Fix RuntimeError in export_phrases (change defaultdict to dict)

CHANGELOG.md

@@ -1,6 +1,10 @@
 Changes
 =======
 
+## Unreleased
+
+- fix RuntimeError in export_phrases (change defaultdict to dict) (PR [#3041](https://github.com/RaRe-Technologies/gensim/pull/3041), [@thalishsajeed](https://github.com/thalishsajeed))
+
 ## 4.0.0beta, 2020-10-31
 
 **⚠️ Gensim 4.0 contains breaking API changes! See the [Migration guide](https://github.com/RaRe-Technologies/gensim/wiki/Migrating-from-Gensim-3.x-to-4) to update your existing Gensim 3.x code and models.**

gensim/models/phrases.py

@@ -62,7 +62,6 @@
 """
 
 import logging
-from collections import defaultdict
 import itertools
 from math import log
 import pickle
@@ -412,7 +411,7 @@ def load(cls, *args, **kwargs):
             if not isinstance(word, str):
                 logger.info("old version of %s loaded, upgrading %i words in memory", cls.__name__, len(model.vocab))
                 logger.info("re-save the loaded model to avoid this upgrade in the future")
-                vocab = defaultdict(int)
+                vocab = {}
                 for key, value in model.vocab.items():  # needs lots of extra RAM temporarily!
                     vocab[str(key, encoding='utf8')] = value
                 model.vocab = vocab
@@ -554,7 +553,7 @@ def __init__(
         self.min_count = min_count
         self.threshold = threshold
         self.max_vocab_size = max_vocab_size
-        self.vocab = defaultdict(int)  # mapping between token => its count
+        self.vocab = {}  # mapping between token => its count
         self.min_reduce = 1  # ignore any tokens with count smaller than this
         self.delimiter = delimiter
         self.progress_per = progress_per
@@ -579,7 +578,7 @@ def __str__(self):
     def _learn_vocab(sentences, max_vocab_size, delimiter, connector_words, progress_per):
         """Collect unigram and bigram counts from the `sentences` iterable."""
         sentence_no, total_words, min_reduce = -1, 0, 1
-        vocab = defaultdict(int)
+        vocab = {}
         logger.info("collecting all words and their counts")
         for sentence_no, sentence in enumerate(sentences):
             if sentence_no % progress_per == 0:
@@ -590,10 +589,11 @@ def _learn_vocab(sentences, max_vocab_size, delimiter, connector_words, progress
             start_token, in_between = None, []
             for word in sentence:
                 if word not in connector_words:
-                    vocab[word] += 1
+                    vocab[word] = vocab.get(word, 0) + 1
                     if start_token is not None:
                         phrase_tokens = itertools.chain([start_token], in_between, [word])
-                        vocab[delimiter.join(phrase_tokens)] += 1
+                        joined_phrase_token = delimiter.join(phrase_tokens)
+                        vocab[joined_phrase_token] = vocab.get(joined_phrase_token, 0) + 1
                     start_token, in_between = word, []  # treat word as both end of a phrase AND beginning of another
                 elif start_token is not None:
                     in_between.append(word)
@@ -654,7 +654,7 @@ def add_vocab(self, sentences):
             logger.info("merging %i counts into %s", len(vocab), self)
             self.min_reduce = max(self.min_reduce, min_reduce)
             for word, count in vocab.items():
-                self.vocab[word] += count
+                self.vocab[word] = self.vocab.get(word, 0) + count
             if len(self.vocab) > self.max_vocab_size:
                 utils.prune_vocab(self.vocab, self.min_reduce)
                 self.min_reduce += 1
@@ -666,17 +666,17 @@ def add_vocab(self, sentences):
 
     def score_candidate(self, word_a, word_b, in_between):
         # Micro optimization: check for quick early-out conditions, before the actual scoring.
-        word_a_cnt = self.vocab[word_a]
+        word_a_cnt = self.vocab.get(word_a, 0)
         if word_a_cnt <= 0:
             return None, None
 
-        word_b_cnt = self.vocab[word_b]
+        word_b_cnt = self.vocab.get(word_b, 0)
         if word_b_cnt <= 0:
             return None, None
 
         phrase = self.delimiter.join([word_a] + in_between + [word_b])
         # XXX: Why do we care about *all* phrase tokens? Why not just score the start+end bigram?
-        phrase_cnt = self.vocab[phrase]
+        phrase_cnt = self.vocab.get(phrase, 0)
         if phrase_cnt <= 0:
             return None, None
 

gensim/test/test_phrases.py

@@ -213,15 +213,34 @@ def dumb_scorer(worda_count, wordb_count, bigram_count, len_vocab, min_count, co
 class TestPhrasesModel(PhrasesCommon, unittest.TestCase):
 
     def test_export_phrases(self):
-        """Test Phrases bigram export phrases."""
+        """Test Phrases bigram and trigram export phrases."""
+        bigram = Phrases(self.sentences, min_count=1, threshold=1, delimiter=' ')
+        trigram = Phrases(bigram[self.sentences], min_count=1, threshold=1, delimiter=' ')
+        seen_bigrams = set(bigram.export_phrases().keys())
+        seen_trigrams = set(trigram.export_phrases().keys())
+
+        assert seen_bigrams == set([
+            'human interface',
+            'response time',
+            'graph minors',
+            'minors survey',
+        ])
+
+        assert seen_trigrams == set([
+            'human interface',
+            'graph minors survey',
+        ])
+
+    def test_find_phrases(self):
+        """Test Phrases bigram find phrases."""
         bigram = Phrases(self.sentences, min_count=1, threshold=1, delimiter=' ')
         seen_bigrams = set(bigram.find_phrases(self.sentences).keys())
 
-        assert seen_bigrams == {
+        assert seen_bigrams == set([
             'response time',
             'graph minors',
             'human interface',
-        }
+        ])
 
     def test_multiple_bigrams_single_entry(self):
         """Test a single entry produces multiple bigrams."""
@@ -441,7 +460,7 @@ def test_multiple_bigrams_single_entry(self):
             'human interface',
         ])
 
-    def test_export_phrases(self):
+    def test_find_phrases(self):
         """Test Phrases bigram export phrases."""
         bigram = Phrases(self.sentences, min_count=1, threshold=1, connector_words=self.connector_words, delimiter=' ')
         seen_bigrams = set(bigram.find_phrases(self.sentences).keys())
@@ -453,6 +472,21 @@ def test_export_phrases(self):
             'lack of interest',
         ])
 
+    def test_export_phrases(self):
+        """Test Phrases bigram export phrases."""
+        bigram = Phrases(self.sentences, min_count=1, threshold=1, delimiter=' ')
+        seen_bigrams = set(bigram.export_phrases().keys())
+        assert seen_bigrams == set([
+            'and graph',
+            'data and',
+            'graph of',
+            'graph survey',
+            'human interface',
+            'lack of',
+            'of interest',
+            'of trees',
+        ])
+
     def test_scoring_default(self):
         """ test the default scoring, from the mikolov word2vec paper """
         bigram = Phrases(self.sentences, min_count=1, threshold=1, connector_words=self.connector_words)
@@ -510,9 +544,9 @@ def test__getitem__(self):
         assert phrased_sentence == ['data_and_graph', 'survey', 'for', 'human_interface']
 
 
-class TestFrozenPhrasesModelCompatibilty(unittest.TestCase):
+class TestFrozenPhrasesModelCompatibility(unittest.TestCase):
 
-    def test_compatibilty(self):
+    def test_compatibility(self):
         phrases = Phrases.load(datapath("phrases-3.6.0.model"))
         phraser = FrozenPhrases.load(datapath("phraser-3.6.0.model"))
         test_sentences = ['trees', 'graph', 'minors']