Use DAWG for fast approximate kNN over Levenshtein distance

gensim/similarities/__init__.py

@@ -6,13 +6,13 @@
 import warnings
 try:
     import Levenshtein  # noqa:F401
-    import vptree  # noqa:F401
+    import lexpy  # noqa:F401
 except ImportError:
     msg = (
         "The gensim.similarities.levenshtein submodule is disabled, because the optional "
         "Levenshtein <https://pypi.org/project/python-Levenshtein/> and "
-        "vptree <https://pypi.org/project/vptree/> packages are unavailable. "
-        "Install Levenhstein and vptree (e.g. `pip install python-Levenshtein vptree`) to "
+        "lexpy <https://pypi.org/project/lexpy/> packages are unavailable. "
+        "Install Levenhstein and lexpy (e.g. `pip install python-Levenshtein lexpy`) to "
         "suppress this warning."
     )
     warnings.warn(msg)

gensim/similarities/levenshtein.py

@@ -10,102 +10,21 @@
 
 import itertools
 import logging
-from math import floor
 
 from gensim.similarities.termsim import TermSimilarityIndex
 
 logger = logging.getLogger(__name__)
 
 
-def levdist(t1, t2, max_distance=float("inf")):
-    """Get the Levenshtein distance between two terms.
-
-    Return the Levenshtein distance between two terms. The distance is a
-    number between <1.0, inf>, higher is less similar.
-
-    Parameters
-    ----------
-    t1 : {bytes, str, unicode}
-        The first compared term.
-    t2 : {bytes, str, unicode}
-        The second compared term.
-    max_distance : {int, float}, optional
-        If you don't care about distances larger than a known threshold, a more
-        efficient code path can be taken. For terms that are clearly "too far
-        apart", we will not compute the distance exactly, but we will return
-        `max(len(t1), len(t2))` more quickly, meaning "more than
-        `max_distance`".
-        Default: always compute distance exactly, no threshold clipping.
-
-    Returns
-    -------
-    int
-        The Levenshtein distance between `t1` and `t2`.
-
-    """
-    import Levenshtein
-
-    distance = Levenshtein.distance(t1, t2)
-    if distance > max_distance:
-        return max(len(t1), len(t2))
-    return distance
-
-
-def levsim(t1, t2, alpha=1.8, beta=5.0, min_similarity=0.0):
-    """Get the Levenshtein similarity between two terms.
-
-    Return the Levenshtein similarity between two terms. The similarity is a
-    number between <0.0, 1.0>, higher is more similar.
-
-    Parameters
-    ----------
-    t1 : {bytes, str, unicode}
-        The first compared term.
-    t2 : {bytes, str, unicode}
-        The second compared term.
-    alpha : float, optional
-        The multiplicative factor alpha defined by Charlet and Damnati (2017).
-    beta : float, optional
-        The exponential factor beta defined by Charlet and Damnati (2017).
-    min_similarity : {int, float}, optional
-        If you don't care about similarities smaller than a known threshold, a
-        more efficient code path can be taken. For terms that are clearly "too
-        far apart", we will not compute the distance exactly, but we will
-        return zero more quickly, meaning "less than `min_similarity`".
-        Default: always compute similarity exactly, no threshold clipping.
-
-    Returns
-    -------
-    float
-        The Levenshtein similarity between `t1` and `t2`.
-
-    Notes
-    -----
-    This notion of Levenshtein similarity was first defined in section 2.2 of
-    `Delphine Charlet and Geraldine Damnati, "SimBow at SemEval-2017 Task 3:
-    Soft-Cosine Semantic Similarity between Questions for Community Question
-    Answering", 2017 <http://www.aclweb.org/anthology/S/S17/S17-2051.pdf>`_.
-
-    """
-    assert alpha >= 0
-    assert beta >= 0
-
-    max_lengths = max(len(t1), len(t2)) or 1
-    min_similarity = float(max(min(min_similarity, 1.0), 0.0))
-    max_distance = int(floor(max_lengths * (1 - (min_similarity / alpha) ** (1 / beta))))
-    distance = levdist(t1, t2, max_distance)
-    similarity = alpha * (1 - distance * 1.0 / max_lengths)**beta
-    return similarity
-
-
 class LevenshteinSimilarityIndex(TermSimilarityIndex):
     r"""
     Computes Levenshtein similarities between terms and retrieves most similar
     terms for a given term.
 
     Notes
     -----
-    This implementation uses a VP-Tree for metric indexing.
+    This implementation uses a Directed Acyclic Word Graph (DAWG)
+    for fast nearest-neighbor retrieval of the most similar terms.
 
     Parameters
     ----------
@@ -115,17 +34,25 @@ class LevenshteinSimilarityIndex(TermSimilarityIndex):
         The multiplicative factor alpha defined by [charletetal17]_.
     beta : float, optional
         The exponential factor beta defined by [charletetal17]_.
+    max_distance : int, optional
+        The maximum Levenshtein distance of the most similar terms.
+        Keeping this value below 3 has a significant impact on the
+        retrieval performance. Default is 1.
 
     Attributes
     ----------
     dictionary : :class:`~gensim.corpora.dictionary.Dictionary`
         A dictionary that specifies the considered terms.
-    alpha : float, optional
+    alpha : float
         The multiplicative factor alpha defined by [charletetal17]_.
-    beta : float, optional
+    beta : float
         The exponential factor beta defined by [charletetal17]_.
-    index : :class:`vptree.VPTree`
-        The VP-Tree metric index.
+    index : :class:`lexpy.dawg.DAWG`
+        The DAWG nearest-neighbor search index.
+    max_distance : int
+        The maximum Levenshtein distance of the most similar terms.
+        Keeping this value below 3 has a significant impact on the
+        retrieval performance.
 
     See Also
     --------
@@ -145,17 +72,29 @@ class LevenshteinSimilarityIndex(TermSimilarityIndex):
        https://www.aclweb.org/anthology/S17-2051/.
 
     """
-    def __init__(self, dictionary, alpha=1.8, beta=5.0):
-        from vptree import VPTree
-
+    def __init__(self, dictionary, alpha=1.8, beta=5.0, max_distance=1):
         self.dictionary = dictionary
         self.alpha = alpha
         self.beta = beta
-        terms = list(self.dictionary.values())
-        self.index = VPTree(terms, levdist)
+        self.max_distance = max_distance
+
+        from lexpy.dawg import DAWG
+
+        self.index = DAWG()
+        terms = sorted(self.dictionary.values())
+        self.index.add_all(terms)
+        self.index.reduce()
+
         super(LevenshteinSimilarityIndex, self).__init__()
 
+    def _levsim(self, t1, t2):
+        from Levenshtein import distance
+
+        max_lengths = max(len(t1), len(t2)) or 1
+        similarity = self.alpha * (1.0 - distance(t1, t2) * 1.0 / max_lengths)**self.beta
+        return similarity
+
     def most_similar(self, t1, topn=10):
-        terms = [term for _, term in self.index.get_n_nearest_neighbors(t1, int(topn + 1))]
-        most_similar = ((t2, levsim(t1, t2, self.alpha, self.beta)) for t2 in terms if t1 != t2)
+        terms = self.index.search_within_distance(t1, self.max_distance)
+        most_similar = ((t2, self._levsim(t1, t2)) for t2 in terms if t1 != t2)
         return itertools.islice(most_similar, topn)

gensim/test/test_similarities.py

@@ -33,7 +33,6 @@
 from gensim.similarities import SparseTermSimilarityMatrix
 from gensim.similarities import LevenshteinSimilarityIndex
 from gensim.similarities.docsim import _nlargest
-from gensim.similarities.levenshtein import levdist, levsim
 
 try:
     from pyemd import emd  # noqa:F401
@@ -1544,74 +1543,12 @@ def test_inner_product_corpus_corpus_true_true(self):
         self.assertTrue(numpy.allclose(expected_result, result.todense()))
 
 
-class TestLevenshteinDistance(unittest.TestCase):
-    @unittest.skipIf(LevenshteinSimilarityIndex is None, "gensim.similarities.levenshtein is disabled")
-    def test_max_distance(self):
-        t1 = "holiday"
-        t2 = "day"
-        max_distance = max(len(t1), len(t2))
-
-        self.assertEqual(4, levdist(t1, t2))
-        self.assertEqual(4, levdist(t1, t2, 4))
-        self.assertEqual(max_distance, levdist(t1, t2, 2))
-        self.assertEqual(max_distance, levdist(t1, t2, -2))
-
-
-class TestLevenshteinSimilarity(unittest.TestCase):
-    @unittest.skipIf(LevenshteinSimilarityIndex is None, "gensim.similarities.levenshtein is disabled")
-    def test_empty_strings(self):
-        t1 = ""
-        t2 = ""
-        alpha = 1.8
-
-        self.assertEqual(alpha, levsim(t1, t2))
-
-    @unittest.skipIf(LevenshteinSimilarityIndex is None, "gensim.similarities.levenshtein is disabled")
-    def test_negative_hyperparameters(self):
-        t1 = "holiday"
-        t2 = "day"
-        alpha = 2.0
-        beta = 2.0
-
-        with self.assertRaises(AssertionError):
-            levsim(t1, t2, -alpha, beta)
-
-        with self.assertRaises(AssertionError):
-            levsim(t1, t2, alpha, -beta)
-
-        with self.assertRaises(AssertionError):
-            levsim(t1, t2, -alpha, -beta)
-
-    @unittest.skipIf(LevenshteinSimilarityIndex is None, "gensim.similarities.levenshtein is disabled")
-    def test_min_similarity(self):
-        t1 = "holiday"
-        t2 = "day"
-        alpha = 2.0
-        beta = 2.0
-        similarity = alpha * (1 - 4.0 / 7)**beta
-        assert similarity > 0.1 and similarity < 0.5
-
-        self.assertAlmostEqual(similarity, levsim(t1, t2, alpha, beta))
-
-        self.assertAlmostEqual(similarity, levsim(t1, t2, alpha, beta, -2))
-        self.assertAlmostEqual(similarity, levsim(t1, t2, alpha, beta, -2.0))
-
-        self.assertAlmostEqual(similarity, levsim(t1, t2, alpha, beta, 0))
-        self.assertAlmostEqual(similarity, levsim(t1, t2, alpha, beta, 0.0))
-
-        self.assertEqual(similarity, levsim(t1, t2, alpha, beta, 0.1))
-        self.assertEqual(0.0, levsim(t1, t2, alpha, beta, 0.5))
-        self.assertEqual(0.0, levsim(t1, t2, alpha, beta, 1.0))
-
-        self.assertEqual(0.0, levsim(t1, t2, alpha, beta, 2))
-        self.assertEqual(0.0, levsim(t1, t2, alpha, beta, 2.0))
-
-
 class TestLevenshteinSimilarityIndex(unittest.TestCase):
     def setUp(self):
         self.documents = [[u"government", u"denied", u"holiday"], [u"holiday", u"slowing", u"hollingworth"]]
         self.dictionary = Dictionary(self.documents)
-        self.index = LevenshteinSimilarityIndex(self.dictionary)
+        max_distance = max(len(term) for term in self.dictionary.values())
+        self.index = LevenshteinSimilarityIndex(self.dictionary, max_distance=max_distance)
 
     @unittest.skipIf(LevenshteinSimilarityIndex is None, "gensim.similarities.levenshtein is disabled")
     def test_most_similar_topn(self):

setup.py

@@ -279,7 +279,7 @@ def run(self):
         'pyemd',
         'nmslib',
         'python-Levenshtein >= 0.10.2',
-        'vptree >= 1.2',
+        'lexpy >= 0.9.8',
     ])
 
 # Add additional requirements for testing on Linux that are skipped on Windows.