{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "fastNLP上手教程\n", "-------\n", "\n", "fastNLP提供方便的数据预处理，训练和测试模型的功能" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import sys\n", "sys.path.append('/Users/yh/Desktop/fastNLP/fastNLP/')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "DataSet & Instance\n", "------\n", "\n", "fastNLP用DataSet和Instance保存和处理数据。每个DataSet表示一个数据集，每个Instance表示一个数据样本。一个DataSet存有多个Instance，每个Instance可以自定义存哪些内容。\n", "\n", "有一些read_*方法，可以轻松从文件读取数据，存成DataSet。" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from fastNLP import DataSet\n", "from fastNLP import Instance\n", "\n", "# 从csv读取数据到DataSet\n", "dataset = DataSet.read_csv('../sentence.csv', headers=('raw_sentence', 'label'), sep='\\t')\n", "print(len(dataset))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# 使用数字索引[k]，获取第k个样本\n", "print(dataset[0])\n", "\n", "# 索引也可以是负数\n", "print(dataset[-3])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Instance\n", "Instance表示一个样本，由一个或多个field（域，属性，特征）组成，每个field有名字和值。\n", "\n", "在初始化Instance时即可定义它包含的域，使用 \"field_name=field_value\"的写法。" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# DataSet.append(Instance)加入新数据\n", "dataset.append(Instance(raw_sentence='fake data', label='0'))\n", "dataset[-1]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## DataSet.apply方法\n", "数据预处理利器" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# 将所有数字转为小写\n", "dataset.apply(lambda x: x['raw_sentence'].lower(), new_field_name='raw_sentence')\n", "print(dataset[0])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# label转int\n", "dataset.apply(lambda x: int(x['label']), new_field_name='label')\n", "print(dataset[0])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# 使用空格分割句子\n", "def split_sent(ins):\n", " return ins['raw_sentence'].split()\n", "dataset.apply(split_sent, new_field_name='words')\n", "print(dataset[0])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# 增加长度信息\n", "dataset.apply(lambda x: len(x['words']), new_field_name='seq_len')\n", "print(dataset[0])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## DataSet.drop\n", "筛选数据" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "dataset.drop(lambda x: x['seq_len'] <= 3)\n", "print(len(dataset))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 配置DataSet\n", "1. 哪些域是特征，哪些域是标签\n", "2. 切分训练集/验证集" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# 设置DataSet中，哪些field要转为tensor\n", "\n", "# set target，loss或evaluate中的golden，计算loss，模型评估时使用\n", "dataset.set_target(\"label\")\n", "# set input，模型forward时使用\n", "dataset.set_input(\"words\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# 分出测试集、训练集\n", "\n", "test_data, train_data = dataset.split(0.3)\n", "print(len(test_data))\n", "print(len(train_data))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Vocabulary\n", "------\n", "\n", "fastNLP中的Vocabulary轻松构建词表，将词转成数字" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from fastNLP import Vocabulary\n", "\n", "# 构建词表, Vocabulary.add(word)\n", "vocab = Vocabulary(min_freq=2)\n", "train_data.apply(lambda x: [vocab.add(word) for word in x['words']])\n", "vocab.build_vocab()\n", "\n", "# index句子, Vocabulary.to_index(word)\n", "train_data.apply(lambda x: [vocab.to_index(word) for word in x['words']], new_field_name='words')\n", "test_data.apply(lambda x: [vocab.to_index(word) for word in x['words']], new_field_name='words')\n", "\n", "\n", "print(test_data[0])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Model\n", "定义一个PyTorch模型" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from fastNLP.models import CNNText\n", "model = CNNText(embed_num=len(vocab), embed_dim=50, num_classes=5, padding=2, dropout=0.1)\n", "model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "这是上述模型的forward方法。如果你不知道什么是forward方法，请参考我们的PyTorch教程。\n", "\n", "注意两点：\n", "1. forward参数名字叫**word_seq**，请记住。\n", "2. forward的返回值是一个**dict**，其中有个key的名字叫**output**。\n", "\n", "```Python\n", " def forward(self, word_seq):\n", " \"\"\"\n", "\n", " :param word_seq: torch.LongTensor, [batch_size, seq_len]\n", " :return output: dict of torch.LongTensor, [batch_size, num_classes]\n", " \"\"\"\n", " x = self.embed(word_seq) # [N,L] -> [N,L,C]\n", " x = self.conv_pool(x) # [N,L,C] -> [N,C]\n", " x = self.dropout(x)\n", " x = self.fc(x) # [N,C] -> [N, N_class]\n", " return {'output': x}\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "这是上述模型的predict方法，是用来直接输出该任务的预测结果，与forward目的不同。\n", "\n", "注意两点：\n", "1. predict参数名也叫**word_seq**。\n", "2. predict的返回值是也一个**dict**，其中有个key的名字叫**predict**。\n", "\n", "```\n", " def predict(self, word_seq):\n", " \"\"\"\n", "\n", " :param word_seq: torch.LongTensor, [batch_size, seq_len]\n", " :return predict: dict of torch.LongTensor, [batch_size, seq_len]\n", " \"\"\"\n", " output = self(word_seq)\n", " _, predict = output['output'].max(dim=1)\n", " return {'predict': predict}\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Trainer & Tester\n", "------\n", "\n", "使用fastNLP的Trainer训练模型" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from fastNLP import Trainer\n", "from copy import deepcopy\n", "from fastNLP.core.losses import CrossEntropyLoss\n", "from fastNLP.core.metrics import AccuracyMetric\n", "\n", "\n", "# 更改DataSet中对应field的名称，与模型的forward的参数名一致\n", "# 因为forward的参数叫word_seq, 所以要把原本叫words的field改名为word_seq\n", "# 这里的演示是让你了解这种**命名规则**\n", "train_data.rename_field('words', 'word_seq')\n", "test_data.rename_field('words', 'word_seq')\n", "\n", "# 顺便把label换名为label_seq\n", "train_data.rename_field('label', 'label_seq')\n", "test_data.rename_field('label', 'label_seq')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### loss\n", "训练模型需要提供一个损失函数\n", "\n", "下面提供了一个在分类问题中常用的交叉熵损失。注意它的**初始化参数**。\n", "\n", "pred参数对应的是模型的forward返回的dict的一个key的名字，这里是\"output\"。\n", "\n", "target参数对应的是dataset作为标签的field的名字，这里是\"label_seq\"。" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "loss = CrossEntropyLoss(pred=\"output\", target=\"label_seq\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Metric\n", "定义评价指标\n", "\n", "这里使用准确率。参数的“命名规则”跟上面类似。\n", "\n", "pred参数对应的是模型的predict方法返回的dict的一个key的名字，这里是\"predict\"。\n", "\n", "target参数对应的是dataset作为标签的field的名字，这里是\"label_seq\"。" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "metric = AccuracyMetric(pred=\"predict\", target=\"label_seq\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# 实例化Trainer，传入模型和数据，进行训练\n", "# 先在test_data拟合\n", "copy_model = deepcopy(model)\n", "overfit_trainer = Trainer(model=copy_model, train_data=test_data, dev_data=test_data,\n", " losser=loss,\n", " metrics=metric,\n", " save_path=None,\n", " batch_size=32,\n", " n_epochs=5)\n", "overfit_trainer.train()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# 用train_data训练，在test_data验证\n", "trainer = Trainer(model=model, train_data=train_data, dev_data=test_data,\n", " losser=CrossEntropyLoss(pred=\"output\", target=\"label_seq\"),\n", " metrics=AccuracyMetric(pred=\"predict\", target=\"label_seq\"),\n", " save_path=None,\n", " batch_size=32,\n", " n_epochs=5)\n", "trainer.train()\n", "print('Train finished!')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# 调用Tester在test_data上评价效果\n", "from fastNLP import Tester\n", "\n", "tester = Tester(data=test_data, model=model, metrics=AccuracyMetric(pred=\"predict\", target=\"label_seq\"),\n", " batch_size=4)\n", "acc = tester.test()\n", "print(acc)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.7" } }, "nbformat": 4, "nbformat_minor": 2 }