Create questiongenerator.py

questiongenerator.py

@@ -0,0 +1,354 @@
+import os
+import sys
+import math
+import numpy as np
+import torch
+import spacy
+import re
+import random
+import json
+import en_core_web_sm
+from transformers import (
+    AutoTokenizer,
+    AutoModelForSeq2SeqLM,
+    AutoModelForSequenceClassification,
+)
+
+
+class QuestionGenerator:
+    def __init__(self, model_dir=None):
+
+        QG_PRETRAINED = "iarfmoose/t5-base-question-generator"
+        self.ANSWER_TOKEN = "<answer>"
+        self.CONTEXT_TOKEN = "<context>"
+        self.SEQ_LENGTH = 512
+
+        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+        self.qg_tokenizer = AutoTokenizer.from_pretrained(QG_PRETRAINED, use_fast=False)
+        self.qg_model = AutoModelForSeq2SeqLM.from_pretrained(QG_PRETRAINED)
+        self.qg_model.to(self.device)
+
+        self.qa_evaluator = QAEvaluator(model_dir)
+
+    def generate(
+        self, article, use_evaluator=True, num_questions=None, answer_style="all"
+    ):
+
+        print("Generating questions...\n")
+
+        qg_inputs, qg_answers = self.generate_qg_inputs(article, answer_style)
+        generated_questions = self.generate_questions_from_inputs(qg_inputs)
+
+        message = "{} questions doesn't match {} answers".format(
+            len(generated_questions), len(qg_answers)
+        )
+        assert len(generated_questions) == len(qg_answers), message
+
+        if use_evaluator:
+
+            print("Evaluating QA pairs...\n")
+
+            encoded_qa_pairs = self.qa_evaluator.encode_qa_pairs(
+                generated_questions, qg_answers
+            )
+            scores = self.qa_evaluator.get_scores(encoded_qa_pairs)
+            if num_questions:
+                qa_list = self._get_ranked_qa_pairs(
+                    generated_questions, qg_answers, scores, num_questions
+                )
+            else:
+                qa_list = self._get_ranked_qa_pairs(
+                    generated_questions, qg_answers, scores
+                )
+
+        else:
+            print("Skipping evaluation step.\n")
+            qa_list = self._get_all_qa_pairs(generated_questions, qg_answers)
+
+        return qa_list
+
+    def generate_qg_inputs(self, text, answer_style):
+
+        VALID_ANSWER_STYLES = ["all", "sentences", "multiple_choice"]
+
+        if answer_style not in VALID_ANSWER_STYLES:
+            raise ValueError(
+                "Invalid answer style {}. Please choose from {}".format(
+                    answer_style, VALID_ANSWER_STYLES
+                )
+            )
+
+        inputs = []
+        answers = []
+
+        if answer_style == "sentences" or answer_style == "all":
+            segments = self._split_into_segments(text)
+            for segment in segments:
+                sentences = self._split_text(segment)
+                prepped_inputs, prepped_answers = self._prepare_qg_inputs(
+                    sentences, segment
+                )
+                inputs.extend(prepped_inputs)
+                answers.extend(prepped_answers)
+
+        if answer_style == "multiple_choice" or answer_style == "all":
+            sentences = self._split_text(text)
+            prepped_inputs, prepped_answers = self._prepare_qg_inputs_MC(sentences)
+            inputs.extend(prepped_inputs)
+            answers.extend(prepped_answers)
+
+        return inputs, answers
+
+    def generate_questions_from_inputs(self, qg_inputs):
+        generated_questions = []
+
+        for qg_input in qg_inputs:
+            question = self._generate_question(qg_input)
+            generated_questions.append(question)
+
+        return generated_questions
+
+    def _split_text(self, text):
+        MAX_SENTENCE_LEN = 128
+
+        sentences = re.findall(".*?[.!\?]", text)
+
+        cut_sentences = []
+        for sentence in sentences:
+            if len(sentence) > MAX_SENTENCE_LEN:
+                cut_sentences.extend(re.split("[,;:)]", sentence))
+        # temporary solution to remove useless post-quote sentence fragments
+        cut_sentences = [s for s in sentences if len(s.split(" ")) > 5]
+        sentences = sentences + cut_sentences
+
+        return list(set([s.strip(" ") for s in sentences]))
+
+    def _split_into_segments(self, text):
+        MAX_TOKENS = 490
+
+        paragraphs = text.split("\n")
+        tokenized_paragraphs = [
+            self.qg_tokenizer(p)["input_ids"] for p in paragraphs if len(p) > 0
+        ]
+
+        segments = []
+        while len(tokenized_paragraphs) > 0:
+            segment = []
+            while len(segment) < MAX_TOKENS and len(tokenized_paragraphs) > 0:
+                paragraph = tokenized_paragraphs.pop(0)
+                segment.extend(paragraph)
+            segments.append(segment)
+        return [self.qg_tokenizer.decode(s) for s in segments]
+
+    def _prepare_qg_inputs(self, sentences, text):
+        inputs = []
+        answers = []
+
+        for sentence in sentences:
+            qg_input = "{} {} {} {}".format(
+                self.ANSWER_TOKEN, sentence, self.CONTEXT_TOKEN, text
+            )
+            inputs.append(qg_input)
+            answers.append(sentence)
+
+        return inputs, answers
+
+    def _prepare_qg_inputs_MC(self, sentences):
+
+        spacy_nlp = en_core_web_sm.load()
+        docs = list(spacy_nlp.pipe(sentences, disable=["parser"]))
+        inputs_from_text = []
+        answers_from_text = []
+
+        for i in range(len(sentences)):
+            entities = docs[i].ents
+            if entities:
+                for entity in entities:
+                    qg_input = "{} {} {} {}".format(
+                        self.ANSWER_TOKEN, entity, self.CONTEXT_TOKEN, sentences[i]
+                    )
+                    answers = self._get_MC_answers(entity, docs)
+                    inputs_from_text.append(qg_input)
+                    answers_from_text.append(answers)
+
+        return inputs_from_text, answers_from_text
+
+    def _get_MC_answers(self, correct_answer, docs):
+
+        entities = []
+        for doc in docs:
+            entities.extend([{"text": e.text, "label_": e.label_} for e in doc.ents])
+
+        # remove duplicate elements
+        entities_json = [json.dumps(kv) for kv in entities]
+        pool = set(entities_json)
+        num_choices = (
+            min(4, len(pool)) - 1
+        )  # -1 because we already have the correct answer
+
+        # add the correct answer
+        final_choices = []
+        correct_label = correct_answer.label_
+        final_choices.append({"answer": correct_answer.text, "correct": True})
+        pool.remove(
+            json.dumps({"text": correct_answer.text, "label_": correct_answer.label_})
+        )
+
+        # find answers with the same NER label
+        matches = [e for e in pool if correct_label in e]
+
+        # if we don't have enough then add some other random answers
+        if len(matches) < num_choices:
+            choices = matches
+            pool = pool.difference(set(choices))
+            choices.extend(random.sample(pool, num_choices - len(choices)))
+        else:
+            choices = random.sample(matches, num_choices)
+
+        choices = [json.loads(s) for s in choices]
+        for choice in choices:
+            final_choices.append({"answer": choice["text"], "correct": False})
+        random.shuffle(final_choices)
+        return final_choices
+
+    def _generate_question(self, qg_input):
+        self.qg_model.eval()
+        encoded_input = self._encode_qg_input(qg_input)
+        with torch.no_grad():
+            output = self.qg_model.generate(input_ids=encoded_input["input_ids"])
+        question = self.qg_tokenizer.decode(output[0], skip_special_tokens=True)
+        return question
+
+    def _encode_qg_input(self, qg_input):
+        return self.qg_tokenizer(
+            qg_input,
+            padding='max_length',
+            max_length=self.SEQ_LENGTH,
+            truncation=True,
+            return_tensors="pt",
+        ).to(self.device)
+
+    def _get_ranked_qa_pairs(
+        self, generated_questions, qg_answers, scores, num_questions=10
+    ):
+        if num_questions > len(scores):
+            num_questions = len(scores)
+            print(
+                "\nWas only able to generate {} questions. For more questions, please input a longer text.".format(
+                    num_questions
+                )
+            )
+
+        qa_list = []
+        for i in range(num_questions):
+            index = scores[i]
+            qa = self._make_dict(
+                generated_questions[index].split("?")[0] + "?", qg_answers[index]
+            )
+            qa_list.append(qa)
+        return qa_list
+
+    def _get_all_qa_pairs(self, generated_questions, qg_answers):
+        qa_list = []
+        for i in range(len(generated_questions)):
+            qa = self._make_dict(
+                generated_questions[i].split("?")[0] + "?", qg_answers[i]
+            )
+            qa_list.append(qa)
+        return qa_list
+
+    def _make_dict(self, question, answer):
+        qa = {}
+        qa["question"] = question
+        qa["answer"] = answer
+        return qa
+
+
+class QAEvaluator:
+    def __init__(self, model_dir=None):
+
+        QAE_PRETRAINED = "iarfmoose/bert-base-cased-qa-evaluator"
+        self.SEQ_LENGTH = 512
+
+        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+        self.qae_tokenizer = AutoTokenizer.from_pretrained(QAE_PRETRAINED)
+        self.qae_model = AutoModelForSequenceClassification.from_pretrained(
+            QAE_PRETRAINED
+        )
+        self.qae_model.to(self.device)
+
+    def encode_qa_pairs(self, questions, answers):
+        encoded_pairs = []
+        for i in range(len(questions)):
+            encoded_qa = self._encode_qa(questions[i], answers[i])
+            encoded_pairs.append(encoded_qa.to(self.device))
+        return encoded_pairs
+
+    def get_scores(self, encoded_qa_pairs):
+        scores = {}
+        self.qae_model.eval()
+        with torch.no_grad():
+            for i in range(len(encoded_qa_pairs)):
+                scores[i] = self._evaluate_qa(encoded_qa_pairs[i])
+
+        return [
+            k for k, v in sorted(scores.items(), key=lambda item: item[1], reverse=True)
+        ]
+
+    def _encode_qa(self, question, answer):
+        if type(answer) is list:
+            for a in answer:
+                if a["correct"]:
+                    correct_answer = a["answer"]
+        else:
+            correct_answer = answer
+        return self.qae_tokenizer(
+            text=question,
+            text_pair=correct_answer,
+            padding="max_length",
+            max_length=self.SEQ_LENGTH,
+            truncation=True,
+            return_tensors="pt",
+        )
+
+    def _evaluate_qa(self, encoded_qa_pair):
+        output = self.qae_model(**encoded_qa_pair)
+        return output[0][0][1]
+
+
+def print_qa(qa_list, show_answers=True):
+    for i in range(len(qa_list)):
+        space = " " * int(np.where(i < 9, 3, 4))  # wider space for 2 digit q nums
+
+        print("{}) Q: {}".format(i + 1, qa_list[i]["question"]))
+
+        answer = qa_list[i]["answer"]
+
+        # print a list of multiple choice answers
+        if type(answer) is list:
+
+            if show_answers:
+                print(
+                    "{}A: 1.".format(space),
+                    answer[0]["answer"],
+                    np.where(answer[0]["correct"], "(correct)", ""),
+                )
+                for j in range(1, len(answer)):
+                    print(
+                        "{}{}.".format(space + "   ", j + 1),
+                        answer[j]["answer"],
+                        np.where(answer[j]["correct"] == True, "(correct)", ""),
+                    )
+
+            else:
+                print("{}A: 1.".format(space), answer[0]["answer"])
+                for j in range(1, len(answer)):
+                    print("{}{}.".format(space + "   ", j + 1), answer[j]["answer"])
+            print("")
+
+        # print full sentence answers
+        else:
+            if show_answers:
+                print("{}A:".format(space), answer, "\n")