Benchmark between numpys bincount and searchsorted functions. ipython notebook.

BincountAndSearchsorted

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   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "import timeit\n",
      "data = np.random.random(500e3)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Digitize performs better on low bin count. Exremest possible example: just 2 bins."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "bins = np.linspace(0,1,2)\n",
      "%timeit np.digitize(data, bins)\n",
      "%timeit np.searchsorted(bins, data, side='right')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "100 loops, best of 3: 2.43 ms per loop\n",
        "100 loops, best of 3: 6.01 ms per loop"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 51
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "With 72 bins, both algorithm have equal runtime. This is independent of number of samples."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "bins = np.linspace(0,1,72)\n",
      "%timeit np.digitize(data, bins)\n",
      "%timeit np.searchsorted(bins, data, side='right')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "10 loops, best of 3: 29.9 ms per loop\n",
        "10 loops, best of 3: 30.5 ms per loop"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 52
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "On higher bin count, searchsorted is much faster"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print 200\n",
      "bins = np.linspace(0,1,200)\n",
      "%timeit np.digitize(data, bins)\n",
      "%timeit np.searchsorted(bins, data, side='right')\n",
      "print 400\n",
      "bins = np.linspace(0,1,400)\n",
      "%timeit np.digitize(data, bins)\n",
      "%timeit np.searchsorted(bins, data, side='right')\n",
      "print 1000\n",
      "bins = np.linspace(0,1,1000)\n",
      "%timeit np.digitize(data, bins)\n",
      "%timeit np.searchsorted(bins, data, side='right')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "200\n",
        "10 loops, best of 3: 71.3 ms per loop"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "10 loops, best of 3: 38.7 ms per loop"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "400\n",
        "10 loops, best of 3: 136 ms per loop"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "10 loops, best of 3: 43.7 ms per loop"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "1000\n",
        "1 loops, best of 3: 331 ms per loop"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "10 loops, best of 3: 49.7 ms per loop"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 54
    },
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     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
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