MD_Feature_extraction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import matplotlib\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from __future__ import division"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "path_to_train = './train.csv'\n",
    "tr = pd.read_csv(path_to_train, encoding='utf-8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>genre_id</th>\n",
       "      <th>ts_listen</th>\n",
       "      <th>media_id</th>\n",
       "      <th>album_id</th>\n",
       "      <th>context_type</th>\n",
       "      <th>release_date</th>\n",
       "      <th>platform_name</th>\n",
       "      <th>platform_family</th>\n",
       "      <th>media_duration</th>\n",
       "      <th>listen_type</th>\n",
       "      <th>user_gender</th>\n",
       "      <th>user_id</th>\n",
       "      <th>artist_id</th>\n",
       "      <th>user_age</th>\n",
       "      <th>is_listened</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>25471</td>\n",
       "      <td>1480597215</td>\n",
       "      <td>222606</td>\n",
       "      <td>41774</td>\n",
       "      <td>12</td>\n",
       "      <td>20040704</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>223</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9241</td>\n",
       "      <td>55164</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>25571</td>\n",
       "      <td>1480544735</td>\n",
       "      <td>250467</td>\n",
       "      <td>43941</td>\n",
       "      <td>0</td>\n",
       "      <td>20060301</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>171</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16547</td>\n",
       "      <td>55830</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>16</td>\n",
       "      <td>1479563953</td>\n",
       "      <td>305197</td>\n",
       "      <td>48078</td>\n",
       "      <td>1</td>\n",
       "      <td>20140714</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>149</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7665</td>\n",
       "      <td>2704</td>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7</td>\n",
       "      <td>1480152098</td>\n",
       "      <td>900502</td>\n",
       "      <td>71521</td>\n",
       "      <td>0</td>\n",
       "      <td>20001030</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>240</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1580</td>\n",
       "      <td>938</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7</td>\n",
       "      <td>1478368974</td>\n",
       "      <td>542335</td>\n",
       "      <td>71718</td>\n",
       "      <td>0</td>\n",
       "      <td>20080215</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1812</td>\n",
       "      <td>2939</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   genre_id   ts_listen  media_id  album_id  context_type  release_date  \\\n",
       "0     25471  1480597215    222606     41774            12      20040704   \n",
       "1     25571  1480544735    250467     43941             0      20060301   \n",
       "2        16  1479563953    305197     48078             1      20140714   \n",
       "3         7  1480152098    900502     71521             0      20001030   \n",
       "4         7  1478368974    542335     71718             0      20080215   \n",
       "\n",
       "   platform_name  platform_family  media_duration  listen_type  user_gender  \\\n",
       "0              1                0             223            0            0   \n",
       "1              2                1             171            0            0   \n",
       "2              2                1             149            1            1   \n",
       "3              0                0             240            0            1   \n",
       "4              0                0             150            0            1   \n",
       "\n",
       "   user_id  artist_id  user_age  is_listened  \n",
       "0     9241      55164        29            0  \n",
       "1    16547      55830        30            1  \n",
       "2     7665       2704        29            1  \n",
       "3     1580        938        30            0  \n",
       "4     1812       2939        24            1  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tr.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "us_gr = tr.groupby(['user_id'])[\"genre_id\"].apply(lambda x: len(x.unique()))\n",
    "#FOR EACH USER"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19918"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(us_gr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "281\n"
     ]
    }
   ],
   "source": [
    "print min(us_gr)   # Мінімальне і максимальне число прослуханих жанрів\n",
    "print max(us_gr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1569\n",
      "7.87729691736 Percent of people who listened just to one genre\n"
     ]
    }
   ],
   "source": [
    "d = [us_gr[i] for i in range(len(us_gr)) if us_gr[i] == 1]\n",
    "print len(d)\n",
    "print len(d)/len(us_gr)*100, \"Percent of people who listened just to one genre\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "8% (1569) користувачів слухали лиш один жанр! можливо, є сенс, що вони прослухають пісню цього ж жанру, але це ще не факт"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.83341700974 Percent of people who listened to more than 100 genres\n"
     ]
    }
   ],
   "source": [
    "large_num_genres = [us_gr[i] for i in range(len(us_gr)) if us_gr[i] >100]\n",
    "print len(large_num_genres)/len(us_gr)*100, \"Percent of people who listened to more than 100 genres\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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xk6Q9gX71D83MzKqmyAMlTwfmA99PVcOAn9czKDMzq6YilxRPA44CNgJExAPAG+sZlJmZ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\nCSWC+bWszAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe6505e9390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plt.subplot(1, 1)\n",
    "#plt.plot(us_gr)\n",
    "us_gr.plot.hist(alpha=0.7, facecolor='green')\n",
    "\n",
    "plt.title('Genres')\n",
    "plt.xlabel('Number of listened genres')\n",
    "plt.ylabel('Number of users')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Більшість юзерів прослухали менше 25 жанрів, хоча є і якийсь дивний меломан, який прослухав 281 жанр. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "usr_alb = tr.groupby(['user_id'])[\"album_id\"].apply(lambda x: len(x.unique()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Maximum albums 2117\n",
      "Minimum albums 1\n"
     ]
    }
   ],
   "source": [
    "print 'Maximum albums', max(usr_alb)\n",
    "print 'Minimum albums', min(usr_alb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "176\n",
      "0.8836228537\n"
     ]
    }
   ],
   "source": [
    "large_num_alb = [usr_alb[i] for i in range(len(usr_alb)) if usr_alb[i] > 800]\n",
    "print len(large_num_alb)\n",
    "print len(large_num_alb)/len(usr_alb)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.22411888744\n"
     ]
    }
   ],
   "source": [
    "small_num_alb = [usr_alb[i] for i in range(len(usr_alb)) if usr_alb[i] == 1]\n",
    "print len(small_num_alb)/len(usr_alb)*100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2.2% слухали лише один альбом\n",
    "Чи варто дивитись, скільки пісень у тих альбомах? Мабуть, більша імовірність, що вони прослухають таки пісні з того алюбому або принаймні того співака."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "91.9419620444\n"
     ]
    }
   ],
   "source": [
    "num_alb = [usr_alb[i] for i in range(len(usr_alb)) if usr_alb[i] < 400]\n",
    "print len(num_alb)/len(usr_alb)*100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "92% юзерів прослухали менше ніж 400 альбомів \n",
    "Як враховувати тих, хто прослуховують набагато більше? Для них складніший collaborative filtering? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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rJa1Igxt+KLfeX0paBKztoD0XpvWvlnRVin2B7EG22ZKu7mwn8//Ll/QO7Xyn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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe650f48f50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "usr_alb.plot.hist(alpha=0.7)\n",
    "\n",
    "plt.title('Albums')\n",
    "plt.xlabel('Number of listened albums')\n",
    "plt.ylabel('Number of users')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1419 Maximum artists\n"
     ]
    }
   ],
   "source": [
    "usr_art = tr.groupby(['user_id'])[\"artist_id\"].apply(lambda x: len(x.unique()))\n",
    "print max(usr_art), \"Maximum artists\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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uc35Tiuhm/wT8UtIXNqPNy4G/pBpq/D7gfNsHqZoU7YPAh8t+46mGkd8HuE7S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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe65067c310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "usr_art.plot.hist(alpha=0.7, facecolor='red')\n",
    "\n",
    "plt.title('Artists')\n",
    "plt.xlabel('Number of artists')\n",
    "plt.ylabel('Number of users')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "96.4353850788\n"
     ]
    }
   ],
   "source": [
    "num_art = [usr_art[i] for i in range(len(usr_art)) if usr_art[i] < 400]\n",
    "print len(num_art)/len(usr_art)*100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "96% юзерів слухали менше ніж 400 співаків"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.70609498946\n"
     ]
    }
   ],
   "source": [
    "num_art = [usr_art[i] for i in range(len(usr_art)) if usr_art[i] == 1]\n",
    "print len(num_art)/len(usr_art)*100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2.7 % слухають лише одного! Чи це означає, що алгоритм їм порадить тільки цього співака? Яка ймовірність, що вони прослухають трек іншого виконавця? "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Flow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1278 Maximum artists\n"
     ]
    }
   ],
   "source": [
    "usr_art_flow = tr.groupby(['user_id', 'listen_type'])[\"artist_id\"].apply(lambda x: len(x.unique()))\n",
    "print max(usr_art_flow), \"Maximum artists\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id  listen_type\n",
       "0        0               987\n",
       "         1               511\n",
       "1        0              1029\n",
       "         1               696\n",
       "2        0               548\n",
       "Name: artist_id, dtype: int64"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "usr_art_flow.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 Min artists\n"
     ]
    }
   ],
   "source": [
    "print min(usr_art_flow), \"Min artists\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    ""
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2.0
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}