def test_save_df(self): print('\nUNIT TEST: DataFrame save') df = data_utils.DataFrame(DB_LOC) df.save('cn_test_save.csv') df_new = data_utils.DataFrame('cn_test_save.csv') self.assertEqual( len(df), len(df_new) ) self.assertEqual( df._string_names, df_new._string_names ) self.assertEqual( df._group_names, df_new._group_names ) self.assertEqual( df._target_names, df_new._target_names ) self.assertEqual( df._input_names, df_new._input_names ) remove('cn_test_save.csv')
def test_use_model(self): print('\nUNIT TEST: use_model') df = data_utils.DataFrame(DB_LOC) df.create_sets(random=True) pd = df.package_sets() config = server_utils.default_config() config['epochs'] = 100 _ = server_utils.train_model(pd, config, 'test', 'rmse', filename='test_use.h5') self.assertEqual( len(server_utils.use_model(pd, 'learn', 'test_use.h5')), len(pd.learn_y)) self.assertEqual( len(server_utils.use_model(pd, 'valid', 'test_use.h5')), len(pd.valid_y)) self.assertEqual( len(server_utils.use_model(pd, 'test', 'test_use.h5')), len(pd.test_y)) self.assertEqual( len(server_utils.use_model(pd, 'train', 'test_use.h5')), len(pd.learn_y) + len(pd.valid_y)) self.assertEqual(len(server_utils.use_model(pd, None, 'test_use.h5')), len(pd.learn_y) + len(pd.valid_y) + len(pd.test_y)) remove('test_use.h5')
def test_package_sets(self): print('\nUNIT TEST: DataFrame package_sets') df = data_utils.DataFrame(DB_LOC) df.shuffle(sets='all', split=[0.7, 0.2, 0.1]) pd = df.package_sets() self.assertEqual(len(pd.learn_x), 337) self.assertEqual(len(pd.valid_x), 96) self.assertEqual(len(pd.test_x), 49) for entry in pd.learn_x: self.assertEqual(len(entry), 15) for entry in pd.valid_x: self.assertEqual(len(entry), 15) for entry in pd.test_x: self.assertEqual(len(entry), 15) self.assertEqual(len(pd.learn_y), 337) self.assertEqual(len(pd.valid_y), 96) self.assertEqual(len(pd.test_y), 49) for entry in pd.learn_y: self.assertEqual(len(entry), 1) for entry in pd.valid_y: self.assertEqual(len(entry), 1) for entry in pd.test_y: self.assertEqual(len(entry), 1)
def test_set_inputs(self): print('\nUNIT TEST: DataFrame set_inputs') df = data_utils.DataFrame(DB_LOC) df.set_inputs(['PHI', 'piPC05']) self.assertEqual(len(df._input_names), 2) df.create_sets(random=True) pd = df.package_sets() self.assertEqual(len(pd.learn_x[0]), 2)
def test_df_init(self): print('\nUNIT TEST: DataFrame init') df = data_utils.DataFrame(DB_LOC) self.assertEqual(len(df._string_names), 7) self.assertEqual(len(df._group_names), 1) self.assertEqual(len(df._target_names), 1) self.assertEqual(len(df._input_names), 15) self.assertEqual(len(df), 482)
def test_open_save_df(self): print('\nUNIT TEST: open/save DataFrame') df = data_utils.DataFrame(DB_LOC) server_utils.save_df(df, 'test_df_saved.d') df = server_utils.open_df('test_df_saved.d') self.assertEqual(len(df), 482) self.assertEqual(len(df._string_names), 7) self.assertEqual(len(df._group_names), 1) self.assertEqual(len(df._target_names), 1) self.assertEqual(len(df._input_names), 15) remove('test_df_saved.d')
def test_get_y(self): print('\nUNIT TEST: get_y') df = data_utils.DataFrame(DB_LOC) df.create_sets(random=True) pd = df.package_sets() self.assertEqual(len(server_utils.get_y(pd, 'learn')), len(pd.learn_y)) self.assertEqual(len(server_utils.get_y(pd, 'valid')), len(pd.valid_y)) self.assertEqual(len(server_utils.get_y(pd, 'test')), len(pd.test_y)) self.assertEqual(len(server_utils.get_y(pd, 'train')), len(pd.learn_y) + len(pd.valid_y)) self.assertEqual(len(server_utils.get_y(pd, None)), len(pd.learn_y) + len(pd.valid_y) + len(pd.test_y))
def test_shuffle(self): print('\nUNIT TEST: DataFrame shuffle') df = data_utils.DataFrame(DB_LOC) df.shuffle(sets='all', split=[0.7, 0.2, 0.1]) self.assertEqual(len(df.learn_set), 337) self.assertEqual(len(df.valid_set), 96) self.assertEqual(len(df.test_set), 49) df.shuffle(sets='train', split=[0.7, 0.2, 0.1]) self.assertEqual(len(df.learn_set), 337) self.assertEqual(len(df.valid_set), 96) self.assertEqual(len(df.test_set), 49)
def test_set_creation(self): print('\nUNIT TEST: DataFrame set creation') df = data_utils.DataFrame(DB_LOC) df.create_sets() self.assertEqual(len(df.learn_set), 329) self.assertEqual(len(df.valid_set), 118) self.assertEqual(len(df.test_set), 35) df.create_sets(random=True, split=[0.7, 0.2, 0.1]) self.assertEqual(len(df.learn_set), 337) self.assertEqual(len(df.valid_set), 96) self.assertEqual(len(df.test_set), 49)
def test_train_model(self): print('\nUNIT TEST: train_model') df = data_utils.DataFrame(DB_LOC) df.create_sets(random=True) pd = df.package_sets() config = server_utils.default_config() config['epochs'] = 100 _ = server_utils.train_model(pd, config, 'test', 'r2', filename='test_train.h5') self.assertTrue(exists('test_train.h5')) remove('test_train.h5')
def test_normalize(self): print('\nUNIT TEST: DataFrame normalize') df = data_utils.DataFrame(DB_LOC) df.normalize() df.create_sets(random=True) pd = df.package_sets() for entry in pd.learn_x: for val in entry: self.assertGreaterEqual(val, 0) self.assertLessEqual(val, 1) for entry in pd.valid_x: for val in entry: self.assertGreaterEqual(val, 0) self.assertLessEqual(val, 1) for entry in pd.test_x: for val in entry: self.assertGreaterEqual(val, 0) self.assertLessEqual(val, 1)