early_penup_experiment.ipynb

{
 "metadata": {
  "name": "early_penup_experiment"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Early penup experiment\n",
      "======================\n",
      "During writing, when the likelihood of the target label is higher than some threshold, the user is notified to stop writng and lift the pen. In this experiment, we compare the performance of early-penup enabled system with the regular system."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from uright.inkutils import (json2array,normalize_ink,filter_bad_ink)\n",
      "import pickle\n",
      "import random\n",
      "\n",
      "filename = \"data/candidate_prototypes_1368819209.p\"\n",
      "candidate_proto = pickle.load(open(filename,\"rb\"))\n",
      "\n",
      "num_test_examples = 30\n",
      "\n",
      "clustered_data = {}\n",
      "label_ink_pairs = []\n",
      "for label in candidate_proto.keys():\n",
      "    clustered_data[label] = []\n",
      "    label_data = []\n",
      "    for _,examples in candidate_proto[label]:\n",
      "        # process all ink associated with the cluster\n",
      "        data = [np.nan_to_num(normalize_ink(json2array(ink)))\n",
      "                for ink in filter_bad_ink(examples)]\n",
      "        weights = [1] * len(data)\n",
      "        clustered_data[label].append(zip(data,weights))\n",
      "        label_data += data\n",
      "            \n",
      "    if len(label_data) > num_test_examples:\n",
      "        sample_data = random.sample(label_data, num_test_examples)\n",
      "    else:\n",
      "        sample_data = label_data\n",
      "    label_ink_pairs += [(label,ink) for ink in sample_data]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from uright.classifier import ClassifierDTW\n",
      "from uright._classifier_beam_early import ClassifierBeamEarly\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "beam = ClassifierBeamEarly()\n",
      "beam.train(clustered_data)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "beam.beam.alpha = 0.5\n",
      "beam.beam.penup_penalty = 10.0\n",
      "beam.beam.beam_width = 1000\n",
      "beam.beam.skips = 3"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "no_early_stop = 0.0\n",
      "all_points = 0\n",
      "for (label, ink) in label_ink_pairs:\n",
      "    (stop_length, prob) = beam.early_stop(ink, label, threshold=-1)\n",
      "    no_early_stop += max(log2(26)+log2(max(prob,1e-6)),0)\n",
      "    all_points += ink.shape[0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "early_stop = 0.0\n",
      "saving = 0\n",
      "for (label, ink) in label_ink_pairs:\n",
      "    (stop_length, prob) = beam.early_stop(ink, label, threshold=1)\n",
      "    early_stop += max(log2(26)+log2(max(prob,1e-6)),0)\n",
      "    saving += (ink.shape[0]-1-stop_length)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The total time of writing (excluding reaction time) is estimated by assuming the sample points were sampled at 60 hz."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "writing_time_no_early_stopping = all_points / 60\n",
      "writing_time_no_early_stopping"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "283"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The total time that could have been saved by stopping early is"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "time_saved_by_early_stopping = saving / 60\n",
      "time_saved_by_early_stopping"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "128"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Using the total log-loss based scores, the BPS of the system with early stopping is"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "bps_with_early_stopping = early_stop / (writing_time_no_early_stopping - time_saved_by_early_stopping)\n",
      "bps_with_early_stopping"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "22.752651626180157"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The BPS of the system without early stopping is"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "bps_no_early_stopping = no_early_stop / writing_time_no_early_stopping\n",
      "bps_no_early_stopping"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "12.312980319233294"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Here are some examples with the early penup positions."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import random\n",
      "from uright.visualization import draw_ink\n",
      "    \n",
      "random.shuffle(label_ink_pairs)\n",
      "\n",
      "for (label,ink) in label_ink_pairs[0:20]:\n",
      "    (stop_length, prob) = beam.early_stop(ink, label, threshold=-1)\n",
      "    figure()\n",
      "    ax = subplot(121)\n",
      "    draw_ink(ax,ink)\n",
      "    ax.set_xlim((-3,3))\n",
      "    ax.set_ylim((2.5,-2.5))\n",
      "    ax.axis('off')\n",
      "    ax.set_title(\"score = %0.2f\"%(max(log2(26)+log2(max(prob,1e-6)),0.0)))\n",
      "    (stop_length, prob) = beam.early_stop(ink, label, threshold=1)\n",
      "    ax = subplot(122)\n",
      "    draw_ink(ax,ink[:stop_length,:])\n",
      "    ax.set_xlim((-3,3))\n",
      "    ax.set_ylim((2.5,-2.5))\n",
      "    ax.set_title(\"score = %0.2f\"%(max(log2(26)+log2(max(prob,1e-6)),0.0)))\n",
      "    ax.axis('off')\n",
      "    "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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      },
      {
       "output_type": "display_data",
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      },
      {
       "output_type": "display_data",
       "png": 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      },
      {
       "output_type": "display_data",
       "png": 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AbW2+j/PViESM33/+/aQkiD0nfdLS7BZ4KGQHfHz80NfJWVEV2mAH97e/be+O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\n"
      },
      {
       "output_type": "display_data",
       "png": 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      },
      {
       "output_type": "display_data",
       "png": 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MYsGCeTo5PsAU2gPk2LFj/Mu//JFIZDH5+Z/H4XATDPayYcNOtm//Ld///t+R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\nC/n/tev8JvKRG9EAAAAASUVORK5CYII=\n"
      },
      {
       "output_type": "display_data",
       "png": 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jDE4AEkGjHtpNTU08/PALFBR8jaNHf0t/fzuzZ/9lOLy93m66up7g3/7t68TE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\nrkJggg==\n"
      },
      {
       "output_type": "display_data",
       "png": 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Cwo089NAL6N9kkYlFoW1TJ06coK0tjczM8ti6urrXCYcHAcjKmkFDg5vTp0/H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\nIiI2otAWEbERhbaIiI0otEVEbOT/A2Ifev9ZmhQyAAAAAElFTkSuQmCC\n"
      },
      {
       "output_type": "display_data",
       "png": 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      },
      {
       "output_type": "display_data",
       "png": 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      },
      {
       "output_type": "display_data",
       "png": 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      },
      {
       "output_type": "display_data",
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      },
      {
       "output_type": "display_data",
       "png": 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3nXFdbrcbgyEwumzfvlf58MOH2LPnJcLDY8nOno7JZMVgiKCvzwXAyJHF/OpX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\ncvJ9PPvsWpqams68IqFb9fX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      },
      {
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K1q8/SE9PEV6vn87OXXR1PY9lZaHrW9G0P6CUH5hMIjGIYWQB41BqPgcOvEFz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\nQYgxStPgppvgqafg8GEDpTQ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      },
      {
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BoBGzOQHwU1ycyTXXzCM3NzfKlf/jnDjpogs774/waXY1M/sbs2nZ3cLWtVsxKsbIcwGs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\nXZKeng5UEQqF3/o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      },
      {
       "output_type": "display_data",
       "png": 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      },
      {
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dQknJXeH2daUUur6TceNG9Pl7G4U0j3wF0dHR/PCHd/D66++xdu3TKJWOUn6i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\n"
      },
      {
       "output_type": "display_data",
       "png": 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      },
      {
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      },
      {
       "output_type": "display_data",
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GWde53BUVZTA4qL3HQMCL3+8hEPCEp2Z1OFooKtLv+xPRKTMzk1CoDaW0y4l1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\nCyGEjvx/Ak8tBnoQ/JcAAAAASUVORK5CYII=\n"
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Data from MySQL as of 7/23\n",
      "\n",
      "<pre>\n",
      "AVG(total_time) = 1.2905858843343052\n",
      "AVG(writing_time) = 0.5811924018138499\n",
      "</pre>"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}