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
 
@@ -9,10 +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
+
+    dataset_names = ['boston', 'iris', 'diabetes', 'digits', 'linnerud', 'wine',
+        'breast_cancer', 'olivetti_faces', '20newsgroups_vectorized', 'lfw_people',
+        'lfw_pairs', 'covtype', 'rcv1', 'california_housing']
+
     @staticmethod
     def load(dataset_parameters):
         name = dataset_parameters.name
@@ -39,23 +56,20 @@ class DatasetLoader(object):
             dataset_loading_func = change_binary_func_load(load_breast_cancer)
             task = Task.BINARYCLASSIFICATION
         elif name == 'olivetti_faces':
-            data = fetch_olivetti_faces(random_state=dataset_parameters.random_state, shuffle=True)
-            task = Task.MULTICLASSIFICATION
-            X, y = data.data, data.target
-        elif name == '20newsgroups':
-            data = fetch_20newsgroups(random_state=dataset_parameters.random_state, shuffle=True)
-            #X, y = 
+            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':
-            data = fetch_lfw_people()
-            X, y = data.data, data.target
+            dataset = fetch_lfw_people()
+            X, y = dataset.data, dataset.target
             task = Task.MULTICLASSIFICATION
         elif name == 'lfw_pairs':
-            data = fetch_lfw_pairs()
-            X, y = data.data, data.target
+            dataset = fetch_lfw_pairs()
+            X, y = dataset.data, dataset.target
             task = Task.MULTICLASSIFICATION
         elif name == 'covtype':
             X, y = fetch_covtype(random_state=dataset_parameters.random_state, shuffle=True, return_X_y=True)
@@ -63,9 +77,6 @@ class DatasetLoader(object):
         elif name == 'rcv1':
             X, y = fetch_rcv1(random_state=dataset_parameters.random_state, shuffle=True, return_X_y=True)
             task = Task.MULTICLASSIFICATION
-        elif name == 'kddcup99':
-            X, y = fetch_kddcup99(random_state=dataset_parameters.random_state, shuffle=True, return_X_y=True)
-            task = Task.MULTICLASSIFICATION
         elif name == 'california_housing':
             X, y = fetch_california_housing(return_X_y=True)
             task = Task.REGRESSION
@@ -73,7 +84,7 @@ class DatasetLoader(object):
             raise ValueError("Unsupported dataset '{}'".format(name))
 
         if X is None:
-            X, y = dataset_loading_func()
+            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,
@@ -99,3 +110,20 @@ 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)