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)
