def test_model_ridge_classifier_cv_multilabel(self):
model, X_test = fit_multilabel_classification_model(
linear_model.RidgeClassifierCV(random_state=42))
model_onnx = convert_sklearn(
model,
"scikit-learn RidgeClassifierCV",
[("input", FloatTensorType([None, X_test.shape[1]]))],
target_opset=TARGET_OPSET)
self.assertTrue(model_onnx is not None)
dump_data_and_model(
X_test, model, model_onnx,
basename="SklearnRidgeClassifierCVMultiLabel")
def test_model_mlp_classifier_multilabel_tanh(self):
model, X_test = fit_multilabel_classification_model(MLPClassifier(
random_state=42, activation="tanh"),
n_labels=3)
model_onnx = convert_sklearn(
model,
"scikit-learn MLPClassifier",
[("input", FloatTensorType([None, X_test.shape[1]]))],
target_opset=TARGET_OPSET)
self.assertTrue(model_onnx is not None)
dump_data_and_model(
X_test,
model,
model_onnx,
basename="SklearnMLPClassifierMultiLabelTanhActivation")
def test_model_mlp_classifier_multilabel_identity(self):
model, X_test = fit_multilabel_classification_model(MLPClassifier(
random_state=42, activation="identity"),
is_int=True)
model_onnx = convert_sklearn(
model,
"scikit-learn MLPClassifier",
[("input", Int64TensorType([None, X_test.shape[1]]))],
target_opset=TARGET_OPSET)
self.assertTrue(model_onnx is not None)
dump_data_and_model(
X_test,
model,
model_onnx,
basename="SklearnMLPClassifierMultiLabelIdentityActivation")
def test_model_ridge_classifier_cv_multilabel(self):
model, X_test = fit_multilabel_classification_model(
linear_model.RidgeClassifierCV(random_state=42))
model_onnx = convert_sklearn(
model,
"scikit-learn RidgeClassifierCV",
[("input", FloatTensorType([None, X_test.shape[1]]))],
)
self.assertTrue(model_onnx is not None)
dump_data_and_model(
X_test,
model,
model_onnx,
basename="SklearnRidgeClassifierCVMultiLabel",
allow_failure="StrictVersion("
"onnxruntime.__version__)<= StrictVersion('0.2.1')",
)
def test_model_extra_trees_classifier_multilabel(self):
model, X_test = fit_multilabel_classification_model(
ExtraTreesClassifier(random_state=42, n_estimators=5))
options = {id(model): {'zipmap': False}}
model_onnx = convert_sklearn(
model,
"scikit-learn ExtraTreesClassifier",
[("input", FloatTensorType([None, X_test.shape[1]]))],
options=options,
target_opset=TARGET_OPSET)
self.assertTrue(model_onnx is not None)
assert 'zipmap' not in str(model_onnx).lower()
dump_data_and_model(
X_test,
model,
model_onnx,
basename="SklearnExtraTreesClassifierMultiLabel-Out0")
def test_model_mlp_classifier_multilabel_tanh(self):
model, X_test = fit_multilabel_classification_model(
MLPClassifier(random_state=42, activation="tanh"), n_labels=3)
model_onnx = convert_sklearn(
model,
"scikit-learn MLPClassifier",
[("input", FloatTensorType([None, X_test.shape[1]]))],
)
self.assertTrue(model_onnx is not None)
dump_data_and_model(
X_test,
model,
model_onnx,
basename="SklearnMLPClassifierMultiLabelTanhActivation",
allow_failure="StrictVersion("
"onnxruntime.__version__)<= StrictVersion('0.2.1')",
)
def test_model_mlp_classifier_multilabel_default(self):
model, X_test = fit_multilabel_classification_model(
MLPClassifier(random_state=42))
model_onnx = convert_sklearn(
model,
"scikit-learn MLPClassifier",
[("input", FloatTensorType([None, X_test.shape[1]]))],
target_opset=TARGET_OPSET
)
self.assertTrue(model_onnx is not None)
dump_data_and_model(
X_test,
model,
model_onnx,
basename="SklearnMLPClassifierMultiLabel",
allow_failure="StrictVersion("
"onnxruntime.__version__)<= StrictVersion('0.2.1')",
)
def test_model_extra_trees_classifier_multilabel_low_samples(self):
model, X_test = fit_multilabel_classification_model(
ExtraTreesClassifier(random_state=42), n_samples=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=TARGET_OPSET)
self.assertTrue(model_onnx is not None)
assert 'zipmap' not in str(model_onnx).lower()
dump_data_and_model(
X_test,
model,
model_onnx,
basename="SklearnExtraTreesClassifierMultiLabelLowSamples-Out0",
allow_failure="StrictVersion("
"onnxruntime.__version__) <= StrictVersion('0.2.1')",
)
def test_model_knn_classifier_multilabel(self):
model, X_test = fit_multilabel_classification_model(
KNeighborsClassifier(),
n_classes=7,
n_labels=3,
n_samples=100,
n_features=10)
options = {id(model): {'zipmap': False}}
model_onnx = convert_sklearn(
model,
"scikit-learn KNN Classifier",
[("input", FloatTensorType([None, X_test.shape[1]]))],
options=options,
target_opset=TARGET_OPSET)
self.assertTrue(model_onnx is not None)
assert 'zipmap' not in str(model_onnx).lower()
dump_data_and_model(X_test,
model,
model_onnx,
basename="SklearnKNNClassifierMultiLabel-Out0")
def test_ovr_rf_multilabel_float(self):
for opset in [12, TARGET_OPSET]:
if opset > TARGET_OPSET:
continue
with self.subTest(opset=opset):
model = OneVsRestClassifier(
RandomForestClassifier(n_estimators=2, max_depth=3))
model, X = fit_multilabel_classification_model(model,
3,
is_int=False,
n_features=5)
model_onnx = convert_sklearn(
model,
initial_types=[('input',
FloatTensorType([None, X.shape[1]]))],
target_opset=opset)
dump_data_and_model(X.astype(np.float32),
model,
model_onnx,
basename="SklearnOVRRFMultiLabelFloat%d" %
opset)
def test_ovr_rf_multilabel_int_11(self):
for opset in [9, 10, 11]:
if opset > TARGET_OPSET:
continue
with self.subTest(opset=opset):
model = OneVsRestClassifier(
RandomForestClassifier(n_estimators=2, max_depth=3))
model, X = fit_multilabel_classification_model(model,
3,
is_int=True,
n_features=5)
model_onnx = convert_sklearn(
model,
initial_types=[('input',
Int64TensorType([None, X.shape[1]]))],
target_opset=opset)
self.assertNotIn('"Clip"', str(model_onnx))
dump_data_and_model(X.astype(np.int64),
model,
model_onnx,
basename="SklearnOVRRFMultiLabelInt64%d" %
opset)