def test_model_dict_vectorizer_sort_false(self):
model = DictVectorizer(sparse=False, sort=False)
data = [{1: 1.0, 2: 200.0}, {1: 3.0, 3: 1.0}]
model.fit_transform(data)
model_onnx = convert_sklearn(
model,
"dictionary vectorizer",
[(
"input",
DictionaryType(Int64TensorType([1]), FloatTensorType([1])),
)],
)
self.assertTrue(model_onnx is not None)
dump_data_and_model(
data,
model,
model_onnx,
basename="SklearnDictVectorizerSortFalse-OneOff-SkipDim1",
allow_failure="StrictVersion(onnxruntime.__version__)"
" <= StrictVersion('0.1.3') or "
"StrictVersion(onnx.__version__)"
" < StrictVersion('1.3.0')",
)
def test_onnx_helper_load_save_init(self):
model = make_pipeline(
Binarizer(),
OneHotEncoder(sparse=False, handle_unknown='ignore'),
StandardScaler())
X = numpy.array([[0.1, 1.1], [0.2, 2.2], [0.4, 2.2], [0.2, 2.4]])
model.fit(X)
model_onnx = convert_sklearn(
model, 'pipe3', [('input', FloatTensorType([None, 2]))])
model_onnx.ir_version = get_ir_version(TARGET_OPSET)
filename = "temp_onnx_helper_load_save.onnx"
save_onnx_model(model_onnx, filename)
model = load_onnx_model(filename)
list(enumerate_model_node_outputs(model))
new_model = select_model_inputs_outputs(model, 'variable')
self.assertTrue(new_model.graph is not None) # pylint: disable=E1101
tr1 = self.get_model(model)
tr2 = self.get_model(new_model)
X = X.astype(numpy.float32)
X1 = tr1(X)
X2 = tr2(X)
self.assertEqual(X1.shape, (4, 2))
self.assertEqual(X2.shape, (4, 2))
def test_model_tfidf_vectorizer11_empty_string_case1(self):
corpus = numpy.array([
'This is the first document.',
'This document is the second document.',
'And this is the third one.',
' ',
]).reshape((4, 1))
vect = TfidfVectorizer(ngram_range=(1, 1), norm=None)
vect.fit(corpus[:3].ravel())
model_onnx = convert_sklearn(vect, 'TfidfVectorizer',
[('input', StringTensorType([1]))],
options=self.get_options(),
target_opset=TARGET_OPSET)
self.assertTrue(model_onnx is not None)
# TfidfVectorizer in onnxruntime fails with empty strings,
# which was fixed in version 0.3.0 afterward
dump_data_and_model(
corpus[2:], vect, model_onnx,
basename="SklearnTfidfVectorizer11EmptyStringSepCase1-"
"OneOff-SklCol",
allow_failure="StrictVersion(onnxruntime.__version__)"
" <= StrictVersion('0.4.0')")
def test_combine_inputs_floats_ints(self):
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline
data = [[0, 0.], [0, 0.], [1, 1.], [1, 1.]]
scaler = StandardScaler()
scaler.fit(data)
model = Pipeline([('scaler1', scaler), ('scaler2', scaler)])
model_onnx = convert_sklearn(model, 'pipeline',
[('input1', Int64TensorType([1, 1])),
('input2', FloatTensorType([1, 1]))])
self.assertTrue(len(model_onnx.graph.node[-1].output) == 1)
self.assertTrue(model_onnx is not None)
data = numpy.array(data)
data = {
'input1': data[:, 0].astype(numpy.int64),
'input2': data[:, 1].astype(numpy.float32)
}
dump_data_and_model(data,
PipeConcatenateInput(model),
model_onnx,
basename="SklearnPipelineScalerMixed-OneOff")
def test_model_multi_class_nocl(self):
model, X = fit_classification_model(SGDClassifier(loss='log',
random_state=42),
2,
label_string=True)
model_onnx = convert_sklearn(
model,
"multi-class nocl",
[("input", FloatTensorType([None, X.shape[1]]))],
options={id(model): {
'nocl': True
}},
target_opset=TARGET_OPSET)
self.assertIsNotNone(model_onnx)
sonx = str(model_onnx)
assert 'classlabels_strings' not in sonx
assert 'cl0' not in sonx
dump_data_and_model(X[6:8],
model,
model_onnx,
classes=model.classes_,
basename="SklearnSGDMultiNoCl",
verbose=False)
def test_simple_imputer_float_inputs(self):
model = SimpleImputer(strategy="mean", fill_value="nan")
data = [[1, 2], [np.nan, 3], [7, 6]]
model.fit(data)
model_onnx = convert_sklearn(model,
"scikit-learn simple imputer",
[("input", FloatTensorType([None, 2]))],
target_opset=TARGET_OPSET)
self.assertTrue(model_onnx.graph.node is not None)
# should contain only node
self.assertEqual(len(model_onnx.graph.node), 1)
# last node should contain the Imputer
outputs = model_onnx.graph.output
self.assertEqual(len(outputs), 1)
self.assertEqual(outputs[0].type.tensor_type.shape.dim[-1].dim_value,
2)
dump_data_and_model(np.array(data, dtype=np.float32),
model,
model_onnx,
basename="SklearnSimpleImputerMeanFloat32")
def test_convert_svr_linear(self):
model, X = self._fit_binary_classification(SVR(kernel="linear"))
model_onnx = convert_sklearn(
model,
"SVR", [("input", FloatTensorType([None, X.shape[1]]))],
target_opset=TARGET_OPSET)
nodes = model_onnx.graph.node
self.assertIsNotNone(nodes)
self._check_attributes(
nodes[0],
{
"coefficients": None,
"kernel_params": None,
"kernel_type": "LINEAR",
"post_transform": None,
"rho": None,
"support_vectors": None,
},
)
dump_data_and_model(X,
model,
model_onnx,
basename="SklearnRegSVRLinear-Dec3")
def test_model_multinomial_nb_multiclass_params(self):
model, X = fit_classification_model(
MultinomialNB(alpha=0.5, fit_prior=False), 5, pos_features=True)
model_onnx = convert_sklearn(
model,
"multinomial naive bayes",
[("input", FloatTensorType([None, X.shape[1]]))],
target_opset=TARGET_OPSET
)
self.assertIsNotNone(model_onnx)
pp = model.predict_proba(X)
col = pp.shape[1]
pps = np.sort(pp, axis=1)
diff = pps[:, col-1] - pps[:, col-2]
ind = diff >= 1e-4
dump_data_and_model(
X[ind],
model,
model_onnx,
basename="SklearnMclMultinomialNBParams-Dec4",
allow_failure="StrictVersion(onnxruntime.__version__)"
"<= StrictVersion('0.2.1')",
)
def test_imputer_float_inputs(self):
model = Imputer(missing_values="NaN", strategy="mean", axis=0)
data = [[1, 2], [np.nan, 3], [7, 6]]
model.fit(data)
model_onnx = convert_sklearn(model, "scikit-learn imputer",
[("input", FloatTensorType([None, 2]))])
self.assertTrue(model_onnx.graph.node is not None)
# should contain only node
self.assertEqual(len(model_onnx.graph.node), 1)
# last node should contain the Imputer
outputs = model_onnx.graph.output
self.assertEqual(len(outputs), 1)
self.assertEqual(outputs[0].type.tensor_type.shape.dim[-1].dim_value,
2)
dump_data_and_model(
np.array(data, dtype=np.float32),
model,
model_onnx,
basename="SklearnImputerMeanFloat32",
)
def test_model_mlp_regressor_tanh(self):
data = load_diabetes()
X, y = data.data, data.target
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.2,
random_state=42)
model = MLPRegressor(random_state=42, activation="tanh").fit(
X_train, y_train)
model_onnx = convert_sklearn(
model,
"scikit-learn MLPRegressor",
[("input", FloatTensorType(X_test.shape))],
)
self.assertTrue(model_onnx is not None)
dump_data_and_model(
X_test.astype(np.float32),
model,
model_onnx,
basename="SklearnMLPRegressorTanhActivation-Dec4",
allow_failure="StrictVersion("
"onnxruntime.__version__)<= StrictVersion('0.2.1')",
)
def test_model_mlp_classifier_multiclass_identity(self):
data = load_digits()
X, y = data.data, data.target
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.2,
random_state=42)
model = MLPClassifier(random_state=42, activation="identity").fit(
X_train, y_train)
model_onnx = convert_sklearn(
model,
"scikit-learn MLPClassifier",
[("input", Int64TensorType(X_test.shape))],
)
self.assertTrue(model_onnx is not None)
dump_data_and_model(
X_test.astype(np.int64),
model,
model_onnx,
basename="SklearnMLPClassifierMultiClassIdentityActivation",
allow_failure="StrictVersion("
"onnxruntime.__version__)<= StrictVersion('0.2.1')",
)
def test_model_mlp_classifier_binary(self):
data = load_iris()
X, y = data.data, data.target
y[y > 1] = 1
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.2,
random_state=42)
model = MLPClassifier(random_state=42).fit(X_train, y_train)
model_onnx = convert_sklearn(
model,
"scikit-learn MLPClassifier",
[("input", FloatTensorType(X_test.shape))],
)
self.assertTrue(model_onnx is not None)
dump_data_and_model(
X_test.astype(np.float32),
model,
model_onnx,
basename="SklearnMLPClassifierBinary",
allow_failure="StrictVersion("
"onnxruntime.__version__)<= StrictVersion('0.2.1')",
)
def test_model_count_vectorizer_wrong_ngram(self):
corpus = numpy.array([
'A AABBB0',
'AAABB B1',
'AA ABBB2',
'AAAB BB3',
'AAA BBB4',
]).reshape((5, 1))
vect = TfidfVectorizer(ngram_range=(1, 2),
token_pattern=r"(?u)\b\w\w+\b")
vect.fit(corpus.ravel())
model_onnx = convert_sklearn(vect, 'TfidfVectorizer',
[('input', StringTensorType([1]))])
self.assertTrue(model_onnx is not None)
dump_data_and_model(
corpus,
vect,
model_onnx,
basename="SklearnTfidfVectorizer12Wngram-OneOff-SklCol",
allow_failure="StrictVersion(onnxruntime.__version__) <= "
"StrictVersion('0.3.0')")
def test_model_sgd_multi_class_log_l1_no_intercept(self):
model, X = fit_classification_model(
SGDClassifier(loss='log',
penalty='l1',
fit_intercept=False,
random_state=42), 5)
X = np.array([X[4], X[4]])
model_onnx = convert_sklearn(
model,
"scikit-learn SGD multi-class classifier",
[("input", FloatTensorType([None, X.shape[1]]))],
)
self.assertIsNotNone(model_onnx)
dump_data_and_model(
X.astype(np.float32),
model,
model_onnx,
basename="SklearnSGDClassifierMultiLogL1NoIntercept-Dec4",
allow_failure="StrictVersion(onnx.__version__)"
" < StrictVersion('1.2') or "
"StrictVersion(onnxruntime.__version__)"
" <= StrictVersion('0.2.1')",
)
def test_model_knn_regressor_double(self):
model, X = self._fit_model(KNeighborsRegressor(n_neighbors=2))
model_onnx = convert_sklearn(model,
"KNN regressor",
[("input", DoubleTensorType([None, 4]))],
target_opset=TARGET_OPSET,
options={id(model): {
'optim': 'cdist'
}},
dtype=numpy.float64)
self.assertIsNotNone(model_onnx)
try:
InferenceSession(model_onnx.SerializeToString())
except OrtImpl as e:
if ("Could not find an implementation for the node "
"To_TopK:TopK(11)") in str(e):
# onnxruntime does not declare TopK(11) for double
return
raise e
dump_data_and_model(X.astype(numpy.float64)[:7],
model,
model_onnx,
basename="SklearnKNeighborsRegressor64")
def test_model_tfidf_vectorizer11_empty_string_case2(self):
corpus = numpy.array([
"This is the first document.",
"This document is the second document.",
"And this is the third one.",
"",
]).reshape((4, 1))
vect = TfidfVectorizer(ngram_range=(1, 1), norm=None)
vect.fit(corpus.ravel())
model_onnx = convert_sklearn(vect, "TfidfVectorizer",
[("input", StringTensorType([1]))],
options=self.get_options(),
target_opset=TARGET_OPSET)
self.assertTrue(model_onnx is not None)
# onnxruntime fails with empty strings
dump_data_and_model(
corpus,
vect,
model_onnx,
basename="SklearnTfidfVectorizer11EmptyString-OneOff-SklCol",
allow_failure="StrictVersion(onnxruntime.__version__)"
" <= StrictVersion('0.4.0')",
)
def test_model_dict_vectorizer(self):
model = DictVectorizer()
data = [{"amy": 1.0, "chin": 200.0}, {"nice": 3.0, "amy": 1.0}]
model.fit_transform(data)
model_onnx = convert_sklearn(
model,
"dictionary vectorizer",
[(
"input",
DictionaryType(StringTensorType([1]), FloatTensorType([1])),
)],
)
self.assertTrue(model_onnx is not None)
dump_data_and_model(
data,
model,
model_onnx,
basename="SklearnDictVectorizer-OneOff-SkipDim1",
allow_failure="StrictVersion(onnxruntime.__version__)"
" <= StrictVersion('0.1.3') or "
"StrictVersion(onnx.__version__)"
" < StrictVersion('1.3.0')",
)
def test_model_mlp_regressor_default(self):
model, X_test = fit_regression_model(MLPRegressor(random_state=42))
exp = model.predict(X_test)
for opv in (1, 2, 7, 8, 9, 10, 11, 12, 13, onnx_opset_version()):
if opv is not None and opv > TARGET_OPSET:
continue
with self.subTest(opv=opv):
try:
onx = convert_sklearn(
model,
"scikit-learn MLPRegressor",
[("input", FloatTensorType([None, X_test.shape[1]]))],
target_opset=opv)
except RuntimeError as e:
if ("is higher than the number of the "
"installed onnx package") in str(e):
continue
raise e
as_string = onx.SerializeToString()
try:
ort = InferenceSession(as_string)
except (RuntimeError, InvalidGraph, Fail) as e:
if opv in (None, 1, 2):
continue
if opv >= onnx_opset_version():
continue
if ("No suitable kernel definition found for "
"op Cast(9)") in str(e):
# too old onnxruntime
continue
raise AssertionError(
"Unable to load opv={}\n---\n{}\n---".format(
opv, onx)) from e
res_out = ort.run(None, {'input': X_test})
assert len(res_out) == 1
res = res_out[0]
assert_almost_equal(exp.ravel(), res.ravel(), decimal=4)
def test_onnxruntime_shapes_clr(self):
iris = load_iris()
X, y = iris.data, iris.target
X_train, X_test, y_train, y_test = train_test_split(X, y)
clr = RandomForestClassifier(max_depth=1)
clr.fit(X_train, y_train)
initial_type = [('float_input', FloatTensorType([None, 4]))]
onx = convert_sklearn(clr,
initial_types=initial_type,
options={id(clr): {
'zipmap': False
}},
target_opset=TARGET_OPSET)
sess = rt.InferenceSession(onx.SerializeToString())
input_name = sess.get_inputs()[0].name
pred_onx = sess.run(None, {input_name: X_test.astype(numpy.float32)})
shape1 = sess.get_inputs()[0].shape
shape2 = sess.get_outputs()[0].shape
assert shape1 == [None, 4]
assert shape2 in ([None, 1], [1], [None])
if len(pred_onx[0].shape) > 1:
assert pred_onx[0].shape[1] == shape2[1]
try:
ishape = onnx.shape_inference.infer_shapes(onx)
except RuntimeError:
# Shape inference does not work?
ishape = None
if ishape is None:
oshape = None
else:
dims = ishape.graph.output[0].type.tensor_type.shape.dim
oshape = [d.dim_value for d in dims]
self.assertIn(oshape, (None, [0]))
dims = ishape.graph.output[1].type.tensor_type.shape.dim
oshape = [d.dim_value for d in dims]
self.assertIn(oshape, (None, [0, 3]))
def test_grid_search(self):
iris = load_iris()
X, y = iris.data, iris.target
X_train, X_test, y_train, y_test = train_test_split(X, y)
pca = PCA(n_components=2)
pca.fit(X_train)
onx = convert_sklearn(pca,
initial_types=[('input',
FloatTensorType(
(1, X.shape[1])))])
onx_bytes = onx.SerializeToString()
tr = OnnxTransformer(onx_bytes)
pipe = make_pipeline(tr, LogisticRegression(solver='liblinear'))
param_grid = [{'logisticregression__penalty': ['l2', 'l1']}]
clf = GridSearchCV(pipe, param_grid, cv=3)
clf.fit(X_train, y_train)
bp = clf.best_params_
self.assertIn(bp, ({
'logisticregression__penalty': 'l1'
}, {
'logisticregression__penalty': 'l2'
}))
tr2 = OnnxTransformer(onx_bytes)
tr2.fit()
self.assertEqualArray(
tr2.transform(X_test),
clf.best_estimator_.steps[0][1].transform(X_test))
y_true, y_pred = y_test, clf.predict(X_test)
cl = classification_report(y_true, y_pred)
self.assertIn('precision', cl)
sc = clf.score(X_test, y_test)
self.assertGreater(sc, 0.70)
def fcts_model(X, y, fit_intercept):
"LogisticRegression."
rf = LogisticRegression(fit_intercept=fit_intercept)
rf.fit(X, y)
initial_types = [('X', FloatTensorType([None, X.shape[1]]))]
onx = convert_sklearn(rf, initial_types=initial_types)
f = BytesIO()
f.write(onx.SerializeToString())
content = f.getvalue()
sess = InferenceSession(content)
outputs = [o.name for o in sess.get_outputs()]
def predict_skl_predict(X, model=rf):
return rf.predict(X)
def predict_skl_predict_proba(X, model=rf):
return rf.predict_proba(X)
def predict_onnxrt_predict(X, sess=sess):
return numpy.array(sess.run(outputs[:1], {'X': X.astype(np.float32)}))
def predict_onnxrt_predict_proba(X, sess=sess):
res = sess.run(outputs[1:], {'X': X.astype(np.float32)})[0]
# do not use DataFrame to convert the output into array,
# it takes too much time
out = numpy.empty((len(res), len(res[0])), dtype=numpy.float32)
for i, row in enumerate(res):
for k, v in row.items():
out[i, k] = v
return out
return {'predict': (predict_skl_predict,
predict_onnxrt_predict),
'predict_proba': (predict_skl_predict_proba,
predict_onnxrt_predict_proba)}
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')")
def test_model_multi_class_nocl(self):
model, X = fit_classification_model(
RandomForestClassifier(random_state=42), 2, label_string=True)
model_onnx = convert_sklearn(
model,
"multi-class nocl",
[("input", FloatTensorType([None, X.shape[1]]))],
options={id(model): {
'nocl': True
}})
self.assertIsNotNone(model_onnx)
sonx = str(model_onnx)
assert 'classlabels_strings' not in sonx
assert 'cl0' not in sonx
dump_data_and_model(X,
model,
model_onnx,
classes=model.classes_,
basename="SklearnRFMultiNoCl",
verbose=False,
allow_failure="StrictVersion(onnx.__version__)"
" < StrictVersion('1.2') or "
"StrictVersion(onnxruntime.__version__)"
" <= StrictVersion('0.2.1')")
def test_model_logistic_regression_multi_class_saga_elasticnet(self):
if _sklearn_version() < StrictVersion('0.21.0'):
model, X = self._fit_model_multiclass_classification(
linear_model.LogisticRegression(solver='saga'))
else:
model, X = self._fit_model_multiclass_classification(
linear_model.LogisticRegression(
solver='saga', penalty='elasticnet', l1_ratio=0.1))
model_onnx = convert_sklearn(
model,
"multi-class logistic regression",
[("input", FloatTensorType([1, 3]))],
)
self.assertIsNotNone(model_onnx)
dump_data_and_model(
X.astype(numpy.float32),
model,
model_onnx,
basename="SklearnLogitisticRegressionMultiSagaElasticnet",
allow_failure="StrictVersion(onnx.__version__)"
" < StrictVersion('1.2') or "
"StrictVersion(onnxruntime.__version__)"
" <= StrictVersion('0.2.1')",
)
def test_model_tfidf_vectorizer11_word4(self):
corpus = numpy.array([
'This is the first document.',
'This document is the second document.',
'And this is the third one.',
'Is this the first document?',
]).reshape((4, 1))
vect = TfidfVectorizer(ngram_range=(1, 1),
norm=None,
token_pattern="[a-zA-Z]{1,4}")
vect.fit(corpus.ravel())
model_onnx = convert_sklearn(vect,
'TfidfVectorizer',
[('input', StringTensorType([1]))],
options=self.get_options(),
target_opset=TARGET_OPSET)
self.assertTrue(model_onnx is not None)
dump_data_and_model(
corpus,
vect,
model_onnx,
basename="SklearnTfidfVectorizer11Regex4-OneOff-SklCol",
allow_failure="StrictVersion(onnxruntime.__version__) <= "
"StrictVersion('0.4.0')")
def test_model_one_hot_encoder(self):
# categorical_features will be removed in 0.22 (this test
# will fail by then). FutureWarning: The handling of integer
# data will change in version 0.22. Currently, the categories
# are determined based on the range [0, max(values)], while
# in the future they will be determined based on the unique values.
# If you want the future behaviour and silence this warning,
# you can specify "categories='auto'".
model = OneHotEncoder()
data = numpy.array([[1, 2, 3], [4, 3, 0], [0, 1, 4], [0, 5, 6]],
dtype=numpy.int64)
model.fit(data)
model_onnx = convert_sklearn(
model,
"scikit-learn one-hot encoder",
[("input", Int64TensorType([1, 3]))],
)
self.assertTrue(model_onnx is not None)
dump_data_and_model(
data,
model,
model_onnx,
basename="SklearnOneHotEncoderInt64-SkipDim1",
)
def test_ada_boost_classifier_samme_r_decision_function(self):
model, X_test = fit_classification_model(AdaBoostClassifier(
n_estimators=10, algorithm="SAMME.R", random_state=42,
base_estimator=DecisionTreeClassifier(
max_depth=2, random_state=42)), 4)
options = {id(model): {'raw_scores': True}}
model_onnx = convert_sklearn(
model,
"AdaBoost classification",
[("input", FloatTensorType((None, X_test.shape[1])))],
target_opset=10,
options=options,
)
self.assertIsNotNone(model_onnx)
dump_data_and_model(
X_test,
model,
model_onnx,
basename="SklearnAdaBoostClassifierSAMMERDecisionFunction",
allow_failure="StrictVersion("
"onnxruntime.__version__)"
"<= StrictVersion('0.2.1')",
methods=['predict', 'decision_function'],
)
def test_model_tfidf_vectorizer11_out_vocabulary(self):
corpus = numpy.array([
'This is the first document.',
'This document is the second document.',
'And this is the third one.',
'Is this the first document?',
]).reshape((4, 1))
vect = TfidfVectorizer(ngram_range=(1, 1), norm=None)
vect.fit(corpus.ravel())
model_onnx = convert_sklearn(vect, 'TfidfVectorizer',
[('input', StringTensorType([1]))],
options=self.get_options())
self.assertTrue(model_onnx is not None)
corpus = numpy.array([
'AZZ ZZ This is the first document.',
'BZZ ZZ This document is the second document.',
'ZZZ ZZ And this is the third one.',
'WZZ ZZ Is this the first document?',
]).reshape((4, 1))
dump_data_and_model(
corpus, vect, model_onnx,
basename="SklearnTfidfVectorizer11OutVocabRegex-OneOff-SklCol",
allow_failure="StrictVersion(onnxruntime.__version__) <= "
"StrictVersion('0.4.0')")
def test_model_logistic_linear_discriminant_analysis_decfunc3(self):
X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]])
y = np.array([1, 1, 1, 2, 2, 3])
X_test = np.array([[-0.8, -1], [0, 1]], dtype=np.float32)
model = LinearDiscriminantAnalysis().fit(X, y)
model_onnx = convert_sklearn(
model,
"linear model",
[("input", FloatTensorType([None, X_test.shape[1]]))],
options={id(model): {
'raw_scores': True
}})
self.assertIsNotNone(model_onnx)
dump_data_and_model(
X_test,
model,
model_onnx,
basename="SklearnLinearDiscriminantAnalysisBinRawScore3-Out0",
# Operator cast-1 is not implemented in onnxruntime
allow_failure="StrictVersion(onnx.__version__)"
" < StrictVersion('1.3') or "
"StrictVersion(onnxruntime.__version__)"
" <= StrictVersion('0.2.1')",
methods=['predict', 'decision_function'])
def common_test_model_hgb_classifier(self, add_nan=False, n_classes=2):
model = HistGradientBoostingClassifier(max_iter=5, max_depth=2)
X, y = make_classification(n_features=10,
n_samples=1000,
n_informative=4,
n_classes=n_classes,
random_state=42)
if add_nan:
rows = numpy.random.randint(0, X.shape[0] - 1, X.shape[0] // 3)
cols = numpy.random.randint(0, X.shape[1] - 1, X.shape[0] // 3)
X[rows, cols] = numpy.nan
X_train, X_test, y_train, _ = train_test_split(X,
y,
test_size=0.5,
random_state=42)
model.fit(X_train, y_train)
model_onnx = convert_sklearn(
model, "unused", [("input", FloatTensorType([None, X.shape[1]]))])
self.assertIsNotNone(model_onnx)
X_test = X_test.astype(numpy.float32)[:5]
dump_data_and_model(X_test, model, model_onnx, folder=self.folder)
