def test_fit1(self):
scale_param = self.get_scale_param()
scale_param.scale_column_idx = []
scale_param.feat_upper = 2
scale_param.feat_lower = 1
scale_obj = MinMaxScale(scale_param)
fit_instance = scale_obj.fit(self.table_instance)
column_min_value = scale_obj.column_min_value
column_max_value = scale_obj.column_max_value
for i, line in enumerate(self.test_data):
for j, value in enumerate(line):
if value > 2:
self.test_data[i][j] = 2
elif value < 1:
self.test_data[i][j] = 1
scaler = MMS()
scaler.fit(self.test_data)
self.assertListEqual(self.get_table_instance_feature(fit_instance),
np.around(scaler.transform(self.test_data), 6).tolist())
data_min = list(scaler.data_min_)
data_max = list(scaler.data_max_)
self.assertListEqual(column_min_value, data_min)
self.assertListEqual(column_max_value, data_max)
transform_data = scale_obj.transform(self.table_instance)
self.assertListEqual(self.get_table_instance_feature(fit_instance),
self.get_table_instance_feature(transform_data))
def test_fit5(self):
scale_column_idx = [1, 2, 4]
scale_names = ['fid1', 'fid2', 'fid1000']
scale_param = self.get_scale_param()
scale_param.mode = "cap"
# scale_param.area = "col"
scale_param.feat_upper = 0.8
scale_param.feat_lower = 0.2
scale_param.scale_names = scale_names
scale_param.scale_col_indexes = [2, 4]
scale_obj = MinMaxScale(scale_param)
fit_instance = scale_obj.fit(self.table_instance)
column_min_value = scale_obj.column_min_value
column_max_value = scale_obj.column_max_value
raw_data = copy.deepcopy(self.test_data)
gt_cap_lower_list = [0, 2, 2, 2, 3, 1]
gt_cap_upper_list = [1, 8, 8, 8, 7, 8]
for i, line in enumerate(self.test_data):
for j, value in enumerate(line):
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 = MMS()
scaler.fit(self.test_data)
sklearn_transform_data = np.around(scaler.transform(self.test_data),
6).tolist()
for i, line in enumerate(sklearn_transform_data):
for j, cols in enumerate(line):
if j not in scale_column_idx:
sklearn_transform_data[i][j] = raw_data[i][j]
fit_data = np.round(self.get_table_instance_feature(fit_instance),
6).tolist()
self.assertListEqual(fit_data, sklearn_transform_data)
for i, line in enumerate(sklearn_transform_data):
for j, cols in enumerate(line):
if j not in scale_column_idx:
sklearn_transform_data[i][j] = raw_data[i][j]
data_min = list(scaler.data_min_)
data_max = list(scaler.data_max_)
self.assertListEqual(column_min_value, data_min)
self.assertListEqual(column_max_value, data_max)
transform_data = scale_obj.transform(self.table_instance)
self.assertListEqual(self.get_table_instance_feature(fit_instance),
self.get_table_instance_feature(transform_data))
def test_fit4(self):
scale_column_idx = [1, 2, 4]
scale_param = self.get_scale_param()
# scale_param.area = "col"
scale_param.feat_upper = 2
scale_param.feat_lower = 1
scale_param.scale_col_indexes = scale_column_idx
scale_obj = MinMaxScale(scale_param)
fit_instance = scale_obj.fit(self.table_instance)
column_min_value = scale_obj.column_min_value
column_max_value = scale_obj.column_max_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 = MMS()
scaler.fit(self.test_data)
sklearn_transform_data = np.around(scaler.transform(self.test_data),
6).tolist()
for i, line in enumerate(sklearn_transform_data):
for j, cols in enumerate(line):
if j not in scale_column_idx:
sklearn_transform_data[i][j] = raw_data[i][j]
self.assertListEqual(self.get_table_instance_feature(fit_instance),
sklearn_transform_data)
for i, line in enumerate(sklearn_transform_data):
for j, cols in enumerate(line):
if j not in scale_column_idx:
sklearn_transform_data[i][j] = raw_data[i][j]
data_min = list(scaler.data_min_)
data_max = list(scaler.data_max_)
self.assertListEqual(column_min_value, data_min)
self.assertListEqual(column_max_value, data_max)
transform_data = scale_obj.transform(self.table_instance)
self.assertListEqual(self.get_table_instance_feature(fit_instance),
self.get_table_instance_feature(transform_data))
def test_fit_instance_default(self):
scale_param = self.get_scale_param()
scale_param.scale_col_indexes = -1
scale_obj = MinMaxScale(scale_param)
fit_instance = scale_obj.fit(self.table_instance)
column_min_value = scale_obj.column_min_value
column_max_value = scale_obj.column_max_value
scaler = MMS()
scaler.fit(self.test_data)
self.assertListEqual(self.get_table_instance_feature(fit_instance),
np.around(scaler.transform(self.test_data), 6).tolist())
data_min = list(scaler.data_min_)
data_max = list(scaler.data_max_)
self.assertListEqual(column_min_value, data_min)
self.assertListEqual(column_max_value, data_max)
transform_data = scale_obj.transform(self.table_instance)
self.assertListEqual(self.get_table_instance_feature(fit_instance),
self.get_table_instance_feature(transform_data))
