Update Ruff workflow to remove type checking

[hadarshavit]

Removed type checking step from Ruff workflow.

[codecov]

❌ 3 Tests Failed:

Tests completed	Failed	Passed	Skipped
585	3	582	8

View the top 3 failed test(s) by shortest run time

test/selectors/test_tuner.py::test_selector_tuner

Stack Traces | 0.149s run time

dummy_performance =     algo1  algo2
0     1.0    5.0
1     2.0    4.0
2     3.0    3.0
3     4.0    2.0
4     5.0    1.0
5     1.2    4.8...
13    3.8    2.2
14    4.8    1.2
15    1.5    4.5
16    2.5    3.5
17    3.5    2.5
18    4.5    1.5
19    5.5    0.5
dummy_features =      f1
0   1.0
1   2.0
2   3.0
3   4.0
4   5.0
5   1.2
6   2.2
7   3.2
8   4.2
9   5.2
10  0.8
11  1.8
12  2.8
13  3.8
14  4.8
15  1.5
16  2.5
17  3.5
18  4.5
19  5.5
validate_predictions = <function validate_predictions.<locals>._validate at 0x7fca5b8bd1c0>

    def test_selector_tuner(dummy_performance, dummy_features, validate_predictions):
        """Test selector tuner with basic dummy data."""
        # Create dummy feature running time
        features_running_time = pd.DataFrame(
            np.random.exponential(0.1, dummy_features.shape),
            columns=dummy_features.columns,
            index=dummy_features.index,
        )
    
        output_dir = "./smac_test_output"
        try:
>           tuned_pipeline = tune_selector(
                X=dummy_features,
                y=dummy_performance,
                features_running_time=features_running_time,
                selector_class=[PairwiseClassifier, PairwiseRegressor],
                runcount_limit=2,
                cv=2,
                seed=42,
                output_dir=output_dir,
                budget=450.0,
            )

test/selectors/test_tuner.py:84: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
asf/selectors/selector_tuner.py:226: in tune_selector
    best_config = smac.optimize()
                  ^^^^^^^^^^^^^^^
.../hostedtoolcache/Python/3.11.15................................................/x64/lib/python3.11.../smac/facade/abstract_facade.py:331: in optimize
    incumbents = self._optimizer.optimize(data_to_scatter=data_to_scatter)
                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.../hostedtoolcache/Python/3.11.15................................................/x64/lib/python3.11.../smac/main/smbo.py:315: in optimize
    trial_info = self.ask()
                 ^^^^^^^^^^
.../hostedtoolcache/Python/3.11.15................................................/x64/lib/python3.11.../smac/main/smbo.py:162: in ask
    trial_info = next(self._trial_generator)
                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^
.../hostedtoolcache/Python/3.11.15................................................/x64/lib/python3.11.../smac/intensifier/intensifier.py:236: in __iter__
    config = next(self.config_generator)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^
.../hostedtoolcache/Python/3.11.15................................................/x64/lib/python3.11.../smac/main/config_selector.py:192: in __iter__
    self._model.train(X, Y)
.../hostedtoolcache/Python/3.11.15................................................/x64/lib/python3.11.../smac/model/abstract_model.py:152: in train
    return self._train(X, Y)
           ^^^^^^^^^^^^^^^^^
.../hostedtoolcache/Python/3.11.15................................................/x64/lib/python3.11.../model/random_forest/random_forest.py:749: in _train
    self._rf.fit(X, y)
.../hostedtoolcache/Python/3.11.15................................................/x64/lib/python3.11.../model/random_forest/random_forest.py:468: in fit
    super().fit(X=X, y=y, sample_weight=sample_weight)
.../hostedtoolcache/Python/3.11.15................................................/x64/lib/python3.11.../site-packages/sklearn/base.py:1336: in wrapper
    return fit_method(estimator, *args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.../hostedtoolcache/Python/3.11.15................................................/x64/lib/python3.11.../sklearn/ensemble/_forest.py:359: in fit
    X, y = validate_data(
.../hostedtoolcache/Python/3.11.15................................................/x64/lib/python3.11.../sklearn/utils/validation.py:2919: in validate_data
    X, y = check_X_y(X, y, **check_params)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.../hostedtoolcache/Python/3.11.15................................................/x64/lib/python3.11.../sklearn/utils/validation.py:1331: in check_X_y
    y = _check_y(y, multi_output=multi_output, y_numeric=y_numeric, estimator=estimator)
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.../hostedtoolcache/Python/3.11.15................................................/x64/lib/python3.11.../sklearn/utils/validation.py:1341: in _check_y
    y = check_array(
.../hostedtoolcache/Python/3.11.15................................................/x64/lib/python3.11.../sklearn/utils/validation.py:1074: in check_array
    _assert_all_finite(
.../hostedtoolcache/Python/3.11.15................................................/x64/lib/python3.11.../sklearn/utils/validation.py:133: in _assert_all_finite
    _assert_all_finite_element_wise(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

X = array([nan])

    def _assert_all_finite_element_wise(
        X, *, xp, allow_nan, msg_dtype=None, estimator_name=None, input_name=""
    ):
        # Cython implementation doesn't support FP16 or complex numbers
        use_cython = (
            xp is np and X.data.contiguous and X.dtype.type in {np.float32, np.float64}
        )
        if use_cython:
            out = cy_isfinite(X.reshape(-1), allow_nan=allow_nan)
            has_nan_error = False if allow_nan else out == FiniteStatus.has_nan
            has_inf = out == FiniteStatus.has_infinite
        else:
            has_inf = xp.any(xp.isinf(X))
            has_nan_error = False if allow_nan else xp.any(xp.isnan(X))
        if has_inf or has_nan_error:
            if has_nan_error:
                type_err = "NaN"
            else:
                msg_dtype = msg_dtype if msg_dtype is not None else X.dtype
                type_err = f"infinity or a value too large for {msg_dtype!r}"
            padded_input_name = input_name + " " if input_name else ""
            msg_err = f"Input {padded_input_name}contains {type_err}."
            if estimator_name and input_name == "X" and has_nan_error:
                # Improve the error message on how to handle missing values in
                # scikit-learn.
                msg_err += (
                    f"\n{estimator_name} does not accept missing values"
                    " encoded as NaN natively. For supervised learning, you might want"
                    " to consider sklearn.ensemble.HistGradientBoostingClassifier and"
                    " Regressor which accept missing values encoded as NaNs natively."
                    " Alternatively, it is possible to preprocess the data, for"
                    " instance by using an imputer transformer in a pipeline or drop"
                    " samples with missing values. See"
                    " https://scikit-learn..../stable/modules/impute.html"
                    " You can find a list of all estimators that handle NaN values"
                    " at the following page:"
                    " https://scikit-learn..../stable/modules/impute.html"
                    "#estimators-that-handle-nan-values"
                )
>           raise ValueError(msg_err)
E           ValueError: Input y contains NaN.

.../hostedtoolcache/Python/3.11.15................................................/x64/lib/python3.11.../sklearn/utils/validation.py:182: ValueError

test/selectors/test_tuner.py::test_tune_selector_with_preprocessing

Stack Traces | 0.41s run time

dummy_data = (         f1        f2        f3
0  0.087047 -0.299007  0.091761
1 -1.987569 -0.219672  0.357113
2  1.477894 -0.518270...33  0.107876  0.169948
7  0.081013  0.075428  0.027688
8  0.009773  0.227512  0.230677
9  0.100267  0.041405  0.042957)

    def test_tune_selector_with_preprocessing(dummy_data):
        """Test tune_selector with preprocessors."""
        features, performance, features_running_time = dummy_data
        preprocessors = [StandardScaler, MinMaxScaler]
    
        output_dir = tempfile.mkdtemp()
    
        try:
>           selector = tune_selector(
                features,
                performance,
                features_running_time=features_running_time,
                selector_class=[
                    (PairwiseClassifier, {"model_class": [SVMClassifierWrapper]}),
                    (PairwiseRegressor, {"model_class": [SVMRegressorWrapper]}),
                ],
                budget=10.0,
                runcount_limit=2,
                preprocessing_class=preprocessors,
                cv=2,
                seed=42,
                output_dir=output_dir,
            )

test/selectors/test_tuner.py:43: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
asf/selectors/selector_tuner.py:226: in tune_selector
    best_config = smac.optimize()
                  ^^^^^^^^^^^^^^^
.../hostedtoolcache/Python/3.11.15................................................/x64/lib/python3.11.../smac/facade/abstract_facade.py:331: in optimize
    incumbents = self._optimizer.optimize(data_to_scatter=data_to_scatter)
                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.../hostedtoolcache/Python/3.11.15................................................/x64/lib/python3.11.../smac/main/smbo.py:315: in optimize
    trial_info = self.ask()
                 ^^^^^^^^^^
.../hostedtoolcache/Python/3.11.15................................................/x64/lib/python3.11.../smac/main/smbo.py:162: in ask
    trial_info = next(self._trial_generator)
                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^
.../hostedtoolcache/Python/3.11.15................................................/x64/lib/python3.11.../smac/intensifier/intensifier.py:236: in __iter__
    config = next(self.config_generator)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^
.../hostedtoolcache/Python/3.11.15................................................/x64/lib/python3.11.../smac/main/config_selector.py:192: in __iter__
    self._model.train(X, Y)
.../hostedtoolcache/Python/3.11.15................................................/x64/lib/python3.11.../smac/model/abstract_model.py:152: in train
    return self._train(X, Y)
           ^^^^^^^^^^^^^^^^^
.../hostedtoolcache/Python/3.11.15................................................/x64/lib/python3.11.../model/random_forest/random_forest.py:749: in _train
    self._rf.fit(X, y)
.../hostedtoolcache/Python/3.11.15................................................/x64/lib/python3.11.../model/random_forest/random_forest.py:468: in fit
    super().fit(X=X, y=y, sample_weight=sample_weight)
.../hostedtoolcache/Python/3.11.15................................................/x64/lib/python3.11.../site-packages/sklearn/base.py:1336: in wrapper
    return fit_method(estimator, *args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.../hostedtoolcache/Python/3.11.15................................................/x64/lib/python3.11.../sklearn/ensemble/_forest.py:359: in fit
    X, y = validate_data(
.../hostedtoolcache/Python/3.11.15................................................/x64/lib/python3.11.../sklearn/utils/validation.py:2919: in validate_data
    X, y = check_X_y(X, y, **check_params)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.../hostedtoolcache/Python/3.11.15................................................/x64/lib/python3.11.../sklearn/utils/validation.py:1331: in check_X_y
    y = _check_y(y, multi_output=multi_output, y_numeric=y_numeric, estimator=estimator)
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.../hostedtoolcache/Python/3.11.15................................................/x64/lib/python3.11.../sklearn/utils/validation.py:1341: in _check_y
    y = check_array(
.../hostedtoolcache/Python/3.11.15................................................/x64/lib/python3.11.../sklearn/utils/validation.py:1074: in check_array
    _assert_all_finite(
.../hostedtoolcache/Python/3.11.15................................................/x64/lib/python3.11.../sklearn/utils/validation.py:133: in _assert_all_finite
    _assert_all_finite_element_wise(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

X = array([nan])

    def _assert_all_finite_element_wise(
        X, *, xp, allow_nan, msg_dtype=None, estimator_name=None, input_name=""
    ):
        # Cython implementation doesn't support FP16 or complex numbers
        use_cython = (
            xp is np and X.data.contiguous and X.dtype.type in {np.float32, np.float64}
        )
        if use_cython:
            out = cy_isfinite(X.reshape(-1), allow_nan=allow_nan)
            has_nan_error = False if allow_nan else out == FiniteStatus.has_nan
            has_inf = out == FiniteStatus.has_infinite
        else:
            has_inf = xp.any(xp.isinf(X))
            has_nan_error = False if allow_nan else xp.any(xp.isnan(X))
        if has_inf or has_nan_error:
            if has_nan_error:
                type_err = "NaN"
            else:
                msg_dtype = msg_dtype if msg_dtype is not None else X.dtype
                type_err = f"infinity or a value too large for {msg_dtype!r}"
            padded_input_name = input_name + " " if input_name else ""
            msg_err = f"Input {padded_input_name}contains {type_err}."
            if estimator_name and input_name == "X" and has_nan_error:
                # Improve the error message on how to handle missing values in
                # scikit-learn.
                msg_err += (
                    f"\n{estimator_name} does not accept missing values"
                    " encoded as NaN natively. For supervised learning, you might want"
                    " to consider sklearn.ensemble.HistGradientBoostingClassifier and"
                    " Regressor which accept missing values encoded as NaNs natively."
                    " Alternatively, it is possible to preprocess the data, for"
                    " instance by using an imputer transformer in a pipeline or drop"
                    " samples with missing values. See"
                    " https://scikit-learn..../stable/modules/impute.html"
                    " You can find a list of all estimators that handle NaN values"
                    " at the following page:"
                    " https://scikit-learn..../stable/modules/impute.html"
                    "#estimators-that-handle-nan-values"
                )
>           raise ValueError(msg_err)
E           ValueError: Input y contains NaN.

.../hostedtoolcache/Python/3.11.15................................................/x64/lib/python3.11.../sklearn/utils/validation.py:182: ValueError

test/selectors/test_selectors.py::test_selector_tuner

Stack Traces | 0.786s run time

dummy_performance =     algo1  algo2  algo3
0     800    250      5
1     200    800    130
2     120    170    500
3     160    150    70...15    480    470    100
16    100    540    560
17    530    100    330
18     25    250    610
19    580    200     10
dummy_features =     feature1  feature2  feature3
0       10.0       5.0       1.0
1       20.0      10.0       2.0
2       15.0       ...      45.0       9.0
17      95.0      48.0       9.5
18     100.0      50.0      10.0
19     105.0      52.0      10.5

    def test_selector_tuner(dummy_performance, dummy_features):
        # Keep runcount_limit and cv low for fast testing
        # Create dummy feature running time (same shape as features)
        features_running_time = pd.DataFrame(
            np.random.exponential(0.1, dummy_features.shape),
            columns=dummy_features.columns,
            index=dummy_features.index,
        )
    
>       tuned_pipeline = tune_selector(
            X=dummy_features,
            y=dummy_performance,
            features_running_time=features_running_time,
            selector_class=[PairwiseClassifier, PairwiseRegressor],
            runcount_limit=2,
            cv=2,
            seed=42,
            output_dir="./smac_test_output",  # Use a test-specific output dir
            smac_scenario_kwargs={},
            budget=450.0,
        )

test/selectors/test_selectors.py:363: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
asf/selectors/selector_tuner.py:226: in tune_selector
    best_config = smac.optimize()
                  ^^^^^^^^^^^^^^^
.../hostedtoolcache/Python/3.11.15................................................/x64/lib/python3.11.../smac/facade/abstract_facade.py:331: in optimize
    incumbents = self._optimizer.optimize(data_to_scatter=data_to_scatter)
                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.../hostedtoolcache/Python/3.11.15................................................/x64/lib/python3.11.../smac/main/smbo.py:315: in optimize
    trial_info = self.ask()
                 ^^^^^^^^^^
.../hostedtoolcache/Python/3.11.15................................................/x64/lib/python3.11.../smac/main/smbo.py:162: in ask
    trial_info = next(self._trial_generator)
                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^
.../hostedtoolcache/Python/3.11.15................................................/x64/lib/python3.11.../smac/intensifier/intensifier.py:236: in __iter__
    config = next(self.config_generator)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^
.../hostedtoolcache/Python/3.11.15................................................/x64/lib/python3.11.../smac/main/config_selector.py:192: in __iter__
    self._model.train(X, Y)
.../hostedtoolcache/Python/3.11.15................................................/x64/lib/python3.11.../smac/model/abstract_model.py:152: in train
    return self._train(X, Y)
           ^^^^^^^^^^^^^^^^^
.../hostedtoolcache/Python/3.11.15................................................/x64/lib/python3.11.../model/random_forest/random_forest.py:749: in _train
    self._rf.fit(X, y)
.../hostedtoolcache/Python/3.11.15................................................/x64/lib/python3.11.../model/random_forest/random_forest.py:468: in fit
    super().fit(X=X, y=y, sample_weight=sample_weight)
.../hostedtoolcache/Python/3.11.15................................................/x64/lib/python3.11.../site-packages/sklearn/base.py:1336: in wrapper
    return fit_method(estimator, *args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.../hostedtoolcache/Python/3.11.15................................................/x64/lib/python3.11.../sklearn/ensemble/_forest.py:359: in fit
    X, y = validate_data(
.../hostedtoolcache/Python/3.11.15................................................/x64/lib/python3.11.../sklearn/utils/validation.py:2919: in validate_data
    X, y = check_X_y(X, y, **check_params)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.../hostedtoolcache/Python/3.11.15................................................/x64/lib/python3.11.../sklearn/utils/validation.py:1331: in check_X_y
    y = _check_y(y, multi_output=multi_output, y_numeric=y_numeric, estimator=estimator)
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.../hostedtoolcache/Python/3.11.15................................................/x64/lib/python3.11.../sklearn/utils/validation.py:1341: in _check_y
    y = check_array(
.../hostedtoolcache/Python/3.11.15................................................/x64/lib/python3.11.../sklearn/utils/validation.py:1074: in check_array
    _assert_all_finite(
.../hostedtoolcache/Python/3.11.15................................................/x64/lib/python3.11.../sklearn/utils/validation.py:133: in _assert_all_finite
    _assert_all_finite_element_wise(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

X = array([nan])

    def _assert_all_finite_element_wise(
        X, *, xp, allow_nan, msg_dtype=None, estimator_name=None, input_name=""
    ):
        # Cython implementation doesn't support FP16 or complex numbers
        use_cython = (
            xp is np and X.data.contiguous and X.dtype.type in {np.float32, np.float64}
        )
        if use_cython:
            out = cy_isfinite(X.reshape(-1), allow_nan=allow_nan)
            has_nan_error = False if allow_nan else out == FiniteStatus.has_nan
            has_inf = out == FiniteStatus.has_infinite
        else:
            has_inf = xp.any(xp.isinf(X))
            has_nan_error = False if allow_nan else xp.any(xp.isnan(X))
        if has_inf or has_nan_error:
            if has_nan_error:
                type_err = "NaN"
            else:
                msg_dtype = msg_dtype if msg_dtype is not None else X.dtype
                type_err = f"infinity or a value too large for {msg_dtype!r}"
            padded_input_name = input_name + " " if input_name else ""
            msg_err = f"Input {padded_input_name}contains {type_err}."
            if estimator_name and input_name == "X" and has_nan_error:
                # Improve the error message on how to handle missing values in
                # scikit-learn.
                msg_err += (
                    f"\n{estimator_name} does not accept missing values"
                    " encoded as NaN natively. For supervised learning, you might want"
                    " to consider sklearn.ensemble.HistGradientBoostingClassifier and"
                    " Regressor which accept missing values encoded as NaNs natively."
                    " Alternatively, it is possible to preprocess the data, for"
                    " instance by using an imputer transformer in a pipeline or drop"
                    " samples with missing values. See"
                    " https://scikit-learn..../stable/modules/impute.html"
                    " You can find a list of all estimators that handle NaN values"
                    " at the following page:"
                    " https://scikit-learn..../stable/modules/impute.html"
                    "#estimators-that-handle-nan-values"
                )
>           raise ValueError(msg_err)
E           ValueError: Input y contains NaN.

.../hostedtoolcache/Python/3.11.15................................................/x64/lib/python3.11.../sklearn/utils/validation.py:182: ValueError

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