def convert_model_to_onnx(model: Any):
"""
Helper function to convert a ML model to onnx format
"""
if isinstance(model, model_classes_keras):
return onnxmltools.convert_keras(model)
if isinstance(model, model_classes_sklearn):
return onnxmltools.convert_sklearn(model)
if 'xgboost' in model.__repr__():
return onnxmltools.convert_sklearn(model)
if isinstance(model, model_classes_scipy):
raise Exception("Pytorch models not yet supported to onnx")
else:
raise Exception(
"Attempt to convert unsupported model to onnx: {model}")
def sk_model(sk_file_path):
sk_load_model = pickle.load(sk_file_path)
sk2onnx = onnxmltools.convert_sklearn(sk_load_model)
# Save as protobuf
sk2onnx = onnxmltools.utils.save_model(sk2onnx,
'.path/to/save/onnx/model.onnx')
return coreml2onnx
def _test_binarizer_converter(self, threshold):
warnings.filterwarnings("ignore")
X = np.array([[1, 2, 3], [4, 3, 0], [0, 1, 4], [0, 5, 6]],
dtype=np.float32)
# Create SKL model for testing
model = Binarizer(threshold=threshold)
model.fit(X)
# Create ONNX-ML model
onnx_ml_model = convert_sklearn(model,
initial_types=[
("float_input",
FloatTensorType_onnx(X.shape))
])
# Create ONNX model by calling converter
onnx_model = convert(onnx_ml_model, "onnx", X)
# Get the predictions for the ONNX-ML model
session = ort.InferenceSession(onnx_ml_model.SerializeToString())
output_names = [
session.get_outputs()[i].name
for i in range(len(session.get_outputs()))
]
inputs = {session.get_inputs()[0].name: X}
onnx_ml_pred = session.run(output_names, inputs)[0]
# Get the predictions for the ONNX model
onnx_pred = onnx_model.transform(X)
return onnx_ml_pred, onnx_pred
def test_label_encoder_converter(self):
model = LabelEncoder()
model.fit(['str3', 'str2', 'str0', 'str1', 'str3'])
model_onnx = convert_sklearn(model, 'scikit-learn label encoder',
[('input', StringTensorType([1, 1]))])
self.assertTrue(model_onnx.graph.node is not None)
def test_model_label_encoder_str_onnxml(self):
model = LabelEncoder()
data = [
"paris",
"milan",
"amsterdam",
"tokyo",
]
model.fit(data)
onnx_ml_model = convert_sklearn(model,
initial_types=[
("input",
StringTensorType_onnx([4]))
])
onnx_model = convert(onnx_ml_model, "onnx", data)
# Get the predictions for the ONNX-ML model
session = ort.InferenceSession(onnx_ml_model.SerializeToString())
output_names = [
session.get_outputs()[i].name
for i in range(len(session.get_outputs()))
]
inputs = {session.get_inputs()[0].name: data}
onnx_ml_pred = session.run(output_names, inputs)
# Get the predictions for the ONNX model
onnx_pred = onnx_model.transform(data)
# Check that predicted values match
np.testing.assert_allclose(onnx_ml_pred[0],
onnx_pred,
rtol=1e-06,
atol=1e-06)
def _test_single_output_core(self, model):
X = [[0, 1], [1, 1], [2, 0]]
y = [100, -10, 50]
model.fit(X, y)
model_onnx = convert_sklearn(model, 'tree-based regressor',
[('input', Int64TensorType([1, 2]))])
self.assertTrue(model_onnx is not None)
def save_sklearn(model, path: str, initial_types=None, prototype=None, shape=None, dtype=None):
"""
Convert a scikit-learn model to onnx first and then save it to disk using `save_onnx`.
We use onnxmltool to do the conversion from scikit-learn to ONNX and currently not all the
scikit-learn models are supported by onnxmltools. A list of supported models can be found
in the documentation.
:param model: Scikit-learn model
:param path: Path to which the object will be serialized
:param initial_types: a python list. Each element is a tuple of a variable name and a type
defined in onnxconverter_common.data_types. If initial type is empty, we'll guess the
required information from prototype or infer it by using shape and dtype.
:param prototype: A numpy array that gives shape and type information. This is ignored if
initial_types is not None
:param shape: Shape of the input to the model. Ignored if initial_types or prototype is not None
:param dtype: redisai.DType object which represents the type of the input to the model.
Ignored if initial_types or prototype is not None
"""
if not utils.is_installed(['onnxmltools', 'skl2onnx', 'pandas']):
raise RuntimeError('Please install onnxmltools, skl2onnx & pandas to use this feature.')
from onnxmltools import convert_sklearn
if initial_types is None:
initial_types = [utils.guess_onnx_tensortype(prototype, shape, dtype)]
if not isinstance(initial_types, list):
raise TypeError((
"`initial_types` has to be a list. "
"If you have only one initial_type, put that into a list"))
serialized = convert_sklearn(model, initial_types=initial_types)
save_onnx(serialized, path)
def _test_scaler_converter(self, model):
warnings.filterwarnings("ignore")
X = np.array([[0.0, 0.0, 3.0], [1.0, -1.0, 0.0], [0.0, 2.0, 1.0],
[1.0, 0.0, -2.0]],
dtype=np.float32)
model.fit(X)
# Create ONNX-ML model
onnx_ml_model = convert_sklearn(
model,
initial_types=[("float_input", FloatTensorType([None,
X.shape[1]]))])
# Create ONNX model by calling converter
onnx_model = convert(onnx_ml_model, "onnx", X)
# Get the predictions for the ONNX-ML model
session = ort.InferenceSession(onnx_ml_model.SerializeToString())
output_names = [
session.get_outputs()[i].name
for i in range(len(session.get_outputs()))
]
inputs = {session.get_inputs()[0].name: X}
onnx_ml_pred = session.run(output_names, inputs)[0]
# Get the predictions for the ONNX model
onnx_pred = onnx_model.transform(X)
return onnx_ml_pred, onnx_pred
def _test_binary_classification_core(self, model):
X = [[0, 1], [1, 1], [2, 0]]
y = ['A', 'B', 'A']
model.fit(X, y)
model_onnx = convert_sklearn(model, 'tree-based binary classifier',
[('input', Int64TensorType([1, 2]))])
self.assertTrue(model_onnx is not None)
def test_one_hot_encoder_onnx_int(self, rtol=1e-06, atol=1e-06):
model = OneHotEncoder()
X = np.array([[1, 2, 3]], dtype=np.int32)
model.fit(X)
# Create ONNX-ML model
onnx_ml_model = convert_sklearn(model,
initial_types=[
("int_input",
IntTensorType_onnx(X.shape))
])
# Create ONNX model by calling converter
onnx_model = convert(onnx_ml_model, "onnx", X)
# Get the predictions for the ONNX-ML model
session = ort.InferenceSession(onnx_ml_model.SerializeToString())
output_names = [
session.get_outputs()[i].name
for i in range(len(session.get_outputs()))
]
inputs = {session.get_inputs()[0].name: X}
onnx_ml_pred = session.run(output_names, inputs)
# Get the predictions for the ONNX model
onnx_pred = onnx_model.transform(X)
# Check that predicted values match
np.testing.assert_allclose(onnx_ml_pred,
onnx_pred,
rtol=rtol,
atol=atol)
def test_model_one_hot_encoder(self):
# categorical_features will be removed in 0.22 (this test will fail by then).
model = OneHotEncoder()
model.fit([[1, 2, 3], [4, 3, 0], [0, 1, 4], [0, 5, 6]])
model_onnx = convert_sklearn(model, 'scikit-learn one-hot encoder',
[('input', Int64TensorType([1, 3]))])
self.assertTrue(model_onnx is not None)
def onnx_sklearn_model(sklearn_model):
import onnxmltools
from skl2onnx.common.data_types import FloatTensorType
initial_type = [('float_input', FloatTensorType([None, 4]))]
onx = onnxmltools.convert_sklearn(sklearn_model, initial_types=initial_type)
return onx
def _test_one_class_classification_core(self, model):
X = [[0., 1.], [1., 1.], [2., 0.]]
y = [1, 1, 1]
model.fit(X, y)
model_onnx = convert_sklearn(model, 'tree-based classifier',
[('input', FloatTensorType([1, 2]))])
self.assertTrue(model_onnx is not None)
def from_sklearn(
model,
inputs: Iterable[IOShape],
opset: int = DEFAULT_OPSET,
):
initial_type = ONNXConverter.convert_initial_type(inputs)
return onnxmltools.convert_sklearn(model, initial_types=initial_type, target_opset=opset)
def test_model_label_encoder_int_onnxml(self):
model = LabelEncoder()
X = np.array([1, 4, 5, 2, 0, 2], dtype=np.int64)
model.fit(X)
# Create ONNX-ML model
onnx_ml_model = convert_sklearn(model,
initial_types=[
("input",
LongTensorType_onnx(X.shape))
])
# Create ONNX model by calling converter
onnx_model = convert(onnx_ml_model, "onnx", X)
# Get the predictions for the ONNX-ML model
session = ort.InferenceSession(onnx_ml_model.SerializeToString())
output_names = [
session.get_outputs()[i].name
for i in range(len(session.get_outputs()))
]
inputs = {session.get_inputs()[0].name: X}
onnx_ml_pred = np.array(session.run(output_names, inputs)).ravel()
# Get the predictions for the ONNX model
onnx_pred = onnx_model.transform(X).ravel()
# Check that predicted values match
np.testing.assert_allclose(onnx_ml_pred,
onnx_pred,
rtol=1e-06,
atol=1e-06)
def onnx_model():
from skl2onnx.common.data_types import FloatTensorType
initial_type = [('float_input', FloatTensorType([None, X.shape[1]]))]
clf = onnxmltools.convert_sklearn(skl_model,
'iris model',
initial_types=initial_type)
onnx.save(clf, 'iris_bdt.onnx')
return clf, 'iris_bdt.onnx'
def test_robust_scaler_floats_no_scaling(self):
model = RobustScaler(with_scaling=False)
data = [[0., 0., 3.], [1., 1., 0.], [0., 2., 1.], [1., 0., 2.]]
model.fit(data)
model_onnx = convert_sklearn(model, 'scaler', [('input', FloatTensorType([1, 3]))])
self.assertTrue(model_onnx is not None)
dump_data_and_model(numpy.array(data, dtype=numpy.float32),
model, basename="SklearnRobustScalerNoScalingFloat32")
def test_max_abs_scaler(self):
model = MaxAbsScaler()
data = [[0., 0., 3.], [1., 1., 0.], [0., 2., 1.], [1., 0., 2.]]
model.fit(data)
model_onnx = convert_sklearn(model, 'scaler', [('input', FloatTensorType([1, 3]))])
self.assertTrue(model_onnx is not None)
dump_data_and_model(numpy.array(data, dtype=numpy.float32),
model, basename="SklearnMaxAbsScaler")
def test_standard_scaler(self):
model = StandardScaler()
data = [[0, 0, 3], [1, 1, 0], [0, 2, 1], [1, 0, 2]]
model.fit(data)
model_onnx = convert_sklearn(model, 'scaler', [('input', Int64TensorType([1, 3]))])
self.assertTrue(model_onnx is not None)
dump_data_and_model(numpy.array(data, dtype=numpy.int64),
model, model_onnx, basename="SklearnStandardScalerInt64")
def save(self):
# Onnx Save (can't save a list of model for now)
onx = convert_sklearn(self.pipe, 'Pipe',
[('input', StringTensorType([1, 1]))])
save_model(onx, "Model.onnx")
print ("Model saved")
def test_one_hot_encoder_mixed_float_int(self):
# categorical_features will be removed in 0.22 (this test will fail by then).
model = OneHotEncoder()
model.fit([[0.4, 0.2, 3], [1.4, 1.2, 0], [0.2, 2.2, 1]])
model_onnx = convert_sklearn(model,
'one-hot encoder mixed-type inputs',
[('input1', FloatTensorType([1, 2])),
('input2', Int64TensorType([1, 1]))])
self.assertTrue(model_onnx is not None)
def test_model_binarizer(self):
model = Binarizer(threshold=0.5)
model_onnx = convert_sklearn(model, 'scikit-learn binarizer',
[('input', FloatTensorType([1, 1]))])
self.assertTrue(model_onnx is not None)
dump_data_and_model(numpy.array([[1, 1]], dtype=numpy.float32),
model,
model_onnx,
basename="SklearnBinarizer-SkipDim1")
def _test_linear(self, classes):
"""
This helper function tests conversion of `ai.onnx.ml.LinearClassifier`
which is created from a scikit-learn LogisticRegression.
This tests `convert_onnx_linear_model` in `hummingbird.ml.operator_converters.onnxml_linear`
"""
n_features = 20
n_total = 100
np.random.seed(0)
warnings.filterwarnings("ignore")
X = np.random.rand(n_total, n_features)
X = np.array(X, dtype=np.float32)
y = np.random.randint(classes, size=n_total)
# Create SKL model for testing
model = LogisticRegression(solver="liblinear",
multi_class="ovr",
fit_intercept=True)
model.fit(X, y)
# Create ONNX-ML model
onnx_ml_model = convert_sklearn(model,
initial_types=[
("float_input",
FloatTensorType_onnx(X.shape))
])
# Create ONNX model by calling converter
onnx_model = convert(onnx_ml_model, "onnx", X)
# Get the predictions for the ONNX-ML model
session = ort.InferenceSession(onnx_ml_model.SerializeToString())
output_names = [
session.get_outputs()[i].name
for i in range(len(session.get_outputs()))
]
onnx_ml_pred = [[] for i in range(len(output_names))]
inputs = {session.get_inputs()[0].name: X}
pred = session.run(output_names, inputs)
for i in range(len(output_names)):
if output_names[i] == "output_label":
onnx_ml_pred[1] = pred[i]
else:
onnx_ml_pred[0] = pred[i]
# Get the predictions for the ONNX model
session = ort.InferenceSession(onnx_model.SerializeToString())
onnx_pred = [[] for i in range(len(output_names))]
pred = session.run(output_names, inputs)
for i in range(len(output_names)):
if output_names[i] == "output_label":
onnx_pred[1] = pred[i]
else:
onnx_pred[0] = pred[i]
return onnx_ml_pred, onnx_pred
def test_model_linear_svc_multi_class(self):
model, X = self._fit_model_multiclass_classification(LinearSVC())
model_onnx = convert_sklearn(model, 'multi-class linear SVC',
[('input', FloatTensorType([1, 3]))])
self.assertIsNotNone(model_onnx)
dump_data_and_model(X.astype(numpy.float32),
model,
model_onnx,
basename="SklearnLinearSVCMulti")
def test_truncated_svd(self):
N, C, K = 2, 3, 2
x = create_tensor(N, C)
svd = TruncatedSVD(n_components=K)
svd.fit(x)
model_onnx = onnxmltools.convert_sklearn(
svd, initial_types=[('input', FloatTensorType(shape=[1, C]))])
self.assertTrue(model_onnx is not None)
dump_data_and_model(x, svd, model_onnx, basename="SklearnTruncatedSVD")
def get_onnx_model(model_format,
model,
initial_types: list = None,
final_types: list = None):
if model_format == ModelFormat.KERAS:
return onnxmltools.convert_keras(model)
if model_format == ModelFormat.SK_LEARN:
return onnxmltools.convert_sklearn(model, initial_types=initial_types)
if model_format == ModelFormat.TENSORFLOW:
return onnxmltools.convert_tensorflow(model)
def test_model_logistic_regression_multi_class(self):
model, X = self._fit_model_multiclass_classification(
linear_model.LogisticRegression())
model_onnx = convert_sklearn(model, 'maximum entropy classifier',
[('input', FloatTensorType([1, 3]))])
self.assertIsNotNone(model_onnx)
dump_data_and_model(X.astype(numpy.float32),
model,
model_onnx,
basename="SklearnLogitisticRegressionMulti")
def test_model_logistic_regression_binary_class(self):
model, X = self._fit_model_binary_classification(
linear_model.LogisticRegression())
model_onnx = convert_sklearn(model, 'logistic regression',
[('input', FloatTensorType([1, 3]))])
self.assertIsNotNone(model_onnx)
dump_data_and_model(X.astype(numpy.float32),
model,
model_onnx,
basename="SklearnLogitisticRegressionBinary")
def test_convert_svmr_linear_multi(self):
model = self._fit_multi_classification(SVR(kernel='linear'))
node = convert_sklearn(model, 'SVR', [('input', FloatTensorType([1, 1]))]).graph.node[0]
self.assertIsNotNone(node)
self._check_attributes(node, {'coefficients': None,
'kernel_params': None,
'kernel_type': 'LINEAR',
'post_transform': None,
'rho': None,
'support_vectors': None})
def test_convert_nusvmr_binary(self):
model = self._fit_binary_classification(NuSVR())
node = convert_sklearn(model, 'SVR', [('input', FloatTensorType([1, 1]))]).graph.node[0]
self.assertIsNotNone(node)
self._check_attributes(node, {'coefficients': None,
'kernel_params': None,
'kernel_type': 'RBF',
'post_transform': None,
'rho': None,
'support_vectors': None})
