Fix deprecations in SoftCosineSimilarity

CHANGELOG.md

@@ -11,6 +11,7 @@ This release contains a major refactoring.
 * No more wheels for x32 platforms (if you need x32 binaries, please build them yourself).
   (__[menshikh-iv](https://github.com/menshikh-iv)__, [#6](https://github.com/RaRe-Technologies/gensim-wheels/pull/6))
 * Speed up random number generation in word2vec model (PR [#2864](https://github.com/RaRe-Technologies/gensim/pull/2864), __[@zygm0nt](https://github.com/zygm0nt)__)
+* Fix deprecations in SoftCosineSimilarity (PR [#2940](https://github.com/RaRe-Technologies/gensim/pull/2940), __[@Witiko](https://github.com/Witiko)__)
 * Remove Keras dependency (PR [#2937](https://github.com/RaRe-Technologies/gensim/pull/2937), __[@piskvorky](https://github.com/piskvorky)__)
 
 ### :books: Tutorial and doc improvements

gensim/matutils.py

@@ -9,12 +9,10 @@
 from __future__ import with_statement
 
 
-from itertools import chain
 import logging
 import math
 
 from gensim import utils
-from gensim.utils import deprecated
 
 import numpy as np
 import scipy.sparse
@@ -193,9 +191,9 @@ def pad(mat, padrow, padcol):
     if padcol < 0:
         padcol = 0
     rows, cols = mat.shape
-    return np.bmat([
-        [mat, np.matrix(np.zeros((rows, padcol)))],
-        [np.matrix(np.zeros((padrow, cols + padcol)))],
+    return np.block([
+        [mat, np.zeros((rows, padcol))],
+        [np.zeros((padrow, cols + padcol))],
     ])
 
 
@@ -819,81 +817,6 @@ def cossim(vec1, vec2):
     return result
 
 
-@deprecated(
-    "Function will be removed in 4.0.0, use "
-    "gensim.similarities.termsim.SparseTermSimilarityMatrix.inner_product instead")
-def softcossim(vec1, vec2, similarity_matrix):
-    """Get Soft Cosine Measure between two vectors given a term similarity matrix.
-
-    Return Soft Cosine Measure between two sparse vectors given a sparse term similarity matrix
-    in the :class:`scipy.sparse.csc_matrix` format. The similarity is a number between `<-1.0, 1.0>`,
-    higher is more similar.
-
-    Notes
-    -----
-    Soft Cosine Measure was perhaps first defined by `Grigori Sidorov et al.,
-    "Soft Similarity and Soft Cosine Measure: Similarity of Features in Vector Space Model"
-    <http://www.cys.cic.ipn.mx/ojs/index.php/CyS/article/view/2043/1921>`_.
-
-    Parameters
-    ----------
-    vec1 : list of (int, float)
-        A query vector in the BoW format.
-    vec2 : list of (int, float)
-        A document vector in the BoW format.
-    similarity_matrix : {:class:`scipy.sparse.csc_matrix`, :class:`scipy.sparse.csr_matrix`}
-        A term similarity matrix. If the matrix is :class:`scipy.sparse.csr_matrix`, it is going
-        to be transposed. If you rely on the fact that there is at most a constant number of
-        non-zero elements in a single column, it is your responsibility to ensure that the matrix
-        is symmetric.
-
-    Returns
-    -------
-    `similarity_matrix.dtype`
-        The Soft Cosine Measure between `vec1` and `vec2`.
-
-    Raises
-    ------
-    ValueError
-        When the term similarity matrix is in an unknown format.
-
-    See Also
-    --------
-    :meth:`gensim.models.keyedvectors.WordEmbeddingsKeyedVectors.similarity_matrix`
-        A term similarity matrix produced from term embeddings.
-    :class:`gensim.similarities.docsim.SoftCosineSimilarity`
-        A class for performing corpus-based similarity queries with Soft Cosine Measure.
-
-    """
-    if not isinstance(similarity_matrix, scipy.sparse.csc_matrix):
-        if isinstance(similarity_matrix, scipy.sparse.csr_matrix):
-            similarity_matrix = similarity_matrix.T
-        else:
-            raise ValueError('unknown similarity matrix format')
-
-    if not vec1 or not vec2:
-        return 0.0
-
-    vec1 = dict(vec1)
-    vec2 = dict(vec2)
-    word_indices = sorted(set(chain(vec1, vec2)))
-    dtype = similarity_matrix.dtype
-    vec1 = np.fromiter((vec1[i] if i in vec1 else 0 for i in word_indices), dtype=dtype, count=len(word_indices))
-    vec2 = np.fromiter((vec2[i] if i in vec2 else 0 for i in word_indices), dtype=dtype, count=len(word_indices))
-    dense_matrix = similarity_matrix[[[i] for i in word_indices], word_indices].todense()
-    vec1len = vec1.T.dot(dense_matrix).dot(vec1)[0, 0]
-    vec2len = vec2.T.dot(dense_matrix).dot(vec2)[0, 0]
-
-    assert \
-        vec1len > 0.0 and vec2len > 0.0, \
-        u"sparse documents must not contain any explicit zero entries and the similarity matrix S " \
-        u"must satisfy x^T * S * x > 0 for any nonzero bag-of-words vector x."
-
-    result = vec1.T.dot(dense_matrix).dot(vec2)[0, 0]
-    result /= math.sqrt(vec1len) * math.sqrt(vec2len)  # rescale by vector lengths
-    return np.clip(result, -1.0, 1.0)
-
-
 def isbow(vec):
     """Checks if a vector is in the sparse Gensim bag-of-words format.
 

gensim/models/keyedvectors.py

@@ -1475,7 +1475,7 @@ def save_word2vec_format(self, fname, fvocab=None, binary=False, total_vec=None,
                 row = self[key]
                 if binary:
                     row = row.astype(REAL)
-                    fout.write(utils.to_utf8(prefix + str(key)) + b" " + row.tostring())
+                    fout.write(utils.to_utf8(prefix + str(key)) + b" " + row.tobytes())
                 else:
                     fout.write(utils.to_utf8("%s%s %s\n" % (prefix, str(key), ' '.join(repr(val) for val in row))))
 

gensim/models/wrappers/wordrank.py

@@ -254,8 +254,7 @@ def load_wordrank_model(cls, model_file, vocab_file=None, context_file=None, sor
             If 1 - use ensemble of word and context vectors.
 
         """
-        glove2word2vec(model_file, model_file + '.w2vformat')
-        model = cls.load_word2vec_format('%s.w2vformat' % model_file)
+        model = cls.load_word2vec_format(model_file, binary=False, no_header=True)
         if ensemble and context_file:
             model.ensemble_embedding(model_file, context_file)
         if sorted_vocab and vocab_file:

gensim/similarities/docsim.py

@@ -77,7 +77,6 @@
 import scipy.sparse
 
 from gensim import interfaces, utils, matutils
-from .termsim import SparseTermSimilarityMatrix
 from six.moves import map, range, zip
 
 
@@ -931,13 +930,7 @@ def __init__(self, corpus, similarity_matrix, num_best=None, chunksize=256):
             A term similarity index that computes cosine similarities between word embeddings.
 
         """
-        if scipy.sparse.issparse(similarity_matrix):
-            logger.warn(
-                "Support for passing an unencapsulated sparse matrix will be removed in 4.0.0, pass "
-                "a SparseTermSimilarityMatrix instance instead")
-            self.similarity_matrix = SparseTermSimilarityMatrix(similarity_matrix)
-        else:
-            self.similarity_matrix = similarity_matrix
+        self.similarity_matrix = similarity_matrix
 
         self.corpus = corpus
         self.num_best = num_best

gensim/similarities/termsim.py

@@ -12,7 +12,6 @@
 from itertools import chain
 import logging
 from math import sqrt
-import warnings
 
 import numpy as np
 from six.moves import range
@@ -457,8 +456,6 @@ class SparseTermSimilarityMatrix(SaveLoad):
         sparse term similarity matrix. If None, then no limit will be imposed.
     dtype : numpy.dtype, optional
         The data type of the sparse term similarity matrix.
-    positive_definite: bool or None, optional
-        A deprecated alias for dominant.
 
     Attributes
     ----------
@@ -472,14 +469,7 @@ class SparseTermSimilarityMatrix(SaveLoad):
 
     """
     def __init__(self, source, dictionary=None, tfidf=None, symmetric=True, dominant=False,
-            nonzero_limit=100, dtype=np.float32, positive_definite=None):
-
-        if positive_definite is not None:
-            warnings.warn(
-                'Parameter positive_definite will be removed in 4.0.0, use dominant instead',
-                category=DeprecationWarning,
-            )
-            dominant = positive_definite
+            nonzero_limit=100, dtype=np.float32):
 
         if not sparse.issparse(source):
             index = source
@@ -529,14 +519,6 @@ def inner_product(self, X, Y, normalized=(False, False)):
         if not X or not Y:
             return self.matrix.dtype.type(0.0)
 
-        if normalized in (True, False):
-            warnings.warn(
-                'Boolean parameter normalized will be removed in 4.0.0, use '
-                'normalized=(%s, %s) instead of normalized=%s' % tuple([normalized] * 3),
-                category=DeprecationWarning,
-            )
-            normalized = (normalized, normalized)
-
         normalized_X, normalized_Y = normalized
         valid_normalized_values = (True, False, 'maintain')
 

gensim/test/test_fasttext.py

@@ -1375,7 +1375,8 @@ def test_in_vocab(self):
 
     def test_out_of_vocab(self):
         model = train_gensim(bucket=0)
-        self.assertRaises(KeyError, model.wv.word_vec, 'streamtrain')
+        with self.assertRaises(KeyError):
+            model.wv.get_vector('streamtrain')
 
     def test_cbow_neg(self):
         """See `gensim.test.test_word2vec.TestWord2VecModel.test_cbow_neg`."""

gensim/test/test_lsimodel.py

@@ -66,7 +66,7 @@ def testTransformFloat32(self):
     def testCorpusTransform(self):
         """Test lsi[corpus] transformation."""
         model = self.model
-        got = np.vstack(matutils.sparse2full(doc, 2) for doc in model[self.corpus])
+        got = np.vstack([matutils.sparse2full(doc, 2) for doc in model[self.corpus]])
         expected = np.array([
             [0.65946639, 0.14211544],
             [2.02454305, -0.42088759],

gensim/test/test_phrases.py

@@ -236,7 +236,7 @@ def testBigramConstructionFromArray(self):
         bigram1_seen = False
         bigram2_seen = False
 
-        for s in self.bigram[np.array(self.sentences)]:
+        for s in self.bigram[np.array(self.sentences, dtype=object)]:
             if not bigram1_seen and self.bigram1 in s:
                 bigram1_seen = True
             if not bigram2_seen and self.bigram2 in s:

gensim/test/test_similarities.py

@@ -974,29 +974,6 @@ def test_dominant(self):
             [0.0, 0.0, 0.0, 0.0, 1.0]])
         self.assertTrue(numpy.all(expected_matrix == matrix))
 
-    def test_positive_definite(self):
-        """Test the positive_definite parameter of the matrix constructor."""
-        negative_index = UniformTermSimilarityIndex(self.dictionary, term_similarity=-0.5)
-        matrix = SparseTermSimilarityMatrix(
-            negative_index, self.dictionary, nonzero_limit=2).matrix.todense()
-        expected_matrix = numpy.array([
-            [1.0, -.5, -.5, 0.0, 0.0],
-            [-.5, 1.0, 0.0, -.5, 0.0],
-            [-.5, 0.0, 1.0, 0.0, 0.0],
-            [0.0, -.5, 0.0, 1.0, 0.0],
-            [0.0, 0.0, 0.0, 0.0, 1.0]])
-        self.assertTrue(numpy.all(expected_matrix == matrix))
-
-        matrix = SparseTermSimilarityMatrix(
-            negative_index, self.dictionary, nonzero_limit=2, positive_definite=True).matrix.todense()
-        expected_matrix = numpy.array([
-            [1.0, -.5, 0.0, 0.0, 0.0],
-            [-.5, 1.0, 0.0, 0.0, 0.0],
-            [0.0, 0.0, 1.0, 0.0, 0.0],
-            [0.0, 0.0, 0.0, 1.0, 0.0],
-            [0.0, 0.0, 0.0, 0.0, 1.0]])
-        self.assertTrue(numpy.all(expected_matrix == matrix))
-
     def test_tfidf(self):
         """Test the tfidf parameter of the matrix constructor."""
         matrix = SparseTermSimilarityMatrix(

gensim/test/test_similarity_metrics.py

@@ -13,7 +13,7 @@
 import unittest
 
 from gensim import matutils
-from scipy.sparse import csr_matrix, csc_matrix
+from scipy.sparse import csr_matrix
 import numpy as np
 import math
 from gensim.corpora.mmcorpus import MmCorpus
@@ -240,36 +240,6 @@ def test_distributions(self):
         self.assertAlmostEqual(expected, result)
 
 
-class TestSoftCosineSimilarity(unittest.TestCase):
-    def test_inputs(self):
-        # checking empty inputs
-        vec_1 = []
-        vec_2 = []
-        similarity_matrix = csc_matrix((0, 0))
-        result = matutils.softcossim(vec_1, vec_2, similarity_matrix)
-        expected = 0.0
-        self.assertEqual(expected, result)
-
-        # checking CSR term similarity matrix format
-        similarity_matrix = csr_matrix((0, 0))
-        result = matutils.softcossim(vec_1, vec_2, similarity_matrix)
-        expected = 0.0
-        self.assertEqual(expected, result)
-
-        # checking unknown term similarity matrix format
-        with self.assertRaises(ValueError):
-            matutils.softcossim(vec_1, vec_2, np.matrix([]))
-
-    def test_distributions(self):
-        # checking bag of words as inputs
-        vec_1 = [(0, 1.0), (2, 1.0)]  # hello world
-        vec_2 = [(1, 1.0), (2, 1.0)]  # hi world
-        similarity_matrix = csc_matrix([[1, 0.5, 0], [0.5, 1, 0], [0, 0, 1]])
-        result = matutils.softcossim(vec_1, vec_2, similarity_matrix)
-        expected = 0.75
-        self.assertAlmostEqual(expected, result)
-
-
 if __name__ == '__main__':
     logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.DEBUG)
     unittest.main()

gensim/test/test_wordrank_wrapper.py

@@ -39,15 +39,13 @@ def testLoadWordrankFormat(self):
         vocab_size, dim = 76, 50
         self.assertEqual(model.vectors.shape, (vocab_size, dim))
         self.assertEqual(len(model), vocab_size)
-        os.remove(self.wr_file + '.w2vformat')
 
     def testEnsemble(self):
         """Test ensemble of two embeddings"""
         if not self.wr_path:
             return
         new_emb = self.test_model.ensemble_embedding(self.wr_file, self.wr_file)
         self.assertEqual(new_emb.shape, (76, 50))
-        os.remove(self.wr_file + '.w2vformat')
 
     def testPersistence(self):
         """Test storing/loading the entire model"""