Baseline.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading Data from local file"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Basic models with basic features to help establish a baseline to compare against other models and additional features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('./XYmat.csv')"
   ]
  },
  {
   "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>name</th>\n",
       "      <th>reviews</th>\n",
       "      <th>FPS</th>\n",
       "      <th>Gore</th>\n",
       "      <th>Action</th>\n",
       "      <th>Demons</th>\n",
       "      <th>Shooter</th>\n",
       "      <th>First-Person</th>\n",
       "      <th>Great Soundtrack</th>\n",
       "      <th>Multiplayer</th>\n",
       "      <th>...</th>\n",
       "      <th>Pool</th>\n",
       "      <th>Documentary</th>\n",
       "      <th>Word Game</th>\n",
       "      <th>Feature Film</th>\n",
       "      <th>Spelling</th>\n",
       "      <th>achievements</th>\n",
       "      <th>prices</th>\n",
       "      <th>Valve</th>\n",
       "      <th>Accounting</th>\n",
       "      <th>Number of genres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DOOM</td>\n",
       "      <td>92</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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>54</td>\n",
       "      <td>19.99</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PLAYERUNKNOWN'S BATTLEGROUNDS</td>\n",
       "      <td>49</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>37</td>\n",
       "      <td>29.99</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BATTLETECH</td>\n",
       "      <td>71</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>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>128</td>\n",
       "      <td>39.99</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DayZ</td>\n",
       "      <td>61</td>\n",
       "      <td>1</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>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>44.99</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>EVE Online</td>\n",
       "      <td>74</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>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>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 383 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                            name  reviews  FPS  Gore  Action  Demons  Shooter  \\\n",
       "0                           DOOM       92    1     1       1       1        1   \n",
       "1  PLAYERUNKNOWN'S BATTLEGROUNDS       49    1     0       1       0        1   \n",
       "2                     BATTLETECH       71    0     0       1       0        0   \n",
       "3                           DayZ       61    1     0       1       0        1   \n",
       "4                     EVE Online       74    0     0       1       0        0   \n",
       "\n",
       "   First-Person  Great Soundtrack  Multiplayer  ...  Pool  Documentary  \\\n",
       "0             1                 1            1  ...     0            0   \n",
       "1             1                 0            1  ...     0            0   \n",
       "2             0                 1            1  ...     0            0   \n",
       "3             0                 0            1  ...     0            0   \n",
       "4             0                 0            1  ...     0            0   \n",
       "\n",
       "   Word Game  Feature Film  Spelling  achievements  prices  Valve  Accounting  \\\n",
       "0          0             0         0            54   19.99      0           0   \n",
       "1          0             0         0            37   29.99      0           0   \n",
       "2          0             0         0           128   39.99      0           0   \n",
       "3          0             0         0             0   44.99      0           0   \n",
       "4          0             0         0             0    0.00      0           0   \n",
       "\n",
       "   Number of genres  \n",
       "0                 1  \n",
       "1                 3  \n",
       "2                 3  \n",
       "3                 3  \n",
       "4                 5  \n",
       "\n",
       "[5 rows x 383 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['name', 'reviews', 'FPS', 'Gore', 'Action', 'Demons', 'Shooter',\n",
       "       'First-Person', 'Great Soundtrack', 'Multiplayer',\n",
       "       ...\n",
       "       'Pool', 'Documentary', 'Word Game', 'Feature Film', 'Spelling',\n",
       "       'achievements', 'prices', 'Valve', 'Accounting', 'Number of genres'],\n",
       "      dtype='object', length=383)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=11766, step=1)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['DOOM', 92, 1, ..., 0, 0, 1],\n",
       "       [\"PLAYERUNKNOWN'S BATTLEGROUNDS\", 49, 1, ..., 0, 0, 3],\n",
       "       ['BATTLETECH', 71, 0, ..., 0, 0, 3],\n",
       "       ...,\n",
       "       ['STAX', 99, 0, ..., 0, 0, 2],\n",
       "       ['Chicken Farm 2K17 - Premium', 96, 0, ..., 0, 0, 3],\n",
       "       ['TANE DLC - ZecRail 2017 Christmas Train', 100, 0, ..., 0, 0, 5]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 1 1 ... 0 0 1]\n",
      " [1 0 1 ... 0 0 3]\n",
      " [0 0 1 ... 0 0 3]\n",
      " ...\n",
      " [0 0 0 ... 0 0 2]\n",
      " [0 0 0 ... 0 0 3]\n",
      " [0 0 1 ... 0 0 5]]\n"
     ]
    }
   ],
   "source": [
    "y = df['reviews'].values\n",
    "array = df.to_numpy()\n",
    "X = array[:, 2:]\n",
    "print(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Split train test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(8232, 381) (3534, 381) (8232,) (3534,)\n"
     ]
    }
   ],
   "source": [
    "num_train = len(df) // 10 * 7\n",
    "\n",
    "Xtrain = X[:num_train]\n",
    "Xtest = X[num_train:]\n",
    "ytrain = y[:num_train]\n",
    "ytest = y[num_train:]\n",
    "print(Xtrain.shape, Xtest.shape, ytrain.shape, ytest.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test data with multiple linear regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy as sp\n",
    "import scipy.sparse.linalg\n",
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(381,)\n"
     ]
    }
   ],
   "source": [
    "out = sp.sparse.linalg.lsqr(Xtrain,ytrain)\n",
    "beta = out[0]\n",
    "print(beta.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compute Baseline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "multiple variable loss=2.33e+01\n"
     ]
    }
   ],
   "source": [
    "yBaseline = np.ones(len(ytest)) * np.mean(ytrain)\n",
    "errs = np.array(abs(ytest - yBaseline))\n",
    "loss = np.sum(errs)\n",
    "avgL = loss/len(ytest)\n",
    "print(\"multiple variable loss=\"+\"{:.2e}\".format(avgL))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Compute loss with linear regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "multiple variable loss=2.356e+01\n",
      "110.80326840456644\n",
      "110.03635757012734\n",
      "106.5454409496329\n",
      "95.34250289431543\n",
      "93.84333453636447\n",
      "93.43438217285066\n",
      "92.81871225985691\n",
      "91.97453299332003\n",
      "90.70044099562236\n",
      "90.64691562348992\n",
      "90.29307527310871\n",
      "90.08951196869847\n",
      "89.91825382082598\n",
      "89.67944815996516\n",
      "89.18390875667345\n",
      "89.06680927930893\n",
      "88.51788592698341\n",
      "88.43999697608496\n",
      "88.32490105844022\n",
      "88.09538186990272\n",
      "87.85303031336616\n",
      "87.37669974809853\n",
      "86.82780608008373\n",
      "86.3474195307129\n",
      "85.91484855194969\n",
      "85.7328028624375\n",
      "85.14148987022764\n",
      "85.01584179429378\n",
      "84.9621235639134\n",
      "84.76659200474484\n",
      "84.64843227508638\n",
      "84.01291630051847\n",
      "83.13544524170166\n",
      "83.06508866607624\n",
      "82.76983120887468\n",
      "82.56446414100174\n",
      "82.0767237877468\n",
      "81.39319184891957\n",
      "81.35060328825806\n",
      "81.30330216171805\n",
      "81.1044453536198\n",
      "79.7799741046897\n",
      "78.35317681288223\n",
      "78.32095515634455\n",
      "77.92288491904827\n",
      "77.51180563328548\n",
      "76.74608997124596\n",
      "76.3254371891392\n",
      "75.99872980337477\n",
      "75.91772305769251\n"
     ]
    }
   ],
   "source": [
    "yp = Xtest@beta\n",
    "\n",
    "errs = np.array(abs(ytest - yp))\n",
    "loss = np.sum(errs)\n",
    "avgL = np.mean(errs)\n",
    "\n",
    "print(\"multiple variable loss=\"+\"{:.3e}\".format(avgL))\n",
    "\n",
    "arr = []\n",
    "for i in range(len(ytest)):\n",
    "    arr.append(abs(yp[i]-ytest[i]))\n",
    "arr.sort(reverse = True)\n",
    "for i in range(50):\n",
    "    print(arr[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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": [
    "\n",
    "\n",
    "plt.plot(yp, label = \"yp\")\n",
    "plt.plot(ytest, label = \"ytest\")\n",
    "plt.legend(loc=\"upper left\")\n",
    "axes = plt.gca()\n",
    "#axes.set_xlim([0,500])\n",
    "#axes.set_ylim([0,150])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x130295460>]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "plt.plot(errs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "23.558472177917505\n"
     ]
    }
   ],
   "source": [
    "print(np.mean(errs))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}