{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Treatise of Medical Image Processing (TMIP)\n",
    "## TMIP_BrainTumour\n",
    "\n",
    "## Problem statement\n",
    "\n",
    "Typically to classify a brain tumour, neurologists would go manually through MRI scans and multiple imaging modalities to locate the tumour and extract information. However going through over a hundred images per patient can be a tedious and time-consuming process.\n",
    "Could we accelerate this process by classifying brain tumours based on their MRI scans before a neurologist starts looking at them? \n",
    "\n",
    "## Medical Approach\n",
    "Brain tumours are among the most fatal cancers in the western population. Gliomas (tumours that arises from glial cells) are the most frequent type of primary brain tumours (70%). Astrocytomas, Oligodendrogliomas and Glioblastomas (GBM) are three classifications of gliomas. In particular GBMs are the most common primary brain tumors among adults. They tend to be very aggressive and grow rapidly. \n",
    "\n",
    "Magnetic Resonnance Imaging (MRI) is the imaging technique of choice for brain tumour diagnosis. It is often used in combination with other imaging techniques such as Positron Emission Tomography (PET) to provide a diagnosis.  \n",
    "A typical brain MRI exam usually includes images of the brain along different axes (axial, cortical or sagital). TMIP_BrainTumour Project will only consider axial images at this stage. \n",
    "\n",
    "MRI examination may be approached using a multiple types of sequences that virtualize tissues to appear in different intensities according to the type of sequence. TMIP_BrainTumour Project utilises four types of sequences used in brain MRI examinations:\n",
    "T1: on these images fat appears brighter and fluids darker\n",
    "T1GD: T1 images after intravenous injection of a contrast agent (gadolinium)\n",
    "T2: on these images fluids appear brighter and fat darker\n",
    "FLAIR: T2 images where fluids are removed   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import os\n",
    "from sqlalchemy import create_engine\n",
    "%matplotlib inline\n",
    "pd.set_option(\"display.max_columns\",101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sample</th>\n",
       "      <th>Radiologist</th>\n",
       "      <th>Tumor_Location</th>\n",
       "      <th>Side_of_Tumor_Epicenter</th>\n",
       "      <th>Eloquent_Brain</th>\n",
       "      <th>Enhancement_Quality</th>\n",
       "      <th>Proportion_Enhancing</th>\n",
       "      <th>Proportion_nCET</th>\n",
       "      <th>Proportion_Necrosis</th>\n",
       "      <th>Cyst</th>\n",
       "      <th>Multifocal_or_Multicentric</th>\n",
       "      <th>T1_FLAIR_Ratio</th>\n",
       "      <th>Thickness_Enhancing_Margin</th>\n",
       "      <th>Definition_Enhancing_Margin</th>\n",
       "      <th>Definition_Non_Enhancing_Margin</th>\n",
       "      <th>Proportion_of_Edema</th>\n",
       "      <th>Edema_Crosses_Midline</th>\n",
       "      <th>Hemorrhage</th>\n",
       "      <th>Diffusion</th>\n",
       "      <th>Pial_Invasion</th>\n",
       "      <th>Ependymal_Invasion</th>\n",
       "      <th>Cortical_Involvement</th>\n",
       "      <th>Deep_WM_Invasion</th>\n",
       "      <th>nCET_Tumor_Crosses_Midline</th>\n",
       "      <th>Enhancing_Tumor_Crosses_Midline</th>\n",
       "      <th>Satellites</th>\n",
       "      <th>Calvarial_Remodeling</th>\n",
       "      <th>Extent_Resection_Enhancing_Tumor</th>\n",
       "      <th>Extent_Resection_nCET</th>\n",
       "      <th>Extent_Resection_Vasogenic_Edema</th>\n",
       "      <th>Lesion_Size_x</th>\n",
       "      <th>Lesion_Size_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>900_00_1961</td>\n",
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       "      <th>1</th>\n",
       "      <td>900_00_1961</td>\n",
       "      <td>Radiologist #2</td>\n",
       "      <td>1, 3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>2</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>900_00_1961</td>\n",
       "      <td>Radiologist #3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>2</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>7</td>\n",
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       "      <th>3</th>\n",
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       "      <td>Radiologist #1</td>\n",
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       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>900_00_5299</td>\n",
       "      <td>Radiologist #2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
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       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Sample     Radiologist Tumor_Location  Side_of_Tumor_Epicenter  \\\n",
       "0  900_00_1961  Radiologist #1        1, 2, 3                        1   \n",
       "1  900_00_1961  Radiologist #2           1, 3                        1   \n",
       "2  900_00_1961  Radiologist #3              3                        1   \n",
       "3  900_00_5299  Radiologist #1              2                        1   \n",
       "4  900_00_5299  Radiologist #2              2                        1   \n",
       "\n",
       "   Eloquent_Brain  Enhancement_Quality  Proportion_Enhancing  Proportion_nCET  \\\n",
       "0               1                    1                     1                7   \n",
       "1               1                    1                     1                8   \n",
       "2               1                    1                     1                8   \n",
       "3               1                    3                     4                3   \n",
       "4               1                    3                     4                3   \n",
       "\n",
       "   Proportion_Necrosis  Cyst  Multifocal_or_Multicentric  T1_FLAIR_Ratio  \\\n",
       "0                    1     1                           1               1   \n",
       "1                    1     1                           1               1   \n",
       "2                    1     1                           1               1   \n",
       "3                    4     1                           1               2   \n",
       "4                    4     1                           2               1   \n",
       "\n",
       "   Thickness_Enhancing_Margin  Definition_Enhancing_Margin  \\\n",
       "0                           1                            1   \n",
       "1                           1                            1   \n",
       "2                           1                            1   \n",
       "3                           4                            2   \n",
       "4                           4                            2   \n",
       "\n",
       "   Definition_Non_Enhancing_Margin  Proportion_of_Edema  \\\n",
       "0                                2                    3   \n",
       "1                                2                    2   \n",
       "2                                2                    2   \n",
       "3                                2                    5   \n",
       "4                                3                    5   \n",
       "\n",
       "   Edema_Crosses_Midline  Hemorrhage  Diffusion  Pial_Invasion  \\\n",
       "0                      2           1          2              1   \n",
       "1                      1           1          4              1   \n",
       "2                      1           1          2              1   \n",
       "3                      2           1          1              2   \n",
       "4                      2           2          1              2   \n",
       "\n",
       "   Ependymal_Invasion  Cortical_Involvement  Deep_WM_Invasion  \\\n",
       "0                   1                     2                 1   \n",
       "1                   1                     2                 1   \n",
       "2                   1                     2                 1   \n",
       "3                   1                     2                 1   \n",
       "4                   1                     2                 1   \n",
       "\n",
       "   nCET_Tumor_Crosses_Midline  Enhancing_Tumor_Crosses_Midline  Satellites  \\\n",
       "0                           2                                1           1   \n",
       "1                           2                                1           1   \n",
       "2                           2                                1           1   \n",
       "3                           2                                2           1   \n",
       "4                           2                                2           2   \n",
       "\n",
       "   Calvarial_Remodeling  Extent_Resection_Enhancing_Tumor  \\\n",
       "0                     1                                 1   \n",
       "1                     1                                 1   \n",
       "2                     1                                 1   \n",
       "3                     1                                 7   \n",
       "4                     2                                 8   \n",
       "\n",
       "   Extent_Resection_nCET  Extent_Resection_Vasogenic_Edema  Lesion_Size_x  \\\n",
       "0                      6                                 2             12   \n",
       "1                      5                                 1             11   \n",
       "2                      4                                 1             11   \n",
       "3                      2                                 3             10   \n",
       "4                      2                                 2             11   \n",
       "\n",
       "   Lesion_Size_y  \n",
       "0              7  \n",
       "1              8  \n",
       "2              7  \n",
       "3              9  \n",
       "4              8  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tumour_features = pd.read_excel('VASARI_MRI_features (gmdi - wiki).xls')\n",
    "tumour_features.head()\n",
    "\n",
    "# Rename columns with meaningful names and drop ID and comments columns\n",
    "\n",
    "cols = ['Sample','Radiologist','Tumor_Location','Side_of_Tumor_Epicenter','Eloquent_Brain','Enhancement_Quality',\n",
    "        'Proportion_Enhancing','Proportion_nCET', 'Proportion_Necrosis','Cyst','Multifocal_or_Multicentric',\n",
    "        'T1_FLAIR_Ratio','Thickness_Enhancing_Margin','Definition_Enhancing_Margin', 'Definition_Non_Enhancing_Margin',\n",
    "        'Proportion_of_Edema','Edema_Crosses_Midline','Hemorrhage','Diffusion','Pial_Invasion','Ependymal_Invasion',\n",
    "        'Cortical_Involvement','Deep_WM_Invasion','nCET_Tumor_Crosses_Midline','Enhancing_Tumor_Crosses_Midline',\n",
    "        'Satellites','Calvarial_Remodeling','Extent_Resection_Enhancing_Tumor','Extent_Resection_nCET',\n",
    "        'Extent_Resection_Vasogenic_Edema','Lesion_Size_x','Lesion_Size_y']\n",
    "\n",
    "tumour_features = tumour_features.drop(['ID', 'comments'], axis=1)\n",
    "tumour_features.columns = cols\n",
    "tumour_features.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sample</th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Survival_months</th>\n",
       "      <th>Disease</th>\n",
       "      <th>Grade</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>900_00_1961</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>15</td>\n",
       "      <td>ASTROCYTOMA</td>\n",
       "      <td>II</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>900_00_5332</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>15</td>\n",
       "      <td>GBM</td>\n",
       "      <td>--</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>900_00_5308</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>17</td>\n",
       "      <td>ASTROCYTOMA</td>\n",
       "      <td>III</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>900_00_5316</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>17</td>\n",
       "      <td>GBM</td>\n",
       "      <td>--</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>900_00_5317</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>17</td>\n",
       "      <td>GBM</td>\n",
       "      <td>--</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "        Sample  Age Gender Survival_months       Disease Grade\n",
       "0  900_00_1961   --     --              15   ASTROCYTOMA    II\n",
       "1  900_00_5332   --     --              15           GBM    --\n",
       "2  900_00_5308   --     --              17   ASTROCYTOMA   III\n",
       "3  900_00_5316   --     --              17           GBM    --\n",
       "4  900_00_5317   --     --              17           GBM    --"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read in CSV and format dataframe\n",
    "\n",
    "patients_info = pd.read_excel('clinical_2014-01-16.xlsx')\n",
    "patients_info.drop(patients_info.columns[6:], axis=1, inplace=True)\n",
    "patients_info.columns = ['Sample','Age','Gender','Survival_months','Disease','Grade']\n",
    "patients_info.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " ASTROCYTOMA          47\n",
       " GBM                  43\n",
       " OLIGODENDROGLIOMA    21\n",
       " --                   15\n",
       " MIXED                 1\n",
       "Name: Disease, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "patients_info['Disease'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Baseline accuracy of the whole dataset: 42.34 %\n"
     ]
    }
   ],
   "source": [
    "# Calculate baseline accuracy ofthe whole dataset (for astrocytoma/gbm/oligodendroglioma diagnosis only)\n",
    "baseline_whole = float(patients_info['Disease'].value_counts()[0])/float(np.sum([patients_info['Disease'].value_counts()[i] for i in range(3)]))\n",
    "print('Baseline accuracy of the whole dataset:', np.round(baseline_whole*100, 2), '%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Merge the two dataframes\n",
    "tumours = pd.merge(patients_info[['Sample','Disease','Survival_months']], tumour_features, on='Sample', how='inner')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Assign survival column type to int\n",
    "tumours['Survival_months'] = tumours['Survival_months'].astype('int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Astocytoma first index: 0\n",
      "GBM first index: 3\n",
      "Oligodendroglioma first index: 21\n"
     ]
    }
   ],
   "source": [
    "# Display first index of each disease\n",
    "\n",
    "print('Astocytoma first index:', tumours.loc[tumours['Disease']==' ASTROCYTOMA', 'Disease'].index[0])\n",
    "print('GBM first index:', tumours.loc[tumours['Disease']==' GBM', 'Disease'].index[0])\n",
    "print('Oligodendroglioma first index:', tumours.loc[tumours['Disease']==' OLIGODENDROGLIOMA', 'Disease'].index[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Sample</th>\n",
       "      <th>Disease</th>\n",
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       "      <th>Thickness_Enhancing_Margin</th>\n",
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       "      <th>Definition_Non_Enhancing_Margin</th>\n",
       "      <th>Proportion_of_Edema</th>\n",
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       "      <th>Hemorrhage</th>\n",
       "      <th>Diffusion</th>\n",
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       "      <td>7</td>\n",
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       "      <td>15</td>\n",
       "      <td>Radiologist #2</td>\n",
       "      <td>1, 3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>11</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>900_00_1961</td>\n",
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       "      <td>15</td>\n",
       "      <td>Radiologist #3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>2</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
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       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Sample  Disease  Survival_months     Radiologist Tumor_Location  \\\n",
       "0  900_00_1961        0               15  Radiologist #1        1, 2, 3   \n",
       "1  900_00_1961        0               15  Radiologist #2           1, 3   \n",
       "2  900_00_1961        0               15  Radiologist #3              3   \n",
       "3  900_00_5332        1               15  Radiologist #1              2   \n",
       "4  900_00_5332        1               15  Radiologist #2              2   \n",
       "\n",
       "   Side_of_Tumor_Epicenter  Eloquent_Brain  Enhancement_Quality  \\\n",
       "0                        1               1                    1   \n",
       "1                        1               1                    1   \n",
       "2                        1               1                    1   \n",
       "3                        3               1                    3   \n",
       "4                        3               3                    3   \n",
       "\n",
       "   Proportion_Enhancing  Proportion_nCET  Proportion_Necrosis  Cyst  \\\n",
       "0                     1                7                    1     1   \n",
       "1                     1                8                    1     1   \n",
       "2                     1                8                    1     1   \n",
       "3                     4                5                    5     1   \n",
       "4                     5                4                    5     1   \n",
       "\n",
       "   Multifocal_or_Multicentric  T1_FLAIR_Ratio  Thickness_Enhancing_Margin  \\\n",
       "0                           1               1                           1   \n",
       "1                           1               1                           1   \n",
       "2                           1               1                           1   \n",
       "3                           1               1                           4   \n",
       "4                           1               2                           4   \n",
       "\n",
       "   Definition_Enhancing_Margin  Definition_Non_Enhancing_Margin  \\\n",
       "0                            1                                2   \n",
       "1                            1                                2   \n",
       "2                            1                                2   \n",
       "3                            2                                2   \n",
       "4                            2                                2   \n",
       "\n",
       "   Proportion_of_Edema  Edema_Crosses_Midline  Hemorrhage  Diffusion  \\\n",
       "0                    3                      2           1          2   \n",
       "1                    2                      1           1          4   \n",
       "2                    2                      1           1          2   \n",
       "3                    4                      2           1          4   \n",
       "4                    3                      2           2          3   \n",
       "\n",
       "   Pial_Invasion  Ependymal_Invasion  Cortical_Involvement  Deep_WM_Invasion  \\\n",
       "0              1                   1                     2                 1   \n",
       "1              1                   1                     2                 1   \n",
       "2              1                   1                     2                 1   \n",
       "3              2                   2                     2                 1   \n",
       "4              2                   2                     2                 1   \n",
       "\n",
       "   nCET_Tumor_Crosses_Midline  Enhancing_Tumor_Crosses_Midline  Satellites  \\\n",
       "0                           2                                1           1   \n",
       "1                           2                                1           1   \n",
       "2                           2                                1           1   \n",
       "3                           2                                2           1   \n",
       "4                           2                                2           1   \n",
       "\n",
       "   Calvarial_Remodeling  Extent_Resection_Enhancing_Tumor  \\\n",
       "0                     1                                 1   \n",
       "1                     1                                 1   \n",
       "2                     1                                 1   \n",
       "3                     1                                 5   \n",
       "4                     1                                 6   \n",
       "\n",
       "   Extent_Resection_nCET  Extent_Resection_Vasogenic_Edema  Lesion_Size_x  \\\n",
       "0                      6                                 2             12   \n",
       "1                      5                                 1             11   \n",
       "2                      4                                 1             11   \n",
       "3                      5                                 2              9   \n",
       "4                      2                                 2             13   \n",
       "\n",
       "   Lesion_Size_y  \n",
       "0              7  \n",
       "1              8  \n",
       "2              7  \n",
       "3             11  \n",
       "4             11  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Encode Disease column\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "tumours['Disease'] = LabelEncoder().fit_transform(tumours['Disease'])\n",
    "\n",
    "tumours.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Astocytoma: 0\n",
      "GBM: 1\n",
      "Oligodendroglioma: 2\n"
     ]
    }
   ],
   "source": [
    "# Display encoded numbers associated with each disease\n",
    "\n",
    "print('Astocytoma:', tumours.iloc[0, 1])\n",
    "print('GBM:', tumours.iloc[3, 1])\n",
    "print('Oligodendroglioma:', tumours.iloc[21, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a new dataframe with only one line per patient: \n",
    "# value is assigned to the most frequent score between the three radiologists \n",
    "# or if there are three different values to score from radiologist #2\n",
    "\n",
    "dicty = {}\n",
    "for col in tumours.columns[4:]:\n",
    "    dicty[col]=[]\n",
    "    for sample in tumours['Sample'].unique():\n",
    "        count = tumours.loc[tumours['Sample']==sample, col].value_counts().sort_values(ascending=False)\n",
    "        if len(count) == 2:\n",
    "            dicty[col].append(count.index[0])\n",
    "        else:\n",
    "            dicty[col].append(tumours.loc[(tumours['Sample']==sample) & (tumours['Radiologist']=='Radiologist #2'), col].values[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
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       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>900_00_5316</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>900_00_5317</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1, 2, 3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Sample  Disease  Survival_months  Calvarial_Remodeling  \\\n",
       "0  900_00_1961        0               15                     1   \n",
       "1  900_00_5332        1               15                     1   \n",
       "2  900_00_5308        0               17                     1   \n",
       "3  900_00_5316        1               17                     1   \n",
       "4  900_00_5317        1               17                     1   \n",
       "\n",
       "   Cortical_Involvement  Cyst  Deep_WM_Invasion  Definition_Enhancing_Margin  \\\n",
       "0                     2     1                 1                            1   \n",
       "1                     2     1                 1                            2   \n",
       "2                     2     1                 1                            2   \n",
       "3                     2     1                 1                            2   \n",
       "4                     2     1                 2                            2   \n",
       "\n",
       "   Definition_Non_Enhancing_Margin  Diffusion  Edema_Crosses_Midline  \\\n",
       "0                                2          2                      1   \n",
       "1                                2          4                      2   \n",
       "2                                2          1                      1   \n",
       "3                                2          1                      2   \n",
       "4                                3          1                      2   \n",
       "\n",
       "   Eloquent_Brain  Enhancement_Quality  Enhancing_Tumor_Crosses_Midline  \\\n",
       "0               1                    1                                1   \n",
       "1               3                    3                                2   \n",
       "2               1                    3                                2   \n",
       "3               1                    3                                2   \n",
       "4               5                    3                                2   \n",
       "\n",
       "   Ependymal_Invasion  Extent_Resection_Enhancing_Tumor  \\\n",
       "0                   1                                 1   \n",
       "1                   2                                 5   \n",
       "2                   2                                 8   \n",
       "3                   1                                 7   \n",
       "4                   2                                 5   \n",
       "\n",
       "   Extent_Resection_Vasogenic_Edema  Extent_Resection_nCET  Hemorrhage  \\\n",
       "0                                 1                      5           1   \n",
       "1                                 2                      5           2   \n",
       "2                                 1                      5           1   \n",
       "3                                 2                      1           1   \n",
       "4                                 2                      4           1   \n",
       "\n",
       "   Lesion_Size_x  Lesion_Size_y  Multifocal_or_Multicentric  Pial_Invasion  \\\n",
       "0             11              7                           1              1   \n",
       "1             13             11                           1              2   \n",
       "2             11              8                           1              1   \n",
       "3             14              9                           1              1   \n",
       "4             18             12                           1              1   \n",
       "\n",
       "   Proportion_Enhancing  Proportion_Necrosis  Proportion_nCET  \\\n",
       "0                     1                    1                8   \n",
       "1                     4                    5                5   \n",
       "2                     4                    4                5   \n",
       "3                     4                    4                5   \n",
       "4                     4                    5                4   \n",
       "\n",
       "   Proportion_of_Edema  Satellites  Side_of_Tumor_Epicenter  T1_FLAIR_Ratio  \\\n",
       "0                    2           1                        1               1   \n",
       "1                    4           1                        3               2   \n",
       "2                    2           1                        1               1   \n",
       "3                    4           1                        3               2   \n",
       "4                    3           1                        1               2   \n",
       "\n",
       "   Thickness_Enhancing_Margin Tumor_Location  nCET_Tumor_Crosses_Midline  \n",
       "0                           1           1, 3                           2  \n",
       "1                           4              2                           2  \n",
       "2                           4              2                           2  \n",
       "3                           4              4                           2  \n",
       "4                           4        1, 2, 3                           2  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target = tumours.iloc[range(0,96,3), 0:3].reset_index()            \n",
    "tumours_clean = target.join(pd.DataFrame(dicty))\n",
    "tumours_clean.drop(['index'], axis=1, inplace=True)\n",
    "tumours_clean.to_csv('tumours_target_features.csv')#ABint\n",
    "tumours_clean.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read clean csv file back in\n",
    "tumours = pd.read_csv('tumours_target_features.csv', encoding='UTF8',index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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mZmYd5MAyM7MiOLDMzKwIDiwzMyuCA8vMzIrgwDIzsyI4sMzMrAgOLDMzK4IDy8zMiuDA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\nspiZNSPpdRHxcm7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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1aba1b3cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot histogramme of tumour type in dataset (32 patients)\n",
    "\n",
    "plt.hist(tumours['Disease'])\n",
    "plt.xticks([0,1,2],['Astrocytoma', 'GBM', 'Oligodendroglioma'])\n",
    "plt.xlabel('Tumour type')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Tumour types occurence in dataset of 32 patients')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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6LBcZ++FRzLFjzKIVzLHHStZHmOUcKVn2bGKOHWN2rWGW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\n/wdJG9wATbwS5wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1ab80b6c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot relations between variables and display tumour types\n",
    "\n",
    "sns.pairplot(tumours, hue='Disease', hue_order=[0,1,2], vars=['Survival_months',\n",
    "                                            'Eloquent_Brain','Proportion_Enhancing','Proportion_nCET',\n",
    "                                            'Proportion_Necrosis','Multifocal_or_Multicentric','T1_FLAIR_Ratio',\n",
    "                                            'Proportion_of_Edema','Extent_Resection_Enhancing_Tumor',\n",
    "                                            'Extent_Resection_nCET','Extent_Resection_Vasogenic_Edema',\n",
    "                                            'Lesion_Size_x', 'Lesion_Size_y'])\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f1ab27e2128>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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+sELI5gZYjiFr/HQBLo+ID9Iwtr1YOt8m73rgj8A3Wno9OQty240s/czdABwG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\n0Z0ctJQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1ab1f99c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot correlation heatmap\n",
    "\n",
    "f, ax = plt.subplots(figsize=(12, 9))\n",
    "sns.heatmap(tumours.corr())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on this first exploratory analysis, the tumor type ('Disease') doesn't seem to be particularly correlated to any specific features (there seems to be a slight correlation to edema features and nCET).  \n",
    "Some features such as Lesion_size_x and Lesion_size_y or Proportions of enhancing tissue, necrosis and nCET seem to be strongly correlated. Therefore we will apply some techniques such as Lasso, PCA and LDA to try and reduce the influence of correlated features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Calvarial_Remodeling</th>\n",
       "      <th>Cortical_Involvement</th>\n",
       "      <th>Cyst</th>\n",
       "      <th>Deep_WM_Invasion</th>\n",
       "      <th>Definition_Enhancing_Margin</th>\n",
       "      <th>Definition_Non_Enhancing_Margin</th>\n",
       "      <th>Diffusion</th>\n",
       "      <th>Edema_Crosses_Midline</th>\n",
       "      <th>Eloquent_Brain</th>\n",
       "      <th>Enhancement_Quality</th>\n",
       "      <th>Enhancing_Tumor_Crosses_Midline</th>\n",
       "      <th>Ependymal_Invasion</th>\n",
       "      <th>Extent_Resection_Enhancing_Tumor</th>\n",
       "      <th>Extent_Resection_Vasogenic_Edema</th>\n",
       "      <th>Extent_Resection_nCET</th>\n",
       "      <th>Hemorrhage</th>\n",
       "      <th>Lesion_Size_x</th>\n",
       "      <th>Lesion_Size_y</th>\n",
       "      <th>Multifocal_or_Multicentric</th>\n",
       "      <th>Pial_Invasion</th>\n",
       "      <th>Proportion_Enhancing</th>\n",
       "      <th>Proportion_Necrosis</th>\n",
       "      <th>Proportion_nCET</th>\n",
       "      <th>Proportion_of_Edema</th>\n",
       "      <th>Satellites</th>\n",
       "      <th>Side_of_Tumor_Epicenter</th>\n",
       "      <th>T1_FLAIR_Ratio</th>\n",
       "      <th>Thickness_Enhancing_Margin</th>\n",
       "      <th>nCET_Tumor_Crosses_Midline</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.214286</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.500</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.357143</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.375</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Calvarial_Remodeling  Cortical_Involvement  Cyst  Deep_WM_Invasion  \\\n",
       "0                   0.0                   1.0   0.0               0.0   \n",
       "1                   0.0                   1.0   0.0               0.0   \n",
       "2                   0.0                   1.0   0.0               0.0   \n",
       "3                   0.0                   1.0   0.0               0.0   \n",
       "4                   0.0                   1.0   0.0               1.0   \n",
       "\n",
       "   Definition_Enhancing_Margin  Definition_Non_Enhancing_Margin  Diffusion  \\\n",
       "0                          0.0                              0.0   0.333333   \n",
       "1                          0.5                              0.0   1.000000   \n",
       "2                          0.5                              0.0   0.000000   \n",
       "3                          0.5                              0.0   0.000000   \n",
       "4                          0.5                              1.0   0.000000   \n",
       "\n",
       "   Edema_Crosses_Midline  Eloquent_Brain  Enhancement_Quality  \\\n",
       "0                    0.0             0.0                  0.0   \n",
       "1                    0.5             0.5                  1.0   \n",
       "2                    0.0             0.0                  1.0   \n",
       "3                    0.5             0.0                  1.0   \n",
       "4                    0.5             1.0                  1.0   \n",
       "\n",
       "   Enhancing_Tumor_Crosses_Midline  Ependymal_Invasion  \\\n",
       "0                              0.0                 0.0   \n",
       "1                              0.5                 1.0   \n",
       "2                              0.5                 1.0   \n",
       "3                              0.5                 0.0   \n",
       "4                              0.5                 1.0   \n",
       "\n",
       "   Extent_Resection_Enhancing_Tumor  Extent_Resection_Vasogenic_Edema  \\\n",
       "0                          0.000000                          0.000000   \n",
       "1                          0.571429                          0.333333   \n",
       "2                          1.000000                          0.000000   \n",
       "3                          0.857143                          0.333333   \n",
       "4                          0.571429                          0.333333   \n",
       "\n",
       "   Extent_Resection_nCET  Hemorrhage  Lesion_Size_x  Lesion_Size_y  \\\n",
       "0                  0.500         0.0       0.363636       0.214286   \n",
       "1                  0.500         1.0       0.545455       0.500000   \n",
       "2                  0.500         0.0       0.363636       0.285714   \n",
       "3                  0.000         0.0       0.636364       0.357143   \n",
       "4                  0.375         0.0       1.000000       0.571429   \n",
       "\n",
       "   Multifocal_or_Multicentric  Pial_Invasion  Proportion_Enhancing  \\\n",
       "0                         0.0            0.0                   0.0   \n",
       "1                         0.0            1.0                   0.6   \n",
       "2                         0.0            0.0                   0.6   \n",
       "3                         0.0            0.0                   0.6   \n",
       "4                         0.0            0.0                   0.6   \n",
       "\n",
       "   Proportion_Necrosis  Proportion_nCET  Proportion_of_Edema  Satellites  \\\n",
       "0                 0.00              1.0             0.000000         0.0   \n",
       "1                 1.00              0.4             0.666667         0.0   \n",
       "2                 0.75              0.4             0.000000         0.0   \n",
       "3                 0.75              0.4             0.666667         0.0   \n",
       "4                 1.00              0.2             0.333333         0.0   \n",
       "\n",
       "   Side_of_Tumor_Epicenter  T1_FLAIR_Ratio  Thickness_Enhancing_Margin  \\\n",
       "0                      0.0             0.0                         0.0   \n",
       "1                      1.0             0.5                         1.0   \n",
       "2                      0.0             0.0                         1.0   \n",
       "3                      1.0             0.5                         1.0   \n",
       "4                      0.0             0.5                         1.0   \n",
       "\n",
       "   nCET_Tumor_Crosses_Midline  \n",
       "0                         0.0  \n",
       "1                         0.0  \n",
       "2                         0.0  \n",
       "3                         0.0  \n",
       "4                         0.0  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "X = tumours.drop(['Sample', 'Disease', 'Survival_months', 'Tumor_Location'], axis=1)\n",
    "X_cols = X.columns\n",
    "X = MinMaxScaler().fit_transform(X)\n",
    "X = pd.DataFrame(X, columns=X_cols)\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Cumulative % of Variance Explained</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>30.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>47.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>59.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>67.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>73.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>78.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>81.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>84.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>87.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>90.27</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Cumulative % of Variance Explained\n",
       "1                                30.74\n",
       "2                                47.93\n",
       "3                                59.78\n",
       "4                                67.76\n",
       "5                                73.62\n",
       "6                                78.13\n",
       "7                                81.67\n",
       "8                                84.97\n",
       "9                                87.79\n",
       "10                               90.27"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "\n",
    "pca = PCA()\n",
    "pca.fit(X)\n",
    "\n",
    "pd.DataFrame(np.cumsum([round(i*100, 2) for i in pca.explained_variance_ratio_]), index=range(1,30), columns=['Cumulative % of Variance Explained']).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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4W2QneiT5t4ZfjtcEuhGYC1zonMsIKTMAuNbvRO8JPFkeOtEPHDjAKQNOY0v1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\nEREJixKIiIiE5f8Bt1FoInQnxEIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1aac5cd6d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_pca = PCA(n_components=2).fit_transform(X)\n",
    "\n",
    "plt.scatter(X_pca[:,0], X_pca[:,1], c=tumours['Disease'], cmap='rainbow')\n",
    "plt.xlabel('pca 1')\n",
    "plt.ylabel('pca 2')\n",
    "plt.title('Tumour types scatter against pca components')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = tumours['Disease']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Baseline accuracy: 0.625\n"
     ]
    }
   ],
   "source": [
    "print('Baseline accuracy:', y.value_counts()[1]/float(len(y)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Store all model scores in a list of tuples to compare them\n",
    "model_scores = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross validated accuracy scores logistic regression C=1, Ridge penalty: [ 0.83333333  0.63636364  0.77777778]\n",
      "Mean cross validated accuracy: 0.749158249158\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import GridSearchCV, cross_val_score, cross_val_predict\n",
    "\n",
    "logreg = LogisticRegression()\n",
    "\n",
    "print('Cross validated accuracy scores logistic regression C=1, Ridge penalty:', cross_val_score(logreg, X, y, cv=3))\n",
    "print('Mean cross validated accuracy:', cross_val_score(logreg, X, y, cv=3).mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Regression parameters: {'penalty': 'l2', 'C': 10} \n",
      "\n",
      "Cross validated accuracy logistic regression C=10, Ridge penalty (optimised): 0.78125\n"
     ]
    }
   ],
   "source": [
    "gs_logreg = GridSearchCV(logreg, {'C': [10e-5, 10e-4, 10e-3, 10e-2, 0.1, 1, 10, 100, 10e3], 'penalty':['l1','l2']})\n",
    "gs_logreg.fit(X,y)\n",
    "\n",
    "print('Regression parameters:', gs_logreg.best_params_, '\\n')\n",
    "\n",
    "print('Cross validated accuracy logistic regression C=10, Ridge penalty (optimised):', gs_logreg.best_score_)\n",
    "\n",
    "model_scores.append(('gs_logreg', gs_logreg.best_score_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 3  1  3]\n",
      " [ 1 19  0]\n",
      " [ 2  0  3]]\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.50      0.43      0.46         7\n",
      "          1       0.95      0.95      0.95        20\n",
      "          2       0.50      0.60      0.55         5\n",
      "\n",
      "avg / total       0.78      0.78      0.78        32\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "\n",
    "print(confusion_matrix(y, cross_val_predict(gs_logreg.best_estimator_, X, y, cv=3)))\n",
    "print(classification_report(y, cross_val_predict(gs_logreg.best_estimator_, X, y, cv=3)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross validated accuracy scores KNN 5 neighbours: [ 0.83333333  0.72727273  0.66666667]\n",
      "Mean cross validated accuracy: 0.742424242424\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "knn = KNeighborsClassifier()\n",
    "\n",
    "print('Cross validated accuracy scores KNN 5 neighbours:', cross_val_score(knn, X, y, cv=3))\n",
    "print('Mean cross validated accuracy:', cross_val_score(knn, X, y, cv=3).mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Optimise model with GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNN parameters: {'n_neighbors': 6} \n",
      "\n",
      "Cross validated accuracy KNN 6 neighbours: 0.8125\n"
     ]
    }
   ],
   "source": [
    "gs_knn = GridSearchCV(knn, {'n_neighbors': range(1, 11)})\n",
    "gs_knn.fit(X,y)\n",
    "\n",
    "print('KNN parameters:', gs_knn.best_params_, '\\n')\n",
    "\n",
    "print('Cross validated accuracy KNN 6 neighbours:', gs_knn.best_score_)\n",
    "\n",
    "model_scores.append(('gs_knn', gs_knn.best_score_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross validated accuracy scores random forest: [ 0.83333333  0.90909091  0.55555556]\n",
      "Mean cross validated accuracy: 0.770202020202\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import classification_report\n",
    "rfc = RandomForestClassifier()\n",
    "\n",
    "print('Cross validated accuracy scores random forest:', cross_val_score(rfc, X, y, cv=3))\n",
    "print('Mean cross validated accuracy:', cross_val_score(rfc, X, y, cv=3).mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random forest parameters: {'n_estimators': 55} \n",
      "\n",
      "Cross validated accuracy Random Forest 15 estimators: 0.875\n"
     ]
    }
   ],
   "source": [
    "gs_rfc = GridSearchCV(rfc, {'n_estimators': range(5, 85, 5)})\n",
    "gs_rfc.fit(X,y)\n",
    "\n",
    "print('Random forest parameters:', gs_rfc.best_params_, '\\n')\n",
    "\n",
    "print('Cross validated accuracy Random Forest 15 estimators:', gs_rfc.best_score_)\n",
    "\n",
    "model_scores.append(('gs_rfc', gs_rfc.best_score_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 5  1  1]\n",
      " [ 0 20  0]\n",
      " [ 2  0  3]]\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.67      0.57      0.62         7\n",
      "          1       0.86      0.95      0.90        20\n",
      "          2       0.50      0.40      0.44         5\n",
      "\n",
      "avg / total       0.76      0.78      0.77        32\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y, cross_val_predict(gs_rfc.best_estimator_, X, y, cv=3)))\n",
    "print(classification_report(y, cross_val_predict(gs_rfc.best_estimator_, X, y, cv=3)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.97163295,  0.02836705])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "\n",
    "lda = LinearDiscriminantAnalysis()\n",
    "\n",
    "X_lda = lda.fit_transform(X, y)\n",
    "lda.explained_variance_ratio_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1aa49ca518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X_lda[:,0], X_lda[:,1], c=y, cmap='rainbow')\n",
    "plt.xlabel('lda1')\n",
    "plt.ylabel('lda2')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross validated accuracy scores LDA: [ 0.83333333  0.72727273  0.22222222]\n",
      "Mean cross validated accuracy: 0.594276094276\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/xenialxerus/anaconda/lib/python3.5/site-packages/sklearn/discriminant_analysis.py:388: UserWarning: Variables are collinear.\n",
      "  warnings.warn(\"Variables are collinear.\")\n",
      "/home/xenialxerus/anaconda/lib/python3.5/site-packages/sklearn/discriminant_analysis.py:388: UserWarning: Variables are collinear.\n",
      "  warnings.warn(\"Variables are collinear.\")\n",
      "/home/xenialxerus/anaconda/lib/python3.5/site-packages/sklearn/discriminant_analysis.py:388: UserWarning: Variables are collinear.\n",
      "  warnings.warn(\"Variables are collinear.\")\n",
      "/home/xenialxerus/anaconda/lib/python3.5/site-packages/sklearn/discriminant_analysis.py:388: UserWarning: Variables are collinear.\n",
      "  warnings.warn(\"Variables are collinear.\")\n",
      "/home/xenialxerus/anaconda/lib/python3.5/site-packages/sklearn/discriminant_analysis.py:388: UserWarning: Variables are collinear.\n",
      "  warnings.warn(\"Variables are collinear.\")\n",
      "/home/xenialxerus/anaconda/lib/python3.5/site-packages/sklearn/discriminant_analysis.py:388: UserWarning: Variables are collinear.\n",
      "  warnings.warn(\"Variables are collinear.\")\n",
      "/home/xenialxerus/anaconda/lib/python3.5/site-packages/sklearn/discriminant_analysis.py:388: UserWarning: Variables are collinear.\n",
      "  warnings.warn(\"Variables are collinear.\")\n",
      "/home/xenialxerus/anaconda/lib/python3.5/site-packages/sklearn/discriminant_analysis.py:388: UserWarning: Variables are collinear.\n",
      "  warnings.warn(\"Variables are collinear.\")\n",
      "/home/xenialxerus/anaconda/lib/python3.5/site-packages/sklearn/discriminant_analysis.py:388: UserWarning: Variables are collinear.\n",
      "  warnings.warn(\"Variables are collinear.\")\n"
     ]
    }
   ],
   "source": [
    "print('Cross validated accuracy scores LDA:', cross_val_score(lda, X, y, cv=3))\n",
    "print('Mean cross validated accuracy:', cross_val_score(lda, X, y, cv=3).mean())\n",
    "\n",
    "model_scores.append(('LDA',cross_val_score(lda, X, y, cv=3).mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 4  1  2]\n",
      " [ 4 13  3]\n",
      " [ 1  1  3]]\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.44      0.57      0.50         7\n",
      "          1       0.87      0.65      0.74        20\n",
      "          2       0.38      0.60      0.46         5\n",
      "\n",
      "avg / total       0.70      0.62      0.65        32\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/xenialxerus/anaconda/lib/python3.5/site-packages/sklearn/discriminant_analysis.py:388: UserWarning: Variables are collinear.\n",
      "  warnings.warn(\"Variables are collinear.\")\n",
      "/home/xenialxerus/anaconda/lib/python3.5/site-packages/sklearn/discriminant_analysis.py:388: UserWarning: Variables are collinear.\n",
      "  warnings.warn(\"Variables are collinear.\")\n",
      "/home/xenialxerus/anaconda/lib/python3.5/site-packages/sklearn/discriminant_analysis.py:388: UserWarning: Variables are collinear.\n",
      "  warnings.warn(\"Variables are collinear.\")\n",
      "/home/xenialxerus/anaconda/lib/python3.5/site-packages/sklearn/discriminant_analysis.py:388: UserWarning: Variables are collinear.\n",
      "  warnings.warn(\"Variables are collinear.\")\n",
      "/home/xenialxerus/anaconda/lib/python3.5/site-packages/sklearn/discriminant_analysis.py:388: UserWarning: Variables are collinear.\n",
      "  warnings.warn(\"Variables are collinear.\")\n",
      "/home/xenialxerus/anaconda/lib/python3.5/site-packages/sklearn/discriminant_analysis.py:388: UserWarning: Variables are collinear.\n",
      "  warnings.warn(\"Variables are collinear.\")\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y, cross_val_predict(lda, X, y, cv=3)))\n",
    "print(classification_report(y, cross_val_predict(lda, X, y, cv=3)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross validated accuracy scores Naive Bayes: [ 0.75        0.72727273  0.77777778]\n",
      "Mean cross validated accuracy: 0.751683501684\n"
     ]
    }
   ],
   "source": [
    "from sklearn.naive_bayes import MultinomialNB\n",
    "\n",
    "nbm = MultinomialNB()\n",
    "\n",
    "print('Cross validated accuracy scores Naive Bayes:', cross_val_score(nbm, X, y, cv=3))\n",
    "print('Mean cross validated accuracy:', cross_val_score(nbm, X, y, cv=3).mean())\n",
    "\n",
    "model_scores.append(('NBm', cross_val_score(nbm, X, y, cv=3).mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 4  2  1]\n",
      " [ 1 19  0]\n",
      " [ 2  2  1]]\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.57      0.57      0.57         7\n",
      "          1       0.83      0.95      0.88        20\n",
      "          2       0.50      0.20      0.29         5\n",
      "\n",
      "avg / total       0.72      0.75      0.72        32\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y, cross_val_predict(nbm, X, y, cv=3)))\n",
    "print(classification_report(y, cross_val_predict(nbm, X, y, cv=3)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model scores [('gs_logreg', 0.78125), ('gs_knn', 0.8125), ('gs_rfc', 0.875), ('LDA', 0.59427609427609429), ('NBm', 0.75168350168350173)]\n"
     ]
    }
   ],
   "source": [
    "print('Model scores', model_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "rfc_opt = gs_rfc.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>columns</th>\n",
       "      <th>feature_importances_rfc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Proportion_nCET</td>\n",
       "      <td>0.121462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Thickness_Enhancing_Margin</td>\n",
       "      <td>0.112232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Proportion_Necrosis</td>\n",
       "      <td>0.106723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Enhancement_Quality</td>\n",
       "      <td>0.092281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Extent_Resection_Vasogenic_Edema</td>\n",
       "      <td>0.057816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Extent_Resection_Enhancing_Tumor</td>\n",
       "      <td>0.055547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Proportion_Enhancing</td>\n",
       "      <td>0.054501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Definition_Enhancing_Margin</td>\n",
       "      <td>0.047747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Lesion_Size_x</td>\n",
       "      <td>0.044729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Edema_Crosses_Midline</td>\n",
       "      <td>0.040110</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             columns  feature_importances_rfc\n",
       "22                   Proportion_nCET                 0.121462\n",
       "27        Thickness_Enhancing_Margin                 0.112232\n",
       "21               Proportion_Necrosis                 0.106723\n",
       "9                Enhancement_Quality                 0.092281\n",
       "13  Extent_Resection_Vasogenic_Edema                 0.057816\n",
       "12  Extent_Resection_Enhancing_Tumor                 0.055547\n",
       "20              Proportion_Enhancing                 0.054501\n",
       "4        Definition_Enhancing_Margin                 0.047747\n",
       "16                     Lesion_Size_x                 0.044729\n",
       "7              Edema_Crosses_Midline                 0.040110"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Feature importance in Random Forest model\n",
    "rfc_top10 = pd.DataFrame({'columns':X.columns, \n",
    "                      'feature_importances_rfc':rfc_opt.feature_importances_}).sort_values('feature_importances_rfc', \n",
    "                                                                                            ascending=False).head(10)\n",
    "rfc_top10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Assess model performances on 3 most important features selected by the Random Forest model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_3cols = X[['Proportion_nCET', 'Thickness_Enhancing_Margin', 'Enhancement_Quality']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross validated accuracy scores logistic regression on RF 3 columns: [ 0.75        0.90909091  0.88888889]\n",
      "Mean cross validated accuracy: 0.849326599327\n"
     ]
    }
   ],
   "source": [
    "print('Cross validated accuracy scores logistic regression on RF 3 columns:', cross_val_score(gs_logreg.best_estimator_, X_3cols, y, cv=3))\n",
    "print('Mean cross validated accuracy:', cross_val_score(gs_logreg.best_estimator_, X_3cols, y, cv=3).mean())\n",
    "\n",
    "model_scores.append(('Logreg_rf3', cross_val_score(gs_logreg.best_estimator_, X_3cols, y, cv=3).mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross validated accuracy scores knn on RF 3 columns: [ 0.75        0.81818182  0.88888889]\n",
      "Mean cross validated accuracy: 0.819023569024\n"
     ]
    }
   ],
   "source": [
    "print('Cross validated accuracy scores knn on RF 3 columns:', cross_val_score(gs_knn.best_estimator_, X_3cols, y, cv=3))\n",
    "print('Mean cross validated accuracy:', cross_val_score(gs_knn.best_estimator_, X_3cols, y, cv=3).mean())\n",
    "\n",
    "model_scores.append(('KNN_rf3', cross_val_score(gs_knn.best_estimator_, X_3cols, y, cv=3).mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross validated accuracy scores random forest on 3 most important features: [ 0.91666667  0.90909091  0.88888889]\n",
      "Mean cross validated accuracy: 0.941919191919\n"
     ]
    }
   ],
   "source": [
    "print('Cross validated accuracy scores random forest on 3 most important features:', cross_val_score(rfc_opt, X_3cols, y, cv=3))\n",
    "print('Mean cross validated accuracy:', cross_val_score(rfc_opt, X_3cols, y, cv=3).mean())\n",
    "\n",
    "model_scores.append(('rfc_rf3', cross_val_score(rfc_opt, X_3cols, y, cv=3).mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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7cG1VDR+wRuo/jfTXwAPpQuz0qro1ycOBvwd+lO5TSWdV1f36dw2voTvwPAo4sr8tLwN+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\npVT3AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1aa49f1dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_scores.sort(key=lambda tup: tup[1])\n",
    "\n",
    "plt.bar(range(8), [tup[1] for tup in model_scores], align='center')\n",
    "plt.xticks(range(8), [tup[0] for tup in model_scores])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>columns</th>\n",
       "      <th>p_values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Enhancement_Quality</td>\n",
       "      <td>1.222770e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Thickness_Enhancing_Margin</td>\n",
       "      <td>7.099996e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Proportion_nCET</td>\n",
       "      <td>2.079979e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Proportion_Necrosis</td>\n",
       "      <td>6.455087e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Proportion_Enhancing</td>\n",
       "      <td>2.704213e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Enhancing_Tumor_Crosses_Midline</td>\n",
       "      <td>5.254509e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Extent_Resection_Enhancing_Tumor</td>\n",
       "      <td>1.535407e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Hemorrhage</td>\n",
       "      <td>1.547425e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Proportion_of_Edema</td>\n",
       "      <td>1.682849e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Definition_Enhancing_Margin</td>\n",
       "      <td>2.458544e-03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             columns      p_values\n",
       "9                Enhancement_Quality  1.222770e-10\n",
       "27        Thickness_Enhancing_Margin  7.099996e-10\n",
       "22                   Proportion_nCET  2.079979e-08\n",
       "21               Proportion_Necrosis  6.455087e-08\n",
       "20              Proportion_Enhancing  2.704213e-07\n",
       "10   Enhancing_Tumor_Crosses_Midline  5.254509e-05\n",
       "12  Extent_Resection_Enhancing_Tumor  1.535407e-03\n",
       "15                        Hemorrhage  1.547425e-03\n",
       "23               Proportion_of_Edema  1.682849e-03\n",
       "4        Definition_Enhancing_Margin  2.458544e-03"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.feature_selection import SelectKBest, f_classif\n",
    "\n",
    "selector = SelectKBest(f_classif, k=5)\n",
    "selected_data = selector.fit_transform(X, y)\n",
    "\n",
    "kbest_columns = X.columns[selector.get_support()]\n",
    "Xtbest = pd.DataFrame(selected_data, columns=kbest_columns)\n",
    "\n",
    "# p-values of each feature in SelectKBest\n",
    "SelectKBest_top10 = pd.DataFrame({'columns':X.columns, 'p_values':selector.pvalues_}).sort_values('p_values').head(10)\n",
    "SelectKBest_top10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f1aa48c2630>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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cq/2ZR0TUyeP8109WW5O2fT+wK4CkfwIesv0FSVsC3x/hPUeN4bz98l34gu0v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\nlDQADEpJA8CglDQADEpJA8CgfgElizy1UCHQBwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1aa49f1630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot correlations between selected 5 features\n",
    "sns.heatmap(tumours[kbest_columns].corr())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross validated accuracy scores logistic regression on KBest columns: [ 0.75        0.90909091  0.88888889]\n",
      "Mean cross validated accuracy: 0.849326599327\n"
     ]
    }
   ],
   "source": [
    "print('Cross validated accuracy scores logistic regression on KBest columns:', cross_val_score(gs_logreg.best_estimator_, Xtbest, y, cv=3))\n",
    "print('Mean cross validated accuracy:', cross_val_score(gs_logreg.best_estimator_, Xtbest, y, cv=3).mean())\n",
    "\n",
    "model_scores.append(('Logreg_kbest', cross_val_score(gs_logreg.best_estimator_, Xtbest, y, cv=3).mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross validated accuracy scores knn on KBest columns: [ 0.75        0.81818182  0.88888889]\n",
      "Mean cross validated accuracy: 0.819023569024\n"
     ]
    }
   ],
   "source": [
    "print('Cross validated accuracy scores knn on KBest columns:', cross_val_score(gs_knn.best_estimator_, Xtbest, y, cv=3))\n",
    "print('Mean cross validated accuracy:', cross_val_score(gs_knn.best_estimator_, Xtbest, y, cv=3).mean())\n",
    "\n",
    "model_scores.append(('KNN_kbest', cross_val_score(gs_knn.best_estimator_, Xtbest, y, cv=3).mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross validated accuracy scores random forest on KBest columns: [ 0.91666667  0.90909091  0.88888889]\n",
      "Mean cross validated accuracy: 0.904882154882\n"
     ]
    }
   ],
   "source": [
    "print('Cross validated accuracy scores random forest on KBest columns:', cross_val_score(gs_rfc.best_estimator_, Xtbest, y, cv=3))\n",
    "print('Mean cross validated accuracy:', cross_val_score(gs_rfc.best_estimator_, Xtbest, y, cv=3).mean())\n",
    "\n",
    "model_scores.append(('rfc_kbest', cross_val_score(gs_rfc.best_estimator_, Xtbest, y, cv=3).mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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8bGY2najf29gbOKm9mY0E/ukx1/cIoj/66e5+h5mNJW71jqBthOa/iV4bZxE9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\niEjGFMRFRDL2/zMu1Faqpn4lAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1aa480dbe0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_scores.sort(key=lambda tup: tup[1])\n",
    "\n",
    "plt.bar(range(11), [tup[1] for tup in model_scores],color=\"green\")\n",
    "plt.xticks(range(11), [tup[0] for tup in model_scores], rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Recursive Feature Elimination (RFE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>columns</th>\n",
       "      <th>ranking_RFE_logreg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Enhancement_Quality</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Proportion_nCET</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Extent_Resection_Vasogenic_Edema</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Hemorrhage</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>T1_FLAIR_Ratio</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Cyst</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Calvarial_Remodeling</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Proportion_of_Edema</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Enhancing_Tumor_Crosses_Midline</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Side_of_Tumor_Epicenter</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             columns  ranking_RFE_logreg\n",
       "9                Enhancement_Quality                   1\n",
       "22                   Proportion_nCET                   2\n",
       "13  Extent_Resection_Vasogenic_Edema                   3\n",
       "15                        Hemorrhage                   4\n",
       "26                    T1_FLAIR_Ratio                   5\n",
       "2                               Cyst                   6\n",
       "0               Calvarial_Remodeling                   7\n",
       "23               Proportion_of_Edema                   8\n",
       "10   Enhancing_Tumor_Crosses_Midline                   9\n",
       "25           Side_of_Tumor_Epicenter                  10"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.feature_selection import RFE\n",
    "\n",
    "# Features rank in logistic regression RFE\n",
    "rfe_logreg = RFE(gs_logreg.best_estimator_, n_features_to_select=1)\n",
    "rfe_logreg.fit(X, y)\n",
    "\n",
    "RFE_logreg_top10 = pd.DataFrame({'columns':X.columns, \n",
    "                              'ranking_RFE_logreg':rfe_logreg.ranking_}).sort_values('ranking_RFE_logreg').head(10)\n",
    "RFE_logreg_top10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define X with only 5 bets features\n",
    "rfe_logreg = RFE(gs_logreg.best_estimator_, n_features_to_select=5)\n",
    "X_rfe_logreg = rfe_logreg.fit_transform(X, y)\n",
    "rfe_logreg_columns = X.columns[rfe_logreg.get_support()]\n",
    "X_rfe_logreg = pd.DataFrame(X_rfe_logreg, columns=rfe_logreg_columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross validated accuracy scores RFE logistic regression: [ 0.83333333  0.81818182  0.44444444]\n",
      "Mean cross validated accuracy: 0.698653198653\n"
     ]
    }
   ],
   "source": [
    "print('Cross validated accuracy scores RFE logistic regression:', cross_val_score(rfe_logreg, X, y, cv=3))\n",
    "print('Mean cross validated accuracy:', cross_val_score(rfe_logreg, X, y, cv=3).mean())\n",
    "\n",
    "model_scores.append(('logreg_rfe', cross_val_score(rfe_logreg, X, y, cv=3).mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>columns</th>\n",
       "      <th>ranking_RFE_rfc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Proportion_nCET</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Thickness_Enhancing_Margin</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Enhancement_Quality</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Edema_Crosses_Midline</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Proportion_Necrosis</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Proportion_Enhancing</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Extent_Resection_nCET</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Extent_Resection_Vasogenic_Edema</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Lesion_Size_x</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Definition_Enhancing_Margin</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             columns  ranking_RFE_rfc\n",
       "22                   Proportion_nCET                1\n",
       "27        Thickness_Enhancing_Margin                2\n",
       "9                Enhancement_Quality                3\n",
       "7              Edema_Crosses_Midline                4\n",
       "21               Proportion_Necrosis                5\n",
       "20              Proportion_Enhancing                6\n",
       "14             Extent_Resection_nCET                7\n",
       "13  Extent_Resection_Vasogenic_Edema                8\n",
       "16                     Lesion_Size_x                9\n",
       "4        Definition_Enhancing_Margin               10"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Features rank in random forest RFE\n",
    "rfe_rf = RFE(rfc_opt, n_features_to_select=1)\n",
    "rfe_rf.fit(X, y)\n",
    "\n",
    "RFE_rfc_top10 = pd.DataFrame({'columns':X.columns, \n",
    "                              'ranking_RFE_rfc':rfe_rf.ranking_}).sort_values('ranking_RFE_rfc').head(10)\n",
    "RFE_rfc_top10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define X with only 5 best features\n",
    "rfe_rf = RFE(rfc_opt, n_features_to_select=5)\n",
    "X_rfe_rf = rfe_rf.fit_transform(X, y)\n",
    "\n",
    "rfe_rf_columns = X.columns[rfe_rf.get_support()]\n",
    "X_rfe_rf = pd.DataFrame(X_rfe_rf, columns=rfe_rf_columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross validated accuracy scores RFE random forest: [ 0.91666667  0.90909091  0.55555556]\n",
      "Mean cross validated accuracy: 0.793771043771\n"
     ]
    }
   ],
   "source": [
    "print('Cross validated accuracy scores RFE random forest:', cross_val_score(rfe_rf, X, y, cv=3))\n",
    "print('Mean cross validated accuracy:', cross_val_score(rfe_rf, X, y, cv=3).mean())\n",
    "\n",
    "model_scores.append(('rfc_rfe', cross_val_score(rfe_rf, X, y, cv=3).mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RFE_logreg_top10</th>\n",
       "      <th>RFE_rfc_top10</th>\n",
       "      <th>SelectKBest_top10</th>\n",
       "      <th>rfc_top10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Enhancement_Quality</td>\n",
       "      <td>Proportion_nCET</td>\n",
       "      <td>Enhancement_Quality</td>\n",
       "      <td>Proportion_nCET</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Proportion_nCET</td>\n",
       "      <td>Thickness_Enhancing_Margin</td>\n",
       "      <td>Thickness_Enhancing_Margin</td>\n",
       "      <td>Thickness_Enhancing_Margin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Extent_Resection_Vasogenic_Edema</td>\n",
       "      <td>Enhancement_Quality</td>\n",
       "      <td>Proportion_nCET</td>\n",
       "      <td>Proportion_Necrosis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Hemorrhage</td>\n",
       "      <td>Edema_Crosses_Midline</td>\n",
       "      <td>Proportion_Necrosis</td>\n",
       "      <td>Enhancement_Quality</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>T1_FLAIR_Ratio</td>\n",
       "      <td>Proportion_Necrosis</td>\n",
       "      <td>Proportion_Enhancing</td>\n",
       "      <td>Extent_Resection_Vasogenic_Edema</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Cyst</td>\n",
       "      <td>Proportion_Enhancing</td>\n",
       "      <td>Enhancing_Tumor_Crosses_Midline</td>\n",
       "      <td>Extent_Resection_Enhancing_Tumor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Calvarial_Remodeling</td>\n",
       "      <td>Extent_Resection_nCET</td>\n",
       "      <td>Extent_Resection_Enhancing_Tumor</td>\n",
       "      <td>Proportion_Enhancing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Proportion_of_Edema</td>\n",
       "      <td>Extent_Resection_Vasogenic_Edema</td>\n",
       "      <td>Hemorrhage</td>\n",
       "      <td>Definition_Enhancing_Margin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Enhancing_Tumor_Crosses_Midline</td>\n",
       "      <td>Lesion_Size_x</td>\n",
       "      <td>Proportion_of_Edema</td>\n",
       "      <td>Lesion_Size_x</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Side_of_Tumor_Epicenter</td>\n",
       "      <td>Definition_Enhancing_Margin</td>\n",
       "      <td>Definition_Enhancing_Margin</td>\n",
       "      <td>Edema_Crosses_Midline</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   RFE_logreg_top10                     RFE_rfc_top10  \\\n",
       "0               Enhancement_Quality                   Proportion_nCET   \n",
       "1                   Proportion_nCET        Thickness_Enhancing_Margin   \n",
       "2  Extent_Resection_Vasogenic_Edema               Enhancement_Quality   \n",
       "3                        Hemorrhage             Edema_Crosses_Midline   \n",
       "4                    T1_FLAIR_Ratio               Proportion_Necrosis   \n",
       "5                              Cyst              Proportion_Enhancing   \n",
       "6              Calvarial_Remodeling             Extent_Resection_nCET   \n",
       "7               Proportion_of_Edema  Extent_Resection_Vasogenic_Edema   \n",
       "8   Enhancing_Tumor_Crosses_Midline                     Lesion_Size_x   \n",
       "9           Side_of_Tumor_Epicenter       Definition_Enhancing_Margin   \n",
       "\n",
       "                  SelectKBest_top10                         rfc_top10  \n",
       "0               Enhancement_Quality                   Proportion_nCET  \n",
       "1        Thickness_Enhancing_Margin        Thickness_Enhancing_Margin  \n",
       "2                   Proportion_nCET               Proportion_Necrosis  \n",
       "3               Proportion_Necrosis               Enhancement_Quality  \n",
       "4              Proportion_Enhancing  Extent_Resection_Vasogenic_Edema  \n",
       "5   Enhancing_Tumor_Crosses_Midline  Extent_Resection_Enhancing_Tumor  \n",
       "6  Extent_Resection_Enhancing_Tumor              Proportion_Enhancing  \n",
       "7                        Hemorrhage       Definition_Enhancing_Margin  \n",
       "8               Proportion_of_Edema                     Lesion_Size_x  \n",
       "9       Definition_Enhancing_Margin             Edema_Crosses_Midline  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Feature importances according to the three techniques: Random Forest, SelectKBest and RFE on Random Forest\n",
    "pd.DataFrame({'rfc_top10':rfc_top10['columns'].values, \n",
    "              'SelectKBest_top10':SelectKBest_top10['columns'].values,\n",
    "              'RFE_logreg_top10':RFE_logreg_top10['columns'].values,\n",
    "              'RFE_rfc_top10':RFE_rfc_top10['columns'].values})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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e1PZmtib1jZ0ZVPq9i6GZXQEMd/ejzawD+D5RohgPXOHuX2/z+rcE9nL3RWa2CfAl4Ahg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\npiAuIpKx/wcvAU9yw/ZougAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1aa47b3630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_scores.sort(key=lambda tup: tup[1])\n",
    "\n",
    "plt.bar(range(13), [tup[1] for tup in model_scores], color=\"green\")\n",
    "plt.xticks(range(13), [tup[0] for tup in model_scores], rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cross-validated results of chosen model: Random forest (n=25) with 5 best features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9049 +/- 0.0117\n"
     ]
    }
   ],
   "source": [
    "acc_cv_mean = cross_val_score(rfc_opt, Xtbest, y, cv=3).mean()\n",
    "acc_cv_std = cross_val_score(rfc_opt, Xtbest, y, cv=3).std()\n",
    "\n",
    "print('Accuracy:', round(acc_cv_mean,4), '+/-', round(acc_cv_std,4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted probabilities of tumour types:\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0 - Astrocytoma</th>\n",
       "      <th>1 - GBM</th>\n",
       "      <th>2 - Oligodendroglioma</th>\n",
       "      <th>Disease</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.981818</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.181818</td>\n",
       "      <td>0.490909</td>\n",
       "      <td>0.327273</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.218182</td>\n",
       "      <td>0.236364</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.018182</td>\n",
       "      <td>0.890909</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.062121</td>\n",
       "      <td>0.774242</td>\n",
       "      <td>0.163636</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.006061</td>\n",
       "      <td>0.266667</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.018182</td>\n",
       "      <td>0.981818</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.981818</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.018182</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.024242</td>\n",
       "      <td>0.193939</td>\n",
       "      <td>0.781818</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.018182</td>\n",
       "      <td>0.981818</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.018182</td>\n",
       "      <td>0.981818</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.076364</td>\n",
       "      <td>0.214545</td>\n",
       "      <td>0.709091</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.963636</td>\n",
       "      <td>0.036364</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.089394</td>\n",
       "      <td>0.874242</td>\n",
       "      <td>0.036364</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.581818</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.418182</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.963636</td>\n",
       "      <td>0.036364</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    0 - Astrocytoma   1 - GBM  2 - Oligodendroglioma  Disease\n",
       "0          0.981818  0.000000               0.018182        0\n",
       "1          0.000000  1.000000               0.000000        1\n",
       "2          0.000000  1.000000               0.000000        0\n",
       "3          0.000000  1.000000               0.000000        1\n",
       "4          0.000000  1.000000               0.000000        1\n",
       "5          0.000000  1.000000               0.000000        1\n",
       "6          0.181818  0.490909               0.327273        1\n",
       "7          0.218182  0.236364               0.545455        2\n",
       "8          0.000000  1.000000               0.000000        1\n",
       "9          0.090909  0.018182               0.890909        2\n",
       "10         0.000000  1.000000               0.000000        1\n",
       "11         0.062121  0.774242               0.163636        1\n",
       "12         0.006061  0.266667               0.727273        2\n",
       "13         0.018182  0.981818               0.000000        1\n",
       "14         0.981818  0.000000               0.018182        0\n",
       "15         0.000000  1.000000               0.000000        1\n",
       "16         0.024242  0.193939               0.781818        0\n",
       "17         0.018182  0.981818               0.000000        1\n",
       "18         0.018182  0.981818               0.000000        1\n",
       "19         0.000000  1.000000               0.000000        1\n",
       "20         0.076364  0.214545               0.709091        2\n",
       "21         0.000000  1.000000               0.000000        1\n",
       "22         0.000000  1.000000               0.000000        1\n",
       "23         0.000000  0.963636               0.036364        1\n",
       "24         0.089394  0.874242               0.036364        1\n",
       "25         1.000000  0.000000               0.000000        0\n",
       "26         0.000000  1.000000               0.000000        1\n",
       "27         1.000000  0.000000               0.000000        0\n",
       "28         0.581818  0.000000               0.418182        2\n",
       "29         1.000000  0.000000               0.000000        0\n",
       "30         0.000000  1.000000               0.000000        1\n",
       "31         0.000000  0.963636               0.036364        1"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Predicted probabilities of tumour types:')\n",
    "pd.DataFrame(cross_val_predict(gs_rfc.best_estimator_, Xtbest, y, cv=3, method='predict_proba'), \n",
    "             columns=['0 - Astrocytoma', '1 - GBM', '2 - Oligodendroglioma']).join(tumours['Disease'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>predicted_Astrocytoma</th>\n",
       "      <th>predicted_GBM</th>\n",
       "      <th>predicted_Oligodendroglioma</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>actual_Astrocytoma</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>actual_GBM</th>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>actual_Oligodendroglioma</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          predicted_Astrocytoma  predicted_GBM  \\\n",
       "actual_Astrocytoma                            5              1   \n",
       "actual_GBM                                    0             20   \n",
       "actual_Oligodendroglioma                      1              0   \n",
       "\n",
       "                          predicted_Oligodendroglioma  \n",
       "actual_Astrocytoma                                  1  \n",
       "actual_GBM                                          0  \n",
       "actual_Oligodendroglioma                            4  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Confusion matrix')\n",
    "pd.DataFrame(confusion_matrix(y, cross_val_predict(gs_rfc.best_estimator_, Xtbest, y, cv=3)), \n",
    "             columns = ['predicted_Astrocytoma', 'predicted_GBM', 'predicted_Oligodendroglioma'],\n",
    "             index = ['actual_Astrocytoma', 'actual_GBM', 'actual_Oligodendroglioma'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification report\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.83      0.71      0.77         7\n",
      "          1       0.95      1.00      0.98        20\n",
      "          2       0.80      0.80      0.80         5\n",
      "\n",
      "avg / total       0.90      0.91      0.90        32\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print('Classification report')\n",
    "print(classification_report(y, cross_val_predict(gs_rfc.best_estimator_, Xtbest, y, cv=3)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Nr7u1iIwHvgs4NGNMAfniCzj1VFetbOe2pjgJXeIVkcdwT6zqB3wmIiNw7+Sd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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1aa4992438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot ROC curve of GBM vs non-GBM (two classes output instead of three)\n",
    "\n",
    "y_bin = [1 if i==1 else 0 for i in y]\n",
    "y_bin_score = cross_val_predict(gs_rfc.best_estimator_, Xtbest, y_bin, cv=3, method='predict_proba')[:,1]\n",
    "\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "\n",
    "fpr, tpr, _ = roc_curve(y_bin, y_bin_score)\n",
    "roc_auc = auc(fpr, tpr)\n",
    "\n",
    "plt.plot(fpr, tpr, color='g',\n",
    "         lw=2, label='ROC curve (area = %0.2f)' % roc_auc)\n",
    "plt.plot([0, 1], [0, 1], color='b', lw=2, linestyle='--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver operating characteristic - Random Forest (n=25) on 5 best features')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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fT2lpKcEPLdauCRMmcNRRR5GTk0ObNm0oKChgxowZtT6dmqRj3jLhuuuuo0mT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\ncYiISCxKHCIiEsv/Bx7wWTtPCFaBAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1aa46c79e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot precision-recall curve of GBM vs non-GBM\n",
    "\n",
    "from sklearn.metrics import precision_recall_curve, average_precision_score\n",
    "\n",
    "precision, recall, _ = precision_recall_curve(y_bin, y_bin_score)\n",
    "average_precision = average_precision_score(y_bin, y_bin_score)\n",
    "\n",
    "plt.plot(recall, precision, color='navy', \n",
    "         label='Precision-Recall curve: AUC={0:0.2f}'.format(average_precision))\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('Precision-Recall  - Random Forest (n=25) on 5 best features')\n",
    "plt.legend(loc=\"lower left\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Extraction of MRI scans "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The aim of this section is to automatically extract the 5 best features from the same 32 patients we studied previously (for which we used manually extracted features) and test our model with these features.\n",
    "If this model gives good results, we will then extend it to the remaining 95 patients for whom we only have a target but no manually extracted features.\n",
    "\n",
    "For image extraction and analysis we will use the SimpleITK library.\n",
    "SimpleITK is a simplified interface to the Insight Segmentation and Registration Toolkit (ITK). ITK is a templated C++ library of image processing algorithms and frameworks for biomedical and other applications. SimpleITK provides an easy to use interface to ITK’s algorithms and is particularly useful to segment and analyse medical images.\n",
    "\n",
    "Ref: Lowekamp BC et al.The Design of SimpleITK.Frontiers in Neuroinformatics.2013;7:45\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "import SimpleITK"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "# int label to assign to the segmented tumour\n",
    "labelTumour = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define a function to display one ITK image\n",
    "\n",
    "def sitk_show(img, title=None, margin=0.0, dpi=40, axis='off'):\n",
    "    nda = SimpleITK.GetArrayFromImage(img)\n",
    "    spacing = img.GetSpacing()\n",
    "    figsize = (1 + margin) * nda.shape[0] / dpi, (1 + margin) * nda.shape[1] / dpi\n",
    "    extent = (0, nda.shape[1]*spacing[1], nda.shape[0]*spacing[0], 0)\n",
    "    fig = plt.figure(figsize=figsize, dpi=dpi)\n",
    "    ax = fig.add_axes([margin, margin, 1 - 2*margin, 1 - 2*margin])\n",
    "\n",
    "    plt.set_cmap(\"gray\")\n",
    "    ax.imshow(nda,extent=extent,interpolation=None)\n",
    "    ax.axis(axis)\n",
    "    \n",
    "    if title:\n",
    "        plt.title(title)\n",
    "    \n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define a function to display the 4 sequences T1, T2, FLAIR and T1 GD\n",
    "\n",
    "sequence_names = ['T1', 'T2', 'FLAIR', 'T1_GD']\n",
    "\n",
    "def sitk_show_4seq(imgs, margin=0.05, dpi=40, axis='off', size=(5,5)):\n",
    "    fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=size)\n",
    "        \n",
    "    for ax, img, seq in zip([ax1,ax2,ax3,ax4], imgs, sequence_names):\n",
    "        nda = SimpleITK.GetArrayFromImage(img)\n",
    "        spacing = img.GetSpacing()\n",
    "        figsize = (1 + margin) * nda.shape[0] / dpi, (1 + margin) * nda.shape[1] / dpi\n",
    "        extent = (0, nda.shape[1]*spacing[1], nda.shape[0]*spacing[0], 0)\n",
    "\n",
    "        plt.set_cmap(\"gray\")\n",
    "        ax.imshow(nda,extent=extent,interpolation=None)\n",
    "        ax.set_title(seq)\n",
    "        ax.axis(axis)\n",
    "    \n",
    "    fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define a function to display several slices of the 4 sequences \n",
    "\n",
    "def sitk_show_slices(imgs, margin=0.05, dpi=40, axis='off', size=(10,10), first_slice=8, last_slice=14):\n",
    "    fig, im = plt.subplots(4, last_slice - first_slice +1, figsize=size)\n",
    "        \n",
    "    for ax, img, seq in zip([im[0],im[1],im[2],im[3]], imgs, sequence_names):\n",
    "        for i in range(first_slice, last_slice+1):\n",
    "            nda = SimpleITK.GetArrayFromImage(img[:,:,i])\n",
    "            spacing = img[:,:,i].GetSpacing()\n",
    "            figsize = (1 + margin) * nda.shape[0] / dpi, (1 + margin) * nda.shape[1] / dpi\n",
    "            extent = (0, nda.shape[1]*spacing[1], nda.shape[0]*spacing[0], 0)\n",
    "\n",
    "            plt.set_cmap(\"gray\")\n",
    "            ax[i-first_slice].imshow(nda,extent=extent,interpolation=None)\n",
    "            ax[i-first_slice].set_title(seq+', slice'+str(i))\n",
    "            ax[i-first_slice].axis(axis)\n",
    "    \n",
    "    fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load in DICOM images for 1 patient\n",
    "\n",
    "# Load in the 4 main sequences: T1, T2, FLAIR and post-IV (= T1 GD)\n",
    "PathDicom_T1 = \"./REMBRANDT/900-00-5299/AX_T2/5-15644/\"\n",
    "PathDicom_T2 = \"./REMBRANDT/900-00-5299/AX_T1/6-93038/\"\n",
    "PathDicom_FLAIR = \"./REMBRANDT/900-00-5299/AX_T2/5-15644/\"\n",
    "PathDicom_T1GD = \"./REMBRANDT/900-00-5299/AX_T1/7-41264/\"\n",
    "\n",
    "# Load in image series\n",
    "def import_img_series(path):\n",
    "    reader = SimpleITK.ImageSeriesReader()\n",
    "    filenamesDICOM = reader.GetGDCMSeriesFileNames(path)\n",
    "    reader.SetFileNames(filenamesDICOM)\n",
    "    return reader.Execute()\n",
    "\n",
    "img_T1_Original = import_img_series(PathDicom_T1)\n",
    "img_T2_Original = import_img_series(PathDicom_T2)\n",
    "img_FLAIR_Original = import_img_series(PathDicom_FLAIR)\n",
    "img_T1GD_Original = import_img_series(PathDicom_T1GD)\n",
    "\n",
    "img_4seq = [img_T1_Original, img_T2_Original, img_FLAIR_Original, img_T1GD_Original]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/xenialxerus/anaconda/lib/python3.5/site-packages/matplotlib/figure.py:397: UserWarning: matplotlib is currently using a non-GUI backend, so cannot show the figure\n",
      "  \"matplotlib is currently using a non-GUI backend, \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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tgT7X6fHHH8fRo0f9ZxqNBmq1mtf5bDaLYrEYyq7x2raUqJl+3TSga0DaaY+X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NhpZM/Ww7YLByHtC/yuzof+xn5AXSexs+WzgzD1oBw5cg8qtTFR8lFSq/3B/s66qP2xdkjt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\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mw9TUFCKRCLq7uzE7O7su7E3FJeR6p7F/0XJ83LlIa0V6XckbJ4nNc889h87OTtTX16Ol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\nh44twv8CANaS6r/jqZ0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1ab24e16d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sitk_show_slices(img_4seq)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "T1\n",
      "Size: (256, 256, 60)\n",
      "Origin: (-114.70562744140625, -114.29615783691406, 16.44131851196289)\n",
      "Spacing: (0.8984375, 0.8984375, 32.5) \n",
      "\n",
      "T2\n",
      "Size: (256, 256, 80)\n",
      "Origin: (-115.0, -115.0, 23.379150390625)\n",
      "Spacing: (0.8984375, 0.8984375, 52.0) \n",
      "\n",
      "FLAIR\n",
      "Size: (256, 256, 60)\n",
      "Origin: (-114.70562744140625, -114.29615783691406, 16.44131851196289)\n",
      "Spacing: (0.8984375, 0.8984375, 32.5) \n",
      "\n",
      "T1_GD\n",
      "Size: (512, 512, 60)\n",
      "Origin: (-143.22894287109375, -138.53262329101562, -84.65164947509766)\n",
      "Spacing: (0.546875, 0.546875, 3.0) \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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Zd1iDq28u3lExF5UH19XofSG/Ub4y8snIZlVHMFo2I+1P7c8Ede9ZZ2ePVDr0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JaOGadGnQDa+78fl+71SMKQyqjvegoMB8NrSE4iTlX8S+O/SndNXP5db+z7ePHvEunW74p0tjZ3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8DRE+Bc7qhAHBVEDghgNQlny7B6xEEntA5dYVJDAQY7AJ6G8NfVPS5XlR2vrLnIis6A4P7T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\nGABsPTw8LEBLzwJiKyhWx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9uNFa6zpxArZeyUU9VG90wT+B41jhJfaY705kyaZ2vKSQBt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AAswIYS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\n4QwAAAAAFkA4AwAAAIAF/D+LAL1WiVvzOgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1ab2364470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Print image sizes and show images using the Tile function (agregates images in real size)\n",
    "for seq, img in zip(sequence_names, img_4seq):\n",
    "    print(seq)\n",
    "    print('Size:', img.GetSize())\n",
    "    print('Origin:', img.GetOrigin())\n",
    "    print('Spacing:', img.GetSpacing(), '\\n')\n",
    "    \n",
    "sitk_show(SimpleITK.Tile(img_T1_Original[:,:,10], img_T2_Original[:,:,10], \n",
    "                         img_FLAIR_Original[:,:,10], img_T1GD_Original[:,:,10], (2,2,0)),\n",
    "         dpi=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define resample filter to match T1 image dimension and settings\n",
    "resample = SimpleITK.ResampleImageFilter()\n",
    "resample.SetReferenceImage(img_T1_Original)\n",
    "resample.SetInterpolator(SimpleITK.sitkBSpline)\n",
    "\n",
    "# Resize all other three images\n",
    "img_T2_Resized = resample.Execute(img_T2_Original)\n",
    "img_FLAIR_Resized = resample.Execute(img_FLAIR_Original)\n",
    "img_T1GD_Resized = resample.Execute(img_T1GD_Original)\n",
    "\n",
    "# Define list of all 4 resized images\n",
    "img_4seq_resized = [img_T1_Original, img_T2_Resized, img_FLAIR_Resized, img_T1GD_Resized]\n",
    "\n",
    "# Define list of images of slice 10 of the 4 sequences (2D images)\n",
    "img_4seq_slice10 = [img_T1_Original[:,:,10], img_T2_Resized[:,:,10], \n",
    "                    img_FLAIR_Resized[:,:,10], img_T1GD_Resized[:,:,10]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "T1\n",
      "Size: (256, 256, 60)\n",
      "Origin: (-114.70562744140625, -114.29615783691406, 16.44131851196289)\n",
      "Spacing: (0.8984375, 0.8984375, 32.5) \n",
      "\n",
      "T2\n",
      "Size: (256, 256, 60)\n",
      "Origin: (-114.70562744140625, -114.29615783691406, 16.44131851196289)\n",
      "Spacing: (0.8984375, 0.8984375, 32.5) \n",
      "\n",
      "FLAIR\n",
      "Size: (256, 256, 60)\n",
      "Origin: (-114.70562744140625, -114.29615783691406, 16.44131851196289)\n",
      "Spacing: (0.8984375, 0.8984375, 32.5) \n",
      "\n",
      "T1_GD\n",
      "Size: (256, 256, 60)\n",
      "Origin: (-114.70562744140625, -114.29615783691406, 16.44131851196289)\n",
      "Spacing: (0.8984375, 0.8984375, 32.5) \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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