JonathanCMitchell/BreastCancerPredictor.ipynb

## BreastCancerPredictor.ipynb
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Jonathan Mitchell\n",
    "### 06/25/2017\n",
    "### Breast Cancer Malignant v. Benign Tumor predictor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import csv\n",
    "import numpy as np\n",
    "from scipy.stats import describe\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.image as mpimg\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "breast-cancer.csv                    field_names.txt\r\n",
      "\u001b[0m\u001b[01;35mdecision_tree_nonlinearBoundary.png\u001b[0m  \u001b[01;35moptimal-hyperplane.png\u001b[0m\r\n",
      "\u001b[01;35mdecision_tree.png\u001b[0m                    \u001b[01;35mSVM_nonlinear_boundary.jpg\u001b[0m\r\n"
     ]
    }
   ],
   "source": [
    "ls data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Part 1: Modeling Challenge"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "* Step 1: Read in the dataframe\n",
    "* Step 2: Add the columns to the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "cols = pd.read_csv('./data/field_names.txt', sep='\\n', header=None)\n",
    "\n",
    "columns = []\n",
    "for i in range(len(cols)):\n",
    "    col = cols.iloc[[i]].values[0][0]\n",
    "    columns.append(col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('./data/breast-cancer.csv')\n",
    "df.columns = columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>diagnosis</th>\n",
       "      <th>radius_mean</th>\n",
       "      <th>radius_sd_error</th>\n",
       "      <th>radius_worst</th>\n",
       "      <th>texture_mean</th>\n",
       "      <th>texture_sd_error</th>\n",
       "      <th>texture_worst</th>\n",
       "      <th>perimeter_mean</th>\n",
       "      <th>perimeter_sd_error</th>\n",
       "      <th>...</th>\n",
       "      <th>concavity_worst</th>\n",
       "      <th>concave_points_mean</th>\n",
       "      <th>concave_points_sd_error</th>\n",
       "      <th>concave_points_worst</th>\n",
       "      <th>symmetry_mean</th>\n",
       "      <th>symmetry_sd_error</th>\n",
       "      <th>symmetry_worst</th>\n",
       "      <th>fractal_dimension_mean</th>\n",
       "      <th>fractal_dimension_sd_error</th>\n",
       "      <th>fractal_dimension_worst</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>842517</td>\n",
       "      <td>M</td>\n",
       "      <td>20.57</td>\n",
       "      <td>17.77</td>\n",
       "      <td>132.90</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>0.08474</td>\n",
       "      <td>0.07864</td>\n",
       "      <td>0.0869</td>\n",
       "      <td>0.07017</td>\n",
       "      <td>...</td>\n",
       "      <td>24.99</td>\n",
       "      <td>23.41</td>\n",
       "      <td>158.80</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>0.1238</td>\n",
       "      <td>0.1866</td>\n",
       "      <td>0.2416</td>\n",
       "      <td>0.1860</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>0.08902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>84300903</td>\n",
       "      <td>M</td>\n",
       "      <td>19.69</td>\n",
       "      <td>21.25</td>\n",
       "      <td>130.00</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>0.10960</td>\n",
       "      <td>0.15990</td>\n",
       "      <td>0.1974</td>\n",
       "      <td>0.12790</td>\n",
       "      <td>...</td>\n",
       "      <td>23.57</td>\n",
       "      <td>25.53</td>\n",
       "      <td>152.50</td>\n",
       "      <td>1709.0</td>\n",
       "      <td>0.1444</td>\n",
       "      <td>0.4245</td>\n",
       "      <td>0.4504</td>\n",
       "      <td>0.2430</td>\n",
       "      <td>0.3613</td>\n",
       "      <td>0.08758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>84348301</td>\n",
       "      <td>M</td>\n",
       "      <td>11.42</td>\n",
       "      <td>20.38</td>\n",
       "      <td>77.58</td>\n",
       "      <td>386.1</td>\n",
       "      <td>0.14250</td>\n",
       "      <td>0.28390</td>\n",
       "      <td>0.2414</td>\n",
       "      <td>0.10520</td>\n",
       "      <td>...</td>\n",
       "      <td>14.91</td>\n",
       "      <td>26.50</td>\n",
       "      <td>98.87</td>\n",
       "      <td>567.7</td>\n",
       "      <td>0.2098</td>\n",
       "      <td>0.8663</td>\n",
       "      <td>0.6869</td>\n",
       "      <td>0.2575</td>\n",
       "      <td>0.6638</td>\n",
       "      <td>0.17300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>84358402</td>\n",
       "      <td>M</td>\n",
       "      <td>20.29</td>\n",
       "      <td>14.34</td>\n",
       "      <td>135.10</td>\n",
       "      <td>1297.0</td>\n",
       "      <td>0.10030</td>\n",
       "      <td>0.13280</td>\n",
       "      <td>0.1980</td>\n",
       "      <td>0.10430</td>\n",
       "      <td>...</td>\n",
       "      <td>22.54</td>\n",
       "      <td>16.67</td>\n",
       "      <td>152.20</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>0.1374</td>\n",
       "      <td>0.2050</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.1625</td>\n",
       "      <td>0.2364</td>\n",
       "      <td>0.07678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>843786</td>\n",
       "      <td>M</td>\n",
       "      <td>12.45</td>\n",
       "      <td>15.70</td>\n",
       "      <td>82.57</td>\n",
       "      <td>477.1</td>\n",
       "      <td>0.12780</td>\n",
       "      <td>0.17000</td>\n",
       "      <td>0.1578</td>\n",
       "      <td>0.08089</td>\n",
       "      <td>...</td>\n",
       "      <td>15.47</td>\n",
       "      <td>23.75</td>\n",
       "      <td>103.40</td>\n",
       "      <td>741.6</td>\n",
       "      <td>0.1791</td>\n",
       "      <td>0.5249</td>\n",
       "      <td>0.5355</td>\n",
       "      <td>0.1741</td>\n",
       "      <td>0.3985</td>\n",
       "      <td>0.12440</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         ID diagnosis  radius_mean  radius_sd_error  radius_worst  \\\n",
       "0    842517         M        20.57            17.77        132.90   \n",
       "1  84300903         M        19.69            21.25        130.00   \n",
       "2  84348301         M        11.42            20.38         77.58   \n",
       "3  84358402         M        20.29            14.34        135.10   \n",
       "4    843786         M        12.45            15.70         82.57   \n",
       "\n",
       "   texture_mean  texture_sd_error  texture_worst  perimeter_mean  \\\n",
       "0        1326.0           0.08474        0.07864          0.0869   \n",
       "1        1203.0           0.10960        0.15990          0.1974   \n",
       "2         386.1           0.14250        0.28390          0.2414   \n",
       "3        1297.0           0.10030        0.13280          0.1980   \n",
       "4         477.1           0.12780        0.17000          0.1578   \n",
       "\n",
       "   perimeter_sd_error           ...             concavity_worst  \\\n",
       "0             0.07017           ...                       24.99   \n",
       "1             0.12790           ...                       23.57   \n",
       "2             0.10520           ...                       14.91   \n",
       "3             0.10430           ...                       22.54   \n",
       "4             0.08089           ...                       15.47   \n",
       "\n",
       "   concave_points_mean  concave_points_sd_error  concave_points_worst  \\\n",
       "0                23.41                   158.80                1956.0   \n",
       "1                25.53                   152.50                1709.0   \n",
       "2                26.50                    98.87                 567.7   \n",
       "3                16.67                   152.20                1575.0   \n",
       "4                23.75                   103.40                 741.6   \n",
       "\n",
       "   symmetry_mean  symmetry_sd_error  symmetry_worst  fractal_dimension_mean  \\\n",
       "0         0.1238             0.1866          0.2416                  0.1860   \n",
       "1         0.1444             0.4245          0.4504                  0.2430   \n",
       "2         0.2098             0.8663          0.6869                  0.2575   \n",
       "3         0.1374             0.2050          0.4000                  0.1625   \n",
       "4         0.1791             0.5249          0.5355                  0.1741   \n",
       "\n",
       "   fractal_dimension_sd_error  fractal_dimension_worst  \n",
       "0                      0.2750                  0.08902  \n",
       "1                      0.3613                  0.08758  \n",
       "2                      0.6638                  0.17300  \n",
       "3                      0.2364                  0.07678  \n",
       "4                      0.3985                  0.12440  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "mal_mask = df['diagnosis'].str.contains('M')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Extract Benign and Malignant Diagnosis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "dMal = df.where(df['diagnosis'] == 'M')\n",
    "dBen = df.where(df['diagnosis'] == 'B')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mal quantity:  568\n",
      "Ben quantity:  568\n"
     ]
    }
   ],
   "source": [
    "print('Mal quantity: ', len(dMal))\n",
    "print('Ben quantity: ', len(dBen))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Extract Pandas Series\n",
    "* Extract series for smoothness_mean and compactness_mean for benign and malignant tumors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "def extract_data_from_df(data, cat):\n",
    "    \"\"\"\n",
    "    Extract data (either Mal or Cat)\n",
    "    and remove null values\n",
    "    \"\"\"\n",
    "    seek = data[cat][~data[cat].isnull()]\n",
    "    nums = np.linspace(0, len(seek - 1), len(seek))\n",
    "    return seek, nums\n",
    "\n",
    "def extract_data_and_plot(data1, data2, cat):\n",
    "    \"\"\"\n",
    "    Extract data (either Mal or Cat)\n",
    "    and remove null values\n",
    "    \"\"\"\n",
    "    seek1 = data1[cat][~data1[cat].isnull()]\n",
    "    nums1 = np.linspace(0, len(seek1 - 1), len(seek1))\n",
    "    \n",
    "    seek2 = data2[cat][~data2[cat].isnull()]\n",
    "    nums2 = np.linspace(0, len(seek2) - 1, len(seek2))\n",
    "\n",
    "    # make sure we order them correctly, we have more benign data than malignant data\n",
    "    if len(seek1) < len(seek2): \n",
    "        color_1 = 'r'\n",
    "        kind_1 = 'Malignant'\n",
    "        color_2 = 'b'\n",
    "        kind_2 = \"Benign\"\n",
    "        id_1 = 'm'\n",
    "        id_2 = 'b'\n",
    "    else:\n",
    "        print('else block hit')\n",
    "        color_1 = 'b'\n",
    "        kind_1 = 'Benign'\n",
    "        color_2 = 'r'\n",
    "        kind_2 = 'Malignant'\n",
    "        id_1 = 'b'\n",
    "        id_2 = 'm'\n",
    "    fix, ax = plt.subplots()\n",
    "    color_1 = 'r'\n",
    "    color_2 = 'b'\n",
    "    \n",
    "    ax.scatter(nums1, seek1, color = color_1, marker = '+', alpha = 0.4)\n",
    "    ax.scatter(nums2, seek2, color = color_2, marker = '+', alpha = 0.4)\n",
    "    plt.title(str(kind_1) + ' vs ' + str(kind_2)+ ' ' + str(cat))\n",
    "    plt.ylabel('Measurement')\n",
    "    plt.xlabel('Number of sampels')\n",
    "    plt.legend(id_1[0]  + id_2[0])\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "len:  211\n",
      "len:  357\n"
     ]
    }
   ],
   "source": [
    "mal_radius_mean, mal_radius_nums = extract_data_from_df(dMal, 'radius_mean')\n",
    "ben_radius_mean, ben_radiun_nums = extract_data_from_df(dBen, 'radius_mean')\n",
    "print('len: ', len(mal_radius_mean))\n",
    "print('len: ', len(ben_radius_mean))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lengths:  211 357\n",
      "total length:  568\n"
     ]
    }
   ],
   "source": [
    "mal_smooth = dMal['smoothness_mean']\n",
    "ben_smooth = dBen['smoothness_mean']\n",
    "mal_compactness = dMal['compactness_mean']\n",
    "ben_compactness = dBen['compactness_mean']\n",
    "\n",
    "# remove the null values, (where the diagnosis is Benign)\n",
    "mal_smooth = mal_smooth[~mal_smooth.isnull()]\n",
    "mal_compactness = mal_compactness[~mal_compactness.isnull()]\n",
    "ben_smooth = ben_smooth[~ben_smooth.isnull()]\n",
    "ben_compactness = ben_compactness[~ben_compactness.isnull()]\n",
    "print('lengths: ', len(mal_smooth), len(ben_smooth))\n",
    "print('total length: ', len(df))\n",
    "# Check for any data loss\n",
    "assert(len(df) == (len(mal_smooth)) + len(ben_smooth))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "I could have just found the indices where benign and malignant tumors were and used only those values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Smoothness median and mean"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "#### Malignant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.654"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mal_smooth.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.3037156398104264"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mal_smooth.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "#### Benign"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.851"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ben_smooth.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.0003212885154062"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ben_smooth.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "#### Average smoothness of both Mal and Ben\n",
    "* Performed using weighted avg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.8559836267605632"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "((len(mal_smooth) * mal_smooth.mean()) + (len(ben_smooth) * ben_smooth.mean())) / (len(mal_smooth) + len(ben_smooth))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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s2gX8vl0/vqe4by1ZPb7x7Jn6FBQejAQXS7HRLW0dbYBzdcGtUa9ZLCpSaDlx\ndZuQCVSHp7vmBjoe0J7pm1dpPEIt5ZQU5xRpUqxCUZrA3FYE/iX12SP+v08TdEHGxMYgTBscGbE9\n1fwE2c3Lp5wm27dnyrpqItVpklAnQLYY9AQ7ZQRYZeW/eJbJlLVWSi6/llG2FawkqODmSTiuUC+1\nZAdV26+OiqdwslpuVlBnuWg19GQY6YXB7TUUU4Pimq096kb0ihs5rnG6eRUVDOGW37u5rQg8vjH0\nDWVSmqAPDvqHMSltsL8fshl715JG4VYbfRrDp64XOs49xSBi4eXY7gPRC2BoH9loCzmyRGQKX7a0\nQeHVhXbexwJs4xNaDA14ymb6dOemJ1tVV1G11mUmb15oTfpefKVWLIogf17Jc5fP2/+9u9xo6L6F\nwOVkOWnLaFCL0OoGJy01vkqpmS3X32zmtiKIv/ReEZR7aaMIdo1AX1+hV527yaZDjlwZZuKstvGA\nGYpUaEO2D8Ig0DPDmTdCBZfiSS9pKwcHyUWPwvgCq0iqWQZ1ptb2OVH2cr53r3HTdMHLxYnCLKkx\nlyJbLs013oplMrb/QGC0Te6DHUMM/nohA/2PlpcnRcW0i595utSzVzzdukhz3EzrudJ+rbYM5rYi\niFPIBJnqsigEfvNHYewOst0/hYEBcjctSS4nGxzo716NUzQW4tbRo2V379k0AFFElp8A4+TGs9AF\nveODjA8tY3jsJD2bbYOUiymogiUQz6evJNfQPmBP9cBCmreiwnkKQeHeuK895Rsb2y/Kryg9ZZLG\nTSlbIj4edOyYVQSLFsHkpO00QLGF7+mZOu9SYWK6CPIroLufAXbTw91Eyy+mp28h2S4/AG9rbXKl\npFnKYqbleh1a728hzHZl2Wg6QxGUS8NMCiB2n2lf9LEx6Okh+5Vs4iFxctHqYm+6AiUPdrQFImD8\n8mS50nDwAESHp46i9UQRDA0VG6yAksvvGbBK6eo8ubFnkU0pQ2Ekcbm01BqwGTXpLj+bBbZssS68\n7m56ugEOQ2609MKSpq9IOkG8pQhjRL29tqE/cgSWLbPrTzttahn5fEGe7MADU2YkzWZGyZKz+4zu\nJbv0ZjhxGG5dBOfkKw+kq1ApjehRt6KBnFE8KFZGuVe81uNq3We6tFpRdYYiKEMxI8c1Qrv2k+3L\nQTfkbr0Atk3YXnVSFk94g0LX0vBPav7MYFF59EyJVfT04LSGjU1kOQmMkotW08M9tgefP52R0Xmw\nbh25aNGCGLn4AAAgAElEQVRU+dLgB5pNHi1/oUmB4oIbJF0AdUpQ2LWVfhAcBNbC1qBnvW2bbSjD\nzK98nujgc2FyGb0rjwEJo6eDkcu5m5ZAf39yv6Cauy/uVkpaHh+HoaOuDvfApk2l+/gT5yeIeAaw\nlOxZP7IKpv+KYvJCjSQ1cvHbF+YTTLd3Xe6RCD9NMdOGzO+/ZYuNo2zeXDS0vV6djuzVGvFUackp\nmK2WRmcqgjDVMWTp6dDVRW78cqLVq8j0P4HNgCnfsyjR5OMLyY3Z1M2kB60wrc6dw4zvepyIl5Dp\ne8i5fShOC10LfevtQK7JEVi+HDIDU0947Ji1Csqkqlj3lFWGIyuXw/795LZNTGk0w0a2cN3jC0vq\noZYXYHAQhsdWuMIDD4p381Q8cBi6u8l0PwpjdzFy93G47LLk6bB9C9hlB60lyurdfX5jMEW3z/jJ\nbs2WtHyF+siMut9ZRlYugUOHyI2dDmTt6bdERPkVZIBsNuusmQjyq6F/Q/H8M6SWuq9kFCdtbyWD\ng6XLaQy76crtH5dK8fpqDobpnjcsq9l0pCIIM3IGh5YRLT2P8ckFDPQtZEs+Q/7gaXQve4yR3vVW\nVwQ3p1z6uH9ao8nzYRfFeW/8kxWtpjAHThL5vO3xZyu4ooKFLKVdMTuo7dHiLjlKu2h+UrVyT6tv\niMYH4dAh6He+7tCXNb6gePFuYFQhIyslJe1yF2Q29RNFTmft2md3OvaYPc3V2+DgAbKXPAALFlil\n5tJtcl2vBsZdHGSM3OIN0BW7fidrbtsETK5hZOUJGB9keGxxiRumxB+97TwyRFMswZILmBJAd/+j\nCFgIy5cTjS0mvy1Wt8QU6N7j5HCupJEHikrHB73dzuW+vJbUeIdx7XAdUMgy86Km1T3lzuPLnJiw\nfZDQMphuoxiG27zxNzZWagzOhLR1WG7/ciTFNmo5vtV0pCIoMDQEo6uBBXBsIYNDyxg7ciqTj8xn\nwfxTUiWbFBq2scVk+g/Tw1Rfve85bt3qHrSuXtcBdeMPsltn3hUo92GUcv6A2JPqG57c2ARwlGzv\nKDBq5xHKn0eUP4/x0ccYWDde4n6qZmqXrHd+94ithS9khnH7rqU2ztC70tVhubCDn44iiuwkd/39\n0D1gW6X4tNHDw8B5RGTYdXs3fWc9adePTx1nWCibw8Cou4asHZiXMEP2lJfeWwaZDD1Afpv1aHV3\n28bSJxz190Ovb5G7HiXaNd8eX4c4SzWmpCzHrqncRLvx4+MMDRWvrVLqZ7UGMvx6qq8if7s9aUI8\nSUHmaucO6wbsvYui8h678Jxuyq0SZTsTmq1QOk8R5HJk3ROS4yA9qw8CEE2eBaufxqYNDzHSmymZ\nmbhC+1l44PIHbfCwt9sqlChaX+xZji8gGlpl3QFAiWVQpitRNljrBQgzgbZvJ1twRyccV6aLljiK\nur/fPs2DVoHkdq0h4nxYis2SCd1PNZLLnwcHDzC+bB/dfesZGiqNY3e5/XrWL3Jyb/ZHFt7qwkR/\n2wdhaITc6AUw+RQb2wFy+WfZa/LjP1zqJtET5I+cQteGtYWGP0yMiiLoGY7oHV/I9juXEO1aA0vz\nZPoPQ2/6AWRRZBVAdzeMjtoq27sXli61htbSpUGj5gL0bImI8mcD/VbpbLfpsNGuNWT6Hir7AZuk\nBi6+LlR2XhF1dVVvrMqVGfYp/LPf1QXr15euL1dmtd522OD7UFsj3Sbxa/NMTNh75wz1ig2xd135\nvsnwcM1hwpa5hDwdoQjKatW+9TbYeuQILF5MZnO/zeqIIoa7MsntZxS5VMV+v8j4OHQve4z8wdPY\ntXc5fattI+RTGkcmTjB+bCFR/lQy/YfZmglN/tVkqWFMQmxqjNzQRfbayu1fyRYeX1A6G6n3X1uz\nBTIZ2yACvZN3MX54IcNjXfSQbA2Uc02wbRvZ/nuI7twAR5/KwKofAKsZW2o/KekbpZ7hw+nrAawG\n6bMxCrqsQmdToDwoulVGxgfpXmZdTnEDqdxLmOk/bHv5lF6nJ+m5KnjYxmHdOqs3w2EH8UbUyraQ\nXXuXFRRIGjUb1nUthL1r32iF96tc1nA8AcsHcz3d3bZvEvaKp3hHo2JvO2nmj/DTFIcOFcuPK75Q\nJr8+Xk5o9aT9tpCvl23brBJYubLU5TXFIxjPl4gt+/NurWDwJ5U506D+dOgIRQBYF0eES/EMnI9L\nT2eENfT22f6o7yWXuwk+s6TgI921j4ljp7CcYxwcWQin2irt7YVh+tmWt4OHJjgFunuJ3MGFDJVM\nxjY0w67A4eI5wgfJU/AXb3LHO3mrZe1MyZAaX8DIxBISM20K58kwjvXbj9PF0IrFdPU/tWz50Tb3\n5maCNMgosimu5MnwONHJpzF8+HQyi+9m84aeYjcKyPbebssavhQIlElPFjJWSRVDAH7KbzftdZQl\nyq8oNkJu2uiC8TUwUJgo1cck/DX7vy1bMuTHoPu0ffT2PQGZ3yzJJ4g3IFMaiJyttezWbElD7d0d\ncXdMJpCtD9uzBujJWEvBx5mqfcCmUmMResi6uqbOqhIyOGhnA4eiaybcL/x0xNhYcRhFV1fl7OVt\nLlYyOWnzFqA0+zZJfl9uswhl8Pdr+dTZZErwdeOvPTYrTYmiDMuG5KEmfvqyVjCnFUE8o2d4bDFM\nWn8sN43ZBmrZMlh5pvVRbLujmAPOKLktq4lwlgG5oAE9wfihfc6nvYBDkws4ySKWLToJi+YxNLoE\nBmFTVwSssG4VYIBBevJ3EO3tsuU4k3+4awDyK6wboty1eLfS+DjR0CqiHUvJrDnASPeZJdca76lP\neeGd+4KxPL3dj7hppImVUXwrBthtZRxdQBewNfMEuWg1OTIlKff5PHRPLmCg74jd3/doekbJ8Rxy\nPIeRvbczvuApjK04H9b8Jlsz94DLWCrL4CAMl07vndgbLnTFS5fLuU9qMcXLWQ5pe+OZTPIM22Hv\nD4rDFbZtww0ijBKvG4o9Z++bruaWCQkDsh6vKHzjGzbsYflbtlgl0N1t4/ee0I3q5fRj6/L5osdx\naMju39c31e8fHhuWFXfNVMtwCgeBx11Z4X7lKMTyUga/4zPY3GhnDSmMPbz6ausevOwyuPVWu83X\nr6/v3t5inCWMj1RzS9WLOa0IPNaHNwDdwOQ+orHTyR+8mM1XHCDbezu5weNw8ijZ/nug3CcJsa6e\n/N4uupeeYODCI/R0PUpEhrE89JOnt/swDAwU850zo861YBvfHiKyXffA5BpgPYNDdnDS0BAwuozx\nyQVw7Bjbvns+fPAAC+Y/Cet62bIFdnztHK64+GHGR5cwNHY6S1c+FVhL70ClaQ5GiSL7FMXN4oLS\n6Zn6ApYokshqiZ5dt7mC102p27HdB1yA/SSDx+YzPJZ3VsBp0GtdUNHY2YwfPpMJltC9ej70P40c\n/a4H7btHLlU3/LDOcPL0C4UeVeAU837wSj7aSkHF4tix9QwD13j3Ui5bcswUV0NBSRc/Weotg/DE\nueHSKajjPvPwugoL2WzRCnKy5/NF14VvPCrlCkBpZnI8SO57wOFA6aEh20iFA6U9PoOnwhc+yeeL\nQV+f8DU2ZmMkcRdZoxu7eB3XEoAtF1RPil34Or76avt/5cri/ocP2zo9ccIue4W4aVPRlQWl80U2\nZdZgx5xWBHETvjAJ6eQCJpetJWItDGOjlJs24fPIc9Fqtt2UKbxs4+MQdWWh3zb4cCo9m1yOfc6n\noN9TmOStx7t5XMuR7cnZD7f095MbvxzYR7brJ0RjZ7uuEsAjidewfz+cPAmPLDmDXZNnMLr/EI+e\nnMdv/85y6Jra48nlnJm5axVRfgW73Mvte2MbNtiZEsb7MrY3EhXryD/0YdAscpZKcYbVdYXvLvje\n7KYrDjAyvpAb/20FJ5+Yx6bfGifb/yPXNYPswANEN53NWM/FdC97jIG+I/QkNB4lLiqfoho4lXMR\nJZPtVZhDsLTcShZSCpIyWUrwPgD/FtfAlF60C1iPTJwgV4jh2B3DT0Hk8/a5WLrUDrqC4r33v0P5\nobTHHOIb+/hMJGFj7t1Eobzhc+LXbdli9+nuts+bdwd5N0sYo0iy7ELLIOl6wu2VZg7J5YoNabmZ\nyD2VYgZVvpOUWI6/H1Fkg+hbtxY7D4XEiK7iOf3z1ddX+lw3M/V0TisCTxgcy4+tp7sbVnqz2g1o\nqtSY3HorHD9ub+rKY4s5NLmAHR+Mp5YV71o2M0pu2wRbbjgdViwn88Jusv0RZJYXPnQPrldeOO/6\nopAct4HaCE660ZXbtxd7ESAl8pX0eKIIxhcydP8KxhbNo2/1PgY4wnDXQKHn6NtW77/2SuDOO0tN\n97Dc3NBFZPv2WCW5Yy39UdHlkc3aQVpLdx+gf82jrofvevWut5zZ3F9QOj0JpnYuR8ENB1YpRPkV\nZLrHC7GMm3Z3c/Dhh1m25CQwNVbR0zPVEog3NkmNQdx1cI3/LoRrBTI4F19X/xTlE0UwTIZM/+Hi\n4Lrgk6XhnEp2XiXrcvQNeyij1ScrrMsugXBg1d699plcsqRUefueeqWM4cLAvdhs7B6fCeSfj3L+\n/0rvjFeY3gro6nJ9rYTgcK2frIhbUknKPdweD3AnxT/CciplKpVTTPF1uVzRJQaxNihfVAReucaz\npZpNRyiC8CUPp3Ox2reffL7YHufIQgau7C0GxRYvhnPO8fngvfS5TJB4alnxIXZq/o4H7MGZDDl3\nhhEGoa+HXKYYAKzUS+3vL/YaurpgvG9lwWxP8n3mIjumYcNZY2T6Hio8cT0ZrMDRYSBTGGjsH9aB\nAasEli61vThwDUB3v22/l64gu2kdkAHXU/Sxky1Xn0eefrqXPUZv9yMl17Ntx1r61xwrmbInipIb\n67BRiMi4xKzDwEIGGYA1sOwR6z6K+1KTmEljA7Dlxt8kP7qc7tXzmTh2CocmbYLZmjVuB28JTB51\nJtejDI6uZriMu6QSxcbAPaBRBCwquJJyuWKe/vg4rHax8GPHprpowrEvYa86NvVRRRn9/a3mbguD\nnGEOfTzmEBLPEEoar5PUuMLUxtxXVblrCc/hs7c8cRdZpZ54OXdauD3Mlooi24Erl/HklawfZxJa\nAtOcaWRGdIQi8MTN8NAvV2iAgodqaKg4z9iVV9r9d++GK66wNy6M8hce3C0R0Y5j7HpgFccmzoHR\neez687tZuuAx+i9eRG/MezDFbeF7iiOlD3f4O5Qxbj5vy9sN3exjhFMYdr3TrVmItrmU1kCG0FTf\nsKEkiafoStu1j6F7nuS37vhPPLIE5s+3DdAHPwjRmvMK5fT2ri+REWBy2VrGu+2AZZg6/U68Ubjx\nRquMCi/6MNy0uxs4QPeyx1gw/ySHji2Egwfo4hg9PVPnDirUxbaia8K7xeINRnLA0RW4LQ9HFkPf\nWvbfCZMuUWt01PbIFx8/g0vWH2FgpU2FGV56Pl1LS68/aU6lar3N4WHIOwUa4jsxY2P2WkI/fXhO\nr/h8BtCVV9r/SXGELVvilm2x8a9kDVRi2zY4eBAuucQu+6yi8B3z+PTarq7KCjq0miYnbdzBPyuT\nk1MD0f5++3Ps3m0V+JVXFhWP7wT54K532yT19uOU67XHB+wlpZCOjxctfD/WBKa+G81kziuCpPQs\n/0L4nkW8V+IbZd9wxP2EPhVwYsIul8szHuMMhHmcxiT9ZzxMpv+0stksnqTRiXF/b1KGSPhS9PUB\nk3DjravZ8ELn2siNkum2A9u6yDM0uozxcete2b691GcZnguglyPAMnYfOYPDIzBvHnDsKCPjwo6R\npVyybpyB7kGG83bqBv+SdXcXA5pHjlgFmvRihWmJJ0+WTicQkeEgcMUam+U0eGw+67uPktnwxNSC\ngvryKYu+UfADucK4UTm/fyGrpvsRersfYbhrLU88Ya1Cr1hOnACWnMEQZzB092KWLn4C1qy1YYLg\nkwXTnhZhjVWgQ7tK5Yy7psKetH82tm+3dekVR3y0cPyZ85ZtSJhqmxTYjfvi41Z3GFCOE3eTeJKC\nxqElsGOHS/JbaccZHDlSjHkV3aYWn5Xkn+E1a6ZO7uqXvZWXZF3G3Wk33VRUchMT8N3vWqPfK720\nA/a8vEuXFo8Lr7fZzHlFEMc/eGFKX9irCpniOy7OQ1Z4wPwL48luzRR8w9u+Zqcz2PxfTpDt/ZU9\n1h1XcBXFMlH8wxua4Un+bCjd7rMxCmZu13rGnHlPZH3uIxMn2HV/N0eeXMojj82jr0wdFR7GQtdt\nnJ6+ET62aQ9btp3HjoP9LHvkOH2rj9PFOOPHFk4pw8ty443FFy3JfA+vYXi46Ef21+wDmdlsv91n\nLE+m/3gx0whivfnAGpm0f6PBeD0fzCznky1J0WSgYJWsWWNNfd94XXhhsWHO73jC9t77ix0EHzcu\nfKYgM/WjOUlxkjDnfnS0+OmD226zsarLLiuWHfasw0Cy7/2GrpPwfKElsG+fPY+f3DXsudbaKCW5\nWvzYhdAK9+/drl1Fi9tbIPHzhr1oPwO4H6HtO2q+Z59k8fgyvVU0PGzrdNOmYq/8kUdsfft7FZ/7\nMeycVZopHEpnPwnrotwzGk8jbRVzXhHEMxzCxI549kH8uEplwtQG25PLWRfM5MknwUCUXwTjZ5Id\neKAwWCweFih8ujAwy8MeXqVriyJ7Xd4H718M7/Iadkqnd/kgfWc9SdeGM4DKPuLCg5sZnRLgtqKu\nZtNmyLKnZH4dKO097dtng+z+RayUKhjvsfv68MdFEYXRvuVkjqLS78ecOAErVhTN/jATJhzBGjYk\nYY+1p6f8pLCFZ+qStfQOFBsZ33v3PepaCXPuTzvNyr7bDuco6WnG75+3an3DNhi446Joat3n81YJ\n+P2TLIOkDJ7Q3RGW5+VJk2njZXvkEXsPjh0rP8jM12HcEvANrncNxQdrVZJp27bi1B++Vx4Gd6FY\nF2HnzD8LfjzFZZcl+/fD+54UA4lbca2yBDxzXhF4wtxrSH5Byw1QSbpJ1W5cZnM/va7x6+kBosPk\nhrvsdMbZYo6Rf6nC/GEo7VVUI8l9Fa7zZQ2PLQZKg7aeeM+lcGCW4pfOssXRvdXSMAcHrQm9ZImt\n83IT+FVK3YOiEixaBlMtgRLfukvDm5iwDYu3lDzxtEbf+Ozalexn9oTPQnzcRWhVxt0xfnu1YLXf\n5hUmFN0FXV1F10O5XmvYoUkT/PS9dB9Hueqq6hPOVaNcXn3B1Ra4yXyco9Igs7BM/6wuX14aExkf\nL83Xr5ROGsoUf++S5hULOyPlLJZyhCO44xlcrW70k+gIRRA+TN7Em+lDX+nY5IyH0UKefBLx0YmV\nAlVpHvSp54cIP2ahfNA00aRN+GhLmCGVZBEND8M73lHaSNYywjPeAKS5T6HLx+dwh8tg67ng0gks\ngSSrK6lhCuWLu7WSYjczIZ7hBskzf3ri5/cWSaV8fO/qqjRJWuinh+pz9qQhPudRKH+58yfFx5Ke\nr0qESin8/HQ8tRWCZInYKOtsdupUHUlWY5qstXZRCh2hCKCyO6dcqlq9sOVOnc446YFqVA5x2JP1\ny1NlLJ6/xMJwlkEtxAcelettlbPC4i9jpZeo3LHl4ilQ2mNPo5DKkeZlr0XxlWtYKjU45cpO23Ot\nl+Iqd96k+xSuS5NqW6nMNBO0JcXbqg3eiivfWlOC/TG1DEprFR2jCKD52rdclkaaY6qtq9dx5Y6Z\niVJMcvPEe3Fp52+fztzulaypWvZJS7u97LV2ctLWQ7zTUo/6q6WMNLGltGVUUqzxfbwiqDSYLM25\n2qX3n4QYY1otQ1U2btxodu7c2WoxZsxseCA8jZA17tOPpyc2S45G0O5y1lO+dr/WNKS5hnLjPGp1\nK7eyvkTkNmPMxmr7dZRFoKSnEQ/tbOohzTXqWddz4b5Nx8qe7nM7G+pLFUETmQ0PRLNJ48KZDcwW\nORUlCVUEStPRRlOZjczl53ZeK04qIptE5B4RuVdE3tUKGRRFURRL0xWBiMwHPga8ELgAeLmIXNBs\nORRFURRLKyyCDHCvMeY+Y8xjwJeBq1ogh6IoikJrFMFa7HfBPPvduhJE5BoR2SkiOx966KGmCaco\nitJptCRGkAZjzKeMMRuNMRtXrVrVanEURVHmLK1QBAeAcJb2dW6doiiK0gJaoQh+DpwrIutF5FTg\nZcC3WiCHoiiKQgvGERhjHheRNwLfA+YDnzPG3NlsORRFURRLSwaUGWO+A3ynFedWFEVRSpkVk86J\nyEPA/TMsphsYq4M4jUblrD+zRVaVs77MFjmhcbKeZYypmm0zKxRBPRCRnWlm4Ws1Kmf9mS2yqpz1\nZbbICa2XtW3TRxVFUZTmoIpAURSlw+kkRfCpVguQEpWz/swWWVXO+jJb5IQWy9oxMQJFURQlmU6y\nCBRFUZQEVBEoiqJ0OHNeEbT7R3BEZEhE7hCR20Vkp1vXJSI/EJFfuf8rWyDX50TkQRHZE6xLlEss\n/8fV8W4R2dBiOa8VkQOuTm8XkRcF297t5LxHRH63iXL2isjNIvJLEblTRN7i1rdVnVaQsx3rdJGI\nRCIy6GR9n1u/XkRudTJ9xU1lg4gsdMv3uu19LZbzOhHZF9TppW598++9MWbO/mGnsNgLnA2cCgwC\nF7RarpiMQ0B3bN0HgXe53+8C/q4Fcv02sAHYU00u4EXAdwEBngnc2mI5rwXenrDvBe4ZWAisd8/G\n/CbJ2QNscL9PB/JOnraq0wpytmOdCrDU/V4A3Orq6qvAy9z6TwKvd7/fAHzS/X4Z8JUWy3kd8NKE\n/Zt+7+e6RTBbP4JzFfB59/vzwEuaLYAx5hZgPLa6nFxXAV8wlp8BK0Skp4VyluMq4MvGmEeNMfuA\ne7HPSMMxxowYY3a530eBu7Df4WirOq0gZzlaWafGGDPpFhe4PwM8F/gXtz5ep76u/wW4UkSkhXKW\no+n3fq4rglQfwWkxBvi+iNwmIte4dauNMSPu96+B1a0RbQrl5GrHen6jM6s/F7jW2kJO55L4DWzP\nsG3rNCYntGGdish8EbkdeBD4AdYiOWyMeTxBnoKsbvsEcEYr5DTG+Dr9a1enHxWRhXE5HQ2v07mu\nCGYDzzbGbMB+w/lPReS3w43G2optl+PbrnI5PgGcA1wKjAAfaa04RURkKfA14K3GmCPhtnaq0wQ5\n27JOjTFPGGMuxX7XJAM8vcUiJRKXU0QuAt6NlfcZQBfwzlbJN9cVQdt/BMcYc8D9fxD4BvZhHvWm\noPv/YOskLKGcXG1Vz8aYUffiPQl8mqKroqVyisgCbOP6JWPM193qtqvTJDnbtU49xpjDwM3As7Cu\nFD+zcihPQVa3fTnwcIvk3OTccMYY8yjwT7SwTue6Imjrj+CIyGkicrr/DbwA2IOV8dVut1cDN7ZG\nwimUk+tbwKtctsMzgYnA3dF0Yv7UP8DWKVg5X+ayR9YD5wJRk2QS4LPAXcaYvw82tVWdlpOzTet0\nlYiscL8XA8/HxjRuBl7qdovXqa/rlwI/clZYK+S8O+gACDaOEdZpc+99o6PRrf7DRuDzWN/he1ot\nT0y2s7EZF4PAnV4+rN/yJuBXwA+BrhbIdgPWBXAS66N8bTm5sNkNH3N1fAewscVyftHJsRv7UvUE\n+7/HyXkP8MImyvlsrNtnN3C7+3tRu9VpBTnbsU4vAX7hZNoD/KVbfzZWGd0L/DOw0K1f5JbvddvP\nbrGcP3J1ugfYRjGzqOn3XqeYUBRF6XDmumtIURRFqYIqAkVRlA5HFYGiKEqHo4pAURSlw1FFoCiK\n0uGoIlBajogYEflIsPx2Ebm2TmVfJyIvrb7njM/zX0XkLhG5udHnmg4iskNEZsWH3JXmo4pAaQce\nBf6ziHS3WpCQYHRqGl4L/Ikx5jmNkkdRGoUqAqUdeBz7zdY/i2+I9+hFZNL9v0JE/k1EbhSR+0Tk\nAyLySjfv+x0ick5QzPNEZKeI5EXk993x80XkQyLyczfp1/8Iyv13EfkW8MsEeV7uyt8jIn/n1v0l\ndiDWZ0XkQ7H9e0TkFrHzze8Rkd9y6z/hZCrMT+/WD4nI37r9d4rIBhH5nojsFZHXBTLeIiLfFvsN\ngE+KyDy37QUi8lMR2SUi/+zmDArlme/qdI+7jil1rnQetfR4FKWRfAzYLSIfrOGYAeB87DTU9wGf\nMcZkxH5M5U3AW91+fdh5XM4BbhaRpwGvwg7df4bYWR9/IiLfd/tvAC4ydlrlAiKyBvg74DeBQ9hZ\nY19ijHm/iDwXO1//zpiMrwC+Z4z5axGZDyxx699jjBl3624SkUuMMbvdtgeMMZeKyEexc9Zfjh0V\nuwc7vz7uei4A7ge2Yy2qHcB7gecZY46JyDuBPwfeH8hzKbDWGHORu6YVVWtZmfOoIlDaAmPMERH5\nAvBm4JGUh/3cuDlYRGQv4BvyO4DQRfNVYydL+5WI3Ied8fEFwCWBtbEcO0/OY0AUVwKOZwA7jDEP\nuXN+CfthnG9WkhH4nNiJ3L5pjLndrf9DsdOOn4L9GMwF2CkIoDgf1h3YaQeOAkdF5NGg4Y6MMfc5\nOW7AWiQnXDk/sdPXcCrw05g89wFni8j/Bb4d1JnSwagiUNqJfwB2YWdi9DyOc2E698epwbZHg99P\nBstPUvpsx+dRMdj5XN5kjPleuEFErgCOTU/8qRhjbhE7tfjvAdeJyN8D/w68HXiGMeaQiFyH7fF7\nwuuIX6O/rnLX9ANjzMsryHNIRAaA3wVeB/wh8MfTuTZl7qAxAqVtMMaMYz8z+Npg9RDWFQPwYuzX\nnWrlv4rIPBc3OBs7Odr3gNe7njoi0i92BthKRMDviEi3c+m8HPi3SgeIyFnAqDHm08BnsG6nZVhl\nMyEiq7HfoqiVjNhZdecBVwM/Bn4GXO5cX3522/6YPN3APGPM17BupKZ9X1ppX9QiUNqNjwBvDJY/\nDdwoIoNYX/h0eusPYBvxZcDrjDEnROQz2NjBLrF+lIeo8klQY8yIiLwLO82xAN82xlSbIvwK4C9E\n5Eq8dPcAAACDSURBVCQwCbzKGLNPRH4B3I39EtVPpnFNPwf+EXiak+cbxpgnReQ1wA1S/NrVe7Gz\n73rWAv/kg8vYj6MoHY7OPqooswznvnq7Meb3Wy2LMjdQ15CiKEqHoxaBoihKh6MWgaIoSoejikBR\nFKXDUUWgKIrS4agiUBRF6XBUESiKonQ4/x8mxT3+DNAdxgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f95d5e06908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "extract_data_and_plot(dMal, dBen, 'smoothness_mean')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "benign smoothness_mean stats:  DescribeResult(nobs=357, minmax=(0.75700000000000001, 5.1180000000000003), mean=2.0003212885154062, variance=0.59470193920844749, skewness=1.1827432067356258, kurtosis=1.70421077885121)\n",
      "malignant smoothness mean stats:  DescribeResult(nobs=211, minmax=(1.3340000000000001, 21.98), mean=4.3037156398104264, variance=6.5418095663732787, skewness=2.8409946067959004, kurtosis=13.991415450198009)\n"
     ]
    }
   ],
   "source": [
    "print('benign smoothness_mean stats: ', describe(ben_smooth))\n",
    "print('malignant smoothness mean stats: ', describe(mal_smooth))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Smoothness is a great indicator, you can clearly see the difference in smoothness between malignant and benign tumors. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Compactness median and mean"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "#### Malignant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.02855"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mal_compactness.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.032201739336492896"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mal_compactness.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "#### Benign"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.016309999999999998"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ben_compactness.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.021438246498599437"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ben_compactness.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "#### Average compactness of both Mal and Ben\n",
    "* Performed using weighted avg, consider the number of malignant tumors and the number of benign tumors. \n",
    "* ((#mal) * (avg_mal) + (#ben) * (avg_ben)) / total_#_terms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.025436656690140846"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "((len(mal_compactness) * mal_compactness.mean()) + (len(ben_compactness) * ben_compactness.mean())) / (len(mal_compactness) + len(ben_compactness))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Generate Bootstrap samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "def bootstrap_sample(data, n = None):\n",
    "    \"\"\"\n",
    "    Bootstrap samples a dataframe\n",
    "    Params:\n",
    "    X: dataframe\n",
    "    n: int, optional length of resampled dataframe, equal to length of data if n is None\n",
    "    Returns sampled_data\n",
    "    \"\"\"\n",
    "    if isinstance(data, pd.DataFrame):\n",
    "        data = data.copy()\n",
    "    \n",
    "    if n == None:\n",
    "        n = len(data)\n",
    "    \n",
    "    return data.sample(n = n, replace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "batch = df.sample(n=10, replace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "#### Variables that are predictive of a malignant tumor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f95d341fc88>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Wg8ltFUlBlyoQ0i8tNckaazBSjOXiiFWrIm9o8eSTPAoRlZQwhfE++1R/2aKF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\nvGsEzp3zvsNWVjoh4uad/YorxGXNDAByk50tnzfdJLOyxAClLldePv7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glsoCH5D8QgMG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\nYkkj/h+RKozEdqMkmgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f95d3467a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "malignant_compactness = np.asarray(mal_compactness, dtype=np.float32)\n",
    "m_nums = np.linspace(0, len(mal_compactness)-1, len(mal_compactness) , endpoint= True)\n",
    "malignant_smoothness = np.asarray(mal_smooth, dtype=np.float32)\n",
    "plt.plot(m_nums, malignant_compactness, 'r--')\n",
    "plt.title('Malignant tumor compactness')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f95d3411cf8>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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XLugcjgEa1WyEdSN4Hrq9wjz011u9bmNLrAsXdA7HCLweuv3CfrP7j+/b2BLrUqagE0J+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\nBCdHnmOxPdaClc/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eQRjbYiz83f2Npi2WVl1SrpJjXOQ4syZn6N2gN7Lez4JKo4LffD+4\ny9zLHGnJwiZuMjdIibRMxyBXnotA90Czp0ijlMJ5njNmdZ2Fj7sJH4KogCj8+9K/esdm6ZMSIoFM\nKivXe1PLrRbiH8ej16pe2Bm9E8+GPmvyvqwlLpVIMab5GHSv192k/Sa2mYjIgEhM2DHBaBmFfGU+\nXB1d8dPAkvOG/nzmZ/x67lfkfJgDb2dvnuVSGSjVSjhKHNG+TntE+EeYtS8b8VaeeGOjmo2wevhq\nwRadZlxyXjJkUhmmd5huUWy7LMZuGYuPDwgvHyEEgR6BSMozPxd9ctvJBsUcED505nyMWga2xMhm\nI3Ho/iHxYddQDeYengvgSWYOI70gHYvPLMa5pHNm210e2tdpj1XDVyHAPcCgoI8IHyF60oaYe2gu\nnOY6QalW6u13MfkiOv3WyWD6rFQihbezt7i9m6MbKGip+e+64wAMpcYWJ0+RBw8nDyTnJeNSyqVS\nt9UlR54DlUalF0Iau2Us1l9e/2QbRY5eq2t4k+Git2sJ225sE/P4zc5D10lbHNB4gFioyxTa12mP\ny29eNjoPb54iz6hjqFArxA+Bt7M3H1hUGSg1SsikMuy/t9/ssq0nXj+BJr5Nyh1vZD+y7oO5+PRi\nRPwcgbiMuEqZaeaf2/+IISMAYi66JSTlJqHTb53w982/9ZYXqYrgIHHAS5tfwtYbW8s8zr2se/jt\n/G/ovrI7LiQLWQCp+amiR6U7IlC3v8OUHPeKxGAeuk4cNtgzWAzLGCJfmQ8KCtlcmV4t7kP3D+FY\nwjFx2jhdTiaehPQzKdouEyo7spZdaWFCSilcHFzgJHWCTCor8znKVeTC3dEd35/4XjxPaWQUZCBX\nnouMQmEcwMz9M8Xzrrm0BmM2j8Hv54VJKKKbR2NGhxnivhue32BxhgsArLi4AgtPCaOozc5D1xlY\nlJKXYnJ98yHrh6DbitJn0/pxwI+I6RhjsM6OrqB7OXlxD70yUKgVcJQ6YnbsbMw5NMfs/dvXaY8w\nH8urp+29sxej/hol2sJgL2r4ovAKn66qQFmA9IJ0veyAAPcAiwR93NZx6LqiK44lHCvh4RepiuDq\n6Iq1l9ea5PF9e/xbzNgzQ7QR0M8W0BV03bQxa6ctfvjfh/D6UoiRtgpshe/6fSeO/gOAjdc2olBZ\naLSTXdd23Ws6m3QWgOFJss8lnYOGanA/+z5+fvZnjGk+Bl2Cu+h56Ltv7UbTRU3FD8K7nd5FwcwC\nOEodTfLQvZy84OfmJ+Shq8suTeA73xehC0P1rkGpVuoJLAtLDmsyDBNaTyj1eOagUCvE3H9zPfQ+\nDftg2eBlcHV0xezY2ej0m2mlbAuUBbiRfgMdlndAbHyswW1kUhmyi7KxPW57idaTrqA3r9VcrNdk\nTap8DL2pX1MUqYqQlJdUqgeTUZABd5m7mKt9Pe063tj5Bub3mY/2ddpbfH5dT85YZktFd4qy+K6u\noMd0jLEo/S+zMFPcz1AeeoB7AAiISR3OuiEnUdBzBUFv6tdUr5iRrijaIsuFhRfCfMNKlENNL0hH\nTMcYo1ksupUZDdUhMSTouq3Aia0nGhyXkJqfiuvp1w1/ECaeK3Pw092pwoeAZW0p1IoyxyYk5yXr\nXcOj3Ed6w+JZdsu9rHvwdPKEj6sQpuz6e1c0rNkQvw+1bBo5uUoODycPZBVlmf1+NK/VHM1rNQcg\neOnm5qGfSDyh17rVZfHpxdh0fRPUVI3HRY/1MnDkarko6J/3sn6GC1ANPPTpHaZjzYg1cJQ4lppa\n5zvfV2/UZkJOAo48OFLu2t3sI9IluIsYkwdKz+8uL4YEvWPdjujVoJfZx1KqlWLzv7id659bj/l9\n5sNRWvq9ZejuzwSdZRB91v0z/ND/B3F9Deca2Pj8RgAl89BXX1yNb49/a/a1mEquIleMB+fKc3Et\n7ZoouGqNGgq1QszuMES+Il8s2czCFcCTsI2hKny6gn404ahB0V56TugwZh+75eeW462/3wIgFHvz\ndvY26fqYiJcVSnyh2QsI9QmFv5s/6noKk4A/yH6g12JitvRa1Qvv/PuO3vLylGxQqBUIdA/EuhHr\nxBIcpnIn847Y7yIl5uWhl9UqWHt5rVjnvXj2zdtt38a3/SrvuTSFKi/ojNLy0A3NVMK+0DP3z8Rz\nG5+z+Lysebpy2Eq94mCV6aFrqAbNazXXy+vOLMzEthvbzH7JFGqF2GlbXLTD/cLR2Kex8LE00UNn\nx2KiMLH1RKS/m47h4cMxtMmTIdpuMjeMbDYS7jL3Eh76y1tfFkM3lUGu4knGxn/3/kOzxc3EOjbs\n95x9cLbROjwDGw/E/7X7PwD6HjobaMOqI+qie41dfu+Cqf9MRZOFTfQGGDFhYs/x8cTj2BYnTOC9\n9OxSbLy60eg1JeclY9C6QThw74A4uUVZselarrWQkpeCyIBI7Bm7B4Ag6HKVXEzbE/PQdT6CgNBR\nW56WlUKtgJezF6Ijos0uqvfNsW/Qf01/AMLHy9Q+MF1BN+ag5CnyxESA4l58x7odMSh0EABg/eX1\naLa4GXLlubiZcRNTdk2xeK5Sc6jyIZfXt7+Oh7kP4ezgbFR0mMjpTkLBYoMUFHez7uJa2jU0W9wM\nlyZdMitbhj1MTg5OekP8RzUbhSJVEVZeXFnhgt6/UX/0b9Rfb9n1tOsY9scw/D3mbwxsPNDkYyk1\nSrg5uqGpX9MSHuCKCysQ6hMKTydPSCXSMo+lUCvg7+aPOT3miNO6EULg4+qD1PxUXEy+iM7BneHi\n6IL0gnRcTL6IY+OPlRgp2KluJ4vKGJhKjjxHDCsUz3LRbS0Y65N4o/UbkKvkyFfk6/W/PC56jO4h\n3RHTMabEPsVFR66SIy4jTm85s4Et080yWnx6MUK8Q4yWqcgoyMDft/7Gy5Evo2u9rlg0cFGpabxq\njRoLTy8Ur7muZ11MbD0R9WvUR8OaDfH4g8d4ddurYiZLjlw/y8XF0aVcyQQbR24EpRSx8bGo51UP\n9WvUN3nfPGWeKMyeTp6Qq+V68W1jDGg0ACqNCpdSLhl9J/MV+WhYoyHyFHklRJ+1CloFtkK+Mh/X\n0q7hcdFj3M68jUWnF2Fsi7EmX4Ol2K2gy1Vyk17qpLwkpOWn4behvxl9wFizWHdi17T8NEiJFP5u\n/kgvSBezMhJzEhHhHwFKKcZtHYfRzUeXKpDBXsGo7VEbtb+tjW2jt2FI2BAAwjD3UJ9QRNSKMNsD\n0eVq6lVcTLlYZolQFge+mXHTLEHvXLczZFIZPu1RsnLgO/+8g3GR4/BohmkDrya1noSRTUfi+abP\ni8sWHFsAd5k7PJ08MWbzGFyffB1NfJvgROIJDF4/GKdeP1XCo82WZ8NB6WDSS2oJfRs8ydMuLui6\nz1BqfqrB/fMV+XB2cMaSwUv0lm8dvdVoP87cnnPxUZePcPbRWfRe3dugp8ied7ZOrpaLgl7WcH7m\nSbvL3BHhH1GmU8L6Ab7p8w0+2PcB1l5ei/T39EMMLD6uUCugUCv03h9nB+dyDf0P9gqGWqNGyA8h\nmN1tNj7p/onJ++bKc8V71LdhX3jIPEyqNDqv1zxkFWZh1+1dBgcOAcJ96Vm/p954CUbMnhioNCoc\nevWQ6Pw8LnoshthquNQw+RosxS5DLscSjsF5njMO3Ct9ZBzwpAhUC/8WRif7ZR76S1teEpd5OXuh\nVWAruDi6CB8PbTO1tmdtAELsd/Wl1WVWGRzYeCD+eUlomut+9eMfx6NAWYAZHWeUGPJeGnvv7NWr\nZR3xcwRe3Pyi3gM7ZtMYTP57st5+Pi4+qOFcw+zRa1/0/sKgmAPm56H3qN8Dzzd9HkceHBHt+Ov6\nX9h8Y7PYucRCFCw2u+HKBvxx5Q+941xJvYILyRcsnlavLN7v/D7e7/w+gJKCHuQRhIKPCuAgcTAq\n6JG/RGLc1nHQUI2eR1/TpSYWHF+Alza/VGIfmVQGb2fvJ4OFtN6zbpiwjmcdhPqEiiNnde9/WVku\n7H56yDyQI8/BheQLpWYPsUFNbjI3ZBZlwtPJE2qNGhkFGTifdB6j/holZmeJpRJ0Oks71+2MrvW6\nGj3+ycSTpYaIfj33K/bc2WPSgKnisHx7AGhbuy2mtp9qcouuhksNXH7zMkY1H2X02MY6n4vnoQNa\nQdepfFrZ2KWg77kjxPPKGuoMPMlDP/3wNLbd2GZwm671umJQ6CCx4wcA3uv0Hk5NOAVnqTOKVEWi\nh8PElA3LNiXl0FAe+vR/p2PQ+kG4lXHLYAeYMfqu6Yveq5+MnGQFv3SzQk4+PFlikA4hBKE+oRYP\nR+69qjd+OvlkdBylVPQQp/0zDT+f/rnMY1xNvYrradcxaN0gcXs2UIN5L8ybYdez+tJqrLm8xuDx\nEnMSLbqWstBNRyteD50QAhdHF9T2qG3cQ1fmw83RDa2Xtkb0pmhx+ezY2Vh9aTVOPypZS3v1xdUY\nv208nl0njIg0lIc+LGwYJj/z5EMtJVLRKy5L0JnousvcERsfi5ZLWuJ62nWj2zMP/c2/38SfV/9E\nTZeaGPnnSHRb0Q33s+9j49WNiN4UjV6rekEmlWF+n/noEtxF3P/DLh+W2kH4cezHpdaLn3d4HjZc\n3WBRITFd0S1QFuBa2jWT5jdt8EMDTNlV+uxGqTGpmNtzLob/MRzfn/heb50hQc+WZ4v56KZ2WpeH\ncgk6ISSeEHKZEHKBEGLe7BHlgIUMDM1zWRyFWgFHiSOWnF2Ct3a9ZXAbQgiCPYMNeixN/ZqiXZ12\n4rqdN3cCgFh8f/Wl1aWe//PDnyNsYZhoC0OpUeJhzkOELgzFjrgdZV4HI9w3HM8EPSP+vWLoCgBP\nmv8aqkFCdgLqedUrsa8lgt5heQdM2D4Bpx+dxp2sJy0Ddi3ODs7YcXMHjiYcRbcV3UqtPT1512RM\n+nsSXB1dxfvJXj5jHrqfm1+J3yVxmiDkLOWxoqn5VU28u+ddAML9/q7fd2LJhvjH8Zi6eyqa+DZB\npH+kwf3zFHlwk7nB29lbvB6lWolPD36K1PxUg1ku2+K24fcLQgijZ/2eaBPUBp2DO+u1gPo07IMl\nZ5dg7aW1AIQQzoFxglNjysAifzd/eDp5mlQ8S9dBkKvl8HH1QR3POkjISdD7PR5kP4CHkwdiOsag\nZaDpE8FQSo2GNQCtOEpkJuXXF+d/Xf+H6e2F2ZSOJxxHs8XNxDEAjL139iJsYZjeOIg8RR7UGjU6\nLO+AX8/9avDYLo4ucHV0xbmkcziffL6kzVpB93P1Q7va7eDi4IK6nnUxsPFAs0pBWEpFxNB7UErN\nq55TTpiQmxIXaxvUFi6OLsgqzDLaKbrr1i4sPrMYBASUUqg0KrRe2hrvdnwX0zpMw7QO08QRccxb\nYILO0tOMofvyFs9scZe5I1+Zb9YDW7xGRJBHEGp71BZfwOS8ZCg1SoMlR9/r9J6YfWEqaflpKKhR\nAJlUpnf/dIedO0gcoNQocej+oVKPxbJcXB1dUaAyUdBd/UqkLQa4B0BKpJXioVNKkSPPEUWvnnc9\nvNP+STpeQnYCfjz1I/aO3WuwxgylFPkKwUOv6VJTzI5hv5mHTMitLl4Dv1BVCF9XX6QXpOPFiBcx\nrMkwDGsyTO/YmYWZuJZ2DSn5JUvrrhy2stTrGh4+HMPDhbRcdt9K83ylRIownzAxRa+mS03U8ayD\nHHmOmHnj7+aPhOwE5Cny8Cj3EYK9gsX7NvO/mVh/Zb2Y+16ctII0XEi+gFMPTxkMhcpVQk63k9TJ\nbEFnmSbAk36x4vH8vmuEfpK/b/0tznHL8vJPPTyF3vVL/rY58hx8fOBjjG4+Gg1qNChRkkI3D72e\ndz2ceP2EuG5c1DizrsFS7DLkcjnlMpykTiaNxPqu/3f4vNfnpeZKs5IAFBQFygIk5CTgcuplve1f\nbfkqwnzCxGUsBMNeTmPI1XJIiRSvt3xdb1ZxpVpp9ki4pNwkHE88jr1394rLTj86jVldZyEyQPAW\n2ctqqPXSvFZztAlqY9K5RDu1IStHiaOenW4yN5x74xyiI6KFe2tiPXRWo724h+7l5IV1I9aJra/n\nwp/Dhuc2wNPJUy/9Ta6SY9LOSVBTdaV46GzYPhOCfEW+XpOd2eLi4GLwdy9SFYGCwk3mBh8XH/ED\nxQS9QY0GUGlUeoOP2H5s6rLDDw4bvJ8sHMM+cDF7YvDjyR8BCMWsdEezloYpHnqEfwRuTLkhPqMD\nGg2Av5swcxerCe7v5o9cRS4O3z+MsIVhOJ/0xGNVqBWljkxmISDmKBWHebu/DPoFr7cyvWolIPSx\nsZILrEO9eFiztofQF7bj5pPWMTunsVZBZmEmfjj5A26k38CwsGG4knpFr0Lot32/xdR2U82ytaIp\nr6BTAHsIIWcJIQYnKSSEvEEIOUMIOZOWZnj0lbn8eU2I6ZkzUKa0XGk2QGDKM1OgoRpxaHWDGg3w\ny5lf0OjHRlBpVKjpUlN8Qdc/tx5f9voSCrWixMupi1wlh6+rL5YNWYbuId3F5eYMbb6VcQuJOYkG\na0OsvbxWT+BlUhm6BHcxKOgFygKsurgKV1KvlHo+XVhxM5lUpveBc5A4oGVgSwS4B4iDtgLcAzCh\nlfHh30q18HHQFfScD3Iwp8ccSCVSREdEo7FPYwBCjvuo5qPg4uii56HnyHPw6/lf0cyvGZ4LL3t8\nQI48B+RTgtUXSw+NMYp38J15dAbNFjfDqYenADwRwe9OfAfvr7xLiDoFxbT209CudjvxeaGUin0a\nUQFRaB3YukSro1BZKM44tOLCCvxz+x+ELwrXq2PDnhNmw/a47eK8q9vjtuOHEz/AGL+f/x3DNgwD\npVTsIDSlhlAtt1qIbh6NlyNfFqdiTM1PhYfMA7XcaqFAWSAWoSqe5VKkKjLq7DCHyNgEMkxchzUZ\nZrYT0mNlDyw+vVjPpuKCzt7ZfXf3ic9iWYLOWo1ujm4YES7Uctl0fZO4fkDjAegU/KTMQL81/fDe\n3vcw/I/hYvmPyqa8gt6ZUtoKwAAAkwkhJbq1KaVLKaVtKKVt/Pz8ynk6gfSCdPi6+po0E3v3Fd3x\nf7v/Dw4SB6MDizIKMxAVEIWfBv4EDycPPUHPkefgTtYdfHH4CxxPPK43UIS9gIaGdzOK1EViDrqu\nvdPaTxOLGRl6eJacWYIVF1YAAEIXhqLud3XFF+fttm+L22UXZWPT9U3itlEBUTj06iGD8UyWamms\nc9gQSo0g6OF+4QhwDxCXJ+YkYvHpxUjKTYKHkwccJY6Y1n5aqaVuWV2dz3p8hg86fQBAGPDFBObU\nw1PYHrcdlFKcTzqPYwnH8NOAn3DwlYPiMdiLOKPDDL2mtTHYYI4pu0vv7GKIsxXJSs9D93b2Ro48\np7ISTaQAACAASURBVIRQuDq64tt+36JH/R7oVq8b3m77NtRULYbe3mj9Bs68cQZ+bvrvQpGqSC89\nk4LiRvoNvdIRTHgM5aFvu7EN3xz/xuh1XUq5hP339oMQgrqedbFwwMJSp9bbEbcDbZa2QYh3CHxd\nfUEpRbhvOD7o9AHm9pyLnA9z0CqwFYaEDREdIt0sFxdHF1BQo85KdPNoNKzREJdSLhnsu7ry1hXM\n6DgDpx+eFlOGTYGlUOrmoQMlBb1AWYCWAS1RpCrCkQdHQCnFiy1eRMuAlkYFnbXS3GXuqOtVF6Ob\nj9br6DwYf1Bv8FBKXgriMuLEjDZrUC5Bp5Q+1P4/FcAWAGWXcKsAMgozcDn1conUPEM8yH6ArKIs\nTGg9QUwfLHG8ggz4uPhAoVZApVHhXtY9OEocUdujtvjCsMyE34b8huMJx9F/TX+E+Ybh+GvHS23q\nRvlHoW+DvpB8JsHcQ3PF5c81fQ7REdGY32e+wfSu709+jwXHF+gtY3HAUc2efO2ZwOk2d43hJnOD\nn6sfEnISytyWMaDRAEQFRGH3i7vxZe8vxeXnk85j8q7JSMhJwMFXDmLzqM1QqBUY9dcooy2huT3n\n4s02b6Jvw77oFtINmYWZmLJriuiF7r2zF0M3DMVPp37CvMPz8MaON1DLrZbeJN3MS6KguJxyuUz7\nWQjKWCbRgmMLsPn6ZvFvV0dXTGg1QRwwYywPnWVEFc90UWlUyFPkQUM1GNB4AL7p+w0cJA7oWb8n\nkmYkGZ34+ej4o1j/3Hp81fsrAE9yzXXvJROUul51RVt00xZL87jZBBQA4OPqg8ltJ5c6/uFh7kOc\nTTqLT7p9gp9O/YS1l9einnc9fNH7CzHUObLZSGwbvU20sbiHrnu/ivN9/+/xbb9voaZqnH10tsT6\nRjUboZZbLUzcORGzDswyamdx2PPBrtXFwQUL+i5Az/o9xW1UGhUUagX6NOiDEeEj4C5zByEEK4et\nxHNNn0NErQiDE8OLHro2A2n9c+vx1jNPEi2G/TFMrBAJCK2b1PxUPC56bJWURaAcgk4IcSOEeLB/\nA+gLwPS2vJlkFmbC+0tvxMbHih5BntLwRK66MA+zUc1GeiEPXTIKM3A36y6c5jphz5098HH1Qb9G\n/SCVSMX884zCDIT7hiMyIBIXUy7i3zv/op5XPbSv077UXOxpHaZh2ZBlkBCJ3st5I/0GknKTENMx\nBs/UfqbEfjfSb5QIjTBROppwFEq1EpRSMUSQWiAIy6qLq9BkYROjrYYgjyCjseftcdvx59U/9Zat\nGr7KYBU9VnmR1QXXUI1oizHxHBE+Aj3r98T1tOs4+uAo0vLTsOj0IjF75sMuH2Jo2FBM/3c6rqVd\ng7vMHfvv7df7EOqmM7b4pUWZKZ9s+4fTDV9zzN4YvdIOdTzrYOngpWgdJAhvcUEfGzkWeR/miS2g\n4mGw80nn4fGFB3bf2g1KKfIUeWLLJMA9AIk5iWi9tLWYestwcXSBu8xdFGWWh87CXJQK/Tuzus4S\nO7Z1BxaZkofOPGiVRoWLyReNpl0COvOiau1hndaPix5j7qG5mLRzkrit7qAlRhPfJkZDYhqqgVKt\nROfgzpAQSYnOdIVagflH5+PMozMmZ7nkyHOw+PRi8Xlg10oIwfQO0/XeMbVGjUmtJ6FH/R7Y9MIm\ndKzbUe9Y+8ftFyfy0LsnyiceOoNSKoaVig9083PzE7OarJGyCJTPQ/cHcIQQchHAKQB/U0oNu8AV\nwOmHp5Etz8bs2NniQCBjM3PrwuK219OuY/3l9QYL9Vx96yr+ekGYni27KBsxHWOwI1roLGEvTGZh\nJhJzEvHb+d9wM+MmZFIZvJy9sOLCihJTpxmi+IM5eP1gvL/vfdzOvF3qhMCUUqwYugJHxx8Vc7Xf\n3/c+0gvSIVfLxYJP7OVMyk1CXEac0Y9MkEeQ0Sn1hm4Yihf+Mjx0fML2CXjnnyfZHmyOTX93f3x9\n9GtM2TUFXx/7GkBJkWOceXQG8Y/j8fWxrzF602jxBWHiJSESfN3na6ipGtfTr4uC/knsJ+JLw0Ie\nTXwED9rQZL26vNjiRdBPKII8gkqsM/QsqDXqUuuhS4gEbjI3UeCKT2Kg68UdfnAYHl944PD9w9h3\ndx9mx86GhEhwLulciQydWftnYfP1zfg4VhASF0eh+BdzAjRUg0+6faLnaTpJnURxcXIoPRtE10PP\nLspG1JIocQIJQ7Df5sdTQqcrSzFs9GMjzDowC3vu7MH+e/sR8E0AfFx8ML/PfL3qkwMbD8RfL/xl\nsG7NzYybkM2VYd/dfbjy5hV82OVDvfUFygK8t+89HHlwRMhD12l5GGv9Tf93OibvmiymFeuK7t2s\nu3o16J0cnPDzoJ/F8hjJeclIyUuB01ynUsdTDAodhLwP88RQ1YF7B+A4xxFHHhwBUFLQa7nWQlJu\nErLl2U+/h04pvUspjdT+14xSOq8iDSsOq61Sy60W/nv5PwS4B5gk6CyzYsfNHRizeYzBJqCDxEF8\n4YuLUW3P2ugR0gNylRy5ily8tv01nE06i/re9VGgLMCr214tdeKM4X8Mx9ANQ0sIOhvBGvlLJOYf\nmy8uKx5rK1IVYVzUOHSs2xG9G/TG+ufWi3Y6OzhD/bEazzZ+VhT0XEUuJERitBqgMUE39KJQSuH7\ntS/mH52Pa+nX9FoMj3IfwdfVFzKpDAfvH9Sbr9SYoPdf0x/zj86Ht5OQelm8eQwIufKs9Km7zB0u\nDi6CR6f1VHvU7wH1x2pxJJ+pmS6fHfwMM/+bqbeMtfQWDnjSTN5wZQOkn0nFWGg973r4rt93CPcN\nByBMHDLtn2kIcA/A6OajS0xgrPuR0k3F3HtnL7488qVYXrZ4C+qnUz/hYPxB8QPi6eSJzsGdxY+4\nVCLFJ90/wY8nfxRDjanvpuKzHp8B0IZctGmIlFJsvbFV74Pl6eQpprKamofOWqfs+ADEUISroysk\nRIKU/BQ09WtqsD6NMXRTOMP9wsVrZrD3pHgHZcyeGPh87QNDsGfaQ+aBlcNWipOXA8I7OP3f6eLf\nGqoR783OmzsRuCAQJxJPQKFWQEIkGLtlLN7f+36Jc7CPOftwuTq6Qk3VyJHnQEM1UGlUeoIe4R+B\nVoGt8GzjZ8VnurKxm7RFNsR+5bCViPCPQAv/FiYJeo/6PdCsVjM4SoSk/uKpi9lF2Zi0c5KYV34/\n+z5qf1tbHLzRu0Fv7B+3H62DWosP+NlHZ1G/Rn3xq1tap2hafhryFHklBN3QwIl+a/rB7XM3PQ8x\nNT8Vcw/NRY+VPZAjz9EbUgwID1m4b7hoC0sDNFRPGwBmdpmJ2HGxJZazAUdsujxAaJpnFGaI+bW6\n9y4pL0kMtzhIHPRE3JigsxTIGi41kKfIEzsKiw+lfjXqVQAQc9YB/aJYEiIR+y3Kqh751t9v4fPD\nnyMuIw7Lzi3TE7kiVRG61usqZtYAwgeRgopN9gD3ALzT/h2xONTxhOP4/uT3aFijIdY/t77EVGW6\nQ+Z1BT2rKAs1XGrAQ+YBKZGWGFzE4uE96/dEhzodEOIdgsOvHhY9cqVaGIh2O/O2mDaoS0zHGDx4\n5wEopdh4dSOG/zFcTGkEhHjvllFbAMCkLBd/N3+0q9MOy4csx1e9vxIzTdh9d5O5iR3Hu2/vLjHH\n6j+3/0HNr2riYvLFEsfWHTmZr8jHpJ2T9BwCZlfxPPTTj04jV5FrMBlibk8hLCchErwc+bI4EAwQ\nPma6oblLKZfgMMcBW29sFfs0mFMmk8qEEc3pJUfR7ru7D9P/nS5+CFmfQa4iV+8jxBjfcjyOjD+C\nnWN2YmSzkSWOVxnYjaBPaDUBB185iAJlARadWoRmfs0wqHHZWQ6bXtiESW0miaO0inuiyXnJWHJ2\nCZJykyCTynA88Tge5T7S6+ABgD9H/olto4XsEA3VINI/Ek4OTnBzdNOreV0cuVqoAzMucpxerI6J\nm66g65Yy+LG/8DJeTr2MWQdmITY+Fm/9/RYGrB0AQHgp7j++j4k7JuLlyJdx6FUhDpkr1y9jWpz6\nNeqXmLABEDzLZ4KeQVRAlJ6NgJDyWTwP/ZdBv2DjyI3ierZtfe/6CPQwPN8miyUzoWNhh/9v78zj\noqreP/45M8MMww6DgoDKIiiggsvXwH03cUfNJUntW5q/1Fyqr6nlVlZmmWW5ZmVpSrmkue9pLmku\nuKGCqCwCssMIA8zc3x+Xc7h3NtBMxO779eKlDHdmzpw59znPeVZjgT4tchr2jtqLKRFTmOmBnlwO\nJR3C+B3j2QZdVUf4HTd24Hr2dfQP6o/7D+7jZMpJ9reGLg1xZPQRzD86n2WGMhtsxRyWlJfg6v2r\n7PHi8mLYKmzZhmkclifU0IUbfm4Jb0clhMBV7SoqzWDgDNDpdazKJA1FFHIz5yZ8lvjgUuYlFJcX\no1BXiGG/DMPeBL5Rs4utC7ydvEEIYadZS1nBCpkCciK3qqFPbzsdR8ccRV37uni73dvs89JYdDsb\nO3Z6+OTEJ5iwc4Lo+QQEuSW5ogxrah6jAt3Z1hl2NnbYeXOnyPwjFI5vt3sbH3bjG3JQW7u5NP4g\nTRACXAOQ9SALf9z9Q6QAOKucRQKdjkmtUKOeYz34uvjiePJx9p42chuz5qtTKaew5NQSlkwojKBR\nyBTYNGSTqAR0TVBrBLqHgweyH2SjwecNMHH3RHT3745ZHWdV/cQKLGnoVBhr7DSY3GYy+5JC64YC\n4EPpGn3RCCeTTzJBFDs0lkV8aOw0VjV0WhVycc/FeLH5i+xxKtzMZcLpOT26+nXFgi4LQFCpaV/M\nqNR28nX5SClIwapzq0QJHAFuAVaLIqUVpmHpqaUmGlUb7zb4qPtHeO/we6J0dQCsxZlwM/Ry9GKR\nIMKU5rUD1iKkTojZ96b+DCrouvt3R8msErPNhHsG9EQb7zbMdEQTei6kX8Cqc6vgpnbD4h6LRXG/\nxpQbyvkMRqcG6B3YG0q5EluvbRVdQwgBBw6nUnkhWqArgIzI2MngRvYNhH4digO3DgCo1KQ5joPL\nRy6sETcl2D0Ybzz3BtzUblDbqKFWqJFdnC2KdIj0iRQ1maYaqa3CFhfSL4ADh0xtJoK/CmaFyYRC\nrKS8BEWlRYi9Esu09TOpZzDvyDwUlxUzgSPMThywcQC+OVfZ7FtYI+Xq/as4kXzC4jwKoQLd3c4d\nfi5++GHQDzgy+gi2DRP3lKVmneLyYpTqS9Hvp37oto7PG6HRWnSDi/SJFK1toUDv2LAjegT0EJ2s\nzPVz3XR5Exb1WIQ69nXQ/tv2IlOcsYYuPEUBQIBrANv8qJJlLglRW6rllZuK9W4s0F8IfUG09i9n\nXgaZR0DmEZbH8E9TawT6yrMrsTdxL9tdNWpNlZ1I9AY9fD7zwRenv2B2L+NYdHpk16g1+KTnJwhy\nC4JaoWa1UMoN5UjMTUSHbzswLUIowN3UblY19JLyEqjkpnHon/T4BIODB4s09O8GfIeufl2RUpCC\nH+J+wMAmA0XOzaTcJPi6+OLnoT+jXf12bGHHZcSh47cdkZCTgJkdZmLD4A0Wx3Ov8B6m7J1iNswx\nvSgdW+O3ssL9Qg3d39WfVYU0cAZ8dPwjlvpsb2MPtUKNOZ3mwMfJx2yrOwNngJ7Tw0Zmgw4NO+Dn\noT/D08ETKoXKai31wSGDkTotldl/qZnN2dYZ09tOF50ojEkrTIOBM6C+c304qZzQ3b87tsZvZVr1\nsj+XIfTrUDSt0xTn752H3qBHTnEO3NRuTCM1F4euVqhBCIGMyEzMS5H1I/H5858zk830yOno0KAD\n0grTWBz/9hHb8W6nylA8YRmFuNfisGPEDnAcH4dO1xbV/B2VjigpLxE9B+DNEXOPzmUmCXsbe1Zr\npkxfhu3Xt4scsZ/3+hzRwdE4nHQYbVa3Qb+f+olOG6/99hrGbBtjMqd9g/piYdeFiB0SC0IIRjUf\nhU6+nUzqlNCTVUl5CVtPVGg2dm+McS3HsQ0uSBOEpNwkpjAEuAXgxkS+zHN8Vjx+v/M7O3UA5qOo\nFp9cjA2XNpj1y5gIdCNnvK+LL4pKixDTPAZ+rn5WE4uEr2uvtMfosNEIdg9GSXkJDtw6IPJPUaUA\nwENVJf071Ip66OWGcry+63U851Pp6Pj05KfYcWMHimfxwqPFyhaIbhItulHKDGVILUyFtlSLISFD\ncOilQyYx43ShaOz4DeJM2hk0cW/ChAz9IvScHgbOgFkdZmH+0fno2LAjfF18sW7gOtzNv4uZB2fi\nVMopHHjpgMjJ082vG4I0QQhbEYZATSA2v8BnltH6ETM7zGQ3+ujw0RgdPhp/pf2Fj//4GD5OPiLz\nibZMizDHMFZPnB7NlXIljt09hpSClCrLIVDnr7FjtNnyZmyB0w1LIVNgUJNBaOTWCJOeq0xmynqQ\nhXcOvgMHpQNa1muJNf3XYE3/NTBwBijmKzC742zmrAN4jUtO5Fjbfy3CPcPRwLkBGjg3wM4bO7E3\ncS+W9FpiUag7KB1EN5G2VAuVXAWFTIGk3CSUG8pFNnAhNP2bbgbDQodh87XNrCNRYk4i7ubfRevn\nWmPVuVVIKUhB2/ptReY2kzh0fWXst7OtM/J0YoFOFQ56My/oyjcm79WoFwvrNMbF1gVF7xRBLpPD\nVmGLZh7NTE5JVKuM8IlAPcd6JgKd2m515Tq83uZ1tKzXEkduH0HvwN5sjQtjq19t9Sp05To8t4a/\np3KKc3Ar9xaLTb96/6qJsxLgC4T1COhh9nMIYRp6WTGrPUMTwTr7dhaFEAe6BULP6ZGUl4QgTRCU\nciX7Tt/c9yZ23NiBnSN3AuDXr7nytRlFGYjPimeZm8L7ZnjT4aJ6McYa+rDQYWju0RyT2kwCIQSB\nboFms7+1ZVr2HIBfG98N/A4AcCfvDnr80ANr+6/F2Ba8D0goa2pD2OITI6UgBXpOj25+lan+Pk4+\nKCkvYRr39azr+O7id6LnCU0GXo5e6OLXxWSnvJN3Bw5KBzRwboDe63vj2N1joqJIQk+/q9oVLrYu\nSMxNZF9QM49m2HhlIz48/iEO3z6Ms2niopPL+y7H1MipUMgUbNfnOA4X0i8gU5uJl1u8zOqXfH7q\nc+y6uYtp3pN2T8K6uHWi13NWOeNQ0iFcyrjErqM3YaY2E3029LFaArSufV3IiEwk0PNK8nA58zIT\n9lSYuNi6YMuwLegd2Fv0GvS5QrNBga4A6UXpcFQ5irRWvUEP1fsqzDgwA2NbjEWLenx23v7E/fjp\n8k9Yfna5WcFBuZN3B3OPzGVhZ0ItadgvwzBp9ySLz31Q9oBtHgDwUthL+HX4r0xgp2vT4engWelg\nLc7GqOajRAlU9PunTvnvBnyHK/93hc2PcdGnmQdnwnNxZUatrlyH7AfZsFXYsuzQD37/AO3XtmfX\nEEJgr7QXrU1qIqTrm24Un/X6DN8P/J6ZS4wFOl1ji08uZm36hMW0KPFZ8UgtTMWF1y6wio3Ctast\n05oVnGX6MnT8tqMogcYcGrUGg4MHw9PBEx72Hvi4+8esZ2xJeYnotBqkCUI9h3rMH5JSkILFJxbj\nbv5dZpKk9vdtw7aZlLUo1ZeKfBIEhJ0QAH4DebnFy+z3ALcAjGs5jvX47RHQA5Ofm8xOZSv6rhAF\nB1Q1J7TPLCB2igo7Qj31YYtPEnoz0cL+QGVxHbrbdvLtZDJpQpNBSkEKNlzaYBJdMK/LPKRNS4NC\npoCzrTOC3YNFSQXCm8zexh6L/uBjralAP5VyCj/G/Yi+QX2hkCmw+epmmEMYJ1yqL0WLlS2w5twa\nJOcn404e38t06t6p6LOhD7bFV9ojm9ZpitX9VqPonSK81uo1RPhEYMTmEfjqzFfsJvd39QdQkWqc\ndd2kFroQuUwOTwdPkY2R1nSnTltzPoEPj32I/j/1x/q49TiTymfNUufnuovr4PyRM7w/8wbHcSKB\nTr+7paeX4kTyCWQUZaBAV4CeP/bExssbrUbkAHxY4ryj81gYoTCt293O3aq5q0dAD9yZcseiTT+9\niBfo/q7+GNRkEFRyFYrLikWmB39Xf/Tw74GFxxaiUFcIuUzOhIVx5UuA31SFaf0vbnkR7p+4Y+qe\nqSx2v0BXgLNpZ/Gg7AFarmyJARsHYOqeqYjLiGPPoyZCuoaD6wRjTqc5bBMtN5TDSeXETgJ04ynV\nl+K9w+9hy7Ut7DumGrJQQx+4cSDeOcjHf4d7hiOmeYwoVl9bKtZGKRfSL+DY3WP46sxXZueU4u3k\njV9e+AXtGrSDt5M33m73Nos8efnXl9FkWaXfJLJ+JNKmp7H1l5CTgLf2v4Wk3CSWAdvGuw22vLAF\njdwamQQ20JBdOmf2SnuRkpBbnItz985hxdkVePfQu4jwicDKfitZCGm5oRwrz64EmUdYTLk5NkRv\nwF/jxFmtYSvCMHzzcLMCXbiuLTXFeNzUCoGelMs7fgJcA9BY0xh9g/oyG2VRaRG2XNuCvQl7TWKS\nhf08z6adxYtbXjTbYYi+lr2NvYkwdFQ5sr6Q9kp7kS0PAGYfmg0AGNl0JLr6dcXWeLHTzeczH8w/\nOl9klxNuNMM3D8d/t/9X9Bxhar6ngydeafkK7JX2WN53Od7r9B4TJBPbTETZu2UIdAuEjMiQqc3k\nNVgb64vH29FbpKFTp2rTuk3hrHJm47uZfROaRRpsvbYVt/NuY8eNHRi1dRTG/cabi6hwEdrjvZ28\nRUKOhn8p5Uq0W9sOW+O3so1Xz+mt9rUEYOIUXd1/NRIn85mlGjtNlVEuQkrKS+C52BNLTi4BwPsT\nPB080cyjGbYM24LQuqEI+ToEY38dy55DCMGqfquwd9ReOKocsfTUUtboI6pRlEntmvSidNHJhTrS\nPz/9ObvB3e3codPrEJcRh/Pp57H9+nZ8fvpz0dq0kdugfYP2TMg2rdsUczvPxZpza9ByZUu09mqN\n/Bn56BnAl4FlJhe9jmWhFugKUKgrhN6gR0PnhqJx2SpsEXslFj1/6Ini8mKsG7QOHRpWNqigjUeM\noZuVJfOROe4V3kOHbzsg9Gs+0CBfl2824YgiFI5UEfJw8ECYZxjcFrmZJETREwj1GXwVJd5stl/f\njlarWmHCzgl4/9j7KNOXiU4IaYVpeG0nn/mqkCkw98hcRG+KNhmXXCYX2cUBfn0W6ArMCnTh79aU\nlsdJ7RDoeUmQERnqO9dH/MR47Bixg9nIikqLsOzPZeDAIaMoQ7R7l+pLERUYhZA6IWajXI7dOYb+\nP/VnGvJvN35DelG6KAKkrn1d7I/Zj8aaxqhrXxfDQodhQuvKEC16Q3f37462Pm1xM+emKL73XtE9\nlkEm1NABceKE0FlLbb8AcD79PP5K+wtT9kzBx8f5Oh9CzVAhU0Auk6NVvVawV9rz3ddVlsMWAWDj\nkI2iIyUV6CF1QpA3I48dT0vKS5BTnAM9pxct1KEhfEwt1dCpZkTjw4XZk30C+6BXQC9mxqJhYVRY\nVKW5UG1Y6Gil9naNWmM1Dn3MtjGYcWAG+10lVyGnOIdtyq28WokSUICKEhNG9k5fF1/mv4m9Gotf\nr/Phq2+1e8skRZxq/RQq0B2UDqIIEQAmtWiEyWAKmQLHxh7DS2EvAeDNYvcK7yG3JJd1yxLSq1Ev\nJE9NRmidUNEml1qYij5BfXB7ym1Ro2Uai37k9hE4KB3AcRzu5t9lgi60bqio3DOFfgY6LktoS7XQ\nLNJg6aml+PTkpzh+9zg7HeeV5Jk0t3jnwDsYv2M8AHEcuoPSAWWGMuy6uYspDsZO0TDPMCRPTcao\n5nxrP2E2LQCTEOQ5R+bA9v3Kk7fwZGIjs8HtvNsmtc43XNqAEZtHmGxkTionFOoKmQnMWKDrZuvA\nzam6b8PjolY4Rd9s+yaGhAwRTVZj98YY32o8bBW2+DP1T9jZ2MFN7YbcklxmE/V18WXOFKq1CAX+\noaRD+O3Gb/gxmm9xNqvDLEzbN80kTZxuJABY2UzK2+3exoTWE+Bs64xwz3BEBUbxWXYKFcoN5TBw\nBqjkKvQN7Mu0TDoGKtDzy/JFC2Vgk4HY9eIuBH4ZiJ+v/oyjd47C08ETcRlxcFQ5MoH+/YXvceX+\nFSzqsQh/vvon9AY9c1Zag5poKB72HogKjDJxGAs3HhrF4Grrim/6f4MFXRYwcxT9m72NPUY0HSGK\nECCEYM+oPUjIScD6S+vZxuqqdrVokxRChRw1L73/+/twUDpgSsQUuNu5s6QOc82i9yTsQZ/APqKx\nuNi6MMGyPppPHuM4Dt6feeO/Lf6LAl2BSfYnwG/22+K3obisWPR340YV6UXpIoFCr/Vy9GLXUS3X\nuFaPtUiIJSeXYP7v8/Fex/dQqi/FyeSTWHp6KRb1WIQGzg1ESVhZD7LQ3KM54jLikFqQajYslL6X\nt5M3ZESGH+N+RMzWGNake++ovWbHobZRQztTW2XUhkrBb54FugJm8qFKSF5JHuq5i3MVUgpTWJy5\ncN0NDRmKZnWbYcmpJcyOXlhaiLiMOGhLtYisHwmFTAEfJx80rdsUMiLD6ZTT8AmptLMLBfqY8DEo\n0BWING1hyQJztf8B4NsL3yIpN8lkvTqpnJBWmIZGbo2wacgmq1FXT4JaoaE7qZxMSn22rNcSK/qu\nQG5JLrRlWqzptwbJU5NFQkloCzWnoR9PPo5mHs3YFz41ciq4OZzoCy43lCPwy0BRP00hMiJjx8cB\nTQZg58idzDZHNQ2VQoU3It7AjPa8tkgXizAOXRhbqy3Vwt3OHXM6zYGrrSucVE5sTHqDnnfG6fJx\nMOkgfr5aWUyLVpCjJiJLlJSXYOqeqfgxjt/I+jXuh50jd8JWYYs3dr+Bj45/JJorWg8d4PuvOqoc\nRclJdG7tlfZ4rfVroq5Icw7PwabLm0SbGMBvDAMaD8AfL/9hdaxMQ6/YDDdf28yy+qICo7Cq7yqz\nNbdzi3ORoc1AcJ1g0eMuti4mkSmEEJQbyhGfzW/a5gT61ftX8c35b5D1IIsJs4XHFsJmgQ3TvjVP\nfAAAGtJJREFUajmOw6Q2k9C7UaUTmTre6L8AX6mxXf12rKCa8WeltFrVip3KtGVaqBVqJoiuZV3D\npiub2EaXnJ+MeUfmIT4rHvm6fPTw74Ejo4+gtVdrzD40Gy//+rLotanNnVaNpG0Nt1/fbvLZjaFp\n/9ZQyBRQyBQoLi9mJhGdXoeS8hLkleSZnIIC3QJxN/8uisuKRQI9UBOIfo37IetBFvxd/aGQKVCo\nK0TYijC0Xcvb3Pcl7sP7v7+Prn5d0bFhRyw9La4LT+/vX4f/irX916KkvMSsf4C+p3HYYqY2E4eS\nDmF40+EmphMaEulu544XQl+wmFT3pKgVAt0SBs7AAvbNVSwc8+sYdPiWtwsax6En5iTiUNIhPB/w\nvNX3kBM5EnISMHnP5IfuMs9s+BU3D42bd7F1wVdRX6Ft/bZs8Xg7eiNlagpGNB2BW3m3MP/ofEyJ\nmILI+pEige5s64w3I9/E11FfiyI+Fh5biGG/DMO+mH0Y0WyEmdFUopQrcTr1NKbtnWbiAD2RcoJp\nSsIoISpIzFWspH+bHjmdxXID/Pfz6clPMXzzcDRd3pS9FgAs6bUEMzvMrLIbu0atQfLUZGYGov06\nAX5Tf7XVq2Zfg9rujbVTero5k3oGPp/5MCeYu507i5M2J9CpGSW5IJmdGtQKNavlAfAbw7wu89An\nqPJUQDtJCfuPhnmG4fjLx+HlID4JGmu9CTkJrKoldVJSoU+1XfqctMI0zD06F2fTzqKeQz0EaYLQ\nybcTnG2dcSrllEkqO910aQRQY/fG6O7fHUtOLUGmNhNNv26Kny79ZDIPDwNtciGs6phXkocxYWPQ\nw18c+kjNO/+36/8QpAnC9YnXmYkoOT8ZF9IvsHIDxiaXnTd2YtEfiyAjMj5T2sjkSAV6TnEOEnIS\nkF6UbmILD3TjwyRdbF2glCuhLdNixoEZuJl9EwdvHYSBM2BAY9Ms0K5+XTEsdBgytZk4cOvAQzV8\n/yeoFSYXc8RnxSP4q2AEuAZAJVehrn1d9N3QFzHNY1jhphvZN9gX18yjGQ69dIjdYIv+WASFTIE3\nIqy3jBLuyFWV8dSV6xD4ZSAmtZkER5Ujjt09hujgaARpghCzNQYnkk8gcXIiHFWOrI7y6LDRyC7O\nhlwmh7eTNzYM3oBxO8Zh+dnlmNB6Ai5nXoa/qz97byeVE9u8Fh5fyHwJGUUZVfb0pMiIDF/3+Rot\nVrbAuovrsOPGDmjUGsQOjRV1ZXJTu2FQk0HwdPDEe53ew6wOs8xm0P2v/f/wv/Z8MaM5h+dg/u/z\nUTq7FHfy70BbpoWzyhn5unys7b+W1c7o5t8Nsw/NRnxWvFV7rFwmF4WpaUu1zOlbXFaMq/evws/V\nz0QI0472tKgWpVdALxa2mVqYytaHxk6DjKIMTGozCaF1Qk3GYexQBASd3Uvy4WLrguKyYhSVFkFj\np2EabGuv1jC8ZzC7dsa1Godu/t3QK6CXqLY5RdhlS1vG9ylt4NwAnRp2Yj4FqizQk4+j0hFp03mH\n97b4bXBUOiJDm4EAV3Ht8z6BfRDhEyH6rDPazUD3H7pj2Z/LcOX+lWrVSrKGWqFGSXkJMrQZaOLe\nBB0adICcyPFBN9M6foOaDMIrLV7B+kvr0c2vG7OHA5VBAkWlRRjfajzCPcOx7Mwy+Lv6g+M4ZD7I\nhIeDB/QGvUkzaID39bip3ZBXkoegZfzGYdzge1W/VUjOT4aHgwfqO9dHc4/m+PiPj3E3/y7sbezh\nrHI2qdsDAKOaj8Ko5qOwLX4bBm0ahPPjz9eo2aXWaujUqTaj/QykTU+Dg9IB+xL3ibqbJOQksIXs\nYuuCLn5d2I0fpAnCW23fMltW1eJ7WjimUVQKFYpKi5CUl4S159diw6UNmNNpDnoH9hbZ5R6UPcDF\n9Iso0BWgX+N+GBM+BtezrmPekXlIK0xDvi4fjkpHjP11LO7m34WzypkJHieVE/JL8rHu4jqkFaYx\nbYQ6I70+9cKR20eq/CzhnuFwt3PH1ftXcTf/LnM0Uj/EqZRTkBEZtgzbwirF0cQXc9wrvIfsB9lM\nyAV8EYDAL3mth6bnD286nIWuxWfF44NjH1RrE9KWajE4djBW/7VadCq5kX0DrVe3Nvt5lXIlWtZr\nybJbKQu6LsC8LvOYI5hq3u527lApVPii9xds0xdCr9sQvQFf9ubNb9TURrXlA7cOoO7iuiYONUKI\n6BTBcRzCV4Rjy7UtiAqMglwmh73S3iS5ykZuw06UNKllYJOBODLmCFuLJolFgsbP7x5+F0tPLzVx\n1AK8kFzZd6WodG1Xv674j9d/WPXPqtZ7VfQI6IFIn0jM7zwfX0V9hVX9VsFV7WpWi1UpVFjdfzXS\n30w30YQjfSLxQdcPsPT5pfiw+4d4IfQFtG/QniUCZRRlwMPew2JympPKCdlvZzPHfGuv1hjedLjo\nms6+nRETFgOAryV0+pXTGNV8FPYm7kUd+zoY1XyUxdcvN5SLHLk1Sa0V6PSm1pZq4aZ2g4zIUM+x\nHgtdLNAVIOtBFsucNHAGbL66mWVXTm87nVVoqy5VhdgBvAM1pSAFe0btgVqhZrZ3YS2Ua/evIXxl\nOI7cPoJMbSZuZN/A5czLmHt0LiLWRCD2SiwcVY7sqDipzSR81O0j9G7UG96O3ojLiMPobaNZEwig\nMiuNHtGrQxP3JojPiudveHv+hne1dcV97X0MiR2CvYnmHWPG7E/cD6/PvNBnQx+mwYd5hmFks5EA\ngA4NeLPX7oTd7GZec24NgOrF59rZ2CE5PxmLTiyCTq9jgob6KsyFLsaExeCvcX9ZvAnvFd0DAWGN\nmbv4dkG7+u0sViD0dPCEjMiYwxuASeVL403CEoQQpBelY+nppbiYfhFhK8JA5hGTkgkKmYLN5/DQ\n4ZjcptI3oZKrYKuwZQKdjmlf4j70/6k/UgpS8Jz3czhw6wCyHmSJkooAYN6ReYhaH2Uyru8GfseK\n0FVnvVtjffR6xITF4NVWr6KrX1cYOAMupl+E80fOolwLIU4qJxOTCSEEMzvMRCO3RjBwBhSXF2PX\nyF1o4NwAt/Nu407+HXaK2/3iblyfaL4/Af2uhwQPYf4sawwJHoKc4hx08e2CZVHmE6nWnFsDmwU2\nLDJOEuiPCL2pp+ydgqO3+Z6T3o7eTKAn5vCxylRDJyCYsHMC1pxbg9t5tx8qjtb4Pa3h4+SDxNzE\nyrT482uwPm69xTj0+Ufno+03bZlTlApmRyUv0D3sPdDNvxtaebXCrhd3obF7Y7St3xZ17OpgWOgw\n/Dz0Z9HzgOonMUQ1ikJjTWMUlRaxpBMvRy/kluQitTAV59PPQ7NIU2VTadpxiBCCngE94eXohU97\nfoofBv2A1Gmp8HPhbaGDYwez5Bkai04dqtYghODtdm8jIScBG6I3sLIC1NFYVQldIQuPLYT7Inek\nF6XD3c6d2fQnPzcZTdybwPYDW5PkM6Aidny2DufTz7MKhw2dG2Js+Fi2sVCBbq0lofD1MrQZWH1u\nNZsTDmLnbqRPpKjd2/jW43H09lEEfRmEdg3a4cHMB8ym7m7nDjmR42DSQey4sQMyIsPwpsOZM9k4\nsunQ7UNILUxlIbuUkDohbJP4uxo6wDsUDycdxoX0C7BZYMOaoFDb/cMStT4KXb/viuzibAyOHYxd\nN3fhTt4d5it5vtHzZsMtAf7zqBVqpBSkWGxeLYTG+MdeibV4Dd30qFIhCfRHRDhxNEnG28mbhTYp\n5UqMbDaSmQsIISyUa+yvY9Hjh6rrUVBW9V2FQLfA6mnoTvV5zXmhAya0ngClXIlwz/Aq49Cp5kr7\nRTqqHJn907grj1wmx8AmA7Hz5k72WkI7s7XyuULe6fAOO3JTrXJ2x9mYHjmdZejlFOeIKj6agzps\n6WdNnZaKIE0QZEQGL0cvUW1q+r2xjjxmbPLmGNRkEAJcA/DJiU/YeNQ2fNSHcbZoTnEOvD71wg8X\nTdO3ZUSG7OJs+Lr4mhztsx9kg4CYTXohhKBMX4blZ5fjfDofDx3gFoC1A9ayNZZelA6NWlOtm1pY\nb51ibM6KHRqLUc1HIWJNBKbsmYKc4hyU6ktxM+cmCnQFIv+Oi60Lrr5+Fa+0eAUAv9l19u2MuvZ1\nMSRkiKjyIlCZwWsuZ4FWazTnHH4YisuK4bHYA13X8W0HDZyBmUSNTWHVxVHliNOpp1kZ6UxtJvJn\n5GNKxJQqnslTx74Olp1ZhpitMVVeq7ZRo4VnC6w5v8bqeABJoD8WaPo/3Z0baxqzGs2hdUOxPnq9\nKLyuuUdzXM68jHP3zpk4RazxaqtXcWPSDZOKcuag5QnKDeUYHDIYutk6hNYNRdv6bVnjBuM49FJ9\nKTsx0DCy9dHr2futPb/W5H0GNRmEotIi1haulVcrrOizAsDDpRnLiExU9pN2u+nu3x2brvClW6v6\n3FTLFta9ERLhE8GaK9Br6cK31FLMGLlMjrHhY3E69bQoG1ej1pgI9H2J+3Cv6J7Zol3UTDImfAxW\n91/NHo+9Eov3j70PDpzFkLw5R+YAECcAcRzHopdobZjqQEMdPR08sW7gOnRo0MHs++64vgOnU0/j\nQvoF7Lq5i22Eo7eNxtJT4vC8IE0Qsouz4aB0gErBFzAbGjIUp1JOmVQZfbcjX8TOXI2RtvXb4trr\n18w6AR8GYRgmvQ9vZN+Ak8rpkWubOCl5M2R8Fh9imlKQAnulfbWLX83pxH+HxlEuljg65qjFfrRA\nZQRNhE8ENg7e+MSKcFmiVgv011rz6bp0sbzf9X2c/C/fwEDYQ5DS3KM5isuLUaArMOv4ssTQn4di\n9LbRVV8I3uvdN6gvgusEizSu6OBofBnF29OFcehqhRo6Pa8J28hsmK1TJVexCnHmtKiufl3hpnYT\nJbF4OHigq19Xq2nVQu7m30XYijD0btSbvdfuhN1IzE1EmEcYXgjl+4tWtfipwLemnRjHodMwMWPn\nlDWmRU7D9MjpouzOT3p8gpfDxTHWuxN2Q6PWsNhqIfSGMzarVOf0RWui0++13FAO1fsqLDy2EACf\nQTuxjeXCaELoxu3p4ImYsBjWoERI9KZoTNw9EfUc6uHw6MMY1XwUe+9bubdwPdvUVrzk1BJRdMqC\nLgtwa/ItUW4FAMzvMh/cHM5sSjohxGwy0qNAM6l9XXzZhu/r4vvIqfDCeyHQLRDfnP8G7xx4p1om\nFIDvIuSgdKi2f8BR5Wg1cIIK9Dr2dTCs6bAnVibXErVaoF+9fxWeDp4mAicxJxGNvmhkosEIk5Me\nJrTol6u/YN3FdVVfWMHF9IsmiVBAZQPikDohWNZ7Gfxc/NjRMyYsBrfeuMVafe28uZOFlFFnjhCV\nQoXst7OZ0AX4ao09/HtUW0Ov51APD8oeiJpct63fFhP/MxEz2s/Aj4N+xO9jfjepbmcMXcS06a4x\neSV5rPk0Ff6tvFphx4gdrJFIdVDbqLG452J4O3mzx4Y1HSaqQWLgDNiTsAc9A3qadYhSgR7ydQhL\noAIq0/GtQZ9LNU+FTAGlXIlNVzZh0q5J6OLbhSkZVUFDNa1p9DRcL8IngglA4emA1uwXsqLPCszr\nPI/97qp2rdbJ8p9i27BtODL6CNzUbnCxdYGNzAavtareHJmDmhOVciVbO7FXY6u9QdCWkI/DPwDw\n99D4VuNxNu0sOzXUJLVaoG+6ssnEuTnn8BwELQsCB86k7GvTuk0xOHgwAKBZ3Wb/yJgScxKRXJBs\nUlZ18YnFUCxQQFumRUOXhni9zevwcPBA+wbtsfT5pfB29IaPkw/TaC5lXGIp08balSWS8pLM9kK0\nBA2L++iPj0RJT19GfQknlRNs5DYiYWmJ6OBocHM4i9op3WAaOjcUhQn2Der7t+2097X3sS9xHxv/\n+XvnkanNFGVrCmno3NCkoBVQmY5P4+TNQQW68Hld/LogtyQXq86tQvcfulc7saRd/XbYH7PfqiZM\nzSTt6ld2ZXK2dWYbvDnH4vjW403qy9Qk9kp7dPLtBIDvFbum/xpM+M+EKp5lGWrS1Kg1bOMyzjWw\nxtwjc/lx/c0IHoqHgweWPr8UX/75JRafWPxYXvPvUKsFeu7/cpH+ZrroseYezWHgDGjt1drE262U\nKzGrwyx8HfV1lQWshBgX97GGv6s/VvdbzerDUKjtuFRfipziHFxMvwhduQ4BbgGY/Nxk7EnYw+Ks\nAf5GoGVtq8vd/LsPdZIQYq1rUFWUG8qRlJvE0tCNUcgUcFA6YFCTQQ81l9Vha/xW9PqxF+vG4+Hg\ngRebvYhejXqZvT64TjCryy3UjqmGPqKp5SxbWo9f2OJvx4gdSJ2WivPjzyNIE2S2KbI5NHYadPfv\nbvU05ah0RGffzpgWWdmx3sfJhxVWe9RIkZpibue56OzbucpOY9bo1agXZneYjW7+3RBaJxQqueqh\nzEM0IqmZx+NT6PYl7kOBroApizVJrc0UBcwL2v6N+6OLbxfWEciYFvVaoEW9Fg/1PilTU0ycSpYg\nhOCVlq+YPE7jhMv0ZdiTsAdjfh2DxMmJ8Hf1R0JOAmYcnAFfF1+msZ5OPY3YIbEIcA0Q1YF/3Jwb\ndw538u9UfaEV9iTsQb+f+mFGuxn4sPuHZq8pLivG7oTd+MTwSbVPHNWB2uITchLQ0KUhfJx8TDZT\nY8zFizsqHTGy2UiL3Y/o9bThsLFdNaROCOtG9bigzYqNzQkPyh7Aw96j1gn0uIw4tFnTBtuGbXvk\nZsocx+F/7f8HW4UtTqWcgk6ve6hTHp0zcybRR6X/xv4ATKs81gS1WqCbw0Zug0OjDz3W13wYbd4S\nwo4yxrWTozdFI60wDc3qNmPxwpE+kSCEVMvkQdk5cqdJa7mqeJQNzhhar95aqrie0+N69nVoS7XV\ndtpWBxqnfTPnJpLyktDGu43Vm7VUX4ou33cBIE7nJ4Sw6ouWiA6OxuCQJ6eFZT/IZnHklJLyEsw4\nOANzO89lIa61hfbf8orJo4YsAnzVy/4b++PMq2eYHZya0KoDFf73tfcf64bo7ehdZV2iJ8EzJ9Cf\nVoQC/bebv8HOxo6FbjVya4RLmZfgqHJEi3otEP96vMXkCGvQVnZPGqpBVmfje9xxut5O3rBV2OJi\n+kV8f/F7jGw2Emv6W44bFr7/w5R9AJ5ckwLK8ZePm5gnlHIlbmTfYKeM2gRVZP6OQKcmqo//+Bg/\nD/35oWuN05P24duH0crLsr/kYbg1+RYz5dQ0tdqGXpsI0gRhfKvx2H9rP7Zf3445neYwDYNms1JH\nTWP3xk9cePwdXmn5Ct5q+xZmdphp8Zr5nfnszscdcSEjMgS4BmDFXytQXF6MF5u9WOVzPB088WrL\nVx/rSeGfwEHpYDJGGqs+7+g8c095qvms52e82epvzDtNRDOXzVsdhoYMxeYXNlc7Eak6+Ln6PXbf\n0KMiCfQnRBvvNljRdwVu5d5CsHuwaEFRs4GwJnptwlZhi0U9Fll18NEbkWYhPk4+f/5zNNY0hkat\nqZaJysXWxWrfVYl/hqmRU3FvevVrDZmD1voXNn1+GAghiA6Ofqx+nKeJZ/NTPcWMDR+LSW0miY7+\nAW68hh7TvOp05NoKbcTxT5w8OjbsiHtF9zAkeEi1btT4rPinImb4UZnfeT4ifCJqehg1QkOXhtC/\np6+ywca/FUmgP2GMO+gAQAvPFlg3cN0zfZPuj9lfZZGvR+VC+gUU6grRr3G/al1/IOaASSGs2sS7\nnd6t6SHUKJIwtwypbsrs46B169bc2bNnn9j7Sfw7yCvJw9rzazH5ucnP7FFa4t8NIeQvjuNaV3Wd\ntPolaj0uti6i5BsJiX8r0tlFQkJC4hlBEugSEhISzwiSQJeQkJB4RpAEuoSEhMQzgiTQJSQkJJ4R\nJIEuISEh8YwgCXQJCQmJZwRJoEtISEg8IzzRTFFCyH0Aj9pNwR1A1mMczrOINEfWkeanaqQ5sk5N\nzU9DjuNMmwsb8UQF+t+BEHK2Oqmv/2akObKOND9VI82RdZ72+ZFMLhISEhLPCJJAl5CQkHhGqE0C\nfVVND6AWIM2RdaT5qRppjqzzVM9PrbGhS0hISEhYpzZp6BISEhISVpAEuoSEhMQzQq0Q6ISQ5wkh\n1wkhCYSQGTU9nqcBQshtQsglQsgFQsjZisfcCCH7CSE3K/51relxPkkIIWsJIZmEkMuCx8zOCeH5\nomJNxRFCWtbcyJ8MFuZnLiEktWIdXSCERAn+9k7F/FwnhPSqmVE/OQgh9QkhhwkhVwkhVwghb1Q8\nXmvW0FMv0AkhcgBfAegNIATACEJISM2O6qmhC8dx4YK42BkADnIcFwjgYMXv/ya+A/C80WOW5qQ3\ngMCKn3EAlj+hMdYk38F0fgBgScU6Cuc4bhcAVNxjwwGEVjzn64p78VmmHMB0juNCAEQAeL1iHmrN\nGnrqBTqANgASOI67xXFcKYCNAAbU8JieVgYA+L7i/98DGFiDY3nicBz3O4Aco4ctzckAAOs4nlMA\nXAgh9Z7MSGsGC/NjiQEANnIcp+M4LglAAvh78ZmF47h7HMedq/h/IYBrALxRi9ZQbRDo3gCSBb+n\nVDz2b4cDsI8Q8hchZFzFYx4cx92r+H86AI+aGdpThaU5kdZVJRMrTAZrBWa6f/X8EEJ8AbQAcBq1\naA3VBoEuYZ72HMe1BH/se50Q0lH4R46PR5ViUgVIc2KW5QACAIQDuAfg05odTs1DCHEAsBnAFI7j\nCoR/e9rXUG0Q6KkA6gt+96l47F8Nx3GpFf9mAtgK/jicQY98Ff9m1twInxoszYm0rgBwHJfBcZye\n4zgDgNWoNKv8K+eHEGIDXpiv5zhuS8XDtWYN1QaBfgZAICHEjxCiBO+o2V7DY6pRCCH2hBBH+n8A\nPQFcBj8voysuGw3g15oZ4VOFpTnZDuClikiFCAD5gmP1vwYjm+8g8OsI4OdnOCFERQjxA+/4+/NJ\nj+9JQgghAL4BcI3juM8Ef6o9a4jjuKf+B0AUgBsAEgHMqunx1PQPAH8AFyt+rtA5AaAB74W/CeAA\nALeaHusTnpefwJsNysDbM/9raU4AEPDRU4kALgFoXdPjr6H5+aHi88eBF1D1BNfPqpif6wB61/T4\nn8D8tAdvTokDcKHiJ6o2rSEp9V9CQkLiGaE2mFwkJCQkJKqBJNAlJCQknhEkgS4hISHxjCAJdAkJ\nCYlnBEmgS0hISDwjSAJdQkJC4hlBEugSEhISzwj/D/521HltsII4AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f95d349b8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(m_nums, malignant_smoothness, 'g--')\n",
    "plt.title('Malignant tumor smoothness')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f95d33805c0>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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CgQcyDUFVxaBBwGGHJd5P3DupBHIziV1Zp6+1nqq1bqO13l9rfV/kf3dorSdF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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f95d3482438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "benign_compactness = np.asarray(ben_compactness, dtype=np.float32)\n",
    "b_nums = np.linspace(0, len(ben_compactness)-1, len(ben_compactness) , endpoint= True)\n",
    "benign_smoothness = np.asarray(ben_smooth, dtype=np.float32)\n",
    "plt.plot(b_nums, benign_compactness, 'r--')\n",
    "plt.title('Benign tumor compactness')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f95d5e06748>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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a/hiYaq3L79dBNPPmaQW8vx/27tXpeuJytMVjsJ2u8A3ruo0+WHzmMP6FuWSf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\nFvgvstEN/W6kMvHSS0GHI4hgtGq+W73Y4eIxaPU8sCJNXnklWK7Ayhq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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f95d334d710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fix, ax = plt.subplots()\n",
    "\n",
    "ax.scatter(b_nums, ben_compactness, color = 'b', marker = '^', alpha = .4)\n",
    "ax.scatter(m_nums, mal_compactness, color = 'r', marker = '^', alpha = 0.4)\n",
    "plt.title('Malignant vs Benign compactness')\n",
    "plt.legend('bm')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Compactness really doesn't tell us a whole lot, as you can see the compactness of malignant vs benign tumors are not that distinguishable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Malignant compactness:  DescribeResult(nobs=211, minmax=(0.0084220000000000007, 0.13539999999999999), mean=0.032201739336492896, variance=0.00033835493948887382, skewness=1.8607616844318295, kurtosis=5.533675360962851)\n",
      "Benign compactness:  DescribeResult(nobs=357, minmax=(0.0022520000000000001, 0.10639999999999999), mean=0.021438246498599437, variance=0.00026737192377052847, skewness=2.2029858518023953, kurtosis=5.903642788584632)\n"
     ]
    }
   ],
   "source": [
    "print('Malignant compactness: ', describe(mal_compactness))\n",
    "print('Benign compactness: ', describe(ben_compactness))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "The mean compactness is only ~0.1 apart, so this is not a very great classification feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f95d3318390>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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4y+NFt8LwhGWhnaK/XCGf3hf0xatSWpTPw5bp0oN4jBFeJkNcCz5pf5fzOa7M\nJzfv/bZ9B3NsnYbiXJFjCzDqhWFoqPIsfLny2W5XA/z0OKv6h7g8meeVjUOczQSGeu/0wgVbQVWY\nv9zHbvsWijEHXsriudS2esy2WWaH4epkjqfnYePe7OI5DuYjwhztjPSvzJucpI8R7qLIBeA4Q9GD\nLOnEzpEt73QO7Fk8lhfhMC4eeupjY+UZUr41E82mcvhrmnUHHXUV0RTDjI9gPf6xsaae3FrKPywK\nb3b0vm4kHT6ur7hRMYr2JYMt+mjRVtp/tb74ZrtPqtGq/XTqfeI9L+ihsIJtshcKwD17GHWDFgvz\nsDGIlVe64X2WyTWTeU6+WOS5+2cZygc55Yu1QJZ8HrZOwysX4Mfrx5kDrszPMIaNyVYcaRpc2TmC\nzJbMEHky9BNMO5CjXJQqjJj0YZ18Hi5tHqf/+BSFiTxnxjJkKIUepg7afY1jM2/mMtnFmQKbjfyG\n8870Fa3GHaHKzTwywqibbuDEoQKv9AfjB3yRR8IugzM5CgXIUcP0CE6oF/P9E/rYMwfcOIJ+cPUb\nL/fDHFmyB7LM5UqmAGQzGc7gO7L7Sz3bLVKCTnp6vngXFmwRB75JS/C3bNRTbze1hnZqXb8WqvUj\ntIueF/SQY9NWPM67G3JiAk6ctOHRV4Kway28PJKheGGWdZHlizWte1lAnizFItxBjv5+OBPIYa1N\nOhsmKsVfllQEdXYKhtMYrDs9wytjGbcst3gOs9OlUHzGjYpt5EbOZm2fYaEAAyN2w/mCTeG7ugDF\nC7OsAt56dD8nDhV4dcMAo5v77AUKPOT8/hyb3CRgF7aOlc4XlsyWOMU4TFnBPb8AR/KlBkt52DxL\nlgohq7BSrTLNw8c/bu+n3btLphQKwF7bOvl7k9ZTv+hE8LLznOf8Ydy5JFUocacbDf17JiZK9Zon\n9HojDZmauxjA7nPzZtsIPHcOdu1qLgYfThUUdn2E51RvWWQr3Ktx2/kWRz3nEZ2os1GRr2fC0VbQ\n84Kedfljx6aLbJqz4cwLF+C703s4dw4e2ZBl82bYWEO2m/+eI0thHnaty9E/Yh/2fD5HhlzZbIxZ\ngIz11PtxHWWZjJ00JLx7q80hUJbAPkt2qMJ2lZ7QbCAWOTuNQR9FRoulSbe+Nj3O4XNw58UZZkcy\nvLg4QjWZJTdh9OmKMDYGmUKeE+fg3IZhNm+GhcNw6hTcsCH5OOtOWoEf2z0MzJY6hf3T4FTgch4e\nPhRcW9d6dnXrAAAYVUlEQVQKCFcdzOfYUYDVA7NMHYSn54cYGwvmc4lU7tEH13vqYFtKW++zn33H\nMcDRo1YsBgaAew8wNFQKdzU0D1AMoYD5xBvfBVAPSULiuxh83To+bpdduQLr15e2rdY5m/R7zISb\nSZNhtpxgvCDFYml8oX9zU5SwMvBT6ofpxPUQrbhqGY/RCnpe0EMGB23c/NHH4NxVeMcu2DycPK9T\npUE6I0/n2PRqnmNkOJmHHe7GvHQWjh+Hp32o9ECwn4SYdmJt7K+6c7WOTdr/o+9PaJ9VqNZLsd3S\nsjNjGVcBWRU6Nm0z9bafybP5SoEnp+25ZTL1ew6FApw4lGddHoaHbStlft4+POPjcIYMJyiJz7dH\n99nyp7wyyOVgcH/O5udfM8Crk0WYLNr+jz17bF3n+z+dcRlsqGU9sHGjPV7YpM3n4W6s0J49C6dP\nw4lzjQmh94b7+uz3p56y/3/91+3//n77Pzz+E5PwjPOe3zJt+xBmJ2cZGYE+lnrqUWFLmrI/Thyh\ndNtFk7SSskuiDT5wlbALUc7P27JzETDy+WQfApbGh0Nh9K+4jWstVOsj8PvK50v92tEJQStltzz0\nkL0+mzfb+8BX+pUI9+2HJ8zMxE8BFQ3Vxp1Tp7Ntek7Ql2SzuLt2FOwd3z/G87dmWBjLksnkIJ9j\npj8b60z6G9inGYfN1pvWwos3Zphcn2VvBsjYkZ99xRxnT8PJsSwbM+X78Q9pxXlcQvyT4FyAK/N1\ndvsHT45/a87i9lmb6WLXGSJHlrOFHFeBk+uHKCxk+KE7t6io+H6EQgGOjGVLAyn9lAkDRa6fO8uG\no6fpO1Vg+ztgyp17Zi7H9ik7I+SdA3B9YYYTJ6F4lx01mp+BTQU3+VmE4rZxrm6kLO3P9y+Un3KW\nV3bDwJR9SGdmlr41bi6T5eBBeMtpu+DFu7LMZWAmoWER98CF8V7P1q1w8mT5uzK86GUy9rh/PQlM\n15xdWhVv28BAeWzbVyaQnGlZKXXQ9UUvUiyWi17YGolWCuF+vTcLpfILXyAFpf026uk3gq+k/DmO\njNhQkn/ek8Q3Ooef75OKhnziyrxSOKup6TpqpOcE3bNYSIG3cuIkXMDe+K8Muw7Rgs0gK6tFKRfC\nHfM5Bt0vNz1mfzMnZ3mtD7bO5ji+ANveb0MrO4Km/Y6J/RQmYGz3cLmgevuCztS4KIV/4QJOHIoD\n1jVLzJN3lM7DepA3LFgv5BXKPXUrzKUbKUOe88ARhlkAbnUdornIXDH5fHwIZlMhz6WnC/xk7QKr\nzl1gvYC5AqufyvPeXZD5VGkEpZ/HKz+TYd2Y/b59Kgf9cGTvAcZCj8qVz5p8jvEMwBATE3Bk/9Jp\nFEK8B1kolMIC/i+XsyJyw3p7P1wT02yOisiiKFH6wTfPw+Z69GEOPeRwP3Z5FvI5bls/w9ieIMQV\neNPRrI89e8rtC88XSpVJXOjA2+Dt9c7G1FRy6ODCBft/etpmcMbNgOyZmLD/Bwbs+pOTdvs77ogX\ndW93M52Cvm+kWCy1tD2VOjr9b97mSqGTuHBLGP+fmiqv7HyFNT3t+o+CFg2UtvHfO/Rmx94R9GjN\nl/eSM1OaaGu+aO++LdN5tkznuXZkmDsHbCYHrpMMIJspzYr37LNw3vV89vfDrbfaz6cvw403Qn/Q\nK7qjYI14ZiSLvzdOnISLU3Y/Z926z4xk6e+368fVyn6uGR+PLz5lr/qLv26f0PDej4vzL3aUZbMc\nydvjrN5o53q5MhRdLwtZL9AZ8nm45ZsHbXhq71CZME1NwdqHclx7FJ5fbzszty7kWOs89bG9GfL5\nDM8dynPDmQLFdQPMbx1n5uoQNxds5+NcWWqOHfkKdpqF/sXQydLzK8OdVxzRJuy+fcmx2LExm910\nxZVp+JAn7dcTF8JaXJcc2czSDs5IBC2oIOLxDsfCQmVvvlLGRFIWTHTbcOBruDy0t9J08/5cFhbs\nfx9K8XH2qGgnebtJ/VbNdD5W22bv3lLoBMpbK3HbhOEW34rxld7Ro3DokB2Ttn69Ffk1a0rl4itj\nKIV8Yuf7bxM9I+iesBkINiVxzdNwJg+Z8dIygDH33T84k+7ByZFlomAfzPPrrABvywB5m0MNwESO\nN46VBCq8CGP5HMxYz/zilA0RbFqAv7tjH9PTkHksx02DsOrCLF+7Hw5/PMeGDXBss401r38Y+m+E\nt77BxuNnyHDz1uTYqff8cy5VMhpLPL9gK5aF/pLARdcDJ+4ZOwhq1L1SLkx1n5mxmXfnF7BNHcfN\nJ727Zudofxi4fBleG+yn/44hbthTMnjxQfYeogv7LE5KNluq0OZch/JiM5Ys+/O2YogOPgknLox6\nyHGZEGEM2bY4Sk9TVES8N/u2ov1hillmp22gym+T1JEWtSmTKXmCax+y21/qm+UnfcOcycMgucV0\nzLCyD8ckxGXshMRVPj7uG3rWU1P23MIKxnd+hpVb2KKIi0VDKX6/ebP9Pz9vBS0Uy6HAmahUecbh\nn+uoJx/a4SuGasKYFFevFCKJq3D8scJzP33anrcX8HXrrKb48FcYjmlX+mclekbQoxfFF9L3B7JM\nj2a5B8DFro/stU//mO+AmslSwNayZ8/afZw8CWwtP0ZZOCS4+Dsm9nPi4y7l7t5xNhVc+pxrEfjO\ntj177Ha3uibYWtch5zl+3H6fW5tlcANcOpzjzBko7MgyMgD9+dLN4/G53BMT1ucNO/p859UzI1k7\nnoVyMQ/XGxtzue4ZYKBoc/4id3MmA5mszbU+O2F/27Y3yzsWjbH/1u3KUChYBRrrJ/Y1fWGWR6Sr\nALBZKpOunOoYcb9IPfHITKY8dTH0jME+oP4avv5YnosUubB+fLHp7T31xfn1h21BRLORotkbg26f\nl46V2xN3jQ4dsvH5Sh23YZgmTqyGhipPEAclkamU9hfXGvQeuR90fc89S4+fZLPfV2hXtEPR36NR\nopVZaFPSu8yT7IirDCpt43/zlRbAffctjaGH92I0VRM6OyK2ZwTdE3oyPna1ebO9SR9+ygrF7b/n\nCi8o9IEBm+Hx6KPwwgs2N/37A1mmF2DusaUD/MKXYhQmbMrdKxfh3BSMu3nUF2/iA/sW494Z14F6\nBRsTHhqFVU7cBiMDJF9xD7r3eqD8pvbT5hafneWVBcjemmMAyG3MsueKPeCRfuv1795tb5yJCXsD\nHjzoKg/3Es9iEZ6Ytp9np8cZGbHpf4UCi2GbxBdBO6/2rPM4vz+QZdwd7wjlYhmKlc9CyJN1cVm7\n/QPFLNfN5tg6m+MH27JMT1tR9XFIKGVFhJ559AH24hXXsllc5ir1cCBVFsiNZRcrT995doUsP3zK\n9jX03zFUFsJKwgt+fCdYtrwicPdUVERGRkrZM2HMutrsBnFx37BS8WXgn5Uw4yOMd9cibj6d8dw5\nf27lAulbg1GqiVjUmw3tivY7hffW7OzSrJ9o3L9SmmJSZRAb2kwg1CL/Ocx5jwuRtZueE/SwoELx\nGx+HmX77kN4erDyHFZzZWetdXHed9YT8AJH+/lIzdMnFy9kZG4tHi8hrfax7eRrz8DwPPzXGul2Z\nxVZCXJM47uEIjzEzA/wPWRa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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f95d3329fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fix, ax = plt.subplots()\n",
    "ax.scatter(b_nums, benign_smoothness, color = 'b', marker = '+', alpha = 0.4)\n",
    "ax.scatter(m_nums, malignant_smoothness, color ='r', marker = '+', alpha = 0.4)\n",
    "plt.title('Malignant vs Benign smoothness')\n",
    "plt.legend('bm')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Feature Analysis and extraction\n",
    "* Our goal when analyzing features is to find features that are very different between Malignant and Benign tumors. In our classification task, we seek to draw a non-linear decision boundary between the two types. In order to do so we need to find what makes them different and learn from that. \n",
    "* In the following section we will identify unique qualities in both Benign and Malignant tumors that are the best for feature analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Feature: Radius"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAL mean radius:  17.4603317536\n",
      "MAL median radius:  17.3\n",
      "BEN mean radius:  12.1465238095\n",
      "Ben median radius:  12.2\n",
      "describe mal radius mean:  DescribeResult(nobs=211, minmax=(10.949999999999999, 28.109999999999999), mean=17.460331753554502, variance=10.312984175129767, skewness=0.49958166675400756, kurtosis=0.3241113765121644)\n",
      "describe ben radius mean:  DescribeResult(nobs=357, minmax=(6.9809999999999999, 17.850000000000001), mean=12.146523809523808, variance=3.1702217220438738, skewness=-0.08344660198964896, kurtosis=-0.028871551594101152)\n",
      "len:  211\n",
      "len:  357\n"
     ]
    }
   ],
   "source": [
    "mal_radius_mean, mal_radius_nums = extract_data_from_df(dMal, 'radius_mean')\n",
    "ben_radius_mean, ben_radius_nums = extract_data_from_df(dBen, 'radius_mean')\n",
    "print('MAL mean radius: ', mal_radius_mean.mean())\n",
    "print('MAL median radius: ', mal_radius_mean.median())\n",
    "print('BEN mean radius: ', ben_radius_mean.mean())\n",
    "print('Ben median radius: ', ben_radius_mean.median())\n",
    "print('describe mal radius mean: ', describe(mal_radius_mean))\n",
    "print('describe ben radius mean: ', describe(ben_radius_mean))\n",
    "\n",
    "print('len: ', len(mal_radius_mean))\n",
    "print('len: ', len(ben_radius_mean))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Mal radius range ~(11-28) where Ben radius range ~(7 to 17)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "It seems that Malignant tumors are generally larger than Benign tumors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f95d3294e10>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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W0n+C3q/UWcSKsGlsTP01HnEWimw51PE+CI2nfwRdmrzlUJd8jRPQqmzrdlXLwVQ2guCZ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\ndEEQhJYggi4IgtASRNAFQRBawv8HCsQJwwK0cAQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f95d32b6ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fix, ax = plt.subplots()\n",
    "ax.scatter(ben_radius_nums, ben_radius_mean, color = 'b', marker = '+', alpha = 0.4)\n",
    "ax.scatter(mal_radius_nums, mal_radius_mean, color ='r', marker = '+', alpha = 0.4)\n",
    "plt.title('Malignant vs Benign radius mean')\n",
    "plt.legend('bm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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yV7q4ssqcrz71nODtYK2rVxX26ryJWAKLGr5ud7GC9Xyre3r5h6GliJF1+xPy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\nU1XLenbqUCTtUgRBTZ933uJ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RP4rIeLWIfE3cGgAfF5H9gn0vFJHvicg2EfliMGdQVJ79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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f95d5559da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "extract_data_and_plot(dMal, dBen, 'radius_mean')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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RVf9ORFYCnwcOBG4B/kRVf1WWHIbRdwygC2wAH2mgKLOzeBdwvKquBo4GThKR3wXeB3xY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\n//5AeBwCZpRXAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f95d31b5dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "extract_data_and_plot(dMal,dBen, 'radius_sd_error')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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quiLvwxRG8EVcu9ZZDv5guLSXtMyXd2TEtTljFS+aliWg3e51g99boepGM2og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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f95d3128278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "extract_data_and_plot(dMal, dBen, 'radius_worst')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "From the two plots above we can determine that <strong>radius</strong> is a very powerful feature in our estimation of Malignant vs Benign tumors. This is because Malignant Tumors seem to be "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Variables that are indicative of tumors are\n",
    "* Radius (size)\n",
    "* Smoothness"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Feature: Perimeter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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WoVN8mr2Yy6mbjI835p/fnpqaUcZ58jftvLzXG6Vg2d8Z1lMogPGznclnbEnO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\nDMMw6phSMAzDMOqYUjAMwzDq/H9kEkQuf1xnugAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f95d30b1630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "extract_data_and_plot(dMal, dBen, 'perimeter_mean')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Perimeter is also related to size, just as the radius is. For a circle Perimeter = 2 * pi * r, where r is the radius. Therefore we already have most of this data from the radius so it isn't completely essential. We can use either because they both describe the size. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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pGUUUZl1Z8xErYkPlmGnLVePAsqVXSDm533Z9tF7MYGqZqTQk8bq4YYPvj6S8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\nxZSFYRiGkYopC8MwDCMVUxaGYRhGKv8PCatoZgLKvWYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f95d302b048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "extract_data_and_plot(dMal, dBen, 'concavity_mean')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Malignant Concavity Mean:  DescribeResult(nobs=211, minmax=(0.0078820000000000001, 0.078950000000000006), mean=0.020427104265402844, variance=0.00010134731916050552, skewness=2.52141930368241, kurtosis=7.94736548849122)\n",
      "Benign Concavity Mean:  DescribeResult(nobs=357, minmax=(0.0095390000000000006, 0.061460000000000001), mean=0.020583806722689075, variance=4.8979544122651307e-05, skewness=1.372019211917399, kurtosis=3.2775887237180905)\n"
     ]
    }
   ],
   "source": [
    "mal_concav_mean, _ = extract_data_from_df(dMal, 'concavity_mean')\n",
    "print('Malignant Concavity Mean: ', describe(mal_concav_mean))\n",
    "ben_concav_mean, _= extract_data_from_df(dBen, 'concavity_mean')\n",
    "print('Benign Concavity Mean: ', describe(ben_concav_mean))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Concavity is not such a great indicator"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Feature: Area"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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F18TJPF3PkXq6D1XdKSLzVfVF4MsichfwF9mJ1t7U1RcRMzgqjkKkkiptRUdw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\nYSTy/wOl65eUWBFGkwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f95d3011588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "extract_data_and_plot(dMal, dBen, 'area_mean')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Area is half decent, but we get better information from `radius_mean`. It also has to do with size so when decising between radius, perimeter, or area I would choose them in that order."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Feature: Symmetry"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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De8nq2vr3wO+FwJ8Dv0655lbgMC/K6iHgZGb6NhIRkUXAdlXdISKLgeOAj6uq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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f95d2f20978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "extract_data_and_plot(dMal, dBen, 'symmetry_mean')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mal symmetry mean stats:  DescribeResult(nobs=211, minmax=(0.088220000000000007, 0.22259999999999999), mean=0.14476298578199051, variance=0.00047912620104265406, skewness=0.44409495835375823, kurtosis=0.7727110366277707)\n",
      "Ben symmetry mean stats:  DescribeResult(nobs=357, minmax=(0.071170000000000011, 0.2006), mean=0.12495949579831933, variance=0.00040053878963270697, skewness=0.3397208879400406, kurtosis=0.3801806565857273)\n"
     ]
    }
   ],
   "source": [
    "mal_sym_mean, _ = extract_data_from_df(dMal, 'symmetry_mean')\n",
    "ben_sym_mean, _ = extract_data_from_df(dBen, 'symmetry_mean')\n",
    "print('Mal symmetry mean stats: ', describe(mal_sym_mean))\n",
    "print('Ben symmetry mean stats: ', describe(ben_sym_mean))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Symmetry is a better indicator than Compactness, but it is worse than Concavity. Again, we want our data to be different for malignant and benign so the greatest differences will allow us to classify between them and draw a non-linear decision boundary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Feature: Fractal Dimensions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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VWXgWwhJVPQ2nMLKyKxBc62EtsH+O+6eJyHLc9q1nqepmQ3dF5ETgRIA5c+bk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\nlIVhGIaRiikLwzAMI5X/Dz/II2aXJ2TbAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f95d2eb8a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "extract_data_and_plot(dMal, dBen, 'fractal_dimension_mean')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mal fractal dimension mean stats:  DescribeResult(nobs=211, minmax=(0.028989999999999998, 0.29100000000000004), mean=0.18184317535545025, variance=0.0021215333322500566, skewness=-0.09645437153881821, kurtosis=0.1324330324246734)\n",
      "Ben fractal dimension mean stats:  DescribeResult(nobs=357, minmax=(0.0, 0.17499999999999999), mean=0.074444344537815121, variance=0.0012814519692320838, skewness=0.11179235799179865, kurtosis=-0.1789984243275069)\n"
     ]
    }
   ],
   "source": [
    "mal_frac_dim_mean, _ = extract_data_from_df(dMal, 'fractal_dimension_mean')\n",
    "ben_frac_dim_mean, _ = extract_data_from_df(dBen, 'fractal_dimension_mean')\n",
    "print('Mal fractal dimension mean stats: ', describe(mal_frac_dim_mean))\n",
    "print('Ben fractal dimension mean stats: ', describe(ben_frac_dim_mean))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "This is great! We can see that our malignant tumors generally have a greater fractal dimension than the Benign tumors. Now let's analyze the error to make sure it wasn't super large and that our data is accurate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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cW26KchnYmWsXk5ZOVYnTpJyrnZBJ9PDmG4eg0jbVvaeoHaRi2Ao154pIbWuZ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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f95d2e36828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "extract_data_and_plot(dMal, dBen, 'fractal_dimension_sd_error')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "This is also a very nice feature because it shows us that malignant tumors have a large deviation in the fractal dimensions they can take, where benign tumors are more uniform"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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C0UEkjdp6WERmAT2q+rEWyzQ1iHzQFSOL6qgEMhkgu6i052WyVjCTRc5JTqd4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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f95d2ef77b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "extract_data_and_plot(dMal, dBen, 'fractal_dimension_worst')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "This is fine because the worse cases are pretty similar"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Feature: Texture"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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IWJJm68YUn1EReRbHauBJ4IPAB4KR44KLac+vULbpR9hY1mouMNrf7xphe8mn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\nD36HFk/Y4Ia+9dAfDa7Xes3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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f95d315e668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "extract_data_and_plot(dMal, dBen, 'texture_mean')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mal texture mean stats:  DescribeResult(nobs=211, minmax=(361.60000000000002, 2501.0), mean=978.26919431279612, variance=136020.56537982397, skewness=1.10802716930194, kurtosis=2.200680886019689)\n",
      "Ben texture mean stats:  DescribeResult(nobs=357, minmax=(143.5, 992.10000000000002), mean=462.79019607843128, variance=18033.030100242344, skewness=0.34082567025503224, kurtosis=0.28842005191069164)\n"
     ]
    }
   ],
   "source": [
    "mal_texture_mean, _  = extract_data_from_df(dMal, 'texture_mean')\n",
    "ben_texture_mean, _ = extract_data_from_df(dBen, 'texture_mean')\n",
    "print('Mal texture mean stats: ', describe(mal_texture_mean))\n",
    "print('Ben texture mean stats: ', describe(ben_texture_mean))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "This is really our best feature, because it is extremely different for our benign vs malignant tumors."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Feature Conclusion\n",
    "Top features for classification\n",
    "* Fractal Dimension\n",
    "* Radius or Perimeter\n",
    "* Texture"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "`Smoothness` also comes in at #4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Prediction and Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>diagnosis</th>\n",
       "      <th>radius_mean</th>\n",
       "      <th>radius_sd_error</th>\n",
       "      <th>radius_worst</th>\n",
       "      <th>texture_mean</th>\n",
       "      <th>texture_sd_error</th>\n",
       "      <th>texture_worst</th>\n",
       "      <th>perimeter_mean</th>\n",
       "      <th>perimeter_sd_error</th>\n",
       "      <th>...</th>\n",
       "      <th>concavity_worst</th>\n",
       "      <th>concave_points_mean</th>\n",
       "      <th>concave_points_sd_error</th>\n",
       "      <th>concave_points_worst</th>\n",
       "      <th>symmetry_mean</th>\n",
       "      <th>symmetry_sd_error</th>\n",
       "      <th>symmetry_worst</th>\n",
       "      <th>fractal_dimension_mean</th>\n",
       "      <th>fractal_dimension_sd_error</th>\n",
       "      <th>fractal_dimension_worst</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>510</th>\n",
       "      <td>915664</td>\n",
       "      <td>B</td>\n",
       "      <td>14.81</td>\n",
       "      <td>14.70</td>\n",
       "      <td>94.66</td>\n",
       "      <td>680.7</td>\n",
       "      <td>0.08472</td>\n",
       "      <td>0.05016</td>\n",
       "      <td>0.03416</td>\n",
       "      <td>0.02541</td>\n",
       "      <td>...</td>\n",
       "      <td>15.61</td>\n",
       "      <td>17.58</td>\n",
       "      <td>101.70</td>\n",
       "      <td>760.2</td>\n",
       "      <td>0.1139</td>\n",
       "      <td>0.1011</td>\n",
       "      <td>0.1101</td>\n",
       "      <td>0.07955</td>\n",
       "      <td>0.2334</td>\n",
       "      <td>0.06142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>511</th>\n",
       "      <td>915691</td>\n",
       "      <td>M</td>\n",
       "      <td>13.40</td>\n",
       "      <td>20.52</td>\n",
       "      <td>88.64</td>\n",
       "      <td>556.7</td>\n",
       "      <td>0.11060</td>\n",
       "      <td>0.14690</td>\n",
       "      <td>0.14450</td>\n",
       "      <td>0.08172</td>\n",
       "      <td>...</td>\n",
       "      <td>16.41</td>\n",
       "      <td>29.66</td>\n",
       "      <td>113.30</td>\n",
       "      <td>844.4</td>\n",
       "      <td>0.1574</td>\n",
       "      <td>0.3856</td>\n",
       "      <td>0.5106</td>\n",
       "      <td>0.20510</td>\n",
       "      <td>0.3585</td>\n",
       "      <td>0.11090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>512</th>\n",
       "      <td>915940</td>\n",
       "      <td>B</td>\n",
       "      <td>14.58</td>\n",
       "      <td>13.66</td>\n",
       "      <td>94.29</td>\n",
       "      <td>658.8</td>\n",
       "      <td>0.09832</td>\n",
       "      <td>0.08918</td>\n",
       "      <td>0.08222</td>\n",
       "      <td>0.04349</td>\n",
       "      <td>...</td>\n",
       "      <td>16.76</td>\n",
       "      <td>17.24</td>\n",
       "      <td>108.50</td>\n",
       "      <td>862.0</td>\n",
       "      <td>0.1223</td>\n",
       "      <td>0.1928</td>\n",
       "      <td>0.2492</td>\n",
       "      <td>0.09186</td>\n",
       "      <td>0.2626</td>\n",
       "      <td>0.07048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>513</th>\n",
       "      <td>91594602</td>\n",
       "      <td>M</td>\n",
       "      <td>15.05</td>\n",
       "      <td>19.07</td>\n",
       "      <td>97.26</td>\n",
       "      <td>701.9</td>\n",
       "      <td>0.09215</td>\n",
       "      <td>0.08597</td>\n",
       "      <td>0.07486</td>\n",
       "      <td>0.04335</td>\n",
       "      <td>...</td>\n",
       "      <td>17.58</td>\n",
       "      <td>28.06</td>\n",
       "      <td>113.80</td>\n",
       "      <td>967.0</td>\n",
       "      <td>0.1246</td>\n",
       "      <td>0.2101</td>\n",
       "      <td>0.2866</td>\n",
       "      <td>0.11200</td>\n",
       "      <td>0.2282</td>\n",
       "      <td>0.06954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>514</th>\n",
       "      <td>916221</td>\n",
       "      <td>B</td>\n",
       "      <td>11.34</td>\n",
       "      <td>18.61</td>\n",
       "      <td>72.76</td>\n",
       "      <td>391.2</td>\n",
       "      <td>0.10490</td>\n",
       "      <td>0.08499</td>\n",
       "      <td>0.04302</td>\n",
       "      <td>0.02594</td>\n",
       "      <td>...</td>\n",
       "      <td>12.47</td>\n",
       "      <td>23.03</td>\n",
       "      <td>79.15</td>\n",
       "      <td>478.6</td>\n",
       "      <td>0.1483</td>\n",
       "      <td>0.1574</td>\n",
       "      <td>0.1624</td>\n",
       "      <td>0.08542</td>\n",
       "      <td>0.3060</td>\n",
       "      <td>0.06783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>515</th>\n",
       "      <td>916799</td>\n",
       "      <td>M</td>\n",
       "      <td>18.31</td>\n",
       "      <td>20.58</td>\n",
       "      <td>120.80</td>\n",
       "      <td>1052.0</td>\n",
       "      <td>0.10680</td>\n",
       "      <td>0.12480</td>\n",
       "      <td>0.15690</td>\n",
       "      <td>0.09451</td>\n",
       "      <td>...</td>\n",
       "      <td>21.86</td>\n",
       "      <td>26.20</td>\n",
       "      <td>142.20</td>\n",
       "      <td>1493.0</td>\n",
       "      <td>0.1492</td>\n",
       "      <td>0.2536</td>\n",
       "      <td>0.3759</td>\n",
       "      <td>0.15100</td>\n",
       "      <td>0.3074</td>\n",
       "      <td>0.07863</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           ID diagnosis  radius_mean  radius_sd_error  radius_worst  \\\n",
       "510    915664         B        14.81            14.70         94.66   \n",
       "511    915691         M        13.40            20.52         88.64   \n",
       "512    915940         B        14.58            13.66         94.29   \n",
       "513  91594602         M        15.05            19.07         97.26   \n",
       "514    916221         B        11.34            18.61         72.76   \n",
       "515    916799         M        18.31            20.58        120.80   \n",
       "\n",
       "     texture_mean  texture_sd_error  texture_worst  perimeter_mean  \\\n",
       "510         680.7           0.08472        0.05016         0.03416   \n",
       "511         556.7           0.11060        0.14690         0.14450   \n",
       "512         658.8           0.09832        0.08918         0.08222   \n",
       "513         701.9           0.09215        0.08597         0.07486   \n",
       "514         391.2           0.10490        0.08499         0.04302   \n",
       "515        1052.0           0.10680        0.12480         0.15690   \n",
       "\n",
       "     perimeter_sd_error           ...             concavity_worst  \\\n",
       "510             0.02541           ...                       15.61   \n",
       "511             0.08172           ...                       16.41   \n",
       "512             0.04349           ...                       16.76   \n",
       "513             0.04335           ...                       17.58   \n",
       "514             0.02594           ...                       12.47   \n",
       "515             0.09451           ...                       21.86   \n",
       "\n",
       "     concave_points_mean  concave_points_sd_error  concave_points_worst  \\\n",
       "510                17.58                   101.70                 760.2   \n",
       "511                29.66                   113.30                 844.4   \n",
       "512                17.24                   108.50                 862.0   \n",
       "513                28.06                   113.80                 967.0   \n",
       "514                23.03                    79.15                 478.6   \n",
       "515                26.20                   142.20                1493.0   \n",
       "\n",
       "     symmetry_mean  symmetry_sd_error  symmetry_worst  fractal_dimension_mean  \\\n",
       "510         0.1139             0.1011          0.1101                 0.07955   \n",
       "511         0.1574             0.3856          0.5106                 0.20510   \n",
       "512         0.1223             0.1928          0.2492                 0.09186   \n",
       "513         0.1246             0.2101          0.2866                 0.11200   \n",
       "514         0.1483             0.1574          0.1624                 0.08542   \n",
       "515         0.1492             0.2536          0.3759                 0.15100   \n",
       "\n",
       "     fractal_dimension_sd_error  fractal_dimension_worst  \n",
       "510                      0.2334                  0.06142  \n",
       "511                      0.3585                  0.11090  \n",
       "512                      0.2626                  0.07048  \n",
       "513                      0.2282                  0.06954  \n",
       "514                      0.3060                  0.06783  \n",
       "515                      0.3074                  0.07863  \n",
       "\n",
       "[6 rows x 32 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[510:515]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "SVM parameters\n",
    "* C is used to set the amount of regularization. C stands for the cost functions power. Low C means low cost for misclassification, High C means high cost for misclassification. If C is low, then you are increasing the regularization. This is good to do when there isn't much data. If there was more data we could increase the value of C without overfitting. High C = more decision points correct, Low C = smoother decision boundary and it will likely capture incorrect decision points but it is less prone to overfitting!\n",
    "* Gamma is the support vector machines influence, we don't tweak this parameter because we don't have enough data to lower it. If we had more data, we could lower Gamma so the SVM would have less influence for each pass. In Scikit learn gamma is denoted as <i>penalty</i> which can be seen [here](http://scikit-learn.org/stable/modules/generated/sklearn.svm.LinearSVC.html#sklearn.svm.LinearSVC)\n",
    "* Small gamma = less prone to overfitting, draws smoother non-linear decision curves that will capture more data points. Low gamma = less smooth, more linear decision curve\n",
    "* L is the loss function, we keep the default at square hinge loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "dataset = df.values\n",
    "X_train = dataset[:, 2:32]\n",
    "y_train = dataset[:, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelBinarizer, StandardScaler\n",
    "from sklearn.svm import LinearSVC\n",
    "from sklearn.model_selection import train_test_split\n",
    "import time\n",
    "\n",
    "label_binarizer = LabelBinarizer()\n",
    "y_one_hot = label_binarizer.fit_transform(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train.shape:  (568, 30)\n",
      "y_train shape:  (568,)\n"
     ]
    }
   ],
   "source": [
    "print('X_train.shape: ', X_train.shape)\n",
    "y_train = y_one_hot.ravel()\n",
    "print('y_train shape: ', y_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "rand_state = np.random.randint(0, 100)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_train, y_train, test_size = 0.2, random_state = rand_state)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train length:  (454, 30)\n",
      "y_train shape:  (454,)\n",
      "X_test length:  (114, 30)\n",
      "y_test shape:  (114,)\n"
     ]
    }
   ],
   "source": [
    "print('X_train length: ', X_train.shape)\n",
    "print('y_train shape: ', y_train.shape)\n",
    "print('X_test length: ', X_test.shape)\n",
    "print('y_test shape: ', y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.03 Seconds to train SVC...\n"
     ]
    }
   ],
   "source": [
    "svc = LinearSVC(C = 0.01)\n",
    "# Use C parameter so that your data is less based on your dataset\n",
    "t = time.time()\n",
    "svc.fit(X_train, y_train)\n",
    "t2 = time.time()\n",
    "print(round(t2-t, 2), 'Seconds to train SVC...')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "def test_accuracy(svc, X_test, y_test):\n",
    "        print('Test Accuracy of SVC = ', round(svc.score(X_test, y_test), 4))\n",
    "        t = time.time()\n",
    "        print('Prediction time for single item: ', t)\n",
    "        n_predict = 10\n",
    "        print('My SVC predicts: ', svc.predict(X_test[0:n_predict]))\n",
    "        print('For these ', n_predict, 'labels ', y_test[0:n_predict])\n",
    "        t2 = time.time()\n",
    "        print(round(t2-t, 5), 'Seconds to predict', n_predict, 'labels with SVC')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy of SVC =  0.9474\n",
      "Prediction time for single item:  1498423691.2724273\n",
      "My SVC predicts:  [1 0 1 0 0 0 0 1 1 1]\n",
      "For these  10 labels  [1 0 0 0 0 0 0 1 1 0]\n",
      "0.00085 Seconds to predict 10 labels with SVC\n"
     ]
    }
   ],
   "source": [
    "test_accuracy(svc, X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "SVM parameters and explanation:\n",
    "The C parameter tells the SVM optimization how much you want to avoid misclassifying each training example. For large values of C, the optimization will choose a smaller-margin hyperplane if that hyperplane does a better job of getting all the training points classified correctly. \n",
    "\n",
    "A small C value will cause the optimizer to look for a large margin separating the hyperplane, even if it misclassifier more points. If C is very small you get misclassified examples which we can see here, but it helps us prevent overfitting.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### SVM with selected features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Train extractions\n",
    "X_train_rad_mean = X_train[:,0]\n",
    "X_train_texture_mean = X_train[:, 3]\n",
    "X_train_fractal_dimension_mean = X_train[:, 27]\n",
    "X_train_smoothness_mean = X_train[:, 12]\n",
    "\n",
    "# Test extractions\n",
    "X_test_rad_mean = X_test[:,0]\n",
    "X_test_texture_mean = X_test[:, 3]\n",
    "X_test_fractal_dimension_mean = X_test[:, 27]\n",
    "X_test_smoothness_mean = X_test[:, 12]\n",
    "\n",
    "# all\n",
    "X_train_all = np.copy(X_train)\n",
    "X_test_all = np.copy(X_test)\n",
    "\n",
    "# 3 features\n",
    "X_train_3_feat = np.dstack((X_train_rad_mean, X_train_texture_mean, X_train_fractal_dimension_mean))\n",
    "X_train_3_feat = np.squeeze(X_train_3_feat)\n",
    "X_test_3_feat = np.dstack((X_test_rad_mean, X_test_texture_mean, X_test_fractal_dimension_mean))\n",
    "X_test_3_feat = np.squeeze(X_test_3_feat)\n",
    "\n",
    "\n",
    "# 4 features\n",
    "X_train_4_feat = np.dstack((X_train_rad_mean, X_train_texture_mean, X_train_fractal_dimension_mean, X_train_smoothness_mean))\n",
    "X_train_4_feat = np.squeeze(X_train_4_feat)\n",
    "X_test_4_feat = np.dstack((X_test_rad_mean, X_test_texture_mean, X_test_fractal_dimension_mean, X_test_smoothness_mean))\n",
    "X_test_4_feat = np.squeeze(X_test_4_feat)\n",
    "\n",
    "# 3 features no size\n",
    "X_train_3_feat_no_size = np.dstack((X_train_texture_mean, X_train_fractal_dimension_mean, X_train_smoothness_mean))\n",
    "X_train_3_feat_no_size = np.squeeze(X_train_3_feat_no_size)\n",
    "X_test_3_feat_no_size = np.dstack((X_test_texture_mean, X_test_fractal_dimension_mean, X_test_smoothness_mean))\n",
    "X_test_3_feat_no_size = np.squeeze(X_test_3_feat_no_size)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Accuracy for 3 features\n",
    "* * Radius, Texture, Fractal Dimension"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy of SVC =  0.8333\n",
      "Prediction time for single item:  1498423691.3192408\n",
      "My SVC predicts:  [0 0 0 0 0 0 0 1 0 1]\n",
      "For these  10 labels  [1 0 0 0 0 0 0 1 1 0]\n",
      "0.00115 Seconds to predict 10 labels with SVC\n"
     ]
    }
   ],
   "source": [
    "clf_3_feat = LinearSVC(C = 0.01)\n",
    "clf_3_feat = clf_3_feat.fit(X_train_3_feat, y_train)\n",
    "test_accuracy(clf_3_feat, X_test_3_feat, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "When we restrict our SVM parameters to only using 3 features: Radius_mean, Texture_mean, and Fractal_dimension_mean we lose ~2% accuracy. When we use all the features we get 92.9% accuracy. The SVM is able to handle a lot of features, so it works well with our dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Accuracy for 4 features\n",
    "* Radius, Texture, Fractal Dimension, and Smoothness"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy of SVC =  0.8596\n",
      "Prediction time for single item:  1498423691.343773\n",
      "My SVC predicts:  [0 0 0 0 0 0 0 1 0 1]\n",
      "For these  10 labels  [1 0 0 0 0 0 0 1 1 0]\n",
      "0.00089 Seconds to predict 10 labels with SVC\n"
     ]
    }
   ],
   "source": [
    "clf_4_feat = LinearSVC(C = 0.01)\n",
    "clf_4_feat = clf_4_feat.fit(X_train_4_feat, y_train)\n",
    "test_accuracy(clf_4_feat, X_test_4_feat, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "When we add the Smoothness feature we increase our accuracy by ~2%"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Accuracy for 3 features no size\n",
    "* Let's remove the size component (radius) and see what we get"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy of SVC =  0.6754\n",
      "Prediction time for single item:  1498423691.368613\n",
      "My SVC predicts:  [0 0 0 0 0 0 0 0 0 0]\n",
      "For these  10 labels  [1 0 0 0 0 0 0 1 1 0]\n",
      "0.00065 Seconds to predict 10 labels with SVC\n"
     ]
    }
   ],
   "source": [
    "clf_3_feat_no_size = LinearSVC(C = 0.01)\n",
    "clf_3_feat_no_size = clf_3_feat_no_size.fit(X_train_3_feat_no_size, y_train)\n",
    "test_accuracy(clf_3_feat_no_size, X_test_3_feat_no_size, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "When we remove the size component from our Support Vector Machine our accuracy drops drastically! This is because `size` is a great indicator of malignancy. We can get this information from either the `area`, the `radius` or the `perimeter`, although radius seems to be the best indicator because the data is more unique for each class"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Decision Tree Regressor"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Our default Tree Regressor uses a 'gini' split criteria. Decision trees are very prone to overfitting data that has many features. You can build bigger classifiers our of decision trees known as ensemble methods that are usually less prone to overfitting. However, I went with a decision tree in this case\n",
    "\n",
    "Control overfitting by limiting the amount of features fed into the decision tree. I chose to use only the essential features, (size, texture, fractal dimensionality means)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "#### Start with original data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "def test_accuracy(svc, X_test, y_test):\n",
    "        print('Test Accuracy of SVC = ', round(svc.score(X_test, y_test), 4))\n",
    "        t = time.time()\n",
    "        print('Prediction time for single item: ', t)\n",
    "        n_predict = 10\n",
    "        print('My SVC predicts: ', svc.predict(X_test[0:n_predict]))\n",
    "        print('For these ', n_predict, 'labels ', y_test[0:n_predict])\n",
    "        t2 = time.time()\n",
    "        print(round(t2-t, 5), 'Seconds to predict', n_predict, 'labels with DTree')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "dataset = df.values\n",
    "X_train_gen = dataset[:, 2:32]\n",
    "y_train_gen = dataset[:, 1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "##### Train with different features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X_train_gen, y_train_gen, test_size = 0.2, random_state = rand_state)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Train extractions\n",
    "X_train_rad_mean = X_train[:,0]\n",
    "X_train_texture_mean = X_train[:, 3]\n",
    "X_train_fractal_dimension_mean = X_train[:, 27]\n",
    "X_train_smoothness_mean = X_train[:, 12]\n",
    "\n",
    "# Test extractions\n",
    "X_test_rad_mean = X_test[:,0]\n",
    "X_test_texture_mean = X_test[:, 3]\n",
    "X_test_fractal_dimension_mean = X_test[:, 27]\n",
    "X_test_smoothness_mean = X_test[:, 12]\n",
    "\n",
    "# all\n",
    "X_train_all = np.copy(X_train)\n",
    "X_test_all = np.copy(X_test)\n",
    "\n",
    "# 3 features\n",
    "X_train_3_feat = np.dstack((X_train_rad_mean, X_train_texture_mean, X_train_fractal_dimension_mean))\n",
    "X_train_3_feat = np.squeeze(X_train_3_feat)\n",
    "X_test_3_feat = np.dstack((X_test_rad_mean, X_test_texture_mean, X_test_fractal_dimension_mean))\n",
    "X_test_3_feat = np.squeeze(X_test_3_feat)\n",
    "\n",
    "\n",
    "# 4 features\n",
    "X_train_4_feat = np.dstack((X_train_rad_mean, X_train_texture_mean, X_train_fractal_dimension_mean, X_train_smoothness_mean))\n",
    "X_train_4_feat = np.squeeze(X_train_4_feat)\n",
    "X_test_4_feat = np.dstack((X_test_rad_mean, X_test_texture_mean, X_test_fractal_dimension_mean, X_test_smoothness_mean))\n",
    "X_test_4_feat = np.squeeze(X_test_4_feat)\n",
    "\n",
    "# 3 features no size\n",
    "X_train_3_feat_no_size = np.dstack((X_train_texture_mean, X_train_fractal_dimension_mean, X_train_smoothness_mean))\n",
    "X_train_3_feat_no_size = np.squeeze(X_train_3_feat_no_size)\n",
    "X_test_3_feat_no_size = np.dstack((X_test_texture_mean, X_test_fractal_dimension_mean, X_test_smoothness_mean))\n",
    "X_test_3_feat_no_size = np.squeeze(X_test_3_feat_no_size)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "from sklearn import tree"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Accuracy for all features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "clf_all = tree.DecisionTreeClassifier()\n",
    "clf_all = clf_all.fit(X_train_all, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy of SVC =  0.9298\n",
      "Prediction time for single item:  1498423691.4289062\n",
      "My SVC predicts:  ['M' 'B' 'B' 'B' 'B' 'B' 'B' 'M' 'M' 'M']\n",
      "For these  10 labels  ['M' 'B' 'B' 'B' 'B' 'B' 'B' 'M' 'M' 'B']\n",
      "0.0005 Seconds to predict 10 labels with DTree\n"
     ]
    }
   ],
   "source": [
    "test_accuracy(clf_all, X_test_all, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Accuracy for 3 features\n",
    "* * Radius, Texture, Fractal Dimension"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy of SVC =  0.9123\n",
      "Prediction time for single item:  1498423691.436558\n",
      "My SVC predicts:  ['M' 'B' 'B' 'B' 'B' 'B' 'B' 'M' 'M' 'M']\n",
      "For these  10 labels  ['M' 'B' 'B' 'B' 'B' 'B' 'B' 'M' 'M' 'B']\n",
      "0.00037 Seconds to predict 10 labels with DTree\n"
     ]
    }
   ],
   "source": [
    "clf_3_feat = tree.DecisionTreeClassifier()\n",
    "clf_3_feat = clf_3_feat.fit(X_train_3_feat, y_train)\n",
    "test_accuracy(clf_3_feat, X_test_3_feat, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "When we restrict our Decision Tree parameters to only using 3 features: Radius_mean, Texture_mean, and Fractal_dimension_mean our error increases ~1%. I suspect the higher accuracy is due to overfitting when using all the features because decision trees tend to overfit when fed more a large amount of features."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Accuracy for 4 features\n",
    "* Radius, Texture, Fractal Dimension, and Smoothness"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy of SVC =  0.9298\n",
      "Prediction time for single item:  1498423691.4427664\n",
      "My SVC predicts:  ['M' 'B' 'B' 'B' 'B' 'B' 'B' 'M' 'M' 'B']\n",
      "For these  10 labels  ['M' 'B' 'B' 'B' 'B' 'B' 'B' 'M' 'M' 'B']\n",
      "0.0005 Seconds to predict 10 labels with DTree\n"
     ]
    }
   ],
   "source": [
    "clf_4_feat = tree.DecisionTreeClassifier()\n",
    "clf_4_feat = clf_4_feat.fit(X_train_4_feat, y_train)\n",
    "test_accuracy(clf_4_feat, X_test_4_feat, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Accuracy for 3 features no size\n",
    "* Let's remove the size component (radius) and see what we get"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy of SVC =  0.9035\n",
      "Prediction time for single item:  1498423691.448662\n",
      "My SVC predicts:  ['M' 'B' 'B' 'B' 'B' 'B' 'B' 'M' 'M' 'B']\n",
      "For these  10 labels  ['M' 'B' 'B' 'B' 'B' 'B' 'B' 'M' 'M' 'B']\n",
      "0.00055 Seconds to predict 10 labels with DTree\n"
     ]
    }
   ],
   "source": [
    "clf_3_feat_no_size = tree.DecisionTreeClassifier()\n",
    "clf_3_feat_no_size = clf_3_feat_no_size.fit(X_train_3_feat_no_size, y_train)\n",
    "test_accuracy(clf_3_feat_no_size, X_test_3_feat_no_size, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Explanation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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UAG99s4yXvlpKvxJh7wGf/tVduD6FMTB+gIdH3kvw8JlBlm+zWLal98+zichc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\nVkqpLPH/AXJO5+AGsiimAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f95d2eed5f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels = 'Malignant', 'Benign'\n",
    "sizes = [len(mal_smooth), len(ben_smooth)]\n",
    "# explode = [0, 0.1, 0, 0]\n",
    "fig1, ax1 = plt.subplots()\n",
    "ax1.pie(sizes, labels = labels, autopct='%1.1f%%', shadow = True, startangle = 90)\n",
    "ax1.axis('equal')\n",
    "plt.title('Percent of data Mal v Benign')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, we have a larger amount of Benign data than Malignant data, specifically ~4:6 ratio. Therefore our predictor is more likely to be biased towards Benign tumors. There are ways to fix this however.\n",
    "\n",
    "You can do stratified sampling where you sample the same amount from each class. You can do this using [stratified k folds](http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.StratifiedKFold.html) which preserves the amount of samples for each class\n",
    "\n",
    "You can also upsample the Malignant data by ~10% so that you have an near-equal distribution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "* We are limit by the amount of data we have, ~368 data points. There is a 4:6 ratio between Malignant : Benign tumors so our predictor is going to be biased towards Benign predictions. To fix this we could upsample the Malignant tumors and random sample our data when we train the SVM using the Bootstrap function.\n",
    "* More Malignant tumor data then Benign tumor data, so we \n",
    "* We also have a different amount of data, so it is likely that the data is swaged in one direction."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Classifier Discussion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "We trained two models, a Support Vector Machine and a Decision Tree. \n",
    "The Support Vector Machine is a great tool for our use case because we are doing performing classification between binary options, either Malignant or Benign. SVM's attempt to draw a hyperplane between each category. Decision trees perform non-linear decision making with a linear decision surface, both are great tools for classification. Decision trees make decisions about your data, \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Non-Technical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.5, 723.5, 570.5, -0.5)"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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H8xNeYuzdu9cjVsx4tPhlZ2fzcU1ivbXy8nJ+MFhM+HJycnDx4kV+fawjB+ul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\nddoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f95d0031da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Dtree_img = mpimg.imread('./data/decision_tree.png')\n",
    "plt.imshow(Dtree_img)\n",
    "plt.axis('off')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A decision tree works by making little micro-decisions at each step. The goal of a decision tree is to draw a non-linear boundary around your data. A decision tree makes a bunch of little micro decisions about your data step by step. Decision trees progress node by node making micro-decisions about the data's features at each stage. So as the decision tree starts out, it can go feature by feature. At the first stage it can decide \"Is this really smooth or not so smooth\", then it answers that question and proceeds to the next node and so on until it has answered all of the feature questions.\n",
    "\n",
    "Decision Trees generally draw a line around your data, smooth or straight, and for that reason it is helpful to have fewer features so the line doesn't try to cover a ton of dimensions/features. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f95cecb4a20>"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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mPUup8xC+jmOJRZoDqjEzm9cRrB+Uxi6s28Xoq6BwdqFoanK5WRah1y6PWiTg3+0dOw5L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\nRZMQQhygaBJCiAMUTUIIceD/ASSZWC2Y6bcPAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f95cf1c8128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Dtree_nonlinear = mpimg.imread('./data/decision_tree_nonlinearBoundary.png')\n",
    "plt.imshow(Dtree_nonlinear)\n",
    "plt.axis('off')\n",
    "plt.title('Example of non-linear decision boundary')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "breast-cancer.csv                    field_names.txt\r\n",
      "\u001b[0m\u001b[01;35mdecision_tree_nonlinearBoundary.png\u001b[0m  \u001b[01;35moptimal-hyperplane.png\u001b[0m\r\n",
      "\u001b[01;35mdecision_tree.png\u001b[0m                    \u001b[01;35mSVM_nonlinear_boundary.jpg\u001b[0m\r\n"
     ]
    }
   ],
   "source": [
    "ls ./data/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.5, 299.5, 295.5, -0.5)"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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jPRLS4mT7Dx48CHd3d6T9lI78/Hz07NkTDx48gL+/P3g8Hry9vbXSEtgRcQxj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\noChKaf8PTK0dr6YIW48AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f95cec7ea90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "SVM_img = mpimg.imread('./data/optimal-hyperplane.png')\n",
    "plt.imshow(SVM_img)\n",
    "plt.axis('off')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A Support Vector Machine works in a similar way. You feed it a bunch of data and it draws a hyperplane one-dimension less than the data's dimension that you fed it. It uses a loss function to determine how 'off' your classes are from each other, and it punishes the 'off' values. In doing so, it draws"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.5, 637.5, 478.5, -0.5)"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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EJsREPlZHKOUjTqFwRNoRKYVx4GMRF++yKQpmzZpFqVSiubm5OpqmurYoHk1q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\nq9dms2s5z+P9lmLEsFRZls5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oOPisFowK+dpXDqS9U/DrFRqLVoo4cphPCbjyp7cx5512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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f95cec9d9e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "SVM_hyp = mpimg.imread('./data/SVM_nonlinear_boundary.jpg')\n",
    "plt.imshow(SVM_hyp)\n",
    "plt.axis('off')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "SVM's are able to handle an unlimited number of features, whereas decision trees are prone to overfitting with many features. Notice how the SVM draws a hyperplane around the data. We say `hyperplane` because it isn't always a line, it is simply one dimension less than its ambient space. So if the data we feed it is 2D it can be a line. If the data we feed it is 30 dimensions, (30 features), it draws a hyperplane with 29 dimensions. This phenomenon is the reason we can use many classes with an SVM. "
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    "The factors that led to Malignant v Benign identification are size, (radius, area, perimeter), texture, and fractal dimensionality. These variables have significant diversity among the two types (Malignant and Benign). Thus, their diversity enables our classifiers to make decisions about the data using these features. \n",
    "\n",
    "Classifier: \"Does this tumor have X size?\" \n",
    "-> \"no\", \n",
    "Classifier: \"okay it is likely Benign.\" \n",
    "-> make sure decision boundary is correctly placed with respect to this benign data point"
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