{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Short quickstart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from lightfm import LightFM\n",
    "from lightfm.datasets import fetch_movielens\n",
    "from lightfm.evaluation import precision_at_k\n",
    "\n",
    "# Load the MovieLens 100k dataset. Only five\n",
    "# star ratings are treated as positive.\n",
    "data = fetch_movielens(min_rating=5.0)\n",
    "\n",
    "# Instantiate and train the model\n",
    "model = LightFM(loss='warp')\n",
    "model.fit(data['train'], epochs=30, num_threads=2)\n",
    "\n",
    "# Evaluate the trained model\n",
    "test_precision = precision_at_k(model, data['test'], k=5).mean()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}