fastNLP/tutorials/fastnlp_10tmin_tutorial.ipynb

{
 "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": "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": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'raw_sentence': A series of escapades demonstrating the adage that what is good for the goose is also good for the gander , some of which occasionally amuses but none of which amounts to much of a story .,\n'label': 1}"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "from fastNLP import DataSet\n",
    "from fastNLP import Instance\n",
    "\n",
    "# 从csv读取数据到DataSet\n",
    "win_path = \"C:\\\\Users\\zyfeng\\Desktop\\FudanNLP\\\\fastNLP\\\\test\\\\data_for_tests\\\\tutorial_sample_dataset.csv\"\n",
    "dataset = DataSet.read_csv(win_path, headers=('raw_sentence', 'label'), sep='\\t')\n",
    "print(dataset[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'raw_sentence': fake data,\n'label': 0}"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# DataSet.append(Instance)加入新数据\n",
    "\n",
    "dataset.append(Instance(raw_sentence='fake data', label='0'))\n",
    "dataset[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# DataSet.apply(func, new_field_name)对数据预处理\n",
    "\n",
    "# 将所有数字转为小写\n",
    "dataset.apply(lambda x: x['raw_sentence'].lower(), new_field_name='raw_sentence')\n",
    "# label转int\n",
    "dataset.apply(lambda x: int(x['label']), new_field_name='label_seq', is_target=True)\n",
    "# 使用空格分割句子\n",
    "dataset.drop(lambda x: len(x['raw_sentence'].split()) == 0)\n",
    "def split_sent(ins):\n",
    "    return ins['raw_sentence'].split()\n",
    "dataset.apply(split_sent, new_field_name='words', is_input=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# DataSet.drop(func)筛除数据\n",
    "# 删除低于某个长度的词语\n",
    "dataset.drop(lambda x: len(x['words']) <= 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train size: "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "54"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test size: "
     ]
    }
   ],
   "source": [
    "# 分出测试集、训练集\n",
    "\n",
    "test_data, train_data = dataset.split(0.3)\n",
    "print(\"Train size: \", len(test_data))\n",
    "print(\"Test size: \", 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": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'raw_sentence': the plot is romantic comedy boilerplate from start to finish .,\n'label': 2,\n'label_seq': 2,\n'words': ['the', 'plot', 'is', 'romantic', 'comedy', 'boilerplate', 'from', 'start', 'to', 'finish', '.'],\n'word_seq': [2, 13, 9, 24, 25, 26, 15, 27, 11, 28, 3]}"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "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='word_seq', is_input=True)\n",
    "test_data.apply(lambda x: [vocab.to_index(word) for word in x['words']], new_field_name='word_seq', is_input=True)\n",
    "\n",
    "\n",
    "print(test_data[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "batch_x has:  {'words': array([list(['this', 'kind', 'of', 'hands-on', 'storytelling', 'is', 'ultimately', 'what', 'makes', 'shanghai', 'ghetto', 'move', 'beyond', 'a', 'good', ',', 'dry', ',', 'reliable', 'textbook', 'and', 'what', 'allows', 'it', 'to', 'rank', 'with', 'its', 'worthy', 'predecessors', '.']),\n",
      "       list(['the', 'entire', 'movie', 'is', 'filled', 'with', 'deja', 'vu', 'moments', '.'])],\n",
      "      dtype=object), 'word_seq': tensor([[  19,  184,    6,    1,  481,    9,  206,   50,   91, 1210, 1609, 1330,\n",
      "          495,    5,   63,    4, 1269,    4,    1, 1184,    7,   50, 1050,   10,\n",
      "            8, 1611,   16,   21, 1039,    1,    2],\n",
      "        [   3,  711,   22,    9, 1282,   16, 2482, 2483,  200,    2,    0,    0,\n",
      "            0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
      "            0,    0,    0,    0,    0,    0,    0]])}\n",
      "batch_y has:  {'label_seq': tensor([3, 2])}\n"
     ]
    }
   ],
   "source": [
    "# 假设你们需要做强化学习或者gan之类的项目，也许你们可以使用这里的dataset\n",
    "from fastNLP.core.batch import Batch\n",
    "from fastNLP.core.sampler import RandomSampler\n",
    "\n",
    "batch_iterator = Batch(dataset=train_data, batch_size=2, sampler=RandomSampler())\n",
    "for batch_x, batch_y in batch_iterator:\n",
    "    print(\"batch_x has: \", batch_x)\n",
    "    print(\"batch_y has: \", batch_y)\n",
    "    break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CNNText(\n  (embed): Embedding(\n    (embed): Embedding(77, 50, padding_idx=0)\n    (dropout): Dropout(p=0.0)\n  )\n  (conv_pool): ConvMaxpool(\n    (convs): ModuleList(\n      (0): Conv1d(50, 3, kernel_size=(3,), stride=(1,), padding=(2,))\n      (1): Conv1d(50, 4, kernel_size=(4,), stride=(1,), padding=(2,))\n      (2): Conv1d(50, 5, kernel_size=(5,), stride=(1,), padding=(2,))\n    )\n  )\n  (dropout): Dropout(p=0.1)\n  (fc): Linear(\n    (linear): Linear(in_features=12, out_features=5, bias=True)\n  )\n)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 定义一个简单的Pytorch模型\n",
    "\n",
    "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": [
    "Trainer & Tester\n",
    "------\n",
    "\n",
    "使用fastNLP的Trainer训练模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from fastNLP import Trainer\n",
    "from copy import deepcopy\n",
    "from fastNLP import CrossEntropyLoss\n",
    "from fastNLP import AccuracyMetric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training epochs started 2018-12-07 14:07:20"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "HBox(children=(IntProgress(value=0, layout=Layout(flex='2'), max=20), HTML(value='')), layout=Layout(display='…"
      ]
     },
     "execution_count": 0,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10. Step:2/20. AccuracyMetric: acc=0.037037"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/10. Step:4/20. AccuracyMetric: acc=0.296296"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 3/10. Step:6/20. AccuracyMetric: acc=0.333333"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 4/10. Step:8/20. AccuracyMetric: acc=0.555556"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 5/10. Step:10/20. AccuracyMetric: acc=0.611111"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 6/10. Step:12/20. AccuracyMetric: acc=0.481481"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 7/10. Step:14/20. AccuracyMetric: acc=0.62963"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 8/10. Step:16/20. AccuracyMetric: acc=0.685185"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 9/10. Step:18/20. AccuracyMetric: acc=0.722222"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 10/10. Step:20/20. AccuracyMetric: acc=0.777778"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    }
   ],
   "source": [
    "# 进行overfitting测试\n",
    "copy_model = deepcopy(model)\n",
    "overfit_trainer = Trainer(model=copy_model, \n",
    "                          train_data=test_data, \n",
    "                          dev_data=test_data,\n",
    "                          loss=CrossEntropyLoss(pred=\"output\", target=\"label_seq\"),\n",
    "                          metrics=AccuracyMetric(),\n",
    "                          n_epochs=10,\n",
    "                          save_path=None)\n",
    "overfit_trainer.train()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training epochs started 2018-12-07 14:08:10"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "HBox(children=(IntProgress(value=0, layout=Layout(flex='2'), max=5), HTML(value='')), layout=Layout(display='i…"
      ]
     },
     "execution_count": 0,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5. Step:1/5. AccuracyMetric: acc=0.037037"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/5. Step:2/5. AccuracyMetric: acc=0.037037"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 3/5. Step:3/5. AccuracyMetric: acc=0.037037"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 4/5. Step:4/5. AccuracyMetric: acc=0.185185"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 5/5. Step:5/5. AccuracyMetric: acc=0.240741"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train finished!"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# 实例化Trainer，传入模型和数据，进行训练\n",
    "trainer = Trainer(model=model, \n",
    "                  train_data=train_data, \n",
    "                  dev_data=test_data,\n",
    "                  loss=CrossEntropyLoss(pred=\"output\", target=\"label_seq\"),\n",
    "                  metrics=AccuracyMetric(),\n",
    "                  n_epochs=5)\n",
    "trainer.train()\n",
    "print('Train finished!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[tester] \nAccuracyMetric: acc=0.240741"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "from fastNLP import Tester\n",
    "\n",
    "tester = Tester(data=test_data, model=model, metrics=AccuracyMetric())\n",
    "acc = tester.test()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# In summary\n",
    "\n",
    "## fastNLP Trainer的伪代码逻辑\n",
    "### 1. 准备DataSet，假设DataSet中共有如下的fields\n",
    "    ['raw_sentence', 'word_seq1', 'word_seq2', 'raw_label','label']\n",
    "    通过\n",
    "        DataSet.set_input('word_seq1', word_seq2', flag=True)将'word_seq1', 'word_seq2'设置为input\n",
    "    通过\n",
    "        DataSet.set_target('label', flag=True)将'label'设置为target\n",
    "### 2. 初始化模型\n",
    "    class Model(nn.Module):\n",
    "        def __init__(self):\n",
    "            xxx\n",
    "        def forward(self, word_seq1, word_seq2):\n",
    "            # (1) 这里使用的形参名必须和DataSet中的input field的名称对应。因为我们是通过形参名, 进行赋值的\n",
    "            # (2) input field的数量可以多于这里的形参数量。但是不能少于。\n",
    "            xxxx\n",
    "            # 输出必须是一个dict\n",
    "### 3. Trainer的训练过程\n",
    "    (1) 从DataSet中按照batch_size取出一个batch，调用Model.forward\n",
    "    (2) 将 Model.forward的结果 与 标记为target的field 传入Losser当中。\n",
    "           由于每个人写的Model.forward的output的dict可能key并不一样，比如有人是{'pred':xxx}, {'output': xxx}; \n",
    "           另外每个人将target可能也会设置为不同的名称, 比如有人是label, 有人设置为target；\n",
    "        为了解决以上的问题，我们的loss提供映射机制\n",
    "           比如CrossEntropyLosser的需要的输入是(prediction, target)。但是forward的output是{'output': xxx}; 'label'是target\n",
    "           那么初始化losser的时候写为CrossEntropyLosser(prediction='output', target='label')即可\n",
    "     (3) 对于Metric是同理的\n",
    "         Metric计算也是从 forward的结果中取值 与 设置target的field中取值。 也是可以通过映射找到对应的值        \n",
    " \n",
    "       \n",
    "\n",
    "## 一些问题.\n",
    "### 1. DataSet中为什么需要设置input和target\n",
    "    只有被设置为input或者target的数据才会在train的过程中被取出来\n",
    "    (1.1) 我们只会在设置为input的field中寻找传递给Model.forward的参数。\n",
    "    (1.2) 我们在传递值给losser或者metric的时候会使用来自: \n",
    "            (a)Model.forward的output\n",
    "            (b)被设置为target的field\n",
    "          \n",
    "\n",
    "### 2. 我们是通过forwad中的形参名将DataSet中的field赋值给对应的参数\n",
    "     (1.1) 构建模型过程中，\n",
    "      例如:\n",
    "          DataSet中x，seq_lens是input，那么forward就应该是\n",
    "          def forward(self, x, seq_lens):\n",
    "              pass\n",
    "          我们是通过形参名称进行匹配的field的\n",
    "       \n",
    "\n",
    "\n",
    "### 1. 加载数据到DataSet\n",
    "### 2. 使用apply操作对DataSet进行预处理\n",
    "      (2.1) 处理过程中将某些field设置为input，某些field设置为target\n",
    "### 3. 构建模型\n",
    "      (3.1) 构建模型过程中，需要注意forward函数的形参名需要和DataSet中设置为input的field名称是一致的。\n",
    "      例如:\n",
    "          DataSet中x，seq_lens是input，那么forward就应该是\n",
    "          def forward(self, x, seq_lens):\n",
    "              pass\n",
    "          我们是通过形参名称进行匹配的field的\n",
    "      (3.2) 模型的forward的output需要是dict类型的。\n",
    "          建议将输出设置为{\"pred\": xx}.\n",
    "          \n"
   ]
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