591 lines
23 KiB
Python
591 lines
23 KiB
Python
import numpy as np
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from scipy.stats.mstats import mquantiles
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import pytest
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from numpy.testing import assert_allclose
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from sklearn.datasets import load_diabetes
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from sklearn.datasets import load_iris
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from sklearn.datasets import make_classification, make_regression
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from sklearn.ensemble import GradientBoostingRegressor
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from sklearn.ensemble import GradientBoostingClassifier
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from sklearn.linear_model import LinearRegression
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from sklearn.utils._testing import _convert_container
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from sklearn.inspection import plot_partial_dependence
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# TODO: Remove when https://github.com/numpy/numpy/issues/14397 is resolved
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pytestmark = pytest.mark.filterwarnings(
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"ignore:In future, it will be an error for 'np.bool_':DeprecationWarning:"
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"matplotlib.*")
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@pytest.fixture(scope="module")
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def diabetes():
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return load_diabetes()
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@pytest.fixture(scope="module")
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def clf_diabetes(diabetes):
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clf = GradientBoostingRegressor(n_estimators=10, random_state=1)
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clf.fit(diabetes.data, diabetes.target)
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return clf
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@pytest.mark.filterwarnings("ignore:A Bunch will be returned")
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@pytest.mark.parametrize("grid_resolution", [10, 20])
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def test_plot_partial_dependence(grid_resolution, pyplot, clf_diabetes,
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diabetes):
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# Test partial dependence plot function.
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# Use columns 0 & 2 as 1 is not quantitative (sex)
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feature_names = diabetes.feature_names
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disp = plot_partial_dependence(clf_diabetes, diabetes.data,
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[0, 2, (0, 2)],
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grid_resolution=grid_resolution,
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feature_names=feature_names,
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contour_kw={"cmap": "jet"})
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fig = pyplot.gcf()
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axs = fig.get_axes()
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assert disp.figure_ is fig
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assert len(axs) == 4
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assert disp.bounding_ax_ is not None
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assert disp.axes_.shape == (1, 3)
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assert disp.lines_.shape == (1, 3)
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assert disp.contours_.shape == (1, 3)
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assert disp.deciles_vlines_.shape == (1, 3)
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assert disp.deciles_hlines_.shape == (1, 3)
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assert disp.lines_[0, 2] is None
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assert disp.contours_[0, 0] is None
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assert disp.contours_[0, 1] is None
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# deciles lines: always show on xaxis, only show on yaxis if 2-way PDP
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for i in range(3):
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assert disp.deciles_vlines_[0, i] is not None
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assert disp.deciles_hlines_[0, 0] is None
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assert disp.deciles_hlines_[0, 1] is None
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assert disp.deciles_hlines_[0, 2] is not None
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assert disp.features == [(0, ), (2, ), (0, 2)]
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assert np.all(disp.feature_names == feature_names)
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assert len(disp.deciles) == 2
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for i in [0, 2]:
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assert_allclose(disp.deciles[i],
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mquantiles(diabetes.data[:, i],
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prob=np.arange(0.1, 1.0, 0.1)))
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single_feature_positions = [(0, (0, 0)), (2, (0, 1))]
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expected_ylabels = ["Partial dependence", ""]
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for i, (feat_col, pos) in enumerate(single_feature_positions):
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ax = disp.axes_[pos]
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assert ax.get_ylabel() == expected_ylabels[i]
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assert ax.get_xlabel() == diabetes.feature_names[feat_col]
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assert_allclose(ax.get_ylim(), disp.pdp_lim[1])
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line = disp.lines_[pos]
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avg_preds = disp.pd_results[i]
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assert avg_preds.average.shape == (1, grid_resolution)
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target_idx = disp.target_idx
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line_data = line.get_data()
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assert_allclose(line_data[0], avg_preds["values"][0])
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assert_allclose(line_data[1], avg_preds.average[target_idx].ravel())
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# two feature position
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ax = disp.axes_[0, 2]
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coutour = disp.contours_[0, 2]
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expected_levels = np.linspace(*disp.pdp_lim[2], num=8)
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assert_allclose(coutour.levels, expected_levels)
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assert coutour.get_cmap().name == "jet"
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assert ax.get_xlabel() == diabetes.feature_names[0]
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assert ax.get_ylabel() == diabetes.feature_names[2]
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@pytest.mark.filterwarnings("ignore:A Bunch will be returned")
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@pytest.mark.parametrize("kind, subsample, shape", [
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('average', None, (1, 3)),
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('individual', None, (1, 3, 442)),
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('both', None, (1, 3, 443)),
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('individual', 50, (1, 3, 50)),
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('both', 50, (1, 3, 51)),
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('individual', 0.5, (1, 3, 221)),
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('both', 0.5, (1, 3, 222))
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])
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def test_plot_partial_dependence_kind(pyplot, kind, subsample, shape,
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clf_diabetes, diabetes):
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disp = plot_partial_dependence(clf_diabetes, diabetes.data, [0, 1, 2],
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kind=kind, subsample=subsample)
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assert disp.axes_.shape == (1, 3)
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assert disp.lines_.shape == shape
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assert disp.contours_.shape == (1, 3)
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assert disp.contours_[0, 0] is None
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assert disp.contours_[0, 1] is None
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assert disp.contours_[0, 2] is None
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@pytest.mark.filterwarnings("ignore:A Bunch will be returned")
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@pytest.mark.parametrize(
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"input_type, feature_names_type",
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[('dataframe', None),
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('dataframe', 'list'), ('list', 'list'), ('array', 'list'),
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('dataframe', 'array'), ('list', 'array'), ('array', 'array'),
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('dataframe', 'series'), ('list', 'series'), ('array', 'series'),
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('dataframe', 'index'), ('list', 'index'), ('array', 'index')]
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)
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def test_plot_partial_dependence_str_features(pyplot, clf_diabetes, diabetes,
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input_type, feature_names_type):
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if input_type == 'dataframe':
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pd = pytest.importorskip("pandas")
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X = pd.DataFrame(diabetes.data, columns=diabetes.feature_names)
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elif input_type == 'list':
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X = diabetes.data.tolist()
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else:
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X = diabetes.data
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if feature_names_type is None:
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feature_names = None
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else:
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feature_names = _convert_container(diabetes.feature_names,
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feature_names_type)
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grid_resolution = 25
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# check with str features and array feature names and single column
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disp = plot_partial_dependence(clf_diabetes, X,
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[('age', 'bmi'), 'bmi'],
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grid_resolution=grid_resolution,
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feature_names=feature_names,
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n_cols=1, line_kw={"alpha": 0.8})
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fig = pyplot.gcf()
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axs = fig.get_axes()
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assert len(axs) == 3
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assert disp.figure_ is fig
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assert disp.axes_.shape == (2, 1)
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assert disp.lines_.shape == (2, 1)
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assert disp.contours_.shape == (2, 1)
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assert disp.deciles_vlines_.shape == (2, 1)
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assert disp.deciles_hlines_.shape == (2, 1)
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assert disp.lines_[0, 0] is None
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assert disp.deciles_vlines_[0, 0] is not None
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assert disp.deciles_hlines_[0, 0] is not None
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assert disp.contours_[1, 0] is None
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assert disp.deciles_hlines_[1, 0] is None
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assert disp.deciles_vlines_[1, 0] is not None
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# line
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ax = disp.axes_[1, 0]
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assert ax.get_xlabel() == "bmi"
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assert ax.get_ylabel() == "Partial dependence"
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line = disp.lines_[1, 0]
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avg_preds = disp.pd_results[1]
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target_idx = disp.target_idx
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assert line.get_alpha() == 0.8
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line_data = line.get_data()
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assert_allclose(line_data[0], avg_preds["values"][0])
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assert_allclose(line_data[1], avg_preds.average[target_idx].ravel())
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# contour
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ax = disp.axes_[0, 0]
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coutour = disp.contours_[0, 0]
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expect_levels = np.linspace(*disp.pdp_lim[2], num=8)
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assert_allclose(coutour.levels, expect_levels)
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assert ax.get_xlabel() == "age"
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assert ax.get_ylabel() == "bmi"
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@pytest.mark.filterwarnings("ignore:A Bunch will be returned")
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def test_plot_partial_dependence_custom_axes(pyplot, clf_diabetes, diabetes):
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grid_resolution = 25
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fig, (ax1, ax2) = pyplot.subplots(1, 2)
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disp = plot_partial_dependence(clf_diabetes, diabetes.data,
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['age', ('age', 'bmi')],
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grid_resolution=grid_resolution,
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feature_names=diabetes.feature_names,
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ax=[ax1, ax2])
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assert fig is disp.figure_
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assert disp.bounding_ax_ is None
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assert disp.axes_.shape == (2, )
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assert disp.axes_[0] is ax1
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assert disp.axes_[1] is ax2
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ax = disp.axes_[0]
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assert ax.get_xlabel() == "age"
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assert ax.get_ylabel() == "Partial dependence"
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line = disp.lines_[0]
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avg_preds = disp.pd_results[0]
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target_idx = disp.target_idx
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line_data = line.get_data()
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assert_allclose(line_data[0], avg_preds["values"][0])
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assert_allclose(line_data[1], avg_preds.average[target_idx].ravel())
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# contour
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ax = disp.axes_[1]
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coutour = disp.contours_[1]
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expect_levels = np.linspace(*disp.pdp_lim[2], num=8)
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assert_allclose(coutour.levels, expect_levels)
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assert ax.get_xlabel() == "age"
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assert ax.get_ylabel() == "bmi"
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@pytest.mark.filterwarnings("ignore:A Bunch will be returned")
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@pytest.mark.parametrize("kind, lines", [
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('average', 1), ('individual', 442), ('both', 443)
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])
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def test_plot_partial_dependence_passing_numpy_axes(pyplot, clf_diabetes,
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diabetes, kind, lines):
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grid_resolution = 25
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feature_names = diabetes.feature_names
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disp1 = plot_partial_dependence(clf_diabetes, diabetes.data,
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['age', 'bmi'], kind=kind,
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grid_resolution=grid_resolution,
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feature_names=feature_names)
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assert disp1.axes_.shape == (1, 2)
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assert disp1.axes_[0, 0].get_ylabel() == "Partial dependence"
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assert disp1.axes_[0, 1].get_ylabel() == ""
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assert len(disp1.axes_[0, 0].get_lines()) == lines
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assert len(disp1.axes_[0, 1].get_lines()) == lines
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lr = LinearRegression()
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lr.fit(diabetes.data, diabetes.target)
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disp2 = plot_partial_dependence(lr, diabetes.data,
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['age', 'bmi'], kind=kind,
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grid_resolution=grid_resolution,
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feature_names=feature_names,
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ax=disp1.axes_)
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assert np.all(disp1.axes_ == disp2.axes_)
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assert len(disp2.axes_[0, 0].get_lines()) == 2 * lines
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assert len(disp2.axes_[0, 1].get_lines()) == 2 * lines
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@pytest.mark.filterwarnings("ignore:A Bunch will be returned")
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@pytest.mark.parametrize("nrows, ncols", [(2, 2), (3, 1)])
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def test_plot_partial_dependence_incorrent_num_axes(pyplot, clf_diabetes,
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diabetes, nrows, ncols):
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grid_resolution = 5
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fig, axes = pyplot.subplots(nrows, ncols)
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axes_formats = [list(axes.ravel()), tuple(axes.ravel()), axes]
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msg = "Expected ax to have 2 axes, got {}".format(nrows * ncols)
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disp = plot_partial_dependence(clf_diabetes, diabetes.data,
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['age', 'bmi'],
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grid_resolution=grid_resolution,
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feature_names=diabetes.feature_names)
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for ax_format in axes_formats:
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with pytest.raises(ValueError, match=msg):
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plot_partial_dependence(clf_diabetes, diabetes.data,
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['age', 'bmi'],
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grid_resolution=grid_resolution,
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feature_names=diabetes.feature_names,
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ax=ax_format)
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# with axes object
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with pytest.raises(ValueError, match=msg):
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disp.plot(ax=ax_format)
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@pytest.mark.filterwarnings("ignore:A Bunch will be returned")
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def test_plot_partial_dependence_with_same_axes(pyplot, clf_diabetes,
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diabetes):
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# The first call to plot_partial_dependence will create two new axes to
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# place in the space of the passed in axes, which results in a total of
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# three axes in the figure.
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# Currently the API does not allow for the second call to
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# plot_partial_dependence to use the same axes again, because it will
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# create two new axes in the space resulting in five axes. To get the
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# expected behavior one needs to pass the generated axes into the second
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# call:
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# disp1 = plot_partial_dependence(...)
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# disp2 = plot_partial_dependence(..., ax=disp1.axes_)
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grid_resolution = 25
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fig, ax = pyplot.subplots()
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plot_partial_dependence(clf_diabetes, diabetes.data, ['age', 'bmi'],
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grid_resolution=grid_resolution,
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feature_names=diabetes.feature_names, ax=ax)
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msg = ("The ax was already used in another plot function, please set "
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"ax=display.axes_ instead")
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with pytest.raises(ValueError, match=msg):
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plot_partial_dependence(clf_diabetes, diabetes.data,
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['age', 'bmi'],
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grid_resolution=grid_resolution,
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feature_names=diabetes.feature_names, ax=ax)
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@pytest.mark.filterwarnings("ignore:A Bunch will be returned")
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def test_plot_partial_dependence_feature_name_reuse(pyplot, clf_diabetes,
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diabetes):
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# second call to plot does not change the feature names from the first
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# call
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feature_names = diabetes.feature_names
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disp = plot_partial_dependence(clf_diabetes, diabetes.data,
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[0, 1],
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grid_resolution=10,
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feature_names=feature_names)
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plot_partial_dependence(clf_diabetes, diabetes.data, [0, 1],
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grid_resolution=10, ax=disp.axes_)
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for i, ax in enumerate(disp.axes_.ravel()):
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assert ax.get_xlabel() == feature_names[i]
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@pytest.mark.filterwarnings("ignore:A Bunch will be returned")
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def test_plot_partial_dependence_multiclass(pyplot):
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grid_resolution = 25
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clf_int = GradientBoostingClassifier(n_estimators=10, random_state=1)
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iris = load_iris()
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# Test partial dependence plot function on multi-class input.
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clf_int.fit(iris.data, iris.target)
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disp_target_0 = plot_partial_dependence(clf_int, iris.data, [0, 1],
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target=0,
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grid_resolution=grid_resolution)
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assert disp_target_0.figure_ is pyplot.gcf()
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assert disp_target_0.axes_.shape == (1, 2)
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assert disp_target_0.lines_.shape == (1, 2)
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assert disp_target_0.contours_.shape == (1, 2)
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assert disp_target_0.deciles_vlines_.shape == (1, 2)
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assert disp_target_0.deciles_hlines_.shape == (1, 2)
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assert all(c is None for c in disp_target_0.contours_.flat)
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assert disp_target_0.target_idx == 0
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# now with symbol labels
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target = iris.target_names[iris.target]
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clf_symbol = GradientBoostingClassifier(n_estimators=10, random_state=1)
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clf_symbol.fit(iris.data, target)
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disp_symbol = plot_partial_dependence(clf_symbol, iris.data, [0, 1],
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target='setosa',
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grid_resolution=grid_resolution)
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assert disp_symbol.figure_ is pyplot.gcf()
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assert disp_symbol.axes_.shape == (1, 2)
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assert disp_symbol.lines_.shape == (1, 2)
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assert disp_symbol.contours_.shape == (1, 2)
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assert disp_symbol.deciles_vlines_.shape == (1, 2)
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assert disp_symbol.deciles_hlines_.shape == (1, 2)
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assert all(c is None for c in disp_symbol.contours_.flat)
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assert disp_symbol.target_idx == 0
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for int_result, symbol_result in zip(disp_target_0.pd_results,
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disp_symbol.pd_results):
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assert_allclose(int_result.average, symbol_result.average)
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assert_allclose(int_result["values"], symbol_result["values"])
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# check that the pd plots are different for another target
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disp_target_1 = plot_partial_dependence(clf_int, iris.data, [0, 1],
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target=1,
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grid_resolution=grid_resolution)
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target_0_data_y = disp_target_0.lines_[0, 0].get_data()[1]
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target_1_data_y = disp_target_1.lines_[0, 0].get_data()[1]
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assert any(target_0_data_y != target_1_data_y)
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multioutput_regression_data = make_regression(n_samples=50, n_targets=2,
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random_state=0)
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@pytest.mark.filterwarnings("ignore:A Bunch will be returned")
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@pytest.mark.parametrize("target", [0, 1])
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def test_plot_partial_dependence_multioutput(pyplot, target):
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# Test partial dependence plot function on multi-output input.
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X, y = multioutput_regression_data
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clf = LinearRegression().fit(X, y)
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grid_resolution = 25
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disp = plot_partial_dependence(clf, X, [0, 1], target=target,
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grid_resolution=grid_resolution)
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fig = pyplot.gcf()
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axs = fig.get_axes()
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assert len(axs) == 3
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assert disp.target_idx == target
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assert disp.bounding_ax_ is not None
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positions = [(0, 0), (0, 1)]
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expected_label = ["Partial dependence", ""]
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for i, pos in enumerate(positions):
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ax = disp.axes_[pos]
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assert ax.get_ylabel() == expected_label[i]
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assert ax.get_xlabel() == "{}".format(i)
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@pytest.mark.filterwarnings("ignore:A Bunch will be returned")
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def test_plot_partial_dependence_dataframe(pyplot, clf_diabetes, diabetes):
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pd = pytest.importorskip('pandas')
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df = pd.DataFrame(diabetes.data, columns=diabetes.feature_names)
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grid_resolution = 25
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plot_partial_dependence(
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clf_diabetes, df, ['bp', 's1'], grid_resolution=grid_resolution,
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feature_names=df.columns.tolist()
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)
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dummy_classification_data = make_classification(random_state=0)
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@pytest.mark.filterwarnings("ignore:A Bunch will be returned")
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@pytest.mark.parametrize(
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|
"data, params, err_msg",
|
|
[(multioutput_regression_data, {"target": None, 'features': [0]},
|
|
"target must be specified for multi-output"),
|
|
(multioutput_regression_data, {"target": -1, 'features': [0]},
|
|
r'target must be in \[0, n_tasks\]'),
|
|
(multioutput_regression_data, {"target": 100, 'features': [0]},
|
|
r'target must be in \[0, n_tasks\]'),
|
|
(dummy_classification_data,
|
|
{'features': ['foobar'], 'feature_names': None},
|
|
'Feature foobar not in feature_names'),
|
|
(dummy_classification_data,
|
|
{'features': ['foobar'], 'feature_names': ['abcd', 'def']},
|
|
'Feature foobar not in feature_names'),
|
|
(dummy_classification_data, {'features': [(1, 2, 3)]},
|
|
'Each entry in features must be either an int, '),
|
|
(dummy_classification_data, {'features': [1, {}]},
|
|
'Each entry in features must be either an int, '),
|
|
(dummy_classification_data, {'features': [tuple()]},
|
|
'Each entry in features must be either an int, '),
|
|
(dummy_classification_data,
|
|
{'features': [123], 'feature_names': ['blahblah']},
|
|
'All entries of features must be less than '),
|
|
(dummy_classification_data,
|
|
{'features': [0, 1, 2], 'feature_names': ['a', 'b', 'a']},
|
|
'feature_names should not contain duplicates'),
|
|
(dummy_classification_data, {'features': [(1, 2)], 'kind': 'individual'},
|
|
'It is not possible to display individual effects for more than one'),
|
|
(dummy_classification_data, {'features': [(1, 2)], 'kind': 'both'},
|
|
'It is not possible to display individual effects for more than one'),
|
|
(dummy_classification_data, {'features': [1], 'subsample': -1},
|
|
'When an integer, subsample=-1 should be positive.'),
|
|
(dummy_classification_data, {'features': [1], 'subsample': 1.2},
|
|
r'When a floating-point, subsample=1.2 should be in the \(0, 1\) range')]
|
|
)
|
|
def test_plot_partial_dependence_error(pyplot, data, params, err_msg):
|
|
X, y = data
|
|
estimator = LinearRegression().fit(X, y)
|
|
|
|
with pytest.raises(ValueError, match=err_msg):
|
|
plot_partial_dependence(estimator, X, **params)
|
|
|
|
|
|
@pytest.mark.filterwarnings("ignore:A Bunch will be returned")
|
|
@pytest.mark.parametrize("params, err_msg", [
|
|
({'target': 4, 'features': [0]},
|
|
'target not in est.classes_, got 4'),
|
|
({'target': None, 'features': [0]},
|
|
'target must be specified for multi-class'),
|
|
({'target': 1, 'features': [4.5]},
|
|
'Each entry in features must be either an int,'),
|
|
])
|
|
def test_plot_partial_dependence_multiclass_error(pyplot, params, err_msg):
|
|
iris = load_iris()
|
|
clf = GradientBoostingClassifier(n_estimators=10, random_state=1)
|
|
clf.fit(iris.data, iris.target)
|
|
|
|
with pytest.raises(ValueError, match=err_msg):
|
|
plot_partial_dependence(clf, iris.data, **params)
|
|
|
|
|
|
def test_plot_partial_dependence_does_not_override_ylabel(pyplot, clf_diabetes,
|
|
diabetes):
|
|
# Non-regression test to be sure to not override the ylabel if it has been
|
|
# See https://github.com/scikit-learn/scikit-learn/issues/15772
|
|
_, axes = pyplot.subplots(1, 2)
|
|
axes[0].set_ylabel("Hello world")
|
|
plot_partial_dependence(clf_diabetes, diabetes.data,
|
|
[0, 1], ax=axes)
|
|
|
|
assert axes[0].get_ylabel() == "Hello world"
|
|
assert axes[1].get_ylabel() == "Partial dependence"
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"kind, expected_shape",
|
|
[("average", (1, 2)), ("individual", (1, 2, 50)), ("both", (1, 2, 51))],
|
|
)
|
|
def test_plot_partial_dependence_subsampling(
|
|
pyplot, clf_diabetes, diabetes, kind, expected_shape
|
|
):
|
|
# check that the subsampling is properly working
|
|
# non-regression test for:
|
|
# https://github.com/scikit-learn/scikit-learn/pull/18359
|
|
matplotlib = pytest.importorskip("matplotlib")
|
|
grid_resolution = 25
|
|
feature_names = diabetes.feature_names
|
|
|
|
disp1 = plot_partial_dependence(
|
|
clf_diabetes,
|
|
diabetes.data,
|
|
["age", "bmi"],
|
|
kind=kind,
|
|
grid_resolution=grid_resolution,
|
|
feature_names=feature_names,
|
|
subsample=50,
|
|
random_state=0,
|
|
)
|
|
|
|
assert disp1.lines_.shape == expected_shape
|
|
assert all(
|
|
[
|
|
isinstance(line, matplotlib.lines.Line2D)
|
|
for line in disp1.lines_.ravel()
|
|
]
|
|
)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"kind, line_kw, label",
|
|
[
|
|
("individual", {}, None),
|
|
("individual", {"label": "xxx"}, None),
|
|
("average", {}, None),
|
|
("average", {"label": "xxx"}, "xxx"),
|
|
("both", {}, "average"),
|
|
("both", {"label": "xxx"}, "xxx"),
|
|
],
|
|
)
|
|
def test_partial_dependence_overwrite_labels(
|
|
pyplot,
|
|
clf_diabetes,
|
|
diabetes,
|
|
kind,
|
|
line_kw,
|
|
label,
|
|
):
|
|
"""Test that make sure that we can overwrite the label of the PDP plot"""
|
|
disp = plot_partial_dependence(
|
|
clf_diabetes,
|
|
diabetes.data,
|
|
[0, 2],
|
|
grid_resolution=25,
|
|
feature_names=diabetes.feature_names,
|
|
kind=kind,
|
|
line_kw=line_kw,
|
|
)
|
|
|
|
for ax in disp.axes_.ravel():
|
|
if label is None:
|
|
assert ax.get_legend() is None
|
|
else:
|
|
legend_text = ax.get_legend().get_texts()
|
|
assert len(legend_text) == 1
|
|
assert legend_text[0].get_text() == label
|