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test_sklearn_glm_regressor_converter.py
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test_sklearn_glm_regressor_converter.py
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"""Tests GLMRegressor converter."""
import unittest
import numpy
from sklearn import datasets
from sklearn import linear_model
from sklearn.model_selection import train_test_split
from sklearn.svm import LinearSVR
from skl2onnx import convert_sklearn
from skl2onnx.common.data_types import FloatTensorType, Int64TensorType
from test_utils import dump_data_and_model
def _fit_model(model, is_int=False):
X, y = datasets.make_regression(n_features=4, random_state=0)
if is_int:
X = X.astype(numpy.int64)
X_train, X_test, y_train, _ = train_test_split(X, y, test_size=0.5,
random_state=42)
model.fit(X_train, y_train)
return model, X_test
def _fit_model_multi(model):
X, y = datasets.make_regression(n_features=4, random_state=0,
n_targets=2, n_samples=10)
X_train, X_test, y_train, _ = train_test_split(X, y, test_size=0.5,
random_state=42)
model.fit(X_train, y_train)
return model, X_test
class TestGLMRegressorConverter(unittest.TestCase):
def test_model_linear_regression(self):
model, X = _fit_model(linear_model.LinearRegression())
model_onnx = convert_sklearn(model, "linear regression",
[("input", FloatTensorType(X.shape))])
self.assertIsNotNone(model_onnx)
dump_data_and_model(
X.astype(numpy.float32),
model,
model_onnx,
basename="SklearnLinearRegression-Dec4",
allow_failure="StrictVersion("
"onnxruntime.__version__)"
"<= StrictVersion('0.2.1')",
)
def test_model_linear_regression_int(self):
model, X = _fit_model(linear_model.LinearRegression(), is_int=True)
model_onnx = convert_sklearn(model, "linear regression",
[("input", Int64TensorType(X.shape))])
self.assertIsNotNone(model_onnx)
dump_data_and_model(
X,
model,
model_onnx,
basename="SklearnLinearRegressionInt-Dec4",
allow_failure="StrictVersion("
"onnxruntime.__version__)"
"<= StrictVersion('0.2.1')",
)
def test_model_linear_regression_nointercept(self):
model, X = _fit_model(
linear_model.LinearRegression(fit_intercept=False))
model_onnx = convert_sklearn(model, "linear regression",
[("input", FloatTensorType(X.shape))])
self.assertIsNotNone(model_onnx)
dump_data_and_model(
X.astype(numpy.float32),
model,
model_onnx,
basename="SklearnLinearRegressionNoIntercept-Dec4",
allow_failure="StrictVersion("
"onnxruntime.__version__)"
"<= StrictVersion('0.2.1')",
)
def test_model_linear_svr(self):
model, X = _fit_model(LinearSVR())
model_onnx = convert_sklearn(model, "linear SVR",
[("input", FloatTensorType(X.shape))])
self.assertIsNotNone(model_onnx)
dump_data_and_model(
X.astype(numpy.float32),
model,
model_onnx,
basename="SklearnLinearSvr-Dec4",
allow_failure="StrictVersion("
"onnxruntime.__version__)"
"<= StrictVersion('0.2.1')",
)
def test_model_linear_svr_int(self):
model, X = _fit_model(LinearSVR(), is_int=True)
model_onnx = convert_sklearn(model, "linear SVR",
[("input", Int64TensorType(X.shape))])
self.assertIsNotNone(model_onnx)
dump_data_and_model(
X,
model,
model_onnx,
basename="SklearnLinearSvrInt-Dec4",
allow_failure="StrictVersion("
"onnxruntime.__version__)"
"<= StrictVersion('0.2.1')",
)
def test_model_ridge(self):
model, X = _fit_model(linear_model.Ridge())
model_onnx = convert_sklearn(model, "ridge regression",
[("input", FloatTensorType(X.shape))])
self.assertIsNotNone(model_onnx)
dump_data_and_model(
X.astype(numpy.float32),
model,
model_onnx,
basename="SklearnRidge-Dec4",
allow_failure="StrictVersion("
"onnxruntime.__version__)"
"<= StrictVersion('0.2.1')",
)
def test_model_ridge_int(self):
model, X = _fit_model(linear_model.Ridge(), is_int=True)
model_onnx = convert_sklearn(model, "ridge regression",
[("input", Int64TensorType(X.shape))])
self.assertIsNotNone(model_onnx)
dump_data_and_model(
X,
model,
model_onnx,
basename="SklearnRidgeInt-Dec4",
allow_failure="StrictVersion("
"onnxruntime.__version__)"
"<= StrictVersion('0.2.1')",
)
def test_model_sgd_regressor(self):
model, X = _fit_model(linear_model.SGDRegressor())
model_onnx = convert_sklearn(
model,
"scikit-learn SGD regression",
[("input", FloatTensorType(X.shape))],
)
self.assertIsNotNone(model_onnx)
dump_data_and_model(
X.astype(numpy.float32),
model,
model_onnx,
basename="SklearnSGDRegressor-Dec4",
allow_failure="StrictVersion("
"onnxruntime.__version__)"
"<= StrictVersion('0.2.1')",
)
def test_model_sgd_regressor_int(self):
model, X = _fit_model(linear_model.SGDRegressor(), is_int=True)
model_onnx = convert_sklearn(model, "SGD regression",
[("input", Int64TensorType(X.shape))])
self.assertIsNotNone(model_onnx)
dump_data_and_model(
X,
model,
model_onnx,
basename="SklearnSGDRegressorInt-Dec4",
allow_failure="StrictVersion("
"onnxruntime.__version__)"
"<= StrictVersion('0.2.1')",
)
def test_model_elastic_net_regressor(self):
model, X = _fit_model(linear_model.ElasticNet())
model_onnx = convert_sklearn(
model,
"scikit-learn elastic-net regression",
[("input", FloatTensorType(X.shape))],
)
self.assertIsNotNone(model_onnx)
dump_data_and_model(
X.astype(numpy.float32),
model,
model_onnx,
basename="SklearnElasticNet-Dec4",
allow_failure="StrictVersion("
"onnxruntime.__version__)"
"<= StrictVersion('0.2.1')",
)
def test_model_elastic_net_cv_regressor(self):
model, X = _fit_model(linear_model.ElasticNetCV())
model_onnx = convert_sklearn(
model,
"scikit-learn elastic-net regression",
[("input", FloatTensorType(X.shape))],
)
self.assertIsNotNone(model_onnx)
dump_data_and_model(
X.astype(numpy.float32),
model,
model_onnx,
basename="SklearnElasticNetCV-Dec4",
allow_failure="StrictVersion("
"onnxruntime.__version__)"
"<= StrictVersion('0.2.1')",
)
def test_model_elastic_net_regressor_int(self):
model, X = _fit_model(linear_model.ElasticNet(), is_int=True)
model_onnx = convert_sklearn(model, "elastic net regression",
[("input", Int64TensorType(X.shape))])
self.assertIsNotNone(model_onnx)
dump_data_and_model(
X,
model,
model_onnx,
basename="SklearnElasticNetRegressorInt-Dec4",
allow_failure="StrictVersion("
"onnxruntime.__version__)"
"<= StrictVersion('0.2.1')",
)
def test_model_lars(self):
model, X = _fit_model(linear_model.Lars())
model_onnx = convert_sklearn(model, "lars",
[("input", FloatTensorType(X.shape))])
self.assertIsNotNone(model_onnx)
dump_data_and_model(
X.astype(numpy.float32),
model,
model_onnx,
basename="SklearnLars-Dec4",
allow_failure="StrictVersion("
"onnxruntime.__version__)"
"<= StrictVersion('0.2.1')",
)
def test_model_lars_cv(self):
model, X = _fit_model(linear_model.LarsCV())
model_onnx = convert_sklearn(model, "lars",
[("input", FloatTensorType(X.shape))])
self.assertIsNotNone(model_onnx)
dump_data_and_model(
X.astype(numpy.float32),
model,
model_onnx,
basename="SklearnLarsCV-Dec4",
allow_failure="StrictVersion("
"onnxruntime.__version__)"
"<= StrictVersion('0.2.1')",
)
def test_model_lasso_lars(self):
model, X = _fit_model(linear_model.LassoLars(alpha=0.01))
model_onnx = convert_sklearn(model, "lasso lars",
[("input", FloatTensorType(X.shape))])
self.assertIsNotNone(model_onnx)
dump_data_and_model(
X.astype(numpy.float32),
model,
model_onnx,
basename="SklearnLassoLars-Dec4",
allow_failure="StrictVersion("
"onnxruntime.__version__)"
"<= StrictVersion('0.2.1')",
)
def test_model_lasso_lars_cv(self):
model, X = _fit_model(linear_model.LassoLarsCV())
model_onnx = convert_sklearn(model, "lasso lars cv",
[("input", FloatTensorType(X.shape))])
self.assertIsNotNone(model_onnx)
dump_data_and_model(
X.astype(numpy.float32),
model,
model_onnx,
basename="SklearnLassoLarsCV-Dec4",
allow_failure="StrictVersion("
"onnxruntime.__version__)"
"<= StrictVersion('0.2.1')",
)
def test_model_lasso_lars_ic(self):
model, X = _fit_model(linear_model.LassoLarsIC())
model_onnx = convert_sklearn(model, "lasso lars cv",
[("input", FloatTensorType(X.shape))])
self.assertIsNotNone(model_onnx)
dump_data_and_model(
X.astype(numpy.float32),
model,
model_onnx,
basename="SklearnLassoLarsIC-Dec4",
allow_failure="StrictVersion("
"onnxruntime.__version__)"
"<= StrictVersion('0.2.1')",
)
def test_model_lasso_cv(self):
model, X = _fit_model(linear_model.LassoCV())
model_onnx = convert_sklearn(model, "lasso cv",
[("input", FloatTensorType(X.shape))])
self.assertIsNotNone(model_onnx)
dump_data_and_model(
X.astype(numpy.float32),
model,
model_onnx,
basename="SklearnLassoCV-Dec4",
allow_failure="StrictVersion("
"onnxruntime.__version__)"
"<= StrictVersion('0.2.1')",
)
def test_model_lasso_lars_int(self):
model, X = _fit_model(linear_model.LassoLars(), is_int=True)
model_onnx = convert_sklearn(model, "lasso lars",
[("input", Int64TensorType(X.shape))])
self.assertIsNotNone(model_onnx)
dump_data_and_model(
X,
model,
model_onnx,
basename="SklearnLassoLarsInt-Dec4",
allow_failure="StrictVersion("
"onnxruntime.__version__)"
"<= StrictVersion('0.2.1')",
)
def test_model_multi_linear_regression(self):
model, X = _fit_model_multi(linear_model.LinearRegression())
model_onnx = convert_sklearn(model, "linear regression",
[("input", FloatTensorType([1, 4]))])
self.assertIsNotNone(model_onnx)
dump_data_and_model(
X.astype(numpy.float32),
model,
model_onnx,
verbose=False,
basename="SklearnLinearRegression-Dec4",
allow_failure="StrictVersion("
"onnxruntime.__version__)"
"<= StrictVersion('0.2.1')",
)
if __name__ == "__main__":
unittest.main()