def test_transform_ts_files(): n_class1 = 4 n_class2 = 8 transform_type = "Train/Test Split" time_series = [TimeSeries(*sample_values(), target='class1') for i in range(n_class1)] time_series += [TimeSeries(*sample_values(), target='class2') for i in range(n_class2)] output = transformation.transform_ts_files(time_series, transform_type) npt.assert_equal(len(output), 2)
def test_train_test_split(): # Mock out unevenly-labeled test data: 4 class1, 8 class2 n_class1 = 4 n_class2 = 8 TS_MOCK_PATHS = [TS_PATHS[0]] * n_class1 + [TS_PATHS[1]] * n_class2 transform_type = "Train/Test Split" time_series = [tslib.from_netcdf(path) for path in TS_MOCK_PATHS] np.random.seed(0) train, test = transformation.transform_ts_files(time_series, transform_type) npt.assert_equal(sum(ts.target == 'class1' for ts in train), 1 * n_class1 / 2) npt.assert_equal(sum(ts.target == 'class1' for ts in test), n_class1 / 2) npt.assert_equal(sum(ts.target == 'class2' for ts in train), 1 * n_class2 / 2) npt.assert_equal(sum(ts.target == 'class2' for ts in test), n_class2 / 2)
def test_train_test_split(): # Mock out unevenly-labeled test data: 4 class1, 8 class2 n_class1 = 4 n_class2 = 8 transform_type = "Train/Test Split" time_series = [TimeSeries(*sample_values(), target='class1') for i in range(n_class1)] time_series += [TimeSeries(*sample_values(), target='class2') for i in range(n_class2)] np.random.seed(0) train, test = transformation.transform_ts_files(time_series, transform_type) npt.assert_equal( sum(ts.target == 'class1' for ts in train), 1 * n_class1 / 2) npt.assert_equal(sum(ts.target == 'class1' for ts in test), n_class1 / 2) npt.assert_equal( sum(ts.target == 'class2' for ts in train), 1 * n_class2 / 2) npt.assert_equal(sum(ts.target == 'class2' for ts in test), n_class2 / 2)