Merge pull request scikit-learn#3786 from AlexanderFabisch/tsne_fix

Fix t-SNE with "non-squarable" metric
TamChen · Oct 21, 2014 · a0e5fcb · a0e5fcb
2 parents d4405cb bf7993e
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diff --git a/sklearn/manifold/t_sne.py b/sklearn/manifold/t_sne.py
@@ -333,7  333,7 @@ class TSNE(BaseEstimator):
         Maximum number of iterations for the optimization. Should be at
         least 200.
 
-    metric : string or callable, (default: "euclidean")
     metric : string or callable, optional
         The metric to use when calculating distance between instances in a
         feature array. If metric is a string, it must be one of the options
         allowed by scipy.spatial.distance.pdist for its metric parameter, or
@@ -342,7  342,8 @@ class TSNE(BaseEstimator):
         Alternatively, if metric is a callable function, it is called on each
         pair of instances (rows) and the resulting value recorded. The callable
         should take two arrays from X as input and return a value indicating
-        the distance between them.
         the distance between them. The default is "euclidean" which is
         interpreted as squared euclidean distance.
 
     init : string, optional (default: "random")
         Initialization of embedding. Possible options are 'random' and 'pca'.
@@ -432,7  433,11 @@ def _fit(self, X):
         else:
             if self.verbose:
                 print("[t-SNE] Computing pairwise distances...")
-            distances = pairwise_distances(X, metric=self.metric, squared=True)
 
             if self.metric == "euclidean":
                 distances = pairwise_distances(X, metric=self.metric, squared=True)
             else:
                 distances = pairwise_distances(X, metric=self.metric)
 
         # Degrees of freedom of the Student's t-distribution. The suggestion
         # alpha = n_components - 1 comes from "Learning a Parametric Embedding

diff --git a/sklearn/manifold/tests/test_t_sne.py b/sklearn/manifold/tests/test_t_sne.py
@@ -254,3  254,12 @@ def test_verbose():
     assert("Finished" in out)
     assert("early exaggeration" in out)
     assert("Finished" in out)
 
 
 def test_chebyshev_metric():
     """t-SNE should allow metrics that cannot be squared (issue #3526)."""
     random_state = check_random_state(0)
     tsne = TSNE(verbose=2, metric="chebyshev")
     X = random_state.randn(5, 2)
     tsne.fit_transform(X)