K-Means and SVM.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is the notebook I used for practicing K-means and SVM (I actually executed most on the lab computers). This is all pretty messy so I'd use it just as reference to see how I did it "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tarfile\n",
    "import urllib\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.metrics import silhouette_score, adjusted_rand_score\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import LabelEncoder, MinMaxScaler, StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import KFold\n",
    "from numpy import sqrt\n",
    "from sklearn.svm import SVC\n",
    "import pickle\n",
    "from sklearn import model_selection\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.metrics import cluster\n",
    "import sklearn.metrics\n",
    "from pathlib import Path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>img_h</th>\n",
       "      <th>error</th>\n",
       "      <th>Clump</th>\n",
       "      <th>Misread</th>\n",
       "      <th>Area</th>\n",
       "      <th>Perimeter</th>\n",
       "      <th>Solidity</th>\n",
       "      <th>Major_axis_length</th>\n",
       "      <th>Minor_axis_length</th>\n",
       "      <th>...</th>\n",
       "      <th>Energy</th>\n",
       "      <th>Correlation</th>\n",
       "      <th>Bounding_box</th>\n",
       "      <th>Entropy</th>\n",
       "      <th>img_area</th>\n",
       "      <th>Area_r</th>\n",
       "      <th>Perimeter_r</th>\n",
       "      <th>Major_axis_length_r</th>\n",
       "      <th>Minor_axis_length_r</th>\n",
       "      <th>Equivalent_diameter_r</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Abies cephalonica1-1</td>\n",
       "      <td>972</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>185475</td>\n",
       "      <td>2148.351514</td>\n",
       "      <td>0.852617</td>\n",
       "      <td>674.933044</td>\n",
       "      <td>420.156991</td>\n",
       "      <td>...</td>\n",
       "      <td>0.339303</td>\n",
       "      <td>0.963486</td>\n",
       "      <td>(56, 763, 690, 1209)</td>\n",
       "      <td>4.849428</td>\n",
       "      <td>1259712</td>\n",
       "      <td>16525.314997</td>\n",
       "      <td>191.412242</td>\n",
       "      <td>60.134687</td>\n",
       "      <td>37.434838</td>\n",
       "      <td>43.297430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Abies cephalonica1-1</td>\n",
       "      <td>972</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>136558</td>\n",
       "      <td>1678.628642</td>\n",
       "      <td>0.899319</td>\n",
       "      <td>538.347768</td>\n",
       "      <td>359.666465</td>\n",
       "      <td>...</td>\n",
       "      <td>0.312159</td>\n",
       "      <td>0.980137</td>\n",
       "      <td>(339, 372, 864, 752)</td>\n",
       "      <td>5.172168</td>\n",
       "      <td>1259712</td>\n",
       "      <td>12166.944145</td>\n",
       "      <td>149.561219</td>\n",
       "      <td>47.965313</td>\n",
       "      <td>32.045298</td>\n",
       "      <td>37.151641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Abies nordmanniana1-1</td>\n",
       "      <td>972</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>565318</td>\n",
       "      <td>2969.695706</td>\n",
       "      <td>0.991946</td>\n",
       "      <td>946.564642</td>\n",
       "      <td>767.424770</td>\n",
       "      <td>...</td>\n",
       "      <td>0.159598</td>\n",
       "      <td>0.983691</td>\n",
       "      <td>(145, 157, 887, 1061)</td>\n",
       "      <td>5.953881</td>\n",
       "      <td>1259712</td>\n",
       "      <td>50368.286956</td>\n",
       "      <td>264.591762</td>\n",
       "      <td>84.336320</td>\n",
       "      <td>68.375447</td>\n",
       "      <td>75.590259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Abies religiosa1-1</td>\n",
       "      <td>972</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5115</td>\n",
       "      <td>352.090404</td>\n",
       "      <td>0.863145</td>\n",
       "      <td>117.359585</td>\n",
       "      <td>59.842103</td>\n",
       "      <td>...</td>\n",
       "      <td>0.351071</td>\n",
       "      <td>0.917417</td>\n",
       "      <td>(34, 547, 154, 615)</td>\n",
       "      <td>5.272603</td>\n",
       "      <td>1259712</td>\n",
       "      <td>455.732504</td>\n",
       "      <td>31.370292</td>\n",
       "      <td>10.456418</td>\n",
       "      <td>5.331768</td>\n",
       "      <td>7.190221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Abies religiosa1-1</td>\n",
       "      <td>972</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>155620</td>\n",
       "      <td>1725.699710</td>\n",
       "      <td>0.904662</td>\n",
       "      <td>562.851959</td>\n",
       "      <td>383.508423</td>\n",
       "      <td>...</td>\n",
       "      <td>0.347042</td>\n",
       "      <td>0.989556</td>\n",
       "      <td>(228, 267, 771, 710)</td>\n",
       "      <td>5.349896</td>\n",
       "      <td>1259712</td>\n",
       "      <td>13865.316187</td>\n",
       "      <td>153.755122</td>\n",
       "      <td>50.148569</td>\n",
       "      <td>34.169551</td>\n",
       "      <td>39.659947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9842</th>\n",
       "      <td>Zornia curvata1-1</td>\n",
       "      <td>972</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>76421</td>\n",
       "      <td>1303.851947</td>\n",
       "      <td>0.904005</td>\n",
       "      <td>367.389517</td>\n",
       "      <td>281.161273</td>\n",
       "      <td>...</td>\n",
       "      <td>0.330122</td>\n",
       "      <td>0.955168</td>\n",
       "      <td>(229, 494, 547, 856)</td>\n",
       "      <td>5.222733</td>\n",
       "      <td>1259712</td>\n",
       "      <td>6808.901994</td>\n",
       "      <td>116.169641</td>\n",
       "      <td>32.733401</td>\n",
       "      <td>25.050700</td>\n",
       "      <td>27.792383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9843</th>\n",
       "      <td>Zornia curvata1-1</td>\n",
       "      <td>972</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3779</td>\n",
       "      <td>377.948268</td>\n",
       "      <td>0.714367</td>\n",
       "      <td>129.472859</td>\n",
       "      <td>47.747114</td>\n",
       "      <td>...</td>\n",
       "      <td>0.522279</td>\n",
       "      <td>0.930787</td>\n",
       "      <td>(789, 549, 918, 613)</td>\n",
       "      <td>3.910655</td>\n",
       "      <td>1259712</td>\n",
       "      <td>336.698560</td>\n",
       "      <td>33.674157</td>\n",
       "      <td>11.535678</td>\n",
       "      <td>4.254137</td>\n",
       "      <td>6.180276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9844</th>\n",
       "      <td>Zornia curvata1-1</td>\n",
       "      <td>972</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1517</td>\n",
       "      <td>165.781746</td>\n",
       "      <td>0.913855</td>\n",
       "      <td>57.978301</td>\n",
       "      <td>36.321790</td>\n",
       "      <td>...</td>\n",
       "      <td>0.333257</td>\n",
       "      <td>0.898527</td>\n",
       "      <td>(813, 1139, 855, 1196)</td>\n",
       "      <td>4.391925</td>\n",
       "      <td>1259712</td>\n",
       "      <td>135.160549</td>\n",
       "      <td>14.770700</td>\n",
       "      <td>5.165708</td>\n",
       "      <td>3.236172</td>\n",
       "      <td>3.915725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9845</th>\n",
       "      <td>Zornia piurensis1-2</td>\n",
       "      <td>972</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>60357</td>\n",
       "      <td>989.761543</td>\n",
       "      <td>0.976398</td>\n",
       "      <td>286.656299</td>\n",
       "      <td>269.424926</td>\n",
       "      <td>...</td>\n",
       "      <td>0.237711</td>\n",
       "      <td>0.927109</td>\n",
       "      <td>(350, 467, 637, 745)</td>\n",
       "      <td>5.678020</td>\n",
       "      <td>1259712</td>\n",
       "      <td>5377.643549</td>\n",
       "      <td>88.185045</td>\n",
       "      <td>25.540292</td>\n",
       "      <td>24.005024</td>\n",
       "      <td>24.699221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9846</th>\n",
       "      <td>Zornia piurensis1-3</td>\n",
       "      <td>972</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>70371</td>\n",
       "      <td>1005.702669</td>\n",
       "      <td>0.995234</td>\n",
       "      <td>346.262154</td>\n",
       "      <td>258.847769</td>\n",
       "      <td>...</td>\n",
       "      <td>0.233972</td>\n",
       "      <td>0.964133</td>\n",
       "      <td>(448, 421, 735, 743)</td>\n",
       "      <td>5.741068</td>\n",
       "      <td>1259712</td>\n",
       "      <td>6269.863548</td>\n",
       "      <td>89.605356</td>\n",
       "      <td>30.851010</td>\n",
       "      <td>23.062628</td>\n",
       "      <td>26.669587</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9847 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Unnamed: 0  img_h  error  Clump  Misread    Area  \\\n",
       "0      Abies cephalonica1-1    972      0      0        0  185475   \n",
       "1      Abies cephalonica1-1    972      0      0        0  136558   \n",
       "2     Abies nordmanniana1-1    972      0      0        0  565318   \n",
       "3        Abies religiosa1-1    972      1      0        1    5115   \n",
       "4        Abies religiosa1-1    972      0      0        0  155620   \n",
       "...                     ...    ...    ...    ...      ...     ...   \n",
       "9842      Zornia curvata1-1    972      1      1        0   76421   \n",
       "9843      Zornia curvata1-1    972      1      0        1    3779   \n",
       "9844      Zornia curvata1-1    972      1      0        1    1517   \n",
       "9845    Zornia piurensis1-2    972      0      0        0   60357   \n",
       "9846    Zornia piurensis1-3    972      0      0        0   70371   \n",
       "\n",
       "        Perimeter  Solidity  Major_axis_length  Minor_axis_length  ...  \\\n",
       "0     2148.351514  0.852617         674.933044         420.156991  ...   \n",
       "1     1678.628642  0.899319         538.347768         359.666465  ...   \n",
       "2     2969.695706  0.991946         946.564642         767.424770  ...   \n",
       "3      352.090404  0.863145         117.359585          59.842103  ...   \n",
       "4     1725.699710  0.904662         562.851959         383.508423  ...   \n",
       "...           ...       ...                ...                ...  ...   \n",
       "9842  1303.851947  0.904005         367.389517         281.161273  ...   \n",
       "9843   377.948268  0.714367         129.472859          47.747114  ...   \n",
       "9844   165.781746  0.913855          57.978301          36.321790  ...   \n",
       "9845   989.761543  0.976398         286.656299         269.424926  ...   \n",
       "9846  1005.702669  0.995234         346.262154         258.847769  ...   \n",
       "\n",
       "        Energy  Correlation            Bounding_box   Entropy  img_area  \\\n",
       "0     0.339303     0.963486    (56, 763, 690, 1209)  4.849428   1259712   \n",
       "1     0.312159     0.980137    (339, 372, 864, 752)  5.172168   1259712   \n",
       "2     0.159598     0.983691   (145, 157, 887, 1061)  5.953881   1259712   \n",
       "3     0.351071     0.917417     (34, 547, 154, 615)  5.272603   1259712   \n",
       "4     0.347042     0.989556    (228, 267, 771, 710)  5.349896   1259712   \n",
       "...        ...          ...                     ...       ...       ...   \n",
       "9842  0.330122     0.955168    (229, 494, 547, 856)  5.222733   1259712   \n",
       "9843  0.522279     0.930787    (789, 549, 918, 613)  3.910655   1259712   \n",
       "9844  0.333257     0.898527  (813, 1139, 855, 1196)  4.391925   1259712   \n",
       "9845  0.237711     0.927109    (350, 467, 637, 745)  5.678020   1259712   \n",
       "9846  0.233972     0.964133    (448, 421, 735, 743)  5.741068   1259712   \n",
       "\n",
       "            Area_r  Perimeter_r  Major_axis_length_r  Minor_axis_length_r  \\\n",
       "0     16525.314997   191.412242            60.134687            37.434838   \n",
       "1     12166.944145   149.561219            47.965313            32.045298   \n",
       "2     50368.286956   264.591762            84.336320            68.375447   \n",
       "3       455.732504    31.370292            10.456418             5.331768   \n",
       "4     13865.316187   153.755122            50.148569            34.169551   \n",
       "...            ...          ...                  ...                  ...   \n",
       "9842   6808.901994   116.169641            32.733401            25.050700   \n",
       "9843    336.698560    33.674157            11.535678             4.254137   \n",
       "9844    135.160549    14.770700             5.165708             3.236172   \n",
       "9845   5377.643549    88.185045            25.540292            24.005024   \n",
       "9846   6269.863548    89.605356            30.851010            23.062628   \n",
       "\n",
       "     Equivalent_diameter_r  \n",
       "0                43.297430  \n",
       "1                37.151641  \n",
       "2                75.590259  \n",
       "3                 7.190221  \n",
       "4                39.659947  \n",
       "...                    ...  \n",
       "9842             27.792383  \n",
       "9843              6.180276  \n",
       "9844              3.915725  \n",
       "9845             24.699221  \n",
       "9846             26.669587  \n",
       "\n",
       "[9847 rows x 27 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "csv = pd.read_csv('FinalDataset.csv')\n",
    "newdf = pd.DataFrame(csv)\n",
    "errordf = newdf[newdf.index.notnull()]\n",
    "area=[]\n",
    "for a in errordf['img_h']:\n",
    "    if a == 972:\n",
    "        area.append(1259712)\n",
    "    if a == 912:\n",
    "        area.append(1108992)\n",
    "    if a ==450:\n",
    "        area.append(270000)\n",
    "errordf['img_area'] = area\n",
    "#Convert all geometric values to ratios\n",
    "errordf['Area_r'] = errordf['Area']/(sqrt(errordf['img_area'])/100)\n",
    "errordf['Perimeter_r'] = errordf['Perimeter']/(sqrt(errordf['img_area'])/100)\n",
    "errordf['Major_axis_length_r'] = errordf['Major_axis_length']/(sqrt(errordf['img_area'])/100)\n",
    "errordf['Minor_axis_length_r'] = errordf['Minor_axis_length']/(sqrt(errordf['img_area'])/100)\n",
    "errordf['Equivalent_diameter_r'] = errordf['Equivalent_diameter']/(sqrt(errordf['img_area'])/100)\n",
    "\n",
    "#I use the image heights to select between datasets, so use ==450 for SAPS or !=450 for GPP or neither for combined\n",
    "#errordf = errordf[errordf['img_h']==450]\n",
    "errordf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>9847.0</td>\n",
       "      <td>0.794714</td>\n",
       "      <td>0.132791</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         0         1         2\n",
       "10  9847.0  0.794714  0.132791"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Trains an SVM model for error detection\n",
    "\n",
    "csv = pd.read_csv('FinalDataset.csv')\n",
    "newdf = pd.DataFrame(csv)\n",
    "errordf = newdf[newdf.index.notnull()]\n",
    "errordf['PE']=errordf['P/E']\n",
    "errordf['Area_r'] = errordf['Area']/(sqrt(errordf['img_area'])/100)\n",
    "errordf['Perimeter_r'] = errordf['Perimeter']/(sqrt(errordf['img_area'])/100)\n",
    "errordf['Major_axis_length_r'] = errordf['Major_axis_length']/(sqrt(errordf['img_area'])/100)\n",
    "errordf['Minor_axis_length_r'] = errordf['Minor_axis_length']/(sqrt(errordf['img_area'])/100)\n",
    "errordf['Equivalent_diameter_r'] = errordf['Equivalent_diameter']/(sqrt(errordf['img_area'])/100)\n",
    "#ppp = ['PE','phyla','polarity','order','family','2D_shape','3D_shape','class','aperture_type','aperture_features','dispersal_unit']#,'viscin','ubisch']\n",
    "fulldf = pd.DataFrame()\n",
    "new=[]\n",
    "\n",
    "ppp=['error']\n",
    "\n",
    "for a in ppp:\n",
    "     for b in np.linspace(0.000001,0.0001,10):\n",
    "        newdf=errordf#[errordf['error']==0]\n",
    "        newdf=newdf[newdf['img_h']==450]\n",
    "        #Remove labels which have fewer than 30 examples\n",
    "        newdf = newdf.groupby(a).filter(lambda x: len(x) >= 30)\n",
    "        newdf = newdf[[a,'Area_r','Eccentricity','Equivalent_diameter_r','Extent','Major_axis_length_r','Minor_axis_length_r','Perimeter_r','ASM','Contrast','Correlation','Dissimilarity','Energy','Entropy','Homogeneity','Solidity']]#.set_index('comb')\n",
    "        #remove NaN\n",
    "        newdf = newdf.set_index(a)\n",
    "        newdf = newdf[newdf.index.notnull()]\n",
    "        #Drop all non-labelled values which are not NaN\n",
    "        try:\n",
    "            newdf = newdf.drop('-')\n",
    "        except:\n",
    "            newdf = newdf\n",
    "        try:\n",
    "            newdf = newdf.drop('no suitable term')\n",
    "        except:\n",
    "            newdf = newdf\n",
    "        try:\n",
    "            newdf = newdf.drop('unplaced')\n",
    "        except:\n",
    "            newdf = newdf\n",
    "        label_encoder = LabelEncoder()\n",
    "        #KFolds - Very important! Splits the dataset 5 times into training and testing data for cross-validation purposes\n",
    "        kf = KFold(n_splits=5, random_state=None, shuffle=True)\n",
    "        rand_scores = []\n",
    "        rands = []\n",
    "        kfolds = []\n",
    "        #The 'pipeline' is really useful to define so you can succinctly define your ML model and make predictions\n",
    "        pipe = Pipeline([(\"scaler\", MinMaxScaler()),(\"SVM\",SVC(C=b,gamma='scale',kernel='rbf',probability=True))])\n",
    "        #This was sort of a mannual K-folds loop, i was just trying to make sure results were valid. You could remove this\n",
    "        for c in range(10):\n",
    "            train_img, test_img, train_lbl, test_lbl = train_test_split(newdf.values, newdf.index, test_size=1/7.0, random_state=None)\n",
    "            pipe[\"scaler\"].fit_transform(train_img,train_lbl)\n",
    "         #Could remove this try as well i was just working around an error which doesn't happen anymore\n",
    "            try:\n",
    "                pipe.fit(newdf.values,newdf.index)\n",
    "            except:\n",
    "                continue\n",
    "         #Measures ARI and appends to dataframe - I used balanced accuracy also in final executions for further validation\n",
    "            kfolds.append(model_selection.cross_val_score(pipe, train_img, train_lbl, scoring='adjusted_rand_score', cv=kf, n_jobs=-1).mean())#sklearn.metrics.adjusted_rand_score(labels,true_labels))\n",
    "            pred=pipe.predict(pipe[\"scaler\"].transform(test_img))\n",
    "            rands.append(sklearn.metrics.adjusted_rand_score(pred,test_lbl))\n",
    "        rand_scores.append(newdf.index.size)\n",
    "        rand_scores.append(sum(kfolds)/len(kfolds))\n",
    "        rand_scores.append(sum(rands)/len(rands))\n",
    "        fulldf=fulldf.append(pd.Series(rand_scores, name=b))\n",
    "        #You can save the models using pickle to make predicitons from again - I would use the next cell for this\n",
    "        #filename = f'SVM_{b}_model.sav'\n",
    "        #pickle.dump(pipe, open(filename, 'wb'))\n",
    "#fulldf.to_csv('output.csv')\n",
    "fulldf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-92-a1680799fc69>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     82\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     83\u001b[0m         \u001b[0mclf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mGridSearchCV\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpipe\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparameters\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mscoring\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'adjusted_rand_score'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mn_jobs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m4\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mcv\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mkf\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mrefit\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mreturn_train_score\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 84\u001b[1;33m         \u001b[0mclf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtrain_img\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrain_lbl\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     85\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     86\u001b[0m         \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mclf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcv_results_\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'CombResults.csv'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36minner_f\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     61\u001b[0m             \u001b[0mextra_args\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mall_args\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     62\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mextra_args\u001b[0m \u001b[1;33m<=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 63\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     64\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     65\u001b[0m             \u001b[1;31m# extra_args > 0\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, groups, **fit_params)\u001b[0m\n\u001b[0;32m    839\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mresults\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    840\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 841\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_run_search\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mevaluate_candidates\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    842\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    843\u001b[0m             \u001b[1;31m# multimetric is determined here because in the case of a callable\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py\u001b[0m in \u001b[0;36m_run_search\u001b[1;34m(self, evaluate_candidates)\u001b[0m\n\u001b[0;32m   1294\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_run_search\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mevaluate_candidates\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1295\u001b[0m         \u001b[1;34m\"\"\"Search all candidates in param_grid\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1296\u001b[1;33m         \u001b[0mevaluate_candidates\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mParameterGrid\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparam_grid\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1297\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1298\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py\u001b[0m in \u001b[0;36mevaluate_candidates\u001b[1;34m(candidate_params, cv, more_results)\u001b[0m\n\u001b[0;32m    793\u001b[0m                               n_splits, n_candidates, n_candidates * n_splits))\n\u001b[0;32m    794\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 795\u001b[1;33m                 out = parallel(delayed(_fit_and_score)(clone(base_estimator),\n\u001b[0m\u001b[0;32m    796\u001b[0m                                                        \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    797\u001b[0m                                                        \u001b[0mtrain\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtrain\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtest\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtest\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, iterable)\u001b[0m\n\u001b[0;32m   1059\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1060\u001b[0m             \u001b[1;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mretrieval_context\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1061\u001b[1;33m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mretrieve\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1062\u001b[0m             \u001b[1;31m# Make sure that we get a last message telling us we are done\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1063\u001b[0m             \u001b[0melapsed_time\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_start_time\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36mretrieve\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    938\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    939\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'supports_timeout'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 940\u001b[1;33m                     \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_output\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mjob\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    941\u001b[0m                 \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    942\u001b[0m                     \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_output\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mjob\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\joblib\\_parallel_backends.py\u001b[0m in \u001b[0;36mwrap_future_result\u001b[1;34m(future, timeout)\u001b[0m\n\u001b[0;32m    540\u001b[0m         AsyncResults.get from multiprocessing.\"\"\"\n\u001b[0;32m    541\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 542\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mfuture\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    543\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mCfTimeoutError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    544\u001b[0m             \u001b[1;32mraise\u001b[0m \u001b[0mTimeoutError\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\concurrent\\futures\\_base.py\u001b[0m in \u001b[0;36mresult\u001b[1;34m(self, timeout)\u001b[0m\n\u001b[0;32m    432\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__get_result\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    433\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 434\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_condition\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    435\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    436\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_state\u001b[0m \u001b[1;32min\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mCANCELLED\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mCANCELLED_AND_NOTIFIED\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\threading.py\u001b[0m in \u001b[0;36mwait\u001b[1;34m(self, timeout)\u001b[0m\n\u001b[0;32m    300\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m    \u001b[1;31m# restore state no matter what (e.g., KeyboardInterrupt)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    301\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mtimeout\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 302\u001b[1;33m                 \u001b[0mwaiter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0macquire\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    303\u001b[0m                 \u001b[0mgotit\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    304\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "#This is the same as the cell above but utilises GridSearchCV - an exhaustive search of rtying lots of different combinations of variables\n",
    "#Annotations same as above\n",
    "csv = pd.read_csv('FinalDataset.csv')\n",
    "newdf = pd.DataFrame(csv)\n",
    "errordf = newdf[newdf.index.notnull()]\n",
    "errordf['PE']=errordf['P/E']\n",
    "errordf['Area_r'] = errordf['Area']/(sqrt(errordf['img_area'])/100)\n",
    "errordf['Perimeter_r'] = errordf['Perimeter']/(sqrt(errordf['img_area'])/100)\n",
    "errordf['Major_axis_length_r'] = errordf['Major_axis_length']/(sqrt(errordf['img_area'])/100)\n",
    "errordf['Minor_axis_length_r'] = errordf['Minor_axis_length']/(sqrt(errordf['img_area'])/100)\n",
    "errordf['Equivalent_diameter_r'] = errordf['Equivalent_diameter']/(sqrt(errordf['img_area'])/100)\n",
    "#ppp = ['PE','phyla','polarity','order','family','2D_shape','3D_shape','class','aperture_type','aperture_features','dispersal_unit']#,'viscin','ubisch']\n",
    "fulldf = pd.DataFrame()\n",
    "new=[]\n",
    "comb = []\n",
    "\n",
    "for b in errordf.values:\n",
    "    #print(b)\n",
    "    if b[4] == 1.0:\n",
    "        comb.append(2.0) \n",
    "    elif b[5] == 1.0:\n",
    "        comb.append(1.0)\n",
    "    else:\n",
    "        comb.append(0.0)\n",
    "errordf['comb'] = comb\n",
    "ppp=['error']\n",
    "\n",
    "l= [0.0001,0.0005,0.001,0.005,0.01,0.05,0.1,0.5,1]\n",
    "\n",
    "for a in ppp:\n",
    "     for b in np.linspace(0.000001,0.0001,10):\n",
    "        newdf=errordf#[errordf['Clump']==0]\n",
    "        newdf=newdf[newdf['img_h']==450]\n",
    "        #newdf = newdf.groupby(a).filter(lambda x: len(x) >= 30)\n",
    "        newdf = newdf[[a,'Area','Eccentricity','Equivalent_diameter','Extent','Major_axis_length','Minor_axis_length','Perimeter','ASM','Contrast','Correlation','Dissimilarity','Energy','Entropy','Homogeneity','Solidity']]#.set_index('comb')\n",
    "        newdf = newdf.set_index(a)\n",
    "        newdf = newdf[newdf.index.notnull()]\n",
    "        newdf\n",
    "\n",
    "        try:\n",
    "            newdf = newdf.drop('-')\n",
    "        except:\n",
    "            newdf = newdf\n",
    "        try:\n",
    "            newdf = newdf.drop('no suitable term')\n",
    "        except:\n",
    "            newdf = newdf\n",
    "        try:\n",
    "            newdf = newdf.drop('unplaced')\n",
    "        except:\n",
    "            newdf = newdf\n",
    "\n",
    "        label_encoder = LabelEncoder()\n",
    "        kf = KFold(n_splits=5, random_state=None, shuffle=True)\n",
    "        \n",
    "        pipe = Pipeline([(\"scaler\", MinMaxScaler()),(\"SVM\",SVC(probability=True))])\n",
    "        train_img, test_img, train_lbl, test_lbl = train_test_split(newdf.values, newdf.index, test_size=1/7.0, random_state=None)\n",
    "        #Paramaters to run through need to be defined for GridSearchCV - there are different kernels and parameters specific to each\n",
    "        parameters = [{'SVM__kernel':['linear'], 'SVM__C':[0.00001,0.0001,0.001,0.01,0.1,1,10,100,1000,10000,100000,1000000]},\n",
    "                     {'SVM__kernel':['rbf'],'SVM_gamma':['scale','auto'], 'SVM__C':[0.00001,0.0001,0.001,0.01,0.1,1,10,100,1000,10000,100000,1000000]},\n",
    "                     {'SVM__kernel':['poly'],'SVM_coef':[0,0.1,0.5,1],'SVM__C':[2,3,4,5,6],'SVM_gamma':['scale','auto'] ,'SVM__C':[0.00001,0.0001,0.001,0.01,0.1,1,10,100,1000,10000,100000,1000000]},\n",
    "                     {'SVM__kernel':['sigmoid'],'SVM_coef':[0,0.1,0.5,1],'SVM_gamma':['scale','auto'], 'SVM__C':[0.00001,0.0001,0.001,0.01,0.1,1,10,100,1000,10000,100000,1000000]},\n",
    "                     ]\n",
    "              \n",
    "\n",
    "        clf = GridSearchCV(pipe, parameters,scoring='adjusted_rand_score',n_jobs = 4,cv=kf,refit=True,return_train_score=True)\n",
    "        clf.fit(train_img, train_lbl)\n",
    "        \n",
    "pd.DataFrame(clf.cv_results_)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_fit_time</th>\n",
       "      <th>std_fit_time</th>\n",
       "      <th>mean_score_time</th>\n",
       "      <th>std_score_time</th>\n",
       "      <th>param_SVM__C</th>\n",
       "      <th>param_SVM__kernel</th>\n",
       "      <th>params</th>\n",
       "      <th>split0_test_score</th>\n",
       "      <th>split1_test_score</th>\n",
       "      <th>split2_test_score</th>\n",
       "      <th>split3_test_score</th>\n",
       "      <th>split4_test_score</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>std_test_score</th>\n",
       "      <th>rank_test_score</th>\n",
       "      <th>split0_train_score</th>\n",
       "      <th>split1_train_score</th>\n",
       "      <th>split2_train_score</th>\n",
       "      <th>split3_train_score</th>\n",
       "      <th>split4_train_score</th>\n",
       "      <th>mean_train_score</th>\n",
       "      <th>std_train_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.526113</td>\n",
       "      <td>0.040151</td>\n",
       "      <td>0.046476</td>\n",
       "      <td>0.007479</td>\n",
       "      <td>1</td>\n",
       "      <td>linear</td>\n",
       "      <td>{'SVM__C': 1, 'SVM__kernel': 'linear'}</td>\n",
       "      <td>0.767236</td>\n",
       "      <td>0.761966</td>\n",
       "      <td>0.796685</td>\n",
       "      <td>0.751442</td>\n",
       "      <td>0.788435</td>\n",
       "      <td>0.773153</td>\n",
       "      <td>0.016845</td>\n",
       "      <td>2</td>\n",
       "      <td>0.776643</td>\n",
       "      <td>0.777304</td>\n",
       "      <td>0.766679</td>\n",
       "      <td>0.784654</td>\n",
       "      <td>0.769372</td>\n",
       "      <td>0.774930</td>\n",
       "      <td>0.006358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.300723</td>\n",
       "      <td>0.157046</td>\n",
       "      <td>0.040092</td>\n",
       "      <td>0.009841</td>\n",
       "      <td>10</td>\n",
       "      <td>linear</td>\n",
       "      <td>{'SVM__C': 10, 'SVM__kernel': 'linear'}</td>\n",
       "      <td>0.799377</td>\n",
       "      <td>0.799394</td>\n",
       "      <td>0.818443</td>\n",
       "      <td>0.772574</td>\n",
       "      <td>0.818288</td>\n",
       "      <td>0.801615</td>\n",
       "      <td>0.016820</td>\n",
       "      <td>1</td>\n",
       "      <td>0.806921</td>\n",
       "      <td>0.808956</td>\n",
       "      <td>0.802171</td>\n",
       "      <td>0.808954</td>\n",
       "      <td>0.802888</td>\n",
       "      <td>0.805978</td>\n",
       "      <td>0.002921</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   mean_fit_time  std_fit_time  mean_score_time  std_score_time param_SVM__C  \\\n",
       "0       1.526113      0.040151         0.046476        0.007479            1   \n",
       "1       1.300723      0.157046         0.040092        0.009841           10   \n",
       "\n",
       "  param_SVM__kernel                                   params  \\\n",
       "0            linear   {'SVM__C': 1, 'SVM__kernel': 'linear'}   \n",
       "1            linear  {'SVM__C': 10, 'SVM__kernel': 'linear'}   \n",
       "\n",
       "   split0_test_score  split1_test_score  split2_test_score  split3_test_score  \\\n",
       "0           0.767236           0.761966           0.796685           0.751442   \n",
       "1           0.799377           0.799394           0.818443           0.772574   \n",
       "\n",
       "   split4_test_score  mean_test_score  std_test_score  rank_test_score  \\\n",
       "0           0.788435         0.773153        0.016845                2   \n",
       "1           0.818288         0.801615        0.016820                1   \n",
       "\n",
       "   split0_train_score  split1_train_score  split2_train_score  \\\n",
       "0            0.776643            0.777304            0.766679   \n",
       "1            0.806921            0.808956            0.802171   \n",
       "\n",
       "   split3_train_score  split4_train_score  mean_train_score  std_train_score  \n",
       "0            0.784654            0.769372          0.774930         0.006358  \n",
       "1            0.808954            0.802888          0.805978         0.002921  "
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Reveals reuslts - I would export to csv to look at results\n",
    "pd.DataFrame(clf.cv_results_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>level_0</th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>index</th>\n",
       "      <th>img_h</th>\n",
       "      <th>error</th>\n",
       "      <th>Clump</th>\n",
       "      <th>Misread</th>\n",
       "      <th>Solidity</th>\n",
       "      <th>Eccentricity</th>\n",
       "      <th>Extent</th>\n",
       "      <th>...</th>\n",
       "      <th>x40</th>\n",
       "      <th>x40SD</th>\n",
       "      <th>x100</th>\n",
       "      <th>x100SD</th>\n",
       "      <th>magnification</th>\n",
       "      <th>Area</th>\n",
       "      <th>Perimeter</th>\n",
       "      <th>Major_axis_length</th>\n",
       "      <th>Minor_axis_length</th>\n",
       "      <th>Equivalent_diameter</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>psilate</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Abies cephalonica</td>\n",
       "      <td>972</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.852617</td>\n",
       "      <td>0.782607</td>\n",
       "      <td>0.655936</td>\n",
       "      <td>...</td>\n",
       "      <td>808.243257</td>\n",
       "      <td>116.115981</td>\n",
       "      <td>193.349882</td>\n",
       "      <td>88.387169</td>\n",
       "      <td>100x</td>\n",
       "      <td>53133.521285</td>\n",
       "      <td>615.444027</td>\n",
       "      <td>193.349882</td>\n",
       "      <td>120.363501</td>\n",
       "      <td>139.213377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>psilate</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Abies cephalonica</td>\n",
       "      <td>972</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.899319</td>\n",
       "      <td>0.744078</td>\n",
       "      <td>0.684501</td>\n",
       "      <td>...</td>\n",
       "      <td>644.680175</td>\n",
       "      <td>92.617749</td>\n",
       "      <td>154.221931</td>\n",
       "      <td>70.500379</td>\n",
       "      <td>100x</td>\n",
       "      <td>39120.136944</td>\n",
       "      <td>480.881255</td>\n",
       "      <td>154.221931</td>\n",
       "      <td>103.034618</td>\n",
       "      <td>119.452942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>psilate</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Abies nordmanniana</td>\n",
       "      <td>972</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.991946</td>\n",
       "      <td>0.585396</td>\n",
       "      <td>0.842792</td>\n",
       "      <td>...</td>\n",
       "      <td>1133.526496</td>\n",
       "      <td>162.847682</td>\n",
       "      <td>271.164915</td>\n",
       "      <td>123.959213</td>\n",
       "      <td>100x</td>\n",
       "      <td>161948.165444</td>\n",
       "      <td>850.736703</td>\n",
       "      <td>271.164915</td>\n",
       "      <td>219.846235</td>\n",
       "      <td>243.043877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>psilate</th>\n",
       "      <td>298</td>\n",
       "      <td>298</td>\n",
       "      <td>Allium cepa</td>\n",
       "      <td>450</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.986618</td>\n",
       "      <td>0.725816</td>\n",
       "      <td>0.763222</td>\n",
       "      <td>...</td>\n",
       "      <td>224.252010</td>\n",
       "      <td>32.217085</td>\n",
       "      <td>53.646101</td>\n",
       "      <td>24.523558</td>\n",
       "      <td>100x</td>\n",
       "      <td>2509.156955</td>\n",
       "      <td>150.660941</td>\n",
       "      <td>53.646101</td>\n",
       "      <td>36.902556</td>\n",
       "      <td>44.461843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>psilate</th>\n",
       "      <td>299</td>\n",
       "      <td>299</td>\n",
       "      <td>Allium cepa</td>\n",
       "      <td>450</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.982017</td>\n",
       "      <td>0.777873</td>\n",
       "      <td>0.712666</td>\n",
       "      <td>...</td>\n",
       "      <td>214.829440</td>\n",
       "      <td>30.863395</td>\n",
       "      <td>51.392012</td>\n",
       "      <td>23.493133</td>\n",
       "      <td>100x</td>\n",
       "      <td>2061.159511</td>\n",
       "      <td>140.910586</td>\n",
       "      <td>51.392012</td>\n",
       "      <td>32.295835</td>\n",
       "      <td>40.297615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>homobrochate</th>\n",
       "      <td>9130</td>\n",
       "      <td>9130</td>\n",
       "      <td>Tilia platyphyllus</td>\n",
       "      <td>450</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.853467</td>\n",
       "      <td>0.898428</td>\n",
       "      <td>0.590966</td>\n",
       "      <td>...</td>\n",
       "      <td>649.256414</td>\n",
       "      <td>93.275192</td>\n",
       "      <td>155.316669</td>\n",
       "      <td>71.000823</td>\n",
       "      <td>100x</td>\n",
       "      <td>11738.275570</td>\n",
       "      <td>435.169569</td>\n",
       "      <td>155.316669</td>\n",
       "      <td>68.202739</td>\n",
       "      <td>96.166917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>homobrochate</th>\n",
       "      <td>9131</td>\n",
       "      <td>9131</td>\n",
       "      <td>Tilia platyphyllus</td>\n",
       "      <td>450</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.987680</td>\n",
       "      <td>0.328196</td>\n",
       "      <td>0.778748</td>\n",
       "      <td>...</td>\n",
       "      <td>267.934662</td>\n",
       "      <td>38.492738</td>\n",
       "      <td>64.095969</td>\n",
       "      <td>29.300568</td>\n",
       "      <td>100x</td>\n",
       "      <td>4911.264797</td>\n",
       "      <td>207.277840</td>\n",
       "      <td>64.095969</td>\n",
       "      <td>60.545669</td>\n",
       "      <td>62.204273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>homobrochate</th>\n",
       "      <td>9132</td>\n",
       "      <td>9132</td>\n",
       "      <td>Tilia platyphyllus</td>\n",
       "      <td>450</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.549047</td>\n",
       "      <td>0.935239</td>\n",
       "      <td>0.369070</td>\n",
       "      <td>...</td>\n",
       "      <td>196.792345</td>\n",
       "      <td>28.272102</td>\n",
       "      <td>47.077135</td>\n",
       "      <td>21.520648</td>\n",
       "      <td>100x</td>\n",
       "      <td>481.411618</td>\n",
       "      <td>108.770405</td>\n",
       "      <td>47.077135</td>\n",
       "      <td>16.666131</td>\n",
       "      <td>19.475196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>satoreticulate</th>\n",
       "      <td>7001</td>\n",
       "      <td>7001</td>\n",
       "      <td>Polemonium caeruleum</td>\n",
       "      <td>972</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.547241</td>\n",
       "      <td>0.797878</td>\n",
       "      <td>0.356481</td>\n",
       "      <td>...</td>\n",
       "      <td>75.012828</td>\n",
       "      <td>10.776691</td>\n",
       "      <td>17.944748</td>\n",
       "      <td>8.203188</td>\n",
       "      <td>unknown</td>\n",
       "      <td>1309.000000</td>\n",
       "      <td>208.574639</td>\n",
       "      <td>62.640345</td>\n",
       "      <td>37.760796</td>\n",
       "      <td>40.824877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>satoreticulate</th>\n",
       "      <td>7002</td>\n",
       "      <td>7002</td>\n",
       "      <td>Polemonium caeruleum</td>\n",
       "      <td>972</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.665756</td>\n",
       "      <td>0.953371</td>\n",
       "      <td>0.351611</td>\n",
       "      <td>...</td>\n",
       "      <td>113.235103</td>\n",
       "      <td>16.267881</td>\n",
       "      <td>27.088372</td>\n",
       "      <td>12.383067</td>\n",
       "      <td>100x</td>\n",
       "      <td>418.823066</td>\n",
       "      <td>71.635337</td>\n",
       "      <td>27.088372</td>\n",
       "      <td>8.175285</td>\n",
       "      <td>12.359815</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11482 rows × 62 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                level_0  Unnamed: 0                 index  img_h  error  \\\n",
       "psilate               0           0     Abies cephalonica    972      0   \n",
       "psilate               1           1     Abies cephalonica    972      0   \n",
       "psilate               2           2    Abies nordmanniana    972      0   \n",
       "psilate             298         298           Allium cepa    450      0   \n",
       "psilate             299         299           Allium cepa    450      0   \n",
       "...                 ...         ...                   ...    ...    ...   \n",
       "homobrochate       9130        9130    Tilia platyphyllus    450      1   \n",
       "homobrochate       9131        9131    Tilia platyphyllus    450      0   \n",
       "homobrochate       9132        9132    Tilia platyphyllus    450      1   \n",
       "satoreticulate     7001        7001  Polemonium caeruleum    972      1   \n",
       "satoreticulate     7002        7002  Polemonium caeruleum    972      1   \n",
       "\n",
       "                Clump  Misread  Solidity  Eccentricity    Extent  ...  \\\n",
       "psilate             0        0  0.852617      0.782607  0.655936  ...   \n",
       "psilate             0        0  0.899319      0.744078  0.684501  ...   \n",
       "psilate             0        0  0.991946      0.585396  0.842792  ...   \n",
       "psilate             0        0  0.986618      0.725816  0.763222  ...   \n",
       "psilate             0        0  0.982017      0.777873  0.712666  ...   \n",
       "...               ...      ...       ...           ...       ...  ...   \n",
       "homobrochate        1        0  0.853467      0.898428  0.590966  ...   \n",
       "homobrochate        0        0  0.987680      0.328196  0.778748  ...   \n",
       "homobrochate        0        1  0.549047      0.935239  0.369070  ...   \n",
       "satoreticulate      0        1  0.547241      0.797878  0.356481  ...   \n",
       "satoreticulate      0        1  0.665756      0.953371  0.351611  ...   \n",
       "\n",
       "                        x40       x40SD        x100      x100SD  \\\n",
       "psilate          808.243257  116.115981  193.349882   88.387169   \n",
       "psilate          644.680175   92.617749  154.221931   70.500379   \n",
       "psilate         1133.526496  162.847682  271.164915  123.959213   \n",
       "psilate          224.252010   32.217085   53.646101   24.523558   \n",
       "psilate          214.829440   30.863395   51.392012   23.493133   \n",
       "...                     ...         ...         ...         ...   \n",
       "homobrochate     649.256414   93.275192  155.316669   71.000823   \n",
       "homobrochate     267.934662   38.492738   64.095969   29.300568   \n",
       "homobrochate     196.792345   28.272102   47.077135   21.520648   \n",
       "satoreticulate    75.012828   10.776691   17.944748    8.203188   \n",
       "satoreticulate   113.235103   16.267881   27.088372   12.383067   \n",
       "\n",
       "                magnification           Area   Perimeter  Major_axis_length  \\\n",
       "psilate                  100x   53133.521285  615.444027         193.349882   \n",
       "psilate                  100x   39120.136944  480.881255         154.221931   \n",
       "psilate                  100x  161948.165444  850.736703         271.164915   \n",
       "psilate                  100x    2509.156955  150.660941          53.646101   \n",
       "psilate                  100x    2061.159511  140.910586          51.392012   \n",
       "...                       ...            ...         ...                ...   \n",
       "homobrochate             100x   11738.275570  435.169569         155.316669   \n",
       "homobrochate             100x    4911.264797  207.277840          64.095969   \n",
       "homobrochate             100x     481.411618  108.770405          47.077135   \n",
       "satoreticulate        unknown    1309.000000  208.574639          62.640345   \n",
       "satoreticulate           100x     418.823066   71.635337          27.088372   \n",
       "\n",
       "                Minor_axis_length Equivalent_diameter  \n",
       "psilate                120.363501          139.213377  \n",
       "psilate                103.034618          119.452942  \n",
       "psilate                219.846235          243.043877  \n",
       "psilate                 36.902556           44.461843  \n",
       "psilate                 32.295835           40.297615  \n",
       "...                           ...                 ...  \n",
       "homobrochate            68.202739           96.166917  \n",
       "homobrochate            60.545669           62.204273  \n",
       "homobrochate            16.666131           19.475196  \n",
       "satoreticulate          37.760796           40.824877  \n",
       "satoreticulate           8.175285           12.359815  \n",
       "\n",
       "[11482 rows x 62 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#This is how you can use ML to classify values with labels which have multiple values (i.e. prefixes, features, class). It makes a copy of each row for each label it has and labels it for each one\n",
    "csv = pd.read_csv('FINAL_measure_adj_atd.csv')\n",
    "newdf = pd.DataFrame(csv)\n",
    "errordf = newdf[newdf.index.notnull()]\n",
    "ornamentation_df = errordf\n",
    "ornamentation_df = ornamentation_df[ornamentation_df['ornamentation_1'].notnull()].reset_index()\n",
    "types = pd.unique(ornamentation_df[['ornamentation_1','ornamentation_2','ornamentation_3','ornamentation_4','ornamentation_5','ornamentation_6','ornamentation_7']].values.ravel('K'))\n",
    "concat_ornamentation_df = pd.DataFrame()\n",
    "f = []\n",
    "for a in types:\n",
    "    i = []\n",
    "    for b,c in ornamentation_df.iterrows():\n",
    "        if c.isin([a]).any():\n",
    "            i.append(b)\n",
    "        else:\n",
    "            continue\n",
    "    d = ornamentation_df.iloc[i]\n",
    "    for e in range(d.index.size):\n",
    "        f.append(a)\n",
    "    concat_ornamentation_df = concat_ornamentation_df.append(pd.DataFrame(d))\n",
    "newdf = concat_ornamentation_df.set_index(pd.Series(f))\n",
    "newdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Save model\n",
    "filename = f'SVM_error_model_SAPS.sav'\n",
    "pickle.dump(pipe, open(filename, 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6866568011263984\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.1461687660850514"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Loading and testing model\n",
    "loaded_model = pickle.load(open('SVM_error_model.sav', 'rb'))\n",
    "csv = pd.read_csv('FINAL_measure_adj_atd.csv')\n",
    "newdf = pd.DataFrame(csv)\n",
    "errordf = newdf[newdf['img_h']==450]\n",
    "errordf = errordf[:500]\n",
    "errordf['PE']=errordf['P/E']\n",
    "errordf['Area_r'] = errordf['Area']/(sqrt(errordf['img_area'])/100)\n",
    "errordf['Perimeter_r'] = errordf['Perimeter']/(sqrt(errordf['img_area'])/100)            \n",
    "errordf['Major_axis_length_r'] = errordf['Major_axis_length']/(sqrt(errordf['img_area'])/100)\n",
    "errordf['Minor_axis_length_r'] = errordf['Minor_axis_length']/(sqrt(errordf['img_area'])/100)\n",
    "errordf['Equivalent_diameter_r'] = errordf['Equivalent_diameter']/(sqrt(errordf['img_area'])/100)\n",
    "newdf = errordf[['error','Area_r','Eccentricity','Equivalent_diameter_r','Extent','Major_axis_length_r','Minor_axis_length_r','Perimeter_r','ASM','Contrast','Correlation','Dissimilarity','Energy','Entropy','Homogeneity','Solidity']]#.set_index('comb')\n",
    "loaded_model = pickle.load(open('SVM_error_model.sav', 'rb'))\n",
    "\n",
    "errordf=newdf.set_index('error')\n",
    "errordf=errordf[errordf.index.notnull()]\n",
    "kf = KFold(n_splits=10, random_state=None, shuffle=True)\n",
    "pred=loaded_model.predict(scaler.transform(errordf.values))\n",
    "\n",
    "print(model_selection.cross_val_score(loaded_model, scaler.transform(errordf.values), errordf.index, scoring='adjusted_rand_score', cv=kf, n_jobs=-1).mean())#sklearn.metrics.adjusted_rand_score(labels,true_labels))\n",
    "\n",
    "sklearn.metrics.adjusted_rand_score(pred,errordf.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-               276\n",
      "oblate          165\n",
      "isodiametric     48\n",
      "prolate          43\n",
      "Name: P/E, dtype: int64\n",
      "Angiospermae    489\n",
      "Gymnospermae     38\n",
      "Name: phyla, dtype: int64\n",
      "isopolar       354\n",
      "heteropolar    157\n",
      "Name: polarity, dtype: int64\n",
      "Asteraceae      46\n",
      "Pinaceae        38\n",
      "Rosaceae        36\n",
      "Asparagaceae    34\n",
      "Name: order, dtype: int64\n",
      "Asparagales    55\n",
      "Asterales      47\n",
      "Lamiales       47\n",
      "Rosales        46\n",
      "Pinales        43\n",
      "Brassicales    33\n",
      "Name: family, dtype: int64\n",
      "circular      363\n",
      "elliptic       90\n",
      "triangular     38\n",
      "Name: 2D_shape, dtype: int64\n",
      "spheroidal    304\n",
      "-             205\n",
      "Name: 3D_shape, dtype: int64\n",
      "colporate    237\n",
      "colpate       86\n",
      "sulcate       81\n",
      "porate        44\n",
      "saccate       33\n",
      "Name: class, dtype: int64\n",
      "colporus    245\n",
      "colpus      105\n",
      "sulcus       67\n",
      "porus        43\n",
      "leptoma      34\n",
      "Name: aperture_type, dtype: int64\n",
      "-                               330\n",
      "aperture membrane ornamented     76\n",
      "Name: aperture_features, dtype: int64\n",
      "monad    522\n",
      "Name: dispersal_unit, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#Get counts of each label for each category\n",
    "for a in ppp:\n",
    "    newdf=errordf[errordf['error']==0]\n",
    "    newdf = newdf.groupby(a).filter(lambda x: len(x) >= 30)\n",
    "    print(newdf[a].value_counts())\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Full dataset testing K-means"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Area_r</th>\n",
       "      <th>Eccentricity</th>\n",
       "      <th>Equivalent_diameter_r</th>\n",
       "      <th>Extent</th>\n",
       "      <th>Major_axis_length_r</th>\n",
       "      <th>Minor_axis_length_r</th>\n",
       "      <th>Perimeter_r</th>\n",
       "      <th>ASM</th>\n",
       "      <th>Contrast</th>\n",
       "      <th>Correlation</th>\n",
       "      <th>Dissimilarity</th>\n",
       "      <th>Energy</th>\n",
       "      <th>Entropy</th>\n",
       "      <th>Homogeneity</th>\n",
       "      <th>Solidity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30348.888889</td>\n",
       "      <td>0.795549</td>\n",
       "      <td>293.035476</td>\n",
       "      <td>0.750673</td>\n",
       "      <td>377.320252</td>\n",
       "      <td>228.614228</td>\n",
       "      <td>1016.230238</td>\n",
       "      <td>0.000504</td>\n",
       "      <td>128.348003</td>\n",
       "      <td>0.962431</td>\n",
       "      <td>7.964537</td>\n",
       "      <td>0.022392</td>\n",
       "      <td>6.952962</td>\n",
       "      <td>0.179542</td>\n",
       "      <td>0.992010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>30682.222222</td>\n",
       "      <td>0.691396</td>\n",
       "      <td>294.640341</td>\n",
       "      <td>0.747132</td>\n",
       "      <td>348.192794</td>\n",
       "      <td>251.560929</td>\n",
       "      <td>1008.419752</td>\n",
       "      <td>0.000539</td>\n",
       "      <td>144.611773</td>\n",
       "      <td>0.942874</td>\n",
       "      <td>8.356267</td>\n",
       "      <td>0.023192</td>\n",
       "      <td>6.785752</td>\n",
       "      <td>0.171641</td>\n",
       "      <td>0.985933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1817.777778</td>\n",
       "      <td>0.901431</td>\n",
       "      <td>71.716518</td>\n",
       "      <td>0.511890</td>\n",
       "      <td>114.007785</td>\n",
       "      <td>49.356468</td>\n",
       "      <td>309.771044</td>\n",
       "      <td>0.004415</td>\n",
       "      <td>5.873426</td>\n",
       "      <td>0.977804</td>\n",
       "      <td>1.811952</td>\n",
       "      <td>0.066281</td>\n",
       "      <td>5.469974</td>\n",
       "      <td>0.418691</td>\n",
       "      <td>0.847668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1446.666667</td>\n",
       "      <td>0.984969</td>\n",
       "      <td>63.978351</td>\n",
       "      <td>0.539801</td>\n",
       "      <td>168.451786</td>\n",
       "      <td>29.096438</td>\n",
       "      <td>341.328844</td>\n",
       "      <td>0.001072</td>\n",
       "      <td>265.956379</td>\n",
       "      <td>0.862711</td>\n",
       "      <td>12.416357</td>\n",
       "      <td>0.032742</td>\n",
       "      <td>6.805995</td>\n",
       "      <td>0.085008</td>\n",
       "      <td>0.741458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>931.111111</td>\n",
       "      <td>0.933540</td>\n",
       "      <td>51.327434</td>\n",
       "      <td>0.566216</td>\n",
       "      <td>96.206402</td>\n",
       "      <td>34.487474</td>\n",
       "      <td>235.740580</td>\n",
       "      <td>0.001651</td>\n",
       "      <td>264.528866</td>\n",
       "      <td>0.824172</td>\n",
       "      <td>12.393500</td>\n",
       "      <td>0.040627</td>\n",
       "      <td>6.600260</td>\n",
       "      <td>0.078870</td>\n",
       "      <td>0.750896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1623</th>\n",
       "      <td>6244.444444</td>\n",
       "      <td>0.472810</td>\n",
       "      <td>132.921644</td>\n",
       "      <td>0.793785</td>\n",
       "      <td>141.676142</td>\n",
       "      <td>124.839955</td>\n",
       "      <td>437.989899</td>\n",
       "      <td>0.000873</td>\n",
       "      <td>127.433283</td>\n",
       "      <td>0.951531</td>\n",
       "      <td>7.017705</td>\n",
       "      <td>0.029522</td>\n",
       "      <td>6.624851</td>\n",
       "      <td>0.183649</td>\n",
       "      <td>0.986311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1624</th>\n",
       "      <td>61891.111111</td>\n",
       "      <td>0.939595</td>\n",
       "      <td>418.468664</td>\n",
       "      <td>0.512570</td>\n",
       "      <td>735.267207</td>\n",
       "      <td>251.673209</td>\n",
       "      <td>1839.285072</td>\n",
       "      <td>0.002135</td>\n",
       "      <td>60.812738</td>\n",
       "      <td>0.990641</td>\n",
       "      <td>4.155558</td>\n",
       "      <td>0.046098</td>\n",
       "      <td>6.479341</td>\n",
       "      <td>0.347500</td>\n",
       "      <td>0.902349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1625</th>\n",
       "      <td>58440.000000</td>\n",
       "      <td>0.633933</td>\n",
       "      <td>406.634204</td>\n",
       "      <td>0.592604</td>\n",
       "      <td>473.228633</td>\n",
       "      <td>365.989276</td>\n",
       "      <td>2047.752043</td>\n",
       "      <td>0.000892</td>\n",
       "      <td>36.170864</td>\n",
       "      <td>0.987476</td>\n",
       "      <td>3.897181</td>\n",
       "      <td>0.029800</td>\n",
       "      <td>6.944964</td>\n",
       "      <td>0.287905</td>\n",
       "      <td>0.858346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1626</th>\n",
       "      <td>18948.888889</td>\n",
       "      <td>0.227458</td>\n",
       "      <td>231.547810</td>\n",
       "      <td>0.773494</td>\n",
       "      <td>234.868945</td>\n",
       "      <td>228.712555</td>\n",
       "      <td>773.441718</td>\n",
       "      <td>0.007192</td>\n",
       "      <td>23.317522</td>\n",
       "      <td>0.989622</td>\n",
       "      <td>2.162627</td>\n",
       "      <td>0.084620</td>\n",
       "      <td>6.105946</td>\n",
       "      <td>0.539631</td>\n",
       "      <td>0.985097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1627</th>\n",
       "      <td>162044.444444</td>\n",
       "      <td>0.314884</td>\n",
       "      <td>677.120365</td>\n",
       "      <td>0.725652</td>\n",
       "      <td>701.292310</td>\n",
       "      <td>665.617692</td>\n",
       "      <td>2316.828282</td>\n",
       "      <td>0.040421</td>\n",
       "      <td>7.647922</td>\n",
       "      <td>0.998416</td>\n",
       "      <td>0.910109</td>\n",
       "      <td>0.200940</td>\n",
       "      <td>4.862573</td>\n",
       "      <td>0.798597</td>\n",
       "      <td>0.989242</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1628 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Area_r  Eccentricity  Equivalent_diameter_r    Extent  \\\n",
       "0      30348.888889      0.795549             293.035476  0.750673   \n",
       "1      30682.222222      0.691396             294.640341  0.747132   \n",
       "2       1817.777778      0.901431              71.716518  0.511890   \n",
       "3       1446.666667      0.984969              63.978351  0.539801   \n",
       "4        931.111111      0.933540              51.327434  0.566216   \n",
       "...             ...           ...                    ...       ...   \n",
       "1623    6244.444444      0.472810             132.921644  0.793785   \n",
       "1624   61891.111111      0.939595             418.468664  0.512570   \n",
       "1625   58440.000000      0.633933             406.634204  0.592604   \n",
       "1626   18948.888889      0.227458             231.547810  0.773494   \n",
       "1627  162044.444444      0.314884             677.120365  0.725652   \n",
       "\n",
       "      Major_axis_length_r  Minor_axis_length_r  Perimeter_r       ASM  \\\n",
       "0              377.320252           228.614228  1016.230238  0.000504   \n",
       "1              348.192794           251.560929  1008.419752  0.000539   \n",
       "2              114.007785            49.356468   309.771044  0.004415   \n",
       "3              168.451786            29.096438   341.328844  0.001072   \n",
       "4               96.206402            34.487474   235.740580  0.001651   \n",
       "...                   ...                  ...          ...       ...   \n",
       "1623           141.676142           124.839955   437.989899  0.000873   \n",
       "1624           735.267207           251.673209  1839.285072  0.002135   \n",
       "1625           473.228633           365.989276  2047.752043  0.000892   \n",
       "1626           234.868945           228.712555   773.441718  0.007192   \n",
       "1627           701.292310           665.617692  2316.828282  0.040421   \n",
       "\n",
       "        Contrast  Correlation  Dissimilarity    Energy   Entropy  Homogeneity  \\\n",
       "0     128.348003     0.962431       7.964537  0.022392  6.952962     0.179542   \n",
       "1     144.611773     0.942874       8.356267  0.023192  6.785752     0.171641   \n",
       "2       5.873426     0.977804       1.811952  0.066281  5.469974     0.418691   \n",
       "3     265.956379     0.862711      12.416357  0.032742  6.805995     0.085008   \n",
       "4     264.528866     0.824172      12.393500  0.040627  6.600260     0.078870   \n",
       "...          ...          ...            ...       ...       ...          ...   \n",
       "1623  127.433283     0.951531       7.017705  0.029522  6.624851     0.183649   \n",
       "1624   60.812738     0.990641       4.155558  0.046098  6.479341     0.347500   \n",
       "1625   36.170864     0.987476       3.897181  0.029800  6.944964     0.287905   \n",
       "1626   23.317522     0.989622       2.162627  0.084620  6.105946     0.539631   \n",
       "1627    7.647922     0.998416       0.910109  0.200940  4.862573     0.798597   \n",
       "\n",
       "      Solidity  \n",
       "0     0.992010  \n",
       "1     0.985933  \n",
       "2     0.847668  \n",
       "3     0.741458  \n",
       "4     0.750896  \n",
       "...        ...  \n",
       "1623  0.986311  \n",
       "1624  0.902349  \n",
       "1625  0.858346  \n",
       "1626  0.985097  \n",
       "1627  0.989242  \n",
       "\n",
       "[1628 rows x 15 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Load measures for k means\n",
    "csv = pd.read_csv('kmeanserror.csv')\n",
    "newdf = pd.DataFrame(csv)\n",
    "#newdf = newdf[newdf['error'] == np.int64(0)]\n",
    "#newdf = newdf[newdf.index.notnull()]\n",
    "newdf['Area_r'] = newdf['Area']/0.450\n",
    "newdf['Perimeter_r'] = newdf['Perimeter']/0.450\n",
    "newdf['Major_axis_length_r'] = newdf['Major_axis_length']/0.450\n",
    "newdf['Minor_axis_length_r'] = newdf['Minor_axis_length']/0.450\n",
    "newdf['Equivalent_diameter_r'] = newdf['Equivalent_diameter']/0.450\n",
    "newdf = newdf[['Area_r','Eccentricity','Equivalent_diameter_r','Extent','Major_axis_length_r','Minor_axis_length_r','Perimeter_r','ASM','Contrast','Correlation','Dissimilarity','Energy','Entropy','Homogeneity','Solidity']]\n",
    "newdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "\"None of ['class'] are in the columns\"",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-15-d2c83dbef9c0>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mfullsetDf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'FINAL_measure_adj_atd.csv'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mfullsetDf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfullsetDf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mfullsetDf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'error'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m==\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_index\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'class'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[0mfullsetDf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfullsetDf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mfullsetDf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'img_h'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m==\u001b[0m\u001b[1;36m450\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mfullsetDf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfullsetDf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mfullsetDf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnotnull\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mfullsetDf\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36mset_index\u001b[1;34m(self, keys, drop, append, inplace, verify_integrity)\u001b[0m\n\u001b[0;32m   4549\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4550\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mmissing\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 4551\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mf\"None of {missing} are in the columns\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   4552\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4553\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0minplace\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: \"None of ['class'] are in the columns\""
     ]
    }
   ],
   "source": [
    "fullsetDf = pd.DataFrame(pd.read_csv('FINAL_measure_adj_atd.csv'))\n",
    "fullsetDf = fullsetDf[fullsetDf['error']==0].set_index('class')\n",
    "fullsetDf = fullsetDf[fullsetDf['img_h']==450]\n",
    "fullsetDf = fullsetDf[fullsetDf.index.notnull()]\n",
    "fullsetDf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Area</th>\n",
       "      <th>Eccentricity</th>\n",
       "      <th>Equivalent_diameter</th>\n",
       "      <th>Extent</th>\n",
       "      <th>Major_axis_length</th>\n",
       "      <th>Minor_axis_length</th>\n",
       "      <th>Perimeter</th>\n",
       "      <th>ASM</th>\n",
       "      <th>Contrast</th>\n",
       "      <th>Correlation</th>\n",
       "      <th>Dissimilarity</th>\n",
       "      <th>Energy</th>\n",
       "      <th>Entropy</th>\n",
       "      <th>Homogeneity</th>\n",
       "      <th>Solidity</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>error</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>53133.521285</td>\n",
       "      <td>0.782607</td>\n",
       "      <td>139.213377</td>\n",
       "      <td>0.655936</td>\n",
       "      <td>193.349882</td>\n",
       "      <td>120.363501</td>\n",
       "      <td>615.444027</td>\n",
       "      <td>0.115128</td>\n",
       "      <td>62.006949</td>\n",
       "      <td>0.963486</td>\n",
       "      <td>1.748401</td>\n",
       "      <td>0.339303</td>\n",
       "      <td>4.849428</td>\n",
       "      <td>0.654054</td>\n",
       "      <td>0.852617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39120.136944</td>\n",
       "      <td>0.744078</td>\n",
       "      <td>119.452942</td>\n",
       "      <td>0.684501</td>\n",
       "      <td>154.221931</td>\n",
       "      <td>103.034618</td>\n",
       "      <td>480.881255</td>\n",
       "      <td>0.097445</td>\n",
       "      <td>51.136255</td>\n",
       "      <td>0.980137</td>\n",
       "      <td>1.856435</td>\n",
       "      <td>0.312159</td>\n",
       "      <td>5.172168</td>\n",
       "      <td>0.614147</td>\n",
       "      <td>0.899319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>161948.165444</td>\n",
       "      <td>0.585396</td>\n",
       "      <td>243.043877</td>\n",
       "      <td>0.842792</td>\n",
       "      <td>271.164915</td>\n",
       "      <td>219.846235</td>\n",
       "      <td>850.736703</td>\n",
       "      <td>0.025473</td>\n",
       "      <td>35.967321</td>\n",
       "      <td>0.983691</td>\n",
       "      <td>1.892064</td>\n",
       "      <td>0.159598</td>\n",
       "      <td>5.953881</td>\n",
       "      <td>0.545196</td>\n",
       "      <td>0.991946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5115.000000</td>\n",
       "      <td>0.860231</td>\n",
       "      <td>80.700807</td>\n",
       "      <td>0.626838</td>\n",
       "      <td>117.359585</td>\n",
       "      <td>59.842103</td>\n",
       "      <td>352.090404</td>\n",
       "      <td>0.123292</td>\n",
       "      <td>590.231175</td>\n",
       "      <td>0.917417</td>\n",
       "      <td>6.415186</td>\n",
       "      <td>0.351071</td>\n",
       "      <td>5.272603</td>\n",
       "      <td>0.505521</td>\n",
       "      <td>0.863145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>155620.000000</td>\n",
       "      <td>0.731942</td>\n",
       "      <td>445.130922</td>\n",
       "      <td>0.646937</td>\n",
       "      <td>562.851959</td>\n",
       "      <td>383.508423</td>\n",
       "      <td>1725.699710</td>\n",
       "      <td>0.120440</td>\n",
       "      <td>48.764527</td>\n",
       "      <td>0.989556</td>\n",
       "      <td>2.580908</td>\n",
       "      <td>0.347042</td>\n",
       "      <td>5.349896</td>\n",
       "      <td>0.566603</td>\n",
       "      <td>0.904662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>76421.000000</td>\n",
       "      <td>0.643680</td>\n",
       "      <td>311.933068</td>\n",
       "      <td>0.663861</td>\n",
       "      <td>367.389517</td>\n",
       "      <td>281.161273</td>\n",
       "      <td>1303.851947</td>\n",
       "      <td>0.108983</td>\n",
       "      <td>212.867950</td>\n",
       "      <td>0.955168</td>\n",
       "      <td>2.973264</td>\n",
       "      <td>0.330122</td>\n",
       "      <td>5.222733</td>\n",
       "      <td>0.613235</td>\n",
       "      <td>0.904005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3779.000000</td>\n",
       "      <td>0.929516</td>\n",
       "      <td>69.365497</td>\n",
       "      <td>0.457728</td>\n",
       "      <td>129.472859</td>\n",
       "      <td>47.747114</td>\n",
       "      <td>377.948268</td>\n",
       "      <td>0.272806</td>\n",
       "      <td>876.628321</td>\n",
       "      <td>0.930787</td>\n",
       "      <td>7.342396</td>\n",
       "      <td>0.522279</td>\n",
       "      <td>3.910655</td>\n",
       "      <td>0.612201</td>\n",
       "      <td>0.714367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1517.000000</td>\n",
       "      <td>0.779444</td>\n",
       "      <td>43.948884</td>\n",
       "      <td>0.633668</td>\n",
       "      <td>57.978301</td>\n",
       "      <td>36.321790</td>\n",
       "      <td>165.781746</td>\n",
       "      <td>0.111159</td>\n",
       "      <td>1628.025744</td>\n",
       "      <td>0.898527</td>\n",
       "      <td>9.486856</td>\n",
       "      <td>0.333257</td>\n",
       "      <td>4.391925</td>\n",
       "      <td>0.599836</td>\n",
       "      <td>0.913855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>60357.000000</td>\n",
       "      <td>0.341482</td>\n",
       "      <td>277.216376</td>\n",
       "      <td>0.756486</td>\n",
       "      <td>286.656299</td>\n",
       "      <td>269.424926</td>\n",
       "      <td>989.761543</td>\n",
       "      <td>0.056511</td>\n",
       "      <td>250.865885</td>\n",
       "      <td>0.927109</td>\n",
       "      <td>3.179826</td>\n",
       "      <td>0.237711</td>\n",
       "      <td>5.678020</td>\n",
       "      <td>0.555621</td>\n",
       "      <td>0.976398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>70371.000000</td>\n",
       "      <td>0.664207</td>\n",
       "      <td>299.331154</td>\n",
       "      <td>0.761476</td>\n",
       "      <td>346.262154</td>\n",
       "      <td>258.847769</td>\n",
       "      <td>1005.702669</td>\n",
       "      <td>0.054746</td>\n",
       "      <td>278.305838</td>\n",
       "      <td>0.964133</td>\n",
       "      <td>3.608922</td>\n",
       "      <td>0.233972</td>\n",
       "      <td>5.741068</td>\n",
       "      <td>0.484351</td>\n",
       "      <td>0.995234</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9847 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Area  Eccentricity  Equivalent_diameter    Extent  \\\n",
       "error                                                               \n",
       "0       53133.521285      0.782607           139.213377  0.655936   \n",
       "0       39120.136944      0.744078           119.452942  0.684501   \n",
       "0      161948.165444      0.585396           243.043877  0.842792   \n",
       "1        5115.000000      0.860231            80.700807  0.626838   \n",
       "0      155620.000000      0.731942           445.130922  0.646937   \n",
       "...              ...           ...                  ...       ...   \n",
       "1       76421.000000      0.643680           311.933068  0.663861   \n",
       "1        3779.000000      0.929516            69.365497  0.457728   \n",
       "1        1517.000000      0.779444            43.948884  0.633668   \n",
       "0       60357.000000      0.341482           277.216376  0.756486   \n",
       "0       70371.000000      0.664207           299.331154  0.761476   \n",
       "\n",
       "       Major_axis_length  Minor_axis_length    Perimeter       ASM  \\\n",
       "error                                                                \n",
       "0             193.349882         120.363501   615.444027  0.115128   \n",
       "0             154.221931         103.034618   480.881255  0.097445   \n",
       "0             271.164915         219.846235   850.736703  0.025473   \n",
       "1             117.359585          59.842103   352.090404  0.123292   \n",
       "0             562.851959         383.508423  1725.699710  0.120440   \n",
       "...                  ...                ...          ...       ...   \n",
       "1             367.389517         281.161273  1303.851947  0.108983   \n",
       "1             129.472859          47.747114   377.948268  0.272806   \n",
       "1              57.978301          36.321790   165.781746  0.111159   \n",
       "0             286.656299         269.424926   989.761543  0.056511   \n",
       "0             346.262154         258.847769  1005.702669  0.054746   \n",
       "\n",
       "          Contrast  Correlation  Dissimilarity    Energy   Entropy  \\\n",
       "error                                                                \n",
       "0        62.006949     0.963486       1.748401  0.339303  4.849428   \n",
       "0        51.136255     0.980137       1.856435  0.312159  5.172168   \n",
       "0        35.967321     0.983691       1.892064  0.159598  5.953881   \n",
       "1       590.231175     0.917417       6.415186  0.351071  5.272603   \n",
       "0        48.764527     0.989556       2.580908  0.347042  5.349896   \n",
       "...            ...          ...            ...       ...       ...   \n",
       "1       212.867950     0.955168       2.973264  0.330122  5.222733   \n",
       "1       876.628321     0.930787       7.342396  0.522279  3.910655   \n",
       "1      1628.025744     0.898527       9.486856  0.333257  4.391925   \n",
       "0       250.865885     0.927109       3.179826  0.237711  5.678020   \n",
       "0       278.305838     0.964133       3.608922  0.233972  5.741068   \n",
       "\n",
       "       Homogeneity  Solidity  \n",
       "error                         \n",
       "0         0.654054  0.852617  \n",
       "0         0.614147  0.899319  \n",
       "0         0.545196  0.991946  \n",
       "1         0.505521  0.863145  \n",
       "0         0.566603  0.904662  \n",
       "...            ...       ...  \n",
       "1         0.613235  0.904005  \n",
       "1         0.612201  0.714367  \n",
       "1         0.599836  0.913855  \n",
       "0         0.555621  0.976398  \n",
       "0         0.484351  0.995234  \n",
       "\n",
       "[9847 rows x 15 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.4186897280538306\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1    5177\n",
       "0    4670\n",
       "Name: error, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = 'error'\n",
    "GPP = pd.DataFrame(pd.read_csv('FINAL_measure_adj_atd.csv'))\n",
    "#GPP = GPP[GPP['error'] == 0]\n",
    "#GPP = GPP[GPP['img_h'] != 450]\n",
    "\n",
    "GPP['Area_r'] = GPP['Area']/(sqrt(GPP['img_area'])/100)\n",
    "GPP['Perimeter_r'] = GPP['Perimeter']/(sqrt(GPP['img_area'])/100)\n",
    "GPP['Major_axis_length_r'] = GPP['Major_axis_length']/(sqrt(GPP['img_area'])/100)\n",
    "GPP['Minor_axis_length_r'] = GPP['Minor_axis_length']/(sqrt(GPP['img_area'])/100)\n",
    "GPP['Equivalent_diameter_r'] = GPP['Equivalent_diameter']/(sqrt(GPP['img_area'])/100)\n",
    "GPP = GPP\n",
    "GPP = GPP[[a,'Area','Eccentricity','Equivalent_diameter','Extent','Major_axis_length','Minor_axis_length','Perimeter','ASM','Contrast','Correlation','Dissimilarity','Energy','Entropy','Homogeneity','Solidity']]\n",
    "GPP = GPP.set_index(a)\n",
    "GPP = GPP[GPP.index.notnull()]\n",
    "\n",
    "GPP = GPP.groupby(a).filter(lambda x: len(x) >= 30)\n",
    "\n",
    "train_img, test_img, train_lbl, test_lbl = train_test_split(GPP.values, GPP.index, test_size=1/7.0, random_state=None)\n",
    "display(GPP)\n",
    "\n",
    "preprocessor = Pipeline([(\"scaler\", MinMaxScaler()),(\"pca\", PCA(n_components=6, random_state=None))])\n",
    "clusterer = Pipeline([(\"kmeans\",KMeans(n_clusters=2,init=\"k-means++\",n_init=50,max_iter=500,random_state=None))])\n",
    "pipe = Pipeline([(\"preprocessor\", preprocessor),(\"clusterer\", clusterer)])    \n",
    "\n",
    "pipe.fit(train_img,train_lbl)\n",
    "\n",
    "pred=pipe.predict(test_img)\n",
    "print(sklearn.metrics.adjusted_rand_score(pred,test_lbl))\n",
    "GPP.index.value_counts()\n",
    "#GPP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "pcadf = pd.DataFrame(pipe[\"preprocessor\"].transform(train_img),columns=[\"Component 1\", \"Component 2\", \"component_3\", \"component_4\", \"component_5\", \"component_6\"])#, \"component_7\", \"component_8\",\"component_9\", \"component_10\"])#, \"component_11\",\"component_12\",\"component_13\", \"component_14\", \"component_15\"])\n",
    "pcadf[\"predicted_cluster\"] = pipe[\"clusterer\"][\"kmeans\"].labels_\n",
    "label_encoder = LabelEncoder()\n",
    "\n",
    "labels = label_encoder.fit_transform(train_lbl)\n",
    "pcadf[\"true_label\"] = label_encoder.inverse_transform(labels)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\pinto\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.style.use(\"fivethirtyeight\")\n",
    "plt.figure(figsize=(8, 8))\n",
    "scat = sns.scatterplot(\"Component 1\",\"Component 2\", s=50, data=pcadf,hue=\"true_label\",palette=\"Set2\")# style=\"predicted_cluster\",\n",
    "scat.set_title(\"K-means of error detection\")\n",
    "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3773772795885345"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sklearn.metrics.adjusted_rand_score(pipe.predict(test_img),test_lbl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\pinto\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:881: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=7.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Measure for the realistic number of clusters you can use\n",
    "sse = []\n",
    "for k in range(1, 101):\n",
    "    kmeans = KMeans(n_clusters=k)#, **kmeans_kwargs)\n",
    "    kmeans.fit(scaled_features)\n",
    "    sse.append(kmeans.inertia_)\n",
    "plt.style.use(\"fivethirtyeight\")\n",
    "plt.plot(range(1, 101), sse)\n",
    "plt.xticks(range(1, 101,10))\n",
    "plt.xlabel(\"Number of Clusters\")\n",
    "plt.ylabel(\"SSE\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAb4AAAEfCAYAAAA+zaOiAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAABnuElEQVR4nO3dd3RU1fbA8e+dSe+9AAEEQidUAVFAULAgCEgR0R9iqGLhiUJQLOjzCWJ9iAgiPhBQQVEQFFQMEEIApShKC9KF9N4z5fdHyCTTkkmYFMj+rOVazr137uxMwuw55+xzjpKRkaFHCCGEaCBUdR2AEEIIUZsk8QkhhGhQJPEJIYRoUCTxCSGEaFAk8QkhhGhQJPEJIYRoUCTxCSGEaFAk8QkhhGhQJPHZQXx8fF2HYCCxWFef4pFYLJNYrKtP8dSnWKpDEp8QQogGRRKfEEKIBkUSnxBCiAZFEp8QQogGRRKfEEKIBkUSnx1dztXy48UCcop1dR2KEEIIKxzqOoAbxbH0Yu75PpnMIj0hrir2jwzG20m+VwghRH0jn8x28tKvmWQWlezpm5CvY9O5/DqOSAghhCWS+OzgdK7Cz/8UGh27kKOto2iEEEJURBKfHaz5x9HsWHK+JD4hhKiPJPFdo8u5WrYnq82OJ+VLgYsQQtRHdZ74VqxYQUREBMHBwfTv35+9e/davTYmJoZx48bRpk0bQkND6dOnD5999pnZdRs2bOC2224jNDSU1q1bM2XKFBITE2sk/o+O5aDRK2bHUwqkxSeEEPVRnSa+jRs3EhUVxaxZs9i9ezc9e/Zk9OjRXLx40eL1Bw4coEOHDqxatYq4uDgiIyOZOXMmGzZsMFyzb98+pk6dyrhx44iLi2Pt2rWcOHGCyZMn2z3+rCId/zuZa/GctPiEEKJ+qtPEt2TJEh566CEmTJhAmzZtWLRoEcHBwaxcudLi9bNmzWLevHn07t2b5s2bExkZydChQ9m8ebPhml9//ZVGjRoxY8YMmjdvzs0338yUKVM4ePCg3eNfdTKXrGK9xXMpBZL4hBCiPqqzxFdUVMSRI0cYOHCg0fGBAweyf/9+m++TnZ2Nj4+P4XGvXr1ITEzkhx9+QK/Xk5qaysaNGxk0aJC9Qjc4k62xei5XoydXJrILIUS9U2eJLzU1Fa1WS2BgoNHxwMBAkpKSbLrHtm3b2LVrF48++qjhWM+ePVmxYgVTpkwhMDCQli1botfrWbp0qT3DB+DdPr7sHxHEsGAN3k4KPk7GY33J0uoTQoh6R8nIyLDcV1fDrly5Qrt27fj+++/p06eP4fiCBQv4+uuv+fXXXyt8/r59+xg9ejSvvPIKkZGRhuMnTpxgxIgRTJ8+nYEDB5KYmMiLL75Ip06dWLZsmdX7XevGivlamPKHCydyy75LrIwooJOXJD8hhKht4eHhVs/V2ZJl/v7+qNVqs9ZdSkqKWSvQVFxcHGPGjGHu3LlGSQ/gnXfeoVu3bjz11FMAdOzYETc3N+655x5efPFFmjRpYvGeFb1JlYmPjyeibThhF1I4kVs2kd0lsBHhTV2rfd/qxnItP4s91adYoH7FI7FYJrFYV5/iqU+xVEeddXU6OTnRpUsXoqOjjY5HR0fTq1cvq8+LjY1l9OjRzJ49m8cff9zsfH5+Pmq18by60sd6fc02bgNcjV83WSo7hRCi3qnTqs4ZM2awbt06Vq9ezcmTJ5kzZw4JCQlMnDgRgPnz5zNs2DDD9TExMYwePZqJEycyZswYEhMTSUxMJCUlxXDN3Xffzffff88nn3zCuXPn2LdvH3PmzKFz586EhYXV6M8T5GL8dsoYnxBC1D91ujvDyJEjSUtLY9GiRSQmJtKuXTvWr19P06ZNAUhISODs2bOG69etW0deXh6LFy9m8eLFhuNhYWEcPXoUgPHjx5OTk8PHH3/MvHnz8PLyom/fvsyfP7/Gf54AV+PElyTLlgkhRL1T59sSTZo0iUmTJlk8Z1qJuXTpUpuqM6dOncrUqVPtEl9VBElXpxBC1Ht1vmTZjcS8q1NafEIIUd9I4rMjKW4RQoj6z+bEFxsba1REYio1NZXY2Fi7BHW9kuIWIYSo/2xOfEOHDjWbelDerl27GDp0qF2Cul75u6gov3ZLWqGOYl2drA8ghBDCCpsTX2Vz4IqKilCpGnbPqYNKwc/Z+D1IlVafEELUKxVWdWZlZZGZmWl4nJaWZnHLoIyMDL7++mtCQ0PtH+F1JtBVRWphWbJLytcS4ma+Ua0QQoi6UWHi+/DDD3nzzTcBUBSFuXPnMnfuXIvX6vV6XnzxRftHeJ0JdFFxotxj2Z5ICCHqlwoT3+23346Liwt6vZ5XX32VkSNH0qlTJ6NrFEXBzc2Nrl270qNHjxoN9noQaFLZKRvSCiFE/VJh4uvduze9e/cGoLCwkGHDhtG+fftaCex6FWha2SmrtwghRL1i88otUVFRNRnHDcNs9Rbp6hRCiHqlSkuWlRaxnDt3jvT0dLNKT0VR+OCDD+wa4PUmUNbrFEKIes3mxLdr1y4eeeQRsrOz8fT0xMfHx+waRVHMn9jAmHZ1SnGLEELULzYnvueffx5fX1+2bt1qVuAiykhxixBC1G82zzg/ffo006dPl6RXCfMWn3R1CiFEfWJz4mvWrBkFBQU1GcsNwXSML7lAV+M7vwshhLCdzYnvmWee4dNPPyU9Pb0m47nuuTmo8HAoG+ss1kFmkSQ+IYSoL2we40tMTMTPz49u3boxYsQImjRpglptPJ6lKApPPfWU3YO83gS4qsjJLuviTMrX4uPcsNcxFUKI+sLmxPfKK68Y/v/TTz+1eI0kvhJBLmrOlUt8yQU6WtdhPEIIIcrYnPh+//33mozjhmI2zieVnUIIUW/YnPiaNm1ak3HcUEwrO2USuxBC1B9VWrkF4OLFi8TGxpKcnGwY69NoNKSnp+Pr64uDQ5VvecMxncsny5YJIUT9UaUs9fzzz7N8+XK0Wi2KohAREUGTJk3Iy8ujW7duREVFMWPGjJqK9bohC1ULIUT9ZXOp4X//+1+WLl3KjBkz+Pbbb43mpnl5eTFkyBC2bNlSI0Feb2ShaiGEqL9sTnyrVq1izJgxzJ8/3+LqLR06dODvv/+2a3DXqwApbhFCiHrL5sR36dIl+vTpY/W8p6cnmZmZdgnqehdk2tUpy5YJIUS9YXPi8/PzIyEhwer5v/76i9DQ0CoHsGLFCiIiIggODqZ///7s3bvX6rUxMTGMGzeONm3aEBoaSp8+ffjss8/MrisqKuL1118nIiKCoKAgOnbsyEcffVTl2KrLtKvzSp6WYp2s3iKEEPWBzYlv8ODBrFq1itTUVLNzv//+O2vWrGHIkCFVevGNGzcSFRXFrFmz2L17Nz179mT06NFcvHjR4vUHDhygQ4cOrFq1iri4OCIjI5k5cyYbNmwwui4yMpIdO3bw/vvv8+uvv/K///2PDh06VCm2a+HtpBBcrruzQAt/pRVbvf5URjErjudwLN36NUIIIeyjStsS7dixgz59+nDXXXehKApr165l1apVbNmyhbCwMJ577rkqvfiSJUt46KGHmDBhAgCLFi1ix44drFy5kpdfftns+lmzZhk9joyMJCYmhs2bNzN69GgAfvnlF3bt2sXhw4fx9/cHShbYrk2KotAzyInvzpct6n0gqYguAU5m1/6dqaHv5iQKteCogp1Dg+jg51ib4QohRINic4svODiYnTt3cvfdd/Pdd9+h1+vZsGEDP//8M2PHjuXHH3+0uDmtNUVFRRw5coSBAwcaHR84cCD79++3+T7Z2dlGr7t161a6du3KkiVLaN++Pd26dWP27Nnk5OTYfE976BlonOQOJBdZvO7bc/kUXh0CLNbBN+fyazo0IYRo0Ko0jy8gIID333+f999/n5SUFHQ6HQEBAahUVV+AOTU1Fa1WS2BgoNHxwMBAkpKSbLrHtm3b2LVrF9u3bzccO3fuHPv27cPZ2ZnVq1eTmZnJ7NmzSUhIYPXq1VWOs7p6BpkkviTLie9KnnHhS0KeFMIIIURNqvYyKwEBAXYJQFEUo8d6vd7smCX79u1j8uTJLFy4kO7duxuO63Q6FEXh448/xtvbGyjpQh05ciRJSUkEBQVZvF98fPw1/BTmz3fXgYPiikZf8rNcyNES91c8pr2dZ5KdKP9rOJeSTXx8il1jqUv1KRaoX/FILJZJLNbVp3jqUyyWhIeHWz1nNfF9/vnnADz44IMoimJ4XJlx48bZdJ2/vz9qtdqsdZeSkmLWCjQVFxfHmDFjmDt3LpGRkUbngoODCQ0NNSQ9gNatS/ZGuHTpktXEV9GbVJn4+HiLz+8Sn8RvyWUFK8nuTbiluavRNfnxyUBZazBP7UJ4ePXXRbUWS12oT7FA/YpHYrFMYrGuPsVTn2KpDquJ7/HHH0dRFB544AGcnJx4/PHHK72Zoig2Jz4nJye6dOlCdHQ0w4cPNxyPjo5m2LBhVp8XGxvL2LFjmTNnjsWYevfuzaZNm8jJycHDwwPAMLE+LCzMptjspWeQk1HiO5BUxDCTxGe6gLWs8iKEEDXLauIr3YbIycnJ6LE9zZgxg6lTp9K9e3d69erFypUrSUhIYOLEiQDMnz+fgwcPsnnzZqBkHt/YsWOJjIxkzJgxJCYmAqBWqw1dr6NGjWLRokXMmDGDqKgoMjMziYqK4v7776+0JWlvPQOd+ZBcw+NfLRS4mK7qkpyvtbm7VwghRNVZTXym2xDVxLZEI0eOJC0tjUWLFpGYmEi7du1Yv3694bUSEhI4e/as4fp169aRl5fH4sWLWbx4seF4WFgYR48eBcDDw4Nvv/2W2bNnM3DgQHx8fBgyZIjF6RE17WaTApfDKUUUavU4q0uSWr5GT1ax8cT2Ai3kaPR4OkriE0KImmBzcUtCQgJnzpyxumzZ3r17admyJcHBwVUKYNKkSUyaNMniuaVLl5o9Nj1mSXh4ON98802V4qgJjd3VNHFXcym3pDuzSAd/pBYbEqK1ffpS8nV4Ola9UlYIIUTlbP50ffHFF3nttdesnn/99dd56aWX7BLUjcR0WsP+pELD/1sbz5ONa4UQoubYnPhiY2MZNGiQ1fN33nknsbGxdgnqRnKzyUT28uN81hKcFLgIIUTNsTnxpaam4uvra/W8j48PycnJdgnqRtLLwkT20r0MrW1XJNsYCSFEzbE58YWGhnL48GGr5w8dOlTrVZPXg45+jriU26zhSp7OMOZnvcUnXZ1CCFFTbE58Q4cOZd26dXz99ddm57755hs+//xzhg4datfgbgROaoWuAZaXL0uy0qUpXZ1CCFFzbK7qfO6554iOjmby5Mm8/fbbtGvXDkVROHbsGCdOnKBt27ZERUXVZKzXrZsDnYhLLBvbO56uAaSrUwgh6oLNLT4vLy9+/PFHw9ZD33//PVu3bgVg9uzZ/Pzzz0bLhIkyrbyNv1+czS5JfNLVKYQQta9Ki1S7ubkxd+5c5s6dW1Px3JBu8jR+m89dTXzWujSlxSeEEDVHZknXguaeaqPHZ7OluEUIIeqK1RbfwoULURSFZ599FpVKxcKFCyu9maIozJ49264B3ggaualxVJVsNAuQVqgjpUBLZpHe4vXphXqKdXocVbJsmRBC2JvVxLdgwQIURWHmzJk4OTmxYMGCSm8mic8ytUqhmYcDp7M0hmO/WtmYtlRKgY5QN3WF1wghhKg6q4kvPT29wseiapp7qo0Sn7Ud2Usl52sl8QkhRA2wOsY3Y8YMfvvtN8Pj2NhYUlKubWfwhqy5SYHLAQtbFJWXInP5hBCiRlhNfOvWrTPaEmjo0KFER0fXSlA3ItMCl8MpxVauLJEklZ1CCFEjrCa+kJAQTp06ZXhcur6kqB7TFl+epuL3Uyo7hRCiZlgd47v77rt566232L59u2Fi+ttvv83q1aut3kxRFMNu6cKYaeIz5eOkkFGuylPm8gkhRM2w+mn8+uuvExwczJ49e0hOTkZRFDIzM9Hp5AO5Oky7Ok2193Vkb7llzWS9TiGEqBlWE5+bm5vR2pu+vr68+uqrjB49ulYCu9F4OKoIdFFZTWgdTBJfimxGK4QQNcLmqs7vvvuOAQMG1EpQNyrTpcvK6+DnaPTY2s4NQgghro3NVZ3Dhg2Tqs5rVFF3Zwdf48SXImN8QghRI6SqsxY1s9LiUynQxsf4XHKBVt5zIYSoAVLVWYtustLi83dW4eWkwt1BIffqNIciHWQW6fFxlvU6hRDCnqSqsxZZm9IQ6FrS8A5wUZGbU1bUklKgxcdZNtAQQgh7kqrOWmQt8QW5lrQEA11VnC+X+JLydbSSvX2FEMKubN6I9rvvvqNNmzY1GcsNL8RNhYsaTBdlCXIpadUFuqiBsqXMZC6fEELYn82J77bbbgPg4sWLxMbGkpyczIgRI2jSpAkajYb09HR8fX1xcKjSpu4Nikop2Z7oZKbG6HhguRZfeckmc/nSC3UsOJxFYr6OFl5q2vg40sbbgU5+jqhl7z4hhLBJlQaQnn/+ebp27cr06dN5+eWX+fvvvwHIy8ujW7duLFu2rMoBrFixgoiICIKDg+nfvz979+61em1MTAzjxo2jTZs2hIaG0qdPHz777DOr18fFxeHv788tt9xS5bhqiqUpDUFXE16Qi/E50xbfk3vSWXY8l2/P5fPOHzlM3Z3O7d8lc8u3SSTmyYR3IYSwhc2J77///S9Lly5lxowZfPvtt0al9l5eXgwZMoQtW7ZU6cU3btxIVFQUs2bNYvfu3fTs2ZPRo0dz8eJFi9cfOHCADh06sGrVKuLi4oiMjGTmzJls2LDB7NqMjAymTZtG//79qxRTTbM0zhd4taszwKTFZ7o10e6EQov3PJWp4Z0/su0UoRBC3NhsTnyrVq1izJgxzJ8/n06dOpmd79Chg6EFaKslS5bw0EMPMWHCBNq0acOiRYsIDg5m5cqVFq+fNWsW8+bNo3fv3jRv3pzIyEiGDh1qcQrFE088wbhx47j55purFFNNs5T4DMUtLsa/jqRyXZ1FWj1ZRdbn9UVftpwUhRBCGLM58V26dIk+ffpYPe/p6UlmZqbNL1xUVMSRI0cYOHCg0fGBAweyf/9+m++TnZ2Nj4+P0bEVK1aQlJTEc889Z/N9astNXuZdnaVje6VjfaXKt/hSC41bf24OCupyw3qnMjXS3SmEEDawuRLFz8+PhIQEq+f/+usvQkNDbX7h1NRUtFotgYGBRscDAwNJSkqy6R7btm1j165dbN++3SiOhQsX8tNPP6FWV7wjQnnx8fE2X3stz1fyFMDV6Fhewnni0yAv1/jcP5kFhvueyjE+F+ykxU2l56+csp/xqyPnGRx47T+LPdWnWKB+xSOxWCaxWFef4qlPsVgSHh5u9ZzNiW/w4MGsWrWKSZMmoSjGFYS///47a9as4bHHHqtycKb30uv1Zscs2bdvH5MnT2bhwoV0794dgMLCQiIjI3nttddo3rx5leKo6E2qTHx8vM3Pb6LRoxy6TGmnpVqBm9u1wkGl4FughcNlXy4ydGrDfS9fLgBSDedCPV24OdCJv/7MMRw7jS+DSbmmn8WeqvK+1Ib6FI/EYpnEYl19iqc+xVIdNnd1Pv/886hUKvr06cMrr7yCoiisXbuWxx57jEGDBtGoUaMqdS36+/ujVqvNWncpKSlmrUBTcXFxjB49mrlz5xIZGWk4npCQwIkTJ5gxYwb+/v74+/vz5ptvcvz4cfz9/fnll19sjq+muDooDGvuYng8tJkrDlenIvg5q4y6L7OK9BRcXcLMtNAlwEVF31Bno2MxV4oQQghRMZtbfMHBwezcuZPXXnuNzZs3o9fr2bBhA56enowdO5ZXXnnFbKytIk5OTnTp0oXo6GiGDx9uOB4dHc2wYcOsPi82NpaxY8cyZ84cHn/8caNzjRo1MpsO8cknnxAdHc2aNWto2rSpzfHVpGV9/egXmgvAw+HuhuMqRSHARUViuZ0Zkgu0hHk4WEh8anoHO6FWQHu1+Xg6S0NyocL1+z1MCCFqXpVmmwcEBPD+++/z/vvvk5KSgk6nIyAgAJWqeutJzpgxg6lTp9K9e3d69erFypUrSUhIYOLEiQDMnz+fgwcPGqo2Y2JiGDt2LJGRkYwZM4bExEQA1Go1AQEBODo60r59e7OYnZ2dzY7XJRcHhci2HhbPBbmqjRNfvo4wD/PiFj8XFR6OKroFOPJrctlqLwczVVgvQRJCCFHtZVacnUu62aqb9ABGjhxJWloaixYtIjExkXbt2rF+/XpDyywhIcFoT8B169aRl5fH4sWLWbx4seF4WFgYR48erXYc9UmQyVy+xKtTGlItdHUC9A11Nkp8v2XaXtAjhBANUZWy1oULF5g6dSotWrSgWbNmNGvWjBYtWjBt2jQuXLhQrQAmTZrE0aNHSUpKYteuXdx6662Gc0uXLjVKaEuXLiUjI8Psv4qS3ty5c4mLi6tWbHUhyGRKQ9LV1l+KyQKfhsQXYjzOdyhTdnMQQoiK2Nzii4+P56677iIzM5Pbb7+dNm3aoNfriY+PZ8OGDfz0009s376dVq1a1WS8N7xgV8uT2E1bfP5XtyvqGeSEowqKr56+WKDin1wtjd2l5SeEEJbYnPjmz5+PXq8nOjqaiIgIo3NHjx7l/vvvZ/78+RWunSkqZzqJvbTFZ5b4rrb43B1VdA9wYl9SWUVnzJVCHmzlVsORCiHE9cnmfrE9e/YwdepUs6QH0KlTJyZPnkxMTIxdg2uIzFp8V7s4LVV1lrrNZFrDHitregohhKhC4isqKsLLy8vqeW9vb4qKZB7ZtQp0MW/x6fR60gott/jAfJwv5ookPiGEsMbmxNe+fXu+/PJL8vPzzc4VFhby5Zdf1qspA9erYDeTqs48LZlFesNcPQBPRwXncjPdewY54VTuaedztCTIup1CCGGRzWN8zzzzDA8//DADBgwgMjLSsFzNqVOnWLlyJfHx8axZs6bGAm0ogl3N9+Qzrej0N9nFwdVBobWPI3+mlU1ruJKnJcRNClyEEMKUzYnv3nvvZfny5cybN4/Zs2cb1tPU6/UEBwezfPly7rnnnhoLtKHwcVKMqjSzi/VcyjFJfM7mDfUgF8vz/4QQQhir0gT2UaNGMXz4cI4cOWKYt9e0aVO6dOmCg0O158KLchRFIchFzT/luiqPZWiMrglwMU98gWbTIHRm1wghhKjGyi0ODg706NGDHj161EQ8AghyUxknvvRio/P+LuZdmKYT35Ml8QkhhEUVFrckJiZy880389prr1V4k9dee42ePXuSkpJi1+AaKtNuy+Nmia/yrs4k6eoUQgiLKkx8H330EWlpacycObPCmzz99NOkpqaybNkye8bWYJm23k7a1NVpXhQjhBDCXIWJ78cff2TkyJF4enpWeBMvLy8eeOABfvjhB7sG11CZLlSdq9EbPfazVNxiZakzIYQQxipMfGfPnqVjx4423ahDhw6cOXPGLkE1dKYtPlM2tfhkjE8IISyqMPEpioJOZ9sHqE6nM0xxENfGdC6fqQALxS2mS53JdAYhhLCswsTXtGlTDh48aNONDh06VG92OL/emU5NMGWpuMXfWYWq3PeOjCI9RVq92XVCCNHQVfgJe9ddd/H1119z6tSpCm9y6tQpvvrqK+6++267BtdQmbbeTFlKfGqVYjaxXQpchBDCXIWfsE888QTu7u4MHTqUr776Co3GuLpQo9Hw1VdfMWzYMDw9PXniiSdqNNiGoqIxPkcVeDla7lI2bSkmS3enEEKYqXACe0BAABs2bGD8+PFMmTKFp556ilatWuHh4UFOTg6nT5+moKCA0NBQvvjiC/z9/Wsr7huap6OCixoKLOQtf2eV1bHUIFc1x9LLvpzI6i1CCGGu0pVbunbtSlxcHJ9++inbtm3j5MmTZGdn4+npSUREBPfccw+PPvoo3t7etRFvg6AoCkGuai7kmGc+S92cpcwmsVvKnEII0cDZtGSZt7c3M2fOrHQiu7CfYFeVxcRnqaKzlExpEEKIytm8H5+oXaZJrFSFLT6Z0iCEEJWSxFdPWavsrDjx2dbiyy3WseHvPI6mFVs8L4QQNzLZS6iestbis7RqSylbli0r1um5bVMSZ7O1qBT48k5/BjVxubZghRDiOiItvnrKaovPwjqdpQJdKp/HF3OlkLPZJQlRp4f5B7OuIUohhLj+SOKrp6zN5auouMX0OZamM1zJM24F/plWLF2eQogGpc4T34oVK4iIiCA4OJj+/fuzd+9eq9fGxMQwbtw42rRpQ2hoKH369OGzzz4zumbz5s2MGDGCli1b0qRJE+644w6+//77mv4x7M50akKpisb4AlxUKJQtU5ZWqKNYZ7xsWXqheTL84nReNaMUQojrT5USn1arZf369TzxxBOMHTuWP//8E4CMjAy++eYbEhISqvTiGzduJCoqilmzZrF792569uzJ6NGjuXjxosXrDxw4QIcOHVi1ahVxcXFERkYyc+ZMNmzYYLgmNjaWfv36sX79enbv3s2gQYN4+OGHK0yo9VGwW9WrOh1UCt4mo7YpJt2dGYXm63duOJOHRifregohGgabi1syMzMZOXIkhw4dwsPDg9zcXB5//HEAPD09eeGFF3jwwQd56aWXbH7xJUuW8NBDDzFhwgQAFi1axI4dO1i5ciUvv/yy2fWzZs0yehwZGUlMTAybN29m9OjRACxcuNDomqioKH788Ue2bt1Knz59bI6trpmO15WqqLgFwM9JT4ambGWXpHwtoeWSaEaReYsvKV/HL/8UMjhMilyEEDc+m1t88+fP58SJE2zYsIEjR46g15e1ENRqNUOHDuWnn36y+YWLioo4cuQIAwcONDo+cOBA9u/fb/N9srOz8fHxqfCanJycSq+pb9wdVXg4mC9N5ltBcQuAn6Nxy810nM9SVyfAF39Ld6cQomGwucW3detWpkyZwp133klaWprZ+ZYtW/Lll1/a/MKpqalotVoCAwONjgcGBpKUlGTTPbZt28auXbvYvn271Ws+/vhjLl++zNixYyu8V3x8vE2vWVPPt8TXwYUcTVmi83LQc+7v0xU+x9/Jyejx0XOXaV5uWsM/6c6AeTfqlnN5HDqeiqedJ7jUxPtyLepTPBKLZRKLdfUpnvoUiyXh4eFWz9n8MZeRkcFNN91k9bxer6eoqKhqkYHZgst6vd6mDW337dvH5MmTWbhwId27d7d4zaZNm3jppZf45JNPKt0rsKI3qTLx8fHX9HxrmsQnc7Gg7D0NcnMkPLxJhc/xP3PO6LHKK5DwcE/D48ITSYB5FWeRXuGoqhETwt2vKebyaup9qa76FI/EYpnEYl19iqc+xVIdNie+pk2bcuzYMavnY2NjadWqlc0v7O/vj1qtNmvdpaSkmLUCTcXFxTFmzBjmzp1LZGSkxWs2bdrEtGnT+Oijj7j33nttjqs+MR3nq6iwpZSfk0lXp8lC1RlWujoB3juaTVaRjh5BTjirFJIKtCTl6/B0VBjQyAWfSrpZhRDiemBz4hs9ejTvvfceQ4cOpV27dkBZa23ZsmVs2bKF//znPza/sJOTE126dCE6Oprhw4cbjkdHRzNs2DCrz4uNjWXs2LHMmTPHUFxj6ptvvmH69OksXbqU+++/3+aY6ptgk3l5NiU+kzE+02XLrI3xAZzN1vLib5YntHfyc+THIYG4Whh3FEKI64nNie9f//oXv/32G8OGDaNVq1YoikJUVBRpaWkkJiYyZMgQpk6dWqUXnzFjBlOnTqV79+706tWLlStXkpCQwMSJE4GSgpqDBw+yefNmoGQe39ixY4mMjGTMmDEkJiYCJcU1AQEBAHz99ddMnTqV1157jT59+hiucXJywtfXt0rx1TXTjWUrq+iEiotbtDo9mUXG53sFObE/qfIu6qNpxWw6l8+DrdwqvVYIIeozmxOfo6Mj69evZ8OGDXz77bcoioJGo6Fz586MHDmSMWPG2DQ2V97IkSNJS0tj0aJFJCYm0q5dO9avX28Yj0tISODs2bOG69etW0deXh6LFy9m8eLFhuNhYWEcPXoUgJUrV6LRaJg7dy5z5841XHPrrbeydevWKsVX13oEGheqdPV3snJlGX8n0xZfWVdnVrGe8mc9HRUW9fbmoR1pXMqtfCeHr87kSeITQlz3qlzDN3r0aMOcOXuYNGkSkyZNsnhu6dKlZo9Nj5m63pJbRW5v5MzMTh5sPpfPLSHOjG3lWulz/ByNHyeWa/GZdnP6OKuI8Hfi8KhgjqYW82tyEb8lF/FnWjFqlYKvk0JMQllrMPpyIcn5WqsLaAshxPXA5mqFzp07V7j017Zt2+jcubNdghIlVIrCKz28OTQqhCW3+eLmUPWuzrRCnWFVFtPCFl+nkvs5qhS6BToxtb0HH/f3I25EMHvuD+K7ewJp71v23Uirh2/P5V/rjyWEEHXK5sR34cIFcnNzrZ7Pzc21utSYqD0OKvArV32pp2zZsnSTVVsqmwwPMKqFcdfmV2ck8Qkhrm9Vqk+vaAzv9OnTeHp6Wj0vao+1fflMuzptSXwjbzLuXt2fVMS5bM01RiiEEHWnwjG+devW8fnnnxsev/XWW6xatcrsuoyMDI4dO8Zdd91l/whFlQW6qDhR7nHpvnxmY3xOlRcjNfd0MKv83Hg2n2ci5EuOEOL6VGHiy83NNUwHgJKFqnU64w9PRVFwc3NjwoQJREVF1UyUokqs7ctXnRYfwKgWrkaJ76u/8yTxCSGuWxUmvsmTJzN58mQAIiIiWLBgwXW7CkpDYjr/r3RKg+nODLYmvuHNXYnan4n2at3MsQwNf6UV08G0hFQIIa4DNo/xLV26lJ49e1o9n5qaSmxsrF2CEtfGtMWXaGjxGVd82roEWaCrmgGNnI2ObThT/d0c8jV6jqQUodPX3B6AGp1e9hgUQlhkc+IbOnQo0dHRVs/v2rWLoUOH2iUocW1M1/i8luKWUqbVndsvFlQrtgs5Gtqvv8Lt3yXTd1MShVr7J6efLxXQYX0CrT6/wpey3ZIQwoTNn3z6Sr6dFxUVoVLJIsb1QSN34xbfP1dXZTGdx+fjZPvv6+4wF8qXwhzP0JBWUPlqL6b+cyjL0PL8K13DxrP2nR6h1+t5bl8Gifk6Mor0PLM3gywLm+8KIRquCsf4srKyyMzMNDxOS0uzOFcvIyODr7/+mtDQUPtHKKqsiZXEdy0tPh9nFR39HDmaVralUVxiEUOama8mo9fr+eFiAdHnHZkYUEx735KxwGKdnm0mLcXfU4sYZ8dl0C7lajmbXZaQczV6oi8Xcn/zyle9EUI0DBUmvg8//JA333wTKKneNF3/sjy9Xs+LL75o/whFlTU2SXyX87To9PpqF7eUuiXYqdLEl16oY3pM+tUE58j6xGR2DwuimacDexMKyTBZJPt4un3nBB5KMd9r8IcL+ZL4hBAGFSa+22+/HRcXF/R6Pa+++iojR46kU6dORteUTmfo2rUrPXr0qNFghW08HFX4OCmGJFOsKylwMW/xVW1R8VtDnFl+vGz1nr2JhUbn9ycWErkr3WjB68wiPcuO5/Cfnj5suWA+Lng8wzxRXYvfks13mvjpUiFaKXQRQlxVYeLr3bs3vXv3BqCwsJBhw4bRvn37WglMXJsmHg5klGudncrQUL7B56wGV3XVEt8twca7Q/yeWkxOsQ4PRxWfncpl5t4MLNWqrI3P44WuXnx/3jzxJeXrSCnQEuBin4WvD1pIfKmFOn5NLsL/6uNCrZ7sYh3+ziqj1Yj+Sitm64V88jV6Itu608Sjymu4CyGuAzb/yy4/OV2r1ZKZmYmXlxcODvLhUB81dlfzZ7nE92e6ccvKx0lV5W2kglzVtPJy4HRWSfekVg+/JhVxk5cD/7KS9KCk1ffir1n8k2e5GOZ4uoa+oVVLfMfTi3ntUBYq4IVuXrTzdUSj03Mk1XIL8ocLBTzsA9su5jMxOp18rZ4gVxU3BzrRysuBn/4p4Fi5btfvzhewf0QQapVsvCvEjaZKgzyHDh1i+PDhNGrUiFatWhnm7aWmpjJmzBh27dpVI0GKqgszGecrnwSh6uN7pfqEGLf6YhOLWHE8F41J0mvnY/yFaOVJ6wucH0+venfnk7HpfH+hgC0XCvi/6DS0Oj0nMjTkmQZy1baLBaQUwZRdJUkPSlqbWy8U8P6fOUZJD+B0loY/0uzbDSuEqB9s/vQ7cOAA9957L2fPnuXBBx80mt7g7+9PTk4On332WY0EKarOtMDFbokv2Hgi+45/Clgdb5zUFvTy5vM7/VGwbVytquN86YU6fksue058poa4pCIOpVjfSf5kpobnTziTVWz7WN+xaiRkIUT9Z/On32uvvUbLli3Zv38/L730ktn5vn378ttvv9k1OFF9plMaTmSYd3VWh+k43+GUYrLKVWr6O6t4tLU7zT0duNXXtvlzFVV2XsrRcNlkd/j4TPOE9M3ZfIuFLUaxZlW1O1V2oRDiRmTzp9+hQ4d4+OGHcXFxsTg21LhxY6MFrUXdauJh/CFfbJKDqtvia+qhNkuq5T3axg0Xh5K/j1GhlltM4d7G3aDHMootLpDw74NZdNyQSKcNCaw8UdaqPJVpnpA2ncvnQJJx4ruzsbPZdaUi/BzZOTSQRb29eaKDB4t6e/NmL2/juKTFJ8QNyeZPP5VKVeHKLImJibi6ylyp+sK0q9OUTxWnMpRSFIU+Jq2+UmoFItt6GB7f4qujmYd5HFPauePhUPb6WUV6LucZZ+ZPTuTw1h/ZQEkRzb8PZRnW9jxtIfGlFOg4kWF8/PmuXpZ/BuDdPj50CXBicjsP/t3Tm8ntPOhnsh5pTSS+31OLmLY7jQ/POZJt+m1ECFErbE58Xbp0Ydu2bRbPFRUVsWHDhgoXsRa1K9RNTUUFib7V7OoEuCXYckvq/uauRsulqRSIbOtudt2Qpq608zVu9ZUvcNl9pZDZ+zKNzqcV6jh/dUUWSy0+Uy291HQLdKK1t3nV8cQ27nQPNE/eLb0cKP+2JOTrrC7LptHp2ZtQyKUc27tD8zQ6RmxP5Yu/8/n0kiP/Pphl83OFEPZj86ffM888w+7du3niiSc4evQoAAkJCfz8888MGzaMs2fPMmvWrBoLVFSNo0ohxNX6r7e6XZ1gXtlZamo78yT3cLgbno5lGbhfqDON3NW08zXe0qg08Z3N0jAhOtXi1IjSVWPibUh8pYnt7jAXo+OBLipe6m65JeioUix0w5q/llan597vU7j3hxRu3phEzJVCs2ss+TWpmLRyiwhsPm/fdUqFELax+dNvwIABLFu2jK1btzJixAgApk+fzujRozlx4gQrVqzg5ptvrrFARdU1cbc+x/JaEl9rbwf8TZ7fxd+RnkHmCdHPRc1nA/3oGejEXU2c+e+tPgC08zFOfMcyNGQX6xi3I9Vs+6RSR9OKKdbpOZtlQ+ILKIllQmt3ys+Nf7O3d4XbMXUwScjHLExp+OmfAg5cLaTJ1+pZdiyn0ngATpoUGF3J05kV7gghal6VZp+PGjWKe++9l19++YUzZ86g0+m46aabuOOOO/Dw8Kj8BqJWNfFQcyDZ8rlrSXyKotAv1JlvzpW1WKa197A6If72Ri7c3si45dXeQlfnG4ezzMbpyvszrZizWRqzOYOWlLb4Wno7sH1IIFvOF9BUk8yImypeELtkQe2yn8vSVItdl41beLZ0vVq77mBKEY3cZWxciNpU5WVX3NzcuO+++2oiFmFnFRW4XEviA5jb1ZM9CYUkF+i4J8yFUS2q9uFt2tV5LL2Yv0yKSboHOHKw3KLTR9OKzZKHl5NiNJ0CwFEFncrtDt/Z34nO/k7Ex1dedWwel3myikkwrh49n6NBp9ejqmQlHNMWH8Ch5CKGWtjhQghRc2xOfJa2I7IkLCys2sEI+6po2kF15/GVau3jyJ9jQkjO11ZrTctAFxX+zipSr455mW6Z18RdzdeDA2j1+RVDC+9SrtZsrt4DN7my9UIBSfllN+jk54hzFdchLWWpJarX6w2t2bQCrdliAIXakm7LyippT1ps8cmUCSFqm82ffhEREXTu3LnS/6pqxYoVREREEBwcTP/+/dm7d6/Va2NiYhg3bhxt2rQhNDSUPn36WFwtZs+ePfTv35/g4GA6d+7MypUrqxzXjaAmW3wAzmql2gs5K4piVtlZ3qs9vPBxVplVZZpuXNvWx9Fsy6HS8b3qaOKuxqtcMU5Wsd5ot4k9CZYnyZ/Nrri7M6NQZ5ScSx1OKTJM0xBC1A6bP7U++OADszEcrVbL+fPn+eKLLwgKCmLSpElVevGNGzcSFRXF22+/Te/evVmxYgWjR49m3759FluOBw4coEOHDjz99NOEhISwY8cOZs6ciYuLC6NHjwbg3LlzjBkzhvHjx7N8+XL27dvHrFmz8Pf35/77769SfNc7ay0+hZIuwrrWztfRYiLpFeTEiJtKkllHf0ejysoLOcbFIOHeDgxu4sKXp/PIKtbjoMAkC9WltipJyI7sLzcZ/li6hrCrCT4mwXIF57lsDbeFWJ8wb6mbEyC7WE98poY2JsU+QoiaY3PiGz9+vNVzM2fOZODAgeTk2FbdVmrJkiU89NBDTJgwAYBFixaxY8cOVq5cycsvv2x2vel0icjISGJiYti8ebMh8X366aeEhISwaNEiANq0acNvv/3GBx980OASX5iFyeNQMnm9svGo2tDeyof9Gz29DV+yOvk6sh7rZf/h3g6EeTiwc1gQuy4X0jfUiVbe15ZE2vk4GCW+4+nF3HV1WsQeK1MXzmVVXJ1pqZuz1KGUYkl8QtSia+/vAjw8PBg/fjwffvihzc8pKiriyJEjDBw40Oj4wIED2b9/v833yc7OxsfHx/D4wIEDZve84447OHz4MMXFDWs8xc9ZhaVt7q5l8ro9WerqHNvSlW7lJpd39LOeENwcFEN3bgsvBya2db/mpAellZ1lSldwScrXctxK1em5Siayn6ygWvVQJWuMCiHsy26b6Tk6OnLlyhWbr09NTUWr1RIYGGh0PDAwkKSkJJvusW3bNnbt2sX27dsNx5KSkrj99tvN7qnRaEhNTSUkJMTiveLj422OvSaeb0/lYwlycuFCvnGic9EX1Vq8Fb2OswYcFVeK9SWtOxeVnkf80oiPTzVc41YEYHkKQpizlr9Pn7ZbPKW88lRA2fSLwwm5xMen8mOyGrDcnXk8KccoblOHLzsDllvgsZeyiI+3Mu+kltTXv9+6Vp9igfoVT32KxZLw8HCr5+yS+I4ePcpHH31EmzZtqvxc03HD8hV0Fdm3bx+TJ09m4cKFdO/evdJ7WjpeXkVvUmXi4+Ov6fn2ZBrLTX+ncCHfuHsu1NuN8PCmtR6LJXOKsvnP4SwU4L+3+XFbS/MkF3L0CgkWCkM6BbsTHm57FbGtvyf/Ai0cTTA8Pl+gonnLVpxOzgDyLD4nodixwvf04pEEwHJ3aHyemqYtWvHl33nM/y2LABcVqwb60baWuj/r899vXapPsUD9iqc+xVIdNie+iIgIi4kjMzOTrKwsPDw8WLJkic0v7O/vj1qtNmvdpaSkmLUCTcXFxTFmzBjmzp1LZGSk0bmgoCCL93RwcMDPz8/m+G4Uprs0gH0qOu3l2c6ePNTKDTcHxeqKKp38HEn4x3xszXR5MXvxc1ET4qoyJNsiHfydpSHmivUuydRCHVlFOryudiPnFutwc1BQFIXcYh0XyxXlqBTwc9SRUlRybbEO3juazRuHsw33GvdzKgdGBuMoO8ALYXc2f3LceuutZolPURR8fHxo0aIFDzzwgNFYW2WcnJzo0qUL0dHRDB8+3HA8OjqaYcOGWX1ebGwsY8eOZc6cOTz++ONm53v27MnWrVuNjkVHR9O1a1ccHRteAYGlys76MsZXqlEl8986+jnyk6XE51UziQ9KKk4TyrWUd/xTyOlyS6U5KBDkqjLaVeJctoYIfyde/jWTJX/lEOKmZuNgf7Nd4Zt7qGnhrOHnlLLfQ2nSK3U2W8uqk7lMaicrIglhbzZ/cixdutTuLz5jxgymTp1K9+7d6dWrFytXriQhIYGJEycCMH/+fA4ePMjmzZuBknl8Y8eOJTIykjFjxhj2/1Or1QQEBAAwceJEPv74Y6Kiopg4cSL79+9n3bp1rFixwu7xXw8szeWraK3K+shagUt4DXYFtvd1JLrc0mTzDhjvFtEtwAkvJ4XLeWXXnM3WoijFvP9nSXXzpVwtT+zJINJkekVrH0daq/P5OaXiGBYeyebBVm54OF5fvy8h6rtqf2XOzi75hurp6VntFx85ciRpaWksWrSIxMRE2rVrx/r162natGSsJCEhgbNnzxquX7duHXl5eSxevJjFixcbjoeFhRl2jGjevDnr16/n+eefZ+XKlYSEhLBw4cIGN5WhVNgNkPg6WUh8CiVbD9UU0xVcTKeY9w11IrNID5QlvvPZGs6ZTGQ/kFyEh6NxT0kbbwfaK5XvxZdcoGPJXznM6WJ5N4madiy9mEKtni7+jjaNuwtxvahS4rtw4QKvv/46P/30ExkZGQD4+PgwePBgnn/+eUPCqopJkyZZnfhu2spcunSpTS3P2267jd27d1c5lhuRpRafbz2YvF4VLb0ccFUr5JfbqyjMQ42bQ80l8HubuuLpmEl2seVVVfqGOpstXXY2W2M0llfqF5NFrdv4ONBWq0OlgM7k9i5qKL8F4OKjOTzWxp1A15pL8qb0ej0v/prFB3+VtFwntnHj3T6+tfb6QtQ0mz854uPjuf322/nqq6/o2rUr06ZNY+rUqXTr1o0NGzYwYMAATlextFzUPIuJ7zpr8alVilkLzNIGs/bk66xi17AgHm3tZrYQQCsvB/oEO9Pc0ziG+EwN+xIrn5PXxscRNzW09TF+vqta4ft7AvEr9/vJ0ehZ9Hu26S1q1H8OZxuSHsCnJ/P4+VJBrcYgRE2y+dNj/vz56PV6oqOjiYiIMDp39OhR7r//fubPn29x7UxRd9wdVfg5q4w2QL3eEh+UjPOVX9C5VQ0nPiiZFP/erb7o9XrOZGnZdaWQPI2Oca3ccFIr3GSS+OISiyxuoGsq3NuBxAwY2MiFY+llCeb1nt50C3Tiuc6ezC03pvjx8VwKtHrmdfMiyFVNdrGOPVcKSSvUcVuIM8087fdeLP0rx2KiffHXTG5v5IyDVJmKG4DN/2L27NnDtGnTzJIeQKdOnZg8eTLLli2za3DCPtr4OBBXriUSVs2FpevSXWEurDpVNofujsYuFVxtX4qi0NLbgZYmybaZp3FL0Jak19hNjZeTikRgZoQHJzKKOZZezCOt3ZnYpmQO42Nt3Vl6LMewLqkeWH0qj2/O5tPRz5Hfkosovvo9xt9ZxbYhAYTbYcWaz0/nGSXc8o5naFh9Ko/H2lZ/HVR7WRufyxuHswl1U/FCNy+zvR6FqIzNX/2Liorw8rI+yO7t7U1RkSy9VB89E+GJm0PJN/VHW7tVun1OfXRPmAv/vtmLOxo780ZPb+5sbH1B6Nri4agi0KVqrefW5bo3A1zUfDU4gGNjQ5nb1ctQQOKsVniztzemjavsYj1xiWVJD0rm/M3el2lYpMHUH6lFPPBjCvd8n8yzcRmsPpXLEQs7QvyVVswTe9IrjP0/h7PINN0/qpZdyNEwc28Gl3K1/JpczPDtqczel0Gepm7jEtcXm//Vtm/fni+//JL8fPMFgwsLC/nyyy9p3769XYMT9jGoiQt/jA7myKhg3rv1+ixSUBSFJzp68vXgAKZ3sL7be20z7e4sb0Aj8+Tcxse21vbdYa58Ncjfpkn60ZcL2XLBfAxOq9Pzf9Fp7PinkLjEIlacyOWp2Axu/y6Z/puTySjX/f3hsRyjFqujCj68zcfwhQkgpUDHu3/U7nijqY1n8o0SP8Dy47n025TM4RT54i1sY3Pie+aZZzh69CgDBgzg448/ZufOnezcuZPly5fTv39//vzzT7PdE0T9EeCiNivGENeuuafl1nOIq4qFvbzNjrepQpfkwMYu7B0exBs9vfE2qcR1Ndlo94UDmeSbTJTfeaWQc9mWl0k7mlZsGMvL0ZQklPLe6+PDQ+HuPNXReAL9h3/lmE3ZqE1fn7W8U8fpLA3DtqVwJqvuYhPXD5sT37333svy5cvJyMhg9uzZjBw5kpEjRzJnzhwyMjJYvnw599xzT03GKkS909zK6jG3hTrT2seRQSZdsjcHVW2TXEeVwvQOHhx+IJi3b/Hmv7f6cGRUML8MDaR87ruQo+W/fxq3xtbFW15XtNT/TuaSVqBlW7KD0VSRxm5qHry6ZuqTHT1o5Fb2MVGkgzWnKr6vPWh1emKuFHI6s6ygKT6zmKNp1ndYyS7WM2dfhqHbt1Cr57WDmdy/LYU18bk1HrO4flSpCTBq1CiGDx/OkSNHuHDhAgBNmzalS5cuODhIa0I0PM2t7HnY9+qmtO/d6svoH1M4naVhRgePCrdZqoifi5rItsatr8nt3PnoWNkH+rt/ZDOulRtNPRzILNKx9YJx62hiGzc2nysg9WoXZ65Gz/LjuXyTYPxv95HWbqivDjC6O6r4V4Qnz+0rK3o5lVmz23vlFOsY9WMq+5KKcFBgWT9fHmjhxkaT1l6vICfa+Diwulwi/umfQn64WMDdYS5Mj0k3PGfXlUIi/ByJ8K/aFw9xY6pytnJwcKBHjx706NGjJuIR4rpyk7UW39XE19hdzd4RwRTr9HZfcDqqixdfncknpaAkkRVoIWp/JmsH+vHt2XyjifBhHmrevsWHmzxzeOm3LMPx94/mkK8ta9GpFHg43HiHDNNdIq7kVbzp7rXQ6+Gp2Az2Xd0IWKOHf+3NoE+Is1l37LhWbkxo7cbpTA17y1UtR+3PZH9ikVmijL5cKIlPANVIfCdPnuTcuXOkp6dbrCQbN26cXQIT4npgady0kZuKFibLqdXELgs+zipe6u7FU7EZhmPfXyhg/Zl8Pj9t3B35YEs3VIrCo23ceeuPbLKKSv7t5pvMwRjU2JkmJtNdGrkZ/yxX8mqugnLdZQezhJVVrOfhHalGu9g7KDC0mQuKorCotw/9NicZinMu5GgN66WWV9FmwKJhsTnxnT9/nqlTp3LgwAGrpdOKokjiEw1KiKvKbJmx20Kda63q9OFwN1afyuW35LLux2fjMsyWWhvXqqQV5+WkYnJbd97+wzwxAExoYz5PL9TduBQgIU+LTq9HZeefMeZKIYvPWu4KLr94AcDtjZzxdylJyB38HM26fS2Jr+EuWnH9sDnx/etf/+KPP/7g9ddf59Zbb63SFkRC3KgURaG5pwMnyrUmSrs5a4NKUfjwNl/6bU4yJF/TpNc7yIkW5bpkp7X3YMlfOUbJGiDUTcXgJuaTwd0cVPg4KWRcbSVq9JCcryPYzX7zQS/nanlsZxpabEumI29yNXo8t6sXG8/mk2Rhw+JSJzM1Nm90LW5sNld1xsXF8eSTTzJ9+nQiIiJo2rSpxf+EaGj6lkt0LuqSeZO1qbWPIy93N586UeohkzG7QFc1j4Sbt+zGh7tbXZLMvLvTvuN8i37PIrnAOGnN6+ZlNokfwEkFQ5oZJz5vJxXze5i/B+UrX7OK9BUmRtFw2Jz4vL298ff3r8lYhLguze3qybhWbtwS7MQn/f0ItWNLyFZT27vTN8S8cMNFDfc3dzU7PqOjh1FSUIBHTBJkeaEmq/1ctnPi22myg8XsLp4829mTKe3ME/SdTVzwtrCZ8oMtXQ0tQZUC83t4mW1pVX6cUDRcNie+hx56iG+//bYGQxHi+uTnomZpX19+uDfQrCVSW1SKwpK+vnia7P13XzNXi0miuacDz3Yu20tzegf3Che7Nk3m9mzxZRTqOFtuor1agX91KonthW5eRvMIwbybs5SiKKzo78uO+wLZPyKIpzt5mu3iIeN8AioY4zt48KDR48GDBxMdHc3QoUOZOHEiTZo0Qa02/2bbvXt3+0cphKhUUw8HFvby5vE9GUBJq2daew+r10d18eS+Zq6cOXeBYV0bVXhvs8SXW9ZleDStmG/O5uHmoKKtjwPtfR1p5qE2zAWsjOmk9DbeDrheXSrN01HF0r5+jNuRSp5GT89AJ4st2FIqRaF7YFnL13TJN6nsFFBB4rvzzjvNBoFLqzljY2PNri8dNE5LS7NziEIIWz0U7o6nk4rdlwu5p6kLPQKtz1tTFIVOfo64pOoqLfgwHeMr7epMyNMyaEuSWaGMv7OKf/f0NlSTVuT3VOM1NiP8jbsn+zdy5vADwVzI0dItwNHmhAol45/lxUtXp6CCxLdkyZLajEMIYSdDm7ky1M5drqZTGkq7OrddLDBLelCya8SsuAzuDnOpdP/H31ONW3ydLUwyD3ZTV6uK1LSr85S0+AQVJL6HHnqoNuMQQtRj1sb4Klq+LE+j5/fUokr3yzNPfNe+t2CpFl4OqJWyvRL/ydOSXazD0/H624xZ2I/89oUQlbLW1Rlv0oIy3TWishZWTrHOqPtRQU8nOyY+Z7VitoPGaenubPCstvgWLlxY5ZspisLs2bOvKSAhRP3j76LCUYVhL7ysIj05xTpOmSSRu8Nc+OZc2ZJjpudN/ZlWTPnp9k1d9XZvjbX2duTvrLL+2FOZGroGyJqdDZnVxLdgwYIq30wSnxA3JpWiEOKm5mJOWQI5m63lQo7xAN89TauW+Ey7Odt62H+CeWtvB364WPb4VIZMaWjorCa+9PT02oxDCFHPNTZJfLEJhUattTAPNV38TasoK04ypomvjXsNJD6TXe8rS8bixidjfEIIm5gWuOwyWW0l3MuBm7wccCg3zHclT0dWkfVkZjqVoWZafDKlQRiTxCeEsInplIbYBJPE5+2Ao0ox26PQWjFJgUZvtLg3QJsaSHymk9j/ztKg0VneYaY++DWpiL6bkmj1+RXG70hlbXwuqZbmjIhqs9rVed9996FSqdi4cSMODg4MHTq00pspisLmzZurFMCKFSv473//S2JiIm3btuWNN96gT58+Fq8tKCjgX//6F7///junTp2iV69ebN261ey6DRs28P777/P333/j6enJ7bffzmuvvUZwcHCVYhNClDFt8WWZ7AJRmmBaezsYtapOZmroZmEi/bH0YspvB9jMQ42VfX2viY+zimBXFYlXF6gu1sG5bA2tvO1XPVoVxTo9W87nsy4+j+xiPQ+FuzG+Vcmu99+dz2fyrjTD3MitFwrYeqEAlQL9fJ1Y1lhr110xGiqrLT69Xo9OV/btS6fTodfrK/yv/PW22LhxI1FRUcyaNYvdu3fTs2dPRo8ezcWLFy1er9VqcXFxYcqUKQwePNjiNfv27WPq1KmMGzeOuLg41q5dy4kTJ5g8eXKVYhNCGDOd0mAq/GoisXV9zJqcv2fKNKa6WLosvVDHwiNZdFqfwMSd6fz0TyH7kop4KjaDvpuTeOnXTP7vlzSLCwLo9LAzzYF7f0jmn1xp/V0rq9+vTFtSllpW12rJkiU89NBDTJgwAYBFixaxY8cOVq5cycsvv2x2vbu7O++++y4Af/31F5mZmWbX/PrrrzRq1IgZM2YA0Lx5c6ZMmcKcOXPsHr8QDUllu06UFpGYdi1am8tnOr5nacUWe2nt40hMQtnr1fY4X2aRjr6bkrhkJWkdS9dwLN3y5sDl/Z2lZcgPyWy+O4CmHjXQPG4g6myMr6ioiCNHjjBw4ECj4wMHDmT//v3Vvm+vXr1ITEzkhx9+QK/Xk5qaysaNGxk0aNC1hixEg9bI3Xri83BQCHEt+ThpY+P6mL+n1V6Lz2yx6lpOfJvP5VtNepaoFXithxfPdTbfYeJctpZ7v0/hXLYU6VRXtb8yxMTEsH79ehISEmjdujXTpk0jLCzM5uenpqai1WoJDAw0Oh4YGEhSUlJ1w6Jnz56sWLGCKVOmkJ+fj0ajYcCAASxdurTC58XHx1f7Ne3xfHuSWKyrT/Fcb7EU6gAsLzod5qLh9OnTAKg0xtedzirm+Ml4HMp9zdbo4M9UVyi347pH5kVwqpn3xSNHBZQtnfbblRxOnUqh/NrcBdqSTW7Lr4Ftr1hizzoCZYndVaXn3iANeVqFH5KNP4ZdVXoWtCukj0seuMCIDvD8SSd2ppZddylXy93fXeF/nQuowYZyherT368l4eHhVs9VmPgWLFjA22+/zZ9//mlUGLJ27VqefPJJw24NP//8M+vXr2fHjh1V3oXd0g4Qla0UX5ETJ04QFRXFc889x8CBA0lMTOTFF19k5syZLFu2zOrzKnqTKhMfH39Nz7cnicW6+hTP9RqL38ErpBWaj+V3CvYgPLzs337I71dIuFpMotUrOIQ0N4wBAnx+Oo9ifdlc4UZuKnp3CK+x98U9Vwt/JRgex+eqiNM3YkJrd/R6PR/8mcOCI9kAPNLajec6e5J28YzdYkk8kwKUVcF+2M+PETeVfDk4klLEy79lsSehkHa+jnxwqw9dTFaW2RCu56GtF/gppewjO6FQxUtnfdh8dwAuDtX/zKyO+vT3Wx0VdnXGxMQwcOBAo6RXWFjI3Llz8fLyYtOmTVy6dImVK1eSk5PDO++8Y/ML+/v7o1arzVp3KSkpZq3AqnjnnXfo1q0bTz31FB07duSOO+7g7bff5ssvv+TSpUvVvq8QAkLdLH9khJuUY1Y0zheXWMjTscYLZNwcVLPNlkbuam4z2aF+7oFM4jOLeeNINi/+lkWuRk+uRs9Hx3Lp+lUi/7voQL7GPtMeTpqsFtPOt+xLQJcAJzbdHcA/DzciZligWdIDcFQpvNqmiDEtjXfdOJBcxFN70w2NEGGbChPfmTNn6NGjh9GxXbt2kZ2dzRNPPEG/fv1wd3dnxIgRjBkzhp07d9r8wk5OTnTp0oXo6Gij49HR0fTq1cv2n8BEfn6+2Qa5pY/lj0OIa2OtstN03ztr43xnsjSM35FG+TntTiqY2cmTmvZ+H1/cy7WM8jR67t6awptXW3rlZRXrWXLeib6bkki7xjl0mUU6LueV/cAOCrSwsNu9i4NSYW+XgwIf3ubLgEbORsfX/53Pe0crL4wRZSpMfOnp6YSEhBgdi4mJQVEU7rrrLqPjXbp0ISEhgaqYMWMG69atY/Xq1Zw8eZI5c+aQkJDAxIkTAZg/fz7Dhg0zes6JEyf4448/SE1NJTc3lz/++IM//vjDcP7uu+/m+++/55NPPuHcuXPs27ePOXPm0Llz5yqNQQohzIVaKXBpVVmLL1NDeqGOMT+lmnWVLr7Nt1YWjW7p7cCCXt5Gx1ItdNuWdzpLw0fHc6/pdU1bey29HHBSV69r0kGl8Ontfmbv7/yDWWy7mG907FRGMU/FprPoSFa1J+wfTili2u40+m5KYva+DP5Ms/86p+eyNTwancaYn1LMFkWoKRWO8QUFBXH58mWjY3FxcXh4eNCxY0ej4yqVCienqv3xjhw5krS0NBYtWkRiYiLt2rVj/fr1hnHChIQEzp49a/Qc03l+/fr1AyAjIwOA8ePHk5OTw8cff8y8efPw8vKib9++zJ8/v0qxCSHMWWrxKZR8mJdntgFsZjHTY9I5nWVciTiniydjW1a+S7u9PBzuxk+XCth8vsDsnJejQmRbd1aezCWzqCxRrIvPI6qLJ6pq1h6YrU7jc23TEHycVXxxhz93bEkio1ycT+7JYN8IJ/xd1JzIKGbgd8nkXe2qTczX8dYtPpXeu0ir559cLb+nFrPseA5xiWVTQI6mFbP8eC49Ah25z0fNU6301X5Pypu8K41fk0sS6o5/CnmjpzeT27lfU61HZSr8DXTr1o1169YxdepUfHx8+PPPPzl8+DD33HOPWVAnT56kcePGVQ5g0qRJTJo0yeI5S5WYR48erfSeU6dOZerUqVWORQhRMUtTGpp5qs2KK0y7Pn9LLgaMWwtjWrgS1aXmuzjLUxSF92/15bfkRKPuR09Hha8HB3BzkBOPd/AgYkMi+VeXlbmUq2X3lcJKN9S1xnSyfFvfa5+20dLbgdUD/Rm5PYXSYcjkAh2z92Wy+DYfJkanGZIewP9O5vJ0Jw/CLMz9K9TqeeFAJj9cKOBynpbK2oa/JRfzW7IzRzXpfNzf95qS34mMYkPSg5INg2fvz+R4RjFv9vbBUVUzya/Crs7nnnuOhIQEunXrxr333su9996Loig8/fTTRtfp9Xq2bNlyTWNzQoj6z9IkdtPCFiip0nSvoNKwe4Aji2/zrdFv9db4OqtY3t/PEJ+Pk8LGq0kPINBVzbDmxkluTXxetV/PtKuzrbd9Jp73C3XmOZMvDl+fzee+H1I4bpJsNXpY8pflccA3j2Sx4kQu/9iQ9Exf69WDWVUN28jmc/kWj396Mo/n4jKu6d4VqTDxdejQgU2bNtGjRw9SUlLo2bMnGzdu5Oabbza6LiYmBg8PD7PxOCHEjcVi4rPQdacoitk4VCk3B4Xl/fxwruY4lz3cFuJM7PAgVg3w49ADwWZVpQ+Huxs9/u58PhlXxwNzi3UcSCqscNeJ8sy7Ou03Uf+ZCE8i/IzvdyjF8jjc6lN5ZoU6ucU6VpyoeAyzf6gzi3p7c7tJUQ3Ae0dzWHWy+mOglrqcAXydFZ6uwYKnSr969O7dm/Xr11d4Tb9+/di7d6/dghJC1E+NLExnMN32p+y4A0dSzT+EX+3hRUs7tXquRXNPB5pbqK4EuDXEicYuOv4pKPl5C7Xw1Zk8egQ6MW5HKlfydAS5loy1WVqAu1RWkc5oxRaVAq3s+LM7qhQ+7OvLgO+SKK4kD+dp9Cw/nktUVy/DsQ1n8o3GM6FkykpTDwc6+TkyoY07na4m1sntPDiRUcywbSkk5Ze92DNxGYR5qBnYuGpdwWeyNEbFMioFGruruZKrZfUAf7NdPuxJtiUSQtjM11mFs0mjz9oHuek4H8CARs5EtnW3cHX9olIUhgYZt9QW/5nD0G0pXLk6NpiUr2P49hR+TSorAMnX6DmaVmyY/2e6XFsLTwe7t3Q7+jkyu7N568hFXVLMU97y47nkXs2Qer2e5ceMuz//1cmD42ND2T4kkLdu8TEkvVJtfRz5/A5/nFVlyVKrhwnRaVzJq9q0D9Nuzj7BTuwcGsjqgX70DTVvXdqTJD4hhM0URaFNuRaeowraW6lSbGdy3NtJ4YM6GterjiHBWspHej5HS7bJVkxZxXpG/pjCV2fyeG5fBm2+vELfTUl0/iqBCzkaTpiO711jRac1MyM8zdY6XdDLh//09MbLqeynSCvU8dnV8co9CUUcK9cNq1LgMRu+lHQPdOK11kVG7012sZ7Vp6rW5bn5vHHiG9bMFX8XNfc2dbXyDPuRxCeEqJK5XT1xd1BQgGc7e+LnYnlu36AmLob5fY4qWHyrL40rWOi6vglx1jOwceUtj+xiPZN2pfPx8VyyrnYbJuXreP1Qltn4Xls7ju+V56hSWDPQj1uCnQh0UfFKdy8mtHbDy0nFZJNktvhoDhdzNCwzae0NaepiserTkgEBWuZ2NW5l/njR8nidJRdyNGZjkUOb13zCK1X3He1CiOvKPU1dOT7WmWKdHn8rSQ/ASa2w+/5Aov8pJNzbwWLXZ333cLgbO/4xn1Q9oJEz0Zcrnmz9zdl8OviZrmhTcx+5YR4O/HCv+XKPU9t78MFfORRe7Yn8J0/LbZuSzFqvU9t7VOn1/q+1O/85XLbqzaGUYpLztQS6Vv7l5juTopZeQU6VbntlT9LiE0JUmZeTqsKkV8rNQcWQZq7XZdIDuLepK0GuZR+TCvDuLT58c1cAz3etuOqwSAeHTVo1NZn4rAlyVTOhtXGrL7NIT/nFXNr7OnBrcNUWIAlxUxt1r+qBny18SbDkO5PxvaHNqjdHsrok8QkhhBXO6pIlwtr6OBDu7cBnA/2YeLXrcHYXL17v6Y3r1WKV20KcGNvSenedSsFoh4raNL+HN3dW0G07tZ1HtcZeBzUxTli2dHfuulyy83x5w2qxmxOkq1MIISp0a4gz+0YEWzw3o4MH41u5oSjg7aQis0jHlvMF5FrY1aG5hxrXWt4+qJSrg8L6Qf4s+SuHVw9mGU198HFSGF1Bwq7IXU1ceOv3su7OHZcLKNbpLa64EpdYyBuHs9l9xbhV2DXAsdZ3k5cWnxBCXAMfZxXeTiUfpd5OKsa1srz2qD0nrleHSlF4sqMnPw0JpIVnWTf1y929cXOoXiroFuCIv3PZc7OK9Bwwac2lFmiJ3JnGPd+nmCU9gJE31W5rDyTxCSGEXU1uZ3lKQE1NZaiqLgFOxI0IZsMgf3YODTR03VaHWqVwZxPjLtTy3Z2bzuXT65skvj5reWmyIU1d6mRepyQ+IYSwozY+jmZ75oF9Fqe2F2e1wqAmLhY3va2qwabjfJcKyCjUMTE6jQnRaaQUmC8p0z/UmW33BrD2Dv9qtzavRf34CiKEEDeQKe3czaY7tKkHy7TVhDsau6BWSlZwATieoeHWb5P4x8JKLp38HFnQy5tbQ2p2ZZbKSItPCCHsbHATF6NxNH9nVZ2P8dUUH2cVPU0W+TZNeo4qeL6rJ78MDazzpAeS+IQQwu7UKoXPBvrTyc+R5p5q3r/Vp84qOmvDXU2sz8Pr7O/IzqFBzO7iVWP761XVjdn2FkKIOtbBz5GY+4PqOoxaMaiJC69Y2Jtvent3XunhXadbUFkiLT4hhBDXpL2vA73KdXd6OymsHejHG7186l3SA2nxCSGEuEaKorDydj+WHcvBUQWPtnG3ecHrulB/IxNCCHHdaOyu5tWbves6DJtIV6cQQogGRRKfEEKIBkUSnxBCiAZFEp8QQogGRRKfEEKIBkUSnxBCiAZFycjIMN8xUQghhLhBSYtPCCFEgyKJTwghRIMiiU8IIUSDIolPCCFEgyKJTwghRIMiia8a3nnnHQYMGEBYWBgtW7Zk7NixHDt2rNZePzY2lgcffJB27drh4+PD2rVrDeeKi4t5+eWX6dOnD40aNaJNmzZMmjSJixcv2j2Oyt6H2owF4OOPP6ZPnz6EhYURFhbGoEGD2L59e53EYurtt9/Gx8eH5557rtbjeeONN/Dx8TH6r3Xr1rUeR6mEhASmTZtGy5YtCQ4OplevXuzZs6dO4unUqZPZe+Pj48OYMWNqPRatVsu///1vIiIiCA4OJiIign//+99oNBqg9t+b7OxsoqKi6NixIyEhIQwePJhDhw7VSSz2JomvGvbs2UNkZCTbt29n8+bNODg4MHz4cNLT02vl9XNzc2nfvj0LFizA1dXV6FxeXh6///47zz77LLt27WLdunX8888/jBo1yvAPyF4qex9qMxaARo0aMX/+fHbt2kV0dDT9+vVj/Pjx/Pnnn7UeS3m//vorq1atokOHDoZjtR1PeHg4J0+eNPy3d+/eOokjIyODu+66C71ez/r169m/fz9vvvkmgYGBdRJPdHS00fuya9cuFEVh+PDhtR7Le++9x4oVK1i4cCEHDhxgwYIFfPzxx7zzzjtA7b83Tz31FL/88gtLly5l7969DBgwgOHDh3P58uU6/fdkDzKPzw5ycnJo2rQpa9eu5Z577qnV127cuDFvvvkm48ePt3rNiRMn6N27N7GxsUYfvvZmy/tQW7GUat68OS+//DITJ06sk1gyMzPp378/77//Pm+++Sbt27dn0aJFFq+tqXjeeOMNNm/eTFxcnE3X1+T78uqrrxIbG2toidd1PKbeeust/vvf/3LixAnc3NxqNZaxY8fi6+vLRx99ZDg2bdo00tPT+fLLLy0+p6biyc/Pp0mTJqxevZohQ4YYjvfv359BgwYxb968WoulJkiLzw5ycnLQ6XT4+PjUdSgWZWdnA9R4fLa8D7UVi1ar5euvvyY3N5eePXvWWSwzZ87k/vvvp3///pVeW5PxnDt3jnbt2hEREcFjjz3GuXPn6iSOrVu30r17dyZOnEirVq247bbbWL58OXq99e/ftfU3o9fr+eyzzxg7dqzFpFfTsfTu3Zs9e/Zw6tQpoCSRxMTEMGjQIKvPqal4NBoNWq0WFxcXo+Ourq5Wv0DV1u/JHmQjWjuIioqiU6dOVj9g61JRURHz5s3j7rvvpnHjxjX6WpW9D7URy19//cXgwYMpKCjA3d2dNWvWWPz2WRuxrFq1ijNnzrBs2bJKr63JeHr06MGHH35IeHg4KSkpLFq0iMGDB7Nv3z78/PxqLQ4oScCffPIJjz/+ODNnzuTo0aPMmTMHgClTpphdX5t/v9HR0Zw/f55HHnnE4vmajmXmzJnk5OTQq1cv1Go1Go2GZ599lkmTJtV6PJ6envTs2ZO33nqLdu3aERwczFdffcWBAwdo0aJFrcZSEyTxXaPnn3+effv2sW3bNtRqdV2HY0Sj0TBlyhQyMzP5/PPPa/S1KnsfaiuW8PBwYmJiyMzMZPPmzUyfPp0tW7bQvn37Wo0lPj6eV199lR9++AEnJ6cKr63peExbDD169KBLly6sW7eOJ554otbiANDpdHTt2pWXX34ZgM6dO3PmzBlWrFhhlvhq8+8XSr6odOvWjYiICLNztRHLxo0b+eKLL1ixYgVt27bl6NGjREVF0bRpU/7v//6v1uNZtmwZM2bMoH379qjVajp37syoUaP4/fffaz0We5PEdw3mzp3Lxo0b+e6772jevHldh2NEo9EQGRnJsWPH2LJli9k3e3uq7H2ozVicnJwM30i7du3KoUOH+PDDD/nggw9qNZYDBw6QmprKLbfcYjim1WrZu3cvK1eu5PLlyzg7O9fqe1PKw8ODtm3bcubMGcOx2oojODiYNm3aGB1r3bo1ly5dMjpW2+9LcnIy33//PW+99ZbZudqK5aWXXuKJJ57ggQceAKBDhw5cvHiRd9991yjx1VY8N910E99//z25ublkZ2cTEhLCxIkTadasWa3HYm+S+Kppzpw5bNy4kS1bthhKw+uL4uJiHnvsMY4fP86WLVsIDg6usdeq7H2ozVgs0el0FBUV1XosQ4YMoWvXrkbHZsyYQcuWLXnmmWdwcnKqs/emoKCA+Ph4+vbtC9Tu+9K7d29Onz5tdOz06dOEhYUZHtfF+7J27VqcnZ0ZOXKk0fHajCUvL8+st0StVqPT6eoknlLu7u64u7uTkZHBjh07ePXVV+ssFnuRxFcNzz77LF9++SVr1qzBx8eHxMREoOQPxMPDo8ZfPycnx/BtXafTcenSJf744w98fX0JDQ1lwoQJHD58mM8//xxFUQzxeXl5mU1/uBaVvQ8ajabWYgF45ZVXGDx4MI0bNyYnJ4evvvqKPXv2sH79+lqPpXQ+WHlubm74+vrSvn37Wo2ndOylSZMmhjG+vLw8xo0bV+vvy+OPP87gwYN56623GDlyJH/88QfLly/nxRdfBKj1eKCkqGX16tWMHDkST09Pw/HajuXuu+/mvffeo1mzZrRt25Y//viDJUuW8OCDD9ZJPDt27ECn0xEeHs7Zs2d58cUXCQ8PZ/z48XXye7Inmc5QDdaqlubMmcPcuXNr/PVjYmIYOnSo2fFx48YRFRVF586dLT5vyZIlFU57qKrK3ofz58/XWiwA06dPJyYmhqSkJLy8vOjQoQNPPfUUd9xxR63HYsmQIUMM0xlqM57HHnuMvXv3kpqaSkBAAD169OCFF16gbdu2dfK+bN++nVdffZXTp0/TpEkTJk+ezNSpU1EUpU7i2b17N8OGDWPHjh10797dcLy2Y8nOzub1119ny5YtpKSkEBwczAMPPMDs2bNxcXGp9Xi++eYb5s+fz+XLl/H19WXYsGHMmzcPb2/vevHv6VpI4hNCCNGgyDw+IYQQDYokPiGEEA2KJD4hhBANiiQ+IYQQDYokPiGEEA2KJD4hhBANiiQ+IezEx8eHf/3rX3Udhs3Onj3LqFGjaNasmdmGxtfi/Pnzdr2fEPYmiU9cN9auXYuPjw9BQUFmazsCPPDAA3Tq1KkOIrs+Pfnkkxw6dIioqCiWLVvGrbfeWulzfvjhB8aOHUurVq0IDAwkPDycBx98kO+++64WIi6Rk5PDG2+8QUxMTK29prixSOIT152ioiLDrtSierRaLXFxcYwZM4bp06czduzYChda1+v1PPHEE4wbN45Lly4xZcoU3nnnHWbMmEFmZiaPPPIIGzZsqJXYc3NzWbhwIXv27KmV1xM3HlmrU1x3OnXqxJo1a3jmmWdo0qRJXYdTq/R6PYWFhWYbhFZVWloaWq0Wb29vm65fvHgxa9asYfLkySxcuBCVquw788yZM/nxxx/RarXXFFNd02q1aLXaSreREtc/afGJ684zzzwDUGmrr6Kxpk6dOjF9+nTD49Ju1D179vD888/TqlUrmjZtyowZMygoKCA3N5eZM2fSokULmjZtyrPPPotGo7H4uhs3bqRXr14EBwfTp08ftm/fbnZNVlYW8+bNo1OnTgQFBdGxY0deeeUVCgsLja4rHTf89ttv6dOnD0FBQXz99dcV/txxcXEMHTqUxo0b06RJE4YPH85vv/1mOP/GG28QHh4OwMKFCy0uqF1efn4+7777LuHh4bzxxhtGSa/U4MGDueeee6zeY/r06Ra7oUvf9/PnzxuOHTlyhNGjR9OyZUtCQkLo3LkzU6dOJTc3l/Pnzxu2NSofe/nfZUJCAk8//TRt27YlKCiIbt268f777xvt8l76t/Huu++yYsUKunXrRlBQEPv37wdK1qkcMGAAYWFhNG3alD59+rBw4UKrP5+4vkiLT1x3mjRpwkMPPVQjrb65c+cSEBDAnDlzOHLkCGvXrsXNzY1z587h6urKCy+8wO7du1mxYgUtWrTg8ccfN3r+/v37+eabb5g6dSoeHh6sWrWK8ePHs2nTJsMYWn5+Pvfddx/nz5/n0Ucf5aabbuLo0aN88MEHnDp1inXr1hndMy4ujk2bNjF58mSCg4Mr3AYrNjaWESNG0KhRI5599ll0Oh2ffvopQ4YMYevWrfTo0YOhQ4cSEBDAc889x3333WdxwfPy9u3bR3p6OtOmTcPBoWY/MlJSUhgxYgT+/v48/fTT+Pj4cOnSJX744Qdyc3MJCAhg0aJFZrHfdNNNQMm+enfeeadh94CQkBDi4uJ4+eWXuXLlCgsWLDB6vfXr15OTk8Ojjz6Kh4cHISEh7Ny5k8cee4x+/frx0ksvoVariY+PZ+/evTX6s4vaI4lPXJdmzZrFunXreOedd+w63ufv78/GjRtRFAWACxcusGLFCkaPHs3y5csBiIyMpFevXqxZs8Ys8R07dozt27fTq1cvAMaPH0+3bt2YP38+P/74IwAffvgh8fHx7Ny502hT1nbt2vHss8+yd+9e+vTpYzh+8uRJdu3aZXFncFMvvPAC7u7u/PzzzwQEBAAlu3b07NmTefPmsW3bNjp27EhgYCDPPfccHTp0YOzYsRXe8+TJk0DJxqg1bf/+/aSnp7Nx40aj/Qyff/55w/8PGzbMauz//ve/KSwsJDY2lqCgIAAmTpxISEgIH3zwAdOnTzfaSPXChQscPHiQkJAQw7GVK1fi6enJxo0bzfbHEzcG6eoU16WwsDBDq89ShWd1Pfzww4akB9CjRw/0ej2PPPKI0XXdu3fn7NmzZs/v2rWrIekB+Pn5MXr0aA4cOEBGRgZQ0o3Wq1cvAgICSE1NNfx3++23AyXb5JTXq1cvm5JeYmIiR44cYdy4cYakB9CoUSNGjRrF/v37DTFURXZ2NoDRXnU1pfQ1tm3bRnFxcZWeq9fr2bRpE3fddRdqtdrovb3jjjvQ6XTExsYaPWfIkCFGSa80htzcXH755Zdr+2FEvSWJT1y3Zs2aBVQ+1lcVpt2mXl5eVo/n5+ebjcm1bNnS7J6lxy5evAjA33//zc6dO2nZsqXRfz169ABKuvvKq6jasrwLFy4AWOwKbdOmDXq93hBDVZQmo9IEWJP69u3L0KFDWbhwIS1atGDs2LH873//Iycnp9LnpqSkkJGRwZo1a8ze2/vvv99wTXmW3tvIyEhatmzJ6NGjadeuHdOmTWPr1q1GY4Ti+iZdneK6Vb7VV1rwUl75lpspnU5n8bi1ri1LBR2A2Yehpdc0vUan09GvXz+LMUNJC608e+xmfS0f2qXdsceOHeO+++6r1j2s/S5MK0EVReGzzz7j4MGDbNu2jZ07dzJz5kzefvttduzYYei+tKT0dzpq1Cgefvhhi9e0aNHC6LGl9zY4OJg9e/YQHR3Nzz//zI4dO/jiiy8YNGgQ69evr/DvSlwfJPGJ61r5sT5Tvr6+AGRmZhodLywsJCEhoUbiOX36tNmxM2fOACWJGkoKMXJycgxdm/bStGlTAE6dOmV2Lj4+HkVRDDFURe/evfHx8eGrr75i1qxZ1Rr38vHxMfs9QFkr1VT37t3p3r07L7zwAj/99BOjR49m9erVPPvss1YTT0BAAF5eXmg0mmt+b52cnLjrrru466670Ov1zJ8/n/fee4/9+/fTu3fva7q3qHvS1Smua+Vbff/884/ROU9PTwICAsxW+Fi5cmWNzTk7fPgwBw4cMDxOS0tjw4YN3HzzzYYpAyNHjuTQoUN8//33Zs/Pz8+3qVvPkuDgYLp06cIXX3xBamqq4fiVK1fYsGEDvXr1qnDagjWurq4888wznDp1ihdeeMFi6/Hnn39m27ZtVu/RokULsrKy+P333w3HcnJy+OKLL4yuy8jIMLt/586dDecA3NzcjB6XUqvVDBs2jC1btnDkyBGzGDIzM20aN0xLSzN6rCiKYYy1OmOkov6RFp+47pW2+k6cOGHWonn00Ud56623ePzxx7n55ps5fPgwu3btwt/fv0Ziad++PWPHjmXKlCmG6QzZ2dm89NJLhmuefPJJfvzxRx555BHGjBlD9+7dKSws5PTp03zzzTeGRFkdr7/+OsOHD+fOO+9kwoQJ6PV6PvnkE4qLi3nttdeq/XM9+eSTnDp1io8++og9e/YwfPhwQkJCSE1N5aeffmLPnj2sWLHC6vNHjRrF/Pnzefjhh5k2bRoajYY1a9YQEBBgVJy0bt06VqxYwX333cdNN91Efn4+a9euRa1WG8bpPDw8CA8PZ+PGjbRq1Qo/Pz+aNWtGjx49eOWVV4iNjeXuu+/mkUceoX379mRnZ3Ps2DG+++47Dh06RHBwcKU/a1paGv369aNx48ZcuXKFjz/+mJCQEJuWdRP1nyQ+cd0LCwtj/PjxfPrpp2bnnn32WdLS0ti4cSPffvstt912G5s2bap07lp19erVi759+7JgwQLOnTtHy5YtWbNmDX379jVc4+rqyubNm3n//ffZuHEjX3/9Ne7u7jRv3pzp06cbJpdXx6233sqmTZv4z3/+w5tvvomiKPTo0YNPP/202skUSlo9H3zwAffeey+ffvopS5cuJTMzE19fX3r06MHnn39e4QR2Hx8f1qxZwwsvvMArr7xCaGgo06dPx8vLixkzZhjFf/jwYb755huSkpLw9PQkIiKCN9980yj+JUuWMHfuXObNm0dhYSHjxo2jR48eBAQEsGPHDhYtWsTWrVv53//+h7e3N61atSIqKsrQ/V2RMWPGsHr1aj799FMyMjIICgpi0KBBzJkzp1YqW0XNUzIyMqRUSQghRIMhY3xCCCEaFEl8QgghGhRJfEIIIRoUSXxCCCEaFEl8QgghGhRJfEIIIRoUSXxCCCEaFEl8QgghGhRJfEIIIRoUSXxCCCEalP8HBKheli/nDQAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Silhouette score also is a robust measure of the realistic number of clusters you could distinguish from this data\n",
    "kmeans_kwargs = {\"init\": \"random\",\"n_init\": 10,\"max_iter\": 300,\"random_state\": 42}\n",
    "\n",
    "silhouette_coefficients = []\n",
    "\n",
    "for k in range(2, 100):\n",
    "    kmeans = KMeans(n_clusters=k, **kmeans_kwargs)\n",
    "    kmeans.fit(scaled_features)\n",
    "    score = silhouette_score(scaled_features, kmeans.labels_)\n",
    "    silhouette_coefficients.append(score)\n",
    "plt.style.use(\"fivethirtyeight\")\n",
    "plt.plot(range(2, 100), silhouette_coefficients)\n",
    "plt.xticks(range(2, 100,10))\n",
    "plt.xlabel(\"Number of Clusters\")\n",
    "plt.ylabel(\"Silhouette Coefficient\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>P/E</th>\n",
       "      <td>0.006509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>phyla</th>\n",
       "      <td>-0.007702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>polarity</th>\n",
       "      <td>-0.010318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>order</th>\n",
       "      <td>0.205648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>family</th>\n",
       "      <td>0.133814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2D_shape</th>\n",
       "      <td>0.079455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3D_shape</th>\n",
       "      <td>0.002420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aperture_prefix</th>\n",
       "      <td>0.057838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>class</th>\n",
       "      <td>0.058160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aperture_type</th>\n",
       "      <td>0.054949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aperture_features</th>\n",
       "      <td>0.007132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dispersal_unit</th>\n",
       "      <td>-0.000331</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          0\n",
       "P/E                0.006509\n",
       "phyla             -0.007702\n",
       "polarity          -0.010318\n",
       "order              0.205648\n",
       "family             0.133814\n",
       "2D_shape           0.079455\n",
       "3D_shape           0.002420\n",
       "aperture_prefix    0.057838\n",
       "class              0.058160\n",
       "aperture_type      0.054949\n",
       "aperture_features  0.007132\n",
       "dispersal_unit    -0.000331"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ppp = ['P/E','phyla','polarity','order','family','2D_shape','3D_shape','aperture_prefix','class','aperture_type','aperture_features','dispersal_unit']\n",
    "effdf=pd.DataFrame()\n",
    "#ppp=['error']\n",
    "\n",
    "for a in ppp:\n",
    "    csv = pd.read_csv('kmeans_in.csv', index_col=a)\n",
    "    newdf = pd.DataFrame(csv)\n",
    "    newdf = newdf[newdf['error'] == np.int64(0)]\n",
    "    newdf = newdf.loc[newdf.index.dropna()]\n",
    "    geometric_ft = ['Area','Eccentricity','Equivalent_diameter','Extent','Major_axis_length','Minor_axis_length','Perimeter']\n",
    "    texture_ft = ['ASM','Contrast','Correlation','Dissimilarity','Energy','Entropy','Homogeneity','Solidity']\n",
    "\n",
    "    newdf =newdf[['Area','Eccentricity','Equivalent_diameter','Extent','Major_axis_length','Minor_axis_length','Perimeter','ASM','Contrast','Correlation','Dissimilarity','Energy','Entropy','Homogeneity','Solidity']]           #.drop(['error','P/E','phyla','order','key','Unnamed: 0','Clump','Misread','family','2D_shape','3D_shape','aperture_prefix','class','aperture_type','aperture_features','ornamentation','other','taxonomy','image','dispersal_unit','min_max_size'],axis=1)\n",
    "    try:\n",
    "        newdf = newdf.drop('-')\n",
    "    except:\n",
    "        newdf = newdf\n",
    "    try:\n",
    "        newdf = newdf.drop('no suitable term')\n",
    "    except:\n",
    "        newdf = newdf\n",
    "    c = newdf.index.size\n",
    "    newdf,xdf,testdf= np.split(newdf,[int(c*0.8),int(c*0.81)])\n",
    "\n",
    "    df_np = newdf.to_numpy()\n",
    "\n",
    "    labels = pd.Series(newdf.index).apply(lambda x: str(x)).to_numpy()\n",
    "    label_encoder = LabelEncoder()\n",
    "    true_labels = label_encoder.fit_transform(labels)\n",
    "    n_clusters = len(label_encoder.classes_)\n",
    "\n",
    "    scaler = StandardScaler()\n",
    "    scaled_features = scaler.fit_transform(df_np)\n",
    "\n",
    "    preprocessor = Pipeline([(\"scaler\", MinMaxScaler()),(\"pca\", PCA(n_components=6, random_state=42))])\n",
    "\n",
    "    clusterer = Pipeline([(\"kmeans\",KMeans(n_clusters=n_clusters,init=\"k-means++\",n_init=50,max_iter=500,random_state=42))])\n",
    "    pipe = Pipeline([(\"preprocessor\", preprocessor),(\"clusterer\", clusterer)])\n",
    "    pipe.fit(df_np)\n",
    "\n",
    "    preprocessed_data = pipe[\"preprocessor\"].transform(df_np)\n",
    "    predicted_labels = pipe[\"clusterer\"][\"kmeans\"].labels_\n",
    "\n",
    "    pcadf = pd.DataFrame(pipe[\"preprocessor\"].transform(df_np),columns=[\"component_1\", \"component_2\", \"component_3\", \"component_4\", \"component_5\", \"component_6\"])\n",
    "    pcadf[\"predicted_cluster\"] = pipe[\"clusterer\"][\"kmeans\"].labels_\n",
    "    pcadf[\"true_label\"] = label_encoder.inverse_transform(true_labels)\n",
    "\n",
    "    pipe.fit(newdf)\n",
    "\n",
    "    preprocessed_data = pipe[\"preprocessor\"].transform(newdf)\n",
    "    predicted_labels = pipe[\"clusterer\"][\"kmeans\"].labels_\n",
    "    d = np.asarray(pipe.fit(newdf).predict(testdf))\n",
    "\n",
    "    labels = pd.Series(testdf.index).apply(lambda x: str(x)).to_numpy()\n",
    "    true_labels = testdf.index\n",
    "    e = sklearn.metrics.adjusted_rand_score(true_labels, d)\n",
    "    effdf = effdf.append(pd.Series(e,name=a))\n",
    "effdf"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}