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  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "[Sebastian Raschka](http://sebastianraschka.com)  \n",
      "last updated: 05/09/2014\n",
      "\n",
      "- [Open in IPython Notebook viewer](http://nbviewer.ipython.org/github/rasbt/One-Python-benchmark-per-day/blob/master/ipython_nbs/day3_dictionary_counting.ipynb?create=1)\n",
      "- [Link to this IPython notebook on Github](https://github.com/rasbt/One-Python-benchmark-per-day/blob/master/ipython_nbs/day3_dictionary_counting.ipynb)  \n",
      "- [Link to the GitHub Repository One-Python-benchmark-per-day](https://github.com/rasbt/One-Python-benchmark-per-day)\n",
      "\n",
      "<br>\n",
      "<br>\n",
      "I would be happy to hear your comments and suggestions.  \n",
      "Please feel free to drop me a note via\n",
      "[twitter](https://twitter.com/rasbt), [email](mailto:bluewoodtree@gmail.com), or [google+](https://plus.google.com/118404394130788869227).\n",
      "<hr>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Day 3 - One Python Benchmark per Day"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### 6 different ways for counting elements using a dictionary\n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<hr>\n",
      "**-> [skip to the results](#Results)**\n",
      "<hr>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br>\n",
      "<br>"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Dictionary counting functions"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from collections import defaultdict\n",
      "from collections import Counter\n",
      "\n",
      "def add_element_check1(elements):\n",
      "    \"\"\"if ele not in dict (v1)\"\"\"\n",
      "    d = dict()\n",
      "    for e in elements:\n",
      "        if e not in d:\n",
      "            d[e] = 1\n",
      "        else:\n",
      "            d[e] += 1\n",
      "    return d\n",
      "            \n",
      "    \n",
      "def add_element_check2(elements):\n",
      "    \"\"\"if ele not in dict (v2)\"\"\"\n",
      "    d = dict()\n",
      "    for e in elements:\n",
      "        if e not in d:\n",
      "            d[e] = 0\n",
      "        d[e] += 1            \n",
      "    return d\n",
      "        \n",
      "    \n",
      "def add_element_except(elements):\n",
      "    \"\"\"try-except\"\"\"\n",
      "    d = dict()\n",
      "    for e in elements:\n",
      "        try:\n",
      "            d[e] += 1\n",
      "        except KeyError:\n",
      "            d[e] = 1\n",
      "    return d\n",
      "            \n",
      "    \n",
      "def add_element_defaultdict(elements):\n",
      "    \"\"\"defaultdict\"\"\"\n",
      "    d = defaultdict(int)\n",
      "    for e in elements:\n",
      "        d[e] += 1\n",
      "    return d\n",
      "\n",
      "\n",
      "def add_element_get(elements):\n",
      "    \"\"\".get() method\"\"\"\n",
      "    d = dict()\n",
      "    for e in elements:\n",
      "        d[e] = d.get(e, 1) + 1\n",
      "    return d\n",
      "\n",
      "\n",
      "def counter_object(elements):\n",
      "    \"\"\"Counter object\"\"\"\n",
      "    return Counter(elements)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br>\n",
      "<br>"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Dataset 1 - \"I have a dream\""
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br>\n",
      "The first data set will be Martin Luther King's popular \"I have a dream\" speech for which we want to count the occurences of the individual words. The data file can be downloaded from [here](https://raw.githubusercontent.com/rasbt/One-Python-benchmark-per-day/master/data/day3_dictionary_counting/i_have_a_dream_speech.txt).\n",
      "<br>"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def read_speech(datafile):\n",
      "    \"\"\" \n",
      "    Reads in a text file and returns individual words\n",
      "    in lowercase and stripped from digits and punctuation\n",
      "    \n",
      "    \"\"\"\n",
      "    def strip_word(word):\n",
      "        out = []\n",
      "        for char in word:\n",
      "            if char.isalpha():\n",
      "                out.append(char.lower())\n",
      "        return \"\".join(out)\n",
      "    \n",
      "    with open(datafile,'r') as infile:\n",
      "        for line in infile:\n",
      "            line = line.strip()\n",
      "            if line:\n",
      "                for word in line.split():\n",
      "                    yield strip_word(word)\n",
      "            \n",
      "speech_file = '../data/day3_dictionary_counting/i_have_a_dream_speech.txt'\n",
      "speech = list(read_speech(speech_file))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br>\n",
      "<br>"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Dataset 2 - DNA string"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Secondly, we will cound the individual nucleotides (\"A\", \"G\", \"C\", or \"T\") in a randomly generated DNA string. The file can be found [here](https://raw.githubusercontent.com/rasbt/One-Python-benchmark-per-day/master/data/day3_dictionary_counting/random_dna.txt)."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def read_dna(datafile):\n",
      "    with open(datafile,'r') as infile:\n",
      "        return list(infile.read())\n",
      "\n",
      "dna_file = '../data/day3_dictionary_counting/random_dna.txt'\n",
      "dna = read_dna(dna_file)  "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br>\n",
      "<br>"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Timing"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import timeit\n",
      "from functools import reduce\n",
      "\n",
      "\n",
      "funcs = ['add_element_check1', 'add_element_check2',\n",
      "         'add_element_except', 'add_element_defaultdict',\n",
      "         'add_element_get', 'counter_object']\n",
      "\n",
      "times_n = {f:[] for f in funcs}\n",
      "\n",
      "for d in [speech, dna]:\n",
      "    for f in funcs:\n",
      "        times_n[f].append(min(timeit.Timer('%s(d)' %f, \n",
      "                      'from __main__ import %s, d' %f)\n",
      "                              .repeat(repeat=3, number=1000)))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br>\n",
      "<br>"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Setting up the plots"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import platform\n",
      "import multiprocessing\n",
      "\n",
      "def print_sysinfo():\n",
      "    \n",
      "    print('\\nPython version:', platform.python_version())\n",
      "    print('compiler:', platform.python_compiler())\n",
      "    \n",
      "    print('\\nsystem     :', platform.system())\n",
      "    print('release    :', platform.release())\n",
      "    print('machine    :', platform.machine())\n",
      "    print('processor  :', platform.processor())\n",
      "    print('interpreter:', platform.architecture()[0])\n",
      "    print('CPU count  :', multiprocessing.cpu_count())\n",
      "    print('\\n\\n')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "labels = [('add_element_check1', 'if-else statements (v1)'), \n",
      "          ('add_element_check2', 'if-else statements (v2)'),\n",
      "          ('add_element_except', 'try-except blocks'),\n",
      "          ('add_element_defaultdict', 'using collections.defaultdict'),\n",
      "          ('add_element_get', 'using the .get() method'),\n",
      "          ('counter_object', 'using collections.Counter'),\n",
      "          ] "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from numpy import arange\n",
      "import matplotlib.pyplot as plt\n",
      "\n",
      "def plot_timings():\n",
      "\n",
      "    plt.rcParams.update({'font.size': 12})\n",
      "\n",
      "    ind = arange(2)  # the x locations for the groups\n",
      "    width = 0.15\n",
      "\n",
      "    fig = plt.figure(figsize=(10,8))\n",
      "    ax = fig.add_subplot(111)\n",
      "    colors = [(0,'b'), (1,'c'), (2,'g'), (3,'r'), (4,'y'), (5, 'm')]\n",
      "\n",
      "    for l,c in zip(labels,colors):\n",
      "        ax.bar(ind + c[0]*width,\n",
      "            times_n[l[0]], \n",
      "            width,\n",
      "            alpha=0.5,\n",
      "            color=c[1],\n",
      "            label=l[1])\n",
      "\n",
      "    ax.set_ylabel('time in milliseconds')\n",
      "    ax.set_title('Methods for counting elements in a dataset using a dictionary')\n",
      "    ax.set_xticks(ind + width)\n",
      "    ax.set_xticklabels(['Luther speech', 'DNA string'])\n",
      "    ax.set_xlim(-0.1,2)\n",
      "    ax.set_ylim(0,1.5)\n",
      "    plt.legend(loc='upper left')\n",
      "    plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 28
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import prettytable\n",
      "\n",
      "def count_unique(input_data):\n",
      "    unique = 0\n",
      "    count_dict = add_element_check1(input_data)\n",
      "    for count in count_dict.values():\n",
      "        if count == 1:\n",
      "            unique += 1\n",
      "    different_ele = len(count_dict)\n",
      "    unique_ele = round(unique / different_ele * 100, 2)\n",
      "    return (different_ele, unique_ele)\n",
      "\n",
      "def summary_table():\n",
      "\n",
      "    speech_different, speech_unique = count_unique(speech)\n",
      "    dna_different, dna_unique = count_unique(dna)\n",
      "\n",
      "    fit_table = prettytable.PrettyTable([\"\", \"total elements\" ,\n",
      "                                     \"different elements\", \n",
      "                                     \"unique elements (%)\"])\n",
      "    fit_table.add_row([\"DNA string\", len(dna), dna_different, dna_unique])\n",
      "    fit_table.add_row([\"Luther speech\", len(speech), speech_different, speech_unique])\n",
      "\n",
      "    print(fit_table)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 29
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Results"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print_sysinfo()\n",
      "summary_table()\n",
      "plot_timings()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Python version: 3.4.1\n",
        "compiler: GCC 4.2.1 (Apple Inc. build 5577)\n",
        "\n",
        "system     : Darwin\n",
        "release    : 13.1.0\n",
        "machine    : x86_64\n",
        "processor  : i386\n",
        "interpreter: 64bit\n",
        "CPU count  : 2\n",
        "\n",
        "\n",
        "\n",
        "+---------------+----------------+--------------------+---------------------+\n",
        "|               | total elements | different elements | unique elements (%) |\n",
        "+---------------+----------------+--------------------+---------------------+\n",
        "|   DNA string  |      4000      |         4          |         0.0         |\n",
        "| Luther speech |      1667      |        536         |        65.67        |\n",
        "+---------------+----------------+--------------------+---------------------+\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Z0QtoAoTnMp0QQgghRIlXFE9T5lQzpkHViI3GuMO+2XlCQkL0//fx8ZHH8oUQ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\nxpXmIUL4Xaxfv54tW7bs8Lto27btTj2/JCkwjO2GTjnlFGb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       "text": [
        "<matplotlib.figure.Figure at 0x10537e668>"
       ]
      }
     ],
     "prompt_number": 30
    },
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      "<br>\n",
      "<br>"
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     "source": [
      "## Conclusion\n",
      "The bar plot indicates that the performance of the different approaches really depends on how many duplicates we have in our data set. But this can be easily explained:\n",
      "\n",
      "### Scenario 1: No or very low number of duplicates\n",
      "Here, expect that the try-except loop would not perform very well, since an error is raised (and eventually the except-block executed) if we encounter an element that is not already in the dictionary. \n",
      "\n",
      "### Scenario 2: High number of duplicates\n",
      "The try-except method performs much better if we have a high number of duplicates, because the sumation in the try-block succeeds if we are encountering elements that already exist as keys in the dictionary - the except-block can be skipped in this case.  \n",
      "\n",
      "Anyway, for a real application, I would use the `Counter` function from the `collection` module, it is the fastest approach in any case."
     ]
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