sklearn_chatbot.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    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)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Chatbot Sederhana dengan Scikit-Learn\n",
    "Author : Afif A. Iskandar ([@afif.ai](https://instagram.com/afif.ai))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notebook ini merupakan implementasi pembuatan chatbot AI  sederhana dengan menggunakan scikit-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import library\n",
    "import string\n",
    "import pickle\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from util import JSONParser"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load Data\n",
    "\n",
    "Data yang digunakan merupakan data JSON sesuai dengan struktur yang dijelaskan didalam video. Tujuannya adalah mengubah bentuk JSON menjadi dalam bentuk tabel agar mudah ditraining menggunakan *scikit-learn*.\n",
    "Saya sudah membuat parser khusus untuk data dengan format tersebut didalam direktori `util/` yang bernama `JSONParser`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] Data JSON converted to DataFrame with shape : (57, 2)\n"
     ]
    }
   ],
   "source": [
    "# load data\n",
    "path = \"data/intents.json\"\n",
    "\n",
    "# buat objek JSONParser dan parse data intents.json\n",
    "jp = JSONParser()\n",
    "jp.parse(path)\n",
    "\n",
    "# simpan dataframe dalam variabel df\n",
    "df = jp.get_dataframe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>text_input</th>\n",
       "      <th>intents</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Hai</td>\n",
       "      <td>salam</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Hi</td>\n",
       "      <td>salam</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Halo</td>\n",
       "      <td>salam</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apa Kabar</td>\n",
       "      <td>salam</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Selamat Pagi</td>\n",
       "      <td>salam</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     text_input intents\n",
       "0           Hai   salam\n",
       "1            Hi   salam\n",
       "2          Halo   salam\n",
       "3     Apa Kabar   salam\n",
       "4  Selamat Pagi   salam"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# lihat 5 data pertama\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "salam                 10\n",
       "bye                    8\n",
       "komunitas              7\n",
       "nama                   6\n",
       "ngodingpython_typo     6\n",
       "pekerjaan              5\n",
       "ngodingpython          5\n",
       "youtube                5\n",
       "kemampuan              5\n",
       "Name: intents, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# hitung jumlah data per tag / inten\n",
    "df.intents.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Praproses Data\n",
    "\n",
    "Pada bagian ini data training akan dibersihkan dan dilakukan praproses apabila diperlukan.\n",
    "\n",
    "Praproses yang dilakukan adalah : \n",
    "- Case Folding : Mengubah semua alfabet menjadi huruf kecil\n",
    "- Menghapus tanda baca\n",
    "\n",
    "Jika dibutuhkan bisa diimplementasikan praproses lain seperti menghapus angka, menghapus spasi berlebih, stemming, lemmatization, POS TAGGING, NER, dsb.\n",
    "\n",
    "Anda juga bisa mengubah metode praproses ini dengan lebih efisien, misal menggunakan Regular Expression atau *RegEx*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess(chat):\n",
    "    \"\"\"\n",
    "    Fungsi yang digunakan untuk melakukan praproses\n",
    "    \"\"\"\n",
    "    # konversi ke lowercase\n",
    "    chat = chat.lower()\n",
    "    # menghapus tanda baca\n",
    "    tandabaca = tuple(string.punctuation)\n",
    "    chat = ''.join(ch for ch in chat if ch not in tandabaca)\n",
    "    return chat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# implementasikan fungsi preprocess ke string\n",
    "df['text_input_prep'] = df.text_input.apply(preprocess)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Apabila kita lihat hasilnya maka kita dapati hal berikut : "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>text_input</th>\n",
       "      <th>text_input_prep</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Hai</td>\n",
       "      <td>hai</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Hi</td>\n",
       "      <td>hi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Halo</td>\n",
       "      <td>halo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apa Kabar</td>\n",
       "      <td>apa kabar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Selamat Pagi</td>\n",
       "      <td>selamat pagi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Selamat Siang</td>\n",
       "      <td>selamat siang</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Selamat Malam</td>\n",
       "      <td>selamat malam</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Salam</td>\n",
       "      <td>salam</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Ping</td>\n",
       "      <td>ping</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>P</td>\n",
       "      <td>p</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      text_input text_input_prep\n",
       "0            Hai             hai\n",
       "1             Hi              hi\n",
       "2           Halo            halo\n",
       "3      Apa Kabar       apa kabar\n",
       "4   Selamat Pagi    selamat pagi\n",
       "5  Selamat Siang   selamat siang\n",
       "6  Selamat Malam   selamat malam\n",
       "7          Salam           salam\n",
       "8           Ping            ping\n",
       "9              P               p"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['text_input', 'text_input_prep']].head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Kita dapat lihat bahwa kolom kiri merupakan kolom data sebelum di praproses sedangkan yang kanan adalah input yang sudah di praproses"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Vektorisasi\n",
    "\n",
    "Sebelum kita masukkan data kita kedalam algoritma machine learning, kita perlu melakukan vektorisasi terlebih dahulu. Karena input yang diterima oleh model machine learning harus berupa matriks sedangkan dataset yang kita proses berupa teks.\n",
    "\n",
    "Metode vektorisasi paling sederhana adalah metode *bag of words* yaitu metode pengumpulan *vocab* yang terdapat pada *corpus*.\n",
    "Metode *bag of words* yang paling sederhana adalah metode `CountVectorizer` yang terdapat pada *scikit-learn*.\n",
    "\n",
    "Anda juga dapat menggunakan metode lain misalnya `TfidfVectorizer`, dengan mengimport dari `sklearn.feature_extraction.text` persis seperti metode `CountVectorizer`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# inisiasi objek CountVectorizer\n",
    "vect = CountVectorizer()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Langkah penting dalam metode *bag of words* adalah mengumpulkan *vocab* yang terdapat pada *corpus* yang kita miliki. Dalam scikit-learn kita dapat lakukan dengan cara :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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      ],
      "text/plain": [
       "CountVectorizer()"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# mengumpulkan vocab dari data teks yang sudah dilakukan praproses\n",
    "vect.fit(df['text_input_prep'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ada',\n",
       " 'aja',\n",
       " 'anda',\n",
       " 'apa',\n",
       " 'apaan',\n",
       " 'bantu',\n",
       " 'bantuin',\n",
       " 'bisa',\n",
       " 'bye',\n",
       " 'channel']"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# lihat list vocab\n",
    "vect.get_feature_names()[:10] #batasi hanya 10 vocab teratas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Selanjutnya konversi data teks menjadi matriks sesuai vocab yang sudah dibuat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<57x81 sparse matrix of type '<class 'numpy.int64'>'\n",
       "\twith 160 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ubah data teks menjadi matriks\n",
    "text_vect = vect.transform(df.text_input_prep)\n",
    "\n",
    "text_vect"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Kita bisa lihat bahwa data teks kita sudah berubah menjadi bentuk *sparse matrix*\n",
    "(Opsional) Jika anda orangnya sangat-sangat penasaran dengan hasil yang dibuat diatas, kita coba lihat hasil matriksnya dengan bantuan pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\project\\chatbot_tutorial\\venv\\lib\\site-packages\\sklearn\\utils\\deprecation.py:87: FutureWarning: Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.\n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    },
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       "      <th>tanyatanya</th>\n",
       "      <th>tinggal</th>\n",
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       "      <th>urlnya</th>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>57 rows × 81 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    ada  aja  anda  apa  apaan  bantu  bantuin  bisa  bye  channel  ...  sih  \\\n",
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       "\n",
       "    tanyatanya  tinggal  tugas  tuh  urlnya  wah  ya  yang  youtube  \n",
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       "\n",
       "[57 rows x 81 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(text_vect.toarray(), columns=vect.get_feature_names())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Jika anda bekerja dengan data yang besar, anda tidak disarankan melihat representasi matriks datanya karena akan membuat komputer kita menjadi berat"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Modelling\n",
    "\n",
    "Setelah data siap, saatnya ditrain kedalam algoritma machine learning.\n",
    "Dalam kasus ini kita menggunakan Multinomial Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>MultinomialNB()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" checked><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MultinomialNB</label><div class=\"sk-toggleable__content\"><pre>MultinomialNB()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "MultinomialNB()"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# deklarasi objek MultinomialNB\n",
    "nb = MultinomialNB()\n",
    "\n",
    "# training data, dengan X : text_vect dan y : intents\n",
    "nb.fit(text_vect, df.intents)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Setelah model dilatih kita coba keluarkan hasil prediksi dari suatu input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Masukkan String : apa sih ngodingpiton itu ?\n",
      "Hasil prediksi : ngodingpython_typo\n"
     ]
    }
   ],
   "source": [
    "# input string dari user\n",
    "chat = input(\"Masukkan String : \")\n",
    "\n",
    "# lakukan preproses\n",
    "chat = preprocess(chat)\n",
    "\n",
    "# ubah teks menjadi vektor\n",
    "chat = vect.transform([chat])\n",
    "\n",
    "# prediksi vektor teks kedalam model machine learning\n",
    "res = nb.predict(chat)\n",
    "\n",
    "# tampilkan hasil prediksi\n",
    "print(f\"Hasil prediksi : {res[0]}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Supaya hasil menjadi lebih natural, kita lakukan prediksi dengan probabilitas sehingga apabila hasil prediksi kurang dari threshold probabilitas yang ditentukan, kita bisa buat bot untuk bilang \"tidak mengerti\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Masukkan String : ngodingpiton apaan sih\n",
      "Max Prob : 0.49028535724847366\n",
      "Max Index: 5\n",
      "Label: ngodingpython_typo\n"
     ]
    }
   ],
   "source": [
    "# input string dari user\n",
    "chat = input(\"Masukkan String : \")\n",
    "\n",
    "# lakukan preproses\n",
    "chat = preprocess(chat)\n",
    "\n",
    "# ubah teks menjadi vektor\n",
    "chat = vect.transform([chat])\n",
    "\n",
    "# prediksi vektor teks kedalam model machine learning\n",
    "res = nb.predict_proba(chat)\n",
    "\n",
    "# ambil nilai probabilitas tertinggi\n",
    "max_prob = max(res[0])\n",
    "max_idx = np.argmax(res[0])\n",
    "print(f\"Max Prob : {max_prob}\\nMax Index: {max_idx}\\nLabel: {nb.classes_[max_idx]}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Efisiensi dengan Pipeline\n",
    "\n",
    "Dari proses diatas kita bisa lihat apabila ada data teks maka kita perlu proses dalam dua langkah, yaitu vektorisasi dan pemodelan. Supaya proses menjadi lebih ringkas dan lebih mudah dalam proses deployment, kita akan buat pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-6 {color: black;background-color: white;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;countvectorizer&#x27;, CountVectorizer()),\n",
       "                (&#x27;multinomialnb&#x27;, MultinomialNB())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;countvectorizer&#x27;, CountVectorizer()),\n",
       "                (&#x27;multinomialnb&#x27;, MultinomialNB())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">CountVectorizer</label><div class=\"sk-toggleable__content\"><pre>CountVectorizer()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MultinomialNB</label><div class=\"sk-toggleable__content\"><pre>MultinomialNB()</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('countvectorizer', CountVectorizer()),\n",
       "                ('multinomialnb', MultinomialNB())])"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Deklarasi pipeline yang mengandung vektorisasi (CountVectorizer) & pemodelan (MultinomialNB) \n",
    "pipe = make_pipeline(CountVectorizer(),\n",
    "                     MultinomialNB())\n",
    "\n",
    "# Training\n",
    "pipe.fit(df.text_input, df.intents)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dapat kita lihat dari proses training diatas, seolah-olah kita langsung memasukkan data teks dan labelnya langsung kedalam \"*black box*\" sehingga proses prediksi akan lebih ringkas.\n",
    "\n",
    "Untuk inference dengan pipeline dapat kita lakukan dengan cara berikut : "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Masukkan String : halo\n",
      "Max Prob : 0.3104999468431081\n",
      "Max Index: 7\n",
      "Label: salam\n"
     ]
    }
   ],
   "source": [
    "# input string dari user\n",
    "chat = input(\"Masukkan String : \")\n",
    "\n",
    "# lakukan preproses\n",
    "chat = preprocess(chat)\n",
    "\n",
    "# prediksi teks kedalam pipeline\n",
    "res = pipe.predict_proba([chat])\n",
    "\n",
    "# ambil nilai probabilitas tertinggi\n",
    "max_prob = max(res[0])\n",
    "max_idx = np.argmax(res[0])\n",
    "print(f\"Max Prob : {max_prob}\\nMax Index: {max_idx}\\nLabel: {nb.classes_[max_idx]}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dapat kita lihat bahwa teks baru setelah praproses bisa langsung masuk kedalam pipeline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Simulasi Inference"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Selanjutnya kita akan simulasikan chatbot mulai dari mendapatkan input sampai ke respon\n",
    "\n",
    "Dalam kasus ini apabila intent yang terdeteksi adalah `bye` maka program berhenti"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Anda Terhubung dengan chatbot Kami\n",
      "Anda : HAI\n",
      "Bot : Halo!\n",
      "Anda : nama lu siapa ?\n",
      "Bot : Namaku Ngopy, salam kenal kak!\n",
      "Anda : bisa apa aja lu ngopy?\n",
      "Bot : Kakak bisa tanya-tanya nama aku, apa yang aku lakukan dan yang berhubungan dengan channel NgodingPython\n",
      "Anda : apaan tuh NgodingPhyton?\n",
      "Bot : NgodingPython kaak, jangan bikin emosi deh\n",
      "Anda : iye2 maap, NgodingPython maksudnya\n",
      "Bot : NgodingPython adalah sebuah channel youtube yang diciptakan oleh kak Afif untuk sharing mengenai Python, Pemrograman dan yang berkaitan dengan itu kak \n",
      "Anda : hoo. minta dong link youtubenya ?\n",
      "Bot : Bisa dilihat di https://youtube.com/NgodingPython, jangan lupa subscribe ya kak!\n",
      "Anda : mantaaap.. kalau mau tanya2 gitu ada komunitas atau grupnya gitu ga ?\n",
      "Bot : Langsung aja join ke telegram komunitas NgodingPython kak disini https://t.me/NgodingPython\n",
      "Anda : sipsip makasih\n",
      "Bot : Sampai jumpa lagi yaa\n"
     ]
    }
   ],
   "source": [
    "print(\"Anda Terhubung dengan chatbot Kami\")\n",
    "while True:\n",
    "    # input user\n",
    "    chat = input(\"Anda : \")\n",
    "    # praproses\n",
    "    chat = preprocess(chat)\n",
    "    # prediksi intent\n",
    "    res = pipe.predict_proba([chat])\n",
    "    # ambil nilai probabilitas & lokasinya\n",
    "    max_prob = max(res[0])\n",
    "    max_idx = np.argmax(res[0])\n",
    "    # kondisi jika probabilitas kurang dari threshold\n",
    "    if max_prob < 0.20:\n",
    "        print(\"Bot : Maaf Kak, aku ga ngerti\")\n",
    "    else:\n",
    "        print(f\"Bot : {jp.get_response(nb.classes_[max_idx])}\")\n",
    "    if nb.classes_[max_idx] == 'bye':\n",
    "        break\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Setelah kita berhasil simulasikan bot nya dalam notebook ini, kita simpan modelnya agar dapat dideploy dengan mudah"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"chatbot_pipeline.pkl\", \"wb\") as model_file:\n",
    "    pickle.dump(pipe, model_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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