def test_pipeline_loaded_from_zip_has_feature_contributions(self):
features = ['age', 'education-num', 'hours-per-week']
model_nimbusml = Pipeline(
steps=[FastLinearBinaryClassifier(feature=features)])
model_nimbusml.fit(train, label)
fc = model_nimbusml.get_feature_contributions(test)
# Save the model to zip
model_filename = get_temp_file(suffix='.zip')
model_nimbusml.save_model(model_filename)
# Load the model from zip
model_nimbusml_zip = Pipeline()
model_nimbusml_zip.load_model(model_filename)
fc_zip = model_nimbusml_zip.get_feature_contributions(test)
assert ['FeatureContributions.' + feature in fc_zip.columns
for feature in features]
assert [fc['FeatureContributions.' + feature].equals(
fc_zip['FeatureContributions.' + feature])
for feature in features]
os.remove(model_filename)
def test_unpickled_pipeline_has_feature_contributions(self):
features = ['age', 'education-num', 'hours-per-week']
model_nimbusml = Pipeline(
steps=[FastLinearBinaryClassifier(feature=features)])
model_nimbusml.fit(train, label)
fc = model_nimbusml.get_feature_contributions(test)
# Save with pickle
pickle_filename = get_temp_file(suffix='.p')
with open(pickle_filename, 'wb') as f:
pickle.dump(model_nimbusml, f)
# Unpickle model
with open(pickle_filename, "rb") as f:
model_nimbusml_pickle = pickle.load(f)
fc_pickle = model_nimbusml_pickle.get_feature_contributions(test)
assert ['FeatureContributions.' + feature in fc_pickle.columns
for feature in features]
assert [fc['FeatureContributions.' + feature].equals(
fc_pickle['FeatureContributions.' + feature])
for feature in features]
os.remove(pickle_filename)
