Update gensim_similarity.py

similarity/gensim_similarity.py

@@ -1,46 +1,42 @@
-
-# Documents Similarity using NLTK and Gensim library
 import gensim
-import nltk
 from nltk.tokenize import word_tokenize
 
-raw_documents = ["I'm taking the show on the road.",
-                 "My socks are a force multiplier.",
-             "I am the barber who cuts everyone's hair who doesn't cut their own.",
-             "Legend has it that the mind is a mad monkey.",
+class GensimSimilarity:
+    def __init__(self):
+        self.raw_documents = ["I'm taking the show on the road.",
+            "My socks are a force multiplier.",
+            "I am the barber who cuts everyone's hair who doesn't cut their own.",
+            "Legend has it that the mind is a mad monkey.",
             "I make my own fun."]
-print("Number of documents:",len(raw_documents))
-
-gen_docs = [[w.lower() for w in word_tokenize(text)] 
-            for text in raw_documents]
-print(gen_docs)
-
-dictionary = gensim.corpora.Dictionary(gen_docs)
-print(dictionary[5])
-print(dictionary.token2id['road'])
-print("Number of words in dictionary:",len(dictionary))
-for i in range(len(dictionary)):
-    print(i, dictionary[i])
-
-corpus = [dictionary.doc2bow(gen_doc) for gen_doc in gen_docs]
-print(corpus)
-
-tf_idf = gensim.models.TfidfModel(corpus)
-print(tf_idf)
-s = 0
-for i in corpus:
-    s += len(i)
-print(s)
-
-sims = gensim.similarities.Similarity('workdir/',tf_idf[corpus],
-                                      num_features=len(dictionary))
-print(sims)
-print(type(sims))
-
-query_doc = [w.lower() for w in word_tokenize("Socks are a force for good.")]
-print(query_doc)
-query_doc_bow = dictionary.doc2bow(query_doc)
-print(query_doc_bow)
-query_doc_tf_idf = tf_idf[query_doc_bow]
-print(query_doc_tf_idf)
-print(sims[query_doc_tf_idf])
+
+    def getSimilarity(gen):
+        gen_docs = [[w.lower() for w in word_tokenize(text)] 
+                for text in gen.raw_documents]
+        print(gen_docs)
+        dictionary = gensim.corpora.Dictionary(gen_docs)
+        print("Number of words in dictionary:",len(dictionary))
+
+        for i in range(len(dictionary)):
+            print(i, dictionary[i])
+
+        corpus = [dictionary.doc2bow(gen_doc) for gen_doc in gen_docs]
+        print(corpus)
+
+        tf_idf = gensim.models.TfidfModel(corpus)
+        print(tf_idf)
+        s = 0
+        for i in corpus:
+            s += len(i)
+        print(s)
+
+        sims = gensim.similarities.Similarity('workdir/',tf_idf[corpus],num_features=len(dictionary))
+
+        query_doc = [w.lower() for w in word_tokenize("Socks are a force for good.")]
+        print(query_doc)
+        query_doc_bow = dictionary.doc2bow(query_doc)
+        print(query_doc_bow)
+        query_doc_tf_idf = tf_idf[query_doc_bow]
+        print(f'Result: {sims[query_doc_tf_idf]}')
+
+similarity = GensimSimilarity()
+similarity.getSimilarity()