def test_partial_dependence_helpers(est, method, target_feature):
# Check that what is returned by _partial_dependence_brute or
# _partial_dependence_recursion is equivalent to manually setting a target
# feature to a given value, and computing the average prediction over all
# samples.
# This also checks that the brute and recursion methods give the same
# output.
X, y = make_regression(random_state=0)
# The 'init' estimator for GBDT (here the average prediction) isn't taken
# into account with the recursion method, for technical reasons. We set
# the mean to 0 to that this 'bug' doesn't have any effect.
y = y - y.mean()
est.fit(X, y)
# target feature will be set to .5 and then to 123
features = np.array([target_feature], dtype=np.int32)
grid = np.array([[.5],
[123]])
if method == 'brute':
pdp = _partial_dependence_brute(est, grid, features, X,
response_method='auto')
else:
pdp = _partial_dependence_recursion(est, grid, features)
mean_predictions = []
for val in (.5, 123):
X_ = X.copy()
X_[:, target_feature] = val
mean_predictions.append(est.predict(X_).mean())
pdp = pdp[0] # (shape is (1, 2) so make it (2,))
assert_allclose(pdp, mean_predictions, atol=1e-3)
def test_partial_dependence_helpers(est, method, target_feature):
# Check that what is returned by _partial_dependence_brute or
# _partial_dependence_recursion is equivalent to manually setting a target
# feature to a given value, and computing the average prediction over all
# samples.
# This also checks that the brute and recursion methods give the same
# output.
X, y = make_regression(random_state=0)
# The 'init' estimator for GBDT (here the average prediction) isn't taken
# into account with the recursion method, for technical reasons. We set
# the mean to 0 to that this 'bug' doesn't have any effect.
y = y - y.mean()
est.fit(X, y)
# target feature will be set to .5 and then to 123
features = np.array([target_feature], dtype=np.int32)
grid = np.array([[.5],
[123]])
if method == 'brute':
pdp = _partial_dependence_brute(est, grid, features, X,
response_method='auto')
else:
pdp = _partial_dependence_recursion(est, grid, features)
mean_predictions = []
for val in (.5, 123):
X_ = X.copy()
X_[:, target_feature] = val
mean_predictions.append(est.predict(X_).mean())
pdp = pdp[0] # (shape is (1, 2) so make it (2,))
assert_allclose(pdp, mean_predictions, atol=1e-3)