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test_sklearn_glm_classifier_converter.py
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test_sklearn_glm_classifier_converter.py
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from distutils.version import StrictVersion
import unittest
import numpy
import sklearn
from sklearn import datasets
from sklearn import linear_model
from sklearn.svm import LinearSVC
from skl2onnx import convert_sklearn
from skl2onnx.common.data_types import FloatTensorType
from skl2onnx.common.data_types import onnx_built_with_ml
from test_utils import dump_data_and_model
def _sklearn_version():
# Remove development version 0.22.dev0 becomes 0.22.
v = ".".join(sklearn.__version__.split('.')[:2])
return StrictVersion(v)
class TestGLMClassifierConverter(unittest.TestCase):
def _fit_model_binary_classification(self, model):
iris = datasets.load_iris()
X = iris.data[:, :3]
y = iris.target
y[y == 2] = 1
model.fit(X, y)
return model, X
def _fit_model_multiclass_classification(self, model):
iris = datasets.load_iris()
X = iris.data[:, :3]
y = iris.target
model.fit(X, y)
return model, X
@unittest.skipIf(not onnx_built_with_ml(),
reason="Requires ONNX-ML extension.")
def test_model_logistic_regression_binary_class(self):
model, X = self._fit_model_binary_classification(
linear_model.LogisticRegression())
model_onnx = convert_sklearn(model, "logistic regression",
[("input", FloatTensorType([1, 3]))])
self.assertIsNotNone(model_onnx)
dump_data_and_model(
X.astype(numpy.float32),
model,
model_onnx,
basename="SklearnLogitisticRegressionBinary",
# Operator cast-1 is not implemented in onnxruntime
allow_failure="StrictVersion(onnx.__version__)"
" < StrictVersion('1.3') or "
"StrictVersion(onnxruntime.__version__)"
" <= StrictVersion('0.2.1')",
)
@unittest.skipIf(not onnx_built_with_ml(),
reason="Requires ONNX-ML extension.")
def test_model_logistic_regression_cv_binary_class(self):
model, X = self._fit_model_binary_classification(
linear_model.LogisticRegressionCV())
model_onnx = convert_sklearn(model, "logistic regression cv",
[("input", FloatTensorType([1, 3]))])
self.assertIsNotNone(model_onnx)
dump_data_and_model(
X.astype(numpy.float32),
model,
model_onnx,
basename="SklearnLogitisticCVRegressionBinary",
# Operator cast-1 is not implemented in onnxruntime
allow_failure="StrictVersion(onnx.__version__)"
" < StrictVersion('1.3') or "
"StrictVersion(onnxruntime.__version__)"
" <= StrictVersion('0.2.1')",
)
@unittest.skipIf(not onnx_built_with_ml(),
reason="Requires ONNX-ML extension.")
def test_model_logistic_regression_binary_class_nointercept(self):
model, X = self._fit_model_binary_classification(
linear_model.LogisticRegression(fit_intercept=False))
model_onnx = convert_sklearn(model, "logistic regression",
[("input", FloatTensorType([1, 3]))])
self.assertIsNotNone(model_onnx)
dump_data_and_model(
X.astype(numpy.float32),
model,
model_onnx,
basename="SklearnLogitisticRegressionBinaryNoIntercept",
# Operator cast-1 is not implemented in onnxruntime
allow_failure="StrictVersion(onnx.__version__)"
" < StrictVersion('1.3') or "
"StrictVersion(onnxruntime.__version__)"
" <= StrictVersion('0.2.1')",
)
@unittest.skipIf(not onnx_built_with_ml(),
reason="Requires ONNX-ML extension.")
def test_model_logistic_regression_multi_class(self):
model, X = self._fit_model_multiclass_classification(
linear_model.LogisticRegression())
model_onnx = convert_sklearn(
model,
"multi-class logistic regression",
[("input", FloatTensorType([1, 3]))],
)
self.assertIsNotNone(model_onnx)
dump_data_and_model(
X.astype(numpy.float32),
model,
model_onnx,
basename="SklearnLogitisticRegressionMulti",
allow_failure="StrictVersion(onnx.__version__)"
" < StrictVersion('1.2') or "
"StrictVersion(onnxruntime.__version__)"
" <= StrictVersion('0.2.1')",
)
@unittest.skipIf(not onnx_built_with_ml(),
reason="Requires ONNX-ML extension.")
def test_model_logistic_regression_multi_class_ovr(self):
model, X = self._fit_model_multiclass_classification(
linear_model.LogisticRegression(multi_class='ovr'))
model_onnx = convert_sklearn(
model,
"multi-class logistic regression",
[("input", FloatTensorType([1, 3]))],
)
self.assertIsNotNone(model_onnx)
dump_data_and_model(
X.astype(numpy.float32),
model,
model_onnx,
basename="SklearnLogitisticRegressionMulti",
allow_failure="StrictVersion(onnx.__version__)"
" < StrictVersion('1.2') or "
"StrictVersion(onnxruntime.__version__)"
" <= StrictVersion('0.2.1')",
)
@unittest.skipIf(not onnx_built_with_ml(),
reason="Requires ONNX-ML extension.")
def test_model_logistic_regression_multi_class_multinomial(self):
model, X = self._fit_model_multiclass_classification(
linear_model.LogisticRegression(
multi_class="multinomial", solver="lbfgs"))
model_onnx = convert_sklearn(
model,
"multi-class logistic regression",
[("input", FloatTensorType([1, 3]))],
)
self.assertIsNotNone(model_onnx)
dump_data_and_model(
X.astype(numpy.float32),
model,
model_onnx,
basename="SklearnLogitisticRegressionMulti",
allow_failure="StrictVersion(onnx.__version__)"
" < StrictVersion('1.2') or "
"StrictVersion(onnxruntime.__version__)"
" <= StrictVersion('0.2.1')",
)
@unittest.skipIf(not onnx_built_with_ml(),
reason="Requires ONNX-ML extension.")
def test_model_logistic_regression_multi_class_no_intercept(self):
model, X = self._fit_model_multiclass_classification(
linear_model.LogisticRegression(fit_intercept=False))
model_onnx = convert_sklearn(
model,
"multi-class logistic regression",
[("input", FloatTensorType([1, 3]))],
)
self.assertIsNotNone(model_onnx)
dump_data_and_model(
X.astype(numpy.float32),
model,
model_onnx,
basename="SklearnLogitisticRegressionMultiNoIntercept",
allow_failure="StrictVersion(onnx.__version__)"
" < StrictVersion('1.2') or "
"StrictVersion(onnxruntime.__version__)"
" <= StrictVersion('0.2.1')",
)
@unittest.skipIf(not onnx_built_with_ml(),
reason="Requires ONNX-ML extension.")
def test_model_logistic_regression_multi_class_lbfgs(self):
penalty = (
'l2'
if _sklearn_version() < StrictVersion('0.21.0')
else 'none')
model, X = self._fit_model_multiclass_classification(
linear_model.LogisticRegression(solver='lbfgs', penalty=penalty))
model_onnx = convert_sklearn(
model,
"multi-class logistic regression",
[("input", FloatTensorType([1, 3]))],
)
self.assertIsNotNone(model_onnx)
dump_data_and_model(
X.astype(numpy.float32),
model,
model_onnx,
basename="SklearnLogitisticRegressionMultiLbfgs",
allow_failure="StrictVersion(onnx.__version__)"
" < StrictVersion('1.2') or "
"StrictVersion(onnxruntime.__version__)"
" <= StrictVersion('0.2.1')",
)
@unittest.skipIf(not onnx_built_with_ml(),
reason="Requires ONNX-ML extension.")
def test_model_logistic_regression_multi_class_liblinear_l1(self):
model, X = self._fit_model_multiclass_classification(
linear_model.LogisticRegression(solver='liblinear', penalty='l1'))
model_onnx = convert_sklearn(
model,
"multi-class logistic regression",
[("input", FloatTensorType([1, 3]))],
)
self.assertIsNotNone(model_onnx)
dump_data_and_model(
X.astype(numpy.float32),
model,
model_onnx,
basename="SklearnLogitisticRegressionMultiLiblinearL1",
allow_failure="StrictVersion(onnx.__version__)"
" < StrictVersion('1.2') or "
"StrictVersion(onnxruntime.__version__)"
" <= StrictVersion('0.2.1')",
)
@unittest.skipIf(not onnx_built_with_ml(),
reason="Requires ONNX-ML extension.")
def test_model_logistic_regression_multi_class_saga_elasticnet(self):
if _sklearn_version() < StrictVersion('0.21.0'):
model, X = self._fit_model_multiclass_classification(
linear_model.LogisticRegression(solver='saga'))
else:
model, X = self._fit_model_multiclass_classification(
linear_model.LogisticRegression(
solver='saga', penalty='elasticnet', l1_ratio=0.1))
model_onnx = convert_sklearn(
model,
"multi-class logistic regression",
[("input", FloatTensorType([1, 3]))],
)
self.assertIsNotNone(model_onnx)
dump_data_and_model(
X.astype(numpy.float32),
model,
model_onnx,
basename="SklearnLogitisticRegressionMultiSagaElasticnet",
allow_failure="StrictVersion(onnx.__version__)"
" < StrictVersion('1.2') or "
"StrictVersion(onnxruntime.__version__)"
" <= StrictVersion('0.2.1')",
)
@unittest.skipIf(not onnx_built_with_ml(),
reason="Requires ONNX-ML extension.")
def test_model_linear_svc_binary_class(self):
model, X = self._fit_model_binary_classification(LinearSVC())
model_onnx = convert_sklearn(model, "linear SVC",
[("input", FloatTensorType([1, 3]))])
self.assertIsNotNone(model_onnx)
dump_data_and_model(
X.astype(numpy.float32),
model,
model_onnx,
basename="SklearnLinearSVCBinary-NoProb",
allow_failure="StrictVersion(onnxruntime.__version__)"
" <= StrictVersion('0.2.1')",
)
@unittest.skipIf(not onnx_built_with_ml(),
reason="Requires ONNX-ML extension.")
def test_model_linear_svc_multi_class(self):
model, X = self._fit_model_multiclass_classification(LinearSVC())
model_onnx = convert_sklearn(
model,
"multi-class linear SVC",
[("input", FloatTensorType([1, 3]))],
)
self.assertIsNotNone(model_onnx)
dump_data_and_model(
X.astype(numpy.float32),
model,
model_onnx,
basename="SklearnLinearSVCMulti",
allow_failure="StrictVersion(onnxruntime.__version__)"
" <= StrictVersion('0.2.1')",
)
if __name__ == "__main__":
unittest.main()