analyze-output.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analyze Results from Evaluation Quickstart\n",
    "\n",
    "This notebook analyzes the results of the evaluation quickstart."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup\n",
    "\n",
    "First, we need to import our libraries:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Import and Preparation\n",
    "\n",
    "LensKit puts its output in a `csv` file:"
   ]
  },
  {
   "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>Partition</th>\n",
       "      <th>Algorithm</th>\n",
       "      <th>BuildTime</th>\n",
       "      <th>TestTime</th>\n",
       "      <th>RMSE.ByUser</th>\n",
       "      <th>RMSE.ByRating</th>\n",
       "      <th>Predict.nDCG</th>\n",
       "      <th>MRR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>Custom</td>\n",
       "      <td>0.618</td>\n",
       "      <td>2.121</td>\n",
       "      <td>0.921526</td>\n",
       "      <td>0.940988</td>\n",
       "      <td>0.949947</td>\n",
       "      <td>0.028879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>PersMean</td>\n",
       "      <td>0.640</td>\n",
       "      <td>2.794</td>\n",
       "      <td>0.921526</td>\n",
       "      <td>0.940988</td>\n",
       "      <td>0.949947</td>\n",
       "      <td>0.029351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>Custom</td>\n",
       "      <td>0.051</td>\n",
       "      <td>0.763</td>\n",
       "      <td>0.934511</td>\n",
       "      <td>0.974935</td>\n",
       "      <td>0.947616</td>\n",
       "      <td>0.030180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>PersMean</td>\n",
       "      <td>0.041</td>\n",
       "      <td>1.357</td>\n",
       "      <td>0.934511</td>\n",
       "      <td>0.974935</td>\n",
       "      <td>0.947616</td>\n",
       "      <td>0.029949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>PersMean</td>\n",
       "      <td>0.049</td>\n",
       "      <td>1.422</td>\n",
       "      <td>0.904862</td>\n",
       "      <td>0.924322</td>\n",
       "      <td>0.951801</td>\n",
       "      <td>0.032978</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Partition Algorithm  BuildTime  TestTime  RMSE.ByUser  RMSE.ByRating  \\\n",
       "0          5    Custom      0.618     2.121     0.921526       0.940988   \n",
       "1          5  PersMean      0.640     2.794     0.921526       0.940988   \n",
       "2          4    Custom      0.051     0.763     0.934511       0.974935   \n",
       "3          4  PersMean      0.041     1.357     0.934511       0.974935   \n",
       "4          3  PersMean      0.049     1.422     0.904862       0.924322   \n",
       "\n",
       "   Predict.nDCG       MRR  \n",
       "0      0.949947  0.028879  \n",
       "1      0.949947  0.029351  \n",
       "2      0.947616  0.030180  \n",
       "3      0.947616  0.029949  \n",
       "4      0.951801  0.032978  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = pd.read_csv('build/eval-results.csv')\n",
    "results.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We ran each algorithm 5 times since we used 5-fold cross-validation.  What we want to do next is compute the average value of each metric for each data set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>BuildTime</th>\n",
       "      <th>TestTime</th>\n",
       "      <th>RMSE.ByUser</th>\n",
       "      <th>RMSE.ByRating</th>\n",
       "      <th>Predict.nDCG</th>\n",
       "      <th>MRR</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algorithm</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Custom</th>\n",
       "      <td>0.1518</td>\n",
       "      <td>1.0968</td>\n",
       "      <td>0.920736</td>\n",
       "      <td>0.941336</td>\n",
       "      <td>0.949690</td>\n",
       "      <td>0.030060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ItemItem</th>\n",
       "      <td>7.2840</td>\n",
       "      <td>16.4518</td>\n",
       "      <td>0.884148</td>\n",
       "      <td>0.898177</td>\n",
       "      <td>0.955747</td>\n",
       "      <td>0.107109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PersMean</th>\n",
       "      <td>0.1772</td>\n",
       "      <td>1.6278</td>\n",
       "      <td>0.920736</td>\n",
       "      <td>0.941336</td>\n",
       "      <td>0.949690</td>\n",
       "      <td>0.030271</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           BuildTime  TestTime  RMSE.ByUser  RMSE.ByRating  Predict.nDCG  \\\n",
       "Algorithm                                                                  \n",
       "Custom        0.1518    1.0968     0.920736       0.941336      0.949690   \n",
       "ItemItem      7.2840   16.4518     0.884148       0.898177      0.955747   \n",
       "PersMean      0.1772    1.6278     0.920736       0.941336      0.949690   \n",
       "\n",
       "                MRR  \n",
       "Algorithm            \n",
       "Custom     0.030060  \n",
       "ItemItem   0.107109  \n",
       "PersMean   0.030271  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agg_results = results.drop(['Partition'], axis=1).groupby('Algorithm').mean()\n",
    "agg_results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting Results\n",
    "\n",
    "Let's start plotting things. What's the RMSE achieved by each algorithm?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x169b518aa58>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEaCAYAAAAcz1CnAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x169b518a4a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results.loc[:,['Algorithm', 'RMSE.ByUser']].boxplot(by='Algorithm')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next up: nDCG"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x169b57477b8>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYgAAAEaCAYAAAAL7cBuAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x169b5756b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results.loc[:,['Algorithm', 'Predict.nDCG']].boxplot(by='Algorithm')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, the build and test times."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x00000169B5759518>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x00000169B5CF7B70>], dtype=object)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXgAAAEaCAYAAAAboUz3AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n3swsU/8fvEGOHxTu2gYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x169b5c24940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results.loc[:,['Algorithm', 'BuildTime', 'TestTime']].boxplot(by='Algorithm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [default]",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}