959 lines
32 KiB
Python
959 lines
32 KiB
Python
"""
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Testing for the partial dependence module.
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"""
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import warnings
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import numpy as np
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import pytest
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import sklearn
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from sklearn.base import BaseEstimator, ClassifierMixin, clone, is_regressor
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from sklearn.cluster import KMeans
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from sklearn.compose import make_column_transformer
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from sklearn.datasets import load_iris, make_classification, make_regression
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from sklearn.dummy import DummyClassifier
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from sklearn.ensemble import (
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GradientBoostingClassifier,
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GradientBoostingRegressor,
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HistGradientBoostingClassifier,
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HistGradientBoostingRegressor,
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RandomForestRegressor,
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)
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from sklearn.exceptions import NotFittedError
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from sklearn.inspection import partial_dependence
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from sklearn.inspection._partial_dependence import (
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_grid_from_X,
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_partial_dependence_brute,
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_partial_dependence_recursion,
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)
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from sklearn.linear_model import LinearRegression, LogisticRegression, MultiTaskLasso
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from sklearn.metrics import r2_score
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from sklearn.pipeline import make_pipeline
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from sklearn.preprocessing import (
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PolynomialFeatures,
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RobustScaler,
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StandardScaler,
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scale,
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)
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from sklearn.tree import DecisionTreeRegressor
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from sklearn.tree.tests.test_tree import assert_is_subtree
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from sklearn.utils import _IS_32BIT
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from sklearn.utils._testing import assert_allclose, assert_array_equal
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from sklearn.utils.validation import check_random_state
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# toy sample
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X = [[-2, -1], [-1, -1], [-1, -2], [1, 1], [1, 2], [2, 1]]
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y = [-1, -1, -1, 1, 1, 1]
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# (X, y), n_targets <-- as expected in the output of partial_dep()
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binary_classification_data = (make_classification(n_samples=50, random_state=0), 1)
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multiclass_classification_data = (
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make_classification(
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n_samples=50, n_classes=3, n_clusters_per_class=1, random_state=0
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),
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3,
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)
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regression_data = (make_regression(n_samples=50, random_state=0), 1)
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multioutput_regression_data = (
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make_regression(n_samples=50, n_targets=2, random_state=0),
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2,
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)
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# iris
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iris = load_iris()
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@pytest.mark.parametrize(
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"Estimator, method, data",
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[
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(GradientBoostingClassifier, "auto", binary_classification_data),
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(GradientBoostingClassifier, "auto", multiclass_classification_data),
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(GradientBoostingClassifier, "brute", binary_classification_data),
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(GradientBoostingClassifier, "brute", multiclass_classification_data),
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(GradientBoostingRegressor, "auto", regression_data),
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(GradientBoostingRegressor, "brute", regression_data),
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(DecisionTreeRegressor, "brute", regression_data),
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(LinearRegression, "brute", regression_data),
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(LinearRegression, "brute", multioutput_regression_data),
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(LogisticRegression, "brute", binary_classification_data),
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(LogisticRegression, "brute", multiclass_classification_data),
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(MultiTaskLasso, "brute", multioutput_regression_data),
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],
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)
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@pytest.mark.parametrize("grid_resolution", (5, 10))
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@pytest.mark.parametrize("features", ([1], [1, 2]))
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@pytest.mark.parametrize("kind", ("average", "individual", "both"))
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def test_output_shape(Estimator, method, data, grid_resolution, features, kind):
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# Check that partial_dependence has consistent output shape for different
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# kinds of estimators:
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# - classifiers with binary and multiclass settings
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# - regressors
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# - multi-task regressors
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est = Estimator()
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if hasattr(est, "n_estimators"):
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est.set_params(n_estimators=2) # speed-up computations
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# n_target corresponds to the number of classes (1 for binary classif) or
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# the number of tasks / outputs in multi task settings. It's equal to 1 for
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# classical regression_data.
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(X, y), n_targets = data
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n_instances = X.shape[0]
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est.fit(X, y)
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result = partial_dependence(
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est,
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X=X,
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features=features,
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method=method,
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kind=kind,
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grid_resolution=grid_resolution,
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)
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pdp, axes = result, result["grid_values"]
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expected_pdp_shape = (n_targets, *[grid_resolution for _ in range(len(features))])
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expected_ice_shape = (
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n_targets,
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n_instances,
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*[grid_resolution for _ in range(len(features))],
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)
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if kind == "average":
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assert pdp.average.shape == expected_pdp_shape
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elif kind == "individual":
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assert pdp.individual.shape == expected_ice_shape
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else: # 'both'
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assert pdp.average.shape == expected_pdp_shape
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assert pdp.individual.shape == expected_ice_shape
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expected_axes_shape = (len(features), grid_resolution)
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assert axes is not None
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assert np.asarray(axes).shape == expected_axes_shape
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def test_grid_from_X():
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# tests for _grid_from_X: sanity check for output, and for shapes.
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# Make sure that the grid is a cartesian product of the input (it will use
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# the unique values instead of the percentiles)
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percentiles = (0.05, 0.95)
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grid_resolution = 100
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is_categorical = [False, False]
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X = np.asarray([[1, 2], [3, 4]])
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grid, axes = _grid_from_X(X, percentiles, is_categorical, grid_resolution)
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assert_array_equal(grid, [[1, 2], [1, 4], [3, 2], [3, 4]])
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assert_array_equal(axes, X.T)
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# test shapes of returned objects depending on the number of unique values
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# for a feature.
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rng = np.random.RandomState(0)
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grid_resolution = 15
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# n_unique_values > grid_resolution
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X = rng.normal(size=(20, 2))
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grid, axes = _grid_from_X(
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X, percentiles, is_categorical, grid_resolution=grid_resolution
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)
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assert grid.shape == (grid_resolution * grid_resolution, X.shape[1])
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assert np.asarray(axes).shape == (2, grid_resolution)
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# n_unique_values < grid_resolution, will use actual values
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n_unique_values = 12
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X[n_unique_values - 1 :, 0] = 12345
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rng.shuffle(X) # just to make sure the order is irrelevant
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grid, axes = _grid_from_X(
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X, percentiles, is_categorical, grid_resolution=grid_resolution
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)
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assert grid.shape == (n_unique_values * grid_resolution, X.shape[1])
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# axes is a list of arrays of different shapes
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assert axes[0].shape == (n_unique_values,)
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assert axes[1].shape == (grid_resolution,)
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@pytest.mark.parametrize(
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"grid_resolution",
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[
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2, # since n_categories > 2, we should not use quantiles resampling
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100,
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],
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)
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def test_grid_from_X_with_categorical(grid_resolution):
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"""Check that `_grid_from_X` always sample from categories and does not
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depend from the percentiles.
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"""
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pd = pytest.importorskip("pandas")
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percentiles = (0.05, 0.95)
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is_categorical = [True]
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X = pd.DataFrame({"cat_feature": ["A", "B", "C", "A", "B", "D", "E"]})
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grid, axes = _grid_from_X(
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X, percentiles, is_categorical, grid_resolution=grid_resolution
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)
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assert grid.shape == (5, X.shape[1])
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assert axes[0].shape == (5,)
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@pytest.mark.parametrize("grid_resolution", [3, 100])
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def test_grid_from_X_heterogeneous_type(grid_resolution):
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"""Check that `_grid_from_X` always sample from categories and does not
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depend from the percentiles.
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"""
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pd = pytest.importorskip("pandas")
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percentiles = (0.05, 0.95)
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is_categorical = [True, False]
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X = pd.DataFrame(
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{
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"cat": ["A", "B", "C", "A", "B", "D", "E", "A", "B", "D"],
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"num": [1, 1, 1, 2, 5, 6, 6, 6, 6, 8],
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}
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)
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nunique = X.nunique()
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grid, axes = _grid_from_X(
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X, percentiles, is_categorical, grid_resolution=grid_resolution
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)
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if grid_resolution == 3:
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assert grid.shape == (15, 2)
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assert axes[0].shape[0] == nunique["num"]
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assert axes[1].shape[0] == grid_resolution
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else:
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assert grid.shape == (25, 2)
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assert axes[0].shape[0] == nunique["cat"]
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assert axes[1].shape[0] == nunique["cat"]
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@pytest.mark.parametrize(
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"grid_resolution, percentiles, err_msg",
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[
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(2, (0, 0.0001), "percentiles are too close"),
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(100, (1, 2, 3, 4), "'percentiles' must be a sequence of 2 elements"),
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(100, 12345, "'percentiles' must be a sequence of 2 elements"),
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(100, (-1, 0.95), r"'percentiles' values must be in \[0, 1\]"),
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(100, (0.05, 2), r"'percentiles' values must be in \[0, 1\]"),
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(100, (0.9, 0.1), r"percentiles\[0\] must be strictly less than"),
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(1, (0.05, 0.95), "'grid_resolution' must be strictly greater than 1"),
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],
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)
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def test_grid_from_X_error(grid_resolution, percentiles, err_msg):
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X = np.asarray([[1, 2], [3, 4]])
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is_categorical = [False]
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with pytest.raises(ValueError, match=err_msg):
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_grid_from_X(X, percentiles, is_categorical, grid_resolution)
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@pytest.mark.parametrize("target_feature", range(5))
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@pytest.mark.parametrize(
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"est, method",
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[
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(LinearRegression(), "brute"),
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(GradientBoostingRegressor(random_state=0), "brute"),
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(GradientBoostingRegressor(random_state=0), "recursion"),
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(HistGradientBoostingRegressor(random_state=0), "brute"),
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(HistGradientBoostingRegressor(random_state=0), "recursion"),
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],
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)
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def test_partial_dependence_helpers(est, method, target_feature):
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# Check that what is returned by _partial_dependence_brute or
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# _partial_dependence_recursion is equivalent to manually setting a target
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# feature to a given value, and computing the average prediction over all
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# samples.
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# This also checks that the brute and recursion methods give the same
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# output.
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# Note that even on the trainset, the brute and the recursion methods
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# aren't always strictly equivalent, in particular when the slow method
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# generates unrealistic samples that have low mass in the joint
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# distribution of the input features, and when some of the features are
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# dependent. Hence the high tolerance on the checks.
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X, y = make_regression(random_state=0, n_features=5, n_informative=5)
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# The 'init' estimator for GBDT (here the average prediction) isn't taken
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# into account with the recursion method, for technical reasons. We set
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# the mean to 0 to that this 'bug' doesn't have any effect.
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y = y - y.mean()
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est.fit(X, y)
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# target feature will be set to .5 and then to 123
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features = np.array([target_feature], dtype=np.int32)
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grid = np.array([[0.5], [123]])
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if method == "brute":
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pdp, predictions = _partial_dependence_brute(
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est, grid, features, X, response_method="auto"
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)
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else:
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pdp = _partial_dependence_recursion(est, grid, features)
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mean_predictions = []
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for val in (0.5, 123):
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X_ = X.copy()
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X_[:, target_feature] = val
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mean_predictions.append(est.predict(X_).mean())
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pdp = pdp[0] # (shape is (1, 2) so make it (2,))
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# allow for greater margin for error with recursion method
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rtol = 1e-1 if method == "recursion" else 1e-3
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assert np.allclose(pdp, mean_predictions, rtol=rtol)
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@pytest.mark.parametrize("seed", range(1))
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def test_recursion_decision_tree_vs_forest_and_gbdt(seed):
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# Make sure that the recursion method gives the same results on a
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# DecisionTreeRegressor and a GradientBoostingRegressor or a
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# RandomForestRegressor with 1 tree and equivalent parameters.
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rng = np.random.RandomState(seed)
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# Purely random dataset to avoid correlated features
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n_samples = 1000
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n_features = 5
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X = rng.randn(n_samples, n_features)
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y = rng.randn(n_samples) * 10
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# The 'init' estimator for GBDT (here the average prediction) isn't taken
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# into account with the recursion method, for technical reasons. We set
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# the mean to 0 to that this 'bug' doesn't have any effect.
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y = y - y.mean()
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# set max_depth not too high to avoid splits with same gain but different
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# features
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max_depth = 5
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tree_seed = 0
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forest = RandomForestRegressor(
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n_estimators=1,
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max_features=None,
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bootstrap=False,
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max_depth=max_depth,
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random_state=tree_seed,
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)
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# The forest will use ensemble.base._set_random_states to set the
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# random_state of the tree sub-estimator. We simulate this here to have
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# equivalent estimators.
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equiv_random_state = check_random_state(tree_seed).randint(np.iinfo(np.int32).max)
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gbdt = GradientBoostingRegressor(
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n_estimators=1,
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learning_rate=1,
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criterion="squared_error",
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max_depth=max_depth,
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random_state=equiv_random_state,
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)
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tree = DecisionTreeRegressor(max_depth=max_depth, random_state=equiv_random_state)
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forest.fit(X, y)
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gbdt.fit(X, y)
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tree.fit(X, y)
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# sanity check: if the trees aren't the same, the PD values won't be equal
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try:
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assert_is_subtree(tree.tree_, gbdt[0, 0].tree_)
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assert_is_subtree(tree.tree_, forest[0].tree_)
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except AssertionError:
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# For some reason the trees aren't exactly equal on 32bits, so the PDs
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# cannot be equal either. See
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# https://github.com/scikit-learn/scikit-learn/issues/8853
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assert _IS_32BIT, "this should only fail on 32 bit platforms"
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return
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grid = rng.randn(50).reshape(-1, 1)
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for f in range(n_features):
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features = np.array([f], dtype=np.int32)
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pdp_forest = _partial_dependence_recursion(forest, grid, features)
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pdp_gbdt = _partial_dependence_recursion(gbdt, grid, features)
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pdp_tree = _partial_dependence_recursion(tree, grid, features)
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np.testing.assert_allclose(pdp_gbdt, pdp_tree)
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np.testing.assert_allclose(pdp_forest, pdp_tree)
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@pytest.mark.parametrize(
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"est",
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(
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GradientBoostingClassifier(random_state=0),
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HistGradientBoostingClassifier(random_state=0),
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),
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)
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@pytest.mark.parametrize("target_feature", (0, 1, 2, 3, 4, 5))
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def test_recursion_decision_function(est, target_feature):
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# Make sure the recursion method (implicitly uses decision_function) has
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# the same result as using brute method with
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# response_method=decision_function
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X, y = make_classification(n_classes=2, n_clusters_per_class=1, random_state=1)
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assert np.mean(y) == 0.5 # make sure the init estimator predicts 0 anyway
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est.fit(X, y)
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preds_1 = partial_dependence(
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est,
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X,
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[target_feature],
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response_method="decision_function",
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method="recursion",
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kind="average",
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)
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preds_2 = partial_dependence(
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est,
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X,
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[target_feature],
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response_method="decision_function",
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method="brute",
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kind="average",
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)
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assert_allclose(preds_1["average"], preds_2["average"], atol=1e-7)
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@pytest.mark.parametrize(
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"est",
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(
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LinearRegression(),
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GradientBoostingRegressor(random_state=0),
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HistGradientBoostingRegressor(
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random_state=0, min_samples_leaf=1, max_leaf_nodes=None, max_iter=1
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),
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DecisionTreeRegressor(random_state=0),
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),
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)
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@pytest.mark.parametrize("power", (1, 2))
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def test_partial_dependence_easy_target(est, power):
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# If the target y only depends on one feature in an obvious way (linear or
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# quadratic) then the partial dependence for that feature should reflect
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# it.
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# We here fit a linear regression_data model (with polynomial features if
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# needed) and compute r_squared to check that the partial dependence
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# correctly reflects the target.
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rng = np.random.RandomState(0)
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n_samples = 200
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target_variable = 2
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X = rng.normal(size=(n_samples, 5))
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y = X[:, target_variable] ** power
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est.fit(X, y)
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pdp = partial_dependence(
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est, features=[target_variable], X=X, grid_resolution=1000, kind="average"
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)
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new_X = pdp["grid_values"][0].reshape(-1, 1)
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new_y = pdp["average"][0]
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# add polynomial features if needed
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new_X = PolynomialFeatures(degree=power).fit_transform(new_X)
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lr = LinearRegression().fit(new_X, new_y)
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r2 = r2_score(new_y, lr.predict(new_X))
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assert r2 > 0.99
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@pytest.mark.parametrize(
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"Estimator",
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(
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sklearn.tree.DecisionTreeClassifier,
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sklearn.tree.ExtraTreeClassifier,
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sklearn.ensemble.ExtraTreesClassifier,
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sklearn.neighbors.KNeighborsClassifier,
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sklearn.neighbors.RadiusNeighborsClassifier,
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sklearn.ensemble.RandomForestClassifier,
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),
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)
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def test_multiclass_multioutput(Estimator):
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# Make sure error is raised for multiclass-multioutput classifiers
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# make multiclass-multioutput dataset
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X, y = make_classification(n_classes=3, n_clusters_per_class=1, random_state=0)
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y = np.array([y, y]).T
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|
|
est = Estimator()
|
|
est.fit(X, y)
|
|
|
|
with pytest.raises(
|
|
ValueError, match="Multiclass-multioutput estimators are not supported"
|
|
):
|
|
partial_dependence(est, X, [0])
|
|
|
|
|
|
class NoPredictProbaNoDecisionFunction(ClassifierMixin, BaseEstimator):
|
|
def fit(self, X, y):
|
|
# simulate that we have some classes
|
|
self.classes_ = [0, 1]
|
|
return self
|
|
|
|
|
|
@pytest.mark.filterwarnings("ignore:A Bunch will be returned")
|
|
@pytest.mark.parametrize(
|
|
"estimator, params, err_msg",
|
|
[
|
|
(
|
|
KMeans(random_state=0, n_init="auto"),
|
|
{"features": [0]},
|
|
"'estimator' must be a fitted regressor or classifier",
|
|
),
|
|
(
|
|
LinearRegression(),
|
|
{"features": [0], "response_method": "predict_proba"},
|
|
"The response_method parameter is ignored for regressors",
|
|
),
|
|
(
|
|
GradientBoostingClassifier(random_state=0),
|
|
{
|
|
"features": [0],
|
|
"response_method": "predict_proba",
|
|
"method": "recursion",
|
|
},
|
|
"'recursion' method, the response_method must be 'decision_function'",
|
|
),
|
|
(
|
|
GradientBoostingClassifier(random_state=0),
|
|
{"features": [0], "response_method": "predict_proba", "method": "auto"},
|
|
"'recursion' method, the response_method must be 'decision_function'",
|
|
),
|
|
(
|
|
LinearRegression(),
|
|
{"features": [0], "method": "recursion", "kind": "individual"},
|
|
"The 'recursion' method only applies when 'kind' is set to 'average'",
|
|
),
|
|
(
|
|
LinearRegression(),
|
|
{"features": [0], "method": "recursion", "kind": "both"},
|
|
"The 'recursion' method only applies when 'kind' is set to 'average'",
|
|
),
|
|
(
|
|
LinearRegression(),
|
|
{"features": [0], "method": "recursion"},
|
|
"Only the following estimators support the 'recursion' method:",
|
|
),
|
|
],
|
|
)
|
|
def test_partial_dependence_error(estimator, params, err_msg):
|
|
X, y = make_classification(random_state=0)
|
|
estimator.fit(X, y)
|
|
|
|
with pytest.raises(ValueError, match=err_msg):
|
|
partial_dependence(estimator, X, **params)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"estimator", [LinearRegression(), GradientBoostingClassifier(random_state=0)]
|
|
)
|
|
@pytest.mark.parametrize("features", [-1, 10000])
|
|
def test_partial_dependence_unknown_feature_indices(estimator, features):
|
|
X, y = make_classification(random_state=0)
|
|
estimator.fit(X, y)
|
|
|
|
err_msg = "all features must be in"
|
|
with pytest.raises(ValueError, match=err_msg):
|
|
partial_dependence(estimator, X, [features])
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"estimator", [LinearRegression(), GradientBoostingClassifier(random_state=0)]
|
|
)
|
|
def test_partial_dependence_unknown_feature_string(estimator):
|
|
pd = pytest.importorskip("pandas")
|
|
X, y = make_classification(random_state=0)
|
|
df = pd.DataFrame(X)
|
|
estimator.fit(df, y)
|
|
|
|
features = ["random"]
|
|
err_msg = "A given column is not a column of the dataframe"
|
|
with pytest.raises(ValueError, match=err_msg):
|
|
partial_dependence(estimator, df, features)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"estimator", [LinearRegression(), GradientBoostingClassifier(random_state=0)]
|
|
)
|
|
def test_partial_dependence_X_list(estimator):
|
|
# check that array-like objects are accepted
|
|
X, y = make_classification(random_state=0)
|
|
estimator.fit(X, y)
|
|
partial_dependence(estimator, list(X), [0], kind="average")
|
|
|
|
|
|
def test_warning_recursion_non_constant_init():
|
|
# make sure that passing a non-constant init parameter to a GBDT and using
|
|
# recursion method yields a warning.
|
|
|
|
gbc = GradientBoostingClassifier(init=DummyClassifier(), random_state=0)
|
|
gbc.fit(X, y)
|
|
|
|
with pytest.warns(
|
|
UserWarning, match="Using recursion method with a non-constant init predictor"
|
|
):
|
|
partial_dependence(gbc, X, [0], method="recursion", kind="average")
|
|
|
|
with pytest.warns(
|
|
UserWarning, match="Using recursion method with a non-constant init predictor"
|
|
):
|
|
partial_dependence(gbc, X, [0], method="recursion", kind="average")
|
|
|
|
|
|
def test_partial_dependence_sample_weight_of_fitted_estimator():
|
|
# Test near perfect correlation between partial dependence and diagonal
|
|
# when sample weights emphasize y = x predictions
|
|
# non-regression test for #13193
|
|
# TODO: extend to HistGradientBoosting once sample_weight is supported
|
|
N = 1000
|
|
rng = np.random.RandomState(123456)
|
|
mask = rng.randint(2, size=N, dtype=bool)
|
|
|
|
x = rng.rand(N)
|
|
# set y = x on mask and y = -x outside
|
|
y = x.copy()
|
|
y[~mask] = -y[~mask]
|
|
X = np.c_[mask, x]
|
|
# sample weights to emphasize data points where y = x
|
|
sample_weight = np.ones(N)
|
|
sample_weight[mask] = 1000.0
|
|
|
|
clf = GradientBoostingRegressor(n_estimators=10, random_state=1)
|
|
clf.fit(X, y, sample_weight=sample_weight)
|
|
|
|
pdp = partial_dependence(clf, X, features=[1], kind="average")
|
|
|
|
assert np.corrcoef(pdp["average"], pdp["grid_values"])[0, 1] > 0.99
|
|
|
|
|
|
def test_hist_gbdt_sw_not_supported():
|
|
# TODO: remove/fix when PDP supports HGBT with sample weights
|
|
clf = HistGradientBoostingRegressor(random_state=1)
|
|
clf.fit(X, y, sample_weight=np.ones(len(X)))
|
|
|
|
with pytest.raises(
|
|
NotImplementedError, match="does not support partial dependence"
|
|
):
|
|
partial_dependence(clf, X, features=[1])
|
|
|
|
|
|
def test_partial_dependence_pipeline():
|
|
# check that the partial dependence support pipeline
|
|
iris = load_iris()
|
|
|
|
scaler = StandardScaler()
|
|
clf = DummyClassifier(random_state=42)
|
|
pipe = make_pipeline(scaler, clf)
|
|
|
|
clf.fit(scaler.fit_transform(iris.data), iris.target)
|
|
pipe.fit(iris.data, iris.target)
|
|
|
|
features = 0
|
|
pdp_pipe = partial_dependence(
|
|
pipe, iris.data, features=[features], grid_resolution=10, kind="average"
|
|
)
|
|
pdp_clf = partial_dependence(
|
|
clf,
|
|
scaler.transform(iris.data),
|
|
features=[features],
|
|
grid_resolution=10,
|
|
kind="average",
|
|
)
|
|
assert_allclose(pdp_pipe["average"], pdp_clf["average"])
|
|
assert_allclose(
|
|
pdp_pipe["grid_values"][0],
|
|
pdp_clf["grid_values"][0] * scaler.scale_[features] + scaler.mean_[features],
|
|
)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"estimator",
|
|
[
|
|
LogisticRegression(max_iter=1000, random_state=0),
|
|
GradientBoostingClassifier(random_state=0, n_estimators=5),
|
|
],
|
|
ids=["estimator-brute", "estimator-recursion"],
|
|
)
|
|
@pytest.mark.parametrize(
|
|
"preprocessor",
|
|
[
|
|
None,
|
|
make_column_transformer(
|
|
(StandardScaler(), [iris.feature_names[i] for i in (0, 2)]),
|
|
(RobustScaler(), [iris.feature_names[i] for i in (1, 3)]),
|
|
),
|
|
make_column_transformer(
|
|
(StandardScaler(), [iris.feature_names[i] for i in (0, 2)]),
|
|
remainder="passthrough",
|
|
),
|
|
],
|
|
ids=["None", "column-transformer", "column-transformer-passthrough"],
|
|
)
|
|
@pytest.mark.parametrize(
|
|
"features",
|
|
[[0, 2], [iris.feature_names[i] for i in (0, 2)]],
|
|
ids=["features-integer", "features-string"],
|
|
)
|
|
def test_partial_dependence_dataframe(estimator, preprocessor, features):
|
|
# check that the partial dependence support dataframe and pipeline
|
|
# including a column transformer
|
|
pd = pytest.importorskip("pandas")
|
|
df = pd.DataFrame(scale(iris.data), columns=iris.feature_names)
|
|
|
|
pipe = make_pipeline(preprocessor, estimator)
|
|
pipe.fit(df, iris.target)
|
|
pdp_pipe = partial_dependence(
|
|
pipe, df, features=features, grid_resolution=10, kind="average"
|
|
)
|
|
|
|
# the column transformer will reorder the column when transforming
|
|
# we mixed the index to be sure that we are computing the partial
|
|
# dependence of the right columns
|
|
if preprocessor is not None:
|
|
X_proc = clone(preprocessor).fit_transform(df)
|
|
features_clf = [0, 1]
|
|
else:
|
|
X_proc = df
|
|
features_clf = [0, 2]
|
|
|
|
clf = clone(estimator).fit(X_proc, iris.target)
|
|
pdp_clf = partial_dependence(
|
|
clf,
|
|
X_proc,
|
|
features=features_clf,
|
|
method="brute",
|
|
grid_resolution=10,
|
|
kind="average",
|
|
)
|
|
|
|
assert_allclose(pdp_pipe["average"], pdp_clf["average"])
|
|
if preprocessor is not None:
|
|
scaler = preprocessor.named_transformers_["standardscaler"]
|
|
assert_allclose(
|
|
pdp_pipe["grid_values"][1],
|
|
pdp_clf["grid_values"][1] * scaler.scale_[1] + scaler.mean_[1],
|
|
)
|
|
else:
|
|
assert_allclose(pdp_pipe["grid_values"][1], pdp_clf["grid_values"][1])
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"features, expected_pd_shape",
|
|
[
|
|
(0, (3, 10)),
|
|
(iris.feature_names[0], (3, 10)),
|
|
([0, 2], (3, 10, 10)),
|
|
([iris.feature_names[i] for i in (0, 2)], (3, 10, 10)),
|
|
([True, False, True, False], (3, 10, 10)),
|
|
],
|
|
ids=["scalar-int", "scalar-str", "list-int", "list-str", "mask"],
|
|
)
|
|
def test_partial_dependence_feature_type(features, expected_pd_shape):
|
|
# check all possible features type supported in PDP
|
|
pd = pytest.importorskip("pandas")
|
|
df = pd.DataFrame(iris.data, columns=iris.feature_names)
|
|
|
|
preprocessor = make_column_transformer(
|
|
(StandardScaler(), [iris.feature_names[i] for i in (0, 2)]),
|
|
(RobustScaler(), [iris.feature_names[i] for i in (1, 3)]),
|
|
)
|
|
pipe = make_pipeline(
|
|
preprocessor, LogisticRegression(max_iter=1000, random_state=0)
|
|
)
|
|
pipe.fit(df, iris.target)
|
|
pdp_pipe = partial_dependence(
|
|
pipe, df, features=features, grid_resolution=10, kind="average"
|
|
)
|
|
assert pdp_pipe["average"].shape == expected_pd_shape
|
|
assert len(pdp_pipe["grid_values"]) == len(pdp_pipe["average"].shape) - 1
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"estimator",
|
|
[
|
|
LinearRegression(),
|
|
LogisticRegression(),
|
|
GradientBoostingRegressor(),
|
|
GradientBoostingClassifier(),
|
|
],
|
|
)
|
|
def test_partial_dependence_unfitted(estimator):
|
|
X = iris.data
|
|
preprocessor = make_column_transformer(
|
|
(StandardScaler(), [0, 2]), (RobustScaler(), [1, 3])
|
|
)
|
|
pipe = make_pipeline(preprocessor, estimator)
|
|
with pytest.raises(NotFittedError, match="is not fitted yet"):
|
|
partial_dependence(pipe, X, features=[0, 2], grid_resolution=10)
|
|
with pytest.raises(NotFittedError, match="is not fitted yet"):
|
|
partial_dependence(estimator, X, features=[0, 2], grid_resolution=10)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"Estimator, data",
|
|
[
|
|
(LinearRegression, multioutput_regression_data),
|
|
(LogisticRegression, binary_classification_data),
|
|
],
|
|
)
|
|
def test_kind_average_and_average_of_individual(Estimator, data):
|
|
est = Estimator()
|
|
(X, y), n_targets = data
|
|
est.fit(X, y)
|
|
|
|
pdp_avg = partial_dependence(est, X=X, features=[1, 2], kind="average")
|
|
pdp_ind = partial_dependence(est, X=X, features=[1, 2], kind="individual")
|
|
avg_ind = np.mean(pdp_ind["individual"], axis=1)
|
|
assert_allclose(avg_ind, pdp_avg["average"])
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"Estimator, data",
|
|
[
|
|
(LinearRegression, multioutput_regression_data),
|
|
(LogisticRegression, binary_classification_data),
|
|
],
|
|
)
|
|
def test_partial_dependence_kind_individual_ignores_sample_weight(Estimator, data):
|
|
"""Check that `sample_weight` does not have any effect on reported ICE."""
|
|
est = Estimator()
|
|
(X, y), n_targets = data
|
|
sample_weight = np.arange(X.shape[0])
|
|
est.fit(X, y)
|
|
|
|
pdp_nsw = partial_dependence(est, X=X, features=[1, 2], kind="individual")
|
|
pdp_sw = partial_dependence(
|
|
est, X=X, features=[1, 2], kind="individual", sample_weight=sample_weight
|
|
)
|
|
assert_allclose(pdp_nsw["individual"], pdp_sw["individual"])
|
|
assert_allclose(pdp_nsw["grid_values"], pdp_sw["grid_values"])
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"estimator",
|
|
[
|
|
LinearRegression(),
|
|
LogisticRegression(),
|
|
RandomForestRegressor(),
|
|
GradientBoostingClassifier(),
|
|
],
|
|
)
|
|
@pytest.mark.parametrize("non_null_weight_idx", [0, 1, -1])
|
|
def test_partial_dependence_non_null_weight_idx(estimator, non_null_weight_idx):
|
|
"""Check that if we pass a `sample_weight` of zeros with only one index with
|
|
sample weight equals one, then the average `partial_dependence` with this
|
|
`sample_weight` is equal to the individual `partial_dependence` of the
|
|
corresponding index.
|
|
"""
|
|
X, y = iris.data, iris.target
|
|
preprocessor = make_column_transformer(
|
|
(StandardScaler(), [0, 2]), (RobustScaler(), [1, 3])
|
|
)
|
|
pipe = make_pipeline(preprocessor, estimator).fit(X, y)
|
|
|
|
sample_weight = np.zeros_like(y)
|
|
sample_weight[non_null_weight_idx] = 1
|
|
pdp_sw = partial_dependence(
|
|
pipe,
|
|
X,
|
|
[2, 3],
|
|
kind="average",
|
|
sample_weight=sample_weight,
|
|
grid_resolution=10,
|
|
)
|
|
pdp_ind = partial_dependence(pipe, X, [2, 3], kind="individual", grid_resolution=10)
|
|
output_dim = 1 if is_regressor(pipe) else len(np.unique(y))
|
|
for i in range(output_dim):
|
|
assert_allclose(
|
|
pdp_ind["individual"][i][non_null_weight_idx],
|
|
pdp_sw["average"][i],
|
|
)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"Estimator, data",
|
|
[
|
|
(LinearRegression, multioutput_regression_data),
|
|
(LogisticRegression, binary_classification_data),
|
|
],
|
|
)
|
|
def test_partial_dependence_equivalence_equal_sample_weight(Estimator, data):
|
|
"""Check that `sample_weight=None` is equivalent to having equal weights."""
|
|
|
|
est = Estimator()
|
|
(X, y), n_targets = data
|
|
est.fit(X, y)
|
|
|
|
sample_weight, params = None, {"X": X, "features": [1, 2], "kind": "average"}
|
|
pdp_sw_none = partial_dependence(est, **params, sample_weight=sample_weight)
|
|
sample_weight = np.ones(len(y))
|
|
pdp_sw_unit = partial_dependence(est, **params, sample_weight=sample_weight)
|
|
assert_allclose(pdp_sw_none["average"], pdp_sw_unit["average"])
|
|
sample_weight = 2 * np.ones(len(y))
|
|
pdp_sw_doubling = partial_dependence(est, **params, sample_weight=sample_weight)
|
|
assert_allclose(pdp_sw_none["average"], pdp_sw_doubling["average"])
|
|
|
|
|
|
def test_partial_dependence_sample_weight_size_error():
|
|
"""Check that we raise an error when the size of `sample_weight` is not
|
|
consistent with `X` and `y`.
|
|
"""
|
|
est = LogisticRegression()
|
|
(X, y), n_targets = binary_classification_data
|
|
sample_weight = np.ones_like(y)
|
|
est.fit(X, y)
|
|
|
|
with pytest.raises(ValueError, match="sample_weight.shape =="):
|
|
partial_dependence(
|
|
est, X, features=[0], sample_weight=sample_weight[1:], grid_resolution=10
|
|
)
|
|
|
|
|
|
def test_partial_dependence_sample_weight_with_recursion():
|
|
"""Check that we raise an error when `sample_weight` is provided with
|
|
`"recursion"` method.
|
|
"""
|
|
est = RandomForestRegressor()
|
|
(X, y), n_targets = regression_data
|
|
sample_weight = np.ones_like(y)
|
|
est.fit(X, y, sample_weight=sample_weight)
|
|
|
|
with pytest.raises(ValueError, match="'recursion' method can only be applied when"):
|
|
partial_dependence(
|
|
est, X, features=[0], method="recursion", sample_weight=sample_weight
|
|
)
|
|
|
|
|
|
# TODO(1.5): Remove when bunch values is deprecated in 1.5
|
|
def test_partial_dependence_bunch_values_deprecated():
|
|
"""Test that deprecation warning is raised when values is accessed."""
|
|
|
|
est = LogisticRegression()
|
|
(X, y), _ = binary_classification_data
|
|
est.fit(X, y)
|
|
|
|
pdp_avg = partial_dependence(est, X=X, features=[1, 2], kind="average")
|
|
|
|
msg = (
|
|
"Key: 'values', is deprecated in 1.3 and will be "
|
|
"removed in 1.5. Please use 'grid_values' instead"
|
|
)
|
|
|
|
with warnings.catch_warnings():
|
|
# Does not raise warnings with "grid_values"
|
|
warnings.simplefilter("error", FutureWarning)
|
|
grid_values = pdp_avg["grid_values"]
|
|
|
|
with pytest.warns(FutureWarning, match=msg):
|
|
# Warns for "values"
|
|
values = pdp_avg["values"]
|
|
|
|
# "values" and "grid_values" are the same object
|
|
assert values is grid_values
|
|
|
|
|
|
def test_mixed_type_categorical():
|
|
"""Check that we raise a proper error when a column has mixed types and
|
|
the sorting of `np.unique` will fail."""
|
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X = np.array(["A", "B", "C", np.nan], dtype=object).reshape(-1, 1)
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y = np.array([0, 1, 0, 1])
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|
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from sklearn.preprocessing import OrdinalEncoder
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|
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clf = make_pipeline(
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OrdinalEncoder(encoded_missing_value=-1),
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LogisticRegression(),
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).fit(X, y)
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with pytest.raises(ValueError, match="The column #0 contains mixed data types"):
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partial_dependence(clf, X, features=[0])
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