""" Testing for the partial dependence module. """ import pytest import numpy as np from numpy.testing import assert_array_equal from sklearn.utils.testing import assert_raises from sklearn.utils.testing import if_matplotlib from sklearn.ensemble.partial_dependence import partial_dependence from sklearn.ensemble.partial_dependence import plot_partial_dependence from sklearn.ensemble import GradientBoostingClassifier from sklearn.ensemble import GradientBoostingRegressor from sklearn import datasets # toy sample X = [[-2, -1], [-1, -1], [-1, -2], [1, 1], [1, 2], [2, 1]] y = [-1, -1, -1, 1, 1, 1] T = [[-1, -1], [2, 2], [3, 2]] true_result = [-1, 1, 1] # also load the boston dataset boston = datasets.load_boston() # also load the iris dataset iris = datasets.load_iris() def test_partial_dependence_classifier(): # Test partial dependence for classifier clf = GradientBoostingClassifier(n_estimators=10, random_state=1) clf.fit(X, y) pdp, axes = partial_dependence(clf, [0], X=X, grid_resolution=5) # only 4 grid points instead of 5 because only 4 unique X[:,0] vals assert pdp.shape == (1, 4) assert axes[0].shape[0] == 4 # now with our own grid X_ = np.asarray(X) grid = np.unique(X_[:, 0]) pdp_2, axes = partial_dependence(clf, [0], grid=grid) assert axes is None assert_array_equal(pdp, pdp_2) def test_partial_dependence_multiclass(): # Test partial dependence for multi-class classifier clf = GradientBoostingClassifier(n_estimators=10, random_state=1) clf.fit(iris.data, iris.target) grid_resolution = 25 n_classes = clf.n_classes_ pdp, axes = partial_dependence( clf, [0], X=iris.data, grid_resolution=grid_resolution) assert pdp.shape == (n_classes, grid_resolution) assert len(axes) == 1 assert axes[0].shape[0] == grid_resolution def test_partial_dependence_regressor(): # Test partial dependence for regressor clf = GradientBoostingRegressor(n_estimators=10, random_state=1) clf.fit(boston.data, boston.target) grid_resolution = 25 pdp, axes = partial_dependence( clf, [0], X=boston.data, grid_resolution=grid_resolution) assert pdp.shape == (1, grid_resolution) assert axes[0].shape[0] == grid_resolution def test_partial_dependecy_input(): # Test input validation of partial dependence. clf = GradientBoostingClassifier(n_estimators=10, random_state=1) clf.fit(X, y) assert_raises(ValueError, partial_dependence, clf, [0], grid=None, X=None) assert_raises(ValueError, partial_dependence, clf, [0], grid=[0, 1], X=X) # first argument must be an instance of BaseGradientBoosting assert_raises(ValueError, partial_dependence, {}, [0], X=X) # Gradient boosting estimator must be fit assert_raises(ValueError, partial_dependence, GradientBoostingClassifier(), [0], X=X) assert_raises(ValueError, partial_dependence, clf, [-1], X=X) assert_raises(ValueError, partial_dependence, clf, [100], X=X) # wrong ndim for grid grid = np.random.rand(10, 2, 1) assert_raises(ValueError, partial_dependence, clf, [0], grid=grid) @pytest.mark.filterwarnings('ignore: Using or importing the ABCs from') # matplotlib Python3.7 warning @if_matplotlib def test_plot_partial_dependence(): # Test partial dependence plot function. clf = GradientBoostingRegressor(n_estimators=10, random_state=1) clf.fit(boston.data, boston.target) grid_resolution = 25 fig, axs = plot_partial_dependence(clf, boston.data, [0, 1, (0, 1)], grid_resolution=grid_resolution, feature_names=boston.feature_names) assert len(axs) == 3 assert all(ax.has_data for ax in axs) # check with str features and array feature names fig, axs = plot_partial_dependence(clf, boston.data, ['CRIM', 'ZN', ('CRIM', 'ZN')], grid_resolution=grid_resolution, feature_names=boston.feature_names) assert len(axs) == 3 assert all(ax.has_data for ax in axs) # check with list feature_names feature_names = boston.feature_names.tolist() fig, axs = plot_partial_dependence(clf, boston.data, ['CRIM', 'ZN', ('CRIM', 'ZN')], grid_resolution=grid_resolution, feature_names=feature_names) assert len(axs) == 3 assert all(ax.has_data for ax in axs) @pytest.mark.filterwarnings('ignore: Using or importing the ABCs from') # matplotlib Python3.7 warning @if_matplotlib def test_plot_partial_dependence_input(): # Test partial dependence plot function input checks. clf = GradientBoostingClassifier(n_estimators=10, random_state=1) # not fitted yet assert_raises(ValueError, plot_partial_dependence, clf, X, [0]) clf.fit(X, y) assert_raises(ValueError, plot_partial_dependence, clf, np.array(X)[:, :0], [0]) # first argument must be an instance of BaseGradientBoosting assert_raises(ValueError, plot_partial_dependence, {}, X, [0]) # must be larger than -1 assert_raises(ValueError, plot_partial_dependence, clf, X, [-1]) # too large feature value assert_raises(ValueError, plot_partial_dependence, clf, X, [100]) # str feature but no feature_names assert_raises(ValueError, plot_partial_dependence, clf, X, ['foobar']) # not valid features value assert_raises(ValueError, plot_partial_dependence, clf, X, [{'foo': 'bar'}]) @pytest.mark.filterwarnings('ignore: Using or importing the ABCs from') # matplotlib Python3.7 warning @if_matplotlib def test_plot_partial_dependence_multiclass(): # Test partial dependence plot function on multi-class input. clf = GradientBoostingClassifier(n_estimators=10, random_state=1) clf.fit(iris.data, iris.target) grid_resolution = 25 fig, axs = plot_partial_dependence(clf, iris.data, [0, 1], label=0, grid_resolution=grid_resolution) assert len(axs) == 2 assert all(ax.has_data for ax in axs) # now with symbol labels target = iris.target_names[iris.target] clf = GradientBoostingClassifier(n_estimators=10, random_state=1) clf.fit(iris.data, target) grid_resolution = 25 fig, axs = plot_partial_dependence(clf, iris.data, [0, 1], label='setosa', grid_resolution=grid_resolution) assert len(axs) == 2 assert all(ax.has_data for ax in axs) # label not in gbrt.classes_ assert_raises(ValueError, plot_partial_dependence, clf, iris.data, [0, 1], label='foobar', grid_resolution=grid_resolution) # label not provided assert_raises(ValueError, plot_partial_dependence, clf, iris.data, [0, 1], grid_resolution=grid_resolution)