From f4057d20c91af9f9a4cb70b57c4799fb9a13dcec Mon Sep 17 00:00:00 2001 From: ChenXin Date: Thu, 27 Feb 2020 21:04:05 +0800 Subject: [PATCH] =?UTF-8?q?=E6=B7=BB=E5=8A=A0=E4=BA=86=20tutorial=5F4=20?= =?UTF-8?q?=E7=9A=84=20ipynb?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- docs/source/tutorials/文本分类.rst | 2 - tutorials/tutorial_4_load_dataset.ipynb | 309 ++++++++++++++++++++++++ 2 files changed, 309 insertions(+), 2 deletions(-) create mode 100644 tutorials/tutorial_4_load_dataset.ipynb diff --git a/docs/source/tutorials/文本分类.rst b/docs/source/tutorials/文本分类.rst index 29e96c20..2f675115 100644 --- a/docs/source/tutorials/文本分类.rst +++ b/docs/source/tutorials/文本分类.rst @@ -19,8 +19,6 @@ .. figure:: ./cn_cls_example.png :alt: jupyter - jupyter - 步骤 ---- diff --git a/tutorials/tutorial_4_load_dataset.ipynb b/tutorials/tutorial_4_load_dataset.ipynb new file mode 100644 index 00000000..f6de83bc --- /dev/null +++ b/tutorials/tutorial_4_load_dataset.ipynb @@ -0,0 +1,309 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 使用Loader和Pipe加载并处理数据集\n", + "\n", + "这一部分是关于如何加载数据集的教程\n", + "\n", + "## Part I: 数据集容器DataBundle\n", + "\n", + "而由于对于同一个任务，训练集，验证集和测试集会共用同一个词表以及具有相同的目标值，所以在fastNLP中我们使用了 DataBundle 来承载同一个任务的多个数据集 DataSet 以及它们的词表 Vocabulary 。下面会有例子介绍 DataBundle 的相关使用。\n", + "\n", + "DataBundle 在fastNLP中主要在各个 Loader 和 Pipe 中被使用。 下面我们先介绍一下 Loader 和 Pipe 。\n", + "\n", + "## Part II: 加载的各种数据集的Loader\n", + "\n", + "在fastNLP中，所有的 Loader 都可以通过其文档判断其支持读取的数据格式，以及读取之后返回的 DataSet 的格式, 例如 ChnSentiCorpLoader \n", + "\n", + "- download() 函数：自动将该数据集下载到缓存地址，默认缓存地址为~/.fastNLP/datasets/。由于版权等原因，不是所有的Loader都实现了该方法。该方法会返回下载后文件所处的缓存地址。\n", + "\n", + "- _load() 函数：从一个数据文件中读取数据，返回一个 DataSet 。返回的DataSet的格式可从Loader文档判断。\n", + "\n", + "- load() 函数：从文件或者文件夹中读取数据为 DataSet 并将它们组装成 DataBundle。支持接受的参数类型有以下的几种\n", + "\n", + " - None, 将尝试读取自动缓存的数据，仅支持提供了自动下载数据的Loader\n", + " - 文件夹路径, 默认将尝试在该文件夹下匹配文件名中含有 train , test , dev 的文件，如果有多个文件含有相同的关键字，将无法通过该方式读取\n", + " - dict, 例如{'train':\"/path/to/tr.conll\", 'dev':\"/to/validate.conll\", \"test\":\"/to/te.conll\"}。" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "In total 3 datasets:\n", + "\ttest has 1944 instances.\n", + "\ttrain has 17196 instances.\n", + "\tdev has 1858 instances.\n", + "\n" + ] + } + ], + "source": [ + "from fastNLP.io import CWSLoader\n", + "\n", + "loader = CWSLoader(dataset_name='pku')\n", + "data_bundle = loader.load()\n", + "print(data_bundle)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "这里表示一共有3个数据集。其中：\n", + "\n", + " 3个数据集的名称分别为train、dev、test，分别有17223、1831、1944个instance\n", + "\n", + "也可以取出DataSet，并打印DataSet中的具体内容" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+----------------------------------------------------------------+\n", + "| raw_words |\n", + "+----------------------------------------------------------------+\n", + "| 迈向 充满 希望 的 新 世纪 —— 一九九八年 新年 讲话 ... |\n", + "| 中共中央 总书记 、 国家 主席 江 泽民 |\n", + "+----------------------------------------------------------------+\n" + ] + } + ], + "source": [ + "tr_data = data_bundle.get_dataset('train')\n", + "print(tr_data[:2])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Part III: 使用Pipe对数据集进行预处理\n", + "\n", + "通过 Loader 可以将文本数据读入，但并不能直接被神经网络使用，还需要进行一定的预处理。\n", + "\n", + "在fastNLP中，我们使用 Pipe 的子类作为数据预处理的类， Loader 和 Pipe 一般具备一一对应的关系，该关系可以从其名称判断， 例如 CWSLoader 与 CWSPipe 是一一对应的。一般情况下Pipe处理包含以下的几个过程，\n", + "1. 将raw_words或 raw_chars进行tokenize以切分成不同的词或字; \n", + "2. 再建立词或字的 Vocabulary , 并将词或字转换为index; \n", + "3. 将target 列建立词表并将target列转为index;\n", + "\n", + "所有的Pipe都可通过其文档查看该Pipe支持处理的 DataSet 以及返回的 DataBundle 中的Vocabulary的情况; 如 OntoNotesNERPipe\n", + "\n", + "各种数据集的Pipe当中，都包含了以下的两个函数:\n", + "\n", + "- process() 函数：对输入的 DataBundle 进行处理, 然后返回处理之后的 DataBundle 。process函数的文档中包含了该Pipe支持处理的DataSet的格式。\n", + "- process_from_file() 函数：输入数据集所在文件夹，使用对应的Loader读取数据(所以该函数支持的参数类型是由于其对应的Loader的load函数决定的)，然后调用相对应的process函数对数据进行预处理。相当于是把Load和process放在一个函数中执行。\n", + "\n", + "接着上面 CWSLoader 的例子，我们展示一下 CWSPipe 的功能：" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "In total 3 datasets:\n", + "\ttest has 1944 instances.\n", + "\ttrain has 17196 instances.\n", + "\tdev has 1858 instances.\n", + "In total 2 vocabs:\n", + "\tchars has 4777 entries.\n", + "\ttarget has 4 entries.\n", + "\n" + ] + } + ], + "source": [ + "from fastNLP.io import CWSPipe\n", + "\n", + "data_bundle = CWSPipe().process(data_bundle)\n", + "print(data_bundle)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "表示一共有3个数据集和2个词表。其中：\n", + "\n", + "- 3个数据集的名称分别为train、dev、test，分别有17223、1831、1944个instance\n", + "- 2个词表分别为chars词表与target词表。其中chars词表为句子文本所构建的词表，一共有4777个不同的字；target词表为目标标签所构建的词表，一共有4种标签。\n", + "\n", + "相较于之前CWSLoader读取的DataBundle，新增了两个Vocabulary。 我们可以打印一下处理之后的DataSet" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+---------------------+---------------------+---------------------+---------+\n", + "| raw_words | chars | target | seq_len |\n", + "+---------------------+---------------------+---------------------+---------+\n", + "| 迈向 充满 希望... | [1224, 178, 674,... | [0, 1, 0, 1, 0, ... | 29 |\n", + "| 中共中央 总书记... | [11, 212, 11, 33... | [0, 3, 3, 1, 0, ... | 15 |\n", + "+---------------------+---------------------+---------------------+---------+\n" + ] + } + ], + "source": [ + "tr_data = data_bundle.get_dataset('train')\n", + "print(tr_data[:2])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "可以看到有两列为int的field: chars和target。这两列的名称同时也是DataBundle中的Vocabulary的名称。可以通过下列的代码获取并查看Vocabulary的 信息" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Vocabulary(['B', 'E', 'S', 'M']...)\n" + ] + } + ], + "source": [ + "vocab = data_bundle.get_vocab('target')\n", + "print(vocab)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Part IV: fastNLP封装好的Loader和Pipe\n", + "\n", + "fastNLP封装了多种任务/数据集的 Loader 和 Pipe 并提供自动下载功能，具体参见文档 [数据集](https://docs.qq.com/sheet/DVnpkTnF6VW9UeXdh?c=A1A0A0)\n", + "\n", + "## Part V: 不同格式类型的基础Loader\n", + "\n", + "除了上面提到的针对具体任务的Loader，我们还提供了CSV格式和JSON格式的Loader\n", + "\n", + "**CSVLoader** 读取CSV类型的数据集文件。例子如下：\n", + "\n", + "```python\n", + "from fastNLP.io.loader import CSVLoader\n", + "data_set_loader = CSVLoader(\n", + " headers=('raw_words', 'target'), sep='\\t'\n", + ")\n", + "```\n", + "\n", + "表示将CSV文件中每一行的第一项将填入'raw_words' field，第二项填入'target' field。其中项之间由'\\t'分割开来\n", + "\n", + "```python\n", + "data_set = data_set_loader._load('path/to/your/file')\n", + "```\n", + "\n", + "文件内容样例如下\n", + "\n", + "```csv\n", + "But it does not leave you with much . 1\n", + "You could hate it for the same reason . 1\n", + "The performances are an absolute joy . 4\n", + "```\n", + "\n", + "读取之后的DataSet具有以下的field\n", + "\n", + "| raw_words | target |\n", + "| --------------------------------------- | ------ |\n", + "| But it does not leave you with much . | 1 |\n", + "| You could hate it for the same reason . | 1 |\n", + "| The performances are an absolute joy . | 4 |\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**JsonLoader** 读取Json类型的数据集文件，数据必须按行存储，每行是一个包含各类属性的Json对象。例子如下\n", + "\n", + "```python\n", + "from fastNLP.io.loader import JsonLoader\n", + "loader = JsonLoader(\n", + " fields={'sentence1': 'raw_words1', 'sentence2': 'raw_words2', 'gold_label': 'target'}\n", + ")\n", + "```\n", + "\n", + "表示将Json对象中'sentence1'、'sentence2'和'gold_label'对应的值赋给'raw_words1'、'raw_words2'、'target'这三个fields\n", + "\n", + "```python\n", + "data_set = loader._load('path/to/your/file')\n", + "```\n", + "\n", + "数据集内容样例如下\n", + "```\n", + "{\"annotator_labels\": [\"neutral\"], \"captionID\": \"3416050480.jpg#4\", \"gold_label\": \"neutral\", ... }\n", + "{\"annotator_labels\": [\"contradiction\"], \"captionID\": \"3416050480.jpg#4\", \"gold_label\": \"contradiction\", ... }\n", + "{\"annotator_labels\": [\"entailment\"], \"captionID\": \"3416050480.jpg#4\", \"gold_label\": \"entailment\", ... }\n", + "```\n", + "\n", + "读取之后的DataSet具有以下的field\n", + "\n", + "| raw_words0 | raw_words1 | target |\n", + "| ------------------------------------------------------ | ------------------------------------------------- | ------------- |\n", + "| A person on a horse jumps over a broken down airplane. | A person is training his horse for a competition. | neutral |\n", + "| A person on a horse jumps over a broken down airplane. | A person is at a diner, ordering an omelette. | contradiction |\n", + "| A person on a horse jumps over a broken down airplane. | A person is outdoors, on a horse. | entailment |" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python Now", + "language": "python", + "name": "now" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}