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author | Liang-Chi Hsieh <viirya@gmail.com> | 2017-04-05 17:46:44 -0700 |
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committer | Joseph K. Bradley <joseph@databricks.com> | 2017-04-05 17:46:44 -0700 |
commit | 12206058e8780e202c208b92774df3773eff36ae (patch) | |
tree | 363db4aa846ad9e7a57285fd9ba57d5921bb7039 /python/pyspark | |
parent | 9d68c67235481fa33983afb766916b791ca8212a (diff) | |
download | spark-12206058e8780e202c208b92774df3773eff36ae.tar.gz spark-12206058e8780e202c208b92774df3773eff36ae.tar.bz2 spark-12206058e8780e202c208b92774df3773eff36ae.zip |
[SPARK-20214][ML] Make sure converted csc matrix has sorted indices
## What changes were proposed in this pull request?
`_convert_to_vector` converts a scipy sparse matrix to csc matrix for initializing `SparseVector`. However, it doesn't guarantee the converted csc matrix has sorted indices and so a failure happens when you do something like that:
from scipy.sparse import lil_matrix
lil = lil_matrix((4, 1))
lil[1, 0] = 1
lil[3, 0] = 2
_convert_to_vector(lil.todok())
File "/home/jenkins/workspace/python/pyspark/mllib/linalg/__init__.py", line 78, in _convert_to_vector
return SparseVector(l.shape[0], csc.indices, csc.data)
File "/home/jenkins/workspace/python/pyspark/mllib/linalg/__init__.py", line 556, in __init__
% (self.indices[i], self.indices[i + 1]))
TypeError: Indices 3 and 1 are not strictly increasing
A simple test can confirm that `dok_matrix.tocsc()` won't guarantee sorted indices:
>>> from scipy.sparse import lil_matrix
>>> lil = lil_matrix((4, 1))
>>> lil[1, 0] = 1
>>> lil[3, 0] = 2
>>> dok = lil.todok()
>>> csc = dok.tocsc()
>>> csc.has_sorted_indices
0
>>> csc.indices
array([3, 1], dtype=int32)
I checked the source codes of scipy. The only way to guarantee it is `csc_matrix.tocsr()` and `csr_matrix.tocsc()`.
## How was this patch tested?
Existing tests.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes #17532 from viirya/make-sure-sorted-indices.
Diffstat (limited to 'python/pyspark')
-rw-r--r-- | python/pyspark/ml/linalg/__init__.py | 3 | ||||
-rw-r--r-- | python/pyspark/mllib/linalg/__init__.py | 3 | ||||
-rw-r--r-- | python/pyspark/mllib/tests.py | 11 |
3 files changed, 17 insertions, 0 deletions
diff --git a/python/pyspark/ml/linalg/__init__.py b/python/pyspark/ml/linalg/__init__.py index b765343251..ad1b487676 100644 --- a/python/pyspark/ml/linalg/__init__.py +++ b/python/pyspark/ml/linalg/__init__.py @@ -72,7 +72,10 @@ def _convert_to_vector(l): return DenseVector(l) elif _have_scipy and scipy.sparse.issparse(l): assert l.shape[1] == 1, "Expected column vector" + # Make sure the converted csc_matrix has sorted indices. csc = l.tocsc() + if not csc.has_sorted_indices: + csc.sort_indices() return SparseVector(l.shape[0], csc.indices, csc.data) else: raise TypeError("Cannot convert type %s into Vector" % type(l)) diff --git a/python/pyspark/mllib/linalg/__init__.py b/python/pyspark/mllib/linalg/__init__.py index 031f22c020..7b24b3c74a 100644 --- a/python/pyspark/mllib/linalg/__init__.py +++ b/python/pyspark/mllib/linalg/__init__.py @@ -74,7 +74,10 @@ def _convert_to_vector(l): return DenseVector(l) elif _have_scipy and scipy.sparse.issparse(l): assert l.shape[1] == 1, "Expected column vector" + # Make sure the converted csc_matrix has sorted indices. csc = l.tocsc() + if not csc.has_sorted_indices: + csc.sort_indices() return SparseVector(l.shape[0], csc.indices, csc.data) else: raise TypeError("Cannot convert type %s into Vector" % type(l)) diff --git a/python/pyspark/mllib/tests.py b/python/pyspark/mllib/tests.py index c519883cdd..523b3f1113 100644 --- a/python/pyspark/mllib/tests.py +++ b/python/pyspark/mllib/tests.py @@ -853,6 +853,17 @@ class SciPyTests(MLlibTestCase): self.assertEqual(sv, serialize(lil.tocsr())) self.assertEqual(sv, serialize(lil.todok())) + def test_convert_to_vector(self): + from scipy.sparse import csc_matrix + # Create a CSC matrix with non-sorted indices + indptr = array([0, 2]) + indices = array([3, 1]) + data = array([2.0, 1.0]) + csc = csc_matrix((data, indices, indptr)) + self.assertFalse(csc.has_sorted_indices) + sv = SparseVector(4, {1: 1, 3: 2}) + self.assertEqual(sv, _convert_to_vector(csc)) + def test_dot(self): from scipy.sparse import lil_matrix lil = lil_matrix((4, 1)) |