def test_constructor(): from sklearn_xarray.utils import convert_to_ndarray coord_1 = ["a"] * 51 + ["b"] * 49 coord_2 = list(range(10)) * 10 X_ds = xr.Dataset( {"var_1": (["sample", "feature"], np.random.random((100, 10)))}, coords={ "sample": range(100), "feature": range(10), "coord_1": (["sample"], coord_1), "coord_2": (["sample"], coord_2), }, ) target = Target(transform_func=convert_to_ndarray) target.assign_to(X_ds) npt.assert_equal(target.values, np.array(X_ds.var_1)) target = Target(coord="coord_1", transformer=LabelBinarizer())(X_ds) npt.assert_equal(target.values, LabelBinarizer().fit_transform(coord_1))
def test_constructor(): from sklearn_xarray.utils import convert_to_ndarray coord_1 = ['a'] * 51 + ['b'] * 49 coord_2 = list(range(10)) * 10 X_ds = xr.Dataset( {'var_1': (['sample', 'feature'], np.random.random((100, 10)))}, coords={ 'sample': range(100), 'feature': range(10), 'coord_1': (['sample'], coord_1), 'coord_2': (['sample'], coord_2) }) target = Target(transform_func=convert_to_ndarray) target.assign_to(X_ds) npt.assert_equal(target.values, np.array(X_ds.var_1)) target = Target(coord='coord_1', transformer=LabelBinarizer())(X_ds) npt.assert_equal(target.values, LabelBinarizer().fit_transform(coord_1))