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{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "#   Medical Treatment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once sequenced, a cancer tumor can have thousands of genetic mutations. But the challenge is distinguishing the mutations that contribute to tumor growth (drivers) from the neutral mutations (passengers). \n",
    "\n",
    "Currently this interpretation of genetic mutations is being done manually. This is a very time-consuming task where a clinical pathologist has to manually review and classify every single genetic mutation based on evidence from text-based clinical literature.\n",
    "\n",
    "We need to develop a Machine Learning algorithm that, using this knowledge base as a baseline, automatically classifies genetic variations."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysis of the problem statement"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets first understand the data set provided and using that dataset we will try to understand the above problem in Machine Learning world. Since, the dataset is huge lets load it using python itself"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/shivank/anaconda/lib/python3.5/site-packages/sklearn/cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "# Loading all required packages\n",
    "# If any of it fails, do not panic. Just install it using \"pip3 install <package_name>\" or by using conda install package_name\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import re\n",
    "import time\n",
    "import warnings\n",
    "import numpy as np\n",
    "from nltk.corpus import stopwords\n",
    "from sklearn.decomposition import TruncatedSVD\n",
    "from sklearn.preprocessing import normalize\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.manifold import TSNE\n",
    "import seaborn as sns\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics.classification import accuracy_score, log_loss\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "from imblearn.over_sampling import SMOTE\n",
    "from collections import Counter\n",
    "from scipy.sparse import hstack\n",
    "from sklearn.multiclass import OneVsRestClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.cross_validation import StratifiedKFold \n",
    "from collections import Counter, defaultdict\n",
    "from sklearn.calibration import CalibratedClassifierCV\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "import math\n",
    "from sklearn.metrics import normalized_mutual_info_score\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "from mlxtend.classifier import StackingClassifier\n",
    "\n",
    "from sklearn import model_selection\n",
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 2 data files provided for solving this problem. I have kept them inside a folder training. So lets load them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loading training_variants. Its a comma seperated file\n",
    "data_variants = pd.read_csv('training_variants')\n",
    "# Loading training_text dataset. This is seperated by ||\n",
    "data_text =pd.read_csv(\"training_text\",sep=\"\\|\\|\",engine=\"python\",names=[\"ID\",\"TEXT\"],skiprows=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>ID</th>\n",
       "      <th>Gene</th>\n",
       "      <th>Variation</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>FAM58A</td>\n",
       "      <td>Truncating Mutations</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>CBL</td>\n",
       "      <td>W802*</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>CBL</td>\n",
       "      <td>Q249E</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ID    Gene             Variation  Class\n",
       "0   0  FAM58A  Truncating Mutations      1\n",
       "1   1     CBL                 W802*      2\n",
       "2   2     CBL                 Q249E      2"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_variants.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>\n",
    "    Let's understand above data. There are 4 fields above: <br>\n",
    "    <ul>\n",
    "        <li><b>ID : </b>row id used to link the mutation to the clinical evidence</li>\n",
    "        <li><b>Gene : </b>the gene where this genetic mutation is located </li>\n",
    "        <li><b>Variation : </b>the aminoacid change for this mutations </li>\n",
    "        <li><b>Class :</b> class value 1-9, this genetic mutation has been classified on</li>\n",
    "    </ul>\n",
    "    \n",
    "Keep doing more analysis  on above data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3321 entries, 0 to 3320\n",
      "Data columns (total 4 columns):\n",
      "ID           3321 non-null int64\n",
      "Gene         3321 non-null object\n",
      "Variation    3321 non-null object\n",
      "Class        3321 non-null int64\n",
      "dtypes: int64(2), object(2)\n",
      "memory usage: 103.9+ KB\n"
     ]
    }
   ],
   "source": [
    "data_variants.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3321.000000</td>\n",
       "      <td>3321.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1660.000000</td>\n",
       "      <td>4.365854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>958.834449</td>\n",
       "      <td>2.309781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>830.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1660.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2490.000000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3320.000000</td>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                ID        Class\n",
       "count  3321.000000  3321.000000\n",
       "mean   1660.000000     4.365854\n",
       "std     958.834449     2.309781\n",
       "min       0.000000     1.000000\n",
       "25%     830.000000     2.000000\n",
       "50%    1660.000000     4.000000\n",
       "75%    2490.000000     7.000000\n",
       "max    3320.000000     9.000000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_variants.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3321, 4)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking dimention of data\n",
    "data_variants.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ID', 'Gene', 'Variation', 'Class'], dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Clecking column in above data set\n",
    "data_variants.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now lets explore about data_text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>TEXT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Cyclin-dependent kinases (CDKs) regulate a var...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Abstract Background  Non-small cell lung canc...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Abstract Background  Non-small cell lung canc...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ID                                               TEXT\n",
       "0   0  Cyclin-dependent kinases (CDKs) regulate a var...\n",
       "1   1   Abstract Background  Non-small cell lung canc...\n",
       "2   2   Abstract Background  Non-small cell lung canc..."
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_text.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So above dataset have 2 columns. ID and Text column. We can also observe column ID which is common in both the dataset. Lets keep exploring it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3321 entries, 0 to 3320\n",
      "Data columns (total 2 columns):\n",
      "ID      3321 non-null int64\n",
      "TEXT    3316 non-null object\n",
      "dtypes: int64(1), object(1)\n",
      "memory usage: 52.0+ KB\n"
     ]
    }
   ],
   "source": [
    "data_text.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3321.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1660.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>958.834449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>830.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1660.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2490.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3320.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                ID\n",
       "count  3321.000000\n",
       "mean   1660.000000\n",
       "std     958.834449\n",
       "min       0.000000\n",
       "25%     830.000000\n",
       "50%    1660.000000\n",
       "75%    2490.000000\n",
       "max    3320.000000"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_text.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ID', 'TEXT'], dtype='object')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_text.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3321, 2)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking the dimentions\n",
    "data_text.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, in short my datasets looks like this\n",
    " * data_variants (ID, Gene, Variations, Class)\n",
    " * data_text(ID, text)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ok, now we understood the dataset. Lets try to understand the same problem from Machine Learning point of view\n",
    "\n",
    "We want to predict about class of cancer. Now question is what kind of data is present in class column. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_variants.Class.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is descrete data so it is ***classification*** problem and since there are multiple descrete output possible so we can call it ***Multi class*** classification problem"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***Important note*** : This is medical related problem so correct results are very important. Error can be really costly here so we would like to have result  for each class in terms of Probablity. We might not be much bothered about time taken by ML algorithm as far as it is reasonable. \n",
    "\n",
    "We also want our model to be highly interpritable because a medical practitionar want to also give proper reasonining on why ML algorithm is predicting any class. \n",
    "\n",
    "We will evaluate our model using Confution matrix and Multi class log-loss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ok, now we understood the problem statement. Let's work on the solution. \n",
    "\n",
    "***Note*** I highly recomend you to attempt the solution on your own first. After that come back and enjoy our solution. \n",
    "\n",
    "We have huge amount of text data. So, we need to pre process it. So lets write a function for the same."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We would like to remove all stop words like a, is, an, the, ... \n",
    "# so we collecting all of them from nltk library\n",
    "stop_words = set(stopwords.words('english'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def data_text_preprocess(total_text, ind, col):\n",
    "    # Remove int values from text data as that might not be imp\n",
    "    if type(total_text) is not int:\n",
    "        string = \"\"\n",
    "        # replacing all special char with space\n",
    "        total_text = re.sub('[^a-zA-Z0-9\\n]', ' ', str(total_text))\n",
    "        # replacing multiple spaces with single space\n",
    "        total_text = re.sub('\\s+',' ', str(total_text))\n",
    "        # bring whole text to same lower-case scale.\n",
    "        total_text = total_text.lower()\n",
    "        \n",
    "        for word in total_text.split():\n",
    "        # if the word is a not a stop word then retain that word from text\n",
    "            if not word in stop_words:\n",
    "                string += word + \" \"\n",
    "        \n",
    "        data_text[col][ind] = string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Below code will take some time because its huge text (took 4 minute on my 16 GB RAM system), so run it and have a cup of coffee :)\n",
    "for index, row in data_text.iterrows():\n",
    "    if type(row['TEXT']) is str:\n",
    "        data_text_preprocess(row['TEXT'], index, 'TEXT')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's merge both the dataset. Remember that ID was common column. So lets use it to merge."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
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       "\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>ID</th>\n",
       "      <th>Gene</th>\n",
       "      <th>Variation</th>\n",
       "      <th>Class</th>\n",
       "      <th>TEXT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>FAM58A</td>\n",
       "      <td>Truncating Mutations</td>\n",
       "      <td>1</td>\n",
       "      <td>cyclin dependent kinases cdks regulate variety...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>CBL</td>\n",
       "      <td>W802*</td>\n",
       "      <td>2</td>\n",
       "      <td>abstract background non small cell lung cancer...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>CBL</td>\n",
       "      <td>Q249E</td>\n",
       "      <td>2</td>\n",
       "      <td>abstract background non small cell lung cancer...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>CBL</td>\n",
       "      <td>N454D</td>\n",
       "      <td>3</td>\n",
       "      <td>recent evidence demonstrated acquired uniparen...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>CBL</td>\n",
       "      <td>L399V</td>\n",
       "      <td>4</td>\n",
       "      <td>oncogenic mutations monomeric casitas b lineag...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ID    Gene             Variation  Class  \\\n",
       "0   0  FAM58A  Truncating Mutations      1   \n",
       "1   1     CBL                 W802*      2   \n",
       "2   2     CBL                 Q249E      2   \n",
       "3   3     CBL                 N454D      3   \n",
       "4   4     CBL                 L399V      4   \n",
       "\n",
       "                                                TEXT  \n",
       "0  cyclin dependent kinases cdks regulate variety...  \n",
       "1  abstract background non small cell lung cancer...  \n",
       "2  abstract background non small cell lung cancer...  \n",
       "3  recent evidence demonstrated acquired uniparen...  \n",
       "4  oncogenic mutations monomeric casitas b lineag...  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#merging both gene_variations and text data based on ID\n",
    "result = pd.merge(data_variants, data_text,on='ID', how='left')\n",
    "result.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's very important to look for missing values. Else they create problem in final analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Gene</th>\n",
       "      <th>Variation</th>\n",
       "      <th>Class</th>\n",
       "      <th>TEXT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1109</th>\n",
       "      <td>1109</td>\n",
       "      <td>FANCA</td>\n",
       "      <td>S1088F</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1277</th>\n",
       "      <td>1277</td>\n",
       "      <td>ARID5B</td>\n",
       "      <td>Truncating Mutations</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1407</th>\n",
       "      <td>1407</td>\n",
       "      <td>FGFR3</td>\n",
       "      <td>K508M</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1639</th>\n",
       "      <td>1639</td>\n",
       "      <td>FLT1</td>\n",
       "      <td>Amplification</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2755</th>\n",
       "      <td>2755</td>\n",
       "      <td>BRAF</td>\n",
       "      <td>G596C</td>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        ID    Gene             Variation  Class TEXT\n",
       "1109  1109   FANCA                S1088F      1  NaN\n",
       "1277  1277  ARID5B  Truncating Mutations      1  NaN\n",
       "1407  1407   FGFR3                 K508M      6  NaN\n",
       "1639  1639    FLT1         Amplification      6  NaN\n",
       "2755  2755    BRAF                 G596C      7  NaN"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result[result.isnull().any(axis=1)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see many rows with missing data. Now the question is what to do with this missing value. One way could be that we can drop these rows having missing values or we can do some imputation in it. Let's go with imputation only. But question is what to impute here :\n",
    "\n",
    "How about merging Gene and Variation column. Let's do it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "result.loc[result['TEXT'].isnull(),'TEXT'] = result['Gene'] +' '+result['Variation']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's cross check it once again if there is any missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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>ID</th>\n",
       "      <th>Gene</th>\n",
       "      <th>Variation</th>\n",
       "      <th>Class</th>\n",
       "      <th>TEXT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [ID, Gene, Variation, Class, TEXT]\n",
       "Index: []"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result[result.isnull().any(axis=1)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Awesome, so all missing values are gone now.\n",
    "\n",
    "## Creating Training, Test and Validation data\n",
    "\n",
    "Before we split the data into taining, test and validation data set. We want to ensure that all spaces in Gene and Variation column to be replaced by _."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_true = result['Class'].values\n",
    "result.Gene      = result.Gene.str.replace('\\s+', '_')\n",
    "result.Variation = result.Variation.str.replace('\\s+', '_')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ok, so we can now start our split process in train, test and validation data set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Splitting the data into train and test set \n",
    "X_train, test_df, y_train, y_test = train_test_split(result, y_true, stratify=y_true, test_size=0.2)\n",
    "# split the train data now into train validation and cross validation\n",
    "train_df, cv_df, y_train, y_cv = train_test_split(X_train, y_train, stratify=y_train, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of data points in train data: 2124\n",
      "Number of data points in test data: 665\n",
      "Number of data points in cross validation data: 532\n"
     ]
    }
   ],
   "source": [
    "print('Number of data points in train data:', train_df.shape[0])\n",
    "print('Number of data points in test data:', test_df.shape[0])\n",
    "print('Number of data points in cross validation data:', cv_df.shape[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at the distribution of data in train, test and validation set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_class_distribution = train_df['Class'].value_counts().sortlevel()\n",
    "test_class_distribution = test_df['Class'].value_counts().sortlevel()\n",
    "cv_class_distribution = cv_df['Class'].value_counts().sortlevel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    363\n",
       "2    289\n",
       "3     57\n",
       "4    439\n",
       "5    155\n",
       "6    176\n",
       "7    609\n",
       "8     12\n",
       "9     24\n",
       "Name: Class, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_class_distribution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, what does above variable suggest us. This means in my train dataset we have class 1 values with count of 363, class 2 values having count of 289 and so on. It will be better idea to visualise it in graph format.\n",
    "\n",
    "*** Visualizing for train class distrubution***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a189c8f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "my_colors = 'rgbkymc'\n",
    "train_class_distribution.plot(kind='bar')\n",
    "plt.xlabel('Class')\n",
    "plt.ylabel(' Number of Data points per Class')\n",
    "plt.title('Distribution of yi in train data')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at distribution in form of percentage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of data points in class 7 : 609 ( 28.672 %)\n",
      "Number of data points in class 4 : 439 ( 20.669 %)\n",
      "Number of data points in class 1 : 363 ( 17.09 %)\n",
      "Number of data points in class 2 : 289 ( 13.606 %)\n",
      "Number of data points in class 6 : 176 ( 8.286 %)\n",
      "Number of data points in class 5 : 155 ( 7.298 %)\n",
      "Number of data points in class 3 : 57 ( 2.684 %)\n",
      "Number of data points in class 9 : 24 ( 1.13 %)\n",
      "Number of data points in class 8 : 12 ( 0.565 %)\n"
     ]
    }
   ],
   "source": [
    "sorted_yi = np.argsort(-train_class_distribution.values)\n",
    "for i in sorted_yi:\n",
    "    print('Number of data points in class', i+1, ':',train_class_distribution.values[i], '(', np.round((train_class_distribution.values[i]/train_df.shape[0]*100), 3), '%)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's visualize the same for test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a194f0940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "my_colors = 'rgbkymc'\n",
    "test_class_distribution.plot(kind='bar')\n",
    "plt.xlabel('Class')\n",
    "plt.ylabel('Number of Data points per Class')\n",
    "plt.title('Distribution of yi in test data')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of data points in class 7 : 191 ( 28.722 %)\n",
      "Number of data points in class 4 : 137 ( 20.602 %)\n",
      "Number of data points in class 1 : 114 ( 17.143 %)\n",
      "Number of data points in class 2 : 91 ( 13.684 %)\n",
      "Number of data points in class 6 : 55 ( 8.271 %)\n",
      "Number of data points in class 5 : 48 ( 7.218 %)\n",
      "Number of data points in class 3 : 18 ( 2.707 %)\n",
      "Number of data points in class 9 : 7 ( 1.053 %)\n",
      "Number of data points in class 8 : 4 ( 0.602 %)\n"
     ]
    }
   ],
   "source": [
    "sorted_yi = np.argsort(-test_class_distribution.values)\n",
    "for i in sorted_yi:\n",
    "    print('Number of data points in class', i+1, ':',test_class_distribution.values[i], '(', np.round((test_class_distribution.values[i]/test_df.shape[0]*100), 3), '%)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's visualize for cross validation set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a12f2db38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "my_colors = 'rgbkymc'\n",
    "cv_class_distribution.plot(kind='bar')\n",
    "plt.xlabel('Class')\n",
    "plt.ylabel('Data points per Class')\n",
    "plt.title('Distribution of yi in cross validation data')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of data points in class 7 : 153 ( 28.759 %)\n",
      "Number of data points in class 4 : 110 ( 20.677 %)\n",
      "Number of data points in class 1 : 91 ( 17.105 %)\n",
      "Number of data points in class 2 : 72 ( 13.534 %)\n",
      "Number of data points in class 6 : 44 ( 8.271 %)\n",
      "Number of data points in class 5 : 39 ( 7.331 %)\n",
      "Number of data points in class 3 : 14 ( 2.632 %)\n",
      "Number of data points in class 9 : 6 ( 1.128 %)\n",
      "Number of data points in class 8 : 3 ( 0.564 %)\n"
     ]
    }
   ],
   "source": [
    "sorted_yi = np.argsort(-train_class_distribution.values)\n",
    "for i in sorted_yi:\n",
    "    print('Number of data points in class', i+1, ':',cv_class_distribution.values[i], '(', np.round((cv_class_distribution.values[i]/cv_df.shape[0]*100), 3), '%)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now question is because we need log-loss as final evaluation metrics how do we say that model we are going to build will be good model. For doing this we will build a random model and will evaluate log loss. Our model should return lower log loss value than this. So, what are you waiting for. Always have a bg smile while solving Machine learning problems :). That helps!!\n",
    "\n",
    "## Building a Random model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ok, so we need to generate 9 random numbers because we have 9 class such that their sum must be equal to 1 because sum of Probablity of all 9 classes must be equivalent to 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data_len = test_df.shape[0]\n",
    "cv_data_len = cv_df.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Log loss on Cross Validation Data using Random Model 2.47716883083\n"
     ]
    }
   ],
   "source": [
    "# we create a output array that has exactly same size as the CV data\n",
    "cv_predicted_y = np.zeros((cv_data_len,9))\n",
    "for i in range(cv_data_len):\n",
    "    rand_probs = np.random.rand(1,9)\n",
    "    cv_predicted_y[i] = ((rand_probs/sum(sum(rand_probs)))[0])\n",
    "print(\"Log loss on Cross Validation Data using Random Model\",log_loss(y_cv,cv_predicted_y, eps=1e-15))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Log loss on Test Data using Random Model 2.51192909174\n"
     ]
    }
   ],
   "source": [
    "# Test-Set error.\n",
    "#we create a output array that has exactly same as the test data\n",
    "test_predicted_y = np.zeros((test_data_len,9))\n",
    "for i in range(test_data_len):\n",
    "    rand_probs = np.random.rand(1,9)\n",
    "    test_predicted_y[i] = ((rand_probs/sum(sum(rand_probs)))[0])\n",
    "print(\"Log loss on Test Data using Random Model\",log_loss(y_test,test_predicted_y, eps=1e-15))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Lets get the index of max probablity\n",
    "predicted_y =np.argmax(test_predicted_y, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4, 8, 5, 8, 2, 2, 4, 7, 7, 2, 3, 1, 4, 2, 5, 6, 3, 3, 5, 2, 0, 2, 8,\n",
       "       8, 2, 6, 3, 3, 7, 7, 4, 2, 0, 3, 8, 6, 0, 8, 0, 4, 1, 2, 1, 3, 5, 7,\n",
       "       5, 3, 1, 0, 4, 2, 1, 6, 0, 7, 4, 1, 1, 7, 7, 5, 3, 8, 7, 2, 4, 6, 5,\n",
       "       3, 7, 1, 0, 5, 4, 8, 3, 6, 8, 2, 4, 7, 5, 7, 8, 4, 6, 6, 6, 5, 7, 7,\n",
       "       7, 1, 3, 7, 8, 5, 3, 3, 7, 2, 2, 2, 3, 6, 8, 0, 7, 2, 3, 5, 8, 6, 0,\n",
       "       4, 6, 5, 4, 8, 0, 8, 7, 1, 0, 1, 8, 0, 3, 8, 4, 4, 5, 8, 6, 5, 8, 1,\n",
       "       4, 2, 5, 8, 0, 1, 3, 4, 5, 5, 6, 5, 0, 7, 7, 0, 2, 4, 7, 0, 4, 6, 5,\n",
       "       0, 0, 8, 1, 8, 8, 3, 1, 4, 5, 3, 8, 6, 0, 5, 0, 1, 2, 2, 2, 7, 8, 3,\n",
       "       2, 0, 8, 7, 1, 1, 5, 0, 5, 8, 5, 6, 0, 0, 7, 6, 2, 8, 0, 7, 0, 3, 1,\n",
       "       3, 7, 5, 0, 4, 8, 4, 8, 0, 4, 7, 0, 7, 8, 6, 5, 5, 6, 7, 6, 6, 0, 6,\n",
       "       4, 3, 7, 6, 3, 1, 0, 8, 5, 4, 2, 7, 3, 5, 3, 7, 1, 0, 4, 0, 2, 7, 0,\n",
       "       3, 7, 1, 0, 6, 6, 0, 0, 2, 0, 0, 0, 4, 6, 7, 1, 2, 3, 3, 1, 5, 4, 7,\n",
       "       6, 3, 5, 4, 5, 2, 0, 5, 5, 7, 5, 8, 7, 6, 7, 5, 4, 3, 2, 8, 0, 4, 4,\n",
       "       4, 4, 5, 0, 0, 3, 5, 5, 2, 4, 0, 2, 4, 5, 2, 2, 6, 5, 8, 5, 2, 1, 3,\n",
       "       7, 8, 3, 6, 4, 8, 4, 4, 5, 3, 3, 8, 0, 8, 3, 2, 0, 8, 0, 4, 1, 8, 8,\n",
       "       3, 4, 3, 3, 7, 3, 0, 1, 6, 5, 3, 4, 6, 3, 7, 2, 3, 2, 4, 8, 5, 5, 2,\n",
       "       0, 2, 4, 1, 1, 0, 8, 4, 3, 7, 5, 2, 7, 8, 5, 2, 3, 1, 8, 4, 0, 3, 7,\n",
       "       3, 3, 7, 1, 8, 3, 2, 5, 0, 5, 3, 2, 4, 8, 3, 0, 2, 5, 8, 7, 0, 3, 6,\n",
       "       3, 0, 2, 1, 4, 2, 0, 2, 0, 4, 5, 0, 1, 8, 2, 3, 5, 6, 3, 6, 2, 8, 1,\n",
       "       6, 3, 4, 8, 0, 7, 7, 0, 5, 3, 8, 8, 1, 3, 8, 0, 3, 1, 0, 2, 5, 1, 3,\n",
       "       3, 0, 8, 8, 2, 2, 2, 8, 3, 8, 6, 7, 3, 5, 3, 7, 5, 8, 8, 7, 6, 1, 4,\n",
       "       3, 3, 1, 8, 7, 2, 8, 4, 0, 0, 0, 6, 0, 8, 2, 3, 5, 8, 2, 1, 4, 7, 2,\n",
       "       5, 4, 7, 0, 8, 7, 7, 7, 0, 6, 6, 2, 5, 3, 4, 0, 4, 2, 0, 0, 8, 6, 3,\n",
       "       4, 3, 6, 5, 4, 1, 8, 6, 5, 7, 7, 3, 1, 8, 3, 7, 0, 8, 6, 4, 4, 6, 5,\n",
       "       3, 5, 4, 6, 5, 2, 3, 5, 7, 3, 2, 5, 4, 8, 8, 4, 5, 2, 7, 8, 8, 5, 1,\n",
       "       0, 2, 1, 6, 3, 3, 8, 4, 4, 8, 6, 1, 1, 1, 8, 6, 2, 6, 6, 8, 2, 8, 7,\n",
       "       5, 2, 6, 2, 7, 2, 0, 3, 2, 4, 8, 4, 1, 3, 0, 1, 5, 4, 8, 7, 1, 5, 8,\n",
       "       4, 4, 3, 0, 8, 4, 1, 0, 6, 8, 7, 0, 4, 1, 3, 4, 7, 5, 1, 2, 8, 1, 1,\n",
       "       2, 0, 4, 5, 7, 5, 1, 3, 7, 0, 2, 4, 2, 4, 6, 7, 6, 7, 8, 3, 5])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Lets see the output. these will be 665 values present in test dataset\n",
    "predicted_y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So you can see the index value ranging from 0 to 8. So, lets make it as 1 to 9 we will increase this value by 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "predicted_y = predicted_y + 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "C = confusion_matrix(y_test, predicted_y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1acea6d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels = [1,2,3,4,5,6,7,8,9]\n",
    "plt.figure(figsize=(20,7))\n",
    "sns.heatmap(C, annot=True, cmap=\"YlGnBu\", fmt=\".3f\", xticklabels=labels, yticklabels=labels)\n",
    "plt.xlabel('Predicted Class')\n",
    "plt.ylabel('Original Class')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Precision matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "B =(C/C.sum(axis=0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a194f0048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,7))\n",
    "sns.heatmap(B, annot=True, cmap=\"YlGnBu\", fmt=\".3f\", xticklabels=labels, yticklabels=labels)\n",
    "plt.xlabel('Predicted Class')\n",
    "plt.ylabel('Original Class')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Recall matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "A =(((C.T)/(C.sum(axis=1))).T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1a5cf828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,7))\n",
    "sns.heatmap(A, annot=True, cmap=\"YlGnBu\", fmt=\".3f\", xticklabels=labels, yticklabels=labels)\n",
    "plt.xlabel('Predicted Class')\n",
    "plt.ylabel('Original Class')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluating Gene Column\n",
    "\n",
    "Now we will look at each independent column to make sure its relavent for my target variable but the question is, how? Let's understand with our first column Gene which is categorial in nature.\n",
    "\n",
    "So, lets explore column ***Gene*** and lets look at its distribution. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Unique Genes : 239\n",
      "BRCA1     163\n",
      "TP53       96\n",
      "EGFR       84\n",
      "PTEN       76\n",
      "BRCA2      74\n",
      "KIT        62\n",
      "BRAF       56\n",
      "ERBB2      51\n",
      "ALK        43\n",
      "PDGFRA     42\n",
      "Name: Gene, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "unique_genes = train_df['Gene'].value_counts()\n",
    "print('Number of Unique Genes :', unique_genes.shape[0])\n",
    "# the top 10 genes that occured most\n",
    "print(unique_genes.head(10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets see the number of unique values present in gene"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "239"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unique_genes.shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets look at the comulative distribution of unique Genes values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1ac3d710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "s = sum(unique_genes.values);\n",
    "h = unique_genes.values/s;\n",
    "c = np.cumsum(h)\n",
    "plt.plot(c,label='Cumulative distribution of Genes')\n",
    "plt.grid()\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, now we need to convert these categorical variable to appropirate format which my machine learning algorithm will be able to take as an input.\n",
    "\n",
    "So we have 2 techniques to deal with it. \n",
    "\n",
    "<ol><li>\n",
    "     ***One-hot encoding*** </li>\n",
    "    <li> ***Response Encoding*** (Mean imputation) </li>\n",
    "</ol>\n",
    "\n",
    "Let's use both of them to see which one work the best. So lets start encoding using one hot encoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# one-hot encoding of Gene feature.\n",
    "gene_vectorizer = CountVectorizer()\n",
    "train_gene_feature_onehotCoding = gene_vectorizer.fit_transform(train_df['Gene'])\n",
    "test_gene_feature_onehotCoding = gene_vectorizer.transform(test_df['Gene'])\n",
    "cv_gene_feature_onehotCoding = gene_vectorizer.transform(cv_df['Gene'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check the number of column generated after one hot encoding. One hot encoding will always return higher number of column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2124, 238)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_gene_feature_onehotCoding.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['abl1',\n",
       " 'acvr1',\n",
       " 'ago2',\n",
       " 'akt1',\n",
       " 'akt2',\n",
       " 'akt3',\n",
       " 'alk',\n",
       " 'apc',\n",
       " 'ar',\n",
       " 'araf',\n",
       " 'arid1a',\n",
       " 'arid1b',\n",
       " 'arid2',\n",
       " 'arid5b',\n",
       " 'asxl1',\n",
       " 'atm',\n",
       " 'atrx',\n",
       " 'aurka',\n",
       " 'aurkb',\n",
       " 'axin1',\n",
       " 'axl',\n",
       " 'b2m',\n",
       " 'bap1',\n",
       " 'bard1',\n",
       " 'bcl2',\n",
       " 'bcl2l11',\n",
       " 'bcor',\n",
       " 'braf',\n",
       " 'brca1',\n",
       " 'brca2',\n",
       " 'brd4',\n",
       " 'brip1',\n",
       " 'btk',\n",
       " 'card11',\n",
       " 'carm1',\n",
       " 'casp8',\n",
       " 'cbl',\n",
       " 'ccnd1',\n",
       " 'ccnd2',\n",
       " 'ccnd3',\n",
       " 'ccne1',\n",
       " 'cdh1',\n",
       " 'cdk12',\n",
       " 'cdk4',\n",
       " 'cdk6',\n",
       " 'cdk8',\n",
       " 'cdkn1a',\n",
       " 'cdkn1b',\n",
       " 'cdkn2a',\n",
       " 'cdkn2b',\n",
       " 'cdkn2c',\n",
       " 'chek2',\n",
       " 'cic',\n",
       " 'crebbp',\n",
       " 'ctcf',\n",
       " 'ctla4',\n",
       " 'ctnnb1',\n",
       " 'ddr2',\n",
       " 'dicer1',\n",
       " 'dnmt3a',\n",
       " 'dnmt3b',\n",
       " 'dusp4',\n",
       " 'egfr',\n",
       " 'eif1ax',\n",
       " 'elf3',\n",
       " 'ep300',\n",
       " 'epas1',\n",
       " 'erbb2',\n",
       " 'erbb3',\n",
       " 'erbb4',\n",
       " 'ercc2',\n",
       " 'ercc3',\n",
       " 'ercc4',\n",
       " 'erg',\n",
       " 'esr1',\n",
       " 'etv1',\n",
       " 'etv6',\n",
       " 'ewsr1',\n",
       " 'ezh2',\n",
       " 'fam58a',\n",
       " 'fanca',\n",
       " 'fancc',\n",
       " 'fat1',\n",
       " 'fbxw7',\n",
       " 'fgf3',\n",
       " 'fgfr1',\n",
       " 'fgfr2',\n",
       " 'fgfr3',\n",
       " 'fgfr4',\n",
       " 'flt1',\n",
       " 'flt3',\n",
       " 'foxa1',\n",
       " 'foxl2',\n",
       " 'foxo1',\n",
       " 'foxp1',\n",
       " 'gata3',\n",
       " 'gli1',\n",
       " 'gnaq',\n",
       " 'gnas',\n",
       " 'h3f3a',\n",
       " 'hist1h1c',\n",
       " 'hla',\n",
       " 'hnf1a',\n",
       " 'hras',\n",
       " 'idh1',\n",
       " 'idh2',\n",
       " 'igf1r',\n",
       " 'ikzf1',\n",
       " 'jak1',\n",
       " 'jak2',\n",
       " 'kdm5a',\n",
       " 'kdm5c',\n",
       " 'kdm6a',\n",
       " 'kdr',\n",
       " 'keap1',\n",
       " 'kit',\n",
       " 'kmt2a',\n",
       " 'kmt2b',\n",
       " 'kmt2c',\n",
       " 'kmt2d',\n",
       " 'knstrn',\n",
       " 'kras',\n",
       " 'lats1',\n",
       " 'lats2',\n",
       " 'map2k1',\n",
       " 'map2k2',\n",
       " 'map2k4',\n",
       " 'map3k1',\n",
       " 'mapk1',\n",
       " 'mdm2',\n",
       " 'med12',\n",
       " 'mef2b',\n",
       " 'men1',\n",
       " 'met',\n",
       " 'mga',\n",
       " 'mlh1',\n",
       " 'mpl',\n",
       " 'msh2',\n",
       " 'msh6',\n",
       " 'mtor',\n",
       " 'myc',\n",
       " 'mycn',\n",
       " 'myd88',\n",
       " 'myod1',\n",
       " 'ncor1',\n",
       " 'nf1',\n",
       " 'nf2',\n",
       " 'nfe2l2',\n",
       " 'nfkbia',\n",
       " 'nkx2',\n",
       " 'notch1',\n",
       " 'notch2',\n",
       " 'npm1',\n",
       " 'nras',\n",
       " 'nsd1',\n",
       " 'ntrk1',\n",
       " 'ntrk2',\n",
       " 'ntrk3',\n",
       " 'nup93',\n",
       " 'pak1',\n",
       " 'pbrm1',\n",
       " 'pdgfra',\n",
       " 'pdgfrb',\n",
       " 'pik3ca',\n",
       " 'pik3cb',\n",
       " 'pik3cd',\n",
       " 'pik3r1',\n",
       " 'pik3r2',\n",
       " 'pik3r3',\n",
       " 'pim1',\n",
       " 'pms2',\n",
       " 'pole',\n",
       " 'ppm1d',\n",
       " 'ppp2r1a',\n",
       " 'ppp6c',\n",
       " 'prdm1',\n",
       " 'ptch1',\n",
       " 'pten',\n",
       " 'ptpn11',\n",
       " 'ptprd',\n",
       " 'ptprt',\n",
       " 'rab35',\n",
       " 'rac1',\n",
       " 'rad21',\n",
       " 'rad50',\n",
       " 'rad51b',\n",
       " 'rad51c',\n",
       " 'rad54l',\n",
       " 'raf1',\n",
       " 'rara',\n",
       " 'rasa1',\n",
       " 'rb1',\n",
       " 'rbm10',\n",
       " 'ret',\n",
       " 'rheb',\n",
       " 'rhoa',\n",
       " 'rictor',\n",
       " 'rit1',\n",
       " 'rnf43',\n",
       " 'ros1',\n",
       " 'rras2',\n",
       " 'runx1',\n",
       " 'rybp',\n",
       " 'sdhb',\n",
       " 'sdhc',\n",
       " 'setd2',\n",
       " 'sf3b1',\n",
       " 'shq1',\n",
       " 'smad2',\n",
       " 'smad3',\n",
       " 'smad4',\n",
       " 'smarca4',\n",
       " 'smarcb1',\n",
       " 'smo',\n",
       " 'sos1',\n",
       " 'sox9',\n",
       " 'spop',\n",
       " 'src',\n",
       " 'srsf2',\n",
       " 'stat3',\n",
       " 'stk11',\n",
       " 'tcf7l2',\n",
       " 'tert',\n",
       " 'tet1',\n",
       " 'tet2',\n",
       " 'tgfbr1',\n",
       " 'tgfbr2',\n",
       " 'tmprss2',\n",
       " 'tp53',\n",
       " 'tp53bp1',\n",
       " 'tsc1',\n",
       " 'tsc2',\n",
       " 'u2af1',\n",
       " 'vhl',\n",
       " 'whsc1',\n",
       " 'xpo1',\n",
       " 'xrcc2',\n",
       " 'yap1']"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#column names after one-hot encoding for Gene column\n",
    "gene_vectorizer.get_feature_names()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, lets also create Response encoding columns for Gene column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# code for response coding with Laplace smoothing.\n",
    "# alpha : used for laplace smoothing\n",
    "# feature: ['gene', 'variation']\n",
    "# df: ['train_df', 'test_df', 'cv_df']\n",
    "# algorithm\n",
    "# ----------\n",
    "# Consider all unique values and the number of occurances of given feature in train data dataframe\n",
    "# build a vector (1*9) , the first element = (number of times it occured in class1 + 10*alpha / number of time it occurred in total data+90*alpha)\n",
    "# gv_dict is like a look up table, for every gene it store a (1*9) representation of it\n",
    "# for a value of feature in df:\n",
    "# if it is in train data:\n",
    "# we add the vector that was stored in 'gv_dict' look up table to 'gv_fea'\n",
    "# if it is not there is train:\n",
    "# we add [1/9, 1/9, 1/9, 1/9,1/9, 1/9, 1/9, 1/9, 1/9] to 'gv_fea'\n",
    "# return 'gv_fea'\n",
    "# ----------------------\n",
    "\n",
    "# get_gv_fea_dict: Get Gene varaition Feature Dict\n",
    "def get_gv_fea_dict(alpha, feature, df):\n",
    "    # value_count: it contains a dict like\n",
    "    # print(train_df['Gene'].value_counts())\n",
    "    # output:\n",
    "    #        {BRCA1      174\n",
    "    #         TP53       106\n",
    "    #         EGFR        86\n",
    "    #         BRCA2       75\n",
    "    #         PTEN        69\n",
    "    #         KIT         61\n",
    "    #         BRAF        60\n",
    "    #         ERBB2       47\n",
    "    #         PDGFRA      46\n",
    "    #         ...}\n",
    "    # print(train_df['Variation'].value_counts())\n",
    "    # output:\n",
    "    # {\n",
    "    # Truncating_Mutations                     63\n",
    "    # Deletion                                 43\n",
    "    # Amplification                            43\n",
    "    # Fusions                                  22\n",
    "    # Overexpression                            3\n",
    "    # E17K                                      3\n",
    "    # Q61L                                      3\n",
    "    # S222D                                     2\n",
    "    # P130S                                     2\n",
    "    # ...\n",
    "    # }\n",
    "    value_count = train_df[feature].value_counts()\n",
    "    \n",
    "    # gv_dict : Gene Variation Dict, which contains the probability array for each gene/variation\n",
    "    gv_dict = dict()\n",
    "    \n",
    "    # denominator will contain the number of time that particular feature occured in whole data\n",
    "    for i, denominator in value_count.items():\n",
    "        # vec will contain (p(yi==1/Gi) probability of gene/variation belongs to perticular class\n",
    "        # vec is 9 diamensional vector\n",
    "        vec = []\n",
    "        for k in range(1,10):\n",
    "            # print(train_df.loc[(train_df['Class']==1) & (train_df['Gene']=='BRCA1')])\n",
    "            #         ID   Gene             Variation  Class  \n",
    "            # 2470  2470  BRCA1                S1715C      1   \n",
    "            # 2486  2486  BRCA1                S1841R      1   \n",
    "            # 2614  2614  BRCA1                   M1R      1   \n",
    "            # 2432  2432  BRCA1                L1657P      1   \n",
    "            # 2567  2567  BRCA1                T1685A      1   \n",
    "            # 2583  2583  BRCA1                E1660G      1   \n",
    "            # 2634  2634  BRCA1                W1718L      1   \n",
    "            # cls_cnt.shape[0] will return the number of rows\n",
    "\n",
    "            cls_cnt = train_df.loc[(train_df['Class']==k) & (train_df[feature]==i)]\n",
    "            \n",
    "            # cls_cnt.shape[0](numerator) will contain the number of time that particular feature occured in whole data\n",
    "            vec.append((cls_cnt.shape[0] + alpha*10)/ (denominator + 90*alpha))\n",
    "\n",
    "        # we are adding the gene/variation to the dict as key and vec as value\n",
    "        gv_dict[i]=vec\n",
    "    return gv_dict\n",
    "\n",
    "# Get Gene variation feature\n",
    "def get_gv_feature(alpha, feature, df):\n",
    "    # print(gv_dict)\n",
    "    #     {'BRCA1': [0.20075757575757575, 0.03787878787878788, 0.068181818181818177, 0.13636363636363635, 0.25, 0.19318181818181818, 0.03787878787878788, 0.03787878787878788, 0.03787878787878788], \n",
    "    #      'TP53': [0.32142857142857145, 0.061224489795918366, 0.061224489795918366, 0.27040816326530615, 0.061224489795918366, 0.066326530612244902, 0.051020408163265307, 0.051020408163265307, 0.056122448979591837], \n",
    "    #      'EGFR': [0.056818181818181816, 0.21590909090909091, 0.0625, 0.068181818181818177, 0.068181818181818177, 0.0625, 0.34659090909090912, 0.0625, 0.056818181818181816], \n",
    "    #      'BRCA2': [0.13333333333333333, 0.060606060606060608, 0.060606060606060608, 0.078787878787878782, 0.1393939393939394, 0.34545454545454546, 0.060606060606060608, 0.060606060606060608, 0.060606060606060608], \n",
    "    #      'PTEN': [0.069182389937106917, 0.062893081761006289, 0.069182389937106917, 0.46540880503144655, 0.075471698113207544, 0.062893081761006289, 0.069182389937106917, 0.062893081761006289, 0.062893081761006289], \n",
    "    #      'KIT': [0.066225165562913912, 0.25165562913907286, 0.072847682119205295, 0.072847682119205295, 0.066225165562913912, 0.066225165562913912, 0.27152317880794702, 0.066225165562913912, 0.066225165562913912], \n",
    "    #      'BRAF': [0.066666666666666666, 0.17999999999999999, 0.073333333333333334, 0.073333333333333334, 0.093333333333333338, 0.080000000000000002, 0.29999999999999999, 0.066666666666666666, 0.066666666666666666],\n",
    "    #      ...\n",
    "    #     }\n",
    "    gv_dict = get_gv_fea_dict(alpha, feature, df)\n",
    "    # value_count is similar in get_gv_fea_dict\n",
    "    value_count = train_df[feature].value_counts()\n",
    "    \n",
    "    # gv_fea: Gene_variation feature, it will contain the feature for each feature value in the data\n",
    "    gv_fea = []\n",
    "    # for every feature values in the given data frame we will check if it is there in the train data then we will add the feature to gv_fea\n",
    "    # if not we will add [1/9,1/9,1/9,1/9,1/9,1/9,1/9,1/9,1/9] to gv_fea\n",
    "    for index, row in df.iterrows():\n",
    "        if row[feature] in dict(value_count).keys():\n",
    "            gv_fea.append(gv_dict[row[feature]])\n",
    "        else:\n",
    "            gv_fea.append([1/9,1/9,1/9,1/9,1/9,1/9,1/9,1/9,1/9])\n",
    "#             gv_fea.append([-1,-1,-1,-1,-1,-1,-1,-1,-1])\n",
    "    return gv_fea"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "#response-coding of the Gene feature\n",
    "# alpha is used for laplace smoothing\n",
    "alpha = 1\n",
    "# train gene feature\n",
    "train_gene_feature_responseCoding = np.array(get_gv_feature(alpha, \"Gene\", train_df))\n",
    "# test gene feature\n",
    "test_gene_feature_responseCoding = np.array(get_gv_feature(alpha, \"Gene\", test_df))\n",
    "# cross validation gene feature\n",
    "cv_gene_feature_responseCoding = np.array(get_gv_feature(alpha, \"Gene\", cv_df))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at columns after applying response encoding. We must be having 9 columns for Gene column after response encoding."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2124, 9)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_gene_feature_responseCoding.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, question is how good is Gene column feature to predict my 9 classes. One idea could be that we will build model having only gene column with one hot encoder with simple model like Logistic regression. If log loss with only one column Gene comes out to be better than random model, than this feature is important."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We need a hyperparemeter for SGD classifier.\n",
    "alpha = [10 ** x for x in range(-5, 1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For values of alpha =  1e-05 The log loss is: 1.3368605233\n",
      "For values of alpha =  0.0001 The log loss is: 1.1538252363\n",
      "For values of alpha =  0.001 The log loss is: 1.17216617485\n",
      "For values of alpha =  0.01 The log loss is: 1.31039471095\n",
      "For values of alpha =  0.1 The log loss is: 1.42650991646\n",
      "For values of alpha =  1 The log loss is: 1.46789693793\n"
     ]
    }
   ],
   "source": [
    "# We will be using SGD classifier\n",
    "# http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.SGDClassifier.html\n",
    "# We will also be using Calibrated Classifier to get the result into probablity format t be used for log loss\n",
    "cv_log_error_array=[]\n",
    "for i in alpha:\n",
    "    clf = SGDClassifier(alpha=i, penalty='l2', loss='log', random_state=42)\n",
    "    clf.fit(train_gene_feature_onehotCoding, y_train)\n",
    "    sig_clf = CalibratedClassifierCV(clf, method=\"sigmoid\")\n",
    "    sig_clf.fit(train_gene_feature_onehotCoding, y_train)\n",
    "    predict_y = sig_clf.predict_proba(cv_gene_feature_onehotCoding)\n",
    "    cv_log_error_array.append(log_loss(y_cv, predict_y, labels=clf.classes_, eps=1e-15))\n",
    "    print('For values of alpha = ', i, \"The log loss is:\",log_loss(y_cv, predict_y, labels=clf.classes_, eps=1e-15))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1c2e81d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Lets plot the same to check the best Alpha value\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(alpha, cv_log_error_array,c='g')\n",
    "for i, txt in enumerate(np.round(cv_log_error_array,3)):\n",
    "    ax.annotate((alpha[i],np.round(txt,3)), (alpha[i],cv_log_error_array[i]))\n",
    "plt.grid()\n",
    "plt.title(\"Cross Validation Error for each alpha\")\n",
    "plt.xlabel(\"Alpha i's\")\n",
    "plt.ylabel(\"Error measure\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For values of best alpha =  0.0001 The train log loss is: 1.03428893664\n",
      "For values of best alpha =  0.0001 The cross validation log loss is: 1.1538252363\n",
      "For values of best alpha =  0.0001 The test log loss is: 1.23969521463\n"
     ]
    }
   ],
   "source": [
    "# Lets use best alpha value as we can see from above graph and compute log loss\n",
    "best_alpha = np.argmin(cv_log_error_array)\n",
    "clf = SGDClassifier(alpha=alpha[best_alpha], penalty='l2', loss='log', random_state=42)\n",
    "clf.fit(train_gene_feature_onehotCoding, y_train)\n",
    "sig_clf = CalibratedClassifierCV(clf, method=\"sigmoid\")\n",
    "sig_clf.fit(train_gene_feature_onehotCoding, y_train)\n",
    "\n",
    "predict_y = sig_clf.predict_proba(train_gene_feature_onehotCoding)\n",
    "print('For values of best alpha = ', alpha[best_alpha], \"The train log loss is:\",log_loss(y_train, predict_y, labels=clf.classes_, eps=1e-15))\n",
    "predict_y = sig_clf.predict_proba(cv_gene_feature_onehotCoding)\n",
    "print('For values of best alpha = ', alpha[best_alpha], \"The cross validation log loss is:\",log_loss(y_cv, predict_y, labels=clf.classes_, eps=1e-15))\n",
    "predict_y = sig_clf.predict_proba(test_gene_feature_onehotCoding)\n",
    "print('For values of best alpha = ', alpha[best_alpha], \"The test log loss is:\",log_loss(y_test, predict_y, labels=clf.classes_, eps=1e-15))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now lets check how many values are overlapping between train, test or between CV and train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_coverage=test_df[test_df['Gene'].isin(list(set(train_df['Gene'])))].shape[0]\n",
    "cv_coverage=cv_df[cv_df['Gene'].isin(list(set(train_df['Gene'])))].shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1. In test data 651 out of 665 : 97.89473684210527\n",
      "2. In cross validation data 519 out of  532 : 97.55639097744361\n"
     ]
    }
   ],
   "source": [
    "print('1. In test data',test_coverage, 'out of',test_df.shape[0], \":\",(test_coverage/test_df.shape[0])*100)\n",
    "print('2. In cross validation data',cv_coverage, 'out of ',cv_df.shape[0],\":\" ,(cv_coverage/cv_df.shape[0])*100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluating Variation column"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Variation is also a categorical variable so we have to deal in same way like we have done for ***Gene*** column. We will again get the one hot encoder and response enoding variable for variation column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Unique Variations : 1922\n",
      "Truncating_Mutations    67\n",
      "Deletion                53\n",
      "Amplification           45\n",
      "Fusions                 19\n",
      "Overexpression           4\n",
      "T58I                     3\n",
      "Y42C                     2\n",
      "G12V                     2\n",
      "T286A                    2\n",
      "E17K                     2\n",
      "Name: Variation, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "unique_variations = train_df['Variation'].value_counts()\n",
    "print('Number of Unique Variations :', unique_variations.shape[0])\n",
    "# the top 10 variations that occured most\n",
    "print(unique_variations.head(10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets look at the comulative distribution of unique ***variation*** values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.03154426  0.05649718  0.07768362 ...,  0.99905838  0.99952919  1.        ]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1c3df4a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "s = sum(unique_variations.values);\n",
    "h = unique_variations.values/s;\n",
    "c = np.cumsum(h)\n",
    "print(c)\n",
    "plt.plot(c,label='Cumulative distribution of Variations')\n",
    "plt.grid()\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets convert the variation column using one hot encoder column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "# one-hot encoding of variation feature.\n",
    "variation_vectorizer = CountVectorizer()\n",
    "train_variation_feature_onehotCoding = variation_vectorizer.fit_transform(train_df['Variation'])\n",
    "test_variation_feature_onehotCoding = variation_vectorizer.transform(test_df['Variation'])\n",
    "cv_variation_feature_onehotCoding = variation_vectorizer.transform(cv_df['Variation'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets look at shape of one hot encoder column for variation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2124, 1953)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_variation_feature_onehotCoding.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets do the same for variation column and generate response encoding for the same."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "# alpha is used for laplace smoothing\n",
    "alpha = 1\n",
    "# train gene feature\n",
    "train_variation_feature_responseCoding = np.array(get_gv_feature(alpha, \"Variation\", train_df))\n",
    "# test gene feature\n",
    "test_variation_feature_responseCoding = np.array(get_gv_feature(alpha, \"Variation\", test_df))\n",
    "# cross validation gene feature\n",
    "cv_variation_feature_responseCoding = np.array(get_gv_feature(alpha, \"Variation\", cv_df))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets look at the shape of this response encoding result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2124, 9)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_variation_feature_responseCoding.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets again build the model with only column name of ***variation*** column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We need a hyperparemeter for SGD classifier.\n",
    "alpha = [10 ** x for x in range(-5, 1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For values of alpha =  1e-05 The log loss is: 1.71725066519\n",
      "For values of alpha =  0.0001 The log loss is: 1.70909981795\n",
      "For values of alpha =  0.001 The log loss is: 1.71633975946\n",
      "For values of alpha =  0.01 The log loss is: 1.7250406318\n",
      "For values of alpha =  0.1 The log loss is: 1.73601806354\n",
      "For values of alpha =  1 The log loss is: 1.73814028659\n"
     ]
    }
   ],
   "source": [
    "# We will be using SGD classifier\n",
    "# http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.SGDClassifier.html\n",
    "# We will also be using Calibrated Classifier to get the result into probablity format t be used for log loss\n",
    "cv_log_error_array=[]\n",
    "for i in alpha:\n",
    "    clf = SGDClassifier(alpha=i, penalty='l2', loss='log', random_state=42)\n",
    "    clf.fit(train_variation_feature_onehotCoding, y_train)\n",
    "    \n",
    "    sig_clf = CalibratedClassifierCV(clf, method=\"sigmoid\")\n",
    "    sig_clf.fit(train_variation_feature_onehotCoding, y_train)\n",
    "    predict_y = sig_clf.predict_proba(cv_variation_feature_onehotCoding)\n",
    "    \n",
    "    cv_log_error_array.append(log_loss(y_cv, predict_y, labels=clf.classes_, eps=1e-15))\n",
    "    print('For values of alpha = ', i, \"The log loss is:\",log_loss(y_cv, predict_y, labels=clf.classes_, eps=1e-15))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a189d1e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Lets plot the same to check the best Alpha value\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(alpha, cv_log_error_array,c='g')\n",
    "for i, txt in enumerate(np.round(cv_log_error_array,3)):\n",
    "    ax.annotate((alpha[i],np.round(txt,3)), (alpha[i],cv_log_error_array[i]))\n",
    "plt.grid()\n",
    "plt.title(\"Cross Validation Error for each alpha\")\n",
    "plt.xlabel(\"Alpha i's\")\n",
    "plt.ylabel(\"Error measure\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For values of best alpha =  0.0001 The train log loss is: 0.747743968009\n",
      "For values of best alpha =  0.0001 The cross validation log loss is: 1.70909981795\n",
      "For values of best alpha =  0.0001 The test log loss is: 1.71064410395\n"
     ]
    }
   ],
   "source": [
    "best_alpha = np.argmin(cv_log_error_array)\n",
    "clf = SGDClassifier(alpha=alpha[best_alpha], penalty='l2', loss='log', random_state=42)\n",
    "clf.fit(train_variation_feature_onehotCoding, y_train)\n",
    "sig_clf = CalibratedClassifierCV(clf, method=\"sigmoid\")\n",
    "sig_clf.fit(train_variation_feature_onehotCoding, y_train)\n",
    "\n",
    "predict_y = sig_clf.predict_proba(train_variation_feature_onehotCoding)\n",
    "print('For values of best alpha = ', alpha[best_alpha], \"The train log loss is:\",log_loss(y_train, predict_y, labels=clf.classes_, eps=1e-15))\n",
    "predict_y = sig_clf.predict_proba(cv_variation_feature_onehotCoding)\n",
    "print('For values of best alpha = ', alpha[best_alpha], \"The cross validation log loss is:\",log_loss(y_cv, predict_y, labels=clf.classes_, eps=1e-15))\n",
    "predict_y = sig_clf.predict_proba(test_variation_feature_onehotCoding)\n",
    "print('For values of best alpha = ', alpha[best_alpha], \"The test log loss is:\",log_loss(y_test, predict_y, labels=clf.classes_, eps=1e-15))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_coverage=test_df[test_df['Variation'].isin(list(set(train_df['Variation'])))].shape[0]\n",
    "cv_coverage=cv_df[cv_df['Variation'].isin(list(set(train_df['Variation'])))].shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1. In test data 70 out of 665 : 10.526315789473683\n",
      "2. In cross validation data 48 out of  532 : 9.022556390977442\n"
     ]
    }
   ],
   "source": [
    "print('1. In test data',test_coverage, 'out of',test_df.shape[0], \":\",(test_coverage/test_df.shape[0])*100)\n",
    "print('2. In cross validation data',cv_coverage, 'out of ',cv_df.shape[0],\":\" ,(cv_coverage/cv_df.shape[0])*100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluating Text column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "# cls_text is a data frame\n",
    "# for every row in data fram consider the 'TEXT'\n",
    "# split the words by space\n",
    "# make a dict with those words\n",
    "# increment its count whenever we see that word\n",
    "\n",
    "def extract_dictionary_paddle(cls_text):\n",
    "    dictionary = defaultdict(int)\n",
    "    for index, row in cls_text.iterrows():\n",
    "        for word in row['TEXT'].split():\n",
    "            dictionary[word] +=1\n",
    "    return dictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "#https://stackoverflow.com/a/1602964\n",
    "def get_text_responsecoding(df):\n",
    "    text_feature_responseCoding = np.zeros((df.shape[0],9))\n",
    "    for i in range(0,9):\n",
    "        row_index = 0\n",
    "        for index, row in df.iterrows():\n",
    "            sum_prob = 0\n",
    "            for word in row['TEXT'].split():\n",
    "                sum_prob += math.log(((dict_list[i].get(word,0)+10 )/(total_dict.get(word,0)+90)))\n",
    "            text_feature_responseCoding[row_index][i] = math.exp(sum_prob/len(row['TEXT'].split()))\n",
    "            row_index += 1\n",
    "    return text_feature_responseCoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of unique words in train data : 52816\n"
     ]
    }
   ],
   "source": [
    "# building a CountVectorizer with all the words that occured minimum 3 times in train data\n",
    "text_vectorizer = CountVectorizer(min_df=3)\n",
    "train_text_feature_onehotCoding = text_vectorizer.fit_transform(train_df['TEXT'])\n",
    "# getting all the feature names (words)\n",
    "train_text_features= text_vectorizer.get_feature_names()\n",
    "\n",
    "# train_text_feature_onehotCoding.sum(axis=0).A1 will sum every row and returns (1*number of features) vector\n",
    "train_text_fea_counts = train_text_feature_onehotCoding.sum(axis=0).A1\n",
    "\n",
    "# zip(list(text_features),text_fea_counts) will zip a word with its number of times it occured\n",
    "text_fea_dict = dict(zip(list(train_text_features),train_text_fea_counts))\n",
    "\n",
    "\n",
    "print(\"Total number of unique words in train data :\", len(train_text_features))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "dict_list = []\n",
    "# dict_list =[] contains 9 dictoinaries each corresponds to a class\n",
    "for i in range(1,10):\n",
    "    cls_text = train_df[train_df['Class']==i]\n",
    "    # build a word dict based on the words in that class\n",
    "    dict_list.append(extract_dictionary_paddle(cls_text))\n",
    "    # append it to dict_list\n",
    "\n",
    "# dict_list[i] is build on i'th  class text data\n",
    "# total_dict is buid on whole training text data\n",
    "total_dict = extract_dictionary_paddle(train_df)\n",
    "\n",
    "\n",
    "confuse_array = []\n",
    "for i in train_text_features:\n",
    "    ratios = []\n",
    "    max_val = -1\n",
    "    for j in range(0,9):\n",
    "        ratios.append((dict_list[j][i]+10 )/(total_dict[i]+90))\n",
    "    confuse_array.append(ratios)\n",
    "confuse_array = np.array(confuse_array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "#response coding of text features\n",
    "train_text_feature_responseCoding  = get_text_responsecoding(train_df)\n",
    "test_text_feature_responseCoding  = get_text_responsecoding(test_df)\n",
    "cv_text_feature_responseCoding  = get_text_responsecoding(cv_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "# https://stackoverflow.com/a/16202486\n",
    "# we convert each row values such that they sum to 1  \n",
    "train_text_feature_responseCoding = (train_text_feature_responseCoding.T/train_text_feature_responseCoding.sum(axis=1)).T\n",
    "test_text_feature_responseCoding = (test_text_feature_responseCoding.T/test_text_feature_responseCoding.sum(axis=1)).T\n",
    "cv_text_feature_responseCoding = (cv_text_feature_responseCoding.T/cv_text_feature_responseCoding.sum(axis=1)).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "# don't forget to normalize every feature\n",
    "train_text_feature_onehotCoding = normalize(train_text_feature_onehotCoding, axis=0)\n",
    "\n",
    "# we use the same vectorizer that was trained on train data\n",
    "test_text_feature_onehotCoding = text_vectorizer.transform(test_df['TEXT'])\n",
    "# don't forget to normalize every feature\n",
    "test_text_feature_onehotCoding = normalize(test_text_feature_onehotCoding, axis=0)\n",
    "\n",
    "# we use the same vectorizer that was trained on train data\n",
    "cv_text_feature_onehotCoding = text_vectorizer.transform(cv_df['TEXT'])\n",
    "# don't forget to normalize every feature\n",
    "cv_text_feature_onehotCoding = normalize(cv_text_feature_onehotCoding, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "#https://stackoverflow.com/a/2258273/4084039\n",
    "sorted_text_fea_dict = dict(sorted(text_fea_dict.items(), key=lambda x: x[1] , reverse=True))\n",
    "sorted_text_occur = np.array(list(sorted_text_fea_dict.values()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Counter({3: 5203, 4: 3624, 6: 3037, 5: 2756, 8: 2185, 7: 1905, 9: 1655, 10: 1461, 12: 1261, 11: 1066, 14: 1046, 13: 929, 16: 856, 15: 773, 18: 730, 17: 593, 20: 576, 21: 555, 24: 521, 41: 473, 19: 456, 23: 450, 22: 414, 30: 400, 26: 388, 28: 359, 27: 347, 25: 322, 32: 300, 33: 282, 29: 276, 31: 274, 34: 246, 36: 239, 40: 236, 37: 235, 35: 233, 42: 218, 39: 194, 48: 192, 50: 185, 38: 176, 44: 168, 52: 168, 43: 164, 47: 163, 46: 153, 45: 152, 51: 151, 49: 146, 55: 146, 60: 146, 56: 138, 57: 133, 62: 131, 54: 128, 53: 123, 59: 120, 74: 119, 64: 115, 69: 115, 58: 114, 61: 107, 63: 107, 68: 105, 65: 101, 72: 101, 80: 98, 71: 97, 66: 95, 70: 92, 67: 90, 84: 90, 82: 85, 75: 83, 79: 81, 76: 80, 85: 80, 90: 79, 77: 77, 78: 71, 73: 70, 100: 70, 81: 69, 89: 68, 93: 64, 96: 64, 88: 63, 98: 63, 83: 62, 86: 61, 91: 61, 97: 61, 87: 58, 92: 56, 109: 54, 95: 53, 112: 53, 111: 52, 101: 51, 105: 50, 94: 49, 103: 49, 115: 49, 117: 49, 99: 48, 107: 48, 104: 47, 108: 47, 135: 47, 120: 46, 106: 45, 125: 45, 102: 44, 130: 44, 113: 43, 116: 43, 110: 42, 128: 42, 136: 42, 159: 42, 118: 41, 123: 41, 134: 40, 114: 39, 121: 39, 119: 38, 126: 38, 137: 37, 143: 37, 154: 37, 129: 36, 140: 36, 142: 36, 141: 35, 144: 35, 133: 33, 153: 33, 156: 33, 147: 32, 149: 32, 131: 31, 148: 31, 158: 31, 132: 30, 124: 29, 127: 29, 139: 29, 206: 29, 138: 28, 155: 28, 170: 28, 214: 28, 122: 27, 160: 27, 164: 27, 180: 27, 185: 27, 196: 27, 204: 27, 205: 27, 162: 26, 167: 26, 176: 26, 171: 25, 210: 25, 234: 25, 146: 24, 150: 24, 161: 24, 163: 24, 166: 24, 183: 24, 186: 24, 187: 24, 198: 24, 222: 24, 259: 24, 157: 23, 169: 23, 175: 23, 182: 23, 190: 23, 221: 23, 151: 22, 184: 22, 217: 22, 243: 22, 189: 21, 209: 21, 173: 20, 191: 20, 192: 20, 195: 20, 200: 20, 201: 20, 230: 20, 271: 20, 145: 19, 168: 19, 172: 19, 179: 19, 197: 19, 207: 19, 254: 19, 272: 19, 152: 18, 181: 18, 199: 18, 220: 18, 223: 18, 228: 18, 237: 18, 246: 18, 247: 18, 248: 18, 256: 18, 264: 18, 286: 18, 273: 18, 165: 17, 178: 17, 208: 17, 211: 17, 216: 17, 235: 17, 242: 17, 244: 17, 282: 17, 188: 16, 202: 16, 224: 16, 227: 16, 229: 16, 240: 16, 262: 16, 285: 16, 306: 16, 231: 15, 238: 15, 249: 15, 267: 15, 280: 15, 299: 15, 312: 15, 174: 14, 245: 14, 250: 14, 281: 14, 288: 14, 291: 14, 297: 14, 333: 14, 338: 14, 353: 14, 203: 13, 226: 13, 232: 13, 265: 13, 283: 13, 284: 13, 287: 13, 289: 13, 301: 13, 315: 13, 320: 13, 365: 13, 392: 13, 438: 13, 213: 13, 241: 13, 212: 12, 233: 12, 251: 12, 252: 12, 253: 12, 277: 12, 278: 12, 279: 12, 290: 12, 300: 12, 314: 12, 330: 12, 348: 12, 352: 12, 377: 12, 177: 11, 194: 11, 215: 11, 219: 11, 236: 11, 255: 11, 257: 11, 292: 11, 294: 11, 295: 11, 302: 11, 313: 11, 316: 11, 318: 11, 319: 11, 325: 11, 328: 11, 337: 11, 343: 11, 356: 11, 360: 11, 393: 11, 397: 11, 406: 11, 218: 10, 225: 10, 260: 10, 266: 10, 268: 10, 269: 10, 274: 10, 378: 10, 414: 10, 417: 10, 424: 10, 451: 10, 497: 10, 193: 9, 239: 9, 258: 9, 270: 9, 275: 9, 276: 9, 298: 9, 309: 9, 321: 9, 326: 9, 331: 9, 335: 9, 344: 9, 347: 9, 358: 9, 407: 9, 422: 9, 426: 9, 460: 9, 462: 9, 469: 9, 490: 9, 508: 9, 540: 9, 261: 8, 263: 8, 293: 8, 307: 8, 317: 8, 322: 8, 323: 8, 324: 8, 327: 8, 336: 8, 340: 8, 341: 8, 342: 8, 346: 8, 350: 8, 351: 8, 363: 8, 368: 8, 380: 8, 385: 8, 399: 8, 402: 8, 420: 8, 431: 8, 435: 8, 444: 8, 456: 8, 463: 8, 499: 8, 596: 8, 303: 7, 304: 7, 308: 7, 332: 7, 339: 7, 361: 7, 366: 7, 369: 7, 373: 7, 376: 7, 379: 7, 382: 7, 383: 7, 390: 7, 391: 7, 395: 7, 410: 7, 455: 7, 458: 7, 464: 7, 466: 7, 476: 7, 486: 7, 491: 7, 511: 7, 512: 7, 525: 7, 535: 7, 554: 7, 573: 7, 609: 7, 674: 7, 793: 7, 868: 7, 403: 7, 436: 7, 494: 7, 676: 7, 310: 6, 311: 6, 349: 6, 354: 6, 359: 6, 370: 6, 371: 6, 374: 6, 381: 6, 386: 6, 388: 6, 396: 6, 411: 6, 413: 6, 415: 6, 421: 6, 423: 6, 428: 6, 439: 6, 440: 6, 445: 6, 447: 6, 453: 6, 457: 6, 459: 6, 465: 6, 470: 6, 472: 6, 473: 6, 483: 6, 524: 6, 532: 6, 556: 6, 563: 6, 570: 6, 592: 6, 595: 6, 623: 6, 644: 6, 651: 6, 662: 6, 701: 6, 706: 6, 754: 6, 781: 6, 813: 6, 876: 6, 904: 6, 477: 6, 362: 6, 1133: 6, 296: 5, 305: 5, 334: 5, 345: 5, 357: 5, 375: 5, 389: 5, 400: 5, 416: 5, 419: 5, 427: 5, 432: 5, 443: 5, 452: 5, 454: 5, 461: 5, 468: 5, 471: 5, 489: 5, 505: 5, 509: 5, 513: 5, 516: 5, 518: 5, 521: 5, 522: 5, 528: 5, 531: 5, 533: 5, 537: 5, 541: 5, 548: 5, 562: 5, 564: 5, 565: 5, 567: 5, 568: 5, 577: 5, 579: 5, 580: 5, 588: 5, 589: 5, 598: 5, 601: 5, 604: 5, 606: 5, 610: 5, 611: 5, 613: 5, 616: 5, 634: 5, 649: 5, 664: 5, 677: 5, 680: 5, 704: 5, 708: 5, 711: 5, 727: 5, 734: 5, 788: 5, 822: 5, 825: 5, 849: 5, 1037: 5, 1082: 5, 1132: 5, 1392: 5, 1529: 5, 355: 5, 474: 5, 702: 4, 364: 4, 367: 4, 372: 4, 398: 4, 405: 4, 409: 4, 412: 4, 418: 4, 429: 4, 430: 4, 433: 4, 434: 4, 446: 4, 448: 4, 449: 4, 450: 4, 482: 4, 492: 4, 493: 4, 501: 4, 503: 4, 504: 4, 515: 4, 520: 4, 523: 4, 534: 4, 536: 4, 538: 4, 539: 4, 546: 4, 549: 4, 550: 4, 553: 4, 560: 4, 566: 4, 569: 4, 574: 4, 581: 4, 587: 4, 591: 4, 597: 4, 602: 4, 614: 4, 618: 4, 624: 4, 626: 4, 627: 4, 629: 4, 638: 4, 646: 4, 654: 4, 663: 4, 671: 4, 679: 4, 681: 4, 684: 4, 688: 4, 698: 4, 709: 4, 712: 4, 714: 4, 719: 4, 748: 4, 750: 4, 768: 4, 770: 4, 772: 4, 782: 4, 792: 4, 795: 4, 800: 4, 805: 4, 807: 4, 820: 4, 821: 4, 823: 4, 853: 4, 861: 4, 865: 4, 894: 4, 896: 4, 899: 4, 911: 4, 927: 4, 942: 4, 960: 4, 578: 4, 1004: 4, 1009: 4, 1023: 4, 1070: 4, 1107: 4, 1134: 4, 1148: 4, 1174: 4, 1176: 4, 1197: 4, 1219: 4, 1224: 4, 1229: 4, 1233: 4, 1296: 4, 1336: 4, 1371: 4, 1376: 4, 1450: 4, 1500: 4, 1196: 4, 527: 4, 802: 4, 637: 4, 650: 4, 668: 4, 4309: 3, 329: 3, 4458: 3, 384: 3, 387: 3, 394: 3, 401: 3, 404: 3, 408: 3, 425: 3, 441: 3, 442: 3, 478: 3, 481: 3, 488: 3, 498: 3, 500: 3, 510: 3, 514: 3, 517: 3, 526: 3, 530: 3, 543: 3, 544: 3, 547: 3, 575: 3, 583: 3, 593: 3, 594: 3, 617: 3, 633: 3, 640: 3, 642: 3, 652: 3, 653: 3, 655: 3, 656: 3, 657: 3, 665: 3, 667: 3, 669: 3, 672: 3, 678: 3, 691: 3, 467: 3, 697: 3, 710: 3, 726: 3, 732: 3, 735: 3, 737: 3, 742: 3, 744: 3, 745: 3, 746: 3, 753: 3, 755: 3, 757: 3, 759: 3, 766: 3, 773: 3, 776: 3, 786: 3, 790: 3, 791: 3, 794: 3, 798: 3, 799: 3, 803: 3, 806: 3, 809: 3, 815: 3, 816: 3, 817: 3, 819: 3, 827: 3, 830: 3, 833: 3, 836: 3, 838: 3, 848: 3, 857: 3, 875: 3, 878: 3, 881: 3, 889: 3, 893: 3, 907: 3, 914: 3, 922: 3, 924: 3, 940: 3, 951: 3, 961: 3, 965: 3, 844: 3, 971: 3, 972: 3, 973: 3, 975: 3, 977: 3, 979: 3, 981: 3, 982: 3, 996: 3, 997: 3, 998: 3, 1007: 3, 1008: 3, 1012: 3, 1013: 3, 1022: 3, 1030: 3, 1032: 3, 1056: 3, 1065: 3, 1076: 3, 1078: 3, 1079: 3, 1104: 3, 1109: 3, 1110: 3, 1116: 3, 1131: 3, 1142: 3, 1143: 3, 1147: 3, 1172: 3, 1222: 3, 1231: 3, 1232: 3, 1241: 3, 1245: 3, 1250: 3, 1251: 3, 1257: 3, 1277: 3, 1288: 3, 1309: 3, 1315: 3, 1319: 3, 1327: 3, 1339: 3, 1361: 3, 1367: 3, 1377: 3, 1383: 3, 1458: 3, 1489: 3, 1501: 3, 1514: 3, 1525: 3, 5655: 3, 1566: 3, 1582: 3, 1584: 3, 1592: 3, 1601: 3, 1611: 3, 1617: 3, 1647: 3, 1683: 3, 1652: 3, 1754: 3, 1765: 3, 1785: 3, 1793: 3, 811: 3, 694: 3, 1853: 3, 1901: 3, 475: 3, 751: 3, 2181: 3, 2195: 3, 2420: 3, 2379: 3, 2510: 3, 1299: 3, 2616: 3, 2623: 3, 1136: 3, 2419: 3, 2149: 3, 3152: 3, 3293: 3, 1333: 3, 661: 3, 2033: 3, 4096: 2, 1091: 2, 437: 2, 12291: 2, 495: 2, 496: 2, 502: 2, 519: 2, 529: 2, 555: 2, 557: 2, 561: 2, 571: 2, 572: 2, 576: 2, 586: 2, 603: 2, 605: 2, 607: 2, 608: 2, 612: 2, 615: 2, 628: 2, 632: 2, 636: 2, 703: 2, 639: 2, 641: 2, 648: 2, 659: 2, 666: 2, 670: 2, 673: 2, 682: 2, 683: 2, 685: 2, 686: 2, 687: 2, 689: 2, 692: 2, 699: 2, 2342: 2, 715: 2, 718: 2, 720: 2, 729: 2, 738: 2, 739: 2, 740: 2, 752: 2, 760: 2, 761: 2, 762: 2, 763: 2, 767: 2, 769: 2, 775: 2, 4873: 2, 778: 2, 783: 2, 785: 2, 796: 2, 797: 2, 4231: 2, 812: 2, 828: 2, 831: 2, 832: 2, 834: 2, 835: 2, 837: 2, 839: 2, 841: 2, 842: 2, 843: 2, 850: 2, 854: 2, 4802: 2, 858: 2, 859: 2, 860: 2, 866: 2, 867: 2, 870: 2, 882: 2, 883: 2, 884: 2, 886: 2, 888: 2, 905: 2, 906: 2, 909: 2, 915: 2, 916: 2, 918: 2, 919: 2, 920: 2, 921: 2, 923: 2, 928: 2, 933: 2, 934: 2, 938: 2, 939: 2, 943: 2, 2888: 2, 946: 2, 949: 2, 955: 2, 956: 2, 958: 2, 959: 2, 962: 2, 963: 2, 957: 2, 967: 2, 968: 2, 978: 2, 990: 2, 1000: 2, 1001: 2, 1003: 2, 1005: 2, 1006: 2, 1014: 2, 1016: 2, 1018: 2, 1019: 2, 1024: 2, 1027: 2, 1537: 2, 1038: 2, 1039: 2, 1539: 2, 1044: 2, 1049: 2, 1057: 2, 1060: 2, 1064: 2, 1067: 2, 1068: 2, 1074: 2, 1075: 2, 1092: 2, 1096: 2, 1102: 2, 1105: 2, 4281: 2, 1112: 2, 1114: 2, 1125: 2, 1139: 2, 1146: 2, 1149: 2, 1156: 2, 1157: 2, 1158: 2, 1161: 2, 1163: 2, 1164: 2, 1175: 2, 1180: 2, 1186: 2, 1189: 2, 1191: 2, 1192: 2, 2381: 2, 1204: 2, 722: 2, 1213: 2, 1218: 2, 1220: 2, 1221: 2, 1235: 2, 1237: 2, 1243: 2, 1248: 2, 1252: 2, 1254: 2, 1255: 2, 1263: 2, 1267: 2, 1268: 2, 1274: 2, 1280: 2, 1285: 2, 1286: 2, 1293: 2, 1297: 2, 1298: 2, 1302: 2, 1304: 2, 1316: 2, 1320: 2, 1322: 2, 1325: 2, 1329: 2, 1332: 2, 1335: 2, 1345: 2, 1358: 2, 1360: 2, 1362: 2, 1374: 2, 1375: 2, 1384: 2, 1386: 2, 1391: 2, 1395: 2, 1396: 2, 1400: 2, 1403: 2, 582: 2, 1406: 2, 1407: 2, 1410: 2, 1416: 2, 1428: 2, 1430: 2, 1442: 2, 1443: 2, 1445: 2, 1457: 2, 1465: 2, 1468: 2, 1471: 2, 1472: 2, 1480: 2, 1485: 2, 4345: 2, 1499: 2, 935: 2, 1522: 2, 1526: 2, 937: 2, 1622: 2, 1543: 2, 1549: 2, 1558: 2, 1563: 2, 1569: 2, 1576: 2, 1580: 2, 1588: 2, 1604: 2, 1607: 2, 1609: 2, 1615: 2, 1616: 2, 5714: 2, 1626: 2, 1630: 2, 1646: 2, 1663: 2, 1672: 2, 1679: 2, 1684: 2, 1693: 2, 1703: 2, 1704: 2, 1705: 2, 1714: 2, 1724: 2, 1725: 2, 1655: 2, 1742: 2, 1745: 2, 1753: 2, 1759: 2, 1767: 2, 1768: 2, 1774: 2, 1781: 2, 1782: 2, 5894: 2, 1801: 2, 3715: 2, 1813: 2, 1822: 2, 1831: 2, 1846: 2, 1847: 2, 6199: 2, 1851: 2, 1854: 2, 1855: 2, 1860: 2, 1864: 2, 1874: 2, 1895: 2, 1427: 2, 1908: 2, 1916: 2, 1921: 2, 1933: 2, 1949: 2, 1962: 2, 1965: 2, 1011: 2, 1987: 2, 1990: 2, 1991: 2, 2015: 2, 2021: 2, 2023: 2, 2090: 2, 2114: 2, 2118: 2, 2129: 2, 1721: 2, 2159: 2, 2170: 2, 2186: 2, 2187: 2, 2194: 2, 2197: 2, 1050: 2, 619: 2, 2223: 2, 2244: 2, 2254: 2, 2256: 2, 5838: 2, 6360: 2, 2292: 2, 2315: 2, 2348: 2, 2361: 2, 2366: 2, 2370: 2, 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4079: 1, 4080: 1, 4083: 1, 1227: 1, 17065: 1, 12283: 1, 4093: 1})\n"
     ]
    }
   ],
   "source": [
    "# Number of words for a given frequency.\n",
    "print(Counter(sorted_text_occur))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets build the model with only ***text*** column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For values of alpha =  1e-05 The log loss is: 1.36360643082\n",
      "For values of alpha =  0.0001 The log loss is: 1.31045987282\n",
      "For values of alpha =  0.001 The log loss is: 1.1529733302\n",
      "For values of alpha =  0.01 The log loss is: 1.24150647749\n",
      "For values of alpha =  0.1 The log loss is: 1.44305887482\n",
      "For values of alpha =  1 The log loss is: 1.66196354012\n"
     ]
    }
   ],
   "source": [
    "cv_log_error_array=[]\n",
    "for i in alpha:\n",
    "    clf = SGDClassifier(alpha=i, penalty='l2', loss='log', random_state=42)\n",
    "    clf.fit(train_text_feature_onehotCoding, y_train)\n",
    "    \n",
    "    sig_clf = CalibratedClassifierCV(clf, method=\"sigmoid\")\n",
    "    sig_clf.fit(train_text_feature_onehotCoding, y_train)\n",
    "    predict_y = sig_clf.predict_proba(cv_text_feature_onehotCoding)\n",
    "    cv_log_error_array.append(log_loss(y_cv, predict_y, labels=clf.classes_, eps=1e-15))\n",
    "    print('For values of alpha = ', i, \"The log loss is:\",log_loss(y_cv, predict_y, labels=clf.classes_, eps=1e-15))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a32f01f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.plot(alpha, cv_log_error_array,c='g')\n",
    "for i, txt in enumerate(np.round(cv_log_error_array,3)):\n",
    "    ax.annotate((alpha[i],np.round(txt,3)), (alpha[i],cv_log_error_array[i]))\n",
    "plt.grid()\n",
    "plt.title(\"Cross Validation Error for each alpha\")\n",
    "plt.xlabel(\"Alpha i's\")\n",
    "plt.ylabel(\"Error measure\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For values of best alpha =  0.001 The train log loss is: 0.758074401927\n",
      "For values of best alpha =  0.001 The cross validation log loss is: 1.1529733302\n",
      "For values of best alpha =  0.001 The test log loss is: 1.13995955716\n"
     ]
    }
   ],
   "source": [
    "best_alpha = np.argmin(cv_log_error_array)\n",
    "clf = SGDClassifier(alpha=alpha[best_alpha], penalty='l2', loss='log', random_state=42)\n",
    "clf.fit(train_text_feature_onehotCoding, y_train)\n",
    "sig_clf = CalibratedClassifierCV(clf, method=\"sigmoid\")\n",
    "sig_clf.fit(train_text_feature_onehotCoding, y_train)\n",
    "\n",
    "predict_y = sig_clf.predict_proba(train_text_feature_onehotCoding)\n",
    "print('For values of best alpha = ', alpha[best_alpha], \"The train log loss is:\",log_loss(y_train, predict_y, labels=clf.classes_, eps=1e-15))\n",
    "predict_y = sig_clf.predict_proba(cv_text_feature_onehotCoding)\n",
    "print('For values of best alpha = ', alpha[best_alpha], \"The cross validation log loss is:\",log_loss(y_cv, predict_y, labels=clf.classes_, eps=1e-15))\n",
    "predict_y = sig_clf.predict_proba(test_text_feature_onehotCoding)\n",
    "print('For values of best alpha = ', alpha[best_alpha], \"The test log loss is:\",log_loss(y_test, predict_y, labels=clf.classes_, eps=1e-15))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets check the overlap of text data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_intersec_text(df):\n",
    "    df_text_vec = CountVectorizer(min_df=3)\n",
    "    df_text_fea = df_text_vec.fit_transform(df['TEXT'])\n",
    "    df_text_features = df_text_vec.get_feature_names()\n",
    "\n",
    "    df_text_fea_counts = df_text_fea.sum(axis=0).A1\n",
    "    df_text_fea_dict = dict(zip(list(df_text_features),df_text_fea_counts))\n",
    "    len1 = len(set(df_text_features))\n",
    "    len2 = len(set(train_text_features) & set(df_text_features))\n",
    "    return len1,len2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "96.549 % of word of test data appeared in train data\n",
      "97.099 % of word of Cross Validation appeared in train data\n"
     ]
    }
   ],
   "source": [
    "len1,len2 = get_intersec_text(test_df)\n",
    "print(np.round((len2/len1)*100, 3), \"% of word of test data appeared in train data\")\n",
    "len1,len2 = get_intersec_text(cv_df)\n",
    "print(np.round((len2/len1)*100, 3), \"% of word of Cross Validation appeared in train data\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, all 3 columns are going to be important.\n",
    "\n",
    "## Data prepration for Machine Learning models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets create few functions which we will be using later"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "def report_log_loss(train_x, train_y, test_x, test_y,  clf):\n",
    "    clf.fit(train_x, train_y)\n",
    "    sig_clf = CalibratedClassifierCV(clf, method=\"sigmoid\")\n",
    "    sig_clf.fit(train_x, train_y)\n",
    "    sig_clf_probs = sig_clf.predict_proba(test_x)\n",
    "    return log_loss(test_y, sig_clf_probs, eps=1e-15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This function plots the confusion matrices given y_i, y_i_hat.\n",
    "def plot_confusion_matrix(test_y, predict_y):\n",
    "    C = confusion_matrix(test_y, predict_y)\n",
    "    \n",
    "    A =(((C.T)/(C.sum(axis=1))).T)\n",
    "    \n",
    "    B =(C/C.sum(axis=0)) \n",
    "    labels = [1,2,3,4,5,6,7,8,9]\n",
    "    # representing A in heatmap format\n",
    "    print(\"-\"*20, \"Confusion matrix\", \"-\"*20)\n",
    "    plt.figure(figsize=(20,7))\n",
    "    sns.heatmap(C, annot=True, cmap=\"YlGnBu\", fmt=\".3f\", xticklabels=labels, yticklabels=labels)\n",
    "    plt.xlabel('Predicted Class')\n",
    "    plt.ylabel('Original Class')\n",
    "    plt.show()\n",
    "\n",
    "    print(\"-\"*20, \"Precision matrix (Columm Sum=1)\", \"-\"*20)\n",
    "    plt.figure(figsize=(20,7))\n",
    "    sns.heatmap(B, annot=True, cmap=\"YlGnBu\", fmt=\".3f\", xticklabels=labels, yticklabels=labels)\n",
    "    plt.xlabel('Predicted Class')\n",
    "    plt.ylabel('Original Class')\n",
    "    plt.show()\n",
    "    \n",
    "    # representing B in heatmap format\n",
    "    print(\"-\"*20, \"Recall matrix (Row sum=1)\", \"-\"*20)\n",
    "    plt.figure(figsize=(20,7))\n",
    "    sns.heatmap(A, annot=True, cmap=\"YlGnBu\", fmt=\".3f\", xticklabels=labels, yticklabels=labels)\n",
    "    plt.xlabel('Predicted Class')\n",
    "    plt.ylabel('Original Class')\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "def predict_and_plot_confusion_matrix(train_x, train_y,test_x, test_y, clf):\n",
    "    clf.fit(train_x, train_y)\n",
    "    sig_clf = CalibratedClassifierCV(clf, method=\"sigmoid\")\n",
    "    sig_clf.fit(train_x, train_y)\n",
    "    pred_y = sig_clf.predict(test_x)\n",
    "\n",
    "    # for calculating log_loss we willl provide the array of probabilities belongs to each class\n",
    "    print(\"Log loss :\",log_loss(test_y, sig_clf.predict_proba(test_x)))\n",
    "    # calculating the number of data points that are misclassified\n",
    "    print(\"Number of mis-classified points :\", np.count_nonzero((pred_y- test_y))/test_y.shape[0])\n",
    "    plot_confusion_matrix(test_y, pred_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "# this function will be used just for naive bayes\n",
    "# for the given indices, we will print the name of the features\n",
    "# and we will check whether the feature present in the test point text or not\n",
    "def get_impfeature_names(indices, text, gene, var, no_features):\n",
    "    gene_count_vec = CountVectorizer()\n",
    "    var_count_vec = CountVectorizer()\n",
    "    text_count_vec = CountVectorizer(min_df=3)\n",
    "    \n",
    "    gene_vec = gene_count_vec.fit(train_df['Gene'])\n",
    "    var_vec  = var_count_vec.fit(train_df['Variation'])\n",
    "    text_vec = text_count_vec.fit(train_df['TEXT'])\n",
    "    \n",
    "    fea1_len = len(gene_vec.get_feature_names())\n",
    "    fea2_len = len(var_count_vec.get_feature_names())\n",
    "    \n",
    "    word_present = 0\n",
    "    for i,v in enumerate(indices):\n",
    "        if (v < fea1_len):\n",
    "            word = gene_vec.get_feature_names()[v]\n",
    "            yes_no = True if word == gene else False\n",
    "            if yes_no:\n",
    "                word_present += 1\n",
    "                print(i, \"Gene feature [{}] present in test data point [{}]\".format(word,yes_no))\n",
    "        elif (v < fea1_len+fea2_len):\n",
    "            word = var_vec.get_feature_names()[v-(fea1_len)]\n",
    "            yes_no = True if word == var else False\n",
    "            if yes_no:\n",
    "                word_present += 1\n",
    "                print(i, \"variation feature [{}] present in test data point [{}]\".format(word,yes_no))\n",
    "        else:\n",
    "            word = text_vec.get_feature_names()[v-(fea1_len+fea2_len)]\n",
    "            yes_no = True if word in text.split() else False\n",
    "            if yes_no:\n",
    "                word_present += 1\n",
    "                print(i, \"Text feature [{}] present in test data point [{}]\".format(word,yes_no))\n",
    "\n",
    "    print(\"Out of the top \",no_features,\" features \", word_present, \"are present in query point\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Combining all 3 features together"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "# merging gene, variance and text features\n",
    "\n",
    "# building train, test and cross validation data sets\n",
    "# a = [[1, 2], \n",
    "#      [3, 4]]\n",
    "# b = [[4, 5], \n",
    "#      [6, 7]]\n",
    "# hstack(a, b) = [[1, 2, 4, 5],\n",
    "#                [ 3, 4, 6, 7]]\n",
    "\n",
    "train_gene_var_onehotCoding = hstack((train_gene_feature_onehotCoding,train_variation_feature_onehotCoding))\n",
    "test_gene_var_onehotCoding = hstack((test_gene_feature_onehotCoding,test_variation_feature_onehotCoding))\n",
    "cv_gene_var_onehotCoding = hstack((cv_gene_feature_onehotCoding,cv_variation_feature_onehotCoding))\n",
    "\n",
    "train_x_onehotCoding = hstack((train_gene_var_onehotCoding, train_text_feature_onehotCoding)).tocsr()\n",
    "train_y = np.array(list(train_df['Class']))\n",
    "\n",
    "test_x_onehotCoding = hstack((test_gene_var_onehotCoding, test_text_feature_onehotCoding)).tocsr()\n",
    "test_y = np.array(list(test_df['Class']))\n",
    "\n",
    "cv_x_onehotCoding = hstack((cv_gene_var_onehotCoding, cv_text_feature_onehotCoding)).tocsr()\n",
    "cv_y = np.array(list(cv_df['Class']))\n",
    "\n",
    "\n",
    "train_gene_var_responseCoding = np.hstack((train_gene_feature_responseCoding,train_variation_feature_responseCoding))\n",
    "test_gene_var_responseCoding = np.hstack((test_gene_feature_responseCoding,test_variation_feature_responseCoding))\n",
    "cv_gene_var_responseCoding = np.hstack((cv_gene_feature_responseCoding,cv_variation_feature_responseCoding))\n",
    "\n",
    "train_x_responseCoding = np.hstack((train_gene_var_responseCoding, train_text_feature_responseCoding))\n",
    "test_x_responseCoding = np.hstack((test_gene_var_responseCoding, test_text_feature_responseCoding))\n",
    "cv_x_responseCoding = np.hstack((cv_gene_var_responseCoding, cv_text_feature_responseCoding))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "One hot encoding features :\n",
      "(number of data points * number of features) in train data =  (2124, 55007)\n",
      "(number of data points * number of features) in test data =  (665, 55007)\n",
      "(number of data points * number of features) in cross validation data = (532, 55007)\n"
     ]
    }
   ],
   "source": [
    "print(\"One hot encoding features :\")\n",
    "print(\"(number of data points * number of features) in train data = \", train_x_onehotCoding.shape)\n",
    "print(\"(number of data points * number of features) in test data = \", test_x_onehotCoding.shape)\n",
    "print(\"(number of data points * number of features) in cross validation data =\", cv_x_onehotCoding.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Response encoding features :\n",
      "(number of data points * number of features) in train data =  (2124, 27)\n",
      "(number of data points * number of features) in test data =  (665, 27)\n",
      "(number of data points * number of features) in cross validation data = (532, 27)\n"
     ]
    }
   ],
   "source": [
    "print(\" Response encoding features :\")\n",
    "print(\"(number of data points * number of features) in train data = \", train_x_responseCoding.shape)\n",
    "print(\"(number of data points * number of features) in test data = \", test_x_responseCoding.shape)\n",
    "print(\"(number of data points * number of features) in cross validation data =\", cv_x_responseCoding.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Building Machine Learning model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets start the first model which is most suitable when we have lot of text column data. So, we will start with Naive Bayes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "for alpha = 1e-05\n",
      "Log Loss : 1.2413811207\n",
      "for alpha = 0.0001\n",
      "Log Loss : 1.2403382721\n",
      "for alpha = 0.001\n",
      "Log Loss : 1.24103020471\n",
      "for alpha = 0.1\n",
      "Log Loss : 1.25945727212\n",
      "for alpha = 1\n",
      "Log Loss : 1.27859979864\n",
      "for alpha = 10\n",
      "Log Loss : 1.37147583426\n",
      "for alpha = 100\n",
      "Log Loss : 1.34176278182\n",
      "for alpha = 1000\n",
      "Log Loss : 1.27271049085\n"
     ]
    }
   ],
   "source": [
    "# http://scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.MultinomialNB.html\n",
    "alpha = [0.00001, 0.0001, 0.001, 0.1, 1, 10, 100,1000]\n",
    "cv_log_error_array = []\n",
    "for i in alpha:\n",
    "    print(\"for alpha =\", i)\n",
    "    clf = MultinomialNB(alpha=i)\n",
    "    clf.fit(train_x_onehotCoding, train_y)\n",
    "    sig_clf = CalibratedClassifierCV(clf, method=\"sigmoid\")\n",
    "    sig_clf.fit(train_x_onehotCoding, train_y)\n",
    "    sig_clf_probs = sig_clf.predict_proba(cv_x_onehotCoding)\n",
    "    cv_log_error_array.append(log_loss(cv_y, sig_clf_probs, labels=clf.classes_, eps=1e-15))\n",
    "    # to avoid rounding error while multiplying probabilites we use log-probability estimates\n",
    "    print(\"Log Loss :\",log_loss(cv_y, sig_clf_probs)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a33644fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.plot(np.log10(alpha), cv_log_error_array,c='g')\n",
    "for i, txt in enumerate(np.round(cv_log_error_array,3)):\n",
    "    ax.annotate((alpha[i],str(txt)), (np.log10(alpha[i]),cv_log_error_array[i]))\n",
    "plt.grid()\n",
    "plt.xticks(np.log10(alpha))\n",
    "plt.title(\"Cross Validation Error for each alpha\")\n",
    "plt.xlabel(\"Alpha i's\")\n",
    "plt.ylabel(\"Error measure\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For values of best alpha =  0.0001 The train log loss is: 0.840306988767\n",
      "For values of best alpha =  0.0001 The cross validation log loss is: 1.2403382721\n",
      "For values of best alpha =  0.0001 The test log loss is: 1.25731298028\n"
     ]
    }
   ],
   "source": [
    "best_alpha = np.argmin(cv_log_error_array)\n",
    "clf = MultinomialNB(alpha=alpha[best_alpha])\n",
    "clf.fit(train_x_onehotCoding, train_y)\n",
    "sig_clf = CalibratedClassifierCV(clf, method=\"sigmoid\")\n",
    "sig_clf.fit(train_x_onehotCoding, train_y)\n",
    "\n",
    "\n",
    "predict_y = sig_clf.predict_proba(train_x_onehotCoding)\n",
    "print('For values of best alpha = ', alpha[best_alpha], \"The train log loss is:\",log_loss(y_train, predict_y, labels=clf.classes_, eps=1e-15))\n",
    "predict_y = sig_clf.predict_proba(cv_x_onehotCoding)\n",
    "print('For values of best alpha = ', alpha[best_alpha], \"The cross validation log loss is:\",log_loss(y_cv, predict_y, labels=clf.classes_, eps=1e-15))\n",
    "predict_y = sig_clf.predict_proba(test_x_onehotCoding)\n",
    "print('For values of best alpha = ', alpha[best_alpha], \"The test log loss is:\",log_loss(y_test, predict_y, labels=clf.classes_, eps=1e-15))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Testing our Naive Bayes model with best found value of alpha on testing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Log Loss : 1.2403382721\n",
      "Number of missclassified point : 0.37781954887218044\n",
      "-------------------- Confusion matrix --------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a32557cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------- Precision matrix (Columm Sum=1) --------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a335f8278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------- Recall matrix (Row sum=1) --------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a32a9cd68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "clf = MultinomialNB(alpha=alpha[best_alpha])\n",
    "clf.fit(train_x_onehotCoding, train_y)\n",
    "sig_clf = CalibratedClassifierCV(clf, method=\"sigmoid\")\n",
    "sig_clf.fit(train_x_onehotCoding, train_y)\n",
    "sig_clf_probs = sig_clf.predict_proba(cv_x_onehotCoding)\n",
    "# to avoid rounding error while multiplying probabilites we use log-probability estimates\n",
    "print(\"Log Loss :\",log_loss(cv_y, sig_clf_probs))\n",
    "print(\"Number of missclassified point :\", np.count_nonzero((sig_clf.predict(cv_x_onehotCoding)- cv_y))/cv_y.shape[0])\n",
    "plot_confusion_matrix(cv_y, sig_clf.predict(cv_x_onehotCoding.toarray()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Interpretability of our model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted Class : 7\n",
      "Predicted Class Probabilities: [[ 0.0863  0.0802  0.0108  0.1088  0.0418  0.0393  0.6242  0.0048  0.0038]]\n",
      "Actual Class : 7\n",
      "--------------------------------------------------\n",
      "15 Text feature [kinase] present in test data point [True]\n",
      "17 Text feature [downstream] present in test data point [True]\n",
      "18 Text feature [inhibitor] present in test data point [True]\n",
      "19 Text feature [activating] present in test data point [True]\n",
      "20 Text feature [activation] present in test data point [True]\n",
      "21 Text feature [presence] present in test data point [True]\n",
      "22 Text feature [well] present in test data point [True]\n",
      "23 Text feature [potential] present in test data point [True]\n",
      "24 Text feature [contrast] present in test data point [True]\n",
      "27 Text feature [expressing] present in test data point [True]\n",
      "28 Text feature [independent] present in test data point [True]\n",
      "29 Text feature [previously] present in test data point [True]\n",
      "30 Text feature [growth] present in test data point [True]\n",
      "31 Text feature [also] present in test data point [True]\n",
      "32 Text feature [recently] present in test data point [True]\n",
      "33 Text feature [however] present in test data point [True]\n",
      "34 Text feature [activated] present in test data point [True]\n",
      "35 Text feature [higher] present in test data point [True]\n",
      "36 Text feature [shown] present in test data point [True]\n",
      "37 Text feature [cell] present in test data point [True]\n",
      "38 Text feature [similar] present in test data point [True]\n",
      "39 Text feature [showed] present in test data point [True]\n",
      "40 Text feature [10] present in test data point [True]\n",
      "41 Text feature [addition] present in test data point [True]\n",
      "42 Text feature [suggest] present in test data point [True]\n",
      "43 Text feature [cells] present in test data point [True]\n",
      "44 Text feature [may] present in test data point [True]\n",
      "45 Text feature [mutations] present in test data point [True]\n",
      "46 Text feature [compared] present in test data point [True]\n",
      "47 Text feature [inhibitors] present in test data point [True]\n",
      "48 Text feature [obtained] present in test data point [True]\n",
      "49 Text feature [inhibition] present in test data point [True]\n",
      "50 Text feature [described] present in test data point [True]\n",
      "51 Text feature [found] present in test data point [True]\n",
      "52 Text feature [approximately] present in test data point [True]\n",
      "53 Text feature [factor] present in test data point [True]\n",
      "54 Text feature [respectively] present in test data point [True]\n",
      "55 Text feature [treated] present in test data point [True]\n",
      "56 Text feature [observed] present in test data point [True]\n",
      "57 Text feature [12] present in test data point [True]\n",
      "58 Text feature [concentrations] present in test data point [True]\n",
      "59 Text feature [1a] present in test data point [True]\n",
      "60 Text feature [reported] present in test data point [True]\n",
      "61 Text feature [studies] present in test data point [True]\n",
      "62 Text feature [using] present in test data point [True]\n",
      "63 Text feature [therapeutic] present in test data point [True]\n",
      "64 Text feature [phosphorylation] present in test data point [True]\n",
      "66 Text feature [fig] present in test data point [True]\n",
      "67 Text feature [total] present in test data point [True]\n",
      "68 Text feature [3a] present in test data point [True]\n",
      "69 Text feature [suggests] present in test data point [True]\n",
      "70 Text feature [mechanism] present in test data point [True]\n",
      "71 Text feature [identified] present in test data point [True]\n",
      "72 Text feature [interestingly] present in test data point [True]\n",
      "73 Text feature [show] present in test data point [True]\n",
      "74 Text feature [consistent] present in test data point [True]\n",
      "75 Text feature [including] present in test data point [True]\n",
      "76 Text feature [although] present in test data point [True]\n",
      "78 Text feature [confirm] present in test data point [True]\n",
      "79 Text feature [followed] present in test data point [True]\n",
      "80 Text feature [various] present in test data point [True]\n",
      "81 Text feature [molecular] present in test data point [True]\n",
      "82 Text feature [3b] present in test data point [True]\n",
      "83 Text feature [sensitive] present in test data point [True]\n",
      "84 Text feature [1b] present in test data point [True]\n",
      "85 Text feature [three] present in test data point [True]\n",
      "86 Text feature [report] present in test data point [True]\n",
      "87 Text feature [confirmed] present in test data point [True]\n",
      "88 Text feature [mutation] present in test data point [True]\n",
      "89 Text feature [without] present in test data point [True]\n",
      "90 Text feature [furthermore] present in test data point [True]\n",
      "91 Text feature [proliferation] present in test data point [True]\n",
      "92 Text feature [either] present in test data point [True]\n",
      "93 Text feature [small] present in test data point [True]\n",
      "94 Text feature [new] present in test data point [True]\n",
      "95 Text feature [active] present in test data point [True]\n",
      "96 Text feature [discussion] present in test data point [True]\n",
      "98 Text feature [additional] present in test data point [True]\n",
      "99 Text feature [results] present in test data point [True]\n",
      "Out of the top  100  features  79 are present in query point\n"
     ]
    }
   ],
   "source": [
    "test_point_index = 1\n",
    "no_feature = 100\n",
    "predicted_cls = sig_clf.predict(test_x_onehotCoding[test_point_index])\n",
    "print(\"Predicted Class :\", predicted_cls[0])\n",
    "print(\"Predicted Class Probabilities:\", np.round(sig_clf.predict_proba(test_x_onehotCoding[test_point_index]),4))\n",
    "print(\"Actual Class :\", test_y[test_point_index])\n",
    "indices = np.argsort(-clf.coef_)[predicted_cls-1][:,:no_feature]\n",
    "print(\"-\"*50)\n",
    "get_impfeature_names(indices[0], test_df['TEXT'].iloc[test_point_index],test_df['Gene'].iloc[test_point_index],test_df['Variation'].iloc[test_point_index], no_feature)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets look at one more point"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted Class : 6\n",
      "Predicted Class Probabilities: [[ 0.0818  0.0756  0.0102  0.2831  0.04    0.4004  0.1006  0.0044  0.0038]]\n",
      "Actual Class : 4\n",
      "--------------------------------------------------\n",
      "10 Text feature [brca] present in test data point [True]\n",
      "13 Text feature [history] present in test data point [True]\n",
      "Out of the top  100  features  2 are present in query point\n"
     ]
    }
   ],
   "source": [
    "test_point_index = 100\n",
    "no_feature = 100\n",
    "predicted_cls = sig_clf.predict(test_x_onehotCoding[test_point_index])\n",
    "print(\"Predicted Class :\", predicted_cls[0])\n",
    "print(\"Predicted Class Probabilities:\", np.round(sig_clf.predict_proba(test_x_onehotCoding[test_point_index]),4))\n",
    "print(\"Actual Class :\", test_y[test_point_index])\n",
    "indices = np.argsort(-clf.coef_)[predicted_cls-1][:,:no_feature]\n",
    "print(\"-\"*50)\n",
    "get_impfeature_names(indices[0], test_df['TEXT'].iloc[test_point_index],test_df['Gene'].iloc[test_point_index],test_df['Variation'].iloc[test_point_index], no_feature)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So Naive Bayes not performing very badly but lets look at other models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# K Nearest Neighbour Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "for alpha = 5\n",
      "Log Loss : 1.02507804255\n",
      "for alpha = 11\n",
      "Log Loss : 1.0116193704\n",
      "for alpha = 15\n",
      "Log Loss : 1.03369059783\n",
      "for alpha = 21\n",
      "Log Loss : 1.04393125152\n",
      "for alpha = 31\n",
      "Log Loss : 1.03163701668\n",
      "for alpha = 41\n",
      "Log Loss : 1.03087550967\n",
      "for alpha = 51\n",
      "Log Loss : 1.03342469652\n",
      "for alpha = 99\n",
      "Log Loss : 1.06785663317\n"
     ]
    }
   ],
   "source": [
    "alpha = [5, 11, 15, 21, 31, 41, 51, 99]\n",
    "cv_log_error_array = []\n",
    "for i in alpha:\n",
    "    print(\"for alpha =\", i)\n",
    "    clf = KNeighborsClassifier(n_neighbors=i)\n",
    "    clf.fit(train_x_responseCoding, train_y)\n",
    "    sig_clf = CalibratedClassifierCV(clf, method=\"sigmoid\")\n",
    "    sig_clf.fit(train_x_responseCoding, train_y)\n",
    "    sig_clf_probs = sig_clf.predict_proba(cv_x_responseCoding)\n",
    "    cv_log_error_array.append(log_loss(cv_y, sig_clf_probs, labels=clf.classes_, eps=1e-15))\n",
    "    # to avoid rounding error while multiplying probabilites we use log-probability estimates\n",
    "    print(\"Log Loss :\",log_loss(cv_y, sig_clf_probs))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a335d2048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.plot(alpha, cv_log_error_array,c='g')\n",
    "for i, txt in enumerate(np.round(cv_log_error_array,3)):\n",
    "    ax.annotate((alpha[i],str(txt)), (alpha[i],cv_log_error_array[i]))\n",
    "plt.grid()\n",
    "plt.title(\"Cross Validation Error for each alpha\")\n",
    "plt.xlabel(\"Alpha i's\")\n",
    "plt.ylabel(\"Error measure\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For values of best alpha =  11 The train log loss is: 0.654238319024\n",
      "For values of best alpha =  11 The cross validation log loss is: 1.0116193704\n",
      "For values of best alpha =  11 The test log loss is: 1.07839089152\n"
     ]
    }
   ],
   "source": [
    "best_alpha = np.argmin(cv_log_error_array)\n",
    "clf = KNeighborsClassifier(n_neighbors=alpha[best_alpha])\n",
    "clf.fit(train_x_responseCoding, train_y)\n",
    "sig_clf = CalibratedClassifierCV(clf, method=\"sigmoid\")\n",
    "sig_clf.fit(train_x_responseCoding, train_y)\n",
    "\n",
    "predict_y = sig_clf.predict_proba(train_x_responseCoding)\n",
    "print('For values of best alpha = ', alpha[best_alpha], \"The train log loss is:\",log_loss(y_train, predict_y, labels=clf.classes_, eps=1e-15))\n",
    "predict_y = sig_clf.predict_proba(cv_x_responseCoding)\n",
    "print('For values of best alpha = ', alpha[best_alpha], \"The cross validation log loss is:\",log_loss(y_cv, predict_y, labels=clf.classes_, eps=1e-15))\n",
    "predict_y = sig_clf.predict_proba(test_x_responseCoding)\n",
    "print('For values of best alpha = ', alpha[best_alpha], \"The test log loss is:\",log_loss(y_test, predict_y, labels=clf.classes_, eps=1e-15))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's test it on testing dataset with our best alpha value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Log loss : 1.0116193704\n",
      "Number of mis-classified points : 0.3383458646616541\n",
      "-------------------- Confusion matrix --------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a33570e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------- Precision matrix (Columm Sum=1) --------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a336d9b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------- Recall matrix (Row sum=1) --------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a33607a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "clf = KNeighborsClassifier(n_neighbors=alpha[best_alpha])\n",
    "predict_and_plot_confusion_matrix(train_x_responseCoding, train_y, cv_x_responseCoding, cv_y, clf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted Class : 2\n",
      "Actual Class : 7\n",
      "The  11  nearest neighbours of the test points belongs to classes [7 2 7 7 7 7 7 7 7 7 7]\n",
      "Fequency of nearest points : Counter({7: 10, 2: 1})\n"
     ]
    }
   ],
   "source": [
    "# Lets look at few test points\n",
    "clf = KNeighborsClassifier(n_neighbors=alpha[best_alpha])\n",
    "clf.fit(train_x_responseCoding, train_y)\n",
    "sig_clf = CalibratedClassifierCV(clf, method=\"sigmoid\")\n",
    "sig_clf.fit(train_x_responseCoding, train_y)\n",
    "\n",
    "test_point_index = 1\n",
    "predicted_cls = sig_clf.predict(test_x_responseCoding[0].reshape(1,-1))\n",
    "print(\"Predicted Class :\", predicted_cls[0])\n",
    "print(\"Actual Class :\", test_y[test_point_index])\n",
    "neighbors = clf.kneighbors(test_x_responseCoding[test_point_index].reshape(1, -1), alpha[best_alpha])\n",
    "print(\"The \",alpha[best_alpha],\" nearest neighbours of the test points belongs to classes\",train_y[neighbors[1][0]])\n",
    "print(\"Fequency of nearest points :\",Counter(train_y[neighbors[1][0]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted Class : 4\n",
      "Actual Class : 4\n",
      "the k value for knn is 11 and the nearest neighbours of the test points belongs to classes [6 4 4 1 4 4 4 4 4 1 4]\n",
      "Fequency of nearest points : Counter({4: 8, 1: 2, 6: 1})\n"
     ]
    }
   ],
   "source": [
    "clf = KNeighborsClassifier(n_neighbors=alpha[best_alpha])\n",
    "clf.fit(train_x_responseCoding, train_y)\n",
    "sig_clf = CalibratedClassifierCV(clf, method=\"sigmoid\")\n",
    "sig_clf.fit(train_x_responseCoding, train_y)\n",
    "\n",
    "test_point_index = 100\n",
    "\n",
    "predicted_cls = sig_clf.predict(test_x_responseCoding[test_point_index].reshape(1,-1))\n",
    "print(\"Predicted Class :\", predicted_cls[0])\n",
    "print(\"Actual Class :\", test_y[test_point_index])\n",
    "neighbors = clf.kneighbors(test_x_responseCoding[test_point_index].reshape(1, -1), alpha[best_alpha])\n",
    "print(\"the k value for knn is\",alpha[best_alpha],\"and the nearest neighbours of the test points belongs to classes\",train_y[neighbors[1][0]])\n",
    "print(\"Fequency of nearest points :\",Counter(train_y[neighbors[1][0]]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logistic Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Balancing all classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "for alpha = 1e-06\n",
      "Log Loss : 1.34230113472\n",
      "for alpha = 1e-05\n",
      "Log Loss : 1.33489075909\n",
      "for alpha = 0.0001\n",
      "Log Loss : 1.25362531452\n",
      "for alpha = 0.001\n",
      "Log Loss : 1.10013828147\n",
      "for alpha = 0.01\n",
      "Log Loss : 1.16806183085\n",
      "for alpha = 0.1\n",
      "Log Loss : 1.46096335713\n",
      "for alpha = 1\n",
      "Log Loss : 1.67414822661\n",
      "for alpha = 10\n",
      "Log Loss : 1.69908593626\n",
      "for alpha = 100\n",
      "Log Loss : 1.70168825357\n"
     ]
    }
   ],
   "source": [
    "alpha = [10 ** x for x in range(-6, 3)]\n",
    "cv_log_error_array = []\n",
    "for i in alpha:\n",
    "    print(\"for alpha =\", i)\n",
    "    clf = SGDClassifier(class_weight='balanced', alpha=i, penalty='l2', loss='log', random_state=42)\n",
    "    clf.fit(train_x_onehotCoding, train_y)\n",
    "    sig_clf = CalibratedClassifierCV(clf, method=\"sigmoid\")\n",
    "    sig_clf.fit(train_x_onehotCoding, train_y)\n",
    "    sig_clf_probs = sig_clf.predict_proba(cv_x_onehotCoding)\n",
    "    cv_log_error_array.append(log_loss(cv_y, sig_clf_probs, labels=clf.classes_, eps=1e-15))\n",
    "    # to avoid rounding error while multiplying probabilites we use log-probability estimates\n",
    "    print(\"Log Loss :\",log_loss(cv_y, sig_clf_probs)) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a3354a390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.plot(alpha, cv_log_error_array,c='g')\n",
    "for i, txt in enumerate(np.round(cv_log_error_array,3)):\n",
    "    ax.annotate((alpha[i],str(txt)), (alpha[i],cv_log_error_array[i]))\n",
    "plt.grid()\n",
    "plt.title(\"Cross Validation Error for each alpha\")\n",
    "plt.xlabel(\"Alpha i's\")\n",
    "plt.ylabel(\"Error measure\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For values of best alpha =  0.001 The train log loss is: 0.617073292064\n",
      "For values of best alpha =  0.001 The cross validation log loss is: 1.10013828147\n",
      "For values of best alpha =  0.001 The test log loss is: 1.06706518612\n"
     ]
    }
   ],
   "source": [
    "best_alpha = np.argmin(cv_log_error_array)\n",
    "clf = SGDClassifier(class_weight='balanced', alpha=alpha[best_alpha], penalty='l2', loss='log', random_state=42)\n",
    "clf.fit(train_x_onehotCoding, train_y)\n",
    "sig_clf = CalibratedClassifierCV(clf, method=\"sigmoid\")\n",
    "sig_clf.fit(train_x_onehotCoding, train_y)\n",
    "\n",
    "predict_y = sig_clf.predict_proba(train_x_onehotCoding)\n",
    "print('For values of best alpha = ', alpha[best_alpha], \"The train log loss is:\",log_loss(y_train, predict_y, labels=clf.classes_, eps=1e-15))\n",
    "predict_y = sig_clf.predict_proba(cv_x_onehotCoding)\n",
    "print('For values of best alpha = ', alpha[best_alpha], \"The cross validation log loss is:\",log_loss(y_cv, predict_y, labels=clf.classes_, eps=1e-15))\n",
    "predict_y = sig_clf.predict_proba(test_x_onehotCoding)\n",
    "print('For values of best alpha = ', alpha[best_alpha], \"The test log loss is:\",log_loss(y_test, predict_y, labels=clf.classes_, eps=1e-15))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets test it on testing data using best alpha value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Log loss : 1.10013828147\n",
      "Number of mis-classified points : 0.3533834586466165\n",
      "-------------------- Confusion matrix --------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a33740908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------- Precision matrix (Columm Sum=1) --------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a3353cd68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------- Recall matrix (Row sum=1) --------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a3361a940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "clf = SGDClassifier(class_weight='balanced', alpha=alpha[best_alpha], penalty='l2', loss='log', random_state=42)\n",
    "predict_and_plot_confusion_matrix(train_x_onehotCoding, train_y, cv_x_onehotCoding, cv_y, clf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_imp_feature_names(text, indices, removed_ind = []):\n",
    "    word_present = 0\n",
    "    tabulte_list = []\n",
    "    incresingorder_ind = 0\n",
    "    for i in indices:\n",
    "        if i < train_gene_feature_onehotCoding.shape[1]:\n",
    "            tabulte_list.append([incresingorder_ind, \"Gene\", \"Yes\"])\n",
    "        elif i< 18:\n",
    "            tabulte_list.append([incresingorder_ind,\"Variation\", \"Yes\"])\n",
    "        if ((i > 17) & (i not in removed_ind)) :\n",
    "            word = train_text_features[i]\n",
    "            yes_no = True if word in text.split() else False\n",
    "            if yes_no:\n",
    "                word_present += 1\n",
    "            tabulte_list.append([incresingorder_ind,train_text_features[i], yes_no])\n",
    "        incresingorder_ind += 1\n",
    "    print(word_present, \"most importent features are present in our query point\")\n",
    "    print(\"-\"*50)\n",
    "    print(\"The features that are most importent of the \",predicted_cls[0],\" class:\")\n",
    "    print (tabulate(tabulte_list, headers=[\"Index\",'Feature name', 'Present or Not']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "testing query point and doing interpretability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted Class : 7\n",
      "Predicted Class Probabilities: [[ 0.019   0.0289  0.0021  0.0486  0.005   0.0018  0.8762  0.0159  0.0026]]\n",
      "Actual Class : 7\n",
      "--------------------------------------------------\n",
      "35 Text feature [activated] present in test data point [True]\n",
      "49 Text feature [transforming] present in test data point [True]\n",
      "61 Text feature [oncogene] present in test data point [True]\n",
      "66 Text feature [cylinders] present in test data point [True]\n",
      "76 Text feature [transformation] present in test data point [True]\n",
      "83 Text feature [extracellular] present in test data point [True]\n",
      "90 Text feature [downstream] present in test data point [True]\n",
      "149 Text feature [resnitzky] present in test data point [True]\n",
      "151 Text feature [activation] present in test data point [True]\n",
      "157 Text feature [infect] present in test data point [True]\n",
      "181 Text feature [138] present in test data point [True]\n",
      "183 Text feature [phospho] present in test data point [True]\n",
      "197 Text feature [mitogen] present in test data point [True]\n",
      "201 Text feature [receptors] present in test data point [True]\n",
      "211 Text feature [technology] present in test data point [True]\n",
      "212 Text feature [transformed] present in test data point [True]\n",
      "218 Text feature [marone] present in test data point [True]\n",
      "228 Text feature [progressed] present in test data point [True]\n",
      "274 Text feature [expressing] present in test data point [True]\n",
      "297 Text feature [oncogenes] present in test data point [True]\n",
      "331 Text feature [ht] present in test data point [True]\n",
      "354 Text feature [nodular] present in test data point [True]\n",
      "366 Text feature [inhibited] present in test data point [True]\n",
      "384 Text feature [activating] present in test data point [True]\n",
      "387 Text feature [attractive] present in test data point [True]\n",
      "396 Text feature [nielsen] present in test data point [True]\n",
      "400 Text feature [overexpression] present in test data point [True]\n",
      "406 Text feature [serum] present in test data point [True]\n",
      "440 Text feature [thymoma] present in test data point [True]\n",
      "451 Text feature [pdk1] present in test data point [True]\n",
      "457 Text feature [dramatic] present in test data point [True]\n",
      "461 Text feature [viral] present in test data point [True]\n",
      "472 Text feature [concentrations] present in test data point [True]\n",
      "481 Text feature [refractory] present in test data point [True]\n",
      "Out of the top  500  features  34 are present in query point\n"
     ]
    }
   ],
   "source": [
    "# from tabulate import tabulate\n",
    "clf = SGDClassifier(class_weight='balanced', alpha=alpha[best_alpha], penalty='l2', loss='log', random_state=42)\n",
    "clf.fit(train_x_onehotCoding,train_y)\n",
    "test_point_index = 1\n",
    "no_feature = 500\n",
    "predicted_cls = sig_clf.predict(test_x_onehotCoding[test_point_index])\n",
    "print(\"Predicted Class :\", predicted_cls[0])\n",
    "print(\"Predicted Class Probabilities:\", np.round(sig_clf.predict_proba(test_x_onehotCoding[test_point_index]),4))\n",
    "print(\"Actual Class :\", test_y[test_point_index])\n",
    "indices = np.argsort(-clf.coef_)[predicted_cls-1][:,:no_feature]\n",
    "print(\"-\"*50)\n",
    "get_impfeature_names(indices[0], test_df['TEXT'].iloc[test_point_index],test_df['Gene'].iloc[test_point_index],test_df['Variation'].iloc[test_point_index], no_feature)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted Class : 6\n",
      "Predicted Class Probabilities: [[ 0.0307  0.0112  0.0023  0.4229  0.0082  0.4827  0.0314  0.0082  0.0024]]\n",
      "Actual Class : 4\n",
      "--------------------------------------------------\n",
      "114 Text feature [motors] present in test data point [True]\n",
      "139 Text feature [unwound] present in test data point [True]\n",
      "161 Text feature [unwind] present in test data point [True]\n",
      "167 Text feature [m299i] present in test data point [True]\n",
      "187 Text feature [duplex] present in test data point [True]\n",
      "216 Text feature [zealand] present in test data point [True]\n",
      "243 Text feature [hospitals] present in test data point [True]\n",
      "413 Text feature [unwinding] present in test data point [True]\n",
      "443 Text feature [unwinds] present in test data point [True]\n",
      "Out of the top  500  features  9 are present in query point\n"
     ]
    }
   ],
   "source": [
    "test_point_index = 100\n",
    "no_feature = 500\n",
    "predicted_cls = sig_clf.predict(test_x_onehotCoding[test_point_index])\n",
    "print(\"Predicted Class :\", predicted_cls[0])\n",
    "print(\"Predicted Class Probabilities:\", np.round(sig_clf.predict_proba(test_x_onehotCoding[test_point_index]),4))\n",
    "print(\"Actual Class :\", test_y[test_point_index])\n",
    "indices = np.argsort(-clf.coef_)[predicted_cls-1][:,:no_feature]\n",
    "print(\"-\"*50)\n",
    "get_impfeature_names(indices[0], test_df['TEXT'].iloc[test_point_index],test_df['Gene'].iloc[test_point_index],test_df['Variation'].iloc[test_point_index], no_feature)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Without class balancing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "for alpha = 1e-06\n",
      "Log Loss : 1.29824487768\n",
      "for alpha = 1e-05\n",
      "Log Loss : 1.29383425203\n",
      "for alpha = 0.0001\n",
      "Log Loss : 1.23997887225\n",
      "for alpha = 0.001\n",
      "Log Loss : 1.09218683253\n",
      "for alpha = 0.01\n",
      "Log Loss : 1.18363670066\n",
      "for alpha = 0.1\n",
      "Log Loss : 1.36189577459\n",
      "for alpha = 1\n",
      "Log Loss : 1.6046737318\n"
     ]
    }
   ],
   "source": [
    "alpha = [10 ** x for x in range(-6, 1)]\n",
    "cv_log_error_array = []\n",
    "for i in alpha:\n",
    "    print(\"for alpha =\", i)\n",
    "    clf = SGDClassifier(alpha=i, penalty='l2', loss='log', random_state=42)\n",
    "    clf.fit(train_x_onehotCoding, train_y)\n",
    "    sig_clf = CalibratedClassifierCV(clf, method=\"sigmoid\")\n",
    "    sig_clf.fit(train_x_onehotCoding, train_y)\n",
    "    sig_clf_probs = sig_clf.predict_proba(cv_x_onehotCoding)\n",
    "    cv_log_error_array.append(log_loss(cv_y, sig_clf_probs, labels=clf.classes_, eps=1e-15))\n",
    "    print(\"Log Loss :\",log_loss(cv_y, sig_clf_probs)) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ezXrj6eHp6jCrDGf27rvdgTJvAW+VVk4ppSqzvPw81hxYgyXOwpztc0jLTKNWtVrc3PZmosKiGNBiAN6e3q4Os0qqcpMeKqVUZWCM4bek34iJi2F2wmxSMlLw8/ZjRJsRRIdFM6jVIKp7VXd1mFWeJimllHKQMYbNKZuxxFuwxFs4cPIA1TyrMaz1MKLCohgWOowaPu41pb2zaZJSSqlSxB2JIyYuBku8hd3Hd+Pt4c3AlgN5td+rjGgzglrVark6xCuWJimllCrCrmO77Ikp4WgCHuJB/+b9eeq6p7i53c0E+Aa4OkS3oElKKaVs9p/YjyXO2pS3JXULgtCrWS8+GvoRt1x9Cw1qNCi9ElWuNEkppdxa8qlkZifMJiYuhnXJ1oG+uzfuzjuD3mH01aNpXKuxiyN0b5qklFJu58iZI8xJmIMl3sKaP9dgMFzT6Bom95/MbWG30bxuc1eHqGw0SSml3MLxs8eZv30+MfEx/LjvR/JNPlfXv5qX+rxEVHgUreu1dnWIqgiapJRSV6xT506xcMdCLPEWlu9ZTk5+Dq0CWvH09U8THR5NeIOLJmhQlYwmKaXUFeVM9hkWJy4mJi6G/yX+j3N552hauymP9HiEqLAoOgV1cnggV+V6mqSUUlVeVm4WS3cvxRJvYdHORWTmZBLkH8TfO/+d6PBougd314FcqyhNUkqpKik3P5cliUuIiY9hwY4FnDp3ikC/QO6KuIuo8Ch6Ne2lA7leATRJKaWqjLz8PFb/udr6I9ttFk6tOUXtarW5pd0tRIVF0a95Px3I9QqjSUopVanlm3x+PfgrljgLsxNmc/jMYfx9/OkR0IOH+j3EwJYDqeZVzdVhKifRJKWUqnSMMWw8tJGYuBhmJcwi6VQS1b2qM7z1cKLDohkaOpR1v6yjT5s+rg5VOZkmKaVUpWCMYdvhbfYRxvem78Xbw5shoUN4Y8Ab3Nj6RmpWq+nqMFUF0ySllHKp7Ue32xPTjrQdeIonA1oM4PkbnuemtjdRp3odV4eoXEiTlFKqwu1N34slzkJMfAzbDm9DEPqE9OGR7o8wqt0o6teo7+oQVSWhSUopVSEOnjzIrPhZWOItbDi0AYCeTXry/uD3ufXqWwmqGeTiCFVlpElKKeU0qRmpzEmYQ0xcDL8c/AWAzkGdeSvyLW4Lu42mtZu6OEJV2WmSUkqVq7TMNOZtn4cl3kLs/ljyTT7tG7Tnlb6vEBUeRauAVq4OUVUhmqSUUpftZNZJFuxYQEx8DD/s/YHc/Fxa12vNc72eIyo8iqvrX+3qEFUVpUlKKVUmGdkZfLfzOyzxFpbsXkJ2XjYhdUL417X/Ijo8mg4NO+hAruqyaZJSSjnsbM5ZluxeQkxcDN/v+p6zuWe5quZVTOw6kaiwKLo17qaJSZUrTVJKqRJl52WzfM9yLPEWFuxYQEZ2Bg1qNGBsx7FEh0dzXdPrdIRx5TSapJRSF8nNz2XVvlXExMUwf8d80rPSqVu9LtFh0USFR9EnpA9eHvr1oZxPP2VKKcA6kOuaP9dgibcwJ2EORzOPUtOnJje1vYno8GgGtBiAj6ePq8NUbkaTlFJuzBjDuuR1xMTFMDthNodOH8LXy5cRbUYQFRbFkNAhVPeq7uowlRvTJKWUmzHGsCV1C5Y463h5f578Ex9PH4aGDiUqLIrhrYfj7+Pv6jCVApyYpETkc2A4cMQYE17E9j7AQmCfbdU8Y8zLzopHKXcXfyQeS7yFmLgYEo8n4uXhRWSLSF7u+zIj24ykdvXarg5RqYs480rqS+BD4L8llFljjBnuxBiUcmuJxxLtI4zHHYnDQzzoG9KXx3s+zqh2o6jnV8/VISpVIqclKWPMTyIS4qz6lVJF+/PEn8yKn0VMfAybUzYDcH3T6/lwyIfcevWtNPRv6OIIlXKcq+9JXSsivwOHgMeMMfEujkepKintXBrv/fYelngLa5PWAtCtcTf+38D/x+irR9OkdhMXR6hU2YgxxnmVW6+kvi/mnlQtIN8YkyEiQ4H3jDGhxdQzHhgP0LBhw84xMTFliicjIwN/f/e6IaznfOU6kX2Cn9J+4scjP7Lt5DYMhpY1WtKvQT/61O/DVb5XuTpEp3KX97mwyznnvn37bjLGdCnnkJzOZUmqiLL7gS7GmLSSynXp0sVs3LixTPHExsbSp0+fMu1bVek5X1nSz6Yzf8d8LPEWVu5dSZ7Jo21gW3rU6MGTw5+kbWBbV4dYYa7k97k4l3POIlIlk5TLmvtEpBFw2BhjRKQb4AEcc1U8SlVWp8+dZtHORcTEx7Bs9zJy8nNoUbcFT173JFHhUbRv0J7Vq1e7VYJS7sOZXdC/BfoAgSKSBLwIeAMYYz4GbgXuF5Fc4CwQbZx5WadUFZKZk8niXYuxxFtYnLiYrNwsmtRqwkPdHyI6PJrOQZ11IFflFpzZu+/2UrZ/iLWLulIKOJd7jmV7lhETF8OinYs4k3OGhjUacl+n+4gKi+LaJtfqQK7K7bi6d59Sbi0nL4eV+1Ziibcwf/t8Tp47ST3fetzR/g6iw6O5odkNeHp4ujpMpVxGk5RSFSwvP4+f/vyJmLgY5m6fy7Gzx6hVrRaj2o0iKiyK/s374+3p7eowlaoUNEkpVQHyTT5rD67FEm9hdsJsUjNSqeFdgxFtRhAdHs2gloOo5lXN1WEqVeloklLKSYwxbErZRExcDLPiZ3Hw1EGqe1VnWOgwosKiGNZ6GH7efq4OU6lKTZOUUuXIGMMfR/6wjzC+J30P3h7eDGo1iNf7v86INiOoWa2mq8NUqsrQJKVUOdiZtpOYuBgs8Ra2p23HUzzp17wfz/R6hpvb3kxd37quDlGpKkmTlFJltC99n33qi98P/44g3NDsBv7R7R/ccvUtNKjRwNUhKlXlaZJS6hIknUpiVvwsLPEW1ievB6BHcA/eHfQuo8NGc1XNK3u8PKUqmiYppUpxOOMwcxLmYIm3sObAGgA6BXXijQFvcFvYbYTUCXFtgEpdwTRJKVWE42ePM2/7PGLiYli1fxX5Jp+w+mH8u++/iQqLIrRekQP2K6XKmSYppWxOZp1k4c6FWOItLN+znNz8XEIDQnnm+meICo8ivEGpg/krpcqZJinl1s5kn+H7Xd8TEx/DksQlnMs7R9PaTXm0x6NEhUdxTaNrdCBXpVxIk5RyO1m5WSxJXIIl3sJ3u74jMyeTIP8gJnSZQHR4NN0bd9fEpFQloUlKuYXsvGx+2PsDMXExLNixgNPZpwn0C2RMhzFEhUVxfdPrdSBXpSohTVLqipWbn0vs/lgscRbmbp9LelY6darXYfTVo4kOj6Zv8754eeh/AaUqM/0fqq4o+SafbSe2MXvxbOZsn8ORM0fw9/HnprY3ERUWxcCWA/Hx9HF1mEopB2mSUlWeMYYNhzbYB3JNPp2Mr5cvw1sPJzo8miGthuDr7evqMJVSZaBJSlVJxhh+P/y7fSDXfSf24ePpw5BWQxjrMZYnb3oSfx9/V4eplLpMmqRUlZJwNMGemHYe24mneBLZMpIXe7/IyLYjqVO9DrGxsZqglLpCaJJSld6e43vsA7n+ceQPBKFPSB8evfZRRrUbRaBfoKtDVEo5SalJSkRaA1OBhsaYcBGJAEYYY15xenTKbR04ecA+kOvGQxsBuK7Jdbw/+H1uvfpWgmoGuThCpVRFcORK6lPgceATAGPMNhH5BtAkpcpVyukU5iTMISY+hl8P/gpAl6u68Hbk24wOG03T2k1dHKFSqqI5kqT8jDHrL/gFfq6T4lFuJi0zjbkJc7HEW4jdH4vBENEwglf7vUpUWBQtA1q6OkSllAs5kqTSRKQlYABE5FYgxalRqSvaiawTLNixgJi4GH7Y+wN5Jo829drwQu8XiAqLol39dq4OUSlVSTiSpCYC04C2IpIM7APucGpU6oqTkZ3Bop2LsMRbWLp7Kdl52TSv05zHez5OdHg0EQ0jdLw8pdRFSkxSIuIBdDHGDBCRGoCHMeZ0xYSmqrqzOWf5X+L/iImPYfGuxZzNPUvjmo15sOuDRIVH0fWqrpqYlFIlKjFJGWPyReRBYJYx5kwFxaSqsHO551i+ZzmWeAsLdy4kIzuDBjUaMO6acUSHR9OzSU88xMPVYSqlqghHmvtWiMhjgAWwJypjzHGnRaWqlNz8XH7c9yMxcTHM3zGfE1knCPAN4Pbw24kKi6J3SG8dyFUpVSaOfHOMs/07sdA6A7Qo/3BUVZGXn8eaA2uwxFmYs30OaZlp1PSpyc3tbiYqLIoBLQboQK5KqctWapIyxjSviEBU5WeM4bek34iJi2F2wmxSMlLw8/bjxtY3Eh0ezeBWg6nuVd3VYSqlriCOjDhxV1HrjTH/LWW/z4HhwBFjTHgJ5boCvwFRxpg5pcWjKpYxhs0pm7HEW8fLO3DyANU8qzE0dChRYVEMbz2cGj41XB2mUuoK5UhzX9dCz6sD/YHNQIlJCvgS+LCkciLiCbwBLHMgDlWB4o7EERMXgyXewu7ju/Hy8GJgy4G80vcVRrYdSa1qtVwdolLKDTjS3PePwssiUhv4yoH9fhKRkFKK/QOYy/mJULnIrmO7sMRZiImPIeFoAh7iQb/m/XjyuicZ1W4UAb4Brg5RKeVmxBhzaTuIeAPbjDGlDgtgS1LfF9XcJyKNgW+AfsBntnJFNveJyHhgPEDDhg07x8TEXFLMBTIyMvD3d68pHEo759SsVFYdWcWqo6tIzEhEENrXbk/f+n25of4NBPhUvcSk77N70HO+NH379t1kjOlSziE5nSP3pL7DNiQS4AFcDcwqh2O/CzxpjMkr7QedxphpWEe9oEuXLqZPnz5lOmBsbCxl3beqKuqck08lMzthNpZ4C78l/QZA98bdmdJzCqPDRhNcK9gFkZYffZ/dg56ze3DkntTbhZ7nAn8aY5LK4dhdgBhbggoEhopIrjFmQTnUrS5w5MwR5ibMJSY+hjV/rsFg6NioI5P7T+a2sNtoXlc7cSqlKh9HktRG4Kxt9InWQCcROWyMybmcAxfu2i4iX2Jt7tMEVY6Onz3O4pTFvPbVa6zct5J8k0+7wHZM6jOJqLAo2gS2cXWISilVIkeS1E9ALxGpC6zEmrSiKGWQWRH5FugDBIpIEvAi4A1gjPn4MmJWJTh17hSLdi4iJi6G5XuWk5OfQ8u6LXn6+qeJCosivEG4jpenlKoyHElSYozJFJF7gA+MMW+KyJbSdjLG3O5oEMaYux0tqy6WmZPJ97u+xxJvYfGuxZzLO0eTWk14uPvDtDrXivHDx2tiUkpVSQ4lKRG5FuuV0z2XsJ9yonO551i6eykx8TF8t/M7zuScoZF/I/7e+e9EhUfRI7gHHuJBbGysJiilVJXlSLJ5GHgamG+MiReRFsAq54alipKTl8MPe3/AEm9h/o75nDp3inq+9bgz4k6iw6Pp1bQXnh6erg5TKaXKTalzJhhjfjLGjDDGvGFb3muMecj5oV15zp49S+/evcnLywNgxowZhIaGEhoayowZM4rcx2KxENI6BPEQAv8ZyNBvhrJgxwJuaXcLS+9YSsq/Uvh4+Mf0CeljT1AhISH2/Z944gnq1KnD8OHDi43r448/pn379nTs2JHrr7+ehIQE+7Zt27Zx7bXXEhYWRvv27cnKyiIzM5Nhw4bRtm1bwsLCeOqpp+zlJ02axJdffgnAY489xo8//ljWl0sppRz6nVR94AkgDOuwSAAYY/o5Ma4r0ueff86oUaPw9PTk+PHjvPTSS2zcuBERoXPnzowYMYK6deuSb/L59eCvWOIsfLvxW44NOoZHrgfXBl/LxJETGdhyINW8qjl0zKioKFq3bs0nn3xSbJn/+7//Y8KECQAsWrSIRx99lKVLl5Kbm8udd97JV199RYcOHTh27Bje3t6cO3eOxx57jL59+5KdnU3//v1ZsmQJQ4YMOa/ef/zjH9x3333066cfFaVU2TjS3DcT61xSw4EJwBjgqDODulLNnDmTb775BoBly5YRGRlJQIB1RIcBAwbwn2/+Q3qrdGYlzCLpVBLVvaozrOswosOjeX/b+7zS7xW6tCn9B+P169e3P+/cuXOp5WvV+mscvjNnztjvYS1fvpyIiAg6dOgAQL169QDw8/Ojb9/vGAoxAAAgAElEQVS+APj4+NCpUyeSkqw/nfP398fX1xeAZs2acezYMVJTU2nUqFGpcSil1IUcSVL1jDGficjDxpjVwGoRWe3swK402dnZ7N27194Ul5ycTHBwML+n/o4l3sKc5Dmk70/Hu5c3g1sNZnL/yYxoM4Ka1WoC8KF86PCxNmzYcMnx/ec//2HKlClkZ2fbm+h27dqFiDBo0CCOHj1KdHQ0TzzxxHn7nThxgu+++46HH34YsDbxFdapUyd++eUXbrnllkuOSSmlHElSBT/aTRGRYcAhoGqPm+MCaWlp1KlTB4AdaTtYtnsZW5O38kL+C3iKJyG+IUS2ieTjxz6mrm/dCo9v4sSJTJw4kW+++YZXXnmFGTNmkJuby88//8yGDRvw8/Ojf//+dO7cmf79+wOQm5vL7bffzkMPPUSLFkXPgdmgQQMOHTpUkaeilLqClNpxAnjFNvL5v4DHgOnAP50a1RXo8LnDHD55mA4fd6Ddf9rxQ9oPeGd4M3XYVFL+lUL/ev25qetNLklQhUVHR7NggXXgj+DgYHr37k1gYCB+fn4MHTqUzZs328uOHz+e0NBQHnnkkWLry8rKsjf/KaXUpXKkd9/3xpiTxpg4Y0xfY0xnY8yiigjuShE9J5pO/+3EicwT+OLLe4PfI+7/xVFtfzWiWkbhle3F8uXLGTRokMN1Jicn269oLldiYqL9+eLFiwkNDQVg0KBBbNu2jczMTHJzc1m9ejVXX301AM899xwnT57k3XffLbHuXbt2ER5e7JyXSilVolKTlIi0FpGVIhJnW44QkeecH9qVYdOhTVjiLfy989+5bcRtvNLqFR7q/hBhzcJ4/vnn6dq1K127duWFF16wd6K499572bhxIwDz588nODiYtWvXMmzYMHsiS0lJwcur9Nbahx56iNGjR7Ny5UqCg4NZtsw6v+QLL7zAokXWvzU+/PBDwsLC6NixI1OmTLF3h69bty6PPvooXbt2pWPHjnTq1Ilhw4aRlJTEq6++SkJCAp06daJjx45Mnz79omPn5OSwe/duunSpcrMDKKUqC2NMiQ9gNdAN2FJoXVxp+znr0blzZ1NWq1atKvO+ZTV+0Xjj+4qvOXH2hNm8ebO58847y6XeDz74wCxcuLDUcq445wLz5s0zzz33XIUf15Xn7Cp6zu7hcs4Z2Ghc9L19OQ9HOk74GWPWXzC0Tm55J8sr0elzp/km7huiw6OpXb0211xzDX379iUvLw9Pz8sbGeLBBx8spyidJzc3l3/961+uDkMpVYU5kqTSRKQltokPReRWIMWpUV0hYuJiyMjOYHzn8fZ148aNc2FEFWv06NGuDkEpVcU5kqQmYp0Vt62IJAP7gDudGtUVYtrmabRv0J7ujbu7OhSllKqSSk1Sxpi9wAARqQF4GGNOOz+sqm9zymY2HtrIB0M+0FHIlVKqjBwZu68OcBcQAngVfOEaHWS2RJ9u+pTqXtW5M0IvOpVSqqwcae77H/Ab8AeQ79xwrgwZ2RnM/GMmUWFR1Klex9XhKKVUleVIkqpujHnU6ZFcQSxxFk5nnz6vw4RSSqlL58iwSF+JyH0iEiQiAQUPp0dWhU3bPI2w+mFcG3ytq0NRSqkqzZErqWzgLeBZbN3Qbf8WPaKom9uaupX1yet5b/B72mFCKaUukyNJ6lGglTEmzdnBXAm0w4RSSpUfR5r74oFMZwdyJTiTfYav//ia0VePJsBXW0SVUupyOXIllQdsFZFVwLmCldoF/WKz4mdx6twp7TChlFLlxJEktcD2UKWYtnka7QLbcV2T61wdilJKXREcGXFiRkUEUtVtO7yN35J+451B72iHCaWUKieO3JNSDvh006dU86zG3yL+5upQlFLqiqFJqhxk5mTy1bavuPXqW6nnV8/V4Sil1BWjxCQlIp4i8lZFBVMRzp49S+/evcnLywNg8ODB1KlTh+HDh5epvqVLl9IytCUn3zxJjXU17OuNMTz77LO0bt2adu3a8f7775dYz/79++nTpw8Ax44do2/fvvj7+5c4b9Tjjz9O27ZtiYiI4Oabb+bEiRMAZGdnM3bsWNq3b88999xDbGysfZ9vv/2W9u3bExERweDBg0lLs/6y4O6777aXi46OPm9KeaWUcpUSk5QxJg/oLFfQTZaPP/6Y5ORk+/KxY8fIzc1lw4YNl1xXXl4eY8eOJf1MOt6e3iz67yISEhIA6NWrF++99x5+fn5Uq1aN8PDwEutKSkpi69atAJw5c4aTJ0+Sm5vLTz/9VOw++/fvtz+Pj4/nxRdfBGDq1Kn8/PPP9hjHjx9Pfn4+ubm5PPzww6xatYqQkBA2bdrEhx9+CMDPP/9sP/6RI0d49tlnL/n1UEqp8uZIc98WYKGI/E1ERhU8nB2Ys3zwwQfccccd9plxJ0+ezDPPPHNRuU2bNtG7d286d+7MoEGDSEm5eJ7HtWvXkn4inXP/d45X5r4CwLRp0wDYsWMHr7/+Olu3bmXr1q3069evxLg8PDzw9vYGoF69erz//vtERUWVuM99991HXFwc27ZtIywsjBUrVgAwd+5cgoKC+OOPP3jnnXdITU1l/fr19umY586di7+/P3l5eVx11VUA+Pj44OVl7Ufz/PPPs2TJEnJzdQJmpZRrOZKkAoBjQD/gRtuj1LYxEflcRI6ISFwx20eKyDYR2SoiG0Xk+ksJvCxycnJISkpi7Nix9nX9+/fHz8/vonJjx44lJycHgNTUVP75z39eVN/q1avxquGFT30fxnUZR69evVi3bh0Ap0+f5tdff6VLly4MGTKk1OazJk2a0KtXLwBq1KjB9ddfb09axRk4cKA9sRw8eJA6dawjrnt4eJCVlUVubi5ZWVlkZmYSGxuLt7c3U6ZMYeLEiaxYsYJz585xzz33ABAZGUmnTp0A6N27N7m5uWzatKnE4yullLOVmqSMMWOLeDgyB/qXwOAStq8EOhhjOgLjgOkORXwZ0tLSyM/PJyQkpMRycXFxxMfHc+rUKfLy8khPT2fNmjUXlTty9Ahn5Sy3tLuFQL9AAgICOHPmDAD5+fmsXLmS7OxsvLy8uPvuu0s8ZpMmTZg3b16ZzuvVV18lKSmJ+++/H4CoqChOnDhB586defvttxER0tPTycnJ4dlnn+WDDz5g/fr1VK9enddffx2A9957j549ewLWJFenTh1+/fXXMsWjlFLlpdQkJSLBIjLfdlV0WETmikhwafsZY34CjpewPcMYUzBgbQ3+GrzWKZJPJbM2da1Dv2Hat28fImK/SqlduzZhYWEcPHiQjh070rFjR+u9rdxk8s/l20eYOH78ODVqWDtPNGvWjHXr1rFhwwYCAgKcdlUyY8YMPvnkE7p168add1rHC7zvvvsYNmwYIkL16tXx9/enSZMmzJo1i8zMTO6//35EhNq1axebiLy9vcnIyHBKzEop5ShHRpz4AvgGGG1bvtO2LvJyDy4iNwOvAw2AYSWUGw+MB2jYsOF5vdUcFXs0lg8Of4BHvgfLly/Hx8fHvm337t1kZ2fb601LS8PDw4P77ruPsLAwcnNzOXjwIGvXrrV/ce/atYstbIGzcOjXQ6xIXMEPP/xAv379iI2NpVu3bkydOpWhQ4dSs2ZNvLy8iI2NZfv27cyfP7/I+2AXSklJISMjo9jzXb9+PW+++Sb16tXjgQceYPXq1QBkZWUxcOBARo4cyZo1a9i6dSv+/v7ExsZy/PhxGjRogDGG48ePF1t/QYeSsrzWrlbSa3al0nN2D+54zvab6cU9gK2OrCtm3xAgzoFyNwA/OFJn586dTVnMiptlmITx9fM1ixcvtq+//vrrTa1atYyHh4dp3LixWbp0qTl37pxp0qSJ6dChg4mIiDDt2rUzkyZNOq+++CPxhucx/gH+JiQkxDRv3tw0atTIxMXFGWOM2b59uxk6dKgJCwszjRo1MmPHjjXGGPPaa6+Z0NDQUuNt1qyZ8fPzM15eXqZx48YmPj7eGGPMPffcYzZs2GCMMSYoKMh4eXmZsLAw06FDB/P3v//dGGNMQkKCadWqlWnbtq1p1aqV6datm73eqVOnmrZt25o2bdqYmjVrmrS0tIuOnZqaanx9fc2hQ4cu5SWuNFatWuXqECqcnrN7uJxzBjYaB75jK9vDkY4TaSJyp+03U54icifWjhTlmSh/AlqKSGB51luUHjf0YMqUKeet8/HxOe/KysfHh0WLFlGrVi0ARMTeC67Ap5s+xdvbm2mfTsPb2xsR4cEHHyQsLAyABx54gAMHDgDWzhkFv5Nau3YtERERDsVarVo1qlWrVux2Pz8/GjVqZG+WLODr64uHh/WtrVWrFrNmzbJvmzBhAtu3b2fp0qU0bdqUevUu/vHxJ598QsOGDQkKCnIoTqWUchZHmvvGAR8C72C9b/Srbd1lEZFWwB5jjBGRToAP5Zz8LjgeAPc/dj9TnptCXl4enp6eRXaIAOjYsWOxv1HKys1ixu8zGNVuFLePup3bR91+UZkff/yxyH3btGnD3/5W+tBJhX8DVdj06X/1L9m9e3eRZUJCQti5cycAsbGxNGvWrMgycXFFdrxk+/btPP3006XGqJRSzlZikhIRT+AWY8yIS61YRL4F+gCBIpIEvAh4AxhjPgZuAe4SkRzgLBBluyR1qnbt23HfffddVh1zE+aSnpVepik53nqr8g/gMXDgQIcSqVJKOVuJScoYkyciI7FeRV0SY8zFlxfnb38DeONS671cxhjGjbu8C8Fpm6fRKqAVfUL6lE9QlUzh35EppZQrOdLc94uIfAhYgDMFK40xm50WlRMI5TOy0460Hfz050+8MeANPETH51VKKWdyJEn1tP37cqF1BusIFG7n002f4u3hzd0d73Z1KEopdcUr7Z6UBzDVGDOrpHJVibmM3wwXdJi4qe1NNKjRoByjUkopVZTSRkHPB4qfK6IKKY+B3Odvn8+xs8fK1GFCKaXUpXPkpsoKEXlMRJqISEDBw+mRVULTNk+jRd0W9Gvuli2dSilV4Rz9nRTAxELrDNCi/MNxvrL2ct91bBex+2N5vf/r2mFCKaUqSKlJyhjTvCICcbbL7d336aZP8fLw0g4TSilVgYq9JBCRJwo9H33BttecGVRlcy73HF/+/iUj24ykkX8jV4ejlFJuo6R2q+hCzy8cI6ekeaIqtbL07luwYwFpmWnaYUIppSpYSUlKinle1HKldzm9+6ZtnkZInRAGtBhQjhEppZQqTUlJyhTzvKjlKuNs5ll69+5NXl4eYJ00MDQ0lNDQUGbMmHFR+cRjifwY/yOeX3vSpnUbIiMjSU9PB6ydMB566CFatWpFREQEmzf/NQhHcfU+++yzNGnSBH9/f4fijY2Ntc/qu2PHDq699lqqVavG22+/Xew+d9xxB23atCE8PJw33niDnJyc87Zv2LABT09P5syZA1gHs+3Tpw8Af/zxR6mzCCulVEUpKUl1EJFTInIaiLA9L1huX0HxlZuCjhMLYxYyatQoPD09OX78OC+99BLr1q1j/fr1vPTSS/YEVGD65unIL8Jtw28jMTGR/v37M3nyZACWLFlCYmIiiYmJTJs2zT59e0n13njjjaxfv75M5xAQEMD777/PY489VmK5O+64gx07dvDHH3+QnZ193sjpeXl5PPnkkwwaNKjIfdu3b09SUpJ9mhGllHKlYpOUMcbTGFPLGFPTGONle16w7F2RQZanJfOWMHLkSACWLVtGZGQkAQEB1K1bl8jISJYuXWovm52XzRdbv6DG3hr8Y/w/ABgzZgwLFiwAYOHChdx1112ICD169ODEiROkpKSUWG+PHj0uaZ4mHx8fateuDUCDBg3o2rUr3t4lv/xDhw5FRBAR2rZtS1JSkn3bBx98wC233EKDBn+NmOHp6UlAwF8/fbvxxhuJiYlxOEallHIW9/rBTy4kH0gmJCQEgOTkZJo0aWLfHBwcTHJysn154Y6FHM08iskw9sQSFBTEkSNHSty/tHovRc+ePXnvvffKtG9OTg4rVqxg8ODB9njnz5/PhAkTzivXpEkT5s2bZ1/u0qVLsfNsKaVURXKbJCUikAn+tf66F1TUD3sLd7CYtnkazWo3w8uj6J+TFbd/afVWlAceeICIiAh69eoFwCOPPMIbb7yBp6dnifs1aNCAQ4cOVUSISilVIrdJUgB4Q/a5bPticHAwBw8etC8nJSXZp4nfm76XH/b+wL2d7qVhw4akpKQAkJKSYm8qK27/kuqtKC+99BJHjx7lgQcesK/buHEj0dHRhISEMGfOHB544AF702VhWVlZ+Pr6VmS4SilVJPdKUr6Qn5dPVlYWAIMGDWL58uWkp6eTnp7O8uXL7R0Kpm+ejqd4MrbjWEaMGGHvoTdjxgz7Pa0RI0bw3//+F2MMv/32G7Vr1yYoKKjEeoszf/78cpuyffr06Sxbtoxvv/0WD4+/3uJ9+/axf/9+9u/fz6233spHH33ETTfddNH+u3btIjw8vFxiUUqpy+E2Saqgd1/33t35+eefAWtvueeff56uXbvStWtXXnjhBQICAsjJy+GdZ97hOs/raFyrMU899RQrVqwgNDSUFStW8NRTTwHWDgotWrSgVatW3HfffXz00Ucl1gvwxBNPEBwcTGZmJsHBwUyaNAmAPXv2UKtWrRLPITU1leDgYKZMmcIrr7xCcHAwp06dssdS0EQ3YcIEDh8+zLXXXsu9997Lyy+/XFK1F1m1ahXDhg27pH2UUsopjDFV6tG5c2dTFt/t/M4wCfPV0q/MnXfeWWLZuQlzDZMw3+/8vkzHKos77rjDHDlypNzrXbVq1SWVz8rKMt27dzc5OTnlHktFudRzvhLoObuHyzlnYKOpBN/hl/pwZBT0K0LBlVTr8NZk980mLy+v2A4E0zZNI7hWMINbVdzoT19//XWFHaskBw4cYPLkyXh5uc1HQylVibnNN1HB9BrGGMaNG1dsuX3p+1i+Zzkv9n4RT4+Se8FdiQpGyVBKqcrAfe5J2bqAlzbA7GdbPkNEGHdN8YlMKaVUxXCfJGVr7ss3+cWWycnL4fMtnzM0dChNajcptpxSSqmK4T5JquBKqoSZeRcnLiYlI4XxnXRKDqWUqgzcJ0lRenPftE3TaFyzMUNCh1RUWEoppUrgNkmqcMeJovx54k+W7l7KPdfcU+wwSEoppSqW2ySpgua+4u5JfbblMwDu6XRPhcWklFKqZO6TpEpo7svNz+WzLZ8xJHQITWs3rejQlFJKFcN9klQJHSf+l/g/Dp0+pB0mlFKqknFakhKRz0XkiIjEFbP9DhHZZnv8KiIdnBULFLonVcSV1LRN0wjyD2JYax2vTimlKhNnXkl9CZQ0rtA+oLcxJgL4NzDNibH81dx3wZXUgZMHWLJ7iXaYUEqpSshp38rGmJ9EJKSE7b8WWvwNCHZWLFB8x4nPt3yOMUY7TCilVCVUWS4d7gGWFLdRRMYD4wEaNmxIbGzsJR/gj5N/ALD19614H/QGIDMrk9ceeI0uD3Vh/9b9fLz0Y/tAr3feead92vXCTp06xcsvv0xqaiqNGjXixRdfpGbNmhw4cIA33niDxMRE7rnnHqKiokqN6ZFHHuGpp56iUaNGTJ8+neXLl3P69GmWLCn6pTh58iSTJk1ix44dDB48mIcffti+beXKlcycORMRoV69ejz77LPUrl2bjIwMYmNjsVgsfPzxxyxYsIDatWuzdOlSUlNTufvuu5k/fz7Vq1dnyJAr4/dhBefsTvSc3YM7nrNTh1gHQoC4Usr0BbYD9Ryps6xTdfxy4BfDJMzSxKX2dRNemGAYjJmXMM8cO3bMNG/e3Bw7dswcP37cNG/e3Bw/fvyieh5//HHz+uuvG2OMef31180TTzxhjDHm8OHDZv369eaZZ54xb731lkMx9e7d2+zbt88YY8zatWvNoUOHTI0aNYotn5GRYdasWWOmTp1qJk6caF+fk5Nj6tevb44ePWqP8cUXXzTGWIf2P3DggBk4cKBp2rSpvcwXX3xhL3PmzBnTsWNHh2KuCnQKB/eg53xpqKJTdbi0d5+IRADTgZHGmGPOPFZRHScs31oI7BTI8NbDWbZsGZGRkQQEBFC3bl0iIyNZunTpRfUsXLiQMWPGADBmzBj79OsNGjSga9eueHt7OxxTQECAfbqQHj16EBQUVGL5GjVqcP3111O9evXz1he8mWfOnMEYw6lTp86brv6f//wnb775pr3JE8DX1xd/f38A/Pz8CAkJYf369Q7HrpRSFcFlzX0i0hSYB/zNGLPL6ce7oOPE3rS9pKek80y/Z/D29CY5OZkmTf4aVDY4OJjk5OSL6jl8+LA9mQQFBXHkyJEyxzRv3rwy71uYt7c3U6dOpX379tSoUYPQ0FD+85//APDLL7/QuHFjOnQ4v/Pkhc2RXbp0Yc2aNXTr1q1cYlJKqfLgzC7o3wJrgTYikiQi94jIBBGZYCvyAlAP+EhEtorIRmfFYosH+KvjxEerP4JqcG+ne4Gifz9V+MqjMsvJyWHq1Kls2bKFQ4cOERERweuvv05mZiZff/21Q9PHN2jQwD79vFJKVRZOS1LGmNuNMUHGGG9jTLAx5jNjzMfGmI9t2+81xtQ1xnS0Pbo4KxY4f8SJvPw8YnbEUF2q07xuc8B65XTw4EF7+aSkpPOazAo0bNiQlJQUAFJSUmjQoIEzw3bI1q1bAWjZsiUiwm233cavv/7Knj17SE1NpUOHDoSEhJCUlESnTp1ITU29qI6srCx8fX0rOnSllCqR24w4UXiA2V8O/kJybjI1PGuQlZUFwKBBg1i+fDnp6emkp6ezfPlyBg0adFE9I0aMYMaMGQDMmDGDkSNHlnrs/v37F9l0WF4aN25MQkICR48eBWDFihW0a9eO9u3bM3/+fPbv38/+/fsJDg5m8+bNNGrU6KI6du3aRXh4uNNiVEqpsnCbJFV4Zt4DJw8A0HdAX37++WfA2onh+eefp2vXrnTt2pUXXniBgIAAAO699142brS2Rj711FOsWLGC0NBQVqxYwVNPPQVAamoqwcHBTJkyhVdeeYXg4GBOnTpFfn4+u3fvttdVnCeeeILg4GAyMzMJDg5m0qRJACxatIgXXnjBXi4kJIRHH32UL7/8kuDgYBISErjqqqt48cUXueGGG4iIiGDr1q0888wzl/T6/PLLLwwYMOCS9lFKKWerLL+TcrrCHSfSMtMAeHDig0z/aLr9y3ncuHGMG3fxtPHTp0+3P69Xrx4rV668qEyjRo1ISkq6aH1cXBy33HJLqU1pb775Jm+++eZF60eMGMGIESPsy/v37y9y/wkTJjBhwoQit5W275YtWwgLCyMwMLDE/ZVSqqK5T5Iq1HEiLTMNT/GkV49e7Nm+h7y8PHtX8PIWHh7OlClTnFJ3eUlLS+Pf//63q8NQSqmLuE2SKvw7qbTMNOr51cNDPIq8cnI3kZGRrg5BKaWK5D73pAo19x07e4xAP23aUkqpys59klShjhNpmWnU863n4oiUUkqVxn2SFOffk9IrKaWUqvzcJ0nJ+b37NEkppVTl5zZJqqDjhF5JKaVU1eE2Saqgue/kuZPk5udqklJKqSrAfZKUrbnv6Bnr0EGapJRSqvJznyRlu5I6csY6tYYmKaWUqvzcJkkV3JM6mqlXUkopVVW4TZKyN/dpklJKqSrDfZIU59+T0h/zKqVU5ec+SarQlZSXhxe1qtVycURKKaVK4z5JqtCVVKBfYJWZGl4ppdyZ2ySpgo4TeSZP70cppVQV4TZJqvCVkyYppZSqGtwnSaFJSimlqhr3SVKFr6R8NUkppVRV4DZJquCeFOiVlFJKVRVuk6QKN/fV89PfSCmlVFXgPklKO04opVSV4z5JSjtOKKVUleM2SUrvSSmlVNXjNklKm/uUUqrqcZ8kpc19SilV5bhPkrJdSVXzrIZHrge9e/cmLy8PgBkzZhAaGkpoaCgzZswocv/jx48TGRlJaGgokZGRpKenA2CM4aGHHqJVq1ZERESwefNm+z6DBw+mTp06DB8+3KEYJ02axJdffgnA7NmzCQsLw8PDg40bNxa7z9KlS2nTpg2tWrVi8uTJ9vU//vgjnTp1YuzYsYwZM4bc3FwAZs6cSUREBBEREfTs2ZPff//dvk9ISAgAR48eZfDgwQ7FrJRSzuS0JCUin4vIERGJK2Z7WxFZKyLnROQxZ8VhP57tSirQL5AvvviCUaNG4enpyfHjx3nppZdYt24d69ev56WXXrInoMImT55M//79SUxMpH///vaEsGTJEhITE0lMTGTatGncf//99n0ef/xxvvrqqzLFGx4ezrx587jhhhuKLZOXl8fEiRNZsmQJCQkJfPvttyQkJJCfn8+YMWOIiYnhiy++oFmzZvbk27x5c1avXs22bdt4/vnnGT9+/EX11q9fn6CgIH755Zcyxa6UUuXFmVdSXwIl/Tl+HHgIeNuJMdgVdJwI9Atk5syZjBw5EoBly5YRGRlJQEAAdevWJTIykqVLl160/8KFCxkzZgwAY8aMYcGCBfb1d911FyJCjx49OHHiBCkpKQD079+fmjVrOhyjv78/vr6+ALRr1442bdqUWH79+vW0atWKFi1a4OPjQ3R0NAsXLuTYsWNUq1aN1q1bAxAZGcncuXMB6NmzJ3Xr1gWgR48eJCUl2eurX7++/flNN93EzJkzHY5dKaWcwWlJyhjzE9ZEVNz2I8aYDUCOs2IorKC5r65PXfbu3Wtv2kpOTqZJkyb2csHBwSQnJ1+0/+HDhwkKCgIgKCiII0eOXNL+jnjssceIiopyuHxxxw4MDCQnJ8feTDhnzhwOHjx40f6fffYZQ4YMsS9v2LDB/rxLly6sWbOmLKehlFLlxsvVAThCRMYD4wEaNmxIbGzsJddxNu8sAOdSz+Hj42OvY/fu3eTk5NiX9+3bR/Xq1S86Rm5u7nnrCpbT0tLYsmWL/Z5PevJn3noAAAm2SURBVHo6mzZtIiMjA4CtW7dy7NixMsUMcOLEifPqKywuLo6UlBR73du3b+fQoUOsXr2aJ554gnHjxpGVlUX37t3Jyso6L4YtW7bwwQcf8P777xcZW25uLgcOHChz3K6UkZFRJeO+HHrO7sEdz7lKJCljzDRgGkCXLl1Mnz59LrmOzJxM+BnatWzHIY9DFNRR8CVfsPztt9/Sq1cvLjxG48aNadOmDUFBQaSkpPz/9u4/tqr6jOP4+zNQ0IiwWgyTMpGAWaUbIFzjsrEV3FrThitzQGSSjSlNEN2SscyULKhh/8yxxWTpjNNoBLYJ27CTLJu0shLFtEbmD1A3kYljBBcZVMdQ6Oye/XEOd13pj9P23nN/nOeV3PTce7733Ofpve3T7/ecfr9cdtllVFdXM2vWLMrLyzPtT506RTqdzvS6AJ566qlzjhfVhAkTmDt3LvPmzTtn35gxY2hvb88cu729nVQqRXV1NdXV1dx+++3s3r2brq4uzpw5k2m3b98+mpqaaG1tzQwJ9nby5EnGjRs37Ljzqef7mRSeczIkMefEXN139pzU5Esn093dzenTpwGora2lpaWFzs5OOjs7aWlpoba29pznp9PpzMUHmzZtypzTSqfTbN68GTOjo6OD8ePH/1+B6su6detobm4ecU6pVIo33niDQ4cO0dXVxdatW0mn0wCZ4ciuri7uvfdeVq9eDcDhw4e58cYb2bJlS78FCuDAgQNUVVWNOEbnnBuJxBSpsaPH0nBFAzd/8mZqamrYs2cPAGVlZaxfv55UKkUqleKuu+6irKwMgFWrVmXO6zQ2NtLa2sqMGTNobW2lsbERgLq6OqZNm8b06dNpaGjg/vvvz7zm/PnzWbp0Kbt27aKiooKdO3cCsH//fiZNmjRgvM3NzVRUVNDe3k59fX2mcB49epS6ujoARo8eTVNTE7W1tVRWVrJs2TJmzpwJwMaNG6msrGTVqlUsWrSIhQsXArBhwwaOHz/OmjVrmD17dp89NIC2tjbq6+uH/o12zrlsMrOc3IDHgLcJLow4AtwKrAZWh/snhY//E3g33L54sOPOnTvXhqutrc3MzF544QVbsWLFsI8zUjU1NbG91tmch2r+/Pl24sSJ7AYTk+HmXMw852QYSc7AXsvR7/tc3nJ2TsrMlg+y/+9ARa5efyBz5sxhwYIFdHd3M2rUqNhf/2yPqlAdO3aMtWvXZi5Vd865fCmKCydy4ZZbbsl3CAVr4sSJLF68ON9hOOdccs5JOeecKz5epJxzzhUsL1LOOecKlhcp55xzBcuLlHPOuYLlRco551zBUvA/XsVD0jHgr8N8ejnwjyyGUww852TwnJNhJDlfbmYTB29WWIquSI2EpL1m1vc8QCXKc04GzzkZkpizD/c555wrWF6knHPOFaykFakH8x1AHnjOyeA5J0Pick7UOSnnnHPFJWk9Keecc0XEi5RzzrmCVZJFStL1kl6XdFBSYx/7x0jaFu5/TtLU+KPMrgg5r5X0mqR9knZJujwfcWbTYDn3aLdEkkkq+kt3o+QsaVn4Xr8q6Rdxx5htET7bH5fUJunF8PNdl484s0XSI5LekfRKP/sl6cfh92OfpKvjjjFW+V51Mds3YBTwF2AacD7wMnBVrzZrgAfC7ZuAbfmOO4acFwAXhtu3JSHnsN044GmgA5iX77hjeJ9nAC8CHw3vX5rvuGPI+UHgtnD7KuCtfMc9wpw/B1wNvNLP/jrg94CAa4Hn8h1zLm+l2JO6BjhoZm+aWRewFbihV5sbgE3h9q+B6yQpxhizbdCczazNzN4P73aQp1WRsyjK+wzwPeAHwOk4g8uRKDk3AD8xs04AM3sn5hizLUrOBlwcbo8HjsYYX9aZ2dPAiQGa3ABstkAHMEHSx+KJLn6lWKQmA3/rcf9I+FifbczsQ+A94JJYosuNKDn3dCvBX2LFbNCcJc0BppjZb+MMLIeivM9XAldKelZSh6TrY4suN6LkfA+wQtIR4HfAN+IJLW+G+vNe1Epx+fi+ekS9r7OP0qaYRM5H0gpgHvD5nEaUewPmLOkjwH3AyrgCikGU93k0wZBfNUFv+RlJVWb2bo5jy5UoOS8HHjWzH0n6NLAlzPk/uQ8vL0rt99eASrEndQSY0uN+Bed2/zNtJI0mGCIYqHtd6KLkjKQvAN8F0mZ2JqbYcmWwnMcBVcBuSW8RjN3vKPKLJ6J+tp8ws3+b2SHgdYKiVayi5Hwr8EsAM2sHxhJMxFqqIv28l4pSLFLPAzMkXSHpfIILI3b0arMD+Fq4vQT4g4VnJIvUoDmHQ18/JShQxX6eAgbJ2czeM7NyM5tqZlMJzsOlzWxvfsLNiiif7d8QXCSDpHKC4b83Y40yu6LkfBi4DkBSJUGROhZrlPHaAXw1vMrvWuA9M3s730HlSskN95nZh5LuAHYSXBn0iJm9KmkDsNfMdgAPEwwJHCToQd2Uv4hHLmLOG4GLgF+F14gcNrN03oIeoYg5l5SIOe8EaiS9BnQD3zGz4/mLemQi5vxt4CFJ3yIY9lpZzH90SnqMYLi2PDzPdjdwHoCZPUBw3q0OOAi8D3w9P5HGw6dFcs45V7BKcbjPOedcifAi5ZxzrmB5kXLOOVewvEg555wrWF6knHPOFSwvUi7RJH0pnCH9Ez0em9rfDNRDaTPI89NnZ/SWdI+klcM9lnOlzIuUS7rlwB5i/l85M9thZt+P8zWdK0ZepFxiSboI+AzBtDp9FilJKyU9IenJcE2ju3vsHiXpoXDdphZJF4TPaZD0vKSXJW2XdGE/x20K7/4L+CB8/Js91v3ams18nStGXqRcki0GnjSzA8CJARaPuwa4GZgNLO0x/98MgmUxZgLvAl8OH3/czFJmNgv4E0ER7JeZ/dDMtoV3G4E5ZvYpYPVwE3OuVHiRckm2nGB9IsKvy/tp12pmx83sA+Bx4LPh44fM7KVw+4/A1HC7StIzkvYTFLeZQ4hpH/DzcLb6D4fwPOdKUsnN3edcFJIuARYSFBQjmBfOJN3ZR/Pec4edvd9zJvlu4IJw+1FgsZm9HF4QUT2E0OoJVmZNA+slzQzXPHMukbwn5ZJqCcHqppeHM6VPAQ7xv15ST1+UVBaec1oMPDvIsccBb0s6j6AnFUm4BtYUM2sD7gQmEEwK7FxieZFySbUcaO712HbgK3203QNsAV4CtkdY7mM98BzQCvx5CDGNAn4WDhO+CNxXxIsVOpcVPgu6cwMIh+vmmdkd+Y7FuSTynpRzzrmC5T0p55xzBct7Us455wqWFynnnHMFy4uUc865guVFyjnnXMHyIuWcc65g/ReRUVs+xFCcNQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a336539e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.plot(alpha, cv_log_error_array,c='g')\n",
    "for i, txt in enumerate(np.round(cv_log_error_array,3)):\n",
    "    ax.annotate((alpha[i],str(txt)), (alpha[i],cv_log_error_array[i]))\n",
    "plt.grid()\n",
    "plt.title(\"Cross Validation Error for each alpha\")\n",
    "plt.xlabel(\"Alpha i's\")\n",
    "plt.ylabel(\"Error measure\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For values of best alpha =  0.001 The train log loss is: 0.612225474329\n",
      "For values of best alpha =  0.001 The cross validation log loss is: 1.09218683253\n",
      "For values of best alpha =  0.001 The test log loss is: 1.07362382408\n"
     ]
    }
   ],
   "source": [
    "best_alpha = np.argmin(cv_log_error_array)\n",
    "clf = SGDClassifier(alpha=alpha[best_alpha], penalty='l2', loss='log', random_state=42)\n",
    "clf.fit(train_x_onehotCoding, train_y)\n",
    "sig_clf = CalibratedClassifierCV(clf, method=\"sigmoid\")\n",
    "sig_clf.fit(train_x_onehotCoding, train_y)\n",
    "\n",
    "predict_y = sig_clf.predict_proba(train_x_onehotCoding)\n",
    "print('For values of best alpha = ', alpha[best_alpha], \"The train log loss is:\",log_loss(y_train, predict_y, labels=clf.classes_, eps=1e-15))\n",
    "predict_y = sig_clf.predict_proba(cv_x_onehotCoding)\n",
    "print('For values of best alpha = ', alpha[best_alpha], \"The cross validation log loss is:\",log_loss(y_cv, predict_y, labels=clf.classes_, eps=1e-15))\n",
    "predict_y = sig_clf.predict_proba(test_x_onehotCoding)\n",
    "print('For values of best alpha = ', alpha[best_alpha], \"The test log loss is:\",log_loss(y_test, predict_y, labels=clf.classes_, eps=1e-15))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets test our model with best hyper param"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Log loss : 1.09218683253\n",
      "Number of mis-classified points : 0.34774436090225563\n",
      "-------------------- Confusion matrix --------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1adaa128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------- Precision matrix (Columm Sum=1) --------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a3372cf60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------- Recall matrix (Row sum=1) --------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a33611da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "clf = SGDClassifier(alpha=alpha[best_alpha], penalty='l2', loss='log', random_state=42)\n",
    "predict_and_plot_confusion_matrix(train_x_onehotCoding, train_y, cv_x_onehotCoding, cv_y, clf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Testing query point and interpretability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted Class : 7\n",
      "Predicted Class Probabilities: [[  3.28000000e-02   2.65000000e-02   4.00000000e-04   5.59000000e-02\n",
      "    2.40000000e-03   1.30000000e-03   8.64200000e-01   1.65000000e-02\n",
      "    0.00000000e+00]]\n",
      "Actual Class : 7\n",
      "--------------------------------------------------\n",
      "75 Text feature [ht] present in test data point [True]\n",
      "127 Text feature [transforming] present in test data point [True]\n",
      "139 Text feature [transformation] present in test data point [True]\n",
      "145 Text feature [activated] present in test data point [True]\n",
      "173 Text feature [extracellular] present in test data point [True]\n",
      "187 Text feature [oncogene] present in test data point [True]\n",
      "207 Text feature [downstream] present in test data point [True]\n",
      "234 Text feature [cylinders] present in test data point [True]\n",
      "245 Text feature [transformed] present in test data point [True]\n",
      "251 Text feature [resnitzky] present in test data point [True]\n",
      "252 Text feature [nodular] present in test data point [True]\n",
      "274 Text feature [mir] present in test data point [True]\n",
      "275 Text feature [mitogen] present in test data point [True]\n",
      "302 Text feature [progressed] present in test data point [True]\n",
      "315 Text feature [phospho] present in test data point [True]\n",
      "322 Text feature [activation] present in test data point [True]\n",
      "344 Text feature [activating] present in test data point [True]\n",
      "349 Text feature [technology] present in test data point [True]\n",
      "368 Text feature [expressing] present in test data point [True]\n",
      "370 Text feature [infect] present in test data point [True]\n",
      "381 Text feature [138] present in test data point [True]\n",
      "426 Text feature [marone] present in test data point [True]\n",
      "427 Text feature [receptors] present in test data point [True]\n",
      "470 Text feature [nielsen] present in test data point [True]\n",
      "482 Text feature [su] present in test data point [True]\n",
      "483 Text feature [501] present in test data point [True]\n",
      "489 Text feature [oncogenes] present in test data point [True]\n",
      "Out of the top  500  features  27 are present in query point\n"
     ]
    }
   ],
   "source": [
    "clf = SGDClassifier(alpha=alpha[best_alpha], penalty='l2', loss='log', random_state=42)\n",
    "clf.fit(train_x_onehotCoding,train_y)\n",
    "test_point_index = 1\n",
    "no_feature = 500\n",
    "predicted_cls = sig_clf.predict(test_x_onehotCoding[test_point_index])\n",
    "print(\"Predicted Class :\", predicted_cls[0])\n",
    "print(\"Predicted Class Probabilities:\", np.round(sig_clf.predict_proba(test_x_onehotCoding[test_point_index]),4))\n",
    "print(\"Actual Class :\", test_y[test_point_index])\n",
    "indices = np.argsort(-clf.coef_)[predicted_cls-1][:,:no_feature]\n",
    "print(\"-\"*50)\n",
    "get_impfeature_names(indices[0], test_df['TEXT'].iloc[test_point_index],test_df['Gene'].iloc[test_point_index],test_df['Variation'].iloc[test_point_index], no_feature)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Linear Support Vector Machines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "for C = 1e-05\n",
      "Log Loss : 1.36102840953\n",
      "for C = 0.0001\n",
      "Log Loss : 1.30529900304\n",
      "for C = 0.001\n",
      "Log Loss : 1.21555400392\n",
      "for C = 0.01\n",
      "Log Loss : 1.13584927473\n",
      "for C = 0.1\n",
      "Log Loss : 1.39091420459\n",
      "for C = 1\n",
      "Log Loss : 1.69470902335\n",
      "for C = 10\n",
      "Log Loss : 1.7021699177\n",
      "for C = 100\n",
      "Log Loss : 1.70216980806\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a3359df98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For values of best alpha =  0.01 The train log loss is: 0.74082256438\n",
      "For values of best alpha =  0.01 The cross validation log loss is: 1.13584927473\n",
      "For values of best alpha =  0.01 The test log loss is: 1.14042661431\n"
     ]
    }
   ],
   "source": [
    "alpha = [10 ** x for x in range(-5, 3)]\n",
    "cv_log_error_array = []\n",
    "for i in alpha:\n",
    "    print(\"for C =\", i)\n",
    "#     clf = SVC(C=i,kernel='linear',probability=True, class_weight='balanced')\n",
    "    clf = SGDClassifier( class_weight='balanced', alpha=i, penalty='l2', loss='hinge', random_state=42)\n",
    "    clf.fit(train_x_onehotCoding, train_y)\n",
    "    sig_clf = CalibratedClassifierCV(clf, method=\"sigmoid\")\n",
    "    sig_clf.fit(train_x_onehotCoding, train_y)\n",
    "    sig_clf_probs = sig_clf.predict_proba(cv_x_onehotCoding)\n",
    "    cv_log_error_array.append(log_loss(cv_y, sig_clf_probs, labels=clf.classes_, eps=1e-15))\n",
    "    print(\"Log Loss :\",log_loss(cv_y, sig_clf_probs)) \n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(alpha, cv_log_error_array,c='g')\n",
    "for i, txt in enumerate(np.round(cv_log_error_array,3)):\n",
    "    ax.annotate((alpha[i],str(txt)), (alpha[i],cv_log_error_array[i]))\n",
    "plt.grid()\n",
    "plt.title(\"Cross Validation Error for each alpha\")\n",
    "plt.xlabel(\"Alpha i's\")\n",
    "plt.ylabel(\"Error measure\")\n",
    "plt.show()\n",
    "\n",
    "\n",
    "best_alpha = np.argmin(cv_log_error_array)\n",
    "# clf = SVC(C=i,kernel='linear',probability=True, class_weight='balanced')\n",
    "clf = SGDClassifier(class_weight='balanced', alpha=alpha[best_alpha], penalty='l2', loss='hinge', random_state=42)\n",
    "clf.fit(train_x_onehotCoding, train_y)\n",
    "sig_clf = CalibratedClassifierCV(clf, method=\"sigmoid\")\n",
    "sig_clf.fit(train_x_onehotCoding, train_y)\n",
    "\n",
    "predict_y = sig_clf.predict_proba(train_x_onehotCoding)\n",
    "print('For values of best alpha = ', alpha[best_alpha], \"The train log loss is:\",log_loss(y_train, predict_y, labels=clf.classes_, eps=1e-15))\n",
    "predict_y = sig_clf.predict_proba(cv_x_onehotCoding)\n",
    "print('For values of best alpha = ', alpha[best_alpha], \"The cross validation log loss is:\",log_loss(y_cv, predict_y, labels=clf.classes_, eps=1e-15))\n",
    "predict_y = sig_clf.predict_proba(test_x_onehotCoding)\n",
    "print('For values of best alpha = ', alpha[best_alpha], \"The test log loss is:\",log_loss(y_test, predict_y, labels=clf.classes_, eps=1e-15))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Testing model with best alpha values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Log loss : 1.13584927473\n",
      "Number of mis-classified points : 0.34210526315789475\n",
      "-------------------- Confusion matrix --------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a325db908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------- Precision matrix (Columm Sum=1) --------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a335bea58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------- Recall matrix (Row sum=1) --------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a33545be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "clf = SGDClassifier(alpha=alpha[best_alpha], penalty='l2', loss='hinge', random_state=42,class_weight='balanced')\n",
    "predict_and_plot_confusion_matrix(train_x_onehotCoding, train_y,cv_x_onehotCoding,cv_y, clf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Querying some correctly classified point"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted Class : 7\n",
      "Predicted Class Probabilities: [[  6.19000000e-02   2.07000000e-02   2.10000000e-03   1.06700000e-01\n",
      "    6.90000000e-03   1.06000000e-02   7.83800000e-01   6.60000000e-03\n",
      "    7.00000000e-04]]\n",
      "Actual Class : 7\n",
      "--------------------------------------------------\n",
      "33 Text feature [transforming] present in test data point [True]\n",
      "35 Text feature [cylinders] present in test data point [True]\n",
      "38 Text feature [activated] present in test data point [True]\n",
      "57 Text feature [transformation] present in test data point [True]\n",
      "58 Text feature [expressing] present in test data point [True]\n",
      "63 Text feature [oncogene] present in test data point [True]\n",
      "68 Text feature [extracellular] present in test data point [True]\n",
      "78 Text feature [activation] present in test data point [True]\n",
      "79 Text feature [downstream] present in test data point [True]\n",
      "90 Text feature [mitogen] present in test data point [True]\n",
      "125 Text feature [transformed] present in test data point [True]\n",
      "130 Text feature [phospho] present in test data point [True]\n",
      "131 Text feature [138] present in test data point [True]\n",
      "135 Text feature [infect] present in test data point [True]\n",
      "143 Text feature [concentrations] present in test data point [True]\n",
      "145 Text feature [oncogenes] present in test data point [True]\n",
      "150 Text feature [thymoma] present in test data point [True]\n",
      "187 Text feature [si] present in test data point [True]\n",
      "194 Text feature [activating] present in test data point [True]\n",
      "196 Text feature [technology] present in test data point [True]\n",
      "202 Text feature [pdk1] present in test data point [True]\n",
      "209 Text feature [inhibited] present in test data point [True]\n",
      "213 Text feature [marone] present in test data point [True]\n",
      "255 Text feature [cysteine] present in test data point [True]\n",
      "257 Text feature [activate] present in test data point [True]\n",
      "266 Text feature [deregulates] present in test data point [True]\n",
      "319 Text feature [kinase] present in test data point [True]\n",
      "329 Text feature [inhibitor] present in test data point [True]\n",
      "335 Text feature [filters] present in test data point [True]\n",
      "337 Text feature [receptors] present in test data point [True]\n",
      "357 Text feature [viral] present in test data point [True]\n",
      "361 Text feature [serum] present in test data point [True]\n",
      "392 Text feature [inhibits] present in test data point [True]\n",
      "442 Text feature [cdc25] present in test data point [True]\n",
      "447 Text feature [elevated] present in test data point [True]\n",
      "460 Text feature [proc] present in test data point [True]\n",
      "Out of the top  500  features  36 are present in query point\n"
     ]
    }
   ],
   "source": [
    "clf = SGDClassifier(alpha=alpha[best_alpha], penalty='l2', loss='hinge', random_state=42)\n",
    "clf.fit(train_x_onehotCoding,train_y)\n",
    "test_point_index = 1\n",
    "# test_point_index = 100\n",
    "no_feature = 500\n",
    "predicted_cls = sig_clf.predict(test_x_onehotCoding[test_point_index])\n",
    "print(\"Predicted Class :\", predicted_cls[0])\n",
    "print(\"Predicted Class Probabilities:\", np.round(sig_clf.predict_proba(test_x_onehotCoding[test_point_index]),4))\n",
    "print(\"Actual Class :\", test_y[test_point_index])\n",
    "indices = np.argsort(-clf.coef_)[predicted_cls-1][:,:no_feature]\n",
    "print(\"-\"*50)\n",
    "get_impfeature_names(indices[0], test_df['TEXT'].iloc[test_point_index],test_df['Gene'].iloc[test_point_index],test_df['Variation'].iloc[test_point_index], no_feature)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random Forest Classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model with One hot encoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "for n_estimators = 100 and max depth =  5\n",
      "Log Loss : 1.22645255649\n",
      "for n_estimators = 100 and max depth =  10\n",
      "Log Loss : 1.15712942023\n",
      "for n_estimators = 200 and max depth =  5\n",
      "Log Loss : 1.20946098825\n",
      "for n_estimators = 200 and max depth =  10\n",
      "Log Loss : 1.14070205202\n",
      "for n_estimators = 500 and max depth =  5\n",
      "Log Loss : 1.19652463791\n",
      "for n_estimators = 500 and max depth =  10\n",
      "Log Loss : 1.141093053\n",
      "for n_estimators = 1000 and max depth =  5\n",
      "Log Loss : 1.18972808447\n",
      "for n_estimators = 1000 and max depth =  10\n",
      "Log Loss : 1.14229737591\n",
      "for n_estimators = 2000 and max depth =  5\n",
      "Log Loss : 1.19030929951\n",
      "for n_estimators = 2000 and max depth =  10\n",
      "Log Loss : 1.14402010029\n"
     ]
    }
   ],
   "source": [
    "alpha = [100,200,500,1000,2000]\n",
    "max_depth = [5, 10]\n",
    "cv_log_error_array = []\n",
    "for i in alpha:\n",
    "    for j in max_depth:\n",
    "        print(\"for n_estimators =\", i,\"and max depth = \", j)\n",
    "        clf = RandomForestClassifier(n_estimators=i, criterion='gini', max_depth=j, random_state=42, n_jobs=-1)\n",
    "        clf.fit(train_x_onehotCoding, train_y)\n",
    "        sig_clf = CalibratedClassifierCV(clf, method=\"sigmoid\")\n",
    "        sig_clf.fit(train_x_onehotCoding, train_y)\n",
    "        sig_clf_probs = sig_clf.predict_proba(cv_x_onehotCoding)\n",
    "        cv_log_error_array.append(log_loss(cv_y, sig_clf_probs, labels=clf.classes_, eps=1e-15))\n",
    "        print(\"Log Loss :\",log_loss(cv_y, sig_clf_probs)) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For values of best estimator =  200 The train log loss is: 0.710284280638\n",
      "For values of best estimator =  200 The cross validation log loss is: 1.14070205202\n",
      "For values of best estimator =  200 The test log loss is: 1.1236273535\n"
     ]
    }
   ],
   "source": [
    "best_alpha = np.argmin(cv_log_error_array)\n",
    "clf = RandomForestClassifier(n_estimators=alpha[int(best_alpha/2)], criterion='gini', max_depth=max_depth[int(best_alpha%2)], random_state=42, n_jobs=-1)\n",
    "clf.fit(train_x_onehotCoding, train_y)\n",
    "sig_clf = CalibratedClassifierCV(clf, method=\"sigmoid\")\n",
    "sig_clf.fit(train_x_onehotCoding, train_y)\n",
    "\n",
    "predict_y = sig_clf.predict_proba(train_x_onehotCoding)\n",
    "print('For values of best estimator = ', alpha[int(best_alpha/2)], \"The train log loss is:\",log_loss(y_train, predict_y, labels=clf.classes_, eps=1e-15))\n",
    "predict_y = sig_clf.predict_proba(cv_x_onehotCoding)\n",
    "print('For values of best estimator = ', alpha[int(best_alpha/2)], \"The cross validation log loss is:\",log_loss(y_cv, predict_y, labels=clf.classes_, eps=1e-15))\n",
    "predict_y = sig_clf.predict_proba(test_x_onehotCoding)\n",
    "print('For values of best estimator = ', alpha[int(best_alpha/2)], \"The test log loss is:\",log_loss(y_test, predict_y, labels=clf.classes_, eps=1e-15))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets test it on testing data using best hyper param"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Log loss : 1.14070205202\n",
      "Number of mis-classified points : 0.38345864661654133\n",
      "-------------------- Confusion matrix --------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a3371e630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------- Precision matrix (Columm Sum=1) --------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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MYd++TPLynAAc2H+I3T8fJCYmklq1arB5UxrZ2TlYlsXaNd/ToGFMuddN5H+la8bFy9eDBea3FvD5nzM1xg70BM6dwGeAry3Lqn2hPOq9scLrxuN3qRvBiI4N8TeGT7dlMGHDXh5tW48tmSf5Ynf+l/zh1zWkc53qhT+ROG/HIfo2juLlrpexI+tMYV6PL/2BbYdPExdamdd7NiG8UiBbD5/i0cXbcXjZTyduSsqu6CIU81XqVpJfmoXL6eKmfu35e1IPJr8xnybN4uiU0IJt3+9h8CNvceJkNkFBAdSoGcons58AYO6sNbw7NX+Kwd33deemfvlrE8z8dBWffJBKQIAfttoRjHjudsLDq1VYHUsSFuSdq9CvWLGeMWOm4nS66N+/Gw8+eBuvvvoBzZvH07VrO3JyHAwalMz27bsICwtm/PjBxMXl//LHxImfMHPmF/j7+zNs2L107tzmvHl6k23HvPOu1YavtvPW+Nm4XBZdb2rLrXd346PJC2nUJJa2nZrz9MOT2JN2kOo1QwGItIUzbOw9AHwxdy0z38v/IDLgrm50vSn/Q0XmwSxefea/nD6VTWh4Nf751J+JtHnX4mFNwxtXdBGKuRTbBUCOs3RryJSnlSu+46UXPsDpctG3X2eSHujDhNdn0LRZfRISryInx8GwIZP4YftuwsKDeWnsw8TGRbFk8TrefH0m/gH++PkZHnq4f2GgYev3uxg+bAo5OQ6u69iKJ5680+uG+PoZ7/yF7NQV3/Li8/k/ndjvlgTuf6Afb7z2Kc2aNyAhsQ05OQ6eGDKB7dt3ExYWzMvj/kVcXDRz56Ty1tS5BAT642cMDzzUn67d8n/i9Y3Xp7NowWr8/f24vEk9Rj13P0FBgRVcU3eBfldWdBEEAO+8fl+q1wxo7F0dp4dFNxnksS929u0vl/t7VVbBgreAdyzLWlXCvo8sy/rrhfLwxmDBpcobgwWXKm8NFlyKvDVYcKnyxmDBpcobgwWXKm8NFlyqFCzwFrp+e5eLO1hgazrEY99pM7a9WO7vVZlcRSzLuuc39l0wUCAiIiIiIiLi23z7xwd9u/QiIiIiIiIi4nEanyYiIiIiIiLiYRW1MKGnKFggIiIiIiIi4mG+Hizw7dKLiIiIiIiIiMdpZIGIiIiIiIiIhxkfvzevYIGIiIiIiIiIh/n6NAQFC0REREREREQ8zBhT0UX4Q3w71CEiIiIiIiIiHqeRBSIiIiIiIiIepmkIIiIiIiIiIuLG1xc49O3Si4iIiIiIiIjHaWSBiIiIiIiIiIdpGoKIiIiIiIiIuPH1YIFvl15EREREREREPE4jC0REREREREQ8zNcXOPTaYMHOf1Sv6CJIgUaPH6/oIkiBn8dVdAnkV5eF1avoIoh4pUr+YRVdBCmw++SPFV0EKaJeyJUVXQQRKW+ahiAiIiIiIiIiFxOvHVkgIiIiIiIi4qt8fYFDBQtEREREREREPMwYU9FF+EN8O9QhIiIiIiIiIh6nkQUiIiIiIiIiHqZfQxARERERERERN76+ZoFvl15EREREREREPE4jC0REREREREQ8zccXOFSwQERERERERMTTfHwcv48XX0REREREREQ8TSMLRERERERERDxN0xBERERERERExI2PBws0DUFERERERERE3GhkgYiIiIiIiIin+fiteQULRERERERERDzM0jQEEREREREREbmYaGSBiIiIiIiIiKf59sACBQt+j5Urv+X50e/gdLkYMKAr9yX1c9vvcOQydMjrbN26i/DwEJKTHyUmNoqvv9pE8rgPyc3NIzAwgMcH30H79i0AGHjH0xw6dJRKlYMAmPbWU9SoEVbudfM1nS6L5Om+LfDzM3yydg+TlqW57R9+czOuaVQTgCpB/tQIrkSr4QsAuKVNHA93iwfgjS928Nn6dADeva89UaGV8PczfLMrixGfbcZllWOlfFhq6gZGj56Ky+Xi1lu7k5R0q9t+hyOXwYOT2bp1J+HhIYwfP5jY2GgAJk+ezowZS/Dz82P48CQ6dmxdqjylZPn91NtF+qlb3Pbn91OvFemnHivST31QpJ+6k/btW5CdncOj/zeW9L0Z+Pn7kZDQhsf+fUcF1c63qF14D50L7/HN1z8waewcnC4XN/Rtx213Jbrt37JxJ5PGzWVX2kGGjb6djt1aAfDd+jQmJ88tTJe+O5NhY/7GtV2akzzqU37ang4WxNSpyePP/JkqVSuVa71E/ij1UxcpP9+OFihYUEpOp5PnRr3FtLefIjo6gttufYKExDY0ahRXmGbmjGWEhgazaPEbzE/5inHjPiB5/GOEVw/lzYlDiYqOYMdPe7nv3udYnjql8LiXXn6E5i0aVkS1fJKfgVG3tOSOyavJOJ7NnP/rxBdbM0iznypM89zcrYX/H3hdfZrG5AdgwqoE8kiPxtw8PhULmPdo/rEnsnN5+D/rOZWTB8CbA9vQq1VtPv/uQLnWzRc5nU5GjZrEO+88S3R0DQYMeIzExHY0alSnMM306YsJDQ1myZIppKSkMnbsu7zyyhDS0vaSkpJKSsoE7PYj3H33UyxaNAnggnlKcfn91FSmvT2C6Oga3HbrEBISrz6nn1pa0E9NYH7KKsaNe5/k8f8mvHoIb058okg/9SzLU6cCcPfdN9OufQscjlz+fvdIUlM30qlT64qqpk9Qu/AeOhfew+l0MeHFWTw/IYma0WH8885Xad+pKXUb2ArTRNqq8+9nbmPG+yvcjr2iTSMmfvQYACeOn+Hufs/Tun1jAO5/7GaqBVcGYHLyXOZ++lWxIISIN1M/Jd6qzNYsMMZcbozpaowJPuf568vqNcvSls1p1KljIy4umqCgQG7o1YFlS9e7pVm29Bv69u0MQI+e7Vmz+nssy6Jp0/pERUcA0Cg+jpycXByO3HKvw8WiVZ3q7DlymvSsM+Q6LeZ9u5/uzWznTX/TlTHM+3Y/AJ0uj2LVT4c4np3LiexcVv10iM6XRwEUBgoC/AxB/n5oUEHpbN68g7p1axEXZyMoKJDevTuxdOlatzTLlq2lX7+uAPTs2YHVqzdhWRZLl66ld+9OBAUFEhdno27dWmzevKNUeUpxZ/spW0E/dR3Lln7jlmbZ0nX07dsFgB49r2HN6i0F/VSDc/opBw5HLlWqVKJdwUiooKBAmjatjz3jSLnWyxepXXgPnQvv8ePWvdSOq0Gt2BoEBgbQpccVrF6x1S2NrXYEDeJr4/cbd+NWLd3M1ddeTuWCUZm/BgosyyInR5+vxPeon7qIGeO5RwUok2CBMeZfwBzgn8D3xpg+RXaPKYvXLGt2exa2WjUKt222CDLt7h+Y7ZlZ2GrlD30PCPAnJKQqx46ddEuzeNEamjStT1BQYOFzTw6bQL++jzPxzRlYlr6iXogtrDIHj2UXbmcc/wVbWJUS08ZUr0JcRFW+3nGo5GOP/YItrHLh9ntJ7Vk/siencvJYsEmjCkrDbj+CzVazcDs6ugb2c9uG/Qi13NpGNY4ePVHCsTWx24+UKk8pLr+fOvu+ebKfAjhx4jTLv1xP+2talFENLh5qF95D58J7HMk8TmR0eOF2zahwDmce/935LF/8LV16Xun23NiRH/PnniNJ351Jnz9f94fLKlKe1E9dxIwHHxWgrEYW3AdcZVlWX6AL8JQx5pGCfeetqjEmyRiz3hizfuqUGWVUtP9NiV/hz4nwlPRF3xSp7o4d6SSP+5BnRiYVPvfS2H8xZ14yH3zwLBvWb2funFRPFfmiVdIfkHWecQA3XhHDgs0HCtceKPHYIocOnLKGtiMXExTgx7XxkX+4rJeCEv/uS9M2jDnP86XLU4orsR387n5qL8nj3ueZkQ+4pcnLc/L4v8fztzt6Exd3/pE8kk/twnvoXHiPkq7Uv/d9O3L4BLvTMmhzzWVuzz/+9J/5aMEI6tSPYsXi7/5AKUXKn/op8VZlFSzwtyzrFIBlWbvJDxjcYIxJ5jeCBZZlTbEsq41lWW3uSxpQRkX739iiI8g4eDYal5GRRVRUxDlpapBx8DCQ/8H65MkzhIUHF6Q/wr8efpnnX3yYOnXOftCOjs4frVAtuAq9b7yOLZt3lHVVfN7B479QK/zsSAJbWGXsx38pMe1NV8Ywt2AKQonHhlfGfsL9WEeeiy+22n9zaoOcZbPVJCPjcOG23X6keNuw1eSgW9s4TXh4SAnHHiYqqkap8pTiivZB8L/2Uy/x/Iv/cuunAJ4eMYm6dWtx58Aby7gWFwe1C++hc+E9akaFcch+rHD7cOYxakSG/q48Upds4tqE5gQE+Bfb5+/vR+fuV7Bq2ZY/XFaR8qR+6iLmZzz3qIjil1G+GcaYK37dKAgc3AjUBHxy/GrzFo3Ys+cg+/bZcThyWTD/KxIS27ilSUhsw+zZ+QvyLF60hnbtm2OM4cSJ0zx4//M8+thfad368sL0eXlOjh49AUBubh4rlm+gUWMtOnIhm9OPUa9mNWIjqhLob7jpyhi+2Govlq5BZDXCqgSycffRwudSf8ikY+NIQqsEElolkI6NI0n9IZOqQf5EhuSvnOzvZ0hoEsXOzJPF8pTiWrSIZ/fuA6SnZ+Bw5JKSkkpiYlu3NImJ7Zg1aykAixZ9Rfv2LTHGkJjYlpSUVByOXNLTM9i9+wAtW8aXKk8prng/taqEfupqZs9eDsDiRavP6adG8+hjt7v1UwCvvvIRp06e5olhd5dXVXye2oX30LnwHpc1jWN/+mEy9h8hNzeP5Yu/o32nZr8rj+WL3KcgWJbF/vTDhf9fs3IbcfWiPFpukbKmfuoi5uNrFpiymCNvjIkF8izLyihhXwfLsr66UB5Oa7PXTd5fsWIjL4x5F5fLRb/+CTzwQH9ef+1jmjVvSGLi1eTkOBgy+HW2b/+Z8LBgxiY/SlxcNJMmzmTqlFnUqXv2Tt20t56iSpVK3Pm3EeTlOXG6XFxzTQuGDB2Iv3/xaHlFavT47oouQjFdLo9iRN/m+BnD9HV7mbB0B4/2vIwt+44VBg4e6XEZlQL9eCllu9uxt7aN46Gu+SsoT/jiJ2Z8k07N4EpMu6ctlQL88fOD1WmHeXbOVpxe9tuJP4+7/MKJKsCKFesZM2YqTqeL/v278eCDt/Hqqx/QvHk8Xbu2IyfHwaBByWzfvouwsGDGjx9cOJR94sRPmDnzC/z9/Rk27F46d25z3jy9idNyVHQRSrRixQZeGPNOQT+VyAMPDOD11/5Ls+aNivRTr53TT9mYNHEGU6d8Rp26tQrzmvbWCHJz80jskkSDBjEEFqxhcPvtNzDg1m4VVcUS+Zugii5CMZdiu/BWl+q52H3yx4ouQjHrVm1nUvIcXE6LHjdfzV/v6cZ7kxbSuEkc13Ruxo9b9zJq0HucPHGGoEqBVK8RwtRPBwGQcSCLx+55gw9ShuPnl3+/y+Vy8e973+TM6V+wLIsGjWvzz6H9Cxc99Cb1Qm6q6CIIAD9VdAFKdKn2U9D4op4bEd/9LY99mdix5J5yf6/KJFjgCd4YLLhUeWOw4FLlrcGCS5G3BgsuVd4YLBCpaN4YLLiUKVjgLbwzWHDpusiDBT08GCxYXP7BgoDyfkERERERERGRi14FrTXgKWW1ZoGIiIiIiIiI+CiNLBARERERERHxNN8eWKBggYiIiIiIiIinWRX0KwaeomkIIiIiIiIiIj7MGHO9MeZHY0yaMWZoCfvrGGO+NMZ8a4zZbIzpdaE8NbJARERERERExNPKaYFDY4w/MAHoDuwDvjHGzLUsa1uRZMOBTy3LmmiMaQrMB+r9Vr4aWSAiIiIiIiLiacaDj9/WFkizLGuXZVkO4GOgzzlpLCC04P9hwIELZapggYiIiIiIiIgXM8YkGWPWF3kkFdkdA6QX2d5X8FxRzwB/M8bsI39UwT8v9JqahiAiIiIiIiLiaR5c4NCyrCnAlPO9UkmHnLP9F+Bdy7LGGWOuAd43xjS3LMt1vtdUsEBERERERETE08ppzQLyRxLEFdmOpfg0g3uA6wEsy1ptjKkM1AQyz5eppiGIiIiIiIiI+K5vgHhjTH1jTBDwZ2DuOWn2Al0BjDFNgMrAod/KVCMLRERERERERDytnAYWWJaVZ4x5GFgE+ANvW5a11RgzClhvWdZc4N/AVGPm1OZ6AAAgAElEQVTMo+RPUbjLsqxzpyq4UbBARERERERExNM8uGbBhViWNZ/8hQuLPjeiyP+3AR1+T56ahiAiIiIiIiIibjSyQERERERERMTTynFkQVnw2mCB8d6iXXLSxtar6CJIgSp1nq7oIkiBM3ufqegiSBE5zuMVXQQpUMk/rKKLIAViqsVWdBFERC5tPj6O38eLLyIiIiIiIiKeptv3IiIiIiIiIp6maQgiIiIiIiIi4sa3YwUKFoiIiIiIiIh4muXn29ECrVkgIiIiIiIiIm40skBERERERETE07RmgYiIiIiIiIi48e1YgaYhiIiIiIiIiIg7jSwQERERERER8TQfX+BQwQIRERERERERT/PxNQs0DUFERERERERE3GhkgYiIiIiIiIin+fbAAgULRERERERERDzOx9cs0DQEEREREREREXGjkQUiIiIiIiIinubjIwsULBARERERERHxMMu3YwWahiAiIiIiIiIi7jSy4HdYuXIjY0a/hcvlYsCAbtyX1N9tv8ORy5Ahr7Jt607Cw0NITn6cmNgojh49wf898jLff59G374JPDUiqfCY++4dxaFDR8lzOmlzVROeGpGEv79/eVfN56xc+S3Pj34Hp8vFgAFduS+pn9t+hyOXoUNeZ+vWXQXn4lFiYqP4+qtNJI/7kNzcPAIDA3h88B20b9+C06ey+dvfnio83p6RxU03d+SJYXeXd9V8zqSX7+eGrldy6MgJ2nQfXGKacSMH0jPhCs5kO0j690S++343ALcP6MTQf/YF4IXXZ/PhjFQArmxRnynjHqBK5SAWffkd/376vXKpy8VgZeoGRo+ehsvlZMCtPUhKGuC23+HIZcjg8WzdmkZ4eCjJ4wcRGxsNwOTJ05k5Ywl+fv48Ofw+OnZsza5d+3js0ZcLj09Pz+Bf//orA+/qU6718kWrVm7ixeffx+V0ccuALtxz381u+x2OXJ4cOoltW38mLDyEl5MfJiYmsnD/wQOH6XvTEB78xy3c9ffeALz/3gI+m7EcjCG+cSzPjk6iUqWg8qyWT0pN3cDo0VNxuVzcemt3kpJuddvvcOQyeHAyWwuu3+PHD3ZrFzNmLMHPz4/hw5Po2LF1qfKU81u18jteGPMeTpeL/gMSufc+9/7E4cjliSET2LbtZ8LDgxmb/AgxMVFs2ZzGM09PBcCyLB76xwC6dW8LwH/eTWHmjC8xBuIb1+G5MQ+obYhPUT91kfLxaQgaWVBKTqeTZ0dNYcrUp5j3+WukpKwiLS3dLc2MGV8QFlqNRYsncufAmxg77j8AVKoUxL8e+QuDBg8slu/4Vx5n9pzxzJv3KllZJ1i48OtyqY8vczqdPDfqLSZPfZJ5n49nfspXxc7FzBnLCA0NZtHiNxg48EbGjfsAgPDqobw5cShz5iXz/AsPM3Tw6wBUC67CrNljCx+1a9eke/d25V43X/T+9BX0ufOF8+7vmXAFDevZaN7pUR4eOpXXRt8DQPWwajz5f7fQ6ean6HjzUzz5f7cQHlYNgNdG/52Hh06jeadHaVjPRo8urcqlLr7O6XQyatRkpk57ms9TJpDyeSppaXvd0syYvoTQ0GAWL5nCwLtuZtzY/EBMWtpe5qes5POUCUyb9jSjRk7C6XTSoEEss+e8yuw5rzLzs2SqVKlEt+7XVET1fIrT6WLMc+8xcfJgZs97iQXz17Azbb9bms9mLic0tBopi5K5Y+D1vDLuY7f9L734Idd1PPu3b7dn8eEHi/nv9GeZNfcFXE4XC+evKZf6+LL8djGJadOeISVlAp+X0C6mT19MaGgwS5ZM4a67+jB27LtAfrtISUklJWUC06Y9w8iRE3E6naXKU0rmdLp47tm3mThlKHPnjWN+ylfsTNvnluazGV8SGhbMgkWvcsedvUke+xEAjeLj+GT6GGbOepHJU55g1DPTyMtzFrSNhXwyYwyz543F5XKxYL4+T4nvUD91ETPGc48KUGbBAmNMW2PM1QX/b2qMecwY06usXq+sbd68gzp1ahEXZyMoKJBeva5j2dJ1bmmWLV1Hn74JAPTseS1rVm/GsiyqVq3MVVc1pVJQ8Qh3cHBVAPLynOTm5mEq6A/Bl2zZnEadOjbi4qIJCgrkhl4dWLZ0vVuaZUu/oW/fzgD06NmeNau/x7IsmjatT1R0BJD/oSMnJxeHI9ft2N27D5KVdYKr2jQpnwr5uK/W/UDWsVPn3X9jj6v4aOZKANZ9m0ZYaFVsUeF079yKpSu3cPT4aY4dP83SlVvo0bkVtqhwQoKrsHbjDgA+mrmSm3q2KZe6+LrNm3dQp26Rfqp3R5YuXeuWZumytfTtlwhAz54dWL16E5ZlsXTpWnr17khQUCCxcTbq1K3F5s073I5dvXozcXE2YmKiyq1Ovur7LTupUyea2LgoAoMCuP6G9ny5bINbmuXLNnJz344AdO/RlrVrtmJZFgDLvlhPbGwkDRvFuB3jdDrJ+cVBXp6TX35xEBlVvXwq5MM2b95B3SLtonfvTsXaxbJla+nXrytQvF307t2JoKBA4uJs1C1oF6XJU0pW9BoeGBTADb2uZdmyc67hy9bTp08nAHr0bFfYNqpUqURAQP7oyxxHrtuH57wibSM7O0dtQ3yK+inxVmUSLDDGPA28Bkw0xjwPvAEEA0ONMU+WxWuWtUx7FrZaNQu3o201sNuPuKWxZx6hVkGagAB/QkKqcuzYyQvmfe89I7muw11Uq1aFnj11x+5C7PYsbLVqFG7bbBFkFjsXZ8/X+c7F4kVraNK0PkFBgW7Pz09ZxfU3XKvAjYfUtkWw7+DZ87M/I4vatghq26qz70DW2ecPZlHbVp3atgj2ZxR5PuMItW0R5VpmX2W3H6GW7Ww/ZYuuWayfyrSf209V49jRkyUcW7yPm5+SSu8bO5VhDS4edvtRoov83UbbIsjMPHreNAEB/gSHVOXYsVOcOfMLb7/1OQ8+dItb+ujoCAbe3YseXR+ha+eHCQ6uyrUdWpR9ZXyc3X4EW5G/7egS/rbtJbSLo0dPlHBsfpsqTZ5SsszMLGy2s9fw6OgIMu1Z7mmKXOfz20aVwmv45k076HPj4/TrM4gRT99DQIA/0dER3HX3jXTr+g8SOj1ASEhVOnTQiDTxHeqnLmJ+xnOPiih+GeU7AOgAdAL+AfS1LGsU0BO47XwHGWOSjDHrjTHrp0z5tIyK9r+xsIo9d+6XSat4klKZ9tbTpK58G4cjlzVrtvxvmVxCSnybi52LEs4XZ9Ps2JFO8rgPeWZkUrF08+d/Re/eHf5oMaVA0ff9V5ZllRiMsSxKSF3y+ZQSlPR3X4q2gaHEhlX0WIcjl2XL1nH99WobpVJiH1SKNAbefOMz7rjzeqpWq+y278Tx03y5bCMLlozni+Wvk52dw+dzV3mw0BenEq8HpblmGHOe50uXp5SsxC6oFJ+nfr2WtGwVz5zPx/Lxp2OYNnUOOTkOjh8/xZfLNrBoyessWzGR7Owc5s1dWRbFFykT6qcuYn4efFSAslrgMM+yLCdwxhiz07KsEwCWZWUbY1znO8iyrCnAFACXtc2rvh1ER9cg4+Dhwm17xhGiotzvdtqia3Dw4GFstprk5Tk5efIM4eEhpcq/UqUgEhOvZtnSdXTocIVHy36xsUVHkFHkTnVGRlaJ5yLj4GFsthqF5yIsPLgg/RH+9fDLPP/iw9SpY3M77ocfduPMc9GsecOyr8glYn/GEWKLjASJsUVw0H6U/Qez6HjN2akeMbUiWLl6O/szsogpckc2xlaDg3b3O7JSsmhbTQ5mnO2nMuyHi7WNaFvNc/qp04SHhxBtq3HOse593MrUDTRt1pCaNTW0tzSibRHYi4yQsWdkFRsW/WuaX/upUyfPEBYWzJbNaXyxeB3jx33MyZNnMMZQqVIgNWqEERsTSUREKABdu7fhu+92cOPN15Vr3XyNzVaTjCJ/23Z7Cdfv87SL4sceJioqvz+7UJ5SsujoCDIyzl7D7faS20bGwSNF2kZ24TX8Vw0bxlClSiV27Ehn/75DxBRtG93a8t23P3HTzR3LvkIiHqB+SrxVWcUoHMaYqgX/v+rXJ40xYcB5gwXerEWLePbsOci+fXYcjlzmz19FQuLVbmkSEq9mzuwvAVi06Gvat2/xmxG806ezyczM/zCZl+dkRepGGjSILbtKXCSat2jkdi4WzP+KhET3Oe0JiW2YPXsFkD/doF375hhjOHHiNA/e/zyPPvZXWre+vFje81NW0UujCjwqZclG/to//wNb2ysbceLkGTIyj7FkxSa6dWxJeFg1wsOq0a1jS5as2ERG5jFOnf6Ftlc2AuCv/Tvy+eINv/USUqBFi3j27D7AvvSM/H4qZSWJie4LdSYmtmX2rGUALFr0Fe3bt8QYQ2JiO+anrMThyGVfegZ7dh+gZcv4wuNSUlbSu7emIJRWs+YN2LMng337Msl15LFwwRq6JLR2S9MloTVzZ+ff/VyyeB1t2zXFGMN7H4xg4RevsPCLV7j9jp7cm3Qzf7m9B7ZaNdi8KY3s7Bwsy2Ltmq00aBBT0stLES1axLN79wHSC9pFSkoqiYlt3dIkJrZj1qylwLntoi0pKak4HLmkp2ewu6BdlCZPKVnzFg3ZW6RtLJj/NQkJV7mlSUi4ijlz8n8dZ/GitbRr3wxjDPv2ZZKX5wTgwP5D7P75IDExkdQq1ja+p0FDtQ3xHeqnLmI+vsBhWY0s6GRZVg6AZVlFgwOBQPGfBPABAQH+DH/qPu69ZyQul4tb+nclPr4Or732Ec2bNyIxsS0DBnRjyOBX6NnjQcLCghmX/O/C47smJnH6dDa5uXksXbqOaW89TXh4CP946HkcjlycLhft27Xgtj/3rMBa+oaAAH+efOoe7rtnNC6Xi379E4iPj+P11z6mWfOGJCZeTf8BiQwZ/Do9ezxMeFgwY5MfBeCjDxeyd28GEyfOYOLEGQBMe+spatQIA2DhgtVMmjKswurmi957/Z90vKYJNauHkLb2DZ5NnkFgYH7XMu2DL1i47Ft6JlzB1pWvcCY7h/sfnwzA0eOnef61Waya9xwAY179jKPHTwPwryffLvzpxMVffseiL7+rmMr5mIAAf54acT/33PsMLqeL/v275fdTr36Y3091bceAAd0ZPCiZHt2TCAsLIXn8IADi4+twww3X0bvXP/D392fEiAcKf8Y1OzuHr77+jpGjHqq4yvmYgAB/hj05kAfvewmny0Xffp1pFB/LhNdn0LRZfRISr6Jf/84MGzKJ3j0fIyw8mJfGPvybebZs1YhuPdpy24Dh+Pv706RJXQb8KaGcauS7AgLy/57vvfdpnIXtoi6vvvoBzZvH07WgXQwalEz37kmEhQUzfnz+z8DGx9flhhuuo1evh4q1i5LylAsLCPBn2PC7uf/eMThdLvrdkkCj+DjeeO1TmjVvQEJiG24ZkMATQyZwQ89HCAsL5uVx/wJg44YfeGvqXAIC/fEzhuEj/k716qFUrx5K957t+FP/J/D39+PyJvW49U9dK7imIqWnfuoi5uM/nWi8dS6wt01DuJRZ5FV0EaRAcN3nK7oIUuDM3mcqughShMN5oqKLIAUq+YdVdBGkQK7rdEUXQYoI9LuyoosgAPxU0QUQN419+9v0BTT412yPfafd9Vrfcn+vympkgYiIiIiIiMgly/LxRSUVLBARERERERHxtAr6FQNP8fHii4iIiIiIiIinaWSBiIiIiIiIiKf5+AKHChaIiIiIiIiIeJqPr1mgaQgiIiIiIiIi4kYjC0REREREREQ8TdMQRERERERERMSNb8cKNA1BRERERERERNxpZIGIiIiIiIiIh1mahiAiIiIiIiIibnw8WKBpCCIiIiIiIiLiRiMLRERERERERDzN+PbIAgULRERERERERDzNx8fx+3jxRURERERERMTTNLJARERERERExNM0DaFs7D61s6KLIAUahFxW0UWQAtl7R1Z0EaRAk7cyKroIUsT2e2wVXQQpkOM8XtFFkAIuK7eiiyBFBGo8r8ilR7+GICIiIiIiIiIXE68dWSAiIiIiIiLis3x8ZIGCBSIiIiIiIiIeZvn4mgWahiAiIiIiIiIibjSyQERERERERMTTfPzWvIIFIiIiIiIiIp6maQgiIiIiIiIicjHRyAIRERERERERT9OvIYiIiIiIiIiIGx8PFmgagoiIiIiIiIi40cgCEREREREREU/z7YEFChaIiIiIiIiIeJqlaQgiIiIiIiIicjHRyAIRERERERERTzO+PbJAwQIRERERERERT/PxaQgKFoiIiIiIiIh4mm/HChQs+D3Wf/0Dk8bOweVycX3fdvzprkS3/Vs27mTyuLn8nHaQoaNvp2O3VgBsWp/GlOS5henSd2cydMzfuLZLcx6/dwLZZ3IAOJZ1isuaxTFi3N3lVykflpq6gdGjp+Jyubj11u4kJd3qtt/hyGXw4GS2bt1JeHgI48cPJjY2GoDJk6czY8YS/Pz8GD48iY4dW5cqTymZzoX3uC6mOsPaN8TPzzDjxwymbU532983PppBV9fHfsYBwEfbDjDjpwwAHr+6Pp3jIjDG8PX+o4xZsxOAXg0iub9VHSwsMs84GLz8B47l5JVvxXyQ2oX3WLVyEy8+/z4up4tbBnThnvtudtvvcOTy5NBJbNv6M2HhIbyc/DAxMZHs33+IvjcOpl69WgC0bNWIp575OwDbtv7M8GGTyfnFQcdOVzBk2B0YHx9uWh6+WrmFl174CJfTRb/+nfj7fb3d9jscuQx/Yirbt+4hLDyYF8c9SExMTQB++jGd50a+x6lT2fj5GT785GkqVQrkoaRxHD50nDynk9ZXNeaJ4Xfg769lucS36Joh3kg9aSk5nS4mvDiLZ1+7l8nTB7F80bfs2ZXhlibKVp1/P3MbCT2vdHu+VZtGTPjoMSZ89BgvTHyASpUDad2+MQBjp/2jcF+TFnW5NqFFudXJlzmdTkaNmsS0ac+QkjKBzz9PJS1tr1ua6dMXExoazJIlU7jrrj6MHfsuAGlpe0lJSSUlZQLTpj3DyJETcTqdpcpTitO58B5+Bp66thFJi7/nppnr6d0gkobhVYulW/DzIW6ZvZFbZm8sDBRcERXKldGh9Jm1gZs/W0+LyBCutoXhb2BY+4YMnL+JvrM28lPWaW5vGlPeVfM5ahfew+l0Mea595g4eTCz573Egvlr2Jm23y3NZzOXExpajZRFydwx8HpeGfdx4b7YuGimzxrD9FljCgMFAM+NeoenR97D5wvHsWdPBqtWbi63Ovkqp9PF86PfZ8KkR/ls7mgWzl9b7FzMmrmS0NBqzFv4In+7swevJn8KQF6ekyeHTuHJEXfy2dzRTHt3KAEB/gC8lPwQn84axcw5z3E06yRLFn1T7nUT+SN0zbh4+fl57lEh5S+vFzLG/Ke8Xqss/LR1L7XjalArtgaBgQF07nEFa1ZsdUsTXTuC+vG1Mb8xN2Xl0s20ufZyKlcOcnv+zOlf2LQ+jWu6NC+T8l9sNm/eQd26tYiLsxEUFEjv3p1YunStW5ply9bSr19XAHr27MDq1ZuwLIulS9fSu3cngoICiYuzUbduLTZv3lGqPKU4nQvv0TIyhL0nstl38hdyXRbzdx0isU6NUh5tUcnfj0A/P4L8/AgwhiPZDgwGA1QNzP9QXi3Qn8yC0VByfmoX3uP7LTupUyea2LgoAoMCuP6G9ny5bINbmuXLNnJz344AdO/RlrVrtmJZ1nnzPHToKKdOZdPqiniMMdzU5zq+XLq+TOtxMfh+yy7i4qIKz0XPXm1Z/uW3bmmWL9vITX06ANCtRxvWrdmOZVms/vp74hvHctnldQAIDw8uHD0QHFwFyA8o5Obm+fp6YnIJ0jXj4mWM5x4VoUyCBcaYuec85gG3/LpdFq9Z1g5nHicyOrxwu2ZUOEcyj//ufFIXf0uXc0YeAHz95fe0uroR1YIr/6FyXirs9iPYbDULt6Oja2C3HymWplat/DQBAf6EhFTj6NETJRxbE7v9SKnylOJ0LrxHVNVKZJw++0XefiaH6GpBxdL1qFeT2f1a80piE2zVKgHwXeZJ1h48Rupf2pP61/as2n+UXcezybMsRn6dxpx+V5H6l3Y0ql6NmT9lFMtT3KldeA+7/SjRtojC7WhbBJmZR8+bJiDAn+CQqhw7dgqA/fsP8adbnuTuO59jw/ofAMi0HyU6ukie0cXzlOIy7Uex1TrnfbO7v2+ZmcewuZ2LKhw7doo9u+0YY3jwvrH8ecDTvPPWfLfjHrxvLImdHqFqtcp063F12VdGxIN0zRBvVVYjC2KBE0AyMK7gcbLI/0tkjEkyxqw3xqz/7zsLy6hoHvQ7QzxZh0/wc1oGV11zWbF9K84TRJCSlXTH59y5oudLU/LzpctTitO58B4lvUPnvpXL9x6h6yfr6DtrI6sPHOP5Tvn9UZ2QyjQMr0rCx2vo8t81tK8dThtbGAHG8Ocmtbhl9kY6/XctP2adIqlVnbKvjI9Tu/AiJb1vpUljIDIynMVLX+HTz0YzaMjtDB38JqdOnSl51IHOxQWVNFajtO3C6XTy7cYdjHnpft55fxhfLt3I2jXbCtNMnPo4Xyx/hVxHHuvWbvd00UXKlK4ZFy+NLChZG2AD8CRw3LKs5UC2ZVkrLMtacb6DLMuaYllWG8uy2vzl7uvLqGj/m5pRYRyyHyvcPpx5jBqRob8rj9Qlm7g2oXnhHLtfnTh2mh+3ptP2uiYeKeulwGarSUbG4cJtu/0IUVERxdIcPJifJi/PycmTpwkPDynh2MNERdUoVZ5SnM6F97CfySkcKQAQXbUSmQULGf7qWE4eua78DxDTfzxIs5rBAHSrV5NNmSc5k+fiTJ6LlelZtIoM4fIa1QBIP/kLAAt/PsyVUb+v77sUqV14j2hbBPaMrMJte0YWkVHVz5smL8/JqZNnCAsLJigokPDwEACaNqtPXFwUe3Zn5Ke3F8nTnkVUZDjy26Kjq5Nx0P19i4wKL57G7VxkExZWjejoCK5qcxnVq4dQpUolruvYku3b9rgdW6lSIJ0TrmD5so1lXxkRD9I14+JljPHYoxSvdb0x5kdjTJoxZuh50vzJGLPNGLPVGPPRhfIsk2CBZVkuy7LGA3cDTxpj3sDHf3mhcdM4DqQfJmP/EXJz81ix+Dvad2r2u/JYvqjk0QMrv9hE2+uaEFQp0FPFvei1aBHP7t0HSE/PwOHIJSUllcTEtm5pEhPbMWvWUgAWLfqK9u1bYowhMbEtKSmpOBy5pKdnsHv3AVq2jC9VnlKczoX32HLoJHVDqxATXJlAP0OvBpF8udd9yGFklbPTEhLr1GDXsTMAHDyVU7igYYAxtKkVxs5jZ7CfcdAovCrVK+f3T9fWDmdnwTFyfmoX3qNZ8wbs2ZPBvn2Z5DryWLhgDV0SWrul6ZLQmrmzVwKwZPE62rZrijGGrKwTOJ0uAPalZ7J3j53Y2CgiI6tTrVplNm1Kw7Is5s1ZRULiVeVeN1/TrHl99u7NZP++Q+Q68lg0fx2dE9w/F3VOuJJ5c74C4IvF67m6XROMMVzboTk7fkonOzuHvDwnG9b/SIOGtTlz+hcOHcq/mZOX52TVys3Ur1+r3Osm8kfomiF/lDHGH5gA3AA0Bf5ijGl6Tpp44Amgg2VZzYD/u1C+ZfoF3rKsfcCtxpje5E9L8Fn+Af48OKgfw/85FafTosfNV1O3oY3/TFpI4yZxtO/cjB+37uXZQe9x6sQZ1q7cxgdTFjP500EA2A9kcdh+jBatGxTLe8Xi74r9DKP8toAAf0aMeIB7730ap9NF//7diI+vy6uvfkDz5vF07dqOAQO6M2hQMt27JxEWFsz48YMBiI+vyw03XEevXg/h75+fj79//miPkvKU36Zz4T2cFjy3Oo1p1zfHzxg++ymDtGNn+Gfrunx/+CRf7s3ib81qk1inBnkui+M5eTyR+iMAi3Yfol3tcObc0gbLsli1/yjL0/Pv7k34di/v925FnsvFgVM5DCs4Rs5P7cJ7BAT4M+zJgTx430s4XS769utMo/hYJrw+g6bN6pOQeBX9+ndm2JBJ9O75GGHhwbw09mEANqz/gTdfn4l/gD9+fobhT99NWHj+aJzhI+5m+LAp5OQ4uK5jK67r1Koiq+kTAgL8Gfrk7TyYNA6Xy0Wffh1p1CiGN1+fRdNm9eiSeCX9+nfiyaFTuOn6IYSGVePFsQ8AEBpWjTsG9uT220ZhjOG6ji3p1LkVRw4f55F/vEpubh5Op4u27Zow4LaECq6pyO+ja8bFqxynD7QF0izL2pX/uuZjoA+wrUia+4AJlmUdBbAsK/NCmZrfWu23Iu06Oc87C3YJahBSfI0FkUtdk7e0yJ832X6PraKLIAVynL9/8V8pGy4rt6KLIEVUCbi2oosgAPxU0QUQN40v6oUU4ieneuw7bdoDne8Hkoo8NcWyrCkAxpgBwPWWZd1bsH0H0M6yrId/TWyMmU1+A+gA+APPWJb1mwsF+vTUABEREREREZGLXUFgYMp5dpe4zvU52wFAPNCF/B8kWGmMaW5Z1rFzDyx6gIiIiPw/e/cdH3WR/3H8NWm0VEo2lIQi0RMBCwhYMaGEYgFB/Xl3nnoKYhdPwYYFRQUpdhRQzzv1lCJFEimCSJEi4AkiKigtQDaUQOhJduf3x8aQJYHEczfZDe/n47EPsrvznXxmh+/s5rMz8xURERHxIeOvywmUlAkkFrvfCNhRSpll1tp8YJMx5ic8yYNvTlZpxYUvIiIiIiIicpqowEsnfgMkG2OaGmMigP8DZpxQZhqQ4onL1AXOBH49VaVKFoiIiIiIiIgEKWttAXAPMBtYD0y01q4zxgw1xlxdWGw2sMcY8wPwJfCwtXZP6TV6aBmCiIiIiIiIiI+FVOD2jdbaDCDjhMeeLPazBR4svJVLmTMLjDG1jPGstjDGnGmMuaVawIwAACAASURBVNoYE17uqEVEREREREROMxW4DMEvyrMMYSFQ3RjTEJgH3Ar8059BiYiIiIiIiEjlKU+ywFhrDwPXAq9Za3sDLfwbloiIiIiIiEjwCvaZBeXZs8AYYy4C/gLc9juOExERERERETktmcr6K99HyjOz4AHgUWBq4Y6KzfDsnigiIiIiIiIiVVCZMwSstV8BXwEUbnS421p7n78DExEREREREQlWpjxfzQew8lwN4SNjTLQxphbwA/CTMeZh/4cmIiIiIiIiEpyCfc+C8uQ6Wlhrc4FeeK7bmATc5NeoRERERERERKTSlGejwnBjTDieZMHr1tp8Y4z1c1wiIiIiIiIiQSvI9zcs18yCt4HNQC1goTGmMZDrz6BEREREREREglmwL0MozwaHrwKvFntoizEmxX8hiYiIiIiIiEhlKs8yBIwxPYFzgOrFHh7ql4gKOWrE+LN6+R1c9mhlhyCFQk31sgtJhVh1c5DPK6tirvnCWdkhSKHpnR2VHYIUOubaX9khiIic1kKC/ONimckCY8xbQE0gBZgA9AVW+DkuERERERERkaB1OuxZcLG19m9AjrX2GeAiING/YYmIiIiIiIhIZSnPMoQjhf8eNsY0APYATf0XkoiIiIiIiEhwC/aZBeVJFsw0xsQCLwGrAYtnOYKIiIiIiIiIlMIE+aYF5bkawrOFP04xxswEqltrtWOOiIiIiIiISBV10mSBMebaUzyHtfZT/4QkIiIiIiIiEtyq8jKEq07xnAWULBAREREREREpRZVNFlhrb63IQEREREREREQkMJxqGcKDwH5r7TsnPH4vEGqtfdnfwYmIiIiIiIgEoyo7swD4O3BBKY+PA74BlCwQERERERERKUWQXwyBkFM8Z621eaU8eAwI8maLiIiIiIiIyMmc8tKJxhiHtdZ54mP+DUlEREREREQkuAX7MoRTzSx4CUg3xnQ0xkQV3q4APgNGVkh0IiIiIiIiIkHIhPjuVhlOdTWEfxljdgFDgZZ4Lpe4DnjKWvt5BcUnIiIiIiIiIhXslMsQCpMCSgyIiIiIiIiI/A7BvgzhlMkCEREREREREfn9TJBnC5Qs+B2WLPqekS9+jMvlpnefy7i1X3ev5/Py8hny6LusX7eF2NhIXhzVnwYN65Ixcxn/end2UbkNP2/no0lPcNbZSeTnFfDisI9Y9c1PhISEcPd9vejUtU1FNy3oLFr0LS8Mew+X203fvp3o17+31/N5efk8Mvg11q37ldjYKEaPHkjDRvF8veQ7Ro/6kPz8AsLDw3ho0E106NCKQweP8Ne/Dik63pm1l6uuvoxHH7u1opsWlBYuXMWwYeNxu91cd10X+ve/zuv5vLx8Bg0azbp1vxAbG8WYMYNo1MizV+rbb09i8uS5hISE8MQT/bnssgvKVaeUbsmi73npxf/gdrnp1ecy/t6vh9fznnHqHdav20JMbCTDR91RNE697zVOZfKfSUM46+ykosfuv/s1tmfuYvL0oRXWnmB2cN33ZE/+D9btJvaSy6jTtUep5XJXr2THO2/ReNAT1GjchP0rlrH3i+N9cWxHJk0GD6F6YhK2oICsiR9xeMNPGGOoe1Vvos/Xe0ZZNEYFjsWLvmP4C//G7XJzbd8ruK3f1V7P5+Xl8/gjb/HDuk3ExEbx0uh7aNiwHtu376LXlYNo0qQ+AK3Pbc6Qp/8OwKsvT+SzGYvJ3X+I5aveqfA2ifiCxikJREoWlJPL5Wb4sI94c/xAHI44/nrDMDqmnEuz5g2Kykybspjo6JrMmPU8szNW8MroKQwfdQc9ruxAjys7AJ4P4A/e+0bRB/AJ49KpXTuKaRnDcLvd7N9/qFLaF0xcLhfPDX2HCe8OweGozQ3XPUpKaluaN08sKjNl8nyioyOZPed1MtKXMGrUB4we8yCxcdG8OfYR4h212fDzVvrd/hwLFo6jVmQNpk47vm9n32sH0aVL+8poXtBxuVwMHfoW7733LA5HHfr2fZDU1PY0b378j8xJk+YQHR3J3LnjSE9fyMiR/+TllwezceNW0tMXkp7+Bk7nHm69dQizZ78FUGadUpLL5ebFYR8ydvyDOBxx/OWG5+iYch5nnDBORUXXYsasF5iVsYJXRk9m+KgBJcapgfe+7pUomDd3FTVrVqvwNgUr63bjnPghifc+SHhsHJtHPEdkq/OoVr+BVznX0aPkLJhH9SbNih6LadeBmHaevji6PZPtb79O9URPX+yelU5YVBRnPDUM63bjOqz3jLJojAocLpeb5597n3ETHsHhqM2NNzzJFSltOKN5w6Iyn05ZQHR0LdJnj+bzjKW8POpjXhp9LwCNEh1Mmvp8iXo7plzAjX/pwpXdHqqwtoj4ksapqivIJxac/GoIxpgHT3WryCADwfdrN9EosR6NEusRHhFGWo8LWfDlf73KLJj/X6685mIAOnVtwzfLfsRa61VmVsYK0nq0K7o/Y+qSom/+QkJCiIuL8nNLgt/aNRtJSkogMdFBREQ43Xtcwvx5K73KzJ/3Db16dQSga1oHli39HmstLVo0Jd5RG4DmyYkcO5ZPXl6+17GbN+9k795c2rQ9u2IaFOTWrNlA48b1SUxMICIinJ49L2fevOVeZebPX07v3p0ASEu7hKVLv8Nay7x5y+nZ83IiIsJJTEygceP6rFmzoVx1Sknfr91EYmJ8sXGqXanj1FWF41Tnrm1YcZJxqluxcerwoaN88P5cbr/jSv83ooo4unkTEfXiiahbDxMWRnSbdhxc898S5XbPnEadLt0w4aXn7g+sXEF02+N9sX/p4qIZCiYkhLBIvWeURWNU4Ph+7S8kJTlolBhPeEQY3bp34Mv5q7zKLJi/mqt7XQZAl67tWL5sXYkx6kTnntucevXi/Ba3iL9pnKq6jPHdrTKc6iIMUWXcys0Yc2lhkqHr/xpoZdvl3EdC/dpF9+MdcWQ793mXyd5HQoLnzSosLJTIqBrs23fQq8zcWSuLPoQfyD0MwJuvTePPfZ9l0MC32LM715/NqBKczr0k1K9TdD8hoTbZzj3eZbL3klC/LuDpi6iomuzbd8CrzJzZyzi7RVMiIsK9Hs9IX0y37hcH/RqjiuJ07iEhoW7RfYejDs4T+8O5h/pe/VGLnJzcUo6ti9O5p1x1SknZzhwc9Y9/YHY44tjlzPEuk51T5jg1Z9Y3dOtxfGbNm69N46ZbulKjRoQfo69a8vflEBZ3vC/CYuPI3+fdF0e3bSU/Zy+Rrc49aT25q78huq2nL1yHPe8Zu2ZOY9OLQ9k+YSwFufv9EH3VojEqcDidOTgSjn+WciTUJjs756RlPGNUzaIxavv2XVx/7ePc+rfnWLXyx4oLXMTPNE5JoDppssBa+8ypbqeq1BizotjP/YDX8SQYnjLGPOKz6CuQpWRW+8S/JUvLfBf/g3Ptml+pXj2C5sme6XYFLhfOrBzOO785H00eQutzmzFm5CTfBl4Flfr9wgmdUWpfcLzMhg3bGD3qQ55+pn+JchkZS+jZ85I/GuZpo6z/96cqU/rj5atTyqlEX5RW5OTj1E/rt7JtazapnS/wa5inhWKvs3W7cU75mPhrrz9p8SObfiUkIoJqDRoWHuOiYF8ONZo1p+kjT1Kj6Rlkf6r3jLJojAogpb43l6OMgXr1Ypkz72UmfjqMhwf/hUcGvcnBg4f9E6dIBdM4VXVV5ZkFABhjqhtj7jbGvGmMefe3WxmHFf+qtj/QpTDB0BX4yyl+V39jzEpjzMp3x88oVwMqSrwjjqyde4vuZztzqBcfW7JMlidDXlDg4uCBI8TE1Cp6fnbGN6T1uLDofmxsJNVrRJDS+XwAOqe15ccftvizGVVCgqM2WTuPZ0azsvYSH1/7hDJ1yNq5G/D0xYEDh4mJjSwsv4f77nmJF4bfQ1JSgtdxP/64GVeBm3NanuHnVlQdCQl1ycraXXTf6dxTsj8S6rLTqz8OERsbVcqxu4mPr1OuOqWkeEcczp3Hv6VzljJOOcocp7yXIHz33S/88MMWenQZzK03DWfLZie33zLCzy0JfuGxcRTkHO+Lgn05hMcc7wv3saPk7djB1pdfYuOQwRzd9Cvb336NI1s2F5XJXbWCqDbH+yK0ViQmIoKocz3vGVEXtOXotq3+b0yQ0xgVOBwJtXFmHf8s5czaS734uJOW8YxRh4mJiSQiIpzYWM/E1hbnNCUxMZ4tm7MqLngRP9I4VXWFGN/dKiX+cpT5N5AApAFfAY2AA6c8AkKMMXHGmDqAsdbuArDWHgIKTnaQtXactbattbbt30/YHbeyndOyCdu2ZrM9cxf5eQXMzviGjineU0c7ppzHzOlfAzBvzioubH9WUQbP7XbzxZyVpHU//sHPGMPlV5zLyhU/AbBi2XqaneG9+ZWU1LJVc7Zs2UlmppO8vHw+z1hCSmpbrzIpqW2ZNu0rwLPcoH2HlhhjyM09xJ13vMDAB//MBRf8qUTdGemL6aFZBb9Lq1bJbN68g23bssjLyyc9fSGpqe28yqSmtmfq1HkAzJ69hA4dWmOMITW1HenpC8nLy2fbtiw2b95B69bJ5apTSjqnZRO2bnUWG6dWcEWJcepcPiscp76Ys4oL2//Ja5yaO2eV1zh1/f+lMHfBKDLmDue9fw+mcRMHE/45qOIaFaSqN25CXraTvN27sAUF5K5a4bXcILRGTZJHvEzzZ4fT/NnhVG/ajIZ33EuNxk0Az8yDA9+u8tqvwBhDZKtzObzB855x6Mf1VKtfv0LbFYw0RgWOc1o2Y8uWLDIzs8nPK2DW58u4IsV71tIVKRcwY9oiAObOWUG79i0wxrB3by4ulxuAzG3ZbN3ipFGj+Apvg4g/aJySQFWeqyE0t9ZeZ4y5xlr7vjHmI2B2GcfEAKvwzC6zxpgEa22WMSaSUmacBYOwsFAGP/5n7u7/Mm635erel3BG84aMfW06Lc5pTMfU8+jV51KGPPIOV3d7jJiYWrww8vgU99UrNxDviKNRYj2veu97sA9DHnmHkcM/IS4uiqefu6WCWxZ8wsJCeXzIbfS7zXMFid59UkhOTuS1Vz/mnJZnkJp6IX36pjJ40Gukdb2H2JhIRo4eCMBHH85i69Ysxo6dzNixkwGY8M4Q6tSJAWDW50t5a9xjlda2YBQWFsqTTw7g9tufwuVy06dPZ5KTG/PKKx/QsmUynTq1p2/fLjz88Gi6dOlPTEwkY8Z4/thMTm5M9+6X0qPHXYSGeuoJDQ0FKLVOObXfxqm7+r+M2+3mmsJx6s3XptHinCZckXoevfpcxhOPTODqbo8SHVOLF0feUXT86pU/4yhlnJLfz4SG4rj+z2x742Vwu4m56BKqNWjIrpnTqJ7UhKjW553y+MMbfyYsNo6Iut59EX9NX3a8P4HsyR8TGhlF/Zt0edeyaIwKHGFhoTz2+M3c2W8ELrebXr070jy5EW+8NpkW5zQlJbUNvft05LHBb9Ez7UFiYiMZMfIeAFat/JE3X5tCaFgoISGGJ566tWjG4OiR/yEj/WuOHs2jc8q9XNvnCu66p09lNlXkd9E4VXVV1owAXzFl7TBrjFlhrW1njFkI3AVkASustc1OeWDpddUEHNbaTWWVPVSw8NSBSYWpHhpbdiGpEKGmemWHIIUOFzgrOwQp5sYFlR2B/GZ6Z0dlhyCFjrm0AWYgqRZ6YdmFpAL8XNkBiJczg/zP6VNLm73YZ3/Tzk67tMJfq/LMLBhnjIkDhgAzgEjgyf/ll1lrDwNlJgpEREREREREglmwzywoM1lgrZ1Q+ONXwO+eTSAiIiIiIiIiwaXMZIExphrQB2hSvLy1dqj/whIREREREREJXuW5mkAgK88yhOnAfjwbFh7zbzgiIiIiIiIiwS/EBPc2fOVJFjSy1nbzeyQiIiIiIiIiEhDKkyz42hjTylq71u/RiIiIiIiIiFQBVX6DQ+BS4BZjzCY8yxAMYK21rf0amYiIiIiIiEiQOh32LOju9yhEREREREREJGCcNFlgjIm21uYCByowHhEREREREZGgV5WXIXwEXInnKggWz/KD31igmR/jEhEREREREQlapqpeDcFae2Xhv00rLhwRERERERERqWxl7llgjLmglIf3A1ustQW+D0lEREREREQkuFXlZQi/eRO4AFiDZylCK+A7oI4xZoC1do4f4xMREREREREJOsF+NYTyxL8ZON9a29Za2wY4D/ge6AyM8GNsIiIiIiIiIlIJyjOz4E/W2nW/3bHW/mCMOd9a+6sxQT6vQkRERERERMQPQqrqBofF/GSMGQt8XHj/BuBnY0w1IN9vkYmIiIiIiIgEqdNhz4JbgLuAB/DsWbAYeAhPoiDFX4GFlCs0EZHKERESXdkhiJfcyg5ACjU579PKDkEK/fJtj8oOQUREgliZf5Fba48AowpvJzro84hEREREREREglywb3B40mSBMWaitfZ6Y8xaoMRiC2tta79GJiIiIiIiIhKkqvIyhPsL/72yIgIRERERERERkcBw0mSBtXanMSYUeMda27kCYxIREREREREJalX6agjWWpcx5rAxJsZau7+ighIREREREREJZlV5GcJvjgJrjTFzgUO/PWitvc9vUYmIiIiIiIhIpSlPsiC98CYiIiIiIiIi5VBlr4ZQzCdAczxXRPjFWnvUvyGJiIiIiIiIBLdg37PgpMkOY0yYMWYEkAm8D3wAbDPGjDDGhFdUgCIiIiIiIiJSsU41M+IloDbQ1Frbxlp7PnAGEAuMrIjgRERERERERIJRiPHdrTKcahnClcCZ1tqiuRPW2lxjzJ3Aj8D9/g5OREREREREJBgF+9UQTjWzwBZPFBR70IVn/wIRERERERERqYJOlSz4wRjztxMfNMb8Fc/MAhEREREREREpRYgPb5XhVMsQ7gY+Ncb8HViFZzbBhUANoHcFxCYiIiIiIiISlKrs1RCstdutte2BocBmYCsw1Frbzlq7vYLiExEREREREZFTMMZ0M8b8ZIzZaIx55BTl+hpjrDGmbVl1nmpmAQDW2vnA/N8Zq4iIiIiIiMhpq6I2ODTGhAJvAF2ATOAbY8wMa+0PJ5SLAu4Dlpen3spa/iAiIiIiIiJSZVXgngXtgI3W2l+ttXnAx8A1pZR7FhgBHC1P/GXOLJDjlixay4gXP8LtctO7z+X8vV9Pr+fz8vJ54tHxrF+3hZjYSIaPupOGDesC8PNP23jumfc5ePAIISGGDz95imrVwrmr/yh279pPgcvFBW3O5NEnbiI0VDmcsixa9C0vDHsPl9tN376d6NffexuNvLx8Hhn8GuvW/UpsbBSjRw+kYaN4vl7yHaNHfUh+fgHh4WE8NOgmOnRoxaGDR/jrX4cUHe/M2stVV1/Go4/dWtFNC0oLF65i2LDxuN1urruuC/37X+f1fF5ePoMGjWbdul+IjY1izJhBNGrkAODttycxefJcQkJCeOKJ/lx22QXlqlNKt2jRf3nxec+50advJ/r16+X1fF5ePo8Ofp11P3jOjVGjH6Bhw3i+XrKGMaOPnxv/ePgmOnRoCcDnGV8z7u1PcbncXN7xAh56+K+V0bSgc3Dd92RP/g/W7Sb2ksuo07VHqeVyV69kxztv0XjQE9Ro3IT9K5ax94vZRc8f25FJk8FDqJ6YhC0oIGviRxze8BPGGOpe1Zvo89tUVJOCVseLm/LkoM6EhoTwydTvGPveMq/nG9aPZsTTPagdV5P9uUd54LHPyMo+AECfq1pyT7+LAXh9/NdM+ex7AFqe7WDk0J5UrxbOl4t/4ZkRX1Rso4KUr9+/AW6+6Sl27cqhWvUIACa8M4Q6dWIqvG0if4Q+S0lZjDH9gf7FHhpnrR1X+HNDYFux5zKB9iccfz6QaK2daYx5qDy/U8mCcnK53Lww7N+8Nf4hHI7a/OWGoXRMOY8zmjcsKjN1yiKio2vx2azhzMpYziujJzJi1F0UFLh4/JFxPPdCP876UxL79h0kLCwUgBGj7yIysgbWWh564A3mzv6Gbj3anywMAVwuF88NfYcJ7w7B4ajNDdc9SkpqW5o3TywqM2XyfKKjI5k953Uy0pcwatQHjB7zILFx0bw59hHiHbXZ8PNW+t3+HAsWjqNWZA2mThtZdHzfawfRpYv6oTxcLhdDh77Fe+89i8NRh759HyQ1tT3NmycVlZk0aQ7R0ZHMnTuO9PSFjBz5T15+eTAbN24lPX0h6elv4HTu4dZbhzB79lsAZdYpJblcboY9+w7j33kCh6MON1z/KCkpbWnevFFRmSmT5xMdU4tZs18jI30Jo0d+yKgxA4mLi+KNsYOJj/ecG/37DePLr95mX84BRo78N5MmD6d27WgefeR1li1dS4eLWlViSwOfdbtxTvyQxHsfJDw2js0jniOy1XlUq9/Aq5zr6FFyFsyjepNmRY/FtOtATLsOABzdnsn2t1+neqLn//7uWemERUVxxlPDsG43rsOHKq5RQSokxDD00a78dcDHZDkPMOPDW5j71QY2/rqnqMxjD6by6czvmfLZ91x0YWMG3deRB5+YSUx0de6/41Ku+vM/sdYy8z+3MnfBBnIPHOO5x9N47NlZrF6zg3++fh1XXNKMBUt+rcSWBj5/vH//ZsRL99Oy1RmV0SyRP0yfpaouXy5DKEwMjDvJ06X9pqLdFY0xIcAY4Jbf8zv98hW2Maa9MSa68OcaxphnjDGfGWOGG2OCMtX7/dpfSUyMp1FiPOERYaT1aMeCL7/1KrNg/mquuuYSADp3bcuKZeux1rL06+9JPrMRZ/3Jc3LGxkYWzR6IjKwBQEGBi/z8AkwFrWsJZmvXbCQpKYHERAcREeF073EJ8+et9Cozf9439OrVEYCuaR1YtvR7rLW0aNGUeEdtAJonJ3LsWD55eflex27evJO9e3Np0/bsimlQkFuzZgONG9cnMTGBiIhweva8nHnzvJdBzZ+/nN69OwGQlnYJS5d+h7WWefOW07Pn5UREhJOYmEDjxvVZs2ZDueqUktau2Uhi0bkRRo8eF/Pl/G+8ysyfv5JrrrkCKDw3lnnOjbNbNCU+vuS5sS3TSZPGDahdOxqAiy5qzZw56ouyHN28iYh68UTUrYcJCyO6TTsOrvlviXK7Z06jTpdumPDSc/cHVq4gum27ovv7ly4umqFgQkIIi4zyTwOqkPNa1mfLthy2bd9PfoGbz2b/QNcrkr3KJDerw5LlWwBY+s0WuhQ+3/Hipixeton9uUfJPXCMxcs2ccUlzahXtxZRtaqxes0OAD6d+T1dU7zrlJL8/f4tEqz0WarqMsb67FaGTCCx2P1GwI5i96OAlsACY8xmoAMwo6xNDv013/1d4HDhz68AMcDwwsfe89Pv9KtsZw4J9WsX3Xc4apPtzPEuk72PhARPmbCwUCKjarBv30G2bHZijOHOfiP5v75P8d47GV7H3dlvJKmX30/NWtXp3PVC/zcmyDmde0moX6fofkJCbbKde7zLZO8lob5nCUhYWChRUTXZt++AV5k5s5dxdoumRESEez2ekb6Ybt0vxihzUy5O5x4SEuoW3Xc46uA8sT+ce6jv1R+1yMnJLeXYujide8pVp5TkzN5L/YTj54bnddvrVSa72Plz0nNjznLOPttzbiQlJbBp03a2b8+moMDFvHkryMra7f/GBLn8fTmExcUV3Q+LjSN/n/d7xtFtW8nP2Utkq3NPWk/u6m+IbuuZ5eQ67Hlb3TVzGpteHMr2CWMpyN3vh+irFkd8FDuyjv8f3+k8gCPeO8my/udsunc+C4C01DOJiqxGbEz1kx6bEB/FTuep65SS/Pn+/fhjb9C710OMfXMy1gb3pcrk9KPPUuID3wDJxpimxpgI4P+AGb89aa3db62ta61tYq1tAiwDrrbWriy9Og9/JQtCrLUFhT+3tdY+YK1dbK19Bmh2soOMMf2NMSuNMSvfGT/dT6H9b0p72znxj8nS3pyMMbhcLr5dvYHnR9zBe/9+jC/nrWb5suMbU44d/xBfLHiZ/LwCVixf7+vQq5xSPwKUpy+Kzc7ZsGEbo0d9yNPP9C9RLiNjCT17XvJHwzxtnOz/fXnKlP54+eqUUvyvfVHs3Ni4YRtjRn3IU8/0AyAmJpIhT93OPx58mb/99UkaNownLDTUx4GfJor1hXW7cU75mPhrrz9p8SObfiUkIoJqDRoWHuOiYF8ONZo1p+kjT1Kj6RlkfzrJ72EHu9KGjhNPg2Gjv6R9m0TSP76VDm2T2OnMxeWyJz22PHVKSf56/x4x8j6mfzaaDz54llUr1zNj+kJfhSxSIfRZquoKMb67nUrh3973ALOB9cBEa+06Y8xQY8zV/2v8/tqz4HtjzK3W2veA74wxba21K40xZwInnTNWfB3GkYKvA+pt1+GII2vn8W/onM691IuPLVkmay+OhNoUFLg4eOAIMTG1cDhq06btWcTFeb51uPSy1qz/YQvtO7QoOrZatXA6ppzHgvmruejicyqmUUEqwVGbrJ3HM6NZWXuLpk8fL1OHrJ27SUioQ0GBiwMHDhMTG1lYfg/33fMSLwy/h6SkBK/jfvxxM64CN+e01LrH8kpIqOv1TbPTuadkfyTUZefO3SQk1C3sj0PExkaVcuxu4uM93zqVVaeU5HDUYWfW8XPD87rFeZdJqEPWzj0nPzfuHcnzL97tdW6kpLQlJcUzS23ixC8ICdEmrGUJj42jIOf4TIKCfTmExxx/z3AfO0rejh1sffklAFy5+9n+9ms0vONeajRuAkDuqhVEtTm+BCG0ViQmIoKoc88HIOqCtuz7enEFtCa4ZTkP0CDh+Lf+9R1RZO/y/qY6e9dBBvxjKgA1a4TTrdOZHDh4jCznATq0TfI6dtnKrex0HqC+49R1Skn+ev92ODzvG7Uia9DzyktZu2YD1xQuZRAJBvosVXVV5Ccma20GkHHCY0+epOwV5anTX/HfDnQ0xvwCtACWGmN+BcYXPhd0zmnZFMzn9AAAIABJREFUlK1bs9meuYv8vAJmZ6ygY8r5XmU6ppzPZ9OXAPDFnJVc2P5sjDFcfElLNvy8jSNHjlFQ4GLVyp9odkYDDh86yq5d+wDPngWLF62hadP6Fd62YNOyVXO2bNlJZqaTvLx8Ps9YQkqq93KblNS2TJv2FeCZrti+Q0uMMeTmHuLOO15g4IN/5oIL/lSi7oz0xfTQrILfpVWrZDZv3sG2bVnk5eWTnr6Q1NR2XmVSU9szdeo8AGbPXkKHDq0xxpCa2o709IWetfHbsti8eQetWyeXq04pqWWrM9i6ZSeZmdnk5RWQkfF10R/5v0lJacP06QuA386Nc46fGwNe5IEHbyxxbuzZ45nqvn//QT7+z2z69k2tkPYEs+qNm5CX7SRv9y5sQQG5q1Z4LTcIrVGT5BEv0/zZ4TR/djjVmzbzShRYt5sD367y2q/AGENkq3M5vOEnAA79uJ5q9fWeUZbv1u2kSVJtGjWIITwshKvSWjD3q41eZeJiaxR9wX3XbRcxcdpaAL76ehOXXdSU6KhqREdV47KLmvLV15vYtfsQBw/ncX4rz4aV117ZkjkLNlRou4KRP96/Cwpc5OTkApCfX8BXC1bR/Ext4CbBRZ+lJFD5ZWaBtXY/cIsxJgrPsoMwINNa6/TH76sIYWGhPPL4X7iz/yjcbjfX9L6M5s0b8uZrU2lxThOuSD2f3n0u5/FHxnFVt8FEx9Ri+MgBAETH1OKmm9P4yw1DMcZw6WWtubzjuezZvZ/7736F/PwCXC437dqfTd8bUiq5pYEvLCyUx4fcRr/bhuF2u+ndJ4Xk5ERee/Vjzml5BqmpF9KnbyqDB71GWtd7iI2JZOTogQB89OEstm7NYuzYyYwdOxnwvsTSrM+X8ta4xyqtbcEoLCyUJ58cwO23P4XL5aZPn84kJzfmlVc+oGXLZDp1ak/fvl14+OHRdOnSn5iYSMaMGQRAcnJjune/lB497iI01FNPaOEU99LqlFMLCwvl8Sf+Tv/bC8+Na1NonpzIa69+UnhutKVP31QeGfw63dLuJSYmkpGjHgA858a2rVm8NXYKb42dAsD4CU9Qp04MLzz/Hj/95Nn87c47+9KkaYOTxiAeJjQUx/V/ZtsbL4PbTcxFl1CtQUN2zZxG9aQmRLU+75THH974M2GxcUTUref1ePw1fdnx/gSyJ39MaGQU9W/S5V3L4nJZnnxxDv8aewOhIYaJ09ew4ZfdDLzzMtb+sJMvvtpIh7ZJDLqvI9bCilXbePKFOQDszz3Kq+O+ZsaHtwDw6rgl7M/1XJr6iWGzCy+dGMaCJb+yYLGuhFAWf7x/16hRjX63PUdBgQuX281FF7Xiuus6VWYzRX43fZaqukLK3pgwoJlA3QQm0JYhnM4iQiMrOwQpFGqqV3YIUqjAfaSyQ5Bi+szPrewQpNB3Dy2p7BCk0C/f9qjsEKSYUNO6skMQAH6u7ADEy5lVeiOFp1Z/4bO/aZ+5oHOFv1ZaeCoiIiIiIiIiXvy1waGIiIiIiIjIaausqxgEOiULRERERERERHws2C82rWUIIiIiIiIiIuJFMwtEREREREREfCzYr4agZIGIiIiIiIiIjwX7ngVahiAiIiIiIiIiXjSzQERERERERMTHgn1mgZIFIiIiIiIiIj4WGuTJAi1DEBEREREREREvmlkgIiIiIiIi4mNahiAiIiIiIiIiXnTpRBERERERERHxEuwzC7RngYiIiIiIiIh40cwCERERERERER8LrewA/qCATRZUC42t7BCk0IH8rZUdghSKiWhW2SFIIYu7skOQYqZ3dlR2CPKb/15b2RFIoeZvZVV2CFLMxgGVHYGIVDQtQxARERERERGRKiVgZxaIiIiIiIiIBCtdDUFEREREREREvIRqGYKIiIiIiIiIVCWaWSAiIiIiIiLiY8G+waGSBSIiIiIiIiI+FuzJAi1DEBEREREREREvmlkgIiIiIiIi4mPBPrNAyQIRERERERERHwsN8ksnahmCiIiIiIiIiHjRzAIRERERERERHwv2b+aVLBARERERERHxsWDfsyDYkx0iIiIiIiIi4mOaWSAiIiIiIiLiY8E+s0DJAhEREREREREf09UQRERERERERKRK0cwCERERERERER8L9mUImlnwOyxatJru3e4mreudjB83pcTzeXn5DBw4krSud3LD9YPYnpkNQE5OLjf/bQhtLriRZ4eO8zqm3+1D6XXNQK688j6efmosLperQtoS7JYuXk/fq4ZxbY9neX/C3BLPr165kZuuf4mLzhvIvDn/9Xpu5vQV9On5LH16PsvM6SuKHp+dsYobe7/In699kfsGjGVfzkG/t6OqWLhwFWlpA+jSpT/jxk0q8XxeXj4PPDCcLl36c911/yAz01n03NtvT6JLl/6kpQ1g0aLV5a5TSrd40X+5svtAuqfdz4Tx00s8n5eXzz8Gvkz3tPu58YbH2b7dM06tXbORPr0H06f3YK7tNYgv5nrOjU2bdhQ93qf3YNq3vZV/v59RoW0KVjovAof6InBcnhjHnP9ry7wbL+SO8xJLLdPjjLrMur4Nn1/fhtGd/lT0eO8zHXxx44V8ceOF9D7TUfT4uz1a8lnfC/j8+jYMvax50H84l9OTxqmqKcT47lYp8VfOrw0+LpeLZ4eOY9z4IXw281XS0xezceM2rzKTJ39BTHQtZs8Zy99uvoqRo/4FQLVqEdx3/408POjmEvWOefkhpk0fw2efvcLevbnMmvV1hbQnmLlcbkYMm8Qrb97BJ9MfZfbnq/n1lyyvMgn143jy2T/TtUcbr8f37z/EhLGzePejB3nvo38wYewscvcfpqDAxejhnzL23Xv46NNHaH5mAyb+Z1FFNitouVwuhg59iwkTniY9/Q1mzlzIxo1bvcpMmjSH6OhI5s4dxy23XMPIkf8EYOPGraSnLyQ9/Q0mTHiaZ57xJMzKU6eU5HK5ee7Zdxk77hFmfDaKjPQl/LIx06vMp5O/JDomks9nv8JNf+vJ6JEfAdA8OZFPJj3PlKnDeXvcowx9egIFBS6aNm3AlKnDmTJ1OBMnv0D1GhF06nxhZTQvqOi8CBzqi8ARYuDpS5tzW/r3dPtkJVc2r0fzuJpeZRrHVGfA+UlcP+07uk9cxXNLfgEgploY97ZNos+n33LtlG+5t20S0RGeCbL3zV3PVZNX033iKmrXCKd7s3oV3jaRP0LjlAQqvyQLjDH3GWNKTxcHqTVrNpCUVJ/ExAQiIsLp0eNS5s9b4VVm/rwVXNMrBYC0tItZtnQN1lpq1qxOmzYtqBYRUaLeyEjPm2RBgYv8/AKMUTq8LOvWbqFRUj0aJtYlPDyMrt0vYOGXa73KNGhYh+SzGhJywuu5bMmPtL/oLGJiahEdU5P2F53F0iXrwYK1liNH8rDWcujgUerVi6nIZgWtNWs20Ljx8XOjZ8/LmTdvuVeZ+fOX07t3JwDS0i5h6dLvsNYyb95yeva8nIiIcBITE2jcuD5r1mwoV51S0to1G0lKSiAx0UF4RBjde1zM/PkrvcrMn7+Sa665HICuae1Zvmwd1lpq1KhGWFgoAMfy8qGUsWjZsrUkJjpo0FAfxMui8yJwqC8Cx7nxUWzJPcK2A0fJd1vSf9lF5yZ1vMrccHZ9Pvh+B7l5BQDsPZoPwGWJcSzJ3Mf+YwXk5hWwJHMflyfFAXAw3zMrMyzEEBESQnBvJyanI41TVZdmFpTuWWC5MWaRMeYuY0zQf7LMdu4loX7dovuOhDo4nXu8yjiz91C/sExYWChRUTXZt+9AmXXfftszXHrJLdSqVYO0tIt8G3gVtCt7P46E2KL78Y5Ydjn3l/vY+BOPzd5PWHgog5+4nj9f+yI9Up9k0y9Orr62g89jr4qczj0kJBQ7NxylnBvOE8+NWuTk5JZybF2czj3lqlNKys7eS0LC8Q/eDkdtsp17vcs495JQ31MmLCyUyKgaRePUmu82cM2VD9H7mod58qnbipIHv/k8Yyk9el7s51ZUDTovAof6InA4alVj58FjRfezDh7DUcv7i5SmMTVoEluDT3qdy+Te53F5Ytwpjq1WdP+9ni1ZfnMHDua7mPXrLj+3RMS3NE5VXaHGd7fK4K9kwa9AIzxJgzbAD8aYWcaYm40xUSc7yBjT3xiz0hizcty4iX4K7X9jS8lTnzgLwP6PqewJ7zzFwkXvkpeXz7Jla8s+4DRnS3uhy3kClXasMVCQ72LKxMX8e9IgMuYPJfnMBvyzlL0QpKTSX9MTz43Sy5ysP8pTp5RU6qlRjnHKFJ5Arc9NZvrMkXw88XkmjJ/OsWN5RWXy8wpYMH8VXdOURCsPnReBQ30ROEp7hU58KUNDDE1iavCXGWt44Isfeb7jmURFhJb+Nl/s4FvTv+eify0jItRwUcPY0kqLBCyNUxKo/JUssNZat7V2jrX2NqAB8CbQDU8i4WQHjbPWtrXWtu3f/3o/hfa/cTjqkLVzd9F9Z9Ye4uNre5VJcNRhZ2GZggIXBw4cJjb2pLkRL9WqRZCaemGJpQ1SUrwjFmfWvqL72c591Isv35KBeEcs2SccW7deDD//5FnX3SixLsYYOqWdx9r/bvJt4FVUQkJdsrKKnRvOUs6NhLonnBuHiI2NKuXY3cTH1ylXnVKSw1GbrKzj3xo4nXupFx/nXSahNlk7PWUKClwcPHCEmNhIrzJnnNGQGjWqsWHD8X1ZFi36L2e3aELduvoQXh46LwKH+iJwZB06Rv3I47MBEiKrkX04z7vMwWN8sXkPBW5L5oGj/LrvME1iapR6rPOEY/Nclnmb95ZY2iAS6DROVV0hxvrsVinx+6ler7SVtTbfWjvDWnsjkOSn3+lXrVols2XLTjIzneTl5ZORsZiUVO9NvlJSL2T6tC8BmD37azp0aHXKDN6hQ0fIzvZMES4ocPHVwtU0a9bIf42oIlq0TGLbll1sz9xDfn4Bcz5fzWVXtCzXsR0u+RPLlv5I7v7D5O4/zLKlP9Lhkj9RLz6WTb84ydnruQLCiqU/0aSZo4zaBDznxubNO9i2LYu8vHzS0xeSmtrOq0xqanumTp0HwOzZS+jQoTXGGFJT25GevpC8vHy2bcti8+YdtG6dXK46paSWrc5g65YsMjOzyc8r4POMr0lJ8d7kMyWlDdOnLwRgzuzltO9wDsYYMjOzKSjwrPvdsX0XmzftpGGxvQky0pfQo+clFdeYIKfzInCoLwLHmuwDNI6pQaOo6oSHGHqeUY95m72nRX+xeQ8dGniSknHVw2gaW5NtuUdZtC2HSxvFER0RRnREGJc2imPRthxqhoVQr6ZnKUOogSuS4vg150iFt03kj9A4VXWF+PBWGUypU7r/aKXGnGmt/fmP1OG2PwTc/jRffbWKF55/B7fbzbV9OjFgwHW8+upHtGzZnNTUdhw7lsfgQS+zfv0mYmIiGTX6HyQmJgDQKbU/hw4dIT+/gKioWkx45yliY6O4c8Aw8vLycbnddGjfikce/XuJdcKV7UB+4O2cumThOkaPmIrb5eaq3h34e/+uvP16Bmefk8jlKa344fstDLr/HXIPHCEiIow6daP5ZNqjAMyYuox/jvcsMbi1Xxeu6u2ZVj1l4mI++WAhYWEhJDSozZPP/YXY2FqV1sbSxEQ0q+wQSvXVVyt5/vnxuFxu+vTpzJ133sArr3xAy5bJdOrUnmPH8nj44dGsX/8rMTGRjBkzqOjcGDv2E6ZM+YLQ0FAee+x2OnZse9I6A0m++1Blh1CqhV99y/AX3sfldtP72hTuGNCb11+dyDktm5GS2pZjx/J4dPAbrF+/mZiYSF4adR+JiQ5mTF/IO+NnEBYeSogxDLirT9FVD44cOUbnlLuZNfdVoqJqlhFB5QgPCaxzFU7P8yJQna590fytrLILVbCOSXE8cfEZhBrDpJ+yGLt6G/e3bcz3uw4wb4vnC5THLmrG5YlxuCy8uXor6b949iDoe5aDOy/wfOf05uqtTPnJSZ0a4Yzv3pKIUEOoMSzdvo9hX/+CK+A+RcLGAZdXdggCwB/6E8VvTtdxCs6s0msjvtie4bPRqHPDHhX+WvklWeALgZgsOF0FYrLgdBWoyYLTUaAmC05XgZgsEKlsgZgsOJ0pWRAoAjNZcPqq2smC+Tt8lyxIbVDxyYKwiv6FIiIiIiIiIlVdZV3FwFcqa/mDiIiIiIiIiAQozSwQERERERER8bHKuoqBryhZICIiIiIiIuJjIVqGICIiIiIiIiJViWYWiIiIiIiIiPhYsM8sULJARERERERExMeCfRp/sMcvIiIiIiIiIj6mmQUiIiIiIiIiPma0DEFEREREREREigvyXIGWIYiIiIiIiIiIN80sEBEREREREfExLUMQERERERERES/BPo0/2OMXERERERERER/TzAIRERERERERHzPGVnYIf4iSBSIiIiIiIiI+FuRbFgRusiDEBGxop52YiGaVHYJIwAk11So7BBGRU9owwFHZIYiISBDTX+QiIiIiIiIiPqarIYiIiIiIiIiIlyDPFehqCCIiIiIiIiLiTTMLRERERERERHwsJMinFihZICIiIiIiIuJjQZ4r0DIEERERERERkWBmjOlmjPnJGLPRGPNIKc8/aIz5wRizxhgzzxjTuKw6lSwQERERERER8TFjfHc79e8xocAbQHegBXCjMabFCcW+Bdpaa1sDk4ERZcWvZIGIiIiIiIiIjxkf3srQDthorf3VWpsHfAxcU7yAtfZLa+3hwrvLgEZlVapkgYiIiIiIiIiP+TJZYIzpb4xZWezWv9ivaghsK3Y/s/Cxk7kN+Lys+LXBoYiIiIiIiEgAs9aOA8ad5OnSJh/YUgsa81egLdCxrN+pZIGIiIiIiIiIj1XgpRMzgcRi9xsBO04sZIzpDDwOdLTWHiurUi1DEBEREREREfGxCtyz4Bsg2RjT1BgTAfwfMMMrFmPOB94GrrbWZpcnfiULRERERERERIKUtbYAuAeYDawHJlpr1xljhhpjri4s9hIQCUwyxvzXGDPjJNUV0TIEERERERERER8zptRtA/zCWpsBZJzw2JPFfu78e+tUskBERERERETExypuywL/0DIEEREREREREfGiZMHvsHDhKtLSBtClS3/GjZtU4vm8vHweeGA4Xbr057rr/kFmprPoubffnkSXLv1JSxvAokWry12nnJz6I3CoLwLHokWr6d7tbtK63sn4cVNKPJ+Xl8/AgSNJ63onN1w/iO2Znv1tcnJyuflvQ2hzwY08O9T7qjx/u+kJune7m969BtK710D27NlXIW0JdjovAof6IrAsWriKbml30rVLf8aNm1zi+by8fAY+MIKuXfpz/XUPleiPrl360y3tTq/+AHC5XPTudT933DHU720Q8TWNU1WTMb67VQYlC8rJ5XIxdOhbTJjwNOnpbzBz5kI2btzqVWbSpDlER0cyd+44brnlGkaO/CcAGzduJT19IenpbzBhwtM888xYXC5XueqU0qk/Aof6InC4XC6eHTqOceOH8NnMV0lPX8zGjdu8ykye/AUx0bWYPWcsf7v5KkaO+hcA1apFcN/9N/LwoJtLrfullwYyddoYpk4bQ506sX5vS7DTeRE41BeBxfPavc34CU8xM/0N0kt57SZPmkt0dCRz5o7j5luuZtTI9wFPf2SkL2Jm+htMmPAUQ595C5fLVXTcv/71Gc3OSEQk2GicqrpCfHirDEoWlNOaNRto3Lg+iYkJRESE07Pn5cybt9yrzPz5y+nduxMAaWmXsHTpd1hrmTdvOT17Xk5ERDiJiQk0blyfNWs2lKtOKZ36I3CoLwLHmjUbSEo6/rr16HEp8+et8Cozf94KrumVAkBa2sUsW7oGay01a1anTZsWVIuIqIzQqxydF4FDfRFY1qzZQFKx165Hz8tKvHbz5i+nV+9UoGR/9Oh5GRER4TRKTCCpsD8AsrJ289WClVzXt0uFt0nkj9I4JYHKL8kCY0yEMeZvxpjOhff/bIx53RhztzEm3B+/09+czj0kJNQtuu9w1MHp3FOiTP36njJhYaFERdUiJye3lGPr4nTuKVedUjr1R+BQXwSObOdeEuoXe90SSumL7BP7oib79h0os+7HHnuN3r0G8uabE7G24nb2DVY6LwKH+iKwOJ17qF/stUsofE2Lyy6lP/blHCjl2OOv+/PPT+Chh2/BhOh7MAk+GqeqLi1DKN17QE/gfmPMv4HrgOXAhcCEkx1kjOlvjFlpjFk5btwnfgrtf1Pah2NzQq+drEzpj5evTimd+iNwqC8Ch6U8ffH7631p5EBmfPYKH3zwPKtW/sD06Qv+xwhPHzovAof6IsD8j/2BgVKGOIwxfPnlN9SpHUPLls19FKRIxdI4VXUZH94qg78undjKWtvaGBMGbAcaWGtdxpgPgO9OdpC1dhxQuLPWzwH11VVCQl2ysnYX3Xc69xAfX7tEmZ07d5OQUJeCAhcHDhwiNjaqlGN3Ex9fB6DMOqV06o/Aob4IHA5HHbJ2FnvdskrpC0edE/riMLGxUWXWC1ArsgZXXnk5a9dsoFfhUgYpnc6LwKG+CCyOhLrsLPbaZTl3l3jtHCfpD0dCnROO9bzu8+cvZ/78FXy1cBV5x/I4ePAwDz80ipdG/qPC2iXyR2ickkDlr5kFIcaYCCAKqAnEFD5eDQjKZQitWiWzefMOtm3LIi8vn/T0haSmtvMqk5ranqlT5wEwe/YSOnRojTGG1NR2pKcvJC8vn23bsti8eQetWyeXq04pnfojcKgvAkerVsls2bKTzEwneXn5ZGQsJiX1Qq8yKakXMn3alwDMnv01HTq0OuU3DQUFLnJycgHIzy9gwYKVJJ+Z5L9GVBE6LwKH+iKwtGqVzJbNO8gsfO0y0heRmtreq0xqajumTZ0PnNgf7clIX0ReXj6Z27LYUtgf//jHzXy18D3mz5/AqNEP075DayUKJKhonKq6gn0Zgr9mFrwD/AiEAo8Dk4wxvwIdgI/99Dv9KiwslCefHMDttz+Fy+WmT5/OJCc35pVXPqBly2Q6dWpP375dePjh0XTp0p+YmEjGjBkEQHJyY7p3v5QePe4iNNRTT2hoKECpdUrZ1B+BQ30ROMLCQnliSD9uv+0Z3G431/bpRHJyEq+++hEtWzYnNbUdfft2ZvCgl0nreicxMZGMGn38A3Wn1P4cOnSE/PwC5s1bwYR3nqJBg3rcftszFBS4cLndXHxRa667ThuIlUXnReBQXwSWsLBQhjx5B7fd/jTuotcuiVdf+dAzThX2x6CHR9O1S39iYqIYPeZhAJKTk+je/VJ69ri7RH+IBDONU1VXsC/8MP7aqMoY0wDAWrvDGBMLdAa2WmtXnPrI3wTWMgQRkeLctqCyQ5BiQoy/ct8iwau0fUyk8hjOquwQBICfKzsA8XJmsP89fUqZhz7z2UDcqNZVFf5a+e3TlbV2R7Gf9wGT/fW7RERERERERAJJSJCnQvRVjIiIiIiIiIiPBXmuwG8bHIqIiIiIiIhIkNLMAhEREREREREfMya4945RskBERERERETEx7QMQURERERERESqFM0sEBEREREREfExE+RTC5QsEBEREREREfGxIM8VaBmCiIiIiIiIiHjTzAIRERERERERHwv2b+aVLBARERERERHxsWDfsyDYkx0iIiIiIiIi4mOaWSAiIiIiIiLic8E9tUDJAhEREREREREfM0GeLNAyBBERERERERHxopkFIiIiIiIiIj5mTHB/Nx+wyQK3LajsEKRQiAnY/yYilcbiruwQRERE5HeqkfRUZYcgxRzZ+p/KDsHPtAxBRERERERERKoQfWUsIiIiIiIi4mPBvsGhkgUiIiIiIiIiPhfcyQItQxARERERERERL5pZICIiIiIiIuJjuhqCiIiIiIiIiJxAyxBEREREREREpArRzAIRERERERERH9PVEERERERERETES7AnC7QMQURERERERES8aGaBiIiIiIiIiM8F93fzShaIiIiIiIiI+JgxWoYgIiIiIiIiIlWIZhaIiIiIiIiI+FxwzyxQskBERERERETEx3Q1BBERERERERGpUjSzQET+v707j6+ivP44/jkCQfYoFi5gwAVrRUDZsRbUIAkIigvaWlvR1kb4uYECP6x1AevWsiitRUOwdam2BdQqoQUEG6JlEaICiv1JrQJKQtlEkZoQzu+PO8bcLBBtkrnJ/b553RfJzDMz55nDTC4nzzNXRERERESqXd3+3Xzdjr6W5ebmMXTIdaSnjWF25vxy6wsLixg3birpaWP47mUT+XDrdgB2797LqCtvp1fPy7l7SmbMNj+5ZgoXjhjH8OE3ctedsyguLq6VvtQHy5evJT19NIMHZ5CZObfc+sLCIsaOfYDBgzO49NJb2Lq1oGTdo4/OZfDgDNLTR5Obm1flfUrFlIv4kZv7OucNuYH0tOuYnflsufWFhUXcPG4a6WnX8d3LJpXcp/7+6puMvHgCI84fx8iLJ7By5XoA9u//nNHX3sOwoTdw/vCbmD7tyVrtT12m6yJ+KBfxJXf5WoakjyFtcAaZmfPKrS8sLGLc2F+QNjiDyy4dXy4faYMzGJI+JiYfAMXFxVx04U1ce+2UGu+DSHV65JfX8kHeI6xZ8otK20ybPIoNy2ewetEDnN71uJLlV4wcyPqc6azPmc4VIweWLO/R7XheW/wAG5bPYNrkUTUZvhyCVeOfMKhYUEXFxcXcPSWTzNm38+KCmWRnv8KmTVti2syb9xKtWjZj0eJZXDnqfKZOewKAxo2TuPGmy5kwsfyFOuPB8Tz/5xm8+OJD7Nq1l7/+9e+10p+6rri4mClTHiEr6y6ysx9mwYLlbNq0OabN3LmLadmyOUuWZHLVVSOYOvV3AGzatJns7OVkZz9MVtZdTJ4cLdJUZZ9SnnIRP4qLi/n5lNk8Ovs2XlzwIAsruE/Nn7eUli2bs2i2X1oRAAATIklEQVTxw4waNZxpwX/+k49qwW9m3cqfX5zBffffwKSJM0u2ufrqC8j+y6+Y/+xU8vL+wfLlsW/QpTxdF/FDuYgv0XP3KLOz7mRB9sNkV3Du5s1dQsuWzVm8JJNRV13AtKmPA9F8LMzOZUH2w2Rl3cmUyY/E/JLliSde5IQTU2q1PyLV4cm5OYy48v5K16efczonHheh68BxXD9pNjPv+TEAR7Vqxm1jL2bgBbcz4ILbuW3sxSS3agbAzHt+xPWTsug6cBwnHhch7ezTaqUvUr/UWLHAzE40s/Fm9pCZTTOz0WbWqqaOV9PWrXuXjh3bkZISISmpEeed9x2WLV0d02bZ0tWMuPAcANLTv83KFetwd5o2PZJevbrQOCmp3H6bN28KwIEDxRQVHajzn8VZW9ate5dOnb7Mx7BhA1m6dFVMm2XLVnHRRYMASE8/kxUr3sTdWbp0FcOGDSQpqREpKRE6dWrHunXvVmmfUp5yET/Wr9tEx46RkvM29LzvsGzpazFtli1dzYUXng1AWvoZrFyxHnenS5cTaNP2aAA6n5TC558XUlhYRJMmjenXvxsASUmN6NLleAryd9Zqv+oiXRfxQ7mIL+vWvUvHUufuvGEDyp27pctWceFFqUD5fJw3bABJSY04NiVCxyAfAPn5O8j52xouHTm41vsk8t96dfU77NrzaaXrh6f14un5uQCsfn0TrVo2JdImmcFnncbS3PXs/ngfez7ex9Lc9aSddRqRNsm0aN6EVXnR6+Pp+bmcn967Vvoiscys2l5hqJFigZndCDwCHAn0AZoAKcAKMzu7Jo5Z07YX7CLS7piS79tGWlNQEPuGuWD7TtoFbRo2bECLFk3Zs+eTw+77mh9P5jtnXkWzZk1ITz+jegOvpwoKdhKJlMpH2wryUVA2H83YvXtvBdseQ0HBzirtU8pTLuJHQZn7VCRyNNvL3ae+bFPZfWrxopWc0uV4kpIaxSzfu3cff3t5Df3P6FZDPag/dF3ED+UivhQU7KRdqXMXCc5padsryMee3Z9UsO2X5/3ee7MYP+Eq7AgNmpX6p33kaLZu+/I6+TB/F+0jR9M+chRbP9r15fJtu2gfOYr2kaP5ML/U8vydtI8cXasxyxesGl+1r6buqD8Bhrj7z4FzgS7ufhswBJhR2UZmlmFma8xsTWbmn2ootK/H8XLLylZ4vHyTKsmacyfLcx+jsLCoZJ6wHJpXcLLL56PiNhUvr9o+pTzlIn5UdJ+iKrko9QPo3Xc3M33ak9w1eXRMmwMHihl/ywx+8MNhpKREqifgekzXRfxQLuLM18wHBhXf4oyXX36N1ke3omvXztUUpEh8qWi+urtXeN9xr/i/lRVeV1LjjCOq7RWGmjzqF5+00BhoAeDum4FGlW3g7pnu3tvde2dkXFaDoX11bdu2Jn/bjpLvC/J30qZNbIUu0rY124I2Bw4U88knn5Gc3KJK+2/cOInU1D7lpjZIxSKRY8jPL5WPggryETmmTD72kZzcooJtd9CmTesq7VPKUy7iR6TMfSo/f1eF96n8MvepVsnNg/Y7ufH6X3DfAzfSsWNsQeDOOx6hU6d2XDlqeA33on7QdRE/lIv40jZyDNtKnbv8gh3lzl3bSvLRNtK6zLbR856X9zbLlq0mNfUabrn5l6xauY4J46fVTodEasGH+Ts5tl3rku87RI5mW8FuPty2i2Pbf3n9dGgXLM/fRYdSIwk6RFqzrWB3rcYs9UNNFQuygNfMLBNYAfwawMy+Aew61Ibxqlu3k/jgg21s3VpAYWERCxe+wjmpfWLanJPahz8//zIAixb9nf79ux3yNw379u1n+/bo6ThwoJic5XmccMKxNdeJeqRbt5N4//2P2LIln8LCIrKzl5Oa2jemTWpqP557bikAixa9Sv/+3TEzUlP7kp29nMLCIrZsyef99z+ie/eTqrRPKU+5iB9du3WOuU/9ZeErnJMaO0fxnNQ+PP/83wBYvGgF/fp3xczYu3cfY669h3E3X0HPnt+K2eahB5/m00/2cetPr66trtR5ui7ih3IRX7p1O4kP3v+IrcG5W5idS2pqv5g2qal9ef65ZUDZfPRjYXYuhYVFbN2SzwdBPm65ZRQ5y3/LsmVZTJs+gX79u/PLqbeE0T2RGpG9JI/vXzIAgL49OrP3k8/I376HJTlvcu6A7iS3akZyq2acO6A7S3LeJH/7Hj7d9x/69oiOtvn+JQNYsHhtmF1IYHV7GoLV1JAUMzsVOAXY4O7vfNXtD/rbcTdWJidnLffdO4eDBw9y8SWDGD36UmbOfJquXTuTmtqXzz8v5H8nPsjGjf+iVavmTJt+S8lw3UGpGezbt5+iogO0aNGMrDl3kpzcgjGj76GwsIjigwfp368bk279EQ0bNgi5p7GOsIaHbxSCnJw13HvvbIqLD3LJJecyZsx3eeihp+ja9SQGDerH558XMmHCdDZufI9WrZozY8bEknzMmvVH5s9/iQYNGvDTn17DWWf1rnSfcniJmItiLww7hArl5Kzl/nt/y8GDB7noklRGjx7Jr2Y+w6ldO5Oa2ie4T81k48Z/kdyqOVOnjyMlJcIjs+YxO/NZOnZqV7KvrDl3UFR0gNSzMzjhhA40Cp5hcMUVQxl56blhdbFCDaz8A2TDlojXRbxK1FxUODUpDkTPXRYHg3M3esxlzHzo99H3U0E+JpbkowXTZ0woyccjs/4Uk4+BZ/WK2feqVet57LHnePTRO8Lo2iEZJ4cdggBNOl4edgjlPP6rGxhwxikcc1QLtu/4mLunz6NRo+j776ynXgJgxt1Xk3b2aXy2/3OuHf8oeeveA+DKy85m4vUjAHjgV8/z5NwcAHp2P4HMaaNpcmQSi19+g3F3/K72O1YF+zc/U6/ncBUeXFNtN+KkI3rX+rmqsWLBfyseiwWJKl6LBSJhitdiQaKKx2KBSNjitViQqFQsiA/xWCxIZCoWVF0YxQL9L1BERERERESk2tXtWoiKBSIiIiIiIiLVLKxPMagudTt6EREREREREal2GlkgIiIiIiIiUu00DUFERERERERESrE6XizQNAQRERERERERiaGRBSIiIiIiIiLVzKxujyxQsUBERERERESk2tXtgfx1O3oRERERERERqXYaWSAiIiIiIiJSzer6Aw5VLBARERERERGpdnW7WKBpCCIiIiIiIiISQyMLRERERERERKpZXf80BI0sEBEREREREal2R1Tj69DMbIiZ/cPMNpnZpArWNzazPwbrV5nZcVWJXkRERERERETqIDNrADwMDAW6AJebWZcyzX4M7Hb3zsAM4IHD7VfFAhEREREREZFqZtX45zD6Apvc/T13LwT+AIwo02YE8Hjw9TxgkB1mnkTcPrPgCOtStyd4BMwsw90zw45DlIt4Uh9y0aBe3KHqRy7qE+UjftSHXNST21S9yEV9UR9ysX/zM2GHUC3qQy4Swzer7VZsZhlARqlFmaX+DXQAtpRatxXoV2YXJW3c/YCZfQy0BnZUdkyNLKh5GYdvIrVEuYgfykX8UC7ii/IRP5SL+KFcxA/lIn4oFwnG3TPdvXepV+liUUVFCS/zfVXaxFCxQERERERERKTu2gqklPr+WOCjytqYWUOgFbDrUDtVsUBERERERESk7noNOMnMjjezJOB7wAtl2rwAjAq+Hgksc/dDjiyI22cW1COaSxQ/lIv4oVzED+Uivigf8UO5iB/KRfxQLuKHciElgmcQXA8sAhoAj7n7W2Y2BVjj7i8Ac4AnzWwT0REF3zvcfu0wxQQRERERERERSTCahiAiIiIiIiIiMVQsEBEREREREZEYKhbUEDN7zMy2m9mGsGNJdGaWYmYvm9lGM3vLzG4KO6ZEZWZHmtlqM3szyMXksGNKdGbWwMxeN7MFYceSyMzsfTNbb2ZvmNmasONJZGaWbGbzzOyd4OfGGWHHlKjM7OTgmvjitdfMxoYdV6Iys3HBz+4NZvaMmR0ZdkyJysxuCvLwlq4JqUl6ZkENMbOBwKfAE+7eNex4EpmZtQPauXuembUA1gIXuvvbIYeWcMzMgGbu/qmZNQJeAW5y95Uhh5awzOxmoDfQ0t2Hhx1PojKz94He7r4j7FgSnZk9DuS6e1bwROmm7r4n7LgSnZk1AD4E+rn7B2HHk2jMrAPRn9ld3H2/mf0JWOjuvws3ssRjZl2BPwB9gULgr8AYd3831MCkXtLIghri7ss5zOdWSu1w923unhd8/QmwEegQblSJyaM+Db5tFLxUsQyJmR0LDAOywo5FJB6YWUtgINEnRuPuhSoUxI1BwD9VKAhVQ6BJ8PnsTSn/Ge5SO04BVrr7Z+5+AMgBLgo5JqmnVCyQhGJmxwE9gFXhRpK4gmHvbwDbgSXurlyE50FgInAw7EAEBxab2Vozywg7mAR2AvBv4LfB9JwsM2sWdlACRD/i65mwg0hU7v4hMBXYDGwDPnb3xeFGlbA2AAPNrLWZNQXOA1JCjknqKRULJGGYWXNgPjDW3feGHU+icvdidz8dOBboGwynk1pmZsOB7e6+NuxYBIAz3b0nMBS4LpjKJrWvIdATmOXuPYB9wKRwQ5JgOsgFwNywY0lUZnYUMAI4HmgPNDOzH4QbVWJy943AA8ASolMQ3gQOhBqU1FsqFkhCCObHzwd+7+7Phh2PQDC092/AkJBDSVRnAhcEc+X/AKSa2VPhhpS43P2j4O/twHNE56JK7dsKbC014mke0eKBhGsokOfuBWEHksDOBf7l7v929yLgWeDbIceUsNx9jrv3dPeBRKc963kFUiNULJB6L3io3hxgo7tPDzueRGZm3zCz5ODrJkTffLwTblSJyd1vdfdj3f04osN7l7m7fksUAjNrFjx8lWDIexrRYaZSy9w9H9hiZicHiwYBehhu+C5HUxDCthnob2ZNg/dVg4g+A0pCYGZtgr87Ahej60NqSMOwA6ivzOwZ4GzgGDPbCtzp7nPCjSphnQn8EFgfzJUH+Km7LwwxpkTVDng8eKr1EcCf3F0f2SeJri3wXPT9Nw2Bp939r+GGlNBuAH4fDH1/D7g65HgSWjAnezBwbdixJDJ3X2Vm84A8okPeXwcyw40qoc03s9ZAEXCdu+8OOyCpn/TRiSIiIiIiIiISQ9MQRERERERERCSGigUiIiIiIiIiEkPFAhERERERERGJoWKBiIiIiIiIiMRQsUBEREREREREYqhYICIiCcfMis3sDTPbYGZzg49n+7r7OtvMFgRfX2Bmkw7RNtnM/udrHOMuMxtfyborg368ZWZvf9HOzH5nZiO/6rFEREREQMUCERFJTPvd/XR37woUAqNLr7Sor/wz0t1fcPf7D9EkGfjKxYLKmNlQYCyQ5u6nAj2Bj6tr/yIiIpK4VCwQEZFElwt0NrPjzGyjmf0GyANSzCzNzFaYWV4wAqE5gJkNMbN3zOwV4OIvdmRmV5nZr4Ov25rZc2b2ZvD6NnA/cGIwquGXQbsJZvaama0zs8ml9nWbmf3DzF4CTq4k9luB8e7+EYC7/8fdZ5dtZGZ3BMfYYGaZZmbB8huD0QjrzOwPwbKzgvjeMLPXzazFf3l+RUREpA5SsUBERBKWmTUEhgLrg0UnA0+4ew9gH/Az4Fx37wmsAW42syOB2cD5wAAgUsnuZwI57n4a0d/4vwVMAv4ZjGqYYGZpwElAX+B0oJeZDTSzXsD3gB5EixF9KjlGV2BtFbr6a3fvE4ykaAIMD5ZPAnq4e3e+HF0xHrjO3U8P+re/CvsXERGRekbFAhERSURNzOwNogWAzcCcYPkH7r4y+Lo/0AV4NWg7CugEfAv4l7u/6+4OPFXJMVKBWQDuXuzuFU0PSAterxMdzfAtosWDAcBz7v6Zu+8FXvivegvnmNkqM1sfxHVqsHwd8Hsz+wFwIFj2KjDdzG4Ekt39QPndiYiISH3XMOwAREREQrA/+M15iWBk/r7Si4Al7n55mXanA15NcRhwn7s/WuYYY6t4jLeAXsCySg8QHQnxG6C3u28xs7uAI4PVw4CBwAXA7WZ2qrvfb2bZwHnASjM7193f+Yr9EhERkTpOIwtEREQqthI408w6A5hZUzP7JvAOcLyZnRi0u7yS7ZcCY4JtG5hZS+AToPQzABYBPyr1LIQOZtYGWA5cZGZNgmcGnF/JMe4DfmFmkWD7xsGIgNK+KAzsCI4zMmh7BJDi7i8DE4k+fLG5mZ3o7uvd/QGiIy++daiTJCIiIvWTRhaIiIhUwN3/bWZXAc+YWeNg8c/c/f/MLAPINrMdwCtEnx1Q1k1Appn9GCgGxrj7CjN71cw2AH8JnltwCrAiGNnwKfADd88zsz8CbwAfEH0IY0UxLjSztsBLwUMLHXisTJs9Zjab6HMZ3gdeC1Y1AJ4ys1ZERzjMCNrebWbnBDG/Dfzlq505ERERqQ8sOt1SRERERERERCRK0xBEREREREREJIaKBSIiIiIiIiISQ8UCEREREREREYmhYoGIiIiIiIiIxFCxQERERERERERiqFggIiIiIiIiIjFULBARERERERGRGP8PD/RWDMAYjdcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a336e5390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------- Recall matrix (Row sum=1) --------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a335987b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "clf = RandomForestClassifier(n_estimators=alpha[int(best_alpha/2)], criterion='gini', max_depth=max_depth[int(best_alpha%2)], random_state=42, n_jobs=-1)\n",
    "predict_and_plot_confusion_matrix(train_x_onehotCoding, train_y,cv_x_onehotCoding,cv_y, clf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted Class : 7\n",
      "Predicted Class Probabilities: [[ 0.1015  0.0948  0.0148  0.0939  0.0418  0.0355  0.6048  0.0069  0.0061]]\n",
      "Actual Class : 7\n",
      "--------------------------------------------------\n",
      "0 Text feature [kinase] present in test data point [True]\n",
      "1 Text feature [activated] present in test data point [True]\n",
      "2 Text feature [activating] present in test data point [True]\n",
      "4 Text feature [activation] present in test data point [True]\n",
      "6 Text feature [kinases] present in test data point [True]\n",
      "7 Text feature [inhibitors] present in test data point [True]\n",
      "8 Text feature [function] present in test data point [True]\n",
      "9 Text feature [oncogenic] present in test data point [True]\n",
      "10 Text feature [phosphorylation] present in test data point [True]\n",
      "11 Text feature [inhibitor] present in test data point [True]\n",
      "12 Text feature [f3] present in test data point [True]\n",
      "15 Text feature [suppressor] present in test data point [True]\n",
      "17 Text feature [activate] present in test data point [True]\n",
      "18 Text feature [tyrosine] present in test data point [True]\n",
      "19 Text feature [missense] present in test data point [True]\n",
      "20 Text feature [loss] present in test data point [True]\n",
      "21 Text feature [inhibited] present in test data point [True]\n",
      "24 Text feature [trials] present in test data point [True]\n",
      "25 Text feature [months] present in test data point [True]\n",
      "26 Text feature [ras] present in test data point [True]\n",
      "27 Text feature [mapk] present in test data point [True]\n",
      "28 Text feature [autophosphorylation] present in test data point [True]\n",
      "29 Text feature [drug] present in test data point [True]\n",
      "30 Text feature [therapy] present in test data point [True]\n",
      "31 Text feature [proteins] present in test data point [True]\n",
      "32 Text feature [therapeutic] present in test data point [True]\n",
      "33 Text feature [kit] present in test data point [True]\n",
      "35 Text feature [receptor] present in test data point [True]\n",
      "37 Text feature [benefit] present in test data point [True]\n",
      "41 Text feature [lines] present in test data point [True]\n",
      "42 Text feature [stability] present in test data point [True]\n",
      "43 Text feature [transforming] present in test data point [True]\n",
      "44 Text feature [treatment] present in test data point [True]\n",
      "46 Text feature [defective] present in test data point [True]\n",
      "47 Text feature [inhibition] present in test data point [True]\n",
      "48 Text feature [activity] present in test data point [True]\n",
      "52 Text feature [signaling] present in test data point [True]\n",
      "54 Text feature [resistance] present in test data point [True]\n",
      "55 Text feature [protein] present in test data point [True]\n",
      "56 Text feature [downstream] present in test data point [True]\n",
      "57 Text feature [mitogen] present in test data point [True]\n",
      "58 Text feature [ba] present in test data point [True]\n",
      "59 Text feature [nuclear] present in test data point [True]\n",
      "61 Text feature [hours] present in test data point [True]\n",
      "63 Text feature [cell] present in test data point [True]\n",
      "64 Text feature [resistant] present in test data point [True]\n",
      "65 Text feature [functional] present in test data point [True]\n",
      "66 Text feature [potential] present in test data point [True]\n",
      "67 Text feature [active] present in test data point [True]\n",
      "69 Text feature [effective] present in test data point [True]\n",
      "70 Text feature [inactivation] present in test data point [True]\n",
      "72 Text feature [treated] present in test data point [True]\n",
      "73 Text feature [sensitive] present in test data point [True]\n",
      "74 Text feature [thyroid] present in test data point [True]\n",
      "75 Text feature [yeast] present in test data point [True]\n",
      "78 Text feature [ligase] present in test data point [True]\n",
      "79 Text feature [transformation] present in test data point [True]\n",
      "80 Text feature [mutants] present in test data point [True]\n",
      "81 Text feature [dna] present in test data point [True]\n",
      "83 Text feature [serum] present in test data point [True]\n",
      "84 Text feature [clinical] present in test data point [True]\n",
      "85 Text feature [extracellular] present in test data point [True]\n",
      "86 Text feature [response] present in test data point [True]\n",
      "91 Text feature [oncogene] present in test data point [True]\n",
      "92 Text feature [patient] present in test data point [True]\n",
      "94 Text feature [src] present in test data point [True]\n",
      "97 Text feature [doses] present in test data point [True]\n",
      "98 Text feature [type] present in test data point [True]\n",
      "Out of the top  100  features  68 are present in query point\n"
     ]
    }
   ],
   "source": [
    "# test_point_index = 10\n",
    "clf = RandomForestClassifier(n_estimators=alpha[int(best_alpha/2)], criterion='gini', max_depth=max_depth[int(best_alpha%2)], random_state=42, n_jobs=-1)\n",
    "clf.fit(train_x_onehotCoding, train_y)\n",
    "sig_clf = CalibratedClassifierCV(clf, method=\"sigmoid\")\n",
    "sig_clf.fit(train_x_onehotCoding, train_y)\n",
    "\n",
    "test_point_index = 1\n",
    "no_feature = 100\n",
    "predicted_cls = sig_clf.predict(test_x_onehotCoding[test_point_index])\n",
    "print(\"Predicted Class :\", predicted_cls[0])\n",
    "print(\"Predicted Class Probabilities:\", np.round(sig_clf.predict_proba(test_x_onehotCoding[test_point_index]),4))\n",
    "print(\"Actual Class :\", test_y[test_point_index])\n",
    "indices = np.argsort(-clf.feature_importances_)\n",
    "print(\"-\"*50)\n",
    "get_impfeature_names(indices[:no_feature], test_df['TEXT'].iloc[test_point_index],test_df['Gene'].iloc[test_point_index],test_df['Variation'].iloc[test_point_index], no_feature)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## RF with Response Coding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "for n_estimators = 10 and max depth =  2\n",
      "Log Loss : 2.12180030037\n",
      "for n_estimators = 10 and max depth =  3\n",
      "Log Loss : 1.7404838211\n",
      "for n_estimators = 10 and max depth =  5\n",
      "Log Loss : 1.45956634309\n",
      "for n_estimators = 10 and max depth =  10\n",
      "Log Loss : 1.81334477685\n",
      "for n_estimators = 50 and max depth =  2\n",
      "Log Loss : 1.68074797837\n",
      "for n_estimators = 50 and max depth =  3\n",
      "Log Loss : 1.42491182941\n",
      "for n_estimators = 50 and max depth =  5\n",
      "Log Loss : 1.3152461495\n",
      "for n_estimators = 50 and max depth =  10\n",
      "Log Loss : 1.70612841413\n",
      "for n_estimators = 100 and max depth =  2\n",
      "Log Loss : 1.514016191\n",
      "for n_estimators = 100 and max depth =  3\n",
      "Log Loss : 1.43821890163\n",
      "for n_estimators = 100 and max depth =  5\n",
      "Log Loss : 1.28392384765\n",
      "for n_estimators = 100 and max depth =  10\n",
      "Log Loss : 1.67237216443\n",
      "for n_estimators = 200 and max depth =  2\n",
      "Log Loss : 1.53734403774\n",
      "for n_estimators = 200 and max depth =  3\n",
      "Log Loss : 1.44171291375\n",
      "for n_estimators = 200 and max depth =  5\n",
      "Log Loss : 1.35300190057\n",
      "for n_estimators = 200 and max depth =  10\n",
      "Log Loss : 1.68569025218\n",
      "for n_estimators = 500 and max depth =  2\n",
      "Log Loss : 1.61190790021\n",
      "for n_estimators = 500 and max depth =  3\n",
      "Log Loss : 1.50307172006\n",
      "for n_estimators = 500 and max depth =  5\n",
      "Log Loss : 1.36710480979\n",
      "for n_estimators = 500 and max depth =  10\n",
      "Log Loss : 1.71138698424\n",
      "for n_estimators = 1000 and max depth =  2\n",
      "Log Loss : 1.59855415851\n",
      "for n_estimators = 1000 and max depth =  3\n",
      "Log Loss : 1.50641942552\n",
      "for n_estimators = 1000 and max depth =  5\n",
      "Log Loss : 1.33835655754\n",
      "for n_estimators = 1000 and max depth =  10\n",
      "Log Loss : 1.6906555468\n",
      "For values of best alpha =  100 The train log loss is: 0.051190149414\n",
      "For values of best alpha =  100 The cross validation log loss is: 1.28392384765\n",
      "For values of best alpha =  100 The test log loss is: 1.30082160359\n"
     ]
    }
   ],
   "source": [
    "alpha = [10,50,100,200,500,1000]\n",
    "max_depth = [2,3,5,10]\n",
    "cv_log_error_array = []\n",
    "for i in alpha:\n",
    "    for j in max_depth:\n",
    "        print(\"for n_estimators =\", i,\"and max depth = \", j)\n",
    "        clf = RandomForestClassifier(n_estimators=i, criterion='gini', max_depth=j, random_state=42, n_jobs=-1)\n",
    "        clf.fit(train_x_responseCoding, train_y)\n",
    "        sig_clf = CalibratedClassifierCV(clf, method=\"sigmoid\")\n",
    "        sig_clf.fit(train_x_responseCoding, train_y)\n",
    "        sig_clf_probs = sig_clf.predict_proba(cv_x_responseCoding)\n",
    "        cv_log_error_array.append(log_loss(cv_y, sig_clf_probs, labels=clf.classes_, eps=1e-15))\n",
    "        print(\"Log Loss :\",log_loss(cv_y, sig_clf_probs)) \n",
    "\n",
    "\n",
    "best_alpha = np.argmin(cv_log_error_array)\n",
    "clf = RandomForestClassifier(n_estimators=alpha[int(best_alpha/4)], criterion='gini', max_depth=max_depth[int(best_alpha%4)], random_state=42, n_jobs=-1)\n",
    "clf.fit(train_x_responseCoding, train_y)\n",
    "sig_clf = CalibratedClassifierCV(clf, method=\"sigmoid\")\n",
    "sig_clf.fit(train_x_responseCoding, train_y)\n",
    "\n",
    "predict_y = sig_clf.predict_proba(train_x_responseCoding)\n",
    "print('For values of best alpha = ', alpha[int(best_alpha/4)], \"The train log loss is:\",log_loss(y_train, predict_y, labels=clf.classes_, eps=1e-15))\n",
    "predict_y = sig_clf.predict_proba(cv_x_responseCoding)\n",
    "print('For values of best alpha = ', alpha[int(best_alpha/4)], \"The cross validation log loss is:\",log_loss(y_cv, predict_y, labels=clf.classes_, eps=1e-15))\n",
    "predict_y = sig_clf.predict_proba(test_x_responseCoding)\n",
    "print('For values of best alpha = ', alpha[int(best_alpha/4)], \"The test log loss is:\",log_loss(y_test, predict_y, labels=clf.classes_, eps=1e-15))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Testing model with best hyper param"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Log loss : 1.28392384765\n",
      "Number of mis-classified points : 0.4868421052631579\n",
      "-------------------- Confusion matrix --------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a325ea278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------- Precision matrix (Columm Sum=1) --------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a335e98d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------- Recall matrix (Row sum=1) --------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a32a952b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "clf = RandomForestClassifier(max_depth=max_depth[int(best_alpha%4)], n_estimators=alpha[int(best_alpha/4)], criterion='gini', max_features='auto',random_state=42)\n",
    "predict_and_plot_confusion_matrix(train_x_responseCoding, train_y,cv_x_responseCoding,cv_y, clf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Query the classified point"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted Class : 7\n",
      "Predicted Class Probabilities: [[  2.00000000e-03   2.44000000e-02   1.70000000e-03   3.00000000e-03\n",
      "    9.00000000e-04   3.10000000e-03   9.61800000e-01   1.40000000e-03\n",
      "    1.70000000e-03]]\n",
      "Actual Class : 7\n",
      "--------------------------------------------------\n",
      "Variation is important feature\n",
      "Variation is important feature\n",
      "Variation is important feature\n",
      "Variation is important feature\n",
      "Gene is important feature\n",
      "Variation is important feature\n",
      "Variation is important feature\n",
      "Text is important feature\n",
      "Text is important feature\n",
      "Gene is important feature\n",
      "Text is important feature\n",
      "Text is important feature\n",
      "Gene is important feature\n",
      "Text is important feature\n",
      "Variation is important feature\n",
      "Gene is important feature\n",
      "Text is important feature\n",
      "Gene is important feature\n",
      "Gene is important feature\n",
      "Variation is important feature\n",
      "Variation is important feature\n",
      "Text is important feature\n",
      "Text is important feature\n",
      "Text is important feature\n",
      "Gene is important feature\n",
      "Gene is important feature\n",
      "Gene is important feature\n"
     ]
    }
   ],
   "source": [
    "clf = RandomForestClassifier(n_estimators=alpha[int(best_alpha/4)], criterion='gini', max_depth=max_depth[int(best_alpha%4)], random_state=42, n_jobs=-1)\n",
    "clf.fit(train_x_responseCoding, train_y)\n",
    "sig_clf = CalibratedClassifierCV(clf, method=\"sigmoid\")\n",
    "sig_clf.fit(train_x_responseCoding, train_y)\n",
    "\n",
    "\n",
    "test_point_index = 1\n",
    "no_feature = 27\n",
    "predicted_cls = sig_clf.predict(test_x_responseCoding[test_point_index].reshape(1,-1))\n",
    "print(\"Predicted Class :\", predicted_cls[0])\n",
    "print(\"Predicted Class Probabilities:\", np.round(sig_clf.predict_proba(test_x_responseCoding[test_point_index].reshape(1,-1)),4))\n",
    "print(\"Actual Class :\", test_y[test_point_index])\n",
    "indices = np.argsort(-clf.feature_importances_)\n",
    "print(\"-\"*50)\n",
    "for i in indices:\n",
    "    if i<9:\n",
    "        print(\"Gene is important feature\")\n",
    "    elif i<18:\n",
    "        print(\"Variation is important feature\")\n",
    "    else:\n",
    "        print(\"Text is important feature\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Stacking model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression :  Log Loss: 1.07\n",
      "Support vector machines : Log Loss: 1.69\n",
      "Naive Bayes : Log Loss: 1.24\n",
      "--------------------------------------------------\n",
      "Stacking Classifer : for the value of alpha: 0.000100 Log Loss: 2.178\n",
      "Stacking Classifer : for the value of alpha: 0.001000 Log Loss: 2.041\n",
      "Stacking Classifer : for the value of alpha: 0.010000 Log Loss: 1.527\n",
      "Stacking Classifer : for the value of alpha: 0.100000 Log Loss: 1.114\n",
      "Stacking Classifer : for the value of alpha: 1.000000 Log Loss: 1.172\n",
      "Stacking Classifer : for the value of alpha: 10.000000 Log Loss: 1.404\n"
     ]
    }
   ],
   "source": [
    "clf1 = SGDClassifier(alpha=0.001, penalty='l2', loss='log', class_weight='balanced', random_state=0)\n",
    "clf1.fit(train_x_onehotCoding, train_y)\n",
    "sig_clf1 = CalibratedClassifierCV(clf1, method=\"sigmoid\")\n",
    "\n",
    "clf2 = SGDClassifier(alpha=1, penalty='l2', loss='hinge', class_weight='balanced', random_state=0)\n",
    "clf2.fit(train_x_onehotCoding, train_y)\n",
    "sig_clf2 = CalibratedClassifierCV(clf2, method=\"sigmoid\")\n",
    "\n",
    "\n",
    "clf3 = MultinomialNB(alpha=0.001)\n",
    "clf3.fit(train_x_onehotCoding, train_y)\n",
    "sig_clf3 = CalibratedClassifierCV(clf3, method=\"sigmoid\")\n",
    "\n",
    "sig_clf1.fit(train_x_onehotCoding, train_y)\n",
    "print(\"Logistic Regression :  Log Loss: %0.2f\" % (log_loss(cv_y, sig_clf1.predict_proba(cv_x_onehotCoding))))\n",
    "sig_clf2.fit(train_x_onehotCoding, train_y)\n",
    "print(\"Support vector machines : Log Loss: %0.2f\" % (log_loss(cv_y, sig_clf2.predict_proba(cv_x_onehotCoding))))\n",
    "sig_clf3.fit(train_x_onehotCoding, train_y)\n",
    "print(\"Naive Bayes : Log Loss: %0.2f\" % (log_loss(cv_y, sig_clf3.predict_proba(cv_x_onehotCoding))))\n",
    "print(\"-\"*50)\n",
    "alpha = [0.0001,0.001,0.01,0.1,1,10] \n",
    "best_alpha = 999\n",
    "for i in alpha:\n",
    "    lr = LogisticRegression(C=i)\n",
    "    sclf = StackingClassifier(classifiers=[sig_clf1, sig_clf2, sig_clf3], meta_classifier=lr, use_probas=True)\n",
    "    sclf.fit(train_x_onehotCoding, train_y)\n",
    "    print(\"Stacking Classifer : for the value of alpha: %f Log Loss: %0.3f\" % (i, log_loss(cv_y, sclf.predict_proba(cv_x_onehotCoding))))\n",
    "    log_error =log_loss(cv_y, sclf.predict_proba(cv_x_onehotCoding))\n",
    "    if best_alpha > log_error:\n",
    "        best_alpha = log_error"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Testing with best hyper param"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Log loss (train) on the stacking classifier : 0.662940243923\n",
      "Log loss (CV) on the stacking classifier : 1.11432017542\n",
      "Log loss (test) on the stacking classifier : 1.11594191877\n",
      "Number of missclassified point : 0.35789473684210527\n",
      "-------------------- Confusion matrix --------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a33653a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------- Precision matrix (Columm Sum=1) --------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a3378f780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------- Recall matrix (Row sum=1) --------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a336c0f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lr = LogisticRegression(C=0.1)\n",
    "sclf = StackingClassifier(classifiers=[sig_clf1, sig_clf2, sig_clf3], meta_classifier=lr, use_probas=True)\n",
    "sclf.fit(train_x_onehotCoding, train_y)\n",
    "\n",
    "log_error = log_loss(train_y, sclf.predict_proba(train_x_onehotCoding))\n",
    "print(\"Log loss (train) on the stacking classifier :\",log_error)\n",
    "\n",
    "log_error = log_loss(cv_y, sclf.predict_proba(cv_x_onehotCoding))\n",
    "print(\"Log loss (CV) on the stacking classifier :\",log_error)\n",
    "\n",
    "log_error = log_loss(test_y, sclf.predict_proba(test_x_onehotCoding))\n",
    "print(\"Log loss (test) on the stacking classifier :\",log_error)\n",
    "\n",
    "print(\"Number of missclassified point :\", np.count_nonzero((sclf.predict(test_x_onehotCoding)- test_y))/test_y.shape[0])\n",
    "plot_confusion_matrix(test_y=test_y, predict_y=sclf.predict(test_x_onehotCoding))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Maximum voting Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Log loss (train) on the VotingClassifier : 0.916449056762\n",
      "Log loss (CV) on the VotingClassifier : 1.1858758572\n",
      "Log loss (test) on the VotingClassifier : 1.20235899773\n",
      "Number of missclassified point : 0.35639097744360904\n",
      "-------------------- Confusion matrix --------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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Ixd+/Dnv3Ou/5sWL5Rvr2va26i+G2LMti2isLuf7GSH7z+zuKw3vc0YqYFc41vzErvqZn71Zl9u3S7Sa2bz3C2ewLnM2+wPatR+jS7SbqhwdT178W+/cdx7Is1qy8vH9V0hWIigojcV8SFy/mYlkW27cd5Iam15WKc0fvW1m5Ig6A9Wt30LlLS4wx3NG7A2tWbyMvL59Tp1I5cSKF1m2a0qr1jZw4kcKpU6nk5xWwZvU27ujdoTqKV6NkZGRz9qxzCeClS7ls3ZrIDTc2LBWnd59OrFi+EYC1a7fSpUtrjDH07tOJ1asTiurCzokTybRp8ytat/4VJ04kc+qUnby8fFavTqB3n05lPlvKqh8egi0qjO+/d95Eetu2AzT9Ven6uKN3R1auiMOyLPbt/ZaAwLqER9SjW7e2/GdLYnG/8Z8tiXTr1pbwiHrU9a/Nvr3fYlkWK1fEqT6qQL+l3IfqwnN52uCBuRo35DPG/AP4p2VZCeVs+8SyrN9cKY0/xG9yizsFOnLz2Dn2RTq8Nh2fus4bLhUWFJD0zw85f/IUxseb6+8bQcjNLcjNyuLYB//ilmefASAjcT/fL3I+qjGiWzca3TUIgEtnznBk7gIKzp+//KhGX1/noxoXvM/5H07i4+/PTU88Ru3w8Arzdq283+O6K0eqBps37+TVV+fjcBRy7739ePLJXzNz5se0atWMvn1vIzc3j7/8ZQaHDn1HcHAA77wzlkaNbADMmbOIzz77Cm9vb8aPf4xevTpWmKY7KXdpSzUbM+ZNvt5xgMzMs4SFhfDMMw9SUOCcWfPAgwOxLIupU+YSH7+b2nVq8eqro2nd2nnF6bOl65k7dwkAT4y6n3vv7QfA/v1HGT9uJpcu5dGj56289NITbjeQlp13rLqzAMDe3d/xxO//l181i8J4OY/Rk6MH06pNE8Y//yEpyZnYourx6tu/JzjYn0MHf+Dzxf9hwmTno+pWLtvOh/O/AuCRx/sxZLhzoObQwR+YMvFTci/lc3v3m3l+/D3ORzVmnS833epW1yeiurNQxuz/XcqXa7bh4+1Ni5ubMHna48x7bwUtW91A7z4dyM3NY9wLczh86ATBwf688fYzNGpU9HjA95az7PPN+Hh7M3bc7+jRsx0AcZv38sZrzkc1Dr+nFyNHDavOIpbLx9Sp7iyUcuTIcca9+L84HIUUWoXceWc3nn76ft5991NatWpKnz6dyc3N44WxMzl06HuCgwN4e8aY4v7ivfeW8vlnsXh7ezNu/B/o2dN5w+PNm3fx2qvORzXec29fRo0aUZ3FLJfDyq/uLJTr8KHjvPzSXPLzC2jUKIKp05/kyzXOp5H8+oH+zkf8Tn2fhIR91Kntx9RXn6RVK+cjfj//bCPz5y0DYOQTwxl+j/NGlwcOHCt6VGM+PXq0Y/zER92q3/D1cs9H2v4Sf0u5q19uXTR3n4Z6FUTe/BeX/Xi3H3qz2o/VVRk8cAV3GTwQ9x08+CVyx8GDXyp3GTwQJ3ccPPilcrfBg18ydx08+CVy18EDkern2YMHtltecNmP95RvXq/2Y+VT3RkQERERERER8TzusdzAVTyrNCIiIiIiIiLicpp5ICIiIiIiIuJi7nKjQ1fR4IGIiIiIiIiIi3na4IFnlUZEREREREREXE4zD0RERERERERczHjYtXoNHoiIiIiIiIi4mKctW9DggYiIiIiIiIiLGWOqOwsu5VlDISIiIiIiIiLicpp5ICIiIiIiIuJiWrYgIiIiIiIiIpXytBsmelZpRERERERERMTlNPNARERERERExMW0bEFEREREREREKuVpgweeVRoRERERERERcTnNPBARERERERFxMU+7YaLbDh78o0dUdWdBihzO+ra6syBFWoQ0r+4sSJEg3ybVnQUpwdOmBYq4wrpTydWdBSkyuFHT6s6CiFQHD/t94lmlERERERERERGXc9uZByIiIiIiIiI1lafNjNTggYiIiIiIiIiLGWOqOwsu5VlDISIiIiIiIiLicpp5ICIiIiIiIuJietqCiIiIiIiIiFTK0+554FmlEREREREREfmFMca8b4xJNcYcKBE2yRjzX2PM3qLXoBLbxhljkowxR4wx0VX5DM08EBEREREREXG1a3vDxA+AWcBHPwl/x7Kst0oGGGNuAR4AWgLXAV8ZY5pbluWo7AM080BERERERETE1bxc+LoCy7LigIwq5mwosNCyrFzLsr4HkoDOVSmOiIiIiIiIiHiePxljEouWNdQrCmsAnCwR51RRWKU0eCAiIiIiIiLiasa47GWMGWmM2VniNbIKOZgDNAXaAcnA2z/mrJy41pUS0z0PRERERERERFzNhfc8sCxrHjDvZ+5jv5wVMx/4oujtKaBRiagNgdNXSk8zD0REREREREQ8jDEmqsTb4cCPT2JYCTxgjKlljLkBaAbsuFJ6mnkgIiIiIiIi4mrX8FK9MeZT4A6gvjHmFPAKcIcxph3OJQnHgScALMs6aIxZDHwDFABPX+lJC6DBAxERERERERGXs67hoxoty3qwnOB/VBJ/OjD953yGli2IiIiIiIiISKU08+BnGD9uJps27SQsLJhVX8wqs92yLKZPn0/c5p3Url2L1/76LC1bNgVg2bJY3puzGIBRT97P8OF9AThwIIlx42aSeymXnr06MmHC45hrOEJVU+Tl5jN+1Gzy8wpwOArp2qcNvxl5Z/H2eW99TuwXX7No02vl7r/0g1jWr9qOl5cXj/95GLd2aQHA7q2HmT9jOYWFhfS/+zZG/N5ZL/bT6bw58WNysi9wY4sGPDfpN/j6qrlUJC5uF9Onz6ewsJD77uvPyJH3ldqel5fP2LEzOHjwGCEhgbzzzlgaNowEYO7cJSxduh4vLy8mThxJjx63VilNKSs3N4+HfjeRvLx8ChyFRA+4nWdGP1AqTl5ePi+8MJNvDn5HSEggM2b8mQYNIwCYN/czPvssFi8vLyZM+CPde7QHID5+N69Of5/CwkJGjOjH4yPvueZlq2mSk8/wwtiZpKVl4eVluP/+ATz8+yGl4jj7jAXEbd5V1GeMLtFnbOC9OUsAGPXkfQwf3gf4sc94l9xLefTs1YEJEx5Tn3EFqgv3UOgo5J2n3ia4fjCPTR/J0T3fsnLuShwFBTRs1ohfP/8A3t7eZfb7et0O1v97HQD9fzuATgOcjyE/+e1JPn3jE/Lz8rm5880Mf/oejDGcP3uef037kAx7BqGRoTz80iPUDax7Tctak6j/dh+qCw/lYd2CZh78DMPv6cv8BZMq3B4Xt4sTx0+zdt1cpkx9msmT5gCQlXWO2bMWsmjxWyxe8jazZy0kOzsHgMmT5jBlytOsXTeXE8dPEx+3+1oUpcbx9fNh6uwnmfnv5/nbx39m97YjHNl/AoCjh05y/tylCvf94bsU4tfvYdanY5k083HmvvE5DkchDkchc9/8nFf+9jizFo4lft0efvguBYAPZ8Vw9wM9ee+zcQQE1uWrlVe8f8gvlsPhYMqU91iwYBIxMbP54os4kpJ+KBVnyZJ1BAUFsH79PB55ZChvvfUBAElJPxATE0dMzGwWLJjE5MlzcDgcVUpTyvLz8+WfH0xm+Yp3WLbsbRIS9rB375FScZYu/YrgoADWrvs7D/9+CG+9/REASUknWb06gVVfzGT+gpeYMmVecV1MnTKfefMnsuqLmcTExJOUdLK8j5cSvL29eeHFR1m9ZhYLF73Bvz9ZU+a4OfuMZNaum8OUqU8xedJ7wI99xiIWLX6DxUveZPasRSX6jLlMmfIUa9fN4cTxZPUZVaC6cA9xyzYT0dj5h05hYSGfvvEJD018mLELXqReZD2+Xvd1mX3Onz3P2o/W8j//+xzPzhrD2o/WcuHcBQCWzlzC/WPuZ/yHE0j77xkOf30IgA0LY2nWvjnjP5xIs/bNiV341bUrZA2j/tt9qC48mJdx3csNXLXBA2NMC2NMX2NMwE/C76xoH3fXqVMrgoMDKtweG7udocN6Y4yhXbsWnD17ntTUDBISdtO1WztCQgIJDg6ga7d2xMfvIjU1g5ycC7Rv3wJjDEOH9ear2G3XsEQ1hzGGOnVrAeAocOAocIABh6OQD95dxe+fuavCfXfEHaRH//b4+vkQeV0YtoZhHP3mB45+8wO2hmHYGoTh6+tDj/7t2RF3EMuySNx5lG592gDQZ3BHtm3ef03KWRMlJh6lSZMoGjWy4efny+DBPYmN3V4qzoYN24tn20RHd2Pr1n1YlkVs7HYGD+6Jn58vjRrZaNIkisTEo1VKU8oyxuDvXweAggIH+QUFZa6Eboj9mqHDegMQHX0727bux7IsNsTuYNCg7vj5+dKwYSSNG0eRmJhEYmISjRtfrotBg7qzIVaDaVcSERFafOU6IKAOTW9siN2eXipObOwOhg67o6jPuKlEn7GHrt3alugz2hIfv7ucPuMOvlK7uCLVRfXLOpPFoe3f0GVQFwAunL2At68PEUWznm7qcBOJ8fvK7Hdk52Gad2iOf5A/dQPr0rxDcw5/fYiz6dnkXrjE9bfcgDGGjv07sX+Ls58+8J/9dBrQCYBOAzpxYIv674qo/3YfqgupKa7K4IExZjSwAngGOGCMGVpi86tX4zPdgd2eTpQtvPi9zRaG3Z6O3Z5BlK3+5fDIMOz2DOz2dGwlw231y/ygkcscjkKe/d3bPHznK7Tr3JybWjVh9ZIEOvdsSWj9oAr3Sz+TTf3IkOL39SNCSE/NJj21dHhYRDDpZ7I5l30e/8A6ePt4F4dnnDl79QpWw/30exwZGVbme2y3pxMV5Yzj4+NNYKA/mZlny9m3flGbuXKaUj6Hw8HwYWPo3u1RunZtS9u2zUttt6emExUVBvxYF3XJyjqH3Z6BLarEMbeFkWpPJ9Wejq0o/o/hdnvGtSmMhzh1ys6hQ9+VrYuf9g22H/uGivqMDGy2sDLxpepUF9Vj+d+XcdfjdxcPZvoH+1NY4ODkEedV0H1x+8hKzSqzX3ZaNvXC6xW/DwkPITstm+y0bILrh5QKP5uWDcC5zHMEhQUDEBQWTE5WzlUrV02n/tt9qC48mDGue7mBq7WI+3Ggg2VZOcaY64GlxpjrLcuaicet/CjBKhtkjAGr7AZjKokv5fL29uJvH/+ZnHMXeW3sPzm45xhbYvcxfc5Tle5nlXv8TQXh5VaXu7RXt1TR8a1KnIrqoLDwymlK+by9vVm2fAZnz57nmT+9zrffnqB58ybF28v7foPBKueEZIyhsKLzl1TJ+fMXGT36dcaN/yMBAT9Zd11h3/Azw6VKVBfV4+C2gwSEBNCoeSOS9h4FnOeWhyY+zPI5yynIL+CmDjfh7V32elb5/bEp/zymCvjZ1H+7D9WFB/OwQ361li14W5aVA2BZ1nGcz5scaIyZQSWH0Bgz0hiz0xizc968RVcpa1dPpC2M5JQzxe9TUtKJiAgtCk+7HG6/HJ5SMjwljYiI0Gua55ooILAOrTs0Zf+uJJJPpTNqxGs8PmwauZfyeeLeshNb6keEkGa/fEUjLTWL0PAgwiKCS4Wnp2YTWj+YoBB/zp+76FwaURRer5KZDb90Nlv9Ut9je9H3+6dxkpOdcQoKHJw7d56QkMBy9k0jIiKsSmlK5YKC/OncuSUJ8XtKhdsiw0hOdl55cNbFBUJCArBFhpGSXOKYp6QTHhFKZGQYKcnppcJVF1WTn1/A6NGvM2RILwYMuL3M9jJ9Q5X6jPQy8eXKVBfV5/sD33Fw6wGm/nYy/5r+EUf3HuXj1/7F9bfcwDN/G81zs8fQtE1T6jcIL7NvSHgwmWcyi99nnckiKCyIkPBgstOyyoQDBNYL5Gy6cxbC2fRsAkIqXm76S6f+232oLqSmuFqDBynGmHY/vikaSLgLqA+0rmgny7LmWZbV0bKsjiNH/voqZe3q6dOnMyuWb8SyLPbuPUxgYF0iIkLp3v1WtiTsITs7h+zsHLYk7KF791uJiAjF378Oe/cexrIsVizfSN++t1V3MdxSdmYOOecuApB7KZ99O47StEUjPlwzifnLJzJ/+URq1fZl7mfjy+zbuWdL4tfvIT+vAPvpdJJPptHslsY0u7kRySfTsJ9OJz+/gPj1e+jcsyXGGFp3+BVbNiQCsCGVTKz8AAAgAElEQVRmJ7f1bHVNy1uTtG7djOPHT3PyZAp5efnExMTRp0/nUnH69LmNZctiAVi7dgtdurTBGEOfPp2JiYkjLy+fkydTOH78NG3aNKtSmlJWRkY2Z8+eB+DSpVy2bk3khhsblorTu08nVizfCMDatVvp0qU1xhh69+nE6tUJ5OXlc+qUnRMnkmnT5le0bv0rTpxI5tQpO3l5+axenUDvPp2uedlqGsuymDhhFk1vbMijjw4tN46zz9hU1GccITDQv6jPaM+WhL0l+oy9dO/evkSfcaSoz9hE375qF1eiuqhedz02hFcWTualf7/CQxMeplm7Zvxu3EOcyzwHQEFeARsWxdJ1SNcy+97UsQXf7jrChXMXuHDuAt/uOsJNHVsQFBZMrTq1OP7NcSzLYuf6r2nV1fnzsuXtrYpvvvj1usvhUpb6b/ehuvBgHnbDxKu1bOFhoKBkgGVZBcDDxpi5V+kzr7oxY97k6x0HyMw8S6+ej/LMMw9SUHR1+oEHB9KrV0fiNu9iQP8nqF2nFq++OhqAkJBAnnrq19w3YgwATz39ACEhgQC8MulJxo+byaVLefToeSs9e3aonsK5ucy0s/xtyqcUFlpYhRbd+ralU/dbKoy/Pe4ASYdO8dsn7qTxjTa69WvHnx54Ay9vL574yz3F0yNHPn8Pk0bPo7DQou+QzjS+0QbA7/90F29N/Bf/nruGG5s3oP/dGtSpiI+PNy+/PIrHHnsFh6OQe+/tR7NmTZg582NatWpG3763MWJEf/7ylxn07z+S4OAA3nlnLADNmjVh4MDuDBr0FN7eznR+fFRXeWlK5c6cyWTci/+Lw1FIoVXInXd2o3fvjrz77qe0atWUPn06M2JEX14YO5PoAU8RHBzA2zOc56VmzRpz58Bu3DV4NN7e3rz08uPFdTHxpcd47I9TKCws5J57+9KsWePqLGaNsHvXIVas2ETz5k0YNvRZAJ4b8zuSTzuvAj3w4J306tWhqM8YVU6fcT/3jXgegKee/nWJPmMU48e9y6VLufTo2UF9RhWoLtzTxsUb+Gb7QaxCi65DutGsvfM+FCeP/MB/vvgPv/7zA/gH+dP/twN45+kZAAz4XTT+Qf4AjPif+/j0zU/Iz82nReebubnzzQD0faAfH037gO1fbqNeRD0efumRailfTaD+232oLjyYhy0VMeWtk3EHFkfcM2O/QEeyjlZ3FqRIi5DmV44k10ShlV/dWZASjNGTh0V+avXJ49WdBSkyuFHT6s6CiJtq7ll/Xf9Es/7/cNnftEfX/7Haj9XVmnkgIiIiIiIi8stV7X/uu5YGD0RERERERERczU3uVeAqmucpIiIiIiIiIpXSzAMRERERERERV/OsiQcaPBARERERERFxNcvDnragZQsiIiIiIiIiUinNPBARERERERFxNQ+7YaIGD0RERERERERczbPGDrRsQUREREREREQqp5kHIiIiIiIiIq7mYTdM1OCBiIiIiIiIiKt52D0PtGxBRERERERERCqlmQciIiIiIiIiruZZEw80eCAiIiIiIiLich52zwMtWxARERERERGRSmnmgYiIiIiIiIiredjMAw0eyBW1CGle3VmQIhaO6s6CFCmwLlZ3FqQEPxNU3VkQcTv9G0RUdxZERH7ZPGyev4cVR0RERERERERcTTMPRERERERERFxNyxZEREREREREpFKeNXagwQMRERERERERV7O8PGv0QPc8EBEREREREZFKaeaBiIiIiIiIiKvpngciIiIiIiIiUinPGjvQsgURERERERERqZxmHoiIiIiIiIi4mofdMFGDByIiIiIiIiKu5mH3PNCyBRERERERERGplGYeiIiIiIiIiLiaZ0080OCBiIiIiIiIiMt52D0PtGxBRERERERERCqlmQciIiIiIiIiruZhMw80eCAiIiIiIiLiYpZnjR1o2cLPMX7cTLre/hBD7vpTudsty2LatHkM6D+Su4c8w8GDx4q3LVsWS/SAJ4ge8ATLlsUWhx84kMSQIc8woP9Ipk2bh2VZV70cniIubhfR0aPo338k8+YtKbM9Ly+fZ599nf79R3LffX/m1Cl78ba5c5fQv/9IoqNHER+/u8ppSlnJyWd4+KGJDBr4J+4a/AwffbiqTBxn25jPgP6juHvI//ykbWwgesCTRA94kmXLNhSHO9vGaAb0H8W0afPVNqroow/WMOyusQwf8gJj/zyL3Ny8Utvz8vJ5/rl3GRQ9ht/8+mX++98zxdsWzFvBoOgxDBn4PFsSEovDE+L3MWTg8wyKHsOC+SuvWVlqOp2j3Ifqwn18//1pRgwfV/zq0vGP/OvDNaXiWJbFa9M/ZFD0c9wz9AW+Ofh98bYVy+MYHP0cg6OfY8XyuOLwgwe/Y/jdLzAo+jlem/6h+owqUttwH6oLqQk0ePAzDL+nL/MXTKpwe1zcLk4cP83adXOZMvVpJk+aA0BW1jlmz1rIosVvsXjJ28yetZDs7BwAJk+aw5QpT7N23VxOHD9NfNzuCtOXyxwOB1OmvMeCBZOIiZnNF1/EkZT0Q6k4S5asIygogPXr5/HII0N5660PAEhK+oGYmDhiYmazYMEkJk+eg8PhqFKaUpa3tzcvvPgoq9fMYuGiN/j3J2tISjpZKo6zbSSzdt0cpkx9ismT3gN+bBuLWLT4DRYveZPZsxaVaBtzmTLlKdaum8OJ48lqG1Vgt2fwycdrWbh0GstWvY6jsJA1q7eWivP50k0EBfuzeu0MHnp4IO+89SkAx5JOsWb1Npavep0588cybco/cTgKcTgKmT71A/4+bywrVr3BmpitHEs6VR3Fq1F0jnIfqgv3csMN17F02WssXfYai5ZOp3YdP/r261gqTnzcXk6cSCHmyxm8Mvkxpk15H4DsrBzmzP6MTxZN5ZPFU5kz+7PiPmPa5Pd5ZfIfiflyBidOpJAQv++al62mUdtwH6oLD+ZlXPdyAxo8+Bk6dWpFcHBAhdtjY7czdFhvjDG0a9eCs2fPk5qaQULCbrp2a0dISCDBwQF07daO+PhdpKZmkJNzgfbtW2CMYeiw3nwVu+0alqjmSkw8SpMmUTRqZMPPz5fBg3sSG7u9VJwNG7YzfHhfAKKju7F16z4syyI2djuDB/fEz8+XRo1sNGkSRWLi0SqlKWVFRITSsmVTAAIC6tD0xobY7eml4sTG7mDosDuK2sZNJdrGHrp2a1uibbQlPn53OW3jDr5SXVRJgcNB7qU8CgocXLqYS0REvVLbN27Yxd1DewLQP7oz27cdxLIsNm7YxcBBXfDz86VhwwgaN45kf+Ix9iceo3HjSBo1isDXz4eBg7qwccOu6ihajaJzlPtQXbiv7dsO0KhRJNc1CC8V7jxP9cAYQ9t2zTh39gJnUjPZsiWR27u2JjgkgODgAG7v2potCYmcSc0kJ+ci7do3xxjD3UN7sCF2ZzWVquZQ23AfqgsPZozrXm7gqg0eGGM6G2M6Ff37FmPMGGPMoKv1ee7Abk8nyna5A7TZwrDb07HbM4iy1b8cHhmG3Z6B3Z6OrWS4rX6ZP7qkfD89dpGRYWWOnd2eTlSUM46PjzeBgf5kZp4tZ9/6RfV05TSlcqdO2Tl06Dvatm1eKrxMG7D92AYqahsZ2GxhZeJL5SIjQ3nk0cH07zuaPj2fJiCwLl27tSkVJ9WeiS0qFHC2i4DAumRl5WC3ZxJZ4phHRoaSmppBamrpuoiMDMVuz7w2BarBdI5yH6oL97Vm9VYGDr69THiqPRObLbT4faQtlNTUTFLtZc9HqfYMUlMziYwM/Um4zlNXorbhPlQXUlNclcEDY8wrwLvAHGPMa8AsIAB40Rgz4Wp8plsoZ3mdMQbKWXdnTCXx5YrKW8v402NXUZzyw6uWplTs/PmLjB79OuPG/5GAgLqlN1bYBn5muFQqO/s8Gzfs4sv1fyN28ywuXsxl1cqEUnHK/Z5XFG5MeVWhdlEFOke5D9WFe8rPK2DThl0MiO5SZlu59yuo5LhXVE9SObUN96G68GBatlAlI4BuQE/gaWCYZVlTgGjg1xXtZIwZaYzZaYzZOW/eoquUtasn0hZGcsrlm4+lpKQTERFaFJ52Odx+OTylZHhKGhERociV2Wz1Sx07e9Ex/Wmc5GRnnIICB+fOnSckJLCcfdOIiAirUppSvvz8AkaPfp0hQ3oxYEDZq0hl2kCV2kZ6mfhSuW1bD9CgQTihoUH4+vrQr18n9u05WipOpC2UlGTnLI6CAgc55y4QHBKAzRaKvcQxt9szCA+vR2RkaKm6sNsziIgIuTYFqsF0jnIfqgv3FB+/l5tvuYH69YPLbIu0hZKScnm2mT0lg4jwemX6Brs9g/CIekUzojLKhEvl1Dbch+rCg3m58OUGrlY2CizLcliWdQE4ZlnWWQDLsi4ChRXtZFnWPMuyOlqW1XHkyArHGNxWnz6dWbF8I5ZlsXfvYQID6xIREUr37reyJWEP2dk5ZGfnsCVhD92730pERCj+/nXYu/cwlmWxYvlG+va9rbqLUSO0bt2M48dPc/JkCnl5+cTExNGnT+dScfr0ua34yRZr126hS5c2GGPo06czMTFx5OXlc/JkCsePn6ZNm2ZVSlPKsiyLiRNm0fTGhjz66NBy4zjbxqaitnGEwED/orbRni0Je0u0jb10796+RNs4UtQ2NtG3r+riSqKiwkjcl8TFi7lYlsX2bQe5oel1peLc0ftWVq5w3qF8/doddO7SEmMMd/TuwJrV28jLy+fUqVROnEihdZumtGp9IydOpHDqVCr5eQWsWb2NO3p3qI7i1Sg6R7kP1YV7WhPzn3KXLAD07t2BlSvisSyLfXuPEhBYh/CIenTr1oatW/YX9xlbt+ynW7c2hEfUw9+/Dvv2HsWyLFauiKd3H52nrkRtw32oLqSm8LlK6eYZY+oWDR4Un72NMcFUMnjg7saMeZOvdxwgM/MsvXo+yjPPPEhBgQOABx4cSK9eHYnbvIsB/Z+gdp1avPrqaABCQgJ56qlfc9+IMQA89fQDhIQEAvDKpCcZP24mly7l0aPnrfTsqc6uKnx8vHn55VE89tgrOByF3HtvP5o1a8LMmR/TqlUz+va9jREj+vOXv8ygf/+RBAcH8M47YwFo1qwJAwd2Z9Cgp/D2dqbj7e0NUG6aUrnduw6xYsUmmjdvwrChzwLw3JjfkXzaOdr9wIN30qtXh6K2MaqctnE/9414HoCnnv51ibYxivHj3uXSpVx69OygtlEFbdr+iv7Rnbn/3gn4eHvT4uYm3Hd/H2a9u5SWrW6gd58O3DPiDsa9MIdB0WMIDvbnjbefAeBXzRoSfedtDL1rLD7e3kx46RG8vZ3jy+MnPsKox5xPbxh+Ty9+1axhdRazRtA5yn2oLtzPxYu5bP3PAV6e/Fhx2OKFXwFw/wP96NGrHXFxexkU/Ry1a9di2qtPABAcEsATTw7nwftfAuCJp4YTHOK8kfVLr/yBiePe41JuHt17tKVHz3bXuFQ1j9qG+1BdeDAPWypirsZzcI0xtSzLyi0nvD4QZVnW/iulYXFED+h1EwbP+tLXZBaO6s6CFMkvPF/dWZAS/LyCqjsLIm4nr/BcdWdBivh5BVZ3FkTcVHOP/kPjxtHLXfY37XfvDqv2Y3VVZh6UN3BQFJ4GpJW3TURERERERETc09VatiAiIiIiIiLyi2V52LIFDR6IiIiIiIiIuJqbPCXBVTysOCIiIiIiIiLiapp5ICIiIiIiIuJqXp61bEEzD0RERERERERczRjXva74UeZ9Y0yqMeZAibA3jTGHjTGJxphlxpiQEtvGGWOSjDFHjDHRVSmOBg9EREREREREarYPgDt/ErYeaGVZVhvgW2AcgDHmFuABoGXRPn83xnhf6QM0eCAiIiIiIiLial7Gda8rsCwrDsj4Sdg6y7IKit5uAxoW/XsosNCyrFzLsr4HkoDOVyzOzym7iIiIiIiIiFSBcd3LGDPSGLOzxGvkz8zNH4A1Rf9uAJwsse1UUVildMNEERERERERETdmWdY8YN7/ZV9jzASgAPj3j0HlfcSV0tHggYiIiIiIiIiLWW7wtAVjzO+Bu4C+lmX9OEBwCmhUIlpD4PSV0tKyBRERERERERFXu4b3PCiPMeZO4AXgbsuyLpTYtBJ4wBhTyxhzA9AM2HGl9DTzQERERERERKQGM8Z8CtwB1DfGnAJewfl0hVrAeuN83OM2y7JGWZZ10BizGPgG53KGpy3LclzpMzR4ICIiIiIiIuJq5totW7As68Fygv9RSfzpwPSf8xkaPBARERERERFxNQ+7SYCHFUdEREREREREXE0zD0RERERERERc7RouW7gW3HbwIPnCt9WdBSkSVbd5dWdBxO30X51b3VmQEtYPOlvdWZAifl5B1Z0FKVJo5Vd3FkREftnc4FGNrqRlCyIiIiIiIiJSKbedeSAiIiIiIiJSY3nYzAMNHoiIiIiIiIi4mOVh9zzQsgURERERERERqZRmHoiIiIiIiIi4moddqtfggYiIiIiIiIiradmCiIiIiIiIiPySaOaBiIiIiIiIiKvpaQsiIiIiIiIiUikPGzzQsgURERERERERqZRmHoiIiIiIiIi4mmdNPNDggYiIiIiIiIirWVq2ICIiIiIiIiK/JJp5ICIiIiIiIuJqxrNmHmjwQERERERERMTVPGzZggYPRERERERERFzNs8YONHhQmdSULF576VMy0s9hjOGue7sw4jc9mPzCvzh5/AwAOecuEhBYhwWLxpTZf8eWw8x6cwWOwkIGD7uN3/yhDwDJ/01nyosfcy77Is1ubsD4aQ/i6+tDXl4Br730Kd8eOkVQcF1eef0hbNeFXtMy1xTjx81k06adhIUFs+qLWWW2W5bF9Onzidu8k9q1a/HaX5+lZcumACxbFst7cxYDMOrJ+xk+vC8ABw4kMW7cTHIv5dKzV0cmTHgc42FTja6G5OQzvDB2JmlpWXh5Ge6/fwAP/35IqTjO+lhA3OZdRfUxukR9bOC9OUsAGPXkfQwf7mwnzvp4l9xLefTs1YEJEx5TfVTgvhuuY3DjSCzL4vtzF/jrvqO0rBfEU7dcj48xfJt9njcSj+Kwyu4b3TCch5s1AuCjoydZe8p5bmse7M+4ts3w8/Zie2om7x78HoBAXx8m3XoTtrq1SLmQyyu7D5OT77hmZa1JPvpgDZ8v3YgxhmbNGzH11ZHUquVXvD0vL5/xL8zhm2+OExISwJsznqFBg3AAFsxbweefbcbby4sXJzxMt+5tAEiI38frr/4LR2Eh94y4g8cev7taylbTxMXtYvr0+RQWFnLfff0ZOfK+Utvz8vIZO3YGBw8eIyQkkHfeGUvDhpEAzJ27hKVL1+Pl5cXEiSPp0ePWKqUpl708YQFxm/cSGhrE5ytfBSA7K4exf/47p/+bxnUN6vPmjKcJCvYvs+/K5QnMf28lAI+Pupu7h3UH4JuD3/PS+AXkXsqje8+2vDD+txhjqpyuOKltuA/VhdQEumFiJby9vXhyzBA+/Hwsf//oGVYs2sLxYym88vpDLFg0hgWLxtCzb2t69GlVZl+Ho5CZf13GX2c9xgef/YXYL/dw/FgKAHNnxnDfb3vy8coXCQysw+plOwBYvXw7gYF1+PfKcdz3257MnRlzTctbkwy/py/zF0yqcHtc3C5OHD/N2nVzmTL1aSZPmgNAVtY5Zs9ayKLFb7F4ydvMnrWQ7OwcACZPmsOUKU+zdt1cThw/TXzc7mtRlBrP29ubF158lNVrZrFw0Rv8+5M1JCWdLBXHWR/JrF03hylTn2LypPeAH+tjEYsWv8HiJW8ye9aiEvUxlylTnmLtujmcOJ6s+qhA/dp+3HtDFCPj9/Fo3F68jKFfg3DGt2vG5N1HeDRuL/aLuUQ3jCizb6CvD480a8yohESeSNjHI80aE+DrDcCY1k15K/EYv924m4b+dbgtPASA3/6qAbvSsvjtxt3O/zdteE3LW1PY7Rl88vFaFi6dxrJVr+MoLGTN6q2l4ny+dBNBwf6sXjuDhx4eyDtvfQrAsaRTrFm9jeWrXmfO/LFMm/JPHI5CHI5Cpk/9gL/PG8uKVW+wJmYrx5JOVUfxahSHw8GUKe+xYMEkYmJm88UXcSQl/VAqzpIl6wgKCmD9+nk88shQ3nrrAwCSkn4gJiaOmJjZLFgwicmT5+BwOKqUplw2dHh35sx7vlTY+wti6NzlFlZ9+Qadu9zCPxZ8UWa/7Kwc3vv7cj5e+DL/XvQK7/19OWezzwMwbcqHvDz5UVZ9+QY/nEhhS3xildMVJ7UN96G68FxeXq57uYNrlg1jzEfX6rNcJSw8iOY3O38Y1/WvTeMbIkk7c7Z4u2VZbFq/j753ti+z7+EDP3BdozCuaxiGr68PfaLbsWXTQSzLYs/XSfTq57yKFD2kIwmbDgCwZdNBood0BKBXvzbs3nEUyyrnUqHQqVMrgoMDKtweG7udocN6Y4yhXbsWnD17ntTUDBISdtO1WztCQgIJDg6ga7d2xMfvIjU1g5ycC7Rv3wJjDEOH9ear2G3XsEQ1V0REaPEsgoCAOjS9sSF2e3qpOLGxOxg67I6i+ripRH3soWu3tiXqoy3x8bvLqY87+Cp2e3UUr0bwNoZa3l54G6jl7cXFgkLyCgs5df4SADvTsugVFVZmv87hIexMy+JcfgE5+Q52pmVxW3g9Qmv5UtfHm4NZ5wBYeyqV7jbn/t0iw/jyVCoAX5YIl7IKHA5yL+VRUODg0sVcIiLqldq+ccMu7h7aE4D+0Z3Zvs3ZR2zcsIuBg7rg5+dLw4YRNG4cyf7EY+xPPEbjxpE0ahSBr58PAwd1YeOGXdVRtBolMfEoTZpE0aiRDT8/XwYP7knsT84nGzZsL56FFh3dja1b92FZFrGx2xk8uCd+fr40amSjSZMoEhOPVilNuaxDxxZlrv5v3LC7eBbB3cO6szG27ADxf7bsp8vtLQkOCSAo2J8ut7dkS0IiZ85kcT7nEm3b/QpjDEOGdmND0f5VSVec1Dbch+rCcxnjupc7uCqDB8aYlT95rQLu+fH91fjMqy3ldAZJR/7Lza0aF4cl7v6OeqGBNGwSXiZ+Wmo2EZEhxe/DI0NIO5PN2awLBATWwdvH+3J4avblfWzOfbx9vAkIqMPZrAtXs1gey25PJ8p2uV5stjDs9nTs9gyibPUvh0eGYbdnYLenYysZbqtf5g9gubJTp+wcOvQdbds2LxVe5rjbfjzuFdVHBrYSf5T+GF/KSruUx8Lv/svivh35vF9nzhc42Jicho8x3FQ0wNYrKoyI2rXK7Fu/th+pF3OL35+5lEv92n6E167FmYt5JcLzqF/bOd2+Xi1fMnLzAcjIzaeen+/VLF6NFRkZyiOPDqZ/39H06fk0AYF16dqtTak4qfZMbFHOpWk+Pt4EBNYlKysHuz2TyBLf/8jIUFJTM0hNLd0uIiNDsdszr02BarCfnt8jI8PKnN/t9nSiopxxfHy8CQz0JzPzbDn71i/qS66cplQuI/0s4UUzmsLDQ8jIOFsmTsk2AhBpCyXVnkmqPZPIyMuDcc42klnldMVJbcN9qC6kprhaMw8aAmeBGcDbRa9zJf5dLmPMSGPMTmPMzo/f//IqZe3nu3ghl5ef/5Cnnx+Kf0Dt4vANX+6l753tyt2nvPkCBlPuTIIf13GXO8nATUaZapxyjqUxptyDbEwl8aXKzp+/yOjRrzNu/B8JCKhbemOFx/1nhksZAb7edI8M5YENO7nnq6+p7e1F/wbhTNn9LX+65Xre696GCwUOHOUd03JOMBY67bhCdvZ5Nm7YxZfr/0bs5llcvJjLqpUJpeKU2x9UFG5MuX2EzlNXVlm/e6U45YdXLU35/1f+76LKf0tJ1altuA/VhefSzIOq6QjsAiYA2ZZlbQIuWpa12bKszRXtZFnWPMuyOlqW1fF3f7jzKmXt5ynId/Dy8x/Sb+Ct9OzbujjcUeAgfsN+ekeXP3gQHhFMqj2r+P0ZexZh4UEE1/Mn59xFHAWOUuEA4ZHBpKZkFaefk3ORoOC6ZROXK4q0hZGccqb4fUpKOhERoUXhaZfD7ZfDU0qGp6QREaGbVVZVfn4Bo0e/zpAhvRgw4PYy28sc9yrVR3qZ+FJWx/ohJF/IJTuvAIdlEZ+cTqt6gRzMOsczWw8wKiGRfelni5cwlHTmUi4RdS7PSAivXYv0S3mkXsolvI5fiXA/0i45ZyJk5uYTWss52yC0li+ZeflXuYQ107atB2jQIJzQ0CB8fX3o168T+/YcLRUn0hZKSrJzRk1BgYOccxcIDgnAZgvFXuL7b7dnEB5ej8jI0FLtwm7PICIiBKmczVa/1Pndbi97PrHZ6pOc7IxTUODg3LnzhIQElrNvGhERYVVKUyoXGhbEmTPO3zxnzmQRGhpUJk6krV5xGwGwpzi/85G20rNunG0kpMrpyv9j787Dqqr2P46/l6ClMikKWJK3UqvrlGbOaaJATqFZt3kuhwZvZZND5ZRlWV1LU9HmW7/UTE3RHNAErTQ1x8wrlQMKBydQ1GRw/f44hBKDZAfZ4Of1POfRs/ba+6y9v2zW5nvWXttN54ZzKBbllzHGYy8nKJHkgbX2pLX2LeB+YIgxZjxl8MkO1lpeGz6dOpcG86+7O+RZtnbVdkL/EUTN4IIv3K5sEMqeXftJ2nOAzMwsli5cT5vrG2CMoWnzuixf4p7YZ+HcNbS9vgEAbTo0YOHcNQAsX7KRptfWdcwPSlkTFtaCObOXYa1l/fqf8fWtQlBQddq1a8bKFT+SlpZOWlo6K1f8SLt2zQgKqk7VqpVZv/5nrLXMmb2MTp1alvZulAnWWoYOGc/ll9Xm/vujCqzjjsc3OfHYhq9v1Zx4NGXlivWnxWM97do1PS0e23Li8Q2dOrU4x3tWNriOn+Cf1Xy5IGcmnWY1AtiZfpyAnNsJKlYw3Luzb/UAACAASURBVFH3YubsTM637up9qVxbMwCfil74VPTi2poBrN6XysETmRzPyuafAe7bHiJrB7Ei57aRla6D3JAz+eINtYNYqSGQBapVK5CNGxI4fvwE1lpWfb+FSy+/KE+d6zs246s5cQAsXriaFq3cfcT1Ha9hwfzvycjIJDExhZ07k2nU+HIaNrqMnTuTSUxMITMjiwXzv+f6jteUxu6VKY0a1WPHjr3s3p1MRkYmMTFxhIXl/X0SFtaSWbNiAVi4cCWtWjXGGENYWAtiYuLIyMhk9+5kduzYS+PG9Yq1TSna9R2b8tVs92icr2avoGNYs3x12rRtxHffbuZw2lEOpx3lu28306ZtI2rWDKBq1QvZuCEBay1z56zMXb842xU3nRvOoVhIWWHOxYR8xphuQFtr7eDirrP32NxSnylw04+/MeCBCVxWr1buH/EPPdaFVtddxasvfs4/G13Cjbe0ya2/PyWNsSNm8Or4hwD4Pn4rE8bO4eRJS5eoa7nroc4A7E08wMjn/8vhw8eod8XFDH75DipV8ibjRCajh/4f27ftwc+vCi+8ehcX1S79ychqVal/5krn2FNPvc4Pqzdz6NBhAgMDePzx28nKGc1x2+1dsNYycsRk4uPXcWHlCxg9egCNGtUDYOYXi5k82f1owL79/kXv3u64bNq0ncGDxvH77xlc174ZL7zQ14HJm5Ol3YB81q75iTvvHEz9+nWoUMF9vJ586i6S9rqz3bfdfkNOPKL/FI+6AMz8YgmTJ38BQN9+t9C7t3syoE2bEhg86G1+//0E17W/hhdecNajM6+f55w5GO6vH0rHi2qQfdKScPgor21M4MEr6tAmqBrGGObsTOKL35IAuMLfhxvrhPD6xgQAuoYGcWdd98Sw/92eyIKcyRCv8Pfh+SZ1ucCrAqv2pTJu868A+FX0Ztg1VxBc+QJcx0/w0tptHMnMKoW9zmtx1/xzOpS2Ce98wdcLvsfby4srr6rD8FEPEz1pDg0aXkrHsGs4cSKDQc9N5OetO/H3r8prbzxOaKg7MRM9aTazvlyOt5cXzw66i+vau0e5xS1fz2uvuB/V2OumDvTp17M0d7FAlSo475ve5cvXMHr0FLKzT9K7d2f697+VceP+S8OG9ejUqSUnTmTwzDNvsnXrr/j7+/DWW88SGhoCwMSJ05g5cwleXl4MHvwQHTo0L3SbTvN7tjN+Tz339LusWf0zqanpVA/0o/9jvQjrdA3PPDmB5KQDhNQKZOxbj+If4MOWzb8xY9pSho18EIBZM+N4L3ouAA/17UHPm9yTjG7Z/BsvDJ7CiRMZtL2uMYOG3I0xhtTU9AK3W9ou9HLmN77n67nhROdvLOo75+KuBNSdFOexv2kT+rUv9WN1TpIHZ8MJyQNxc2Ly4PzlvOTB+cpJyQNxZvLgfOXE5MH5yinJA3Fu8kCk9JXv5EG9yZ5LHmzvW/rJA4c8MVJEREREREREnKrMzUMgIiIiIiIi4nSmnH1Vr+SBiIiIiIiIiIc5aLoujyhnuRARERERERER8bQzjjwwxlQFjltrTxpj6gNXAgustXq4t4iIiIiIiEgBKpyHIw/igAuNMRcDscD9wIcl2SgRERERERGRsswYz72coDjJA2OtPQbcBLxjre0F/LNkmyUiIiIiIiIiTlGcCRONMaY1cCfw4F9YT0REREREROS85JQRA55SnCTAE8AgYJa1dosx5jJgWck2S0RERERERKTsMuUse3DG5IG1djmwHMAYUwHYb60dUNINExERERERERFnOOOcB8aYz4wxfjlPXfgJ2GaMeabkmyYiIiIiIiJSNpkKnns5QXGa8U9r7WGgJzAfuAS4u0RbJSIiIiIiIlKGnY9PW6hojKmIO3kwx1qbCdiSbZaIiIiIiIiIOEVxkgeTgR1AVSDOGFMHOFySjRIREREREREpy8rbyIPiTJj4NvD2aUU7jTEdS65JIiIiIiIiImWbU/7o95TiPKoRY0w3oAFw4WnFI0qkRSIiIiIiIiLiKGdMHhhjJgFVgI7AVOBmYHUJt4tqlQJL+iOkmAzlLGVWpnmVdgMkx9Ju/qXdBDnN0ayk0m6C5KhYoWppN0FyZNsTpd0EEZHzWoVy9mdUceY8aGOtvQc4ZK0dDrQGQku2WSIiIiIiIiJlV3mb86A4yYPjOf8eM8ZcBGQCl5Zck0RERERERETESYoz58E8Y0wA8DqwDvdjGqeWaKtEREREREREyjCnjBjwlOI8bWFkzn9nGmPmARdaa9NKtlkiIiIiIiIiZZcpZ5MeFJo8MMbcVMQyrLVflkyTRERERERERMRJihp50KOIZRZQ8kBERERERESkAOfNbQvW2vvPZUNEREREREREyovyljwo9GkLxpinjDEPFlD+uDHmiZJtloiIiIiIiIg4RVG3LTwANCugPBr4AfhPibRIREREREREpIwrbyMPikoeWGttRgGFJ4wpb4dBRERERERExHPK2cMWCr9tAcAYE1ycMhEREREREREpHcaYfxtjNhtjtvwxzYAxproxZrExZnvOv9X+zmcUlTx4HYgxxnQwxvjmvK4H5gJj/86HioiIiIiIiJRnxnjuVfTnmIbAw0ALoAnQ3RhTD3geiLXW1gNic96ftaKetvCxMWYfMAJoiPvxjFuAl6y1C/7Oh4qIiIiIiIiUZ6bIcf4edRXwvbX2GIAxZjnQC4gCrs+p8xHwDfDc2X5IUXMekJMkUKJARERERERExJk2Ay8bYwKB40BXYA0QbK1NArDWJhljgv7OhxSZPBARERERERGRv86TjxkwxvQB+pxWFG2tjQaw1m41xowBFgPpwAYgy3Of7qbkgYiIiIiIiIiHefIhhTmJgugilr8HvJfzuaOBRMBljKmVM+qgFpDyd9pw7u7CKKNeGvoeHa8bQO+oobllixb+wE03DqFpwwfYsvm3QtddGb+JqG6D6HHDc7w/JSa3fE/iPu66bSQ9ujzHswPfJTPDnRTKyMjk2YHv0uOG57jrtpHs2bO/5HasHIiLW0tkZD/Cw/sQHT0j3/KMjEyeeGIM4eF9uOWWgSQmunKXTZ48g/DwPkRG9iM+fl2xtykFUyycJTs7m5t6PU3/vqPzLcvIyOSpJ98gMuJRbv3X8+xJPNWHRE/+ksiIR+l6w+OsiP8xtzw+/ke63vA4kRGPMiX6y3OyD2XRiKH/JaL989za8+XcskED3+eO3q9wR+9XuDHiRe7o/UqB63674id6dx9Bry7D+HDqotzyPYn7ue/217mp63AGDXyfzMxT/cWgge/Tq8sw7rv9dfbuOVCyO1eGJSXt4567h9K1y2N07/Y4H380N18day2jRk0hIrwfN/b4N1u2/JK7bNaspURG9Ccyoj+zZi3NLd+8OYEePQYQEd6PUaOmYK09J/tTFg0b+iGdrhvILVHD8i37+INFNGvQh0OHjhS47tzZ3xLVZShRXYYyd/a3ueU/bdnJv3oO48YbhvDa6M9zj39a6lH6P/QWUV2G0v+htzicdrRE9qm8UP/tHIqF/F1/3JJgjLkEuAn4P+Ar4N6cKvcCc/7OZxSaPDDGPFXU6+98aFlyY892vDs57+7WrXsxb457jGbN6xe6Xnb2SV55+RMmTHqSL796ma/nr+KXhD0A/OfNGdx1TwRzF4zBz68qs76MA2DWzHj8/Koy9+sx3HVPBOPenF5yO1bGZWdnM2LEJKZOHUZMzATmzYsjIWFXnjozZizCz8+HxYujue++KMaO/RCAhIRdxMTEERMzgalThzF8+ESys7OLtU3JT7Fwnk8+juHyyy4ucNnML2Lx8/Nh4aIJ3Htvd9544xMAEhJ2s2D+CubO+w/RU4cycsSU3FiMGjGFyVOGMHfef5gfs4KEhN3ncnfKjO49W/H2pEfzlL3yxgN8NnMQn80cRMfwq+nY+ep862Vnn+S1UdMZN/ERpn81lEXz1/LrL0kAjH9rDnfc3ZEv57+En19l5sz8DoA5X36Hn19lZi0Yxh13d+SdN//WtUC55uXlxXPP38/8BeP5fNprfPrZgnw/w3Fxa9m5I4mFiyYyYuQjDB82CYDU1CNMGD+NadNfY/qM15kwfhppaekADB82mREjHmHhoons3JFEfNy6fJ8tbj16tmH85AH5ypOTDvL9tz8RUqt6geulpR4leuI8Pv6/QXzy+SCiJ87LTQa8MuJThgy7mzkLRrFrp4tvV2wG4IOpC2jR8krmLBhFi5ZX8sHUr0tux8o49d/OoViUX+fqaQs5ZhpjfsL9dMRHrbWHgFeBcGPMdiA85/1ZK2rkge8ZXsVmjGmXk3SIONuGlpZrml+Bn79PnrLLLr+If1xaq8j1Nm/6ldDQIGqHBlGxkjeRXVvwzbIfsdbyw6qtdI5oDkCPqLYsi3VfcHyzdB09otoC0DmiOau/36pvMgqxceN26tSpRWhoCJUqVaRbt/bExq7KU2fp0lX06tUJgMjItnz33QastcTGrqJbt/ZUqlSR0NAQ6tSpxcaN24u1TclPsXCW5OQDLF++jt63dC5w+dLY1fTseT0AEZGt+f67TVhrWRr7A126tqNSpYrUrh3MJZeEsGljAps2JnDJJSG5sejStR1LY384h3tUdjRrXhc//yoFLrPWsuTrdUR2vSbfsi2bdhB6SQ1qh9agYkVvwrs0Y/nSjTn9xf8Ii2gKQLeolixfugGAuKUb6RbVEoCwiKb8sGqb+otCBAVVp0GDywHw8anM5ZfVxuXKO1IjNnY1UT2vxxjD1VdfweHDR0lJOciKFT/Spm0TAgJ88ff3oU3bJsTHryMl5SDp6cdo2vRKjDFE9byeJfodVahrmtfH379qvvI3xkzniYG9Cx3W+93KLbRsfRX+AVXx869Ky9ZX8e2KLezbl8rRo8dpcvXlGGPofmNrlsWuB2D5sg1079kagO49W/PN0vUlt2NlnPpv51Asyq9zmTyw1l5nrf2ntbaJtTY2p+yAtbaTtbZezr8H/87+FJo8sNYOL+pV1EaNMatP+//DwHjcCYeXjDF/69mSZUWK61CeTHpwcHVSXIdITU3H17cK3t5eOeXVSElJda+TkkpIiHsdb28vfHwrk5qafu4bXwa4XAcICamR+z44ODDfxaDLdYBatdx1vL298PWtyqFDhwtYtwYu14FibVPyUyyc5dXR7/P003dToZBexpVykJA8sahCauoRUlwHCKkVmFsvOCQQl+sgLtep+gAhIdVJUSz+sh/X/kJgoC+X1Mk/yfG+lDSCQ6rlvg8Orsa+lDTSUo/i61s5t78ICq5GSkoaACmnrePt7YWPT2XSUjU8+0wSE11s3forTZrkHTnoch2kVsjpP+enfv7zlAefdl6EBOarL8W3fOl6goIDqH9laKF1Tr8uglPXTPtcqQQFnzpngkJOXUsdOHCYmjUDAKhZM4CDBwu+HULUfzuJYiFlxRknTDTGXAg8CDQALvyj3Fr7QBGrVTzt/32AcGvtPmPMWOB7/uZwibKgoO9/jDEFfjP0xzV+wcs8OEVnOVKcY1VYncJicPKkjv/ZUCyc45tla6ge6E+DhpezetXmAusUeMwxhfzOgpMFfZutWPxli+avIaJr8wKXFXYe/NX+AoWlSEePHmfAgDEMGvwgPj5/GiFS2LH+q+VSLMePn+C96PlMmPJEkfUKPzfy1zU6Af4y9d/OoViUX+XtkBdnwsRPgBAgElgO1AbOlMatYIyplvOcSWOt3QdgrT1KEY+MMMb0McasMcaseW9K2b5/Mzi4GslJp76FcLkOUjMogGrVfDly5BhZWdk55YdyM+TBwdVITnavk5WVTfqR4wUO8xMICalBcvKpCSVdrgMEBVXPVycpyV0nKyubI0eOEhDgW8C6+wkKCizWNiU/xcI51q37mWVLf6BzWD8GDnyLVas28ewz4/LUCQkOJDlPLI7hH+BDcHAgyUmnvpFwJbuP+en1AZKTDyoWf1FWVjbLlmwg/IZmBS4PCg7AlXwo973LdYgaNf0JqObDkSPHc/uLFNchatb0ByD4tHWysrJJT1d/UZTMzCwGDBhDjx4diIhonW95cEggScmn/5y7f/7zlbtOlScnH8hXX4oncfc+9uw5wG03jaRb+CBSXIe48+ZR7N+Xlqfe6ddFcOqaKSgkgBTXqXMmJfkQNYPc50ZgoB/79rlHIezbl0r16n/pTtvzivpv51Asyq8KxnMvJyhO8qCutfYF4Ki19iOgG9DoDOv4A2uBNUB1Y0wIgDHGhyK+G7HWRltrm1trmz/4cFSxdsCpGjS8lF27UtiTuI/MjCwWzl9Nh45NMcbQvMWVLFm0BoC5c1ZyfZj7grJDx6bMnbMSgCWL1nBty6uUISxEo0b12LFjL7t3J5ORkUlMTBxhYS3y1AkLa8msWbEALFy4klatGmOMISysBTExcWRkZLJ7dzI7duylceN6xdqm5KdYOMdTA+9i2fIpLFk6iTfeeJKWLRvx2uv/zlOnY9i1zJ79DQCLFn5Hy1YNMcbQMaw5C+avICMjk8REFzt3JtGocV0aNqrLzp1JJCa6yMjIZMH8FXQMK/gbdCnY6u+3Ueey4Dy3Jpzunw3rsGvXPvYk7iczM4vFC9bRvmPjnP6iPksXuZ98ETNnFe3DGgNwXcdGxMxx37u6dNGPXNuyvvqLQlhrGTpkPJdfVpv77y/42iIsrAVzZn+DtZb167fh61uVoKDqtGvXlJUr1pOWlk5aWjorV6ynXbumBAVVp2rVyqxf755rYs7sb+jUSb+jiqte/drExr9BzOJXiFn8CkHB1fj0i6HUyEmO/aF12wZ8/+1PHE47yuG0o3z/7U+0btuAmjUDqFLlQjZu+BVrLfO++o7rw9yTkbbv2IR5s90Ti86b/R0dOjY55/tXVqj/dg7FQsoKc6YJlowxq621LYwxccAjQDKw2lp72V/+MGOqAMHW2sKfb5jjeNa3jpj56fmnJ7Hmh59JTU2neqAf/R/tib9/VV4d/SmHDh7B168KV1wRysQpT5OScojhL37AhEnupzPEx23g9Vf/j5MnTxLV6zoe7tsDgMTdKTz39CQOpx3liqsuYfSYPlSqVJETJzIZ8nw027buws+/KmPG9qN2aP77Y8+1yt41zlypFCxfvobRo6eQnX2S3r0707//rYwb918aNqxHp04tOXEig2eeeZOtW3/F39+Ht956ltDQEAAmTpzGzJlL8PLyYvDgh+jQoXmh25QzOx9jkW0zSrsJRVq9ajMfvP8VEycP5p23/48GDesSFnYtJ05k8Nyzb7N1628E+Psw9s0nc2MxadIXzJq5FC8vL54ffD/t27sTm8uXr+XV0R9w8uRJevUOo1+/m0tz1wp0NCuptJvAkGc+YO0P20lNTScw0I8+j3Qlqncbhg35hEaN/0HvW6/LrbsvJZVRL33GuImPALAybgtvjvmC7GzLjb1a8UDfGwBI3L2fIc98kNNfhDLi1Xty+4uXBn3Mtq278fOvysuv30/tUGf8rvatWLu0m5DH2jU/ceedg6lfvw4Vcr66efKpu0ja6/5G7rbbb8Bay8gR0cTHr+PCyhcwevQAGjWqC8DML5YwefIXAPTtdwu9e7snLNu0KYHBg97m999PcF37a3jhhYcdl8A5lvW3HuftMYOensLaH7blXkv1e/RGevZul7u8W/gg/jt9MNWq+fLT5h18MT2OF0fcA8DsL1fwfvQCAB7s25WoXu6JpX/avIOXhnzIiRMZtGnXkOeG3I4xhtTUdJ57KprkpIOE1KrOa2/2xT+g9EflVPUueqLt0nI+9t9Odf7Gor6zfnF6WPjXKz32N+3iG9qW+rEqTvLgIWAm0Bj4APABXrTWTirJhjkleSDOTR6IlCanJw/ON05IHoib05IH5zOnJA/EuckDkdJXvpMHkQtXeOxv2oWR7Ur9WJ1xwkRr7dSc/y4H/vJoAxEREREREZHzjVPmKvCU4jxt4QKgN/CP0+tba0eUXLNERERERERExCnOmDwA5gBpuCdAPFGyzREREREREREp+4rzdIKypDjJg9rW2htKvCUiIiIiIiIi5UQFU76m8StOMuRbY8yZHs0oIiIiIiIiIuVUcUYetAPuM8b8hvu2BQNYa23jEm2ZiIiIiIiISBl13k2YCHQp8VaIiIiIiIiIlCPnzZwHxhg/a+1h4Mg5bI+IiIiIiIiIOExRIw8+A7rjfsqCxX27wh8scFkJtktERERERESkzDpvbluw1nbP+ffSc9ccERERERERkbLPlLOnLZxxzgNjTLMCitOAndbaLM83SUREREREREScpDgTJr4LNAM24r51oRGwAQg0xvSz1i4qwfaJiIiIiIiIlDnl7baF4kwAuQNoaq1tbq29Brga2Ax0Bl4rwbaJiIiIiIiIlEkVPPhyguK040pr7ZY/3lhrf8KdTPi15JolIiIiIiIiIk5RnNsWthljJgKf57y/FfifMeYCILPEWiYiIiIiIiJSRlU43yZMBO4DHgGewD3nwQrgadyJg44l1jIRERERERGRMqq8zXlwxuSBtfY48EbO68/SPd4iEZEywMtUKu0myGl8K9Yu7SZIjoS0X0q7CZLjEp+g0m6CiIiUI4UmD4wx0621/zLGbALyjbew1jYu0ZaJiIiIiIiIlFFOmejQU4oaefDvnH+7n4uGiIiIiIiIiJQX581tC9baJGOMF/CetbbzOWyTiIiIiIiIiDhIkXMeWGuzjTHHjDH+1tq0c9UoERERERERkbLsfHzawu/AJmPMYuDoH4XW2gEl1ioRERERERGRMuy8uW3hNDE5LxERERERERE5DxUneTANqIv7iQu/WGt/L9kmiYiIiIiIiJRt583TFowx3sBo4AFgJ+59r22M+QAYYq3NPDdNFBERERERESlbytucB0UlQ14HqgOXWmuvsdY2BS4HAoCx56JxIiIiIiIiIlL6irptoTtQ31qbmy6x1h42xvQHfgb+XdKNExERERERESmLzqcJE+3piYPTCrONKWfjL0REREREREQ8qLwlD4q6beEnY8w9fy40xtyFe+SBiIiIiIiIiJwHihp58CjwpTHmAWAt7qctXAtUBnqdg7aJiIiIiIiIlEnnzdMWrLV7gJbGmDCgAWCABdba2HPVOBEREREREZGyqLw9baGokQcAWGuXAkvPQVtERERERERExIHOmDwQERERERERkb/mfJowUYCXhr5Hx+sG0DtqaG7ZooU/cNONQ2ja8AG2bP6t0HVXxm8iqtsgetzwHO9Pickt35O4j7tuG0mPLs/x7MB3yczIAiAjI5NnB75Ljxue467bRrJnz/6S27FyIC5uLZGR/QgP70N09Ix8yzMyMnniiTGEh/fhllsGkpjoyl02efIMwsP7EBnZj/j4dcXephRMsXAOxcIZkpL2cc/dQ+na5TG6d3ucjz+am6+OtZZRo6YQEd6PG3v8my1bfsldNmvWUiIj+hMZ0Z9Zs04N/tu8OYEePQYQEd6PUaOmUMBDkQTIOJHJk/f9h8fuGMsjt77Gp9FfAzBu5DQeu2Msj90xltHPf8TxYycKXH/6h7E8fNNo+t78Kmu/OzVH9Nrvfqbvza/y8E2jmfHRqbs4k/cc4Kn7x/Fw71cYM/hjMjOzSnYHy7D/frKQXjc+T68ez/PJx1/nW26t5dWXP6Zb5EB69xzMTz/tyF02Z3Y83W94mu43PM2c2fG55T9t+Y2bogbRLXIgr778sc6Lv0B9hnMoFuVTBQ++nMAp7XCsG3u2493JT+Upq1v3Yt4c9xjNmtcvdL3s7JO88vInTJj0JF9+9TJfz1/FLwl7APjPmzO4654I5i4Yg59fVWZ9GQfArJnx+PlVZe7XY7jrngjGvTm95HasjMvOzmbEiElMnTqMmJgJzJsXR0LCrjx1ZsxYhJ+fD4sXR3PffVGMHfshAAkJu4iJiSMmZgJTpw5j+PCJZGdnF2ubkp9i4RyKhXN4eXnx3PP3M3/BeD6f9hqffraAhITdeerExa1l544kFi6ayIiRjzB82CQAUlOPMGH8NKZNf43pM15nwvhppKWlAzB82GRGjHiEhYsmsnNHEvFx6/J9tkDFSt6Mfrc/4z97mrc/Hcja77bx86adPPxkFOM/e5rxnz1NzeAA5s1YkW/dXb8mE7foR979/FmGj3uYia99SXb2SbKzTzLxtS8ZPu5h3p32LMsX/siuX5MB+HB8DFG3t2fKzEFU9a3C4jmrz/Uulwnbt+9m5oxlfDZtODNmvUzcN+vZuSM5T50VcRvYudPFvK/H8uLwBxg1/AMA0lLTmfTuLD79fBifTRvOpHdncTjtKACjRnzIS8MfYN7XY9m508WK+I3nfN/KIvUZzqFYSFmh5MEZXNP8Cvz8ffKUXXb5Rfzj0lpFrrd506+EhgZROzSIipW8iezagm+W/Yi1lh9WbaVzRHMAekS1ZVms++Lvm6Xr6BHVFoDOEc1Z/f1WZc8LsXHjdurUqUVoaAiVKlWkW7f2xMauylNn6dJV9OrVCYDIyLZ8990GrLXExq6iW7f2VKpUkdDQEOrUqcXGjduLtU3JT7FwDsXCOYKCqtOgweUA+PhU5vLLauNyHchTJzZ2NVE9r8cYw9VXX8Hhw0dJSTnIihU/0qZtEwICfPH396FN2ybEx68jJeUg6enHaNr0SowxRPW8niWKRYGMMVSucgEAWVnZZGdlYwxU8bkQcH+7nXEiE0P+8aTfx22hfURTKlbyJuTiQGrVDuR/W3bxvy27qFU7kJCLA6lY0Zv2EU35Pm4L1lo2rtlOu7DGAHTq1pzvlm86dztbhvz2y14aN6lL5coX4O3tRfNrryQ2dk2eOsuWrqNHVDuMMTRpUpcjR46xb18qK1duonXrhvgH+ODnX5XWrRuyYsVG9u1LJT39OE2urocxhh5R7VgWu7aU9rBsUZ/hHIpF+VXBeO7lBCWSPDDGtDTG+OX8v7IxZrgxZq4xZowxdjq5RgAAIABJREFUxr8kPtNpUlyHCKlVPfd9cHB1UlyHSE1Nx9e3Ct7eXjnl1UhJSXWvk5JKSIh7HW9vL3x8K5Oamn7uG18GuFwHCAmpkfs+ODgw34W5y3WAWrXcdby9vfD1rcqhQ4cLWLcGLteBYm1T8lMsnEOxcKbERBdbt/5KkyZ5R6u5XAepddqxDQkJxOU6mL88+FR5SEhgvvpSsOzskzx+5xvcFfkSV7eozxUN6wDwnxGfc3eXYSTuTKH7re3yrXdgXxo1gwNy39cICuDAvrQCyv05sC+Nw2lHqepbGa+cfr1GsD8H9h0u4b0rm+rWq826NdtITT3C8eMniI/bgCsp789wSsqh3Gsh+OP66SAproN5r6tCTpUHB/+pfsqhkt+ZckB9hnMoFuWXMdZjLycoqZEH7wPHcv4/DvAHxuSUfVBCn+koBYXXGFPgSAKTk0kqeJlD0kwOU5xjVVidwmKg4392FAvnUCyc5+jR4wwYMIZBgx/Ex6dK3oWF9Qd/tVwK5OVVgXc+HciH817kfz/tYscvSQA88eJtfBTzEqH/CCZ+8fr8Kxb2M19Ax26g4HLFpUCXXX4x9z/UjT4PjqF/n9e54opL8PLOeyla+O+o/NsrrLyAASVSAPUZzqFYSFlRUsmDCtbaP2YLam6tfcJau8JaOxy4rLCVjDF9jDFrjDFr3psyp4Sadm4EB1cj+bRsust1kJpBAVSr5suRI8fIysrOKT9EzZoBp9ZJdq+TlZVN+pHj+PtXPfeNLwNCQmqQnHxqQkmX6wBBQdXz1UlKctfJysrmyJGjBAT4FrDufoKCAou1TclPsXAOxcJZMjOzGDBgDD16dCAionW+5cEhgSSddmyTk93HNl+561R5cvKBfPWlaD6+lWnU7HLWnTbxoZdXBa4Lv5pvl+a/Nz4wKIB9rtTc9/tTUqlew4/AIP8/ladRvaY/fgFVOXrkONk5/fp+VxrVa/iV4B6VbTf1vp7pM0fx4SdD8fOvyiV1QvIsDw6unnstBH9cP1UjOKR63uuq5FPlp4/AcbkOElSzWsnvSDmgPsM5FIvyS7ctFM9mY8z9Of/fYIxpDmCMqQ9kFraStTbaWtvcWtv8wYejSqhp50aDhpeya1cKexL3kZmRxcL5q+nQsSnGGJq3uJIli9z3+M2ds5Lrw5oB0KFjU+bOWQnAkkVruLblVcoQFqJRo3rs2LGX3buTycjIJCYmjrCwFnnqhIW1ZNYs92zYCxeupFWrxhhjCAtrQUxMHBkZmezencyOHXtp3LhesbYp+SkWzqFYOIe1lqFDxnP5ZbW5//6C+7OwsBbMmf0N1lrWr9+Gr29VgoKq065dU1auWE9aWjppaemsXLGedu2aEhRUnapVK7N+/TastcyZ/Q2dOikWBUk7lE76keMAnPg9k/Wrt3NxnSD27nZfSFtrWR2/hdr/CMq3bsvrGhC36EcyM7JI3nOAvbv3U7/BJdT/Zyh7d+8nec8BMjOziFv0Iy2va4AxhkbX1GVFTiIiNmYNrTo0PHc7W8YcOJAGQNLe/cQuWUPXrnkTa9eHNWPunBVYa9mwIQFf3yrUrBlA27aN+PbbTRxOO8rhtKN8++0m2rZtRM2aAVSteiEbNiRgrWXunBV0zLmukqKpz3AOxaL8Km9PWzAlMSFfzrwG44DrgP1AM2B3zmuAtXbDmbZxPOtbR9zY8fzTk1jzw8+kpqZTPdCP/o/2xN+/Kq+O/pRDB4/g61eFK64IZeKUp0lJOcTwFz9gwiT30xni4zbw+qv/x8mTJ4nqdR0P9+0BQOLuFJ57ehKH045yxVWXMHpMHypVqsiJE5kMeT6abVt34edflTFj+1E7NP+FzblW2bvGmSuVguXL1zB69BSys0/Su3dn+ve/lXHj/kvDhvXo1KklJ05k8Mwzb7J166/4+/vw1lvPEhrq/oZj4sRpzJy5BC8vLwYPfogOHZoXuk05M8XCOc7XWFiyS7sJeaxd8xN33jmY+vXrUCHn64Inn7qLpL3uP15vu/0GrLWMHBFNfPw6Lqx8AaNHD6BRo7oAzPxiCZMnfwFA33630Lu3e5KsTZsSGDzobX7//QTXtb+GF1542HFJ5oS0X85cqYT9tn0vbw3/P06etJw8abmucxNufaAzz/WZwLGjv2MtXFqvFo8+dzNVfC5kVdxmtm9N5K6+NwAw7f0lLJ67Gi+vCjz8VBTN21wFwA8rtzLlzdmcPGkJ79GCWx/oDLgf1ThmyCekHz7GZfUv5ukRd1Kxknep7f8fLvEp/WuIP7v3rpGkpabjXdGLp5+9k1atGzD9c/cfRP+6rRPWWkaP+oiVKzZx4YWVGPnywzRo6B60OmvmcqZGfwXAw32j6HlTewC2bP6VoYOjOXEik3bXNWbQkHscd15c4BVw5kql4HztM5zo/I1FfWedrB42ZE2sx/6mfbl5p1I/ViWSPMjduDG+uG9T8AYSrbWuM6ySyynJA3Fu8kBE5A9OSx6cz5yQPBA3JyYPzldOTR6IlL7ynTx4Ye0Sj/1NO/KazqV+rEo0LW6tPQKccZSBiIiIiIiISHnilLkKPMUpt0+IiIiIiIiIiEOV/g15IiIiIiIiIuVMeRt5oOSBiIiIiIiIiId5lXYDPEy3LYiIiIiIiIhIkTTyQERERERERMTDKpjy9QBBJQ9EREREREREPKy8zXmg2xZEREREREREpEgaeSAiIiIiIiLiYRp5ICIiIiIiIiJF8jKeexWHMSbAGPOFMeZnY8xWY0xrY0x1Y8xiY8z2nH+rne3+KHkgIiIiIiIiUvaNA7621l4JNAG2As8DsdbaekBszvuzouSBiIiIiIiIiIdVMJ57nYkxxg9oD7wHYK3NsNamAlHARznVPgJ6nu3+aM4DEREREREREQ87x49qvAzYB3xgjGkCrAX+DQRba5MArLVJxpigs/0AjTwQERERERER8TBPjjwwxvQxxqw57dXnTx/nDTQDJlprmwJH+Ru3KBREIw9EREREREREHMxaGw1EF1ElEUi01q7Kef8F7uSByxhTK2fUQS0g5WzboJEHIiIiIiIiIh7m5cHXmVhrk4Hdxpgrcoo6AT8BXwH35pTdC8w52/1x7MiDC70DS7sJkuNI5u7SboLk8K0YWtpNEHEkU6xuVc6Fev71S7sJkqPyJS+VdhMkx/Fdw0u7CSJSCooz0aGHPQ58aoypBPwK3I97wMB0Y8yDwC7glrPduGOTByIiIiIiIiJSPNba9UDzAhZ18sT2lTwQERERERER8bBz/LSFEqfkgYiIiIiIiIiHeZ372xZKlCZMFBEREREREZEiaeSBiIiIiIiIiIeVwoSJJUrJAxEREREREREPK2/JA922ICIiIiIiIiJF0sgDEREREREREQ8rbyMPlDwQERERERER8TCvcvaoRt22ICIiIiIiIiJF0sgDEREREREREQ8rb9/UK3kgIiIiIiIi4mHlbc6D8pYMEREREREREREP08gDEREREREREQ8rbyMPlDwQERERERER8TA9bUFEREREREREzisaeSAiIiIiIiLiYeXttgWNPPgLBg8aR5vWd9Oj+2MFLrfWMmpUNBHhfbixx+Ns2fJL7rJZs2KJjOhLZERfZs2KzS3fvDmBHj0eJyK8D6NGRWNt+Rra4knDh35CePtn+VfPkXnKP/90GTd1H8a/okYy7o0vC1z32xVbuKn7MHp2eYkPpy7MLd+TuJ97b3+NXl1fYtDAqWRmZgGQkZHJoIFT6dnlJe69/TX27jlQcjtWDsTFrSUysh/h4X2Ijp6Rb3lGRiZPPDGG8PA+3HLLQBITXbnLJk+eQXh4HyIj+xEfv67Y25SCKRbOoVg4h2Jx7k16vS87101izeLXcss+mTCA7xe8wvcLXuHnlW/z/YJX8qwTelEg+7Z+wBN9uhW4zTqhNYmbM5JNy9/kkwkDqFjRC4BKlbz5ZMIANse9RdyckVxSu0buOk8/GsXmuLfYsOwNOrdvXAJ7Wrbp3HAOxaJ8qmA893ICJQ/+gl43dWLK1GGFLo+LW8vOHXtZuGgyI0Y+yvBhEwFITT3ChPGfM236WKbPeIMJ4z8nLS0dgOHDJjJixKMsXDSZnTv2Eh+3rtDtn+969GzFO5PyJm7WrN5G3LKNfP7lEKbPeYG77wvPt1529knGjJrG2xMfY8ZXL7Bw/hp+/SUJgHfems0dd4cxa/5wfP2qMGfmtwDM+fJbfP2qMHvBcO64O4x33pxV8jtYRmVnZzNixCSmTh1GTMwE5s2LIyFhV546M2Ysws/Ph8WLo7nvvijGjv0QgISEXcTExBETM4GpU4cxfPhEsrOzi7VNyU+xcA7FwjkUi9LxyYzlRN3zap6yux99m1ZdBtGqyyBmL1jNnK9/yLP8tRfvZtE36wvd5suD7uCdqfNp1OEpDqUd5b5bOwJw360dOZR2lIbtn+SdqfN5edAdAFxZ72Ju6dGaZp2f4cZ7XmXcyw9QwSlX4A6gc8M5FAspK0okeWCMGWCMCS2JbZema69tiL+/T6HLY2NXEdWzI8YYrr76Sg4fPkpKykFWrFhHm7ZXExDgi7+/D23aXk18/FpSUg6Snn6Mpk2vxBhDVM+OLIn9/hzuUdnSrHk9/Pyr5in7Ylo89z4YSaVKFQGoHuibb70tm3YQeklNaofWoGJFbyK6XMPypRuw1vLDqm10imgKQPeoVnyzdAMAy5dupHtUKwA6RTRl9aptGhVSiI0bt1OnTi1CQ0OoVKki3bq1JzZ2VZ46S5euolevTgBERrblu+/cxz82dhXdurWnUqWKhIaGUKdOLTZu3F6sbUp+ioVzKBbOoViUjpWrf+Zganqhy3t3b8X0Od/mvu8R0ZzfdqXw0/8SC12nQ5sGfDnffZw//SKOHpHNAegecQ2ffhEHwJfzV3F924Y55c2ZMfc7MjKy2Ll7H7/sSObaq+v+7X0rL3RuOIdiUX5p5EHxjARWGWPijTGPGGNqltDnOIrLdYBaIad2NSQkEJfrAC7XQWqFnBpCFxIciMt1EJfrACGnl4fUwOXS8Pi/YteOFNavTeDe21+jz31vsmXTjnx1UlJSCQ6plvs+KLgaKSlppKUexde3Ct7eXjnlAaSkpOZbx9vbCx+fyqSlHi35HSqD/vxzHBwcmO/n2OU6QK1a7jre3l74+lbl0KHDBaxbI+ecOfM2JT/FwjkUC+dQLJynbYsrce1P45cdyQBUqXwBA/v34OX/zCx0ncBqvqQdPkp29kkA9iQd4KKQ6gBcFFKdxL3u45+dfZLDR44RWM2Xi4Or5Za71znIRaddD5zvdG44h2JRfnkZz72coKSSB78CtXEnEa4BfjLGfG2MudcYk/+r4RzGmD7GmDXGmDXR0dNKqGklqIAvpo0xUMA31sYUUV+KLSs7m8OHj/HhZ88wYOBNDHr6vfwjBAo8zhQ4kiD3+Bc0yEChKVCRx/EMdQouL942JT/FwjkUC+dQLJznX1FtmHHaqIMXnrqZd95bwNFjJwpdp6DD+0ccCjr21toCV9IgwlN0bjiHYiFlRUk9bcFaa08Ci4BFxpiKQBfgdmAsUOBIBGttNBANYNlW5n69B4cEkpS8L/d9cvIBgoKqExwSyOrVm0+Vuw7QokVDgkMCSU7ef1r9/QQFVT+nbS7rgoOr0bHz1RhjaNjoHxhjSD2UTrXqp3JUQcEBuJIP5b5PcR2iZk1/Aqr5cOTIMbKysvH29iLFlUrNmv551gkOqUZWVjbp6cfx/9MtE+IWElIjz8+xy3Ug389xSEgNkpL2ExJSg6ysbI4cOUpAgG8B6+4nKCgQ4IzblPwUC+dQLJxDsXAWL68KRN3QgrbdBueWXdu0Lr26tuTlQXfg71eFk9by+4lMJn20KLfO/oNH8PeripdXBbKzT3JxrUCSXO6+fU/SAWpfFMie5IN4eVXAz7cKB1PT2ZN8kNoXBeZu4+Ja1XPXEZ0bTqJYlF8VTJn7k7ZIJTXyIE9ay1qbaa39ylp7O3BJCX1mqQsLa8Gc2cuw1rJ+/c/4+lYhKKg67do1Y+WKH0lLSyctLZ2VK36kXbtmBAVVp2rVyqxf/zPWWubMXkanTi1LezfKlA5hjVmzehsAO3e4yMrMIqBa3nkp/tmwDrt3pbAncT+ZmVksWrCW9h0bY4yheYv6xC76EYB5c76nQ5h7Jub2HRszb457/onYRT9ybcsrlK0tRKNG9dixYy+7dyeTkZFJTEwcYWEt8tQJC2uZ+5SRhQtX0qqV+/iHhbUgJiaOjIxMdu9OZseOvTRuXK9Y25T8FAvnUCycQ7FwlrB2jfjfL3vZk3wwt6zzzcO5su0Armw7gPHvL+D18bPzJA7+EPfdFm7q6r5OuvPm9sxbtBaAmMVrufPm9gDc1LUly7/dklt+S4/WVKrkTZ3QmtS9NIQf1ieU9C6WGTo3nEOxKL8qePDlBKYkJoEzxtS31v7v72zDiSMPnnrqdX5YvZlDhw4TGBjA44/fTlZWNgC33d4Fay0jR0wmPn4dF1a+gNGjB9CoUT0AZn6xmMmT3Y9I6dvvX/Tu3RmATZu2M3jQOH7/PYPr2jfjhRf6Ou6P1PTMwicvOpcGP/M+a3/4H6mp6QQG+tHnkW50u7ElI4Z+wrZtiVSs6M0TT9/EtS2vYF9KKiNf+pS3Jz4KwIq4zbw55guys09yY6/WPNi3CwCJu/cz+Jn3OJx2jCuuqs3IV++jUqWKnDiRyYuDPmTb1kT8/Ksw+vUHqR1ao6jmnRO+FZ05D+ny5WsYPXoK2dkn6d27M/3738q4cf+lYcN6dOrUkhMnMnjmmTfZuvVX/P19eOutZwkNDQFg4sRpzJy5BC8vLwYPfogOHZoXuk05M8XCORQL5zhfY1H5kpdK7bM/eudxrmt9FTWq+ZKyP42Rb37BR9O+IfqNfqz+MYGp/11S4HpDnuzN0aO/85/oGABmffgsjzw3hSTXIf5xSRCfjH+cagE+bNiyg/v/PYGMjCwuuKAi7//nEZo0+AeHUtO5+7F32LErBYBnH+vJvbdeT1ZWNs8M/5hF32w4Z8fgdMd3DS+Vzz2T8/XccKLzNxb1nfWHj4ct2TPfY3/Tdr64a6kfqxJJHniCE5MH5yunJA/EuckDERFxntJMHkheTk0eiJS+8p08WLrXc8mDsItKP3lQUnMeiIiIiIiIiJy3nPKUBE9xyu0TIiIiIiIiIuJQGnkgIiIiIiIi4mHl7WkLSh6IiIiIiIiIeFgF3bYgIiIiIiIiIucTjTwQERERERER8bDyNvJAyQMRERERERERDytvw/zL2/6IiIiIiIiIiIdp5IGIiIiIiIiIhxndtiAiIiIiIiIiRSlnuQPdtiAiIiIiIiIiRdPIAxEREREREREP020LIiIiIiIiIlKk8jbMv7ztj4iIiIiIiIh4mEYeiIiIiIiIiHiYMba0m+BRSh6IiIiIiIiIeFg5m/LAuckDU+4OddnlU7F2aTdBRERE/qJju14s7SaIOI6lfH0TXNbpL76yxbHJAxEREREREZGySk9bEBEREREREZEilbPcgZ62ICIiIiIiIiJF08gDEREREREREQ+rUM6GHih5ICIiIiIiIuJh5Sx3oNsWRERERERERKRoGnkgIiIiIiIi4mF62oKIiIiIiIiIFKmc5Q6UPBARERERERHxtPKWPNCcByIiIiIiIiJSJI08EBEREREREfEwPapRRERERERERIpUznIHum1BRERERERERIqmkQciIiIiIiIiHmaMLe0meJSSByIiIiIiIiIeptsWRERERERERMQxjDEXGmNWG2M2GGO2GGOG55RfaoxZZYzZboyZZoypdLafoeTBXxAXt5bIyH6Eh/chOnpGvuUZGZk88cQYwsP7cMstA0lMdOUumzx5BuHhfYiM7Ed8/Lpib1MKNnjQONq0vpse3R8rcLm1llGjookI78ONPR5ny5ZfcpfNmhVLZERfIiP6MmtWbG755s0J9OjxOBHhfRg1Khpry9cwo5Kkc8M5FAvnUCycQ7FwjqSkfdxz91C6dnmM7t0e5+OP5uar4+7DpxAR3o8be/z7T334UiIj+hMZ0Z9Zs5bmlrv78AFEhPdj1Kgp6sOLSeeGM+i6tvwyxnOvYjgBhFlrmwBXAzcYY1oBY4C3rLX1gEPAg2e7P0oeFFN2djYjRkxi6tRhxMRMYN68OBISduWpM2PGIvz8fFi8OJr77oti7NgPAUhI2EVMTBwxMROYOnUYw4dPJDs7u1jblIL1uqkTU6YOK3R5XNxadu7Yy8JFkxkx8lGGD5sIQGrqESaM/5xp08cyfcYbTBj/OWlp6QAMHzaRESMeZeGiyezcsZf4uHWFbl9O0bnhHIqFcygWzqFYOIuXlxfPPX8/8xeM5/Npr/HpZwtISNidp467D09i4aKJjBj5CMOHTQL+6MOnMW36a0yf8ToTxk87rQ+fzIgRj7Bw0UR27khSH14MOjecQ9e15VcFD77OxLql57ytmPOyQBjwRU75R0DPv7M/HmeMqWSMuccY0znn/R3GmPHGmEeNMRVL4jNL2saN26lTpxahoSFUqlSRbt3aExu7Kk+dpUtX0atXJwAiI9vy3XcbsNYSG7uKbt3aU6lSRUJDQ6hTpxYbN24v1jalYNde2xB/f59Cl8fGriKqZ0eMMVx99ZUcPnyUlJSDrFixjjZtryYgwBd/fx/atL2a+Pi1pKQcJD39GE2bXokxhqieHVkS+/053KOyS+eGcygWzqFYOIdi4SxBQdVp0OByAHx8KnP5ZbVxuQ7kqRMbu5qontfn9OFXnNaH/0ibtk1O68ObEB+/roA+/HqWKB5npHPDOXRdK8VhjOljjFlz2qtPAXW8jDHrgRRgMfALkGqtzcqpkghcfLZtKKmRBx8A3YB/G2M+AW4BVgHXAlNL6DNLlMt1gJCQGrnvg4MD83V2LtcBatVy1/H29sLXtyqHDh0uYN0auFwHirVNOTsu1wFqhdTMfR8SEphzzA9S67RjHhIciMt1MF8sQkJqKBbFpHPDORQL51AsnEOxcK7ERBdbt/5Kkyb185Tn66tD/uirC+vDDxISEpivvhRN50bZoevassuTty1Ya6Ottc1Pe0X/+fOstdnW2quB2kAL4KoCmnXW97CUVPKgkbX2VqAXEAHcbK39BLgfaFrYSqdnU6Kjp5VQ085OQfcJmT/dfFJYnYLLi7dNOUsFnBLGfdALKC+ivpyRzg3nUCycQ7FwDsXCmY4ePc6AAWMYNPhBfHyq5F1YaF/9F8ulSDo3yhBd15ZZxoOvv8Jamwp8A7QCAowxfzxlsTaw92z3p6SSBxVyZnH0BaoA/jnlF+C+96JAp2dT+vS5tYSadnZCQmqQnLw/973LdYCgoOr56iQluetkZWVz5MhRAgJ8C1h3P0FBgcXappyd4JBAkpL35b5PTnYfW3f5qWOe7DpVfnoskpP3KxbFpHPDORQL51AsnEOxcJ7MzCwGDBhDjx4diIhonW95vr66WH34gXz1pWg6N8oOXddKcRhjahpjAnL+XxnoDGwFlgE351S7F5hztp9RUsmD94CfgfXAEGCGMWYK8APweQl9Zolq1KgeO3bsZffuZDIyMomJiSMsrEWeOmFhLXNnOV24cCWtWjXGGENYWAtiYuLIyMhk9+5kduzYS+PG9Yq1TTk7YWEtmDN7GdZa1q//GV/fKgQFVaddu2asXPEjaWnppKWls3LFj7Rr14ygoOpUrVqZ9et/xlrLnNnL6NSpZWnvRpmgc8M5FAvnUCycQ7FwFmstQ4eM5/LLanP//VEF1nH34d/k9OHb8PWtmtOHN2XlivWn9eHradeu6Wl9+LacPvwbOnVSPM5E50bZoevasuscP22hFrDMGLMR99/di62184DngKeMMQlAIO6/1c9uf0rqsR3GmIsArLV7czIgnYFd1trVxdvC/xz3PJHly9cwevQUsrNP0rt3Z/r3v5Vx4/5Lw4b16NSpJSdOZPD/7d17mJV1ufDx783gAQHhleLwJll5qjybmnnABDl4wEN00KvDrr13iJrHN00p31fRy06mW7dGDNaurtp5gOggtFHRBLzwiAQomlSgJAyJZwsHxvv9Yy1xYIYRas2sp7W+H691MfOs3zzP/ax7Hn9r7vX7/Z4LL7yGJUv+SJ8+vbj22osYPHggABMn3sLUqXfR0NDA+PH/zlFHHbTZfRZN/v3TYjrNBRd8m4ceXMwLL7xMv359Ofvs01i/vgWAU087lszkigmTmDNnPtv32I6rrjqHffbZHYCpU+5k0qTSrYNOH/dJxow5BoBFi55i/CXXsXZtM0cOOZBLLz29cEO8YqsHLXWNer02ishcFIe5KI56zUXSUu0Q2njk4cf59KfHs8ceu9CtW6lPO/+Cz7Dy2dKnpKeeNqrchzdu0ofvBsDUKXcxaVJp0fDTx32CMWNKi/ktWrSU8Zdcz9q1r3PkkA9x6aVfLFQfHjRUO4R21eO14fvaYgn2LF5QFbTitV9X7Bdu556jq/5adVrx4B9XvOJBvSri/2TrVVGLB5Kk4ili8aBeFbV4UI98X1ssFg+2XBGKB93fvokkSZIkSdoa3ar+535lWTyQJEmSJKnCaqx20GkLJkqSJEmSpBrhyANJkiRJkiosorbW2LB4IEmSJElShTltQZIkSZIk1RVHHkiSJEmSVGFRY0MPLB5IkiRJklRhNVY7cNqCJEmSJEnqmCMPJEmSJEmqsFr7pN7igSRJkiRJFVZrax7UWjFEkiRJkiRVmCMPJEmSJEmquNoaemDxQJIkSZKkCosaKx44bUGSJEmSJHXIkQeSJEmSJFVYRG19Vl/Y4kGS1Q5BkiRJUg3JXF/tENRKrd2NoK3aOsHaKoVIkiRJkqSKK+zIA0mSJEmS/lnV2oKJFg8kSZIkSaq42ioeOG1BkiRJkiR1yJEHkiRJkiRVmHdbkCRJkiRJb8NpC5IkSZIkqY448kCSJElYal3sAAATmUlEQVSSpArzbguSJEmSJKlDtVY8cNqCJEmSJEnqkCMPJEmSJEmquNr6rN7igSRJkiRJFRbhtAVJkiRJklRHHHkgSZIkSVLF1dbIA4sHkiRJkiRVmHdbkCRJkiRJdcXiwVYYf8l1HPaRzzL6hC+1+3xmcuWVjYwYPpYTR5/NY4/9YcNz06bNYuSI0xk54nSmTZu1YfvixUsZPfpsRgwfy5VXNpKZnX4etcBcFMvs2Y8wcuQ4hg8fS2PjbW2eb25ex3nnfZPhw8fyiU/8H1asaNrw3KRJtzF8+FhGjhzHnDnzt3ifap+5KA5zURzmojhWrvwLn/vs1zju2C9xwvFn8+Mf/bpNm1IfPpkRw8dx4uhzN+nD72bkiDMYOeIMpk27e8P2Uh9+DiOGj+PKKyfbh28hr41ieP31Zj75iYs4+aTzOeGEc/nP629u06a5eR3nn381I0ecyac++RX+vGL1hucaJ01l5IgzOXbUl5g759EN2+fMmc+xo77EyBFnMrnx511yLtpUtwo+qq8YUfyTOOVjw5h802WbfX727EdYvuxZZt4xiQlXnMXll00E4MUXX+HGG27mlluv5tbbvsONN9zMSy+9CsDll01kwoSzmHnHJJYve5Y5s+dvdv96i7kojpaWFiZM+B433XQZ06ffyO23z2bp0qc3anPbbXew4469uPPORj7/+ZO4+uofArB06dNMnz6b6dNv5KabLuPyyyfS0tKyRftUW+aiOMxFcZiLYmloaOArF3+BGb+5gZtv+RY//e/fsHTpMxu1KfXhK5l5x0QmXHEml1/2PeDNPvwWbrn1W9x627e58YZbWvXhk5gw4Uxm3jGR5ctW2odvAa+N4th22234rx9ezi9+eS3Tpn2HuXMfZcGCJzdqM2XKXfTZsRcz7/gun/uX0Vz9nR8DsHTpM8yYMZdf334dk2+6lAkTGjfk4ooJk2mc/DV+fft1TJ8+p821ps4XFfyvCDqteBARu0bElyPiuoj4TkSMi4g+nXW8rnDwwXvTp0+vzT4/a9YDnHTy0UQE++//fl5++TVWr36euXPnc9jh+9O3b2/69OnFYYfvz5w5j7B69fO8+upfOeCA9xMRnHTy0dw16/4uPKN/XuaiOBYufIpddhnE4MED2XbbbTj++CHMmvXARm3uvvsBTjllGAAjRx7OvHm/IzOZNesBjj9+CNtuuw2DBw9kl10GsXDhU1u0T7VlLorDXBSHuSiW/v13Yq+9dgWgV68e7Pq+nWlqWrNRm1mzHuSkkz9a7sP3bNWHP8phh+/Xqg/fjzlz5rfTh3+Uu8zH2/LaKI6IoGfPHgCsX9/CuvXr29zi7+5ZD3HSyUcDMHLkR7h/3iIyk7tnPchxxx3Btttuw847D+Dd7x7EwoVLWbhwKe9+91u5OO64I7h71oNdfm6qLZ1SPIiIc4DvAdsDBwM9gMHAvIj4aGccswiamtYwaOA7N3w/cGA/mprW0NT0PIMGvuOt7QP60dT0PE1NaxjYevvAd7TpQPX3MRddZ9PXbsCAfm1eu6amNQwaVGrTvXsDvXv35IUXXm7nZ99RztPb71NtmYviMBfFYS6Ka8WKJpYs+SP77bfHRtvb9NUD3+yrN9eHP8/Agf3atFfHvDaKpaWlhVNOvoAjDv8Chx22X9vrYvUaBg0q/Z6XcrEDL774Sun3f1Cr13xgP1Y3rWF10xoGDuq30Xavi64XERV7FEFnjTz4IjAqM68EjgE+mJlfBUYB13bSMauvnel1EQHtzLuL6KC9/nHmosu0N69009duc23a375l+1Rb5qI4zEVxmItieu21v3HOOd/kkvH/Rq9eO2z85Gb76q3crg55bRRLQ0MD035xDff8djKLFi7l979fvtHz7S/jEWQ7b2Ijor23tl4XVREVfFRfZ6558OZtILcDegNk5tPANpv7gYgYGxEPR8TDjY23dGJonWPAwH6sXPWXDd+vWrWG/v13Km9/7q3tTW9tX9V6+6rn6N9/py6NuVaZi64zcOA7Nnrtmsqv6aZtVq4stVm/voVXXnmNvn17t/Ozz9G/f78t2qfaMhfFYS6Kw1wUz7p16znnnG8yevRRjBjxkTbPt+mrt6gPX9OmvTrmtVFMO+7Yk0MO2WujhQ+hNNJm5crS73kpF3+lb99eDBzQj1UrW73mq9bwzv47MWBAP1atXLPRdnPR9YJuFXsUQWdFcRPwUEQ0AvOAGwAi4p3AZsfLZGZjZh6UmQeNHfupTgqt8wwdegi//MU9ZCYLFjxB79470L//ThxxxIHcN/dRXnrpVV566VXum/soRxxxIP3770TPnj1YsOAJMpNf/uIehg37cLVPoyaYi66zzz67s2zZszzzzCqam9cxffpshg49ZKM2Q4d+eMOdLWbOvI9DD92XiGDo0EOYPn02zc3reOaZVSxb9iz77rv7Fu1TbZmL4jAXxWEuiiUz+dpXb2DX9+3MF75wUrttSn34b8t9+JP07t2z3IcfwH1zF7TqwxdwxBEHtOrDnyz34b9l2DDz8Xa8Norj+edf4uWXXwNg7drXmTdvIe99384btTl66MH88hf3ADBz5jwOPXQfIoKjhx7MjBlzaW5ex4oVTSxfvpJ9992NffbZjeXLV7JiRRPNzeuYMWMuRw89uMvPTbUlOutWNhGxF/ABYHFmPrG1P588Wbh77Fxwwbd56MHFvPDCy/Tr15ezzz6N9etbADj1tGPJTK6YMIk5c+azfY/tuOqqc9hnn90BmDrlTiZNKt2u5vRxn2TMmGMAWLToKcZfch1r1zZz5JADufTS0x3etQXqNRdFWWl1U/fe+zBXXTWZlpY3GDPmGM4441Ncd91P2Hvv3Rk27MO8/nozF154DUuW/JE+fXpx7bUXMXjwQAAmTryFqVPvoqGhgfHj/52jjjpos/vU2zMXxWEuiqNec5G0VDuENh55+HE+/enx7LHHLnTrVurTzr/gM6x8tvTJ6amnjSr34Y2b9OG7ATB1yl1MmjQFgNPHfYIxY0qL+S1atJTxl1zP2rWvc+SQD3HppV8sVB8eNFQ7hHbV47XxRq6rdghtPPnkMi65+D9paXmDN/INRo06nLPO+iTXX/8z9t57V4YOPYTXX2/mKxddx5Ilf6JPn15855oLNuTie9+bws+nzqKhoYFLxv8rQ4YcCMC99z7C16/6AW+88QYfGzOMceM+Xs3TbFe32Ks4F2oneL3loYr9Tbtdw8FVf606rXjwjypi8UCqtqIWDyRJxVPE4kG9KmrxoB4VsXhQz2q9eND8xsMV+5t2224HVf21KsbkCUmSJEmSVFjd376JJEmSJEnaOlUfLFBRFg8kSZIkSaqwotwloVJq62wkSZIkSVLFOfJAkiRJkqSKc9qCJEmSJEnqQK3dKc1pC5IkSZIkqUOOPJAkSZIkqcIiamvkgcUDSZIkSZIqrrYG+tfW2UiSJEmSpIpz5IEkSZIkSRVWawsmWjyQJEmSJKniaqt44LQFSZIkSZLUIYsHkiRJkiRVWERU7LEFxxoVEU9GxNKIuLgzzsdpC5IkSZIkVVzXfFYfEQ3AjcBwYAXwUET8KjMfr+RxHHkgSZIkSdI/r0OApZn5x8xsBm4GTqr0QRx5IEmSJElShXXh3RbeBTzT6vsVwIcrfZDCFg+CPWtiacqIGJuZjdWOQ+aiSMxFcZiLYjEfxVELuaiJN1LURi5qRS3koluNXBi1kIv6sEfFfuMiYiwwttWmxla/A+0dJyt17Dc5baHzjX37Juoi5qI4zEVxmItiMR/FYS6Kw1wUh7koDnNRZzKzMTMPavVoXTxaAQxu9f3OwLOVjsHigSRJkiRJ/7weAnaPiPdGxLbAqcCvKn2Qwk5bkCRJkiRJHcvM9RHxJWAm0AD8IDMfq/RxLB50PuciFYe5KA5zURzmoljMR3GYi+IwF8VhLorDXGgjmTkDmNGZx4jMiq+jIEmSJEmSaohrHkiSJEmSpA5ZPOgkEfGDiFgdEYurHUu9i4jBEXFPRCyJiMci4txqx1SvImL7iHgwIn5XzsXl1Y6p3kVEQ0Q8GhG3VzuWehYRyyJiUUQsiIiHqx1PPYuIvhExJSKeKPcbH6l2TPUqIvYsXxNvPl6OiPOqHVe9iojzy3334oj4WURsX+2Y6lVEnFvOw2NeE+pKTlvoJBExBHgV+HFm7l3teOpZRAwCBmXm/IjoDTwCnJyZj1c5tLoTEQH0zMxXI2IbYC5wbmbeX+XQ6lZEXAAcBOyYmSdUO556FRHLgIMy87lqx1LvIuJHwJzMvKm8YvUOmfliteOqdxHRAPwZ+HBmLq92PPUmIt5Fqc/+YGb+LSJuBWZk5g+rG1n9iYi9gZuBQ4Bm4H+AMzLzqaoGprrgyINOkpmzgeerHYcgM1dm5vzy168AS4B3VTeq+pQlr5a/3ab8sIJZJRGxM3A8cFO1Y5GKICJ2BIYA3wfIzGYLB4UxDPiDhYOq6g70iIjuwA50wj3ktUU+ANyfmX/NzPXAvcApVY5JdcLigepKRLwHOAB4oLqR1K/yMPkFwGrgzsw0F9XzH8BFwBvVDkQkcEdEPBIRY6sdTB17H/AX4L/K03luioie1Q5KQOme5T+rdhD1KjP/DFwNPA2sBF7KzDuqG1XdWgwMiYh+EbEDcBwwuMoxqU5YPFDdiIhewFTgvMx8udrx1KvMbMnM/YGdgUPKw+/UxSLiBGB1Zj5S7VgEwOGZeSBwLHBWeeqbul534EBgYmYeALwGXFzdkFSePnIicFu1Y6lXEfG/gJOA9wL/G+gZEZ+pblT1KTOXAN8E7qQ0ZeF3wPqqBqW6YfFAdaE8v34q8NPM/Hm14xGUhwL/FhhV5VDq1eHAieW59jcDQyPiJ9UNqX5l5rPlf1cD0yjNZVXXWwGsaDUiagqlYoKq61hgfmY2VTuQOnYM8KfM/EtmrgN+DhxW5ZjqVmZ+PzMPzMwhlKZJu96BuoTFA9W88iJ93weWZOY11Y6nnkXEOyOib/nrHpTejDxR3ajqU2Zekpk7Z+Z7KA0Hvjsz/RSpCiKiZ3kxV8pD5EdQGpaqLpaZq4BnImLP8qZhgIvrVt9pOGWh2p4GDo2IHcrvq4ZRWkNKVRAR/cv/vhv4GF4f6iLdqx1ArYqInwEfBd4RESuA/5eZ369uVHXrcOCzwKLyXHuA8Zk5o4ox1atBwI/Kq2Z3A27NTG8RqHo3AJhWej9Od+C/M/N/qhtSXTsb+Gl5qPwfgS9UOZ66Vp7TPRw4vdqx1LPMfCAipgDzKQ2RfxRorG5UdW1qRPQD1gFnZeYL1Q5I9cFbNUqSJEmSpA45bUGSJEmSJHXI4oEkSZIkSeqQxQNJkiRJktQhiweSJEmSJKlDFg8kSZIkSVKHLB5IkupORLRExIKIWBwRt5VvB/f37uujEXF7+esTI+LiDtr2jYgz/45jXBYRX97Mc58rn8djEfH4m+0i4ocR8fGtPZYkSVJ7LB5IkurR3zJz/8zcG2gGxrV+Mkq2uo/MzF9l5jc6aNIX2OriweZExLHAecCIzNwLOBB4qVL7lyRJepPFA0lSvZsD7BYR74mIJRHxXWA+MDgiRkTEvIiYXx6h0AsgIkZFxBMRMRf42Js7iojPR8QN5a8HRMS0iPhd+XEY8A1g1/Koh2+X210YEQ9FxMKIuLzVvr4aEU9GxF3AnpuJ/RLgy5n5LEBmrs3MyZs2ioj/Wz7G4ohojIgobz+nPFphYUTcXN52VDm+BRHxaET0/gdfX0mSVAMsHkiS6lZEdAeOBRaVN+0J/DgzDwBeA74GHJOZBwIPAxdExPbAZGA0cCQwcDO7vx64NzP3ozQi4DHgYuAP5VEPF0bECGB34BBgf+BDETEkIj4EnAocQKk4cfBmjrE38MgWnOoNmXlweaRFD+CE8vaLgQMyc1/eGn3xZeCszNy/fH5/24L9S5KkGmfxQJJUj3pExAJKBYGnge+Xty/PzPvLXx8KfBC4r9z2X4BdgPcDf8rMpzIzgZ9s5hhDgYkAmdmSme1NJxhRfjxKabTD+ykVE44EpmXmXzPzZeBX/9DZwtER8UBELCrHtVd5+0LgpxHxGWB9edt9wDURcQ7QNzPXt92dJEmqN92rHYAkSVXwt/In6xuUR/K/1noTcGdmnrZJu/2BrFAcAXw9MydtcozztvAYjwEfAu7e7AFKIyW+CxyUmc9ExGXA9uWnjweGACcCl0bEXpn5jYiYDhwH3B8Rx2TmE1t5XpIkqcY48kCSpPbdDxweEbsBRMQOEbEH8ATw3ojYtdzutM38/CzgjPLPNkTEjsArQOs1BGYC/9pqLYV3RUR/YDZwSkT0KK85MHozx/g68K2IGFj++e3KIwZae7NQ8Fz5OB8vt+0GDM7Me4CLKC3m2Csids3MRZn5TUojM97f0YskSZLqgyMPJElqR2b+JSI+D/wsIrYrb/5aZv4+IsYC0yPiOWAupbUHNnUu0BgR/wa0AGdk5ryIuC8iFgO/Ka978AFgXnnkw6vAZzJzfkTcAiwAllNa1LG9GGdExADgrvIiiAn8YJM2L0bEZErrOiwDHio/1QD8JCL6UBoBcW257RURcXQ55seB32zdKydJkmpRlKZrSpIkSZIktc9pC5IkSZIkqUMWDyRJkiRJUocsHkiSJEmSpA5ZPJAkSZIkSR2yeCBJkiRJkjpk8UCSJEmSJHXI4oEkSZIkSeqQxQNJkiRJktSh/w86HJXIM0/3wgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a33738710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------- Precision matrix (Columm Sum=1) --------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a3371ac88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------- Recall matrix (Row sum=1) --------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1ff49e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Refer:http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.VotingClassifier.html\n",
    "from sklearn.ensemble import VotingClassifier\n",
    "vclf = VotingClassifier(estimators=[('lr', sig_clf1), ('svc', sig_clf2), ('rf', sig_clf3)], voting='soft')\n",
    "vclf.fit(train_x_onehotCoding, train_y)\n",
    "print(\"Log loss (train) on the VotingClassifier :\", log_loss(train_y, vclf.predict_proba(train_x_onehotCoding)))\n",
    "print(\"Log loss (CV) on the VotingClassifier :\", log_loss(cv_y, vclf.predict_proba(cv_x_onehotCoding)))\n",
    "print(\"Log loss (test) on the VotingClassifier :\", log_loss(test_y, vclf.predict_proba(test_x_onehotCoding)))\n",
    "print(\"Number of missclassified point :\", np.count_nonzero((vclf.predict(test_x_onehotCoding)- test_y))/test_y.shape[0])\n",
    "plot_confusion_matrix(test_y=test_y, predict_y=vclf.predict(test_x_onehotCoding))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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