Eliminate obsolete step parameter from doc2vec infer_vector and simil…

…arity_unseen_docs (#3176)

* Fix: eliminate step params

* Fix: typo in doc2vec.infer_vector() documentation

* Update CHANGELOG.md

Co-authored-by: Michael Penkov <m@penkov.dev>

CHANGELOG.md

@@ -6,8 +6,9 @@ Changes
 ### :red_circle: Bug fixes
 
 * [#3116](https://github.com/RaRe-Technologies/gensim/pull/3116): Fix bug where saved Phrases model did not load its connector_words, by [@aloknayak29](https://github.com/aloknayak29)
-* [#3136](https://github.com/RaRe-Technologies/gensim/pull/3136): fix indexing error in word2vec_inner.pyx, by [@bluekura](https://github.com/bluekura)
+* [#3136](https://github.com/RaRe-Technologies/gensim/pull/3136): Fix indexing error in word2vec_inner.pyx, by [@bluekura](https://github.com/bluekura)
 * [#3174](https://github.com/RaRe-Technologies/gensim/pull/3174): Fix a bug when upgrading phraser from gensim 3.x to 4.0, by [@emgucv](https://github.com/emgucv)
+* [#3176](https://github.com/RaRe-Technologies/gensim/pull/3176): Eliminate obsolete step parameter from doc2vec infer_vector and similarity_unseen_docs, by [@rock420](https://github.com/rock420)
 
 ### :+1: Improvements
 

gensim/models/doc2vec.py

@@ -581,13 +581,13 @@ def estimated_lookup_memory(self):
         """
         return 60 * len(self.dv) + 140 * len(self.dv)
 
-    def infer_vector(self, doc_words, alpha=None, min_alpha=None, epochs=None, steps=None):
+    def infer_vector(self, doc_words, alpha=None, min_alpha=None, epochs=None):
         """Infer a vector for given post-bulk training document.
 
         Notes
         -----
         Subsequent calls to this function may infer different representations for the same document.
-        For a more stable representation, increase the number of steps to assert a stricket convergence.
+        For a more stable representation, increase the number of epochs to assert a stricter convergence.
 
         Parameters
         ----------
@@ -1047,7 +1047,7 @@ def scan_vocab(self, corpus_iterable=None, corpus_file=None, progress_per=10000,
 
         return total_words, corpus_count
 
-    def similarity_unseen_docs(self, doc_words1, doc_words2, alpha=None, min_alpha=None, steps=None):
+    def similarity_unseen_docs(self, doc_words1, doc_words2, alpha=None, min_alpha=None, epochs=None):
         """Compute cosine similarity between two post-bulk out of training documents.
 
         Parameters
@@ -1062,7 +1062,7 @@ def similarity_unseen_docs(self, doc_words1, doc_words2, alpha=None, min_alpha=N
             The initial learning rate.
         min_alpha : float, optional
             Learning rate will linearly drop to `min_alpha` as training progresses.
-        steps : int, optional
+        epochs : int, optional
             Number of epoch to train the new document.
 
         Returns
@@ -1071,8 +1071,8 @@ def similarity_unseen_docs(self, doc_words1, doc_words2, alpha=None, min_alpha=N
             The cosine similarity between `doc_words1` and `doc_words2`.
 
         """
-        d1 = self.infer_vector(doc_words=doc_words1, alpha=alpha, min_alpha=min_alpha, steps=steps)
-        d2 = self.infer_vector(doc_words=doc_words2, alpha=alpha, min_alpha=min_alpha, steps=steps)
+        d1 = self.infer_vector(doc_words=doc_words1, alpha=alpha, min_alpha=min_alpha, epochs=epochs)
+        d2 = self.infer_vector(doc_words=doc_words2, alpha=alpha, min_alpha=min_alpha, epochs=epochs)
         return np.dot(matutils.unitvec(d1), matutils.unitvec(d2))
 
 

gensim/test/test_doc2vec.py

@@ -718,8 +718,8 @@ def __str__(self):
     def epochs(self):
         return self.models[0].epochs
 
-    def infer_vector(self, document, alpha=None, min_alpha=None, epochs=None, steps=None):
-        return np.concatenate([model.infer_vector(document, alpha, min_alpha, epochs, steps) for model in self.models])
+    def infer_vector(self, document, alpha=None, min_alpha=None, epochs=None):
+        return np.concatenate([model.infer_vector(document, alpha, min_alpha, epochs) for model in self.models])
 
     def train(self, *ignore_args, **ignore_kwargs):
         pass  # train subcomponents individually