def test_model_extra_trees_classifier_multilabel(self):
model, X_test = fit_multilabel_classification_model(
ExtraTreesClassifier(random_state=42, n_estimators=10))
options = {id(model): {'zipmap': False}}
model_onnx = convert_sklearn(
model,
"scikit-learn ExtraTreesClassifier",
[("input", FloatTensorType([None, X_test.shape[1]]))],
options=options,
target_opset=get_opset_number_from_onnx())
self.assertTrue(model_onnx is not None)
self.assertNotIn('zipmap', str(model_onnx).lower())
dump_data_and_model(
X_test,
model,
model_onnx,
basename="SklearnExtraTreesClassifierMultiLabel-Out0",
folder=self.folder)
def test_model_random_forest_classifier_multilabel_low_samples(self):
model, X_test = fit_multilabel_classification_model(
RandomForestClassifier(random_state=42, n_estimators=10),
n_samples=4)
options = {id(model): {'zipmap': False}}
model_onnx = convert_sklearn(
model,
"scikit-learn RandomForestClassifier",
[("input", FloatTensorType([None, X_test.shape[1]]))],
options=options,
target_opset=TARGET_OPSET)
self.assertTrue(model_onnx is not None)
self.assertNotIn('zipmap', str(model_onnx).lower())
dump_data_and_model(
X_test,
model,
model_onnx,
basename="SklearnRandomForestClassifierMultiLabelLowSamples-Out0",
folder=self.folder)
def test_random_forest_classifier_fit_simple_multi(self):
res = fit_multilabel_classification_model(ExtraTreesClassifier())
self.assertEqual(len(res), 2)