# -------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for
# license information.
# --------------------------------------------------------------------------
import unittest
from distutils.version import StrictVersion
import numpy as np
from sklearn.datasets import load_digits, load_iris
from sklearn.decomposition import PCA, TruncatedSVD
from sklearn.model_selection import train_test_split
from sklearn.pipeline import FeatureUnion
from sklearn.preprocessing import StandardScaler, MinMaxScaler
from onnxruntime import __version__ as ort_version
from skl2onnx import convert_sklearn
from skl2onnx.common.data_types import FloatTensorType, Int64TensorType
from test_utils import dump_data_and_model
class TestSklearnAdaBoostModels(unittest.TestCase):
@unittest.skipIf(
StrictVersion(ort_version) <= StrictVersion('0.4.0'),
reason="onnxruntime too old")
def test_feature_union_default(self):
data = load_iris()
X, y = data.data, data.target
X = X.astype(np.float32)
X_train, X_test, *_ = train_test_split(X, y, test_size=0.5,
random_state=42)
model = FeatureUnion([('standard', StandardScaler()),
('minmax', MinMaxScaler())]).fit(X_train)
model_onnx = convert_sklearn(
model, 'feature union',
[('input', FloatTensorType([None, X_test.shape[1]]))])
self.assertTrue(model_onnx is not None)
dump_data_and_model(X_test,
model,
model_onnx,
basename="SklearnFeatureUnionDefault")
@unittest.skipIf(
StrictVersion(ort_version) <= StrictVersion('0.4.0'),
reason="onnxruntime too old")
def test_feature_union_transformer_weights_0(self):
data = load_iris()
X, y = data.data, data.target
X = X.astype(np.float32)
X_train, X_test, *_ = train_test_split(X, y, test_size=0.5,
random_state=42)
model = FeatureUnion([('standard', StandardScaler()),
('minmax', MinMaxScaler())],
transformer_weights={'standard': 2, 'minmax': 4}
).fit(X_train)
model_onnx = convert_sklearn(
model, 'feature union',
[('input', FloatTensorType([None, X_test.shape[1]]))])
self.assertTrue(model_onnx is not None)
dump_data_and_model(X_test,
model,
model_onnx,
basename="SklearnFeatureUnionTransformerWeights0")
def test_feature_union_transformer_weights_1(self):
data = load_digits()
X, y = data.data, data.target
X = X.astype(np.int64)
X_train, X_test, *_ = train_test_split(X, y, test_size=0.5,
random_state=42)
model = FeatureUnion([('pca', PCA()),
('svd', TruncatedSVD())],
transformer_weights={'pca': 10, 'svd': 3}
).fit(X_train)
model_onnx = convert_sklearn(
model, 'feature union',
[('input', Int64TensorType([None, X_test.shape[1]]))])
self.assertTrue(model_onnx is not None)
dump_data_and_model(
X_test,
model,
model_onnx,
basename="SklearnFeatureUnionTransformerWeights1-Dec4",
allow_failure="StrictVersion("
"onnxruntime.__version__)"
"<= StrictVersion('0.2.1')",
)
def test_feature_union_transformer_weights_2(self):
data = load_digits()
X, y = data.data, data.target
X = X.astype(np.float32)
X_train, X_test, *_ = train_test_split(X, y, test_size=0.5,
random_state=42)
model = FeatureUnion([('pca', PCA()),
('svd', TruncatedSVD())],
transformer_weights={'pca1': 10, 'svd2': 3}
).fit(X_train)
model_onnx = convert_sklearn(
model, 'feature union',
[('input', FloatTensorType([None, X_test.shape[1]]))])
self.assertTrue(model_onnx is not None)
dump_data_and_model(
X_test,
model,
model_onnx,
basename="SklearnFeatureUnionTransformerWeights2-Dec4",
allow_failure="StrictVersion("
"onnxruntime.__version__)"
"<= StrictVersion('0.2.1')",
)
if __name__ == "__main__":
unittest.main()
