Skip to content

Test GTIL with IsolationForest #370

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
chyunsu3 merged 2 commits into from
Feb 23, 2022
Merged

Test GTIL with IsolationForest #370

Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
9b8c88b
Test GTIL with IsolationForest
chyunsu3 Feb 22, 2022
9090c21
Fix formatting
chyunsu3 Feb 22, 2022
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
14 changes: 13 additions & 1 deletion tests/python/test_gtil.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -9,7 +9,7 @@
import scipy
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier, \
ExtraTreesClassifier, RandomForestRegressor, GradientBoostingRegressor, \
ExtraTreesRegressor
ExtraTreesRegressor, IsolationForest
from sklearn.datasets import load_iris, load_breast_cancer, load_boston, load_svmlight_file
from sklearn.model_selection import train_test_split

Expand Down Expand Up @@ -80,6 +80,18 @@ def test_skl_multiclass_classifier(clazz):
np.testing.assert_almost_equal(out_prob, expected_prob, decimal=5)


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)


@pytest.mark.parametrize('objective', ['reg:linear', 'reg:squarederror', 'reg:squaredlogerror',
'reg:pseudohubererror'])
@pytest.mark.parametrize('model_format', ['binary', 'json'])
Expand Down