cancer_classification.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import xml.etree.cElementTree as etree\n",
    "import re\n",
    "from tqdm import tqdm\n",
    "import os\n",
    "import nltk\n",
    "from bs4 import BeautifulSoup\n",
    "from html2text import html2text \n",
    "import re\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib notebook\n",
    "np.random.seed(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def clean_html(html):\n",
    "    \"\"\"\n",
    "    Copied from NLTK package.\n",
    "    Remove HTML markup from the given string.\n",
    "\n",
    "    :param html: the HTML string to be cleaned\n",
    "    :type html: str\n",
    "    :rtype: str\n",
    "    \"\"\"\n",
    "\n",
    "    # First we remove inline JavaScript/CSS:\n",
    "    cleaned = re.sub(r\"(?is)<(script|style).*?>.*?(</\\1>)\", \"\", html.strip())\n",
    "    # Then we remove html comments. This has to be done before removing regular\n",
    "    # tags since comments can contain '>' characters.\n",
    "    cleaned = re.sub(r\"(?s)<!--(.*?)-->[\\n]?\", \"\", cleaned)\n",
    "    # Next we can remove the remaining tags:\n",
    "    cleaned = re.sub(r\"(?s)<.*?>\", \" \", cleaned)\n",
    "    # Finally, we deal with whitespace\n",
    "    cleaned = re.sub(r\"&nbsp;\", \" \", cleaned)\n",
    "    cleaned = re.sub(r\"  \", \" \", cleaned)\n",
    "    cleaned = re.sub(r\"  \", \" \", cleaned)\n",
    "    return cleaned.strip()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def loadFiles(directory):\n",
    "    DEBUG =False\n",
    "    l = []\n",
    "    for file in os.listdir(directory):\n",
    "        data = directory + file\n",
    "        if DEBUG : print (data)\n",
    "        l.append(data)\n",
    "    l=sorted(l)\n",
    "    \n",
    "    return l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cancer_file_loc = \"./Dataset/train/cancer/\"\n",
    "non_cancer_file_loc = \"./Dataset/train/noncancer/\" "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "files_cancer = loadFiles(cancer_file_loc)\n",
    "files_noncancer = loadFiles(non_cancer_file_loc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 300/300 [00:50<00:00,  5.95it/s]\n"
     ]
    }
   ],
   "source": [
    "cancer=[]\n",
    "for i in tqdm(files_cancer):\n",
    "    xmlDoc = open(i, 'r')\n",
    "    xmlDocData = xmlDoc.read()\n",
    "    html=xmlDocData\n",
    "    cleanhtml = clean_html(html)\n",
    "    text = html2text(cleanhtml)\n",
    "    soup = BeautifulSoup(html,\"lxml\")\n",
    "    text2 = soup.get_text()\n",
    "    cancer.append([text2,'c'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 150/150 [00:14<00:00, 10.45it/s]\n"
     ]
    }
   ],
   "source": [
    "noncancer=[]\n",
    "for i in tqdm(files_noncancer):\n",
    "    xmlDoc = open(i, 'r')\n",
    "    xmlDocData = xmlDoc.read()\n",
    "    html=xmlDocData\n",
    "    cleanhtml = clean_html(html)\n",
    "    text = html2text(cleanhtml)\n",
    "    soup = BeautifulSoup(html,\"lxml\")\n",
    "    text2 = soup.get_text()\n",
    "    noncancer.append([text2,'nc'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cnT  = pd.DataFrame(cancer,columns=['text','lable'])\n",
    "NcnT = pd.DataFrame(noncancer,columns=['text','lable'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cm = [cnT,NcnT]\n",
    "df = pd.concat(cm,ignore_index=True)\n",
    "df = df.sample(frac=1).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>lable</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Case Reports ImmunolCase Reports ImmunolCRIICa...</td>\n",
       "      <td>nc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Case Reports ImmunolCase Reports ImmunolCRIICa...</td>\n",
       "      <td>nc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Cancer Immunol ImmunotherCancer Immunol. Immun...</td>\n",
       "      <td>c</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Cancer Biol MedCancer Biol MedCBMCancer Biolog...</td>\n",
       "      <td>c</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Cancer ImagingCancer ImagingCancer Imaging1740...</td>\n",
       "      <td>c</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                text lable\n",
       "0  Case Reports ImmunolCase Reports ImmunolCRIICa...    nc\n",
       "1  Case Reports ImmunolCase Reports ImmunolCRIICa...    nc\n",
       "2  Cancer Immunol ImmunotherCancer Immunol. Immun...     c\n",
       "3  Cancer Biol MedCancer Biol MedCBMCancer Biolog...     c\n",
       "4  Cancer ImagingCancer ImagingCancer Imaging1740...     c"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Clean Text Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from nltk.tokenize import word_tokenize,sent_tokenize\n",
    "import re\n",
    "import string\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.tokenize import RegexpTokenizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data  = df\n",
    "data  = np.array(data['text'])\n",
    "lable  = df['lable'].map({\"c\":1,\"nc\":0})\n",
    "lable  = np.array(lable)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def cleanDatset(holdtext):\n",
    "    dataset=[]\n",
    "    for i in tqdm(holdtext):        \n",
    "        sent_tokenize_list = sent_tokenize(i)\n",
    "        tokenized_reports = [word_tokenize(report) for report in sent_tokenize_list]\n",
    "        regex = re.compile('[%s]' % re.escape(string.punctuation)) #see documentation here: http://docs.python.org/2/library/string.html\n",
    "        tokenized_reports_no_punctuation = []\n",
    "        \n",
    "        for review in tokenized_reports:\n",
    "            new_review = []\n",
    "            for token in review: \n",
    "                new_token = regex.sub(u'', token)\n",
    "                if not new_token == u'':\n",
    "                    new_review.append(new_token)\n",
    "            tokenized_reports_no_punctuation.append(new_review)    \n",
    "        tokenized_reports_no_stopwords = []\n",
    "        for report in tokenized_reports_no_punctuation:\n",
    "            new_term_vector = []\n",
    "            for word in report:\n",
    "                if not word in stopwords.words('english'):\n",
    "                    new_term_vector.append(word)\n",
    "            tokenized_reports_no_stopwords.append(new_term_vector)\n",
    "        v=[]\n",
    "        for i in tokenized_reports_no_stopwords:\n",
    "            v.append(\" \".join(i))\n",
    "        gt=\" \".join(v)\n",
    "        text2=''.join([i for i in gt if not i.isdigit()])\n",
    "        toker = RegexpTokenizer(r'((?<=[^\\w\\s])\\w(?=[^\\w\\s])|(\\W))+', gaps=True)\n",
    "        fnlTxt=''.join(toker.tokenize(text2))\n",
    "        dataset.append(\" \".join(fnlTxt.split()))\n",
    "    return dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 450/450 [05:40<00:00,  1.32it/s]\n"
     ]
    }
   ],
   "source": [
    "dataset = cleanDatset(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "t1=pd.DataFrame(dataset,columns=[\"text\"])\n",
    "t2=pd.DataFrame(lable,columns=[\"lable\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>lable</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Case Reports ImmunolCase Reports ImmunolCRIICa...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Case Reports ImmunolCase Reports ImmunolCRIICa...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Cancer Immunol ImmunotherCancer Immunol Immuno...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Cancer Biol MedCancer Biol MedCBMCancer Biolog...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Cancer ImagingCancer ImagingCancer ImagingBioM...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Cancer ImagingCancer ImagingCICancer ImagingCa...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Cancer ImagingCancer ImagingCancer ImagingBioM...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Clin Drug InvestigClin Drug InvestigClinical D...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Cancer ImagingCancer ImagingCancer ImagingBioM...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Cancer ImagingCICancer ImagingCancer ImagingeM...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                text  lable\n",
       "0  Case Reports ImmunolCase Reports ImmunolCRIICa...      0\n",
       "1  Case Reports ImmunolCase Reports ImmunolCRIICa...      0\n",
       "2  Cancer Immunol ImmunotherCancer Immunol Immuno...      1\n",
       "3  Cancer Biol MedCancer Biol MedCBMCancer Biolog...      1\n",
       "4  Cancer ImagingCancer ImagingCancer ImagingBioM...      1\n",
       "5  Cancer ImagingCancer ImagingCICancer ImagingCa...      1\n",
       "6  Cancer ImagingCancer ImagingCancer ImagingBioM...      1\n",
       "7  Clin Drug InvestigClin Drug InvestigClinical D...      0\n",
       "8  Cancer ImagingCancer ImagingCancer ImagingBioM...      1\n",
       "9  Cancer ImagingCICancer ImagingCancer ImagingeM...      1"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trData = pd.concat([t1,t2],axis=1)\n",
    "trData.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "holdtext = np.array(trData['text'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "vect = CountVectorizer(max_features=3000)\n",
    "vect.fit(holdtext)\n",
    "simple_train_dtm = vect.transform(holdtext)\n",
    "std   =  simple_train_dtm.toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>ab</th>\n",
       "      <th>abdomen</th>\n",
       "      <th>abdominal</th>\n",
       "      <th>ability</th>\n",
       "      <th>ablation</th>\n",
       "      <th>ablative</th>\n",
       "      <th>able</th>\n",
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       "      <th>abnormalities</th>\n",
       "      <th>abnormality</th>\n",
       "      <th>...</th>\n",
       "      <th>yield</th>\n",
       "      <th>york</th>\n",
       "      <th>young</th>\n",
       "      <th>younger</th>\n",
       "      <th>zebrafish</th>\n",
       "      <th>zero</th>\n",
       "      <th>zr</th>\n",
       "      <th>μgml</th>\n",
       "      <th>μl</th>\n",
       "      <th>μm</th>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
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       "      <th>3</th>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\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>...</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</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>...</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>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 3000 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   ab  abdomen  abdominal  ability  ablation  ablative  able  abnormal  \\\n",
       "0   0        0          0        0         0         0     0         0   \n",
       "1   4        0          0        0         0         0     1         0   \n",
       "2   0        0          0        0         0         0     0         0   \n",
       "3   0        0          0        1         0         0     0         0   \n",
       "4   0        0          0        1         0         0     0         0   \n",
       "5   0        0          0        0         1         0     1         1   \n",
       "6   0        0          0        0         5         0     0         0   \n",
       "7   1        0          2        1         0         0     2         0   \n",
       "8   0        1          0        1         0         0     0         0   \n",
       "9   0        2          2        5         0         0     0         0   \n",
       "\n",
       "   abnormalities  abnormality ...  yield  york  young  younger  zebrafish  \\\n",
       "0              0            0 ...      0     0      0        0          0   \n",
       "1              0            0 ...      0     0      0        0          0   \n",
       "2              0            0 ...      0     0      0        0          0   \n",
       "3              0            0 ...      0     0      0        0          0   \n",
       "4              0            0 ...      0     0      0        0          0   \n",
       "5              0            0 ...      0     0      1        0          0   \n",
       "6              0            0 ...      0     0      0        0          0   \n",
       "7              0            0 ...      0     0      0        0          0   \n",
       "8              0            0 ...      0     0      0        0          0   \n",
       "9              0            0 ...      0     0      0        0          0   \n",
       "\n",
       "   zero  zr  μgml  μl  μm  \n",
       "0     0   0     0   0   0  \n",
       "1     0   0     0   6   0  \n",
       "2     0   0     0   0   0  \n",
       "3     0   0     0   0   0  \n",
       "4     0   0     0   0   0  \n",
       "5     0   0     0   0   0  \n",
       "6     0   0     0   0   0  \n",
       "7     0   0     0   0   0  \n",
       "8     0   0     0   0   0  \n",
       "9     1   0     0   0   0  \n",
       "\n",
       "[10 rows x 3000 columns]"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tab = pd.DataFrame(simple_train_dtm.toarray(),columns=vect.get_feature_names())\n",
    "tab.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Reduction < Select Important Features >"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "def dimensionReduction(nof):\n",
    "    pca = PCA(n_components=nof)\n",
    "    fit = pca.fit(std)\n",
    "    tx = pd.DataFrame(pca.components_,columns=tab.columns)\n",
    "    td  = np.array(tx)\n",
    "    tmp = td[-1]\n",
    "    newA = np.copy(tmp)\n",
    "    pos=[]\n",
    "    for i in range(nof):\n",
    "        mx = np.where(newA==np.max(newA))\n",
    "        pos.append(mx[0][0])\n",
    "        newA[mx[0][0]]=-999\n",
    "    newcolname = tx.columns[pos]\n",
    "    val = []\n",
    "    for i in newcolname:\n",
    "        val.append(i)\n",
    "    redu_features = tab[val]\n",
    "    #print(tab.shape)\n",
    "    #print(redu_features.shape)\n",
    "    return redu_features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Split to train-test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>able</th>\n",
       "      <th>absolute</th>\n",
       "      <th>ab</th>\n",
       "      <th>affects</th>\n",
       "      <th>absorption</th>\n",
       "      <th>academy</th>\n",
       "      <th>pe</th>\n",
       "      <th>abnormality</th>\n",
       "      <th>ablation</th>\n",
       "      <th>activated</th>\n",
       "      <th>...</th>\n",
       "      <th>emotional</th>\n",
       "      <th>mabs</th>\n",
       "      <th>fentanyl</th>\n",
       "      <th>observed</th>\n",
       "      <th>obstruction</th>\n",
       "      <th>surgery</th>\n",
       "      <th>process</th>\n",
       "      <th>challenging</th>\n",
       "      <th>bowel</th>\n",
       "      <th>fractures</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</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>...</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</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>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>0</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>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>53</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 500 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   able  absolute  ab  affects  absorption  academy  pe  abnormality  \\\n",
       "0     0         0   0        2           0        0   0            0   \n",
       "1     1         4   4        0           0        0   0            0   \n",
       "2     0         1   0        0           0        3   0            0   \n",
       "3     0         0   0        0           0        1   0            0   \n",
       "4     0         0   0        0           0        0   0            0   \n",
       "5     1         0   0        1           0        0   0            0   \n",
       "6     0         0   0        0           0        0   0            0   \n",
       "7     2         1   1        0           1        1   0            0   \n",
       "8     0         0   0        0           0        0   0            0   \n",
       "9     0         0   0        0           0        0   0            0   \n",
       "\n",
       "   ablation  activated    ...      emotional  mabs  fentanyl  observed  \\\n",
       "0         0          0    ...              0     0         0         1   \n",
       "1         0          0    ...              0     0         0         2   \n",
       "2         0          2    ...              0     0         0         8   \n",
       "3         0          0    ...              0     0         0         2   \n",
       "4         0          0    ...              0     0         0         0   \n",
       "5         1          0    ...              0     0         0         1   \n",
       "6         5          0    ...              0     0         0         0   \n",
       "7         0          0    ...              1     0         1         1   \n",
       "8         0          0    ...              0     0         0         1   \n",
       "9         0          0    ...              0     0         0         1   \n",
       "\n",
       "   obstruction  surgery  process  challenging  bowel  fractures  \n",
       "0            0        0        0            0      0          0  \n",
       "1            0        0        0            1      2          0  \n",
       "2            0        0        0            0      0          0  \n",
       "3            0        4        2            0      0          0  \n",
       "4            0        2        2            0      0          0  \n",
       "5            1        1        0            2      1          0  \n",
       "6            0        0        0            0      0          0  \n",
       "7            0        0        1            1     53          0  \n",
       "8            0        0        0            0      0          0  \n",
       "9            0        2        0            0      2          0  \n",
       "\n",
       "[10 rows x 500 columns]"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "redu_features = dimensionReduction(500)\n",
    "redu_features.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X = np.array(redu_features)\n",
    "y = np.array(trData['lable'].astype('int'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "collapsed": true
   },
   "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=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn import svm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "clf = svm.SVC() # svm classifer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape=None, degree=3, gamma='auto', kernel='rbf',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(X_train, y_train) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import confusion_matrix \n",
    "from sklearn.metrics import precision_recall_curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "result1 = clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 17,  29],\n",
       "       [  0, 103]])"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test,result1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.80536912751677847"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test,result1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn import metrics\n",
    "from sklearn.metrics import average_precision_score\n",
    "from sklearn.metrics import precision_recall_curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_score = clf.decision_function(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average precision-recall score: 0.99\n"
     ]
    }
   ],
   "source": [
    "average_precision = average_precision_score(y_test, y_score)\n",
    "\n",
    "print('Average precision-recall score: {0:0.2f}'.format(\n",
    "      average_precision))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        this.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"639.9999861283738\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fa0e39d4d30>"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "precision, recall, _ = precision_recall_curve(y_test, y_score)\n",
    "\n",
    "plt.step(recall, precision, color='b', alpha=0.2,\n",
    "         where='post')\n",
    "plt.fill_between(recall, precision, step='post', alpha=0.2,\n",
    "                 color='b')\n",
    "\n",
    "plt.xlabel('Recall')\n",
    "plt.ylabel('Precision')\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.title('2-class Precision-Recall curve: AP={0:0.2f}'.format(\n",
    "          average_precision))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Boosted tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import xgboost as xgb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "       colsample_bytree=1, gamma=0, learning_rate=0.1, max_delta_step=0,\n",
       "       max_depth=3, min_child_weight=1, missing=None, n_estimators=100,\n",
       "       n_jobs=1, nthread=None, objective='binary:logistic', random_state=0,\n",
       "       reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,\n",
       "       silent=True, subsample=1)"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_xgboost = xgb.XGBClassifier() # boosted tree classifire\n",
    "model_xgboost.fit(X_train,y_train) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.979865771812\n"
     ]
    }
   ],
   "source": [
    "pred = model_xgboost.predict(X_test)\n",
    "print(accuracy_score(pred,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 43,   3],\n",
       "       [  0, 103]])"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test,pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average precision-recall score: 0.99\n"
     ]
    }
   ],
   "source": [
    "average_precision = average_precision_score(y_test,pred)\n",
    "\n",
    "print('Average precision-recall score: {0:0.2f}'.format(\n",
    "      average_precision))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        this.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"639.9999861283738\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fa0e39e21d0>"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "precision, recall, _ = precision_recall_curve(y_test,pred )\n",
    "\n",
    "plt.step(recall, precision, color='b', alpha=0.2,\n",
    "         where='post')\n",
    "plt.fill_between(recall, precision, step='post', alpha=0.2,\n",
    "                 color='b')\n",
    "\n",
    "plt.xlabel('Recall')\n",
    "plt.ylabel('Precision')\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.title('2-class Precision-Recall curve: AP={0:0.2f}'.format(\n",
    "          average_precision))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Analysis of effect Reduce the dimension of feature and compare between SVM and Boosted Tree\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Total extracted dimesion is 3000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2510, 2010, 1510, 1010, 510, 10]"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v= np.arange(10,3005,500)\n",
    "sample = sorted(v,reverse=True)\n",
    "sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 6/6 [00:10<00:00,  1.42s/it]\n"
     ]
    }
   ],
   "source": [
    "svmAc  = []\n",
    "boostAcc = []\n",
    "for i in tqdm(sample):\n",
    "    redu = dimensionReduction(i)\n",
    "    X = np.array(redu)\n",
    "    y = np.array(trData['lable'].astype('int'))\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=4)\n",
    "    clf.fit(X_train, y_train)\n",
    "    result1 = clf.predict(X_test)\n",
    "    svmAc.append(accuracy_score(y_test,result1))\n",
    "    model_xgboost.fit(X_train,y_train) \n",
    "    pred = model_xgboost.predict(X_test)\n",
    "    boostAcc.append(accuracy_score(pred,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import plotly.plotly as py\n",
    "import plotly.graph_objs as go"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~ayon.mi1/10.embed\" height=\"525px\" width=\"100%\"></iframe>"
      ],
      "text/plain": [
       "<plotly.tools.PlotlyDisplay object>"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trace1 = go.Bar(\n",
    "    x = sample,\n",
    "    y = svmAc,\n",
    "    name='SVM_acc'\n",
    ")\n",
    "trace2 = go.Bar(\n",
    "    x = sample,\n",
    "    y = boostAcc,\n",
    "    name='Boost_acc'\n",
    ")\n",
    "\n",
    "data = [trace1, trace2]\n",
    "layout = go.Layout(\n",
    "    barmode='group',\n",
    "    title='Accuracy_Changes_by_reduce_the_feature',\n",
    "    xaxis=dict(\n",
    "        title='No_of_feature',\n",
    "        titlefont=dict(\n",
    "            family='Courier New, monospace',\n",
    "            size=18,\n",
    "            color='#7f7f7f'\n",
    "        )\n",
    "    ),\n",
    "    yaxis=dict(\n",
    "        title='Accuracy',\n",
    "        titlefont=dict(\n",
    "            family='Courier New, monospace',\n",
    "            size=18,\n",
    "            color='#7f7f7f'\n",
    "        )\n",
    "    )\n",
    ")\n",
    "\n",
    "fig = go.Figure(data=data, layout=layout)\n",
    "py.iplot(fig, filename='Accuracy_Changes_by_reduce_the_feature')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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t6NChRWHhH58D+krC8OHDbZdddmlaFFx55ZV2wQUX2Jtvvlm08ULife97n82e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\nicD/AAWxlJgECnpjAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(filename='fig.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [default]",
   "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": "3.5.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}