"""
Tests scikit-learn's feature selection converters
"""
import unittest
import numpy as np
from sklearn.datasets import load_breast_cancer
from sklearn.feature_selection import (
GenericUnivariateSelect,
RFE,
RFECV,
SelectFdr,
SelectFpr,
SelectFromModel,
)
from sklearn.feature_selection import (
SelectFwe,
SelectKBest,
SelectPercentile,
VarianceThreshold,
)
from sklearn.svm import SVR
from skl2onnx import convert_sklearn
from skl2onnx.common.data_types import Int64TensorType, FloatTensorType
from test_utils import dump_data_and_model
class TestSklearnFeatureSelectionConverters(unittest.TestCase):
def test_generic_univariate_select_int(self):
model = GenericUnivariateSelect()
X = np.array(
[[1, 2, 3, 1], [0, 3, 1, 4], [3, 5, 6, 1], [1, 2, 1, 5]],
dtype=np.int64,
)
y = np.array([0, 1, 0, 1])
model.fit(X, y)
model_onnx = convert_sklearn(
model,
"generic univariate select",
[("input", Int64TensorType([None, X.shape[1]]))],
)
self.assertTrue(model_onnx is not None)
dump_data_and_model(
X,
model,
model_onnx,
basename="SklearnGenericUnivariateSelect",
# Operator cast-1 is not implemented in onnxruntime
allow_failure="StrictVersion(onnx.__version__)"
" < StrictVersion('1.2') or "
"StrictVersion(onnxruntime.__version__)"
" <= StrictVersion('0.2.1')",
)
def test_rfe_int(self):
model = RFE(estimator=SVR(kernel="linear"))
X = np.array(
[[1, 2, 3, 1], [0, 3, 1, 4], [3, 5, 6, 1], [1, 2, 1, 5]],
dtype=np.int64,
)
y = np.array([0, 1, 0, 1])
model.fit(X, y)
model_onnx = convert_sklearn(
model, "rfe", [("input", Int64TensorType([None, X.shape[1]]))])
self.assertTrue(model_onnx is not None)
dump_data_and_model(
X,
model,
model_onnx,
basename="SklearnRFE",
methods=["transform"],
allow_failure="StrictVersion(onnx.__version__)"
" < StrictVersion('1.2') or "
"StrictVersion(onnxruntime.__version__)"
" <= StrictVersion('0.2.1')",
)
def test_rfecv_int(self):
model = RFECV(estimator=SVR(kernel="linear"), cv=3)
X = np.array(
[[1, 2, 3, 1], [0, 3, 1, 4], [3, 5, 6, 1], [1, 2, 1, 5]],
dtype=np.int64,
)
y = np.array([0, 1, 0, 1])
model.fit(X, y)
model_onnx = convert_sklearn(
model, "rfecv", [("input", Int64TensorType([None, X.shape[1]]))])
self.assertTrue(model_onnx is not None)
dump_data_and_model(
X,
model,
model_onnx,
basename="SklearnRFECV",
methods=["transform"],
allow_failure="StrictVersion(onnx.__version__)"
" < StrictVersion('1.2') or "
"StrictVersion(onnxruntime.__version__)"
" <= StrictVersion('0.2.1')",
)
def test_select_fdr_int(self):
model = SelectFdr()
X, y = load_breast_cancer(return_X_y=True)
model.fit(X, y)
model_onnx = convert_sklearn(
model, "select fdr",
[("input", Int64TensorType([None, X.shape[1]]))])
self.assertTrue(model_onnx is not None)
dump_data_and_model(
X.astype(np.int64),
model,
model_onnx,
basename="SklearnSelectFdr",
allow_failure="StrictVersion(onnx.__version__)"
" < StrictVersion('1.2') or "
"StrictVersion(onnxruntime.__version__)"
" <= StrictVersion('0.2.1')",
)
def test_select_fpr_int(self):
model = SelectFpr()
X = np.array(
[[1, 2, 3, 1], [0, 3, 1, 4], [3, 5, 6, 1], [1, 2, 1, 5]],
dtype=np.int64,
)
y = np.array([0, 1, 0, 1])
model.fit(X, y)
model_onnx = convert_sklearn(
model, "select fpr",
[("input", Int64TensorType([None, X.shape[1]]))])
self.assertTrue(model_onnx is not None)
dump_data_and_model(
X,
model,
model_onnx,
basename="SklearnSelectFpr",
allow_failure="StrictVersion(onnx.__version__)"
" < StrictVersion('1.2') or "
"StrictVersion(onnxruntime.__version__)"
" <= StrictVersion('0.2.1')",
)
def test_select_from_model_int(self):
model = SelectFromModel(estimator=SVR(kernel="linear"))
X = np.array(
[[1, 2, 3, 1], [0, 3, 1, 4], [3, 5, 6, 1], [1, 2, 1, 5]],
dtype=np.int64,
)
y = np.array([0, 1, 0, 1])
model.fit(X, y)
model_onnx = convert_sklearn(
model,
"select from model",
[("input", Int64TensorType([None, X.shape[1]]))],
)
self.assertTrue(model_onnx is not None)
dump_data_and_model(
X,
model,
model_onnx,
basename="SklearnSelectFromModel",
allow_failure="StrictVersion(onnx.__version__)"
" < StrictVersion('1.2') or "
"StrictVersion(onnxruntime.__version__)"
" <= StrictVersion('0.2.1')",
)
def test_select_fwe_int(self):
model = SelectFwe()
X, y = load_breast_cancer(return_X_y=True)
model.fit(X, y)
model_onnx = convert_sklearn(
model, "select fwe",
[("input", Int64TensorType([None, X.shape[1]]))])
self.assertTrue(model_onnx is not None)
dump_data_and_model(
X.astype(np.int64),
model,
model_onnx,
basename="SklearnSelectFwe",
allow_failure="StrictVersion(onnx.__version__)"
" < StrictVersion('1.2') or "
"StrictVersion(onnxruntime.__version__)"
" <= StrictVersion('0.2.1')",
)
def test_select_k_best_int(self):
model = SelectKBest(k="all")
X = np.array(
[[1, 2, 3, 1], [0, 3, 1, 4], [3, 5, 6, 1], [1, 2, 1, 5]],
dtype=np.int64,
)
y = np.array([0, 1, 0, 1])
model.fit(X, y)
model_onnx = convert_sklearn(
model,
"select k best",
[("input", Int64TensorType([None, X.shape[1]]))],
)
self.assertTrue(model_onnx is not None)
dump_data_and_model(
X,
model,
model_onnx,
basename="SklearnSelectKBest",
allow_failure="StrictVersion(onnx.__version__)"
" < StrictVersion('1.2') or "
"StrictVersion(onnxruntime.__version__)"
" <= StrictVersion('0.2.1')",
)
def test_select_percentile_int(self):
model = SelectPercentile()
X = np.array(
[[1, 2, 3, 1], [0, 3, 1, 4], [3, 5, 6, 1], [1, 2, 1, 5]],
dtype=np.int64,
)
y = np.array([0, 1, 0, 1])
model.fit(X, y)
model_onnx = convert_sklearn(
model,
"select percentile",
[("input", Int64TensorType([None, X.shape[1]]))],
)
self.assertTrue(model_onnx is not None)
dump_data_and_model(
X,
model,
model_onnx,
basename="SklearnSelectPercentile",
allow_failure="StrictVersion(onnx.__version__)"
" < StrictVersion('1.2') or "
"StrictVersion(onnxruntime.__version__)"
" <= StrictVersion('0.2.1')",
)
def test_variance_threshold_int(self):
model = VarianceThreshold()
X = np.array(
[[1, 2, 3, 1], [0, 3, 1, 4], [3, 5, 6, 1], [1, 2, 1, 5]],
dtype=np.int64,
)
y = np.array([0, 1, 0, 1])
model.fit(X, y)
model_onnx = convert_sklearn(
model,
"variance threshold",
[("input", Int64TensorType([None, X.shape[1]]))],
)
self.assertTrue(model_onnx is not None)
dump_data_and_model(
X,
model,
model_onnx,
basename="SklearnVarianceThreshold",
allow_failure="StrictVersion(onnx.__version__)"
" < StrictVersion('1.2') or "
"StrictVersion(onnxruntime.__version__)"
" <= StrictVersion('0.2.1')",
)
def test_generic_univariate_select_float(self):
model = GenericUnivariateSelect()
X = np.array(
[[1, 2, 3, 1], [0, 3, 1, 4], [3, 5, 6, 1], [1, 2, 1, 5]],
dtype=np.float32,
)
y = np.array([0, 1, 0, 1])
model.fit(X, y)
model_onnx = convert_sklearn(
model,
"generic univariate select",
[("input", FloatTensorType([None, X.shape[1]]))],
)
self.assertTrue(model_onnx is not None)
dump_data_and_model(
X,
model,
model_onnx,
basename="SklearnGenericUnivariateSelect",
allow_failure="StrictVersion(onnx.__version__)"
" < StrictVersion('1.2') or "
"StrictVersion(onnxruntime.__version__)"
" <= StrictVersion('0.2.1')",
)
def test_rfe_float(self):
model = RFE(estimator=SVR(kernel="linear"))
X = np.array(
[[1, 2, 3, 1], [0, 3, 1, 4], [3, 5, 6, 1], [1, 2, 1, 5]],
dtype=np.float32,
)
y = np.array([0, 1, 0, 1])
model.fit(X, y)
model_onnx = convert_sklearn(
model, "rfe", [("input", FloatTensorType([None, X.shape[1]]))])
self.assertTrue(model_onnx is not None)
dump_data_and_model(
X,
model,
model_onnx,
basename="SklearnRFE",
methods=["transform"],
allow_failure="StrictVersion(onnx.__version__)"
" < StrictVersion('1.2') or "
"StrictVersion(onnxruntime.__version__)"
" <= StrictVersion('0.2.1')",
)
def test_rfecv_float(self):
model = RFECV(estimator=SVR(kernel="linear"), cv=3)
X = np.array(
[[1, 2, 3, 1], [0, 3, 1, 4], [3, 5, 6, 1], [1, 2, 1, 5]],
dtype=np.float32,
)
y = np.array([0, 1, 0, 1])
model.fit(X, y)
model_onnx = convert_sklearn(
model, "rfecv", [("input", FloatTensorType([None, X.shape[1]]))])
self.assertTrue(model_onnx is not None)
dump_data_and_model(
X,
model,
model_onnx,
basename="SklearnRFECV",
methods=["transform"],
allow_failure="StrictVersion(onnx.__version__)"
" < StrictVersion('1.2') or "
"StrictVersion(onnxruntime.__version__)"
" <= StrictVersion('0.2.1')",
)
def test_select_fdr_float(self):
model = SelectFdr()
X, y = load_breast_cancer(return_X_y=True)
model.fit(X, y)
model_onnx = convert_sklearn(
model, "select fdr",
[("input", FloatTensorType([None, X.shape[1]]))])
self.assertTrue(model_onnx is not None)
dump_data_and_model(
X.astype(np.float32),
model,
model_onnx,
basename="SklearnSelectFdr",
allow_failure="StrictVersion(onnx.__version__)"
" < StrictVersion('1.2') or "
"StrictVersion(onnxruntime.__version__)"
" <= StrictVersion('0.2.1')",
)
def test_select_fpr_float(self):
model = SelectFpr()
X = np.array(
[[1, 2, 3, 1], [0, 3, 1, 4], [3, 5, 6, 1], [1, 2, 1, 5]],
dtype=np.float32,
)
y = np.array([0, 1, 0, 1])
model.fit(X, y)
model_onnx = convert_sklearn(
model, "select fpr",
[("input", FloatTensorType([None, X.shape[1]]))])
self.assertTrue(model_onnx is not None)
dump_data_and_model(
X,
model,
model_onnx,
basename="SklearnSelectFpr",
allow_failure="StrictVersion(onnx.__version__)"
" < StrictVersion('1.2') or "
"StrictVersion(onnxruntime.__version__)"
" <= StrictVersion('0.2.1')",
)
def test_select_from_model_float(self):
model = SelectFromModel(estimator=SVR(kernel="linear"))
X = np.array(
[[1, 2, 3, 1], [0, 3, 1, 4], [3, 5, 6, 1], [1, 2, 1, 5]],
dtype=np.float32,
)
y = np.array([0, 1, 0, 1])
model.fit(X, y)
model_onnx = convert_sklearn(
model,
"select from model",
[("input", FloatTensorType([None, X.shape[1]]))],
)
self.assertTrue(model_onnx is not None)
dump_data_and_model(
X,
model,
model_onnx,
basename="SklearnSelectFromModel",
allow_failure="StrictVersion(onnx.__version__)"
" < StrictVersion('1.2') or "
"StrictVersion(onnxruntime.__version__)"
" <= StrictVersion('0.2.1')",
)
def test_select_from_model_float_nomodel(self):
model = SelectFromModel(
estimator=SVR(kernel="linear"), threshold=1e5)
X = np.array(
[[1, 2, 3, 1], [0, 3, 1, 4], [3, 5, 6, 1], [1, 2, 1, 5]],
dtype=np.float32,
)
y = np.array([0, 1, 0, 1])
model.fit(X, y)
with self.assertRaises(RuntimeError):
convert_sklearn(
model, "select from model",
[("input", FloatTensorType([None, X.shape[1]]))])
def test_select_fwe_float(self):
model = SelectFwe()
X, y = load_breast_cancer(return_X_y=True)
model.fit(X, y)
model_onnx = convert_sklearn(
model, "select fwe",
[("input", FloatTensorType([None, X.shape[1]]))])
self.assertTrue(model_onnx is not None)
dump_data_and_model(
X.astype(np.float32),
model,
model_onnx,
basename="SklearnSelectFwe",
allow_failure="StrictVersion(onnx.__version__)"
" < StrictVersion('1.2') or "
"StrictVersion(onnxruntime.__version__)"
" <= StrictVersion('0.2.1')",
)
def test_select_k_best_float(self):
model = SelectKBest(k="all")
X = np.array(
[[1, 2, 3, 1], [0, 3, 1, 4], [3, 5, 6, 1], [1, 2, 1, 5]],
dtype=np.float32,
)
y = np.array([0, 1, 0, 1])
model.fit(X, y)
model_onnx = convert_sklearn(
model,
"select k best",
[("input", FloatTensorType([None, X.shape[1]]))],
)
self.assertTrue(model_onnx is not None)
dump_data_and_model(
X,
model,
model_onnx,
basename="SklearnSelectKBest",
allow_failure="StrictVersion(onnx.__version__)"
" < StrictVersion('1.2') or "
"StrictVersion(onnxruntime.__version__)"
" <= StrictVersion('0.2.1')",
)
def test_select_percentile_float(self):
model = SelectPercentile()
X = np.array(
[[1, 2, 3, 1], [0, 3, 1, 4], [3, 5, 6, 1], [1, 2, 1, 5]],
dtype=np.float32,
)
y = np.array([0, 1, 0, 1])
model.fit(X, y)
model_onnx = convert_sklearn(
model,
"select percentile",
[("input", FloatTensorType([None, X.shape[1]]))],
)
self.assertTrue(model_onnx is not None)
dump_data_and_model(
X,
model,
model_onnx,
basename="SklearnSelectPercentile",
allow_failure="StrictVersion(onnx.__version__)"
" < StrictVersion('1.2') or "
"StrictVersion(onnxruntime.__version__)"
" <= StrictVersion('0.2.1')",
)
def test_variance_threshold_float(self):
model = VarianceThreshold()
X = np.array(
[[1, 2, 3, 1], [0, 3, 1, 4], [3, 5, 6, 1], [1, 2, 1, 5]],
dtype=np.float32,
)
y = np.array([0, 1, 0, 1])
model.fit(X, y)
model_onnx = convert_sklearn(
model,
"variance threshold",
[("input", FloatTensorType([None, X.shape[1]]))],
)
self.assertTrue(model_onnx is not None)
dump_data_and_model(
X,
model,
model_onnx,
basename="SklearnVarianceThreshold",
allow_failure="StrictVersion(onnx.__version__)"
" < StrictVersion('1.2') or "
"StrictVersion(onnxruntime.__version__)"
" <= StrictVersion('0.2.1')",
)
if __name__ == "__main__":
unittest.main()
