cv_load_CatBoost.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import Counter\n",
    "\n",
    "dtype = {\n",
    "    'Usage': 'category',\n",
    "    'Description': 'category',\n",
    "    'status': 'category',\n",
    "}\n",
    "df = pd.read_csv('data/reservations.csv.gz', dtype=dtype, parse_dates=['created', 'arrival', 'departure'])\n",
    "df.drop(columns='Usage', inplace=True)\n",
    "\n",
    "df.loc[df['cancel_date'] == '0001-01-01T00:00:00', ['cancel_date']] = None\n",
    "df['cancel_date'] = pd.to_datetime(df['cancel_date'])\n",
    "\n",
    "df['arrival_year'] = df['arrival'].dt.year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "appearances = {}\n",
    "for contract_id in df['contract_id'].unique():\n",
    "    subset_df = df.loc[df['contract_id'] == contract_id].sort_values(by=['arrival', 'created'])\n",
    "    # save the last known state\n",
    "    # fill in blanks for bad years\n",
    "    # handle case where cancel year might come after a series of misses\n",
    "    yearly_state = {arrival_year: status for (created, arrival_year, status) in subset_df[['created', 'arrival_year', 'status']].itertuples(index=False, name=None)}\n",
    "    earliest = subset_df['arrival_year'].min()\n",
    "    latest = min(subset_df['arrival_year'].max(), 2019)\n",
    "    activity = [(year, yearly_state.get(year, 'no-show')) for year in range(earliest, latest + 1)]    \n",
    "    if activity:\n",
    "        resort_id = subset_df['resort_id'].values[0]\n",
    "        appearances[str(contract_id)] = [resort_id] + activity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "rows = []\n",
    "for r in list(appearances.values()):\n",
    "    resort_id, activity = r[0], r[1:]\n",
    "    row = [None] * 5\n",
    "    row[-len(activity):] = [s for year, s in activity]\n",
    "    rows.append([resort_id] + row)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(rows, columns=['resort_id', 'year_2015', 'year_2016', 'year_2017', 'year_2018', 'year_2019'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "resort_id    0.000000\n",
       "year_2015    0.515846\n",
       "year_2016    0.205276\n",
       "year_2017    0.098678\n",
       "year_2018    0.048311\n",
       "year_2019    0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum() / df.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.fillna('missing', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "resort_id    57\n",
       "year_2015     4\n",
       "year_2016     4\n",
       "year_2017     4\n",
       "year_2018     4\n",
       "year_2019     3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop(\"year_2019\", axis=1)\n",
    "y = df[\"year_2019\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 1, 2, 3, 4]\n"
     ]
    }
   ],
   "source": [
    "cat_features = list(range(0, X.shape[1]))\n",
    "print(cat_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "\n",
    "X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.3, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0:\tlearn: 0.9089395\ttest: 0.9088238\tbest: 0.9088238 (0)\ttotal: 83.9ms\tremaining: 755ms\n",
      "5:\tlearn: 0.6921539\ttest: 0.6930650\tbest: 0.6930650 (5)\ttotal: 219ms\tremaining: 146ms\n",
      "9:\tlearn: 0.6811982\ttest: 0.6819978\tbest: 0.6819978 (9)\ttotal: 319ms\tremaining: 0us\n",
      "\n",
      "bestTest = 0.6819978385\n",
      "bestIteration = 9\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<catboost.core.CatBoostClassifier at 0x7f2c8c533390>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from catboost import CatBoostClassifier\n",
    "\n",
    "clf = CatBoostClassifier(iterations=10, verbose=5, learning_rate=0.5)\n",
    "clf.fit(X_train, y_train, cat_features=cat_features, eval_set=(X_val, y_val))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_prob = clf.predict_proba(data=X_val)\n",
    "y_pred = clf.predict(data=X_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.7176913425345044\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "accuracy = accuracy_score(y_val, y_pred)\n",
    "print('Accuracy:', accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC: 0.8310445038554471\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "auc = roc_auc_score(y_val, y_prob, multi_class=\"ovo\", average=\"macro\")\n",
    "print('AUC:', auc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "      active       0.75      0.87      0.80     11582\n",
      "   cancelled       0.63      0.52      0.57      5562\n",
      "     no-show       0.73      0.52      0.61      3578\n",
      "\n",
      "    accuracy                           0.72     20722\n",
      "   macro avg       0.70      0.64      0.66     20722\n",
      "weighted avg       0.71      0.72      0.71     20722\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "\n",
    "print(classification_report(y_val, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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