Resolve "Experiment pipeline"

Closes #12 (closed)

code/bolsonaro/data/dataset_loader.py

 from bolsonaro.data.dataset import Dataset
+from bolsonaro.data.dataset_parameters import DatasetParameters
 from bolsonaro.data.task import Task
 from bolsonaro.utils import change_binary_func_load
 
@@ -9,13 +10,26 @@ from sklearn.datasets import fetch_olivetti_faces, fetch_20newsgroups, \
     fetch_covtype, fetch_rcv1, fetch_kddcup99, fetch_california_housing
 from sklearn.model_selection import train_test_split
 from sklearn import preprocessing
+import random
 
 
 class DatasetLoader(object):
 
+    DEFAULT_DATASET_NAME = 'boston'
+    DEFAULT_NORMALIZE_D = False
+    DEFAULT_DATASET_NORMALIZER = 'standard'
+    DEFAULT_FOREST_SIZE = 100
+    DEFAULT_EXTRACTED_FOREST_SIZE = 10
+    DEFAULT_DEV_SIZE = 0.2
+    DEFAULT_TEST_SIZE = 0.2
+    DEFAULT_RANDOM_SEED_NUMBER = 1
+    DEFAULT_SUBSETS_USED = 'train,dev'
+    DEFAULT_NORMALIZE_WEIGHTS = False
+
     @staticmethod
     def load(dataset_parameters):
         name = dataset_parameters.name
+        X, y = None, None
         if name == 'boston':
             dataset_loading_func = load_boston
             task = Task.REGRESSION
@@ -37,37 +51,37 @@ class DatasetLoader(object):
         elif name == 'breast_cancer':
             dataset_loading_func = change_binary_func_load(load_breast_cancer)
             task = Task.BINARYCLASSIFICATION
-        elif name == 'olivetti_faces':  # bug (no return X_y)
-            dataset_loading_func = fetch_olivetti_faces
-            task = Task.MULTICLASSIFICATION
-        elif name == '20newsgroups':  # bug (no return X_y)
-            dataset_loading_func = fetch_20newsgroups
+        elif name == 'olivetti_faces':
+            dataset = fetch_olivetti_faces(random_state=dataset_parameters.random_state, shuffle=True)
             task = Task.MULTICLASSIFICATION
+            X, y = dataset.data, dataset.target
         elif name == '20newsgroups_vectorized':
-            dataset_loading_func = fetch_20newsgroups_vectorized
+            dataset = fetch_20newsgroups_vectorized()
+            X, y = dataset.data, dataset.target
             task = Task.MULTICLASSIFICATION
-        elif name == 'lfw_people':  # needs PIL (image dataset)
-            dataset_loading_func = fetch_lfw_people
+        elif name == 'lfw_people':
+            dataset = fetch_lfw_people()
+            X, y = dataset.data, dataset.target
             task = Task.MULTICLASSIFICATION
         elif name == 'lfw_pairs':
-            dataset_loading_func = fetch_lfw_pairs
+            dataset = fetch_lfw_pairs()
+            X, y = dataset.data, dataset.target
             task = Task.MULTICLASSIFICATION
         elif name == 'covtype':
-            dataset_loading_func = fetch_covtype
+            X, y = fetch_covtype(random_state=dataset_parameters.random_state, shuffle=True, return_X_y=True)
             task = Task.MULTICLASSIFICATION
         elif name == 'rcv1':
-            dataset_loading_func = fetch_rcv1
-            task = Task.MULTICLASSIFICATION
-        elif name == 'kddcup99':
-            dataset_loading_func = fetch_kddcup99
+            X, y = fetch_rcv1(random_state=dataset_parameters.random_state, shuffle=True, return_X_y=True)
             task = Task.MULTICLASSIFICATION
         elif name == 'california_housing':
-            dataset_loading_func = fetch_california_housing
+            X, y = fetch_california_housing(return_X_y=True)
             task = Task.REGRESSION
         else:
             raise ValueError("Unsupported dataset '{}'".format(name))
 
-        X, y = dataset_loading_func(return_X_y=True)
+        if X is None:
+            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)
@@ -92,3 +106,20 @@ class DatasetLoader(object):
 
         return Dataset(task, X_train,
             X_dev, X_test, y_train, y_dev, y_test)
+
+    @staticmethod
+    def load_default(dataset_name, seed):
+        begin_random_seed_range = 1
+        end_random_seed_range = 2000
+
+        seed = seed if seed else random.randint(begin_random_seed_range, end_random_seed_range)
+
+        dataset_parameters = DatasetParameters(
+            name=dataset_name,
+            test_size=DatasetLoader.DEFAULT_TEST_SIZE,
+            dev_size=DatasetLoader.DEFAULT_DEV_SIZE,
+            random_state=seed,
+            dataset_normalizer=DatasetLoader.DEFAULT_DATASET_NORMALIZER
+        )
+
+        return DatasetLoader.load(dataset_parameters)