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.assertEqual(bp, {'logisticregression__penalty': 'l1'})
tr2 = OnnxTransformer(onx_bytes)
tr2.fit()
assert_almost_equal(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)
assert 'precision' in cl
sc = clf.score(X_test, y_test)
assert sc >= 0.80
def test_transform_dict(self):
x = {'X': np.array([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]])}
name = self.get_name("mul_1.pb")
with open(name, "rb") as f:
content = f.read()
tr = OnnxTransformer(content)
tr.fit()
res = tr.transform(x)
exp = np.array([[1., 4.], [9., 16.], [25., 36.]], dtype=np.float32)
self.assertEqual(list(res.ravel()), list(exp.ravel()))
def test_transform_dataframe(self):
x = pandas.DataFrame(data=[[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]])
x.columns = "X1 X2".split()
name = self.get_name("mul_1.pb")
with open(name, "rb") as f:
content = f.read()
tr = OnnxTransformer(content)
tr.fit()
try:
tr.transform(x)
except RuntimeError:
pass
initial_type = [('float_input', FloatTensorType([1, 4]))]
onx = convert_sklearn(clr, initial_types=initial_type)
with open("rf_iris.onnx", "wb") as f:
f.write(onx.SerializeToString())
###################################
# Compute ONNX prediction similarly as scikit-learn transformer
# +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
with open("rf_iris.onnx", "rb") as f:
content = f.read()
ot = OnnxTransformer(content, output_name="output_probability")
ot.fit(X_train, y_train)
print(ot.transform(X_test[:5].astype(np.float32)))
###################################
# .. index:: transfer learning, MobileNet, ImageNet
#
# Transfer Learning with MobileNet
# ++++++++++++++++++++++++++++++++
#
# Deep learning models started to win
# the `ImageNet <http://www.image-net.org/>`_
# competition in 2012 and most the winners
# are available on the web as pre-trained models.
# Transfer Learning is computed by wrapping
# a backend into a *scikit-learn*