https://github.com/bojone/ape210k_baseline
py2.7+requirements.txt
absl-py==0.11.0 astor==0.8.1 astroid==1.5.3 backports.functools-lru-cache==1.5 backports.weakref==1.0.post1 bert4keras==0.8.8 certifi==2020.6.20 configparser==4.0.2 enum34==1.1.10 funcsigs==1.0.2 futures==3.3.0 gast==0.4.0 google-pasta==0.2.0 grpcio==1.33.2 h5py==2.10.0 isort==4.3.21 Keras==2.3.1 Keras-Applications==1.0.8 Keras-Preprocessing==1.1.2 lazy-object-proxy==1.4.3 Markdown==3.1.1 mock==3.0.5 mpmath==1.1.0 numpy==1.16.0 pandas==0.20.3 protobuf==3.13.0 pylint==1.7.2 python-dateutil==2.8.1 pytz==2020.1 PyYAML==5.3.1 scipy==1.2.3 six==1.15.0 sympy==1.1.1 tensorboard==1.14.0 tensorflow==1.14.0 tensorflow-estimator==1.14.0 termcolor==1.1.0 Werkzeug==1.0.1 wrapt==1.12.1 tqdm==4.50.2
#! -*- coding: utf-8 -*- # 用Seq2Seq做小学数学应用题 # 数据集为ape210k：https://github.com/Chenny0808/ape210k # Base版准确率为70%+，Large版准确率为73%+ # 实测环境：tensorflow 1.14 + keras 2.3.1 + bert4keras 0.8.8 # 介绍链接：https://kexue.fm/archives/7809 from __future__ import division import json, re import numpy as np import pandas as pd from tqdm import tqdm from bert4keras.backend import keras, K from bert4keras.layers import Loss from bert4keras.models import build_transformer_model from bert4keras.tokenizers import Tokenizer, load_vocab from bert4keras.optimizers import Adam from bert4keras.snippets import sequence_padding, open from bert4keras.snippets import DataGenerator, AutoRegressiveDecoder from keras.models import Model from sympy import Integer # 基本参数 maxlen = 192 batch_size = 32 epochs = 100 # bert配置 config_path = '/Users/war/Downloads/uer/mixed_corpus_bert_base_model/bert_config.json' checkpoint_path = '/Users/war/Downloads/uer/mixed_corpus_bert_base_model/bert_model.ckpt' dict_path = '/Users/war/Downloads/uer/mixed_corpus_bert_base_model/vocab.txt' def is_equal(a, b): """比较两个结果是否相等 """ a = round(float(a), 6) b = round(float(b), 6) return a == b def remove_bucket(equation): """去掉冗余的括号 """ l_buckets, buckets = [], [] for i, c in enumerate(equation): if c == '(': l_buckets.append(i) elif c == ')': buckets.append((l_buckets.pop(), i)) eval_equation = eval(equation) for l, r in buckets: new_equation = '%s %s %s' % ( equation[:l], equation[l + 1:r], equation[r + 1:] ) try: if is_equal(eval(new_equation.replace(' ', '')), eval_equation): equation = new_equation except: pass return equation.replace(' ', '') def load_data(filename): """读取训练数据，并做一些标准化，保证equation是可以eval的 参考：https://kexue.fm/archives/7809 """ D = [] for l in open(filename): l = json.loads(l) question, equation, answer = l['original_text'], l['equation'], l['ans'] # 处理带分数 question = re.sub('(d+)((d+/d+))', '(\1+\2)', question) equation = re.sub('(d+)((d+/d+))', '(\1+\2)', equation) answer = re.sub('(d+)((d+/d+))', '(\1+\2)', answer) equation = re.sub('(d+)(', '\1+(', equation) answer = re.sub('(d+)(', '\1+(', answer) # 分数去括号 question = re.sub('((d+/d+))', '\1', question) # 处理百分数 equation = re.sub('([.d]+)%', '(\1/100)', equation) answer = re.sub('([.d]+)%', '(\1/100)', answer) # 冒号转除号、剩余百分号处理 equation = equation.replace(':', '/').replace('%', '/100') answer = answer.replace(':', '/').replace('%', '/100') if equation[:2] == 'x=': equation = equation[2:] try: if is_equal(eval(equation), eval(answer)): D.append((question, remove_bucket(equation), answer)) except: continue return D # 加载数据集 train_data = load_data('/Users/war/Downloads/ape210k-master/data/train.ape.json') valid_data = load_data('/Users/war/Downloads/ape210k-master/data/valid.ape.json') test_data = load_data('/Users/war/Downloads/ape210k-master/data/test.ape.json') # 加载并精简词表，建立分词器 token_dict, keep_tokens = json.load(open('/Users/war/Downloads/苏--训练好的模型权重/token_dict_keep_tokens.json')) # token_dict, keep_tokens = load_vocab( # dict_path=dict_path, # simplified=True, # startswith=['[PAD]', '[UNK]', '[CLS]', '[SEP]'], # ) tokenizer = Tokenizer(token_dict, do_lower_case=True) class data_generator(DataGenerator): """数据生成器 """ def __iter__(self, random=False): batch_token_ids, batch_segment_ids = [], [] for is_end, (question, equation, answer) in self.sample(random): token_ids, segment_ids = tokenizer.encode( question, equation, maxlen=maxlen ) batch_token_ids.append(token_ids) batch_segment_ids.append(segment_ids) if len(batch_token_ids) == self.batch_size or is_end: batch_token_ids = sequence_padding(batch_token_ids) batch_segment_ids = sequence_padding(batch_segment_ids) yield [batch_token_ids, batch_segment_ids], None batch_token_ids, batch_segment_ids = [], [] class CrossEntropy(Loss): """交叉熵作为loss，并mask掉输入部分 """ def compute_loss(self, inputs, mask=None): y_true, y_mask, y_pred = inputs y_true = y_true[:, 1:] # 目标token_ids y_mask = y_mask[:, 1:] # segment_ids，刚好指示了要预测的部分 y_pred = y_pred[:, :-1] # 预测序列，错开一位 loss = K.sparse_categorical_crossentropy(y_true, y_pred) loss = K.sum(loss * y_mask) / K.sum(y_mask) return loss model = build_transformer_model( config_path, checkpoint_path, application='unilm', keep_tokens=keep_tokens, # 只保留keep_tokens中的字，精简原字表 ) output = CrossEntropy(2)(model.inputs + model.outputs) model = Model(model.inputs, output) model.compile(optimizer=Adam(2e-5)) model.summary() class AutoSolve(AutoRegressiveDecoder): """seq2seq解码器 """ @AutoRegressiveDecoder.wraps(default_rtype='probas') def predict(self, inputs, output_ids, states): token_ids, segment_ids = inputs token_ids = np.concatenate([token_ids, output_ids], 1) segment_ids = np.concatenate([segment_ids, np.ones_like(output_ids)], 1) return model.predict([token_ids, segment_ids])[:, -1] def generate(self, text, topk=1): token_ids, segment_ids = tokenizer.encode(text, maxlen=maxlen) output_ids = self.beam_search([token_ids, segment_ids], topk) # 基于beam search return tokenizer.decode(output_ids).replace(' ', '') autosolve = AutoSolve(start_id=None, end_id=tokenizer._token_end_id, maxlen=64) class Evaluator(keras.callbacks.Callback): """评估与保存 """ def __init__(self): self.best_acc = 0. def on_epoch_end(self, epoch, logs=None): metrics = self.evaluate(valid_data) # 评测模型 if metrics['acc'] >= self.best_acc: self.best_acc = metrics['acc'] model.save_weights('./best_model.weights') # 保存模型 metrics['best_acc'] = self.best_acc print('valid_data:', metrics) def evaluate(self, data, topk=1): total, right = 0.0, 0.0 for question, equation, answer in tqdm(data): total += 1 pred_equation = autosolve.generate(question, topk) try: right += int(is_equal(eval(pred_equation), eval(answer))) except: pass return {'acc': right / total} def predict(in_file, out_file, topk=1): """输出预测结果到文件 该函数主要为比赛 https://www.datafountain.cn/competitions/467 所写， 主要是读取该比赛的测试集，然后预测equation，并且根据不同的问题输出不同格式的答案， out_file可以直接提交到线上评测，线上准确率可以达到38%+。 """ fw = open(out_file, 'w', encoding='utf-8') raw_data = pd.read_csv(in_file, header=None, encoding='utf-8') for i, question in tqdm(raw_data.values): question = re.sub('(d+)_(d+/d+)', '(\1+\2)', question) pred_equation = autosolve.generate(question, topk) if '.' not in pred_equation: pred_equation = re.sub('([d]+)', 'Integer(\1)', pred_equation) try: pred_answer = eval(pred_equation) except: pred_answer = np.random.choice(21) + 1 if '.' in pred_equation: if u'百分之几' in question: pred_answer = pred_answer * 100 pred_answer = round(pred_answer, 2) if int(pred_answer) == pred_answer: pred_answer = int(pred_answer) if ( re.findall(u'多少[辆|人|个|只|箱|包本|束|头|盒|张]', question) or re.findall(u'几[辆|人|个|只|箱|包|本|束|头|盒|张]', question) ): if re.findall(u'至少|最少', question): pred_answer = np.ceil(pred_answer) elif re.findall(u'至多|最多', question): pred_answer = np.floor(pred_answer) else: pred_answer = np.ceil(pred_answer) pred_answer = int(pred_answer) pred_answer = str(pred_answer) if u'百分之几' in question: pred_answer = pred_answer + '%' else: pred_answer = str(pred_answer) if '/' in pred_answer: if re.findall('d+/d+', question): a, b = pred_answer.split('/') a, b = int(a), int(b) if a > b: pred_answer = '%s_%s/%s' % (a // b, a % b, b) else: if re.findall(u'至少|最少', question): pred_answer = np.ceil(eval(pred_answer)) elif re.findall(u'至多|最多', question): pred_answer = np.floor(eval(pred_answer)) else: pred_answer = np.ceil(eval(pred_answer)) pred_answer = str(int(pred_answer)) fw.write(str(i) + ',' + pred_answer + ' ') fw.flush() fw.close() if __name__ == '__main__': evaluator = Evaluator() train_generator = data_generator(train_data, batch_size) model.fit_generator( train_generator.forfit(), steps_per_epoch=len(train_generator), epochs=epochs, callbacks=[evaluator] ) Input = load_data("/Users/war/Downloads/test.csv") Output = load_data("/Users/war/Downloads/submit_example.csv") predict(Input, Output) else: Input = load_data("/Users/war/Downloads/test.csv") Output = load_data("/Users/war/Downloads/submit_example.csv") model.load_weights('/Users/war/Downloads/苏--训练好的模型权重/best_model.weights') predict(Input, Output)