def test_cols_select_fit_and_transform(self):
scale_param = self.get_scale_param()
scale_param.scale_column_idx = [1, 2, 4]
standard_scaler = StandardScale(scale_param)
fit_data = standard_scaler.fit(self.table_instance)
scale_column_idx = standard_scaler.scale_column_idx
scaler = SSL(with_mean=True, with_std=True)
scaler.fit(self.test_data)
transform_data = np.around(scaler.transform(self.test_data),
4).tolist()
for i, line in enumerate(transform_data):
for j, cols in enumerate(line):
if j not in scale_column_idx:
transform_data[i][j] = self.test_data[i][j]
self.assertListEqual(self.get_table_instance_feature(fit_data),
transform_data)
std_scale_transform_data = standard_scaler.transform(
self.table_instance)
self.assertListEqual(
self.get_table_instance_feature(std_scale_transform_data),
transform_data)
def test_cols_select_fit_and_transform_repeat(self):
scale_column_idx = [1, 1, 2, 2, 4, 5, 5]
standard_scaler = StandardScaler(area='col',
scale_column_idx=scale_column_idx,
with_mean=True,
with_std=True)
fit_data, scale_conf = standard_scaler.fit(self.table_instance)
mean, std, scale_column_idx = scale_conf[0], scale_conf[1], scale_conf[
2]
scaler = SSL(with_mean=True, with_std=True)
scaler.fit(self.test_data)
transform_data = np.around(scaler.transform(self.test_data),
4).tolist()
for i, line in enumerate(transform_data):
for j, cols in enumerate(line):
if j not in scale_column_idx:
transform_data[i][j] = self.test_data[i][j]
self.assertListEqual(self.get_table_instance_feature(fit_data),
transform_data)
std_scale_transform_data = standard_scaler.transform(
self.table_instance, mean, std, scale_column_idx)
self.assertListEqual(
self.get_table_instance_feature(std_scale_transform_data),
transform_data)
def test_fit10(self):
scale_column_idx = [1, 2, 4]
scale_param = self.get_scale_param()
scale_param.scale_column_idx = []
scale_param.feat_upper = 0.8
scale_param.feat_lower = 0.2
scale_param.with_mean = True
scale_param.with_std = True
scale_param.mode = "cap"
scale_param.scale_column_idx = scale_column_idx
scale_param.area = "col"
standard_scaler = StandardScale(scale_param)
fit_instance = standard_scaler.fit(self.table_instance)
mean = standard_scaler.mean
std = standard_scaler.std
column_max_value = standard_scaler.column_max_value
column_min_value = standard_scaler.column_min_value
gt_cap_lower_list = [0, 2, 2, 2, 3, 1]
gt_cap_upper_list = [1, 8, 8, 8, 7, 8]
raw_data = copy.deepcopy(self.test_data)
for i, line in enumerate(self.test_data):
for j, value in enumerate(line):
if j in scale_column_idx:
if value > gt_cap_upper_list[j]:
self.test_data[i][j] = gt_cap_upper_list[j]
elif value < gt_cap_lower_list[j]:
self.test_data[i][j] = gt_cap_lower_list[j]
scaler = SSL(with_mean=True, with_std=True)
scaler.fit(self.test_data)
transform_data = np.around(scaler.transform(self.test_data),
4).tolist()
for i, line in enumerate(transform_data):
for j, cols in enumerate(line):
if j not in scale_column_idx:
transform_data[i][j] = raw_data[i][j]
self.assertListEqual(self.get_table_instance_feature(fit_instance),
transform_data)
self.assertEqual(column_max_value, gt_cap_upper_list)
self.assertEqual(column_min_value, gt_cap_lower_list)
self.assertListEqual(list(np.around(mean, 6)),
list(np.around(scaler.mean_, 6)))
self.assertListEqual(list(np.around(std, 6)),
list(np.around(scaler.scale_, 6)))
raw_data_transform = standard_scaler.transform(self.table_instance)
self.assertListEqual(
self.get_table_instance_feature(fit_instance),
self.get_table_instance_feature(raw_data_transform))
def test_fit4(self):
standard_scaler = StandardScaler(with_mean=False, with_std=False)
fit_instance, mean, std = standard_scaler.fit(self.table_instance)
scaler = SSL(with_mean=False, with_std=False)
scaler.fit(self.test_data)
self.assertListEqual(
self.get_table_instance_feature(fit_instance),
np.around(scaler.transform(self.test_data), 4).tolist())
self.assertEqual(mean, [0 for _ in range(len(self.test_data[0]))])
self.assertEqual(std, [1 for _ in range(len(self.test_data[0]))])
def test_fit3(self):
standard_scaler = StandardScaler(with_mean=True, with_std=False)
fit_instance, mean, std = standard_scaler.fit(self.table_instance)
scaler = SSL(with_std=False)
scaler.fit(self.test_data)
self.assertListEqual(
self.get_table_instance_feature(fit_instance),
np.around(scaler.transform(self.test_data), 4).tolist())
self.assertListEqual(list(np.around(mean, 4)),
list(np.around(scaler.mean_, 4)))
self.assertListEqual(list(np.around(std, 4)), [1 for _ in std])
def test_fit2(self):
standard_scaler = StandardScaler(with_mean=False, with_std=True)
fit_instance, scale_conf = standard_scaler.fit(self.table_instance)
mean, std = scale_conf[0], scale_conf[1]
scaler = SSL(with_mean=False)
scaler.fit(self.test_data)
self.assertListEqual(
self.get_table_instance_feature(fit_instance),
np.around(scaler.transform(self.test_data), 4).tolist())
self.assertListEqual(list(np.around(mean, 4)), [0 for _ in mean])
self.assertListEqual(list(np.around(std, 4)),
list(np.around(scaler.scale_, 4)))
def test_fit9(self):
scale_column_idx = [1, 2, 4]
scale_param = self.get_scale_param()
scale_param.scale_column_idx = []
scale_param.feat_upper = [2, 2, 2, 2, 2, 2]
scale_param.feat_lower = [1, 1, 1, 1, 1, 1]
scale_param.with_mean = True
scale_param.with_std = True
scale_param.scale_column_idx = scale_column_idx
scale_param.area = "col"
standard_scaler = StandardScale(scale_param)
fit_instance = standard_scaler.fit(self.table_instance)
mean = standard_scaler.mean
std = standard_scaler.std
column_max_value = standard_scaler.column_max_value
column_min_value = standard_scaler.column_min_value
raw_data = copy.deepcopy(self.test_data)
for i, line in enumerate(self.test_data):
for j, value in enumerate(line):
if j in scale_column_idx:
if value > 2:
self.test_data[i][j] = 2
elif value < 1:
self.test_data[i][j] = 1
scaler = SSL(with_mean=True, with_std=True)
scaler.fit(self.test_data)
transform_data = np.around(scaler.transform(self.test_data),
4).tolist()
for i, line in enumerate(transform_data):
for j, cols in enumerate(line):
if j not in scale_column_idx:
transform_data[i][j] = raw_data[i][j]
self.assertListEqual(self.get_table_instance_feature(fit_instance),
transform_data)
self.assertListEqual(list(np.around(mean, 6)),
list(np.around(scaler.mean_, 6)))
self.assertListEqual(list(np.around(std, 6)),
list(np.around(scaler.scale_, 6)))
self.assertEqual(column_max_value, [1, 2, 2, 10, 2, 10])
self.assertEqual(column_min_value, [0, 1, 1, 2, 2, -100])
raw_data_transform = standard_scaler.transform(self.table_instance)
self.assertListEqual(
self.get_table_instance_feature(fit_instance),
self.get_table_instance_feature(raw_data_transform))
def test_fit1(self):
scale_param = self.get_scale_param()
standard_scaler = StandardScale(scale_param)
fit_instance = standard_scaler.fit(self.table_instance)
mean = standard_scaler.mean
std = standard_scaler.std
scaler = SSL()
scaler.fit(self.test_data)
self.assertListEqual(
self.get_table_instance_feature(fit_instance),
np.around(scaler.transform(self.test_data), 4).tolist())
self.assertListEqual(list(np.around(mean, 4)),
list(np.around(scaler.mean_, 4)))
self.assertListEqual(list(np.around(std, 4)),
list(np.around(scaler.scale_, 4)))
def test_fit6(self):
standard_scaler = StandardScaler(area='col',
with_mean=True,
with_std=True)
fit_instance, scale_conf = standard_scaler.fit(self.table_instance)
mean, std, scale_column_idx = scale_conf[0], scale_conf[1], scale_conf[
2]
scaler = SSL()
scaler.fit(self.test_data)
self.assertListEqual(
self.get_table_instance_feature(fit_instance),
np.around(scaler.transform(self.test_data), 4).tolist())
self.assertListEqual(list(np.around(mean, 4)),
list(np.around(scaler.mean_, 4)))
self.assertListEqual(list(np.around(std, 4)),
list(np.around(scaler.scale_, 4)))
def test_fit6(self):
scale_param = self.get_scale_param()
scale_param.scale_column_idx = []
scale_param.area = "col"
standard_scaler = StandardScale(scale_param)
fit_instance = standard_scaler.fit(self.table_instance)
mean = standard_scaler.mean
std = standard_scaler.std
scaler = SSL()
scaler.fit(self.test_data)
self.assertListEqual(self.get_table_instance_feature(fit_instance),
np.around(self.test_data, 4).tolist())
self.assertListEqual(list(np.around(mean, 4)),
list(np.around(scaler.mean_, 4)))
self.assertListEqual(list(np.around(std, 4)),
list(np.around(scaler.scale_, 4)))