DNACLASS.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Classification Of DNA of E.Coli\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing Dataset from UCI ML repository"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = 'https://archive.ics.uci.edu/ml/machine-learning-databases/molecular-biology/promoter-gene-sequences/promoters.data'\n",
    "names = ['Class', 'id', 'Sequence']\n",
    "data = pd.read_csv(url, names = names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Class</th>\n",
       "      <th>id</th>\n",
       "      <th>Sequence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>+</td>\n",
       "      <td>S10</td>\n",
       "      <td>\\t\\ttactagcaatacgcttgcgttcggtggttaagtatgtataat...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>+</td>\n",
       "      <td>AMPC</td>\n",
       "      <td>\\t\\ttgctatcctgacagttgtcacgctgattggtgtcgttacaat...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>+</td>\n",
       "      <td>AROH</td>\n",
       "      <td>\\t\\tgtactagagaactagtgcattagcttatttttttgttatcat...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>+</td>\n",
       "      <td>DEOP2</td>\n",
       "      <td>\\taattgtgatgtgtatcgaagtgtgttgcggagtagatgttagaa...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>+</td>\n",
       "      <td>LEU1_TRNA</td>\n",
       "      <td>\\ttcgataattaactattgacgaaaagctgaaaaccactagaatgc...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>101</td>\n",
       "      <td>-</td>\n",
       "      <td>799</td>\n",
       "      <td>\\t\\tcctcaatggcctctaaacgggtcttgaggggttttttgctga...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>102</td>\n",
       "      <td>-</td>\n",
       "      <td>987</td>\n",
       "      <td>\\t\\tgtattctcaacaagattaaccgacagattcaatctcgtggat...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>103</td>\n",
       "      <td>-</td>\n",
       "      <td>1226</td>\n",
       "      <td>\\t\\tcgcgactacgatgagatgcctgagtgcttccgttactggatt...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>104</td>\n",
       "      <td>-</td>\n",
       "      <td>794</td>\n",
       "      <td>\\t\\tctcgtcctcaatggcctctaaacgggtcttgaggggtttttt...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>105</td>\n",
       "      <td>-</td>\n",
       "      <td>1442</td>\n",
       "      <td>\\t\\ttaacattaataaataaggaggctctaatggcactcattagcc...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>106 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Class         id                                           Sequence\n",
       "0       +        S10  \\t\\ttactagcaatacgcttgcgttcggtggttaagtatgtataat...\n",
       "1       +       AMPC  \\t\\ttgctatcctgacagttgtcacgctgattggtgtcgttacaat...\n",
       "2       +       AROH  \\t\\tgtactagagaactagtgcattagcttatttttttgttatcat...\n",
       "3       +      DEOP2  \\taattgtgatgtgtatcgaagtgtgttgcggagtagatgttagaa...\n",
       "4       +  LEU1_TRNA  \\ttcgataattaactattgacgaaaagctgaaaaccactagaatgc...\n",
       "..    ...        ...                                                ...\n",
       "101     -        799  \\t\\tcctcaatggcctctaaacgggtcttgaggggttttttgctga...\n",
       "102     -        987  \\t\\tgtattctcaacaagattaaccgacagattcaatctcgtggat...\n",
       "103     -       1226  \\t\\tcgcgactacgatgagatgcctgagtgcttccgttactggatt...\n",
       "104     -        794  \\t\\tctcgtcctcaatggcctctaaacgggtcttgaggggtttttt...\n",
       "105     -       1442  \\t\\ttaacattaataaataaggaggctctaatggcactcattagcc...\n",
       "\n",
       "[106 rows x 3 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Class</th>\n",
       "      <th>id</th>\n",
       "      <th>Sequence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>106</td>\n",
       "      <td>106</td>\n",
       "      <td>106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>unique</td>\n",
       "      <td>2</td>\n",
       "      <td>106</td>\n",
       "      <td>106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>top</td>\n",
       "      <td>-</td>\n",
       "      <td>RECA</td>\n",
       "      <td>\\t\\tcgaacgagtcaatcagaccgctttgactctggtattactgtg...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>freq</td>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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      ],
      "text/plain": [
       "       Class    id                                           Sequence\n",
       "count    106   106                                                106\n",
       "unique     2   106                                                106\n",
       "top        -  RECA  \\t\\tcgaacgagtcaatcagaccgctttgactctggtattactgtg...\n",
       "freq      53     1                                                  1"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Droping Id column to prevent unnecessary skewing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 1 Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    +\n",
      "1    +\n",
      "2    +\n",
      "3    +\n",
      "4    +\n",
      "Name: Class, dtype: object\n"
     ]
    }
   ],
   "source": [
    "classes = data.loc[:, 'Class']\n",
    "print(classes[:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   0   1   2   3   4   5   6   7   8   9    ... 96  97  98  99  100 101 102  \\\n",
      "0    t   t   g   a   t   a   c   t   c   t  ...   c   c   t   a   g   c   g   \n",
      "1    a   g   t   a   c   g   a   t   g   t  ...   c   g   a   g   a   c   t   \n",
      "2    c   c   a   t   g   g   g   t   a   t  ...   g   c   t   a   g   t   a   \n",
      "3    t   t   c   t   a   g   g   c   c   t  ...   a   t   g   g   a   c   t   \n",
      "4    a   a   t   g   t   g   g   t   t   a  ...   g   a   a   g   g   a   t   \n",
      "5    g   t   a   t   a   c   g   a   t   a  ...   t   g   c   g   c   a   c   \n",
      "6    c   c   g   g   a   a   g   c   a   a  ...   a   g   c   t   a   t   t   \n",
      "7    a   c   a   a   t   a   t   a   a   t  ...   g   a   g   g   t   g   c   \n",
      "8    a   t   g   t   t   g   g   a   t   t  ...   a   c   a   t   g   g   a   \n",
      "9    t   g   a   g   a   g   g   a   a   t  ...   c   t   a   a   t   c   a   \n",
      "10   a   a   a   t   a   a   a   a   t   c  ...   c   t   c   c   c   c   c   \n",
      "11   c   c   c   g   c   g   g   c   a   c  ...   c   t   g   t   a   t   a   \n",
      "12   g   a   t   t   t   g   g   a   c   t  ...   t   c   a   c   g   c   a   \n",
      "13   c   g   a   a   a   a   a   c   t   c  ...   t   t   g   c   c   t   g   \n",
      "14   t   t   g   t   t   t   t   t   g   t  ...   a   t   t   a   c   a   a   \n",
      "15   t   t   t   c   t   g   t   t   c   t  ...   g   g   c   a   t   a   t   \n",
      "16   g   g   g   g   g   g   t   g   g   g  ...   a   t   a   g   c   a   t   \n",
      "17   c   t   c   a   a   a   a   a   a   t  ...   g   t   a   a   g   c   a   \n",
      "18   g   c   a   a   c   a   a   t   c   c  ...   a   g   t   a   a   g   a   \n",
      "19   t   a   t   g   g   a   g   a   a   a  ...   g   a   c   g   c   g   c   \n",
      "20   t   c   t   t   a   g   c   c   g   g  ...   c   t   a   a   a   g   c   \n",
      "21   c   g   a   g   a   a   c   t   g   g  ...   a   t   g   g   a   t   g   \n",
      "22   g   c   g   t   a   g   a   g   a   c  ...   t   t   a   g   c   c   a   \n",
      "23   g   t   c   g   a   g   t   t   c   c  ...   g   t   c   a   t   t   c   \n",
      "24   t   g   t   t   g   t   c   a   g   g  ...   t   c   c   a   t   t   a   \n",
      "25   g   a   t   t   c   t   t   t   t   g  ...   c   c   g   g   g   g   g   \n",
      "26   g   t   a   g   t   g   c   g   c   a  ...   a   a   c   a   c   a   a   \n",
      "27   t   t   t   c   g   c   c   a   c   a  ...   g   t   t   t   a   g   t   \n",
      "28   t   g   t   g   a   c   t   g   g   t  ...   c   g   t   g   t   g   t   \n",
      "29   a   g   t   g   a   g   g   c   t   a  ...   c   c   t   a   a   g   c   \n",
      "30   a   t   t   a   a   t   a   a   t   a  ...   t   g   g   g   a   g   a   \n",
      "31   g   g   t   g   a   a   t   t   c   c  ...   c   g   a   g   a   t   a   \n",
      "32   t   t   t   t   c   t   g   a   t   t  ...   g   t   c   c   t   t   t   \n",
      "33   a   c   t   a   c   a   a   c   g   c  ...   a   g   t   t   g   t   c   \n",
      "34   t   g   g   g   a   a   c   a   t   c  ...   c   t   c   a   c   t   t   \n",
      "35   g   t   t   a   c   a   g   g   g   c  ...   a   t   t   g   t   t   c   \n",
      "36   t   t   t   t   t   g   c   t   t   t  ...   a   t   g   a   t   t   g   \n",
      "37   a   a   a   g   a   a   a   a   a   a  ...   c   t   g   c   t   g   t   \n",
      "38   t   c   t   t   g   a   t   t   a   t  ...   t   g   t   g   c   c   g   \n",
      "39   a   a   c   t   a   a   a   a   a   a  ...   t   c   a   t   t   t   g   \n",
      "40   a   a   a   a   a   c   g   a   t   a  ...   g   g   t   c   t   g   a   \n",
      "41   t   t   t   g   t   t   t   t   c   t  ...   c   c   t   t   g   a   t   \n",
      "42   g   c   g   a   g   a   c   t   g   g  ...   a   a   a   c   t   a   g   \n",
      "43   c   t   c   a   c   g   a   g   c   c  ...   t   a   c   t   a   a   g   \n",
      "44   g   a   t   t   g   a   g   c   a   g  ...   a   t   t   g   g   g   a   \n",
      "45   c   a   a   a   c   g   c   t   a   c  ...   a   g   g   c   a   g   c   \n",
      "46   g   c   a   c   c   t   c   t   t   c  ...   a   t   t   a   c   a   g   \n",
      "47   g   g   c   t   t   c   c   c   g   a  ...   t   t   g   t   g   g   t   \n",
      "48   g   c   c   a   c   c   a   a   a   c  ...   g   a   a   g   t   g   t   \n",
      "49   c   a   a   a   c   g   t   a   a   c  ...   c   a   a   g   g   a   c   \n",
      "50   t   t   c   c   g   t   c   c   a   a  ...   t   t   c   a   c   a   a   \n",
      "51   t   c   c   a   t   t   a   a   t   c  ...   t   c   a   g   c   c   a   \n",
      "52   g   g   c   a   g   t   t   g   g   t  ...   t   g   t   t   c   t   c   \n",
      "53   t   c   g   a   g   a   g   a   g   g  ...   c   c   t   a   t   a   a   \n",
      "54   c   c   g   c   t   g   a   a   t   a  ...   t   t   a   t   a   t   t   \n",
      "55   g   a   c   t   a   g   a   c   t   c  ...   t   t   t   g   c   a   t   \n",
      "56   t   a   g   c   g   t   t   a   t   a  ...   g   t   t   a   g   t   g   \n",
      "57   +   +   +   +   +   +   +   +   +   +  ...   -   -   -   -   -   -   -   \n",
      "\n",
      "   103 104 105  \n",
      "0    c   c   t  \n",
      "1    g   t   a  \n",
      "2    c   c   a  \n",
      "3    g   g   c  \n",
      "4    a   t   a  \n",
      "5    c   c   t  \n",
      "6    t   c   t  \n",
      "7    a   t   a  \n",
      "8    c   c   a  \n",
      "9    g   a   t  \n",
      "10   a   a   a  \n",
      "11   t   t   a  \n",
      "12   g   g   a  \n",
      "13   a   g   t  \n",
      "14   g   c   a  \n",
      "15   a   c   a  \n",
      "16   t   t   g  \n",
      "17   g   c   g  \n",
      "18   c   t   a  \n",
      "19   c   a   g  \n",
      "20   t   a   g  \n",
      "21   g   a   c  \n",
      "22   a   c   t  \n",
      "23   g   g   c  \n",
      "24   t   g   t  \n",
      "25   g   g   a  \n",
      "26   c   t   a  \n",
      "27   t   c   t  \n",
      "28   t   t   g  \n",
      "29   c   t   g  \n",
      "30   c   g   c  \n",
      "31   g   a   a  \n",
      "32   t   g   c  \n",
      "33   t   g   t  \n",
      "34   a   g   c  \n",
      "35   c   g   a  \n",
      "36   t   t   t  \n",
      "37   g   t   t  \n",
      "38   g   t   a  \n",
      "39   a   t   g  \n",
      "40   t   t   c  \n",
      "41   t   t   c  \n",
      "42   g   g   a  \n",
      "43   t   c   a  \n",
      "44   c   t   t  \n",
      "45   a   g   c  \n",
      "46   c   a   a  \n",
      "47   c   a   a  \n",
      "48   a   a   t  \n",
      "49   a   g   c  \n",
      "50   g   g   a  \n",
      "51   g   a   a  \n",
      "52   c   g   g  \n",
      "53   t   g   a  \n",
      "54   t   a   a  \n",
      "55   c   a   c  \n",
      "56   c   c   t  \n",
      "57   -   -   -  \n",
      "\n",
      "[58 rows x 106 columns]\n"
     ]
    }
   ],
   "source": [
    "# generate list of DNA sequences\n",
    "sequences = list(data.loc[:, 'Sequence'])\n",
    "dataset = {}\n",
    "\n",
    "# loop through sequences and split into individual nucleotides\n",
    "for i, seq in enumerate(sequences):\n",
    "    \n",
    "    # split into nucleotides, remove tab characters\n",
    "    nucleotides = list(seq)\n",
    "    nucleotides = [x for x in nucleotides if x != '\\t']\n",
    "    \n",
    "    # append class assignment\n",
    "    nucleotides.append(classes[i])\n",
    "    \n",
    "    # add to dataset\n",
    "    dataset[i] = nucleotides\n",
    "    \n",
    "dframe = pd.DataFrame(dataset)\n",
    "print(dframe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  0  1  2  3  4  5  6  7  8  9   ... 48 49 50 51 52 53 54 55 56 57\n",
      "0  t  a  c  t  a  g  c  a  a  t  ...  g  c  t  t  g  t  c  g  t  +\n",
      "1  t  g  c  t  a  t  c  c  t  g  ...  c  a  t  c  g  c  c  a  a  +\n",
      "2  g  t  a  c  t  a  g  a  g  a  ...  c  a  c  c  c  g  g  c  g  +\n",
      "3  a  a  t  t  g  t  g  a  t  g  ...  a  a  c  a  a  a  c  t  c  +\n",
      "4  t  c  g  a  t  a  a  t  t  a  ...  c  c  g  t  g  g  t  a  g  +\n",
      "\n",
      "[5 rows x 58 columns]\n"
     ]
    }
   ],
   "source": [
    "# transpose since sequence is represented in column . Change it to row\n",
    "df = dframe.transpose()\n",
    "print(df.iloc[:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   0  1  2  3  4  5  6  7  8  9  ... 48 49 50 51 52 53 54 55 56 Class\n",
      "0  t  a  c  t  a  g  c  a  a  t  ...  g  c  t  t  g  t  c  g  t     +\n",
      "1  t  g  c  t  a  t  c  c  t  g  ...  c  a  t  c  g  c  c  a  a     +\n",
      "2  g  t  a  c  t  a  g  a  g  a  ...  c  a  c  c  c  g  g  c  g     +\n",
      "3  a  a  t  t  g  t  g  a  t  g  ...  a  a  c  a  a  a  c  t  c     +\n",
      "4  t  c  g  a  t  a  a  t  t  a  ...  c  c  g  t  g  g  t  a  g     +\n",
      "\n",
      "[5 rows x 58 columns]\n"
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      ],
      "text/plain": [
       "          0    1    2    3    4    5    6    7    8    9  ...   48   49   50  \\\n",
       "count   106  106  106  106  106  106  106  106  106  106  ...  106  106  106   \n",
       "unique    4    4    4    4    4    4    4    4    4    4  ...    4    4    4   \n",
       "top       t    a    a    c    a    a    a    a    a    a  ...    c    c    c   \n",
       "freq     38   34   30   30   36   42   38   34   33   36  ...   36   42   31   \n",
       "\n",
       "         51   52   53   54   55   56 Class  \n",
       "count   106  106  106  106  106  106   106  \n",
       "unique    4    4    4    4    4    4     2  \n",
       "top       t    t    c    c    t    t     -  \n",
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       "\n",
       "[4 rows x 58 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# lets rename the 57 dataframe column to class\n",
    "df.rename(columns = {57: 'Class'}, inplace = True) \n",
    "print(df.iloc[:5])\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
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      ],
      "text/plain": [
       "   0_a  0_c  0_g  0_t  1_a  1_c  1_g  1_t  2_a  2_c  ...  55_a  55_c  55_g  \\\n",
       "0    0    0    0    1    1    0    0    0    0    1  ...     0     0     1   \n",
       "1    0    0    0    1    0    0    1    0    0    1  ...     1     0     0   \n",
       "2    0    0    1    0    0    0    0    1    1    0  ...     0     1     0   \n",
       "3    1    0    0    0    1    0    0    0    0    0  ...     0     0     0   \n",
       "4    0    0    0    1    0    1    0    0    0    0  ...     1     0     0   \n",
       "\n",
       "   55_t  56_a  56_c  56_g  56_t  Class_+  Class_-  \n",
       "0     0     0     0     0     1        1        0  \n",
       "1     0     1     0     0     0        1        0  \n",
       "2     0     0     0     1     0        1        0  \n",
       "3     1     0     1     0     0        1        0  \n",
       "4     0     0     0     1     0        1        0  \n",
       "\n",
       "[5 rows x 230 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# One hot encoding categorical Sequence \n",
    "numerical_df = pd.get_dummies(df)\n",
    "numerical_df.iloc[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   0_a  0_c  0_g  0_t  1_a  1_c  1_g  1_t  2_a  2_c  ...  54_t  55_a  55_c  \\\n",
      "0    0    0    0    1    1    0    0    0    0    1  ...     0     0     0   \n",
      "1    0    0    0    1    0    0    1    0    0    1  ...     0     1     0   \n",
      "2    0    0    1    0    0    0    0    1    1    0  ...     0     0     1   \n",
      "3    1    0    0    0    1    0    0    0    0    0  ...     0     0     0   \n",
      "4    0    0    0    1    0    1    0    0    0    0  ...     1     1     0   \n",
      "\n",
      "   55_g  55_t  56_a  56_c  56_g  56_t  Class  \n",
      "0     1     0     0     0     0     1      1  \n",
      "1     0     0     1     0     0     0      1  \n",
      "2     0     0     0     0     1     0      1  \n",
      "3     0     1     0     1     0     0      1  \n",
      "4     0     0     0     0     1     0      1  \n",
      "\n",
      "[5 rows x 229 columns]\n"
     ]
    }
   ],
   "source": [
    "# Preventing the Dummy Variable Trap\n",
    "df = numerical_df.drop(columns=['Class_-'])\n",
    "\n",
    "df.rename(columns = {'Class_+': 'Class'}, inplace = True)\n",
    "print(df.iloc[:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create X and Y datasets for training\n",
    "X = np.array(df.drop(['Class'], 1))\n",
    "y = np.array(df['Class'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training with different algorithms and evaluating each model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Nearest Neighbors: 0.842857 (0.118666)\n",
      "Gaussian Process: 0.842857 (0.134771)\n",
      "Decision Tree: 0.732143 (0.174416)\n",
      "Random Forest: 0.664286 (0.150000)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/rion/anaconda3/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:585: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/home/rion/anaconda3/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:585: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/home/rion/anaconda3/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:585: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/home/rion/anaconda3/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:585: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/home/rion/anaconda3/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:585: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/home/rion/anaconda3/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:585: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/home/rion/anaconda3/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:585: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/home/rion/anaconda3/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:585: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/home/rion/anaconda3/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:585: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/home/rion/anaconda3/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:585: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Neural Net: 0.930357 (0.069826)\n",
      "AdaBoost: 0.916071 (0.093831)\n",
      "Naive Bayes: 0.873214 (0.134688)\n",
      "SVM Linear: 0.916071 (0.068721)\n",
      "SVM RBF: 0.887500 (0.139709)\n",
      "SVM Sigmoid: 0.873214 (0.076786)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.gaussian_process import GaussianProcessClassifier\n",
    "from sklearn.gaussian_process.kernels import RBF\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.metrics import classification_report, accuracy_score\n",
    "\n",
    "# Define models to train\n",
    "names = [\"Nearest Neighbors\", \"Gaussian Process\",\n",
    "         \"Decision Tree\", \"Random Forest\", \"Neural Net\", \"AdaBoost\",\n",
    "         \"Naive Bayes\", \"SVM Linear\", \"SVM RBF\", \"SVM Sigmoid\"]\n",
    "\n",
    "classifiers = [\n",
    "    KNeighborsClassifier(n_neighbors = 3),\n",
    "    GaussianProcessClassifier(1.0 * RBF(1.0)),\n",
    "    DecisionTreeClassifier(max_depth=5),\n",
    "    RandomForestClassifier(max_depth=5, n_estimators=10, max_features=1),\n",
    "    MLPClassifier(alpha=1),\n",
    "    AdaBoostClassifier(),\n",
    "    GaussianNB(),\n",
    "    SVC(kernel = 'linear'), \n",
    "    SVC(kernel = 'rbf'),\n",
    "    SVC(kernel = 'sigmoid')\n",
    "]\n",
    "\n",
    "models = zip(names, classifiers)\n",
    "\n",
    "from sklearn.model_selection import KFold, cross_val_score\n",
    "\n",
    "# evaluate each model in turn\n",
    "results = []\n",
    "names = []\n",
    "\n",
    "for name, model in models:\n",
    "    kfold = KFold(n_splits=10, random_state = None)\n",
    "    cv_results = cross_val_score(model, X_train, y_train, cv=kfold, scoring='accuracy')\n",
    "    results.append(cv_results)\n",
    "    names.append(name)\n",
    "    msg = \"%s: %f (%f)\" % (name, cv_results.mean(), cv_results.std())\n",
    "    print(msg)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Lets evaluate the models on test data\n",
    "from sklearn.metrics import accuracy_score,classification_report,confusion_matrix\n",
    "\n",
    "for name, model in models:\n",
    "    model.fit(X_train, y_train)\n",
    "    predictions = model.predict(X_test)\n",
    "    print(name)\n",
    "    print(accuracy_score(y_test, predictions))\n",
    "    print(confusion_matrix(y_test, predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score,classification_report,confusion_matrix\n",
    "for name,model in models:\n",
    "    model.fit(X_train, y_train)\n",
    "    predictions = model.predict(X_test)\n",
    "    print(name)\n",
    "    print(accuracy_score(y_test, predictions))\n",
    "    print(classification_report(y_test, predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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