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test_sklearn_pipeline_within_pipeline.py
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test_sklearn_pipeline_within_pipeline.py
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"""
Tests pipeline within pipelines.
"""
import numpy as np
import unittest
try:
from sklearn.compose import ColumnTransformer
except ImportError:
# not available in 0.19
ColumnTransformer = None
try:
from sklearn.impute import SimpleImputer
except ImportError:
from sklearn.preprocessing import Imputer as SimpleImputer
from sklearn.decomposition import PCA
from sklearn.linear_model import LogisticRegression
from sklearn.naive_bayes import MultinomialNB
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import RobustScaler, StandardScaler
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
class TestSklearnPipelineWithinPipeline(unittest.TestCase):
@unittest.skipIf(not onnx_built_with_ml(),
reason="Requires ONNX-ML extension.")
def test_pipeline_pca_pipeline_minmax(self):
model = Pipeline(
memory=None,
steps=[
(
"PCA",
PCA(
copy=True,
iterated_power="auto",
n_components=0.15842105263157896,
random_state=None,
tol=0.0,
svd_solver="auto",
whiten=False,
),
),
(
"Pipeline",
Pipeline(
memory=None,
steps=[(
"MinMax scaler",
MinMaxScaler(
copy=True,
feature_range=(0, 3.7209871159509307),
),
)],
),
),
],
)
data = np.array([[0, 0], [0, 0], [1, 1], [1, 1]], dtype=np.float32)
y = [0, 0, 1, 1]
model.fit(data, y)
model_onnx = convert_sklearn(
model,
"pipelinewithinpipeline",
[("input", FloatTensorType(data.shape))],
)
self.assertTrue(model_onnx is not None)
dump_data_and_model(
data,
model,
model_onnx,
basename="SklearnPipelinePcaPipelineMinMax",
allow_failure="StrictVersion(onnxruntime.__version__)"
" <= StrictVersion('0.2.1')",
)
@unittest.skipIf(not onnx_built_with_ml(),
reason="Requires ONNX-ML extension.")
def test_pipeline_pca_pipeline_none_lin(self):
model = Pipeline(
memory=None,
steps=[
(
"PCA",
PCA(
copy=True,
iterated_power="auto",
n_components=0.15842105263157896,
random_state=None,
tol=0.0,
svd_solver="auto",
whiten=False,
),
),
(
"Pipeline",
Pipeline(
memory=None,
steps=[
(
"MinMax scaler",
MinMaxScaler(
copy=True,
feature_range=(0, 3.7209871159509307),
),
),
("logreg", LogisticRegression(solver="liblinear")),
],
),
),
],
)
data = np.array([[0, 0], [0, 0], [1, 1], [1, 1]], dtype=np.float32)
y = [0, 0, 1, 1]
model.fit(data, y)
model_onnx = convert_sklearn(
model,
"pipelinewithinpipeline",
[("input", FloatTensorType(data.shape))],
)
self.assertTrue(model_onnx is not None)
dump_data_and_model(
data,
model,
model_onnx,
basename="SklearnPipelinePcaPipelineMinMaxLogReg",
allow_failure="StrictVersion(onnxruntime.__version__)"
" <= StrictVersion('0.2.1')",
)
@unittest.skipIf(not onnx_built_with_ml(),
reason="Requires ONNX-ML extension.")
def test_pipeline_pca_pipeline_multinomial(self):
model = Pipeline(
memory=None,
steps=[
(
"PCA",
PCA(
copy=True,
iterated_power="auto",
n_components=2,
random_state=None,
svd_solver="auto",
tol=0.0,
whiten=False,
),
),
(
"Pipeline",
Pipeline(
memory=None,
steps=[
(
"MinMax scaler",
MinMaxScaler(
copy=True,
feature_range=(0, 3.7209871159509307),
),
),
(
"MultinomialNB",
MultinomialNB(
alpha=0.7368421052631579,
class_prior=None,
fit_prior=True,
),
),
],
),
),
],
)
data = np.array(
[[0, 0, 0], [0, 0, 0.1], [1, 1, 1.1], [1, 1.1, 1]],
dtype=np.float32,
)
y = [0, 0, 1, 1]
model.fit(data, y)
model_onnx = convert_sklearn(
model,
"pipelinewithinpipeline",
[("input", FloatTensorType(data.shape))],
)
self.assertTrue(model_onnx is not None)
dump_data_and_model(
data,
model,
model_onnx,
basename="SklearnPipelinePcaPipelineMinMaxNB2",
allow_failure="StrictVersion(onnxruntime.__version__)"
" <= StrictVersion('0.2.1')",
)
@unittest.skipIf(not onnx_built_with_ml(),
reason="Requires ONNX-ML extension.")
def test_pipeline_pca_pipeline_multinomial_none(self):
model = Pipeline(
memory=None,
steps=[
(
"PCA",
PCA(
copy=True,
iterated_power="auto",
n_components=0.15842105263157896,
random_state=None,
tol=0.0,
svd_solver="auto",
whiten=False,
),
),
(
"Pipeline",
Pipeline(
memory=None,
steps=[
(
"MinMax scaler",
MinMaxScaler(
copy=True,
feature_range=(0, 3.7209871159509307),
),
),
(
"MultinomialNB",
MultinomialNB(
alpha=0.7368421052631579,
class_prior=None,
fit_prior=True,
),
),
],
),
),
],
)
data = np.array([[0, 0], [0, 0], [1, 1], [1, 1]], dtype=np.float32)
y = [0, 0, 1, 1]
model.fit(data, y)
model_onnx = convert_sklearn(
model,
"pipelinewithinpipeline",
[("input", FloatTensorType(data.shape))],
)
self.assertTrue(model_onnx is not None)
dump_data_and_model(
data,
model,
model_onnx,
basename="SklearnPipelinePcaPipelineMinMaxNBNone",
allow_failure="StrictVersion(onnxruntime.__version__)"
" <= StrictVersion('0.2.1')",
)
@unittest.skipIf(
ColumnTransformer is None,
reason="ColumnTransformer not available in 0.19",
)
@unittest.skipIf(not onnx_built_with_ml(),
reason="Requires ONNX-ML extension.")
def test_pipeline_column_transformer_pipeline_imputer_scaler_lr(self):
X = np.array([[1, 2], [3, np.nan], [3, 0]], dtype=np.float32)
y = np.array([1, 0, 1])
model = Pipeline([
(
"ct",
ColumnTransformer([
(
"pipeline1",
Pipeline([
("imputer", SimpleImputer()),
("scaler", StandardScaler()),
]),
[0],
),
(
"pipeline2",
Pipeline([
("imputer", SimpleImputer()),
("scaler", RobustScaler()),
]),
[1],
),
]),
),
("lr", LogisticRegression(solver="liblinear")),
])
model.fit(X, y)
model_onnx = convert_sklearn(
model,
"pipelinewithinpipeline",
[("input", FloatTensorType([None, X.shape[1]]))],
)
self.assertTrue(model_onnx is not None)
dump_data_and_model(
X,
model,
model_onnx,
basename="SklearnPipelineCTPipelineImputerScalerLR",
allow_failure="StrictVersion(onnxruntime.__version__)"
" <= StrictVersion('0.2.1')",
)
if __name__ == "__main__":
unittest.main()