def main():
ds = DataSet()
with open(INPUT_DATA_FILE, "r", newline='', encoding="utf8") as csv_file:
ds.extract_from_csv(csv_file)
# X_train, X_test, y_train, y_test = train_test_split(ds.X, ds.y, test_size=0.3, random_state=1)
clf = MLPClassifier(algorithm='l-bfgs', max_iter=50, alpha=1e-6, hidden_layer_sizes=10000, random_state=1)
# classifier = clf.fit(X_train, y_train)
print(cross_val_score(clf, ds.X, ds.y, cv=10, n_jobs=-1))
def main():
ds = DataSet()
with open(INPUT_DATA_FILE, "r", newline='', encoding="utf8") as csv_file:
ds.extract_from_csv(csv_file)
print("Ranking (descending)", ds.create_features_ranking(use_names=True))
experiment_results = {}
final_counter = Counter()
for layer_size in HIDDEN_LAYER_SIZES:
experiment_results[layer_size] = {}
for n_features in range(1, ds.number_of_features, 1):
result = run_experiment(ds.X, ds.y, hidden_layer_size=layer_size, n_features=n_features)
experiment_results[layer_size][n_features] = result
final_counter.update(result.counter)
print_result(result, layer_size, n_features)
print("\nNum of times features were selected: {}".format(final_counter))
generate_plots(experiment_results, ds.number_of_features, ds.col_names, final_counter)
class TestDataSet(TestCase):
def setUp(self):
self.init_column_names = ["feat_001", "X-coord", "h,std,dev", "Grade"]
self.init_features = [[0., 0.1, 3.], [0., 0.2, 0.], [0., 0.3, 0.5]]
self.init_classes = ["G3", "G1", "G1"]
self.expected_extracted_column_names = self.init_column_names
self.expected_extracted_features = [[1.7, 3., 0.09], [-5., -1.12, 0.]]
self.expected_extracted_classes = ["G2", "G3"]
self.data_set_dir = path.join(path.dirname(__file__), "data_sets")
self.data_set = DataSet(X=self.init_features,
y=self.init_classes,
col_names=self.init_column_names)
def check_extracted(self):
self.assertListEqual(self.expected_extracted_column_names,
self.data_set.col_names)
self.assertListEqual(self.expected_extracted_features, self.data_set.X)
self.assertListEqual(self.expected_extracted_classes, self.data_set.y)
def test_should_initialize_properly(self):
self.assertListEqual(self.init_column_names, self.data_set.col_names)
self.assertListEqual(self.init_features, self.data_set.X)
self.assertListEqual(self.init_classes, self.data_set.y)
def test_should_extract_features_and_classes_from_csv_with_header(self):
with open(path.join(DATA_SETS_DIR, "mock_data_set_with_header.csv"),
"r",
newline='',
encoding="utf8") as csv_file:
self.data_set.extract_from_csv(csv_file)
self.check_extracted()
def test_should_raise_error_on_feature_size_mismatch(self):
with self.assertRaises(RuntimeError) as cm:
DataSet(X=[[1], [1, 2]])
with self.assertRaises(RuntimeError):
DataSet(y=[1])
with self.assertRaises(RuntimeError):
DataSet(X=[[1], [1, 2]], y=[1])
with self.assertRaises(RuntimeError):
DataSet(X=[[1], [1, 2]], y=[1, 1])
with self.assertRaises(RuntimeError):
DataSet(X=[[1], [1, 2]], y=[1, 1, 1])
with self.assertRaises(RuntimeError):
DataSet(col_names=["a"])
with self.assertRaises(RuntimeError):
DataSet(X=[[1, 2], [1, 2]], y=[1, 1], col_names=["a"])
with open(path.join(DATA_SETS_DIR, "mock_data_set_corrupted.csv"),
"r",
newline='',
encoding="utf8") as csv_file:
with self.assertRaises(RuntimeError):
self.data_set.extract_from_csv(csv_file)
def test_should_return_number_of_features(self):
self.assertEqual(len(self.init_features[0]),
self.data_set.number_of_features)
def test_should_return_column_name(self):
for index, element in enumerate(self.init_column_names):
self.assertEqual(element, self.data_set.col_names[index])
def test_should_create_ranking(self):
ranking = [2, 1, 0]
self.assertListEqual(
self.data_set.create_features_ranking(use_names=False), ranking)