BUG: type conversion in spectral_embedding

Under some version of numpy, inplace operations with different types
lead to errors rather than implicit casting.

Fixes scikit-learn#2153

Author: GaelVaroquaux

sklearn/manifold/tests/test_spectral_embedding.py

@@ -69,6 +69,9 @@ def test_spectral_embedding_two_components(seed=36):
     se_precomp = SpectralEmbedding(n_components=1, affinity="precomputed",
                                    random_state=np.random.RandomState(seed))
     embedded_coordinate = se_precomp.fit_transform(affinity)
+    # Some numpy versions are touchy with types
+    embedded_coordinate = \
+        se_precomp.fit_transform(affinity.astype(np.float32))
     # thresholding on the first components using 0.
     label_ = np.array(embedded_coordinate.ravel() < 0, dtype="float")
     assert_equal(normalized_mutual_info_score(true_label, label_), 1.0)

sklearn/utils/graph.py

@@ -173,9 +173,9 @@ def _laplacian_dense(graph, normed=False, return_diag=False):
         w[w_zeros] = 1
         lap /= w
         lap /= w[:, np.newaxis]
-        lap.flat[::n_nodes + 1] = 1 - w_zeros
+        lap.flat[::n_nodes + 1] = (1 - w_zeros).astype(lap.dtype)
     else:
-        lap.flat[::n_nodes + 1] = w
+        lap.flat[::n_nodes + 1] = w.astype(lap.dtype)
 
     if return_diag:
         return lap, w