8000 Fix a regression in GridSearchCV for parameter grids that have arrays… · scikit-learn/scikit-learn@f2f4eeb · GitHub
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Fix a regression in GridSearchCV for parameter grids that have arrays of different sizes as parameter values
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‎doc/whats_new/v1.5.rst‎

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@@ -58,6 +58,9 @@ Changelog
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:mod:`sklearn.utils`
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....................
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- |Fix| Fix a regression in :class:`model_selection.GridSearchCV` for parameter
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grids that have arrays of different sizes as parameter values.
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:pr:`29314` by :user:`Marco Gorelli<MarcoGorelli>`.
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- |API| :func:`utils.validation.check_array` has a new parameter, `force_writeable`, to
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control the writeability of the output array. If set to `True`, the output array will

‎sklearn/model_selection/_search.py‎

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Original file line numberDiff line numberDiff line change
@@ -1086,36 +1086,33 @@ def _store(key_name, array, weights=None, splits=False, rank=False):
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for key, param_result in param_results.items():
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param_list = list(param_result.values())
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try:
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with warnings.catch_warnings():
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warnings.filterwarnings(
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"ignore",
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message="in the future the `.dtype` attribute",
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category=DeprecationWarning,
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)
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# Warning raised by NumPy 1.20+
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arr_dtype = np.result_type(*param_list)
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arr = np.array(param_list)
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except (TypeError, ValueError):
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arr_dtype = np.dtype(object)
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else:
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if any(np.min_scalar_type(x) == object for x in param_list):
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# `np.result_type` might get thrown off by `.dtype` properties
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# (which some estimators have).
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# If finding the result dtype this way would give object,
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# then we use object.
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# https://github.com/scikit-learn/scikit-learn/issues/29157
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arr_dtype = np.dtype(object)
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if len(param_list) == n_candidates and arr_dtype != object:
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# Exclude `object` else the numpy constructor might infer a list of
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# tuples to be a 2d array.
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results[key] = MaskedArray(param_list, mask=False, dtype=arr_dtype)
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else:
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# Use one MaskedArray and mask all the places where the param is not
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# applicable for that candidate (which may not contain all the params).
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ma = MaskedArray(np.empty(n_candidates), mask=True, dtype=arr_dtype)
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for index, value in param_result.items():
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# Setting the value at an index unmasks that index
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ma[index] = value
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results[key] = ma
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arr_dtype = arr.dtype if (arr.dtype.kind != "U") else object
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if len(param_list) == n_candidates:
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try:
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ma = MaskedArray(param_list, mask=False, dtype=arr_dtype)
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except ValueError:
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# Fall back to iterating over `param_result.items()` below
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pass
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else:
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if ma.ndim > 1:
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# If ndim > 1, then a list of tuples might be turned into
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# a 2D array, so we use the fallback below for that case too.
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arr_dtype = object
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else:
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results[key] = ma
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continue
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1109+
# Use one MaskedArray and mask all the places where the param is not
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# applicable for that candidate (which may not contain all the params).
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ma = MaskedArray(np.empty(n_candidates), mask=True, dtype=arr_dtype)
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for index, value in param_result.items():
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# Setting the value at an index unmasks that index
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ma[index] = value
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results[key] = ma
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# Store a list of param dicts at the key 'params'
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results["params"] = candidate_params

‎sklearn/model_selection/tests/test_search.py‎

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@@ -65,8 +65,13 @@
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from sklearn.model_selection.tests.common import OneTimeSplitter
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from sklearn.naive_bayes import ComplementNB
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from sklearn.neighbors import KernelDensity, KNeighborsClassifier, LocalOutlierFactor
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from sklearn.pipeline import Pipeline
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from sklearn.preprocessing import OneHotEncoder, OrdinalEncoder, StandardScaler
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from sklearn.pipeline import Pipeline, make_pipeline
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from sklearn.preprocessing import (
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OneHotEncoder,
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OrdinalEncoder,
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SplineTransformer,
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StandardScaler,
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)
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from sklearn.svm import SVC, LinearSVC
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from sklearn.tests.metadata_routing_common import (
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ConsumingScorer,
@@ -2724,6 +2729,39 @@ def test_search_with_estimators_issue_29157():
27242729
assert grid_search.cv_results_["param_enc__enc"].dtype == object
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def test_cv_results_multi_size_array_29277():
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x = np.linspace(-np.pi * 2, np.pi * 5, 1000)
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y_true = np.sin(x)
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y_train = y_true[(0 < x) & (x < np.pi * 2)]
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x_train = x[(0 < x) & (x < np.pi * 2)]
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y_train_noise = y_train + np.random.normal(size=y_train.shape, scale=0.5)
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x = x.reshape((-1, 1))
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x_train = x_train.reshape((-1, 1))
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2743+
spline_reg_pipe = make_pipeline(
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SplineTransformer(extrapolation="periodic"),
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LinearRegression(fit_intercept=False),
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)
2747+
2748+
spline_reg_pipe_cv = GridSearchCV(
2749+
estimator=spline_reg_pipe,
2750+
param_grid={
2751+
"splinetransformer__knots": [
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np.linspace(0, np.pi * 2, n_knots).reshape((-1, 1))
2753+
for n_knots in range(10, 21, 5)
2754+
],
2755+
},
2756+
verbose=1,
2757+
)
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2759+
spline_reg_pipe_cv.fit(X=x_train, y=y_train_noise)
2760+
assert (
2761+
spline_reg_pipe_cv.cv_results_["param_splinetransformer__knots"].dtype == object
2762+
)
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@pytest.mark.parametrize(
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"array_namespace, device, dtype", yield_namespace_device_dtype_combinations()
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)

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