def test_transfer_transformer(self):
_register_converters_mlinsights(True)
iris = load_iris()
X, y = iris.data, iris.target
pipe = TransferTransformer(StandardScaler(), trainable=True)
pipe.fit(X, y)
model_def = to_onnx(pipe, X[:1].astype(numpy.float32))
sess = OnnxInference(model_def)
res = sess.run({'X': X.astype(numpy.float32)})
exp = pipe.transform(X.astype(numpy.float32))
self.assertEqualArray(exp, res['variable'], decimal=5)
def test_transfer_logistic_regression(self):
_register_converters_mlinsights(True)
iris = load_iris()
X, y = iris.data, iris.target
pipe = TransferTransformer(LogisticRegression(solver='liblinear'),
trainable=True)
pipe.fit(X, y)
model_def = to_onnx(pipe, X[:1])
sess = OnnxInference(model_def)
res = sess.run({'X': X})
exp = pipe.transform(X)
self.assertEqualArray(exp, res['probabilities'], decimal=5)
def test_transfer_transformer_pickle(self):
X = numpy.array([[0.1], [0.2], [0.3], [0.4], [0.5]])
Y = numpy.array([1., 1.1, 1.2, 10, 1.4])
norm = StandardScaler()
norm.fit(X)
X2 = norm.transform(X)
clr = LinearRegression()
clr.fit(X2, Y)
pipe = make_pipeline(TransferTransformer(norm),
TransferTransformer(clr))
pipe.fit(X)
test_sklearn_pickle(lambda: pipe, X, Y)
def test_pipeline_pickable(self):
_register_converters_mlinsights(True)
iris = load_iris()
X, y = iris.data, iris.target
pipe = OnnxPipeline(
[('gm', TransferTransformer(StandardScaler(), trainable=True)),
('lr', LogisticRegression())],
enforce_float32=True,
op_version=TARGET_OPSET)
pipe.fit(X, y)
pipe.fit(X, y)
self.assertTrue(hasattr(pipe, 'raw_steps_'))
self.assertEqual(len(pipe.steps), 2)
self.assertEqual(len(pipe.raw_steps_), 2)
self.assertIsInstance(pipe.steps[0][1], OnnxTransformer)
X = X.astype(numpy.float32)
model_def = to_onnx(pipe,
X[:1],
target_opset=pipe.op_version,
options={id(pipe): {
'zipmap': False
}})
sess = OnnxInference(model_def)
res = sess.run({'X': X})
self.assertEqual(list(sorted(res)), ['label', 'probabilities'])
self.assertEqualArray(res["label"], pipe.predict(X))
self.assertEqualArray(res["probabilities"], pipe.predict_proba(X))
def test_transfer_transformer_diff_trainable(self):
X = numpy.array([[0.1], [0.2], [0.3], [0.4], [0.5]])
Y = numpy.array([1., 1.1, 1.2, 10, 1.4])
norm = StandardScaler()
norm.fit(X)
X2 = norm.transform(X)
clr = LinearRegression()
clr.fit(X2, Y)
exp = clr.predict(X2)
pipe = make_pipeline(TransferTransformer(norm, trainable=True),
TransferTransformer(clr, trainable=True))
pipe.fit(X, Y)
got = pipe.transform(X)
self.assertEqual(exp, got)
def test_transfer_transformer_logreg(self):
X = numpy.array([[0.1], [0.2], [0.3], [0.4], [0.5]])
Y = numpy.array([0, 0, 1, 1, 1])
norm = StandardScaler()
norm.fit(X)
X2 = norm.transform(X)
clr = LogisticRegression()
clr.fit(X2, Y)
exp = clr.predict_proba(X2)
pipe = make_pipeline(TransferTransformer(norm),
TransferTransformer(clr))
pipe.fit(X)
got = pipe.transform(X)
self.assertEqual(exp, got)
def test_transfer_transformer_decision_function(self):
X = numpy.array([[0.1], [0.2], [0.3], [0.4], [0.5]])
Y = numpy.array([0, 0, 1, 1, 1])
norm = StandardScaler()
norm.fit(X)
X2 = norm.transform(X)
clr = LogisticRegression()
clr.fit(X2, Y)
exp = clr.decision_function(X2)
pipe = make_pipeline(
TransferTransformer(norm),
TransferTransformer(clr, method="decision_function"))
pipe.fit(X)
got = pipe.transform(X)
self.assertEqual(exp, got)
def test_transfer_transformer_sample_weight(self):
X = numpy.array([[0.1], [0.2], [0.3], [0.4], [0.5]])
Y = numpy.array([1., 1.1, 1.2, 10, 1.4])
sw = numpy.array([1, 1, 1.5, 1.5, 1.])
norm = StandardScaler()
norm.fit(X)
X2 = norm.transform(X)
clr = LinearRegression()
clr.fit(X2, Y)
exp = clr.predict(X2)
pipe = Pipeline(steps=[
('scaler', TransferTransformer(norm)),
('model', TransferTransformer(clr))])
pipe.fit(X, model__sample_weight=sw)
got = pipe.transform(X)
self.assertEqual(exp, got)
def test_transfer_transformer_cloned0(self):
X = numpy.array([[0.1], [0.2], [0.3], [0.4], [0.5]])
norm = StandardScaler()
norm.fit(X)
tr1 = TransferTransformer(norm, copy_estimator=True)
test_sklearn_clone(lambda: tr1, ext=self, copy_fitted=False)
tr1.fit(X)
test_sklearn_clone(lambda: tr1, ext=self, copy_fitted=False)
tr1 = TransferTransformer(norm, copy_estimator=True)
test_sklearn_clone(lambda: tr1, ext=self, copy_fitted=True)
tr1.fit(X)
test_sklearn_clone(lambda: tr1, ext=self, copy_fitted=True)
def test_pipeline_pickable_options(self):
_register_converters_mlinsights(True)
iris = load_iris()
X, y = iris.data, iris.target
pipe = OnnxPipeline([('gm',
TransferTransformer(GaussianMixture(
n_components=5, random_state=2),
trainable=True,
method='predict_proba')),
('lr', LogisticRegression(random_state=2))],
enforce_float32=True,
op_version=TARGET_OPSET,
options={
'gm__score_samples': True,
'lr__zipmap': False
})
pipe.fit(X, y)
pipe.fit(X, y)
self.assertTrue(hasattr(pipe, 'raw_steps_'))
self.assertEqual(len(pipe.steps), 2)
self.assertEqual(len(pipe.raw_steps_), 2)
self.assertIsInstance(pipe.steps[0][1], OnnxTransformer)
X = X.astype(numpy.float32)
model_def = to_onnx(pipe,
X[:1],
target_opset=pipe.op_version,
options={id(pipe): {
'zipmap': False
}})
sess = OnnxInference(model_def, runtime="python_compiled")
self.assertIn("'probabilities': probabilities,", str(sess))
sess = InferenceSession(model_def.SerializeToString())
r = sess.run(None, {'X': X})
self.assertEqual(len(r), 2)
sess = OnnxInference(model_def)
res = sess.run({'X': X})
self.assertEqual(list(sorted(res)), ['label', 'probabilities'])
self.assertEqualArray(res["probabilities"], pipe.predict_proba(X))
self.assertEqualArray(res["label"], pipe.predict(X))
def test_transfer_transformer_clone(self):
X = numpy.array([[0.1], [0.2], [0.3], [0.4], [0.5]])
Y = numpy.array([1., 1.1, 1.2, 10, 1.4])
norm = StandardScaler()
norm.fit(X)
X2 = norm.transform(X)
clr = LinearRegression()
clr.fit(X2, Y)
tr1 = TransferTransformer(norm, copy_estimator=False)
test_sklearn_clone(lambda: tr1, ext=self, copy_fitted=True)
tr1.fit(X)
test_sklearn_clone(lambda: tr1, ext=self, copy_fitted=True)
tr1 = TransferTransformer(norm, copy_estimator=True)
test_sklearn_clone(lambda: tr1, ext=self, copy_fitted=True)
tr1.fit(X)
test_sklearn_clone(lambda: tr1, ext=self, copy_fitted=True)
tr1 = TransferTransformer(norm, copy_estimator=True)
tr2 = TransferTransformer(clr, copy_estimator=True)
pipe = make_pipeline(tr1, tr2)
pipe.fit(X)
self.maxDiff = None
test_sklearn_clone(lambda: tr1, ext=self, copy_fitted=True)
test_sklearn_clone(lambda: tr2, ext=self, copy_fitted=True)
test_sklearn_clone(lambda: pipe, ext=self, copy_fitted=True)