模型预测类

[maoding1]

初步写了个FastText的配置类：https://github.com/maoding1/sinaCrawler/blob/master/eval.py 其他模型的预测可以改改init方法里的配置试试

[JiuJiaozgmzb]

初步写了个FastText的配置类：https://github.com/maoding1/sinaCrawler/blob/master/eval.py 其他模型的预测可以改改init方法里的配置试试

你好，这个网址打不开，显示404

[maoding1]

初步写了个FastText的配置类：https://github.com/maoding1/sinaCrawler/blob/master/eval.py 其他模型的预测可以改改init方法里的配置试试

你好，这个网址打不开，显示404

之前因为一些原因设成private了 现在开放了

[AriesYB]

抄了别人的代码，然后改了下，支持项目里所有模型，并且打印出预测结果前5个的标签和概率。完整的预测项目可参考：https://github.com/AriesYB/keyword_classifier.git

# coding: UTF-8
import os
import pickle as pkl
from importlib import import_module

import numpy as np
import torch

from train_eval import init_network

class MyClassifier:
    def __init__(self, model_name, dataset, embedding, word):
        print("品目分类器!")
        self.dataset = dataset  # 数据集目录
        self.model_name = model_name  # 模型
        self.embedding = embedding  # embedding
        self.word = word  # 数据集是否已分词
        self.labels = []
        # 读取类别
        with open(self.dataset + '/data/class.txt', 'r', encoding='utf-8') as file:
            for line in file:
                s = line.strip()
                self.labels.append(s)
                print("%s" % s)

        print("一共读取到%s个类别" % len(self.labels))

        # 创建模型配置
        x = import_module('models.' + self.model_name)
        self.config = x.Config(self.dataset, self.embedding)
        np.random.seed(1)
        torch.manual_seed(1)
        torch.cuda.manual_seed_all(1)
        torch.backends.cudnn.deterministic = True  # 保证每次结果一样

        print("加载词汇表vocab.pkl...")
        self.vocab = self.build_dataset(self.config, self.word)

        # eval
        self.config.n_vocab = len(self.vocab)
        self.model = x.Model(self.config).to(self.config.device)
        if self.model_name != 'Transformer':
            init_network(self.model)

        print("加载模型参数ckpt文件...")

        # 加载模型权重
        self.model.load_state_dict(torch.load(self.config.save_path, map_location='cpu'))
        self.model.eval()

    def build_dataset(self, config, ues_word):
        if ues_word:
            print("按空格分词生成向量")
            tokenizer = lambda x: x.split(' ')  # 以空格隔开，word-level
        else:
            print("按字生成向量")
            tokenizer = lambda x: [y for y in x]  # char-level
        if os.path.exists(config.vocab_path):
            print("读取已生成的词汇表vocab.pkl")
            vocab = pkl.load(open(config.vocab_path, 'rb'))
        else:
            print("读取训练集生成词汇表")
            vocab = build_vocab(config.train_path, tokenizer=tokenizer, max_size=MAX_VOCAB_SIZE, min_freq=1)
        print(f"词汇大小: {len(vocab)}")
        return vocab

    def my_to_tensor(self, config, datas):
        x = torch.LongTensor([_[0] for _ in datas]).to(config.device)
        y = torch.LongTensor([_[1] for _ in datas]).to(config.device)

        # pad前的长度(超过pad_size的设为pad_size)
        seq_len = torch.LongTensor([_[2] for _ in datas]).to(config.device)
        return (x, seq_len), y
    def my_to_tensorFastText(self, config, datas):
        # xx = [xxx[2] for xxx in datas]
        # indexx = np.argsort(xx)[::-1]
        # datas = np.array(datas)[indexx]
        x = torch.LongTensor([_[0] for _ in datas]).to(config.device)
        y = torch.LongTensor([_[1] for _ in datas]).to(config.device)
        bigram = torch.LongTensor([_[3] for _ in datas]).to(config.device)
        trigram = torch.LongTensor([_[4] for _ in datas]).to(config.device)

        # pad前的长度(超过pad_size的设为pad_size)
        seq_len = torch.LongTensor([_[2] for _ in datas]).to(config.device)
        return (x, seq_len, bigram, trigram)

    def str2numpy(self, text, config):
        UNK, PAD = '<UNK>', '<PAD>'
        tokenizer = lambda x: [y for y in x]  # char-level
        vocab = self.vocab

        def to_numpy(content, pad_size=32):
            word_line = []
            token = tokenizer(content)
            seq_len = len(token)
            if pad_size:
                if len(token) < pad_size:
                    token.extend([PAD] * (pad_size - len(token)))
                else:
                    token = token[:pad_size]
                    seq_len = pad_size
            # word to id
            for word in token:
                word_line.append(vocab.get(word, vocab.get(UNK)))
            # 文本转换为向量，标签设置为-1
            return [(word_line, -1, len(token))]

        npy = to_numpy(text, config.pad_size)
        return DatasetIterater(npy, config.batch_size, config.device)
    def str2numpyFastText(self, text, config):
        UNK, PAD = '<UNK>', '<PAD>'
        tokenizer = lambda x: [y for y in x]  # char-level
        vocab = pkl.load(open(config.vocab_path, 'rb'))

        def biGramHash(sequence, t, buckets):
            t1 = sequence[t - 1] if t - 1 >= 0 else 0
            return (t1 * 14918087) % buckets

        def triGramHash(sequence, t, buckets):
            t1 = sequence[t - 1] if t - 1 >= 0 else 0
            t2 = sequence[t - 2] if t - 2 >= 0 else 0
            return (t2 * 14918087 * 18408749 + t1 * 14918087) % buckets

        def to_numpy(content, pad_size=32):
            words_line = []
            token = tokenizer(content)
            seq_len = len(token)
            if pad_size:
                if len(token) < pad_size:
                    token.extend([PAD] * (pad_size - len(token)))
                else:
                    token = token[:pad_size]
                    seq_len = pad_size
            # word to id
            for word in token:
                words_line.append(vocab.get(word, vocab.get(UNK)))

            # fasttext ngram
            buckets = config.n_gram_vocab
            bigram = []
            trigram = []
            # ------ngram------
            for i in range(pad_size):
                bigram.append(biGramHash(words_line, i, buckets))
                trigram.append(triGramHash(words_line, i, buckets))
            # -----------------
            return [(words_line, -1, seq_len, bigram, trigram)]

        npy = to_numpy(text, config.pad_size)
        npy = self.my_to_tensorFastText(config, npy)
        return npy
    def classify(self, text):
        # FastText
        if self.model_name == 'FastText':
            data = self.str2numpyFastText(text, self.config)
            outputs = self.model(data)
            probabilities = torch.softmax(outputs, dim=1)

            # 获取前5个最大概率及其索引
            topk_values, topk_indices = torch.topk(probabilities, k=5, dim=1)

            # 打印结果
            for i in range(len(topk_indices[0])):
                print(
                    f"{[self.labels[topk_indices[0].cpu().numpy()[i]]]} {topk_values[0].cpu().detach().numpy()[i]:.4f}")
            # 概率值最大的预测结果
            predict_result = torch.max(outputs.data, 1)[1].cpu().numpy()[0]
            # 对应的分类
            cls = self.labels[predict_result]
            return cls
        # 除了FastText
        else:
            data = self.str2numpy(text, self.config)
            for texts, labels in data:
                outputs = self.model(texts)

                probabilities = torch.softmax(outputs, dim=1)

                # 获取前5个最大概率及其索引
                topk_values, topk_indices = torch.topk(probabilities, k=5, dim=1)

                # 打印结果
                for i in range(len(topk_indices[0])):
                    print(
                        f"{[self.labels[topk_indices[0].cpu().numpy()[i]]]} {topk_values[0].cpu().detach().numpy()[i]:.4f}")
                # 概率值最大的预测结果
                predict_result = torch.max(outputs.data, 1)[1].cpu().numpy()[0]
                # 对应的分类
                cls = self.labels[predict_result]
                return cls

if __name__ == '__main__':
    model_name = 'TextCNN'  # TextCNN, TextRNN, FastText, TextRCNN, TextRNN_Att, DPCNN, Transformer
    embedding = 'random'
    word = False
    dataset = 'goods'  # 数据集目录

    # fastText的embedding方式不一样
    if model_name == 'FastText':
        from utils_fasttext import build_vocab, MAX_VOCAB_SIZE, DatasetIterater
        embedding = 'random'
    else:
        from utils import build_vocab, MAX_VOCAB_SIZE, DatasetIterater

    classifier = MyClassifier(model_name=model_name, dataset=dataset, embedding=embedding, word=word)

    while True:
        # 输入关键字
        keyword = input("请输入关键字（输入 q 退出）：")

        # 如果输入 q，则退出循环
        if keyword.lower() == 'q':
            print("程序已退出。")
            break

        # 对关键字进行分词
        print("%s 预测:%s" % (keyword, classifier.classify(keyword)))