def test_fit_run_is_called_for_single_processor(self):
pipeline = Pipeline('name_pipeline', [
Concreter(),
])
X = (0, 1)
Y = pipeline.fit_run(X)
Concreter.fit_run.assert_called_once_with(X)
self.assertEqual(Y, (1, 0))
def test_fit_run_call_for_several_processors(self):
pipeline = Pipeline('name_pipeline', [
Concreter(),
Concreter(),
])
X = (0, 1)
Y = pipeline.fit_run(X)
Concreter.fit.assert_has_calls([call(X), call((1, 0))])
Concreter.run.assert_has_calls([call(X), call((1, 0))])
self.assertEqual(Y, (2, 1))
def main():
data = load_breast_cancer()
features = data['data']
labels = data['target']
features_train, features_test, labels_train, labels_test = train_test_split(features,
labels,
test_size=0.2,
random_state=42)
x_train = {
'features': features_train,
'labels': labels_train
}
x_test = {
'features': features_test,
'labels': labels_test
}
save_path = '/tmp/pipeline_example'
intermediate_features_path = os.path.join(save_path, 'features')
pipeline_train = Pipeline('Pca_and_linear_svc_pipeline',[Pca(n_components=0.95), LinearSvc(C=1.0)])
print ('Training pipeline description: ', pipeline_train)
y_train = pipeline_train.fit_run(x_train)
print('AUC for training set: {}'.format(str(roc_auc_score(y_train['labels'], y_train['scores']))))
pipeline_train.save(save_path)
pca = Pca()
pca.load(save_path)
svc = LinearSvc()
svc.load(save_path)
pipeline_test = Pipeline('Pca_and_linear_svc_pipeline', [pca,
PipelineSaver(intermediate_features_path,
'PCA_test_features'),
svc])
pipeline_test.load(save_path)
print('Test pipeline description: ', pipeline_test)
y_test = pipeline_test.run(x_test)
print('AUC for training set: {}'.format(str(roc_auc_score(y_test['labels'], y_test['scores']))))