def test_pipeline_new_with_params():
p = Pipeline([
steps.SelectCanvas('band_1'),
steps.Flatten(),
('pca', steps.Transform(IncrementalPCA(n_components=3))),
('kmeans', KMeans(n_clusters=4))
])
p.fit(random_elm_store())
p.predict(random_elm_store())
assert p.steps[-1][-1].cluster_centers_.shape[0] == 4
p2 = p.new_with_params(kmeans__n_clusters=7, pca__n_components=2)
with pytest.raises(NotFittedError):
p2.predict(random_elm_store())
p2.fit(random_elm_store())
assert p2.steps[-1][-1].cluster_centers_.shape[0] == 7
def test_poly():
s = flat_poly_var_kmeans
p = Pipeline(s[:1])
flat, y, sample_weight = p.fit_transform(**data_source)
assert hasattr(flat, 'flat')
p = Pipeline(s[:2])
more_cols, _, _ = p.fit_transform(**data_source)
assert more_cols.flat.shape[1] > flat.flat.shape[1]
p = Pipeline(s[:3])
feat_sel = p.fit_transform(**data_source)
assert isinstance(feat_sel, tuple)
p = Pipeline(s) # thru KMeans
# fit should always return a Pipeline instance (self after fitting)
fitted = p.fit(**data_source)
assert isinstance(fitted, Pipeline)
assert isinstance(fitted.steps[-1][-1], KMeans)
assert fitted._estimator.cluster_centers_.shape[0] == fitted.get_params(
)['kmeans__n_clusters']
# predict should return KMeans's predict output
pred = p.predict(**data_source)
# fit_transform here should return the transform of the KMeans,
# the distances in each dimension to the cluster centers.
out = p.fit_transform(**data_source)
assert isinstance(out, tuple) and len(out) == 3
X, _, _ = out
assert X.shape[0] == pred.size