def test_skl_converter_iforest(tmpdir, toolchain): # pylint: disable=W0212
# pylint: disable=too-many-locals
"""Convert scikit-learn regressor"""
X = load_boston(return_X_y=True)[0]
clf = IsolationForest(max_samples=64, random_state=0, n_estimators=10)
clf.fit(X)
expected_pred = clf._compute_chunked_score_samples(X) # pylint: disable=W0212
model = treelite.sklearn.import_model(clf)
assert model.num_feature == clf.n_features_
assert model.num_class == 1
assert model.num_tree == clf.n_estimators
dtrain = treelite_runtime.DMatrix(X, dtype='float32')
annotation_path = os.path.join(tmpdir, 'annotation.json')
annotator = treelite.Annotator()
annotator.annotate_branch(model=model, dmat=dtrain, verbose=True)
annotator.save(path=annotation_path)
libpath = os.path.join(tmpdir, 'skl' + _libext())
model.export_lib(toolchain=toolchain,
libpath=libpath,
params={'annotate_in': annotation_path},
verbose=True)
predictor = treelite_runtime.Predictor(libpath=libpath)
assert predictor.num_feature == clf.n_features_
assert model.num_class == 1
assert predictor.pred_transform == 'exponential_standard_ratio'
assert predictor.global_bias == 0.0
assert predictor.sigmoid_alpha == 1.0
assert predictor.ratio_c > 0
assert predictor.ratio_c < 64
out_pred = predictor.predict(dtrain)
np.testing.assert_almost_equal(out_pred, expected_pred, decimal=2)
def test_skl_converter_iforest():
"""Scikit-learn isolation forest"""
X, _ = load_boston(return_X_y=True)
clf = IsolationForest(max_samples=64, random_state=0, n_estimators=10)
clf.fit(X)
expected_pred = clf._compute_chunked_score_samples(X) # pylint: disable=W0212
tl_model = treelite.sklearn.import_model(clf)
out_pred = treelite.gtil.predict(tl_model, X)
np.testing.assert_almost_equal(out_pred, expected_pred, decimal=2)
