top250.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import json\n",
    "import numpy as np\n",
    "from imdb import IMDb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "movies = pd.read_csv(\"ml-latest-small/linked_movies.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "ratings = pd.read_csv(\"ml-latest-small/ratings.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "movie_genre = pd.DataFrame()\n",
    "movie_language = pd.DataFrame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "for i in range(0,len(movies)):\n",
    "    movie =  movies.loc[i]\n",
    "    movie_id = movie['movieId']\n",
    "    genres = movie['genres'].split('|')\n",
    "    df = pd.DataFrame({movie_id:np.ones(len(genres))}, index = genres)\n",
    "    movie_genre = pd.concat([movie_genre, df], axis=1, sort=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "ia = IMDb()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "top250 = ia.get_top250_movies()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "top250_movies = {}\n",
    "for movie in top250:\n",
    "    top250_movies[int(movie.getID())] = movie.data['rating']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "top250_movies = pd.DataFrame.from_dict(top250_movies, orient='index', columns=['imdb_rating'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "top250_movies_ratings = top250_movies['imdb_rating'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "links = pd.read_csv(\"ml-latest-small/links.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
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       "  </tbody>\n",
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       "<p>250 rows × 1 columns</p>\n",
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      ],
      "text/plain": [
       "         imdb_rating\n",
       "111161           9.2\n",
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       "110413           8.5\n",
       "120689           8.5\n",
       "...              ...\n",
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       "\n",
       "[250 rows x 1 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top250_movies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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     "text": [
      "Z:\\Nikhil\\IntrotoDS\\small\\Project\\top250_imdb.py:22: RuntimeWarning: invalid value encountered in true_divide\n",
      "  return (data-min(data))/(max(data) - min(data))\n"
     ]
    },
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     "name": "stdout",
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     ]
    }
   ],
   "source": [
    "from top250_imdb import Content\n",
    "model = Content(ratings, movie_genre, top250_movies, links, movies)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "recs = model.recommendations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('recommendations/top250_imdb/top250_imdb.json', 'w') as json_file:\n",
    "    json.dump(recs, json_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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