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": {
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yHB4eZvXq1QCMjIwwmlY6i7cC7iLrLH4IWADMjohluTRTgKciYq2kk4DDImJQ0guBtRHx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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": 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SriNrsroVmBERZ6Sw70q6fKAE9PT00NnZCUBHRwddXV10d3cDm9sIW325sq4o6fHyaJaX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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": 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Q/r0JuDki7k7r/QTYB1hXUlufDxwg6aj0fGdgP4qPgtd27ffStjaN9the6zzX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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": {
  "kernelspec": {
   "display_name": "Python 3",
   "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
}