def test_prediction_to_csv_class(): """Test util.prediction_to_csv""" with create_test_project() as p, create_test_dataset(p) as ds,\ create_test_featureset(p) as fs,\ create_test_model(fs, model_type='LinearSGDClassifier') as m,\ create_test_prediction(ds, m) as pred: pred = featureset.from_netcdf(pred.file.uri) assert util.prediction_to_csv(pred) ==\ [['ts_name', 'true_target', 'prediction'], ['0', 'Mira', 'Mira'], ['1', 'Classical_Cepheid', 'Classical_Cepheid'], ['2', 'Mira', 'Mira'], ['3', 'Classical_Cepheid', 'Classical_Cepheid'], ['4', 'Mira', 'Mira']]
def test_prediction_to_csv_regr(): """Test util.prediction_to_csv""" with create_test_project() as p, create_test_dataset(p, label_type='regr') as ds,\ create_test_featureset(p, label_type='regr') as fs,\ create_test_model(fs, model_type='LinearRegressor') as m,\ create_test_prediction(ds, m) as pred: pred = xr.open_dataset(pred.file.uri) results = util.prediction_to_csv(pred) assert results[0] == ['ts_name', 'true_target', 'prediction'] npt.assert_array_almost_equal( [[float(e) for e in row] for row in results[1:]], [[0, 2.2, 2.2], [1, 3.4, 3.4], [2, 4.4, 4.4], [3, 2.2, 2.2], [4, 3.1, 3.1]])
def test_prediction_to_csv_regr(): """Test util.prediction_to_csv""" with create_test_project() as p, create_test_dataset(p, label_type='regr') as ds,\ create_test_featureset(p, label_type='regr') as fs,\ create_test_model(fs, model_type='LinearRegressor') as m,\ create_test_prediction(ds, m) as pred: pred = featureset.from_netcdf(pred.file.uri) results = util.prediction_to_csv(pred) assert results[0] == ['ts_name', 'true_target', 'prediction'] npt.assert_array_almost_equal( [[float(e) for e in row] for row in results[1:]], [[0, 2.2, 2.2], [1, 3.4, 3.4], [2, 4.4, 4.4], [3, 2.2, 2.2], [4, 3.1, 3.1]])