113 lines
3.5 KiB
Python
113 lines
3.5 KiB
Python
import unittest
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import numpy as np
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import pandas as pd
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from lib import utils
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from lib.utils import StandardScaler
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class MyTestCase(unittest.TestCase):
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def test_separate_seasonal_trend_and_residual(self):
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data = np.array([
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[2, 1, 2, 3, 0, 1, 2, 1, 2, 3, 4, 3, 0, 3, 4, 1]
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], dtype=np.float32).T
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trends = np.array([
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[1, 2, 3, 2, 1, 2, 3, 2, 1, 2, 3, 2, 1, 2, 3, 2]
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], dtype=np.float32).T
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residual = np.array([
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[1, -1, -1, 1, -1, -1, -1, -1, 1, 1, 1, 1, -1, 1, 1, -1]
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], dtype=np.float32).T
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df = pd.DataFrame(data)
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df_trend, df_residual = utils.separate_seasonal_trend_and_residual(df, period=4, test_ratio=0, null_val=-1)
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self.assertTrue(np.array_equal(df_trend.values, trends))
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self.assertTrue(np.array_equal(df_residual.values, residual))
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def test_get_rush_hours_bool_index(self):
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index = pd.date_range('2017-02-27', '2017-03-06', freq='1min')
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data = np.zeros((len(index), 3))
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df = pd.DataFrame(data, index=index)
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ind = utils.get_rush_hours_bool_index(df)
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df = df[ind]
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self.assertEqual(6 * 5 * 60, df.shape[0])
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class IODataPreparationTest(unittest.TestCase):
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from lib import utils
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def test_generate_io_data_with_time(self):
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data = np.array([
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[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15],
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[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]
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], dtype=np.float32).T
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df = pd.DataFrame(data, index=pd.date_range('2017-10-18', '2017-10-19 23:59', freq='3h'))
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xs, ys = utils.generate_io_data_with_time(df, batch_size=2, seq_len=3, horizon=3, output_type='range', )
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self.assertTupleEqual(xs.shape, (3, 2, 9))
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self.assertTupleEqual(ys.shape, (3, 2, 6))
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class StandardScalerTest(unittest.TestCase):
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def test_transform(self):
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data = np.array([
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[35., 0.],
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[0., 17.5],
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[70., 35.]]
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)
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expected_result = np.array([
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[0., -1.],
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[-1, -0.5],
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[1., 0.]]
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)
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scaler = StandardScaler(mean=35., std=35.)
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result = scaler.transform(data)
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self.assertTrue(np.array_equal(expected_result, result))
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def test_transform_df(self):
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df = pd.DataFrame([
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[35., 0.],
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[0., 17.5],
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[70., 35.]]
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)
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expected_result = np.array([
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[0., -1.],
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[-1, -0.5],
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[1., 0.]]
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)
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scaler = StandardScaler(mean=35., std=35.)
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result = scaler.transform(df)
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self.assertTrue(np.array_equal(expected_result, result.values))
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def test_reverse_transform(self):
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data = np.array([
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[0., -1.],
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[-1, -0.5],
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[1., 0.]]
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)
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expected_result = np.array([
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[35., 0.],
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[0., 17.5],
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[70., 35.]]
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)
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scaler = StandardScaler(mean=35., std=35.)
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result = scaler.inverse_transform(data)
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self.assertTrue(np.array_equal(expected_result, result))
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def test_reverse_transform_df(self):
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df = pd.DataFrame([
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[0., -1.],
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[-1, -0.5],
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[1., 0.]]
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)
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expected_result = np.array([
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[35., 0.],
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[0., 17.5],
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[70., 35.]]
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)
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scaler = StandardScaler(mean=35., std=35.)
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result = scaler.inverse_transform(df)
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self.assertTrue(np.array_equal(expected_result, result.values))
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if __name__ == '__main__':
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unittest.main()
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