Features Visualization-Final.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#ucitavanje paketa\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "from nltk.corpus import words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#citanje\n",
    "data1 = pd.read_csv(\"spam.csv\",encoding='latin-1')\n",
    "\n",
    "\n",
    "#spajanje zadnja cetri stupca kako bi ostavili poruke u potpunosti spojenima\n",
    "data1 = data1.rename(columns={\"Unnamed: 2\":\"two\", \"Unnamed: 3\":\"tri\",\"Unnamed: 4\":\"cetr\"})\n",
    "a =  data1[data1[\"two\"].notnull()][\"v2\"].map(str) + data1[data1[\"two\"].notnull()][\"two\"].map(str)\n",
    "data1.loc[data1[\"two\"].notnull(),\"v2\"] = a\n",
    "a =  data1[data1[\"tri\"].notnull()][\"v2\"].map(str) + data1[data1[\"tri\"].notnull()][\"tri\"].map(str)\n",
    "data1.loc[data1[\"tri\"].notnull(),\"v2\"] = a\n",
    "a =  data1[data1[\"cetr\"].notnull()][\"v2\"].map(str) + data1[data1[\"cetr\"].notnull()][\"cetr\"].map(str)\n",
    "data1.loc[data1[\"cetr\"].notnull(),\"v2\"] = a\n",
    "data1 = data1[[\"v1\",\"v2\"]]\n",
    "#data1.to_csv(\"proba.csv\")\n",
    "\n",
    "\n",
    "\n",
    "data1 = data1.rename(columns={\"v1\":\"category_class\", \"v2\":\"sms\"})\n",
    "codes = {'ham':0, 'spam':1}\n",
    "data1[\"class\"] = data1[\"category_class\"].map(codes)\n",
    "#data1.tail()\n",
    "\n",
    "#a.to_csv(\"proba.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       upper_letters  small_words  capital_words  punctuation      urls  \\\n",
      "class                                                                     \n",
      "0           4.173472     0.094153       0.042398     0.062885  0.003731   \n",
      "1          15.483266     0.061207       0.049184     0.041112  0.170013   \n",
      "\n",
      "           length  numeric_chars  non_alpha_num  englishPercentage  \n",
      "class                                                               \n",
      "0       71.627979       0.305285      17.500518           0.138482  \n",
      "1      139.148594      15.812584      29.132530           0.100496  \n"
     ]
    }
   ],
   "source": [
    "# **NOVO - dodani featuri u dataframe i imas statistike prosjecne za svaki\n",
    "from string import punctuation\n",
    "spunctuation = set(punctuation)\n",
    "swords = set(words.words())\n",
    "def englishWordsPercentage(message):\n",
    "    return sum(1 for c in message.split() if not c.isdigit() and c.lower() in swords)/len(message)\n",
    "def countUpperLetters(message):\n",
    "    return sum(1 for c in message if c.isupper())\n",
    "def countDigits(message):\n",
    "    return sum(c.isdigit() for c in message)\n",
    "def countNonAlphaNumerical(message):\n",
    "    return sum(not c.isalnum() for c in message)\n",
    "def smallWordsRatio(message):\n",
    "    return sum(1 for c in message.split() if len(c) <= 3)/len(message)\n",
    "def capitalWordsRatio(message):\n",
    "    return sum(1 for c in message.split() if c[0].isupper())/len(message)\n",
    "def punctuationRatio(message):\n",
    "    return sum(c in spunctuation for c in message)/len(message)\n",
    "def urls(message):\n",
    "    return sum('www' in c or '.net' in c or 'http' in c or '.com' in c for c in message.split())\n",
    "\n",
    "data1[\"upper_letters\"] = data1[\"sms\"].apply(countUpperLetters)\n",
    "data1[\"small_words\"] = data1[\"sms\"].apply(smallWordsRatio)\n",
    "data1[\"capital_words\"] = data1[\"sms\"].apply(capitalWordsRatio)\n",
    "data1[\"punctuation\"] = data1[\"sms\"].apply(punctuationRatio)\n",
    "data1[\"urls\"] = data1[\"sms\"].apply(urls)\n",
    "\n",
    "data1['length'] = data1['sms'].apply(len)\n",
    "data1[\"numeric_chars\"] = data1[\"sms\"].apply(countDigits)\n",
    "data1[\"non_alpha_num\"] = data1[\"sms\"].apply(countNonAlphaNumerical)\n",
    "data1[\"englishPercentage\"] = data1[\"sms\"].apply(englishWordsPercentage)\n",
    "print(data1.groupby(\"class\").mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def twoColloredPlot(data_ham, data_spam, title, mbins = 100, mrange = [0,250], mnormed= True):\n",
    "    plt.figure(figsize=(12,8))\n",
    "    plt.hist(data_ham, bins=mbins, range=mrange, normed=mnormed, label='ham')\n",
    "    plt.hist(data_spam, bins=mbins, range=mrange,normed=mnormed, alpha=0.5, label='spam')\n",
    "    plt.title(title, fontsize=15)\n",
    "    plt.legend()\n",
    "\n",
    "    plt.axvline(data_spam.mean(), color='k', linestyle='dashed', linewidth=1) \n",
    "    plt.axvline(data_ham.mean(), color='b', linestyle='dashed', linewidth=1) \n",
    "    \n",
    "    print('# Summary statistics')\n",
    "    print('mean-ham  {:.2f}   mean-spam {:.2f} \\nstd-ham   {:.2f}   std-spam   {:.2f} \\nmin-ham    {:.2f}   min-ham    {:.2f} \\nmax-ham  {:.2f}   max-spam  {:.2f}'.format(data_ham.mean(), \n",
    "                         data_spam.mean(), data_ham.std(), data_spam.std(), data_ham.min(), data_spam.min(), data_ham.max(), data_spam.max()))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# Summary statistics\n",
      "mean-ham  0.00   mean-spam 0.17 \n",
      "std-ham   0.07   std-spam   0.39 \n",
      "min-ham    0.00   min-ham    0.00 \n",
      "max-ham  2.00   max-spam  2.00\n"
     ]
    },
    {
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AHVMSAwAAAAB0TEkMAAAAANAxJTEAAAAAQMeU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\nWwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x21b5de1d908>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 483,
       "width": 708
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "twoColloredPlot(data1[data1['class'] == 0][\"urls\"],\n",
    "                data1[data1['class'] == 1][\"urls\"],\n",
    "                'Urls', 2, [0,2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# Summary statistics\n",
      "mean-ham  0.06   mean-spam 0.04 \n",
      "std-ham   0.06   std-spam   0.02 \n",
      "min-ham    0.00   min-ham    0.00 \n",
      "max-ham  0.86   max-spam  0.18\n"
     ]
    },
    {
     "data": {
      "image/png": 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dGzOXq+qIqrqoqm7qY91W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\nAAAAYJD/B2taZMkbLti4AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x21b5ddecef0>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 483,
       "width": 715
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "twoColloredPlot(data1[data1['class'] == 0][\"punctuation\"],\n",
    "                data1[data1['class'] == 1][\"punctuation\"],\n",
    "                'Punctuation words', 100, [0,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# Summary statistics\n",
      "mean-ham  0.04   mean-spam 0.05 \n",
      "std-ham   0.04   std-spam   0.02 \n",
      "min-ham    0.00   min-ham    0.00 \n",
      "max-ham  0.50   max-spam  0.17\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x21b5cd879b0>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 483,
       "width": 705
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "twoColloredPlot(data1[data1['class'] == 0][\"capital_words\"],\n",
    "                data1[data1['class'] == 1][\"capital_words\"],\n",
    "                'Capital words', 100, [0,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# Summary statistics\n",
      "mean-ham  0.09   mean-spam 0.06 \n",
      "std-ham   0.05   std-spam   0.02 \n",
      "min-ham    0.00   min-ham    0.00 \n",
      "max-ham  0.50   max-spam  0.14\n"
     ]
    },
    {
     "data": {
      "image/png": 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AAAAAVRTLAAAAAABUmWodAGBI1twy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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x21b5cabf470>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 483,
       "width": 715
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "twoColloredPlot(data1[data1['class'] == 0][\"small_words\"],\n",
    "                data1[data1['class'] == 1][\"small_words\"],\n",
    "                'Small words', 100, [0,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# Summary statistics\n",
      "mean-ham  71.63   mean-spam 139.15 \n",
      "std-ham   58.44   std-spam   28.98 \n",
      "min-ham    2.00   min-ham    13.00 \n",
      "max-ham  910.00   max-spam  224.00\n"
     ]
    },
    {
     "data": {
      "image/png": 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VueKKK1JK2dz2jW98Y2bNmpUnP/nJOfjgg3PnnXfmC1/4Ql772tfm+uuvz2WX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\ncWs6gwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x21b5d36d940>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 483,
       "width": 715
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "twoColloredPlot(data1[data1['class'] == 0][\"length\"],\n",
    "                data1[data1['class'] == 1][\"length\"],\n",
    "                'Length histogram', 100, [0,400])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# Summary statistics\n",
      "mean-ham  0.14   mean-spam 0.10 \n",
      "std-ham   0.05   std-spam   0.03 \n",
      "min-ham    0.00   min-ham    0.00 \n",
      "max-ham  0.31   max-spam  0.20\n"
     ]
    },
    {
     "data": {
      "image/png": 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33313Vq9enb333ntaE8JtJINv7/z9alciOElSa72ilHJNkv2TzE9yxXQHB1uz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\nAAAAgJbMmjUre++9d1auXJl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AaIAxGAAAAACgAcZgAAAAAIAGjNQOAGbukduu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\nMRgAAAAAoAHGYAAAAACABhiDAQAAAAAaMFI7AP4fD36qdsG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5STb2GBsAAAAAwKrSy8zg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\nAAAwAP8PfVt/Fkdus1oAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x21b5e195080>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 483,
       "width": 705
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "twoColloredPlot(data1[data1['class'] == 0][\"englishPercentage\"],\n",
    "                data1[data1['class'] == 1][\"englishPercentage\"],\n",
    "                'EnglishPercentage histogram', 100, [0,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# Summary statistics\n",
      "mean-ham  4.17   mean-spam 15.48 \n",
      "std-ham   10.83   std-spam   12.30 \n",
      "min-ham    0.00   min-ham    0.00 \n",
      "max-ham  129.00   max-spam  128.00\n"
     ]
    },
    {
     "data": {
      "image/png": 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RLAMAAAAA0EWwDAAAAABAF8EyAAAAAABddsYuAE7SqVNj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\n348luT/JW6vq5gxh73OS3JjhGIrXHPn8BxZznX+xqr49yY8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rXnzSsIEw0R717NcnSdrV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\nAACgF4llAAAAAAB6+f9fHJV448Ub9gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x21b5d74b128>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 483,
       "width": 715
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "twoColloredPlot(data1[data1['class'] == 0][\"upper_letters\"],\n",
    "                data1[data1['class'] == 1][\"upper_letters\"],\n",
    "                'Uppercase histogram', 100, [0,100])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# Summary statistics\n",
      "mean-ham  0.31   mean-spam 15.81 \n",
      "std-ham   1.07   std-spam   8.72 \n",
      "min-ham    0.00   min-ham    0.00 \n",
      "max-ham  23.00   max-spam  47.00\n"
     ]
    },
    {
     "data": {
      "image/png": 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vmDnvbU9hAAAAAACegrZi8M0z51eX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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x21b5cf34978>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 483,
       "width": 705
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "twoColloredPlot(data1[data1['class'] == 0][\"numeric_chars\"],\n",
    "                data1[data1['class'] == 1][\"numeric_chars\"],\n",
    "                'Numeric_chars histogram', 100, [0,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# Summary statistics\n",
      "mean-ham  17.50   mean-spam 29.13 \n",
      "std-ham   15.18   std-spam   7.30 \n",
      "min-ham    0.00   min-ham    1.00 \n",
      "max-ham  253.00   max-spam  50.00\n"
     ]
    },
    {
     "data": {
      "image/png": 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dKqm8efPmfO5znxtQVLObxDIAAAAAMK8ddNBB+fa3v52bb775vn2ttbzpTW/K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\nwO6YsQwAAAAAwFAUlgEAAAAAGIqlMACAqfLC39vcdwrACnDZeaf1nQI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IckiS61prJy9F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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1e374754da0>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 483,
       "width": 715
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "twoColloredPlot(data1[data1['class'] == 0][\"non_alpha_num\"],\n",
    "                data1[data1['class'] == 1][\"non_alpha_num\"],\n",
    "                'Non_alpha_num histogram', 50, [0,50])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "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": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}