8000 [MRG+1] Fixes incorrect output when input is precomputed sparse matrix in DBSCAN. by Akshay0724 · Pull Request #8339 · scikit-learn/scikit-learn · GitHub
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[MRG+1] Fixes incorrect output when input is precomputed sparse matrix in DBSCAN. #8339

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Merged
merged 6 commits into from
Feb 23, 2017
Merged

[MRG+1] Fixes incorrect output when input is precomputed sparse matrix in DBSCAN. #8339

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11f0307
Fixes incorrect output in DBSCAN
Akshay0724 Feb 13, 2017
fe924f9
Added support for multiple initial row all zero
Akshay0724 Feb 15, 2017
4875516
Added a test for issue #8306
Akshay0724 Feb 15, 2017
749733b
Added entry in whats_new.rst for issue #8306
Akshay0724 Feb 15, 2017
dcb5433
Removed axis=0
Akshay0724 Feb 15, 2017
62ff80f
Wording in changelog
jnothman Feb 23, 2017
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4 changes: 4 additions & 0 deletions doc/whats_new.rst
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Expand Up @@ -149,6 +149,10 @@ Enhancements

Bug fixes
.........
- Fixed a bug where :class:`sklearn.cluster.DBSCAN` gives incorrect
result when input is a precomputed sparse matrix with initial
rows all zero.
:issue:`8306` by :user:`Akshay Gupta <Akshay0724>`

- Fixed a bug where :func:`sklearn.datasets.make_moons` gives an
incorrect result when ``n_samples`` is odd.
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3 changes: 2 additions & 1 deletion sklearn/cluster/dbscan_.py
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Expand Up @@ -124,7 +124,8 @@ def dbscan(X, eps=0.5, min_samples=5, metric='minkowski', metric_params=None,
X.sum_duplicates() # XXX: modifies X's internals in-place
X_mask = X.data <= eps
masked_indices = astype(X.indices, np.intp, copy=False)[X_mask]
masked_indptr = np.cumsum(X_mask)[X.indptr[1:] - 1]
masked_indptr = np.concatenate(([0], np.cumsum(X_mask)))[X.indptr[1:]]

# insert the diagonal: a point is its own neighbor, but 0 distance
# means absence from sparse matrix data
masked_indices = np.insert(masked_indices, masked_indptr,
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17 changes: 17 additions & 0 deletions sklearn/cluster/tests/test_dbscan.py
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Expand Up @@ -350,3 +350,20 @@ def test_dbscan_precomputed_metric_with_degenerate_input_arrays():
X = np.zeros((10, 10))
labels = DBSCAN(eps=0.5, metric='precomputed').fit(X).labels_
assert_equal(len(set(labels)), 1)


def test_dbscan_precomputed_metric_with_initial_rows_zero():
# sample matrix with initial two row all zero
ar = np.array([
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.1, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.1, 0.0, 0.0],
[0.0, 0.0, 0.1, 0.1, 0.0, 0.0, 0.3],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.1],
[0.0, 0.0, 0.0, 0.0, 0.3, 0.1, 0.0]
])
matrix = sparse.csr_matrix(ar)
labels = DBSCAN(eps=0.2, metric='precomputed',
min_samples=2).fit(matrix).labels_
assert_array_equal(labels, [-1, -1, 0, 0, 0, 1, 1])
0