def test_reshaping_transformer(self):
estimator = wrap(ReshapingEstimator(new_shape=(-1, 2)),
reshapes='feat_1')
# test DataArray
X_da = self.X.var_3d
y = X_da[:, :2].drop('feat_1')
y['dummy'] = y.dummy[:, 0]
estimator.fit(X_da)
yp = estimator.transform(X_da)
assert_allclose(yp, y)
# test Dataset
X_ds = self.X.var_2d.to_dataset()
y = X_ds.var_2d[:, :2].drop('feat_1')
y['dummy'] = y.dummy[:, 0]
estimator.fit(X_ds)
yp = estimator.transform(X_ds).var_2d
assert_allclose(yp, y)
def test_ndim_reshaping_estimator(self):
estimator = wrap(ReshapingEstimator(new_shape=(-1, 5, 0)),
reshapes={'feature': ['feat_1', 'feat_2']})
# test DataArray
X_da = self.X.var_3d
Xt = X_da[:, :5, 0].drop(['feat_1',
'feat_2']).rename({'feat_1': 'feature'})
Xt['dummy'] = Xt.dummy[:, 0]
estimator.fit(X_da)
Xt_da = estimator.transform(X_da)
Xr_da = estimator.inverse_transform(Xt_da)
assert_allclose(Xt_da, Xt)
# test Dataset
X_ds = self.X.var_3d.to_dataset()
y = X_ds.var_3d[:, :5, 0].drop(['feat_1', 'feat_2'])
y = y.rename({'feat_1': 'feature'})
y['dummy'] = y.dummy[:, 0]
estimator.fit(X_ds)
yp = estimator.predict(X_ds).var_3d
assert_allclose(yp, y)
def test_reshaping_estimator_singleton(self):
estimator = wrap(
ReshapingEstimator(new_shape=(-1, 0)),
reshapes='feat_1'
)
# test DataArray
X_da = self.X.var_2d
y = X_da[:, 0].drop('feat_1')
estimator.fit(X_da)
yp = estimator.predict(X_da)
assert_allclose(yp, y)
# test Dataset
X_ds = self.X
y = X_ds.var_2d[:, 0].drop('feat_1')
estimator.fit(X_ds)
yp = estimator.predict(X_ds).var_2d
assert_allclose(yp, y)
def test_update_restore_dims(self):
estimator = wrap(ReshapingEstimator(new_shape=(-1, 0, 5)),
reshapes={'feature': ['feat_1', 'feat_2']})
X = self.X.var_3d
estimator.fit(X)
X_out = estimator.estimator_.transform(X.values)
dims_new = estimator._update_dims(X, X_out)
Xt = xr.DataArray(X_out, dims=dims_new)
assert dims_new == ['sample', 'feature']
Xr_out = estimator.estimator_.inverse_transform(X_out)
dims_old = estimator._restore_dims(Xt, Xr_out)
assert dims_old == ['sample', 'feat_1', 'feat_2']