +]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]12.5;+inf[ ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 1 +]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 1 +]40.5;59.5] {Craft-repair, Transport-moving, Farming-fishing, ...} ]12.5;+inf[ ]36.5;43.5] {Married-civ-spouse} {Female} 1 +]40.5;59.5] {Exec-managerial} ]-inf;8.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 1 +]40.5;59.5] {Exec-managerial} ]-inf;8.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 1 +]40.5;59.5] {Exec-managerial} ]-inf;8.5] ]36.5;43.5] {Married-civ-spouse} {Female} 1 +]40.5;59.5] {Exec-managerial} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 1 +]40.5;59.5] {Exec-managerial} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 1 +]40.5;59.5] {Exec-managerial} ]-inf;8.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 1 +]40.5;59.5] {Exec-managerial} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 1 +]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 1 +]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 1 +]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 1 +]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 1 +]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]12.5;+inf[ ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 1 +]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]12.5;+inf[ ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 1 +]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 1 +]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 1 +]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]12.5;+inf[ ]36.5;43.5] {Married-civ-spouse} {Female} 1 +]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 1 +]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]12.5;+inf[ ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 1 +]40.5;59.5] {Machine-op-inspct, Handlers-cleaners} ]12.5;+inf[ ]43.5;+inf[ {Married-civ-spouse} {Female} 1 +]40.5;59.5] {Other-service, Priv-house-serv} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 1 +]40.5;59.5] {Other-service, Priv-house-serv} ]12.5;+inf[ ]36.5;43.5] {Married-civ-spouse} {Female} 1 +]40.5;59.5] {Prof-specialty} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 1 +]40.5;59.5] {Prof-specialty} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 1 +]40.5;59.5] {Prof-specialty} ]-inf;8.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 1 +]40.5;59.5] {Prof-specialty} ]-inf;8.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 1 +]40.5;59.5] {Sales} ]-inf;8.5] ]36.5;43.5] {Married-civ-spouse} {Female} 1 +]40.5;59.5] {Sales} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 1 +]40.5;59.5] {Sales} ]-inf;8.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 1 +]40.5;59.5] {Sales} ]-inf;8.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 1 +]40.5;59.5] {Sales} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 1 +]40.5;59.5] {Sales} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 1 +]59.5;+inf[ {Adm-clerical, Tech-support} ]-inf;8.5] ]-inf;36.5] {Married-civ-spouse} {Female} 1 +]59.5;+inf[ {Adm-clerical, Tech-support} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 1 +]59.5;+inf[ {Adm-clerical, Tech-support} ]-inf;8.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 1 +]59.5;+inf[ {Adm-clerical, Tech-support} ]-inf;8.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 1 +]59.5;+inf[ {Adm-clerical, Tech-support} ]8.5;9.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 1 +]59.5;+inf[ {Adm-clerical, Tech-support} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 1 +]59.5;+inf[ {Adm-clerical, Tech-support} ]8.5;9.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 1 +]59.5;+inf[ {Adm-clerical, Tech-support} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 1 +]59.5;+inf[ {Adm-clerical, Tech-support} ]9.5;12.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 1 +]59.5;+inf[ {Adm-clerical, Tech-support} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 1 +]59.5;+inf[ {Adm-clerical, Tech-support} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 1 +]59.5;+inf[ {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]36.5;43.5] {Married-civ-spouse} {Female} 1 +]59.5;+inf[ {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 1 +]59.5;+inf[ {Craft-repair, Transport-moving, Farming-fishing, ...} ]-inf;8.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 1 +]59.5;+inf[ {Craft-repair, Transport-moving, Farming-fishing, ...} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 1 +]59.5;+inf[ {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]-inf;36.5] {Married-civ-spouse} {Female} 1 +]59.5;+inf[ {Craft-repair, Transport-moving, Farming-fishing, ...} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 1 +]59.5;+inf[ {Craft-repair, Transport-moving, Farming-fishing, ...} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 1 +]59.5;+inf[ {Craft-repair, Transport-moving, Farming-fishing, ...} ]12.5;+inf[ ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 1 +]59.5;+inf[ {Craft-repair, Transport-moving, Farming-fishing, ...} ]12.5;+inf[ ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 1 +]59.5;+inf[ {Exec-managerial} ]-inf;8.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 1 +]59.5;+inf[ {Exec-managerial} ]-inf;8.5] ]-inf;36.5] {Married-civ-spouse} {Female} 1 +]59.5;+inf[ {Exec-managerial} ]-inf;8.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 1 +]59.5;+inf[ {Exec-managerial} ]-inf;8.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 1 +]59.5;+inf[ {Exec-managerial} ]-inf;8.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 1 +]59.5;+inf[ {Exec-managerial} ]-inf;8.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 1 +]59.5;+inf[ {Exec-managerial} ]8.5;9.5] ]-inf;36.5] {Married-civ-spouse} {Female} 1 +]59.5;+inf[ {Exec-managerial} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 1 +]59.5;+inf[ {Exec-managerial} ]8.5;9.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 1 +]59.5;+inf[ {Exec-managerial} ]8.5;9.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 1 +]59.5;+inf[ {Exec-managerial} ]8.5;9.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 1 +]59.5;+inf[ {Exec-managerial} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 1 +]59.5;+inf[ {Exec-managerial} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 1 +]59.5;+inf[ {Exec-managerial} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 1 +]59.5;+inf[ {Exec-managerial} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 1 +]59.5;+inf[ {Exec-managerial} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 1 +]59.5;+inf[ {Exec-managerial} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 1 +]59.5;+inf[ {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 1 +]59.5;+inf[ {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 1 +]59.5;+inf[ {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 1 +]59.5;+inf[ {Machine-op-inspct, Handlers-cleaners} ]-inf;8.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 1 +]59.5;+inf[ {Machine-op-inspct, Handlers-cleaners} ]8.5;9.5] ]36.5;43.5] {Married-civ-spouse} {Female} 1 +]59.5;+inf[ {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Female} 1 +]59.5;+inf[ {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 1 +]59.5;+inf[ {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 1 +]59.5;+inf[ {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 1 +]59.5;+inf[ {Machine-op-inspct, Handlers-cleaners} ]9.5;12.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 1 +]59.5;+inf[ {Machine-op-inspct, Handlers-cleaners} ]12.5;+inf[ ]-inf;36.5] {Divorced, Widowed, Separated, ...} {Male} 1 +]59.5;+inf[ {Machine-op-inspct, Handlers-cleaners} ]12.5;+inf[ ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 1 +]59.5;+inf[ {Machine-op-inspct, Handlers-cleaners} ]12.5;+inf[ ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 1 +]59.5;+inf[ {Machine-op-inspct, Handlers-cleaners} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Male} 1 +]59.5;+inf[ {Machine-op-inspct, Handlers-cleaners} ]12.5;+inf[ ]43.5;+inf[ {Married-civ-spouse} {Male} 1 +]59.5;+inf[ {Other-service, Priv-house-serv} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 1 +]59.5;+inf[ {Other-service, Priv-house-serv} ]-inf;8.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 1 +]59.5;+inf[ {Other-service, Priv-house-serv} ]-inf;8.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 1 +]59.5;+inf[ {Other-service, Priv-house-serv} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 1 +]59.5;+inf[ {Other-service, Priv-house-serv} ]8.5;9.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Male} 1 +]59.5;+inf[ {Other-service, Priv-house-serv} ]8.5;9.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 1 +]59.5;+inf[ {Other-service, Priv-house-serv} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 1 +]59.5;+inf[ {Other-service, Priv-house-serv} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Female} 1 +]59.5;+inf[ {Other-service, Priv-house-serv} ]9.5;12.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 1 +]59.5;+inf[ {Other-service, Priv-house-serv} ]9.5;12.5] ]43.5;+inf[ {Divorced, Widowed, Separated, ...} {Female} 1 +]59.5;+inf[ {Other-service, Priv-house-serv} ]9.5;12.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 1 +]59.5;+inf[ {Other-service, Priv-house-serv} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 1 +]59.5;+inf[ {Other-service, Priv-house-serv} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 1 +]59.5;+inf[ {Other-service, Priv-house-serv} ]12.5;+inf[ ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Female} 1 +]59.5;+inf[ {Other-service, Priv-house-serv} ]12.5;+inf[ ]36.5;43.5] {Married-civ-spouse} {Male} 1 +]59.5;+inf[ {Other-service, Priv-house-serv} ]12.5;+inf[ ]43.5;+inf[ {Married-civ-spouse} {Male} 1 +]59.5;+inf[ {Prof-specialty} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 1 +]59.5;+inf[ {Prof-specialty} ]-inf;8.5] ]36.5;43.5] {Married-civ-spouse} {Female} 1 +]59.5;+inf[ {Prof-specialty} ]-inf;8.5] ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 1 +]59.5;+inf[ {Prof-specialty} ]-inf;8.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 1 +]59.5;+inf[ {Prof-specialty} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 1 +]59.5;+inf[ {Prof-specialty} ]9.5;12.5] ]36.5;43.5] {Married-civ-spouse} {Female} 1 +]59.5;+inf[ {Prof-specialty} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 1 +]59.5;+inf[ {Prof-specialty} ]9.5;12.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Male} 1 +]59.5;+inf[ {Sales} ]-inf;8.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 1 +]59.5;+inf[ {Sales} ]-inf;8.5] ]36.5;43.5] {Divorced, Widowed, Separated, ...} {Male} 1 +]59.5;+inf[ {Sales} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 1 +]59.5;+inf[ {Sales} ]8.5;9.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 1 +]59.5;+inf[ {Sales} ]8.5;9.5] ]43.5;+inf[ {Married-civ-spouse} {Female} 1 +]59.5;+inf[ {Sales} ]8.5;9.5] ]43.5;+inf[ {Never-married, Married-AF-spouse} {Female} 1 +]59.5;+inf[ {Sales} ]9.5;12.5] ]-inf;36.5] {Never-married, Married-AF-spouse} {Female} 1 +]59.5;+inf[ {Sales} ]12.5;+inf[ ]-inf;36.5] {Never-married, Married-AF-spouse} {Male} 1 +]59.5;+inf[ {Sales} ]12.5;+inf[ ]36.5;43.5] {Never-married, Married-AF-spouse} {Female} 1 + diff --git a/tests/resources/coclustering_results/ref_reports/AdultLegacy.khc b/tests/resources/coclustering_results/ref_reports/AdultLegacy.khc index b5e5fc72..603dd108 100644 --- a/tests/resources/coclustering_results/ref_reports/AdultLegacy.khc +++ b/tests/resources/coclustering_results/ref_reports/AdultLegacy.khc @@ -1,13 +1,13 @@ #Khiops 9.0.1 Short description Dimensions 6 -Name Type Parts Initial parts Values Interest Description -age Numerical 6 6 48842 1 -occupation Categorical 6 6 14 1 -education_num Numerical 4 4 48842 1 -hours_per_week Numerical 3 3 48842 1 -marital_status Categorical 3 3 7 1 -sex Categorical 2 2 2 1 +Name Is variable part Type Parts Initial parts Values Interest Description +age False Numerical 6 6 48842 1 +occupation False Categorical 6 6 14 1 +education_num False Numerical 4 4 48842 1 +hours_per_week False Numerical 3 3 48842 1 +marital_status False Categorical 3 3 7 1 +sex False Categorical 2 2 2 1 Coclustering stats Instances 48842 diff --git a/tests/resources/coclustering_results/ref_reports/AnsiGreek_Coclustering.khc b/tests/resources/coclustering_results/ref_reports/AnsiGreek_Coclustering.khc index 9eb550d1..02e4d0a0 100644 --- a/tests/resources/coclustering_results/ref_reports/AnsiGreek_Coclustering.khc +++ b/tests/resources/coclustering_results/ref_reports/AnsiGreek_Coclustering.khc @@ -1,9 +1,9 @@ #Khiops 10.0.0.3i Short description Dimensions 2 -Name Type Parts Initial parts Values Interest Description -Char Categorical 3 3 3 1 -Type Categorical 3 3 3 1 +Name Is variable part Type Parts Initial parts Values Interest Description +Char False Categorical 3 3 3 1 +Type False Categorical 3 3 3 1 Coclustering stats Instances 30 diff --git a/tests/resources/coclustering_results/ref_reports/AnsiLatinGreek_Coclustering.khc b/tests/resources/coclustering_results/ref_reports/AnsiLatinGreek_Coclustering.khc index 2c4f3a3d..5ba13ef4 100644 --- a/tests/resources/coclustering_results/ref_reports/AnsiLatinGreek_Coclustering.khc +++ b/tests/resources/coclustering_results/ref_reports/AnsiLatinGreek_Coclustering.khc @@ -1,9 +1,9 @@ #Khiops 10.0.0.3i Short description Dimensions 2 -Name Type Parts Initial parts Values Interest Description -Char Categorical 4 4 4 1 -Type Categorical 4 4 4 1 +Name Is variable part Type Parts Initial parts Values Interest Description +Char False Categorical 4 4 4 1 +Type False Categorical 4 4 4 1 Coclustering stats Instances 40 diff --git a/tests/resources/coclustering_results/ref_reports/AnsiLatin_Coclustering.khc b/tests/resources/coclustering_results/ref_reports/AnsiLatin_Coclustering.khc index 29368372..222ea5ef 100644 --- a/tests/resources/coclustering_results/ref_reports/AnsiLatin_Coclustering.khc +++ b/tests/resources/coclustering_results/ref_reports/AnsiLatin_Coclustering.khc @@ -1,9 +1,9 @@ #Khiops 10.0.0.3i Short description Dimensions 2 -Name Type Parts Initial parts Values Interest Description -Char Categorical 3 3 3 1 -Type Categorical 3 3 3 1 +Name Is variable part Type Parts Initial parts Values Interest Description +Char False Categorical 3 3 3 1 +Type False Categorical 3 3 3 1 Coclustering stats Instances 30 diff --git a/tests/resources/coclustering_results/ref_reports/Ansi_Coclustering.khc b/tests/resources/coclustering_results/ref_reports/Ansi_Coclustering.khc index 43dd38d7..c6f8d04f 100644 --- a/tests/resources/coclustering_results/ref_reports/Ansi_Coclustering.khc +++ b/tests/resources/coclustering_results/ref_reports/Ansi_Coclustering.khc @@ -1,9 +1,9 @@ #Khiops 10.0.0.3i Short description Dimensions 2 -Name Type Parts Initial parts Values Interest Description -Char Categorical 2 2 2 1 -Type Categorical 2 2 2 1 +Name Is variable part Type Parts Initial parts Values Interest Description +Char False Categorical 2 2 2 1 +Type False Categorical 2 2 2 1 Coclustering stats Instances 20 diff --git a/tests/resources/coclustering_results/ref_reports/Greek_Coclustering.khc b/tests/resources/coclustering_results/ref_reports/Greek_Coclustering.khc index bc1300c8..1f5cb6b9 100644 --- a/tests/resources/coclustering_results/ref_reports/Greek_Coclustering.khc +++ b/tests/resources/coclustering_results/ref_reports/Greek_Coclustering.khc @@ -1,9 +1,9 @@ #Khiops 10.0.0.3i Short description Dimensions 2 -Name Type Parts Initial parts Values Interest Description -Char Categorical 2 2 2 1 -Type Categorical 2 2 2 1 +Name Is variable part Type Parts Initial parts Values Interest Description +Char False Categorical 2 2 2 1 +Type False Categorical 2 2 2 1 Coclustering stats Instances 20 diff --git a/tests/resources/coclustering_results/ref_reports/Iris.khc b/tests/resources/coclustering_results/ref_reports/Iris.khc index 4b24fbbc..6471fa88 100644 --- a/tests/resources/coclustering_results/ref_reports/Iris.khc +++ b/tests/resources/coclustering_results/ref_reports/Iris.khc @@ -1,69 +1,69 @@ -#Khiops 10.0.0.3i -Short description -Dimensions 3 -Name Type Parts Initial parts Values Interest Description -PetalLength Numerical 3 3 150 1 -PetalWidth Numerical 3 3 150 1 -Class Categorical 3 3 3 1 - -Coclustering stats -Instances 150 -Cells 7 -Null cost 1379.668177 -Cost 1164.273132 -Level 0.1561209056 -Initial dimensions 3 -Frequency variable -Dictionary Iris -Database ../../../datasets/Iris/Iris.txt -Sample percentage 100 -Sampling mode Include sample -Selection variable -Selection value - -Bounds -Name Min Max -PetalLength 1 6.9 -PetalWidth 0.1 2.5 - -Hierarchy PetalLength -Cluster ParentCluster Frequency Interest HierarchicalLevel Rank HierarchicalRank -]-inf;2.4] ]-inf;+inf[ 50 0.557285 1 1 9 -]2.4;4.75] ]2.4;+inf[ 45 1 1 3 9 -]4.75;+inf[ ]2.4;+inf[ 55 0.442715 1 5 9 -]-inf;+inf[ 150 0.648091 -0.0145257 2 4 -]2.4;+inf[ ]-inf;+inf[ 100 0.693493 0.856289 4 8 - -Hierarchy PetalWidth -Cluster ParentCluster Frequency Interest HierarchicalLevel Rank HierarchicalRank -]-inf;0.8] ]-inf;+inf[ 50 0.569949 1 1 9 -]0.8;1.75] ]0.8;+inf[ 54 1 1 3 9 -]1.75;+inf[ ]0.8;+inf[ 46 0.430051 1 5 9 -]-inf;+inf[ 150 0.681865 0.374198 2 5 -]0.8;+inf[ ]-inf;+inf[ 100 0.737824 0.704564 4 7 - -Hierarchy Class -Cluster ParentCluster Frequency Interest HierarchicalLevel Rank HierarchicalRank -{Iris-setosa} C1 50 1 1 1 9 -{Iris-versicolor} C4 50 0.912663 1 3 9 -{Iris-virginica} C4 50 0.912663 1 5 9 -C1 150 0.941775 0 2 3 -C4 C1 100 0.912663 0.739904 4 6 - -Composition Class -Cluster Value Frequency Typicality -{Iris-setosa} Iris-setosa 50 1 -{Iris-versicolor} Iris-versicolor 50 1 -{Iris-virginica} Iris-virginica 50 1 -{Iris-virginica} * 0 0 - -Cells -PetalLength PetalWidth Class Frequency -]-inf;2.4] ]-inf;0.8] {Iris-setosa} 50 -]4.75;+inf[ ]1.75;+inf[ {Iris-virginica} 45 -]2.4;4.75] ]0.8;1.75] {Iris-versicolor} 44 -]4.75;+inf[ ]0.8;1.75] {Iris-versicolor} 5 -]4.75;+inf[ ]0.8;1.75] {Iris-virginica} 4 -]2.4;4.75] ]0.8;1.75] {Iris-virginica} 1 -]4.75;+inf[ ]1.75;+inf[ {Iris-versicolor} 1 - +#Khiops 10.0.0.3i +Short description +Dimensions 3 +Name Is variable part Type Parts Initial parts Values Interest Description +PetalLength False Numerical 3 3 150 1 +PetalWidth False Numerical 3 3 150 1 +Class False Categorical 3 3 3 1 + +Coclustering stats +Instances 150 +Cells 7 +Null cost 1379.668177 +Cost 1164.273132 +Level 0.1561209056 +Initial dimensions 3 +Frequency variable +Dictionary Iris +Database ../../../datasets/Iris/Iris.txt +Sample percentage 100 +Sampling mode Include sample +Selection variable +Selection value + +Bounds +Name Min Max +PetalLength 1 6.9 +PetalWidth 0.1 2.5 + +Hierarchy PetalLength +Cluster ParentCluster Frequency Interest HierarchicalLevel Rank HierarchicalRank +]-inf;2.4] ]-inf;+inf[ 50 0.557285 1 1 9 +]2.4;4.75] ]2.4;+inf[ 45 1 1 3 9 +]4.75;+inf[ ]2.4;+inf[ 55 0.442715 1 5 9 +]-inf;+inf[ 150 0.648091 -0.0145257 2 4 +]2.4;+inf[ ]-inf;+inf[ 100 0.693493 0.856289 4 8 + +Hierarchy PetalWidth +Cluster ParentCluster Frequency Interest HierarchicalLevel Rank HierarchicalRank +]-inf;0.8] ]-inf;+inf[ 50 0.569949 1 1 9 +]0.8;1.75] ]0.8;+inf[ 54 1 1 3 9 +]1.75;+inf[ ]0.8;+inf[ 46 0.430051 1 5 9 +]-inf;+inf[ 150 0.681865 0.374198 2 5 +]0.8;+inf[ ]-inf;+inf[ 100 0.737824 0.704564 4 7 + +Hierarchy Class +Cluster ParentCluster Frequency Interest HierarchicalLevel Rank HierarchicalRank +{Iris-setosa} C1 50 1 1 1 9 +{Iris-versicolor} C4 50 0.912663 1 3 9 +{Iris-virginica} C4 50 0.912663 1 5 9 +C1 150 0.941775 0 2 3 +C4 C1 100 0.912663 0.739904 4 6 + +Composition Class +Cluster Value Frequency Typicality +{Iris-setosa} Iris-setosa 50 1 +{Iris-versicolor} Iris-versicolor 50 1 +{Iris-virginica} Iris-virginica 50 1 +{Iris-virginica} * 0 0 + +Cells +PetalLength PetalWidth Class Frequency +]-inf;2.4] ]-inf;0.8] {Iris-setosa} 50 +]4.75;+inf[ ]1.75;+inf[ {Iris-virginica} 45 +]2.4;4.75] ]0.8;1.75] {Iris-versicolor} 44 +]4.75;+inf[ ]0.8;1.75] {Iris-versicolor} 5 +]4.75;+inf[ ]0.8;1.75] {Iris-virginica} 4 +]2.4;4.75] ]0.8;1.75] {Iris-virginica} 1 +]4.75;+inf[ ]1.75;+inf[ {Iris-versicolor} 1 + diff --git a/tests/resources/coclustering_results/ref_reports/LatinGreek_Coclustering.khc b/tests/resources/coclustering_results/ref_reports/LatinGreek_Coclustering.khc index a84b807a..d6474e3c 100644 --- a/tests/resources/coclustering_results/ref_reports/LatinGreek_Coclustering.khc +++ b/tests/resources/coclustering_results/ref_reports/LatinGreek_Coclustering.khc @@ -1,9 +1,9 @@ #Khiops 10.0.0.3i Short description Dimensions 2 -Name Type Parts Initial parts Values Interest Description -Char Categorical 3 3 3 1 -Type Categorical 3 3 3 1 +Name Is variable part Type Parts Initial parts Values Interest Description +Char False Categorical 3 3 3 1 +Type False Categorical 3 3 3 1 Coclustering stats Instances 30 diff --git a/tests/resources/coclustering_results/ref_reports/Latin_Coclustering.khc b/tests/resources/coclustering_results/ref_reports/Latin_Coclustering.khc index 7f0d64b5..27756d1e 100644 --- a/tests/resources/coclustering_results/ref_reports/Latin_Coclustering.khc +++ b/tests/resources/coclustering_results/ref_reports/Latin_Coclustering.khc @@ -1,9 +1,9 @@ #Khiops 10.0.0.3i Short description Dimensions 2 -Name Type Parts Initial parts Values Interest Description -Char Categorical 2 2 2 1 -Type Categorical 2 2 2 1 +Name Is variable part Type Parts Initial parts Values Interest Description +Char False Categorical 2 2 2 1 +Type False Categorical 2 2 2 1 Coclustering stats Instances 20 diff --git a/tests/resources/coclustering_results/ref_reports/MushroomAnnotated.khc b/tests/resources/coclustering_results/ref_reports/MushroomAnnotated.khc index 3f10d5bf..49d04dd4 100644 --- a/tests/resources/coclustering_results/ref_reports/MushroomAnnotated.khc +++ b/tests/resources/coclustering_results/ref_reports/MushroomAnnotated.khc @@ -1,140 +1,140 @@ -#Khiops 10.0.0.3i -Short description -Dimensions 2 -Name Type Parts Initial parts Values Interest Description -CapColor Categorical 7 7 10 1 -Odor Categorical 7 7 9 1 - -Coclustering stats -Instances 8416 -Cells 25 -Null cost 27983.3 -Cost 24901.7 -Level 0.1101228233 -Initial dimensions 2 -Frequency variable -Dictionary Mushroom -Database C:\Temp\Mushroom\Mushroom.txt -Sample percentage 100 -Sampling mode Include sample -Selection variable -Selection value - -Hierarchy CapColor -Cluster ParentCluster Frequency Interest HierarchicalLevel Rank HierarchicalRank -{YELLOW} A2 1072 1 1 1 14 -{GRAY, BUFF} A6 2264 0.739123 1 3 14 -{WHITE} A9 1040 0.626873 1 5 14 -{PINK} A12 144 0.276019 1 7 14 -{CINNAMON, GREEN, PURPLE} A12 76 0.14494 1 9 14 -{RED} A10 1500 0.680039 1 11 14 -{BROWN} A10 2320 0.668346 1 13 14 -A2 8416 0.71515 -0.00326863 2 3 -A6 A3 3524 0.674258 0.651661 4 7 -A9 A6 1260 0.557706 0.895619 6 10 -A12 A9 220 0.230737 0.995743 8 13 -A3 A2 7344 0.673571 0.358859 10 4 -A10 A3 3820 0.672937 0.942919 12 11 - -Hierarchy Odor -Cluster ParentCluster Frequency Interest HierarchicalLevel Rank HierarchicalRank -{ALMOND, ANISE} B5 800 0.91958 1 1 14 -{FOUL} B5 2160 0.876044 1 3 14 -{FISHY, SPICY} B11 1152 1 1 5 14 -{MUSTY} B11 48 0.145564 1 7 14 -{NONE} B7 3808 0.865195 1 9 14 -{CREOSOTE} B8 192 0.46277 1 11 14 -{PUNGENT} B8 256 0.378504 1 13 14 -B5 B1 2960 0.88781 0.633019 2 6 -B1 8416 0.863512 0 4 2 -B11 B4 1200 0.965823 0.98921 6 12 -B4 B1 5456 0.85033 0.405738 8 5 -B7 B4 4256 0.817766 0.80499 10 8 -B8 B7 448 0.414618 0.859857 12 9 - -Composition CapColor -Cluster Value Frequency Typicality -{YELLOW} YELLOW 1072 1 -{GRAY, BUFF} GRAY 2096 1 -{GRAY, BUFF} BUFF 168 0.186724 -{WHITE} WHITE 1040 1 -{PINK} PINK 144 1 -{CINNAMON, GREEN, PURPLE} CINNAMON 44 1 -{CINNAMON, GREEN, PURPLE} GREEN 16 0.32908 -{CINNAMON, GREEN, PURPLE} PURPLE 16 0.286005 -{RED} RED 1500 1 -{BROWN} BROWN 2320 1 -{BROWN} * 0 0 - -Composition Odor -Cluster Value Frequency Typicality -{ALMOND, ANISE} ALMOND 400 1 -{ALMOND, ANISE} ANISE 400 1 -{FOUL} FOUL 2160 1 -{FISHY, SPICY} FISHY 576 1 -{FISHY, SPICY} SPICY 576 1 -{MUSTY} MUSTY 48 1 -{NONE} NONE 3808 1 -{CREOSOTE} CREOSOTE 192 1 -{PUNGENT} PUNGENT 256 1 -{PUNGENT} * 0 0 - -Cells -CapColor Odor Frequency -{GRAY, BUFF} {NONE} 1360 -{BROWN} {NONE} 1208 -{GRAY, BUFF} {FOUL} 840 -{YELLOW} {FOUL} 648 -{RED} {NONE} 624 -{BROWN} {FISHY, SPICY} 576 -{RED} {FISHY, SPICY} 576 -{WHITE} {NONE} 448 -{YELLOW} {ALMOND, ANISE} 400 -{WHITE} {ALMOND, ANISE} 304 -{BROWN} {FOUL} 288 -{RED} {FOUL} 288 -{BROWN} {PUNGENT} 128 -{WHITE} {PUNGENT} 128 -{BROWN} {ALMOND, ANISE} 96 -{WHITE} {FOUL} 96 -{PINK} {NONE} 80 -{CINNAMON, GREEN, PURPLE} {NONE} 64 -{GRAY, BUFF} {CREOSOTE} 64 -{PINK} {CREOSOTE} 64 -{WHITE} {CREOSOTE} 64 -{BROWN} {MUSTY} 24 -{YELLOW} {NONE} 24 -{CINNAMON, GREEN, PURPLE} {MUSTY} 12 -{RED} {MUSTY} 12 - -Annotation CapColor -Cluster Expand Selected ShortDescription Description -{YELLOW} FALSE FALSE Jaune -{GRAY, BUFF} FALSE FALSE Gris Gris¤comme¤Gandalf -{WHITE} FALSE FALSE Blanc Blanc¤comme¤Saruman -{PINK} FALSE FALSE Rose -{CINNAMON, GREEN, PURPLE} FALSE FALSE Cannelle -{RED} FALSE FALSE Rouge Rouge!!!°Ah¤la¤la,¤ça¤craint... -{BROWN} FALSE FALSE Brun -A2 FALSE FALSE -A6 FALSE FALSE -A9 FALSE FALSE -A12 FALSE FALSE -A3 FALSE FALSE -A10 FALSE FALSE - -Annotation Odor -Cluster Expand Selected ShortDescription Description -{ALMOND, ANISE} FALSE FALSE -{FOUL} FALSE FALSE -{FISHY, SPICY} FALSE FALSE -{MUSTY} FALSE FALSE -{NONE} FALSE FALSE -{CREOSOTE} FALSE FALSE -{PUNGENT} FALSE FALSE -B5 FALSE FALSE -B1 FALSE FALSE -B11 FALSE FALSE -B4 FALSE FALSE -B7 FALSE FALSE -B8 FALSE FALSE +#Khiops 10.0.0.3i +Short description +Dimensions 2 +Name Is variable part Type Parts Initial parts Values Interest Description +CapColor False Categorical 7 7 10 1 +Odor False Categorical 7 7 9 1 + +Coclustering stats +Instances 8416 +Cells 25 +Null cost 27983.3 +Cost 24901.7 +Level 0.1101228233 +Initial dimensions 2 +Frequency variable +Dictionary Mushroom +Database C:\Temp\Mushroom\Mushroom.txt +Sample percentage 100 +Sampling mode Include sample +Selection variable +Selection value + +Hierarchy CapColor +Cluster ParentCluster Frequency Interest HierarchicalLevel Rank HierarchicalRank +{YELLOW} A2 1072 1 1 1 14 +{GRAY, BUFF} A6 2264 0.739123 1 3 14 +{WHITE} A9 1040 0.626873 1 5 14 +{PINK} A12 144 0.276019 1 7 14 +{CINNAMON, GREEN, PURPLE} A12 76 0.14494 1 9 14 +{RED} A10 1500 0.680039 1 11 14 +{BROWN} A10 2320 0.668346 1 13 14 +A2 8416 0.71515 -0.00326863 2 3 +A6 A3 3524 0.674258 0.651661 4 7 +A9 A6 1260 0.557706 0.895619 6 10 +A12 A9 220 0.230737 0.995743 8 13 +A3 A2 7344 0.673571 0.358859 10 4 +A10 A3 3820 0.672937 0.942919 12 11 + +Hierarchy Odor +Cluster ParentCluster Frequency Interest HierarchicalLevel Rank HierarchicalRank +{ALMOND, ANISE} B5 800 0.91958 1 1 14 +{FOUL} B5 2160 0.876044 1 3 14 +{FISHY, SPICY} B11 1152 1 1 5 14 +{MUSTY} B11 48 0.145564 1 7 14 +{NONE} B7 3808 0.865195 1 9 14 +{CREOSOTE} B8 192 0.46277 1 11 14 +{PUNGENT} B8 256 0.378504 1 13 14 +B5 B1 2960 0.88781 0.633019 2 6 +B1 8416 0.863512 0 4 2 +B11 B4 1200 0.965823 0.98921 6 12 +B4 B1 5456 0.85033 0.405738 8 5 +B7 B4 4256 0.817766 0.80499 10 8 +B8 B7 448 0.414618 0.859857 12 9 + +Composition CapColor +Cluster Value Frequency Typicality +{YELLOW} YELLOW 1072 1 +{GRAY, BUFF} GRAY 2096 1 +{GRAY, BUFF} BUFF 168 0.186724 +{WHITE} WHITE 1040 1 +{PINK} PINK 144 1 +{CINNAMON, GREEN, PURPLE} CINNAMON 44 1 +{CINNAMON, GREEN, PURPLE} GREEN 16 0.32908 +{CINNAMON, GREEN, PURPLE} PURPLE 16 0.286005 +{RED} RED 1500 1 +{BROWN} BROWN 2320 1 +{BROWN} * 0 0 + +Composition Odor +Cluster Value Frequency Typicality +{ALMOND, ANISE} ALMOND 400 1 +{ALMOND, ANISE} ANISE 400 1 +{FOUL} FOUL 2160 1 +{FISHY, SPICY} FISHY 576 1 +{FISHY, SPICY} SPICY 576 1 +{MUSTY} MUSTY 48 1 +{NONE} NONE 3808 1 +{CREOSOTE} CREOSOTE 192 1 +{PUNGENT} PUNGENT 256 1 +{PUNGENT} * 0 0 + +Cells +CapColor Odor Frequency +{GRAY, BUFF} {NONE} 1360 +{BROWN} {NONE} 1208 +{GRAY, BUFF} {FOUL} 840 +{YELLOW} {FOUL} 648 +{RED} {NONE} 624 +{BROWN} {FISHY, SPICY} 576 +{RED} {FISHY, SPICY} 576 +{WHITE} {NONE} 448 +{YELLOW} {ALMOND, ANISE} 400 +{WHITE} {ALMOND, ANISE} 304 +{BROWN} {FOUL} 288 +{RED} {FOUL} 288 +{BROWN} {PUNGENT} 128 +{WHITE} {PUNGENT} 128 +{BROWN} {ALMOND, ANISE} 96 +{WHITE} {FOUL} 96 +{PINK} {NONE} 80 +{CINNAMON, GREEN, PURPLE} {NONE} 64 +{GRAY, BUFF} {CREOSOTE} 64 +{PINK} {CREOSOTE} 64 +{WHITE} {CREOSOTE} 64 +{BROWN} {MUSTY} 24 +{YELLOW} {NONE} 24 +{CINNAMON, GREEN, PURPLE} {MUSTY} 12 +{RED} {MUSTY} 12 + +Annotation CapColor +Cluster Expand Selected ShortDescription Description +{YELLOW} FALSE FALSE Jaune +{GRAY, BUFF} FALSE FALSE Gris Gris¤comme¤Gandalf +{WHITE} FALSE FALSE Blanc Blanc¤comme¤Saruman +{PINK} FALSE FALSE Rose +{CINNAMON, GREEN, PURPLE} FALSE FALSE Cannelle +{RED} FALSE FALSE Rouge Rouge!!!°Ah¤la¤la,¤ça¤craint... +{BROWN} FALSE FALSE Brun +A2 FALSE FALSE +A6 FALSE FALSE +A9 FALSE FALSE +A12 FALSE FALSE +A3 FALSE FALSE +A10 FALSE FALSE + +Annotation Odor +Cluster Expand Selected ShortDescription Description +{ALMOND, ANISE} FALSE FALSE +{FOUL} FALSE FALSE +{FISHY, SPICY} FALSE FALSE +{MUSTY} FALSE FALSE +{NONE} FALSE FALSE +{CREOSOTE} FALSE FALSE +{PUNGENT} FALSE FALSE +B5 FALSE FALSE +B1 FALSE FALSE +B11 FALSE FALSE +B4 FALSE FALSE +B7 FALSE FALSE +B8 FALSE FALSE diff --git a/tests/resources/scenario_generation/api/build_dictionary_from_data_table/ref/Adult._kh b/tests/resources/scenario_generation/api/build_dictionary_from_data_table/ref/Adult._kh index dfaf4e4a..8fc726b0 100644 --- a/tests/resources/scenario_generation/api/build_dictionary_from_data_table/ref/Adult._kh +++ b/tests/resources/scenario_generation/api/build_dictionary_from_data_table/ref/Adult._kh @@ -1,10 +1,11 @@ // Scenario for task build_dictionary_from_data_table // Dictionary building settings -ClassManagement.BuildClassDefButton -SourceDataTable.DatabaseName Adult.csv -SourceDataTable.HeaderLineUsed true -SourceDataTable.FieldSeparator -SourceDataTable.DatabaseFormatDetector.DetectFileFormat +ClassManagement.ManageClasses +BuildClassDefButton +SourceDataTable.DatabaseSpec.Data.DatabaseName Adult.csv +SourceDataTable.DatabaseSpec.Data.HeaderLineUsed true +SourceDataTable.DatabaseSpec.Data.FieldSeparator +SourceDataTable.DatabaseSpec.Data.DatabaseFormatDetector.DetectFileFormat BuildClassDef ClassName Adult OK @@ -13,6 +14,7 @@ Exit // Save dictionary ClassFileName Adult.kdic OK +Exit // End of scenario for task build_dictionary_from_data_table diff --git a/tests/resources/scenario_generation/api/build_dictionary_from_data_table/ref/Customer._kh b/tests/resources/scenario_generation/api/build_dictionary_from_data_table/ref/Customer._kh index cb5121ed..3483f5b0 100644 --- a/tests/resources/scenario_generation/api/build_dictionary_from_data_table/ref/Customer._kh +++ b/tests/resources/scenario_generation/api/build_dictionary_from_data_table/ref/Customer._kh @@ -1,10 +1,11 @@ // Scenario for task build_dictionary_from_data_table // Dictionary building settings -ClassManagement.BuildClassDefButton -SourceDataTable.DatabaseName Customer.csv -SourceDataTable.HeaderLineUsed true -SourceDataTable.FieldSeparator -SourceDataTable.DatabaseFormatDetector.DetectFileFormat +ClassManagement.ManageClasses +BuildClassDefButton +SourceDataTable.DatabaseSpec.Data.DatabaseName Customer.csv +SourceDataTable.DatabaseSpec.Data.HeaderLineUsed true +SourceDataTable.DatabaseSpec.Data.FieldSeparator +SourceDataTable.DatabaseSpec.Data.DatabaseFormatDetector.DetectFileFormat BuildClassDef ClassName Customer OK @@ -13,6 +14,7 @@ Exit // Save dictionary ClassFileName Customer.kdic OK +Exit // End of scenario for task build_dictionary_from_data_table diff --git a/tests/resources/scenario_generation/api/build_dictionary_from_data_table/ref/SpliceJunction._kh b/tests/resources/scenario_generation/api/build_dictionary_from_data_table/ref/SpliceJunction._kh index fd20cf8a..a5bb876a 100644 --- a/tests/resources/scenario_generation/api/build_dictionary_from_data_table/ref/SpliceJunction._kh +++ b/tests/resources/scenario_generation/api/build_dictionary_from_data_table/ref/SpliceJunction._kh @@ -1,10 +1,11 @@ // Scenario for task build_dictionary_from_data_table // Dictionary building settings -ClassManagement.BuildClassDefButton -SourceDataTable.DatabaseName SpliceJunction.csv -SourceDataTable.HeaderLineUsed true -SourceDataTable.FieldSeparator -SourceDataTable.DatabaseFormatDetector.DetectFileFormat +ClassManagement.ManageClasses +BuildClassDefButton +SourceDataTable.DatabaseSpec.Data.DatabaseName SpliceJunction.csv +SourceDataTable.DatabaseSpec.Data.HeaderLineUsed true +SourceDataTable.DatabaseSpec.Data.FieldSeparator +SourceDataTable.DatabaseSpec.Data.DatabaseFormatDetector.DetectFileFormat BuildClassDef ClassName SpliceJunction OK @@ -13,6 +14,7 @@ Exit // Save dictionary ClassFileName SpliceJunction.kdic OK +Exit // End of scenario for task build_dictionary_from_data_table diff --git a/tests/resources/scenario_generation/api/build_multi_table_dictionary/ref/Adult._kh b/tests/resources/scenario_generation/api/build_multi_table_dictionary/ref/Adult._kh deleted file mode 100644 index d9d31a14..00000000 --- a/tests/resources/scenario_generation/api/build_multi_table_dictionary/ref/Adult._kh +++ /dev/null @@ -1,20 +0,0 @@ -// Scenario for task build_multi_table_dictionary -// Dictionary file and class settings -ClassManagement.OpenFile -ClassFileName Adult.kdic -OK - -// Multi-table dictionary creation -// warning: Overwrites the loaded dictionary file -LearningTools.ExtractKeysFromDataTable -BuildMultiTableClass -MultiTableClassName RootAdult -TableVariableName SecondaryAdult -OK -Exit - -// End of scenario for task build_multi_table_dictionary - -// Exit Khiops -ClassManagement.Quit -OK diff --git a/tests/resources/scenario_generation/api/build_multi_table_dictionary/ref/Customer._kh b/tests/resources/scenario_generation/api/build_multi_table_dictionary/ref/Customer._kh deleted file mode 100644 index 8952eac1..00000000 --- a/tests/resources/scenario_generation/api/build_multi_table_dictionary/ref/Customer._kh +++ /dev/null @@ -1,20 +0,0 @@ -// Scenario for task build_multi_table_dictionary -// Dictionary file and class settings -ClassManagement.OpenFile -ClassFileName Customer.kdic -OK - -// Multi-table dictionary creation -// warning: Overwrites the loaded dictionary file -LearningTools.ExtractKeysFromDataTable -BuildMultiTableClass -MultiTableClassName RootCustomer -TableVariableName SecondaryCustomer -OK -Exit - -// End of scenario for task build_multi_table_dictionary - -// Exit Khiops -ClassManagement.Quit -OK diff --git a/tests/resources/scenario_generation/api/build_multi_table_dictionary/ref/SpliceJunction._kh b/tests/resources/scenario_generation/api/build_multi_table_dictionary/ref/SpliceJunction._kh deleted file mode 100644 index 418c2560..00000000 --- a/tests/resources/scenario_generation/api/build_multi_table_dictionary/ref/SpliceJunction._kh +++ /dev/null @@ -1,20 +0,0 @@ -// Scenario for task build_multi_table_dictionary -// Dictionary file and class settings -ClassManagement.OpenFile -ClassFileName SpliceJunction.kdic -OK - -// Multi-table dictionary creation -// warning: Overwrites the loaded dictionary file -LearningTools.ExtractKeysFromDataTable -BuildMultiTableClass -MultiTableClassName RootSpliceJunction -TableVariableName SecondarySpliceJunction -OK -Exit - -// End of scenario for task build_multi_table_dictionary - -// Exit Khiops -ClassManagement.Quit -OK diff --git a/tests/resources/scenario_generation/api/check_database/ref/Adult._kh b/tests/resources/scenario_generation/api/check_database/ref/Adult._kh index ad3f5737..b592205b 100644 --- a/tests/resources/scenario_generation/api/check_database/ref/Adult._kh +++ b/tests/resources/scenario_generation/api/check_database/ref/Adult._kh @@ -3,18 +3,18 @@ ClassManagement.OpenFile ClassFileName Adult.kdic OK -ClassManagement.ClassName Adult // Train database settings -TrainDatabase.DatabaseFiles.List.Key Adult -TrainDatabase.DatabaseFiles.DataTableName Adult.csv -TrainDatabase.HeaderLineUsed true -TrainDatabase.FieldSeparator -TrainDatabase.DatabaseFormatDetector.DetectFileFormat -TrainDatabase.SampleNumberPercentage 100.0 -TrainDatabase.SamplingMode Include sample -TrainDatabase.SelectionAttribute -TrainDatabase.SelectionValue +TrainDatabase.ClassName Adult +TrainDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key +TrainDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName Adult.csv +TrainDatabase.DatabaseSpec.Data.HeaderLineUsed true +TrainDatabase.DatabaseSpec.Data.FieldSeparator +TrainDatabase.DatabaseSpec.Data.DatabaseFormatDetector.DetectFileFormat +TrainDatabase.DatabaseSpec.Sampling.SampleNumberPercentage 100.0 +TrainDatabase.DatabaseSpec.Sampling.SamplingMode Include sample +TrainDatabase.DatabaseSpec.Selection.SelectionAttribute +TrainDatabase.DatabaseSpec.Selection.SelectionValue // Log messages limit AnalysisSpec.SystemParameters.MaxErrorMessageNumberInLog 20 diff --git a/tests/resources/scenario_generation/api/check_database/ref/Customer._kh b/tests/resources/scenario_generation/api/check_database/ref/Customer._kh index c259d7dc..5a46d5c5 100644 --- a/tests/resources/scenario_generation/api/check_database/ref/Customer._kh +++ b/tests/resources/scenario_generation/api/check_database/ref/Customer._kh @@ -3,30 +3,30 @@ ClassManagement.OpenFile ClassFileName Customer.kdic OK -ClassManagement.ClassName Customer // Train database settings -TrainDatabase.DatabaseFiles.List.Key Customer -TrainDatabase.DatabaseFiles.DataTableName Customer.csv -TrainDatabase.DatabaseFiles.List.Key Customer`Services -TrainDatabase.DatabaseFiles.DataTableName ServicesBidon.csv -TrainDatabase.DatabaseFiles.List.Key Customer`Services`Usages -TrainDatabase.DatabaseFiles.DataTableName UsagesBidon.csv -TrainDatabase.DatabaseFiles.List.Key Customer`Address -TrainDatabase.DatabaseFiles.DataTableName AddressBidon.csv -TrainDatabase.DatabaseFiles.List.Key City -TrainDatabase.DatabaseFiles.DataTableName CityBidon.csv -TrainDatabase.DatabaseFiles.List.Key Country -TrainDatabase.DatabaseFiles.DataTableName CountryBidon.csv -TrainDatabase.DatabaseFiles.List.Key Product -TrainDatabase.DatabaseFiles.DataTableName ProductBidon.csv -TrainDatabase.HeaderLineUsed true -TrainDatabase.FieldSeparator -TrainDatabase.DatabaseFormatDetector.DetectFileFormat -TrainDatabase.SampleNumberPercentage 100.0 -TrainDatabase.SamplingMode Include sample -TrainDatabase.SelectionAttribute -TrainDatabase.SelectionValue +TrainDatabase.ClassName Customer +TrainDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key +TrainDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName Customer.csv +TrainDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key Services +TrainDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName ServicesBidon.csv +TrainDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key Services/Usages +TrainDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName UsagesBidon.csv +TrainDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key Address +TrainDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName AddressBidon.csv +TrainDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key /City +TrainDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName CityBidon.csv +TrainDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key /Country +TrainDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName CountryBidon.csv +TrainDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key /Product +TrainDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName ProductBidon.csv +TrainDatabase.DatabaseSpec.Data.HeaderLineUsed true +TrainDatabase.DatabaseSpec.Data.FieldSeparator +TrainDatabase.DatabaseSpec.Data.DatabaseFormatDetector.DetectFileFormat +TrainDatabase.DatabaseSpec.Sampling.SampleNumberPercentage 100.0 +TrainDatabase.DatabaseSpec.Sampling.SamplingMode Include sample +TrainDatabase.DatabaseSpec.Selection.SelectionAttribute +TrainDatabase.DatabaseSpec.Selection.SelectionValue // Log messages limit AnalysisSpec.SystemParameters.MaxErrorMessageNumberInLog 20 diff --git a/tests/resources/scenario_generation/api/check_database/ref/SpliceJunction._kh b/tests/resources/scenario_generation/api/check_database/ref/SpliceJunction._kh index 1fb31a84..65b21df7 100644 --- a/tests/resources/scenario_generation/api/check_database/ref/SpliceJunction._kh +++ b/tests/resources/scenario_generation/api/check_database/ref/SpliceJunction._kh @@ -3,20 +3,20 @@ ClassManagement.OpenFile ClassFileName SpliceJunction.kdic OK -ClassManagement.ClassName SpliceJunction // Train database settings -TrainDatabase.DatabaseFiles.List.Key SpliceJunction -TrainDatabase.DatabaseFiles.DataTableName SpliceJunction.csv -TrainDatabase.DatabaseFiles.List.Key SpliceJunction`DNA -TrainDatabase.DatabaseFiles.DataTableName SpliceJunctionDNABidon.csv -TrainDatabase.HeaderLineUsed true -TrainDatabase.FieldSeparator -TrainDatabase.DatabaseFormatDetector.DetectFileFormat -TrainDatabase.SampleNumberPercentage 100.0 -TrainDatabase.SamplingMode Include sample -TrainDatabase.SelectionAttribute -TrainDatabase.SelectionValue +TrainDatabase.ClassName SpliceJunction +TrainDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key +TrainDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName SpliceJunction.csv +TrainDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key DNA +TrainDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName SpliceJunctionDNABidon.csv +TrainDatabase.DatabaseSpec.Data.HeaderLineUsed true +TrainDatabase.DatabaseSpec.Data.FieldSeparator +TrainDatabase.DatabaseSpec.Data.DatabaseFormatDetector.DetectFileFormat +TrainDatabase.DatabaseSpec.Sampling.SampleNumberPercentage 100.0 +TrainDatabase.DatabaseSpec.Sampling.SamplingMode Include sample +TrainDatabase.DatabaseSpec.Selection.SelectionAttribute +TrainDatabase.DatabaseSpec.Selection.SelectionValue // Log messages limit AnalysisSpec.SystemParameters.MaxErrorMessageNumberInLog 20 diff --git a/tests/resources/scenario_generation/api/deploy_model/ref/Adult._kh b/tests/resources/scenario_generation/api/deploy_model/ref/Adult._kh index a8b2602b..d5a25770 100644 --- a/tests/resources/scenario_generation/api/deploy_model/ref/Adult._kh +++ b/tests/resources/scenario_generation/api/deploy_model/ref/Adult._kh @@ -9,21 +9,21 @@ LearningTools.TransferDatabase ClassName Adult // Input database settings -SourceDatabase.DatabaseFiles.List.Key Adult -SourceDatabase.DatabaseFiles.DataTableName Adult.csv -SourceDatabase.HeaderLineUsed true -SourceDatabase.FieldSeparator -SourceDatabase.DatabaseFormatDetector.DetectFileFormat -SourceDatabase.SampleNumberPercentage 100.0 -SourceDatabase.SamplingMode Include sample -SourceDatabase.SelectionAttribute -SourceDatabase.SelectionValue +SourceDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key +SourceDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName Adult.csv +SourceDatabase.DatabaseSpec.Data.HeaderLineUsed true +SourceDatabase.DatabaseSpec.Data.FieldSeparator +SourceDatabase.DatabaseSpec.Data.DatabaseFormatDetector.DetectFileFormat +SourceDatabase.DatabaseSpec.Sampling.SampleNumberPercentage 100.0 +SourceDatabase.DatabaseSpec.Sampling.SamplingMode Include sample +SourceDatabase.DatabaseSpec.Selection.SelectionAttribute +SourceDatabase.DatabaseSpec.Selection.SelectionValue // Output database settings -TargetDatabase.DatabaseFiles.List.Key Adult -TargetDatabase.DatabaseFiles.DataTableName .\AdultDeployed.csv -TargetDatabase.HeaderLineUsed true -TargetDatabase.FieldSeparator +TargetDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key +TargetDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName ./AdultDeployed.csv +TargetDatabase.DatabaseSpec.Data.HeaderLineUsed true +TargetDatabase.DatabaseSpec.Data.FieldSeparator // Transfer TransferDatabase diff --git a/tests/resources/scenario_generation/api/deploy_model/ref/Customer._kh b/tests/resources/scenario_generation/api/deploy_model/ref/Customer._kh index f333c028..b85abba2 100644 --- a/tests/resources/scenario_generation/api/deploy_model/ref/Customer._kh +++ b/tests/resources/scenario_generation/api/deploy_model/ref/Customer._kh @@ -9,45 +9,45 @@ LearningTools.TransferDatabase ClassName Customer // Input database settings -SourceDatabase.DatabaseFiles.List.Key Customer -SourceDatabase.DatabaseFiles.DataTableName Customer.csv -SourceDatabase.DatabaseFiles.List.Key Customer`Services -SourceDatabase.DatabaseFiles.DataTableName ServicesBidon.csv -SourceDatabase.DatabaseFiles.List.Key Customer`Services`Usages -SourceDatabase.DatabaseFiles.DataTableName UsagesBidon.csv -SourceDatabase.DatabaseFiles.List.Key Customer`Address -SourceDatabase.DatabaseFiles.DataTableName AddressBidon.csv -SourceDatabase.DatabaseFiles.List.Key City -SourceDatabase.DatabaseFiles.DataTableName CityBidon.csv -SourceDatabase.DatabaseFiles.List.Key Country -SourceDatabase.DatabaseFiles.DataTableName CountryBidon.csv -SourceDatabase.DatabaseFiles.List.Key Product -SourceDatabase.DatabaseFiles.DataTableName ProductBidon.csv -SourceDatabase.HeaderLineUsed true -SourceDatabase.FieldSeparator -SourceDatabase.DatabaseFormatDetector.DetectFileFormat -SourceDatabase.SampleNumberPercentage 100.0 -SourceDatabase.SamplingMode Include sample -SourceDatabase.SelectionAttribute -SourceDatabase.SelectionValue +SourceDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key +SourceDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName Customer.csv +SourceDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key Services +SourceDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName ServicesBidon.csv +SourceDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key Services/Usages +SourceDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName UsagesBidon.csv +SourceDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key Address +SourceDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName AddressBidon.csv +SourceDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key /City +SourceDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName CityBidon.csv +SourceDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key /Country +SourceDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName CountryBidon.csv +SourceDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key /Product +SourceDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName ProductBidon.csv +SourceDatabase.DatabaseSpec.Data.HeaderLineUsed true +SourceDatabase.DatabaseSpec.Data.FieldSeparator +SourceDatabase.DatabaseSpec.Data.DatabaseFormatDetector.DetectFileFormat +SourceDatabase.DatabaseSpec.Sampling.SampleNumberPercentage 100.0 +SourceDatabase.DatabaseSpec.Sampling.SamplingMode Include sample +SourceDatabase.DatabaseSpec.Selection.SelectionAttribute +SourceDatabase.DatabaseSpec.Selection.SelectionValue // Output database settings -TargetDatabase.DatabaseFiles.List.Key Customer -TargetDatabase.DatabaseFiles.DataTableName .\CustomerDeployed.csv -TargetDatabase.DatabaseFiles.List.Key Customer`Services -TargetDatabase.DatabaseFiles.DataTableName .\TransferServicesBidon.csv -TargetDatabase.DatabaseFiles.List.Key Customer`Services`Usages -TargetDatabase.DatabaseFiles.DataTableName .\TransferUsagesBidon.csv -TargetDatabase.DatabaseFiles.List.Key Customer`Address -TargetDatabase.DatabaseFiles.DataTableName .\TransferAddressBidon.csv -TargetDatabase.DatabaseFiles.List.Key City -TargetDatabase.DatabaseFiles.DataTableName .\TransferCityBidon.csv -TargetDatabase.DatabaseFiles.List.Key Country -TargetDatabase.DatabaseFiles.DataTableName .\TransferCountryBidon.csv -TargetDatabase.DatabaseFiles.List.Key Product -TargetDatabase.DatabaseFiles.DataTableName .\TransferProductBidon.csv -TargetDatabase.HeaderLineUsed true -TargetDatabase.FieldSeparator +TargetDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key +TargetDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName ./CustomerDeployed.csv +TargetDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key Services +TargetDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName ./TransferServicesBidon.csv +TargetDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key Services/Usages +TargetDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName ./TransferUsagesBidon.csv +TargetDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key Address +TargetDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName ./TransferAddressBidon.csv +TargetDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key /City +TargetDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName ./TransferCityBidon.csv +TargetDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key /Country +TargetDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName ./TransferCountryBidon.csv +TargetDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key /Product +TargetDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName ./TransferProductBidon.csv +TargetDatabase.DatabaseSpec.Data.HeaderLineUsed true +TargetDatabase.DatabaseSpec.Data.FieldSeparator // Transfer TransferDatabase diff --git a/tests/resources/scenario_generation/api/deploy_model/ref/SpliceJunction._kh b/tests/resources/scenario_generation/api/deploy_model/ref/SpliceJunction._kh index e83e9565..753883f0 100644 --- a/tests/resources/scenario_generation/api/deploy_model/ref/SpliceJunction._kh +++ b/tests/resources/scenario_generation/api/deploy_model/ref/SpliceJunction._kh @@ -9,25 +9,25 @@ LearningTools.TransferDatabase ClassName SpliceJunction // Input database settings -SourceDatabase.DatabaseFiles.List.Key SpliceJunction -SourceDatabase.DatabaseFiles.DataTableName SpliceJunction.csv -SourceDatabase.DatabaseFiles.List.Key SpliceJunction`DNA -SourceDatabase.DatabaseFiles.DataTableName SpliceJunctionDNABidon.csv -SourceDatabase.HeaderLineUsed true -SourceDatabase.FieldSeparator -SourceDatabase.DatabaseFormatDetector.DetectFileFormat -SourceDatabase.SampleNumberPercentage 100.0 -SourceDatabase.SamplingMode Include sample -SourceDatabase.SelectionAttribute -SourceDatabase.SelectionValue +SourceDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key +SourceDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName SpliceJunction.csv +SourceDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key DNA +SourceDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName SpliceJunctionDNABidon.csv +SourceDatabase.DatabaseSpec.Data.HeaderLineUsed true +SourceDatabase.DatabaseSpec.Data.FieldSeparator +SourceDatabase.DatabaseSpec.Data.DatabaseFormatDetector.DetectFileFormat +SourceDatabase.DatabaseSpec.Sampling.SampleNumberPercentage 100.0 +SourceDatabase.DatabaseSpec.Sampling.SamplingMode Include sample +SourceDatabase.DatabaseSpec.Selection.SelectionAttribute +SourceDatabase.DatabaseSpec.Selection.SelectionValue // Output database settings -TargetDatabase.DatabaseFiles.List.Key SpliceJunction -TargetDatabase.DatabaseFiles.DataTableName .\SpliceJunctionDeployed.csv -TargetDatabase.DatabaseFiles.List.Key SpliceJunction`DNA -TargetDatabase.DatabaseFiles.DataTableName .\TransferSpliceJunctionDNABidon.csv -TargetDatabase.HeaderLineUsed true -TargetDatabase.FieldSeparator +TargetDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key +TargetDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName ./SpliceJunctionDeployed.csv +TargetDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key DNA +TargetDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName ./TransferSpliceJunctionDNABidon.csv +TargetDatabase.DatabaseSpec.Data.HeaderLineUsed true +TargetDatabase.DatabaseSpec.Data.FieldSeparator // Transfer TransferDatabase diff --git a/tests/resources/scenario_generation/api/detect_data_table_format/ref/Adult._kh b/tests/resources/scenario_generation/api/detect_data_table_format/ref/Adult._kh index 50d9e933..66cd46af 100644 --- a/tests/resources/scenario_generation/api/detect_data_table_format/ref/Adult._kh +++ b/tests/resources/scenario_generation/api/detect_data_table_format/ref/Adult._kh @@ -3,12 +3,13 @@ ClassManagement.OpenFile ClassFileName Adult.kdic OK -ClassManagement.ClassName Adult + +TrainDatabase.ClassName Adult // Detect format the data table format on the "Extract Keys" window LearningTools.ExtractKeysFromDataTable -SourceDataTable.DatabaseName Adult.csv -SourceDataTable.DatabaseFormatDetector.DetectFileFormat +SourceDataTable.DatabaseSpec.Data.DatabaseName Adult.csv +SourceDataTable.DatabaseSpec.Data.DatabaseFormatDetector.DetectFileFormat Exit // End of scenario for task detect_data_table_format_with_dictionary diff --git a/tests/resources/scenario_generation/api/detect_data_table_format/ref/Customer._kh b/tests/resources/scenario_generation/api/detect_data_table_format/ref/Customer._kh index 7ce31069..bc173716 100644 --- a/tests/resources/scenario_generation/api/detect_data_table_format/ref/Customer._kh +++ b/tests/resources/scenario_generation/api/detect_data_table_format/ref/Customer._kh @@ -1,9 +1,8 @@ // Scenario for task detect_data_table_format // Detect format on the "Build Dictionary" window -ClassManagement.BuildClassDefButton -SourceDataTable.DatabaseName Customer.csv -SourceDataTable.DatabaseFormatDetector.DetectFileFormat -Exit +TrainDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key // List item selection +TrainDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName Customer.csv +TrainDatabase.DatabaseSpec.Data.DatabaseFormatDetector.DetectFileFormat // End of scenario for task detect_data_table_format diff --git a/tests/resources/scenario_generation/api/detect_data_table_format/ref/SpliceJunction._kh b/tests/resources/scenario_generation/api/detect_data_table_format/ref/SpliceJunction._kh index e0f7aeb9..1be59a83 100644 --- a/tests/resources/scenario_generation/api/detect_data_table_format/ref/SpliceJunction._kh +++ b/tests/resources/scenario_generation/api/detect_data_table_format/ref/SpliceJunction._kh @@ -3,12 +3,13 @@ ClassManagement.OpenFile ClassFileName SpliceJunctionDNA.kdic OK -ClassManagement.ClassName SpliceJunctionDNA + +TrainDatabase.ClassName SpliceJunctionDNA // Detect format the data table format on the "Extract Keys" window LearningTools.ExtractKeysFromDataTable -SourceDataTable.DatabaseName SpliceJunction.csv -SourceDataTable.DatabaseFormatDetector.DetectFileFormat +SourceDataTable.DatabaseSpec.Data.DatabaseName SpliceJunction.csv +SourceDataTable.DatabaseSpec.Data.DatabaseFormatDetector.DetectFileFormat Exit // End of scenario for task detect_data_table_format_with_dictionary diff --git a/tests/resources/scenario_generation/api/evaluate_predictor/ref/Adult._kh b/tests/resources/scenario_generation/api/evaluate_predictor/ref/Adult._kh index b5214ffa..2c8ae528 100644 --- a/tests/resources/scenario_generation/api/evaluate_predictor/ref/Adult._kh +++ b/tests/resources/scenario_generation/api/evaluate_predictor/ref/Adult._kh @@ -6,17 +6,19 @@ OK // Evaluate predictor settings LearningTools.EvaluatePredictors -MainTargetModality -EvaluationDatabase.DatabaseFiles.List.Key Adult -EvaluationDatabase.DatabaseFiles.DataTableName Adult.csv -EvaluationDatabase.HeaderLineUsed true -EvaluationDatabase.FieldSeparator -EvaluationDatabase.DatabaseFormatDetector.DetectFileFormat -EvaluationDatabase.SampleNumberPercentage 100.0 -EvaluationDatabase.SamplingMode Include sample -EvaluationDatabase.SelectionAttribute -EvaluationDatabase.SelectionValue -EvaluationFileName .\AdultResults\EvaluationReport.xls +MainTargetModality +EvaluationDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key +EvaluationDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName Adult.csv +EvaluationDatabase.DatabaseSpec.Data.HeaderLineUsed true +EvaluationDatabase.DatabaseSpec.Data.FieldSeparator +EvaluationDatabase.DatabaseSpec.Data.DatabaseFormatDetector.DetectFileFormat +EvaluationDatabase.DatabaseSpec.Sampling.SampleNumberPercentage 100.0 +EvaluationDatabase.DatabaseSpec.Sampling.SamplingMode Include sample +EvaluatedPredictors.List.Key Adult +EvaluationDatabase.DatabaseSpec.Selection.SelectionAttribute +EvaluationDatabase.DatabaseSpec.Selection.SelectionValue +ExportAsXls false +EvaluationFileName AdultResults\AdultAnalysisResults.khj // Evaluate predictor EvaluatePredictors diff --git a/tests/resources/scenario_generation/api/evaluate_predictor/ref/Customer._kh b/tests/resources/scenario_generation/api/evaluate_predictor/ref/Customer._kh index d88bb62b..57b1cd7b 100644 --- a/tests/resources/scenario_generation/api/evaluate_predictor/ref/Customer._kh +++ b/tests/resources/scenario_generation/api/evaluate_predictor/ref/Customer._kh @@ -6,29 +6,31 @@ OK // Evaluate predictor settings LearningTools.EvaluatePredictors -MainTargetModality -EvaluationDatabase.DatabaseFiles.List.Key Customer -EvaluationDatabase.DatabaseFiles.DataTableName Customer.csv -EvaluationDatabase.DatabaseFiles.List.Key Customer`Services -EvaluationDatabase.DatabaseFiles.DataTableName ServicesBidon.csv -EvaluationDatabase.DatabaseFiles.List.Key Customer`Services`Usages -EvaluationDatabase.DatabaseFiles.DataTableName UsagesBidon.csv -EvaluationDatabase.DatabaseFiles.List.Key Customer`Address -EvaluationDatabase.DatabaseFiles.DataTableName AddressBidon.csv -EvaluationDatabase.DatabaseFiles.List.Key City -EvaluationDatabase.DatabaseFiles.DataTableName CityBidon.csv -EvaluationDatabase.DatabaseFiles.List.Key Country -EvaluationDatabase.DatabaseFiles.DataTableName CountryBidon.csv -EvaluationDatabase.DatabaseFiles.List.Key Product -EvaluationDatabase.DatabaseFiles.DataTableName ProductBidon.csv -EvaluationDatabase.HeaderLineUsed true -EvaluationDatabase.FieldSeparator -EvaluationDatabase.DatabaseFormatDetector.DetectFileFormat -EvaluationDatabase.SampleNumberPercentage 100.0 -EvaluationDatabase.SamplingMode Include sample -EvaluationDatabase.SelectionAttribute -EvaluationDatabase.SelectionValue -EvaluationFileName .\CustomerResults\EvaluationReport.xls +MainTargetModality +EvaluationDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key +EvaluationDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName Customer.csv +EvaluationDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key Services +EvaluationDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName ServicesBidon.csv +EvaluationDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key Services/Usages +EvaluationDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName UsagesBidon.csv +EvaluationDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key Address +EvaluationDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName AddressBidon.csv +EvaluationDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key /City +EvaluationDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName CityBidon.csv +EvaluationDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key /Country +EvaluationDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName CountryBidon.csv +EvaluationDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key /Product +EvaluationDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName ProductBidon.csv +EvaluationDatabase.DatabaseSpec.Data.HeaderLineUsed true +EvaluationDatabase.DatabaseSpec.Data.FieldSeparator +EvaluationDatabase.DatabaseSpec.Data.DatabaseFormatDetector.DetectFileFormat +EvaluationDatabase.DatabaseSpec.Sampling.SampleNumberPercentage 100.0 +EvaluationDatabase.DatabaseSpec.Sampling.SamplingMode Include sample +EvaluatedPredictors.List.Key Customer +EvaluationDatabase.DatabaseSpec.Selection.SelectionAttribute +EvaluationDatabase.DatabaseSpec.Selection.SelectionValue +ExportAsXls false +EvaluationFileName CustomerResults\CustomerAnalysisResults.khj // Evaluate predictor EvaluatePredictors diff --git a/tests/resources/scenario_generation/api/evaluate_predictor/ref/SpliceJunction._kh b/tests/resources/scenario_generation/api/evaluate_predictor/ref/SpliceJunction._kh index 92d6f5aa..ed1e0770 100644 --- a/tests/resources/scenario_generation/api/evaluate_predictor/ref/SpliceJunction._kh +++ b/tests/resources/scenario_generation/api/evaluate_predictor/ref/SpliceJunction._kh @@ -6,19 +6,21 @@ OK // Evaluate predictor settings LearningTools.EvaluatePredictors -MainTargetModality -EvaluationDatabase.DatabaseFiles.List.Key SpliceJunction -EvaluationDatabase.DatabaseFiles.DataTableName SpliceJunction.csv -EvaluationDatabase.DatabaseFiles.List.Key SpliceJunction`DNA -EvaluationDatabase.DatabaseFiles.DataTableName SpliceJunctionDNABidon.csv -EvaluationDatabase.HeaderLineUsed true -EvaluationDatabase.FieldSeparator -EvaluationDatabase.DatabaseFormatDetector.DetectFileFormat -EvaluationDatabase.SampleNumberPercentage 100.0 -EvaluationDatabase.SamplingMode Include sample -EvaluationDatabase.SelectionAttribute -EvaluationDatabase.SelectionValue -EvaluationFileName .\SpliceJunctionResults\EvaluationReport.xls +MainTargetModality +EvaluationDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key +EvaluationDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName SpliceJunction.csv +EvaluationDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key DNA +EvaluationDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName SpliceJunctionDNABidon.csv +EvaluationDatabase.DatabaseSpec.Data.HeaderLineUsed true +EvaluationDatabase.DatabaseSpec.Data.FieldSeparator +EvaluationDatabase.DatabaseSpec.Data.DatabaseFormatDetector.DetectFileFormat +EvaluationDatabase.DatabaseSpec.Sampling.SampleNumberPercentage 100.0 +EvaluationDatabase.DatabaseSpec.Sampling.SamplingMode Include sample +EvaluatedPredictors.List.Key SpliceJunction +EvaluationDatabase.DatabaseSpec.Selection.SelectionAttribute +EvaluationDatabase.DatabaseSpec.Selection.SelectionValue +ExportAsXls false +EvaluationFileName SpliceJunctionResults\SpliceJunctionAnalysisResults.khj // Evaluate predictor EvaluatePredictors diff --git a/tests/resources/scenario_generation/api/extract_clusters/ref/Adult._kh b/tests/resources/scenario_generation/api/extract_clusters/ref/Adult._kh index 2b67e95a..9087711a 100644 --- a/tests/resources/scenario_generation/api/extract_clusters/ref/Adult._kh +++ b/tests/resources/scenario_generation/api/extract_clusters/ref/Adult._kh @@ -4,13 +4,13 @@ LearningTools.ExtractClusters SelectInputCoclustering InputCoclusteringFileName AdultCoclustering.khc OK -CoclusteringAttributeSpec.CoclusteringAttribute age +CoclusteringAttribute age PostProcessingSpec.MaxPreservedInformation 0 PostProcessingSpec.MaxCellNumber 0 +PostProcessingSpec.MaxTotalPartNumber 0 // Output settings -AnalysisResults.ResultFilesDirectory . -AnalysisResults.ClusterFileName AdultClusters.txt +ClusterFileName AdultClusters.txt // Extract clusters ExtractClusters diff --git a/tests/resources/scenario_generation/api/extract_clusters/ref/Customer._kh b/tests/resources/scenario_generation/api/extract_clusters/ref/Customer._kh index 4b300caa..5e4fe549 100644 --- a/tests/resources/scenario_generation/api/extract_clusters/ref/Customer._kh +++ b/tests/resources/scenario_generation/api/extract_clusters/ref/Customer._kh @@ -4,13 +4,13 @@ LearningTools.ExtractClusters SelectInputCoclustering InputCoclusteringFileName CustomerCoclustering.khc OK -CoclusteringAttributeSpec.CoclusteringAttribute id_customer +CoclusteringAttribute id_customer PostProcessingSpec.MaxPreservedInformation 0 PostProcessingSpec.MaxCellNumber 0 +PostProcessingSpec.MaxTotalPartNumber 0 // Output settings -AnalysisResults.ResultFilesDirectory . -AnalysisResults.ClusterFileName CustomerClusters.txt +ClusterFileName CustomerClusters.txt // Extract clusters ExtractClusters diff --git a/tests/resources/scenario_generation/api/extract_clusters/ref/SpliceJunction._kh b/tests/resources/scenario_generation/api/extract_clusters/ref/SpliceJunction._kh index 97db599a..25ba84b8 100644 --- a/tests/resources/scenario_generation/api/extract_clusters/ref/SpliceJunction._kh +++ b/tests/resources/scenario_generation/api/extract_clusters/ref/SpliceJunction._kh @@ -4,13 +4,13 @@ LearningTools.ExtractClusters SelectInputCoclustering InputCoclusteringFileName SpliceJunctionCoclustering.khc OK -CoclusteringAttributeSpec.CoclusteringAttribute SampleId +CoclusteringAttribute SampleId PostProcessingSpec.MaxPreservedInformation 0 PostProcessingSpec.MaxCellNumber 0 +PostProcessingSpec.MaxTotalPartNumber 0 // Output settings -AnalysisResults.ResultFilesDirectory . -AnalysisResults.ClusterFileName SpliceJunctionClusters.txt +ClusterFileName SpliceJunctionClusters.txt // Extract clusters ExtractClusters diff --git a/tests/resources/scenario_generation/api/extract_keys_from_data_table/ref/Adult._kh b/tests/resources/scenario_generation/api/extract_keys_from_data_table/ref/Adult._kh index e023c1cc..5ed98be6 100644 --- a/tests/resources/scenario_generation/api/extract_keys_from_data_table/ref/Adult._kh +++ b/tests/resources/scenario_generation/api/extract_keys_from_data_table/ref/Adult._kh @@ -7,13 +7,13 @@ OK // Extract keys settings LearningTools.ExtractKeysFromDataTable ClassName Adult -SourceDataTable.DatabaseName Adult.csv -SourceDataTable.HeaderLineUsed true -SourceDataTable.FieldSeparator -SourceDataTable.DatabaseFormatDetector.DetectFileFormat -TargetDataTable.DatabaseName .\AdultKeys.csv -TargetDataTable.HeaderLineUsed true -TargetDataTable.FieldSeparator +SourceDataTable.DatabaseSpec.Data.DatabaseName Adult.csv +SourceDataTable.DatabaseSpec.Data.HeaderLineUsed true +SourceDataTable.DatabaseSpec.Data.FieldSeparator +SourceDataTable.DatabaseSpec.Data.DatabaseFormatDetector.DetectFileFormat +TargetDataTable.DatabaseSpec.Data.DatabaseName ./AdultKeys.csv +TargetDataTable.DatabaseSpec.Data.HeaderLineUsed true +TargetDataTable.DatabaseSpec.Data.FieldSeparator ExtractKeysFromDataTable Exit diff --git a/tests/resources/scenario_generation/api/extract_keys_from_data_table/ref/Customer._kh b/tests/resources/scenario_generation/api/extract_keys_from_data_table/ref/Customer._kh index f890afb8..a9d4ddef 100644 --- a/tests/resources/scenario_generation/api/extract_keys_from_data_table/ref/Customer._kh +++ b/tests/resources/scenario_generation/api/extract_keys_from_data_table/ref/Customer._kh @@ -7,13 +7,13 @@ OK // Extract keys settings LearningTools.ExtractKeysFromDataTable ClassName Customer -SourceDataTable.DatabaseName Customer.csv -SourceDataTable.HeaderLineUsed true -SourceDataTable.FieldSeparator -SourceDataTable.DatabaseFormatDetector.DetectFileFormat -TargetDataTable.DatabaseName .\CustomerKeys.csv -TargetDataTable.HeaderLineUsed true -TargetDataTable.FieldSeparator +SourceDataTable.DatabaseSpec.Data.DatabaseName Customer.csv +SourceDataTable.DatabaseSpec.Data.HeaderLineUsed true +SourceDataTable.DatabaseSpec.Data.FieldSeparator +SourceDataTable.DatabaseSpec.Data.DatabaseFormatDetector.DetectFileFormat +TargetDataTable.DatabaseSpec.Data.DatabaseName ./CustomerKeys.csv +TargetDataTable.DatabaseSpec.Data.HeaderLineUsed true +TargetDataTable.DatabaseSpec.Data.FieldSeparator ExtractKeysFromDataTable Exit diff --git a/tests/resources/scenario_generation/api/extract_keys_from_data_table/ref/SpliceJunction._kh b/tests/resources/scenario_generation/api/extract_keys_from_data_table/ref/SpliceJunction._kh index a5be26db..1e7e7b50 100644 --- a/tests/resources/scenario_generation/api/extract_keys_from_data_table/ref/SpliceJunction._kh +++ b/tests/resources/scenario_generation/api/extract_keys_from_data_table/ref/SpliceJunction._kh @@ -7,13 +7,13 @@ OK // Extract keys settings LearningTools.ExtractKeysFromDataTable ClassName SpliceJunction -SourceDataTable.DatabaseName SpliceJunction.csv -SourceDataTable.HeaderLineUsed true -SourceDataTable.FieldSeparator -SourceDataTable.DatabaseFormatDetector.DetectFileFormat -TargetDataTable.DatabaseName .\SpliceJunctionKeys.csv -TargetDataTable.HeaderLineUsed true -TargetDataTable.FieldSeparator +SourceDataTable.DatabaseSpec.Data.DatabaseName SpliceJunction.csv +SourceDataTable.DatabaseSpec.Data.HeaderLineUsed true +SourceDataTable.DatabaseSpec.Data.FieldSeparator +SourceDataTable.DatabaseSpec.Data.DatabaseFormatDetector.DetectFileFormat +TargetDataTable.DatabaseSpec.Data.DatabaseName ./SpliceJunctionKeys.csv +TargetDataTable.DatabaseSpec.Data.HeaderLineUsed true +TargetDataTable.DatabaseSpec.Data.FieldSeparator ExtractKeysFromDataTable Exit diff --git a/tests/resources/scenario_generation/api/prepare_coclustering_deployment/ref/Adult._kh b/tests/resources/scenario_generation/api/prepare_coclustering_deployment/ref/Adult._kh index 2bdc3548..b42e9491 100644 --- a/tests/resources/scenario_generation/api/prepare_coclustering_deployment/ref/Adult._kh +++ b/tests/resources/scenario_generation/api/prepare_coclustering_deployment/ref/Adult._kh @@ -3,7 +3,6 @@ ClassManagement.OpenFile ClassFileName Adult.kdic OK -ClassManagement.ClassName Adult // Prepare deployment window LearningTools.PrepareDeployment @@ -16,6 +15,7 @@ OK // Simplification settings PostProcessingSpec.MaxPreservedInformation 0 PostProcessingSpec.MaxCellNumber 0 +PostProcessingSpec.MaxTotalPartNumber 0 PostProcessingSpec.PostProcessedAttributes.List.Key age PostProcessingSpec.PostProcessedAttributes.MaxPartNumber 2 PostProcessingSpec.PostProcessedAttributes.List.Key workclass @@ -26,16 +26,16 @@ PostProcessingSpec.PostProcessedAttributes.List.Key sex PostProcessingSpec.PostProcessedAttributes.MaxPartNumber 16 // Deployment dictionary settings +DeploymentSpec.InputClassName Adult DeploymentSpec.InputObjectArrayAttributeName age DeploymentSpec.DeployedAttributeName workclass DeploymentSpec.BuildPredictedClusterAttribute true DeploymentSpec.BuildClusterDistanceAttributes false DeploymentSpec.BuildFrequencyRecodingAttributes false -DeploymentSpec.OutputAttributesPrefix +DeploymentSpec.OutputAttributesPrefix // Output settings -AnalysisResults.ResultFilesDirectory .\AdultResults -AnalysisResults.ResultFilesPrefix +CoclusteringDictionaryFileName AdultResults/AdultCoclusteringResults.khcj // Execute prepare deployment PrepareDeployment diff --git a/tests/resources/scenario_generation/api/prepare_coclustering_deployment/ref/Customer._kh b/tests/resources/scenario_generation/api/prepare_coclustering_deployment/ref/Customer._kh index 347d004f..730188b8 100644 --- a/tests/resources/scenario_generation/api/prepare_coclustering_deployment/ref/Customer._kh +++ b/tests/resources/scenario_generation/api/prepare_coclustering_deployment/ref/Customer._kh @@ -3,7 +3,6 @@ ClassManagement.OpenFile ClassFileName Customer.kdic OK -ClassManagement.ClassName Customer // Prepare deployment window LearningTools.PrepareDeployment @@ -16,18 +15,19 @@ OK // Simplification settings PostProcessingSpec.MaxPreservedInformation 0 PostProcessingSpec.MaxCellNumber 0 +PostProcessingSpec.MaxTotalPartNumber 0 // Deployment dictionary settings +DeploymentSpec.InputClassName Customer DeploymentSpec.InputObjectArrayAttributeName id_customer DeploymentSpec.DeployedAttributeName Name DeploymentSpec.BuildPredictedClusterAttribute true DeploymentSpec.BuildClusterDistanceAttributes false DeploymentSpec.BuildFrequencyRecodingAttributes false -DeploymentSpec.OutputAttributesPrefix +DeploymentSpec.OutputAttributesPrefix // Output settings -AnalysisResults.ResultFilesDirectory .\CustomerResults -AnalysisResults.ResultFilesPrefix +CoclusteringDictionaryFileName CustomerResults/CustomerCoclusteringResults.khcj // Execute prepare deployment PrepareDeployment diff --git a/tests/resources/scenario_generation/api/prepare_coclustering_deployment/ref/SpliceJunction._kh b/tests/resources/scenario_generation/api/prepare_coclustering_deployment/ref/SpliceJunction._kh index ab71fc1d..04e3b47b 100644 --- a/tests/resources/scenario_generation/api/prepare_coclustering_deployment/ref/SpliceJunction._kh +++ b/tests/resources/scenario_generation/api/prepare_coclustering_deployment/ref/SpliceJunction._kh @@ -3,7 +3,6 @@ ClassManagement.OpenFile ClassFileName SpliceJunction.kdic OK -ClassManagement.ClassName SpliceJunction // Prepare deployment window LearningTools.PrepareDeployment @@ -16,22 +15,23 @@ OK // Simplification settings PostProcessingSpec.MaxPreservedInformation 0 PostProcessingSpec.MaxCellNumber 0 +PostProcessingSpec.MaxTotalPartNumber 0 PostProcessingSpec.PostProcessedAttributes.List.Key SampleId PostProcessingSpec.PostProcessedAttributes.MaxPartNumber 32 PostProcessingSpec.PostProcessedAttributes.List.Key NonExistentVar PostProcessingSpec.PostProcessedAttributes.MaxPartNumber 64 // Deployment dictionary settings +DeploymentSpec.InputClassName SpliceJunction DeploymentSpec.InputObjectArrayAttributeName SampleId DeploymentSpec.DeployedAttributeName NonExistentVar DeploymentSpec.BuildPredictedClusterAttribute true DeploymentSpec.BuildClusterDistanceAttributes false DeploymentSpec.BuildFrequencyRecodingAttributes false -DeploymentSpec.OutputAttributesPrefix +DeploymentSpec.OutputAttributesPrefix // Output settings -AnalysisResults.ResultFilesDirectory .\SpliceJunctionResults -AnalysisResults.ResultFilesPrefix +CoclusteringDictionaryFileName SpliceJunctionResults/SpliceJunctionCoclusteringResults.khcj // Execute prepare deployment PrepareDeployment diff --git a/tests/resources/scenario_generation/api/simplify_coclustering/ref/Adult._kh b/tests/resources/scenario_generation/api/simplify_coclustering/ref/Adult._kh index 9f2a9bfe..feac4085 100644 --- a/tests/resources/scenario_generation/api/simplify_coclustering/ref/Adult._kh +++ b/tests/resources/scenario_generation/api/simplify_coclustering/ref/Adult._kh @@ -17,9 +17,7 @@ PostProcessingSpec.PostProcessedAttributes.List.Key sex PostProcessingSpec.PostProcessedAttributes.MaxPartNumber 16 // Output settings -AnalysisResults.ResultFilesDirectory .\AdultResults -AnalysisResults.ResultFilesPrefix -AnalysisResults.PostProcessedCoclusteringFileName SimplifiedAdult._khc +PostProcessedCoclusteringFileName AdultResults\AdultSimplifiedCoclusteringResults.khcj // Simplify Coclustering PostProcessCoclustering diff --git a/tests/resources/scenario_generation/api/simplify_coclustering/ref/Customer._kh b/tests/resources/scenario_generation/api/simplify_coclustering/ref/Customer._kh index 57b125ad..7a09f08b 100644 --- a/tests/resources/scenario_generation/api/simplify_coclustering/ref/Customer._kh +++ b/tests/resources/scenario_generation/api/simplify_coclustering/ref/Customer._kh @@ -9,9 +9,7 @@ PostProcessingSpec.MaxCellNumber 0 PostProcessingSpec.MaxTotalPartNumber 0 // Output settings -AnalysisResults.ResultFilesDirectory .\CustomerResults -AnalysisResults.ResultFilesPrefix -AnalysisResults.PostProcessedCoclusteringFileName SimplifiedCustomer._khc +PostProcessedCoclusteringFileName CustomerResults\CustomerSimplifiedCoclusteringResults.khcj // Simplify Coclustering PostProcessCoclustering diff --git a/tests/resources/scenario_generation/api/simplify_coclustering/ref/SpliceJunction._kh b/tests/resources/scenario_generation/api/simplify_coclustering/ref/SpliceJunction._kh index 4c09c9c7..39c114dd 100644 --- a/tests/resources/scenario_generation/api/simplify_coclustering/ref/SpliceJunction._kh +++ b/tests/resources/scenario_generation/api/simplify_coclustering/ref/SpliceJunction._kh @@ -13,9 +13,7 @@ PostProcessingSpec.PostProcessedAttributes.List.Key NonExistentVar PostProcessingSpec.PostProcessedAttributes.MaxPartNumber 64 // Output settings -AnalysisResults.ResultFilesDirectory .\SpliceJunctionResults -AnalysisResults.ResultFilesPrefix -AnalysisResults.PostProcessedCoclusteringFileName SimplifiedSpliceJunction._khc +PostProcessedCoclusteringFileName SpliceJunctionResults\SpliceJunctionSimplifiedCoclusteringResults.khcj // Simplify Coclustering PostProcessCoclustering diff --git a/tests/resources/scenario_generation/api/sort_data_table/ref/Adult._kh b/tests/resources/scenario_generation/api/sort_data_table/ref/Adult._kh index fa5d9c5d..c9955f4d 100644 --- a/tests/resources/scenario_generation/api/sort_data_table/ref/Adult._kh +++ b/tests/resources/scenario_generation/api/sort_data_table/ref/Adult._kh @@ -3,10 +3,10 @@ ClassManagement.OpenFile ClassFileName Adult.kdic OK -ClassManagement.ClassName Adult // Sort table settings LearningTools.SortDataTableByKey +ClassName Adult SortAttributes.SelectDefaultKeyAttributes SortAttributes.RemoveItem SortAttributes.RemoveItem @@ -26,15 +26,15 @@ SortAttributes.InsertItemAfter SortAttributes.Name race // Source table settings -SourceDataTable.DatabaseName Adult.csv -SourceDataTable.HeaderLineUsed true -SourceDataTable.FieldSeparator -SourceDataTable.DatabaseFormatDetector.DetectFileFormat +SourceDataTable.DatabaseSpec.Data.DatabaseName Adult.csv +SourceDataTable.DatabaseSpec.Data.HeaderLineUsed true +SourceDataTable.DatabaseSpec.Data.FieldSeparator +SourceDataTable.DatabaseSpec.Data.DatabaseFormatDetector.DetectFileFormat // Target table settings -TargetDataTable.HeaderLineUsed true -TargetDataTable.DatabaseName .\AdultSorted.csv -TargetDataTable.FieldSeparator +TargetDataTable.DatabaseSpec.Data.HeaderLineUsed true +TargetDataTable.DatabaseSpec.Data.DatabaseName ./AdultSorted.csv +TargetDataTable.DatabaseSpec.Data.FieldSeparator // Sort table SortDataTableByKey diff --git a/tests/resources/scenario_generation/api/sort_data_table/ref/Customer._kh b/tests/resources/scenario_generation/api/sort_data_table/ref/Customer._kh index 841f4e5c..3c91f881 100644 --- a/tests/resources/scenario_generation/api/sort_data_table/ref/Customer._kh +++ b/tests/resources/scenario_generation/api/sort_data_table/ref/Customer._kh @@ -3,22 +3,22 @@ ClassManagement.OpenFile ClassFileName Customer.kdic OK -ClassManagement.ClassName Customer // Sort table settings LearningTools.SortDataTableByKey +ClassName Customer SortAttributes.SelectDefaultKeyAttributes // Source table settings -SourceDataTable.DatabaseName Customer.csv -SourceDataTable.HeaderLineUsed true -SourceDataTable.FieldSeparator -SourceDataTable.DatabaseFormatDetector.DetectFileFormat +SourceDataTable.DatabaseSpec.Data.DatabaseName Customer.csv +SourceDataTable.DatabaseSpec.Data.HeaderLineUsed true +SourceDataTable.DatabaseSpec.Data.FieldSeparator +SourceDataTable.DatabaseSpec.Data.DatabaseFormatDetector.DetectFileFormat // Target table settings -TargetDataTable.HeaderLineUsed true -TargetDataTable.DatabaseName .\CustomerSorted.csv -TargetDataTable.FieldSeparator +TargetDataTable.DatabaseSpec.Data.HeaderLineUsed true +TargetDataTable.DatabaseSpec.Data.DatabaseName ./CustomerSorted.csv +TargetDataTable.DatabaseSpec.Data.FieldSeparator // Sort table SortDataTableByKey diff --git a/tests/resources/scenario_generation/api/sort_data_table/ref/SpliceJunction._kh b/tests/resources/scenario_generation/api/sort_data_table/ref/SpliceJunction._kh index 1e785382..702a175f 100644 --- a/tests/resources/scenario_generation/api/sort_data_table/ref/SpliceJunction._kh +++ b/tests/resources/scenario_generation/api/sort_data_table/ref/SpliceJunction._kh @@ -3,10 +3,10 @@ ClassManagement.OpenFile ClassFileName SpliceJunction.kdic OK -ClassManagement.ClassName SpliceJunction // Sort table settings LearningTools.SortDataTableByKey +ClassName SpliceJunction SortAttributes.SelectDefaultKeyAttributes SortAttributes.RemoveItem SortAttributes.RemoveItem @@ -22,15 +22,15 @@ SortAttributes.InsertItemAfter SortAttributes.Name SampleId // Source table settings -SourceDataTable.DatabaseName SpliceJunction.csv -SourceDataTable.HeaderLineUsed true -SourceDataTable.FieldSeparator -SourceDataTable.DatabaseFormatDetector.DetectFileFormat +SourceDataTable.DatabaseSpec.Data.DatabaseName SpliceJunction.csv +SourceDataTable.DatabaseSpec.Data.HeaderLineUsed true +SourceDataTable.DatabaseSpec.Data.FieldSeparator +SourceDataTable.DatabaseSpec.Data.DatabaseFormatDetector.DetectFileFormat // Target table settings -TargetDataTable.HeaderLineUsed true -TargetDataTable.DatabaseName .\SpliceJunctionSorted.csv -TargetDataTable.FieldSeparator +TargetDataTable.DatabaseSpec.Data.HeaderLineUsed true +TargetDataTable.DatabaseSpec.Data.DatabaseName ./SpliceJunctionSorted.csv +TargetDataTable.DatabaseSpec.Data.FieldSeparator // Sort table SortDataTableByKey diff --git a/tests/resources/scenario_generation/api/train_coclustering/ref/Adult._kh b/tests/resources/scenario_generation/api/train_coclustering/ref/Adult._kh index 8036b436..15f9a8e7 100644 --- a/tests/resources/scenario_generation/api/train_coclustering/ref/Adult._kh +++ b/tests/resources/scenario_generation/api/train_coclustering/ref/Adult._kh @@ -3,18 +3,18 @@ ClassManagement.OpenFile ClassFileName Adult.kdic OK -ClassManagement.ClassName Adult // Train database settings -Database.DatabaseFiles.List.Key Adult -Database.DatabaseFiles.DataTableName Adult.csv -Database.HeaderLineUsed true -Database.FieldSeparator -Database.DatabaseFormatDetector.DetectFileFormat -Database.SampleNumberPercentage 100.0 -Database.SamplingMode Include sample -Database.SelectionAttribute -Database.SelectionValue +Database.ClassName Adult +Database.DatabaseSpec.Data.DatabaseFiles.List.Key +Database.DatabaseSpec.Data.DatabaseFiles.DataTableName Adult.csv +Database.DatabaseSpec.Data.HeaderLineUsed true +Database.DatabaseSpec.Data.FieldSeparator +Database.DatabaseSpec.Data.DatabaseFormatDetector.DetectFileFormat +Database.DatabaseSpec.Sampling.SampleNumberPercentage 100.0 +Database.DatabaseSpec.Sampling.SamplingMode Include sample +Database.DatabaseSpec.Selection.SelectionAttribute +Database.DatabaseSpec.Selection.SelectionValue // Coclustering variables settings AnalysisSpec.CoclusteringParameters.Attributes.InsertItemAfter @@ -25,14 +25,13 @@ AnalysisSpec.CoclusteringParameters.Attributes.InsertItemAfter AnalysisSpec.CoclusteringParameters.Attributes.Name race AnalysisSpec.CoclusteringParameters.Attributes.InsertItemAfter AnalysisSpec.CoclusteringParameters.Attributes.Name sex -AnalysisSpec.CoclusteringParameters.FrequencyAttribute +AnalysisSpec.CoclusteringParameters.FrequencyAttributeName // Minimum optimization time AnalysisSpec.SystemParameters.OptimizationTime 0 // Output settings -AnalysisResults.ResultFilesDirectory .\AdultResults -AnalysisResults.ResultFilesPrefix +AnalysisResults.CoclusteringFileName AdultResults/AdultCoclusteringResults._khcj // Train BuildCoclustering diff --git a/tests/resources/scenario_generation/api/train_coclustering/ref/Customer._kh b/tests/resources/scenario_generation/api/train_coclustering/ref/Customer._kh index 7a912bb4..bb5e7125 100644 --- a/tests/resources/scenario_generation/api/train_coclustering/ref/Customer._kh +++ b/tests/resources/scenario_generation/api/train_coclustering/ref/Customer._kh @@ -3,44 +3,43 @@ ClassManagement.OpenFile ClassFileName Customer.kdic OK -ClassManagement.ClassName Customer // Train database settings -Database.DatabaseFiles.List.Key Customer -Database.DatabaseFiles.DataTableName Customer.csv -Database.DatabaseFiles.List.Key Customer`Services -Database.DatabaseFiles.DataTableName ServicesBidon.csv -Database.DatabaseFiles.List.Key Customer`Services`Usages -Database.DatabaseFiles.DataTableName UsagesBidon.csv -Database.DatabaseFiles.List.Key Customer`Address -Database.DatabaseFiles.DataTableName AddressBidon.csv -Database.DatabaseFiles.List.Key City -Database.DatabaseFiles.DataTableName CityBidon.csv -Database.DatabaseFiles.List.Key Country -Database.DatabaseFiles.DataTableName CountryBidon.csv -Database.DatabaseFiles.List.Key Product -Database.DatabaseFiles.DataTableName ProductBidon.csv -Database.HeaderLineUsed true -Database.FieldSeparator -Database.DatabaseFormatDetector.DetectFileFormat -Database.SampleNumberPercentage 100.0 -Database.SamplingMode Include sample -Database.SelectionAttribute -Database.SelectionValue +Database.ClassName Customer +Database.DatabaseSpec.Data.DatabaseFiles.List.Key +Database.DatabaseSpec.Data.DatabaseFiles.DataTableName Customer.csv +Database.DatabaseSpec.Data.DatabaseFiles.List.Key Services +Database.DatabaseSpec.Data.DatabaseFiles.DataTableName ServicesBidon.csv +Database.DatabaseSpec.Data.DatabaseFiles.List.Key Services/Usages +Database.DatabaseSpec.Data.DatabaseFiles.DataTableName UsagesBidon.csv +Database.DatabaseSpec.Data.DatabaseFiles.List.Key Address +Database.DatabaseSpec.Data.DatabaseFiles.DataTableName AddressBidon.csv +Database.DatabaseSpec.Data.DatabaseFiles.List.Key /City +Database.DatabaseSpec.Data.DatabaseFiles.DataTableName CityBidon.csv +Database.DatabaseSpec.Data.DatabaseFiles.List.Key /Country +Database.DatabaseSpec.Data.DatabaseFiles.DataTableName CountryBidon.csv +Database.DatabaseSpec.Data.DatabaseFiles.List.Key /Product +Database.DatabaseSpec.Data.DatabaseFiles.DataTableName ProductBidon.csv +Database.DatabaseSpec.Data.HeaderLineUsed true +Database.DatabaseSpec.Data.FieldSeparator +Database.DatabaseSpec.Data.DatabaseFormatDetector.DetectFileFormat +Database.DatabaseSpec.Sampling.SampleNumberPercentage 100.0 +Database.DatabaseSpec.Sampling.SamplingMode Include sample +Database.DatabaseSpec.Selection.SelectionAttribute +Database.DatabaseSpec.Selection.SelectionValue // Coclustering variables settings AnalysisSpec.CoclusteringParameters.Attributes.InsertItemAfter AnalysisSpec.CoclusteringParameters.Attributes.Name id_customer AnalysisSpec.CoclusteringParameters.Attributes.InsertItemAfter AnalysisSpec.CoclusteringParameters.Attributes.Name Name -AnalysisSpec.CoclusteringParameters.FrequencyAttribute +AnalysisSpec.CoclusteringParameters.FrequencyAttributeName // Minimum optimization time AnalysisSpec.SystemParameters.OptimizationTime 0 // Output settings -AnalysisResults.ResultFilesDirectory .\CustomerResults -AnalysisResults.ResultFilesPrefix +AnalysisResults.CoclusteringFileName CustomerResults/CustomerCoclusteringResults._khcj // Train BuildCoclustering diff --git a/tests/resources/scenario_generation/api/train_coclustering/ref/SpliceJunction._kh b/tests/resources/scenario_generation/api/train_coclustering/ref/SpliceJunction._kh index cd3ff165..0348ec1d 100644 --- a/tests/resources/scenario_generation/api/train_coclustering/ref/SpliceJunction._kh +++ b/tests/resources/scenario_generation/api/train_coclustering/ref/SpliceJunction._kh @@ -3,34 +3,33 @@ ClassManagement.OpenFile ClassFileName SpliceJunction.kdic OK -ClassManagement.ClassName SpliceJunction // Train database settings -Database.DatabaseFiles.List.Key SpliceJunction -Database.DatabaseFiles.DataTableName SpliceJunction.csv -Database.DatabaseFiles.List.Key SpliceJunction`DNA -Database.DatabaseFiles.DataTableName SpliceJunctionDNABidon.csv -Database.HeaderLineUsed true -Database.FieldSeparator -Database.DatabaseFormatDetector.DetectFileFormat -Database.SampleNumberPercentage 100.0 -Database.SamplingMode Include sample -Database.SelectionAttribute -Database.SelectionValue +Database.ClassName SpliceJunction +Database.DatabaseSpec.Data.DatabaseFiles.List.Key +Database.DatabaseSpec.Data.DatabaseFiles.DataTableName SpliceJunction.csv +Database.DatabaseSpec.Data.DatabaseFiles.List.Key DNA +Database.DatabaseSpec.Data.DatabaseFiles.DataTableName SpliceJunctionDNABidon.csv +Database.DatabaseSpec.Data.HeaderLineUsed true +Database.DatabaseSpec.Data.FieldSeparator +Database.DatabaseSpec.Data.DatabaseFormatDetector.DetectFileFormat +Database.DatabaseSpec.Sampling.SampleNumberPercentage 100.0 +Database.DatabaseSpec.Sampling.SamplingMode Include sample +Database.DatabaseSpec.Selection.SelectionAttribute +Database.DatabaseSpec.Selection.SelectionValue // Coclustering variables settings AnalysisSpec.CoclusteringParameters.Attributes.InsertItemAfter AnalysisSpec.CoclusteringParameters.Attributes.Name SampleId AnalysisSpec.CoclusteringParameters.Attributes.InsertItemAfter AnalysisSpec.CoclusteringParameters.Attributes.Name NonExistentVar -AnalysisSpec.CoclusteringParameters.FrequencyAttribute +AnalysisSpec.CoclusteringParameters.FrequencyAttributeName // Minimum optimization time AnalysisSpec.SystemParameters.OptimizationTime 0 // Output settings -AnalysisResults.ResultFilesDirectory .\SpliceJunctionResults -AnalysisResults.ResultFilesPrefix +AnalysisResults.CoclusteringFileName SpliceJunctionResults/SpliceJunctionCoclusteringResults._khcj // Train BuildCoclustering diff --git a/tests/resources/scenario_generation/api/train_predictor/ref/Adult._kh b/tests/resources/scenario_generation/api/train_predictor/ref/Adult._kh index d68769f2..753bfec3 100644 --- a/tests/resources/scenario_generation/api/train_predictor/ref/Adult._kh +++ b/tests/resources/scenario_generation/api/train_predictor/ref/Adult._kh @@ -3,35 +3,33 @@ ClassManagement.OpenFile ClassFileName Adult.kdic OK -ClassManagement.ClassName Adult // Train/test database settings -TrainDatabase.DatabaseFiles.List.Key Adult -TrainDatabase.DatabaseFiles.DataTableName Adult.csv -TrainDatabase.HeaderLineUsed true -TrainDatabase.FieldSeparator -TrainDatabase.DatabaseFormatDetector.DetectFileFormat -TrainDatabase.SampleNumberPercentage 70.0 -TrainDatabase.SamplingMode Include sample -TrainDatabase.SelectionAttribute -TrainDatabase.SelectionValue +TrainDatabase.ClassName Adult +TrainDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key +TrainDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName Adult.csv +TrainDatabase.DatabaseSpec.Data.HeaderLineUsed true +TrainDatabase.DatabaseSpec.Data.FieldSeparator +TrainDatabase.DatabaseSpec.Data.DatabaseFormatDetector.DetectFileFormat +TrainDatabase.DatabaseSpec.Sampling.SampleNumberPercentage 70.0 +TrainDatabase.DatabaseSpec.Sampling.SamplingMode Include sample +TrainDatabase.DatabaseSpec.Selection.SelectionAttribute +TrainDatabase.DatabaseSpec.Selection.SelectionValue TrainDatabase.TestDatabaseSpecificationMode Complementary // Target variable AnalysisSpec.TargetAttributeName class -AnalysisSpec.MainTargetModality +AnalysisSpec.MainTargetModality -// Predictors to train -AnalysisSpec.PredictorsSpec.SelectiveNaiveBayesPredictor true -AnalysisSpec.PredictorsSpec.AdvancedSpec.UnivariatePredictorNumber 0 +// Do data preparation only +AnalysisSpec.PredictorsSpec.AdvancedSpec.DataPreparationOnly false // Selective Naive Bayes settings -AnalysisSpec.PredictorsSpec.AdvancedSpec.InspectSelectiveNaiveBayesParameters -TrainParameters.MaxEvaluatedAttributeNumber 0 -SelectionParameters.MaxSelectedAttributeNumber 0 -Exit +AnalysisSpec.PredictorsSpec.AdvancedSpec.SelectiveNaiveBayesParameters.TrainParameters.MaxEvaluatedAttributeNumber 0 +AnalysisSpec.PredictorsSpec.AdvancedSpec.SelectiveNaiveBayesParameters.SelectionParameters.MaxSelectedAttributeNumber 0 // Feature engineering +AnalysisSpec.PredictorsSpec.ConstructionSpec.MaxTextFeatureNumber 10000 AnalysisSpec.PredictorsSpec.ConstructionSpec.MaxTreeNumber 10 AnalysisSpec.PredictorsSpec.ConstructionSpec.MaxAttributePairNumber 0 AnalysisSpec.PredictorsSpec.AdvancedSpec.InspectAttributePairsParameters @@ -41,30 +39,36 @@ SpecificAttributePairs.FirstName age SpecificAttributePairs.SecondName rage SpecificAttributePairs.InsertItemAfter SpecificAttributePairs.FirstName Label -SpecificAttributePairs.SecondName +SpecificAttributePairs.SecondName SpecificAttributePairs.InsertItemAfter -SpecificAttributePairs.FirstName +SpecificAttributePairs.FirstName SpecificAttributePairs.SecondName capital_gain Exit -AnalysisSpec.PredictorsSpec.ConstructionSpec.MaxConstructedAttributeNumber 100 +AnalysisSpec.PredictorsSpec.ConstructionSpec.KeepSelectedAttributesOnly true +AnalysisSpec.PredictorsSpec.ConstructionSpec.MaxConstructedAttributeNumber 1000 AnalysisSpec.PredictorsSpec.AdvancedSpec.InspectConstructionDomain UnselectAll Exit +// Text feature parameters +AnalysisSpec.PredictorsSpec.AdvancedSpec.InspectTextFeaturesParameters +TextFeatures words +Exit + + // Data preparation (discretization & grouping) settings AnalysisSpec.PreprocessingSpec.TargetGrouped false -AnalysisSpec.PreprocessingSpec.DiscretizerSpec.SupervisedMethodName MODL -AnalysisSpec.PreprocessingSpec.DiscretizerSpec.UnsupervisedMethodName MODL -AnalysisSpec.PreprocessingSpec.DiscretizerSpec.MinIntervalFrequency 0 -AnalysisSpec.PreprocessingSpec.DiscretizerSpec.MaxIntervalNumber 0 -AnalysisSpec.PreprocessingSpec.GrouperSpec.SupervisedMethodName MODL -AnalysisSpec.PreprocessingSpec.GrouperSpec.UnsupervisedMethodName MODL -AnalysisSpec.PreprocessingSpec.GrouperSpec.MinGroupFrequency 0 -AnalysisSpec.PreprocessingSpec.GrouperSpec.MaxGroupNumber 0 + +AnalysisSpec.PreprocessingSpec.InspectAdvancedParameters // Advanced parameters +DiscretizerUnsupervisedMethodName MODL +GrouperUnsupervisedMethodName MODL +Exit + +// Max parts +AnalysisSpec.PreprocessingSpec.MaxPartNumber 0 // Output settings -AnalysisResults.ResultFilesDirectory .\AdultResults -AnalysisResults.ResultFilesPrefix +AnalysisResults.ReportFileName AdultResults/AdultAnalysisResults._khj // Build model ComputeStats diff --git a/tests/resources/scenario_generation/api/train_predictor/ref/Customer._kh b/tests/resources/scenario_generation/api/train_predictor/ref/Customer._kh index 88ecbb9f..2407ca45 100644 --- a/tests/resources/scenario_generation/api/train_predictor/ref/Customer._kh +++ b/tests/resources/scenario_generation/api/train_predictor/ref/Customer._kh @@ -3,70 +3,74 @@ ClassManagement.OpenFile ClassFileName Customer.kdic OK -ClassManagement.ClassName Customer // Train/test database settings -TrainDatabase.DatabaseFiles.List.Key Customer -TrainDatabase.DatabaseFiles.DataTableName Customer.csv -TrainDatabase.DatabaseFiles.List.Key Customer`Services -TrainDatabase.DatabaseFiles.DataTableName ServicesBidon.csv -TrainDatabase.DatabaseFiles.List.Key Customer`Services`Usages -TrainDatabase.DatabaseFiles.DataTableName UsagesBidon.csv -TrainDatabase.DatabaseFiles.List.Key Customer`Address -TrainDatabase.DatabaseFiles.DataTableName AddressBidon.csv -TrainDatabase.DatabaseFiles.List.Key City -TrainDatabase.DatabaseFiles.DataTableName CityBidon.csv -TrainDatabase.DatabaseFiles.List.Key Country -TrainDatabase.DatabaseFiles.DataTableName CountryBidon.csv -TrainDatabase.DatabaseFiles.List.Key Product -TrainDatabase.DatabaseFiles.DataTableName ProductBidon.csv -TrainDatabase.HeaderLineUsed true -TrainDatabase.FieldSeparator -TrainDatabase.DatabaseFormatDetector.DetectFileFormat -TrainDatabase.SampleNumberPercentage 70.0 -TrainDatabase.SamplingMode Include sample -TrainDatabase.SelectionAttribute -TrainDatabase.SelectionValue +TrainDatabase.ClassName Customer +TrainDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key +TrainDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName Customer.csv +TrainDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key Services +TrainDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName ServicesBidon.csv +TrainDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key Services/Usages +TrainDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName UsagesBidon.csv +TrainDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key Address +TrainDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName AddressBidon.csv +TrainDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key /City +TrainDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName CityBidon.csv +TrainDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key /Country +TrainDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName CountryBidon.csv +TrainDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key /Product +TrainDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName ProductBidon.csv +TrainDatabase.DatabaseSpec.Data.HeaderLineUsed true +TrainDatabase.DatabaseSpec.Data.FieldSeparator +TrainDatabase.DatabaseSpec.Data.DatabaseFormatDetector.DetectFileFormat +TrainDatabase.DatabaseSpec.Sampling.SampleNumberPercentage 70.0 +TrainDatabase.DatabaseSpec.Sampling.SamplingMode Include sample +TrainDatabase.DatabaseSpec.Selection.SelectionAttribute +TrainDatabase.DatabaseSpec.Selection.SelectionValue TrainDatabase.TestDatabaseSpecificationMode Complementary // Target variable -AnalysisSpec.TargetAttributeName -AnalysisSpec.MainTargetModality +AnalysisSpec.TargetAttributeName +AnalysisSpec.MainTargetModality -// Predictors to train -AnalysisSpec.PredictorsSpec.SelectiveNaiveBayesPredictor true -AnalysisSpec.PredictorsSpec.AdvancedSpec.UnivariatePredictorNumber 0 +// Do data preparation only +AnalysisSpec.PredictorsSpec.AdvancedSpec.DataPreparationOnly false // Selective Naive Bayes settings -AnalysisSpec.PredictorsSpec.AdvancedSpec.InspectSelectiveNaiveBayesParameters -TrainParameters.MaxEvaluatedAttributeNumber 0 -SelectionParameters.MaxSelectedAttributeNumber 0 -Exit +AnalysisSpec.PredictorsSpec.AdvancedSpec.SelectiveNaiveBayesParameters.TrainParameters.MaxEvaluatedAttributeNumber 0 +AnalysisSpec.PredictorsSpec.AdvancedSpec.SelectiveNaiveBayesParameters.SelectionParameters.MaxSelectedAttributeNumber 0 // Feature engineering +AnalysisSpec.PredictorsSpec.ConstructionSpec.MaxTextFeatureNumber 10000 AnalysisSpec.PredictorsSpec.ConstructionSpec.MaxTreeNumber 10 AnalysisSpec.PredictorsSpec.ConstructionSpec.MaxAttributePairNumber 0 AnalysisSpec.PredictorsSpec.AdvancedSpec.InspectAttributePairsParameters AllAttributePairs true Exit -AnalysisSpec.PredictorsSpec.ConstructionSpec.MaxConstructedAttributeNumber 100 +AnalysisSpec.PredictorsSpec.ConstructionSpec.KeepSelectedAttributesOnly true +AnalysisSpec.PredictorsSpec.ConstructionSpec.MaxConstructedAttributeNumber 1000 AnalysisSpec.PredictorsSpec.AdvancedSpec.InspectConstructionDomain Exit +// Text feature parameters +AnalysisSpec.PredictorsSpec.AdvancedSpec.InspectTextFeaturesParameters +TextFeatures words +Exit + + // Data preparation (discretization & grouping) settings AnalysisSpec.PreprocessingSpec.TargetGrouped false -AnalysisSpec.PreprocessingSpec.DiscretizerSpec.SupervisedMethodName EqualWidth -AnalysisSpec.PreprocessingSpec.DiscretizerSpec.UnsupervisedMethodName EqualWidth -AnalysisSpec.PreprocessingSpec.DiscretizerSpec.MinIntervalFrequency 0 -AnalysisSpec.PreprocessingSpec.DiscretizerSpec.MaxIntervalNumber 0 -AnalysisSpec.PreprocessingSpec.GrouperSpec.SupervisedMethodName BasicGrouping -AnalysisSpec.PreprocessingSpec.GrouperSpec.UnsupervisedMethodName BasicGrouping -AnalysisSpec.PreprocessingSpec.GrouperSpec.MinGroupFrequency 0 -AnalysisSpec.PreprocessingSpec.GrouperSpec.MaxGroupNumber 0 + +AnalysisSpec.PreprocessingSpec.InspectAdvancedParameters // Advanced parameters +DiscretizerUnsupervisedMethodName MODL +GrouperUnsupervisedMethodName MODL +Exit + +// Max parts +AnalysisSpec.PreprocessingSpec.MaxPartNumber 0 // Output settings -AnalysisResults.ResultFilesDirectory .\CustomerResults -AnalysisResults.ResultFilesPrefix +AnalysisResults.ReportFileName CustomerResults/CustomerAnalysisResults._khj // Build model ComputeStats diff --git a/tests/resources/scenario_generation/api/train_predictor/ref/SpliceJunction._kh b/tests/resources/scenario_generation/api/train_predictor/ref/SpliceJunction._kh index 39b82573..b7b26f5d 100644 --- a/tests/resources/scenario_generation/api/train_predictor/ref/SpliceJunction._kh +++ b/tests/resources/scenario_generation/api/train_predictor/ref/SpliceJunction._kh @@ -3,43 +3,42 @@ ClassManagement.OpenFile ClassFileName SpliceJunction.kdic OK -ClassManagement.ClassName SpliceJunction // Train/test database settings -TrainDatabase.DatabaseFiles.List.Key SpliceJunction -TrainDatabase.DatabaseFiles.DataTableName SpliceJunction.csv -TrainDatabase.DatabaseFiles.List.Key SpliceJunction`DNA -TrainDatabase.DatabaseFiles.DataTableName SpliceJunctionDNABidon.csv -TrainDatabase.HeaderLineUsed true -TrainDatabase.FieldSeparator -TrainDatabase.DatabaseFormatDetector.DetectFileFormat -TrainDatabase.SampleNumberPercentage 70.0 -TrainDatabase.SamplingMode Include sample -TrainDatabase.SelectionAttribute -TrainDatabase.SelectionValue +TrainDatabase.ClassName SpliceJunction +TrainDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key +TrainDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName SpliceJunction.csv +TrainDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key DNA +TrainDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName SpliceJunctionDNABidon.csv +TrainDatabase.DatabaseSpec.Data.HeaderLineUsed true +TrainDatabase.DatabaseSpec.Data.FieldSeparator +TrainDatabase.DatabaseSpec.Data.DatabaseFormatDetector.DetectFileFormat +TrainDatabase.DatabaseSpec.Sampling.SampleNumberPercentage 70.0 +TrainDatabase.DatabaseSpec.Sampling.SamplingMode Include sample +TrainDatabase.DatabaseSpec.Selection.SelectionAttribute +TrainDatabase.DatabaseSpec.Selection.SelectionValue TrainDatabase.TestDatabaseSpecificationMode Complementary // Target variable AnalysisSpec.TargetAttributeName Class -AnalysisSpec.MainTargetModality +AnalysisSpec.MainTargetModality -// Predictors to train -AnalysisSpec.PredictorsSpec.SelectiveNaiveBayesPredictor true -AnalysisSpec.PredictorsSpec.AdvancedSpec.UnivariatePredictorNumber 0 +// Do data preparation only +AnalysisSpec.PredictorsSpec.AdvancedSpec.DataPreparationOnly false // Selective Naive Bayes settings -AnalysisSpec.PredictorsSpec.AdvancedSpec.InspectSelectiveNaiveBayesParameters -TrainParameters.MaxEvaluatedAttributeNumber 0 -SelectionParameters.MaxSelectedAttributeNumber 0 -Exit +AnalysisSpec.PredictorsSpec.AdvancedSpec.SelectiveNaiveBayesParameters.TrainParameters.MaxEvaluatedAttributeNumber 0 +AnalysisSpec.PredictorsSpec.AdvancedSpec.SelectiveNaiveBayesParameters.SelectionParameters.MaxSelectedAttributeNumber 0 // Feature engineering +AnalysisSpec.PredictorsSpec.ConstructionSpec.MaxTextFeatureNumber 10000 AnalysisSpec.PredictorsSpec.ConstructionSpec.MaxTreeNumber 10 AnalysisSpec.PredictorsSpec.ConstructionSpec.MaxAttributePairNumber 0 AnalysisSpec.PredictorsSpec.AdvancedSpec.InspectAttributePairsParameters AllAttributePairs true Exit -AnalysisSpec.PredictorsSpec.ConstructionSpec.MaxConstructedAttributeNumber 100 +AnalysisSpec.PredictorsSpec.ConstructionSpec.KeepSelectedAttributesOnly true +AnalysisSpec.PredictorsSpec.ConstructionSpec.MaxConstructedAttributeNumber 1000 AnalysisSpec.PredictorsSpec.AdvancedSpec.InspectConstructionDomain UnselectAll ConstructionRules.List.Key TableMode @@ -48,20 +47,25 @@ ConstructionRules.List.Key TableSelection ConstructionRules.Used true Exit +// Text feature parameters +AnalysisSpec.PredictorsSpec.AdvancedSpec.InspectTextFeaturesParameters +TextFeatures words +Exit + + // Data preparation (discretization & grouping) settings AnalysisSpec.PreprocessingSpec.TargetGrouped false -AnalysisSpec.PreprocessingSpec.DiscretizerSpec.SupervisedMethodName MODL -AnalysisSpec.PreprocessingSpec.DiscretizerSpec.UnsupervisedMethodName MODL -AnalysisSpec.PreprocessingSpec.DiscretizerSpec.MinIntervalFrequency 0 -AnalysisSpec.PreprocessingSpec.DiscretizerSpec.MaxIntervalNumber 0 -AnalysisSpec.PreprocessingSpec.GrouperSpec.SupervisedMethodName MODL -AnalysisSpec.PreprocessingSpec.GrouperSpec.UnsupervisedMethodName MODL -AnalysisSpec.PreprocessingSpec.GrouperSpec.MinGroupFrequency 0 -AnalysisSpec.PreprocessingSpec.GrouperSpec.MaxGroupNumber 0 + +AnalysisSpec.PreprocessingSpec.InspectAdvancedParameters // Advanced parameters +DiscretizerUnsupervisedMethodName MODL +GrouperUnsupervisedMethodName MODL +Exit + +// Max parts +AnalysisSpec.PreprocessingSpec.MaxPartNumber 0 // Output settings -AnalysisResults.ResultFilesDirectory .\SpliceJunctionResults -AnalysisResults.ResultFilesPrefix +AnalysisResults.ReportFileName SpliceJunctionResults/SpliceJunctionAnalysisResults._khj // Build model ComputeStats diff --git a/tests/resources/scenario_generation/api/train_recoder/ref/Adult._kh b/tests/resources/scenario_generation/api/train_recoder/ref/Adult._kh index 71510521..7b2d2ea9 100644 --- a/tests/resources/scenario_generation/api/train_recoder/ref/Adult._kh +++ b/tests/resources/scenario_generation/api/train_recoder/ref/Adult._kh @@ -3,29 +3,29 @@ ClassManagement.OpenFile ClassFileName Adult.kdic OK -ClassManagement.ClassName Adult +TrainDatabase.ClassName Adult // Train/test database settings -TrainDatabase.DatabaseFiles.List.Key Adult -TrainDatabase.DatabaseFiles.DataTableName Adult.csv -TrainDatabase.HeaderLineUsed true -TrainDatabase.FieldSeparator -TrainDatabase.DatabaseFormatDetector.DetectFileFormat -TrainDatabase.SampleNumberPercentage 100.0 -TrainDatabase.SamplingMode Include sample -TrainDatabase.SelectionAttribute -TrainDatabase.SelectionValue +TrainDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key +TrainDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName Adult.csv +TrainDatabase.DatabaseSpec.Data.HeaderLineUsed true +TrainDatabase.DatabaseSpec.Data.FieldSeparator +TrainDatabase.DatabaseSpec.Data.DatabaseFormatDetector.DetectFileFormat +TrainDatabase.DatabaseSpec.Sampling.SampleNumberPercentage 100.0 +TrainDatabase.DatabaseSpec.Sampling.SamplingMode Include sample +TrainDatabase.DatabaseSpec.Selection.SelectionAttribute +TrainDatabase.DatabaseSpec.Selection.SelectionValue // Target variable AnalysisSpec.TargetAttributeName class -// Disable predictors -AnalysisSpec.PredictorsSpec.SelectiveNaiveBayesPredictor false -AnalysisSpec.PredictorsSpec.NaiveBayesPredictor false -AnalysisSpec.PredictorsSpec.AdvancedSpec.UnivariatePredictorNumber 0 +// Disable predictors: do data preparation only +AnalysisSpec.PredictorsSpec.AdvancedSpec.DataPreparationOnly true + // Feature engineering -AnalysisSpec.PredictorsSpec.ConstructionSpec.MaxTreeNumber 0 +AnalysisSpec.PredictorsSpec.ConstructionSpec.MaxTextFeatureNumber 10000 +AnalysisSpec.PredictorsSpec.ConstructionSpec.MaxTreeNumber 10 AnalysisSpec.PredictorsSpec.ConstructionSpec.MaxAttributePairNumber 0 AnalysisSpec.PredictorsSpec.AdvancedSpec.InspectAttributePairsParameters AllAttributePairs true @@ -34,9 +34,9 @@ SpecificAttributePairs.FirstName age SpecificAttributePairs.SecondName rage SpecificAttributePairs.InsertItemAfter SpecificAttributePairs.FirstName Label -SpecificAttributePairs.SecondName +SpecificAttributePairs.SecondName SpecificAttributePairs.InsertItemAfter -SpecificAttributePairs.FirstName +SpecificAttributePairs.FirstName SpecificAttributePairs.SecondName capital_gain Exit AnalysisSpec.PredictorsSpec.ConstructionSpec.MaxConstructedAttributeNumber 100 @@ -44,16 +44,20 @@ AnalysisSpec.PredictorsSpec.AdvancedSpec.InspectConstructionDomain UnselectAll Exit +// Text feature parameters +AnalysisSpec.PredictorsSpec.AdvancedSpec.InspectTextFeaturesParameters +TextFeatures words +Exit + // Data preparation (discretization & grouping) settings AnalysisSpec.PreprocessingSpec.TargetGrouped false -AnalysisSpec.PreprocessingSpec.DiscretizerSpec.SupervisedMethodName MODL -AnalysisSpec.PreprocessingSpec.DiscretizerSpec.UnsupervisedMethodName MODL -AnalysisSpec.PreprocessingSpec.DiscretizerSpec.MinIntervalFrequency 0 -AnalysisSpec.PreprocessingSpec.DiscretizerSpec.MaxIntervalNumber 0 -AnalysisSpec.PreprocessingSpec.GrouperSpec.SupervisedMethodName MODL -AnalysisSpec.PreprocessingSpec.GrouperSpec.UnsupervisedMethodName MODL -AnalysisSpec.PreprocessingSpec.GrouperSpec.MinGroupFrequency 0 -AnalysisSpec.PreprocessingSpec.GrouperSpec.MaxGroupNumber 0 + +AnalysisSpec.PreprocessingSpec.MaxPartNumber 0 +AnalysisSpec.PreprocessingSpec.InspectAdvancedParameters +DiscretizerUnsupervisedMethodName MODL +GrouperUnsupervisedMethodName MODL +Exit + // Recoder Settings AnalysisSpec.RecodersSpec.Recoder true @@ -66,8 +70,7 @@ AnalysisSpec.RecodersSpec.RecodingSpec.RecodeContinuousAttributes part Id AnalysisSpec.RecodersSpec.RecodingSpec.RecodeBivariateAttributes part Id // Output settings -AnalysisResults.ResultFilesDirectory .\AdultResults -AnalysisResults.ResultFilesPrefix +AnalysisResults.ReportFileName AdultResults/AdultAnalysisResults._khj // Train recoder ComputeStats diff --git a/tests/resources/scenario_generation/api/train_recoder/ref/Customer._kh b/tests/resources/scenario_generation/api/train_recoder/ref/Customer._kh index b1838e59..bf43ae73 100644 --- a/tests/resources/scenario_generation/api/train_recoder/ref/Customer._kh +++ b/tests/resources/scenario_generation/api/train_recoder/ref/Customer._kh @@ -3,41 +3,41 @@ ClassManagement.OpenFile ClassFileName Customer.kdic OK -ClassManagement.ClassName Customer +TrainDatabase.ClassName Customer // Train/test database settings -TrainDatabase.DatabaseFiles.List.Key Customer -TrainDatabase.DatabaseFiles.DataTableName Customer.csv -TrainDatabase.DatabaseFiles.List.Key Customer`Services -TrainDatabase.DatabaseFiles.DataTableName ServicesBidon.csv -TrainDatabase.DatabaseFiles.List.Key Customer`Services`Usages -TrainDatabase.DatabaseFiles.DataTableName UsagesBidon.csv -TrainDatabase.DatabaseFiles.List.Key Customer`Address -TrainDatabase.DatabaseFiles.DataTableName AddressBidon.csv -TrainDatabase.DatabaseFiles.List.Key City -TrainDatabase.DatabaseFiles.DataTableName CityBidon.csv -TrainDatabase.DatabaseFiles.List.Key Country -TrainDatabase.DatabaseFiles.DataTableName CountryBidon.csv -TrainDatabase.DatabaseFiles.List.Key Product -TrainDatabase.DatabaseFiles.DataTableName ProductBidon.csv -TrainDatabase.HeaderLineUsed true -TrainDatabase.FieldSeparator -TrainDatabase.DatabaseFormatDetector.DetectFileFormat -TrainDatabase.SampleNumberPercentage 100.0 -TrainDatabase.SamplingMode Include sample -TrainDatabase.SelectionAttribute -TrainDatabase.SelectionValue +TrainDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key +TrainDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName Customer.csv +TrainDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key Services +TrainDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName ServicesBidon.csv +TrainDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key Services/Usages +TrainDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName UsagesBidon.csv +TrainDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key Address +TrainDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName AddressBidon.csv +TrainDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key /City +TrainDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName CityBidon.csv +TrainDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key /Country +TrainDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName CountryBidon.csv +TrainDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key /Product +TrainDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName ProductBidon.csv +TrainDatabase.DatabaseSpec.Data.HeaderLineUsed true +TrainDatabase.DatabaseSpec.Data.FieldSeparator +TrainDatabase.DatabaseSpec.Data.DatabaseFormatDetector.DetectFileFormat +TrainDatabase.DatabaseSpec.Sampling.SampleNumberPercentage 100.0 +TrainDatabase.DatabaseSpec.Sampling.SamplingMode Include sample +TrainDatabase.DatabaseSpec.Selection.SelectionAttribute +TrainDatabase.DatabaseSpec.Selection.SelectionValue // Target variable -AnalysisSpec.TargetAttributeName +AnalysisSpec.TargetAttributeName + +// Disable predictors: do data preparation only +AnalysisSpec.PredictorsSpec.AdvancedSpec.DataPreparationOnly true -// Disable predictors -AnalysisSpec.PredictorsSpec.SelectiveNaiveBayesPredictor false -AnalysisSpec.PredictorsSpec.NaiveBayesPredictor false -AnalysisSpec.PredictorsSpec.AdvancedSpec.UnivariatePredictorNumber 0 // Feature engineering -AnalysisSpec.PredictorsSpec.ConstructionSpec.MaxTreeNumber 0 +AnalysisSpec.PredictorsSpec.ConstructionSpec.MaxTextFeatureNumber 10000 +AnalysisSpec.PredictorsSpec.ConstructionSpec.MaxTreeNumber 10 AnalysisSpec.PredictorsSpec.ConstructionSpec.MaxAttributePairNumber 0 AnalysisSpec.PredictorsSpec.AdvancedSpec.InspectAttributePairsParameters AllAttributePairs true @@ -46,16 +46,20 @@ AnalysisSpec.PredictorsSpec.ConstructionSpec.MaxConstructedAttributeNumber 100 AnalysisSpec.PredictorsSpec.AdvancedSpec.InspectConstructionDomain Exit +// Text feature parameters +AnalysisSpec.PredictorsSpec.AdvancedSpec.InspectTextFeaturesParameters +TextFeatures words +Exit + // Data preparation (discretization & grouping) settings AnalysisSpec.PreprocessingSpec.TargetGrouped false -AnalysisSpec.PreprocessingSpec.DiscretizerSpec.SupervisedMethodName EqualWidth -AnalysisSpec.PreprocessingSpec.DiscretizerSpec.UnsupervisedMethodName EqualWidth -AnalysisSpec.PreprocessingSpec.DiscretizerSpec.MinIntervalFrequency 0 -AnalysisSpec.PreprocessingSpec.DiscretizerSpec.MaxIntervalNumber 0 -AnalysisSpec.PreprocessingSpec.GrouperSpec.SupervisedMethodName BasicGrouping -AnalysisSpec.PreprocessingSpec.GrouperSpec.UnsupervisedMethodName BasicGrouping -AnalysisSpec.PreprocessingSpec.GrouperSpec.MinGroupFrequency 0 -AnalysisSpec.PreprocessingSpec.GrouperSpec.MaxGroupNumber 0 + +AnalysisSpec.PreprocessingSpec.MaxPartNumber 0 +AnalysisSpec.PreprocessingSpec.InspectAdvancedParameters +DiscretizerUnsupervisedMethodName MODL +GrouperUnsupervisedMethodName MODL +Exit + // Recoder Settings AnalysisSpec.RecodersSpec.Recoder true @@ -68,8 +72,7 @@ AnalysisSpec.RecodersSpec.RecodingSpec.RecodeContinuousAttributes part Id AnalysisSpec.RecodersSpec.RecodingSpec.RecodeBivariateAttributes part Id // Output settings -AnalysisResults.ResultFilesDirectory .\CustomerResults -AnalysisResults.ResultFilesPrefix +AnalysisResults.ReportFileName CustomerResults/CustomerAnalysisResults._khj // Train recoder ComputeStats diff --git a/tests/resources/scenario_generation/api/train_recoder/ref/SpliceJunction._kh b/tests/resources/scenario_generation/api/train_recoder/ref/SpliceJunction._kh index b07db0d5..477b0b99 100644 --- a/tests/resources/scenario_generation/api/train_recoder/ref/SpliceJunction._kh +++ b/tests/resources/scenario_generation/api/train_recoder/ref/SpliceJunction._kh @@ -3,31 +3,31 @@ ClassManagement.OpenFile ClassFileName SpliceJunction.kdic OK -ClassManagement.ClassName SpliceJunction +TrainDatabase.ClassName SpliceJunction // Train/test database settings -TrainDatabase.DatabaseFiles.List.Key SpliceJunction -TrainDatabase.DatabaseFiles.DataTableName SpliceJunction.csv -TrainDatabase.DatabaseFiles.List.Key SpliceJunction`DNA -TrainDatabase.DatabaseFiles.DataTableName SpliceJunctionDNABidon.csv -TrainDatabase.HeaderLineUsed true -TrainDatabase.FieldSeparator -TrainDatabase.DatabaseFormatDetector.DetectFileFormat -TrainDatabase.SampleNumberPercentage 100.0 -TrainDatabase.SamplingMode Include sample -TrainDatabase.SelectionAttribute -TrainDatabase.SelectionValue +TrainDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key +TrainDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName SpliceJunction.csv +TrainDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key DNA +TrainDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName SpliceJunctionDNABidon.csv +TrainDatabase.DatabaseSpec.Data.HeaderLineUsed true +TrainDatabase.DatabaseSpec.Data.FieldSeparator +TrainDatabase.DatabaseSpec.Data.DatabaseFormatDetector.DetectFileFormat +TrainDatabase.DatabaseSpec.Sampling.SampleNumberPercentage 100.0 +TrainDatabase.DatabaseSpec.Sampling.SamplingMode Include sample +TrainDatabase.DatabaseSpec.Selection.SelectionAttribute +TrainDatabase.DatabaseSpec.Selection.SelectionValue // Target variable AnalysisSpec.TargetAttributeName Class -// Disable predictors -AnalysisSpec.PredictorsSpec.SelectiveNaiveBayesPredictor false -AnalysisSpec.PredictorsSpec.NaiveBayesPredictor false -AnalysisSpec.PredictorsSpec.AdvancedSpec.UnivariatePredictorNumber 0 +// Disable predictors: do data preparation only +AnalysisSpec.PredictorsSpec.AdvancedSpec.DataPreparationOnly true + // Feature engineering -AnalysisSpec.PredictorsSpec.ConstructionSpec.MaxTreeNumber 0 +AnalysisSpec.PredictorsSpec.ConstructionSpec.MaxTextFeatureNumber 10000 +AnalysisSpec.PredictorsSpec.ConstructionSpec.MaxTreeNumber 10 AnalysisSpec.PredictorsSpec.ConstructionSpec.MaxAttributePairNumber 0 AnalysisSpec.PredictorsSpec.AdvancedSpec.InspectAttributePairsParameters AllAttributePairs true @@ -41,16 +41,20 @@ ConstructionRules.List.Key TableSelection ConstructionRules.Used true Exit +// Text feature parameters +AnalysisSpec.PredictorsSpec.AdvancedSpec.InspectTextFeaturesParameters +TextFeatures words +Exit + // Data preparation (discretization & grouping) settings AnalysisSpec.PreprocessingSpec.TargetGrouped false -AnalysisSpec.PreprocessingSpec.DiscretizerSpec.SupervisedMethodName MODL -AnalysisSpec.PreprocessingSpec.DiscretizerSpec.UnsupervisedMethodName MODL -AnalysisSpec.PreprocessingSpec.DiscretizerSpec.MinIntervalFrequency 0 -AnalysisSpec.PreprocessingSpec.DiscretizerSpec.MaxIntervalNumber 0 -AnalysisSpec.PreprocessingSpec.GrouperSpec.SupervisedMethodName MODL -AnalysisSpec.PreprocessingSpec.GrouperSpec.UnsupervisedMethodName MODL -AnalysisSpec.PreprocessingSpec.GrouperSpec.MinGroupFrequency 0 -AnalysisSpec.PreprocessingSpec.GrouperSpec.MaxGroupNumber 0 + +AnalysisSpec.PreprocessingSpec.MaxPartNumber 0 +AnalysisSpec.PreprocessingSpec.InspectAdvancedParameters +DiscretizerUnsupervisedMethodName MODL +GrouperUnsupervisedMethodName MODL +Exit + // Recoder Settings AnalysisSpec.RecodersSpec.Recoder true @@ -63,8 +67,7 @@ AnalysisSpec.RecodersSpec.RecodingSpec.RecodeContinuousAttributes part Id AnalysisSpec.RecodersSpec.RecodingSpec.RecodeBivariateAttributes part Id // Output settings -AnalysisResults.ResultFilesDirectory .\SpliceJunctionResults -AnalysisResults.ResultFilesPrefix +AnalysisResults.ReportFileName SpliceJunctionResults/SpliceJunctionAnalysisResults._khj // Train recoder ComputeStats diff --git a/tests/resources/scenario_generation/system_settings/ref/default._kh b/tests/resources/scenario_generation/system_settings/ref/default._kh index 09628cf1..8b8b4b91 100644 --- a/tests/resources/scenario_generation/system_settings/ref/default._kh +++ b/tests/resources/scenario_generation/system_settings/ref/default._kh @@ -11,18 +11,18 @@ AnalysisSpec.SystemParameters.TemporaryDirectoryName /another/tmp ClassManagement.OpenFile ClassFileName a.kdic OK -ClassManagement.ClassName dict_name // Train database settings -TrainDatabase.DatabaseFiles.List.Key dict_name -TrainDatabase.DatabaseFiles.DataTableName data.txt -TrainDatabase.HeaderLineUsed true -TrainDatabase.FieldSeparator -TrainDatabase.DatabaseFormatDetector.DetectFileFormat -TrainDatabase.SampleNumberPercentage 100.0 -TrainDatabase.SamplingMode Include sample -TrainDatabase.SelectionAttribute -TrainDatabase.SelectionValue +TrainDatabase.ClassName dict_name +TrainDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key +TrainDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName data.txt +TrainDatabase.DatabaseSpec.Data.HeaderLineUsed true +TrainDatabase.DatabaseSpec.Data.FieldSeparator +TrainDatabase.DatabaseSpec.Data.DatabaseFormatDetector.DetectFileFormat +TrainDatabase.DatabaseSpec.Sampling.SampleNumberPercentage 100.0 +TrainDatabase.DatabaseSpec.Sampling.SamplingMode Include sample +TrainDatabase.DatabaseSpec.Selection.SelectionAttribute +TrainDatabase.DatabaseSpec.Selection.SelectionValue // Log messages limit AnalysisSpec.SystemParameters.MaxErrorMessageNumberInLog 20 diff --git a/tests/test_core.py b/tests/test_core.py index 7f615b70..ccd5efa9 100644 --- a/tests/test_core.py +++ b/tests/test_core.py @@ -11,7 +11,6 @@ import shutil import textwrap import unittest -import warnings from pathlib import Path from unittest import mock @@ -47,22 +46,18 @@ def test_analysis_results(self): reports = [ "Adult", "AdultEvaluation", - "AdultLegacy", "Ansi", "AnsiGreek", "AnsiLatin", "AnsiLatinGreek", "AnyChar", - "AnyCharLegacy", "BadTool", "Deft2017ChallengeNGrams1000", "EmptyDatabase", "Greek", "Iris2D", - "Iris2DLegacy", "IrisC", "IrisG", - "IrisMAPLegacy", "IrisR", "IrisU", "IrisU2D", @@ -77,12 +72,8 @@ def test_analysis_results(self): "XORRegression", ] reports_warn = [ - "AdultLegacy", "AnsiLatin", "AnsiLatinGreek", - "AnyCharLegacy", - "Iris2DLegacy", - "IrisMAPLegacy", ] reports_ko = ["BadTool", "NoVersion"] for report in reports: @@ -117,7 +108,6 @@ def test_coclustering_results(self): # Read then json reports, dump them as txt reports and compare to the reference reports = [ "Adult", - "AdultLegacy", "Iris", "Ansi_Coclustering", "AnsiGreek_Coclustering", @@ -129,7 +119,6 @@ def test_coclustering_results(self): "MushroomAnnotated", ] reports_warn = [ - "AdultLegacy", "AnsiLatin_Coclustering", "AnsiLatinGreek_Coclustering", ] @@ -229,7 +218,6 @@ def test_dictionary(self): "AIDSBondCounts", "Adult", "AdultKey", - "AdultLegacy", "AdultModeling", "Ansi", "AnsiGreek", @@ -254,7 +242,6 @@ def test_dictionary(self): ] dictionaries_warn = [ - "AdultLegacy", "AnsiLatin", "AnsiLatinGreek", "AnsiLatinGreek_Modeling", @@ -284,28 +271,26 @@ def _build_mock_api_method_parameters(self): datasets = ["Adult", "SpliceJunction", "Customer"] additional_data_tables = { "Adult": None, - "SpliceJunction": {"SpliceJunction`DNA": "SpliceJunctionDNABidon.csv"}, + "SpliceJunction": {"DNA": "SpliceJunctionDNABidon.csv"}, "Customer": { - "Customer`Services": "ServicesBidon.csv", - "Customer`Services`Usages": "UsagesBidon.csv", - "Customer`Address": "AddressBidon.csv", - "City": "CityBidon.csv", - "Country": "CountryBidon.csv", - "Product": "ProductBidon.csv", + "Services": "ServicesBidon.csv", + "Services/Usages": "UsagesBidon.csv", + "Address": "AddressBidon.csv", + "/City": "CityBidon.csv", + "/Country": "CountryBidon.csv", + "/Product": "ProductBidon.csv", }, } output_additional_data_tables = { "Adult": None, - "SpliceJunction": { - "SpliceJunction`DNA": "TransferSpliceJunctionDNABidon.csv" - }, + "SpliceJunction": {"DNA": "TransferSpliceJunctionDNABidon.csv"}, "Customer": { - "Customer`Services": "TransferServicesBidon.csv", - "Customer`Services`Usages": "TransferUsagesBidon.csv", - "Customer`Address": "TransferAddressBidon.csv", - "City": "TransferCityBidon.csv", - "Country": "TransferCountryBidon.csv", - "Product": "TransferProductBidon.csv", + "Services": "TransferServicesBidon.csv", + "Services/Usages": "TransferUsagesBidon.csv", + "Address": "TransferAddressBidon.csv", + "/City": "TransferCityBidon.csv", + "/Country": "TransferCountryBidon.csv", + "/Product": "TransferProductBidon.csv", }, } target_variables = {"Adult": "class", "SpliceJunction": "Class", "Customer": ""} @@ -430,7 +415,7 @@ def _build_mock_api_method_parameters(self): f"Modeling{dataset}.kdic", dataset, f"{dataset}.csv", - f"{dataset}Results", + f"{dataset}Results/{dataset}AnalysisResults.khj", ], "kwargs": { "additional_data_tables": additional_data_tables[dataset] @@ -476,7 +461,7 @@ def _build_mock_api_method_parameters(self): f"{dataset}._khc", coclustering_variables[dataset][0], coclustering_variables[dataset][1], - f"{dataset}Results", + f"{dataset}Results/{dataset}CoclusteringResults.khcj", ], "kwargs": { "max_part_numbers": max_part_numbers[dataset], @@ -488,8 +473,7 @@ def _build_mock_api_method_parameters(self): dataset: { "args": [ f"{dataset}._khc", - f"Simplified{dataset}._khc", - f"{dataset}Results", + f"{dataset}Results/{dataset}SimplifiedCoclusteringResults.khcj", ], "kwargs": { "max_part_numbers": max_part_numbers[dataset], @@ -518,7 +502,7 @@ def _build_mock_api_method_parameters(self): dataset, f"{dataset}.csv", coclustering_variables[dataset], - f"{dataset}Results", + f"{dataset}Results/{dataset}CoclusteringResults._khcj", ], "kwargs": { "additional_data_tables": additional_data_tables[dataset], @@ -533,7 +517,7 @@ def _build_mock_api_method_parameters(self): dataset, f"{dataset}.csv", target_variables[dataset], - f"{dataset}Results", + f"{dataset}Results/{dataset}AnalysisResults._khj", ], "kwargs": { "additional_data_tables": additional_data_tables[dataset], @@ -550,7 +534,7 @@ def _build_mock_api_method_parameters(self): dataset, f"{dataset}.csv", target_variables[dataset], - f"{dataset}Results", + f"{dataset}Results/{dataset}AnalysisResults._khj", ], "kwargs": { "additional_data_tables": additional_data_tables[dataset], @@ -793,7 +777,7 @@ def __enter__(self): self.mock_context = mock.patch.object( self.mocked_runner, "_get_khiops_version", - return_value=KhiopsVersion("10.2.2"), + return_value=KhiopsVersion("10.6.0-b.0"), ) self.mock_context.__enter__() @@ -1754,38 +1738,38 @@ def test_dictionary_extract_data_paths(self): expected_data_paths = { "Adult": {"Adult": []}, "SpliceJunction": { - "SpliceJunction": ["SpliceJunction`DNA"], + "SpliceJunction": ["DNA"], "SpliceJunctionDNA": [], }, "SpliceJunctionModeling": { - "SNB_SpliceJunction": ["SNB_SpliceJunction`SpliceJunctionDNA"], + "SNB_SpliceJunction": ["SpliceJunctionDNA"], "SNB_SpliceJunctionDNA": [], }, "Customer": { "Address": [], "Customer": [ - "Customer`Services", - "Customer`Services`Usages", - "Customer`Address", + "Services", + "Services/Usages", + "Address", ], - "Service": ["Service`Usages"], + "Service": ["Usages"], "Usage": [], }, "CustomerExtended": { - "Address": ["City", "Country"], - "City": ["Country"], + "Address": ["/City", "/Country"], + "City": ["/Country"], "Country": [], "Customer": [ - "Customer`Services", - "Customer`Services`Usages", - "Customer`Address", - "City", - "Country", - "Product", + "Services", + "Services/Usages", + "Address", + "/City", + "/Country", + "/Product", ], "Product": [], - "Service": ["Service`Usages", "Product"], - "Usage": ["Product"], + "Service": ["Usages", "/Product"], + "Usage": ["/Product"], }, } dictionaries_by_domain = { @@ -1830,24 +1814,20 @@ def test_dictionary_get_dictionary_at_data_path(self): # Set the expected outputs expected_dictionary_names = { - "SpliceJunction": {"SpliceJunction`DNA": "SpliceJunctionDNA"}, - "SpliceJunctionModeling": { - "SNB_SpliceJunction`SpliceJunctionDNA": "SNB_SpliceJunctionDNA" - }, + "SpliceJunction": {"DNA": "SpliceJunctionDNA"}, + "SpliceJunctionModeling": {"SpliceJunctionDNA": "SNB_SpliceJunctionDNA"}, "Customer": { - "Customer`Services": "Service", - "Customer`Services`Usages": "Usage", - "Customer`Address": "Address", - "Service`Usages": "Usage", + "Services": "Service", + "Address": "Address", + "Services/Usages": "Usage", }, "CustomerExtended": { - "City": "City", - "Country": "Country", - "Customer`Services": "Service", - "Customer`Services`Usages": "Usage", - "Customer`Address": "Address", - "Product": "Product", - "Service`Usages": "Usage", + "/City": "City", + "/Country": "Country", + "Services": "Service", + "Address": "Address", + "/Product": "Product", + "Services/Usages": "Usage", }, } @@ -1881,14 +1861,12 @@ def test_dictionary_get_dictionary_at_data_path(self): with self.assertRaises(ValueError): domain.get_dictionary_at_data_path("INVALID DATA PATH") with self.assertRaises(ValueError): - domain.get_dictionary_at_data_path("InexistentDictionary`Some`Path") - first_data_path = list(expected_dictionary_names_by_data_path.keys())[0] - data_path_parts = first_data_path.split("`") + domain.get_dictionary_at_data_path("Some/Path") with self.assertRaises(ValueError): - domain.get_dictionary_at_data_path(f"{data_path_parts[0]}`Some`Path") + domain.get_dictionary_at_data_path("Some/Path") with self.assertRaises(ValueError): domain.get_dictionary_at_data_path( - f"{data_path_parts[0]}`{valid_non_table_vars[domain_name]}`Path" + f"{valid_non_table_vars[domain_name]}/Path" ) @@ -1928,7 +1906,7 @@ def __init__(self, test_case, root_dir): self._write_version = False def _initialize_khiops_version(self): - self._khiops_version = KhiopsVersion("10.1.0") + self._khiops_version = KhiopsVersion("10.6.0-b.0") @property def ref_scenario_dir(self): @@ -2283,62 +2261,6 @@ def _build_multi_table_dictionary_args(): "output_dictionary_file_path": multi_table_dict_out_path, } - def test_build_multi_table_dictionary_deprecation(self): - """Test that `api.build_multi_table_dictionary` raises deprecation warning""" - in_args = KhiopsCoreVariousTests._build_multi_table_dictionary_args() - - with warnings.catch_warnings(record=True) as warning_list: - kh.build_multi_table_dictionary(**in_args) - - self.assertEqual(len(warning_list), 1) - warning = warning_list[0] - self.assertTrue(issubclass(warning.category, UserWarning)) - warning_message = warning.message - self.assertEqual(len(warning_message.args), 1) - message = warning_message.args[0] - self.assertTrue( - "'build_multi_table_dictionary'" in message and "deprecated" in message - ) - - def test_build_multi_table_dictionary_behavior(self): - """Test that the helper function is called with the right parameters""" - parameter_trace = KhiopsTestHelper.create_parameter_trace() - - in_args = KhiopsCoreVariousTests._build_multi_table_dictionary_args() - helper_name = "build_multi_table_dictionary_domain" - KhiopsTestHelper.wrap_with_parameter_trace( - "khiops.core.api", helper_name, parameter_trace - ) - with self.assertWarns(UserWarning): - kh.build_multi_table_dictionary(**in_args) - # Test that at least one trace has been created, so that the assertions can fail - self.assertTrue(any(True for _ in parameter_trace.items())) - for _, function_parameters in parameter_trace.items(): - # Test that at least a traced function has been called - self.assertTrue(any(True for _ in function_parameters.items())) - for function_name, parameters in function_parameters.items(): - # Test that the helper has been called - self.assertEqual(function_name, helper_name) - first_call_parameters = parameters[0] - args = first_call_parameters["args"] - - # Test that the parameters have been passed - self.assertEqual(args[1], in_args["root_dictionary_name"]) - self.assertEqual(args[2], in_args["secondary_table_variable_name"]) - - # Test that the first argument passed is a DictionaryDomain - domain = args[0] - self.assertTrue(isinstance(domain, kh.DictionaryDomain)) - - # Shallowly test that the domain passed to the helper reflects - # the source dictionary - # N.B. We do not test the function for reading a dictionary file - # into a domain here - self.assertEqual(len(domain.dictionaries), 1) - dictionary = domain.dictionaries[0] - self.assertEqual(dictionary.name, "SpliceJunctionDNA") - self.assertEqual(dictionary.key, ["SampleId"]) - def test_scenario_generation(self): """Test the scenario generation from template and arguments""" templates = { @@ -2606,7 +2528,7 @@ def test_raise_exception_on_error_case_without_a_message(self): dictionary_name="Iris", data_table_path="/tmp/Iris.txt", target_variable="Class", - results_dir="/tmp", + analysis_report_file_path="/tmp/IrisAnalysisResults.khj", trace=True, ) expected_msg = ( diff --git a/tests/test_dataset_class.py b/tests/test_dataset_class.py index 102cfd07..ab94dfa4 100644 --- a/tests/test_dataset_class.py +++ b/tests/test_dataset_class.py @@ -8,7 +8,6 @@ import os import shutil import unittest -import warnings import numpy as np import pandas as pd @@ -258,141 +257,67 @@ def create_multitable_snowflake_data_files( tertiary_table.to_csv(tertiary_table_path, sep="\t", index=False) quaternary_table.to_csv(quaternary_table_path, sep="\t", index=False) - def create_fixture_ds_spec(self, output_dir, data_type, multitable, schema): + def create_fixture_ds_spec(self, multitable, schema): if not multitable: - if data_type == "df": - ref_table = self.create_monotable_dataframe() - features = ref_table.drop(["class"], axis=1) - ds_spec = { - "main_table": "Reviews", - "tables": {"Reviews": (features, "User_ID")}, - } - label = ref_table["class"] - else: - assert data_type == "file" - ref_table_path = os.path.join(output_dir, "Reviews.csv") - self.create_monotable_data_file(ref_table_path) - ds_spec = { - "main_table": "Reviews", - "tables": {"Reviews": (ref_table_path, "User_ID")}, - "format": ("\t", True), - } - label = "class" + ref_table = self.create_monotable_dataframe() + features = ref_table.drop(["class"], axis=1) + ds_spec = { + "main_table": "Reviews", + "tables": {"Reviews": (features, "User_ID")}, + } + label = ref_table["class"] elif schema == "star": - if data_type == "df": - ( - ref_main_table, - ref_secondary_table, - ) = self.create_multitable_star_dataframes() - features_ref_main_table = ref_main_table.drop("class", axis=1) - ds_spec = { - "main_table": "id_class", - "tables": { - "id_class": (features_ref_main_table, "User_ID"), - "logs": (ref_secondary_table, "User_ID"), - }, - } - label = ref_main_table["class"] - else: - assert data_type == "file" - ref_main_table_path = os.path.join(output_dir, "id_class.csv") - ref_secondary_table_path = os.path.join(output_dir, "logs.csv") - self.create_multitable_star_data_files( - ref_main_table_path, ref_secondary_table_path - ) - ds_spec = { - "main_table": "id_class", - "tables": { - "id_class": (ref_main_table_path, "User_ID"), - "logs": (ref_secondary_table_path, "User_ID"), - }, - "format": ("\t", True), - } - label = "class" + ( + ref_main_table, + ref_secondary_table, + ) = self.create_multitable_star_dataframes() + features_ref_main_table = ref_main_table.drop("class", axis=1) + ds_spec = { + "main_table": "id_class", + "tables": { + "id_class": (features_ref_main_table, "User_ID"), + "logs": (ref_secondary_table, "User_ID"), + }, + } + label = ref_main_table["class"] else: assert schema == "snowflake" - if data_type == "df": - ( - ref_main_table, - ref_secondary_table_1, - ref_secondary_table_2, - ref_tertiary_table, - ref_quaternary_table, - ) = self.create_multitable_snowflake_dataframes() - - features_ref_main_table = ref_main_table.drop("class", axis=1) - ds_spec = { - "main_table": "A", - "tables": { - "D": ( - ref_tertiary_table, - ["User_ID", "VAR_1", "VAR_2"], - ), - "B": (ref_secondary_table_1, ["User_ID", "VAR_1"]), - "E": ( - ref_quaternary_table, - ["User_ID", "VAR_1", "VAR_2", "VAR_3"], - ), - "C": (ref_secondary_table_2, ["User_ID"]), - "A": (features_ref_main_table, "User_ID"), - }, - "relations": [ - ("B", "D", False), - ("A", "C", True), - ("D", "E"), - ("A", "B", False), - ], - } - label = ref_main_table["class"] - else: - assert data_type == "file" - ref_main_table_path = os.path.join(output_dir, "A.csv") - ref_secondary_table_path_1 = os.path.join(output_dir, "B.csv") - ref_secondary_table_path_2 = os.path.join(output_dir, "C.csv") - ref_tertiary_table_path = os.path.join(output_dir, "D.csv") - ref_quaternary_table_path = os.path.join(output_dir, "E.csv") - - self.create_multitable_snowflake_data_files( - ref_main_table_path, - ref_secondary_table_path_1, - ref_secondary_table_path_2, - ref_tertiary_table_path, - ref_quaternary_table_path, - ) - ds_spec = { - "main_table": "A", - "tables": { - "B": ( - ref_secondary_table_path_1, - ["User_ID", "VAR_1"], - ), - "E": ( - ref_quaternary_table_path, - ["User_ID", "VAR_1", "VAR_2", "VAR_3"], - ), - "C": ( - ref_secondary_table_path_2, - ["User_ID"], - ), - "A": (ref_main_table_path, "User_ID"), - "D": ( - ref_tertiary_table_path, - ["User_ID", "VAR_1", "VAR_2"], - ), - }, - "relations": [ - ("B", "D", False), - ("A", "B", False), - ("D", "E"), - ("A", "C", True), - ], - "format": ("\t", True), - } - label = "class" + ( + ref_main_table, + ref_secondary_table_1, + ref_secondary_table_2, + ref_tertiary_table, + ref_quaternary_table, + ) = self.create_multitable_snowflake_dataframes() + + features_ref_main_table = ref_main_table.drop("class", axis=1) + ds_spec = { + "main_table": "A", + "tables": { + "D": ( + ref_tertiary_table, + ["User_ID", "VAR_1", "VAR_2"], + ), + "B": (ref_secondary_table_1, ["User_ID", "VAR_1"]), + "E": ( + ref_quaternary_table, + ["User_ID", "VAR_1", "VAR_2", "VAR_3"], + ), + "C": (ref_secondary_table_2, ["User_ID"]), + "A": (features_ref_main_table, "User_ID"), + }, + "relations": [ + ("B", "D", False), + ("A", "C", True), + ("D", "E"), + ("A", "B", False), + ], + } + label = ref_main_table["class"] return ds_spec, label - def get_ref_var_types(self, multitable, data_type="df", schema=None): + def get_ref_var_types(self, multitable, schema=None): ref_var_types = {} if not multitable: ref_var_types["Reviews"] = { @@ -406,14 +331,6 @@ def get_ref_var_types(self, multitable, data_type="df", schema=None): "Positive Feedback average": "Numerical", "class": "Categorical", } - # Special type changes for file datasets: - # - "Date" field from "Timestamp" to "Date", the type Khiops detects - # - "Recommended IND" field from "Numerical" to "Categorical" because - # Khiops doesn't parse it well - if data_type == "file": - ref_var_types["Reviews"]["Date"] = "Date" - ref_var_types["Reviews"]["Recommended IND"] = "Categorical" - warnings.warn("Changed field `Recommended IND` to avoid a Khiops bug") elif schema == "star": ref_var_types["id_class"] = { "User_ID": "Categorical", @@ -427,10 +344,6 @@ def get_ref_var_types(self, multitable, data_type="df", schema=None): "VAR_3": "Numerical", "VAR_4": "Numerical", } - # Special change for the file type: - # - logs.VAR_3 is binary and detected as "Categorical" by Khiops - if data_type == "file": - ref_var_types["logs"]["VAR_3"] = "Categorical" else: assert ( schema == "snowflake" @@ -470,19 +383,13 @@ def get_ref_var_types(self, multitable, data_type="df", schema=None): "VAR_3": "Categorical", "VAR_4": "Categorical", } - # Special change for the file type: - # - B.VAR_3 is binary and detected as "Categorical" by Khiops - # - C.VAR_3 is binary and detected as "Categorical" by Khiops - if data_type == "file": - ref_var_types["B"]["VAR_3"] = "Categorical" - ref_var_types["C"]["VAR_3"] = "Categorical" return ref_var_types def test_dataset_is_correctly_built(self): """Test that the dataset structure is consistent with the input spec""" ds_spec, label = self.create_fixture_ds_spec( - output_dir=None, data_type="df", multitable=True, schema="snowflake" + multitable=True, schema="snowflake" ) dataset = Dataset(ds_spec, label) @@ -509,9 +416,7 @@ def test_out_file_from_dataframe_monotable(self): to that of the csv file created by khiops.sklearn. """ # Create a monotable dataset object from fixture data - spec, y = self.create_fixture_ds_spec( - output_dir=None, data_type="df", multitable=False, schema=None - ) + spec, y = self.create_fixture_ds_spec(multitable=False, schema=None) dataset = Dataset(spec, y=y) # Create and load the intermediary Khiops file @@ -654,30 +559,6 @@ def test_out_file_from_sparse_matrix_monotable_specification(self): ), ) - def test_out_file_from_data_file_monotable(self): - """Test consistency of the created data file with the input data file - - - This test verifies that the content of the input data file is equal - to that of the csv file created by khiops.sklearn. - """ - # Create the test dataset - ds_spec, label = self.create_fixture_ds_spec( - output_dir=self.output_dir, data_type="file", multitable=False, schema=None - ) - dataset = Dataset(ds_spec, label) - - out_table_path, _ = dataset.create_table_files_for_khiops(self.output_dir) - out_table = pd.read_csv(out_table_path, sep="\t") - - ref_table_path = ds_spec["tables"]["Reviews"][0] - ref_table = pd.read_csv(ref_table_path, sep="\t") - - # Check that the dataframes are equal - assert_frame_equal( - ref_table.sort_values(by="User_ID").reset_index(drop=True), - out_table, - ) - def test_out_files_from_dataframes_multitable_star(self): """Test consistency of the created data files with the input dataframes @@ -686,9 +567,7 @@ def test_out_files_from_dataframes_multitable_star(self): schema of the dataset is "star". """ # Create the test dataset - ds_spec, label = self.create_fixture_ds_spec( - output_dir=None, data_type="df", multitable=True, schema="star" - ) + ds_spec, label = self.create_fixture_ds_spec(multitable=True, schema="star") dataset = Dataset(ds_spec, label) # Create the Khiops intermediary files @@ -722,47 +601,6 @@ def test_out_files_from_dataframes_multitable_star(self): ).reset_index(drop=True), ) - def test_out_files_from_data_files_multitable_star(self): - """Test consistency of the created data files with the input data files - - - This test verifies that the content of the input data files, defined - through a dictionary, is equal to that of the csv files created by - khiops.sklearn. The schema of the dataset is "star". - """ - ds_spec, label = self.create_fixture_ds_spec( - output_dir=self.output_dir, data_type="file", multitable=True, schema="star" - ) - - dataset = Dataset(ds_spec, label) - main_table_path, dico_secondary_table = dataset.create_table_files_for_khiops( - self.output_dir - ) - secondary_table_path = dico_secondary_table["logs"] - out_main_table = pd.read_csv(main_table_path, sep="\t") - out_secondary_table = pd.read_csv(secondary_table_path, sep="\t") - - ref_table_path = ds_spec["tables"]["id_class"][0] - ref_main_table = pd.read_csv(ref_table_path, sep="\t") - ref_secondary_table_path = ds_spec["tables"]["logs"][0] - ref_secondary_table = pd.read_csv(ref_secondary_table_path, sep="\t") - - # assertions - assert_frame_equal( - ref_main_table.sort_values(by="User_ID", ascending=True).reset_index( - drop=True - ), - out_main_table, - ) - - assert_frame_equal( - ref_secondary_table.sort_values( - by=ref_secondary_table.columns.tolist(), ascending=True - ).reset_index(drop=True), - out_secondary_table.sort_values( - by=out_secondary_table.columns.tolist(), ascending=True - ).reset_index(drop=True), - ) - def test_out_files_from_dataframes_multitable_snowflake(self): """Test consistency of the created data files with the input dataframes @@ -771,7 +609,7 @@ def test_out_files_from_dataframes_multitable_snowflake(self): khiops.sklearn. The schema of the dataset is "snowflake". """ ds_spec, label = self.create_fixture_ds_spec( - output_dir=None, data_type="df", multitable=True, schema="snowflake" + multitable=True, schema="snowflake" ) dataset = Dataset(ds_spec, label) @@ -806,88 +644,18 @@ def test_out_files_from_dataframes_multitable_snowflake(self): ).reset_index(drop=True), ) - def test_out_files_from_data_files_multitable_snowflake(self): - """Test consistency of the created s with the input data files - - - This test verifies that the content of the input data files, defined - through a dictionary, is equal to that of the csv files created - by khiops.sklearn. The schema of the dataset is "snowflake". - """ - ds_spec, label = self.create_fixture_ds_spec( - output_dir=self.output_dir, - data_type="file", - multitable=True, - schema="snowflake", - ) - - dataset = Dataset(ds_spec, label) - main_table_path, additional_table_paths = dataset.create_table_files_for_khiops( - self.output_dir - ) - - out_main_table = pd.read_csv(main_table_path, sep="\t") - ref_main_table_path = ds_spec["tables"]["A"][0] - ref_main_table = pd.read_csv(ref_main_table_path, sep="\t") - - # assertions - assert_frame_equal( - ref_main_table.sort_values(by="User_ID", ascending=True).reset_index( - drop=True - ), - out_main_table, - ) - - additional_table_names = list(additional_table_paths.keys()) - for name in additional_table_names: - additional_table_path = additional_table_paths[name] - out_additional_table = pd.read_csv(additional_table_path, sep="\t") - ref_additional_table_path = ds_spec["tables"][name][0] - ref_additional_table = pd.read_csv(ref_additional_table_path, sep="\t") - assert_frame_equal( - out_additional_table.sort_values( - by=out_additional_table.columns.tolist(), ascending=True - ).reset_index(drop=True), - ref_additional_table.sort_values( - by=ref_additional_table.columns.tolist(), ascending=True - ).reset_index(drop=True), - ) - def test_create_khiops_domain(self): """Test consistency of the dataset method create_khiops_domain""" fixtures = [ { - "output_dir": None, - "data_type": "df", "multitable": False, "schema": None, }, { - "output_dir": self.output_dir, - "data_type": "file", - "multitable": False, - "schema": None, - }, - { - "output_dir": None, - "data_type": "df", "multitable": True, "schema": "star", }, { - "output_dir": self.output_dir, - "data_type": "file", - "multitable": True, - "schema": "star", - }, - { - "output_dir": None, - "data_type": "df", - "multitable": True, - "schema": "snowflake", - }, - { - "output_dir": self.output_dir, - "data_type": "file", "multitable": True, "schema": "snowflake", }, @@ -898,7 +666,6 @@ def test_create_khiops_domain(self): ds = Dataset(*self.create_fixture_ds_spec(**fixture)) ref_var_types = self.get_ref_var_types( multitable=fixture["multitable"], - data_type=fixture["data_type"], schema=fixture["schema"], ) self._test_domain_coherence(ds, ref_var_types) diff --git a/tests/test_dataset_errors.py b/tests/test_dataset_errors.py index 20ab1c57..7749bbbe 100644 --- a/tests/test_dataset_errors.py +++ b/tests/test_dataset_errors.py @@ -14,7 +14,7 @@ import pandas as pd from khiops.core.internals.common import type_error_message -from khiops.sklearn.dataset import Dataset, FileTable, PandasTable +from khiops.sklearn.dataset import Dataset, PandasTable # Disable PEP8 variable names because of scikit-learn X,y conventions @@ -50,163 +50,74 @@ def tearDown(self): #################### # Helper functions # #################### - def create_fixture_dataset_spec( - self, data_type="df", multitable=True, schema="snowflake", output_dir=None - ): + def create_fixture_dataset_spec(self, multitable=True, schema="snowflake"): """Creates a fixture dataset specification Parameters ---------- - data_type : ["df", "file"], default "df" - The desired type of the dataset tables. multitable : bool, default ``True`` Whether the dataset is multitable or not. schema : ["snowflake", "star"], default "snowflake" The type of multi-table schema. - output_dir: str, optional - The output directory for file based datasets. Mandatory if ``data_type == - "file"``. """ if not multitable: - if data_type == "df": - reference_table = self.create_monotable_dataframe() - features = reference_table.drop(["class"], axis=1) - dataset_spec = { - "main_table": "Reviews", - "tables": {"Reviews": (features, "User_ID")}, - } - label = reference_table["class"] - else: - assert data_type == "file" - assert isinstance(output_dir, str) - reference_table_path = os.path.join(output_dir, "Reviews.csv") - self.create_monotable_data_file(reference_table_path) - dataset_spec = { - "main_table": "Reviews", - "tables": {"Reviews": (reference_table_path, "User_ID")}, - "format": ("\t", True), - } - label = "class" + reference_table = self.create_monotable_dataframe() + features = reference_table.drop(["class"], axis=1) + dataset_spec = { + "main_table": "Reviews", + "tables": {"Reviews": (features, "User_ID")}, + } + label = reference_table["class"] elif schema == "star": - if data_type == "df": - ( - reference_main_table, - reference_secondary_table, - ) = self.create_multitable_star_dataframes() - features_reference_main_table = reference_main_table.drop( - "class", axis=1 - ) - dataset_spec = { - "main_table": "id_class", - "tables": { - "id_class": (features_reference_main_table, "User_ID"), - "logs": (reference_secondary_table, "User_ID"), - }, - } - label = reference_main_table["class"] - else: - assert data_type == "file" - assert isinstance(output_dir, str) - reference_main_table_path = os.path.join(output_dir, "id_class.csv") - reference_secondary_table_path = os.path.join(output_dir, "logs.csv") - self.create_multitable_star_data_files( - reference_main_table_path, reference_secondary_table_path - ) - dataset_spec = { - "main_table": "id_class", - "tables": { - "id_class": (reference_main_table_path, "User_ID"), - "logs": (reference_secondary_table_path, "User_ID"), - }, - "format": ("\t", True), - } - label = "class" - + ( + reference_main_table, + reference_secondary_table, + ) = self.create_multitable_star_dataframes() + features_reference_main_table = reference_main_table.drop("class", axis=1) + dataset_spec = { + "main_table": "id_class", + "tables": { + "id_class": (features_reference_main_table, "User_ID"), + "logs": (reference_secondary_table, "User_ID"), + }, + } + label = reference_main_table["class"] else: assert schema == "snowflake" - if data_type == "df": - ( - reference_main_table, - reference_secondary_table_1, - reference_secondary_table_2, - reference_tertiary_table, - reference_quaternary_table, - ) = self.create_multitable_snowflake_dataframes() - - features_reference_main_table = reference_main_table.drop( - "class", axis=1 - ) - dataset_spec = { - "main_table": "A", - "tables": { - "D": ( - reference_tertiary_table, - ["User_ID", "VAR_1", "VAR_2"], - ), - "B": (reference_secondary_table_1, ["User_ID", "VAR_1"]), - "E": ( - reference_quaternary_table, - ["User_ID", "VAR_1", "VAR_2", "VAR_3"], - ), - "C": (reference_secondary_table_2, "User_ID"), - "A": (features_reference_main_table, "User_ID"), - }, - "relations": [ - ("B", "D", False), - ("A", "C", True), - ("D", "E", False), - ("A", "B"), - ], - } - label = reference_main_table["class"] - else: - assert data_type == "file" - assert isinstance(output_dir, str) - reference_main_table_path = os.path.join(output_dir, "A.csv") - reference_secondary_table_path_1 = os.path.join(output_dir, "B.csv") - reference_secondary_table_path_2 = os.path.join(output_dir, "C.csv") - reference_tertiary_table_path = os.path.join(output_dir, "D.csv") - reference_quaternary_table_path = os.path.join(output_dir, "E.csv") - - self.create_multitable_snowflake_data_files( - reference_main_table_path, - reference_secondary_table_path_1, - reference_secondary_table_path_2, - reference_tertiary_table_path, - reference_quaternary_table_path, - ) - dataset_spec = { - "main_table": "A", - "tables": { - "B": ( - reference_secondary_table_path_1, - ["User_ID", "VAR_1"], - ), - "E": ( - reference_quaternary_table_path, - ["User_ID", "VAR_1", "VAR_2", "VAR_3"], - ), - "C": ( - reference_secondary_table_path_2, - "User_ID", - ), - "A": (reference_main_table_path, "User_ID"), - "D": ( - reference_tertiary_table_path, - ["User_ID", "VAR_1", "VAR_2"], - ), - }, - "relations": [ - ("B", "D"), - ("A", "B", False), - ("D", "E", False), - ("A", "C", True), - ], - "format": ("\t", True), - } - label = "class" + ( + reference_main_table, + reference_secondary_table_1, + reference_secondary_table_2, + reference_tertiary_table, + reference_quaternary_table, + ) = self.create_multitable_snowflake_dataframes() + + features_reference_main_table = reference_main_table.drop("class", axis=1) + dataset_spec = { + "main_table": "A", + "tables": { + "D": ( + reference_tertiary_table, + ["User_ID", "VAR_1", "VAR_2"], + ), + "B": (reference_secondary_table_1, ["User_ID", "VAR_1"]), + "E": ( + reference_quaternary_table, + ["User_ID", "VAR_1", "VAR_2", "VAR_3"], + ), + "C": (reference_secondary_table_2, "User_ID"), + "A": (features_reference_main_table, "User_ID"), + }, + "relations": [ + ("B", "D", False), + ("A", "C", True), + ("D", "E", False), + ("A", "B"), + ], + } + label = reference_main_table["class"] return dataset_spec, label @@ -431,30 +342,19 @@ def assert_dataset_fails( # Basic X, y tests # #################### - def test_x_must_be_df_or_tuple_or_sequence_or_mapping(self): + def test_x_must_be_df_or_sequence_or_mapping(self): """Test that `.Dataset` init raises TypeError when X has a wrong type""" bad_spec = AnotherType() y = "class" expected_msg = type_error_message( - "X", bad_spec, "array-like", tuple, Sequence, Mapping + "X", bad_spec, "array-like", Mapping, Sequence ) self.assert_dataset_fails(bad_spec, y, TypeError, expected_msg) def test_y_type_must_be_str_or_array_like_1d(self): """Test that `.Dataset` init raises TypeError when y has a wrong type""" - # Test when X is a tuple: expects str - table_path = os.path.join(self.output_dir, "Reviews.csv") - dataframe = self.create_monotable_dataframe() - dataframe.to_csv(table_path, sep="\t", index=False) - tuple_spec = (table_path, "\t") - bad_y = dataframe["class"] - expected_msg = ( - type_error_message("y", bad_y, str) - + " (X's tables are of type str [file paths])" - ) - self.assert_dataset_fails(tuple_spec, bad_y, TypeError, expected_msg) - # Test when X is a dataframe: expects array-like + dataframe = self.create_monotable_dataframe() bad_y = "TargetColumn" expected_msg = ( type_error_message("y", bad_y, "array-like") @@ -475,17 +375,6 @@ def test_df_dataset_fails_if_target_column_is_already_in_the_features(self): ) self.assertIn(expected_msg_prefix, output_error_msg) - def test_file_dataset_fails_if_table_does_not_contain_the_target_column(self): - """Test FileTable failing if the table does not contain the target column""" - table_path = os.path.join(self.output_dir, "table.csv") - table = pd.DataFrame({"a": [1, 2], "b": [3, 4]}) - table.to_csv(table_path, sep="\t", index=False) - with self.assertRaises(ValueError) as context: - Dataset({"tables": {"main_table": (table_path, None)}}, y="TargetColumn") - output_error_msg = str(context.exception) - expected_msg_prefix = "Target column 'TargetColumn' not present in" - self.assertIn(expected_msg_prefix, output_error_msg) - ##################################### # Tests for dictionary dataset spec # ##################################### @@ -520,13 +409,6 @@ def test_dict_spec_table_input_type_must_be_a_tuple(self): ) self.assert_dataset_fails(bad_spec, y, TypeError, expected_msg) - def test_dict_spec_table_input_tuple_must_have_size_2(self): - """Test Dataset raising ValueError when a table entry is a tuple of size != 2""" - bad_spec, y = self.create_fixture_dataset_spec() - bad_spec["tables"]["D"] = (*bad_spec["tables"]["D"], "AnotherT", "YetAnotherT") - expected_msg = "'D' table entry must have size 2, not 4" - self.assert_dataset_fails(bad_spec, y, ValueError, expected_msg) - def test_dict_spec_source_table_type_must_be_adequate(self): """Test Dataset raising TypeError when a table entry is not str nor DataFrame""" bad_spec, y = self.create_fixture_dataset_spec() @@ -619,19 +501,6 @@ def test_dict_spec_secondary_table_key_must_be_specified(self): ) self.assert_dataset_fails(bad_spec, y, ValueError, expected_msg) - def test_dict_spec_secondary_tables_must_have_the_same_type_as_the_main_table(self): - """Test Dataset raising ValueError if main and sec. table's types don't match""" - bad_spec, _ = self.create_fixture_dataset_spec() - alt_spec, _ = self.create_fixture_dataset_spec( - data_type="file", output_dir=self.output_dir - ) - bad_spec["tables"]["D"] = alt_spec["tables"]["D"] - expected_msg = ( - "Secondary table 'D' has type 'str' which is different " - "from the main table's type 'DataFrame'." - ) - self.assert_dataset_fails(bad_spec, None, ValueError, expected_msg) - def test_dict_spec_format_must_be_tuple(self): """Test Dataset raising a TypeError if the format field is not a tuple""" bad_spec, y = self.create_fixture_dataset_spec() @@ -657,10 +526,7 @@ def test_dict_spec_format_tuple_1st_element_must_be_str(self): def test_dict_spec_format_tuple_2nd_element_must_be_bool(self): """Test Dataset raising a TypeError if any of the format fields are not bool""" - bad_spec, y = self.create_fixture_dataset_spec( - output_dir=self.output_dir, - data_type="file", - ) + bad_spec, y = self.create_fixture_dataset_spec() bad_spec["format"] = (",", AnotherType()) expected_msg = type_error_message( "'format' tuple's 2nd element (header)", bad_spec["format"][1], bool @@ -669,10 +535,7 @@ def test_dict_spec_format_tuple_2nd_element_must_be_bool(self): def test_dict_spec_format_tuple_1st_element_must_be_a_single_character(self): """Test Dataset raising a ValueError if the format sep. is not a single char""" - bad_spec, y = self.create_fixture_dataset_spec( - output_dir=self.output_dir, - data_type="file", - ) + bad_spec, y = self.create_fixture_dataset_spec() bad_spec["format"] = (";;", True) expected_msg = "'format' separator must be a single char, got ';;'" self.assert_dataset_fails(bad_spec, y, ValueError, expected_msg) @@ -687,18 +550,6 @@ def test_dict_spec_y_type_must_be_series_or_df_when_x_is_df_spec(self): ) self.assert_dataset_fails(spec, bad_y, TypeError, expected_msg) - def test_dict_spec_y_must_be_str_when_x_is_file_spec(self): - """Test Dataset raising TypeError for a file-dict-spec and y not a str""" - spec, _ = self.create_fixture_dataset_spec( - output_dir=self.output_dir, data_type="file" - ) - bad_y = np.array([1, 2, 3]) - expected_msg = ( - type_error_message("y", bad_y, str) - + " (X's tables are of type str [file paths])" - ) - self.assert_dataset_fails(spec, bad_y, TypeError, expected_msg) - def test_dict_spec_table_name_must_be_str(self): """Test Dataset raising TypeError when a table name is not a str""" spec, _ = self.create_fixture_dataset_spec(multitable=False, schema=None) @@ -902,81 +753,3 @@ def test_pandas_table_column_ids_must_all_be_int_or_str(self): "strings. Column id at index 0 ('1') is of type 'int'" ) self.assertEqual(output_error_msg, expected_msg) - - def test_file_table_fails_with_non_existent_table_file(self): - """Test FileTable failing when it is created with a non-existent file""" - with self.assertRaises(ValueError) as context: - FileTable("reviews", "Review.csv") - output_error_msg = str(context.exception) - expected_msg = "Non-existent data table file: Review.csv" - self.assertEqual(expected_msg, output_error_msg) - - def test_file_table_internal_file_creation_fails_on_an_existing_path(self): - """Test FileTable failing to create an internal file to a existing path""" - spec, _ = self.create_fixture_dataset_spec( - output_dir=self.output_dir, data_type="file", multitable=False, schema=None - ) - old_file_path = spec["tables"]["Reviews"][0] - new_file_path = old_file_path.replace("Reviews.csv", "copy_Reviews.txt") - os.rename(old_file_path, new_file_path) - file_table = FileTable("Reviews", new_file_path, key="User_ID") - with self.assertRaises(ValueError) as context: - file_table.create_table_file_for_khiops(self.output_dir, sort=False) - output_error_msg = str(context.exception) - expected_msg_prefix = "Cannot overwrite this table's path" - self.assertIn(expected_msg_prefix, output_error_msg) - - ########################################################## - # Tests for tuple and sequence dataset spec (deprecated) # - ########################################################## - - def test_tuple_spec_must_have_length_2(self): - """Test that `.Dataset` raises `ValueError` when the tuple is not of size 2""" - # Test pour la tuple de taille 3 - bad_spec = ("a", "b", "\t") - y = "class" - self.assert_dataset_fails( - bad_spec, y, ValueError, "'X' tuple input must have length 2 not 3" - ) - - # Test pour une tuple de taille 1 - bad_spec = ("a",) - self.assert_dataset_fails( - bad_spec, y, ValueError, "'X' tuple input must have length 2 not 1" - ) - - def test_tuple_spec_elements_must_be_str(self): - """Test Dataset raising TypeError when the tuple spec has non-strings""" - # Test for the first element - bad_spec = (AnotherType(), "/some/path") - y = "class" - expected_msg = type_error_message("X[0]", bad_spec[0], str) - self.assert_dataset_fails(bad_spec, y, TypeError, expected_msg) - - # Test for the second element - bad_spec = ("table-name", AnotherType()) - expected_msg = type_error_message("X[1]", bad_spec[1], str) - self.assert_dataset_fails(bad_spec, y, TypeError, expected_msg) - - def test_sequence_spec_must_be_a_non_empty(self): - """Test that Datasets raises `ValueError` when X is an empty sequence""" - bad_spec = [] - y = "class" - expected_msg = "'X' must be a non-empty sequence" - self.assert_dataset_fails(bad_spec, y, ValueError, expected_msg) - - def test_sequence_spec_must_be_str_or_df(self): - """Test Dataset raising TypeError when it is a sequence with bad types""" - # Test that the first element is not str or df - bad_spec = [AnotherType(), "table_1"] - y = "class" - expected_msg = type_error_message("X[0]", bad_spec[0], str, pd.DataFrame) - self.assert_dataset_fails(bad_spec, y, TypeError, expected_msg) - - # Test that the second element is not str - bad_spec = ["table_1", AnotherType()] - expected_msg = ( - type_error_message("Table at index 1", bad_spec[1], str) - + " as the first table in X" - ) - self.assert_dataset_fails(bad_spec, y, TypeError, expected_msg) diff --git a/tests/test_estimator_attributes.py b/tests/test_estimator_attributes.py index ac68e165..3a3fe0db 100644 --- a/tests/test_estimator_attributes.py +++ b/tests/test_estimator_attributes.py @@ -105,10 +105,10 @@ def assert_attribute_values_ok(self, model, X, y): else: pair_feature_evaluated_names_ = [] pair_feature_evaluated_levels_ = [] - if "treePreparationReport" in model.model_report_raw_: - tree_preparation_report = model.model_report_raw_["treePreparationReport"][ - "variablesStatistics" - ] + if "treePreparationReport" in model.model_report_.json_data: + tree_preparation_report = model.model_report_.json_data[ + "treePreparationReport" + ]["variablesStatistics"] tree_feature_evaluated_names_ = [ tree_preparation_report[i]["name"] for i in range(0, len(tree_preparation_report)) diff --git a/tests/test_helper.py b/tests/test_helper.py index 29377cf8..f328bac1 100644 --- a/tests/test_helper.py +++ b/tests/test_helper.py @@ -41,56 +41,42 @@ class CoreApiFunctionMock: api_function_specs = { ("khiops.core.api", "export_dictionary_as_json"): { "output_path_arg_index": 1, - "output_path_is_dir": False, - "output_file_keys": ["kdicj_path"], + "output_file_key": "kdicj_path", "return_value_number": 1, }, ("khiops.core", "train_predictor"): { "output_path_arg_index": 4, - "output_path_is_dir": True, - "output_file_keys": ["report_path", "predictor_kdic_path"], + "output_file_key": "report_path", "return_value_number": 2, }, ("khiops.core", "train_recoder"): { "output_path_arg_index": 4, - "output_path_is_dir": True, - "output_file_keys": ["report_path", "predictor_kdic_path"], + "output_file_key": "report_path", "return_value_number": 2, }, ("khiops.core", "deploy_model"): { "output_path_arg_index": 3, - "output_path_is_dir": False, - "output_file_keys": ["output_data_table"], + "output_file_key": "output_data_table", "return_value_number": 0, }, ("khiops.core", "train_coclustering"): { "output_path_arg_index": 4, - "output_path_is_dir": True, - "output_file_keys": ["report_path"], + "output_file_key": "report_path", "return_value_number": 1, }, ("khiops.core", "simplify_coclustering"): { - "output_path_arg_index": 2, - "output_path_is_dir": True, - "output_file_keys": ["report_path"], + "output_path_arg_index": 1, + "output_file_key": "report_path", "return_value_number": 0, }, ("khiops.core", "prepare_coclustering_deployment"): { "output_path_arg_index": 5, - "output_path_is_dir": True, - "output_file_keys": ["deploy_kdic_path"], - "return_value_number": 0, - }, - ("khiops.core", "build_multi_table_dictionary"): { - "output_path_arg_index": 3, - "output_path_is_dir": False, - "output_file_keys": ["kdic_path"], + "output_file_key": "deploy_kdic_path", "return_value_number": 0, }, ("khiops.core", "extract_keys_from_data_table"): { "output_path_arg_index": 3, - "output_path_is_dir": False, - "output_file_keys": ["keys_table_path"], + "output_file_key": "keys_table_path", "return_value_number": 0, }, } @@ -130,13 +116,12 @@ def _check_fixture(self): raise ValueError("Missing 'output_file_paths' key in fixture") # Check contents of containers - for output_file_key in self.output_file_keys: - if output_file_key not in self.fixture["output_file_paths"]: - raise ValueError( - f"Missing output file key '{output_file_key}' " - f"for function '{self.function_name}' " - f"of module '{self.module_name}'." - ) + if self.output_file_key not in self.fixture["output_file_paths"]: + raise ValueError( + f"Missing output file key '{self.output_file_key}' " + f"for function '{self.function_name}' " + f"of module '{self.module_name}'." + ) if len(self.fixture["return_values"]) != self.return_value_number: raise ValueError( f"Found {len(self.fixture['return_values'])} " @@ -162,9 +147,9 @@ def output_path_arg_index(self): ] @property - def output_file_keys(self): + def output_file_key(self): return self.api_function_specs[(self.module_name, self.function_name)][ - "output_file_keys" + "output_file_key" ] @property @@ -187,27 +172,26 @@ def __enter__(self): def function_mock(_mocked, _instance, args, kwargs): # Function with output dir: Copy the output_files to the specified directory copied_output_file_paths = {} - if self.output_path_is_dir: - # Create the directory if non-existent - output_dir = args[self.output_path_arg_index] - os.makedirs(output_dir, exist_ok=True) - - # Copy the output files from the fixture - for output_file_key in self.output_file_keys: - resource_file_path = self.fixture["output_file_paths"][ - output_file_key - ] - output_file_name = os.path.basename(resource_file_path) - output_file_path = os.path.join(output_dir, output_file_name) - shutil.copyfile(resource_file_path, output_file_path) - copied_output_file_paths[output_file_key] = output_file_path - # Function with output file: Copy the only resource to the specified path - else: - output_file_key = self.output_file_keys[0] - output_file_path = args[self.output_path_arg_index] - resource_file_path = self.fixture["output_file_paths"][output_file_key] - shutil.copyfile(resource_file_path, output_file_path) - copied_output_file_paths[output_file_key] = output_file_path + output_file_path = args[self.output_path_arg_index] + resource_file_path = self.fixture["output_file_paths"][self.output_file_key] + shutil.copyfile(resource_file_path, output_file_path) + copied_output_file_paths[self.output_file_key] = output_file_path + + # Attempt to copy resource predictor file + if "predictor_kdic_path" in self.fixture["output_file_paths"]: + resource_predictor_file_path = self.fixture["output_file_paths"][ + "predictor_kdic_path" + ] + if os.path.exists(resource_predictor_file_path): + output_predictor_file_path = ( + KhiopsTestHelper.model_path_of_report_path(output_file_path) + ) + shutil.copyfile( + resource_predictor_file_path, output_predictor_file_path + ) + copied_output_file_paths["predictor_kdic_path"] = ( + output_predictor_file_path + ) # Copy the log file if specified in the fixture if "log_file_path" in self.fixture["extra_file_paths"]: @@ -437,3 +421,7 @@ def predict_helper(data, pickled=True, kind="simple"): # 'proba' accepted "and data should be an iterable of two elements" ) return predictions + + @staticmethod + def model_path_of_report_path(report_path): + return f"{os.path.splitext(report_path)[0]}.model.kdic" diff --git a/tests/test_khiops_integrations.py b/tests/test_khiops_integrations.py index cbeac770..da0a8d68 100644 --- a/tests/test_khiops_integrations.py +++ b/tests/test_khiops_integrations.py @@ -189,6 +189,27 @@ def test_runner_environment_initialization(self): self.assertTrue(os.path.isfile(mpiexec_path)) self.assertTrue(os.access(mpiexec_path, os.X_OK)) + # Check that runner creation sets `KHIOPS_API_MODE` to `true` + + # Store original KHIOPS_API_MODE if any, then delete it from the + # environment if present + original_khiops_api_mode = os.environ.get("KHIOPS_API_MODE") + if original_khiops_api_mode is not None: + del os.environ["KHIOPS_API_MODE"] + + # Create fresh runner + _ = KhiopsLocalRunner() + + # Get KHIOPS_API_MODE as set after runner intialization + env_khiops_api_mode = os.environ.get("KHIOPS_API_MODE") + + # Restore original KHIOPS_API_MODE, if any + if original_khiops_api_mode is not None: + os.environ["KHIOPS_API_MODE"] = original_khiops_api_mode + + self.assertTrue(env_khiops_api_mode is not None) + self.assertEqual(env_khiops_api_mode, "true") + class KhiopsMultitableFitTests(unittest.TestCase): """Test if Khiops estimator can be fitted on multi-table data""" @@ -197,35 +218,38 @@ def setUp(self): KhiopsTestHelper.skip_expensive_test(self) def test_estimator_multiple_create_and_fit_does_not_raise_exception(self): - """Test if estimator can be fitted from paths several times""" + """Test if estimator can be fitted from dataframes several times""" # Set upt the file based dataset - dataset_name = "SpliceJunction" - samples_dir = kh.get_runner().samples_dir + ( + root_table_data, + secondary_table_data, + ) = KhiopsTestHelper.get_two_table_data( + "SpliceJunction", "SpliceJunction", "SpliceJunctionDNA" + ) + root_data, _ = KhiopsTestHelper.prepare_data(root_table_data, "Class") + secondary_data = KhiopsTestHelper.prepare_data( + secondary_table_data, "SampleId", primary_table=root_data[0] + )[0] dataset = { "main_table": "SpliceJunction", "tables": { "SpliceJunction": ( - os.path.join(samples_dir, dataset_name, "SpliceJunction.txt"), + root_data[0], "SampleId", ), "SpliceJunctionDNA": ( - os.path.join(samples_dir, dataset_name, "SpliceJunctionDNA.txt"), + secondary_data[0], "SampleId", ), }, - "format": ("\t", True), } # Train classifier output_dir = os.path.join("resources", "tmp", "test_multitable_fit_predict") + khiops_classifier = KhiopsClassifier(output_dir=output_dir) try: for _ in range(2): - KhiopsTestHelper.fit_helper( - KhiopsClassifier, - data=(dataset, "Class"), - pickled=False, - output_dir=output_dir, - ) + khiops_classifier.fit(X=dataset, y=root_data[1]) # Remove data files created during the test finally: if os.path.exists(output_dir): diff --git a/tests/test_parallel_execution.py b/tests/test_parallel_execution.py index 38e8edd0..19a0eb1f 100644 --- a/tests/test_parallel_execution.py +++ b/tests/test_parallel_execution.py @@ -108,8 +108,10 @@ def test_parallel_coclustering_fit(self): callback=partial( KhiopsTestHelper.fit_helper, KhiopsCoclustering, - key="SampleId", - variables=["SampleId", "Pos", "Char"], + fit_kwargs={ + "id_column": "SampleId", + "columns": ["SampleId", "Pos", "Char"], + }, ), arg_sequence=(secondary_train_data,) * n_clusterers, n_cpus=self._n_cpus, @@ -201,8 +203,10 @@ def test_parallel_coclustering_fit_predict(self): callback=partial( KhiopsTestHelper.fit_helper, KhiopsCoclustering, - key="SampleId", - variables=["SampleId", "Pos", "Char"], + fit_kwargs={ + "id_column": "SampleId", + "columns": ["SampleId", "Pos", "Char"], + }, ), arg_sequence=(secondary_train_data,) * n_clusterers, n_cpus=self._n_cpus, diff --git a/tests/test_remote_access.py b/tests/test_remote_access.py index 70cacc54..5be06beb 100644 --- a/tests/test_remote_access.py +++ b/tests/test_remote_access.py @@ -5,7 +5,6 @@ # see the "LICENSE.md" file for more details. # ###################################################################################### """Integration tests with remote filesystems and Khiops runners""" -import io import os import shutil import signal @@ -17,13 +16,10 @@ from contextlib import suppress from urllib.request import Request, urlopen -import pandas as pd - import khiops.core as kh import khiops.core.internals.filesystems as fs from khiops.core.internals.runner import KhiopsLocalRunner from khiops.extras.docker import KhiopsDockerRunner -from khiops.sklearn import KhiopsClassifier, KhiopsCoclustering from tests.test_helper import KhiopsTestHelper @@ -173,92 +169,32 @@ def test_train_predictor_with_remote_access(self): f"test_{self.remote_access_test_case()}_remote_files_{uuid.uuid4()}", ) + # Attempt to make directory if not existing: + # - make dir if local + # - else, write "dir-like" object + if not fs.exists(output_dir): + if fs.is_local_resource(output_dir): + fs.make_dir(output_dir) + else: + fs.write(f"{output_dir}/", "") + + report_file_path = fs.get_child_path(output_dir, "IrisAnalysisResults.khj") + # When using `kh`, the log file will be by default # in the runner `root_temp_dir` folder that can be remote - kh.train_predictor( + _, model_file_path = kh.train_predictor( fs.get_child_path(iris_data_dir, "Iris.kdic"), dictionary_name="Iris", data_table_path=fs.get_child_path(iris_data_dir, "Iris.txt"), target_variable="Class", - results_dir=output_dir, + analysis_report_file_path=report_file_path, temp_dir=self._khiops_temp_dir, trace=True, ) # Check the existence of the training files - self.assertTrue(fs.exists(fs.get_child_path(output_dir, "AllReports.khj"))) - self.assertTrue(fs.exists(fs.get_child_path(output_dir, "Modeling.kdic"))) - - def test_khiops_classifier_with_remote_access(self): - """Test the training of a khiops_classifier with remote resources""" - - # Setup paths - # note : the current implementation forces the khiops.log file - # to be created in the output_dir (thus local) - # (any attempt to override it as an arg - # for the fit method will be ignored) - - # ask for folder cleaning during tearDown - self.folder_name_to_clean_in_teardown = output_dir = ( - self._khiops_temp_dir + f"/KhiopsClassifier_output_dir_{uuid.uuid4()}/" - ) - iris_data_dir = fs.get_child_path(kh.get_runner().samples_dir, "Iris") - iris_data_file_path = fs.get_child_path(iris_data_dir, "Iris.txt") - iris_dataset = { - "tables": {"Iris": (iris_data_file_path, None)}, - "format": ("\t", True), - } - - # Test if the 'fit' output files were created - classifier = KhiopsClassifier(output_dir=output_dir) - classifier.fit(iris_dataset, "Class") - self.assertTrue(fs.exists(fs.get_child_path(output_dir, "AllReports.khj"))) - self.assertTrue(fs.exists(fs.get_child_path(output_dir, "Modeling.kdic"))) - - # Test if the 'predict' output file was created - with io.BytesIO(fs.read(iris_data_file_path)) as iris_data_file: - iris_df = pd.read_csv(iris_data_file, sep="\t") - iris_df.pop("Class") - classifier.predict(iris_df) - predict_path = fs.get_child_path(output_dir, "predict.txt") - self.assertTrue(fs.exists(predict_path), msg=f"Path: {predict_path}") - - def test_khiops_coclustering_with_remote_access(self): - """Test the training of a khiops_coclustering with remote resources""" - - # Setup paths - # note : the current implementation forces the khiops.log file - # to be created in the output_dir (thus local) - # (any attempt to override it as an arg - # for the fit method will be ignored) - - # ask for folder cleaning during tearDown - self.folder_name_to_clean_in_teardown = output_dir = ( - self._khiops_temp_dir - + f"/KhiopsCoclustering_output_dir_{uuid.uuid4()}/" - ) - splice_data_dir = fs.get_child_path( - kh.get_runner().samples_dir, "SpliceJunction" - ) - splice_data_file_path = fs.get_child_path( - splice_data_dir, "SpliceJunctionDNA.txt" - ) - - # Read the splice junction secondary datatable - with io.BytesIO(fs.read(splice_data_file_path)) as splice_data_file: - splice_df = pd.read_csv(splice_data_file, sep="\t") - - # Fit the coclustering - khcc = KhiopsCoclustering(output_dir=output_dir) - khcc.fit(splice_df, id_column="SampleId") - - # Test if the 'fit' files were created - self.assertTrue( - fs.exists(fs.get_child_path(output_dir, "Coclustering.kdic")) - ) - self.assertTrue( - fs.exists(fs.get_child_path(output_dir, "Coclustering.khcj")) - ) + self.assertTrue(fs.exists(report_file_path)) + self.assertTrue(fs.exists(model_file_path)) def test_train_predictor_fail_and_log_with_remote_access(self): """Test train_predictor failure and access to a remote log""" @@ -268,11 +204,25 @@ def test_train_predictor_fail_and_log_with_remote_access(self): # no cleaning required as an exception would be raised # without any result produced - output_dir = fs.get_child_path( + self.folder_name_to_clean_in_teardown = output_dir = fs.get_child_path( self.results_dir_root(), f"test_{self.remote_access_test_case()}_remote_files_{uuid.uuid4()}", ) + # Attempt to make directory if not existing: + # - make dir if local + # - else, write "dir-like" object + if not fs.exists(output_dir): + if fs.is_local_resource(output_dir): + fs.make_dir(output_dir) + else: + fs.write(f"{output_dir}/", "") + + report_file_path = fs.get_child_path( + output_dir, + "IrisAnalysisResults.khj", + ) + iris_data_dir = fs.get_child_path(kh.get_runner().samples_dir, "Iris") with self.assertRaises(kh.KhiopsRuntimeError): kh.train_predictor( @@ -280,7 +230,7 @@ def test_train_predictor_fail_and_log_with_remote_access(self): dictionary_name="Iris", data_table_path=fs.get_child_path(iris_data_dir, "Iris.txt"), target_variable="Class", - results_dir=output_dir, + analysis_report_file_path=report_file_path, log_file_path=log_file_path, ) # Check and remove log file diff --git a/tests/test_samples.py b/tests/test_samples.py index d19b505d..b412cee4 100644 --- a/tests/test_samples.py +++ b/tests/test_samples.py @@ -7,7 +7,6 @@ """Test run all samples""" import unittest -import khiops.core as kh from khiops.samples import samples, samples_sklearn from tests.test_helper import KhiopsTestHelper @@ -20,25 +19,11 @@ def setUp(self): def test_samples(self): """Test if all samples run without problems""" - # Obtain the runner version and set the minimal requirements for some samples - min_version = { - samples.detect_data_table_format: kh.KhiopsVersion("10.0.1"), - samples.deploy_coclustering: kh.KhiopsVersion("10.0.1"), - } - # Run the samples for sample in samples.exported_samples: with self.subTest(sample=sample.__name__): print(f"\n>>> Testing sample.{sample.__name__}") - if sample not in min_version: - sample.__call__() - elif kh.get_runner().khiops_version >= min_version[sample]: - sample.__call__() - else: - print( - f"Ignored sample {sample.__name__}, " - f"minimum version required: {min_version[sample]}" - ) + sample.__call__() print("> Done") def test_samples_sklearn(self): diff --git a/tests/test_sklearn.py b/tests/test_sklearn.py index 157e35dd..8379eae2 100644 --- a/tests/test_sklearn.py +++ b/tests/test_sklearn.py @@ -6,7 +6,6 @@ ###################################################################################### """Tests parameter transfer between Khiops sklearn and core APIs""" import contextlib -import copy import os import shutil import unittest @@ -79,18 +78,6 @@ def setUpClass(cls): if not os.path.isdir(cls.output_dir): os.makedirs(cls.output_dir) - ( - train_multitable_file_dataset_classif, - test_multitable_file_dataset_classif, - ) = cls._create_train_test_multitable_file_dataset() - - ( - train_multitable_file_dataset_reg, - test_multitable_file_dataset_reg, - ) = cls._create_train_test_multitable_file_dataset( - transform_for_regression=True - ) - ( X_train_multitable_dataframe_classif, y_train_multitable_dataframe_classif, @@ -103,16 +90,6 @@ def setUpClass(cls): X_test_multitable_dataframe_reg, ) = cls._create_train_test_multitable_dataframe(transform_for_regression=True) - ( - train_monotable_file_dataset_classif, - test_monotable_file_dataset_classif, - ) = cls._create_train_test_monotable_file_dataset("class") - - ( - train_monotable_file_dataset_reg, - test_monotable_file_dataset_reg, - ) = cls._create_train_test_monotable_file_dataset("age") - ( X_train_monotable_dataframe_classif, y_train_monotable_dataframe_classif, @@ -144,20 +121,6 @@ def setUpClass(cls): # data) cls.datasets = { "multitable": { - "file_dataset": { - KhiopsClassifier: { - "train": train_multitable_file_dataset_classif, - "test": test_multitable_file_dataset_classif, - }, - KhiopsEncoder: { - "train": train_multitable_file_dataset_classif, - "test": test_multitable_file_dataset_classif, - }, - KhiopsRegressor: { - "train": train_multitable_file_dataset_reg, - "test": test_multitable_file_dataset_reg, - }, - }, "dataframe": { KhiopsClassifier: { "X_train": X_train_multitable_dataframe_classif, @@ -177,20 +140,6 @@ def setUpClass(cls): }, }, "monotable": { - "file_dataset": { - KhiopsClassifier: { - "train": train_monotable_file_dataset_classif, - "test": test_monotable_file_dataset_classif, - }, - KhiopsEncoder: { - "train": train_monotable_file_dataset_classif, - "test": test_monotable_file_dataset_classif, - }, - KhiopsRegressor: { - "train": train_monotable_file_dataset_reg, - "test": test_monotable_file_dataset_reg, - }, - }, "dataframe": { KhiopsClassifier: { "X_train": X_train_monotable_dataframe_classif, @@ -352,17 +301,6 @@ def setUpClass(cls): "return_values": [], }, ), - CoreApiFunctionMock( - module_name="khiops.core", - function_name="build_multi_table_dictionary", - fixture={ - "output_file_paths": { - "kdic_path": resources["tmp_model_kdic_path"] - }, - "extra_file_paths": {}, - "return_values": [], - }, - ), CoreApiFunctionMock( module_name="khiops.core", function_name="prepare_coclustering_deployment", @@ -418,12 +356,6 @@ def setUpClass(cls): "not_applicable": { ("dataframe",): { KhiopsCoclustering: { - "build_multi_table_dictionary": { - "expected_n_dictionaries": 1, - "expected_main_table_key": None, - "expected_main_dictionary_name": "CC_main_table", - "expected_additional_data_table_names": [], - }, "train_coclustering": { "expected_n_dictionaries": 1, "expected_main_table_key": None, @@ -444,34 +376,6 @@ def setUpClass(cls): }, } }, - ("file_dataset",): { - KhiopsCoclustering: { - "build_multi_table_dictionary": { - "expected_n_dictionaries": 1, - "expected_main_table_key": None, - "expected_main_dictionary_name": "CC_main_table", - "expected_additional_data_table_names": [], - }, - "train_coclustering": { - "expected_n_dictionaries": 1, - "expected_main_table_key": None, - "expected_main_dictionary_name": "main_table", - "expected_additional_data_table_names": [], - }, - "deploy_model": { - "expected_n_dictionaries": 2, - "expected_main_table_key": "SampleId", - "expected_main_dictionary_name": ("CC_Keys_main_table"), - "expected_additional_data_table_names": ["CC_main_table"], - }, - "extract_keys_from_data_table": { - "expected_n_dictionaries": 1, - "expected_main_table_key": "SampleId", - "expected_main_dictionary_name": "main_table", - "expected_additional_data_table_names": [], - }, - } - }, }, "monotable": { ("dataframe", "dataframe_xy"): { @@ -504,76 +408,8 @@ def setUpClass(cls): }, }, }, - ("file_dataset",): { - KhiopsPredictor: { - "train_predictor": { - "expected_n_dictionaries": 1, - "expected_main_table_key": None, - "expected_main_dictionary_name": "main_table", - "expected_additional_data_table_names": [], - }, - "deploy_model": { - "expected_n_dictionaries": 1, - "expected_main_table_key": None, - "expected_main_dictionary_name": "SNB_main_table", - "expected_additional_data_table_names": [], - }, - }, - KhiopsEncoder: { - "train_recoder": { - "expected_n_dictionaries": 1, - "expected_main_table_key": None, - "expected_main_dictionary_name": "main_table", - "expected_additional_data_table_names": [], - }, - "deploy_model": { - "expected_n_dictionaries": 1, - "expected_main_table_key": None, - "expected_main_dictionary_name": "R_main_table", - "expected_additional_data_table_names": [], - }, - }, - }, }, "multitable": { - ("file_dataset",): { - KhiopsPredictor: { - "train_predictor": { - "expected_n_dictionaries": 2, - "expected_main_table_key": "SampleId", - "expected_main_dictionary_name": "SpliceJunction", - "expected_additional_data_table_names": [ - "SpliceJunctionDNA" - ], - }, - "deploy_model": { - "expected_n_dictionaries": 2, - "expected_main_table_key": "SampleId", - "expected_main_dictionary_name": "SNB_SpliceJunction", - "expected_additional_data_table_names": [ - "SNB_SpliceJunctionDNA" - ], - }, - }, - KhiopsEncoder: { - "train_recoder": { - "expected_n_dictionaries": 2, - "expected_main_table_key": "SampleId", - "expected_main_dictionary_name": "SpliceJunction", - "expected_additional_data_table_names": [ - "SpliceJunctionDNA" - ], - }, - "deploy_model": { - "expected_n_dictionaries": 2, - "expected_main_table_key": "SampleId", - "expected_main_dictionary_name": "R_SpliceJunction", - "expected_additional_data_table_names": [ - "R_SpliceJunctionDNA" - ], - }, - }, - }, ("dataframe",): { KhiopsPredictor: { "train_predictor": { @@ -627,13 +463,11 @@ def setUpClass(cls): "fit": [ ("khiops.core", "train_coclustering"), ("khiops.core", "read_coclustering_results_file"), - ("khiops.core", "build_multi_table_dictionary"), ("khiops.core", "prepare_coclustering_deployment"), ("khiops.core", "simplify_coclustering"), ], "simplify": [ ("khiops.core", "simplify_coclustering"), - ("khiops.core", "build_multi_table_dictionary"), ("khiops.core", "prepare_coclustering_deployment"), ], "predict": [ @@ -648,16 +482,19 @@ def setUpClass(cls): KhiopsCoclustering: { "fit": { ("khiops.core", "prepare_coclustering_deployment"): { - 2: os.path.join(cls.output_dir, "Coclustering.khcj"), + 2: os.path.join( + cls.output_dir, "main_tableCoclusteringResults.khcj" + ), 3: "CC_main_table", 4: "SampleId", - 5: cls.output_dir, + 5: os.path.join( + cls.output_dir, "CC_Keys_main_table_deployed.kdic" + ), }, ("khiops.core", "read_coclustering_results_file"): { - 0: os.path.join(cls.output_dir, "Coclustering.khcj") - }, - ("khiops.core", "build_multi_table_dictionary"): { - 2: "CC_main_table" + 0: os.path.join( + cls.output_dir, "main_tableCoclusteringResults.khcj" + ) }, ("khiops.core", "train_coclustering"): { 1: "main_table", @@ -668,50 +505,17 @@ def setUpClass(cls): }, "simplify": { ("khiops.core", "simplify_coclustering"): { - 2: cls.output_dir - }, - ("khiops.core", "prepare_coclustering_deployment"): { - 2: os.path.join(cls.output_dir, "Coclustering.khcj"), - 3: "CC_main_table", - 4: "SampleId", - 5: cls.output_dir, - }, - ("khiops.core", "build_multi_table_dictionary"): { - 2: "CC_main_table" + 1: os.path.join(cls.output_dir, "Coclustering.khcj") }, - }, - "predict": { - ("khiops.core", "deploy_model"): { - 1: "CC_Keys_main_table", - 3: cls.output_dir, - }, - ("khiops.core", "extract_keys_from_data_table"): { - 1: "main_table", - 2: "copy_main_table.txt", - 3: "keys_main_table.txt", - }, - }, - }, - }, - ("file_dataset",): { - KhiopsCoclustering: { - "fit": { ("khiops.core", "prepare_coclustering_deployment"): { - 2: os.path.join(cls.output_dir, "Coclustering.khcj"), + 2: os.path.join( + cls.output_dir, "main_tableCoclusteringResults.khcj" + ), 3: "CC_main_table", 4: "SampleId", - 5: cls.output_dir, - }, - ("khiops.core", "read_coclustering_results_file"): { - 0: os.path.join(cls.output_dir, "Coclustering.khcj") - }, - ("khiops.core", "build_multi_table_dictionary"): { - 2: "CC_main_table" - }, - ("khiops.core", "train_coclustering"): { - 1: "main_table", - 3: ("SampleId", "Pos", "Char"), - 4: cls.output_dir, + 5: os.path.join( + cls.output_dir, "CC_Keys_main_table_deployed.kdic" + ), }, }, "predict": { @@ -777,57 +581,6 @@ def setUpClass(cls): }, }, }, - ("file_dataset",): { - KhiopsRegressor: { - "fit": { - ("khiops.core", "train_predictor"): { - 1: "main_table", - 2: "main_table.txt", - 3: "age", - 4: cls.output_dir, - } - }, - "predict": { - ("khiops.core", "deploy_model"): { - 1: "SNB_main_table", - 2: "copy_main_table.txt", - 3: cls.output_dir, - } - }, - }, - KhiopsClassifier: { - "fit": { - ("khiops.core", "train_predictor"): { - 1: "main_table", - 2: "main_table.txt", - 3: "class", - 4: cls.output_dir, - } - }, - "predict": { - ("khiops.core", "deploy_model"): { - 1: "SNB_main_table", - 2: "copy_main_table.txt", - 3: cls.output_dir, - } - }, - }, - KhiopsEncoder: { - "fit": { - ("khiops.core", "train_recoder"): { - 1: "main_table", - 3: "class", - } - }, - "predict": { - ("khiops.core", "deploy_model"): { - 1: "R_main_table", - 2: "copy_main_table.txt", - 3: cls.output_dir, - } - }, - }, - }, }, "multitable": { ("dataframe",): { @@ -878,55 +631,6 @@ def setUpClass(cls): }, }, }, - ("file_dataset",): { - KhiopsRegressor: { - "fit": { - ("khiops.core", "train_predictor"): { - 1: "SpliceJunction", - 3: "Class", - 4: cls.output_dir, - } - }, - "predict": { - ("khiops.core", "deploy_model"): { - 1: "SNB_SpliceJunction", - 2: "copy_SpliceJunction.txt", - 3: cls.output_dir, - } - }, - }, - KhiopsClassifier: { - "fit": { - ("khiops.core", "train_predictor"): { - 1: "SpliceJunction", - 3: "Class", - 4: cls.output_dir, - } - }, - "predict": { - ("khiops.core", "deploy_model"): { - 1: "SNB_SpliceJunction", - 2: "copy_SpliceJunction.txt", - 3: cls.output_dir, - } - }, - }, - KhiopsEncoder: { - "fit": { - ("khiops.core", "train_recoder"): { - 1: "SpliceJunction", - 3: "Class", - } - }, - "predict": { - ("khiops.core", "deploy_model"): { - 1: "R_SpliceJunction", - 2: "copy_SpliceJunction.txt", - 3: cls.output_dir, - } - }, - }, - }, }, } cls.special_arg_checkers = { @@ -994,9 +698,6 @@ def setUpClass(cls): "build_frequency_variables": False, }, ("khiops.core", "read_coclustering_results_file"): {}, - ("khiops.core", "build_multi_table_dictionary"): { - "overwrite_dictionary_file": True - }, ("khiops.core", "train_coclustering"): { "log_file_path": os.path.join( cls.output_dir, "khiops_train_cc.log" @@ -1009,274 +710,50 @@ def setUpClass(cls): "simplify": { ("khiops.core", "simplify_coclustering"): { "max_part_numbers": {"SampleId": 2}, - "max_preserved_information": 3, - "max_cells": 4, - "max_total_parts": 1, - }, - ("khiops.core", "prepare_coclustering_deployment"): { - "build_cluster_variable": True, - "build_distance_variables": False, - "build_frequency_variables": False, - }, - ("khiops.core", "build_multi_table_dictionary"): { - "overwrite_dictionary_file": True - }, - }, - "predict": { - ("khiops.core", "deploy_model"): { - "detect_format": False, - "header_line": True, - "additional_data_tables": { - "CC_Keys_main_table`CC_main_table" - }, - "log_file_path": os.path.join( - cls.output_dir, "khiops.log" - ), - }, - ("khiops.core", "extract_keys_from_data_table"): { - "header_line": True, - "output_header_line": True, - }, - }, - }, - }, - ("file_dataset",): { - KhiopsCoclustering: { - "fit": { - ("khiops.core", "prepare_coclustering_deployment"): { - "build_cluster_variable": True, - "build_distance_variables": False, - "build_frequency_variables": False, - }, - ("khiops.core", "read_coclustering_results_file"): {}, - ("khiops.core", "build_multi_table_dictionary"): { - "overwrite_dictionary_file": True - }, - ("khiops.core", "train_coclustering"): { - "log_file_path": os.path.join( - cls.output_dir, "khiops_train_cc.log" - ) - }, - }, - "predict": { - ("khiops.core", "deploy_model"): { - "detect_format": False, - "header_line": True, - "additional_data_tables": { - "CC_Keys_main_table`CC_main_table" - }, - "log_file_path": os.path.join( - cls.output_dir, "khiops.log" - ), - }, - ("khiops.core", "extract_keys_from_data_table"): { - "header_line": True, - "output_header_line": True, - }, - }, - }, - }, - }, - "monotable": { - ("dataframe", "dataframe_xy"): { - KhiopsClassifier: { - "fit": { - ("khiops.core", "train_predictor"): { - "field_separator": "\t", - "detect_format": False, - "header_line": True, - "max_pairs": 1, - "max_trees": 5, - "max_selected_variables": 1, - "max_evaluated_variables": 3, - "specific_pairs": [("age", "race")], - "all_possible_pairs": False, - "construction_rules": ["TableMode", "TableSelection"], - "group_target_value": False, - "additional_data_tables": {}, - } - }, - "predict": { - ("khiops.core", "deploy_model"): { - "field_separator": "\t", - "detect_format": False, - "header_line": True, - "log_file_path": os.path.join( - cls.output_dir, "khiops.log" - ), - "additional_data_tables": {}, - } - }, - }, - KhiopsRegressor: { - "fit": { - ("khiops.core", "train_predictor"): { - "field_separator": "\t", - "detect_format": False, - "header_line": True, - "max_trees": 0, - "max_selected_variables": 1, - "max_evaluated_variables": 3, - "construction_rules": ["TableMode", "TableSelection"], - "additional_data_tables": {}, - } - }, - "predict": { - ("khiops.core", "deploy_model"): { - "field_separator": "\t", - "detect_format": False, - "header_line": True, - "log_file_path": os.path.join( - cls.output_dir, "khiops.log" - ), - "additional_data_tables": {}, - } - }, - }, - KhiopsEncoder: { - "fit": { - ("khiops.core", "train_recoder"): { - "field_separator": "\t", - "detect_format": False, - "header_line": True, - "max_pairs": 1, - "max_trees": 5, - "specific_pairs": [("age", "race")], - "all_possible_pairs": False, - "construction_rules": ["TableMode", "TableSelection"], - "informative_variables_only": True, - "group_target_value": False, - "keep_initial_categorical_variables": False, - "keep_initial_numerical_variables": False, - "categorical_recoding_method": "part Id", - "numerical_recoding_method": "part Id", - "pairs_recoding_method": "part Id", - "additional_data_tables": {}, - } - }, - "predict": { - ("khiops.core", "deploy_model"): { - "field_separator": "\t", - "detect_format": False, - "header_line": True, - "log_file_path": os.path.join( - cls.output_dir, "khiops.log" - ), - "additional_data_tables": {}, - } - }, - }, - }, - ("file_dataset",): { - KhiopsClassifier: { - "fit": { - ("khiops.core", "train_predictor"): { - "field_separator": "\t", - "detect_format": False, - "header_line": True, - "max_pairs": 1, - "max_trees": 5, - "max_selected_variables": 1, - "max_evaluated_variables": 3, - "specific_pairs": [("age", "race")], - "all_possible_pairs": False, - "construction_rules": ["TableMode", "TableSelection"], - "group_target_value": False, - "additional_data_tables": {}, - } - }, - "predict": { - ("khiops.core", "deploy_model"): { - "field_separator": "\t", - "detect_format": False, - "header_line": True, - "log_file_path": os.path.join( - cls.output_dir, "khiops.log" - ), - "additional_data_tables": {}, - } - }, - }, - KhiopsRegressor: { - "fit": { - ("khiops.core", "train_predictor"): { - "field_separator": "\t", - "detect_format": False, - "header_line": True, - "max_trees": 0, - "max_selected_variables": 1, - "max_evaluated_variables": 3, - "construction_rules": ["TableMode", "TableSelection"], - "additional_data_tables": {}, - } - }, - "predict": { - ("khiops.core", "deploy_model"): { - "field_separator": "\t", - "detect_format": False, - "header_line": True, - "log_file_path": os.path.join( - cls.output_dir, "khiops.log" - ), - "additional_data_tables": {}, - } - }, - }, - KhiopsEncoder: { - "fit": { - ("khiops.core", "train_recoder"): { - "field_separator": "\t", - "detect_format": False, - "header_line": True, - "max_pairs": 1, - "max_trees": 5, - "specific_pairs": [("age", "race")], - "all_possible_pairs": False, - "construction_rules": ["TableMode", "TableSelection"], - "informative_variables_only": True, - "group_target_value": False, - "keep_initial_categorical_variables": False, - "keep_initial_numerical_variables": False, - "categorical_recoding_method": "part Id", - "numerical_recoding_method": "part Id", - "pairs_recoding_method": "part Id", - "additional_data_tables": {}, - } + "max_preserved_information": 3, + "max_cells": 4, + "max_total_parts": 1, + }, + ("khiops.core", "prepare_coclustering_deployment"): { + "build_cluster_variable": True, + "build_distance_variables": False, + "build_frequency_variables": False, + }, }, "predict": { ("khiops.core", "deploy_model"): { - "field_separator": "\t", "detect_format": False, "header_line": True, + "additional_data_tables": {"CC_main_table"}, "log_file_path": os.path.join( cls.output_dir, "khiops.log" ), - "additional_data_tables": {}, - } + }, + ("khiops.core", "extract_keys_from_data_table"): { + "header_line": True, + "output_header_line": True, + }, }, }, }, }, - "multitable": { - ("dataframe",): { + "monotable": { + ("dataframe", "dataframe_xy"): { KhiopsClassifier: { "fit": { ("khiops.core", "train_predictor"): { "field_separator": "\t", "detect_format": False, "header_line": True, - "max_constructed_variables": 10, "max_pairs": 1, "max_trees": 5, "max_selected_variables": 1, "max_evaluated_variables": 3, - "specific_pairs": [], + "specific_pairs": [("age", "race")], "all_possible_pairs": False, "construction_rules": ["TableMode", "TableSelection"], "group_target_value": False, - "additional_data_tables": { - "SpliceJunction`SpliceJunctionDNA" - }, + "additional_data_tables": {}, } }, "predict": { @@ -1287,9 +764,7 @@ def setUpClass(cls): "log_file_path": os.path.join( cls.output_dir, "khiops.log" ), - "additional_data_tables": { - "SNB_SpliceJunction`SpliceJunctionDNA" - }, + "additional_data_tables": {}, } }, }, @@ -1299,14 +774,11 @@ def setUpClass(cls): "field_separator": "\t", "detect_format": False, "header_line": True, - "max_constructed_variables": 10, "max_trees": 0, "max_selected_variables": 1, "max_evaluated_variables": 3, "construction_rules": ["TableMode", "TableSelection"], - "additional_data_tables": { - "SpliceJunction`SpliceJunctionDNA" - }, + "additional_data_tables": {}, } }, "predict": { @@ -1317,9 +789,7 @@ def setUpClass(cls): "log_file_path": os.path.join( cls.output_dir, "khiops.log" ), - "additional_data_tables": { - "SNB_SpliceJunction`SpliceJunctionDNA" - }, + "additional_data_tables": {}, } }, }, @@ -1329,10 +799,9 @@ def setUpClass(cls): "field_separator": "\t", "detect_format": False, "header_line": True, - "max_constructed_variables": 10, "max_pairs": 1, "max_trees": 5, - "specific_pairs": [], + "specific_pairs": [("age", "race")], "all_possible_pairs": False, "construction_rules": ["TableMode", "TableSelection"], "informative_variables_only": True, @@ -1342,9 +811,7 @@ def setUpClass(cls): "categorical_recoding_method": "part Id", "numerical_recoding_method": "part Id", "pairs_recoding_method": "part Id", - "additional_data_tables": { - "SpliceJunction`SpliceJunctionDNA" - }, + "additional_data_tables": {}, } }, "predict": { @@ -1355,14 +822,14 @@ def setUpClass(cls): "log_file_path": os.path.join( cls.output_dir, "khiops.log" ), - "additional_data_tables": { - "R_SpliceJunction`SpliceJunctionDNA" - }, + "additional_data_tables": {}, } }, }, }, - ("file_dataset",): { + }, + "multitable": { + ("dataframe",): { KhiopsClassifier: { "fit": { ("khiops.core", "train_predictor"): { @@ -1378,12 +845,7 @@ def setUpClass(cls): "all_possible_pairs": False, "construction_rules": ["TableMode", "TableSelection"], "group_target_value": False, - "log_file_path": os.path.join( - cls.output_dir, "khiops.log" - ), - "additional_data_tables": { - "SpliceJunction`SpliceJunctionDNA" - }, + "additional_data_tables": {"SpliceJunctionDNA"}, } }, "predict": { @@ -1394,9 +856,7 @@ def setUpClass(cls): "log_file_path": os.path.join( cls.output_dir, "khiops.log" ), - "additional_data_tables": { - "SNB_SpliceJunction`SpliceJunctionDNA" - }, + "additional_data_tables": {"SpliceJunctionDNA"}, } }, }, @@ -1411,12 +871,7 @@ def setUpClass(cls): "max_selected_variables": 1, "max_evaluated_variables": 3, "construction_rules": ["TableMode", "TableSelection"], - "log_file_path": os.path.join( - cls.output_dir, "khiops.log" - ), - "additional_data_tables": { - "SpliceJunction`SpliceJunctionDNA" - }, + "additional_data_tables": {"SpliceJunctionDNA"}, } }, "predict": { @@ -1427,9 +882,7 @@ def setUpClass(cls): "log_file_path": os.path.join( cls.output_dir, "khiops.log" ), - "additional_data_tables": { - "SNB_SpliceJunction`SpliceJunctionDNA" - }, + "additional_data_tables": {"SpliceJunctionDNA"}, } }, }, @@ -1452,9 +905,7 @@ def setUpClass(cls): "categorical_recoding_method": "part Id", "numerical_recoding_method": "part Id", "pairs_recoding_method": "part Id", - "additional_data_tables": { - "SpliceJunction`SpliceJunctionDNA" - }, + "additional_data_tables": {"SpliceJunctionDNA"}, } }, "predict": { @@ -1465,9 +916,7 @@ def setUpClass(cls): "log_file_path": os.path.join( cls.output_dir, "khiops.log" ), - "additional_data_tables": { - "R_SpliceJunction`SpliceJunctionDNA" - }, + "additional_data_tables": {"SpliceJunctionDNA"}, } }, }, @@ -1647,84 +1096,6 @@ def _check_kwargs( ), f"Special checker for '{key}' is not callable." special_checker(self, actual_value, expected_value) - @classmethod - def _create_train_test_multitable_file_dataset(cls, transform_for_regression=False): - ( - root_table_data, - secondary_table_data, - ) = KhiopsTestHelper.get_two_table_data( - "SpliceJunction", "SpliceJunction", "SpliceJunctionDNA" - ) - root_table_file_name_suffix = "" - if transform_for_regression: - root_table_data.replace({"Class": {"EI": 1, "IE": 2, "N": 3}}, inplace=True) - root_table_file_name_suffix = "_R" - (root_train_data, root_labels), ( - root_test_data, - _, - ) = KhiopsTestHelper.prepare_data(root_table_data, "Class") - root_train_data["Class"] = root_labels - secondary_train_data = ( - root_train_data["SampleId"] - .to_frame() - .merge(secondary_table_data, on="SampleId") - ) - secondary_test_data = ( - root_test_data["SampleId"] - .to_frame() - .merge(secondary_table_data, on="SampleId") - ) - root_train_data_path = os.path.join( - cls.output_dir, f"SpliceJunction_train{root_table_file_name_suffix}.txt" - ) - root_test_data_path = os.path.join( - cls.output_dir, f"SpliceJunction_test{root_table_file_name_suffix}.txt" - ) - secondary_train_data_path = os.path.join( - cls.output_dir, "SpliceJunctionDNA_train.txt" - ) - secondary_test_data_path = os.path.join( - cls.output_dir, "SpliceJunctionDNA_test.txt" - ) - root_train_data.to_csv(root_train_data_path, sep="\t", header=True, index=False) - root_test_data.to_csv(root_test_data_path, sep="\t", header=True, index=False) - secondary_train_data.to_csv( - secondary_train_data_path, sep="\t", header=True, index=False - ) - secondary_test_data.to_csv( - secondary_test_data_path, sep="\t", header=True, index=False - ) - train_dataset = { - "main_table": "SpliceJunction", - "tables": { - "SpliceJunction": ( - root_train_data_path, - "SampleId", - ), - "SpliceJunctionDNA": ( - secondary_train_data_path, - "SampleId", - ), - }, - "format": ("\t", True), - } - test_dataset = { - "main_table": "SpliceJunction", - "tables": { - "SpliceJunction": ( - root_test_data_path, - "SampleId", - ), - "SpliceJunctionDNA": ( - secondary_test_data_path, - "SampleId", - ), - }, - "format": ("\t", True), - } - - return (train_dataset, test_dataset) - @classmethod def _create_train_test_multitable_dataframe(cls, transform_for_regression=False): dataset_name = "SpliceJunction" @@ -1762,31 +1133,6 @@ def _create_train_test_multitable_dataframe(cls, transform_for_regression=False) } return (X_train_data, y_train_data, X_test_data) - @classmethod - def _create_train_test_monotable_file_dataset(cls, label): - data = KhiopsTestHelper.get_monotable_data("Adult") - (train_data, train_labels), ( - test_data, - _, - ) = KhiopsTestHelper.prepare_data(data, label) - - train_data_path = os.path.join(cls.output_dir, f"Adult_train_for_{label}.txt") - test_data_path = os.path.join(cls.output_dir, f"Adult_test_for_{label}.txt") - train_data[label] = train_labels - train_data.to_csv(train_data_path, sep="\t", header=True, index=False) - test_data.to_csv(test_data_path, sep="\t", header=True, index=False) - train_dataset = { - "main_table": "main_table", - "tables": {"main_table": (train_data_path, None)}, - "format": ("\t", True), - } - test_dataset = { - "main_table": "main_table", - "tables": {"main_table": (test_data_path, None)}, - "format": ("\t", True), - } - return (train_dataset, test_dataset) - @classmethod def _create_train_test_monotable_dataframe(cls, label, y_as_dataframe=False): data = KhiopsTestHelper.get_monotable_data("Adult") @@ -1879,7 +1225,6 @@ def _test_template( - "dataframe": input data is a Pandas DataFrame, input labels are a Pandas Series, - "dataframe_xy": input data and labels are both Pandas DataFrames, - - "file_dataset": input data is specified as paths to files The template also takes custom keyword arguments that can be passed to specific estimator methods, viz. `fit` and `predict`. @@ -1906,46 +1251,12 @@ def _test_template( estimation_process=KhiopsClassifier, ) # choose train and test sets according to the source type - if source_type == "dataframe": - X_train_data = data["X_train"]["tables"]["SpliceJunctionDNA"][ - 0 - ] # XXX leaky - y_train_data = None - X_test_data = data["X_test"]["tables"]["SpliceJunctionDNA"][ - 0 - ] # XXX leaky - else: - assert source_type == "file_dataset" - dataset = copy.deepcopy(data) - X_train_data = dataset["train"] - del X_train_data["tables"]["SpliceJunction"] # XXX leaky - - # set the key to None, as the dataset is monotable - X_train_data["tables"]["SpliceJunctionDNA"] = ( - X_train_data["tables"]["SpliceJunctionDNA"][0], - None, - ) - X_train_data["main_table"] = "main_table" - X_train_data["tables"]["main_table"] = X_train_data["tables"][ - "SpliceJunctionDNA" - ] - del X_train_data["tables"]["SpliceJunctionDNA"] - - y_train_data = None - - X_test_data = dataset["test"] - del X_test_data["tables"]["SpliceJunction"] # XXX leaky - - # set the key to None, as the dataset is monotable - X_test_data["tables"]["SpliceJunctionDNA"] = ( - X_test_data["tables"]["SpliceJunctionDNA"][0], - None, - ) - X_test_data["main_table"] = "main_table" - X_test_data["tables"]["main_table"] = X_test_data["tables"][ - "SpliceJunctionDNA" - ] - del X_test_data["tables"]["SpliceJunctionDNA"] + assert source_type == "dataframe" + X_train_data = data["X_train"]["tables"]["SpliceJunctionDNA"][ + 0 + ] # XXX leaky + y_train_data = None + X_test_data = data["X_test"]["tables"]["SpliceJunctionDNA"][0] # XXX leaky else: assert issubclass(estimator_type, KhiopsSupervisedEstimator) data = self._retrieve_data( @@ -1953,22 +1264,9 @@ def _test_template( source_type=source_type, estimation_process=estimator_type, ) - if source_type in ("dataframe", "dataframe_xy"): - X_train_data = data["X_train"] - y_train_data = data["y_train"] - X_test_data = data["X_test"] - else: - assert source_type == "file_dataset" - X_train_data = data["train"] - if schema_type == "multitable": - y_train_data = "Class" - else: - assert schema_type == "monotable" - if estimator_type == KhiopsRegressor: - y_train_data = "age" - else: - y_train_data = "class" - X_test_data = data["test"] + X_train_data = data["X_train"] + y_train_data = data["y_train"] + X_test_data = data["X_test"] dataset = self.dataset_of_schema_type[schema_type] resources = self._define_resources(dataset, estimator_type, estimator_method) @@ -2138,25 +1436,6 @@ def test_parameter_transfer_classifier_fit_from_monotable_dataframe_with_df_y( }, ) - def test_parameter_transfer_classifier_fit_from_monotable_file_dataset(self): - """Test parameter transfer from monotable file dataset fit to core API""" - self._test_template( - estimator_type=KhiopsClassifier, - estimator_method="fit", - schema_type="monotable", - source_type="file_dataset", - extra_estimator_kwargs={ - "n_pairs": 1, - "n_trees": 5, - "n_selected_features": 1, - "n_evaluated_features": 3, - "specific_pairs": [("age", "race")], - "all_possible_pairs": False, - "construction_rules": ["TableMode", "TableSelection"], - "group_target_value": False, - }, - ) - def test_parameter_transfer_classifier_fit_from_multitable_dataframe(self): """Test parameter transfer from multitable dataframe fit to core API""" self._test_template( @@ -2177,26 +1456,6 @@ def test_parameter_transfer_classifier_fit_from_multitable_dataframe(self): }, ) - def test_parameter_transfer_classifier_fit_from_multitable_file_dataset(self): - """Test parameter transfer from file dataset fit to core API""" - self._test_template( - estimator_type=KhiopsClassifier, - estimator_method="fit", - schema_type="multitable", - source_type="file_dataset", - extra_estimator_kwargs={ - "n_features": 10, - "n_pairs": 1, - "n_trees": 5, - "n_selected_features": 1, - "n_evaluated_features": 3, - "specific_pairs": [], - "all_possible_pairs": False, - "construction_rules": ["TableMode", "TableSelection"], - "group_target_value": False, - }, - ) - def test_parameter_transfer_classifier_predict_from_monotable_dataframe(self): """Test parameter transfer from monotable dataframe predict to core API""" self._test_template( @@ -2206,17 +1465,6 @@ def test_parameter_transfer_classifier_predict_from_monotable_dataframe(self): source_type="dataframe", ) - def test_parameter_transfer_classifier_predict_from_monotable_file_dataset( - self, - ): - """Test parameter transfer from monotable file dataset predict to core api""" - self._test_template( - estimator_type=KhiopsClassifier, - estimator_method="predict", - schema_type="monotable", - source_type="file_dataset", - ) - def test_parameter_transfer_classifier_predict_from_multitable_dataframe(self): """Test parameter transfer from dataframe predict to core API""" self._test_template( @@ -2226,17 +1474,6 @@ def test_parameter_transfer_classifier_predict_from_multitable_dataframe(self): source_type="dataframe", ) - def test_parameter_transfer_classifier_predict_from_multitable_file_dataset( - self, - ): - """Test parameter transfer from file dataset predict to core API""" - self._test_template( - estimator_type=KhiopsClassifier, - estimator_method="predict", - schema_type="multitable", - source_type="file_dataset", - ) - def test_parameter_transfer_encoder_fit_from_monotable_dataframe(self): """Test parameter transfer from monotable dataframe fit to core API""" self._test_template( @@ -2283,28 +1520,6 @@ def test_parameter_transfer_encoder_fit_from_monotable_dataframe_with_df_y( }, ) - def test_parameter_transfer_encoder_fit_from_monotable_file_dataset(self): - """Test parameter transfer from monotable file dataset fit to core API""" - self._test_template( - estimator_type=KhiopsEncoder, - estimator_method="fit", - schema_type="monotable", - source_type="file_dataset", - extra_estimator_kwargs={ - "n_pairs": 1, - "n_trees": 5, - "specific_pairs": [("age", "race")], - "all_possible_pairs": False, - "construction_rules": ["TableMode", "TableSelection"], - "informative_features_only": True, - "group_target_value": False, - "keep_initial_variables": False, - "transform_type_categorical": "part_id", - "transform_type_numerical": "part_id", - "transform_type_pairs": "part_id", - }, - ) - def test_parameter_transfer_encoder_fit_from_multitable_dataframe(self): """Test parameter transfer from multitable dataframe fit to core API""" self._test_template( @@ -2328,29 +1543,6 @@ def test_parameter_transfer_encoder_fit_from_multitable_dataframe(self): }, ) - def test_parameter_transfer_encoder_fit_from_multitable_file_dataset(self): - """Test parameter transfer from multitable file dataset fit to core API""" - self._test_template( - estimator_type=KhiopsEncoder, - estimator_method="fit", - schema_type="multitable", - source_type="file_dataset", - extra_estimator_kwargs={ - "n_features": 10, - "n_pairs": 1, - "n_trees": 5, - "specific_pairs": [], - "all_possible_pairs": False, - "construction_rules": ["TableMode", "TableSelection"], - "informative_features_only": True, - "group_target_value": False, - "keep_initial_variables": False, - "transform_type_categorical": "part_id", - "transform_type_numerical": "part_id", - "transform_type_pairs": "part_id", - }, - ) - def test_parameter_transfer_encoder_predict_from_monotable_dataframe(self): """Test parameter transfer from monotable dataframe predict to core API""" self._test_template( @@ -2360,15 +1552,6 @@ def test_parameter_transfer_encoder_predict_from_monotable_dataframe(self): source_type="dataframe", ) - def test_parameter_transfer_encoder_predict_from_monotable_file_dataset(self): - """Test parameter transfer from monotable file dataset predict to core API""" - self._test_template( - estimator_type=KhiopsEncoder, - estimator_method="predict", - schema_type="monotable", - source_type="file_dataset", - ) - def test_parameter_transfer_encoder_predict_from_multitable_dataframe(self): """Test parameter transfer from dataframe predict to core API""" self._test_template( @@ -2378,15 +1561,6 @@ def test_parameter_transfer_encoder_predict_from_multitable_dataframe(self): source_type="dataframe", ) - def test_parameter_transfer_encoder_predict_from_multitable_file_dataset(self): - """Test parameter transfer from file dataset predict to core API""" - self._test_template( - estimator_type=KhiopsEncoder, - estimator_method="predict", - schema_type="multitable", - source_type="file_dataset", - ) - def test_parameter_transfer_regressor_fit_from_monotable_dataframe(self): """Test parameter transfer from monotable dataframe fit to core API""" self._test_template( @@ -2417,20 +1591,6 @@ def test_parameter_transfer_regressor_fit_from_monotable_dataframe_with_df_y( }, ) - def test_parameter_transfer_regressor_fit_from_monotable_file_dataset(self): - """Test parameter transfer from monotable file dataset fit to core API""" - self._test_template( - estimator_type=KhiopsRegressor, - estimator_method="fit", - schema_type="monotable", - source_type="file_dataset", - extra_estimator_kwargs={ - "n_selected_features": 1, - "n_evaluated_features": 3, - "construction_rules": ["TableMode", "TableSelection"], - }, - ) - def test_parameter_transfer_regressor_fit_from_multitable_dataframe(self): """Test parameter transfer from multitable dataframe fit to core API""" self._test_template( @@ -2447,22 +1607,6 @@ def test_parameter_transfer_regressor_fit_from_multitable_dataframe(self): }, ) - def test_parameter_transfer_regressor_fit_from_multitable_file_dataset(self): - """Test parameter transfer from file dataset fit to core API""" - self._test_template( - estimator_type=KhiopsRegressor, - estimator_method="fit", - schema_type="multitable", - source_type="file_dataset", - extra_estimator_kwargs={ - "n_features": 10, - "n_trees": 0, - "n_selected_features": 1, - "n_evaluated_features": 3, - "construction_rules": ["TableMode", "TableSelection"], - }, - ) - def test_parameter_transfer_regressor_predict_from_monotable_dataframe(self): """Test parameter transfer from monotable dataframe predict to core API""" self._test_template( @@ -2472,15 +1616,6 @@ def test_parameter_transfer_regressor_predict_from_monotable_dataframe(self): source_type="dataframe", ) - def test_parameter_transfer_regressor_predict_from_monotable_file_dataset(self): - """Test parameter transfer from monotable file dataset predict to core API""" - self._test_template( - estimator_type=KhiopsRegressor, - estimator_method="predict", - schema_type="monotable", - source_type="file_dataset", - ) - def test_parameter_transfer_regressor_predict_from_multitable_dataframe(self): """Test parameter transfer from dataframe predict to core API""" self._test_template( @@ -2490,17 +1625,6 @@ def test_parameter_transfer_regressor_predict_from_multitable_dataframe(self): source_type="dataframe", ) - def test_parameter_transfer_regressor_predict_from_multitable_file_dataset( - self, - ): - """Test parameter transfer from file dataset predict to core API""" - self._test_template( - estimator_type=KhiopsRegressor, - estimator_method="predict", - schema_type="multitable", - source_type="file_dataset", - ) - def test_parameter_transfer_coclustering_fit_from_dataframe(self): """Test parameter transfer from dataframe coclustering fit to core API""" self._test_template( @@ -2517,21 +1641,6 @@ def test_parameter_transfer_coclustering_fit_from_dataframe(self): }, ) - def test_parameter_transfer_coclustering_fit_from_file_dataset(self): - """Test parameter transfer from file dataset coclustering fit to core API""" - self._test_template( - estimator_type=KhiopsCoclustering, - estimator_method="fit", - schema_type="not_applicable", - source_type="file_dataset", - custom_kwargs={ - "fit": { - "columns": ("SampleId", "Pos", "Char"), - "id_column": "SampleId", - } - }, - ) - def test_parameter_transfer_coclustering_simplify_from_dataframe(self): """Test parameter transfer from dataframe coclustering simplify to core API""" self._test_template( @@ -2572,25 +1681,6 @@ def test_parameter_transfer_coclustering_predict_from_dataframe(self): }, ) - def test_parameter_transfer_coclustering_predict_from_file_dataset(self): - # prepare two-table data for coclustering fit from file dataset - # wrap core coclustering predict with parameter trace - # (namely, two relevant core API methods) - # launch sklearn coclustering fit to build a coclustering model - # probe parameters passed to core's coclustering predict - self._test_template( - estimator_type=KhiopsCoclustering, - estimator_method="predict", - schema_type="not_applicable", - source_type="file_dataset", - custom_kwargs={ - "fit": { - "columns": ("SampleId", "Pos", "Char"), - "id_column": "SampleId", - } - }, - ) - class KhiopsSklearnEstimatorStandardTests(unittest.TestCase): """Tests to comply with `sklearn.util.estimator_checks.check_estimator`"""