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736 lines
31 KiB
736 lines
31 KiB
#! -*- coding: utf-8 -*-
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import os
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from config.predict_sim_config import DropSimBertConfig
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config = DropSimBertConfig()
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# os.environ["TF_KERAS"] = "1"
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os.environ["CUDA_VISIBLE_DEVICES"] = config.cuda_id
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import glob
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import random
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from tqdm import tqdm
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import numpy as np
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import pandas as pd
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from bert4keras.backend import keras, K
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from bert4keras.layers import Loss
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from bert4keras.models import build_transformer_model
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from bert4keras.tokenizers import Tokenizer, load_vocab
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from bert4keras.optimizers import Adam
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from bert4keras.snippets import sequence_padding, open
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from bert4keras.snippets import DataGenerator, AutoRegressiveDecoder
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from keras.models import Model
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import tensorflow as tf
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from keras.backend import set_session
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tfconfig = tf.ConfigProto()
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tfconfig.gpu_options.allow_growth = True
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set_session(tf.Session(config=tfconfig)) # 此处不同
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global graph
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graph = tf.get_default_graph()
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sess = tf.Session(graph=graph)
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set_session(sess)
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# global graph,model
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# graph = tf.get_default_graph()
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# sess = tf.Session(graph=graph)
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# K.set_session(sess)
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# 基本参数
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class TotalLoss(Loss):
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"""loss分两部分，一是seq2seq的交叉熵，二是相似度的交叉熵。
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"""
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def compute_loss(self, inputs, mask=None):
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loss1 = self.compute_loss_of_seq2seq(inputs, mask)
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loss2 = self.compute_loss_of_similarity(inputs, mask)
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self.add_metric(loss1, name='seq2seq_loss')
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self.add_metric(loss2, name='similarity_loss')
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return loss1 + loss2
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def compute_loss_of_seq2seq(self, inputs, mask=None):
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y_true, y_mask, _, y_pred = inputs
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y_true = y_true[:, 1:] # 目标token_ids
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y_mask = y_mask[:, 1:] # segment_ids，刚好指示了要预测的部分
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y_pred = y_pred[:, :-1] # 预测序列，错开一位
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loss = K.sparse_categorical_crossentropy(y_true, y_pred)
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loss = K.sum(loss * y_mask) / K.sum(y_mask)
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return loss
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def compute_loss_of_similarity(self, inputs, mask=None):
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_, _, y_pred, _ = inputs
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y_true = self.get_labels_of_similarity(y_pred) # 构建标签
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y_pred = K.l2_normalize(y_pred, axis=1) # 句向量归一化
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similarities = K.dot(y_pred, K.transpose(y_pred)) # 相似度矩阵
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similarities = similarities - K.eye(K.shape(y_pred)[0]) * 1e12 # 排除对角线
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similarities = similarities * 30 # scale
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loss = K.categorical_crossentropy(
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y_true, similarities, from_logits=True
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)
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return loss
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def get_labels_of_similarity(self, y_pred):
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idxs = K.arange(0, K.shape(y_pred)[0])
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idxs_1 = idxs[None, :]
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idxs_2 = (idxs + 1 - idxs % 2 * 2)[:, None]
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labels = K.equal(idxs_1, idxs_2)
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labels = K.cast(labels, K.floatx())
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return labels
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class GenerateModel(object):
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def __init__(self, config_path, checkpoint_path, dict_path, maxlen, savemodel_path):
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self.config_path = config_path
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self.checkpoint_path = checkpoint_path
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self.dict_path = dict_path
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self.maxlen = maxlen
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self.savemodel_path = savemodel_path
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def device_setup(self):
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token_dict, keep_tokens = load_vocab(
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dict_path=self.dict_path,
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simplified=True,
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startswith=['[PAD]', '[UNK]', '[CLS]', '[SEP]'],
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)
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tokenizer = Tokenizer(token_dict, do_lower_case=True)
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# model = build_transformer_model(
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# self.config_path,
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# self.checkpoint_path,
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# application='unilm',
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# keep_tokens=keep_tokens, # 只保留keep_tokens中的字，精简原字表
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# )
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bert = build_transformer_model(
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self.config_path,
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self.checkpoint_path,
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with_pool='linear',
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application='unilm',
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keep_tokens=keep_tokens,
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return_keras_model=False,
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)
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encoder = keras.models.Model(bert.model.inputs, bert.model.outputs[0])
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seq2seq = keras.models.Model(bert.model.inputs, bert.model.outputs[1])
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# output = CrossEntropy(2)(model.inputs + model.outputs)
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#
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# model = Model(model.inputs, output)
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# model = Model(model.inputs, model.outputs)
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outputs = TotalLoss([2, 3])(bert.model.inputs + bert.model.outputs)
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model = keras.models.Model(bert.model.inputs, outputs)
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path_model = self.savemodel_path
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model.load_weights(path_model)
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return encoder,seq2seq, tokenizer
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class CrossEntropy(Loss):
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"""交叉熵作为loss，并mask掉输入部分
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"""
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def compute_loss(self, inputs, mask=None):
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y_true, y_mask, y_pred = inputs
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y_true = y_true[:, 1:] # 目标token_ids
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y_mask = y_mask[:, 1:] # segment_ids，刚好指示了要预测的部分
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y_pred = y_pred[:, :-1] # 预测序列，错开一位
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loss = K.sparse_categorical_crossentropy(y_true, y_pred)
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loss = K.sum(loss * y_mask) / K.sum(y_mask)
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return loss
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class AutoTitle(AutoRegressiveDecoder):
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"""seq2seq解码器
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"""
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def __init__(self, model, tokenizer, start_id, end_id, maxlen, minlen=1):
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super(AutoTitle, self).__init__(start_id, end_id, maxlen, minlen)
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self.model = model
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self.tokenizer = tokenizer
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self.start_id = start_id
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self.end_id = end_id
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self.minlen = minlen
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self.models = {}
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if start_id is None:
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self.first_output_ids = np.empty((1, 0), dtype=int)
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else:
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self.first_output_ids = np.array([[self.start_id]])
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def data_generator(self, inputs, output_ids):
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batch_token_ids, batch_segment_ids = [], []
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if output_ids == []:
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for txt in inputs:
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token_ids, segment_ids = self.tokenizer.encode(txt, maxlen=120)
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batch_token_ids.append(token_ids)
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batch_segment_ids.append(segment_ids)
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else:
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for txt,output_id in zip(inputs, output_ids):
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token_ids, segment_ids = self.tokenizer.encode(txt, output_id)
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batch_token_ids.append(token_ids[:-1])
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batch_segment_ids.append(segment_ids[:-1])
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batch_token_ids = sequence_padding(batch_token_ids)
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batch_segment_ids = sequence_padding(batch_segment_ids)
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return batch_token_ids, batch_segment_ids
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def beam_search_batch(self, inputs, topk, states=None, temperature=1, min_ends=1):
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"""beam search解码
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说明：这里的topk即beam size；
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返回：最优解码序列。
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"""
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inputs = [np.array([i]) for i in inputs]
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output_ids, output_scores = self.first_output_ids, np.zeros(1)
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for step in range(self.maxlen):
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scores, states = self.predict(
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inputs, output_ids, states, temperature, 'logits'
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) # 计算当前得分
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if step == 0: # 第1步预测后将输入重复topk次
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inputs = [np.repeat(i, topk, axis=0) for i in inputs]
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scores = output_scores.reshape((-1, 1)) + scores # 综合累积得分
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indices = scores.argpartition(-topk, axis=None)[-topk:] # 仅保留topk
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indices_1 = indices // scores.shape[1] # 行索引
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indices_2 = (indices % scores.shape[1]).reshape((-1, 1)) # 列索引
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output_ids = np.concatenate([output_ids[indices_1], indices_2],
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1) # 更新输出
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output_scores = np.take_along_axis(
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scores, indices, axis=None
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) # 更新得分
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is_end = output_ids[:, -1] == self.end_id # 标记是否以end标记结束
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end_counts = (output_ids == self.end_id).sum(1) # 统计出现的end标记
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if output_ids.shape[1] >= self.minlen: # 最短长度判断
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best = output_scores.argmax() # 得分最大的那个
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if is_end[best] and end_counts[best] >= min_ends: # 如果已经终止
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return output_ids[best] # 直接输出
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else: # 否则，只保留未完成部分
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flag = ~is_end | (end_counts < min_ends) # 标记未完成序列
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if not flag.all(): # 如果有已完成的
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inputs = [i[flag] for i in inputs] # 扔掉已完成序列
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output_ids = output_ids[flag] # 扔掉已完成序列
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output_scores = output_scores[flag] # 扔掉已完成序列
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end_counts = end_counts[flag] # 扔掉已完成end计数
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topk = flag.sum() # topk相应变化
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# 达到长度直接输出
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return output_ids[output_scores.argmax()]
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def random_sample_batch(
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self,
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inputs,
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n,
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topk=None,
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topp=None,
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states=None,
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temperature=1,
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min_ends=1
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):
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"""随机采样n个结果
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说明：非None的topk表示每一步只从概率最高的topk个中采样；而非None的topp
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表示每一步只从概率最高的且概率之和刚好达到topp的若干个token中采样。
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返回：n个解码序列组成的list。
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"""
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inputs = [np.array([i for j in i]) for i in inputs]
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output_ids = self.first_output_ids
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results = []
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for step in range(self.maxlen):
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probas, states = self.predict(
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inputs, output_ids, states, temperature, 'probas'
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) # 计算当前概率
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probas /= probas.sum(axis=1, keepdims=True) # 确保归一化
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if step == 0: # 第1步预测后将结果重复n次
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probas = np.repeat(probas, n, axis=0)
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inputs = [np.repeat(i, n, axis=0) for i in inputs]
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output_ids = np.repeat(output_ids, n, axis=0)
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if topk is not None:
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k_indices = probas.argpartition(-topk,
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axis=1)[:, -topk:] # 仅保留topk
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probas = np.take_along_axis(probas, k_indices, axis=1) # topk概率
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probas /= probas.sum(axis=1, keepdims=True) # 重新归一化
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if topp is not None:
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p_indices = probas.argsort(axis=1)[:, ::-1] # 从高到低排序
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probas = np.take_along_axis(probas, p_indices, axis=1) # 排序概率
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cumsum_probas = np.cumsum(probas, axis=1) # 累积概率
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flag = np.roll(cumsum_probas >= topp, 1, axis=1) # 标记超过topp的部分
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flag[:, 0] = False # 结合上面的np.roll，实现平移一位的效果
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probas[flag] = 0 # 后面的全部置零
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probas /= probas.sum(axis=1, keepdims=True) # 重新归一化
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sample_func = lambda p: np.random.choice(len(p), p=p) # 按概率采样函数
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sample_ids = np.apply_along_axis(sample_func, 1, probas) # 执行采样
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sample_ids = sample_ids.reshape((-1, 1)) # 对齐形状
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if topp is not None:
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sample_ids = np.take_along_axis(
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p_indices, sample_ids, axis=1
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) # 对齐原id
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if topk is not None:
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sample_ids = np.take_along_axis(
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k_indices, sample_ids, axis=1
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) # 对齐原id
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output_ids = np.concatenate([output_ids, sample_ids], 1) # 更新输出
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is_end = output_ids[:, -1] == self.end_id # 标记是否以end标记结束
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end_counts = (output_ids == self.end_id).sum(1) # 统计出现的end标记
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if output_ids.shape[1] >= self.minlen: # 最短长度判断
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flag = is_end & (end_counts >= min_ends) # 标记已完成序列
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if flag.any(): # 如果有已完成的
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for ids in output_ids[flag]: # 存好已完成序列
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results.append(ids)
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flag = (flag == False) # 标记未完成序列
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inputs = [i[flag] for i in inputs] # 只保留未完成部分输入
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output_ids = output_ids[flag] # 只保留未完成部分候选集
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end_counts = end_counts[flag] # 只保留未完成部分end计数
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if len(output_ids) == 0:
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break
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# 如果还有未完成序列，直接放入结果
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for ids in output_ids:
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results.append(ids)
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# 返回结果
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return results
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def random_sample_and_beam_search(
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self,
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inputs,
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n,
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topk=None,
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topp=None,
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states=None,
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temperature=1,
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min_ends=1
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):
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"""随机采样n个结果
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说明：非None的topk表示每一步只从概率最高的topk个中采样；而非None的topp
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表示每一步只从概率最高的且概率之和刚好达到topp的若干个token中采样。
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返回：n个解码序列组成的list。
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"""
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whether_end_b = False
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results_r = []
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results_b = []
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# index_r = [i for i in range(n)]
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# index_b = [i for i in range(topk)]
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index_r = np.arange(n)
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index_b = np.arange(topk)
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inputs = [np.array([i]) for i in inputs]
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output_ids, output_scores = self.first_output_ids, np.zeros(1)
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results = []
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for step in range(self.maxlen):
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beam_n = len(index_b)
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probas, states = self.predict(
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inputs, output_ids, states, temperature, 'probas'
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) # 计算当前概率
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probas = probas / probas.sum(axis=1, keepdims=True) # 确保归一化
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if step == 0: # 第1步预测后将结果重复n次
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probas = np.repeat(probas, n + topk, axis=0)
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inputs_r = [np.repeat(i, n, axis=0) for i in inputs]
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output_ids = np.repeat(output_ids, n + topk, axis=0)
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inputs_b = [np.repeat(i, topk, axis=0) for i in inputs]
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else:
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if whether_end_b == False:
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inputs_r = [i[:-beam_n, :] for i in inputs]
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inputs_b = [i[-beam_n:, :] for i in inputs]
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else:
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inputs_r = inputs
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if whether_end_b == False:
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probas_r = probas[:-beam_n, :]
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else:
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probas_r = probas
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if step == 0:
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probas_b = probas[0,:]
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else:
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probas_b = probas[-beam_n:, :]
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if whether_end_b == False:
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output_ids_r = output_ids[:-beam_n, :]
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output_ids_b = output_ids[-beam_n:, :]
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else:
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output_ids_r = output_ids
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k_indices = probas_r.argpartition(-topk,
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axis=1)[:, -topk:] # 仅保留topk
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probas_r = np.take_along_axis(probas_r, k_indices, axis=1) # topk概率
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probas_r /= probas_r.sum(axis=1, keepdims=True) # 重新归一化
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if whether_end_b == False:
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scores = output_scores.reshape((-1, 1)) + probas_b # 综合累积得分
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indices = scores.argpartition(-topk, axis=None)[-topk:] # 仅保留topk
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indices_1 = indices // scores.shape[1] # 行索引
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indices_2 = (indices % scores.shape[1]).reshape((-1, 1)) # 列索引
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try:
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output_ids_b = np.concatenate([output_ids_b[indices_1], indices_2],
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1) # 更新输出
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except:
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print(output_ids_b.shape)
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print(indices_1)
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print(indices_2)
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exit()
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output_scores = np.take_along_axis(
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scores, indices, axis=None
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) # 更新得分
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sample_func = lambda p: np.random.choice(len(p), p=p) # 按概率采样函数
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try:
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sample_ids = np.apply_along_axis(sample_func, 1, probas_r) # 执行采样
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except:
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print(probas_r)
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sample_ids = sample_ids.reshape((-1, 1)) # 对齐形状
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if topk is not None:
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sample_ids = np.take_along_axis(
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k_indices, sample_ids, axis=1
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) # 对齐原id
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output_ids_r = np.concatenate([output_ids_r, sample_ids], 1) # 更新输出
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# output_ids = np.concatenate([output_ids_r, output_ids_b], 0)
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if whether_end_b == False:
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is_end_r = output_ids_r[:, -1] == self.end_id # 标记是否以end标记结束
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is_end_b = output_ids_b[:, -1] == self.end_id # 标记是否以end标记结束
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else:
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is_end_r = output_ids_r[:, -1] == self.end_id
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if whether_end_b == False:
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end_counts_r = (output_ids_r == self.end_id).sum(1) # 统计出现的end标记
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end_counts_b = (output_ids_b == self.end_id).sum(1) # 统计出现的end标记
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else:
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end_counts_r = (output_ids_r == self.end_id).sum(1)
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# random_serach
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if output_ids_r.shape[1] >= self.minlen: # 最短长度判断
|
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flag = is_end_r & (end_counts_r >= min_ends) # 标记已完成序列
|
|
if flag.any(): # 如果有已完成的
|
|
for ids in output_ids_r[flag]: # 存好已完成序列
|
|
results_r.append(ids)
|
|
flag = (flag == False) # 标记未完成序列
|
|
try:
|
|
index_r = index_r[flag]
|
|
except:
|
|
print("flag",flag)
|
|
print("index_r",index_r)
|
|
inputs_r = [i[flag] for i in inputs_r] # 只保留未完成部分输入
|
|
output_ids_r = output_ids_r[flag] # 只保留未完成部分候选集
|
|
end_counts_r = end_counts_r[flag] # 只保留未完成部分end计数
|
|
|
|
# beam_serach
|
|
if whether_end_b == False:
|
|
if output_ids_b.shape[1] >= self.minlen: # 最短长度判断
|
|
best = output_scores.argmax() # 得分最大的那个
|
|
if is_end_b[best] and end_counts_b[best] >= min_ends: # 如果已经终止
|
|
results_b.append(output_ids_b[best]) # 直接输出
|
|
whether_end_b = True
|
|
else: # 否则，只保留未完成部分
|
|
flag_b = ~is_end_b | (end_counts_b < min_ends) # 标记未完成序列
|
|
if not flag_b.all(): # 如果有已完成的
|
|
index_b = index_b[flag_b]
|
|
inputs_b = [i[flag_b] for i in inputs_b] # 扔掉已完成序列
|
|
output_ids_b = output_ids_b[flag_b] # 扔掉已完成序列
|
|
output_scores = output_scores[flag_b] # 扔掉已完成序列
|
|
end_counts_b = end_counts_b[flag_b] # 扔掉已完成end计数
|
|
topk = flag_b.sum() # topk相应变化
|
|
|
|
if whether_end_b == False and len(output_ids_r) != 0:
|
|
token_r = inputs_r[0]
|
|
sample_ids_r = inputs_r[1]
|
|
token_b = inputs_b[0]
|
|
sample_ids_b = inputs_b[1]
|
|
token = np.concatenate([token_r,token_b],0)
|
|
sample_ids = np.concatenate([sample_ids_r,sample_ids_b],0)
|
|
inputs = [token,sample_ids]
|
|
output_ids = np.concatenate([output_ids_r, output_ids_b], 0)
|
|
elif whether_end_b == True and len(output_ids_r) != 0:
|
|
inputs = inputs_r
|
|
output_ids = output_ids_r
|
|
elif whether_end_b == False and len(output_ids_r) == 0:
|
|
inputs = inputs_b
|
|
output_ids = output_ids_b
|
|
else:
|
|
break
|
|
|
|
|
|
# 如果还有未完成序列，直接放入结果
|
|
for ids in output_ids:
|
|
results.append(ids)
|
|
# 返回结果
|
|
return results_r, results_b
|
|
|
|
|
|
def top_batch(
|
|
self,
|
|
inputs_str,
|
|
temperature=1,
|
|
min_ends=1
|
|
):
|
|
"""随机采样n个结果
|
|
说明：非None的topk表示每一步只从概率最高的topk个中采样；而非None的topp
|
|
表示每一步只从概率最高的且概率之和刚好达到topp的若干个token中采样。
|
|
返回：n个解码序列组成的list。
|
|
"""
|
|
output_str = []
|
|
# token_ids, segment_ids = self.data_generator(inputs, output_ids)
|
|
batch_nums = len(inputs_str)
|
|
output_ids =self.first_output_ids_batch = np.empty((batch_nums, 0), dtype=int)
|
|
|
|
results = [[] for i in range(batch_nums)]
|
|
index_data = [i for i in range(batch_nums)]
|
|
for step in range(self.maxlen):
|
|
|
|
token_ids, segment_ids = self.data_generator(inputs_str, output_str)
|
|
inputs = [token_ids, segment_ids]
|
|
probas = self.predict_batch(
|
|
inputs
|
|
) # 计算当前概率
|
|
# probas /= probas.sum(axis=1, keepdims=True) # 确保归一化
|
|
|
|
probas_new = []
|
|
probas_bool = np.array(token_ids, dtype=bool)
|
|
# np.array(np.where(probas_bool == True))
|
|
for i,sentence in enumerate(probas_bool):
|
|
lie = np.array(np.where(sentence == True))[0]
|
|
probas_new.append(probas[i,lie[-1]])
|
|
probas = np.array(probas_new)
|
|
k_indices = np.argmax(probas,axis=1) # 仅保留topk
|
|
k_indices = k_indices.reshape(-1,1)
|
|
|
|
sample_ids = k_indices
|
|
output_ids = np.concatenate([output_ids, sample_ids], 1) # 更新输出
|
|
is_end = output_ids[:, -1] == self.end_id # 标记是否以end标记结束
|
|
end_counts = (output_ids == self.end_id).sum(1) # 统计出现的end标记
|
|
if output_ids.shape[1] >= self.minlen: # 最短长度判断
|
|
flag = is_end & (end_counts >= min_ends) # 标记已完成序列
|
|
if flag.any(): # 如果有已完成的
|
|
index = np.array(np.where(flag == True))[0]
|
|
pop_index = []
|
|
for i in index:
|
|
results[index_data[i]] = output_ids[i]
|
|
pop_index.append(index_data[i])
|
|
for i in pop_index:
|
|
index_data.remove(i)
|
|
# for ids in output_ids[flag]: # 存好已完成序列
|
|
# results.append(ids)
|
|
flag = (flag == False) # 标记未完成序列
|
|
inputs_str = [inputs_str[i] for i in index_data] # 只保留未完成部分输入
|
|
output_ids = output_ids[flag] # 只保留未完成部分候选集
|
|
if len(output_ids) == 0:
|
|
break
|
|
else:
|
|
output_str = [tokenizer.decode(ids) for ids in output_ids]
|
|
else:
|
|
output_str = [tokenizer.decode(ids) for ids in output_ids]
|
|
# 如果还有未完成序列，直接放入结果
|
|
# for ids in output_ids:
|
|
# results.append(ids)
|
|
# 返回结果
|
|
return results
|
|
|
|
|
|
@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)
|
|
with graph.as_default():
|
|
K.set_session(sess)
|
|
nodes = self.last_token(self.model).predict([token_ids, segment_ids])
|
|
return nodes
|
|
# return self.last_token(self.model).predict([token_ids, segment_ids])
|
|
|
|
def predict_batch(self, inputs):
|
|
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)
|
|
with graph.as_default():
|
|
K.set_session(sess)
|
|
nodes = self.model.predict([token_ids, segment_ids])
|
|
return nodes
|
|
# return self.last_token(self.model).predict([token_ids, segment_ids])
|
|
|
|
def generate(self, text, topk=5):
|
|
token_ids, segment_ids = self.tokenizer.encode(text, maxlen=256)
|
|
output_ids = self.beam_search([token_ids, segment_ids],
|
|
topk=topk) # 基于beam search
|
|
return self.tokenizer.decode(output_ids)
|
|
|
|
def generate_random(self, text, n=20, topk=5):
|
|
if isinstance(text, list):
|
|
text = text[0]
|
|
token_ids, segment_ids = self.tokenizer.encode(text, maxlen=120)
|
|
output_ids = self.random_sample([token_ids, segment_ids], n, topk) # 基于随机采样
|
|
return [tokenizer.decode(ids) for ids in output_ids]
|
|
|
|
def generate_random_topp(self, text, n=20, topp=0.98):
|
|
if isinstance(text, list):
|
|
text = text[0]
|
|
token_ids, segment_ids = self.tokenizer.encode(text, maxlen=120)
|
|
output_ids = self.random_sample([token_ids, segment_ids], n, topp=topp) # 基于随机采样
|
|
return [tokenizer.decode(ids) for ids in output_ids]
|
|
|
|
def generate_top(self, text):
|
|
output_ids = self.top_batch(text) # 基于随机采样
|
|
return [tokenizer.decode(ids) for ids in output_ids]
|
|
|
|
def generate_random_sample_and_beam_search(self, text, n=20, topk=5):
|
|
text = text[0]
|
|
token_ids, segment_ids = self.tokenizer.encode(text, maxlen=120)
|
|
output_ids_r, output_ids_b = self.random_sample_and_beam_search([token_ids, segment_ids], n=n,
|
|
topk=topk) # 基于beam search
|
|
output_str_r = [self.tokenizer.decode(ids) for ids in output_ids_r]
|
|
output_str_b = [self.tokenizer.decode(ids) for ids in output_ids_b]
|
|
return output_str_r, output_str_b
|
|
|
|
def gen_synonyms(self, text, n=20):
|
|
""""含义： 产生sent的n个相似句，然后返回最相似的k个。
|
|
做法：用seq2seq生成，并用encoder算相似度并排序。
|
|
"""
|
|
r = self.generate_random_topp(text, n)
|
|
r = [i for i in set(r) if i != text]
|
|
r = [text] + r
|
|
X, S = [], []
|
|
for t in r:
|
|
x, s = tokenizer.encode(t)
|
|
X.append(x)
|
|
S.append(s)
|
|
X = sequence_padding(X)
|
|
S = sequence_padding(S)
|
|
Z = encoder.predict([X, S])
|
|
Z /= (Z ** 2).sum(axis=1, keepdims=True) ** 0.5
|
|
argsort = np.dot(Z[1:], -Z[0]).argsort()
|
|
return [r[i + 1] for i in argsort]
|
|
|
|
|
|
def gen_synonyms_short(self, text, n=20, len_s = 0.9):
|
|
""""含义： 产生sent的n个相似句，然后返回最相似的k个。
|
|
做法：用seq2seq生成，并用encoder算相似度并排序。
|
|
"""
|
|
if isinstance(text, list):
|
|
text = text[0]
|
|
new_text_len = int(len(text) * len_s)
|
|
r = self.generate_random(text, n)
|
|
r = [i for i in set(r) if i != text]
|
|
r = [text] + r
|
|
X, S = [], []
|
|
for t in r:
|
|
x, s = tokenizer.encode(t)
|
|
X.append(x)
|
|
S.append(s)
|
|
X = sequence_padding(X)
|
|
S = sequence_padding(S)
|
|
with graph.as_default():
|
|
K.set_session(sess)
|
|
Z = encoder.predict([X, S])
|
|
Z /= (Z ** 2).sum(axis=1, keepdims=True) ** 0.5
|
|
argsort = np.dot(Z[1:], -Z[0]).argsort()
|
|
sentence_list = [r[i + 1] for i in argsort]
|
|
|
|
return_list = []
|
|
for i in sentence_list:
|
|
if len(i) < new_text_len:
|
|
return_list.append(i)
|
|
break
|
|
|
|
for i in sentence_list:
|
|
if new_text_len <len(i) < len(text):
|
|
return_list.append(i)
|
|
break
|
|
if return_list != []:
|
|
return return_list[0]
|
|
else:
|
|
return sentence_list[0]
|
|
|
|
|
|
def just_show(file):
|
|
data = []
|
|
try:
|
|
with open(file, 'r', encoding="utf-8") as f:
|
|
lines = [x.strip() for x in f if x.strip() != '']
|
|
except:
|
|
with open(file, 'r', encoding="gbk") as f:
|
|
lines = [x.strip() for x in f if x.strip() != '']
|
|
# s2 = u'她只能应下来。'
|
|
# lines = pd.read_csv(file,encoding="gbk").values.tolist()
|
|
# random.shuffle(lines)
|
|
# lines = lines[:20]
|
|
for s in tqdm(lines[:2]):
|
|
print(s)
|
|
pre = autotitle.generate_random(s)
|
|
print(s)
|
|
print(pre)
|
|
# data.append([s, pre])
|
|
# pd.DataFrame(data,columns=["原始文本","生成文本"]).to_csv("data/text_测试一万字_unilm_修正数据_小说预训练_全部数据_epoch72_反向训练.csv")
|
|
|
|
|
|
def just_show_sentence(file: list) -> object:
|
|
"""
|
|
@param file:list
|
|
"""
|
|
text = file[0]
|
|
pre = autotitle.generate(text)
|
|
return pre
|
|
|
|
def just_show_sentence_batch(file: list) -> object:
|
|
text = file
|
|
pre = autotitle.generate_top(text)
|
|
return pre
|
|
|
|
def just_show_csv_random(file):
|
|
data_new = []
|
|
data = pd.read_csv(file).values.tolist()
|
|
for sentence in tqdm(data):
|
|
sentence = sentence[1]
|
|
print(sentence)
|
|
data_new_dan = []
|
|
data_new_dan.extend([sentence, len(sentence)])
|
|
pre = autotitle.generate_random(sentence)
|
|
for i in pre:
|
|
data_new_dan.extend([i, len(i)])
|
|
|
|
data_new.append(data_new_dan)
|
|
pd.DataFrame(data_new).to_csv("data/###第3章 非常尴尬_sim_topK_5.csv")
|
|
# return pre
|
|
|
|
|
|
def just_show_chachong_random(file):
|
|
text = file[0]
|
|
pre = autotitle.gen_synonyms(text)
|
|
return pre
|
|
|
|
|
|
def just_show_csv_beam(file):
|
|
data_new = []
|
|
data = pd.read_csv(file).values.tolist()
|
|
for sentence in tqdm(data):
|
|
sentence = sentence[1]
|
|
print(sentence)
|
|
data_new_dan = []
|
|
data_new_dan.extend([sentence, len(sentence)])
|
|
pre = autotitle.generate([sentence])
|
|
print(pre)
|
|
data_new_dan.extend([pre, len(pre)])
|
|
data_new.append(data_new_dan)
|
|
pd.DataFrame(data_new).to_csv("data/###第3章 非常尴尬_sim_topK_1.csv")
|
|
|
|
|
|
|
|
if __name__ == '__main__':
|
|
|
|
generatemodel = GenerateModel(config.config_path,
|
|
config.checkpoint_path,
|
|
config.dict_path,
|
|
config.maxlen,
|
|
config.savemodel_path)
|
|
encoder, seq2seq, tokenizer = generatemodel.device_setup()
|
|
autotitle = AutoTitle(seq2seq, tokenizer, start_id=None, end_id=tokenizer._token_end_id, maxlen=120)
|
|
text = ["随着经济的发展，人们生活水平的提高，环境问题也日益突出。"]
|
|
print(just_show_sentence(text))
|
|
#
|
|
# print(just_show_sentence_batch(text))
|
|
# print(type(just_show_sentence_batch(text)))
|
|
|
|
|
|
# path = "./data/700条论文测试.xlsx"
|
|
# df_list = pd.read_excel(path).values.tolist()
|
|
#
|
|
# df_list_new = []
|
|
# print(len(df_list))
|
|
# for i in tqdm(df_list):
|
|
# try:
|
|
# pre = just_show_sentence([i[0]])
|
|
# df_list_new.append([i[0], i[1]] + [pre])
|
|
# except:
|
|
# print(i[0])
|
|
# continue
|
|
# df = pd.DataFrame(df_list_new)
|
|
# df.to_excel("./data/700条论文测试_19.xlsx", index=None)
|
|
|
|
|
|
|
|
|