diff --git a/code/bolsonaro/data/dataset_loader.py b/code/bolsonaro/data/dataset_loader.py
index dc6382c364d9ef4518b877a5035124f09f3e9bfb..6ad4b1f769d35b67b9ebcec6dae6b03ed68607e7 100644
--- a/code/bolsonaro/data/dataset_loader.py
+++ b/code/bolsonaro/data/dataset_loader.py
@@ -1,79 +1,79 @@
-from bolsonaro.data import Dataset
-from bolsonaro.data import Task
-
-from sklearn.datasets import load_boston, load_iris, load_diabetes, load_digits, load_linnerud, load_wine, load_breast_cancer
-from sklearn.datasets import fetch_olivetti_faces, fetch_20newsgroups, \
- fetch_20newsgroups_vectorized, fetch_lfw_people, fetch_lfw_pairs, \
- fetch_covtype, fetch_rcv1, fetch_kddcup99, fetch_california_housing
-from sklearn.model_selection import train_test_split
-
-
-class DatasetLoader(object):
-
- @staticmethod
- def load_from_name(dataset_parameters):
- name = dataset_parameters.name
- if name == 'boston':
- dataset_loading_func = load_boston
- task = Task.REGRESSION
- elif name == 'iris':
- dataset_loading_func = load_iris
- task = Task.CLASSIFICATION
- elif name == 'diabetes':
- dataset_loading_func = load_diabetes
- task = Task.REGRESSION
- elif name == 'digits':
- dataset_loading_func = load_digits
- task = Task.CLASSIFICATION
- elif name == 'linnerud':
- dataset_loading_func = load_linnerud
- task = Task.REGRESSION
- elif name == 'wine':
- dataset_loading_func = load_wine
- task = Task.CLASSIFICATION
- elif name == 'breast_cancer':
- dataset_loading_func = load_breast_cancer
- task = Task.CLASSIFICATION
- elif name == 'olivetti_faces':
- dataset_loading_func = fetch_olivetti_faces
- task = Task.CLASSIFICATION
- elif name == '20newsgroups':
- dataset_loading_func = fetch_20newsgroups
- task = Task.CLASSIFICATION
- elif name == '20newsgroups_vectorized':
- dataset_loading_func = fetch_20newsgroups_vectorized
- task = Task.CLASSIFICATION
- elif name == 'lfw_people':
- dataset_loading_func = fetch_lfw_people
- task = Task.CLASSIFICATION
- elif name == 'lfw_pairs':
- dataset_loading_func = fetch_lfw_pairs
- elif name == 'covtype':
- dataset_loading_func = fetch_covtype
- task = Task.CLASSIFICATION
- elif name == 'rcv1':
- dataset_loading_func = fetch_rcv1
- task = Task.CLASSIFICATION
- elif name == 'kddcup99':
- dataset_loading_func = fetch_kddcup99
- task = Task.CLASSIFICATION
- elif name == 'california_housing':
- dataset_loading_func = fetch_california_housing
- task = Task.REGRESSION
- else:
- raise ValueError("Unsupported dataset '{}'".format(name))
-
- X, y = dataset_loading_func(return_X_y=True)
- X_train, X_test, y_train, y_test = train_test_split(X, y,
- test_size=dataset_parameters.test_size,
- random_state=dataset_parameters.random_state)
- X_train, X_dev, y_train, y_dev = train_test_split(X_train, y_train,
- test_size=dataset_parameters.dev_size,
- random_state=dataset_parameters.random_state)
-
- # TODO
- if dataset_parameters.normalize:
- pass
-
- return Dataset(task, dataset_parameters, X_train,
- X_dev, X_test, y_train, y_dev, y_test)
+from bolsonaro.data.dataset import Dataset
+from bolsonaro.data.task import Task
+
+from sklearn.datasets import load_boston, load_iris, load_diabetes, load_digits, load_linnerud, load_wine, load_breast_cancer
+from sklearn.datasets import fetch_olivetti_faces, fetch_20newsgroups, \
+ fetch_20newsgroups_vectorized, fetch_lfw_people, fetch_lfw_pairs, \
+ fetch_covtype, fetch_rcv1, fetch_kddcup99, fetch_california_housing
+from sklearn.model_selection import train_test_split
+
+
+class DatasetLoader(object):
+
+ @staticmethod
+ def load_from_name(dataset_parameters):
+ name = dataset_parameters.name
+ if name == 'boston':
+ dataset_loading_func = load_boston
+ task = Task.REGRESSION
+ elif name == 'iris':
+ dataset_loading_func = load_iris
+ task = Task.CLASSIFICATION
+ elif name == 'diabetes':
+ dataset_loading_func = load_diabetes
+ task = Task.REGRESSION
+ elif name == 'digits':
+ dataset_loading_func = load_digits
+ task = Task.CLASSIFICATION
+ elif name == 'linnerud':
+ dataset_loading_func = load_linnerud
+ task = Task.REGRESSION
+ elif name == 'wine':
+ dataset_loading_func = load_wine
+ task = Task.CLASSIFICATION
+ elif name == 'breast_cancer':
+ dataset_loading_func = load_breast_cancer
+ task = Task.CLASSIFICATION
+ elif name == 'olivetti_faces':
+ dataset_loading_func = fetch_olivetti_faces
+ task = Task.CLASSIFICATION
+ elif name == '20newsgroups':
+ dataset_loading_func = fetch_20newsgroups
+ task = Task.CLASSIFICATION
+ elif name == '20newsgroups_vectorized':
+ dataset_loading_func = fetch_20newsgroups_vectorized
+ task = Task.CLASSIFICATION
+ elif name == 'lfw_people':
+ dataset_loading_func = fetch_lfw_people
+ task = Task.CLASSIFICATION
+ elif name == 'lfw_pairs':
+ dataset_loading_func = fetch_lfw_pairs
+ elif name == 'covtype':
+ dataset_loading_func = fetch_covtype
+ task = Task.CLASSIFICATION
+ elif name == 'rcv1':
+ dataset_loading_func = fetch_rcv1
+ task = Task.CLASSIFICATION
+ elif name == 'kddcup99':
+ dataset_loading_func = fetch_kddcup99
+ task = Task.CLASSIFICATION
+ elif name == 'california_housing':
+ dataset_loading_func = fetch_california_housing
+ task = Task.REGRESSION
+ else:
+ raise ValueError("Unsupported dataset '{}'".format(name))
+
+ X, y = dataset_loading_func(return_X_y=True)
+ X_train, X_test, y_train, y_test = train_test_split(X, y,
+ test_size=dataset_parameters.test_size,
+ random_state=dataset_parameters.random_state)
+ X_train, X_dev, y_train, y_dev = train_test_split(X_train, y_train,
+ test_size=dataset_parameters.dev_size,
+ random_state=dataset_parameters.random_state)
+
+ # TODO
+ if dataset_parameters.normalize:
+ pass
+
+ return Dataset(task, dataset_parameters, X_train,
+ X_dev, X_test, y_train, y_dev, y_test)