{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "5ede69a1",
   "metadata": {},
   "source": [
    "# Classification Challenge using CatBoost\n",
    "\n",
    "## INF2179 Fall 2021\n",
    "### Hamid Yuksel\n",
    "\n",
    "This submission uses [CatBoost](https://catboost.ai/).\n",
    "CatBoost was chosen for its listed benefits, mainly in requiring less hyperparameter tuning and preprocessing of categorical and text features. It is also fast and fairly easy to set up.\n",
    "\n",
    "<img src=\"https://cdn.britannica.com/39/7139-050-A88818BB/Himalayan-chocolate-point.jpg\"\n",
    "     alt=\"Markdown Monster icon\"\n",
    "     style=\"float: left; margin-right: 10px;\" />\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "ee82451e",
   "metadata": {},
   "outputs": [
    {
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      "\u001b[33mWARNING: You are using pip version 21.2.4; however, version 21.3.1 is available.\n",
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      "\u001b[33mWARNING: You are using pip version 21.2.4; however, version 21.3.1 is available.\n",
      "You should consider upgrading via the '/Users/yuksel/opt/anaconda3/bin/python -m pip install --upgrade pip' command.\u001b[0m\n",
      "Enabling notebook extension jupyter-js-widgets/extension...\n",
      "      - Validating: \u001b[32mOK\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "#Installing and Importing required libraries\n",
    "! pip3 install --user catboost\n",
    "! pip3 install --user ipywidgets\n",
    "! jupyter nbextension enable --py widgetsnbextension\n",
    "\n",
    "import pandas as pd \n",
    "import numpy as np\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.metrics import accuracy_score \n",
    "from catboost import Pool, CatBoostClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "1853f85d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Rock       24486\n",
       "Pop        16251\n",
       "Hip Hop     9263\n",
       "unknown     5000\n",
       "Name: Genre, dtype: int64"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reading data\n",
    "df = pd.read_csv('data.csv')\n",
    "\n",
    "# Splitting\n",
    "training = df.head(50000)\n",
    "holdout_set = training.sample(5000, random_state=1) # pick 5000 observations randomly\n",
    "training = training.drop(holdout_set.index) # Remove holdout from training data\n",
    "testing = df.tail(5000)\n",
    "\n",
    "#Looking at counts per genre\n",
    "df['Genre'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "f0a20f7a",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Lyric</th>\n",
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       "    <tr>\n",
       "      <th>50000</th>\n",
       "      <td>Feels so good,. Feels so good,. Feels so good ...</td>\n",
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       "      <th>50001</th>\n",
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       "      <th>50002</th>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <th>54995</th>\n",
       "      <td>I can't believe what you did to me. Down on my...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54996</th>\n",
       "      <td>Have all the songs been written?. Have all the...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54997</th>\n",
       "      <td>Everything you do you do so right. The clothes...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54998</th>\n",
       "      <td>(trecho). (Rule Number Two. Understanding what...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54999</th>\n",
       "      <td>As fall rides off in the Sunset. I sweep the S...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5000 rows × 1 columns</p>\n",
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      ],
      "text/plain": [
       "                                                   Lyric\n",
       "50000  Feels so good,. Feels so good,. Feels so good ...\n",
       "50001  Shadow of a doubt. I heard your heart,. you he...\n",
       "50002  Slaves. Hebrews born to serve to the pharaoh. ...\n",
       "50003  You've been picked and it's over. What's the c...\n",
       "50004  Magic happens. But only if you are open to the...\n",
       "...                                                  ...\n",
       "54995  I can't believe what you did to me. Down on my...\n",
       "54996  Have all the songs been written?. Have all the...\n",
       "54997  Everything you do you do so right. The clothes...\n",
       "54998  (trecho). (Rule Number Two. Understanding what...\n",
       "54999  As fall rides off in the Sunset. I sweep the S...\n",
       "\n",
       "[5000 rows x 1 columns]"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Splitting training/testing set to feature (X) and labels (y)\n",
    "train_y = training.Genre\n",
    "train_X = training.drop('Genre', axis=1)\n",
    "\n",
    "test_X = testing.drop('Genre', axis=1)\n",
    "\n",
    "test_X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "1c8eb420",
   "metadata": {},
   "outputs": [
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     ]
    },
    {
     "data": {
      "text/plain": [
       "<catboost.core.CatBoostClassifier at 0x7ff1e82e72e0>"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Build a classifier\n",
    "text_features = ['Lyric']\n",
    "\n",
    "\n",
    "train_dataset = Pool(data=train_X,\n",
    "                     label=train_y,\n",
    "                     text_features=text_features)\n",
    "\n",
    "model = CatBoostClassifier(iterations=100,\n",
    "                           learning_rate=1,\n",
    "                           depth=3,\n",
    "                           loss_function='MultiClass')\n",
    "\n",
    "model.fit(train_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "f770a8f2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6794\n"
     ]
    }
   ],
   "source": [
    "# Estimate accuracy\n",
    "pred = model.predict(holdout_set.drop('Genre',axis=1))\n",
    "estimated_accuracy = accuracy_score(holdout_set['Genre'], pred)\n",
    "print(estimated_accuracy)\n",
    "pd.Series(estimated_accuracy).to_csv('ea.csv', index=False, header=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "035f58c6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Pop' 'Rock' 'Rock' ... 'Rock' 'Pop' 'Rock']\n"
     ]
    }
   ],
   "source": [
    "# Predict testing set\n",
    "pred = model.predict(test_X)\n",
    "print(pred.flatten())\n",
    "pred = pd.Series(pred.flatten()).to_csv('pred.csv', index=False, header=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "2137c958",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array(['Hip Hop', 'Pop', 'Rock'], dtype=object), array([ 802, 1307, 2891]))"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# to check number of instances of each genre in pred\n",
    "np.unique(model.predict(test_X), return_counts=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "83aa22c4",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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