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Add support to Python 3.12, Sync Fabric till dc382961 (#1467)
* Merged PR 1686010: Bump version to 2.3.5.post2, Distribute source and wheel, Fix license-file, Only log better models - Fix license-file - Bump version to 2.3.5.post2 - Distribute source and wheel - Log better models only - Add artifact_path to register_automl_pipeline - Improve logging of _automl_user_configurations ---- This pull request fixes the project’s configuration by updating the license metadata for compliance with FLAML OSS 2.3.5. The changes in `/pyproject.toml` update the project’s license and readme metadata by replacing deprecated keys with the new structured fields. - `/pyproject.toml`: Replaced `license_file` with `license = { text = "MIT" }`. - `/pyproject.toml`: Replaced `description-file` with `readme = "README.md"`. <!-- GitOpsUserAgent=GitOps.Apps.Server.pullrequestcopilot --> Related work items: #4252053 * Merged PR 1688479: Handle feature_importances_ is None, Catch RuntimeError and wait for spark cluster to recover - Add warning message when feature_importances_ is None (#3982120) - Catch RuntimeError and wait for spark cluster to recover (#3982133) ---- Bug fix. This pull request prevents an AttributeError in the feature importance plotting function by adding a check for a `None` value with an informative warning message. - `flaml/fabric/visualization.py`: Checks if `result.feature_importances_` is `None`, logs a warning with possible reasons, and returns early. - `flaml/fabric/visualization.py`: Imports `logger` from `flaml.automl.logger` to support the warning message. <!-- GitOpsUserAgent=GitOps.Apps.Server.pullrequestcopilot --> Related work items: #3982120, #3982133 * Removed deprecated metadata section * Fix log_params, log_artifact doesn't support run_id in mlflow 2.6.0 * Remove autogen * Remove autogen * Remove autogen * Merged PR 1776547: Fix flaky test test_automl Don't throw error when time budget is not enough ---- #### AI description (iteration 1) #### PR Classification Bug fix addressing a failing test in the AutoML notebook example. #### PR Summary This PR fixes a flaky test by adding a conditional check in the AutoML test that prints a message and exits early if no best estimator is set, thereby preventing unpredictable test failures. - `test/automl/test_notebook_example.py`: Introduced a check to print "Training budget is not sufficient" and return if `automl.best_estimator` is not found. <!-- GitOpsUserAgent=GitOps.Apps.Server.pullrequestcopilot --> Related work items: #4573514 * Merged PR 1777952: Fix unrecognized or malformed field 'license-file' when uploading wheel to feed Try to fix InvalidDistribution: Invalid distribution metadata: unrecognized or malformed field 'license-file' ---- Bug fix addressing package metadata configuration. This pull request fixes the error with unrecognized or malformed license file fields during wheel uploads by updating the setup configuration. - In `setup.py`, added `license="MIT"` and `license_files=["LICENSE"]` to provide proper license metadata. <!-- GitOpsUserAgent=GitOps.Apps.Server.pullrequestcopilot --> Related work items: #4560034 * Cherry-pick Merged PR 1879296: Add support to python 3.12 and spark 4.0 * Cherry-pick Merged PR 1890869: Improve time_budget estimation for mlflow logging * Cherry-pick Merged PR 1879296: Add support to python 3.12 and spark 4.0 * Disable openai workflow * Add python 3.12 to test envs * Manually trigger openai * Support markdown files with underscore-prefixed file names * Improve save dependencies * SynapseML is not installed * Fix syntax error:Module !flaml/autogen was never imported * macos 3.12 also hangs * fix syntax error * Update python version in actions * Install setuptools for using pkg_resources * Fix test_automl_performance in Github actions * Fix test_nested_run
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‎.coveragerc‎

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[run]
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branch = True
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source = flaml
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source =
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flaml
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omit =
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*test*
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*/test/*
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*/flaml/autogen/*

‎.github/workflows/CD.yml‎

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strategy:
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matrix:
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os: ["ubuntu-latest"]
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python-version: ["3.10"]
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python-version: ["3.12"]
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runs-on: ${{ matrix.os }}
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environment: package
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steps:

‎.github/workflows/deploy-website.yml‎

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- name: setup python
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uses: actions/setup-python@v4
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with:
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python-version: "3.10"
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python-version: "3.12"
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- name: pydoc-markdown install
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run: |
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python -m pip install --upgrade pip
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pip install pydoc-markdown==4.7.0
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pip install pydoc-markdown==4.7.0 setuptools
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- name: pydoc-markdown run
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run: |
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pydoc-markdown
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- name: setup python
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uses: actions/setup-python@v4
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with:
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python-version: "3.10"
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python-version: "3.12"
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- name: pydoc-markdown install
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run: |
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python -m pip install --upgrade pip
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pip install pydoc-markdown==4.7.0
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pip install pydoc-markdown==4.7.0 setuptools
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- name: pydoc-markdown run
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run: |
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pydoc-markdown

‎.github/workflows/openai.yml‎

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name: OpenAI
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on:
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pull_request:
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branches: ['main']
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paths:
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- 'flaml/autogen/**'
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- 'test/autogen/**'
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- 'notebook/autogen_openai_completion.ipynb'
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- 'notebook/autogen_chatgpt_gpt4.ipynb'
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- '.github/workflows/openai.yml'
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workflow_dispatch:
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# pull_request:
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# branches: ['main']
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# paths:
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# - 'flaml/autogen/**'
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# - 'test/autogen/**'
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# - 'notebook/autogen_openai_completion.ipynb'
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# - 'notebook/autogen_chatgpt_gpt4.ipynb'
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# - '.github/workflows/openai.yml'
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permissions: {}
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‎.github/workflows/python-package.yml‎

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fail-fast: false
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matrix:
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os: [ubuntu-latest, macos-latest, windows-latest]
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python-version: ["3.10", "3.11"]
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python-version: ["3.10", "3.11", "3.12"]
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exclude:
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- os: macos-latest
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python-version: "3.10"
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python-version: "3.10" # macOS runners will hang on python 3.10 for unknown reasons
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- os: macos-latest
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python-version: "3.12" # macOS runners will hang on python 3.12 for unknown reasons
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steps:
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- uses: actions/checkout@v4
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- name: Set up Python ${{ matrix.python-version }}
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pip install -e .
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python -c "import flaml"
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pip install -e .[test]
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- name: On Ubuntu python 3.10, install pyspark 3.4.1
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if: matrix.python-version == '3.10' && matrix.os == 'ubuntu-latest'
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run: |
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pip install pyspark==3.4.1
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pip list | grep "pyspark"
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- name: On Ubuntu python 3.11, install pyspark 3.5.1
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if: matrix.python-version == '3.11' && matrix.os == 'ubuntu-latest'
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run: |
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run: |
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pip cache purge
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- name: Test with pytest
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if: matrix.python-version != '3.10'
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if: matrix.python-version != '3.11'
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run: |
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pytest test/ --ignore=test/autogen --reruns 2 --reruns-delay 10
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- name: Coverage
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if: matrix.python-version == '3.10'
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if: matrix.python-version == '3.11'
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run: |
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pip install coverage
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coverage run -a -m pytest test --ignore=test/autogen --reruns 2 --reruns-delay 10
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coverage xml
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- name: Upload coverage to Codecov
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if: matrix.python-version == '3.10'
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if: matrix.python-version == '3.11'
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uses: codecov/codecov-action@v3
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with:
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file: ./coverage.xml
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BRANCH=unit-tests-installed-dependencies
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git fetch origin
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git checkout -B "$BRANCH"
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if git show-ref --verify --quiet "refs/remotes/origin/$BRANCH"; then
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git rebase "origin/$BRANCH"
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fi
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git checkout -B "$BRANCH" "origin/$BRANCH"
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pip freeze > installed_all_dependencies_${{ matrix.python-version }}_${{ matrix.os }}.txt
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python test/check_dependency.py > installed_first_tier_dependencies_${{ matrix.python-version }}_${{ matrix.os }}.txt
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git add installed_*dependencies*.txt
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mv coverage.xml ./coverage_${{ matrix.python-version }}_${{ matrix.os }}.xml || true
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git add -f ./coverage_${{ matrix.python-version }}_${{ matrix.os }}.xml || true
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git commit -m "Update installed dependencies for Python ${{ matrix.python-version }} on ${{ matrix.os }}" || exit 0
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git push origin "$BRANCH"
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git push origin "$BRANCH" --force

‎.gitignore‎

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.hypothesis/
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.pytest_cache/
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cover/
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junit
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# Translations
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*.mo

‎flaml/autogen/__init__.py‎

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import warnings
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from .agentchat import *
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from .code_utils import DEFAULT_MODEL, FAST_MODEL
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from .oai import *
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warnings.warn(
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"The `flaml.autogen` module is deprecated and will be removed in a future release. "
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"Please refer to `https://github.com/microsoft/autogen` for latest usage.",
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DeprecationWarning,
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stacklevel=2,
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)

‎flaml/automl/automl.py‎

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# * project root for license information.
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from __future__ import annotations
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import inspect
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import json
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import logging
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import os
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['auto', 'cv', 'holdout'].
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split_ratio: A float of the valiation data percentage for holdout.
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n_splits: An integer of the number of folds for cross - validation.
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log_type: A string of the log type, one of
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['better', 'all'].
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'better' only logs configs with better loss than previos iters
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'all' logs all the tried configs.
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log_type: Specifies which logs to save. One of ['better', 'all']. Default is 'better'.
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- 'better': Logs configs and models (if `model_history` is True) only when the loss improves,
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to `log_file_name` and MLflow, respectively.
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- 'all': Logs all configs and models (if `model_history` is True), regardless of performance.
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Note: Configs are always logged to MLflow if MLflow logging is enabled.
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model_history: A boolean of whether to keep the best
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model per estimator. Make sure memory is large enough if setting to True. Default False.
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log_training_metric: A boolean of whether to log the training
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use_spark=True,
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force_cancel=self._force_cancel,
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mlflow_exp_name=self._mlflow_exp_name,
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automl_info=(mlflow_log_latency,), # pass automl info to tune.run
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automl_info=(mlflow_log_latency, self._log_type), # pass automl info to tune.run
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extra_tag=self.autolog_extra_tag,
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# raise_on_failed_trial=False,
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# keep_checkpoints_num=1,
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if better or self._log_type == "all":
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self._log_trial(search_state, estimator)
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if self.mlflow_integration:
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self.mlflow_integration.record_state(self, search_state, estimator)
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self.mlflow_integration.record_state(
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self, search_state, estimator, better or self._log_type == "all"
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)
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def _log_trial(self, search_state, estimator):
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if self._training_log:
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if better or self._log_type == "all":
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self._log_trial(search_state, estimator)
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if self.mlflow_integration:
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self.mlflow_integration.record_state(self, search_state, estimator)
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self.mlflow_integration.record_state(
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self, search_state, estimator, better or self._log_type == "all"
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)
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logger.info(
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" at {:.1f}s,\testimator {}'s best error={:.4f},\tbest estimator {}'s best error={:.4f}".format(

‎flaml/automl/data.py‎

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import json
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import os
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import random
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import re
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import uuid
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from datetime import datetime, timedelta
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from decimal import ROUND_HALF_UP, Decimal
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"""
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if na_values is None:
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na_values = {"NA", "na", "NULL", "null", ""}
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# Remove the empty string separately (handled by the regex `^\s*$`)
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vals = [re.escape(v) for v in na_values if v != ""]
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# Build inner alternation group
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inner = "|".join(vals) if vals else ""
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if inner:
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pattern = re.compile(rf"^\s*(?:{inner})?\s*$")
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else:
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pattern = re.compile(r"^\s*$")
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df_converted = df.convert_dtypes()
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schema = {}
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721730
for col in df.columns:
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series = df[col]
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# Replace NA-like values if string
724-
series_cleaned = series.map(lambda x: np.nan if isinstance(x, str) and x.strip() in na_values else x)
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if series.dtype == object:
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mask = series.astype(str).str.match(pattern)
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series_cleaned = series.where(~mask, np.nan)
736+
else:
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series_cleaned = series
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# Skip conversion if already non-object data type, except bool which can potentially be categorical
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if (

‎flaml/automl/model.py‎

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params = super().config2params(config)
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params["tol"] = params.get("tol", 0.0001)
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params["loss"] = params.get("loss", None)
2350-
if params["loss"] is None and self._task.is_classification():
2351-
params["loss"] = "log_loss" if SKLEARN_VERSION >= "1.1" else "log"
2350+
if params["loss"] is None:
2351+
if self._task.is_classification():
2352+
params["loss"] = "log_loss" if SKLEARN_VERSION >= "1.1" else "log"
2353+
else:
2354+
params["loss"] = "squared_error"
23522355
if not self._task.is_classification() and "n_jobs" in params:
23532356
params.pop("n_jobs")
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