category_03_climate_environment/01_NLP_with_Disaster_Tweets/nlp_disaster_tweets.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# NLP with Disaster Tweets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```\n",
    "Step 1. Library Import & Data Load\n",
    "Step 2. Data Preprocessing\n",
    "     2-a. Drop Columns\n",
    "     2-b. Tokenizer\n",
    "     2-c. Pad Sequences\n",
    "     2-d. Match Data type to numpy.ndarray\n",
    "Step 3. Modeling\n",
    "Step 4. Model Compile\n",
    "Step 5. Callbacks\n",
    "Step 6. Model Fit\n",
    "Step 7. Model Evaluate & Save\n",
    "Step 8. Reload Model\n",
    "Step 9. Predict Test Data\n",
    "```\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 1. Library Import & Data Load"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df = pd.read_csv('./data/train_data.csv')\n",
    "test_df = pd.read_csv('./data/test_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>id</th>\n      <th>keyword</th>\n      <th>location</th>\n      <th>text</th>\n      <th>target</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Our Deeds are the Reason of this  # earthquake...</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>4</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Forest fire near La Ronge Sask .  Canada</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>5</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>All residents asked to  ' shelter in place '  ...</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>6</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>13,000 people receive  # wildfires evacuation ...</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>7</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Just got sent this photo from Ruby  # Alaska a...</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
      "text/plain": "   id keyword location                                               text  \\\n0   1     NaN      NaN  Our Deeds are the Reason of this  # earthquake...   \n1   4     NaN      NaN           Forest fire near La Ronge Sask .  Canada   \n2   5     NaN      NaN  All residents asked to  ' shelter in place '  ...   \n3   6     NaN      NaN  13,000 people receive  # wildfires evacuation ...   \n4   7     NaN      NaN  Just got sent this photo from Ruby  # Alaska a...   \n\n   target  \n0       1  \n1       1  \n2       1  \n3       1  \n4       1  "
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 7613 entries, 0 to 7612\n",
      "Data columns (total 5 columns):\n",
      " #   Column    Non-Null Count  Dtype \n",
      "---  ------    --------------  ----- \n",
      " 0   id        7613 non-null   int64 \n",
      " 1   keyword   7552 non-null   object\n",
      " 2   location  5080 non-null   object\n",
      " 3   text      7613 non-null   object\n",
      " 4   target    7613 non-null   int64 \n",
      "dtypes: int64(2), object(3)\n",
      "memory usage: 297.5+ KB\n"
     ]
    }
   ],
   "source": [
    "train_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "id          7613\nkeyword      221\nlocation    3341\ntext        6981\ntarget         2\ndtype: int64"
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>id</th>\n      <th>keyword</th>\n      <th>location</th>\n      <th>text</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Just happened a terrible car crash</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Heard about  # earthquake is different cities,...</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>there is a forest fire at spot pond, geese are...</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>9</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Apocalypse lighting .   # Spokane  # wildfires</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>11</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Typhoon Soudelor kills 28 in China and Taiwan</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
      "text/plain": "   id keyword location                                               text\n0   0     NaN      NaN                 Just happened a terrible car crash\n1   2     NaN      NaN  Heard about  # earthquake is different cities,...\n2   3     NaN      NaN  there is a forest fire at spot pond, geese are...\n3   9     NaN      NaN     Apocalypse lighting .   # Spokane  # wildfires\n4  11     NaN      NaN      Typhoon Soudelor kills 28 in China and Taiwan"
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3263 entries, 0 to 3262\n",
      "Data columns (total 4 columns):\n",
      " #   Column    Non-Null Count  Dtype \n",
      "---  ------    --------------  ----- \n",
      " 0   id        3263 non-null   int64 \n",
      " 1   keyword   3237 non-null   object\n",
      " 2   location  2158 non-null   object\n",
      " 3   text      3263 non-null   object\n",
      "dtypes: int64(1), object(3)\n",
      "memory usage: 102.1+ KB\n"
     ]
    }
   ],
   "source": [
    "test_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "id          3263\nkeyword      221\nlocation    1602\ntext        3109\ndtype: int64"
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2. Data Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2-a. Drop Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>id</th>\n      <th>keyword</th>\n      <th>location</th>\n      <th>text</th>\n      <th>target</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Our Deeds are the Reason of this  # earthquake...</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>4</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Forest fire near La Ronge Sask .  Canada</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>5</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>All residents asked to  ' shelter in place '  ...</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>6</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>13,000 people receive  # wildfires evacuation ...</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>7</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Just got sent this photo from Ruby  # Alaska a...</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
      "text/plain": "   id keyword location                                               text  \\\n0   1     NaN      NaN  Our Deeds are the Reason of this  # earthquake...   \n1   4     NaN      NaN           Forest fire near La Ronge Sask .  Canada   \n2   5     NaN      NaN  All residents asked to  ' shelter in place '  ...   \n3   6     NaN      NaN  13,000 people receive  # wildfires evacuation ...   \n4   7     NaN      NaN  Just got sent this photo from Ruby  # Alaska a...   \n\n   target  \n0       1  \n1       1  \n2       1  \n3       1  \n4       1  "
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df.drop(columns=['id','keyword','location'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>id</th>\n      <th>keyword</th>\n      <th>location</th>\n      <th>text</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Just happened a terrible car crash</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Heard about  # earthquake is different cities,...</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>there is a forest fire at spot pond, geese are...</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>9</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Apocalypse lighting .   # Spokane  # wildfires</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>11</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Typhoon Soudelor kills 28 in China and Taiwan</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
      "text/plain": "   id keyword location                                               text\n0   0     NaN      NaN                 Just happened a terrible car crash\n1   2     NaN      NaN  Heard about  # earthquake is different cities,...\n2   3     NaN      NaN  there is a forest fire at spot pond, geese are...\n3   9     NaN      NaN     Apocalypse lighting .   # Spokane  # wildfires\n4  11     NaN      NaN      Typhoon Soudelor kills 28 in China and Taiwan"
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_df.drop(columns=['keyword','location'],axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(7613, 2) (3263, 2)\n"
     ]
    }
   ],
   "source": [
    "print(train_df.shape, test_df.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2-b. Tokenizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_valid, y_train, y_valid = train_test_split(train_df['text'],train_df['target'], test_size=0.2, random_state=111)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(6090,) (6090,) (1523,) (1523,)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape, y_train.shape, X_valid.shape, y_valid.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.preprocessing.text import Tokenizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "vocab_size = 1000\n",
    "oov_token = \"<OOV>\"\n",
    "tokenizer = Tokenizer(num_words=vocab_size, oov_token=oov_token)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "tokenizer.fit_on_texts(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = tokenizer.texts_to_sequences(X_train)\n",
    "X_valid = tokenizer.texts_to_sequences(X_valid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11\n",
      "16\n",
      "10\n",
      "28\n",
      "16\n",
      "20\n",
      "15\n",
      "20\n",
      "10\n",
      "8\n"
     ]
    }
   ],
   "source": [
    "for i in range(10):\n",
    "    print(len(X_train[i]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "[132, 1, 6, 253, 10, 15, 685, 12, 618, 157, 1]"
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11\n",
      "26\n",
      "27\n",
      "21\n",
      "12\n",
      "7\n",
      "12\n",
      "7\n",
      "7\n",
      "11\n"
     ]
    }
   ],
   "source": [
    "for i in range(10):\n",
    "    print(len(X_valid[i]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "[32, 11, 230, 494, 18, 758, 402, 923, 178, 2, 1]"
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_valid[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2-c. Pad Sequences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.preprocessing.sequence import pad_sequences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "max_length = 120\n",
    "trunc_type = 'post'\n",
    "pad_type = 'post'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_padded = pad_sequences(X_train, maxlen=max_length, truncating=trunc_type, padding=pad_type)\n",
    "X_valid_padded = pad_sequences(X_valid, maxlen=max_length, truncating=trunc_type, padding=pad_type)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "array([[132,   1,   6, 253,  10,  15, 685,  12, 618, 157,   1,   0,   0,\n          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n          0,   0,   0],\n       [821,   1, 138,  26,   1,   5,   3,   1,  25, 125,   1,  70, 174,\n         13,   1,   1,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n          0,   0,   0]], dtype=int32)"
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_padded[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "array([[ 32,  11, 230, 494,  18, 758, 402, 923, 178,   2,   1,   0,   0,\n          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n          0,   0,   0],\n       [ 17,   1,  50, 558,  57,   5, 834,   3,   1,   1,   9,  80,  54,\n        437,  36,  20,   3,   1,   9,  54,   1,  12,   1,  33,   1,   1,\n          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n          0,   0,   0]], dtype=int32)"
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_valid_padded[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(6090, 120) (1523, 120)\n"
     ]
    }
   ],
   "source": [
    "print(X_train_padded.shape, X_valid_padded.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2-d. Match Data type to numpy.ndarray"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.ndarray'> <class 'numpy.ndarray'>\n",
      "<class 'pandas.core.series.Series'> <class 'pandas.core.series.Series'>\n"
     ]
    }
   ],
   "source": [
    "print(type(X_train_padded), type(X_valid_padded))\n",
    "print(type(y_train), type(y_valid))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train = np.array(y_train)\n",
    "y_valid = np.array(y_valid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.ndarray'> <class 'numpy.ndarray'>\n",
      "<class 'numpy.ndarray'> <class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "print(type(X_train_padded), type(X_valid_padded))\n",
    "print(type(y_train), type(y_valid))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 3. Modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Embedding, LSTM, Dense, Bidirectional, Flatten"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "embedding_dim = 16\n",
    "# vocab_size = 1000\n",
    "# max_length = 120"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential([\n",
    "    Embedding(vocab_size, embedding_dim, input_length=max_length),\n",
    "    Bidirectional(LSTM(64, return_sequences=True)),\n",
    "    Bidirectional(LSTM(64, return_sequences=True)),\n",
    "    Bidirectional(LSTM(64, dropout=0.5)),\n",
    "    Dense(32, activation='relu'),\n",
    "    Dense(16, activation='relu'),\n",
    "    Dense(1, activation='sigmoid')\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "embedding (Embedding)        (None, 120, 16)           16000     \n",
      "_________________________________________________________________\n",
      "bidirectional (Bidirectional (None, 120, 128)          41472     \n",
      "_________________________________________________________________\n",
      "bidirectional_1 (Bidirection (None, 120, 128)          98816     \n",
      "_________________________________________________________________\n",
      "bidirectional_2 (Bidirection (None, 128)               98816     \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 32)                4128      \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 16)                528       \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 1)                 17        \n",
      "=================================================================\n",
      "Total params: 259,777\n",
      "Trainable params: 259,777\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 4. Model Compile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['acc'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 5. Callbacks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "filepath = 'my_checkpoint.ckpt'\n",
    "cp = ModelCheckpoint(\n",
    "    filepath=filepath,\n",
    "    save_weights_only=True,\n",
    "    save_best_only=True,\n",
    "    monitor='val_loss',\n",
    "    verbose=1\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "ep = EarlyStopping(\n",
    "    monitor='val_loss', \n",
    "    patience=5,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 6. Model Fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "191/191 [==============================] - ETA: 0s - loss: 0.5478 - acc: 0.7194\n",
      "Epoch 00001: val_loss improved from inf to 0.47684, saving model to my_checkpoint.ckpt\n",
      "191/191 [==============================] - 37s 195ms/step - loss: 0.5478 - acc: 0.7194 - val_loss: 0.4768 - val_acc: 0.7774\n",
      "Epoch 2/30\n",
      "191/191 [==============================] - ETA: 0s - loss: 0.4245 - acc: 0.8189\n",
      "Epoch 00002: val_loss improved from 0.47684 to 0.44872, saving model to my_checkpoint.ckpt\n",
      "191/191 [==============================] - 30s 158ms/step - loss: 0.4245 - acc: 0.8189 - val_loss: 0.4487 - val_acc: 0.7945\n",
      "Epoch 3/30\n",
      "191/191 [==============================] - ETA: 0s - loss: 0.3991 - acc: 0.8312\n",
      "Epoch 00003: val_loss did not improve from 0.44872\n",
      "191/191 [==============================] - 30s 157ms/step - loss: 0.3991 - acc: 0.8312 - val_loss: 0.4723 - val_acc: 0.7833\n",
      "Epoch 4/30\n",
      "191/191 [==============================] - ETA: 0s - loss: 0.3813 - acc: 0.8386\n",
      "Epoch 00004: val_loss did not improve from 0.44872\n",
      "191/191 [==============================] - 31s 162ms/step - loss: 0.3813 - acc: 0.8386 - val_loss: 0.4488 - val_acc: 0.7984\n",
      "Epoch 5/30\n",
      "191/191 [==============================] - ETA: 0s - loss: 0.3623 - acc: 0.8547\n",
      "Epoch 00005: val_loss improved from 0.44872 to 0.44845, saving model to my_checkpoint.ckpt\n",
      "191/191 [==============================] - 32s 170ms/step - loss: 0.3623 - acc: 0.8547 - val_loss: 0.4485 - val_acc: 0.7965\n",
      "Epoch 6/30\n",
      "191/191 [==============================] - ETA: 0s - loss: 0.3450 - acc: 0.8627\n",
      "Epoch 00006: val_loss did not improve from 0.44845\n",
      "191/191 [==============================] - 34s 178ms/step - loss: 0.3450 - acc: 0.8627 - val_loss: 0.4671 - val_acc: 0.7892\n",
      "Epoch 7/30\n",
      "191/191 [==============================] - ETA: 0s - loss: 0.3295 - acc: 0.8714\n",
      "Epoch 00007: val_loss did not improve from 0.44845\n",
      "191/191 [==============================] - 31s 164ms/step - loss: 0.3295 - acc: 0.8714 - val_loss: 0.4890 - val_acc: 0.7827\n",
      "Epoch 8/30\n",
      "191/191 [==============================] - ETA: 0s - loss: 0.3038 - acc: 0.8846\n",
      "Epoch 00008: val_loss did not improve from 0.44845\n",
      "191/191 [==============================] - 31s 165ms/step - loss: 0.3038 - acc: 0.8846 - val_loss: 0.5213 - val_acc: 0.7708\n",
      "Epoch 9/30\n",
      "191/191 [==============================] - ETA: 0s - loss: 0.2889 - acc: 0.8929\n",
      "Epoch 00009: val_loss did not improve from 0.44845\n",
      "191/191 [==============================] - 32s 165ms/step - loss: 0.2889 - acc: 0.8929 - val_loss: 0.5495 - val_acc: 0.7735\n",
      "Epoch 10/30\n",
      "191/191 [==============================] - ETA: 0s - loss: 0.2759 - acc: 0.8967\n",
      "Epoch 00010: val_loss did not improve from 0.44845\n",
      "191/191 [==============================] - 32s 168ms/step - loss: 0.2759 - acc: 0.8967 - val_loss: 0.5851 - val_acc: 0.7649\n"
     ]
    },
    {
     "data": {
      "text/plain": "<tensorflow.python.keras.callbacks.History at 0x7f912a097590>"
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "epochs=30\n",
    "model.fit(\n",
    "    X_train_padded, y_train,\n",
    "    validation_data = (X_valid_padded, y_valid),\n",
    "    callbacks=[cp,ep],\n",
    "    epochs=epochs\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 7. Model Evaluate & Save"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "<tensorflow.python.training.tracking.util.CheckpointLoadStatus at 0x7f912a575590>"
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.load_weights(filepath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "48/48 [==============================] - 2s 32ms/step - loss: 0.4485 - acc: 0.7965\n"
     ]
    },
    {
     "data": {
      "text/plain": "[0.4484510123729706, 0.7964543700218201]"
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(X_valid_padded, y_valid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "[32, 11, 230, 494, 18, 758, 402, 923, 178, 2, 1]"
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_valid[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save('./model/basic_val_loss_0.4484.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 8. Reload Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "mymodel = tf.keras.models.load_model('./model/basic_val_loss_0.4484.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "embedding (Embedding)        (None, 120, 16)           16000     \n",
      "_________________________________________________________________\n",
      "bidirectional (Bidirectional (None, 120, 128)          41472     \n",
      "_________________________________________________________________\n",
      "bidirectional_1 (Bidirection (None, 120, 128)          98816     \n",
      "_________________________________________________________________\n",
      "bidirectional_2 (Bidirection (None, 128)               98816     \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 32)                4128      \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 16)                528       \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 1)                 17        \n",
      "=================================================================\n",
      "Total params: 259,777\n",
      "Trainable params: 259,777\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "mymodel.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 9. Predict Test Data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test = tokenizer.texts_to_sequences(test_df['text'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test_padded = pad_sequences(X_test, maxlen=max_length, truncating=trunc_type, padding=pad_type)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test_raw = model.predict(X_test_padded)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "array([[0.8736457 ],\n       [0.6497478 ],\n       [0.8904932 ],\n       ...,\n       [0.9764315 ],\n       [0.84007984],\n       [0.99754155]], dtype=float32)"
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test_raw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test = list(map(lambda x : 1 if x > 0.5 else 0, y_test_raw))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "{0, 1}"
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "[1, 1, 1, 0, 1]"
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_df['predict'] = y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>id</th>\n      <th>text</th>\n      <th>predict</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>Just happened a terrible car crash</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>Heard about  # earthquake is different cities,...</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>there is a forest fire at spot pond, geese are...</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>9</td>\n      <td>Apocalypse lighting .   # Spokane  # wildfires</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>11</td>\n      <td>Typhoon Soudelor kills 28 in China and Taiwan</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>3258</th>\n      <td>10861</td>\n      <td>EARTHQUAKE SAFETY LOS ANGELES  SAFETY FASTENER...</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3259</th>\n      <td>10865</td>\n      <td>Storm in RI worse than last hurricane .  My ci...</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3260</th>\n      <td>10868</td>\n      <td>Green Line derailment in Chicago</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3261</th>\n      <td>10874</td>\n      <td>MEG issues Hazardous Weather Outlook  ( HWO )</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3262</th>\n      <td>10875</td>\n      <td># City of Calgary has activated its Municipal...</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n<p>3263 rows × 3 columns</p>\n</div>",
      "text/plain": "         id                                               text  predict\n0         0                 Just happened a terrible car crash        1\n1         2  Heard about  # earthquake is different cities,...        1\n2         3  there is a forest fire at spot pond, geese are...        1\n3         9     Apocalypse lighting .   # Spokane  # wildfires        0\n4        11      Typhoon Soudelor kills 28 in China and Taiwan        1\n...     ...                                                ...      ...\n3258  10861  EARTHQUAKE SAFETY LOS ANGELES  SAFETY FASTENER...        1\n3259  10865  Storm in RI worse than last hurricane .  My ci...        1\n3260  10868                  Green Line derailment in Chicago         1\n3261  10874    MEG issues Hazardous Weather Outlook  ( HWO )          1\n3262  10875   # City of Calgary has activated its Municipal...        1\n\n[3263 rows x 3 columns]"
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>id</th>\n      <th>text</th>\n      <th>predict</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>Just happened a terrible car crash</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>Heard about  # earthquake is different cities,...</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>there is a forest fire at spot pond, geese are...</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>11</td>\n      <td>Typhoon Soudelor kills 28 in China and Taiwan</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>12</td>\n      <td>We are shaking . It is an earthquake</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>3258</th>\n      <td>10861</td>\n      <td>EARTHQUAKE SAFETY LOS ANGELES  SAFETY FASTENER...</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3259</th>\n      <td>10865</td>\n      <td>Storm in RI worse than last hurricane .  My ci...</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3260</th>\n      <td>10868</td>\n      <td>Green Line derailment in Chicago</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3261</th>\n      <td>10874</td>\n      <td>MEG issues Hazardous Weather Outlook  ( HWO )</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3262</th>\n      <td>10875</td>\n      <td># City of Calgary has activated its Municipal...</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n<p>1190 rows × 3 columns</p>\n</div>",
      "text/plain": "         id                                               text  predict\n0         0                 Just happened a terrible car crash        1\n1         2  Heard about  # earthquake is different cities,...        1\n2         3  there is a forest fire at spot pond, geese are...        1\n4        11      Typhoon Soudelor kills 28 in China and Taiwan        1\n5        12               We are shaking . It is an earthquake        1\n...     ...                                                ...      ...\n3258  10861  EARTHQUAKE SAFETY LOS ANGELES  SAFETY FASTENER...        1\n3259  10865  Storm in RI worse than last hurricane .  My ci...        1\n3260  10868                  Green Line derailment in Chicago         1\n3261  10874    MEG issues Hazardous Weather Outlook  ( HWO )          1\n3262  10875   # City of Calgary has activated its Municipal...        1\n\n[1190 rows x 3 columns]"
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df[test_df['predict']==1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission = test_df[['id','predict']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>id</th>\n      <th>predict</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>9</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>11</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>3258</th>\n      <td>10861</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3259</th>\n      <td>10865</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3260</th>\n      <td>10868</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3261</th>\n      <td>10874</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3262</th>\n      <td>10875</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n<p>3263 rows × 2 columns</p>\n</div>",
      "text/plain": "         id  predict\n0         0        1\n1         2        1\n2         3        1\n3         9        0\n4        11        1\n...     ...      ...\n3258  10861        1\n3259  10865        1\n3260  10868        1\n3261  10874        1\n3262  10875        1\n\n[3263 rows x 2 columns]"
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission.columns = ['id', 'target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>id</th>\n      <th>target</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>9</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>11</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>3258</th>\n      <td>10861</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3259</th>\n      <td>10865</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3260</th>\n      <td>10868</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3261</th>\n      <td>10874</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3262</th>\n      <td>10875</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n<p>3263 rows × 2 columns</p>\n</div>",
      "text/plain": "         id  target\n0         0       1\n1         2       1\n2         3       1\n3         9       0\n4        11       1\n...     ...     ...\n3258  10861       1\n3259  10865       1\n3260  10868       1\n3261  10874       1\n3262  10875       1\n\n[3263 rows x 2 columns]"
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission.to_csv('./sample_submission.csv', index=False)"
   ]
  }
 ],
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   "display_name": "Python 3.7.7 64-bit ('dataScience': conda)",
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