save model

word_vector/word_to_vec.py

 import os
 
-from gensim.models import Word2Vec
+from gensim.models import word2vec
 
 base_dir = os.getcwd()
 print("base_dir: " + base_dir)
@@ -8,5 +8,24 @@ model_dir = os.path.join(base_dir, "_models")
 data_dir = os.path.join(base_dir, "_data")
 
 
-def w2v_train():
-    pass
+class W2vSentences:
+    def __init__(self, f_name):
+        self.f_name = f_name
+
+    def __iter__(self):
+        for line in open(self.f_name, "r"):
+            yield line.split()
+
+
+def w2v_train(f_name, model_output_name):
+    input_file = os.path.join(data_dir, f_name)
+    print("input: " + input_file)
+    sentences = W2vSentences(input_file)
+    w2v_model = word2vec.Word2Vec(sentences, min_count=2, workers=2, size=100, window=10)
+    model_path = os.path.join(model_dir, model_output_name)
+    print("output: " + model_path)
+    w2v_model.save(model_path)
+
+
+if __name__ == "__main__":
+    w2v_train("dispose_problem.txt", "w2v_model")