ok

train_valid_split.py

@@ -27,21 +27,39 @@ def filter_valid(questions):
     return new_questions
 
 
+# def format_to_valid(questions):
+#     answers_txt = [e["answer"] for e in questions]
+#     questions_txt = [e["question"] for e in questions]
+#     vectorizer = TfidfVectorizer()
+#     vectorizer.fit(answers_txt + questions_txt)
+#     answer_vectors = vectorizer.transform(answers_txt)
+#     for i, question in enumerate(questions):
+#         similarities = linear_kernel(answer_vectors[[i]], answer_vectors).flatten()
+#         answer_scores = [(j, sim) for j, sim in enumerate(similarities) if sim != 1]
+#         answer_scores = sorted(answer_scores, key=lambda x: x[1], reverse=True)
+#         sorted_answers = [questions[j]["answer"] for j, _ in answer_scores if questions[j]["answer"] != question["answer"]]
+#         negative_answer = sorted_answers[len(sorted_answers) // 2]
+#         assert question["answer"] not in sorted_answers
+#         question["candidates"] = [question["answer"]] + sorted_answers
+#         question["negative_example"] = negative_answer
+#     return questions
+
+
 def format_to_valid(questions):
+    answers = [e["answer"] for e in questions]
+    for question in questions:
+        answer = question["answer"]
+        candidates = [e for e in answers if e != answer]
+        candidates = [answer] + candidates
+        question["candidates"] = candidates
+    return questions
+
+
+def format_to_train(questions):
     answers_txt = [e["answer"] for e in questions]
-    questions_txt = [e["question"] for e in questions]
-    vectorizer = TfidfVectorizer()
-    vectorizer.fit(answers_txt + questions_txt)
-    answer_vectors = vectorizer.transform(answers_txt)
-    for i, question in enumerate(questions):
-        similarities = linear_kernel(answer_vectors[[i]], answer_vectors).flatten()
-        answer_scores = [(j, sim) for j, sim in enumerate(similarities) if sim != 1]
-        answer_scores = sorted(answer_scores, key=lambda x: x[1], reverse=True)
-        sorted_answers = [questions[j]["answer"] for j, _ in answer_scores if questions[j]["answer"] != question["answer"]]
-        negative_answer = sorted_answers[0]
-        assert question["answer"] not in sorted_answers
-        question["candidates"] = [question["answer"]] + sorted_answers
-        question["negative_example"] = negative_answer
+    answers_shifted = answers_txt[1:] + [answers_txt[0]]
+    for question, answer in zip(questions, answers_shifted):
+        question["negative"] = answer
     return questions
 
 
@@ -57,18 +75,19 @@ def valid_train_split(filename, mapping=None):
             line = json.loads(line_txt.strip())
             domain = line["domain"]
             if domain != previous_domain and previous_domain != "":
+                form_questions = format_to_train(domain_data["questions"])
                 if len(mapping[previous_domain]) > 1:
-                    train.extend(domain_data["questions"])
+                    train.extend(form_questions)
                 elif len(valid) > 2000:
-                    train.extend(domain_data["questions"])
+                    train.extend(form_questions)
                 elif len(domain_data["pages"]) > 1:
-                    train.extend(domain_data["questions"])
+                    train.extend(form_questions)
                 elif len(domain_data["questions"]) < 15:
-                    train.extend(domain_data["questions"])
+                    train.extend(form_questions)
                 else:
                     questions = filter_valid(domain_data["questions"])
                     if len(questions) < 15:
-                        train.extend(questions)
+                        train.extend(form_questions)
                     else:
                         questions = format_to_valid(questions)
                         valid.extend(questions)
@@ -76,7 +95,7 @@ def valid_train_split(filename, mapping=None):
             domain_data["questions"].append(line)
             domain_data["pages"].add(line["domain_index"])
             previous_domain = domain
-        train.extend(domain_data["questions"])
+        # train.extend(form_questions)
     return train, valid, filename