207 lines
6.8 KiB
Python
207 lines
6.8 KiB
Python
|
"""
|
||
|
|
Testing for the partial dependence module.
|
||
|
|
"""
|
||
|
|
|
||
|
|
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)
|
||
|
|
|
||
|
|
|
||
|
|
@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)
|
||
|
|
|
||
|
|
|
||
|
|
@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'}])
|
||
|
|
|
||
|
|
|
||
|
|
@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)
|