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TSNE in t_sne.py Replaced nan with 0 in distance matrix. Issue #4475 #4492

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TSNE in t_sne.py Replaced nan with 0 in distance matrix. Issue #4475 #4492

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3 changes: 3 additions & 0 deletions sklearn/manifold/t_sne.py
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Expand Up @@ -442,6 +442,9 @@ def fit(self, X, y=None):
else:
distances = pairwise_distances(X, metric=self.metric)

# Replacing any nan entries with zero to avoid undefined distances
distances = np.nan_to_num(distances)

# Degrees of freedom of the Student's t-distribution. The suggestion
# alpha = n_components - 1 comes from "Learning a Parametric Embedding
# by Preserving Local Structure" Laurens van der Maaten, 2009.
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16 changes: 16 additions & 0 deletions sklearn/manifold/tests/test_t_sne.py
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Expand Up @@ -271,3 +271,19 @@ def test_reduction_to_one_component():
X = random_state.randn(5, 2)
X_embedded = tsne.fit_transform(X)
assert(np.all(np.isfinite(X_embedded)))

def test_undefined_correlation():
# t-SNE throws an exception with undefined correlation (issue #4475)
from sklearn.manifold import TSNE
import numpy as np
np.random.seed(42)

data = np.random.rand(10, 3)
data[-1, :] = 0

try:
model = TSNE(metric="correlation")
model.fit_transform(data)
assert True
except ValueError as e:
assert False