Feature-Encoding(LR).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import roc_auc_score\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import numpy as np "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv(\"./Data/train.csv\")\n",
    "test = pd.read_csv(\"./Data/test.csv\")\n",
    "\n",
    "labels = train.pop('target')\n",
    "train_id = train.pop(\"id\")\n",
    "test_id = test.pop(\"id\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(300000, 23)\n",
      "(200000, 23)\n"
     ]
    }
   ],
   "source": [
    "print(train.shape)\n",
    "print(test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(labels)\n",
    "plt.title(\"labels counts\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def count_plot(data, columns):\n",
    "    \n",
    "    fig, axes = plt.subplots(nrows=len(columns), figsize=(10,10))\n",
    "    fig.subplots_adjust(hspace=0.3)\n",
    "\n",
    "    for (i, col) in enumerate(columns):\n",
    "        sns.countplot(data[col], ax=axes[i])\n",
    "        axes[i].set_xlabel(None)\n",
    "        axes[i].set_ylabel(\"count \" + str(col), fontsize=12)\n",
    "    \n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bin_col = ['bin_0', 'bin_2', 'bin_3', 'bin_4']\n",
    "\n",
    "count_plot(train, bin_col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "nom_col = ['nom_0', 'nom_2', 'nom_3', 'nom_4']\n",
    "\n",
    "count_plot(train, nom_col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ord_col = ['ord_0', 'ord_1', 'ord_2', 'ord_3', 'ord_4']\n",
    "\n",
    "count_plot(train, ord_col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = labels.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.concat([train, test])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "data[\"ord_5a\"] = data[\"ord_5\"].str[0]\n",
    "data[\"ord_5b\"] = data[\"ord_5\"].str[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop([\"bin_0\", \"ord_5\"], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = [i for i in data.columns]\n",
    "\n",
    "dummies = pd.get_dummies(data,\n",
    "                         columns=columns,\n",
    "                         drop_first=True,\n",
    "                         sparse=True)\n",
    "\n",
    "del data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = dummies.iloc[:train.shape[0], :]\n",
    "test = dummies.iloc[train.shape[0]:, :]\n",
    "\n",
    "del dummies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(300000, 16438)\n",
      "(200000, 16438)\n"
     ]
    }
   ],
   "source": [
    "print(train.shape)\n",
    "print(test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = train.sparse.to_coo().tocsr()\n",
    "test = test.sparse.to_coo().tocsr()\n",
    "\n",
    "train = train.astype(\"float32\")\n",
    "test = test.astype(\"float32\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score:  0.8284135463297031\n"
     ]
    }
   ],
   "source": [
    "lr = LogisticRegression(C=0.1338,\n",
    "                        solver=\"lbfgs\",\n",
    "                        tol=0.0003,\n",
    "                        max_iter=5000)\n",
    "\n",
    "lr.fit(train, labels)\n",
    "\n",
    "lr_pred = lr.predict_proba(train)[:, 1]\n",
    "score = roc_auc_score(labels, lr_pred)\n",
    "\n",
    "print(\"score: \", score)"
   ]
  }
 ],
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