Data_Preprocessing.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define constants\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "BASE_DIR = 'data' \n",
    "MOVIELENS_DIR = BASE_DIR + '/ml-1m/'\n",
    "USER_DATA_FILE = 'users.dat'\n",
    "MOVIE_DATA_FILE = 'movies.dat'\n",
    "RATING_DATA_FILE = 'ratings.dat'\n",
    "\n",
    "#from http://files.grouplens.org/datasets/movielens/ml-1m-README.txt\n",
    "\n",
    "AGES = { 1: \"Under 18\", 18: \"18-24\", 25: \"25-34\", 35: \"35-44\", 45: \"45-49\", 50: \"50-55\", 56: \"56+\" }\n",
    "OCCUPATIONS = { 0: \"other or not specified\", 1: \"academic/educator\", 2: \"artist\", 3: \"clerical/admin\",\n",
    "                4: \"college/grad student\", 5: \"customer service\", 6: \"doctor/health care\",\n",
    "                7: \"executive/managerial\", 8: \"farmer\", 9: \"homemaker\", 10: \"K-12 student\", 11: \"lawyer\",\n",
    "                12: \"programmer\", 13: \"retired\", 14: \"sales/marketing\", 15: \"scientist\", 16: \"self-employed\",\n",
    "                17: \"technician/engineer\", 18: \"tradesman/craftsman\", 19: \"unemployed\", 20: \"writer\" }\n",
    "RATINGS_CSV_FILE = 'ml1m_ratings.csv'\n",
    "USERS_CSV_FILE = 'ml1m_users.csv'\n",
    "MOVIES_CSV_FILE = 'ml1m_movies.csv'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Download MovieLens 1M data\n",
    "\n",
    "The MovieLens 1M Dataset can be downloaded from http://files.grouplens.org/datasets/movielens/ml-1m.zip."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1000209 ratings loaded\n",
      "Saved to ml1m_ratings.csv\n"
     ]
    }
   ],
   "source": [
    "ratings = pd.read_csv(os.path.join(MOVIELENS_DIR, RATING_DATA_FILE), \n",
    "                    sep='::', \n",
    "                    engine='python', \n",
    "                    encoding='latin-1',\n",
    "                    names=['userid', 'movieid', 'rating', 'timestamp'])\n",
    "\n",
    "max_userid = ratings['userid'].drop_duplicates().max()\n",
    "max_movieid = ratings['movieid'].drop_duplicates().max()\n",
    "ratings['user_emb_id'] = ratings['userid'] - 1\n",
    "ratings['movie_emb_id'] = ratings['movieid'] - 1\n",
    "print(len(ratings), 'ratings loaded')\n",
    "ratings.to_csv(RATINGS_CSV_FILE, \n",
    "               sep='\\t', \n",
    "               header=True, \n",
    "               encoding='latin-1', \n",
    "               columns=['user_emb_id', 'movie_emb_id', 'rating', 'timestamp'])\n",
    "print('Saved to', RATINGS_CSV_FILE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6040 descriptions of 6040 users loaded.\n",
      "Saved to ml1m_users.csv\n"
     ]
    }
   ],
   "source": [
    "users = pd.read_csv(os.path.join(MOVIELENS_DIR, USER_DATA_FILE), \n",
    "                    sep='::', \n",
    "                    engine='python', \n",
    "                    encoding='latin-1',\n",
    "                    names=['userid', 'gender', 'age', 'occupation', 'zipcode'])\n",
    "users['age_desc'] = users['age'].apply(lambda x: AGES[x])\n",
    "users['occ_desc'] = users['occupation'].apply(lambda x: OCCUPATIONS[x])\n",
    "print(len(users), 'descriptions of', max_userid, 'users loaded.')\n",
    "users['user_emb_id'] = users['userid'] - 1\n",
    "users.to_csv(USERS_CSV_FILE, \n",
    "             sep='\\t', \n",
    "             header=True, \n",
    "             encoding='latin-1',\n",
    "             columns=['user_emb_id', 'gender', 'age', 'occupation', 'zipcode', 'age_desc', 'occ_desc'])\n",
    "print('Saved to', USERS_CSV_FILE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3883 descriptions of 3952 movies loaded.\n",
      "Saved to ml1m_movies.csv\n"
     ]
    }
   ],
   "source": [
    "movies = pd.read_csv(os.path.join(MOVIELENS_DIR, MOVIE_DATA_FILE), \n",
    "                    sep='::', \n",
    "                    engine='python', \n",
    "                    encoding='latin-1',\n",
    "                    names=['movieid', 'title', 'genre'])\n",
    "print(len(movies), 'descriptions of', max_movieid, 'movies loaded.')\n",
    "movies['movie_emb_id'] = movies['movieid'] - 1\n",
    "movies.to_csv(MOVIES_CSV_FILE, \n",
    "              sep='\\t', \n",
    "              header=True, \n",
    "              columns=['movie_emb_id', 'title', 'genre'])\n",
    "print('Saved to', MOVIES_CSV_FILE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6040 of the 6040 users rate at least one movie.\n"
     ]
    }
   ],
   "source": [
    "print(len(ratings['userid'].drop_duplicates()), 'of the', max_userid, 'users rate at least one movie.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3706 of the 3952 movies are rated.\n"
     ]
    }
   ],
   "source": [
    "print(len(ratings['movieid'].drop_duplicates()), 'of the', max_movieid, 'movies are rated.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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