data analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "path = 'Data/'\n",
    "dataset = 'pinterest-20'\n",
    "training_rating_name = path+dataset+'.train.rating'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def load_rating_file_as_list(filename):\n",
    "    ratingList = []\n",
    "    with open(filename, \"r\") as f:\n",
    "        line = f.readline()\n",
    "        user = -1\n",
    "        while line != None and line != \"\":\n",
    "            arr = line.split(\"\\t\")\n",
    "            if int(arr[0])==user:\n",
    "                ratingList[user].append(item)\n",
    "            else:\n",
    "                user, item = int(arr[0]), int(arr[1])\n",
    "                ratingList.append([item])\n",
    "                line = f.readline()\n",
    "    return ratingList"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# train_rating = load_rating_file_as_list(training_rating_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_rating_df = pd.read_csv(training_rating_name, sep='\\t', names=['user','item','rating','other'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>user</th>\n",
       "      <th>item</th>\n",
       "      <th>rating</th>\n",
       "      <th>other</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.445622e+06</td>\n",
       "      <td>1.445622e+06</td>\n",
       "      <td>1445622.0</td>\n",
       "      <td>1445622.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.754274e+04</td>\n",
       "      <td>3.780221e+03</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.588175e+04</td>\n",
       "      <td>2.474970e+03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.386800e+04</td>\n",
       "      <td>1.594000e+03</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.752000e+04</td>\n",
       "      <td>3.548000e+03</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.121000e+04</td>\n",
       "      <td>5.783000e+03</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.518600e+04</td>\n",
       "      <td>9.915000e+03</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               user          item     rating      other\n",
       "count  1.445622e+06  1.445622e+06  1445622.0  1445622.0\n",
       "mean   2.754274e+04  3.780221e+03        1.0        0.0\n",
       "std    1.588175e+04  2.474970e+03        0.0        0.0\n",
       "min    0.000000e+00  0.000000e+00        1.0        0.0\n",
       "25%    1.386800e+04  1.594000e+03        1.0        0.0\n",
       "50%    2.752000e+04  3.548000e+03        1.0        0.0\n",
       "75%    4.121000e+04  5.783000e+03        1.0        0.0\n",
       "max    5.518600e+04  9.915000e+03        1.0        0.0"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_rating_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_rating = train_rating_df.groupby(['user'])['item'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user\n",
       "0    24\n",
       "1    19\n",
       "2    21\n",
       "3    27\n",
       "4    22\n",
       "Name: item, dtype: int64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_rating.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "55187"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_rating.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    55187.000000\n",
       "mean        26.194973\n",
       "std          7.527052\n",
       "min         14.000000\n",
       "25%         21.000000\n",
       "50%         24.000000\n",
       "75%         29.000000\n",
       "max        136.000000\n",
       "Name: item, dtype: float64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_rating.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    9911.000000\n",
       "mean      145.860357\n",
       "std       134.639853\n",
       "min         1.000000\n",
       "25%        54.000000\n",
       "50%       115.000000\n",
       "75%       194.000000\n",
       "max      1608.000000\n",
       "Name: user, dtype: float64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_rating_df.groupby(['item'])['user'].count().describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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