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]])
