model: Add doc2vec (#48)

* fix .gitignore

* add doc2vec_train.py

* rm doc2vec.py

models/.gitignore

@@ -1,2 +1,3 @@
 .ACCESS_TOKEN
-*.txt
+*.txt
+training_data/

models/doc2vec/doc2vec_train.py

@@ -0,0 +1,58 @@
+import argparse
+import json
+import re
+import gensim
+from gensim import models
+from gensim.models.doc2vec import Doc2Vec, TaggedDocument
+
+parser = argparse.ArgumentParser()
+parser.add_argument("-i","--issues", type=str, default='../training_data/issues.json', help="path to issues")
+parser.add_argument("-c","--comments", type=str, default='../training_data/comments.json', help="path to comments")
+
+args = parser.parse_args()
+
+# load training sentences
+issue_open = open(args.issues, "r")
+issue_load = json.load(issue_open)
+comment_open = open(args.comments, "r")
+comment_load = json.load(comment_open)
+
+trains = []
+for issue in issue_load:
+    if 'pull_request' in issue:
+        continue
+    train = {}
+    train['html_url'] = issue['html_url']
+    train['title'] = issue['title']
+    train['body'] = issue['body']
+    trains.append(train)
+
+for comment in comment_load:
+    if comment['html_url'].split('/')[-2] == "issues":
+        for train in trains:
+            if train['html_url'].split('/')[-1] == comment['issue_url'].split('/')[-1]:
+                train['body'] = f"{train['body']} {comment['body']}"
+
+# add label
+terms = [TaggedDocument(f"{train['title']} {train['body']}", [str(i)]) for i, train in enumerate(trains)]
+
+# model train
+model = models.Doc2Vec(terms, dm=0, vector_size=100, window=2, min_count=0, workers=4, epoch=20)
+#model.save('doc2vec_model')
+model = Doc2Vec.load('doc2vec_model')
+
+# output results
+results = model.docvecs.most_similar(len(trains)-1)
+
+suggestions = []
+for result in results:
+    index = int(result[0])
+    suggestion = {}
+    suggestion['html_url'] = trains[index]['html_url']
+    suggestion['title'] = trains[index]['title']
+    suggestion['number'] = int(trains[index]['html_url'].split('/')[-1])
+    suggestion['probability'] = result[1]
+    suggestions.append(suggestion)
+
+suggestions = json.dumps(suggestions, indent=4)
+print(suggestions)