{
 "cells": [
  {
   "cell_type": "markdown",
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   },
   "source": [
    "# FastNLP 1分钟上手教程"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## step 1\n",
    "读取数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yh/miniconda2/envs/python3/lib/python3.6/site-packages/tqdm/autonotebook/__init__.py:14: TqdmExperimentalWarning: Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)\n",
      "  \" (e.g. in jupyter console)\", TqdmExperimentalWarning)\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "sys.path.append(\"../\")\n",
    "\n",
    "from fastNLP import DataSet\n",
    "\n",
    "# linux_path = \"../test/data_for_tests/tutorial_sample_dataset.csv\"\n",
    "win_path = \"../test/data_for_tests/tutorial_sample_dataset.csv\"\n",
    "ds = DataSet.read_csv(win_path, headers=('raw_sentence', 'label'), sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'raw_sentence': this quiet , introspective and entertaining independent is worth seeking .,\n",
       "'label': 4,\n",
       "'label_seq': 4,\n",
       "'words': ['this', 'quiet', ',', 'introspective', 'and', 'entertaining', 'independent', 'is', 'worth', 'seeking', '.']}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## step 2\n",
    "数据预处理\n",
    "1. 类型转换\n",
    "2. 切分验证集\n",
    "3. 构建词典"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 将所有数字转为小写\n",
    "ds.apply(lambda x: x['raw_sentence'].lower(), new_field_name='raw_sentence')\n",
    "# label转int\n",
    "ds.apply(lambda x: int(x['label']), new_field_name='label_seq', is_target=True)\n",
    "\n",
    "def split_sent(ins):\n",
    "    return ins['raw_sentence'].split()\n",
    "ds.apply(split_sent, new_field_name='words', is_input=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train size:  54\n",
      "Test size:  23\n"
     ]
    }
   ],
   "source": [
    "# 分割训练集/验证集\n",
    "train_data, dev_data = ds.split(0.3)\n",
    "print(\"Train size: \", len(train_data))\n",
    "print(\"Test size: \", len(dev_data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from fastNLP import Vocabulary\n",
    "vocab = Vocabulary(min_freq=2)\n",
    "train_data.apply(lambda x: [vocab.add(word) for word in x['words']])\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",
    "dev_data.apply(lambda x: [vocab.to_index(word) for word in x['words']], new_field_name='word_seq', is_input=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## step 3\n",
    " 定义模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## step 4\n",
    "开始训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training epochs started 2018-12-07 14:03:41\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "HBox(children=(IntProgress(value=0, layout=Layout(flex='2'), max=6), HTML(value='')), layout=Layout(display='i…"
      ]
     },
     "execution_count": 0,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/3. Step:2/6. AccuracyMetric: acc=0.26087\n",
      "Epoch 2/3. Step:4/6. AccuracyMetric: acc=0.347826\n",
      "Epoch 3/3. Step:6/6. AccuracyMetric: acc=0.608696\n",
      "Train finished!\n"
     ]
    }
   ],
   "source": [
    "from fastNLP import Trainer, CrossEntropyLoss, AccuracyMetric\n",
    "trainer = Trainer(model=model, \n",
    "                  train_data=train_data, \n",
    "                  dev_data=dev_data,\n",
    "                  loss=CrossEntropyLoss(),\n",
    "                  metrics=AccuracyMetric()\n",
    "                  )\n",
    "trainer.train()\n",
    "print('Train finished!')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 本教程结束。更多操作请参考进阶教程。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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