Luc new archi

code/bolsonaro/data/dataset_loader.py

-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)