def max_onnxruntime_opset():
"""
See `Versioning.md
<https://github.com/microsoft/onnxruntime/blob/
master/docs/Versioning.md>`_.
"""
vi = pv.Version(ort_version.split('+')[0])
if vi >= pv.Version("1.12.0"):
return 17
if vi >= pv.Version("1.11.0"):
return 16
if vi >= pv.Version("1.10.0"):
return 15
if vi >= pv.Version("1.9.0"):
return 15
if vi >= pv.Version("1.8.0"):
return 14
if vi >= pv.Version("1.6.0"):
return 13
if vi >= pv.Version("1.3.0"):
return 12
if vi >= pv.Version("1.0.0"):
return 11
if vi >= pv.Version("0.4.0"):
return 10
if vi >= pv.Version("0.3.0"):
return 9
return 8
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.exceptions import ConvergenceWarning
try:
from sklearn.utils._testing import ignore_warnings
except ImportError:
from sklearn.utils.testing import ignore_warnings
from skl2onnx import convert_sklearn
from skl2onnx.common.data_types import (FloatTensorType, Int64TensorType)
from test_utils import (dump_data_and_model, dump_multiple_classification,
fit_classification_model,
fit_multilabel_classification_model, TARGET_OPSET)
warnings_to_skip = (DeprecationWarning, FutureWarning, ConvergenceWarning)
ort_version = '.'.join(ort_version.split('.')[:2])
class TestOneVsRestClassifierConverter(unittest.TestCase):
@ignore_warnings(category=warnings_to_skip)
def test_ovr(self):
model = OneVsRestClassifier(LogisticRegression())
dump_multiple_classification(model, target_opset=TARGET_OPSET)
@unittest.skipIf(pv.Version(ort_version) <= pv.Version('1.4.0'),
reason="onnxruntime too old")
@ignore_warnings(category=warnings_to_skip)
def test_ovr_rf(self):
model = OneVsRestClassifier(
RandomForestClassifier(n_estimators=2, max_depth=2))
model, X = fit_classification_model(model,
"""Tests scikit-LabelEncoder converter"""
import unittest
import packaging.version as pv
import numpy as np
from onnxruntime import __version__ as ort_version
from sklearn.preprocessing import LabelEncoder
from skl2onnx import convert_sklearn
from skl2onnx.common.data_types import (
FloatTensorType,
Int64TensorType,
StringTensorType,
)
from test_utils import dump_data_and_model, TARGET_OPSET
ort_version = ".".join(ort_version.split('.')[:2])
class TestSklearnLabelEncoderConverter(unittest.TestCase):
@unittest.skipIf(pv.Version(ort_version) < pv.Version("0.3.0"),
reason="onnxruntime too old")
def test_model_label_encoder(self):
model = LabelEncoder()
data = ["str3", "str2", "str0", "str1", "str3"]
model.fit(data)
model_onnx = convert_sklearn(model,
"scikit-learn label encoder",
[("input", StringTensorType([None]))],
target_opset=TARGET_OPSET)
self.assertTrue(model_onnx is not None)
self.assertTrue(model_onnx.graph.node is not None)
from skl2onnx.common._apply_operation import apply_less
except ImportError:
# onnxconverter-common is too old
apply_less = None
from skl2onnx import convert_sklearn
from skl2onnx.common.data_types import (
BooleanTensorType,
FloatTensorType,
Int64TensorType,
)
from skl2onnx.operator_converters.ada_boost import _scikit_learn_before_022
from onnxruntime import __version__ as ort_version
from test_utils import (dump_data_and_model, fit_regression_model,
TARGET_OPSET)
ort_version = ort_version.split('+')[0]
class TestSklearnSVM(unittest.TestCase):
def _fit_binary_classification(self, model):
iris = load_iris()
X = iris.data[:, :3]
y = iris.target
y[y == 2] = 1
model.fit(X, y)
return model, X[:5].astype(numpy.float32)
def _fit_one_class_svm(self, model):
iris = load_iris()
X = iris.data[:, :3]
model.fit(X)
import unittest
import numpy as np
try:
from sklearn.linear_model import GammaRegressor
except ImportError:
GammaRegressor = None
from onnxruntime import __version__ as ort_version
from skl2onnx import convert_sklearn
from skl2onnx.common.data_types import (
FloatTensorType, )
from test_utils import (dump_data_and_model, TARGET_OPSET)
ort_version = ".".join(ort_version.split(".")[:2])
class TestGammaRegressorConverter(unittest.TestCase):
@unittest.skipIf(GammaRegressor is None, reason="scikit-learn<1.0")
def test_gamma_regressor(self):
model = GammaRegressor()
X = np.array([[1, 2], [2, 3], [3, 4], [4, 3]])
y = np.array([19, 26, 33, 30])
model.fit(X, y)
test_x = np.array([[1, 0], [2, 8]])
model_onnx = convert_sklearn(
model,
"scikit-learn Gamma Regressor",
