classifying-dna-sequences-markov-models-knn-svm.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {},
   "source": [
    "# Classifying DNA Sequences\n",
    "\n",
    "During this tutorial, we will explore the world of bioinformatics by using Markov models, K-nearest neighbor (KNN) algorithms, support vector machines, and other common classifiers to classify short E. Coli DNA sequences. This project will use a dataset from the UCI Machine Learning Repository that has 106 DNA sequences, with 57 sequential nucleotides (“base-pairs”) each.   \n",
    "\n",
    "You will learn how to:\n",
    "* Import data from the UCI repository\n",
    "* Convert text inputs to numerical data\n",
    "* Build and train classification algorithms\n",
    "* Compare and contrast classification algorithms\n",
    "\n",
    "# Step 1: Importing the Dataset\n",
    "\n",
    "The following code cells will import necessary libraries and import the dataset from the UCI repository as a Pandas DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python: 3.6.6 |Anaconda, Inc.| (default, Oct  9 2018, 12:34:16) \n",
      "[GCC 7.3.0]\n",
      "Numpy: 1.16.2\n",
      "Sklearn: 0.20.3\n",
      "Pandas: 0.23.4\n"
     ]
    }
   ],
   "source": [
    "# To make sure all of the correct libraries are installed, import each module and print the version number\n",
    "\n",
    "import sys\n",
    "import numpy\n",
    "import sklearn\n",
    "import pandas\n",
    "\n",
    "print('Python: {}'.format(sys.version))\n",
    "print('Numpy: {}'.format(numpy.__version__))\n",
    "print('Sklearn: {}'.format(sklearn.__version__))\n",
    "print('Pandas: {}'.format(pandas.__version__))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import, change module names\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# import the uci Molecular Biology (Promoter Gene Sequences) Data Set\n",
    "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": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Class                                                       +\n",
      "id                                                        S10\n",
      "Sequence    \\t\\ttactagcaatacgcttgcgttcggtggttaagtatgtataat...\n",
      "Name: 0, dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(data.iloc[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {},
   "source": [
    "# Step 2: Preprocessing the Dataset\n",
    "\n",
    "The data is not in a usable form; as a result, we will need to process it before using it to train our algorithms."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    +\n",
      "1    +\n",
      "2    +\n",
      "3    +\n",
      "4    +\n",
      "Name: Class, dtype: object\n"
     ]
    }
   ],
   "source": [
    "# Building our Dataset by creating a custom Pandas DataFrame\n",
    "# Each column in a DataFrame is called a Series. Lets start by making a series for each column.\n",
    "\n",
    "classes = data.loc[:, 'Class']\n",
    "print(classes[:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['t', 'a', 'c', 't', 'a', 'g', 'c', 'a', 'a', 't', 'a', 'c', 'g', 'c', 't', 't', 'g', 'c', 'g', 't', 't', 'c', 'g', 'g', 't', 'g', 'g', 't', 't', 'a', 'a', 'g', 't', 'a', 't', 'g', 't', 'a', 't', 'a', 'a', 't', 'g', 'c', 'g', 'c', 'g', 'g', 'g', 'c', 't', 't', 'g', 't', 'c', 'g', 't', '+']\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",
    "print(dataset[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "   0   1   2   3   4   5   6   7   8   ... 97  98  99  100 101 102 103 104 105\n",
      "0    t   t   g   a   t   a   c   t   c ...   c   t   a   g   c   g   c   c   t\n",
      "1    a   g   t   a   c   g   a   t   g ...   g   a   g   a   c   t   g   t   a\n",
      "2    c   c   a   t   g   g   g   t   a ...   c   t   a   g   t   a   c   c   a\n",
      "3    t   t   c   t   a   g   g   c   c ...   t   g   g   a   c   t   g   g   c\n",
      "4    a   a   t   g   t   g   g   t   t ...   a   a   g   g   a   t   a   t   a\n",
      "5    g   t   a   t   a   c   g   a   t ...   g   c   g   c   a   c   c   c   t\n",
      "6    c   c   g   g   a   a   g   c   a ...   g   c   t   a   t   t   t   c   t\n",
      "7    a   c   a   a   t   a   t   a   a ...   a   g   g   t   g   c   a   t   a\n",
      "8    a   t   g   t   t   g   g   a   t ...   c   a   t   g   g   a   c   c   a\n",
      "9    t   g   a   g   a   g   g   a   a ...   t   a   a   t   c   a   g   a   t\n",
      "10   a   a   a   t   a   a   a   a   t ...   t   c   c   c   c   c   a   a   a\n",
      "11   c   c   c   g   c   g   g   c   a ...   t   g   t   a   t   a   t   t   a\n",
      "12   g   a   t   t   t   g   g   a   c ...   c   a   c   g   c   a   g   g   a\n",
      "13   c   g   a   a   a   a   a   c   t ...   t   g   c   c   t   g   a   g   t\n",
      "14   t   t   g   t   t   t   t   t   g ...   t   t   a   c   a   a   g   c   a\n",
      "15   t   t   t   c   t   g   t   t   c ...   g   c   a   t   a   t   a   c   a\n",
      "16   g   g   g   g   g   g   t   g   g ...   t   a   g   c   a   t   t   t   g\n",
      "17   c   t   c   a   a   a   a   a   a ...   t   a   a   g   c   a   g   c   g\n",
      "18   g   c   a   a   c   a   a   t   c ...   g   t   a   a   g   a   c   t   a\n",
      "19   t   a   t   g   g   a   g   a   a ...   a   c   g   c   g   c   c   a   g\n",
      "20   t   c   t   t   a   g   c   c   g ...   t   a   a   a   g   c   t   a   g\n",
      "21   c   g   a   g   a   a   c   t   g ...   t   g   g   a   t   g   g   a   c\n",
      "22   g   c   g   t   a   g   a   g   a ...   t   a   g   c   c   a   a   c   t\n",
      "23   g   t   c   g   a   g   t   t   c ...   t   c   a   t   t   c   g   g   c\n",
      "24   t   g   t   t   g   t   c   a   g ...   c   c   a   t   t   a   t   g   t\n",
      "25   g   a   t   t   c   t   t   t   t ...   c   g   g   g   g   g   g   g   a\n",
      "26   g   t   a   g   t   g   c   g   c ...   a   c   a   c   a   a   c   t   a\n",
      "27   t   t   t   c   g   c   c   a   c ...   t   t   t   a   g   t   t   c   t\n",
      "28   t   g   t   g   a   c   t   g   g ...   g   t   g   t   g   t   t   t   g\n",
      "29   a   g   t   g   a   g   g   c   t ...   c   t   a   a   g   c   c   t   g\n",
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      "31   g   g   t   g   a   a   t   t   c ...   g   a   g   a   t   a   g   a   a\n",
      "32   t   t   t   t   c   t   g   a   t ...   t   c   c   t   t   t   t   g   c\n",
      "33   a   c   t   a   c   a   a   c   g ...   g   t   t   g   t   c   t   g   t\n",
      "34   t   g   g   g   a   a   c   a   t ...   t   c   a   c   t   t   a   g   c\n",
      "35   g   t   t   a   c   a   g   g   g ...   t   t   g   t   t   c   c   g   a\n",
      "36   t   t   t   t   t   g   c   t   t ...   t   g   a   t   t   g   t   t   t\n",
      "37   a   a   a   g   a   a   a   a   a ...   t   g   c   t   g   t   g   t   t\n",
      "38   t   c   t   t   g   a   t   t   a ...   g   t   g   c   c   g   g   t   a\n",
      "39   a   a   c   t   a   a   a   a   a ...   c   a   t   t   t   g   a   t   g\n",
      "40   a   a   a   a   a   c   g   a   t ...   g   t   c   t   g   a   t   t   c\n",
      "41   t   t   t   g   t   t   t   t   c ...   c   t   t   g   a   t   t   t   c\n",
      "42   g   c   g   a   g   a   c   t   g ...   a   a   c   t   a   g   g   g   a\n",
      "43   c   t   c   a   c   g   a   g   c ...   a   c   t   a   a   g   t   c   a\n",
      "44   g   a   t   t   g   a   g   c   a ...   t   t   g   g   g   a   c   t   t\n",
      "45   c   a   a   a   c   g   c   t   a ...   g   g   c   a   g   c   a   g   c\n",
      "46   g   c   a   c   c   t   c   t   t ...   t   t   a   c   a   g   c   a   a\n",
      "47   g   g   c   t   t   c   c   c   g ...   t   g   t   g   g   t   c   a   a\n",
      "48   g   c   c   a   c   c   a   a   a ...   a   a   g   t   g   t   a   a   t\n",
      "49   c   a   a   a   c   g   t   a   a ...   a   a   g   g   a   c   a   g   c\n",
      "50   t   t   c   c   g   t   c   c   a ...   t   c   a   c   a   a   g   g   a\n",
      "51   t   c   c   a   t   t   a   a   t ...   c   a   g   c   c   a   g   a   a\n",
      "52   g   g   c   a   g   t   t   g   g ...   g   t   t   c   t   c   c   g   g\n",
      "53   t   c   g   a   g   a   g   a   g ...   c   t   a   t   a   a   t   g   a\n",
      "54   c   c   g   c   t   g   a   a   t ...   t   a   t   a   t   t   t   a   a\n",
      "55   g   a   c   t   a   g   a   c   t ...   t   t   g   c   a   t   c   a   c\n",
      "56   t   a   g   c   g   t   t   a   t ...   t   t   a   g   t   g   c   c   t\n",
      "57   +   +   +   +   +   +   +   +   + ...   -   -   -   -   -   -   -   -   -\n",
      "\n",
      "[58 rows x 106 columns]\n"
     ]
    }
   ],
   "source": [
    "# turn dataset into pandas DataFrame\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  10 11 ... 46 47 48 49 50 51 52 53 54 55 56 57\n",
      "0  t  a  c  t  a  g  c  a  a  t  a  c ...  g  g  g  c  t  t  g  t  c  g  t  +\n",
      "1  t  g  c  t  a  t  c  c  t  g  a  c ...  c  g  c  a  t  c  g  c  c  a  a  +\n",
      "2  g  t  a  c  t  a  g  a  g  a  a  c ...  a  c  c  a  c  c  c  g  g  c  g  +\n",
      "3  a  a  t  t  g  t  g  a  t  g  t  g ...  c  t  a  a  c  a  a  a  c  t  c  +\n",
      "4  t  c  g  a  t  a  a  t  t  a  a  c ...  c  t  c  c  g  t  g  g  t  a  g  +\n",
      "\n",
      "[5 rows x 58 columns]\n"
     ]
    }
   ],
   "source": [
    "# transpose the DataFrame\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 10  ...  47 48 49 50 51 52 53 54 55 56 Class\n",
      "0  t  a  c  t  a  g  c  a  a  t  a  ...   g  g  c  t  t  g  t  c  g  t     +\n",
      "1  t  g  c  t  a  t  c  c  t  g  a  ...   g  c  a  t  c  g  c  c  a  a     +\n",
      "2  g  t  a  c  t  a  g  a  g  a  a  ...   c  c  a  c  c  c  g  g  c  g     +\n",
      "3  a  a  t  t  g  t  g  a  t  g  t  ...   t  a  a  c  a  a  a  c  t  c     +\n",
      "4  t  c  g  a  t  a  a  t  t  a  a  ...   t  c  c  g  t  g  g  t  a  g     +\n",
      "\n",
      "[5 rows x 58 columns]\n"
     ]
    }
   ],
   "source": [
    "# for clarity, lets rename the last dataframe column to class\n",
    "df.rename(columns = {57: 'Class'}, inplace = True) \n",
    "print(df.iloc[:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
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       "      <td>t</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>38</td>\n",
       "      <td>34</td>\n",
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       "      <td>35</td>\n",
       "      <td>37</td>\n",
       "      <td>31</td>\n",
       "      <td>41</td>\n",
       "      <td>39</td>\n",
       "      <td>27</td>\n",
       "      <td>34</td>\n",
       "      <td>43</td>\n",
       "      <td>32</td>\n",
       "      <td>35</td>\n",
       "      <td>45</td>\n",
       "      <td>41</td>\n",
       "      <td>33</td>\n",
       "      <td>42</td>\n",
       "      <td>36</td>\n",
       "      <td>34</td>\n",
       "      <td>35</td>\n",
       "      <td>31</td>\n",
       "      <td>32</td>\n",
       "      <td>36</td>\n",
       "      <td>42</td>\n",
       "      <td>31</td>\n",
       "      <td>33</td>\n",
       "      <td>35</td>\n",
       "      <td>32</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>34</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0    1    2    3    4    5  ...    52   53   54   55   56 Class\n",
       "count   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     2\n",
       "top       t    a    a    c    a    a  ...     t    c    c    t    t     -\n",
       "freq     38   34   30   30   36   42  ...    35   32   29   29   34    53\n",
       "\n",
       "[4 rows x 58 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# looks good! Let's start to familiarize ourselves with the dataset so we can pick the most suitable \n",
    "# algorithms for this data\n",
    "\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      0     1     2     3     4  ...      53    54    55    56  Class\n",
      "t  38.0  26.0  27.0  26.0  22.0  ...    30.0  23.0  29.0  34.0    NaN\n",
      "c  27.0  22.0  21.0  30.0  19.0  ...    32.0  29.0  29.0  17.0    NaN\n",
      "a  26.0  34.0  30.0  22.0  36.0  ...    22.0  26.0  24.0  27.0    NaN\n",
      "g  15.0  24.0  28.0  28.0  29.0  ...    22.0  28.0  24.0  28.0    NaN\n",
      "-   NaN   NaN   NaN   NaN   NaN  ...     NaN   NaN   NaN   NaN   53.0\n",
      "+   NaN   NaN   NaN   NaN   NaN  ...     NaN   NaN   NaN   NaN   53.0\n",
      "\n",
      "[6 rows x 58 columns]\n"
     ]
    }
   ],
   "source": [
    "# desribe does not tell us enough information since the attributes are text. Lets record value counts for each sequence\n",
    "series = []\n",
    "for name in df.columns:\n",
    "    series.append(df[name].value_counts())\n",
    "    \n",
    "info = pd.DataFrame(series)\n",
    "details = info.transpose()\n",
    "print(details)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   0_a  0_c  0_g  0_t  1_a   ...     56_c  56_g  56_t  Class_+  Class_-\n",
       "0    0    0    0    1    1   ...        0     0     1        1        0\n",
       "1    0    0    0    1    0   ...        0     0     0        1        0\n",
       "2    0    0    1    0    0   ...        0     1     0        1        0\n",
       "3    1    0    0    0    1   ...        1     0     0        1        0\n",
       "4    0    0    0    1    0   ...        0     1     0        1        0\n",
       "\n",
       "[5 rows x 230 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Unfortunately, we can't run machine learning algorithms on the data in 'String' formats. As a result, we need to switch\n",
    "# it to numerical data. This can easily be accomplished using the pd.get_dummies() function\n",
    "numerical_df = pd.get_dummies(df)\n",
    "numerical_df.iloc[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   0_a  0_c  0_g  0_t  1_a  1_c  ...    55_t  56_a  56_c  56_g  56_t  Class\n",
      "0    0    0    0    1    1    0  ...       0     0     0     0     1      1\n",
      "1    0    0    0    1    0    0  ...       0     1     0     0     0      1\n",
      "2    0    0    1    0    0    0  ...       0     0     0     1     0      1\n",
      "3    1    0    0    0    1    0  ...       1     0     1     0     0      1\n",
      "4    0    0    0    1    0    1  ...       0     0     0     1     0      1\n",
      "\n",
      "[5 rows x 229 columns]\n"
     ]
    }
   ],
   "source": [
    "# We don't need both class columns.  Lets drop one then rename the other to simply 'Class'.\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": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use the model_selection module to separate training and testing datasets\n",
    "from sklearn import model_selection\n",
    "\n",
    "# Create X and Y datasets for training\n",
    "X = np.array(df.drop(['Class'], 1))\n",
    "y = np.array(df['Class'])\n",
    "\n",
    "# define seed for reproducibility\n",
    "seed = 1\n",
    "\n",
    "# split data into training and testing datasets\n",
    "X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=0.25, random_state=seed)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {},
   "source": [
    "# Step 3: Training and Testing the Classification Algorithms\n",
    "\n",
    "Now that we have preprocessed the data and built our training and testing datasets, we can start to deploy different classification algorithms. It's relatively easy to test multiple models; as a result, we will compare and contrast the performance of ten different algorithms."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Nearest Neighbors: 0.823214 (0.113908)\n",
      "Test--  Nearest Neighbors :  0.7777777777777778\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.65      0.79        17\n",
      "           1       0.62      1.00      0.77        10\n",
      "\n",
      "   micro avg       0.78      0.78      0.78        27\n",
      "   macro avg       0.81      0.82      0.78        27\n",
      "weighted avg       0.86      0.78      0.78        27\n",
      "\n",
      "Gaussian Process: 0.873214 (0.056158)\n",
      "Test--  Gaussian Process :  0.8888888888888888\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.82      0.90        17\n",
      "           1       0.77      1.00      0.87        10\n",
      "\n",
      "   micro avg       0.89      0.89      0.89        27\n",
      "   macro avg       0.88      0.91      0.89        27\n",
      "weighted avg       0.91      0.89      0.89        27\n",
      "\n",
      "Decision Tree: 0.735714 (0.179427)\n",
      "Test--  Decision Tree :  0.7777777777777778\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.92      0.71      0.80        17\n",
      "           1       0.64      0.90      0.75        10\n",
      "\n",
      "   micro avg       0.78      0.78      0.78        27\n",
      "   macro avg       0.78      0.80      0.78        27\n",
      "weighted avg       0.82      0.78      0.78        27\n",
      "\n",
      "Random Forest: 0.708929 (0.209173)\n",
      "Test--  Random Forest :  0.7037037037037037\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.85      0.65      0.73        17\n",
      "           1       0.57      0.80      0.67        10\n",
      "\n",
      "   micro avg       0.70      0.70      0.70        27\n",
      "   macro avg       0.71      0.72      0.70        27\n",
      "weighted avg       0.74      0.70      0.71        27\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/opt/conda/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/opt/conda/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/opt/conda/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/opt/conda/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/opt/conda/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/opt/conda/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/opt/conda/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/opt/conda/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/opt/conda/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:562: 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: 0.875000 (0.096825)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:562: 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": [
      "Test--  Neural Net :  0.9259259259259259\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.88      0.94        17\n",
      "           1       0.83      1.00      0.91        10\n",
      "\n",
      "   micro avg       0.93      0.93      0.93        27\n",
      "   macro avg       0.92      0.94      0.92        27\n",
      "weighted avg       0.94      0.93      0.93        27\n",
      "\n",
      "AdaBoost: 0.912500 (0.112500)\n",
      "Test--  AdaBoost :  0.8518518518518519\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.76      0.87        17\n",
      "           1       0.71      1.00      0.83        10\n",
      "\n",
      "   micro avg       0.85      0.85      0.85        27\n",
      "   macro avg       0.86      0.88      0.85        27\n",
      "weighted avg       0.89      0.85      0.85        27\n",
      "\n",
      "Naive Bayes: 0.837500 (0.137500)\n",
      "Test--  Naive Bayes :  0.9259259259259259\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.88      0.94        17\n",
      "           1       0.83      1.00      0.91        10\n",
      "\n",
      "   micro avg       0.93      0.93      0.93        27\n",
      "   macro avg       0.92      0.94      0.92        27\n",
      "weighted avg       0.94      0.93      0.93        27\n",
      "\n",
      "SVM Linear: 0.850000 (0.108972)\n",
      "Test--  SVM Linear :  0.9629629629629629\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.94      0.97        17\n",
      "           1       0.91      1.00      0.95        10\n",
      "\n",
      "   micro avg       0.96      0.96      0.96        27\n",
      "   macro avg       0.95      0.97      0.96        27\n",
      "weighted avg       0.97      0.96      0.96        27\n",
      "\n",
      "SVM RBF: 0.737500 (0.117925)\n",
      "Test--  SVM RBF :  0.7777777777777778\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.65      0.79        17\n",
      "           1       0.62      1.00      0.77        10\n",
      "\n",
      "   micro avg       0.78      0.78      0.78        27\n",
      "   macro avg       0.81      0.82      0.78        27\n",
      "weighted avg       0.86      0.78      0.78        27\n",
      "\n",
      "SVM Sigmoid: 0.569643 (0.159209)\n",
      "Test--  SVM Sigmoid :  0.4444444444444444\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.12      0.21        17\n",
      "           1       0.40      1.00      0.57        10\n",
      "\n",
      "   micro avg       0.44      0.44      0.44        27\n",
      "   macro avg       0.70      0.56      0.39        27\n",
      "weighted avg       0.78      0.44      0.34        27\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/sklearn/svm/base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "/opt/conda/lib/python3.6/site-packages/sklearn/svm/base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "/opt/conda/lib/python3.6/site-packages/sklearn/svm/base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "/opt/conda/lib/python3.6/site-packages/sklearn/svm/base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "/opt/conda/lib/python3.6/site-packages/sklearn/svm/base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "/opt/conda/lib/python3.6/site-packages/sklearn/svm/base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "/opt/conda/lib/python3.6/site-packages/sklearn/svm/base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "/opt/conda/lib/python3.6/site-packages/sklearn/svm/base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "/opt/conda/lib/python3.6/site-packages/sklearn/svm/base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "/opt/conda/lib/python3.6/site-packages/sklearn/svm/base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "/opt/conda/lib/python3.6/site-packages/sklearn/svm/base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "/opt/conda/lib/python3.6/site-packages/sklearn/svm/base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "/opt/conda/lib/python3.6/site-packages/sklearn/svm/base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "/opt/conda/lib/python3.6/site-packages/sklearn/svm/base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "/opt/conda/lib/python3.6/site-packages/sklearn/svm/base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "/opt/conda/lib/python3.6/site-packages/sklearn/svm/base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "/opt/conda/lib/python3.6/site-packages/sklearn/svm/base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "/opt/conda/lib/python3.6/site-packages/sklearn/svm/base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "/opt/conda/lib/python3.6/site-packages/sklearn/svm/base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "/opt/conda/lib/python3.6/site-packages/sklearn/svm/base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "/opt/conda/lib/python3.6/site-packages/sklearn/svm/base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "/opt/conda/lib/python3.6/site-packages/sklearn/svm/base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "# Now that we have our dataset, we can start building algorithms! We'll need to import each algorithm we plan on using\n",
    "# from sklearn.  We also need to import some performance metrics, such as accuracy_score and classification_report.\n",
    "\n",
    "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 scoring method\n",
    "scoring = 'accuracy'\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",
    "# evaluate each model in turn\n",
    "results = []\n",
    "names = []\n",
    "\n",
    "for name, model in models:\n",
    "    kfold = model_selection.KFold(n_splits=10, random_state = seed)\n",
    "    cv_results = model_selection.cross_val_score(model, X_train, y_train, cv=kfold, scoring=scoring)\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",
    "    model.fit(X_train, y_train)\n",
    "    predictions = model.predict(X_test)\n",
    "    print('Test-- ',name,': ',accuracy_score(y_test, predictions))\n",
    "    print()\n",
    "    print(classification_report(y_test, predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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