def test_pipeline_lr(self):
data = numpy.array([[0.0, 0.0], [0.0, 0.0], [1.0, 1.0], [1.0, 1.0]],
dtype=numpy.float32)
yd = numpy.array([0, 1, 0, 2], dtype=numpy.float32)
pipe = Pipeline([('norm', MinMaxScaler()),
('clr', LogisticRegression())])
pipe.fit(data, yd)
options = {'clr__raw_scores': True, 'clr__zipmap': False}
new_options = _process_options(pipe, options)
exp = {'raw_scores': True, 'zipmap': False}
op = pipe.steps[1][1]
self.assertIn(id(op), new_options)
self.assertEqual(new_options[id(op)], exp)
model_def = to_onnx(pipe,
data,
options={
'clr__raw_scores': True,
'clr__zipmap': False
})
sonx = str(model_def)
assert "SOFTMAX" not in sonx
def test_pipeline_column_transformer(self):
iris = load_iris()
X = iris.data[:, :3]
y = iris.target
X_train = pandas.DataFrame(X, columns=["vA", "vB", "vC"])
X_train["vcat"] = X_train["vA"].apply(lambda x: "cat1"
if x > 0.5 else "cat2")
X_train["vcat2"] = X_train["vB"].apply(lambda x: "cat3"
if x > 0.5 else "cat4")
y_train = y % 2
numeric_features = [0, 1, 2] # ["vA", "vB", "vC"]
categorical_features = [3, 4] # ["vcat", "vcat2"]
classifier = LogisticRegression(C=0.01,
class_weight=dict(
zip([False, True], [0.2, 0.8])),
n_jobs=1,
max_iter=10,
solver="lbfgs",
tol=1e-3)
numeric_transformer = Pipeline(
steps=[("imputer", SimpleImputer(
strategy="median")), ("scaler", StandardScaler())])
categorical_transformer = Pipeline(steps=[(
"onehot", OneHotEncoder(sparse=True, handle_unknown="ignore")
), ("tsvd",
TruncatedSVD(n_components=1, algorithm="arpack", tol=1e-4))])
preprocessor = ColumnTransformer(
transformers=[("num", numeric_transformer, numeric_features),
("cat", categorical_transformer,
categorical_features)])
model = Pipeline(steps=[("precprocessor",
preprocessor), ("classifier", classifier)])
model.fit(X_train, y_train)
names = list(enumerate_model_names(model, short=False))
simple = [_[0] for _ in names]
assert len(set(simple)) == len(simple)
names = list(enumerate_model_names(model))
simple2 = [_[0] for _ in names]
assert len(simple2) == len(simple)
exp = [
'', 'precprocessor', 'precprocessor__num',
'precprocessor__num__imputer', 'precprocessor__num__scaler',
'precprocessor__cat', 'precprocessor__cat__onehot',
'precprocessor__cat__onehot__categories___0',
'precprocessor__cat__onehot__categories___1',
'precprocessor__cat__tsvd', 'classifier'
]
self.assertEqual(simple2[:len(exp) - 2], exp[:-2])
initial_type = [("numfeat", FloatTensorType([None, 3])),
("strfeat", StringTensorType([None, 2]))]
model_onnx = convert_sklearn(model,
initial_types=initial_type,
target_opset=TARGET_OPSET)
dump_data_and_model(
X_train,
model,
model_onnx,
basename="SklearnPipelineColumnTransformerPipelinerOptions1")
options = {'classifier': {'zipmap': False}}
new_options = _process_options(model, options)
assert len(new_options) == 2
model_onnx = convert_sklearn(model,
initial_types=initial_type,
options={'classifier': {
'zipmap': False
}},
target_opset=TARGET_OPSET)
assert 'zipmap' not in str(model_onnx).lower()
dump_data_and_model(
X_train,
model,
model_onnx,
basename="SklearnPipelineColumnTransformerPipelinerOptions2")
options = {'classifier__zipmap': False}
new_options = _process_options(model, options)
assert len(new_options) == 2
model_onnx = convert_sklearn(model,
initial_types=initial_type,
options=options,
target_opset=TARGET_OPSET)
assert 'zipmap' not in str(model_onnx).lower()
dump_data_and_model(
X_train,
model,
model_onnx,
basename="SklearnPipelineColumnTransformerPipelinerOptions2")
options = {id(model): {'zipmap': False}}
new_options = _process_pipeline_options(model, options)
model_onnx = convert_sklearn(model,
initial_types=initial_type,
options={id(model): {
'zipmap': False
}},
target_opset=TARGET_OPSET)
assert 'zipmap' not in str(model_onnx).lower()
dump_data_and_model(
X_train,
model,
model_onnx,
basename="SklearnPipelineColumnTransformerPipelinerOptions2")