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Personalized Medicine.ipynb
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{
"cells": [
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" <script type=\"text/javascript\">\n",
" window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
" if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
" if (typeof require !== 'undefined') {\n",
" require.undef(\"plotly\");\n",
" requirejs.config({\n",
" paths: {\n",
" 'plotly': ['https://cdn.plot.ly/plotly-2.20.0.min']\n",
" }\n",
" });\n",
" require(['plotly'], function(Plotly) {\n",
" window._Plotly = Plotly;\n",
" });\n",
" }\n",
" </script>\n",
" "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Loading all required packages\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 import classification_report, 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.model_selection 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",
"from chart_studio import plotly\n",
"from plotly import __version__\n",
"import plotly.graph_objs as go\n",
"import cufflinks as cf\n",
"cf.go_offline()\n",
"from plotly.offline import download_plotlyjs,init_notebook_mode,plot,iplot\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": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## Loading Data Files"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true,
"jupyter": {
"outputs_hidden": true
}
},
"outputs": [],
"source": [
"# Loading training_variants. Its a comma seperated file\n",
"data_variants = pd.read_csv('training_variants')\n",
"\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": 5,
"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",
" <tr>\n",
" <th>3</th>\n",
" <td>3</td>\n",
" <td>CBL</td>\n",
" <td>N454D</td>\n",
" <td>3</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",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>5</td>\n",
" <td>CBL</td>\n",
" <td>V391I</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>6</td>\n",
" <td>CBL</td>\n",
" <td>V430M</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>7</td>\n",
" <td>CBL</td>\n",
" <td>Deletion</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>8</td>\n",
" <td>CBL</td>\n",
" <td>Y371H</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>9</td>\n",
" <td>CBL</td>\n",
" <td>C384R</td>\n",
" <td>4</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",
"5 5 CBL V391I 4\n",
"6 6 CBL V430M 5\n",
"7 7 CBL Deletion 1\n",
"8 8 CBL Y371H 4\n",
"9 9 CBL C384R 4"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Exploring data_variants\n",
"data_variants.head(10)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"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",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 ID 3321 non-null int64 \n",
" 1 Gene 3321 non-null object\n",
" 2 Variation 3321 non-null object\n",
" 3 Class 3321 non-null int64 \n",
"dtypes: int64(2), object(2)\n",
"memory usage: 103.9+ KB\n"
]
}
],
"source": [
"#Getting data information\n",
"data_variants.info()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
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" vertical-align: middle;\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>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": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Description for numeric columns\n",
"data_variants.describe()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(3321, 4)"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Checking dimension of data\n",
"data_variants.shape"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['ID', 'Gene', 'Variation', 'Class'], dtype='object')"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Checking column in above data_variants\n",
"data_variants.columns"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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" <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",
" <tr>\n",
" <th>3</th>\n",
" <td>3</td>\n",
" <td>Recent evidence has demonstrated that acquired...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4</td>\n",
" <td>Oncogenic mutations in the monomeric Casitas B...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>5</td>\n",
" <td>Oncogenic mutations in the monomeric Casitas B...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>6</td>\n",
" <td>Oncogenic mutations in the monomeric Casitas B...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>7</td>\n",
" <td>CBL is a negative regulator of activated recep...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>8</td>\n",
" <td>Abstract Juvenile myelomonocytic leukemia (JM...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>9</td>\n",
" <td>Abstract Juvenile myelomonocytic leukemia (JM...</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...\n",
"3 3 Recent evidence has demonstrated that acquired...\n",
"4 4 Oncogenic mutations in the monomeric Casitas B...\n",
"5 5 Oncogenic mutations in the monomeric Casitas B...\n",
"6 6 Oncogenic mutations in the monomeric Casitas B...\n",
"7 7 CBL is a negative regulator of activated recep...\n",
"8 8 Abstract Juvenile myelomonocytic leukemia (JM...\n",
"9 9 Abstract Juvenile myelomonocytic leukemia (JM..."
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Exploring data_text\n",
"data_text.head(10)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"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",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 ID 3321 non-null int64 \n",
" 1 TEXT 3316 non-null object\n",
"dtypes: int64(1), object(1)\n",
"memory usage: 52.0+ KB\n"
]
}
],
"source": [
"#Getting data information\n",
"data_text.info()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
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" <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",
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"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": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Description for numeric columns\n",
"data_text.describe()"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['ID', 'TEXT'], dtype='object')"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Checking column in above data_text\n",
"data_text.columns"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(3321, 2)"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Checking the dimensions\n",
"data_text.shape"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([1, 2, 3, 4, 5, 6, 7, 8, 9], dtype=int64)"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# We are trying to predict the class of cancer,hence we will check all the unique values that can be determined\n",
"data_variants.Class.unique()"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": true,
"jupyter": {
"outputs_hidden": true
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"[nltk_data] Downloading package stopwords to C:\\Users\\ANUJ\n",
"[nltk_data] OJHA\\AppData\\Roaming\\nltk_data...\n",
"[nltk_data] Package stopwords is already up-to-date!\n"
]
}
],
"source": [
"# We would like to remove all stop words like a, is, an, the, ... so we are collecting all of them from nltk(Natural Language\n",
"# Toolkit) library\n",
"import nltk\n",
"nltk.download('stopwords')\n",
"stop_words = set(stopwords.words('english'))"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": true,
"jupyter": {
"outputs_hidden": true
}
},
"outputs": [],
"source": [
"def data_text_preprocess(total_text, ind, col):\n",
" # Removing integer values from text data as that might not be important\n",
" if type(total_text) is not int:\n",
" string = \"\"\n",
" # replacing all special characters 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",
" # bringing the whole text to lower-case\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": 18,
"metadata": {
"collapsed": true,
"jupyter": {
"outputs_hidden": true
}
},
"outputs": [],
"source": [
"for index, row in data_text.iterrows():\n",
" if type(row['TEXT']) is str:\n",
" data_text_preprocess(row['TEXT'], index, 'TEXT')"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
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" <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",
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" <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",
" <tr>\n",
" <th>5</th>\n",
" <td>5</td>\n",
" <td>CBL</td>\n",
" <td>V391I</td>\n",
" <td>4</td>\n",
" <td>oncogenic mutations monomeric casitas b lineag...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>6</td>\n",
" <td>CBL</td>\n",
" <td>V430M</td>\n",
" <td>5</td>\n",
" <td>oncogenic mutations monomeric casitas b lineag...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>7</td>\n",
" <td>CBL</td>\n",
" <td>Deletion</td>\n",
" <td>1</td>\n",
" <td>cbl negative regulator activated receptor tyro...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>8</td>\n",
" <td>CBL</td>\n",
" <td>Y371H</td>\n",
" <td>4</td>\n",
" <td>abstract juvenile myelomonocytic leukemia jmml...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>9</td>\n",
" <td>CBL</td>\n",
" <td>C384R</td>\n",
" <td>4</td>\n",
" <td>abstract juvenile myelomonocytic leukemia jmml...</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",
"5 5 CBL V391I 4 \n",
"6 6 CBL V430M 5 \n",
"7 7 CBL Deletion 1 \n",
"8 8 CBL Y371H 4 \n",
"9 9 CBL C384R 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... \n",
"5 oncogenic mutations monomeric casitas b lineag... \n",
"6 oncogenic mutations monomeric casitas b lineag... \n",
"7 cbl negative regulator activated receptor tyro... \n",
"8 abstract juvenile myelomonocytic leukemia jmml... \n",
"9 abstract juvenile myelomonocytic leukemia jmml... "
]
},
"execution_count": 19,
"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(10)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"# Cleaning the data, handling missing values"
]
},
{
"cell_type": "code",
"execution_count": 20,
"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",
" <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": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"result[result.isnull().any(axis=1)]"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": true,
"jupyter": {
"outputs_hidden": true
}
},
"outputs": [],
"source": [
"result.loc[result['TEXT'].isnull(),'TEXT'] = result['Gene'] +' '+result['Variation']"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>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": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Cross checking the null values after imputation\n",
"result[result.isnull().any(axis=1)]"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": true,
"jupyter": {
"outputs_hidden": true
}
},
"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": "code",
"execution_count": 24,
"metadata": {
"collapsed": true,
"jupyter": {
"outputs_hidden": true
}
},
"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",
"\n",
"# split the train data now into train 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": 25,
"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])"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"# Distribution of data in train, test and validation set"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"8 12\n",
"9 24\n",
"3 57\n",
"5 155\n",
"6 176\n",
"2 289\n",
"1 363\n",
"4 439\n",
"7 609\n",
"Name: Class, dtype: int64"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_class_distribution = train_df['Class'].value_counts().sort_values()\n",
"train_class_distribution"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"data": {
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"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"class_counts = train_class_distribution.value_counts()\n",
"class_percentages = class_counts / class_counts.sum() * 100\n",
"class_counts.plot(kind='bar', color='red')\n",
"plt.xlabel('Class')\n",
"plt.ylabel('Number of Data Points per Class')\n",
"plt.title('Distribution of yi in train data')\n",
"plt.xticks(rotation=0)\n",
"plt.grid()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of data points in class 9 : 609 ( 28.672 %)\n",
"Number of data points in class 8 : 439 ( 20.669 %)\n",
"Number of data points in class 7 : 363 ( 17.09 %)\n",
"Number of data points in class 6 : 289 ( 13.606 %)\n",
"Number of data points in class 5 : 176 ( 8.286 %)\n",
"Number of data points in class 4 : 155 ( 7.298 %)\n",
"Number of data points in class 3 : 57 ( 2.684 %)\n",
"Number of data points in class 2 : 24 ( 1.13 %)\n",
"Number of data points in class 1 : 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": "code",
"execution_count": null,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"8 4\n",
"9 7\n",
"3 18\n",
"5 48\n",
"6 55\n",
"2 91\n",
"1 114\n",
"4 137\n",
"7 191\n",
"Name: Class, dtype: int64"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test_class_distribution = test_df['Class'].value_counts().sort_values()\n",
"test_class_distribution"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
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"Number of data points in class 9 : 191 ( 28.722 %)\n",
"Number of data points in class 8 : 137 ( 20.602 %)\n",
"Number of data points in class 7 : 114 ( 17.143 %)\n",
"Number of data points in class 6 : 91 ( 13.684 %)\n",
"Number of data points in class 5 : 55 ( 8.271 %)\n",
"Number of data points in class 4 : 48 ( 7.218 %)\n",
"Number of data points in class 3 : 18 ( 2.707 %)\n",
"Number of data points in class 2 : 7 ( 1.053 %)\n",
"Number of data points in class 1 : 4 ( 0.602 %)\n"
]
}
],
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]
},
"metadata": {},
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}
],
"source": [
"df_train= pd.DataFrame(cv_class_distribution)\n",
"df_train.iplot(kind = 'bar',xTitle='Class',yTitle='Data points',title = 'Validation Class Distribution',color = 'blue')"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'cv_class_distribution' is not defined",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32mC:\\Users\\ANUJOJ~1\\AppData\\Local\\Temp/ipykernel_2760/85583956.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[0msorted_yi\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0margsort\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m-\u001b[0m\u001b[0mtrain_class_distribution\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msort_values\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0msorted_yi\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Number of data points in class'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mi\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m':'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mcv_class_distribution\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'('\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mround\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcv_class_distribution\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m/\u001b[0m\u001b[0mcv_df\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;36m100\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m3\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'%)'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;31mNameError\u001b[0m: name 'cv_class_distribution' is not defined"
]
}
],
"source": [
"sorted_yi = np.argsort(-train_class_distribution.sort_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), '%)')"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"Looking at the data point distribution for train, test and validation data we can safely say that the data distribution is similar in all the sets."
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## Building a random model"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"jupyter": {
"outputs_hidden": true
}
},
"outputs": [],
"source": [
"test_data_len = test_df.shape[0]\n",
"cv_data_len = cv_df.shape[0]"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"### Log loss of validation set "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Log loss on Cross Validation Data using Random Model 2.489739234636341\n"
]
}
],
"source": [
"# Creating an output array that has exactly same size as the CV data\n",
"cv_predicted_y = np.zeros((cv_data_len,9))\n",
"\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))"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"### Log loss of test set"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Log loss on Test Data using Random Model 2.488292493876385\n"
]
}
],
"source": [
"# Creating an 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))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
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" 7, 0, 6, 0, 3, 7, 2, 7, 2, 6, 1, 6, 1, 1, 6, 5, 8, 5, 7, 3, 6, 0,\n",
" 3, 7, 6, 6, 3, 4, 1, 7, 0, 2, 4, 3, 4, 2, 8, 4, 8, 7, 3, 7, 5, 2,\n",
" 8, 0, 6, 7, 2, 3, 5, 7, 0, 3, 6, 5, 7, 1, 0, 8, 8, 3, 8, 8, 1, 7,\n",
" 7, 6, 5, 3, 8, 2, 7, 6, 7, 4, 6, 4, 0, 5, 2, 4, 6, 5, 5, 6, 0, 1,\n",
" 5, 6, 0, 4, 4, 3, 0, 4, 8, 5, 6, 0, 3, 7, 3, 2, 5, 7, 1, 5, 7, 4,\n",
" 4, 4, 4, 5, 3, 1, 2, 5, 0, 1, 8, 4, 3, 6, 5, 1, 1, 5, 8, 1, 0, 1,\n",
" 3, 8, 0, 0, 8], dtype=int64)"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Getting the index of max probablity\n",
"predicted_y =np.argmax(test_predicted_y, axis=1)\n",
"\n",
"predicted_y"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"The index value ranges from 0 to 8. To rectify the value is increased by 1"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"jupyter": {
"outputs_hidden": true
}
},
"outputs": [],
"source": [
"predicted_y = predicted_y + 1"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## Confusion Matrix"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"jupyter": {
"outputs_hidden": true
}
},
"outputs": [],
"source": [
"C = confusion_matrix(y_test, predicted_y)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
"<Figure size 1440x504 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"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()"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## Precision Matrix"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"jupyter": {
"outputs_hidden": true
}
},
"outputs": [],
"source": [
"B =(C/C.sum(axis=0))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 1440x504 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"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()"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## Recall Matrix"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"jupyter": {
"outputs_hidden": true
}
},
"outputs": [],
"source": [
"A =(((C.T)/(C.sum(axis=1))).T)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 1440x504 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"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()"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## Evaluating Gene Column\n",
"\n",
"Determining if the independent column gene is relevant to the target variable."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of Unique Genes : 234\n",
"BRCA1 164\n",
"TP53 107\n",
"EGFR 95\n",
"BRCA2 81\n",
"PTEN 81\n",
"KIT 65\n",
"BRAF 54\n",
"ERBB2 48\n",
"ALK 43\n",
"PIK3CA 35\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",
"\n",
"# Top 10 genes\n",
"print(unique_genes.head(10))"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"Cumulative distribution of unique Genes values"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 720x432 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.set_style('whitegrid')\n",
"s = sum(unique_genes.values);\n",
"h = unique_genes.values/s;\n",
"c = np.cumsum(h)\n",
"plt.figure(figsize =(10,6))\n",
"plt.plot(c,label='Cumulative distribution of Genes')\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"jupyter": {
"outputs_hidden": true
}
},
"outputs": [],
"source": [
"# One-hot encoding of gene feature\n",
"gene_vectorizer = CountVectorizer()\n",
"# Train gene feature\n",
"train_gene_feature_onehotCoding = gene_vectorizer.fit_transform(train_df['Gene'])\n",
"# Test gene feature\n",
"test_gene_feature_onehotCoding = gene_vectorizer.transform(test_df['Gene'])\n",
"# CV gene feature\n",
"cv_gene_feature_onehotCoding = gene_vectorizer.transform(cv_df['Gene'])"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"### Column generated after one hot encoding"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(2124, 234)"
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_gene_feature_onehotCoding.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"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",
" 'arid1b',\n",
" 'arid2',\n",
" 'arid5b',\n",
" 'atm',\n",
" 'atr',\n",
" 'atrx',\n",
" 'aurka',\n",
" 'axin1',\n",
" 'axl',\n",
" 'b2m',\n",
" 'bap1',\n",
" 'bard1',\n",
" 'bcl10',\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",
" 'ccnd3',\n",
" 'ccne1',\n",
" 'cdh1',\n",
" 'cdk12',\n",
" 'cdk4',\n",
" 'cdk8',\n",
" 'cdkn1a',\n",
" 'cdkn1b',\n",
" 'cdkn2a',\n",
" 'cdkn2b',\n",
" 'chek2',\n",
" 'cic',\n",
" 'crebbp',\n",
" 'ctcf',\n",
" 'ctnnb1',\n",
" 'ddr2',\n",
" 'dicer1',\n",
" 'dnmt3a',\n",
" 'dnmt3b',\n",
" 'dusp4',\n",
" 'egfr',\n",
" 'eif1ax',\n",
" 'elf3',\n",
" 'ep300',\n",
" 'epas1',\n",
" 'epcam',\n",
" 'erbb2',\n",
" 'erbb3',\n",
" 'erbb4',\n",
" 'ercc2',\n",
" 'ercc4',\n",
" 'erg',\n",
" 'errfi1',\n",
" 'esr1',\n",
" 'etv1',\n",
" 'etv6',\n",
" 'ewsr1',\n",
" 'ezh2',\n",
" 'fam58a',\n",
" 'fanca',\n",
" 'fancc',\n",
" 'fat1',\n",
" 'fbxw7',\n",
" 'fgf3',\n",
" 'fgf4',\n",
" 'fgfr1',\n",
" 'fgfr2',\n",
" 'fgfr3',\n",
" 'fgfr4',\n",
" 'flt3',\n",
" 'foxa1',\n",
" 'foxl2',\n",
" 'foxo1',\n",
" 'foxp1',\n",
" 'gata3',\n",
" 'gna11',\n",
" 'gnaq',\n",
" 'gnas',\n",
" 'h3f3a',\n",
" 'hist1h1c',\n",
" 'hla',\n",
" 'hnf1a',\n",
" 'hras',\n",
" 'idh1',\n",
" 'idh2',\n",
" 'igf1r',\n",
" 'ikbke',\n",
" 'jak1',\n",
" 'jak2',\n",
" 'jun',\n",
" 'kdm5a',\n",
" 'kdm5c',\n",
" 'kdm6a',\n",
" 'kdr',\n",
" 'keap1',\n",
" 'kit',\n",
" 'klf4',\n",
" 'kmt2a',\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",
" 'mdm4',\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",
" '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",
" 'pax8',\n",
" 'pbrm1',\n",
" 'pdgfra',\n",
" 'pdgfrb',\n",
" 'pik3ca',\n",
" 'pik3cb',\n",
" 'pik3cd',\n",
" 'pik3r1',\n",
" 'pik3r2',\n",
" 'pik3r3',\n",
" 'pim1',\n",
" 'pms1',\n",
" 'pms2',\n",
" 'pole',\n",
" 'ppm1d',\n",
" 'ppp2r1a',\n",
" 'ppp6c',\n",
" 'prdm1',\n",
" 'pten',\n",
" 'ptpn11',\n",
" 'ptprd',\n",
" 'ptprt',\n",
" 'rab35',\n",
" 'rac1',\n",
" 'rad21',\n",
" 'rad50',\n",
" 'rad51c',\n",
" 'rad51d',\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",
" 'rxra',\n",
" 'rybp',\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",
" 'stag2',\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": 54,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Column names after one-hot encoding for gene column\n",
"gene_vectorizer.get_feature_names()"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"Function for Response encoding of gene column"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"jupyter": {
"outputs_hidden": true
}
},
"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",
"\n",
"# ALGORITHIM\n",
"# -------------------------------------------------------------------------------------------------\n",
"# Considering all unique values and the number of occurences 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 /\n",
"# 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 in 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",
"\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 particular class\n",
" # vec is 9 dimensional 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"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"### Response-coding of the gene feature"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"jupyter": {
"outputs_hidden": true
}
},
"outputs": [],
"source": [
"# 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))"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"Columns after applying response encoding"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(2124, 9)"
]
},
"execution_count": 57,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_gene_feature_responseCoding.shape"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"Determining if gene column is a good feature for predicting the target variable Class"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"For values of alpha = 1e-05 The log loss is: 1.2099619723792538\n",
"For values of alpha = 0.0001 The log loss is: 1.1953178455364262\n",
"For values of alpha = 0.001 The log loss is: 1.2511239445115685\n",
"For values of alpha = 0.01 The log loss is: 1.3696013964523224\n",
"For values of alpha = 0.1 The log loss is: 1.4492726650492624\n",
"For values of alpha = 1 The log loss is: 1.4788684089492758\n"
]
}
],
"source": [
"# Hyperparemeter for SGD(Stochastic Gradient Descent) classifier\n",
"alpha = [10 ** x for x in range(-5, 1)]\n",
"\n",
"# Using SGD classifier\n",
"# Using Calibrated Classifier to get the result into probablity format to 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))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
"<Figure size 720x432 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Determining the best Alpha value\n",
"sns.set_style('whitegrid')\n",
"fig, ax = plt.subplots(figsize =(10,6))\n",
"ax.plot(alpha, cv_log_error_array,c='red')\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.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": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"For values of best alpha = 0.0001 The train log loss is: 0.9930117400277069\n",
"For values of best alpha = 0.0001 The cross validation log loss is: 1.1953178455364262\n",
"For values of best alpha = 0.0001 The test log loss is: 1.1758558546014406\n"
]
}
],
"source": [
"# Using best alpha value from the above graph to 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"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"Checking overlapping between train, test or between validation and train"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"jupyter": {
"outputs_hidden": true
}
},
"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": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1. In test data 649 out of 665 : 97.59398496240601\n",
"2. In cross validation data 515 out of 532 : 96.80451127819549\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)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## Evaluating Variation column"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"Variation is also a categorical variable and can be dealt with the same way as Gene column"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of Unique Variations : 1925\n",
"Truncating_Mutations 55\n",
"Deletion 52\n",
"Amplification 46\n",
"Fusions 21\n",
"Overexpression 5\n",
"G12V 3\n",
"Q61H 2\n",
"TMPRSS2-ETV1_Fusion 2\n",
"G35R 2\n",
"T73I 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))"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"Cumulative distribution of unique variation values"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
"<Figure size 720x432 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"s = sum(unique_variations.values);\n",
"h = unique_variations.values/s;\n",
"c = np.cumsum(h)\n",
"plt.figure(figsize = (10,6))\n",
"plt.plot(c,label='Cumulative distribution of Variations')\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"Converting variation column using one hot encoder"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"jupyter": {
"outputs_hidden": true
}
},
"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'])"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"Shape of one hot encoder column for variation"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(2124, 1952)"
]
},
"execution_count": 66,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_variation_feature_onehotCoding.shape"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"Generating response encoding for variation column"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"jupyter": {
"outputs_hidden": true
}
},
"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))"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"Shape of response encoding result"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(2124, 9)"
]
},
"execution_count": 68,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_variation_feature_responseCoding.shape"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"Building the model with variation column"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"For values of alpha = 1e-05 The log loss is: 1.7187728320000426\n",
"For values of alpha = 0.0001 The log loss is: 1.7078935081231914\n",
"For values of alpha = 0.001 The log loss is: 1.70727434399397\n",
"For values of alpha = 0.01 The log loss is: 1.7167347249716214\n",
"For values of alpha = 0.1 The log loss is: 1.7366487145276648\n",
"For values of alpha = 1 The log loss is: 1.7379499696427028\n"
]
}
],
"source": [
"# Hyperparemeter for SGD classifier.\n",
"alpha = [10 ** x for x in range(-5, 1)]\n",
"\n",
"# Using SGD classifier\n",
"# 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": null,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 720x432 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Plot to check the best Alpha value\n",
"fig, ax = plt.subplots(figsize =(10,6))\n",
"ax.plot(alpha, cv_log_error_array,c='magenta')\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.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": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"For values of best alpha = 0.001 The train log loss is: 1.068158095988339\n",
"For values of best alpha = 0.001 The cross validation log loss is: 1.70727434399397\n",
"For values of best alpha = 0.001 The test log loss is: 1.7079620722228164\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": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1. In test data 68 out of 665 : 10.225563909774436\n",
"2. In cross validation data 53 out of 532 : 9.962406015037594\n"
]
}
],
"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]\n",
"\n",
"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)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## Evaluating Text column"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"jupyter": {
"outputs_hidden": true
}
},
"outputs": [],
"source": [
"\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": null,
"metadata": {
"collapsed": true,
"jupyter": {
"outputs_hidden": true
}
},
"outputs": [],
"source": [
"import math\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": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Total number of unique words in train data : 52008\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",
"\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",
"print(\"Total number of unique words in train data :\", len(train_text_features))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"jupyter": {
"outputs_hidden": true
}
},
"outputs": [],
"source": [
"dict_list = []\n",
"# dict_list =[] contains 9 dictionaries 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 dictionary 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 build 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": null,
"metadata": {
"collapsed": true,
"jupyter": {
"outputs_hidden": true
}
},
"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": null,
"metadata": {
"collapsed": true,
"jupyter": {
"outputs_hidden": true
}
},
"outputs": [],
"source": [
"# Converting each row value 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": null,
"metadata": {
"collapsed": true,
"jupyter": {
"outputs_hidden": true
}
},
"outputs": [],
"source": [
"# Normalizing every feature\n",
"train_text_feature_onehotCoding = normalize(train_text_feature_onehotCoding, axis=0)\n",
"\n",
"# Using the same vectorizer that was trained on train data\n",
"test_text_feature_onehotCoding = text_vectorizer.transform(test_df['TEXT'])\n",
"\n",
"# Normalizing every feature\n",
"test_text_feature_onehotCoding = normalize(test_text_feature_onehotCoding, axis=0)\n",
"\n",
"# Using the same vectorizer that was trained on train data\n",
"cv_text_feature_onehotCoding = text_vectorizer.transform(cv_df['TEXT'])\n",
"\n",
"# Normalizing every feature\n",
"cv_text_feature_onehotCoding = normalize(cv_text_feature_onehotCoding, axis=0)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"jupyter": {
"outputs_hidden": true
}
},
"outputs": [],
"source": [
"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": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Counter({3: 4856, 4: 3581, 5: 2794, 6: 2460, 8: 2423, 9: 1890, 7: 1719, 10: 1450, 12: 1329, 11: 1097, 15: 988, 16: 934, 13: 878, 14: 772, 18: 665, 20: 594, 17: 584, 19: 529, 24: 505, 21: 498, 22: 428, 23: 413, 32: 376, 30: 369, 25: 353, 28: 352, 26: 343, 44: 317, 27: 317, 33: 297, 40: 274, 29: 273, 49: 260, 36: 251, 34: 251, 31: 225, 35: 219, 42: 218, 37: 208, 39: 194, 38: 194, 46: 178, 41: 173, 50: 170, 48: 168, 45: 167, 58: 161, 43: 161, 52: 152, 51: 148, 57: 143, 56: 143, 54: 143, 60: 141, 55: 135, 47: 133, 80: 121, 61: 119, 64: 118, 69: 116, 59: 113, 53: 113, 70: 109, 66: 109, 72: 104, 62: 104, 65: 100, 63: 99, 68: 91, 73: 90, 74: 89, 71: 88, 75: 87, 67: 84, 77: 83, 79: 82, 91: 77, 76: 75, 90: 74, 84: 73, 83: 73, 104: 69, 81: 69, 88: 68, 89: 67, 87: 66, 85: 66, 78: 65, 93: 64, 82: 64, 98: 62, 86: 60, 96: 59, 95: 57, 102: 56, 112: 55, 101: 55, 100: 55, 94: 54, 105: 52, 92: 52, 138: 49, 108: 49, 128: 48, 121: 48, 109: 48, 103: 48, 99: 48, 118: 46, 116: 46, 126: 45, 120: 45, 111: 45, 144: 44, 131: 43, 110: 43, 107: 43, 97: 43, 150: 42, 130: 42, 115: 42, 145: 41, 106: 40, 132: 39, 135: 38, 122: 38, 113: 37, 143: 36, 139: 36, 154: 35, 152: 35, 151: 35, 114: 35, 146: 34, 134: 34, 156: 33, 155: 33, 125: 33, 127: 32, 124: 32, 140: 31, 123: 31, 159: 30, 133: 30, 200: 29, 170: 29, 147: 29, 119: 29, 203: 28, 185: 28, 183: 28, 161: 28, 149: 28, 136: 28, 129: 28, 169: 27, 160: 27, 117: 27, 189: 26, 180: 26, 168: 26, 158: 26, 148: 26, 141: 26, 195: 25, 173: 25, 153: 25, 142: 25, 182: 24, 165: 24, 164: 24, 187: 23, 176: 23, 171: 23, 256: 22, 235: 22, 216: 22, 210: 22, 193: 22, 186: 22, 166: 22, 163: 22, 137: 22, 249: 21, 230: 21, 206: 21, 184: 21, 181: 21, 178: 21, 174: 21, 167: 21, 162: 21, 231: 20, 212: 20, 157: 20, 304: 19, 273: 19, 253: 19, 245: 19, 227: 19, 226: 19, 208: 19, 202: 19, 201: 19, 191: 19, 175: 19, 172: 19, 287: 18, 232: 18, 217: 18, 211: 18, 204: 18, 192: 18, 190: 18, 179: 18, 303: 17, 294: 17, 289: 17, 279: 17, 276: 17, 272: 17, 255: 17, 248: 17, 241: 17, 220: 17, 214: 17, 199: 17, 194: 17, 188: 17, 335: 16, 319: 16, 300: 16, 247: 16, 238: 16, 224: 16, 223: 16, 218: 16, 213: 16, 209: 16, 205: 16, 197: 16, 346: 15, 290: 15, 271: 15, 267: 15, 252: 15, 236: 15, 234: 15, 222: 15, 196: 15, 325: 14, 305: 14, 296: 14, 286: 14, 281: 14, 280: 14, 278: 14, 265: 14, 260: 14, 259: 14, 254: 14, 250: 14, 229: 14, 221: 14, 177: 14, 336: 13, 320: 13, 313: 13, 309: 13, 306: 13, 288: 13, 277: 13, 257: 13, 244: 13, 237: 13, 207: 13, 422: 12, 392: 12, 330: 12, 307: 12, 285: 12, 269: 12, 264: 12, 262: 12, 258: 12, 246: 12, 243: 12, 233: 12, 228: 12, 219: 12, 525: 11, 444: 11, 440: 11, 372: 11, 367: 11, 363: 11, 329: 11, 312: 11, 310: 11, 295: 11, 293: 11, 292: 11, 291: 11, 268: 11, 261: 11, 251: 11, 225: 11, 215: 11, 198: 11, 479: 10, 446: 10, 353: 10, 344: 10, 332: 10, 323: 10, 317: 10, 301: 10, 299: 10, 263: 10, 242: 10, 240: 10, 239: 10, 469: 9, 449: 9, 415: 9, 414: 9, 401: 9, 394: 9, 391: 9, 380: 9, 352: 9, 349: 9, 348: 9, 339: 9, 338: 9, 326: 9, 302: 9, 297: 9, 284: 9, 282: 9, 266: 9, 931: 8, 696: 8, 557: 8, 512: 8, 506: 8, 476: 8, 471: 8, 466: 8, 459: 8, 451: 8, 438: 8, 437: 8, 434: 8, 428: 8, 418: 8, 404: 8, 396: 8, 389: 8, 387: 8, 383: 8, 382: 8, 379: 8, 377: 8, 375: 8, 371: 8, 368: 8, 366: 8, 359: 8, 358: 8, 347: 8, 340: 8, 337: 8, 334: 8, 331: 8, 316: 8, 275: 8, 270: 8, 907: 7, 720: 7, 691: 7, 683: 7, 671: 7, 637: 7, 625: 7, 587: 7, 567: 7, 562: 7, 545: 7, 538: 7, 530: 7, 524: 7, 477: 7, 461: 7, 454: 7, 432: 7, 431: 7, 427: 7, 421: 7, 411: 7, 402: 7, 399: 7, 393: 7, 376: 7, 373: 7, 369: 7, 364: 7, 361: 7, 360: 7, 343: 7, 333: 7, 324: 7, 321: 7, 315: 7, 311: 7, 1124: 6, 922: 6, 894: 6, 787: 6, 772: 6, 734: 6, 710: 6, 674: 6, 647: 6, 615: 6, 606: 6, 602: 6, 599: 6, 598: 6, 581: 6, 575: 6, 550: 6, 536: 6, 526: 6, 515: 6, 513: 6, 511: 6, 503: 6, 496: 6, 495: 6, 489: 6, 482: 6, 453: 6, 450: 6, 447: 6, 443: 6, 436: 6, 435: 6, 433: 6, 430: 6, 429: 6, 426: 6, 420: 6, 416: 6, 408: 6, 405: 6, 390: 6, 386: 6, 385: 6, 381: 6, 374: 6, 370: 6, 362: 6, 350: 6, 342: 6, 341: 6, 327: 6, 322: 6, 318: 6, 314: 6, 308: 6, 298: 6, 283: 6, 274: 6, 1632: 5, 1230: 5, 1180: 5, 1005: 5, 982: 5, 930: 5, 928: 5, 923: 5, 882: 5, 855: 5, 843: 5, 833: 5, 827: 5, 814: 5, 807: 5, 798: 5, 795: 5, 781: 5, 759: 5, 743: 5, 739: 5, 728: 5, 716: 5, 709: 5, 704: 5, 699: 5, 697: 5, 692: 5, 686: 5, 678: 5, 677: 5, 648: 5, 643: 5, 638: 5, 634: 5, 630: 5, 628: 5, 613: 5, 610: 5, 608: 5, 597: 5, 591: 5, 589: 5, 586: 5, 577: 5, 571: 5, 564: 5, 558: 5, 540: 5, 539: 5, 531: 5, 522: 5, 517: 5, 508: 5, 502: 5, 501: 5, 490: 5, 486: 5, 481: 5, 480: 5, 468: 5, 457: 5, 455: 5, 445: 5, 442: 5, 441: 5, 417: 5, 400: 5, 397: 5, 388: 5, 378: 5, 365: 5, 354: 5, 328: 5, 3283: 4, 2177: 4, 1322: 4, 1283: 4, 1270: 4, 1235: 4, 1188: 4, 1175: 4, 1152: 4, 1142: 4, 1119: 4, 1087: 4, 1082: 4, 1057: 4, 1051: 4, 987: 4, 980: 4, 976: 4, 971: 4, 924: 4, 921: 4, 913: 4, 898: 4, 895: 4, 893: 4, 888: 4, 887: 4, 884: 4, 874: 4, 873: 4, 865: 4, 852: 4, 844: 4, 842: 4, 838: 4, 805: 4, 796: 4, 770: 4, 763: 4, 756: 4, 754: 4, 747: 4, 742: 4, 740: 4, 732: 4, 726: 4, 723: 4, 714: 4, 706: 4, 698: 4, 682: 4, 675: 4, 670: 4, 669: 4, 660: 4, 654: 4, 652: 4, 644: 4, 636: 4, 621: 4, 619: 4, 611: 4, 595: 4, 576: 4, 572: 4, 565: 4, 555: 4, 554: 4, 553: 4, 551: 4, 534: 4, 521: 4, 520: 4, 519: 4, 516: 4, 509: 4, 504: 4, 499: 4, 491: 4, 487: 4, 483: 4, 475: 4, 474: 4, 464: 4, 463: 4, 460: 4, 456: 4, 448: 4, 439: 4, 424: 4, 423: 4, 412: 4, 410: 4, 407: 4, 403: 4, 398: 4, 356: 4, 355: 4, 351: 4, 2985: 3, 2688: 3, 2643: 3, 2468: 3, 2191: 3, 1899: 3, 1819: 3, 1798: 3, 1773: 3, 1717: 3, 1716: 3, 1714: 3, 1680: 3, 1657: 3, 1651: 3, 1645: 3, 1621: 3, 1620: 3, 1600: 3, 1596: 3, 1581: 3, 1569: 3, 1566: 3, 1561: 3, 1527: 3, 1512: 3, 1499: 3, 1487: 3, 1486: 3, 1472: 3, 1471: 3, 1467: 3, 1450: 3, 1410: 3, 1395: 3, 1393: 3, 1392: 3, 1382: 3, 1375: 3, 1352: 3, 1344: 3, 1330: 3, 1313: 3, 1310: 3, 1291: 3, 1286: 3, 1285: 3, 1282: 3, 1280: 3, 1272: 3, 1254: 3, 1242: 3, 1233: 3, 1225: 3, 1222: 3, 1216: 3, 1215: 3, 1203: 3, 1200: 3, 1199: 3, 1186: 3, 1183: 3, 1166: 3, 1145: 3, 1144: 3, 1128: 3, 1121: 3, 1115: 3, 1114: 3, 1112: 3, 1089: 3, 1088: 3, 1084: 3, 1083: 3, 1067: 3, 1028: 3, 1026: 3, 1020: 3, 1010: 3, 984: 3, 981: 3, 969: 3, 958: 3, 957: 3, 954: 3, 949: 3, 944: 3, 934: 3, 927: 3, 920: 3, 903: 3, 899: 3, 891: 3, 886: 3, 878: 3, 876: 3, 871: 3, 867: 3, 854: 3, 832: 3, 826: 3, 825: 3, 816: 3, 815: 3, 797: 3, 794: 3, 791: 3, 784: 3, 778: 3, 775: 3, 773: 3, 771: 3, 764: 3, 761: 3, 753: 3, 751: 3, 750: 3, 745: 3, 744: 3, 738: 3, 731: 3, 724: 3, 722: 3, 707: 3, 700: 3, 684: 3, 681: 3, 668: 3, 666: 3, 662: 3, 661: 3, 657: 3, 653: 3, 651: 3, 649: 3, 646: 3, 642: 3, 632: 3, 629: 3, 624: 3, 623: 3, 620: 3, 612: 3, 609: 3, 607: 3, 604: 3, 603: 3, 601: 3, 593: 3, 592: 3, 590: 3, 588: 3, 585: 3, 580: 3, 574: 3, 570: 3, 568: 3, 563: 3, 560: 3, 556: 3, 549: 3, 548: 3, 547: 3, 533: 3, 529: 3, 527: 3, 523: 3, 514: 3, 510: 3, 507: 3, 505: 3, 498: 3, 497: 3, 478: 3, 473: 3, 467: 3, 465: 3, 462: 3, 458: 3, 425: 3, 406: 3, 395: 3, 384: 3, 357: 3, 345: 3, 20524: 2, 10128: 2, 9087: 2, 7702: 2, 7623: 2, 7081: 2, 6967: 2, 6363: 2, 5503: 2, 5247: 2, 5098: 2, 4772: 2, 4701: 2, 4684: 2, 4391: 2, 4150: 2, 4148: 2, 4143: 2, 4000: 2, 3964: 2, 3860: 2, 3728: 2, 3639: 2, 3629: 2, 3621: 2, 3545: 2, 3539: 2, 3454: 2, 3379: 2, 3373: 2, 3344: 2, 3341: 2, 3294: 2, 3257: 2, 3242: 2, 3207: 2, 3164: 2, 3151: 2, 3056: 2, 2997: 2, 2907: 2, 2792: 2, 2754: 2, 2660: 2, 2646: 2, 2631: 2, 2618: 2, 2607: 2, 2589: 2, 2552: 2, 2549: 2, 2543: 2, 2507: 2, 2499: 2, 2497: 2, 2471: 2, 2448: 2, 2435: 2, 2405: 2, 2398: 2, 2359: 2, 2321: 2, 2308: 2, 2295: 2, 2293: 2, 2285: 2, 2259: 2, 2251: 2, 2242: 2, 2241: 2, 2220: 2, 2213: 2, 2212: 2, 2193: 2, 2174: 2, 2126: 2, 2109: 2, 2104: 2, 2102: 2, 2068: 2, 2065: 2, 2063: 2, 2052: 2, 2039: 2, 2027: 2, 2023: 2, 2018: 2, 2002: 2, 1993: 2, 1984: 2, 1981: 2, 1975: 2, 1974: 2, 1973: 2, 1960: 2, 1954: 2, 1953: 2, 1936: 2, 1927: 2, 1923: 2, 1922: 2, 1909: 2, 1896: 2, 1888: 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1, 1404: 1, 1400: 1, 1399: 1, 1398: 1, 1394: 1, 1391: 1, 1389: 1, 1384: 1, 1383: 1, 1381: 1, 1380: 1, 1379: 1, 1378: 1, 1377: 1, 1376: 1, 1374: 1, 1372: 1, 1370: 1, 1368: 1, 1365: 1, 1361: 1, 1354: 1, 1350: 1, 1345: 1, 1343: 1, 1342: 1, 1335: 1, 1334: 1, 1331: 1, 1329: 1, 1327: 1, 1321: 1, 1317: 1, 1315: 1, 1312: 1, 1309: 1, 1306: 1, 1304: 1, 1303: 1, 1302: 1, 1294: 1, 1293: 1, 1292: 1, 1287: 1, 1277: 1, 1276: 1, 1275: 1, 1271: 1, 1269: 1, 1268: 1, 1262: 1, 1252: 1, 1250: 1, 1246: 1, 1243: 1, 1239: 1, 1238: 1, 1237: 1, 1232: 1, 1229: 1, 1227: 1, 1226: 1, 1224: 1, 1218: 1, 1213: 1, 1212: 1, 1211: 1, 1209: 1, 1205: 1, 1202: 1, 1201: 1, 1198: 1, 1197: 1, 1196: 1, 1192: 1, 1191: 1, 1189: 1, 1184: 1, 1182: 1, 1179: 1, 1178: 1, 1171: 1, 1170: 1, 1164: 1, 1162: 1, 1161: 1, 1160: 1, 1159: 1, 1149: 1, 1143: 1, 1135: 1, 1133: 1, 1129: 1, 1118: 1, 1116: 1, 1111: 1, 1110: 1, 1109: 1, 1104: 1, 1103: 1, 1100: 1, 1099: 1, 1097: 1, 1095: 1, 1094: 1, 1092: 1, 1090: 1, 1080: 1, 1071: 1, 1066: 1, 1064: 1, 1060: 1, 1059: 1, 1058: 1, 1055: 1, 1050: 1, 1042: 1, 1038: 1, 1036: 1, 1033: 1, 1032: 1, 1023: 1, 1021: 1, 1019: 1, 1017: 1, 1016: 1, 1015: 1, 1009: 1, 1008: 1, 1007: 1, 1004: 1, 996: 1, 993: 1, 992: 1, 989: 1, 986: 1, 983: 1, 979: 1, 978: 1, 967: 1, 966: 1, 961: 1, 956: 1, 955: 1, 953: 1, 952: 1, 947: 1, 946: 1, 945: 1, 936: 1, 926: 1, 925: 1, 914: 1, 910: 1, 909: 1, 904: 1, 901: 1, 900: 1, 896: 1, 890: 1, 889: 1, 885: 1, 883: 1, 881: 1, 879: 1, 872: 1, 869: 1, 863: 1, 862: 1, 861: 1, 856: 1, 853: 1, 851: 1, 840: 1, 836: 1, 835: 1, 831: 1, 830: 1, 821: 1, 820: 1, 819: 1, 818: 1, 813: 1, 810: 1, 809: 1, 792: 1, 786: 1, 785: 1, 768: 1, 767: 1, 766: 1, 765: 1, 760: 1, 758: 1, 752: 1, 749: 1, 746: 1, 741: 1, 729: 1, 721: 1, 719: 1, 713: 1, 712: 1, 702: 1, 685: 1, 680: 1, 673: 1, 664: 1, 659: 1, 658: 1, 655: 1, 641: 1, 639: 1, 631: 1, 622: 1, 618: 1, 617: 1, 614: 1, 605: 1, 596: 1, 582: 1, 578: 1, 573: 1, 559: 1, 542: 1, 532: 1, 528: 1, 500: 1, 493: 1, 472: 1, 409: 1})\n"
]
}
],
"source": [
"# Number of words for a given frequency\n",
"print(Counter(sorted_text_occur))"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"Building the model with text column"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"For values of alpha = 1e-05 The log loss is: 1.4672449292435714\n",
"For values of alpha = 0.0001 The log loss is: 1.3245547472347057\n",
"For values of alpha = 0.001 The log loss is: 1.3343111613693481\n",
"For values of alpha = 0.01 The log loss is: 1.4464352762341575\n",
"For values of alpha = 0.1 The log loss is: 1.5743123005824804\n",
"For values of alpha = 1 The log loss is: 1.7187018999654895\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))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
"<Figure size 720x432 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(figsize = (10,6))\n",
"ax.plot(alpha, cv_log_error_array,c='orange')\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.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": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"For values of best alpha = 0.0001 The train log loss is: 0.637191874613802\n",
"For values of best alpha = 0.0001 The cross validation log loss is: 1.3245547472347057\n",
"For values of best alpha = 0.0001 The test log loss is: 1.2058882337099506\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"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"Checking the overlap of text data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"jupyter": {
"outputs_hidden": true
}
},
"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": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"95.958 % of word of test data appeared in train data\n",
"97.408 % 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\")"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## Data Preparation for Machine Learning models"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"Functions that will be used"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"jupyter": {
"outputs_hidden": true
}
},
"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": null,
"metadata": {
"collapsed": true,
"jupyter": {
"outputs_hidden": true
}
},
"outputs": [],
"source": [
"# Used to plot the confusion matrices\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 an array of probabilities is provided that 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": null,
"metadata": {
"collapsed": true,
"jupyter": {
"outputs_hidden": true
}
},
"outputs": [],
"source": [
"# Function for naive bayes\n",
"# for the given indices,the name of the features would be printed\n",
"# and it would be checked 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\")"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## Combining all 3 features together"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"jupyter": {
"outputs_hidden": true
}
},
"outputs": [],
"source": [
"# Merging gene, variation 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",
"### One-hot encoding\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",
"### Response Encoding\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": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"One hot encoding features :\n",
"(number of data points,number of features) in train data = (2124, 54194)\n",
"(number of data points,number of features) in test data = (665, 54194)\n",
"(number of data points,number of features) in cross validation data = (532, 54194)\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": null,
"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)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## Building Machine Learning models"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"### Naive Bayes"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"For alpha = 1e-05\n",
"Log Loss : 1.3405879958818532\n",
"For alpha = 0.0001\n",
"Log Loss : 1.333729328103857\n",
"For alpha = 0.001\n",
"Log Loss : 1.327982517200206\n",
"For alpha = 0.1\n",
"Log Loss : 1.3010906608923245\n",
"For alpha = 1\n",
"Log Loss : 1.3349564796492244\n",
"For alpha = 10\n",
"Log Loss : 1.4476484407604788\n",
"For alpha = 100\n",
"Log Loss : 1.443107768409862\n",
"For alpha = 1000\n",
"Log Loss : 1.4199037213305592\n"
]
}
],
"source": [
"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 log-probability estimates are being used\n",
" print(\"Log Loss :\",log_loss(cv_y, sig_clf_probs)) "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
"<Figure size 720x432 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(figsize = (10,6))\n",
"ax.plot(np.log10(alpha), cv_log_error_array,c='navy')\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.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": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"For values of best alpha = 0.1 The train log loss is: 0.8338042990402318\n",
"For values of best alpha = 0.1 The cross validation log loss is: 1.3010906608923245\n",
"For values of best alpha = 0.1 The test log loss is: 1.2380580225379638\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))"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Testing Naive Bayes model with best found value of alpha on testing data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Log Loss : 1.3010906608923245\n",
"Number of Misclassified points : 0.41353383458646614\n",
"-------------------- Confusion matrix --------------------\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 1440x504 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"-------------------- Precision matrix (Columm Sum=1) --------------------\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 1440x504 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"-------------------- Recall matrix (Row sum=1) --------------------\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 1440x504 with 2 Axes>"
]
},
"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 Misclassified points :\", 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()))"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"### Interpretability of the model"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Predicted Class : 2\n",
"Predicted Class Probabilities: [[0.087 0.5553 0.0155 0.1114 0.0452 0.035 0.1397 0.006 0.0047]]\n",
"Actual Class : 2\n",
"--------------------------------------------------\n",
"18 Text feature [identified] present in test data point [True]\n",
"21 Text feature [sequencing] present in test data point [True]\n",
"22 Text feature [novel] present in test data point [True]\n",
"23 Text feature [detection] present in test data point [True]\n",
"24 Text feature [another] present in test data point [True]\n",
"25 Text feature [kinase] present in test data point [True]\n",
"26 Text feature [clinical] present in test data point [True]\n",
"27 Text feature [achieved] present in test data point [True]\n",
"28 Text feature [mutations] present in test data point [True]\n",
"29 Text feature [identification] present in test data point [True]\n",
"30 Text feature [including] present in test data point [True]\n",
"31 Text feature [molecular] present in test data point [True]\n",
"32 Text feature [using] present in test data point [True]\n",
"33 Text feature [present] present in test data point [True]\n",
"35 Text feature [confirmed] present in test data point [True]\n",
"36 Text feature [may] present in test data point [True]\n",
"37 Text feature [inhibitor] present in test data point [True]\n",
"38 Text feature [different] present in test data point [True]\n",
"39 Text feature [identify] present in test data point [True]\n",
"40 Text feature [case] present in test data point [True]\n",
"42 Text feature [new] present in test data point [True]\n",
"43 Text feature [patient] present in test data point [True]\n",
"44 Text feature [therapy] present in test data point [True]\n",
"45 Text feature [therapeutic] present in test data point [True]\n",
"46 Text feature [well] present in test data point [True]\n",
"47 Text feature [12] present in test data point [True]\n",
"49 Text feature [also] present in test data point [True]\n",
"51 Text feature [harboring] present in test data point [True]\n",
"53 Text feature [studies] present in test data point [True]\n",
"54 Text feature [harbor] present in test data point [True]\n",
"56 Text feature [common] present in test data point [True]\n",
"57 Text feature [found] present in test data point [True]\n",
"58 Text feature [previously] present in test data point [True]\n",
"59 Text feature [recently] present in test data point [True]\n",
"61 Text feature [others] present in test data point [True]\n",
"63 Text feature [mutation] present in test data point [True]\n",
"64 Text feature [heterogeneous] present in test data point [True]\n",
"65 Text feature [one] present in test data point [True]\n",
"66 Text feature [performed] present in test data point [True]\n",
"67 Text feature [complete] present in test data point [True]\n",
"68 Text feature [need] present in test data point [True]\n",
"69 Text feature [pcr] present in test data point [True]\n",
"70 Text feature [mutational] present in test data point [True]\n",
"71 Text feature [additional] present in test data point [True]\n",
"72 Text feature [10] present in test data point [True]\n",
"73 Text feature [table] present in test data point [True]\n",
"75 Text feature [specific] present in test data point [True]\n",
"76 Text feature [analysis] present in test data point [True]\n",
"77 Text feature [study] present in test data point [True]\n",
"78 Text feature [presence] present in test data point [True]\n",
"79 Text feature [number] present in test data point [True]\n",
"81 Text feature [sequenced] present in test data point [True]\n",
"84 Text feature [time] present in test data point [True]\n",
"85 Text feature [treated] present in test data point [True]\n",
"87 Text feature [40] present in test data point [True]\n",
"88 Text feature [approved] present in test data point [True]\n",
"89 Text feature [higher] present in test data point [True]\n",
"90 Text feature [respectively] present in test data point [True]\n",
"93 Text feature [subsequent] present in test data point [True]\n",
"95 Text feature [treatment] present in test data point [True]\n",
"97 Text feature [reported] present in test data point [True]\n",
"98 Text feature [33] present in test data point [True]\n",
"99 Text feature [similarly] present in test data point [True]\n",
"Out of the top 100 features 63 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": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Predicted Class : 2\n",
"Predicted Class Probabilities: [[0.0893 0.4833 0.0159 0.1144 0.0464 0.0358 0.2038 0.0062 0.0049]]\n",
"Actual Class : 7\n",
"--------------------------------------------------\n",
"18 Text feature [identified] present in test data point [True]\n",
"20 Text feature [harbored] present in test data point [True]\n",
"21 Text feature [sequencing] present in test data point [True]\n",
"22 Text feature [novel] present in test data point [True]\n",
"24 Text feature [another] present in test data point [True]\n",
"25 Text feature [kinase] present in test data point [True]\n",
"26 Text feature [clinical] present in test data point [True]\n",
"28 Text feature [mutations] present in test data point [True]\n",
"30 Text feature [including] present in test data point [True]\n",
"31 Text feature [molecular] present in test data point [True]\n",
"32 Text feature [using] present in test data point [True]\n",
"33 Text feature [present] present in test data point [True]\n",
"35 Text feature [confirmed] present in test data point [True]\n",
"36 Text feature [may] present in test data point [True]\n",
"37 Text feature [inhibitor] present in test data point [True]\n",
"39 Text feature [identify] present in test data point [True]\n",
"41 Text feature [potential] present in test data point [True]\n",
"42 Text feature [new] present in test data point [True]\n",
"43 Text feature [patient] present in test data point [True]\n",
"44 Text feature [therapy] present in test data point [True]\n",
"45 Text feature [therapeutic] present in test data point [True]\n",
"46 Text feature [well] present in test data point [True]\n",
"47 Text feature [12] present in test data point [True]\n",
"48 Text feature [revealed] present in test data point [True]\n",
"49 Text feature [also] present in test data point [True]\n",
"50 Text feature [15] present in test data point [True]\n",
"51 Text feature [harboring] present in test data point [True]\n",
"53 Text feature [studies] present in test data point [True]\n",
"54 Text feature [harbor] present in test data point [True]\n",
"55 Text feature [observed] present in test data point [True]\n",
"57 Text feature [found] present in test data point [True]\n",
"58 Text feature [previously] present in test data point [True]\n",
"60 Text feature [activating] present in test data point [True]\n",
"63 Text feature [mutation] present in test data point [True]\n",
"65 Text feature [one] present in test data point [True]\n",
"66 Text feature [performed] present in test data point [True]\n",
"69 Text feature [pcr] present in test data point [True]\n",
"71 Text feature [additional] present in test data point [True]\n",
"72 Text feature [10] present in test data point [True]\n",
"75 Text feature [specific] present in test data point [True]\n",
"76 Text feature [analysis] present in test data point [True]\n",
"77 Text feature [study] present in test data point [True]\n",
"78 Text feature [presence] present in test data point [True]\n",
"80 Text feature [gene] present in test data point [True]\n",
"83 Text feature [characterized] present in test data point [True]\n",
"84 Text feature [time] present in test data point [True]\n",
"85 Text feature [treated] present in test data point [True]\n",
"86 Text feature [three] present in test data point [True]\n",
"87 Text feature [40] present in test data point [True]\n",
"89 Text feature [higher] present in test data point [True]\n",
"91 Text feature [established] present in test data point [True]\n",
"95 Text feature [treatment] present in test data point [True]\n",
"96 Text feature [respond] present in test data point [True]\n",
"97 Text feature [reported] present in test data point [True]\n",
"99 Text feature [similarly] present in test data point [True]\n",
"Out of the top 100 features 55 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)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Naive Bayes is not performing very badly but lets look at other models"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## K Nearest Neighbour Classification"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"for alpha = 5\n",
"Log Loss : 1.1327114515572707\n",
"for alpha = 11\n",
"Log Loss : 1.1432493383891909\n",
"for alpha = 15\n",
"Log Loss : 1.1297040104382379\n",
"for alpha = 21\n",
"Log Loss : 1.1438168558181883\n",
"for alpha = 31\n",
"Log Loss : 1.1262465658600036\n",
"for alpha = 41\n",
"Log Loss : 1.1249640092746829\n",
"for alpha = 51\n",
"Log Loss : 1.1361516603992405\n",
"for alpha = 99\n",
"Log Loss : 1.1468029582365218\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 log-probability estimates are being used\n",
" print(\"Log Loss :\",log_loss(cv_y, sig_clf_probs))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
"<Figure size 720x432 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(figsize = (10,6))\n",
"ax.plot(alpha, cv_log_error_array,c='purple')\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.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": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"For values of best alpha = 41 The train log loss is: 0.8347333358605703\n",
"For values of best alpha = 41 The cross validation log loss is: 1.1249640092746829\n",
"For values of best alpha = 41 The test log loss is: 1.1300864348562587\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))"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"Testing the model on test dataset with our best alpha value"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Log loss : 1.1249640092746829\n",
"Number of mis-classified points : 0.41729323308270677\n",
"-------------------- Confusion matrix --------------------\n"
]
},
{
"data": {
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"text/plain": [
"<Figure size 1440x504 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"-------------------- Precision matrix (Columm Sum=1) --------------------\n"
]
},
{
"data": {
"image/png": 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"text/plain": [
"<Figure size 1440x504 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"-------------------- Recall matrix (Row sum=1) --------------------\n"
]
},
{
"data": {
"image/png": 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8xiLG2xW4qKo3HeGxcx3ZNsVj57oQjycHAN5++22io6Pp1avXPz7WV5MD5ZGvJgfKK19MDpRXvpgcKK98MTlQXvlqcqA88tXkQHnli8kBEe9TcqCkSjM5cFG+CrrvvvsuxmlFREREREREfJxvjkS+EN8cJyoiIiIiIiLih0prrYDCaSljSEtLw2azMWHCBKKjC9ea2L59OxMnTnTvm5yczOuvv06XLl3Oez4lB0RERERERET8zLJly3A6ncTHx5OcnMzkyZOZNWsWAE2aNGHu3LkAfP3111StWvVvEwOg5ICIiIiIiIiIx3hy5EB8fDzx8fHu7b59+9K3b18AkpKS6Ny58M45sbGxpKSk/OX4rKwsXn31VebNu/DafkoOiIiIiIiIiHiIyYNrDpydDPhvmZmZ2O1297bFYiE/Px/rWXeZWrx4Mb1796ZSpUoXfC3/XClBRERERERExAeZTGaPPf6O3W7H4XC4t10uV7HEAMDnn3/OrbfeWqJ6KzkgIiIiIiIi4mfi4uJITEwEChccjIkpfovIjIwMnE4nkZGRJTqfphWIiIiIiIiIeIjJZCqV1+nVqxdr1qyhX79+GIbBxIkTmTNnDlFRUfTo0YP09HRq1qxZ4vMpOSAiIiIiIiLiIaV1K0Oz2cy4ceOKPVe/fn33/1u2bMnMmTNLfj6P1UxERERERERE/JJGDoiIiIiIiIh4iCfvVlCalBwQERERERER8ZDSmlbgaf5ZaxERERERERHxGI0cEBEREREREfEQfx05oOSAiIiIiIiIiIf465oD/llrEREREREREfEYjRwQERERERER8RRNK/CM7YuaebsKckaDaSe8XQU5y74nw71dBTnj2dhLvF0FOcNqDvZ2FeQMK4qFr2jwxhFvV0HOsutB9Q2R8sZf1xzwz1qLiIiIiIiIiMf43MgBEREREREREX9lMpm8XYX/iZIDIiIiIiIiIh6iuxWIiIiIiIiIiF/SyAERERERERERD/HXBQmVHBARERERERHxFD9dc8A/UxoiIiIiIiIi4jEaOSAiIiIiIiLiKX76FbySAyIiIiIiIiKeomkFIiIiIiIiIuKPNHJARERERERExFP8dOSAkgMiIiIiIiIinuKn4/P9tNoiIiIiIiIi4ikaOSAiIiIiIiLiIYamFYiIiIiIiIiUc/6ZG9C0AhEREREREZHyTiMHSsjlcjF+3LvsSN1HgM3KuPGDiYqu7i5fnLCchITlWC1mHnjwJrp1b8PhQ8d47pk3yC8oAANGj7ufunVr8MXnq3l/zheYLWZu6tONfrdf6cWW+R8TMKFXI5pWtZOb72LEN6nsO5n9l33eu7kV3+46yvxNhwizWXjtuuaE2Cw4C1w89uU2jjqcRFcMZuKVjQgwm3EWuHj48xRO5uR7pV3+yOVyMWbMLNLS0rHZApgw4RGio2u4yxMSvmHhwqVYrRaGDLmN7t3bc/z4KYYPf4mcHCdVq1Zi0qShBAcHnXNfKTm9R/kW9Q3foVj4DhMwtnMDmlxix+lyMeqHHew7nfOXfd6+ujnL9v7Bh9sOA7B6YAf2nSq8zm88cpqX1u3l+oZVubdlTQoMWJx6hAVn9pWSUb/wHYpFGWX2z6EDSg6U0PJl63HmOpm/cDybkncy9cW5vPr6kwAcO3qS+fOWEr94Irm5eQwaMJqOnVry6isJ3D7gKnr0bMea1ZuYMf1D/vPqMF56cR6ffv4SISFBXH/dMP51dUcqVLB7uYX+46qGVQi0mrlpfhKtI8N5tlsD7v9kS7F9hneuR3hQ0a/3Lc0jST2WyaSVu+nXsgaD20Ux4YddTL6yMS+u2s3Gw6f5V0wV6lUKYcOh06XdJL+1bNlPOJ1O4uNfIjk5lcmT32XWrGcBOHr0BHPnfs6SJS+Tm+ukf/8RdOrUmpkzF3LttV3p06cns2cvIj5+Kddc0/Wc+9psAV5uof/Qe5RvUd/wHYqF7+hV9xICrWZu/SSZ2KphjLysHg9+s63YPk+0r0OFwKLrd3R4ENuOZvLA0q3F9nv60rr8KyGJrLwClvZtyxe7jnLaqeR+Salf+A7Foozy0zUHSmVagdPpLI2Xuag2bkil0+WxALSKbcjWlD3usi1bdhEb1wibLYCwsBBqR1UnLe1XnhxxB126tgYgP7+AwMDCzhnTKIqMzCxynU4Mw8Dkp7883tKuVgVWpv8BwMbDp2lZPbxY+dUxVTAMWJl+3P1c2jEHoTYLAGE2C3kug0CrmUtCAuhZvzIL+7YmrkYFkg8rMfBPJCVto3PnNgDExjYmJWWnu2zz5h20bt3kTL8IJSoqktTU9GLHdOnSlrVrN513Xyk5vUf5FvUN36FY+I621SuQ+OsJAJJ/z6B51bBi5b3rVcZlQOL+E+7nmlUJo1qojXnXteTtq5tTt0IwAGnHHYTZrARazJgAA6PU2lEWqF/4DsVCfIlHkwMrVqyge/fu9OrVi6+++sr9/H333efJl/GKzMxswsKC3dtmi5n8/IKiMntRWWhoEJkZWUREhBMQYCU9/RAvTZ3HkH/fAkDDhrW57ZaR3HDdcLp2iyM8PLR0G+Pn7DYrGblF3w4UGAaWM3+8xFQO5YYm1Zi2ek+xY05k59GlziUsu7sDD7SPIn7zISoGWWlUxc7qfcfpF7+RCoFWbmkWWapt8XeZmVnY7SHubUuxfpFFWFjR73ZoaDCZmVln+lKI+7mMDMd595WS03uUb1Hf8B2Khe+w2yxknPXtvstlYDmTe2wYEcJ1Daoy45e9xY45muXkjY37uePzzcza8CvTejQGYMfxLD65uTVf923Lil+Pk+EsKK1mlAnqF75DsSijTB58lCKPTit44403+OSTT3C5XAwdOpTc3FxuuukmDMP/s7l2ezAOR9G8OMNlYLVazlnmcOQQFl7YYdf9vJXx495h0pR/U7duDdLS9pG4ciPffPcqISFBPP3Ua3yz9Ceu6n1p6TbIj2U68wm1Ff3qmk2FCQKAm5tVp5o9kA/7tqZWhSDyCgwOnMqhf6savLFuHws2HaJxlVDeuLEFN8xbT0ZuPj/uPwnA8j3H6BxdiYQUzVssKbs9BIejaL0HV7F+EYLDUXRRcjiyCQsLPdNfsgkKCsThyCY8PPS8+0rJ6T3Kt6hv+A7FwndkOguwnxnFB2A2mSg48xHxpkbVCkcIXN+SmmFB5BW4OJiRw7rDpyhwFe6UdOQ0VUNtNL4klG5Rlei2YB1ZeQVMu6Ix/6pXma/3HPNGs/yS+oXvUCzKKD9dc8CjIwcCAgKoUKECERERzJw5k3nz5vHTTz+ViSGpreMasSpxIwCbknfSMKa2u6xFiwZsSEolN9dJRkYW6XsO0rBhbdb9vJVJE9/nzdkjad68PgBh9hACg2wEBdqwWMxUuiSc06czvdImf7X+4Cm617sEgNaR4aQddbjLJq3czY3zk+gXv5HFKUd4e/2vrNx7nFM5+e7RBn9k5WG3WcjNd5F+Iot2NSsA0L5WRXb84fjrC8p5xcU1ITFxPQDJyanExES7y1q2jCEpaduZfuFg9+79xMREExfXlJUrC49JTFxPmzbNzruvlJzeo3yL+obvUCx8R9KR03SNqgRAbNUw0o4XXXNf/CmdWz5OZsBnm/ko7Tfe3XyQxP0neKRNNHe1rAlA40tCOZKZy+ncfHILXOTmu3AZ8EdOHuGBWkbrn1C/8B2KhfgSk+HBr/WfeuopIiIiGDp0KCEhIRw+fJh7772X06dPs3r16hKdI8+10VPV8Sj3SuBp+8CA8RMfZFViMlFR1eh+RVsWJyxn0aLlGC6D+wffSK8rO9DnxqfIc+ZzSeXCPz7r1q3B6LH3E7/wOz7+6AcCAqzUrl2NseMeIMDmexe1BtNOXHgnL/jzbgVNqtgxmWD419vpXu8S9p7IZtnuom8NHutYl6OOXOZvOkTVUBsv9m5MSICVAIuJaav3sHrfCZpUsTO+ZwwWs4n9p3IY9tU28ly+OdJl35O1vF2Fv/hzhd0dO/ZiGAYTJw4lMTGJqKhIevToQELCN8THL8UwDAYPvpWrrurEsWMnGDFiBg5HNhER4UybNpyQkKBz7uur8ly+l0Qqj+9RAAFm3/xWpLz2DV9UHmPR4I0j3q7COf15t4LGl4RiwsSIH9LoFlWJfaeyWb6vaJ2gR9tGczTLyYfbDhNuszKtRyNCAyzkuwzGrN7FnpPZ3N40klsaVyOvwODX0zk8s3KHz16/dz1Y/cI7lbLy2C98VfmNRYy3K3BRNez1jsfOtfO7ez12rgvxaHIgPz+fzz77jH/9618EBxfObz127BhvvvkmzzzzTInO4avJgfLIV5MD5ZUvJgfKK19MDpRXvpocEPEmX00OlFe+mBwQ8b4ynhy40oPJgW9LLzng0a+CrFYrffr0KfZc5cqVS5wYEBEREREREZHS55vjREVERERERET8kZ8uSKjkgIiIiIiIiIin+GduwLN3KxARERERERER/6ORAyIiIiIiIiIeYpj8c+iAkgMiIiIiIiIinuKnaw5oWoGIiIiIiIhIOaeRAyIiIiIiIiKe4p8DB5QcEBEREREREfEYrTkgIiIiIiIiIqXB5XIxZswY0tLSsNlsTJgwgejoaHf5ypUref311zEMg2bNmjF69GhMf5O40JoDIiIiIiIiIp5iNnnu8TeWLVuG0+kkPj6eYcOGMXnyZHdZZmYmU6dO5Y033mDRokXUrFmTEydO/O35NHJARERERERExFNKaVZBUlISnTt3BiA2NpaUlBR32caNG4mJiWHKlCns37+fW2+9lUqVKv3t+ZQcEBEREREREfFB8fHxxMfHu7f79u1L3759gcLRAXa73V1msVjIz8/HarVy4sQJfv75Zz755BNCQkIYMGAAsbGx1K1b97yvpeSAiIiIiIiIiKd4cEHCs5MB/81ut+NwONzbLpcLq7XwT/yKFSvSokULqlSpAkDbtm3Zvn373yYHtOaAiIiIiIiIiKeYTJ57/I24uDgSExMBSE5OJiYmxl3WrFkzduzYwfHjx8nPz2fTpk00aNDgb8+nkQMiIiIiIiIifqZXr16sWbOGfv36YRgGEydOZM6cOURFRdGjRw+GDRvGfffdB0Dv3r2LJQ/ORckBEREREREREU8ppfH5ZrOZcePGFXuufv367v9fc801XHPNNSU+n88lBwLMod6ugpyx70nFwpeczvvV21WQM6Zt8XYN5E/DWvzh7SqI+Jy8Y4a3qyAiUr55cM2B0uRzyQERERERERERv+WfuQEtSCgiIiIiIiJS3mnkgIiIiIiIiIiHGGb/HDqg5ICIiIiIiIiIp/jpmgOaViAiIiIiIiJSzmnkgIiIiIiIiIin+OfAASUHRERERERERDzGT9cc0LQCERERERERkXJOIwdEREREREREPMVPFyRUckBERERERETEU/wzN6BpBSIiIiIiIiLlnUYOiIiIiIiIiHiKny5IqOSAiIiIiIiIiKf4aXJA0wpEREREREREyjmNHBARERERERHxEMM/Bw4oOSAiIiIiIiLiMX46rUDJgRJyuVyMGTOLtLR0bLYAJkx4hOjoGu7yhIRvWLhwKVarhSFDbqN79/YcP36K4cNfIifHSdWqlZg0aSjBwUHn3FdKTrHwHS6Xiynj49m54yABAVaeHdef2lFV3eUfL17DxwmrsVjN3PNAbzp3a8G0yYvZkXoAgD/+OE1YWDBzFjwJwInjGdw3cDoLPhpFYGCAV9rkrwyXi/XvxnPy14OYrVbaP9CfsOpFsUj7agX71q4HoEZsM5rfcg3OrGx+fOVd8nJysQRYufTfdxJcsQIZR35n/TsLceXnY7YG0PHRuwkMs3uraX5JfcN3KBa+wwRM+FcTmlazk1vgYsQX29h3IttdPvrKRrSrXZFMZz4A9ydswmo28cpNLQiymvktI5fhn28lJ99Fv9Y1GdC6FvkuF6+uTmfFrmNeapV/0mcp36FYiC9RcqCEli37CafTSXz8SyQnpzJ58rvMmvUsAEePnmDu3M9ZsuRlcnOd9O8/gk6dWjNz5kKuvbYrffr0ZPbsRcTHL+Waa7qec1+bTR8wSkqx8B0/LN9MrjOPd+cPZ8umdGZM/Yhprz4IwLFjp4if/wMfxD+FMzef+wZNp0PHxgx7+hYA8vMKuG/QNJ4Z0x+AH9ds47WXP+WPY6e91h5/dmD9Zgry8ug1bjjHdqaTPO8jOg8vjEXmb8fYu/oXek14EpPJxPIx06nVLpbftu2gQu0axA64id3L15D6+TJaD7yZX976kJb9rqdyw7rs/3kjGYd/V3LgH1Lf8B2Khe+4qlFVAq1mbnrvF1rXrMCzPWO4f9Emd3mLyDAGLtjAiew893NjrmzEpymHWbz5MEM61mFAXC0+23qEu9vV5rp3fibQamHxnW1Znf4HzgLDG83yS/os5TsUizLK5J8jB7QgYQklJW2jc+c2AMTGNiYlZae7bPPmHbRu3QSbLYCwsFCioiJJTU0vdkyXLm1Zu3bTefeVklMsfMemjbvp2KkpAC1a1WX71l/dZVu37KNVbD1stgDsYcHUrl2FnWmH3OXxC36gQ8cmNIipCYDZZOL1tx8hvEJI6TaijDiWtpvIVoWxqNywLsf3FMUi5JIIuj39b8xmMyaTCVdBAWablYq1a5CfkwNAXnY2ZquFfKeT3NMZHNqwheXjZnBsZzqVGtTxRpP8mvqG71AsfEe72hVZubvwG/6NB0/RMjLcXWYC6kSEMPmaJiy5sx23tapx1jF/APDDrmNcXrcSrWpUYP3+UzgLDDJy89l7PJvGVcNKvT3+TJ+lfIdiUUaZTZ57lGa1L/YL5OTk4HQ6L/bLXHSZmVnY7UUfBiwWM/n5Be6ysLBQd1loaDCZmVlkZmYTFhbifi4jw3HefaXkFAvf4cjMITQs2L1tNhfFwpGZg/2sspDQQDIzC4eP5uXl89Gi1Qy8q6e7vEPHJlSsqG+n/1d52TkEhBT9vE1mM66CwliYrRYCw+0YhsHGeR8RUacW4ZHVsIWFcnhzKl8NH0/qF8uo160jzswsTh04TLXmjbniuaE4HVnsXfmTt5rlt9Q3fIdi4TvsgVYycvPd2wWGgeXMt2shNgvvr9/P0E9SGPThBga2qU3jqnbsgVZOnzkm01lAWJCVsEALGblFowscznzCgjQY9p/QZynfoViIL/F4cmDXrl089NBDjBw5krVr13L11Vdz9dVX8/3333v6pUqV3R6Cw1E0L87lMrBaLWeVFXU+hyObsLBQ7PZg9zEORzbh4aHn3VdKTrHwHaH2ILIcOe5twyiKRag9CMdZZVmOXMLOfAhf92Mqrds0KPahXP5/AoKDyM8uHguzxeLeLnDm8eNr75Gfk0Obe/oBsHXJVzS5ridXv/Qc3UY+wuqX38JmD8EaHES1ZjGYTCZqxjUvNgpBSkZ9w3coFr4jMzefUFvRH/Fmk4kCo3AqQHZeAe+u+5WcfBcOZwFr9x6nSbUwMnPzsdvOXONtFk7n5JORW4D9rPOE2qyczslHSk6fpXyHYlFGmT34KOVqe9To0aO56667aN++PY8++iiLFi3ik08+4c033/T0S5WquLgmJCYWLuaVnJxKTEy0u6xlyxiSkraRm+skI8PB7t37iYmJJi6uKStXFh6TmLieNm2anXdfKTnFwne0al2PNau2ArBlUzr1GxYtoNOsRTTJG3aTm5tHZkY26elH3OXrfkqj4+XNvFLnsqpyTD0OJRfG4tjOdCrWLoqFYRismvYmEdE1aXdff8zmwrf+gNAQ92iDwAp28rJzsNpshFWvyu+puwD4ffsuKtSOLOXW+D/1Dd+hWPiO9QdO0r1BZQBa16xA2u+Z7rJ6lUJZcmc7zCawmk20q12RlMOnix3TrUFl1v16kk2HTtEuqiKBFjNhgVYaVA5lx1nnkgvTZynfoViUUSaT5x6lyONjsFwuF+3bF66M+fPPP3PJJZcUvpDVv4d79ep1GWvWJNOv35MYhsHEiUOZM+cToqIi6dGjAwMHXkf//iMwDIPHHx9IYKCNIUNuY8SIGSQkfEtERDjTpg0nJCTonPtKySkWvqNbj1b8vDaVewa8BMDz4+9g/vvLqRVVha7dW9J3QDfuHzQdwzB46NHr3Ct770v/jauv1wq6nlSrXSuObEnlu+cLY9Fh8B2kfrmcsGpVMAwXv2/fSUFePoeStwHQqt/1tLz1Wta9NZ9d3yXiKnDR/v7CRdfaDx5A0px4jAIXoVUuoVX/G73VLL+lvuE7FAvfsTT1dy6vewkf3dkOkwmGf76V+zpEsfd4Nst2HuXjLYf55O725BcYLNlymJ3HHLy6Op1p1zfj9ta1OJ7l5NFPtpCd52LOL/tZdGdbzCYTL/2wi9wCl7eb51f0Wcp3KBbiS0yGYXh0addRo0ZhMpkYP368+9up2bNns23bNmbMmFGCM+zwZHVEyozTeRra7SumbfF2DeRPw1p4uwYivqfFFK3a70v2Patvb0X+KsbbFbio6j36icfOteeVGz12rgvx+Nf5EyZMYMWKFe7EAEC1atUYOHCgp19KRERERERExKcYfnorQ48nB8xmMz179iz23A033ODplxERERERERERD/HvhQBEREREREREfEkp32XAU5QcEBEREREREfEUs39OK/DTnIaIiIiIiIiIeIpGDoiIiIiIiIh4ihYkFBERERERESnnNK1ARERERERERPyRRg6IiIiIiIiIeIp/DhxQckBERERERETEUwxNKxARERERERERf6SRAyIiIiIiIiKe4qcjB5QcEBEREREREfEUP72VoaYViIiIiIiIiJRzGjkgIiIiIiIi4il++hW8kgMiIiIiIiIinqJpBSIiIiIiIiLij3xu5MCPv+/0dhXkjFaVwr1dBTmL3VrD21WQM+bet9TbVZAznl3f3dtVkDMCzKHeroKcseeZHG9XQcQn5bkc3q6CnBFQ1r+iLqW7FbhcLsaMGUNaWho2m40JEyYQHR3tLp8wYQIbNmwgNLTwGj1z5kzCwsLOez6fSw6IiIiIiIiI+K1SSg4sW7YMp9NJfHw8ycnJTJ48mVmzZrnLt27dyttvv02lSpVKdL6ynrMRERERERERKXOSkpLo3LkzALGxsaSkpLjLXC4X+/bt4/nnn6dfv34sXrz4gufTyAERERERERERDzE8uCBhfHw88fHx7u2+ffvSt29fADIzM7Hb7e4yi8VCfn4+VquVrKws7rjjDu6++24KCgoYNGgQzZs3p3Hjxud9LSUHRERERERERDzFg+Pzz04G/De73Y7DUbSWhsvlwmot/BM/ODiYQYMGERwcDMCll15Kamrq3yYHNK1ARERERERExM/ExcWRmJgIQHJyMjExMe6yvXv3cvvtt1NQUEBeXh4bNmygWbNmf3s+jRwQERERERER8RQPTiv4O7169WLNmjX069cPwzCYOHEic+bMISoqih49enDDDTdw2223ERAQwA033EDDhg3/9nxKDoiIiIiIiIh4SindrcBsNjNu3Lhiz9WvX9/9//vuu4/77ruv5OfzWM1ERERERERExC9p5ICIiIiIiIiIp5TSyAFPU3JARERERERExFP8MzegaQUiIiIiIiIi5Z1GDoiIiIiIiIh4iKFpBSIiIiIiIiLlXCndytDTlBwQERERERER8RQ/HTmgNQdEREREREREyjmNHBARERERERHxFP8cOKDkgIiIiIiIiIinmP10fL6SAyXkcrn4YPoS9u86hDXAyj0jbqNarSrF9jl9IpMXHnqV8e8NxxYYQOZpB7PHzyfbkYu9Qgh3P3Ub4RFhAOTmOJn6+Bvc83RfakRX80aT/JbL5WLi+PnsSNuPzWbl+bF3EnXWz/CjRYksXrQSq8XCfYOvoUu3Vpw6mcmN1zxL/YY1ALiiRxz9B/bkxUkfkrxhJyGhQQC8/OrDhIWFeKVd/sjlcjFu7Jukpu7FZgtg/IR/Ex0d6S5PSPiWhPhvsVgtPPjgLXTv3o4TJ04zfNh0cnOdVKlaiYkTHyE4OJC33vqIr75cRag9hHvvvZHu3dt5sWX+x2SC8SN70DimCk5nASPHf8e+/Sfd5V071uHRwZdhAlK2/8bzk1fw4F3t6NKxDgDhYYFUuSSUDle+Se8rGvLg3e0wDPj06+289+FGr7TJn7lcLsaPe5cdqfsIsFkZN34wUdHV3eWLE5aTkLAcq8XMAw/eRLfubTh86BjPPfMG+QUFYMDocfcTZg9h+LD/uI9LS93HY0/cTt9+vbzRLL/kcrkYM2YWaWnp2GwBTJjwCNHRNdzlCQnfsHDhUqxWC0OG3Eb37u05fvwUw4e/RE6Ok6pVKzFp0lCCg4POua+UXOE1423Szlwzxk14sNg1Y1HCMhLivztzzbiZbt3buMs+eP9Ljh07wRPD7gDg889X8d6cz7GYzfS5uTv9br+q1Nvjz9QvfIeuF+JLlBwooQ2rUsjLzee5N4aya+teFr7+GUMn3esu3/JzKove/JJTx0+7n/ti7nIatqjHdYN6snX9Dha/+RX3PN2X9NT9vP/SIo4fPeWNpvi975dvxJmbxwcLRrF5026mT13EjNceBuDY0VN8OH858xOeJTc3j3sGTuHSjk3Zvv1Xrrq6PU8/07/YubZv3cfrsx8n4kzSRv6ZZct+Jjc3j4XxU0hOTuPFKXN4feYoAI4ePcG8uV+yeMlL5OY6GdB/FJ06xTLz9QSuvbYLN/W5grdmLyE+/hs6XtaKL79YRXzCFABuv30kl17akuDgQG82z69c2b0BNpuVW+5aSGyLSEY93oXBT3wGQGhIAE8/1oX+DyRw4mQOD9zZlkoVg3njvV94471fAHj7Pzcy+T+rMJtNPPXo5dxwx3wcWXl8u/hOPv16OydO5nizeX5n+bL1OHOdzF84nk3JO5n64lxeff1JAI4dPcn8eUuJXzyR3Nw8Bg0YTcdOLXn1lQRuH3AVPXq2Y83qTcyY/iH/eXUY730wGoDkjTt45T/x3HJrD282ze8sW/YTTqeT+PiXSE5OZfLkd5k161mg8H1q7tzPWbLkZXJznfTvP4JOnVozc+ZCrr22K3369GT27EXExy/lmmu6nnNfmy3Ayy30H8uX/YIz18mH8RPZlLyDF6d8wOszRwB/XjO+YtGSKeTmOrmj/3N07NQSl8vFc8++wZYtu7jyyg7uc0198QM++3w6ISFBXHft4/zr6k5UqGD3VtP8jvqF79D1omzy05sVXNwFCf/444+LefpStXNzOi06NAagQbM6pKfuL1ZuMpt46uUHCQ0v+tb50N4jtLy08JiGLeqwY0s6APnOfB554W4io6qWUu3Llo0bdtHx8uYAtGxVn21b97rLUrak06p1A2y2AMLCQqgdVZWdaQfYvnUf27ft4947X+TJx2dx9OhJXC4Xv/76GxPGfMBdAybxyUervdQi/7UhaTuXd24NQGxsI1JSdrvLtmzZSVxc4zOxCCUqOpK0tL0kbSg6pnOXOH78cTO79xygfftmBAbaCAy0EX1mXym5trE1SVy7F4DkLYdp0bToW4e4VjXYsesYox7vSvw7t3HsjyyOn8x2l191RQNOnc5h9U/7cLkMet38HhmZTiIqBGG2mMnLc5V2c/zexg2pdLo8FoBWsQ3ZmrLHXbZlyy5i4xqd9T5VnbS0X3lyxB106VrYN/LzCwgMLPpwbRgGE194j+dG34vF4qdjFb0kKWkbnTsXfgMdG9uYlJSd7rLNm3fQunWTovepqEhSU9OLHdOlS1vWrt103n2l5M6+ZrSKjWFrsWvGLloXu2ZUJy1tH7m5edx4YzcGD+5T7FyNYqLJzMzC6cwDA0z++kncS9QvfIeuF2WTyeS5R2ny6G9Menp6sceQIUPc//d32Y4cQuxB7m2z2UxBfoF7u3m7RtgrhBY7JqpBTTau2QrAxtVbceY4AWjYsi6XVIsohVqXTQ5HNvawYPe2xWwm/0wsHI5swuxFZSGhQWRkZlOnXnWGPHwD77z/FN17tGbKCx+Sne2kX/8eTJh8H6/PfpyEhd+zI23/X15Pzi/TkV1sGobFUhSLzMws7GFFfSI0NJiMjCwcmVnuY0JDg8nMcBATE8X69dtwZGZz4sRpkjemkp2dW7qN8XP2UBsZmUU/M1eBC4ul8IpSqWIwl7atzZRXVnH3wx9zd/846kZVdO875O72vDL7J/d2QYHBVVc04MuFA/l5/X6ysvNKrR1lRWZmNmFnvU+Zi/WN4u9ToaFBZGZkERERTkCAlfT0Q7w0dR5D/n2Le58fvk+iQYNa1K1bNOxXSiYzMwu7/fzvU2H/9T6VmZl1Jn5F71MZGY7z7isll+nIxn7WNeMv/eKssj+vGRUq2Ol0eau/nKtBw9rccvMIrr/2Cbp2iyM8PPQv+8j5qV/4Dl0vxJd4dFrB3XffTVBQEFWrVsUwDNLT03n++ecxmUx88MEHnnypUhccGkROVtEHb8MwsFgtf3vMNQN7MH/Gx0x8+DVaXdaESlUrXuRalg+hocFkOYqGOLsMA+uZWISGBuM4qyzLkUNYWAgtWtYjKMgGQPcerZn12qcEBdnoP7Cne+h6+/aN2ZF2gJhGtUuxNf7N/l8/b5erKBZ2ewgOR9G30w5HNuFhoYSeeT4oKLAwmRMWSv36tek/4Gruv38ckZGVadkyRlM9/qFMh5PQUJt722Q2UVBgAHDiVA6btx3h2B+FH9jWbThAk0ZVSf/1JA3qVuJ0Rm6x9QkAvlmxi2+/38XUsb3pc21TFn+2tdTaUhbY7cX7hlGsbxQvczhyCDsz6mzdz1sZP+4dJk35d7EPdl98vpoBA/9VSrUvW/77veiv71NFf8j8+Z5UGKOi96nw8NDz7islV3jNKIrFX/vFX68Z55KWto/ElRv4btnrhIQEMeLJV1m69Ed6977s4jagDFG/8B26XpRN/jqayaMjB5YsWUKDBg0YPHgwc+fOpXHjxsydO9fvEwMADVrUYdOP2wHYtXUvtepFXuAISEveQ9frLmXUaw9TtWZlGraoe7GrWS7Etm7A6sQtAGzetJsGDWu6y5q3qMvGDTvJzc0jIyOL9D2HadCwJuOef4/l3yUBsO6n7TRpGs2+vUe4+45JFBS4yMvLZ+PGXTRpGuWVNvmruLgmJK4s/LkmJ6cRE1P082vRoiFJ67eRm+skI8PBnt0HaBgTRVzrxiSu3ADAqsQNtGnblOPHT+FwZLPgw0mMGfsgR44co2FDxeKfSEo+RLdOhe8xsS0iSdt1zF22dftvxNSvTETFICwWE61bRLJrT+G0r04dolm5pmh0lz3Uxodv3YYtwIJhQHZ2Hi6XUbqNKQNaxzViVWLhQo6bknfSMKYo6diiRQM2JKWe6RtZpO85SMOGtVn381YmTXyfN2ePpHnz+sXOtzVlD61bx5RqG8qKuLgmJCauByA5OZWYmGh3WcuWMSQlFb1P7d69n5iYaOLimrJyZeExiYnradOm2Xn3lZJrHdeYVWfe/zcl76BhsWtGA5LWbz/rmnGwWL85m90eQmBQ4TQ0i8VCpUvCOX0qs1TaUFaoX/gOXS/KJn+dVuDRkQOXXHIJM2bMYMqUKWzZssWTp/a6Nl1asHX9DiYMeQXDMLh3ZD+WLvyBarUq0/rM/Pf/FhlVhbde+BCAipUrcO/TfUuzymXWFT1b89OP27hzwCQMw2DshLuZ+9631I6qSrcrYrl9QA/uGTgFw3Dx70dvIjAwgEcfv5kxz75HwsLvCQ4O5Plxd1KlSkWuue4yBt0+EWuAhWuvv4z6DWpeuALi1rNXB9auTeb2fk8XznGb9AjvzfmUqOhIrriiPXcMvIY7BjyDy+XisccGEBho48EhtzLy6VdYtOg7IiLCmPrSEwQHB7Jn9wFuveVJAgKsDH/yTiyWvx+ZI8V98/1OLr80ikVz+mEywVNjvuHeAXHs3X+S5Yl7mPrqat57/WYAvvp2Bzt2FyYH6tWJYPVP+9znyXQ4+fTr7Sx8+zby812k7jzKJ19t90qb/FmPnu1Yu3YLA25/DgwYP/FB3n/vS6KiqtH9irYMuKM3g+4Yg+EyePSxvgQG2pg86X3y8/IZNXImAHXr1mD02Ps5fvw0ofZgv/0Wwtt69bqMNWuS6dfvycL3qYlDmTPnE6KiIunRowMDB15H//4jMAyDxx8fSGCgjSFDbmPEiBkkJHxLREQ406YNJyQk6Jz7Ssn17NWetWs307/fMxiGwQuT/s17cz4nKro6V1zRjjsGXs3AAc/jcrkY+tjt5/351qxZhdv69mLggOcICLBSu3Z1brypW6m2xd+pX/gOXS/El5gMw7goXwl99NFHfPTRR8ybN+8fHffj719ejOrI/6BVpXBvV0HOEmS5xNtVkDMatFnq7SrIGWnru3u7CnJGgFlDiX1FgaG7i/gSiynowjtJqchzObxdBTkjwNza21W4qBq+meixc+0c3MVj57qQi3Yrwz59+tCnT58L7ygiIiIiIiJSRpj89EYRflptEREREREREfGUizZyQERERERERKS88ddlH5QcEBEREREREfEQs58mBzStQERERERERKSc08gBEREREREREQ/RtAIRERERERGRcs5fkwOaViAiIiIiIiJSzpUoOeByuSgoKGD9+vU4nc6LXScRERERERERv2QymTz2KE0XnFbwwgsvUL9+fQ4dOsTWrVupXLkyU6ZMKY26iYiIiIiIiPgVk5+Oz79gtbds2UK/fv3YuHEj77zzDkeOHCmNeomIiIiIiIhIKbngyAGXy0VKSgq1atXC6XTicDhKo14iIiIiIiIifqfMLkh4ww03MHbsWO655x6mTp1K3759S6NeIiIiIiIiIn7HZPLcozRdcOTAgAEDGDBgAAD33HMPkZGRF71SIiIiIiIiIlJ6LpgcePvttwkPD+f06dN89NFHdO7cmZEjR5ZG3URERERERET8SpmdVvDtt99y4403kpiYyFdffcW2bdtKo14iIiIiIiIifsds8tyjNF1w5IDZbObYsWNUrlwZgNzc3ItaoY3HAi7q+aXkmkXkeLsKcpYCw+ntKsgZyT+38HYV5IyP9/7m7SrIGb1rW7xdBTlj3k7D21WQszzUtI63qyBnBJhDvV0FEZ92wZEDHTp0YODAgdxxxx1MnDiRrl27lka9RERERERERPxOmV2Q8PHHH+fxxx8HoEWLFgQE6Jt9ERERERERkXPx1zUHLpgcWL58OQsWLCAvLw/DMDh58iSff/55adRNRERERERERErBBacVzJgxg4cffpjIyEhuuukmGjVqVBr1EhEREREREfE7JrPJY4/SdMHkQNWqVWndujUAffr04bfftPiTiIiIiIiIyLn465oDF0wOBAQE8Msvv5Cfn8+qVas4ceJEadRLRERERERERErJBZMDY8eOJT8/nyFDhpCQkMCQIUNKo14iIiIiIiIifsdfRw6cd0HC9PR09/+rV68OwBNPPHHxayQiIiIiIiLip0rrj3qXy8WYMWNIS0vDZrMxYcIEoqOj/7LPAw88QI8ePbj99tv/9nznTQ48//zz53zeZDLxwQcf/A9VFxERERERERFPWLZsGU6nk/j4eJKTk5k8eTKzZs0qts+MGTM4ffp0ic533uTA3LlzKSgowGKxAJCZmUlQUBBW6wXvfigiIiIiIiJSLpXWTQaSkpLo3LkzALGxsaSkpBQrX7p0KSaTyb3PhZx3zYEdO3bQu3dvTp06BcBPP/1E79692bVr1/9adxEREREREZEyzZNrDsTHx9OnTx/3Iz4+3v06mZmZ2O1297bFYiE/Px8o/Hv+iy++YOjQoSWu93mHAbzwwgtMnz6dChUqANCzZ08qVarEhAkTeO+99/7pz0dERERERERE/oG+ffvSt2/fc5bZ7XYcDod72+VyuUf6f/LJJ/z222/ceeedHDx4kICAAGrWrEmXLl3O+1rnTQ64XC5atGhR7Lm4uDjy8vL+UWNEREREREREygvTBe8J6BlxcXF8//33XH311SQnJxMTE+Mue+qpp9z/f/XVV6lcufLfJgbgAsmBc/lzmIKIiIiIiIiIFFdadyvo1asXa9asoV+/fhiGwcSJE5kzZw5RUVH06NHjH5/vvMmBLl26MGXKFB566CHCwsJwOBy89tprXHrppf+vBoiIiIiIiIjI/4/ZbGbcuHHFnqtfv/5f9nvkkUdKdL7zJgceeOAB3nrrLW666SZycnKoUKECN954I/fee+8/rLKIiIiIiIhI+WAqraEDHnbe5IDJZOKBBx7ggQceKM36iIiIiIiIiPgtP80NnD85IMUZLhcr3kzg2N6DWAKs9Px3fypGVnGXb/hsBTtWbwCgTpumXNr3agzD4J37nnPvF9moLp0GXs+RnftYNecjDANCI8K56rFBWG0BXmmXP3K5XEwZH8/OHQcJCLDy7LgB1I4qisXHi9fwccJqLFYL9zxwFZ27tWDa5MXsSD0AwB9/ZBAWFszTz/dj+uTF7uNSNu9l6isP0PHypqXeJn/lcrkYP+4ddqTuI8AWwLjxg4mKru4uX5ywnISEZVgtZh54sA/durfh8KFjPPfMLPILXGAYjB73AHXr1mDLll1MnTIXwzCoXLkik198mMBAmxdb51/UL3yHy+Xii9cXcWTPISwBVm58rB+X1CiKxdqPv2fLyo0ANGzXhCsG/AtnTi6LpnxAdmY2FquFm4cNILxyxcLzFbiIn/QebXtfRsO2TbzRJL+mvuE79FnKd7hcLsaMmUVaWjo2WwATJjxCdHQNd3lCwjcsXLgUq9XCkCG30b17e44fP8Xw4S+Rk+OkatVKTJo0lODgoHPuKyWnWIgvUXKghHb/vJmCvDz6ThnG4bR0Vs35mOtGFY6qOHXkGGmJ6+k7ZTgms4lFo16mfodWBATaqFqvNtc/M9h9HsMwWD7zQ6556l4qRlYh5bu1ZBw9TkTNat5qmt/5Yflmcp35vDt/OFs2pTNj6kdMe7XwZ3zs2Gni5//AB/FP4czN575B0+nQsTHDnr4FgPy8Au4bNJ1nxtxOg5iavPneYwAs+2YDVapV1Ie8f2j5sl9w5uYxf+EENiXvYOqLc3n19ScBOHb0JPPnfU384knk5uYxaMDzdOzUkldfief2Ab3p0bMda1YnM2P6Ama8Mowxz8/m5RlPEBVdncWLlnPo0DHq1q1xgRrIn9QvfMf2H7eQ78zngZcfZ//2vSx96xMGjL4fgOOHj7Hp+yQGv/wEJrOJt4f/h6YdW7IneSc1GtSm+4DebPjuZ1YtXs41D97M8UPHWDJtHqeOnaRt78u83DL/pL7hO/RZyncsW/YTTqeT+PiXSE5OZfLkd5k161kAjh49wdy5n7Nkycvk5jrp338EnTq1ZubMhVx7bVf69OnJ7NmLiI9fyjXXdD3nvjYlakpMsSibytzIAafTed6DbLaSfZvncrk4evQoVapUwWwupfs5XCSHtu8hunXhh4DIRnX5bfev7jJ75QhufP4hzJbCNrryC7AGBPD77l/JPH6SJc+9gsUWQNd7+gAQFBbKxs++549fD1OnbTNdzP6hTRt307FT4bdnLVrVZfvWolhs3bKXVrH1sNkCsNkCqF27CjvTDtGsRTQA8Qt+oEPHJjSIqek+Jjsrl9mvf8Xs9x8r1XaUBRs3pNHp8lYAtIqNYWvKbnfZli27iI1rVBSLqOqkpe3jyREDsdtDAMjPdxEYaGPv3sNUrBjGB+9/ya6d++nStbUSA/+Q+oXv+HXrHhq0KYxF7SZ1OLhzv7usQpUIBo1/0H29KDhzveh4UzdcBYV3CTr1+wmCQoMByM3J5Yah/Vi9aHnpNqIMUd/wHfos5TuSkrbRuXMbAGJjG5OSstNdtnnzDlq3buLuF1FRkaSmppOUtI3Bg28DoEuXtkyf/gG1a0eec9+WLWPO+bryV4pF2VTmkgO9e/fGZDJhGEax500mE8uXn/9DyqhRo5g4cSKbNm1i+PDhVKxYEYfDwcSJE4mNjfVYxUubMzuHwJAg97bJbMZVUIDZYsFitRAcbscwDFa//wlV6tUmomZVsk6epl2fK2nYqTUHt+1m6YwP6HrPzRxOS6f7/bdSIbIKn73wBtXq16Z2y0ZebJ1/cWTmEBoW7N42m83k5xdgtVpwZOZgP6ssJDSIzMxsAPLy8vlo0Rre//DJYuf79KMf6XFlaypG2EunAWVIZmYWYWEh7m2zpSgWmZnZhNmLykJDg8jMyCIiIhyA9PRDvDR1Lq+89iQnTpwmeWMao569m6io6vx7yBSaNa9Ph0ubl3qb/JX6he/IzcohKLToemE2mygoKMBy5noRWqHwevHN258SWb8WlWtVLdzPYubdp1/jt/RD3DXxIQAi69U852tIyalv+A59lvIdmZlZ7kQ9gKXY9TuLsLBQd1loaDCZmVmF1/Uz1/zQ0GAyMhzn3VdKTrEQX3Le5MCKFSv+pxMeOFA4R+/ll1/mrbfeok6dOvz2228MGzaMefPm/W+19AG24CCcOblFTxgGZovFvZnvzOO71+ZjCw6i+wOFmbyqDaLcIyZqNq2P4/gpguwhVKxemUq1C+dlR7duwm+79+uC9g+E2oPIchTFwjAMrFaLu8xxVlmWI4ewMx/81v2YRus2DYp9EARY+uUvTJ5+XynUvOyx20NwOHLc24arKBZ2ezAOR7a7zOHIISy88KK17ucUxo97h0lTHqZu3RoYhkFUVHXq168FwOWXx7I1ZbeSA/+A+oXvCAwJIjf7rFi4DCxnXS/ynHl8/PICAoODuO7ftxY79p7JD3N0/2/Mff5NnpjzfKnVuSxT3/Ad+izlOwqv30XXaFex63cIDkfRH5UORzZhYaHu63pQUCAORzbh4aHn3VdKTrEom8x+OnLggmP9ly9fzr333sugQYMYOHAg1113XYlObLFYqFOnDgDVqlXD5XL9vyrqbZFN6rE3aSsAh9PSuSQq0l1mGAafT5pNlTo16TGkn3tI3M/xX7Pxix8AOJp+gLDKEVSoXpm8nFxOHj4KwKFtu7mkdnWk5Fq1rseaVYWx2LIpnfoNi4afN2tRh+QNu8jNzSMzI5v09N/c5et+Sv3L/NDMjGycznyqR0aUXgPKkNZxjViVWLiw2qbkHTSMiXKXtWjRgA1JqeTmOsnIyCJ9z0EaNqzNup9TmDTxfd6cPYrmzQvvw1q7VjWysnL4dd8RAJKStlO/Qe3Sb5AfU7/wHVFN67Lzl20A7N++l2pnTZExDIMFY9+met2a3PBoX/f1YmX8dyQv/wUAW1Cg+3n5/1Pf8B36LOU74uKakJi4HoDk5FRiYqLdZS1bxpCUtO3M9dvB7t37iYmJJi6uKStXFh6TmLieNm2anXdfKTnFomwymzz3KE0XXJBwxowZjBs3joULF9KhQwfWrFnzt/tnZmbSp08fsrKyWLRoEddffz2TJ0+mRg3/nj/coENLfk1OJeHp6RiGQa9HBrDh0xVUjKyCy+Xi4NZdFOTls3dD4QfCTndcT9s+vfhmxvvsXb8Vk8VMr0cGuFfnXTr9PQygRqO61G2rb0f/iW49WvHz2lTuGTANMHh+/B3Mf385taKq0LV7S/oO6Mb9g17GMAweevRaAgMLF2LZl/47V1/fodi59u39nRo1L/FCK8qGHj3bsXbtZgbc/hwYBuMnDuH9974gKqo63a9oy4A7/sWgO0ZjuAwefawfgYE2Jk96n/y8fEaNnAlA3bqRjB77AOMmPMhTT76CYRjEtm5E125xXm6df1G/8B1NOrZk98Y0Zj/xMhhw0xP9WfPR91SqURmjwMXeLbvIz8tn5/rtAPS6+1raXNmBJdPmk/TNTxguFzc93t/LrSg71Dd8hz5L+Y5evS5jzZpk+vV7EsMwmDhxKHPmfEJUVCQ9enRg4MDr6N9/BIZh8PjjAwkMtDFkyG2MGDGDhIRviYgIZ9q04YSEBJ1zXyk5xUJ8icn470UF/su9997LO++8w4gRI5gyZQoDBw5k7ty5f3tSp9NJamoqQUFB1KlThyVLlnDLLbcQEHDh1TJnbvv2n7VALpo7GvrpeJgyKthS5cI7SanILjjq7SrIGUv3F3i7CnJG79qWC+8kpWLezr/9aCel7KGmdbxdBREfVLYXSrzqm9UeO9c3V13usXNdyAVHDgQEBPDLL7+Qn5/PqlWrOHHixAVParPZaNmypXv79ttv///VUkRERERERMQPlNk1B8aOHUt+fj5DhgwhISGBIUOGlEa9RERERERERPyO2YOP0nTBkQPVqlXDarWSm5vLyJEjS6NOIiIiIiIiIlKKLpgcGDNmDImJiVStWhXDMDCZTCxcuLA06iYiIiIiIiLiV8wm/1z75YLJgc2bN7Ns2TL3PWZFRERERERE5NzK7JoD0dHR5ObmlkZdRERERERERMQLLjhy4PDhw3Tv3p3o6GgATSsQEREREREROQ9/HXN/weTAtGnTSqMeIiIiIiIiIn7PX6cVnDc5sGjRIm699VYWLlyIyVS8dU888cRFr5iIiIiIiIiIlI7zJgeqV68OQL169UqtMiIiIiIiIiL+zOSndys473SIzp07A4VrDJz9CAgIYP369aVWQRERERERERF/YTZ57lGaLrjmwJdffklOTg6xsbFs3ryZ3NxcLBYLzZo1Y9SoUaVRRxERERERERG5iC6YHMjPz+f999/HbDbjcrm4//77eeedd+jXr19p1E9ERERERETEb5TZuxWcPHmS/Px8bDYb+fn5nDp1CgCn03nRKyciIiIiIiLiT8x+uubABZMD/fv357rrrqNhw4bs2bOH++67jzfeeMO9JoGIiIiIiIiI+LcLJgduvfVWevbsya+//kpUVBQREREUFBRgsVhKo34iIiIiIiIifqO0FxL0lPMmB2bOnMlDDz3EE088gclUvHXTpk276BUTERERERER8Tdlbs2BK664AoCrr76a8PDw0quQ2T/nZ5RF4QF1vF0FEZ+UmZfr7SrIGZ2r65rhK677JszbVZAzZnY64e0qiIiIHzpvcqBx48YAvPPOO3z44YelViERERERERERf1XmphX8qUKFCrz//vvUrVsXs7lwgMTll19+0SsmIiIiIiIi4m/K7N0KIiIiSE1NJTU11f2ckgMiIiIiIiIiZccFkwMPP/wwhw4dIjIyklq1apVGnURERERERET8UpmbVuBwOBg2bBgnT56kZs2a7Nu3j0qVKjF9+nTsdntp1lFERERERETEL5S5uxVMmzaN3r17c+ONN7qfW7RoES+++CLjxo0rjbqJiIiIiIiISCk4b1IjNTW1WGIA4NZbbyUtLe1i10lERERERETEL5lNhscepem8Iwes1nMXWSyWi1YZEREREREREX/mr2sOnHfkQMWKFdmyZUux57Zs2UKFChUueqVEREREREREpPScd+TAU089xZAhQ+jQoQO1a9fmwIED/Pjjj8yaNas06yciIiIiIiLiN8rcyIFatWqxePFi2rVrR15eHi1btiQhIYHatWuXZv1ERERERERE/IbZg4/SdN6RAwCBgYFcddVVpVUXEREREREREfGCv00OiIiIiIiIiEjJlfZdBjxFyQERERERERERDylzaw6IiIiIiIiISPmgkQMiIiIiIiIiHlJa38C7XC7GjBlDWloaNpuNCRMmEB0d7S6fP38+H330ESaTiXvuuYerr776b8+n5EAJGS4Xy95YxNG9B7EEWLny4duJiKziLk/69HtSV20AoG7bpnTs9y8Mw2D2Pc9TsUbhfjUa1aHzoOvZvW4LP8Z/g9lipnnPS2l5ZUevtMlfFXaCWaSlpWOzBTBhwiNER9dwlyckfMPChUuxWi0MGXIb3bu35/jxUwwf/hI5OU6qVq3EpElDCQ4OOue+UnKKhe9wuVxMfeEjdqYdIsBmZdSY26gdVdld/snin/hk8Y9YLGbufqAXl3dtyqEDfzDu2YUYhkH1GhGMfP5WgoJtAJw4nskDd77KvMXDCQwM8Faz/JbL5eLliR+xe8dhAmwWnnz+NmqdFQ+Ak8czefju13gnYZj7Z3zg12M8N+w95iwaXmzf5PW7eeHZBSxa+lyptaGsMAGPt6hPg/AQnC6DqZt2cTArx11+Y3R1/lW7KgYQv/sg3x/+g1CrhdFxjQi2mHG6DF5I3sHx3DxqhgTxRIv6BJhN5LkMxm5I43Revtfa5m9cLhezp37E3p2HCAiw8tCo24isXbxfnDqRyagHXuXlecOxBQZgGAb3Xz+OyFqFn6UatYjmjoeu4Yev1/PpvB8IsQfR/Zp29Ly+gzea5Ld0/fYdikXZVFrTCpYtW4bT6SQ+Pp7k5GQmT57MrFmzADh+/DgffvghH3/8Mbm5uVxzzTX861//wmQ6f+WUHCihXT9voSAvj/4vPsGhtHRWvvsxNz7zAAAnjxxj+8r19J86DJPZxMKnZ9Dw0pZYA21UrV+Lm54d7D5PQX4BP7zzMQOmDScg0MaHT8+gfvvmhFYM91bT/M6yZT+d6QQvkZycyuTJ7zJr1rMAHD16grlzP2fJkpfJzXXSv/8IOnVqzcyZC7n22q706dOT2bMXER+/lGuu6XrOfW02/SFUUoqF71i5IoXc3DzenvcoKZv28cpLnzH1lXsA+OPYaRYtWMWchY/jzM1j8J2v0f6yGF6d/gU33XoZV10Tx6dLfmLB3JXc80AvflqTysz/fMkfxzK83Cr/tfr7rTid+cz84BG2bt7HrOmf88KMu93l69amMfuVLzn+R9HP+Nsvkli8YBUnTziKnev3IydZNC+R/HxXqdW/LLm8eiVsZhMPrdlC04p2Hmpah2fWpwJQIcDKDdHVuW/VJmxmMx90a833h/+gd+2q7Mlw8Mb2fVwbVY1+9Woyc/tehresz1up+9h2MpMu1S+hdmgwW0+qn5TUupUp5OXmMfntR0lL2cd7r3zGyKn3uMs3/pTKvJlfcvKsfnHkwB/Ui6nFqGn3up87fTKTD99cykvvP0FoWBBjHnmTlm0bUrVGpVJtjz/T9dt3KBZyIfHx8cTHx7u3+/btS9++fQFISkqic+fOAMTGxpKSkuLer1KlSnzyySdYrVYOHjxIYGDg3yYGQGsOlNjBbbup07oJADUa1eW3XfvdZWGVI+gzZghmixmTyYSroABLQAC/7dpP5h+nSHjmFT4a9wbHD/zG8QNHqBhZmSB7CJYAKzWb1OPA1t3eapZfSkraRufObQCIjW1MSspOd9nmzTto3boJNlsAYWGhREVFkpqaXuyYLl3asnbtpvPuKyWnWPiOTRvTuaxTYwCat4omdVvRe9TWLb/SsnVdbDYr9rBgakVVZteOQ6Tv+Y3LOhce07J1XTZvKPyZm8wmXp39IOEVQkq/IWXElo3ptO/YCIBmLaNJOyseAGaziWlvDCYsvOhnbA8P5j/vDCm2X25uHtNfWMxjo/pc/EqXUS0rhbPu6EkAtp3MpFFFu7vsVF4+961KpsAwqBQUgNNVmIDZczqLEIsFgBCrhXzDwGY2U9EWQMdqlZhxWXOaRYSxXYmBf2T7pnRaX1b4ntOoeTS7U4v3C5PJxJhXH8R+Vr/YnbqfP46e4vmHZjLh8bc4uO93fjt4nDoNaxBWIQSz2UyDJrXZsXVfqbbF3+n67TsUi7LJZDI89ujbty8fffSR+/FnYgAgMzMTu73oumaxWMjPLxrRZrVamTdvHn379uX666+/YL0venLg+PHjGIZ/3srhbLlZOQSGBru3TWYzroICACxWCyHhdgzD4Ic5n1C1Xi0q1ayKvVI47W/pxW0vPEr7W3rx1ctzyc3KwRZSdB5bcCC5juxSb48/y8zMwm4v+uBgsZjJzy9wl4WFhbrLQkODyczMIjMzm7CwEPdzGRmO8+4rJadY+A5HZg6h9iD3ttlcFAuHI7dYWUhIIJmZOcQ0qsGq77cCsOqHrWRnOwHocFkjKlQMRf53DkcO9rPjcVbfAGh7acxffsYduzQlODiw2HP/mfwxfQd1o0rVChe3wmVYiNWK46yh/y4DLGd9cVJgwE11qjOrU0u+PXAUgNPOPNpWqcj7XVvTr15Nvtz/G+E2K/XCQ0k6dorHfkwhLMBK79pVS7s5fi3LkUNIaPH3qYKz+kVsh0aEVSjeLyIqh3PznT0YN/Mhbr6zBzPGLCCydmX2px/h5B8Z5OY42bJ+Jzln3r+kZHT99h2KRdlkNnnu8XfsdjsOR9GIQ5fLhdVafHLAHXfcwapVq/jll1/46aef/r7e/3OLz2PJkiW89tprbN26ld69e3P33XfTu3dv1q5d6+mXKlWBIUE4s4vmKBqGC/OZbxUA8p15fDX9A/Kyc+gx+DYAqjWIokH7FgDUalofx/FT2IKDyMvJdR/nzM4l6Kykg1yY3R6C46yEistlYLVazioreiN0OLIJCwvFbg92H+NwZBMeHnrefaXkFAvfEWoPIiur6L3l7FiEhgaS5Sgqy8rKJSwsmEeHX8+qH7Yy5J6ZmICKEfqZe0po6PnjUVLHfj/Flo3pvPfmtwy9byYZp7IYO2Kep6ta5mXl5xNy1s/eRGFC4Gwf7z1Cn+9+odUl4bS+pAJ3xUTx4e6D3LlyI8N/3sr4No057czHkZfPxj9OAfDj78dpVMGOlFxIaBDZ/9UvLBfoFw2a1KZdl2YANImtx4ljpwgNC+buoTfw4sj3mP7cPOo1qkW4Epr/iK7fvkOxkP+PuLg4EhMTAUhOTiYmJsZdtmfPHh5++GEMwyAgIACbzYbZ/Pd//ns8ObBgwQLuueceXnzxRWbNmsWnn37KBx98wLRp0zz9UqWqRpN6pCdtA+BQWjqVz1ooxDAMPnnhLarUqUGvh/phthT+WH9c+DVJn/8AwO/pBwmrXJFLalfnxKGjZGc4KMjL58C2XUQ2rlvazfFrcXFNSExcD0BycioxMUUrcrZsGUNS0jZyc51kZDjYvXs/MTHRxMU1ZeXKwmMSE9fTpk2z8+4rJadY+I6WsXVZu2o7ACmb9lG/YaS7rFmLKDZt2ENubh6ZGdns3fMb9RpUZ92POxjy6NXMevchzBYz7S6NOd/p5R9qHluHn1YXzmvfunkf9RpU/8fnqFy1AnM/GcF/3n6I/7z9EGEVQhg95Q5PV7XM23I8gw5VIwBoWtFOekbRh+faocGMb1M4zD3fMMhzuXAZBhl5+TjOfHN30plHiNWC0+VivyOHlpUK1whqWalCsXPJhTVuWZcNawvfp9JS9hFdP/ICR0D829/yxcLCD77pOw9xSdWKuApc7Ek7wAtvPszwFwZxcN/vNG5Z52JWvczR9dt3KBZlk9mDj7/Tq1cvbDYb/fr1Y9KkSYwcOZI5c+awfPly6tWrR+PGjenbty/9+vWjVatWtG//94tUenxBwoCAAEJCQggNDaV27doAVKtW7YKLH/i6hpe2ZF9yGguemg7AVY8OYP2nK6hYvQqGy8WBrbsoyM8nfUPhRa/zwOtof3Mvvpr+Aenrt2K2WLhq6B1YrBa63XMjS8bMwjBcNO9xKWGXVPRiy/xPr16XsWZNMv36PYlhGEycOJQ5cz4hKiqSHj06MHDgdfTvPwLDMHj88YEEBtoYMuQ2RoyYQULCt0REhDNt2nBCQoLOua+UnGLhO7r1aM4vP+3g/oGvYBjw7Pi+LPhgJbVqX0KX7s25tX9nHrzrdVwug8GPXE1gYABRdaoweuR8AmxW6tWvzpOa1+4xna9ozvqfdvDvO1/FMGDE2L4kzF1JzdqV6dStmberV66sOvIHbatU5PWOLTCZYHLyLm6rW4MDWTms/e04u047mNmpJWDw8+8n2XT8NAccOTzVqj43RlfHajbx0uZdALy4aSePtaiPxWTiSFYOb27f69W2+ZsO3Zqz6ZcdjLy/8H3q4Wf78tmClVSvdQntuzQ/5zF9Bl3BjDELSFq7HYvFzCPP9XOPNhh+53QCbAFc378r4RU1iuOf0PXbdygWZZPZVDrT6s1mM+PGjSv2XP369d3/f/jhh3n44YdLfD6T4eEFAWbPns3GjRuJiYkhJSWFzp07s2rVKpo0acLw4cMvfHzqN56sjvw/PKARDSLndCJ3h7erIGfk/Pf4cPGafiu0UryvmNnphLerIGdpFqFRWSJ/Vbb7xXNJyzx2rvFtenrsXBfi8ZEDDzzwAOvWrWP16tXUqFGDP/74g4EDB9KtWzdPv5SIiIiIiIiIT7nQQoK+yuPJAYD27dtfcD6DiIiIiIiISFnjr8mBi34rQxERERERERHxbRdl5ICIiIiIiIhIefTPbl7sO5QcEBEREREREfGQ0rpbgadpWoGIiIiIiIhIOaeRAyIiIiIiIiIe4q8LEio5ICIiIiIiIuIh/poc0LQCERERERERkXJOIwdEREREREREPMTipyMHlBwQERERERER8RBNKxARERERERERv6SRAyIiIiIiIiIeYjYZ3q7C/0TJAREREREREREP8ddpBUoOiIiIiIiIiHiIxdsV+B9pzQERERERERGRck4jB0REREREREQ8RNMKPOT+xlHeroKcseLQLm9XQc7SNbKWt6sgZ1Sw1fV2FeSMCFOAt6sgZ6y81ts1kD8FR73m7SrIWbJ/HevtKohIKfPXBQk1rUBERERERESknPO5kQMiIiIiIiIi/sqiaQUiIiIiIiIi5Zu/rjmgaQUiIiIiIiIi5ZxGDoiIiIiIiIh4iL+OHFByQERERERERMRD/DU5oGkFIiIiIiIiIuWcRg6IiIiIiIiIeIjFZHi7Cv8TJQdEREREREREPMRfh+f7a71FRERERERExEM0ckBERERERETEQ/x1QUIlB0REREREREQ8xF+TA5pWICIiIiIiIlLOaeSAiIiIiIiIiIfobgUiIiIiIiIi5ZymFYiIiIiIiIiIX9LIAREREREREREP8deRA0oOlJDL5WLsmDdJTduLzWZlwoSHiY6OdJcnJHxL/MJvsFotPDjkVrp3b8eJ46cZNnwauTlOqlatxMRJj7J370EmTnzHfdym5B28/vpIOneJ80az/JLL5WLhjMUc2H0Ia4CVO57sS9WaVdzlyxf9wPoVGwFodmkTrr2zN85cJ3NemE/GyQyCQoK48+n+hFW0s2HlJr79cDmYTLTvEccVt3T1VrP8ksvlYtzYt0lL3YvNFsC4CQ8W6xeLEpaREP8dFquFBx+8mW7d23Do0FGefWYWBfkFGIbB2HGDqVuvJt+vWM+smYuxWMz0ufkKbr2tpxdb5n8KYzGb1DOxGD/hof96j/qOhPhvsVjNPPjgrXTv3tZd9v77n3Ps2EmGDRsIwLff/Mhbb32EyWTi2uu6MGjQtaXeHn/ncrkYM2YWaWnp2GwBTJjwCNHRNdzlCQnfsHDhUqxWC0OG3Eb37u05fvwUw4e/RM6Za8akSUMJDg46575ScoqF7zCZTPznhXto2SSKXGc+Q56azZ59vwHQsmk0U0cPcu/bvnUDbrt/OuuTd7F55ctsS9sPwGff/MLr7y5l6P3XcNuNHTFcBi++9gmffbPeK23yV+oXvkOxKJuUHCjjli37mVynk/j4KSQnpzFl8hxmzhoFwNGjJ5g79wuWLJlGbq6T/v1H0qlTLK/PjOfaa7vQp08PZs9eQnz8N9x11/XMnfsCAEu/XkO1qpcoMfAPbVqdQp4zn6def4w92/ayZOZnDHnhXgCOHjrGumVJjJj5OCaziZceeYXYy1uSmrSDmvUiufauu/llxQa+nvsttzx0I5+89QUj33iCwOBAxt49mfa92mCvYPdyC/3H8mW/4Mx18mH8RDYl7+DFKR/w+swRQGG/mDf3KxYtmUJurpM7+j9Hx04tefU/8fQf0JuePduzelUyL7+8gGnTH2fy5PdIWDSZ4OBA7uj/HN2vaEvlyhW920A/smzZOnJz81gYP5nk5DRenPIer88cCfwZiy9ZvGQqublOBvR/hk6dWuFyuXju2Zls3rKLK6+8FICCggKmTZ/H4sUvEhISxLXXDOW667oQERHuzeb5nWXLfsLpdBIf/xLJyalMnvwus2Y9C/x5zficJUtePnPNGEGnTq2ZOXMh117blT59ejJ79iLi45dyzTVdz7mvzRbg5Rb6D8XCd1x/VVuCAgPodtNo2rduwOTn7uC2+6YBsHnbPq7qOx6APtd04NCR43y3chPdL2/Ook/X8sTo99znqRAewr/v6U2zLo8RGhLEz19PUnLgH1K/8B2KhfgSj685kJmZ6elT+oSkpO107lz4R3xsbCNSUna5y7Zs3klc6ybYbAGEhYUSHRVJWupeNpx1TJcucfy4dpP7mKysHF599UOeeea+0m1IGbB7yx6atm8MQL2mddi3Y7+7rFLVCB55cTBmixmTyURBgYsAm5XdKUXHNG/fhNSkHZgtZka//zTB9mAyTzswCgwsVuXL/okNSdu5vHNrAFrFxrA1Zbe7bMuWXbSOa+zuF1HR1UlL28dTTw+ia9fCflFQUECgLYA9ew4SHVWdChXs2GwBxLVpzPr1273SJn91diwK36POjsVO4v4Si73k5uZx443deXDwze59LRYLX375CmFhoZw8mYHL5SIgQP3in0pK2kbnzm0AiI1tTErKTnfZ5s07aH3WNSMqKpLU1PRix3Tp0pa1azedd18pOcXCd3Rs14jvfij8LLRu4y7atKz3l31CggN59olbGD7mAwDiWtSldYu6fJvwPPNnDaV61Yo4snL59eAxQkOCCA0OxOXyz1XBvUn9wncoFmWTxeS5R2nyeHKgU6dOLFq0yNOn9TpHZhZh9hD3tsViJj+/AIDMzCzCworKQkODycjMKvZ8aGgwGRlZ7n0WL17GVb07EVFJ38b9U9lZOQSHBru3zWYTBQWFsbBYLdgr2DEMgyWzPqV2g5pUq12VbEfRMYEhgWQ7cgr3t1jYmLiZF+6bSsPY+gQG2Uq/QX4s05GN/azffXOxfpH9136RkUVERDgBAVbS9xxk6otzeejft5KZmVXsPKGhQWSe1V/kwjIdxd+HLP8VC/s5YlGhgp1Ol8f+5VxWq4Vvv/2JG298gnbtmxEcHHjR61/WZGZmYf/ba0aouyw0NJjMzKxifaYwRo7z7islp1j4jjB7MKfOem8vKHBhsRT/KHpXv2589OXP/HEiA4C0XYcYN30RV942js+/Wc/0cXcBcODQH2xYPpW1X01k5pylpdaGskL9wncoFmWT2WR47FGq9fb0CRs3bsz27dsZNGgQ69at8/TpvSbUHoLDke3edrkMrFYLAPb/KnM4sgkLCy32vMORTVh4UYf94vOV3Hqr5lT/L4JDgsjNynFvGy4Di8Xi3s5z5vHuhHnkZOVy+2O3FB4TWnRMblYuwfai5ELrLi2ZtGgMBXkF/PTtL6XUirLBHhpc7HffKNYvgv/SL8LPXLR+/imFRx6eyuQpj1C3Xs0zfSXnrH1ziv2hKxdmD/3v9yhXiWJxPldeeSkrV75NXl4+n37yw0Wpc1n239eFv14zij6wFV0zgotdM8LDQ8+7r5ScYuE7MjKzCbP/d3LfVWyffjdeznsfrnBv/7B2KyvXbgXg06W/0KpZHa7qHkv1qhVp0mkoMZc9wnVXtaNtq/ql04gyQv3CdygW4ks8nhwIDAzk+eef58knn2Tu3Llcd911vPDCC3zwwQeefqlSFRfXmJWJSQAkJ6cRExPtLmvRsiHrk7aRm+skI8PB7t0HiImJonVcY1auLDwmMXEDbds0BSAjw4HTmUdkZJW/vpBcUL3mdUn5uXDI+Z5te6lRr2jRNcMwmPXMO9RqUIMBw27DfOYbifpnHZOybjsNWtYj25HD9KGvkefMx2w2Ywu2YTL56eohXtI6rjGrVm4AChfXbBgT5S5r0aIBSeu3u/vFnt0HaRhTm59/SmHSxDm8+dYzNG9R+GGuXr2a7Nt3mJMnM3A681j/yzZiW8d4pU3+Ki6uMYlnYvGX96gWDc8Ri6hzniczM4uBdzyL05mH2WwmODgIs1l3vf2n4uKakJhYOAc6OTm1WDxatowhqdg1Yz8xMdHExTVl5crCYxIT19OmTbPz7islp1j4jh/X7+Cq7rFA4YKDKan7i5WHhwVjs1k5cPi4+7lZLz7ATVd3AKD75c3ZuCWdk6cyyc5xkpubR25uHidPO6gQroTyP6F+4TsUi7LJ7MFHaTIZhuHRsQoDBw5k7ty57u2MjAx++eUX0tPTuffeey94vIFvzjP+824FaTv2YhgwaeIjrExMIjoqkit6tCch4VsS4r/FZbgYPPgWrrqqI8eOneTpEf/B4cgmIiKcl6Y9QUhIEFs27+SNNxbx+sxR3m7W3/r+kG/OU/rzbgUH9xzGMAwGjbidlJ+2U6VmZQyXi3fGz6Vu06I3wxvvv5Za9Wvw/uQFnPrjNJYAC/c8O5AKlcJZ9fla1n71MxarhZr1atD30T7uhIKv6RpZy9tV+Is/71awI20fhmHwwqR/k7hyA1HR1bniinYsSljGooRluFwuHhjchyuvupSbbhiO05nnXmywTt0ajB032H23ApfLRZ+br6D/gN7ebdzfMGG58E6l7M+7FaSdicXESQ+fFYv2JCR8x6KEb3G5DAYPvpkrr7rMfezHH61gT/pB990KEuK/ZcmS5VitFmIaRfPss/cVG53jS8wm31xo6c/Vp3fs2FsYj4lDSUxMIioqkh49OpCQ8A3x8UsxDIPBg2/lqqs6cezYCUaMmOG+ZkybNpyQkKBz7islVx5jERw12ttVOKc/71bQonEUJhM8MPxNenePZfe+3/jyuyTatKzHiEdu5Lb7p7uPia5dhTenDsZkMuHIyuWhEbM58vtJnn3iFq7sWriw6tpf0hg1cYEXW/b3sn8d6+0q/EV57Be+qvzGomx/CbTi0FceO9cVNa4+b1nh788Y0tLSsNlsTJgwgejoor+D3nvvPb788ksAunbtysMPP/y3r+Xx5MDHH3/MTTfd9D8f76vJgfLIV5MD5ZUvJgfKK19MDpRXvpocEPEmX00OlFe+mBwQ8T4lB0rq75ID3377LStWrGDy5MkkJyfz5ptvMmvWLAD279/P0KFDWbRoEWazmdtvv50xY8bQuHHj857P40tQ/38SAyIiIiIiIiL+zJN3GYiPjyc+Pt693bdvX/r27QtAUlISnTt3BiA2NpaUlBT3ftWrV+ftt992j/7Mz88nMPDvF5nW/alEREREREREPMSTdxk4Oxnw3zIzM7Hb7e5ti8VCfn4+VquVgIAAKlWqhGEYvPjiizRt2pS6dev+fb09VmsRERERERERKRV2ux2Hw+HeLrxbVdH3/7m5uQwfPhyHw8Ho0ReecqbkgIiIiIiIiIiHmE2ee/yduLg4EhMTAUhOTiYmpmgtB8MweOihh2jUqBHjxo0r0eLSmlYgIiIiIiIi4iEX+qPeU3r16sWaNWvo16/fmbtdTGTOnDlERUXhcrlYt24dTqeTVatWAfDEE0/QunXr855PyQERERERERERP2M2mxk3blyx5+rXr+/+/5YtW/7R+ZQcEBEREREREfEQf527r+SAiIiIiIiIiIeYSmlagaf5a1JDRERERERERDxEIwdEREREREREPMRPBw4oOSAiIiIiIiLiKZpWICIiIiIiIiJ+SSMHRERERERERDzEX7+BV3JARERERERExENMJsPbVfif+GtSQ0REREREREQ8RCMHRERERERERDzET9cj9L3kQMO233q7CnLGrvX/8nYVRERE5B/qO+9Bb1dBRKRc090KRERERERERMQv+dzIARERERERERF/5acDB5QcEBEREREREfEUs59mBzStQERERERERKSc08gBEREREREREQ/x04EDSg6IiIiIiIiIeIruViAiIiIiIiIifkkjB0REREREREQ8xE8HDig5ICIiIiIiIuIpSg6IiIiIiIiIlHO6laGIiIiIiIiI+CWNHBARERERERHxED8dOKDkgIiIiIiIiIinmEyGt6vwP9G0AhEREREREZFyTiMHRERERERERDxE0wpEREREREREyjmTn2YHNK1AREREREREpJzTyIESMplg7NNX0KRhZZx5BYwav4x9B065y7t0jObR+zuAycTW7b8zesr3mM0mRj3emRZNq2ELsPDK7J/5fnU689+82X1cvegIPvpiO1NfW+ONZvkll8vFmDGzSEtLx2YLYMKER4iOruEuT0j4hoULl2K1Whgy5Da6d2/P8eOnGD78JXJynFStWolJk4YSHBx0zn2l5BQL36FY+BbFw3coFr7DcLlIn78Ax4EDmK1W6t05iOCqVd3lR77/nt/XrAWTiRpXXknldm0pyM1l51tvk5+Vhdlqof7ddxMYEcHpHTvZt3gRYCI8JoboW24+/wvLX6hf+A7Fomzy12/g/bXepa5Xt/oE2izcek8CU19dw8jHO7vLQkMCeHro5dz/2Gfcclc8Bw6fplLFYG68ujEBVgt9713Eg8M+J7p2BQAGDF7CgMFLeHrcdxz5PZPX31nnrWb5pWXLfsLpdBIf/xLDht3J5MnvusuOHj3B3Lmfs3Dhi7zzzlimT/8ApzOPmTMXcu21XVmwYApNm9YjPn7pefeVklMsfIdi4VsUD9+hWPiO48nJuPLyaDHyaaL69GFfwiJ3WV5GBkd+WEnzp0fQbNgT7Fu0CMMw+H3VKkKjo2n+1JNU7nAph775BoC98fE0vP8BWowaSebedBy//uqtZvkl9QvfoViUTSaT5x6l6aInB5xOJzk5ORf7ZS66trE1SPxxHwDJKUdo3qSauyyuZSRpu/5g5OOd+fCtWzj2RxbHT2bT+bJojhzN5K0Z1/PCsz1ZkZhe7JzPDuvKi6+uIStbHfefSEraRufObQCIjW1MSspOd9nmzTto3boJNlsAYWGhREVFkpqaXuyYLl3asnbtpvPuKyWnWPgOxcK3KB6+Q7HwHRk7d1GxeTMAwurXI3PfPndZQFgYrZ5/DrPVivPUKcwBAZhMJiJ79qTWNVcDkHv8ONbgEABajBpJUJXKFOTkUJCdjTkwsPQb5MfUL3yHYiG+xOPJgfT0dB599FGGDRtGcnIy1113Hddccw1fffWVp1+qVNlDbWRk5rq3XS4Di6UwlRNRMZhL29TixVfXcO+jn3J3/1jqRFUkomIw0bUqcv9jnzH7/fVMHt3LfXyjBpWxh9r48Zf9pd4Wf5eZmYXdHuLetljM5OcXuMvCwkLdZaGhwWRmZpGZmU1YWIj7uYwMx3n3lZJTLHyHYuFbFA/foVj4joKcHCzBwe5tk9mEUVBQtG2xcHjFClImTaZyhw5n7Wdm60vTOLJiBZVat3bvm7F7D5vGjCUgvAK2iIjSa0gZoH7hOxSLssnkwUdp8nhy4LnnnqNfv35ceeWVDB48mA8++IDPP/+c999/39MvVaoyHU7sITb3ttkEBQUGACdP5bBl228c+yOLrOw81m04SNOYKpw8lc33qwszdus2HKRuVEX38Tdc3Yj4j1NKtQ1lhd0egsOR7d52uQysVstZZUVvhA5HNmFhodjtwe5jHI5swsNDz7uvlJxi4TsUC9+iePgOxcJ3WIKCKMgp+qIFl4HJYim2T+QVV9Dmpamc3rmTU6mp7uebDR9G86eeJO2NWe7nwurXI27yJEKjojj09dKLXv+yRP3CdygWZZOmFZyRn59Px44dufLKK6lYsSLVqlUjJCQEq9W/1z5M2nSYrp3qABDbvDppu/5wl6Wk/k7D+pcQUSEIi8VEbItIdqYfJyn5EN3OHNO4YWUO/ZbhPqZju9ruaQryz8TFNSExcT0AycmpxMREu8tatowhKWkbublOMjIc7N69n5iYaOLimrJyZeExiYnradOm2Xn3lZJTLHyHYuFbFA/foVj4jrAG9Tm5ZQsAGbv3EFKrprss+8gR0mbOwjAKEwZmqxVMJg5+9TVHf/wRAHNgECaTGcMwSJnyIvkOB1CYdPDb+4Z5ifqF71AsxJeYDMMwPHnCYcOG4XK5KCgo4MCBA3Tu3Bm73c7WrVuZMWPGBY9v0PY/nqyOx/x5t4LGDS7BZDIxYux3dLu8Dvv2n2R5YjrXXBnD/QPjAPhq2U5mv5+ELcDC2JHdaVi3EphMjJ60gq1pRwFY/dW9XH71O95s0gXtWv8vb1fhnP5c1XXHjr0YhsHEiUNJTEwiKiqSHj06kJDwDfHxSzEMg8GDb+Wqqzpx7NgJRoyYgcORTUREONOmDSckJOic+0rJKRa+Q7HwLYqH7yiPsbgr8bC3q3BO7rsVHDwABjS4605ObEkhqGoVKsXGsv+zzzmZkgImExWbN6f2ddfiPH2aXe/OwcjLw3C5iLq5D+ENGnA8OZmDX32NKcCKrUIF6g8aVJgk8EHvdYn0dhX+ojz2C19VfmMR4+0KXFQHHJ977Fy1Qq/z2LkuxOPJgfz8fFauXEmdOnUIDQ3lvffeo0KFCtx5552EhIRc8HhfTQ6UR76aHBAREZHz89XkQHnli8kBEe8r28mBQ1meSw7UCCm95IDHx/pbrVZ69Ojh3n766ac9/RIiIiIiIiIi4kH+vRCAiIiIiIiIiA/x11VQlBwQERERERER8RCTyaMz90uNx+9WICIiIiIiIiL+RSMHRERERERERDxE0wpEREREREREyjmTn2YHNK1ARERERERExM+4XC6ef/55+vbty8CBA9m3b99f9jl+/DhXXXUVubm5FzyfkgMiIiIiIiIiHmLy4OPvLFu2DKfTSXx8PMOGDWPy5MnFyletWsU999zD0aNHS1RvJQdEREREREREPMTswcffSUpKonPnzgDExsaSkpJSvB5mM3PmzKFixYolqrfWHBARERERERHxQfHx8cTHx7u3+/btS9++fQHIzMzEbre7yywWC/n5+VithX/md+rU6R+9lpIDIiIiIiIiIh7iyQUJz04G/De73Y7D4XBvu1wud2Lgf6FpBSIiIiIiIiIeUzqrDsTFxZGYmAhAcnIyMTEx/69aa+SAiIiIiIiIiJ/p1asXa9asoV+/fhiGwcSJE5kzZw5RUVH06NHjH59PyQERERERERERDzFd8D4DnmE2mxk3blyx5+rXr/+X/VasWFGi8yk5ICIiIiIiIuIhJpN/zt73z1qLiIiIiIiIiMdo5ICIiIiIiIiIx5TOtAJPMxmGYXi7EsXt8HYFREREREQ84q7Ew96ugpyx+olkb1dBzti1fqi3q3BRnXIu9di5Kth6e+xcF6JpBSIiIiIiIiLlnKYViIiIiIiIiHiMf04rUHJARERERERExEN0twIRERERERER8UsaOSAiIiIiIiLiMZpWICIiIiIiIlKumfw0OaBpBSIiIiIiIiLlnEYOiIiIiIiIiHiIv44cUHJARERERERExGP8c4C+f9ZaRERERERERDxGIwdEREREREREPMRk0rQCERERERERkXLOP5MDmlYgIiIiIiIiUs5p5ICIiIiIiIiIh+huBSIiIiIiIiLlnn8O0PfPWouIiIiIiIiIx2jkQAm5XC7GjJlFWlo6NlsAEyY8QnR0DXd5QsI3LFy4FKvVwpAht9G9e3uOHz/F8OEvkZPjpGrVSkyaNJTg4KBz7islp1j4DsXCdygWvkXx8B2Khe9QLHyH4XKRPn8BjgMHMFut1LtzEMFVq7rLj3z/Pb+vWQsmEzWuvJLK7dpSkJvLzrfeJj8rC7PVQv277yYwIoLTO3ayb/EiwER4TAzRt9zsvYb5IZMJxj59BU0aVsaZV8Co8cvYd+CUu7xLx2gevb8DmExs3f47o6d8j9lsYtTjnWnRtBq2AAuvzP6Z71enM//Nop99vegIPvpiO1NfW+ONZpV7/jqtQCMHSmjZsp9wOp3Ex7/EsGF3Mnnyu+6yo0dPMHfu5yxc+CLvvDOW6dM/wOnMY+bMhVx7bVcWLJhC06b1iI9fet59peQUC9+hWPgOxcK3KB6+Q7HwHYqF7zienIwrL48WI58mqk8f9iUscpflZWRw5IeVNH96BM2GPcG+RYswDIPfV60iNDqa5k89SeUOl3Lom28A2BsfT8P7H6DFqJFk7k3H8euv3mqWX+rVrT6BNgu33pPA1FfXMPLxzu6y0JAAnh56Ofc/9hm33BXPgcOnqVQxmBuvbkyA1ULfexfx4LDPia5dAYABg5cwYPASnh73HUd+z+T1d9Z5q1nlnslk8tijNF3U5IBhGBfz9KUqKWkbnTu3ASA2tjEpKTvdZZs376B16ybYbAGEhYUSFRVJamp6sWO6dGnL2rWbzruvlJxi4TsUC9+hWPgWxcN3KBa+Q7HwHRk7d1GxeTMAwurXI3PfPndZQFgYrZ5/DrPVivPUKcwBAZhMJiJ79qTWNVcDkHv8ONbgEABajBpJUJXKFOTkUJCdjTkwsPQb5MfaxtYg8cfCn39yyhGaN6nmLotrGUnarj8Y+XhnPnzrFo79kcXxk9l0viyaI0czeWvG9bzwbE9WJBb//X92WFdefHUNWdlKmsk/4/FpBb/++itjx45lz549/P777zRr1ozatWvz9NNPU6VKFU+/XKnJzMzCbg9xb1ssZvLzC7BaLWRmZhEWFuouCw0NJjMzi8zMbMLCQtzPZWQ4zruvlJxi4TsUC9+hWPgWxcN3KBa+Q7HwHQU5OViCg93bJrMJo6AAk8VSuG2xcHjFCg589jnVr7jirP3MbH1pGlkHD9L08cfd+2bs3sPOt94iODISW0RE6TbGz9lDbWRk5rq3XS4Di8VEQYFBRMVgLm1Ti+sGLCArK4+Fb9/Cxi2HiagYTHStitz/2Ge0j6vJ5NG96P/AYgAaNaiMPdTGj7/s91aTBEDTCgqNHTuWZ599lu+//5758+fToUMH7r77bp555hlPv1SpsttDcDiy3dsul4HVajmrrOii5HBkExYWit0e7D7G4cgmPDz0vPtKySkWvkOx8B2KhW9RPHyHYuE7FAvfYQkKoiCn6A9SXIY7MfCnyCuuoM1LUzm9cyenUlPdzzcbPozmTz1J2huz3M+F1a9H3ORJhEZFcejrpRe9/mVJpsOJPcTm3jaboKCgcPT1yVM5bNn2G8f+yCIrO491Gw7SNKYKJ09l8/3qwtEC6zYcpG5URffxN1zdiPiPU0q1DfJXJswee5Qmj79aZmYmdevWBSA2NpYNGzbQvHlzTp8+7emXKlVxcU1ITFwPQHJyKjEx0e6yli1jSEraRm6uk4wMB7t37ycmJpq4uKasXFl4TGLietq0aXbefaXkFAvfoVj4DsXCtygevkOx8B2Khe8Ia1Cfk1u2AJCxew8htWq6y7KPHCFt5iwMozBhYLZawWTi4Fdfc/THHwEwBwZhMpkxDIOUKS+S73AAhUkHSnmOtL9L2nSYrp3qABDbvDppu/5wl6Wk/k7D+pcQUSEIi8VEbItIdqYfJyn5EN3OHNO4YWUO/ZbhPqZju9ruaQriTSYPPkqx1oaHFwYYNmwYoaGhdOnShR9++IHQ0FAuu+wy3n//febMmVOCM+zwZHU85s8Vdnfs2IthGEycOJTExCSioiLp0aMDCQnfEB+/FMMwGDz4Vq66qhPHjp1gxIgZOBzZRESEM23acEJCgs65r5ScYuE7FAvfoVj4FsXDdygWvqO8xuKuxMPersJfuO9WcPAAGNDgrjs5sSWFoKpVqBQby/7PPudkSgqYTFRs3pza112L8/Rpdr07ByMvD8PlIurmPoQ3aMDx5GQOfvU1pgArtgoVqD9oUGGSwAetfiLZ21X4iz/vVtC4wSWYTCZGjP2ObpfXYd/+kyxPTOeaK2O4f2AcAF8t28ns95OwBVgYO7I7DetWApOJ0ZNWsDXtKACrv7qXy69+x5tNKpFd64d6uwoXVW7BLx47V6ClncfOdSEeTw44nU4WLVrErl27aNKkCTfffDNbtmwhOjqaiBLNQfLN5ICIiIiIyD/li8mB8soXkwPlVVlPDjhd6z12Lpu5rcfOdSEeX5DQZrMxYMCAYs/FxsZ6+mVEREREREREfJB/Tq8p3RUORERERERERMTneHzkgIiIiIiIiEh5Vdp3GfAUJQdEREREREREPEbTCkRERERERETED2nkgIiIiIiIiIiHmPx05ICSAyIiIiIiIiIeYjL5Z3JA0wpEREREREREyjmNHBARERERERHxGP/8Dl7JAREREREREfm/9u4/pqr68eP48+ANRH5Kpa4pCaaNtRHYWjKRb/4orfUTf6DEdaaz/NEQXQaSMsxlahZpJohKrqsimGSulWTLMm0xvwx/bTiHiIo6METxKgGXez5/tA/fL5l9PnPUUc/rsfHHPW/umxfn7v7Y67zPPdJF7tTvHLgzKw0RERERERER6TJaOSAiIiIiIiLSZe7MlQMqB0RERERERES6iK5WICIiIiIiIiJ3JJUDIiIiIiIiIl3Gpwt/bs7r9ZKVlUVSUhJOp5PTp093Gi8uLiYxMZEJEyawd+/e/5hapxWIiIiIiIiIdJF/6moF3333Ha2trRQVFXHo0CGWLVtGbm4uABcvXsTlcrFjxw5aWlpITk5m6NCh+Pr63nQ+rRwQERERERERucOUl5czbNgwAGJiYjh27FjH2JEjR4iNjcXX15egoCDCw8M5fvz4X853G64cGGR1ABERERGRLrEpQZ9tbxv/+z9WJxDb6LrnfVFREUVFRR23k5KSSEpKAsDtdhMYGNgx1q1bNzweDw6HA7fbTVBQUMdYQEAAbrf7L//WbVgOiIiIiIiIiMj/LwP+KDAwkGvXrnXc9nq9OByOPx27du1ap7Lgz+i0AhEREREREZE7zODBg9m3bx8Ahw4dYtCg/1uxEB0dTXl5OS0tLVy9epWTJ092Gv8zhmma5t+aWERERERERES6lNfrJTs7mxMnTmCaJkuXLmXfvn2Eh4czcuRIiouLKSoqwjRNXn/9dUaPHv2X86kcEBEREREREbE5nVYgIiIiIiIiYnMqB0RERERERERsTuWAiIiIiIiIiM3pUoZ/g8OHD7Ny5UpcLpfVUWyrra2NzMxMzp07R2trKzNnzmTkyJFWx7Kt9vZ2Fi5cyKlTpzAMg8WLF//Hb0uVv1dDQwOJiYkUFBQwYMAAq+PY1ssvv9xxfeK+ffvy3nvvWZzIvtatW8f3339PW1sbkyZNYvz48VZHsq2SkhK++OILAFpaWqisrOTAgQMEBwdbnMx+2trayMjI4Ny5c/j4+LBkyRK9Z1iktbWVBQsWcPbsWQIDA8nKyqJ///5Wx5K7jMqBLrZ+/Xp27dqFv7+/1VFsbdeuXYSGhvL+++9z+fJlXnrpJZUDFtq7dy8A27Zto6ysjJycHHJzcy1OZV9tbW1kZWXRvXt3q6PYWktLC6Zpqki+DZSVlVFRUUFhYSHNzc0UFBRYHcnWEhMTSUxMBGDx4sWMHTtWxYBFfvzxRzweD9u2bePAgQN89NFHfPzxx1bHsqXi4mJ69OhBcXEx1dXVLFmyhI0bN1odS+4yOq2gi4WHh+tF8zYwZswY5syZA4BpmnTr1s3iRPY2atQolixZAsD58+f1Ic9iy5cvZ+LEifTq1cvqKLZ2/PhxmpubmTp1KpMnT+bQoUNWR7Kt/fv3M2jQIGbPns2MGTN48sknrY4kwNGjR6mqqiIpKcnqKLYVERFBe3s7Xq8Xt9uNw6HjilapqqoiISEBgMjISE6ePGlxIrkb6RnexUaPHk1tba3VMWwvICAAALfbTWpqKmlpadYGEhwOB+np6ezZs4fVq1dbHce2SkpKCAsLY9iwYeTn51sdx9a6d+/OtGnTGD9+PDU1NUyfPp3du3frw7cFGhsbOX/+PHl5edTW1jJz5kx2796NYRhWR7O1devWMXv2bKtj2FqPHj04d+4czzzzDI2NjeTl5VkdybaioqLYu3cvo0aN4vDhw9TV1dHe3q4DYNKltHJA7loXLlxg8uTJvPjiizz//PNWxxF+P2JdWlrKokWLuH79utVxbGnHjh38/PPPOJ1OKisrSU9P5+LFi1bHsqWIiAheeOEFDMMgIiKC0NBQPRYWCQ0NJT4+Hl9fXyIjI/Hz8+PSpUtWx7K1pqYmTp06xZAhQ6yOYmubNm0iPj6e0tJSvvzySzIyMmhpabE6li2NHTuWwMBAkpOT2bNnD4888oiKAelyKgfkrvTrr78ydepU5s+fz7hx46yOY3s7d+5k3bp1APj7+2MYBj4+evmxwpYtW9i8eTMul4uoqCiWL1/O/fffb3UsW/r8889ZtmwZAHV1dbjdbj0WFnnsscf46aefME2Turo6mpubCQ0NtTqWrR08eJC4uDirY9hecHAwQUFBAISEhODxeGhvb7c4lT0dPXqUuLg4CgsLGTNmDP369bM6ktyFtHZR7kp5eXk0NTWxdu1a1q5dC/z+ZZH6AjZrPP300yxYsIBXXnkFj8dDZmamHguxvXHjxrFgwQImTZqEYRgsXbpUpxRYZPjw4Rw8eJBx48ZhmiZZWVk6ImexU6dO0bdvX6tj2N6UKVPIzMwkOTmZtrY25s6dS48ePayOZUsPPvggq1atIi8vj6CgIN59912rI8ldyDBN07Q6hIiIiIiIiIhYR+t6RURERERERGxO5YCIiIiIiIiIzakcEBEREREREbE5lQMiIiIiIiIiNqdyQERERERERMTmVA6IiIjtlJWVERcXh9PpxOl0MmHCBFwu1y3NtXLlSkpKSqisrGTNmjU3/b09e/ZQV1f3X825b98+MjIybth+4cIF5syZg9PpZPz48WRnZ9Pa2kptbS0TJky4pfwiIiIioHJARERsasiQIbhcLlwuF5s3b+bTTz+lqanplueLiorijTfeuOn4Z599htvtvuX529vbmTVrFlOnTsXlcrF9+3YcDgerV6++5TlFRERE/s1hdQARERGrud1ufHx86NatG06nk7CwMK5cuUJ+fj7Z2dmcPn0ar9dLWloaTzzxBKWlpeTm5hIWFkZbWxuRkZGUlZWxbds2cnJy2L59O4WFhXi9XkaMGEF0dDSVlZWkp6ezdetWioqK+OqrrzAMg2effZbJkydz8uRJMjMz8ff3x9/fn5CQkE4Zy8vL6dOnD48++mjHtvnz5+P1emloaOjYtnv3brZs2YLH48EwjI7VDGlpaZimSUtLC4sXLyYyMpI5c+bgdrtpbm5m7ty5xMfH/zM7XERERG47KgdERMSWfvnlF5xOJ4ZhcM8997Bo0SICAgIAeO6553jqqafYunUrPXv2ZOnSpTQ2NpKSksLOnTtZtmwZJSUlhIaG8tprr3Wat6GhgfXr17Nr1y78/Pz44IMPePzxx4mKiiI7O5szZ87w9ddfs3XrVgBeffVV4uPjWbFiBampqQwdOpT8/Hyqq6s7zVtfX0+/fv06bfPz87vh/6qpqSE/Px9/f3+ysrLYv38/wcHBhIaGsmLFCqqqqrh+/Tpnzpzh8uXLbNiwgYaGBmpqarpw74qIiMidRuWAiIjY0pAhQ8jJyfnTsYiICABOnDhBeXk5R44cAcDj8XDx4kVCQkLo2bMnALGxsZ3ue/bsWQYOHEj37t0BePPNNzuNnzhxgvPnzzNlyhQArly5wunTp6mpqSE6OhqAwYMH31AOPPDAA3z77bedtjU2NlJRUcGgQYM6tt17772kp6cTEBBAdXU1MTExJCQkUFNTw6xZs3A4HMycOZOBAweSlJTEvHnz8Hg8OJ3O/3rfiYiIyN1H5YCIiMgfGIYBQGRkJH369GHGjBn89ttv5Obmct9999HU1MSlS5cICwvj6NGj9OnTp+O+4eHhVFdX09raiq+vL6mpqbz99tsYhoFpmkRGRvLQQw+xYcMGDMNg06ZNPPzwwwwYMICKigoSEhI4duzYDZliYmKora3lyJEjREdHY5oma9aswc/Pr6McuHr1KqtXr+aHH34Afl+VYJomZWVl9OrVi4KCAioqKvjwww9ZuHAh165dIz8/n/r6eiZOnMjw4cP//p0rIiIityWVAyIiIjcxceJEFi5cSEpKCm63m+TkZHx9fcnKymLatGmEhITgcHR+Kw0LC2P69OmkpKRgGAbDhw+nd+/exMbG8tZbb1FQUEBcXByTJk2itbWV6OhoevfuTUZGBunp6WzcuJGwsLAbThnw8fFh1apVvPPOOzQ3N3P9+nViYmJIS0ujvr4egMDAQAYPHkxSUhIOh4Pg4GDq6+sZMWIE8+bNo7CwEI/Hw+zZs+nfvz+ffPIJ33zzDV6vl9TU1H9sv4qIiMjtxzBN07Q6hIiIiIiIiIhYR5cyFBEREREREbE5lQMiIiIiIiIiNqdyQERERERERMTmVA6IiIiIiIiI2JzKARERERERERGbUzkgIiIiIiIiYnMqB0RERERERERs7l9ZBuXuNbtr/gAAAABJRU5ErkJggg==",
"text/plain": [
"<Figure size 1440x504 with 2 Axes>"
]
},
"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)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"Testing some data points"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Predicted Class : 1\n",
"Actual Class : 2\n",
"The 41 nearest neighbours of the test points belongs to classes [2 7 1 1 7 7 7 7 7 7 7 7 7 7 1 7 2 7 7 7 7 7 6 6 7 2 1 1 2 7 7 2 5 7 6 2 2\n",
" 7 7 7 6]\n",
"Fequency of nearest points : Counter({7: 24, 2: 7, 1: 5, 6: 4, 5: 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 = 6\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": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Predicted Class : 2\n",
"Actual Class : 2\n",
"the k value for knn is 41 and the nearest neighbours of the test points belongs to classes [2 2 2 2 2 2 2 2 2 2 2 2 2 7 7 7 1 7 2 7 7 1 1 5 2 2 6 6 6 7 7 2 2 2 2 7 7\n",
" 7 2 2 2]\n",
"Fequency of nearest points : Counter({2: 23, 7: 11, 1: 3, 6: 3, 5: 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 = 105\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]]))"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## Logistic Regression"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Balancing all classes"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"For alpha = 1e-06\n",
"Log Loss : 1.525529377492278\n",
"For alpha = 1e-05\n",
"Log Loss : 1.4848914297937406\n",
"For alpha = 0.0001\n",
"Log Loss : 1.3207725565546995\n",
"For alpha = 0.001\n",
"Log Loss : 1.2844140351567999\n",
"For alpha = 0.01\n",
"Log Loss : 1.3220281007822212\n",
"For alpha = 0.1\n",
"Log Loss : 1.5381778317979788\n",
"For alpha = 1\n",
"Log Loss : 1.7388545410586238\n",
"For alpha = 10\n",
"Log Loss : 1.756079435353909\n",
"For alpha = 100\n",
"Log Loss : 1.757840928330266\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": null,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 720x432 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(figsize = (10,6))\n",
"ax.plot(alpha, cv_log_error_array,c='magenta')\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": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"For values of best alpha = 0.001 The train log loss is: 0.5081490656894474\n",
"For values of best alpha = 0.001 The cross validation log loss is: 1.2844140351567999\n",
"For values of best alpha = 0.001 The test log loss is: 1.1169496603919944\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))"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"### Testing on test data using best alpha value"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Log loss : 1.2844140351567999\n",
"Number of mis-classified points : 0.38533834586466165\n",
"-------------------- Confusion matrix --------------------\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 1440x504 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"-------------------- Precision matrix (Columm Sum=1) --------------------\n"
]
},
{
"data": {
"image/png": 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6YTw1/DYiMpwwa+Efm7byNnKzc7GEhlC5ZhV2pRUOMd2VtpNqMdVKuTbBrUmLi1n/QzoA6Wl7ubjeub8/W1QE1lN3uMtVsBWOPrCFYTGbObT/CB6Phw1rM2gar7Uf/ouGzeuw8cftAGRu2Ut0veLnxctDPyCmfg0eHHq797zYv/s3Xn1hFo+OvJv4ywun6oSEWqgeXZntqYXr12xP3UXtulWR/yYuvj7fJ6cBsHnTTuo3KAoOX9KsLhs3ZOJ05pGVlc3uXYeo36AWcQkN+D55MwApKZnUq1+0z7ate2gRX790K1FGJCQ0JnlVCgCpqRnExhZNk2nWrAEp67f9o/8+kz59u7N4yWRmzxnLAw/04Pru7bmlR5dSqUNZkZDQmOTk9QCkpqYTe9pvqebNY0lJKWqLnTv3ERsbQ0JCE1atKtwnOXk9LVs2PWteKTm1hQQSg8fj8elS+X369CEvL6/Yex6PB4PBwIIFC865f7PZq31ZHJ/562kFsRWsGAzw4g+ZtK9ZkV+zcli5/yj3Nq1Jt5jK5Ls9fLbrdxZm/gbA1C5NeWPjHjKOOf5xzK96tOLGT9YH7NMKUvtU8HcRzuivpxVkZuzF4/Hw0viHSV61geiY6nTp0oqFSd+yMOlb3G4PDw7owdXdLueWmwbjcuVRqVJhnS6uU4NRoweSlPgNixevwGI2EdswhheG9St25zuQfLkv8O6ke59WsPsgeDz0eupOtq/bRqUalXG73cwbN5voxkUd03X9ulOhSkUWTlmAM9eJO7+AbvdcR2zLhhzceYAlby7CXeCmYrWK9H7mLswBugZEk/KBNy3or6cV7NlxCA/w5PBe/PzDdmrUqsRlHYsW67r3xpd4b+EzhIRa+PPwCV4fu5CcHCcF+W7uHtCNhDaxpKft5d0pn+IucJNwWUPueeha/1XsHLLyAi+g99fTCn795SAeDwx6oRcbf0ynWq2LcLs9vDFiLg2aFp0Xdwy8jk/nrGDvLwepXL0wCB1hDePpSfezZ8dBPpi8mIICN1WqV+ThF+8I2PMitlzg9hnjxsxlR+Y+PB4YNfZ+vk/eTO3oKnTqEs+ShatYvHAVHo+H+x+4nquuvpQTx+2MHvEhhw+fwGw2MXZ8f2rUrMTRoycZ1H8yiUtG+bta/yrMFLhtMXrUu2Sc6r/HjX+U5FUpp/rv1iQlfXOq/3YzYMBtXN3tcu++Hy9Zwa7d+//xVIKzvR9IjIbAO2f/WiE/M3NPYVuMe5zk5BSio6tz5ZVtSEr6msTEr/B4PAwYcDvdurXlyJFjDB36Gg5HDhUqRDF58lNERISdMa+U3IXbFrH+LsB5VbXx0z471u/bX/bZsc7F58GBTZs2MWzYMKZOnfqPP7Jq1jz3ateBGhy4EAVqcOBCFYjBgQtVIAYHLlSBGBy4UAVqcOBCFKjBgQtVIAYHRPyvbAcHqjUZ6rNj/bZtos+OdS4+v1q1aNGCm266iYyMDLp27errw4uIiIiIiIiIj52XUGb//v3Px2FFREREREREAlxwLu2ncU4iIiIiIiIiPlLaTxnwleAstYiIiIiIiIj4jEYOiIiIiIiIiPhIsI4cUHBARERERERExEcMQTpAX8EBERERERERER8J1pEDwVlqEREREREREfEZjRwQERERERER8RGDweDvIvxPFBwQERERERER8RFNKxARERERERGRoKSRAyIiIiIiIiI+oqcViIiIiIiIiFzgNK1ARERERERERIKSRg6IiIiIiIiI+EiwjhxQcEBERERERETER4J1zYHgLLWIiIiIiIiI+IxGDoiIiIiIiIj4iqYV+Mb6u0L9XQQ5JX7uMX8XQU6zuU89fxdBTvFQ4O8iyCkGTP4ugpySnX/Y30WQU3ae3OXvIshpGpSL9XcRRKSUBeuaA8FZahERERERERHxmYAbOSAiIiIiIiISrAwGg7+L8D9RcEBERERERETER/S0AhEREREREREJSho5ICIiIiIiIuIjwbogoYIDIiIiIiIiIr4SpGsOBGdIQ0RERERERER8RiMHRERERERERHwlSG/BKzggIiIiIiIi4iuaViAiIiIiIiIiwUgjB0RERERERER8JUhHDig4ICIiIiIiIuIrQTo+P0iLLSIiIiIiIiK+opEDIiIiIiIiIj7i0bQCERERERERkQtccMYGNK1ARERERERE5EKnkQMl5Ha7eWn0LDIyfiUkxMzI0f2JjqnqTV+08DsWJX2HyWTkwYE30bFTPCeO27nhuqep36AWAF2uupS7+3QD4OjRk9xz1xgWffISoaEhfqlTsDIAL7SpT8MKVlwFbkau3cG+rFxv+tBL6xJfJQpHXgEAj6/cRoHHw7DW9alpC8NiMjB+3U62/GnnujqVuadxLQo8Hj7Z+TtJmYf8VKvg5Ha7GTnybTIydhMSYmHs2EeJianhTU9K+poFC77CbDYxaFBPOnduzdGjJ3jqqVfIzXVRpUpFxo9/nPDwsDPmlZJzu92MGvku6Rl7CAkxM3bsI8TEVPemJyV9Q+KCrzGbTQwcdDudO7fi2NGTDHlqMs5TbTFu/GOEh4cy/b0lfPFFMjZbBP3630Lnzq38WLPgpHMjcLjdbsaNmUtmxj5CQswMH3Vvsf57ycJVLFq4ErPJRP8B3enQKY6cbCcvjZnNwf1HyMvLZ+jzd1Gt+kU8+/Q73v0y0n/lsSdv4/Zenf1RraDkdruZNnEJu3ccxBJi5rEXelKjdqVieU4cs/N0/zd5a/5ThIRacNhzmPTCXHKynVhCzDw16k4qVIoidV0mH771BSazkbhWsfQZdK2fahWcdI0KHGqLMsoYnEMHFBwooRXLU3C6XMz9aASbNv3CK5Pm88bUJwE4cvg48+d+w4KFo3E687jn7jFcfsUlbN+2h2uvu5znhvUtdqwfvt/M61OSOHLkuB9qEvy61L6IUJORPl9tonmlSJ5qWZfHV27zpje+yMbA5Vs47sz3vjeoeTS/HM/mhTWZNCgfQcMKNrb8aWdIQl1uWZpCdn4Bn9zQkv/bc5gsV/6ZPlbOYNmytbhcLhITXyE1NZ0JEz7g7beHAXD48DHmzFnK4sWv4nS6uPPOobRtG8+0aQvo3r0jPXpcxXvvLSQx8Suuv77jGfOGhFj8XMPgsWzZTzhdLhITJ5KamsHECTOZ9vbzwF9t8TmLF08+9f0+R9u2cUydlkj37h3o0eNK3ntvMYmJX3P55c35/PNkkhZOAuCO3s9y2WXNCQ8P9Wf1go7OjcDx3fKNuJx5zJ7/Aps37WTKy4m89tZjABw5fIKP5i1jXtJwnM487u8znsuuaMqsmf9H/fq1GDv+ATIz9pGZsY9Lmtfl/Q+HArAp9Remvr6EHrd19GfVgs7aVVvIc+Ux+YPHSE/by4zXP+PFV+73pqf8mM6sqV9w7GiW971ln/9MTL1q3P/YDXz1yVoWz11J/ydu5IM3lvLU6LuoXacqQx98iz2/HOLi+tXP9LFyBrpGBQ61RRkVpGsOlMq0ApfLVRofc15t3JBJ23bNAWjRoj7btu72pqWl7SI+PpaQEAuRkRFER1clM2Mf27btYdu23dzXdyxDnniDw4ePA2A0Gnjvg6GUK2fzR1WCXnyVKH44eAyAzUeyaHJR0fdoAGIiwxl+WQNmdWvOzfUK7w5dUaMCeW43b195CQOaR7PmUOH+mccdRIaYCDUZC89hj6e0qxPUUlK20b59SwDi4hqxZcsOb9rmzZnExzc+dV5YiY6uTnr67mL7dOhwKWvWbDprXim5lJTttG+fAEBcXEO2bPnFm5a2eQcJp32/MdHVyUjfw4bT9unQIYEf12xi1879tG59CaGhIYSGhhATU52MjD3+qFJQ07kRODZu2MEV7S4BoHmLemzbusebtiVtFy3iG3j779rRVdiRsZ81P2zFYjHx0AOTmf7OUq5oe4l3H4/Hw8Rx83h+eB9MJs3O/C+2pu4m4fJGADRqFsOO7fuKpRuNBsa+NZDIqAjvexfXq05OthOAbEcuZnPhd163YU2yTmaTn1+Ay5mPMUjv0vmLrlGBQ20hgcSnvdqKFSvo3LkzXbt25csvv/S+379/f19+jF/Y7TnYbEWdldFoJD+/cNi6w56DLTLcmxZhDcOelU2dOtV5+JFbmTl7GF2ubMn4l2YDcPkVzShfPrJ0K1CG2Cxm7Kfd3Xd7PJhO/SYIN5uYn3GQ57/PYNDyrfRqWJ0G5SMoH2ohKsTMoOVbWLX/KEMS6gDwy3EHC66L5+MbWpK8/yhZp6YiSMnY7dnFzguTqei8sNuziYy0etOs1nDs9mzs9hwiIyO872VlOc6aV0rOYc8m8l/boijNag0ny55d7P3CtsgmtmEM69dvxW7P4dixk2zcmE5OTi7y3+jcCBwOR/E+2nR6/+3IJdJWvP/Osmdz/FgWJ09mM236EDp0asGUVxK9eVZ9l0q9ejW5uI7uUv9XOY5crLYw77bJaKQgv6jfjW/TkKjy1mL7RJazsvGnTAb1msSSOSvpemMboDBoMHrwDAb1nESlquWpdXGVUqlDWaFrVOBQW5RRBh++SpFPgwPvvPMOn3zyCUlJSSxYsICPP/4YKIyyBzubLZxsR9EPZLfHjdlsAsBqC8dxWlq2I5fIqAhaX9aEVm2aAIXrDaRv31u6hS6j7Hn5RFhM3m0jBgpO/RPLLShg3vaD5Ba4yc4vYN1vx2lYwcYJZx7f7T8KwMr9R2lyUSQNykfQoWZFrv34Z675eB0Vwyx0ja50po+Us7DZInA4crzbbrfHe14UphV1Sg5HDpGRVmy2cO8+DkcOUVHWs+aVkrOesy2K0oraIqJYW0RGWalXrzZ33XUdD/QfxZgx02neIpYKFaJKtzJlgM6NwGG1/r3/LmoLqzXsn/13ZATly9vo2DkOgI6d4ti2ZY83z5ef/8itt2s6wf8i3BpGjsPp3XZ7PJjMpn/ZAz56/xtu7dOZtxOfYcybDzL+2VnYs3JYOGsFUxc8w/sfP0+N2pX4eN6q8138MkXXqMChtiijjAbfvUqz2L48mMVioVy5clSoUIFp06Yxd+5c1q5diyFI51ycLi4+ltWrUwHYtOkXGjSo7U1r1qwuG1IycDpdZGVls2vXQeo3qMXIF2ew7JufAfhp7VaaNLnYDyUve1IPn6R9zYoANK8UyY7jDm9aTGQ4s69pgdEAZoOB+Mrl2H7UzsY/TtK+ZgUAWlaJYucJB/a8AnIL3OQWuHF74GhuHlGhWobjv0hIaExy8noAUlPTiY2N8aY1bx5LSsq2U+eFg5079xEbG0NCQhNWrSrcJzl5PS1bNj1rXim5hIRGrEpOASA1NaPY99eseQPWF/t+9xMbG018QiNWrSrcJzl5A5e2bMLRoydwOHL4aMEERo0ayG+HjtCgQbRf6hTMdG4Ejrj4+nyfnAbA5k07qd+gpjftkmZ12bghE6czj6ysbHbvOkT9BrWIS2jA98mbAUhJyaRe/aJ9tm3dQ4v4+qVbiTKiSYs6rF+zHYD0tL1cXO/coy9skeFEnBptUL6CjWxHLqGhFsLCQwiPKFzQuWKlKOxZOf92GPkbXaMCh9pCAonB48Pb+s888wwVKlTg8ccfJyIigkOHDtGvXz9OnjzJ999/X6JjOAvW+ao4PvXX0woyM3/F44ExLz3A6uRN1I6uSucuCSxa+B2LF36H2+2h/4M30vXqVuzf/wcjhr2Px+MhPDyUkWP6U7lyee8xr7nqST79YmLAPq2g1XznuTP5wV9PK4gtb8VggBfXZNK+ZkX2ZeWwcv9R7m1Sk6tjKpPv9rB01+8s3PEbUSFmRl7egMrhIeS7PbzwQwYHHU5ub1CNm+tXI8/tZn9WLiPX7iDfHZgjXTb3qXruTKXsrxV2MzP34PF4GDfucZKTU4iOrs6VV7YhKelrEhO/wuPxMGDA7XTr1pYjR44xdOhrOBw5VKgQxeTJTxEREXbGvIHKQ+BNP/nraQUZmXvweGD8uEdZlZxCTHR1ulzZmqSkb0hK/Aa3x82AAbfRrdsVHDlynGeHvu5ti1cmDyY8PJQRI95m29ZdWCxmBg/pQ6tWTf1dvbMy8O93Hf3lQjw3svMP+7sIZ/TX0wp2ZO7D44FRY+/n++TN1I6uQqcu8SxZuIrFC1fh8Xi4/4HruerqSzlx3M7oER9y+PAJzGYTY8f3p0bNShw9epJB/SeTuGSUv6v1rw44jvm7CGf019MK9vxyEI8Hnhjei/U/pFOj9kW06VC0rsP9N43lnaShhIRa+PPwCd54KYncbBf5+QXcPaAb8W0asua7NBbNXkFIiBlrZDhPDu+N7bS1CgJJg3Kx/i7CP1yI16hAdeG2ReCdF77UoOsMnx1rx7f9fHasc/FpcCA/P5/PPvuMa6+9lvDwwjl8R44c4d133+WFF14o0TECNThwIQrU4MCFKhCDAxeqQAwOXKgCNThwIQrU4MCFKFCDAxeqQAwOiPhf2T4vGlztw+DAN6UXHPDpGGqz2UyPHj2KvVepUqUSBwZEREREREREpPRpgrWIiIiIiIiIrwTp41UVHBARERERERHxleCMDfj2aQUiIiIiIiIiEnw0ckBERERERETERzyG4Bw6oOCAiIiIiIiIiK+U0poDhY/CHElGRgYhISGMHTuWmJgYb/oHH3zA559/jsFgYODAgXTt2vVfj6fggIiIiIiIiEiQWbZsGS6Xi8TERFJTU5kwYQJvv/02ACdPnmT27Nl888035OTkcPPNNys4ICIiIiIiIlJqSmlWQUpKCu3btwcgLi6OLVu2eNPCw8OpUaMGOTk55OTkYCjBVAcFB0RERERERER8xYdrDiQmJpKYmOjd7tWrF7169QLAbrdjs9m8aSaTifz8fMzmwj/zq1evzvXXX09BQQEDBgw452cpOCAiIiIiIiISgE4PBvydzWbD4XB4t91utzcwkJyczB9//MHy5csB6NevHwkJCTRv3vysn6VHGYqIiIiIiIj4itHgu9e/SEhIIDk5GYDU1FRiY2O9aeXKlSMsLIyQkBBCQ0OJjIzk5MmT/3o8jRwQERERERER8ZVSWnOga9eu/PDDD/Tu3RuPx8O4ceOYOXMm0dHRXHnllaxZs4aePXtiNBpJSEigbdu2/15sj8fjKZ2il4yzYJ2/iyCntJrv9HcR5DSb+1T1dxHkFA8F/i6CnGLA5O8iyCnZ+Yf9XQQ55YDjmL+LIKdpUC723JlELjhl+7yof8tsnx3rl4/7+uxY56KRAyIiIiIiIiK+4sMFCUuTggMiIiIiIiIivhKkwQEtSCgiIiIiIiJygdPIARERERERERFfCdJb8AEXHAg1lfd3EeSUzX38XQI53dJfd/q7CHJKt1qV/V0EOSXEGOXvIsgpEWadF4Hi4kirv4sgInJhC9JpBQEXHBAREREREREJWsEZGwjWAQ8iIiIiIiIi4isaOSAiIiIiIiLiIx5jcA4dUHBARERERERExFeCdM0BTSsQERERERERucBp5ICIiIiIiIiIrwTnwAEFB0RERERERER8JkjXHNC0AhEREREREZELnEYOiIiIiIiIiPhKkC5IqOCAiIiIiIiIiK8EZ2xA0wpERERERERELnQaOSAiIiIiIiLiK0G6IKGCAyIiIiIiIiK+EqTBAU0rEBEREREREbnAaeSAiIiIiIiIiI94gnPggIIDIiIiIiIiIj4TpNMKFBwoIbfbzciRb5ORsZuQEAtjxz5KTEwNb3pS0tcsWPAVZrOJQYN60rlza44ePcFTT71Cbq6LKlUqMn7844SHh50xr5Sc2iJwuN1ulryxiEO7DmCymOk5uDeValb2picvXsnGlRsAaNy6CVf3uYYcRw5zX5qFK8eF2WLmjmfvJqpiFEcOHGbx6wvJz8/HbDFz9wv3YI2y+qtqQcftdjN29Ewy0n8lJMTCqDH9iY6p5k1flLSChUkrMJuMPDjwZjp2TvCm/bxuO88Nncay794EYPaH/8eSRd9RoWIUAMNH3U+dOjWQktN1KnCoLQKH2+1mzOgZZKbvxRJiYfSYAX+7Ti0nKWnZqetUDzp1bsnhP47x7NC3yMvLp1w5GxMmPYLVGu7dZ+Tw9yhXzsaTQ+70R5WCls6LwKG2kECiNQdKaNmytbhcLhITX2HIkHuYMOEDb9rhw8eYM2cpCxZMYsaMUUyZMhuXK49p0xbQvXtH5s+fSJMmdUlM/OqseaXk1BaBY+sPaeS78nj0jSe5vt8NLH33U2/an4eOsGF5Co++9gSPvfEkmeszOLjrIOu/Xkf1OjV4+NXHaNEpnpVJKwBY+FoS19x7HQ9PeYzLu7fl8P4//FWtoLRiWQpOZx7zFoziicG9eHnSPG/akcPHmTf3a+bMH8E77z/La68mev+t/3boT2bP+pL8/AJv/m3bdvPSxEHMnD2MmbOHKTDwP9B1KnCoLQLH8mU/43LmMW/BWJ4cfAcvT5rjTSu8Tv0fc+eP5t33X+D1Vz/C5cpjxvufcuNNHZg9dxSNGl/M4oUrvPskJX7Ljsxf/VGVoKfzInCoLcoog8F3r1J03oMDubm5uFyu8/0x511Kyjbat28JQFxcI7Zs2eFN27w5k/j4xoSEWIiMtBIdXZ309N3F9unQ4VLWrNl01rxScmqLwLF76y4atmoMQEyTi9mXuc+bVr5yBR4YPwCjyYjBYKCgoABLiJlqdarjzM4FINeRi8lsIs/pwn48i21rtzJtyJvs3baH6IYxfqlTsNqwIYN27VoA0CKuAdu2FP1bTkvbSXxC7Kl/6xFER1cjM+NXnE4Xo0d+wLDh9xU71ratu5nx3qf0vWsU77/3KfLf6ToVONQWgWPjhgzaeq9TsWzdstOblpb2C3EJDb3XqdrR1cjI2MvQ5+7hhhvb43a7+e3Qn0RGRRQea2MGmzf/wu29rvJLXYKdzovAobYoo4wG371Ks9i+PuAvv/zCQw89xHPPPceaNWu47rrruO666/juu+98/VGlym7PxmaL8G6bTEbvnTa7PZvIyKLhz1ZrOHZ7NnZ7DpGREd73srIcZ80rJae2CBy5Didh1jDvttFYGAQAMJlNWMvZ8Hg8LH33U2rWr0nlWlWwRlnJTMlgUr/xrFq4gtbXXEZ2Vja/7/mNBgmxDHrlEXLs2az/dp2/qhWUHPYcbJFFQ22Nxc6LnGLnjNUaRlZWDuPGzOLe+6+jatWKxY517XWX8eLIfsyY+QIbUjJZ9d2G0qlEGaLrVOBQWwSOwu+wqC3+fp2K/Nt1yp6VfSq47ObmG5/i53VbadPmEg7/cYy3py7ihWH3l3odygqdF4FDbSGBxOdrDowYMYLHH3+cAwcO8Nhjj/H1118TGhpK//796dy5s68/rtTYbBE4HDnebbfbg9lsOi2t6ORzOHKIjLRis4XjcOQQFhaKw5FDVJT1rHml5NQWgSPMGoozx+nd9ng8mEwm73aeK4+kVz4iNCKUHo/eDsA3c76mU88uXN69LQd3HWT26A947M0nCY0IpX5cAwAat2lK5oYMWl9zWelWKIhZbeE4HLnebbfbfdp5EU72aWkORy4Wi4kNKRn8+uvvvD31Y06csPP04DeZNPkR7u57rfdHR4eOcWzfvrfYGgVybrpOBQ61ReAo/A6LrkWeYm0RXqydHI5cIk+tO2OxmPns8yn8uGYzzz87lauubsPxY1kMGjCBP48cJyfXSZ26Nbj5lk6lWZ2gpvMicKgtyqggnbzv82K73W5at27NLbfcwlVXXcVFF12EzWbDbA7utQ8TEhqTnLwegNTUdGJji4Y8N28eS0rKNpxOF1lZDnbu3EdsbAwJCU1Ytapwn+Tk9bRs2fSseaXk1BaB4+KmdUn/aRsAe7ftoVqd6t40j8fDzOHvU71uDW57ohdGU+HlJiIynLBTi0nZytvIzc7FEhpC5ZpV2JVWOMR0V9pOqp22SJWcW3xCLKuTUwHYlLqDBrG1vWnNmtUjJSX91L/1bHbtOkCz5vVY+n+veNcVKFfOxstTHsVuz+GWG4eS7cjF4/Gw7qetNGlax0+1Cl66TgUOtUXgiE9oyOrkjQBsSs2kQWy0N61Zs/psOO06tXvXARo0qM2YUe+z7qctQOGdUIPRwN19riVp8QQ+nD2Cfg/cxPXXt1Ng4D/SeRE41BZlVJCuOWDweDweXx7w+eefx2AwMGbMGIzGwj8G3nvvPbZt28Zrr71WgiNk+rI4PvPXSqKZmXvweDyMG/c4yckpREdX58or25CU9DWJiV/h8XgYMOB2unVry5Ejxxg69DUcjhwqVIhi8uSniIgIO2NeKbkLtS2W/rrz3JlKmfdpBbsPgsdDr6fuZPu6bVSqURm32828cbOJblzUMV3XrzsVqlRk4ZQFOHOduPML6HbPdcS2bMjBnQdY8uYi3AVuKlarSO9n7sJsCcygYrdalc+dqZT99bSCzIx9eDwexowbwOrkVKKjq9K5S0sWJa1g0cLvcLvdPDDgJrpeXXwF407tH2Ll6mkALP10NfPmfkNIiJk2lzXl4Udv80eVSiTEGOXvIpzRhXqdCkQXYlvkuQNzKLH3aQUZv4LHw5hxg1idvJHo6Gp07nIpi5KWs3DhMjxuDw8MuIWuV7dh164DjB75PgYDGI1Gnh92H/Xq1fIe85OPV7J718GAflqBxRhx7kyl7EI8LwLVhdsWsf4uwHlV95GPfXasXW/d4rNjnYvPgwNut5sVK1Zw1VVFC8R8+umnXH311YSHh//Lnn8JzOCAiL8FYnDgQhWIwYELVaAGB0T8KVCDAxeqQAwOiPhfGQ8OPPaJz461642bfXasc/H5bTmj0VgsMABw0003+fpjRERERERERAKOp5SnA/hKkC6VICIiIiIiIiK+EpgTekVERERERESCUZDegldwQERERERERMRXjJpWICIiIiIiIiJBSCMHRERERERERHwlSBckVHBARERERERExFc0rUBEREREREREgpFGDoiIiIiIiIj4SnAOHFBwQERERERERMRXPJpWICIiIiIiIiLBSCMHRERERERERHwlSEcOKDggIiIiIiIi4itB+ihDTSsQERERERERucBp5ICIiIiIiIiIrwTpLXgFB0RERERERER8RdMKRERERERERCQYBdzIgT1ZGf4ugpxS21bH30UQCUixIw/7uwhyyuqhh/xdBDmltrWhv4sgp3jI93cR5DTh0SP8XQQ5JefXUf4uglwo9LQCERERERERkQtckAYHNK1ARERERERE5AKnkQMiIiIiIiIiPuIJ0gUJFRwQERERERER8ZUgHZ8fpMUWEREREREREV/RyAERERERERERX9G0AhEREREREZELnJ5WICIiIiIiIiLBSCMHRERERERERHwlSEcOKDggIiIiIiIi4ivBGRvQtAIRERERERGRC51GDoiIiIiIiIj4iEfTCkREREREREQucHqUoYiIiIiIiMgFLkhHDmjNAREREREREZELnEYOiIiIiIiIiPhKcA4cUHCgpNxuN29OWMLuHYewWEw88WJPatauVCzP8WN2Bvd7i3c+GkJIqIXED1ewfk06AHZ7Lsf+zGLB1yNYvXwzSbNWAAa6XJvALXe090ONgpfb7Wb0qOlkpO8hJMTC6LGDiImp7k1fmPQtSYnfYjIbGTjwNjp1vpSDBw8z7IWpFOS78Xg8jBo9kDp1a/LN1z/y/vSPMRgMdL+hPX36dvdjzYKP2+1myRuLOLTrACaLmZ6De1OpZmVvevLilWxcuQGAxq2bcHWfa8hx5DD3pVm4clyYLWbuePZuoipGceTAYRa/vpD8/HzMFjN3v3AP1iirv6oWdAwGGNu9KY2rReLKdzP00y3sPZrtTe/UoBKPd2qAwQBpB0/w4ufbGNS+Lh3rF17HosIsVLaF0urlFTSvUY5h1zbCABy2u3hy8Sac+W4/1Sz4uN1u3hi/hJ2Zh7CEmBjyYk9qRv+zv3j8vreYnljYXxQUuHlnymdkbttPniufvgOu5rIOTQAoKHAz9tk5XHtzG1q3beSPKgU1t9vNyJFvk5Gxm5AQC2PHPkpMTA1velLS1yxY8BVms4lBg3rSuXNrjh49wVNPvUJurosqVSoyfvzjhIeHnTGvlJzb7Wbs6JlkpP9KSIiFUWP6Ex1TzZu+KGkFC5NWYDYZeXDgzXTsnOBN+3nddp4bOo1l370JwLffrGPG9KUYDHB997bc3feaUq9PWdAqrh5jn7uTbr3GFHv/uqsSeP7xHuTnFzAraRUzP1pBWKiFma8/TOVK5ciy5/DA4Lc5cjTrjHml5HSNKpuMQTo+P0iLXfrWrNxKniuf12Y+yv2PXs97ry4tlr7+xwyef/g9jv2Z5X2v171dePm9h3j5vYeoVKUcT4/qTUGBmw/e+pIJ0wbw2sxH+XzhGk4cd5R2dYLa8mXrcDldfJQ4nsFD7mbSxFnetMOHjzF3zpfM++glpr//Iq9OmYfLlcebry/gzruuZdac0Tw4oAevvjqPgoICpkyZy4yZI5i/YBwfzf+aY8dO+rFmwWfrD2nku/J49I0nub7fDSx991Nv2p+HjrBheQqPvvYEj73xJJnrMzi46yDrv15H9To1ePjVx2jRKZ6VSYU/Iha+lsQ1917Hw1Me4/LubTm8/w9/VSsoXd2oKqFmIz2mr2Xit5kM61b0R6Q1xMRzVzfi/nnrufm9H9l/PIeKESG8vXoXvWeuo/fMdRw6mcvgJZsBmHDTJTz9cRq3z/iJVTsOU7NcuL+qFZR++G4rLlc+b856lP6PXs87f+svfl6TwdCHivcXy75IIT+/gNdnPsLoV+/jwL4jABzcd4TB/aeRsXVfqdahLFm2bC0ul4vExFcYMuQeJkz4wJt2+PAx5sxZyoIFk5gxYxRTpszG5cpj2rQFdO/ekfnzJ9KkSV0SE786a14puRXLUnA685i3YBRPDO7Fy5PmedOOHD7OvLlfM2f+CN55/1leezXR+/3+duhPZs/6kvz8AqAwYPba5AVM/+A55n40isSPlnHsWNYZP1PObvDAG5g26UHCQi3F3jebTUwa3ofud4+na8/R9LuzC1UqlePBPl3ZkrGPq24bxfzFq3n2sVvOmldKTtcoCSTnNTjw559/ns/Dl6qtqbu59PKGADRuFsOO7cV/qBkMBiZMG0BkVMQ/9v1+RRqRkeG0vKwhJpOR9xc+jdUWTtYJBwVuN2azqVTqUFZsSNlOu/bxALSIi2Xrlp3etLS0X4hPaERIiIXISCvRMdXIyNjLM8/eQ8eOLYHCHxWhIRZMJhOff/EGkZFWjh+3U+B2Y7FoMM1/sXvrLhq2agxATJOL2ZdZdF6Ur1yBB8YPwGgyYjAYKCgowBJiplqd6jizcwHIdeRiMpvIc7qwH89i29qtTBvyJnu37SG6YYxf6hSsWsVUYNWOwj8oN+4/TrOaRT/OWkZXIOP3LIZ1a0RSvzYcsbs4mu3ypndrXJUTOXms3nmEuhdZOZbjot/lF5N4fxvKR1jY9acCmP/FltTdtLqisL9o0jyGzG3F+wuj0cCkt4v3F+t/zKBS5XI8/9j7TBmzkMtPjRrIyXEx+MXbiWtVv/QqUMakpGyjffvC639cXCO2bNnhTdu8OZP4+MZFfUZ0ddLTdxfbp0OHS1mzZtNZ80rJbdiQQbt2LQBoEdeAbVuKvr+0tJ3EJ8Se+n4jiI6uRmbGrzidLkaP/IBhw+/z5jWZjHz6xctERkZw/HjWqf5bv6X+q117f6f3g6/+4/1G9Wuyc8/vHD/hIC+vgDU/Z9CuTSOuaNWQb1duAuDrlal0btfsrHml5HSNKpsMBt+9SpNPgwO7d+8u9ho0aJD3/4NdtiMXqy3Mu200Gik4FcEGaHlZLFHlzzwEOvHD5dz14NXebZPZxPcr0hh0xxRatKxHWHjI+St4GWR35GCLLPpRbTQZvXcT7PZsIk9Ls1rDycpyUKFCFBaLmd27DvDypFk89HBPoDA6/u03a7nl5sG0bt2U8PDQ0q1MkMt1OAmznn5eFAYBoPDfubWcDY/Hw9J3P6Vm/ZpUrlUFa5SVzJQMJvUbz6qFK2h9zWVkZ2Xz+57faJAQy6BXHiHHns36b9f5q1pByRZqJstZdIegwO3BdGql3AoRIVxe5yImfJvBvXPWc//lF1PnoqLz5KEOdXl95S+Fea0WWtauwKyf9nLXh+toW/ciLq9TsXQrE+T+0V+Y/tlflPtbf3HiuIOD+47w0uv96HVvZ14emQhAvdgaxNStWjoFL6Ps9mxstqJ/76Z/9BlFbWG1hmO3Z2O353j7kr/6kbPllZJz2HOwRRaNRCref+cUayerNYysrBzGjZnFvfdfR9Wqxa9DZrOJZd/8zG03P0+rVo0JDw9D/ptP/m8defn5/3g/KjKck1lF/7az7DlERUYQGRnOiVPvZ9lzKRcZfta8UnK6RpVNCg4A9913H4MGDWLEiBEMHz6c3bt3M3z4cEaMGOHLj/GLCGsY2dlO77bH48FUgjv+e3f9htUW/o/1Cdp1aca8/3uRvLwCln2x3uflLcts1nAcjhzvtue00Rc2W0SxNIcjh6hTF8qf1qbx6CMTmTDxMerUrenN0/Xqy1i5ajp5efl8+smqUqpF2RBmDcWZ87fzwlR0XuS58pg/fg7OnFx6PHo7AN/M+ZpOPbvwzIzneGDCIGaP/oCIyAhCI0KpH9cAg8FA4zZNi41CkHOzO/OxhhSNfDEaDBS4PQAcz3ax6eAJDttdZLsKWLfnKE2qRQFQv7KNk7n53vUJjmfnsedoNjuPOMh3e1i14wjNa2qI6H8RYQ0j23HaeeE+d38RVc5Kmw5NMBgMtGhZj/2/Hjnfxbxg/L1fcLs9f+szin48Oxw5REZasdmK+hmHI4eoKOtZ80rJWW3hOBy53m13sf47nOzT0hyOXCwWExtSMnh76sfc13csJ07YeXrwm948V13diuWr3iQvL5/PPl1dehUp405m5WA7LfAfaQvnxEkHWVk5RFrDT70XxvGT2WfNKyWna5QEEp8GBxYvXkz9+vUZMGAAc+bMoVGjRsyZM4fZs2f78mP8okmLi/n5h8LFBben7eXi+tXOsUehjet20OqKouFVDnsuTz04DZcrH6PRSFh4CMYgfQ6mv8QnNGL1qsJF7jalZtIgtmj4ebNm9UlZvx2n00VWloNdO/fTIDaan9amMX7cB7w7fRiXNCscnmu3Z9P37hdxufIwGo2Eh4eqLf6ji5vWJf2nbQDs3baHanWKFob0eDzMHP4+1evW4LYnemE0FV5uIiLDCTv148JW3kZudi6W0BAq16zCrrTCKSK70nZSLaZk55gUWv/rMTrHFi4GGV+rPBl/FM2/3XLoJA2r2KgQYcFkNBBfuzw7DtsBaFf3IlbuOOzN++uxbKwhJmIqFt6RaBVTgcw/7KVYk+DXNO5i1p3qL7Zt3kudEvQXl8RdzLrvC/fZmXmQKtXKn88iXlASEhqTnFwYhE9NTSf2tD6jefNYUlK2efuMnTv3ERsbQ0JCE1atKtwnOXk9LVs2PWteKbn4hFhWJ6cCsCl1Bw1ia3vTmjWrR0pK+qnvN5tduw7QrHk9lv7fK8ycPYyZs4dRrpyNl6c8it2ezb19xqj/Pk/SfzlA/TrVqFDOisViom2bRvyUsoMf12fSrUscAN06xfHDuvSz5pWS0zWqbDIYDD57/Ru3283w4cPp1asXffr0Ye/evcXSV61aRc+ePbn99tsZOXIkHo/nX4/n0wnWF110Ea+99hoTJ04kLS3Nl4f2u7adL2HDT5k8cf+b4IHBI3qxeO4qatSuxOUdm551v/17D5PQJta7bbWF0eWaBJ56YCpms4k6DarT5dqWpVGFMuOqrm1Ys2Yzd/Z+Ho/Hw0vjH+bDmZ8RHVOdLl1acXef6+hz1zDcbg+PP3EnoaEhTBg/k7y8fJ5/9i0ALq5Tg1GjBxY+oeDuF7GYTcQ2jOGGGzv4uXbB5ZK2zchMyeDNx18Dj4deT93JqkXfUalGZdxuN7s27yQ/L5/0n7cDcF2/7nS75zoWTlnAmqXf484v4PYnewPQc0hvlry5CHeBm4rVKnJ9/xv8WLPg8/X232lfrxKL+1+GwQBPf5xGvysuZu+f2SzL+INJ32Yyu28rAL7Y8pv3D/66lax8v7PoLnVegYdnPknj9dtaYDDAhl+P813m4TN+ppxZu86XsGFtJo/d+yYeDzw9sheLTvUXV5ylv7iux2W8Pm4xj/R9A/DwxPO3lm6hy7CuXS/nhx9S6d37aTweD+PGPc7MmZ8QHV2dK69sQ58+N3DnnUPxeDw8+WQfQkNDGDSoJ0OHvkZS0jdUqBDF5MlPERERdsa8UnJXXnUpP65J4+47Cn+gjhk3gFkffkl0dFU6d2nJXXd34567x+B2u3nsiZ5n/X5ttgiuv6Et9/YZg9lsIjY2mu43tCvl2pQ9vW66Aqs1jA/mr2DomLksnfscBqOB2YkrOfj7Md6b8y3vvzqI5YtH4HIVcO9jb5KfX3DGvFJyukaVTaU1HWDZsmWnFrRMJDU1lQkTJvD2228DYLfbefnll5k9ezYVK1Zk+vTpHDt2jIoVzz5d1OA5V/jgf7RkyRKWLFnC3Llz/9N+e7KWnjuTlIratjr+LoKc5st9GmYfKB59398lkL+sHvrP+bLiH7WtDf1dBDnF5daTdwJJuYsn+7sIckrOr6P8XQTxij13liBW/51knx3rl4Fnv3k5fvx4mjdvzvXXXw9A+/btWb26cJrV6tWr+fjjj7FYLOzbt4/bb7+dW2655V8/67wtzd6jRw969Ohxvg4vIiIiIiIiEnB8OXIgMTGRxMRE73avXr3o1asXUDg6wGazedNMJhP5+fmYzWaOHTvGTz/9xCeffEJERAR33XUXcXFx1Klz9hvAem6biIiIiIiIiI8YfLiy3+nBgL+z2Ww4HEWLgBYu9Fr4J3758uVp1qwZlSsXrkl16aWXsn379n8NDvh0QUIREREREREROf8SEhJITi6cwpCamkpsbNF0jaZNm5KZmcnRo0fJz89n06ZN1K9f/1+Pp5EDIiIiIiIiIj5SWgsSdu3alR9++IHevXufWtByHDNnziQ6Oporr7ySIUOG0L9/fwCuueaaYsGDM1FwQERERERERMRHSuvpqkajkdGjRxd7r169et7/v/76672LFZboeD4rmYiIiIiIiIgEJY0cEBEREREREfGR0ppW4GsKDoiIiIiIiIj4SLAGB0o0rcDtdlNQUMD69etxuVznu0wiIiIiIiIiUorOOXLgpZdeol69ehw8eJCtW7dSqVIlJk6cWBplExEREREREQkqhiAdOnDOkQNpaWn07t2bjRs3MmPGDH777bfSKJeIiIiIiIhI0DEYffcqTef8OLfbzZYtW6hVqxYulwuHw1Ea5RIRERERERGRUnLO4MBNN93EqFGjuP/++3n55Zfp1atXaZRLREREREREJOgYDL57laZzrjlw1113cddddwFw//33U7169fNeKBEREREREZFgFKRLDpw7OPD+++8TFRXFyZMnWbJkCe3bt+e5554rjbKJiIiIiIiISCk457SCb775hptvvpnk5GS+/PJLtm3bVhrlEhEREREREQk6ZXZagdFo5MiRI1SqVAkAp9N53gslIiIiIiIiEoyMZXVaQZs2bejTpw8vv/wy48aNo2PHjue1QBVCQ87r8UWC1Q3R9fxdBDnlmpF6akugGL7hqL+LIKcMviTd30WQU8qH1PZ3EeQ0Ob+O8ncRRERK5JzBgSeffJInn3wSgGbNmmGxWM57oURERERERESCUZldkHD58uXMnz+fvLw8PB4Px48fZ+nSpaVRNhEREREREZGgEqzBgXMuSPjaa6/xyCOPUL16dW655RYaNmxYGuUSERERERERkVJyzuBAlSpViI+PB6BHjx78/vvv571QIiIiIiIiIsHIYDT47FWazjmtwGKx8PPPP5Ofn8/q1as5duxYaZRLREREREREJOiU2WkFo0aNIj8/n0GDBpGUlMSgQYNKo1wiIiIiIiIiUkrOOnJg9+7d3v+vVq0aAIMHDz7/JRIREREREREJUsE6cuCswYHhw4ef8X2DwcDs2bPPW4FEREREREREglWZCw7MmTOHgoICTCYTAHa7nbCwMMzmcy5TICIiIiIiIiJB5KxrDmRmZnLNNddw4sQJANauXcs111zDL7/8UmqFExEREREREQkmRoPvXqXprMMAXnrpJaZMmUK5cuUAuOqqq6hYsSJjx47lww8/LK3yiYiIiIiIiASNYJ1WcNaRA263m2bNmhV7LyEhgby8vPNeKBEREREREREpPWcdOeB2u8/4fn5+/nkrjIiIiIiIiEgwM5z1FnxgO2uxO3TowMSJE8nKygLA4XAwceJELrvsslIrnIiIiIiIiEgwMRh89ypNZw0OPPjgg1SoUIFbbrmFdu3a0bNnTypWrMjjjz9emuUTERERERERkfPsrNMKDAYDDz74IA8++GBplkdEREREREQkaBmCdEXCswYHpDi3283EsQvZkXGAkBAzL4y6g9rRlYvlOXY0i/59X2P+4mcJDbWQm+tixHNzOHo0C2tEGCNeuosKFSP5+ssUFsxdiclkpF6DGgwddjtGY5BOTPEDt9vN6FHTyUjfQ0iIhdFjBxETU92bvjDpW5ISv8VkNjJw4G106nwpBw8eZtgLUynId+PxeBg1eiB16tbkm69/5P3pH2MwGOh+Q3v69O3ux5oFH7fbzciRb5ORsZuQEAtjxz5KTEwNb3pS0tcsWPAVZrOJQYN60rlza44ePcFTT71Cbq6LKlUqMn7844SHh50xr5Sc2+1mzOgPyEzfiyXEzOgxA4iOqeZNX5S0nKSk5ZhNRh4ceAudOrfk8B/HeHboW+Tl5VOunI0Jkx7Bag0nLW0nL0+cjccDlSqVY8KkRwgNDfFj7YKLx+0mdeYCTvx6AKPFTEL/u7BVq+JN3/nNKvau/hEDBhpcfxW1LmtJXnYO696cQb7TidFsptVD9xJWvhzJY1/17pd18HdiOlzGJb1v9kOtgpfb7WbySx/zS+ZBLCFmnh1xO7WiKxXLc+yonUH3TGXWosGEhlrIyXYx6rl5ZJ3MwWwxMWxMbypXLcemDbt4a/LnGAwG4lrW5aEnr/dTrYKTL69Tn32azMwPPicyMpybbu7Irbd18WPNgo/678ChtiibgjQ2cPZpBVLcqhVpuJx5fDBvMA8/cQOvv/xxsfQff9jOowOmcfTISe97ixO/p16D6kyf9QTX3diKD977htxcF++8+Tlvz3iU9+c8icOew/ertpZ2dYLa8mXrcDldfJQ4nsFD7mbSxFnetMOHjzF3zpfM++glpr//Iq9OmYfLlcebry/gzruuZdac0Tw4oAevvjqPgoICpkyZy4yZI5i/YBwfzf+aY8dO/ssny98tW7YWl8tFYuIrDBlyDxMmfOBNO3z4GHPmLGXBgknMmDGKKVNm43LlMW3aArp378j8+RNp0qQuiYlfnTWvlNzyZetxOV3MWzCGJwffycuT5njTjhw+zry5XzF3/ijeff95Xn91AS5XHjPe/4wbb+rI7LmjaNT4YhYvXIHH42Hk8PcY+9Ig5swbRdt2cRw8eMSPNQs+B1M2UZCXT6dRT3NJr5tJm7fEm+bMsrN7eTKdRjxNu+cfJ23eYjweD3uTfySqdg06Dh9Crctakvn5MgA6DHuSDsOepOWDfQivWJ5GN1/jr2oFrdUrtuJy5fHunEcZ+Ph1vDV5abH0n37IYPDA6Rz9M8v73tIlP9GwcS2mznyIbtcnMO/DlQC8PukzRk28m/fmPsr2LfvI3H6gNKsS9Hx1nTp27CRvvZHEh7OG8+HsEXzx+Q8cOPCHH2sWfNR/Bw61hQSSswYHXC7XWV8XotQNO7m8XWMAmrWow/Zt+4qlGw0G3pr+CFHlrN73Nm3cxeVtC/e5ol0T1q3NICTEzPtzniQsvPAuXH6+m5BQDeD4LzakbKdd+3gAWsTFsnXLTm9aWtovxCc0IiTEQmSkleiYamRk7OWZZ++hY8eWABQUuAkNsWAymfj8izeIjLRy/LidArcbi0Vt8V+kpGyjffvC7zUurhFbtuzwpm3enEl8fOOitoiuTnr67mL7dOhwKWvWbDprXim5jRvSadsuDoAWcQ3YumWXNy0t7RfiEhqe+n4jqB1djYyMXxn6XF9uuLEdbreb3w79SWSUlT17DlG+vI3Zs77k3j6jOHnCTp06Nc7yqXImf2bspGqLJgBUbFCHY7v3etNCI210Gfc8RrMJ54mTGC0WDAYDUbVrkp/rBCA/Jxej2VTsmJvmLOSS3jdjDgsrvYqUEZs37qbNFY0AuKR5DOlb9xdLNxoNvPbeg0SVi/C+1/Pu9vR94EoAfj90nMjIwu/9vbmPUqNWRbKznTjsuYRHaETNf+Gr69T+fX/QsFEM5crbMBqNXHJJXTal/uKnWgUn9d+BQ21RNgXrgoRn/UvommuuwWAw4PF4ir1vMBhYvnx5iQ7udrs5fPgwlStXDvph8w5HLjZbuHfbaDSSn1+A+dQPuL9+eBTbx56LLbJwnwhrKI6sHIxGIxdVigIgcd4qcrKdtLn8n/vK2dkdOdgii37EGU1FbWG3ZxN5WprVGk5WloMKFQq/8927DvDypFm8+dZQAMxmE99+s5YxY6bTsWNLwsNDS7cyQc5uz8ZmK/q+Tf9oi6JgmdUajt2ejd2e422jv9rnbHml5Aq/19OuUcXaIofI065fVmsY9qxsDAYD+fkF3HrLUFzOPAY9dCu//X6U1I2ZPD/sfqKjq/LwoEk0vaQubS67xB/VCkp5OblYwou+b4PRiLugAKOpsL8wmkzs/GYl2xd/Qb2rOwEQYrPyR9p2vn16NC5HNh2HD/buf+LX/eTn5FLlEvUV/wuHw4k1siiocvq5AdDq8tgz7mcyGXms/zvs+uU3Xn3nAaCwz9iyeS8jh87j4rpVqVK1/Hkvf1niq+uU1RbOL7/s58iR41it4axdu5WYi6uf6SPlLNR/Bw61RdlU5qYVrFixguXLl7NixYpir3MFBp5//nkANm3aRLdu3XjkkUfo3r07qampPi14abNaw3A4cr3bHrfb+8PirPvYwsh2FN4JynY4sUUVnsRut5vXX/mEdWszmPhqv6BdsMJfbNZwHI4c7/bpbWGzRRRLczhyiDp1ofxpbRqPPjKRCRMfo07dmt48Xa++jJWrppOXl8+nn6wqpVqUDX//vt1uz9/aoqhTcjhyiIy0YrMVtZ/DkUNUlPWseaXkCr/X069Rp7dF8TSHI5fIU9cji8XMZ59PZsSoB3j+2WmUL28jOroa9erVxGIx065di2J39+TcLOFh5OcWb4u/AgN/qXd1J66bOp4jGb9weGsG6Uu+oEH3rnR9eTjtnn2Eta+958376/fruLhz21Irf1ljtYZ6+2Iofm6cyxvvD2TqzId4Ychs73uXNI9h0f89T2zjmsz9YIXPy1uW+eo6Va6cjaHP9uXJx1/lmafeoEmTi703AaRk1H8HDrWFBJJz3s5fvnw5/fr1o2/fvvTp04cbbrjhX/Pv3184XO/VV19l+vTpLFy4kJkzZ/LKK6/4psR+0iK+LmtWbwMgbdNu6jU49zDbFvF1+WF14XoCa77fRlxCXQDGj07E5czj5df7e6cXSMnFJzRi9aoNAGxKzaRBbIw3rVmz+qSs347T6SIry8GunftpEBvNT2vTGD/uA96dPoxLmtUHCiO1fe9+EZcrD6PRSHh4KEajAjX/RUJCY5KT1wOQmppO7Glt0bx5LCkp27xtsXPnPmJjY0hIaMKqVYX7JCevp2XLpmfNKyUXn9CQ1ckbAdiUuoMGsbW9ac2a1WdDSvqp7zeb3bsO0KBBbcaMmsG6nwqvUVZrGAajgdq1qpKdncuve38DICUlnXr1a5V+hYLYRbH1+C218Hs9umM35WoX9RdZB39n7avv4vF4MJhMGM1mMBqwWCOwRBTeNQ2NiiQ/p+iPpMNbM6javEnpVqIMaRZ/MWu/3w7Als17qdug2jn2gDkzVvDV0hQAwiNCMBqNeDweHrp3GidPFv74jogIxaA+4z/x1XUqP7+Abdt2M3vuSCa/+gS7dx0kPuHMI0DkzNR/Bw61RdlkNPjuVZoMnr/PG/ibG264gdGjR7NgwQLatGnDDz/8wOTJk8+av2/fvsyePZt+/foxY8YM7/t33nkn8+fPP2eBTri+/g/FLz1/Pa3gl8wDeDwwfMxd/LB6K7WjK9OhczNvvpu6jSTpsxcKn1aQ42LkC3P588hJzBYTYybew5E/TnBP71eIS6jrHTHQ6+6OdL6yhb+qdlY2S81zZ/KDv55WkJmxF4/Hw0vjHyZ51QaiY6rTpUsrFiZ9y8Kkb3G7PTw4oAdXd7ucW24ajMuVR6VKFQC4uE4NRo0eSFLiNyxevAKL2URswxheGNYPk6lkd5RKm8kQeIGkv1bYzczcg8fjYdy4x0lOTiE6ujpXXtmGpKSvSUz8Co/Hw4ABt9OtW1uOHDnG0KGv4XDkUKFCFJMnP0VERNgZ8waqPLfD30X4B+8q4Bl7wQNjxg1kdXIq0dFV6dzlUhYlLWfhwuV43B4eGHAzXa9uw65dBxg98n0MBgNGo4Hnh91PvXo1+WntFl6d8hEej4e4+Fiee/5ef1fvrIZvOOrvIvyD92kF+w6AB1oO6MNvqVuxVq1MjZbN2b74C37btBWDwUDVFk1p3OM6co4dZ8P0eeTnOvEUFND4tu5UbVa4Zs2XjzzHdW+N93Otzm3wJYF3XkDR0wp27jiEx+Ph+dG9+HH1dmpFV6Jdp6befLddO455nzxNaKiFo39mMXbYAlyufNwFHgY+fi3N4+uw+rstzJnxHSEhZi6qFMnQkbcTERF409HKh9Q+dyY/8OV1atrURaxYvp7QEAv33Hc9V3e7zN/VOyuLMfDu3l6o/XcgunDbomwH9Lp+9YPPjvXtNaXXjucMDvz1R/7QoUOZOHEiffr0Yc6cOWfN36NHDwCys7Pp168fN954IxMmTCArK6tEowcCNThwIQrU4MCFKhCDAxeqQAwOXKgCMThwoQrU4MCFKFCDAxeqQAwOiPifggMlVZrBgXMuzW6xWPj555/Jz89n9erVHDt27F/zL1myBJfLRXp6OmFhYRgMBmJjY7ntttt8VmgRERERERGRQGQ0/Ov994B1zuDAqFGj2LVrF4MGDeL1119n0KBB5zxoSEgIzZs3927fcccd/3+lFBEREREREQkCwbokzTmDA1WrVsVsNuN0OnnuuedKo0wiIiIiIiIiQemcq/4HqHMGB0aOHElycjJVqlQpXF3ZYGDBggWlUTYRERERERERKQXnDA5s3ryZZcuWYTQGa/xDREREREREpHSU2TUHYmJicDqdhIeHl0Z5RERERERERIJWmV1z4NChQ3Tu3JmYmBgATSsQERERERERKWPOGRyYPHlyaZRDREREREREJOgF64T8swYHFi5cyO23386CBQswGIqPixg8ePB5L5iIiIiIiIhIsClz0wqqVasGQN26dUutMCIiIiIiIiJS+s464qF9+/ZA4RoDp78sFgvr168vtQKKiIiIiIiIBAuDweOzV2k655oDX3zxBbm5ucTFxbF582acTicmk4mmTZvy/PPPl0YZRURERERERIJCmZtW8Jf8/HxmzZqF0WjE7XbzwAMPMGPGDHr37l0a5RMRERERERGR8+ycwYHjx4+Tn59PSEgI+fn5nDhxAgCXy3XeCyciIiIiIiISTMrc0wr+cuedd3LDDTfQoEEDdu3aRf/+/XnnnXe8axKIiIiIiIiISCFjKa8V4CvnDA7cfvvtXHXVVfz6669ER0dToUIFCgoKMJlMpVE+ERERERERETnPzhocmDZtGg899BCDBw/GYCi+osLkyZPPe8FEREREREREgk2ZW5CwS5cuAFx33XVERUWVWoFEREREREREglWZW3OgUaNGAMyYMYOPPvqo9ApkDCu1z5J/V+DJ9XcR5DRHcnf5uwhySoXQGv4ugpzyXIvgnNNXFrV41t8lkL/sfCXE30UQEZEgdM41B8qVK8esWbOoU6cORmNhDKRdu3bnvWAiIiIiIiIiwabMTSv4S4UKFUhPTyc9Pd37noIDIiIiIiIiIv9UZp9W8Mgjj3Dw4EGqV69OrVq1SqNMIiIiIiIiIlKKzhoccDgcDBkyhOPHj1OzZk327t1LxYoVmTJlCjabrTTLKCIiIiIiIhIUyty0gsmTJ3PNNddw8803e99buHAhkyZNYvTo0aVRNhEREREREZGgEqxPKzhrudPT04sFBgBuv/12MjIyzneZRERERERERKQUnXXkgNl85iSTyXTeCiMiIiIiIiISzIJ1QcKzjhwoX748aWlpxd5LS0ujXLly571QIiIiIiIiIsHIaPDdqzSddeTAM888w6BBg2jTpg21a9dm//79/Pjjj7z99tulWT4REREREREROc/OOnKgVq1aLFq0iFatWpGXl0fz5s1JSkqidu3apVk+ERERERERkaBR5kYOAISGhtKtW7fSKouIiIiIiIhIUCtzTysQERERERERkQvDv44cEBEREREREZGSK3NPKxARERERERGR/6a01hxwu90MHz6cXr160adPH/bu3XvGPP379+ejjz46d7n/1wqLiIiIiIiIiH8sW7YMl8tFYmIiQ4YMYcKECf/I89prr3Hy5MkSHU/TCkRERERERER8pLTuwKekpNC+fXsA4uLi2LJlS7H0r776CoPB4M1zLgoOlJDb7Wb8mPlkZuwnJMTMi6P6Eh1TxZu+ZOFqFi9MxmQy0n/A9XTo1JxDB//kxec+wOOBcuUieGlSf8LDQ/nqi3XMn7MMk8lE/diaPPfinRiNGsRRUm63m7GjZ5KR/ishIRZGjelPdEw1b/qipBUsTFqB2WTkwYE307Fzgjft53XbeW7oNJZ99yYAsz/8P5Ys+o4KFaMAGD7qfurUqVG6FQpibrebKeM+ZmfmQSwWM8+MuJ1a0ZWK5Tl+1M5D905l5sLBhIZavO/v3f0HA/u8ySfLhxMaamHThl1Mm/I5BoOBFi3rMuiJ60u7OkFN50VgcbvdTByTyI7MA1gsZoaNvpPa0UV9xseLfuDjpO8xmY3c/+A1tO/UjAP7jzDqhdl4PFCtekVeGHkne/f+zpQJi737bdm8m5ffeJAr2jX1R7WCksEAY3o0p3GNKFz5bp5N2sTePx0ANK4RxfCbLvHmjY+pwIMz15G2/wSv35VAmMXE7ydzeXpBKrl5BfRuE80dl19MgdvNW9/uYMX23/1VraDkdrsZPeo90tP3EBJiYczYh4iJqe5NT0r6lqTEbzCZjQwceDudO1/qTZs1aylHjhxnyJA+AHz66Uo+mPEpkZER3HxLZ2677apSr08wc7vdjBz5NhkZuwkJsTB27KPExBRd55OSvmbBgq8wm00MGtSTzp1bc/ToCZ566hVyc11UqVKR8eMfJzw87Ix5peTUFmWTLx9BmJiYSGJione7V69e9OrVCwC73Y7NZvOmmUwm8vPzMZvNZGZm8vnnn/PGG28wderUEn2WggMl9N3yVFzOPGbNf5bNm3bx6ssLefWthwE4cvgEC+YtZ27SCzidefTrM4nLrmjMvNnLuPqaVvS8oxNvvf4xny75gZtvbce0Nz8h8eMRhIeH8txT01m9cjMdu8T5t4JBZMWyFJzOPOYtGMWm1B28PGkeb04dAsCRw8eZN/drEheNxenMo+9do7i8bTNCQiz8duhPZs/6kvz8Au+xtm3bzUsTB9G0aR1/VSeorf5uKy5nHm/PfpStm/cydcpSxr92nzd93ZoM3n39S47+mVVsP4c9l6mTl2KxmLzvvfnyZ4x+pS81albk8f7vkJl+gNhGNUutLsFO50VgWbl8M05XHh/Me4q0Tbt57eUlTH5zIABHjpwgcd5KZic+g8uZT/++U2hzRSPemPwxPXq255rrW/HJoh+YN3s5/QZcy7sfPgHAsq83ULlqOQUG/qOrL6lOqMXIrW9+T1x0BV64sQkPzvwZgO0HT3LH22sAuK55dX47mUtyxmFG3HwJn248wOKf9zGwS33uvDyGzzYe4J72dbnp1WRCLUaSHmnH95mHcRW4/Vm9oLJs2TqczjwWJE4gNTWDSRM/ZOq05wA4fPgYc+d8waLFL+N0urjrzhdo27YFbrebF4dNY3PaL1x99WUAHDt2kjde/4jFS14hKsrK/feN5PLLmlOzVpV/+3g5zbJla08NRX6F1NR0Jkz4gLffHgYUtsWcOUtZvPhVnE4Xd945lLZt45k2bQHdu3ekR4+reO+9hSQmfsX113c8Y96QEMs5SiB/UVvIuZweDPg7m82Gw+Hwbrvdbszmwj/xP/nkE37//XfuueceDhw4gMVioWbNmnTo0OGsn3Xeb1cfPXoUjyc4V2s8XeqGX7w/yJq3qMu2rUWLPWxN202L+PqEhFiIjIygdnQVdmQcoGGj2pw8WdhYDnsuZrOJkBAzM+c+S3h4KAAFBQWEhOqk/S82bMigXbsWALSIa8C2Lbu9aWlpO4lPiPW2RXR0NTIzfsXpdDF65AcMG35fsWNt27qbGe99St+7RvH+e5+Waj3KgrSNu2nTthEATZvHkLF1f7F0g8HAlHcfJCoqwvuex+Ph5TGLePDRawkLC/G+/86cR6lRsyLZ2U7s9lzCw0OQktN5EVg2bdzJFW2bANCsRR22b/3Vm7Y1bS8t4uoSEmLBFhlO7dqV2ZFxkN07f/P2My3i65G6Yad3n5xsJ+9N/YKnnr29dCtSBlxapyKr0v8AIPXXYzSrXf4fecJDTDzRrRGjP9nyj31Wbf+Dtg0qExddnpTdR3EVuMnKzWfvEQeNakSVWj3Kgg0p22nXPh6AuLiGbNlS9G88LW0HCQmNTl2nrETHVCMjYw9OZx4339yZgQNu9ebdt+93GjW6mPLlIzEajVzSrD6pmzJLvT7BLCVlG+3btwQgLq4RW7bs8KZt3pxJfHzjoraIrk56+u5i+3TocClr1mw6a14pObVF2WQweHz2+jcJCQkkJycDkJqaSmxsrDftmWeeYeHChcyZM4dbbrmFe++9918DA3AeggOLFy/mrbfeYuvWrVxzzTXcd999XHPNNaxZs8bXH1WqHI5cbJHh3m2T0eC902Z35GKzFaVFWMOw27OpUq0CSfO/47YbR/DD6i1c1a0lRqORiyoV/phYMG8F2dlOLruiSelWJsg57DnF2sJoMha1hT0Hm63oD1GrNYysrBzGjZnFvfdfR9WqFYsd69rrLuPFkf2YMfMFNqRksuq7DaVTiTLC4XBitYV5t09vC4BWl8dSrry12D4z3/mWy9s3pn7D4sPUzWYTWzfv5d5bJ1PxokiqVC1/Xste1ui8CCwOey7W09vDWNQeDnvx/iTCGordnkNso1okr9wMQPLKzeTmuLx5Pl2yhiuvjqd8haKhg1IykWFmsnLzvdsFbg+mv4337NU6mi83H+SYo/A7t4VZyMrJA8DuzCcy3Iwt1EJWbp53H4czn8gwDcD8L+yObCIji65Fpr9fpyJPv06Fk5WVTblyNtq2iyt2nJiY6vzyyz6OHDlOTo6TtT+mkZOTWyp1KCvs9uxi/ULxtsgmMrKo77Zaw7Hbs7Hbc7ztV9g+jrPmlZJTW5RNpfW0gq5duxISEkLv3r0ZP348zz33HDNnzmT58uX/U7l93qvNnz+fOXPmMGjQIN5++23q1KnD77//zkMPPcQVV1zh648rNVZrGA6H07vt9ngwmwuHRNusYWQ7ijqlbEcukZERjB05h5Ev3ccV7ZqyetVmhj/3AW+8/Rhut5vXJy9m757fefm1gRgMPpyUcgGw2sJxnPZ9Fw6fOdUWtvBibeFw5GKxmNiQksGvv/7O21M/5sQJO08PfpNJkx/h7r7Xei+uHTrGsX373mJzseXfWa2hZJ92XnjcRefF2Xz75QYqVynHFx+v4+ifWQwZNJ23PngIKBx9kPR/zzP9ra+Y98EK7n+o23ktf1mi8yKwWG3F+wXPaX2G1RZWrK2yHU4iI8N54ukeTHopiaUfr6Vth6aUL18UCPjqi5+ZMOWB0qtAGZKVm48ttOjnjtFgoMBd/E7MTQm1eGj2z95te24etjAzTrsLW6iZkzl52J15WE87jvXU+1JyNmsEDkeOd/vv16nT0xyOHKIirf84BkC5cjaeffY+Hn9sEuXLR9KkSV0qVNAojv/CZvt7W5z2u9YWgcNR9Eelw5FDZKTV20ZhYaGF7RNlPWteKTm1hfz/MBqNjB49uth79erV+0e+Rx99tGTH80mpTmOxWIiIiMBqtVK7dm0AqlatGvR/AMfF1+OH5DQANm/aRf0GRXOhmzarw8YNO3A688jKymb3rkPUa1CTyCir9+5Q5SrlOXmy8IR9aeRcnM48prz5kHd6gZRcfEIsq5NTAdiUuoMGsbW9ac2a1SMlJR2n00VWVja7dh2gWfN6LP2/V5g5exgzZw+jXDkbL095FLs9h1tuHEq2IxePx8O6n7bSRHOs/5NL4i5m7ffbAdi6eS91G1Q7xx7w0dJneWPGIN6YMYiKF0Uy+e0H8Hg8PHLfNLJOnSMR1lAMvlzJ5QKg8yKwtIivyw+rtwKQtmk39RoUjZRp2iyG1A07cTrzsGflsHv3b9RrUIOf1qTz0OM38u6HT2A0Gmh9ReGUHXtWDi5XPtWqV/BLXYJdyu6jdGpcOBc9LroCGYeKP84pMsxMiNnIoeNFAZuUPUfp1LgqAB0bV+HnXUdJ/fU4repeRIjZSGSYmfpVbWT8Vnw9Ffl3CQmNSF5VOBIpNTWD2NgYb1qzZg1IWb/91HXKwa6dB2gQG33G4+TnF7Bt2y7mznuJV197il27D5CQ0KhU6lBWJCQ0Jjl5PQCpqenF2qJ581hSUrZ522Lnzn3ExsaQkNCEVasK90lOXk/Llk3PmldKTm1RNhl9+CpNPh850KVLFwYNGkRsbCwDBgygffv2rF69mssuu8zXH1WqOl8Vz9oft3PvXRPweGDk2HuY++G31I6uTMcucfS+60r69ZmE2+Ph4cduJjTUwtDnezPhpY9wu914PPDssDvZvm0vnyz5gfiW9Rlw/xQA7rj7SrpcFe/nGgaPK6+6lB/XpHH3HSPxeDyMGTeAWR9+SXR0VTp3acldd3fjnrvH4Ha7eeyJnoSGnnnuemRkBI8/0ZP7732JkBAzbS5rSoeOcaVbmSDXocslrF+7g0F93wI8PDuqF4lzVlGzdiXadSr5omkGg4FefTvw9MMzsISYuahSJM+M0Nzq/0LnRWDpdGULflqTzv13vQLA8DF3M2/WcmpFV6Zj5+b0uqsTD/Sdgsfj4aHHbiA01EJMnSoMH/ohlhAzdetXZ+gLhYsP7d3zBzVqXuTP6gS1r7ccol1sZRY92g4D8HRiKv061GXvnw6Wbf2dOpVt7D9WfOjtW9/u4JU74undJoZjDiePz9tAjquAWat3kfRwW4wGA698mY4rX4sR/hdXdW3DmjWbuKP3c3g8HsaNf4QPZ35GdEw1unRpzd19rufuu17A7fbwxBN3nvU69ddd1Vt7PEVIiIX77rtRIwf+o65dL+eHH1Lp3fvpwrYY9zgzZ35CdHR1rryyDX363MCddw7F4/Hw5JN9CA0NYdCgngwd+hpJSd9QoUIUkyc/RURE2BnzSsmpLcom4znWCghUBs95WC1w3bp1fP/99xw7dozy5cvTsmVLOnXqVKJ9HfmrfF0c+R9ZjBqKFEiOOQ/6uwhySoVQPdYvUOQWHPd3EeSUFs9qbmug2PnKP4eUiv8YDVp4WuSfYs+dJYi9mLLMZ8ca07L0HtV6XlbSad26Na1b67maIiIiIiIicmEJ1tmxWmZXRERERERExEeCNThQ2msciIiIiIiIiEiA0cgBERERERERER/59wd7By4FB0RERERERER8JFifVqBpBSIiIiIiIiIXOI0cEBEREREREfGRYF2QUMEBERERERERER8J1uCAphWIiIiIiIiIXOA0ckBERERERETER0xBOnJAwQERERERERERH9G0AhEREREREREJSho5ICIiIiIiIuIjRoPH30X4nyg4ICIiIiIiIuIjwTqtQMEBERERERERER8x+bsA/yOtOSAiIiIiIiJygdPIAREREREREREf0bQCHwkzXeTvIsgpn+3d5+8iyGluubiRv4sgp3go8HcR5JTcguP+LoKckvlytL+LIKfUeeV3fxdBTrP36Vr+LoKIlLJgXZBQ0wpERERERERELnABN3JAREREREREJFiZNK1ARERERERE5MIWrGsOaFqBiIiIiIiIyAVOIwdEREREREREfCRYRw4oOCAiIiIiIiLiI8EaHNC0AhEREREREZELnEYOiIiIiIiIiPiIyeDxdxH+JwoOiIiIiIiIiPhIsA7PD9Zyi4iIiIiIiIiPaOSAiIiIiIiIiI8E64KECg6IiIiIiIiI+EiwBgc0rUBERERERETkAqeRAyIiIiIiIiI+oqcViIiIiIiIiFzgNK1ARERERERERIKSRg6UkNvtZvSo6WSk7yEkxMLosYOIianuTV+Y9C1Jid9iMhsZOPA2OnW+lIMHDzPshakU5LvxeDyMGj2QOnVr8t2Kn3l72kJMJhM9bu3C7T27+rFmwcftdvPpm4s4tPsAJouZW5/oTaWalb3pq5esZPPKDQA0bN2Eq+6+huyTDhInzSU3O5eISCu3PtkLW/lIVi9Zyc//txZreSsAPR7rSeXaVf1Sr2DkdrsZOfJtMjJ2ExJiYezYR4mJqeFNT0r6mgULvsJsNjFoUE86d27N0aMneOqpV8jNdVGlSkXGj3+c8PCwM+aVknO73Ywa+S7pGXsICTEzduwjxa5RSUnfkLjga8xmEwMH3U7nzq04dvQkQ56ajPNUW4wb/xjh4aFMf28JX3yRjM0WQb/+t9C5cys/1iw4ud1uJo5JZEfmASwWM8NG30nt6Cre9I8X/cDHSd9jMhu5/8FraN+pGQf2H2HUC7PxeKBa9Yq8MPJO9u79nSkTFnv327J5Ny+/8SBXtGvqj2oFJbfbzZjRM8hM34slxMLoMQOIjqnmTV+UtJykpGWYTUYeHNiDTp1bcviPYzw79C3y8vIpV87GhEmPYLWG8+03P/H+9E8wGAxc370dffpe58eaBR8DMLZrQ5pUseHMdzP063T2Hs/xpneqU5HHr6iDAdjyexbDlmV60+pVjOCTuy/l0qnf4yxw06pmOV7oVB8P8NO+40xI3lnq9Qlm6r8Dh9qibArWkQMKDpTQ8mXrcDldfJQ4nk2pmUyaOIup054F4PDhY8yd8yULF0/C6XRx953DuKJtC958fQF33nUtV13Vhu9Xb+TVV+cxecpgJkz4kKSFEwkPD+XuO1+gc5dWVKpU3r8VDCLb1qSRl5fHQ689ya/b9/DFe59yz6j+APx56AipK1J4+PUnMRgNvDP4DZpe0ZwNy37m4qZ16XxHV3ZsyOCrmV9w25O9ObBjHz2fuYtaDWr7uVbBadmytbhcLhITXyE1NZ0JEz7g7beHAYXnxZw5S1m8+FWcThd33jmUtm3jmTZtAd27d6RHj6t4772FJCZ+xfXXdzxj3pAQi59rGDyWLfsJp8tFYuJEUlMzmDhhJtPefh74qy0+Z/Hiyae+3+do2zaOqdMS6d69Az16XMl77y0mMfFrLr+8OZ9/nkzSwkkA3NH7WS67rDnh4aH+rF7QWbl8M05XHh/Me4q0Tbt57eUlTH5zIABHjpwgcd5KZic+g8uZT/++U2hzRSPemPwxPXq255rrW/HJoh+YN3s5/QZcy7sfPgHAsq83ULlqOQUG/qPly37G5cxj3oKxbErN5OVJc3hz6tMAHDl8nHlz/4/EReNxOvPoe9dwrmjbnBnvf8qNN3Xgpps7MvWthSxeuIK7+lzLq5Pnk7hoPBERYdzUfTDdb2hHhQpRfq5h8OjWoDKhZiO3zEshvnoUwzrV54FP0gCwWkw837E+vRI3ciwnjwGto6kYbuFoTh62EBPDOtXHle/2HmtElwYM+mwL+07ksqBXPE2r2Nj6h91fVQs66r8Dh9qibArW4IDPpxXY7WXzwrwhZTvt2scD0CIulq1biiLUaWm/EJ/QiJAQC5GRVqJjqpGRsZdnnr2Hjh1bAlBQ4CY0xMKuXfuJia5GuXI2QkIsJLRszPr12/xSp2C1Z+suGl7aGIDoxhdzYMc+b1r5yhW4/6UBGE1GDAYDBfkFmEPM/PHrb8S2Ktzn4qZ12LtlFwAHduxj5YJlvD34db5b8G3pVybIpaRso337wn/jcXGN2LJlhzdt8+ZM4uMbF50X0dVJT99dbJ8OHS5lzZpNZ80rJZeSsp327RMAiItryJYtv3jT0jbvIOG07zcmujoZ6XvYcNo+HTok8OOaTezauZ/WrS8hNDSE0NAQYmKqk5Gxxx9VCmqbNu7kirZNAGjWog7bt/7qTduatpcWcXUJCbFgiwyndu3K7Mg4yO6dv3n/8G8RX4/UDUX9TE62k/emfsFTz95euhUpAzZuyKBtuxbAmfvvuISGp86NCGpHF/bfQ5+7hxtubI/b7ea3Q38SGRWByWTksy+mEBkZwfHjWRS43VgsusfyX7SqVY5Vu/8EYOOhkzSvVhRYaVmzHOlHHAzrVJ+FdyRwxOHiaE4eAOOvbsSk1bvIyS/w5r9pbgr7TuQSYTERGWrG4SpASk79d+BQW0gg8XlwoG3btixcuNDXh/U7uyMHW2SEd9toMpJ/qpOy27OJPC3Nag0nK8tBhQpRWCxmdu86wMuTZvHQwz2x24sfx2oNw56VXXoVKQNys52EWcO82wajgYKCwrYwmU1Yy9nweDx88d6n1Khfk8q1qlC9bk22r90CwLYft+ByugBo0SmBWx67nQcmPsyerbvZvnZr6VcoiNnt2dhsRf+eTf84L6zeNKs1HLs9G7s9x3u+/HWunC2vlJzDnk3kv7bF365R9uxi7xe2RTaxDWNYv34rdnsOx46dZOPGdHJycku3MmWAw56LNTLcu200FrWHw56L7bS0CGsodnsOsY1qkbxyMwDJKzeTm+Py5vl0yRquvDqe8hVspVSDsuPv//6L9985xc6bv/pkg8FAQYGbm298ip/XbaVNm0sAMJtNfPvNT9x68zO0atWE8PAwpORsIWaynPne7QKPB5Oh8PZaxXALl0eXZ0LyTu5ZtIl+LWtTp0I4T1xRhxW7/mT74eI3nwo8HuKrR/HNfa057HByyO4s1boEO/XfgUNtUTaZDL57lSafBwcaNWrE9u3b6du3L+vWrfP14f3GZg3H4SiaF+dxuzGbTYVptohiaQ5HDlGnTs6f1qbx6CMTmTDxMerUrYnNFv63vLnFTmQ5t7CIUJzZRT8CPB4PJpPJu53nymPBhDk4c3K5+ZHCu2yde1/Fsd+O8s6QNzj2+1HKV66Ax+Oh3S0dsZazYbaYadS6CQd37i/1+gSzv//bd7s9fzsvijolhyOHyEhrsXPA4cghKsp61rxSctZztkXxa1RhW0QUa4vIKCv16tXmrruu44H+oxgzZjrNW8Rq2PT/wGoLI9tRFFTxeIraw2oLw3FaWrbDSWRkOE883YPk79IYcO9rGAwGypcvCgR89cXP3HRr29KrQBlS+O/8tLYodm6coU+OKrz2WCxmPvt8CiNGPcDzz0715ul6dRtWrHqbvLx8Pvt0VSnVomywu/KxhhSNtjAaCv/IBziWk8fmQ1kcdrjIzivgp/3HaVIlkluaVKVXs+os6BVPZWsIc26P8+6/8dBJ2r33I1t+t/NQm5jSrk5QU/8dONQWZZPR4PHZq1TL7esDhoaGMnz4cJ5++mnmzJnDDTfcwEsvvcTs2bN9/VGlKj6hEatXFS5ytyk1kwaxRZ1Qs2b1SVm/HafTRVaWg10799MgNpqf1qYxftwHvDt9GJc0qw9A3bq12Lv3EMePZ+Fy5bH+523Excf6pU7BKqZJXdJ/LpyK8ev2PVS7uGjRNY/Hw+yR71O9bg16PN4Lo6nwn/jutJ20vvZyBk5+jItqVCamaR2c2bm8+uBEnDlOPB4PO1N3UFNrD/wnCQmNSU5eD0Bqajqxp50XzZvHkpKyzXte7Ny5j9jYGBISmrBqVeE+ycnradmy6VnzSsklJDRiVXIKAKmpGcW+v2bNG7C+2Pe7n9jYaOITGrFqVeE+yckbuLRlE44ePYHDkcNHCyYwatRAfjt0hAYNov1Sp2DWIr4uP6wuHImUtmk39RoULS7VtFkMqRt24nTmYc/KYffu36jXoAY/rUnnocdv5N0Pn8BoNND6ikYA2LNycLnyqVa9gl/qEuziExqyOnkj8Ff/XfTvuVmz+mxIST91bmSze9cBGjSozZhR77Pup8LRZlZrOAajAbs9m3v7jMTlysNoNBIeHobRqIc+/RfrD5ygc92LAIivHkXGYYc3bcsfWTSsZKVCuAWTwUB89Sh2/Omg4/tr6Z24kd6JGznscNFnYSoAC+9IICq0MNBgd+Xj9gTnM8X9Rf134FBbSCAxeDy+vZr26dOHOXPmeLezsrL4+eef2b17N/369Tvn/gWeLb4sjs/89bSCzIy9eDweXhr/MMmrNhAdU50uXVqxMOlbFiZ9i9vt4cEBPbi62+XcctNgXK48KlUq/EF3cZ0ajBo90Pu0ArfbQ49bu3DnXdf6uXZn9tnefefO5AdFTys4CHi4bfCdZPy8jYtqVMbtdrNg/GxqNy66GF5zX3es5WwkvTwPgHKVynHrk3cQZg1jw7KfWfNpMiaLmfpxsXTtG5htAXDLxfX8XYR/+GuF3czMPXg8HsaNe5zk5BSio6tz5ZVtSEr6msTEr/B4PAwYcDvdurXlyJFjDB36Gg5HDhUqRDF58lNERISdMW+g8hB4c1v/elpBRuYePB4YP+5RViWnEBNdnS5XtiYp6RuSEr/B7XEzYMBtdOt2BUeOHOfZoa972+KVyYMJDw9lxIi32bZ1FxaLmcFD+tCqVeAugJeVd8DfRTij059WADB8zN38sHortaIr07Fz88KnFSz8Ho/Hw30PdKNL13i2bN7NpLFJWELM1K1fnaEv9MJsMbE1bS8zp3/FK28M8HOt/l24qZK/i3BG3qcVZPwKHg9jxg1idfJGoqOr0bnLpSxKWs7ChcvwuD08MOAWul7dhl27DjB65PsYDIVTQp4fdh/16tViYdIyliz+DrPZRGxsNM8Pux+TKfACBPUnH/V3Ec7or6cVNK5sw2CAp/5vO53rXsSeYzks23mEGxpVYUCrwuDN5xl/8M66X4vt//2Dl3PljJ9wFrjpWr8SD7WJwVXg5g+7i6Ffp5OdF3jXZoC9T9fydxH+4ULtvwPRhdsWZfvm6LIDX/rsWFfVLL0n4/g8OPDxxx9zyy23/M/7B2pw4EIUqMGBC1UgBgcuVIEYHLhQBWpw4EIUqMGBC1GgBgcuVIEYHBDxv7IdHFhx0HfBgS41Si844PNw9/9PYEBERERERERESp+ewSMiIiIiIiLiI6X9lAFfUXBARERERERExEdK+ykDvhJ4q+iIiIiIiIiISKnSyAERERERERERHzFqWoGIiIiIiIjIhS1YgwOaViAiIiIiIiJygdPIAREREREREREfCdY78AoOiIiIiIiIiPiIQdMKRERERERERCQYaeSAiIiIiIiIiI8E6cABBQdEREREREREfEXTCkREREREREQkKGnkgIiIiIiIiIiPBOsdeAUHRERERERERHzEYPD4uwj/k2ANaoiIiIiIiIiIj2jkgIiIiIiIiIiPBOl6hIEXHDAZQvxdBDnlxpja/i6CSEAyYPJ3EeSUKEu0v4sgEnD2PB3q7yKIiFzQ9LQCEREREREREQlKATdyQERERERERCRYBenAAQUHRERERERERHzFGKTRAU0rEBEREREREbnAaeSAiIiIiIiIiI8E6cABBQdEREREREREfKW0nlbgdrsZOXIkGRkZhISEMHbsWGJiYrzpH374IV988QUAHTt25JFHHvnX42lagYiIiIiIiEiQWbZsGS6Xi8TERIYMGcKECRO8afv27eOzzz5jwYIFJCUl8f3335Oenv6vx9PIAREREREREREfKa1pBSkpKbRv3x6AuLg4tmzZ4k2rVq0a77//PiaTCYD8/HxCQ0P/9XgKDoiIiIiIiIj4iC+DA4mJiSQmJnq3e/XqRa9evQCw2+3YbDZvmslkIj8/H7PZjMVioWLFing8HiZNmkSTJk2oU6fOv36WggMiIiIiIiIiPuLLRxmeHgz4O5vNhsPh8G673W7M5qI/8Z1OJ88//zxWq5URI0ac87O05oCIiIiIiIhIkElISCA5ORmA1NRUYmNjvWkej4eHHnqIhg0bMnr0aO/0gn+jkQMiIiIiIiIiPlJaaw507dqVH374gd69e+PxeBg3bhwzZ84kOjoat9vNunXrcLlcrF69GoDBgwcTHx9/9nJ7PB5PKZW9hDL9XQA5pcDj8ncR5DQmQ4i/iyAiIkHAQ4G/iyCnMXDuu3UiF57Yc2cJYr+cXOqzY9WPusFnxzoXTSsQERERERERucBpWoGIiIiIiIiIj5TWtAJfU3BARERERERExEcMQRodUHCghNxuNyNHvk1Gxm5CQiyMHfsoMTE1vOlJSV+zYMFXmM0mBg3qSefOrTl69ARPPfUKubkuqlSpyPjxjxMeHnbGvFJybreb0aOmk5G+h5AQC6PHDiImpro3fWHStyQlfovJbGTgwNvo1PlSDh48zLAXplKQ78bj8TBq9EDq1K3JN1//yPvTP8ZgMND9hvb06dvdjzULPjovAofaIrCoPQKH2iJwuN1uRo18l/SMPYSEmBk79pFi/XdS0jckLvgas9nEwEG307lzK2/arA8/48iR4wx5qi8AK1asY9rUJExmE7feeiU9e15d6vUJZjovAofaQgKJ1hwooWXL1uJyuUhMfIUhQ+5hwoQPvGmHDx9jzpylLFgwiRkzRjFlymxcrjymTVtA9+4dmT9/Ik2a1CUx8auz5pWSW75sHS6ni48SxzN4yN1MmjjLm3b48DHmzvmSeR+9xPT3X+TVKfNwufJ48/UF3HnXtcyaM5oHB/Tg1VfnUVBQwJQpc5kxcwTzF4zjo/lfc+zYST/WLPjovAgcaovAovYIHGqLwLFs2U84XS4SEycyZEhfJk6Y6U0r/H4/56MFE3h/xgimTJmDy5VHbq6Tp4ZMYf78//PmzcvLZ8L4D5jxwUjmzBlLUuI3HDly3A81Cl46LwKH2qJsMvrwVdrllhJISdlG+/YtAYiLa8SWLTu8aZs3ZxIf35iQEAuRkVaio6uTnr672D4dOlzKmjWbzppXSm5DynbatS98BEeLuFi2btnpTUtL+4X4hEZF329MNTIy9vLMs/fQsWNhWxQUuAkNsWAymfj8izeIjLRy/LidArcbi0WDaf4LnReBQ20RWNQegUNtEThSUrbTvn0CAHFxDdmy5RdvWtrmHSSc9v3GRFcnI30PTmceN9/SmQEDb/Pm3bVzP9HR1SlXzkZIiIWWLRuz/uetpV6fYKbzInCoLcomg8F3r9J03oMDLpeL3Nzc8/0x553dno3NFuHdNpmM5OcXeNMiI63eNKs1HLs9G7s9h8jICO97WVmOs+aVkrM7crBFFrWF8R9tUZT21/deoUIUFouZ3bsO8PKkWTz0cE8AzGYT336zlltuHkzr1k0JDw8t3coEOZ0XgUNtEVjUHoFDbRE4HPZsIv+1Lf7Wf9uzKVfORrt2xZ/Jfba8UnI6LwKH2kICic+DA7t37+axxx5jyJAhpKamcsMNN3D99dfz5Zdf+vqjSpXNFoHDkePddrs9mM2m09KKTj6HI4fISCs2W7h3H4cjh6go61nzSsnZrOHF2sLjdv+tLYrSHI4cok59vz+tTePRRyYyYeJj1Klb05un69WXsXLVdPLy8vn0k1WlVIuyQedF4FBbBBa1R+BQWwQO6znbonj/fbbv97/klTPTeRE41BZlk8GHr9Lk8+DAiy++SO/evbn66qsZMGAAs2fPZunSpcyaNevcOwewhITGJCevByA1NZ3Y2BhvWvPmsaSkbMPpdJGV5WDnzn3ExsaQkNCEVasK90lOXk/Llk3PmldKLj6hEatXbQBgU2omDU77/po1q0/K+u3e73fXzv00iI3mp7VpjB/3Ae9OH8YlzeoDhdHYvne/iMuVh9FoJDw8FKMxSJcW9ROdF4FDbRFY1B6BQ20ROBISGrEqOQWA1NSMYt9fs+YNWF/s+91PbGz0GY9Tt14t9u49xPHjWbhcefy8fivx8Q1LpQ5lhc6LwKG2KJuCdVqBwePxeHx5wN69e/+/9u48Pqry3uP4d5IQkkw2UoroxQSiRNPaGEItIEsNoFhfruxbcOGlgFhAwAYQIwHL5oJQZJNNA2RBqOV6lU2BuNwiRhDwAimEsHrZIZkkZJLMuX+gU1OIpN4hZ4bzeb9e+WPO88wzv3MeDoTvPOccZWVlyTAM3X///Vq3bp0kqV+/flq+fHktRsj3ZDke88OdRPPzC2UYhiZPHq7c3DxFR9+oTp1aKSdnnbKz18owDA0a1ENdurTV6dPnlJr6pkpKytSgQbhef320QkKCrtjXG1UZTrNLuKIfnlaQv++QDMPQn6cMVe6WrxUdc6M6drxLK3M2aGXOBrlchp4Z1FX3dWmjxx4ZKaezQg0bNpAkNW12k9InDlZO9nqtWvWJ6gX4K+62GL04fqD8/f1N3sMr87cFml3CZax4Xngr5sK7MB/ew4pzYajK7BKu6IenFezLL5RhSFMm/1FbcvMUE32jOnb6nXJy1isne71chkuDBnVXly53u9+7evXHOlhw7LKnFbgMl7p166x+/R4wa7euyibv+73CiueFt7LuXMSZXcA1daTkPz021s32hzw21tV4PBwYNWqUXC6XqqqqdPToUbVv316hoaH69ttv9eabb9ZiBO8MB6zIW8MBq/LGcAAA4H28NRywKm8MBwDzXd/hwFEPhgNNfDkcqKys1JYtW9S0aVPZ7XYtXbpUERERevzxxxUSEnL1AQgHvAbhgHchHAAA1AbhgHchHACu5PoOB46Xei4cuCnEh8OB/z/CAW9BOOBdCAcAALVBOOBdCAeAKyEcqK26DAd4qDsAAAAAAB7iq7c4JxwAAAAAAMBDbDYvW5xfSx5/lCEAAAAAAPAtrBwAAAAAAMBDuKwAAAAAAACLs/loOsBlBQAAAAAAWBwrBwAAAAAA8BAfXThAOAAAAAAAgKf46vJ8X60bAAAAAAB4CCsHAAAAAADwEF+9ISHhAAAAAAAAHuOb6QCXFQAAAAAAYHGsHAAAAAAAwENsPrpygHAAAAAAAAAPsdl8c4G+b1YNAAAAAAA8hpUDAAAAAAB4DJcV4Drjbws0uwQA+EkVrlKzS8D36vmFmF0CAPyk4OiXzS4B3ys7nGl2CdeUr95zgMsKAAAAAACwOFYOAAAAAADgMb65coBwAAAAAAAAD+FpBQAAAAAAwCexcgAAAAAAAI/hsgIAAAAAACyNpxUAAAAAAACfxMoBAAAAAAA8xFdXDhAOAAAAAADgMb65QN83qwYAAAAAAB7DygEAAAAAADzEZuOyAgAAAAAALM43wwEuKwAAAAAAwOJYOQAAAAAAgIfwtAIAAAAAACzPNxfo+2bVAAAAAADAY1g5UEsul0sTJszVvn0HFRhYT6+88kfFxNzkbs/JWaesrLUKCPDXkCE9lZz8O509e0GjR7+mixedatQoSlOmDFdwcNAV+6L2mAvvwVx4D+bCe7hcLk2auEj5ew+pXmA9TZw0SNExjd3t7+V8rJycjQrw99Mzg7vqnuSWOnXynMakzlZFRaUiIkI1dfpzstuD9eF/fa6Mdz+Uv7+fmsdF66W0gfLzI9f/d3BueA+Xy6X0CfO1d1+hAgMD9Morzykm5kZ3e07OemVnrVNAgL8GD+mh5OS73G3vLF2j06fPa9ToAZKkTz75UnPeypF/gL+6deuknj3vq/P98WWcF97nrsRb9MrYvurSa1K17Q90TtK44V1VWVmld3K2aEnmJwqqX09LZg7VLxtGqNhRpqdHztXps8VX7Atz+OplBfyGUUsbN/5dTqdT2dmvadSoxzV16mJ326lT55SR8Z/KypquRYvS9cYb78rprNCcOVl68MHfa8WKafrVr2KVnb22xr6oPebCezAX3oO58B4fb9wmZ3mFlme9oudH9tGr0zPcbadPndfyZR9p2YqJmr/wRc2ckSmns0KLFv5NDz/SQe8uS9ft8U21auUnunjRqVkzs7V4aZqWrZgkR3Gptmz+2sQ9802cG95j48atKnc6lZ09TaNGDdC0qUvcbZeO7wfKzJqqhYte1htvZMjprNDFi+UaPeoNrVjxkbtvRUWlpk5ZrEWLJygj4xXlZK/X6dPnTdgj38V54V1GDn5Ic6Y/o6D69aptDwjw1/S0FD3Yf4ru7TlRA/t2VKOGEXom5V7t3ndEnbuna8WqTzVm2GM19oU5bDabx37q0jUNBwzDuJbD16m8vP9R+/YtJUmJibdr9+5/uNt27sxXixbxCgysp7Awu6Kjb9TevQervadDh9/qiy++qbEvao+58B7MhfdgLrzH9q/3qW27OyVJdybG6dvdB9xtu3btV2LSbd8f3xDdHN1Y+/YdUurYx/XQw+3lcrn0v9+dUVh4iAIDA7RsxUQFB9eXJFVVVSnwX35xxNVxbniPvLw9at8+SZKUmHibdu/e727btfMfSvrR8Y2JvlH79haqvLxCjz6WrEGDu7v7Fhw4qujoGxUREarAwHpq2TJeX237ts73x5dxXniXgkMn1PuZGZdtv/3W/9CBwhM6f6FEFRVV+mLbPrVrdbvuvus2bdj8jSRp3eYdSm73mxr7Av8Oj4cDhw8f1sCBA5WcnKw77rhDPXv21KhRo3Tq1ClPf1SdcjhKFRoa4n7t7++nysoqd1tYmN3dZrcHy+EolcNRprCwEPe24uKSGvui9pgL78FceA/mwntcOob/nAu/anNRprAfzZPdHiRHcalsNpuqqlx69OHR2vblt2rV6g75+fmpYcNISdLyZR+ptLRcd9+dUKf7cj3g3PAeJY7San/+L5+LH58bwSp2lCoiIlTt2rWoNk5NfVF7nBfe5f2PvlRFZeVl28PDglVU/M/jWewoU3hYiMLCgnXh++3FjouKCAuusS/MYvPgT93xeDiQnp6u8ePHa9OmTVq+fLlatWqlJ598Ui+++KKnP6pOhYaGqKSkzP3a5TIUEOD/o7Z/nowlJWUKC7MrNDTY/Z6SkjKFh9tr7IvaYy68B3PhPZgL73HpGF50vzaqzUVwtXkqKbmosPBLx7devQCt+eANvZz+tMaNeUvSpeuCX52eof/+YpdmzBxZ58sLrwecG97DftW5+PG5UfPx/Xf64so4L3xDUXGZQu1B7tdhocG6UFSi4uIyhdmDv98WpPNFpTX2hTls8vPYT13y+Kc5HA41a9ZMkpSYmKivv/5ad9xxh4qKijz9UXUqKSleublfSZJ27NiruLgYd1tCQpzy8v5H5eVOFReX6MCBI4qLi1FS0q+0Zcul9+TmfqWWLX9dY1/UHnPhPZgL78FceI8WSbfp09ztkqRvduSreVy0u+03v7lVX+ft/f74lupgwTE1b36zJqUv1Jdbd0u69M2bze9SCJD+8ttylldo1uzR7ssL8O/h3PAeSUm3a0tuniRpx4591Y7fbxKa66tqx/eo4n507vxY7C1NdOjQdzp/vlhOZ4W2ffWtWrS4rU724XrBeeEb9u4/plubNVaDCLvq1fNX21a3a2veP/TfX+WrS8dESVKXexL1+Zd7a+wLs/jmygGb4eEbA4waNUp2u10dOnTQ5s2bZbfb1aZNG73zzjtasmTJ1QdQvifL8Zgf7uqan18owzA0efJw5ebmKTr6RnXq1Eo5OeuUnb1WhmFo0KAe6tKlrU6fPqfU1DdVUlKmBg3C9frroxUSEnTFvqg95sJ7MBfew6pzUeHyvuWr7qcV7DssGYYmTR6iT3O3Kzq6sZI7/lbv5XyslSs3ynAZenrQY7r3vlYqKDimiRMWymaT/Pz8NG78kyq/6FSvHuPUsuXt0vcrBvqn/EGd7/XOO4HX8/PO5atWPDcMVZldwhX98LSCffmFMgxpyuQ/aktunmKib1THTr9TTs565WSvl8twadCg7urS5W73e1ev/lgHC45d9rQCl+FSt26d1a/fA2bt1lXZ5G92CZex4nkhScHRL5tdQo2imzRUxuxh+v2jaer1yN2y24O0eMUn7icQ2Pxsejd7s+a/u0HBQYFaOGOIGjeKlNNZpSeG/UUnTl24Yl9vVXY40+wSrqnyqm0eG6u+/11X7+QhHg8HnE6nVq5cqf379ys+Pl7dunXTrl27FBMTowYNGtRiBO8MBwAA3scbwwGr8tZwwIq8NRywKm8MB6zKm8MBq7newwGn6yuPjRXo91uPjXU1AZ4eMDAwUP369au2LTEx0dMfAwAAAACAF/LNewTV7R0OAAAAAACA1/H4ygEAAAAAAKyqrp8y4CmEAwAAAAAAeAyXFQAAAAAAAB/EygEAAAAAADzE5qMrBwgHAAAAAADwEJvNN8MBLisAAAAAAMDiWDkAAAAAAIDH+OZ38IQDAAAAAAB4iK/ec8A3Iw0AAAAAAOAxrBwAAAAAAMBjfHPlAOEAAAAAAAAewtMKAAAAAACATyIcAAAAAADAY/w8+FMzl8ultLQ09erVSykpKTp06FC19pycHHXt2lU9e/bUpk2brlo1lxUAAAAAAOAhdfW0go0bN8rpdCo7O1s7duzQ1KlTNXfuXEnSqVOnlJGRoVWrVqm8vFx9+/ZV27ZtFRgYWON4rBwAAAAAAMDH5OXlqX379pKkxMRE7d692922c+dOtWjRQoGBgQoLC1N0dLT27t37k+N54cqBOLMLAAD4iHpE3MBlfPM2WMC1V3Y40+wSYBme+z9tdna2srOz3a979eqlXr16SZIcDodCQ0Pdbf7+/qqsrFRAQIAcDofCwsLcbXa7XQ6H4yc/ywvDAQAAAAAA8OMw4F+FhoaqpKTE/drlcikgIOCKbSUlJdXCgivhOxcAAAAAAHxMUlKScnNzJUk7duxQXNw/VywkJCQoLy9P5eXlKi4u1oEDB6q1X4nNMAzjmlYMAAAAAAA8yuVyacKECcrPz5dhGJo8ebJyc3MVHR2tTp06KScnR9nZ2TIMQ4MGDVKXLl1+cjzCAQAAAAAALI7LCgAAAAAAsDjCAQAAAAAALI6nFVwD33zzjV577TVlZGSYXYplVVRUaNy4cTp27JicTqeGDBmiTp06mV2WZVVVVWn8+PE6ePCgbDab0tPTr3pDFFxbZ86cUdeuXbV48WLdcsstZpdjWY899pj7EURNmjTRlClTTK7IuubPn69PPvlEFRUV6tOnj3r06GF2SZa1evVq/fWvf5UklZeXa8+ePfr8888VHh5ucmXWU1FRoTFjxujYsWPy8/PTpEmT+DfDJE6nU2PHjtWRI0cUGhqqtLQ0NW3a1OyycJ0hHPCwt99+W2vWrFFwcLDZpVjamjVrFBkZqVdffVXnz5/Xo48+Sjhgok2bNkmSsrKytHXrVs2YMUNz5841uSrrqqioUFpamoKCgswuxdLKy8tlGAZBshfYunWrtm/frszMTJWVlWnx4sVml2RpXbt2VdeuXSVJ6enp6tatG8GASbZs2aLKykplZWXp888/15tvvqm//OUvZpdlSTk5OQoJCVFOTo4KCgo0adIkLVq0yOyycJ3hsgIPi46O5i9NL3D//fdr+PDhkiTDMOTv729yRdbWuXNnTZo0SZJ0/Phxfskz2bRp09S7d281atTI7FIsbe/evSorK9NTTz2lAQMGaMeOHWaXZFmfffaZ4uLiNHToUA0ePFj33HOP2SVB0q5du7R///4an++Na69Zs2aqqqqSy+WSw+FwPz8ddW///v3q0KGDJCk2NlYHDhwwuSJcjzjDPaxLly46evSo2WVYnt1ulyQ5HA4NGzZMI0aMMLcgKCAgQKmpqdqwYYNmzZpldjmWtXr1akVFRal9+/ZasGCB2eVYWlBQkAYOHKgePXqosLBQTz/9tNauXcsv3yY4d+6cjh8/rnnz5uno0aMaMmSI1q5dK5vNZnZpljZ//nwNHTrU7DIsLSQkRMeOHdMf/vAHnTt3TvPmzTO7JMuKj4/Xpk2b1LlzZ33zzTc6ceKEqqqq+AIMHsXKAVy3vvvuOw0YMECPPPKIHnroIbPLgS59Y71u3Tq99NJLKi0tNbscS1q1apW++OILpaSkaM+ePUpNTdWpU6fMLsuSmjVrpocfflg2m03NmjVTZGQkc2GSyMhItWvXToGBgYqNjVX9+vV19uxZs8uytKKiIh08eFCtW7c2uxRLW7p0qdq1a6d169bpb3/7m8aMGaPy8nKzy7Kkbt26KTQ0VH379tWGDRv061//mmAAHkc4gOvS6dOn9dRTT+mFF15Q9+7dzS7H8t5//33Nnz9fkhQcHCybzSY/P/76McPy5cu1bNkyZWRkKD4+XtOmTdMvf/lLs8uypPfee09Tp06VJJ04cUIOh4O5MEnLli316aefyjAMnThxQmVlZYqMjDS7LEvbtm2b2rRpY3YZlhceHq6wsDBJUkREhCorK1VVVWVyVda0a9cutWnTRpmZmbr//vt18803m10SrkOsXcR1ad68eSoqKtKcOXM0Z84cSZduFskN2Mxx3333aezYserXr58qKys1btw45gKW1717d40dO1Z9+vSRzWbT5MmTuaTAJMnJydq2bZu6d+8uwzCUlpbGN3ImO3jwoJo0aWJ2GZb3xBNPaNy4cerbt68qKir0/PPPKyQkxOyyLCkmJkYzZ87UvHnzFBYWpj//+c9ml4TrkM0wDMPsIgAAAAAAgHlY1wsAAAAAgMURDgAAAAAAYHGEAwAAAAAAWBzhAAAAAAAAFkc4AAAAAACAxREOAAAsZ+vWrWrTpo1SUlKUkpKinj17KiMj42eN9dprr2n16tXas2ePZs+eXWO/DRs26MSJE7UaMzc3V2PGjLls+3fffafhw4c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"text/plain": [
"<Figure size 1440x504 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"-------------------- Recall matrix (Row sum=1) --------------------\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 1440x504 with 2 Axes>"
]
},
"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)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"### Feature importance"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"jupyter": {
"outputs_hidden": true
}
},
"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']))"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Testing query point and doing interpretability"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Predicted Class : 2\n",
"Predicted Class Probabilities: [[0.0055 0.9462 0.0047 0.0054 0.0139 0.0048 0.0102 0.0054 0.0039]]\n",
"Actual Class : 2\n",
"--------------------------------------------------\n",
"110 Text feature [e255k] present in test data point [True]\n",
"115 Text feature [y253h] present in test data point [True]\n",
"154 Text feature [ccyr] present in test data point [True]\n",
"158 Text feature [t315i] present in test data point [True]\n",
"206 Text feature [gimema] present in test data point [True]\n",
"299 Text feature [v299l] present in test data point [True]\n",
"310 Text feature [f317l] present in test data point [True]\n",
"332 Text feature [interindividual] present in test data point [True]\n",
"334 Text feature [y253f] present in test data point [True]\n",
"350 Text feature [leukemianet] present in test data point [True]\n",
"398 Text feature [advise] present in test data point [True]\n",
"424 Text feature [pcyr] present in test data point [True]\n",
"440 Text feature [abstracts] present in test data point [True]\n",
"488 Text feature [sound] present in test data point [True]\n",
"Out of the top 500 features 14 are present in query point\n"
]
}
],
"source": [
"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": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Predicted Class : 7\n",
"Predicted Class Probabilities: [[0.1102 0.2074 0.0294 0.1462 0.0703 0.0451 0.3752 0.0061 0.0101]]\n",
"Actual Class : 7\n",
"--------------------------------------------------\n",
"28 Text feature [3t3] present in test data point [True]\n",
"45 Text feature [oncogene] present in test data point [True]\n",
"95 Text feature [activated] present in test data point [True]\n",
"205 Text feature [activation] present in test data point [True]\n",
"252 Text feature [transformation] present in test data point [True]\n",
"289 Text feature [technology] present in test data point [True]\n",
"320 Text feature [activating] present in test data point [True]\n",
"388 Text feature [expressing] present in test data point [True]\n",
"447 Text feature [downstream] 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)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"### Without class balancing"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"for alpha = 1e-06\n",
"Log Loss : 1.446130870473239\n",
"for alpha = 1e-05\n",
"Log Loss : 1.463124556854643\n",
"for alpha = 0.0001\n",
"Log Loss : 1.3261385058624722\n",
"for alpha = 0.001\n",
"Log Loss : 1.3016277903025404\n",
"for alpha = 0.01\n",
"Log Loss : 1.3993859298193954\n",
"for alpha = 0.1\n",
"Log Loss : 1.5216381208836347\n",
"for alpha = 1\n",
"Log Loss : 1.6944725926729756\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)) "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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Ru3btG+Y4JyeH33//nffee48ff/yRMWPGsHTpUjIyMjh+/Dj33XcfsbGxNGjQgLVr1+Lj40NQUFC+kOfr62t5HBAQwK5duxwv5B08eJBmzZpZnjdr1owtW7bQuHFjYmNjad68uT3KEhERkTIoNTU1X4CyBm9vbxo1aoTJZKJly5acPHkSk8nErFmzmDhxIm5ubtSvX5/KlSvzySefYDKZ2LFjB/Hx8YwaNYoFCxZQtWpVy/aqVq3KDz/8UOCYNrmEyvr161m5ciWQ1xL18vLCZDJZXg8PD+eXX34hPDyclStXMmTIEFuUJSIiImXc+fOQnu5LamqqVcdp3rw5e/bsAeDw4cPUrFkTyDvx4oMPPuD999/n1KlT/PWvf+Xjjz9m6dKlxMTEEBgYyMyZM/MFPMgLpj4+PgWOabVOXq1atVi1ahUAXbt2tSz38fHhs88+y7eup6cn8+bNs1YpIiIiIgDk5MCuXbBxY96fPXugQoUneOyxZQW+LzExkWnTpjFnzpxijdevXz/effddevXqxfbt2+nVqxeGYTBp0iQAqlWrRkhICB4eHnTt2pX777+/SNuNi4ujTZs2Ba5jl921IiIiIrZy/jx89VVeqPvqK0hKAicnePhhmDwZmjRJZNeuRvz888+Ws2UBYmJiLI8rV65M9erVCx1rxowZ+Z4vXrzY8njo0KE3HKvXq1cvevXqdcvtXV/DNdnZ2fz888+MHDmywFoU8kRERKRUuVm3zjCgWjXo0gU6dYInnoBrezvj47No3fol5syZw5QpU266TcMw6N+/vw0/xa2tXLmSQYMG4ezsXOB6CnkiIiJS4hXWrevUCZo2zVt2M1WqVLllwANwdXW94bg4e4mMjCzSegp5IiIiUuIUt1tXFinkiYiISIlwp926skYhT0RERBySunV3RiFPREREHIa6dXePQp6IiIjYTU4O/PgjbNigbt3dppAnIiIiNnV9t+7rr+HiRXXrrEEhT0RERKzqWrdu48a8jt313brOndWtsxaFPBEREbnr1K2zP4U8ERERuWPq1jkehTwRERG5LerWOTaFPBERESkSdetKFoU8ERERuSV160ouhTwRERGxULeu9FDIExERKeNu1a1r1UrdupJMIU9ERKSMUbeubFDIExERKQMK6tZNmgRPPaVuXWmjkCciIlIKqVsnCnkiIiKlhLp1cj2FPBERkRJK3TopiEKeiIhICaJunRSVQp6IiIgDU7dObpdCnoiIiINRt07uBoU8ERERO7u+W7dxI+zerW6d3DmFPBERETtQt06sTSFPRETEBq516z76yJfdu9WtE+tTyBMREbGSm3frfNWtE5tQyBMREblLinJsXZ06R2ndOsDepUoZYLWQFxcXx5tvvklMTEy+5fv372fGjBkYhkHVqlV54403cHd355lnnsHLywuAWrVqMX36dGuVJiIictcU99i6+Phc+xYsZYZVQt6iRYtYt24dnp6e+ZYbhsG4ceOYN28ederUYfXq1Zw9e5Z7770XwzBuCIQiIiKORmfCSklhlZDn5+dHdHQ0I0eOzLf8119/xdvbmyVLlvDLL7/Qrl07/P39iYuLIz09nX79+pGdnc3w4cNp0qSJNUoTEREpNp0JKyWRyTAMwxobPnPmDMOHD2fVqlWWZXv27OEf//gH//d//4efnx+DBw/m2WefxcfHh7i4OEJCQjh58iQDBgzgyy+/xMWl4Ay6b98+3N3drVG+RUZGBh4eHlYdQ4pP8+J4NCeOR3Ny+3Jy4OBBD2Jjvdi2zYuDBz0wDBM+PtkEBZkJCkrjr39Nw9s7p9jb1rw4HlvOSWBgoE3GARufeOHt7U2dOnWoV68eAEFBQRw8eJC+fftSp04dTCYTdevWxdvbm8TERGrWrFng9tzd3a3+ZcXHx9t0QqRoNC+OR3PieDQnxVNYt65TJ2jWzAUnJ2/A+7bH0bw4HlvNSXx8vNXHuJ5NQ17t2rVJS0sjISGBOnXqsHv3bnr27MmaNWs4evQoEydO5Ny5c5jNZqpWrWrL0kREpIy51bF1Vav+79i6v/0NqlSxd6Uit8cmIW/9+vVcuXKF0NBQpk6dyogRIzAMg6ZNm9K+fXuysrIYM2YM4eHhmEwmpk2bVuiuWhERkeIqWrdOx9ZJ6WC1JFWrVi3L8Xhdu3a1LG/dujVr1qzJt66bmxtvvfWWtUoREZEySt06KcvULhMRkVJF3TqRPAp5IiJSoqlbJ3JzCnkiIlLiqFsnUjiFPBERcXjq1okUn0KeiIg4pMTEvG7dhg3q1oncDoU8ERFxCOrWidxdCnkiImI317p1Gzfm/VS3TuTuUcgTERGbUbdOxHYU8kRExKrUrROxD4U8ERG5q9StE3EMCnkiInLH1K0TcTwKeSIiUmzq1ok4PoU8EREpEnXrREoWhTwREbmpgrp1Tz2VF+qeeELdOhFHpZAnIiIW6taJlB4KeSIiZZi6dSKll0KeiEgZo26dSNmgkCciUsqpWydSNinkiYiUQomJsG5dRaZMUbdOpKxSyBMRKQVu3q27V906kTJMIU9EpIQq7Ni6Bg1+JTi4rrp1ImWUQp6ISAlR3GPr4uMzFPBEyjCFPBERB6YzYUXkdinkiYg4kJycvA7dhg06E1ZE7oxCnoiInalbJyLWoJAnImJj6taJiC0o5ImI2IC6dSJiawp5IiJWoG6diNibQp6IyF1ys26dyaRunYjYh0KeiMhtUrdORByZ1UJeXFwcb775JjExMfmW79+/nxkzZmAYBlWrVuWNN97A1dWViRMncuTIEdzc3JgyZQp16tSxVmkiIrdN3ToRKSmsEvIWLVrEunXr8PT0zLfcMAzGjRvHvHnzqFOnDqtXr+bs2bMcO3aMrKwsVq5cyb59+5gxYwYLFiywRmkiIsWibp2IlFRWCXl+fn5ER0czcuTIfMt//fVXvL29WbJkCb/88gvt2rXD39+flStXEhQUBECTJk04ePCgNcoSESkSdetEpDSwSsjr2LEjZ86cuWF5cnIye/fuZfz48fj5+TF48GAaNWqE2WzGy8vLsp6zszPZ2dm4uBRcXmZmJvHx8Xe9/utlZGRYfQwpPs2L4ynJc5KTAwcPehAb68W2bV4cPOiBYZjw8cnmkUfMBAWl0aZNGt7eOZb3HDlix4KLqCTPSWmmeXE8pXVObHrihbe3N3Xq1KFevXoABAUFcfDgQby8vEhLS7Osl5ubW2jAA3B3dycwMNBq9QLEx8dbfQwpPs2L4ylpc1K0bp0LTk7egLedq709JW1OygrNi+Ox1ZzYOkjaNOTVrl2btLQ0EhISqFOnDrt376Znz574+fmxefNmnnrqKfbt20f9+vVtWZaIlAE6tk5EyhqbhLz169dz5coVQkNDmTp1KiNGjMAwDJo2bUr79u3Jzc3l+++/JywsDMMwmDZtmi3KEpFSTsfWiUhZZrWQV6tWLVatWgVA165dLctbt27NmjVr8q3r5OTE5MmTrVWKiJQR6taJiPyPLoYsIiWaunUiIjenkCciJcq1bt3GjXkdO3XrRERuTiFPRByeunUiIsWnkCciDuf6bt3GjfDjj+rWiYgUl0KeiDgEdetERO4uhTwRsQt160RErEshT0RspqBu3cSJecGueXN160RE7gaFPBGxGnXrRETsRyFPRO4qdetERByDQp6I3JFr3bqPPvJl925160REHIVCnogU2827db7q1omIOBCFPBEpVFGOrbvvvl9o3bq+vUsVEZH/UsgTkZsq7rF18fE5dq1XRETyU8gTEUBnwoqIlDYKeSJlmM6EFREpvRTyRMoQdetERMoOhTyRUk7dOhGRskkhT6SUUbdORERAIU+kVFC3TkRE/kwhT6QEUrdOREQKo5AnUkJcuJDXpduwQd06EREpnEKeiINSt05ERO6EQp6IA1G3TkRE7haFPBE7Kqhb16lTXsdO3ToREbkdCnkiNqZunYiI2IJCnoiVqVsnIiL2oJAnYgXq1omIiL0p5IncBerWiYiIo1HIE7lN6taJiIgjU8gTKSJ160REpCSxWsiLi4vjzTffJCYmJt/yJUuWsHr1anx8fACYNGkSdevWpW3bttx3330ANGnShBEjRlirNJEiU7dORERKKquEvEWLFrFu3To8PT1veO3gwYPMnDmTRo0aWZYlJCTwwAMP8O6771qjHJEiU7dORERKC6uEPD8/P6Kjoxk5cuQNrx06dIiFCxeSmJhI+/btGTRoEIcOHeLcuXNERUXh4eHBmDFj8Pf3t0ZpIjdQt05EREojk2EYhjU2fObMGYYPH86qVavyLZ8/fz4RERF4eXkxZMgQwsPD8fLy4sKFC3Tq1Indu3czffp0Pvnkk0LH2LdvH+7u7tYo3yIjIwMPDw+rjiHFdyfzkpMDBw96sHWrF1u3enHwoAeGYcLHJ5s2bdJo29ZMmzZpeHvn3OWqSzf9XXE8mhPHpHlxPLack8DAQJuMAzY+8cIwDPr27UuFChUAaNeuHT///DP9+vXD2dkZgBYtWnD+/HkMw8BkMhW4PXd3d6t/WfHx8TadECma4s5L0bp1Ljg5VQIqWa3u0kx/VxyP5sQxaV4cj63mJD4+3upjXM+mIc9sNtOlSxc2bNhAuXLl2LlzJ8HBwcyfPx9vb28GDBjA4cOHqVmzZqEBT6QgOrZORETKOpuEvPXr13PlyhVCQ0MZNmwYffr0wc3NjdatW9OuXTuaNGnCP//5T7Zs2YKzszPTp0+3RVlSylzr1m3cmPfzwgUdWyciImWX1UJerVq1LMfjde3a1bK8e/fudO/ePd+6lSpVYuHChdYqRUqpgrp1Tz6pbp2IiJRtuhiylCjXunUrVtzDDz+oWyciInIrCnni0G7VrfPxKc9TT+WFuieeAF9fe1cqIiLiWBTyxOEU5di6cuV+4YEHdHaaiIjIrRQa8s6dO8cbb7xBUlISTz75JAEBATz44IO2qE3KiMKOrbtZt87GZ6GLiIiUOIUeuTRu3DiCg4O5evUqLVq0YOrUqbaoS0q5Cxfg44+hd2+oUQMefhgmT847lm7iRNi1C/74A2JiICJCu2NFRESKq9BOXkZGBq1bt2bBggX4+/tb/Q4TUjrdTrdOREREbl+hIc/d3Z2tW7eSm5vLvn37cHNzs0VdUgrounUiIiL2U2jIe/3115k5cybJycksXryYiRMn2qAsKYnUrRMREXEchYa8JUuWMGfOHFvUIiWQunUiIiKOqdCQd+zYMVJTU6lYsaIt6hEHp26diIhIyVBoyDt+/DitWrXCx8cHk8kEwLZt26xemFhHcnIyc+bMYfLkyWzatIm3334bFxcXgoOD6dWr103fM27cNMzmuiQmht+yW/evf0UxZMgQWrVqBeR1gC9cuMArr7xyw/aee+45kpOTcXV1xd3dnffff5/4+Hhef/11nJ2dcXNzY+bMmfj6+rJkyRK++OILANq1a8eQIUMYPXo0tWrVwsnJiW+++YYhQ4ZY7fsSEREpqQoNeZs3b7ZFHWIjc+fOJSIigqtXrzJ9+nTWrFmDp6cn4eHhPPbYY/j6+lq6df/3f0msXz+SK1dOkpzcHze3W3frqlb1pVq1amRkZPDaa69x4MABnnjiiZvWkJCQwBdffGH5nwaAqVOnMm7cOAIDA1mxYgWLFi2id+/erFu3jtWrV+Pk5ER4eDgdOnTA19cXHx8fAgICeP/99zl16hR+fn7W/upERERKlEJD3pgxY25YNn36dKsUI9ZlNps5cOAAkyZN4vDhw/j5+VGpUiUAGjZszpw5P5KQ0MnSrXN1TePBB4cSGBhLixYwcuStj60bN24clStXJjU1lWeeeYY2bdpw4sSJG9a7cOECqampDB48mNTUVAYOHMijjz7K7NmzqVatGgA5OTm4u7tTo0YN3n//fZydnQHIzs7G3d2dgQMHcvLkSQA6derExx9/fNPfUxERkbKs0JD31FNPAWAYBj///DPnz5+3elFiHfv27aNu3boApKaaycqqwMSJecfWHT9enqtXzbi7X9+tq42vb22io2Px9S345AkfHx8AKlWqxCOPPMLatWtvut7Vq1fp168fffr04dKlS4SHh9O4cWNLwPvpp59YunQpH3/8Ma6urvj4+GAYBrNmzaJhw4aW+l1dXQEICAggOjr6Ln1DIiIipUehIS8oKMjyuG3btvTr18+qBYn1nDqVTGKiL717w7ffeuHiksbHH+cdW9emTRpPPFGB556z7pmwvr6+hIWF4eLiQpUqVQgMDOTXX3+lSpUqbNiwgQULFrBw4UJLaMzMzOTVV1+lfPnyTJgw4YbtVa1alZSUFOsVLCIiUkIVGvKuP8kiMTGRCxcuWLUguftycqBnT/j66ypUqJBKbi787W/1OHEigdjYFGrVKkdY2G569uxv9UudbN++naVLl7Jo0SLS0tL45Zdf8Pf357PPPmPlypXExMTg7e0N5HWPn3/+eVq1asXAgQNvur3U1FRLIBQREZH/KTTkXTuzEfLufjFt2jSrFiR335o18OmnMHjwg5w48SYbN4KTkyubNo3mn//sj2EYBAcHU716dY4dO8bSpUsLvOj1sGHDePXVV6latWqRa5g1axZPPvkk7dq1Y9u2bfTq1QsnJyeGDx9OpUqVmDp1KjVr1mTo0KEAPPTQQwQGBrJr1y6ysrLYunUrAMOHD6dp06aW7cbFxdG6devb+l5ERERKM5NhGEZhK+Xk5GAYBvv27aNx48YOc2uz+Ph4AgMDS/wY1pSbC40b5/08cAAmTRpPWFgYDRs2vO1tzp49m8GDB1OuXLm7WGnxXJuXESNG8PLLL1O7dm271SJ5SvrfldJIc+KYNC+Ox1ZzYuu5L7STN3XqVOrVq8dvv/3GoUOH8PX1ZebMmbaoTe6CtWvh0CFYtgycneGll15izpw5TJky5ba3GRYWZteAd821M4QV8ERERG5U6BFYBw4cICwsjL179/LBBx/wxx9/2KIuuQtyc2HyZAgIgGvXOa5SpcodBTyAe+655y5Ud+caNGjASy+9ZO8yREREHFKhnbzc3FwOHjxIrVq1yMrKIi0tzRZ1yV3w6ad5u2hjYvK6eCIiIlJ2FNrJ69atG5MmTaJfv3688cYbhIaG2qIuuUOGkdfFu/9+CAuzdzUiIiJia4V28iIjI4mMjATgtdde4+rVq1YvSu7cunUQFwdLloBLobMsIiIipU2h//yvWLGCDz/8kOzsbAzDwNXVla+++soWtcltMgyYNAnq1YP/5nMREREpYwrdXfvxxx8TExND27ZtmT59OvXq1bNFXXIHvvgC9u6F115TF09ERKSsKjTkVatWjWrVqpGWlkarVq24fPmyLeqS23Sti1e3LvTube9qRERExF4K7fNUqFCBb7/9FpPJxIoVK3SfUAe3cSPs3g2LFoGrq72rEREREXsptJM3ZcoU7r33XoYPH87JkycZO3asLeqS23DtjNo6daBPH3tXIyIiIvZUaCfP09OTgwcP8ttvv/Hoo49y//3326IuuQ1ffw07d8K774KD3HlORERE7KTQTt748eP57bff2L59O2lpaYwaNapIG46LiyMqKuqG5UuWLKFz585ERUURFRXFiRMnyMjIYOjQoURERDBgwACSkpKK/0nKuGvH4tWuDf/4h72rEREREXsrNOSdOnWKl156CXd3dx577LEinXixaNEixo4dS2Zm5g2vHTx4kJkzZxITE0NMTAz+/v4sX76c+vXrs2zZMrp3784777xze5+mDPvuO9ixA8aMURdPREREihDycnJyLJ01s9mMk1Ohb8HPz4/o6Oibvnbo0CEWLlxIeHg47733HgB79uwhKCgIgLZt27Jjx44ifwD5Xxfv3nuhXz97VyMiIiKOoNBj8l5++WXCw8NJTEwkNDSUV199tdCNduzYkTNnztz0tc6dOxMREYGXlxdDhgxh8+bNmM1mKlSoAED58uWLfJmWzMxM4uPji7Tu7crIyLD6GHfqhx/KsW1bHV577Q9OnEi2dzk2URLmpazRnDgezYlj0rw4ntI6J4WGvJYtW/LVV1+RlJSEj4/PHQ1mGAZ9+/a1BLp27drx888/4+XlRVpaGgBpaWlUrFixSNtzd3cnMDDwjmoqTHx8vNXHuFPPPQc1a8LYsTXw8Khh73JsoiTMS1mjOXE8mhPHpHlxPLaaE1sHySLd1mzlypX5jq/bsGHDbQ1mNpvp0qULGzZsoFy5cuzcuZPg4GA8PT3ZsmULjRs3JjY2lubNm9/W9suiLVvy/sydCx4e9q5GREREHEWhIe+jjz5i4cKFVKpU6bYHWb9+PVeuXCE0NJRhw4bRp08f3NzcaN26Ne3ataNly5aMGjWK8PBwXF1deeutt257rLJm0iSoUQMGDrR3JSIiIuJICg15AQEB1KxZE2dn52JtuFatWqxatQqArl27WpZ3796d7t2751vX09OTefPmFWv7Alu3wubNMHs2eHrauxoRERFxJIWGvIcffpgOHTpQu3ZtDMPAZDLx0Ucf2aI2KcTkyVCtGgwaZO9KRERExNEUGvJWrlzJ3LlzLSdLiGPYvh2+/RbeeAPKlbN3NSIiIuJoCg151atX5y9/+UuRro8ntjN5Mvj65p1ZKyIiIvJnhYa8rKwsunXrxv3334/JZALQiRF2tnMnfPUVzJgB5cvbuxoRERFxRIWGvEE64MvhTJ4MVarACy/YuxIRERFxVEW6GLI4jh9/hA0bYNo08PKydzUiIiLiqHSgXQkzeTJUrqwunoiIiBSs0JD3ww8/2KIOKYKffoLPP4fhw6GId34TERGRMqrQkBcdHW2LOqQIJk8Gb28YOtTelYiIiIijK/SYPJPJxAsvvEDdunUtl1EZPny41QuT/Pbtg88+g4kT4Q7uMCciIiJlRKEhLzg42BZ1SCFefz0v3L30kr0rERERkZKg0N21Xbt25cqVK+zfv5/U1FQ6d+5si7rkOvv3w9q1eQHP29ve1YiIiEhJUGjIGz9+PKdPn6ZNmzacPXuWsWPH2qIuuc7rr0OFCuriiYiISNEVurs2ISGBjz/+GIAOHToQFhZm9aLkfw4ehDVr4LXXwMfH3tWIiIhISVFoJy8zM5P09HQAMjIyyMnJsXpR8j9TpuRd9HjYMHtXIiIiIiVJoZ28vn37Wu5de+zYMV588UVb1CXAzz/DqlUwalTebcxEREREiqrQkFe1alVWrVrF6dOnqVWrFpUrV7ZFXQJMnQrlysGIEfauREREREqaIl0M2dvbm7/85S9lNuAlJyczfvx4y/P09HTCwsI4fvx4kbeRkZHB0KFDiYiIYMCAASQlJQGwZMkSOnfuTFRUFFFRUZw4cQKAI0dgxYq825f5+sKZM2d47LHHilXDrl27aNeuneX5/v37iYiIIDw8nBdffJHMzExycnIYM2YMYWFhhIeHc/ToUQAee+wxzpw5w/Lly9mxY0eRP6eIiIg4Bl0MuQjmzp1LREQEAAcOHGDChAmcO3euWNtYvnw59evXZ+jQoXzxxRe88847jB07loMHDzJz5kwaNWqUb/0pU8DD439dPB8fH2rUqFHkGn7//Xc+/PBDsrOzATAMg3HjxjFv3jzq1KnD6tWrOXv2rCVUrlixgp07dzJnzhwWLFhA9erVqVKlCiEhIfTr14+WLVvi7OxcrM8sIiIi9lNoJ6979+506NCBevXqUbduXerWrWuLuhzGlStXOHDgAA0aNAAgKyuLt99+G39/f8s6V69e5dVXXyUyMpLw8HB27tx5w3b27NlDUFAQAG3btrV0xw4dOsTChQsJDw/nvffeA+CXX2DZMnjuOahWLe/95cqVY968ebes4XqZmZlMmDCBiRMnWpb9+uuveHt7s2TJEnr37k1KSgr+/v506NCB119/HYDffvuNiv+9KW50dDSenp64uLjQsGFD/vOf/9zmNygiIiL2UGgnb8OGDSxevNgWtTikI0eO5Au2zZs3v2Gd1atXU7lyZaZNm0ZycjK9e/fmiy++yLeO2WymQoUKAJQvX57Lly8D0LlzZyIiIvDy8mLIkCFs3ryZf//7Udzc4J//zD+Or6/vLWu43uTJk+nXrx/Vq1e3LEtOTmbv3r2MHz8ePz8/Bg8eTKNGjWjdujUuLi6MGjWKb775xhIkr40FEBAQwK5du3j88ccL+7pERETEQRQa8ipWrMh3333HfffdZ9ldW5a6eampqfkCz80cPXqUPXv2sH//fgCys7M5cOAAs2bNAuDpp5/Gy8uLtLQ0ANLS0qhYsSKGYdC3b19L+GvXrh3btv3M0qWPMnQoXJfRiuzcuXPs3r2bU6dO8fbbb3Pp0iWGDRvGkCFDqFOnDvXq1QMgKCiIgwcP0rp1awBmzpzJK6+8Qq9evfjiiy8oV66cZZtVq1blhx9+KH4xIiIiYjeFhryLFy+yZMkSy3OTycRHH31kzZocxtdfw5Ej9+DhkVDgev7+/tSoUYPBgweTkZHBggULeOCBB4iJibGsc/nyZbZs2ULjxo2JjY2lefPmmM1munTpwoYNGyhXrhw7d+7k9OlgXF1h5Mjbq7l69ep89dVXludt2rRhzpw5ZGVlkZaWRkJCAnXq1GH37t307NmTTz/9lHPnzjFo0CA8PT0xmUyWMH9NamoqProSs4iISIlSaMi7PqhA3vFeZUXHjmAyPU737ksKXC8sLIyxY8fSu3dvzGYzERERNwSl8PBwRo0aRXh4OK6urrz11ltUqFCBYcOG0adPH9zc3AgMbM2CBe3o3TuexYvX8tprrxW51pSUFMaOHcv8+fNv+rqbmxtTp05lxIgRGIZB06ZNad++PVeuXGHMmDFERkaSnZ3Nq6++ioeHR773xsXF0aZNmyLXIiIiIg7AuIWXXnrJ8viDDz6wPI6KirrVW2zu559/tur2Ie/PuHHjjEOHDll1LMMwjGefNQw3N8P45Zc0Y/bs2VYfryiuXr1q9O7d28jOzrZ3KflYe+6l+DQnjkdz4pg0L47HVnNi67m/5dm1Fy9etDy+/sxKwzCsGjod0UsvvcSyZcusOkZCAixZAgMGQPXqOQwYMMCq4xXVypUrGTRokC6fIiIiUsIUursW8gc7k8lktWIcVZUqVZgyZYpVx5g+HZycYPRoLCdiOILIyEh7lyAiIiK34ZadvOvDXFkMdrZ06hQsXgz9+kGtWvauRkREREqDW3byjh07ZjlI//rHxbmVlxTNzJl5P8eMsW8dIiIiUnrcMuTNnTvX8jgsLOymj+XOnTkD778P//gH+PnZuxoREREpLW65u7Zly5a3/FMUcXFxREVF3fL1cePG8eabb1qeP/PMM0RFRREVFcUYB2tpJScnM378eMvz9PR0wsLCitXVzMjIYOjQoURERDBgwACSkpIAePXV/VSvHkFiYjgvvvhigZeoCQgIsDzOycnhxRdfJDY29pbrHz9+nObNm9+wzXfffZdhw4ZZnq9du5aQkBB69OjB22+/DUBUVBQ7d+5ky5YtrF69usifU0RERBxDofeuvR2LFi1i7NixtwwsK1as4OjRo5bnmZmZGIZBTEwMMTExTJ8+3Rpl3ba5c+cSEREBwIEDB4iMjOT06dPF2sby5cupX78+y5Yto3v37rzzzjucPWuwdes4goKms3btcoKCgjh79uwtt9GwYUMATp06RWRkJAcOHLjlumazmZkzZ+Lm5pZv+ZYtW/KdLX3q1CmWL19OTEwMa9as4erVq1y9ehVfX1+qVatGu3bt+OqrrzCbzcX6vCIiImJfVgl5fn5+REdH3/S1n376ibi4OEJDQy3LDh8+THp6Ov369aNPnz7s27fPGmXdFicnMwcOHKBBgwYAZGVl8fbbb+Pv729Z5+rVq7z66qtERkYSHh7Ozp07b9jOnj17CAoKAqBt27bs2LGDCRN+JTvbmxo1ltC7d29SUlLybffPFi1aBMCVK1eYOnUqrVq1uul6hmEwbtw4hg8fjqenp2V5QkICK1eu5MUXX7Qs2759O40aNWLUqFH07t2bZs2a4erqyrhx47jvvvuAvNutrV27tojfmIiIiDiCIl1Cpbg6duzImTNnblh+/vx53n77bebPn8/GjRstyz08POjfvz8hISGcPHmSAQMG8OWXX+LiUnB5mZmZxMfH3/X6/ycQD499VKlSxTJOuXLlSElJIS0tjRMnTpCVlcXGjRvJzc1l7NixpKam8tprr90Qcs+dO8f58+eJj48nNzeXCxeS2bYtiZo199KxYyQ1a/ZgypQpVKxYkcaNG9+yosTERCAvbKakpHD69OkbvoPly5dz//33YxgGWVlZHD58mJycHGbOnMnLL7/M6dOnSU1NJT4+nqNHj7Jt2zZmzpxJZmYmr776KrNmzcLLy4tz584BefPz+eef89BDD93NL/eOZGRkWHnupbg0J45Hc+KYNC+Op7TOiVVC3q18+eWXJCcnM3DgQBITE8nIyMDf358uXbpQp04dTCYTdevWxdvbm8TERGrWrFng9tzd3QkMDLRqzc7Oyfj7+98wTvny5fH396devXqsXLmSQ4cOMW3atP++x5ns7GxmzZoFwNNPP0316tWpUaMGgYGBXL58mZwcH7KyKuPnV4cnnngCgE6dOpGamlrkz+Tt7U3t2rVvWH/Hjh3UqFGDHTt2cOnSJWbNmkXfvn1JT0/nnXfeITU1lfPnz7N161buv/9+TCYTzZo1A6BBgwY3fK8eHh589tlnVv+uiyM+Pt6h6hHNiSPSnDgmzYvjsdWc2DpI2jTk9enThz59+gB5B/ufOHGCHj16sGzZMo4ePcrEiRM5d+4cZrOZqlWr2rK0W8rJqUJqamqB6/j7+1OjRg0GDx5MRkYGCxYs4IEHHsh339/Lly+zZcsWGjduzPr1sfz+e3N69qzN2bNpJCQkUKdOHXbv3k3Pnj3vuOZvvvnG8vixxx5j8eLFuLu7W8Lkzp07WbFiBQMHDuTIkSMsW7aMzMxMcnJyOH78OH5/Os03NTUVHx+fO65LREREbMcqx+T92fr161m5cuUtX+/ZsyeXL18mPDycYcOGMW3atEJ31dpChQqQnv4gR44cKXC9sLAwTpw4Qe/evQkLC+Pee+/FySn/VxseHs4vv/xCeHg4b7+9ksTEIYwb58bUqVMZMWIEwcHB1KhRg/bt25OYmJjv7Nei6tevH1lZWcV6T0BAAMHBwYSHhxMZGcnzzz+Pt7d3vnXi4uJo3bp1sesRERER+zEZJfhmtNZur/boAYcOZRAaOo2wsDDL2a13IjER7rsPnnkGli69+TrZ2dm8+eabjB49+o7Huxv69+/Pv/71L7y8vOxdioV2dzgezYnj0Zw4Js2L47Hl7lpbzr1NOnkl3UsvvcSyZcvuyrbeegvS02Hs2FuvYxgG/fv3vyvj3an//Oc/dOzY0aECnoiIiBTO/vtES4AqVaowZcqUO97OhQswfz6EhsJ/r8hyU66urg5zTGL79u3tXYKIiIjcBnXyCnE3d2bPmQNXrsC4cXdvmyIiIiI3o5BXAJPp7m0rKQmioyEkBO7CoX0iIiIiBVLIs5G5c+Hy5YKPxRMRERG5WxTybCA5Gf71LwgOhr/8xd7ViIiISFmgkGcD//oXpKbqWDwRERGxHYU8K7t0KW9Xbffu8OCD9q5GREREygqFPCubNy8v6I0fb+9KREREpCxRyLOi1NS8y6Y8/TQ0bWrvakRERKQsUcgrxJ1cJ2/+/LyTLtTFExEREVtTyCvAnVwn7/LlvFuYde4MzZvfvZpEREREikIhz0refjvvAsjq4omIiIg9KORZgdmc18V78klo2dLe1YiIiEhZpJBnBQsWwIULMGGCvSsRERGRskoh7y5LS4M33oAnnoCHH7Z3NSIiIlJWKeTdZe+9B4mJOhZPRERE7Esh7y66cgVmzYLHH4c2bexdjYiIiJRlLvYuoDRZuBDOnYPVq+1diYiIiJR16uQVwjCKdrG89HSYORPat4egIOvWJCIiIlIYdfIKUJyLIb//PvzxByxfbr16RERERIpKnby7ICMDZsyAtm3zOnkiIiIi9qZO3l2weDH89ht89JG9KxERERHJo07eHcrMhOnT886mfewxe1cjIiIikkedvDv04Ydw5kxeN684x/CJiIiIWJM6eXcgKyuvi/fww9Chg72rEREREfkfdfLuwL//DadO5d3lQl08ERERcSTq5N2mxMRkZs4cT8uW4Oq6ieDgYEJDQ1m1atUN6yYkJBAeHk5ERAQTJkwgNzc332tdu3YtcKydO3cSFRVleZ6UlETHjh3JzMy8Yd1jx44RHh5OWFgYo0ePJjs7G4CFCxfSrVs3IiMj2bx5MwCnT58mMjKSiIgIXnnlFdLT0y1jGYbBqFGjyMjIuK3vR0REROxLIa8QhnHz5UOGzOX06Qhee+0qM2ZMZ/HixcTExLBy5UouXLiQb93p06fz8ssvs2zZMgzD4LvvvgPg008/ZdiwYSQlJRVYg6+vL9WqVQNg69at9OvXj8TExJuuO3v2bIYPH86KFSsA2Lx5M0eOHOHzzz9n1apVLF68mHnz5pGens4bb7xBWFgYy5Yto1WrVnz44YeWsUwmE126dOH9998vztclIiIiDkIhrwC32gWbnGxm794DNG7cgPr1j+Pn50elSpVwc3OjefPm/Pjjj/nWP3ToEC1btgSgbdu2bN++HYBKlSqxdOnSQuvw9/fntddeA8DJyYkPP/wQb2/vm64bHR3NQw89RFZWFomJiXh5eXH8+HFatmyJu7s77u7u1KlThyNHjnDs2DHatm0LQLNmzdizZ0++sf7617+ycePGfJ1HERERKRmsFvLi4uLy7WL8s3HjxvHmm28CkJuby/jx4wkNDSUqKoqEhARrlXVXzJ27j8uX6zJ+PJjNZipUqGB5rXz58pjN5nzrG4aB6b+JsXz58ly+fBmARx99lHLlyhU6nslkwsfHB4A2bdpQuXLlW67r7OzM2bNn6dKlC8nJyTRo0ICAgAB2796N2WwmOTmZvXv3kp6eTmBgIJs2bQLgu+++Iz09Pd9Yzs7O+Pj4cPTo0WJ8OyIiIuIIrBLyFi1axNixY296zBjAihUr8gWHb7/9lqysLFauXMmIESOYMWOGNcq6K7KzYdmyZHx8fOnSBby8vEhLS7O8npaWli/0QV737frXK1asaNUa7733Xr7++mvCw8OZMWMG9erVIzIykmeffZbXX3+dBx98kMqVKzNq1Cg2bdpEVFQUJpPppuGxWrVqpKSkWLVeERERufusEvL8/PyIjo6+6Ws//fQTcXFxhIaGWpbt2bOHoKAgAJo0acLBgwetUdZdsWIF/PZbFZo0ScVkgnr16pGQkEBKSgpZWVns3r2bpk2b5ntPw4YN2blzJwCxsbG0aNHCavUNHjyYkydPAnldQycnJ5KSkkhLS2PFihVMmjSJ33//nfvvv5/t27czbNgwYmJicHZ25q9//esN27t06RJVqlSxWr0iIiJiHVa5hErHjh05c+bMDcvPnz/P22+/zfz589m4caNludlsxsvLy/Lc2dmZ7OxsXFwKLi8zM5P4+Pi7V/ifpKbei2G45htj1ap7KF/+AZKSXrcsj4yMJDIyktzcXDp06EBSUhJxcXF88cUXDB48mJ49ezJz5kyys7OpVasWfn5++baZnZ1tef7TTz/x66+/EhwcXGBtWVlZHD58GDc3N06fPm0Zq2PHjrz00ku4uLjg7u7OCy+8wB9//MGePXtYvXo1Li4uREVFcfToUUwmE0OGDMHV1ZXatWszaNCgfHXl5uZy6tQprl69atXv+XZkZGQ4XE1lnebE8WhOHJPmxfGU1jmx6XXyvvzyS5KTkxk4cCCJiYlkZGTg7+9/wy7P3NzcQgMegLu7O4GBgVart2JFMJky841hGFCrFjz00EMYhkHDhg0JDAykb9+++d4bGBjIE088YXncoYCrJV/r8kHe7tHVq1cX+rm2bdt2y7GeeeaZG9a/WWc1MDCQbt263XKMLVu20L17dxo2bFhgLfYQHx9v1bmX4tOcOB7NiWPSvDgeW82JrYOkTc+u7dOnD2vXriUmJoaBAwfSpUsXevToQbNmzYiNjQVg37591K9f35ZlFUtKCnh7w0svvcSyZcvu+vYNw6Bfv353fbu3U8f69ev5+9//bu9SRERE5DbYpJO3fv16rly5ku84vOv97W9/4/vvvycsLAzDMJg2bZotyrotKSlw//1QpUoVpkyZcte37+vre9e3eTtMJpPl7GcREREpeawW8mrVqmW5+8PN7ujQo0cPy2MnJycmT55srVJu282uk3etkyciIiLiyHQx5GK6dAkqVbJ3FSIiIiIFU8grhuxsuHxZnTwRERFxfAp5xZCamvdTIU9EREQcnUJeMVy78YNCnoiIiDg6hbxiUMgTERGRkkIhrxgU8kRERKSkUMgrhmshT2fXioiIiKNTyCuEYfzv8aVLeT/VyRMRERFHp5BXgD9fDFm7a0VERKSkUMgrhpSUvOBXsaK9KxEREREpmEJeMaSk5AU8J31rIiIi4uAUV4pB960VERGRkkIhrxhSUnRmrYiIiJQMCnnFcOmSOnkiIiJSMijkFYN214qIiEhJoZBXDAp5IiIiUlIo5BXi+oshK+SJiIhISaGQV4DrL4acmwupqQp5IiIiUjIo5BVRampeV09n14qIiEhJoJBXRLqlmYiIiJQkCnlFdOlS3k+FPBERESkJFPKKSJ08ERERKUkU8opIIU9ERERKEoW8IlLIExERkZJEIa8Q166Tdy3k6exaERERKQkU8gpw/XXyFPJERESkJFHIK6JLl8DLC1xc7F2JiIiISOEU8opItzQTERGRkkQhr4gU8kRERKQkUcgrIoU8ERERKUmsdoRZXFwcb775JjExMfmWf/XVVyxcuBCTyUTXrl3p27cvAM888wxeXl4A1KpVi+nTp1urtNuSkgL33GPvKkRERESKxiohb9GiRaxbtw5PT898y3Nycnjrrbf45JNPKFeuHE899RRdu3alfPnyGIZxQyB0JCkp0LChvasQERERKRqrhDw/Pz+io6MZOXJkvuXOzs5s2LABFxcXLl68SG5uLm5ubhw+fJj09HT69etHdnY2w4cPp0mTJoWOk5mZSXx8vDU+AgCpqfdgGO7Ex8dz8WJ9DOMS8fHnrDaeFF1GRoZV516KT3PieDQnjknz4nhK65xYJeR17NiRM2fO3HxAFxe+/vprJk+eTLt27fD09MTDw4P+/fsTEhLCyZMnGTBgAF9++SUuhVyvxN3dncDAQGt8BAAqVgTIokGDQMxmqFvXh8BAH6uNJ0UXHx9v1bmX4tOcOB7NiWPSvDgeW82JrYOkXU68eOKJJ4iNjeXq1at8+umn1K1bl6effhqTyUTdunXx9vYmMTHRHqXlc+1iyGYz5ObqxAsREREpOWwa8sxmM7179yYrKwsnJyc8PT1xcnJizZo1zJgxA4Bz585hNpupWrWqLUsrkO5bKyIiIiWNTe7fsH79eq5cuUJoaChdu3YlMjISFxcXAgICePrpp8nJyWHMmDGEh4djMpmYNm1aobtqbUm3NBMREZGSxmpJqlatWqxatQqArl27WpaHhoYSGhqab11nZ2feeusta5Vyx9TJExERkZJGF0MuAoU8ERERKWkU8org0qW8nwp5IiIiUlIo5BWBOnkiIiJS0ijkFcIwdOKFiIiIlDwKeQW4dp28lBTw9AQ3N7uWIyIiIlJkCnlFkJKiXbUiIiJSsijkFcGFC8lUrDgegE2bNhEcHExoaKjlEjHXS0hIIDw8nIiICCZMmEBubi4A8+fPp2fPnoSFhbF///4C17322vWXnrmZnTt3EhUVZXmelJREx44dyczMvGHdY8eOER4eTlhYGKNHjyY7OxuAJUuWEBISQkhICPPnzwcgJyeHKVOmEBYWRo8ePdi8eTMAAQEBAMybN49jx44V7csTERERu1DIK4JTp+ZSsWIEV69eZfr06SxevJiYmBhWrlzJhQsX8q07ffp0Xn75ZZYtW4ZhGHz33XccOnSIXbt2sXr1ambPns2kSZNuuS7Ap59+yrBhw0hKSiqwLl9fX6pVqwbA1q1b6dev3y1vBzd79myGDx/OihUrANi8eTOnT59m3bp1rFixglWrVrFt2zYOHz7MZ599RnZ2NitWrGDBggUkJCQA0LBhQwD+/ve/M3PmzNv8NkVERMQWFPIKZSY9/QC+vg04fvw4fn5+VKpUCTc3N5o3b86PP/6Yb+1Dhw7RsmVLANq2bcv27dvZs2cPjzzyCCaTiXvuuYecnBySkpJuui5ApUqVWLp0aaGV+fv789prrwHg5OTEhx9+iPct9itHR0fz0EMPkZWVRWJiIl5eXtSoUYP3338fZ2dnTCYT2dnZuLu7s23bNqpXr87AgQMZO3Ysjz32GACLFi0CoGLFinh4eHD48OHif50iIiJiEwp5hXBy2k9OTl28vfPuvVuhQgXLa+XLl8dsNudb3zAMTP89Y6N8+fJcvnwZs9mMl5dXvvddvnz5pusCPProo5QrV67Q2kwmEz4+PgC0adOGypUr33JdZ2dnzp49S5cuXUhOTqZBgwa4urri4+ODYRjMnDmThg0bUrduXZKTkzl16hTvvfceAwYMYMyYMUBe5/CagIAAdu3aVWiNIiIiYh8KeYUwmZLJzPSlUiXw8vIiLS3N8lpaWlq+0Ad5HbXrX69YseIt33ezda3p3nvv5euvvyY8PJwZM2YAkJmZySuvvEJaWhoTJkwAwNvbm/bt22MymWjZsiUnT568YVtVq1Yl5dq1ZURERMThKOQVwjB8uHo1FW9vqFevHgkJCaSkpJCVlcXu3btp2rRpvvUbNmzIzp07AYiNjaVFixY0a9aMbdu2kZuby2+//UZubi4+Pj43XddaBg8ebAlr5cuXx8nJCcMweP755wkICGDy5Mk4OzsD0Lx5c7Zs2QLA4cOHqVmz5g3bu3TpElWqVLFavSIiInJnXOxdgCMzmcBsbkL58v/C2xtcXV0ZPXo0/fv3xzAMgoODqV69OseOHWPp0qVMnDiRUaNGMW7cOGbPno2/vz8dO3bE2dmZFi1aEBoaSm5uLuPH552pe7N1byU2NpbDhw8zcODAItd/fV0DBw5k9OjRuLq64unpyZQpU/j222/ZtWsXWVlZbN26FYDhw4fTq1cvJkyYQK9evTAMw3KiyPX279/PsGHDiveFioiIiM2YDMMw7F3E7YqPjycwMNBq2+/fH/79b4MqVSYwZEgY48Y1tNpYhbl48SKrV69m8ODBdqvhmpSUFEaPHs27775rtxqsPfdSfJoTx6M5cUyaF8djqzmx9dxrd20B3NwgJ8fExYsvER+/zK61GIZBv3797FrDNUuWLFEXT0RExMFpd20Brt3GLCenCn37TrFrLdef2WpvL7/8sr1LEBERkUKok1cAd/f/Pa5UyX51iIiIiBSXQl4BrnXyQPeuFRERkZJFIa8ACnkiIiJSUinkFeD63bUKeSIiIlKSKOQV4Fonz90dPDzsW4uIiIhIcSjkFeBayFMXT0REREoahbwCXNtdq5AnIiIiJY1CXgGudfJ0+RQREREpaRTyCqDdtSIiIlJSKeQV4PrdtcnJyYwfPx6ATZs2ERwcTGhoKKtWrbrhfQkJCYSHhxMREcGECRPIzc0FYP78+fTs2ZOwsDD279+f7z3Tpk1j+fLlBdYTFRXFzp07Lc+XLFnCm2++edN158yZQ0hICL169bK8JykpiX79+hEREcHLL79Menq6ZTshISGEhIQwf/58AEaPHs3atWs5cuSIZZmIiIiUHAp5Bbi+kzd37lwiIiK4evUq06dPZ/HixcTExLBy5UouXLiQ733Tp0/n5ZdfZtmyZRiGwXfffcehQ4fYtWsXq1evZvbs2UyaNAnIC17PPvssmzZtKrQeX19fqlWrRkZGBiNGjGDZspvfT/fnn39m3759rFq1itmzZzN16lQA3nnnHbp06cKyZcto2LAhK1eu5PTp06xbt44VK1awatUqtm3bxuHDhy1jBQQEkJCQwKlTp27/ixQRERGbU8grwLWQV768mQMHDtCgQQOOHz+On58flSpVws3NjebNm/Pjjz/me9+hQ4do2bIlAG3btmX79u3s2bOHRx55BJPJxD333ENOTg5JSUmkpaUxdOhQunXrVmg948aN47777iMzM5NnnnmGwYMH33S9hg0b8sEHH2Aymfjtt9+oWLEiAHv27CEoKChfXTVq1OD999/H2dkZk8lEdnY27u7uDBw40PIZOnXqxMcff3xb36GIiIjYh0JeAa7trs3K2kfdunUBMJvNVKhQwbJO+fLlMZvN+d5nGAYmk8ny+uXLlzGbzXh5eeV73+XLl6lduzYPPvhgkerx8fHBZDJRqVIlHnnkkQLXdXFxYc6cOQwaNIgePXrcUPu18V1dXfHx8cEwDGbOnEnDhg2pW7cuFStWxO2/KTcgIIBdu3YVqUYRERFxDFYLeXFxcURFRd2w/KuvviI4OJiePXvy73//G4Dc3FzGjx9PaGgoUVFRJCQkWKusYrnWyXNySsbX1xcALy8v0tLSLOukpaXlC3156zvle71ixYpFet/dNmzYMLZu3coHH3zAqVOn8tVwrS6AzMxMXnnlFdLS0pgwYcIN26latSopKSlWrVVERETuLquEvEWLFjF27FgyMzPzLc/JyeGtt95iyZIlrFy5kmXLlpGUlMS3335LVlYWK1euZMSIEcyYMcMaZRXbtZBXrVoVUlNTAahXrx4JCQmkpKSQlZXF7t27adq0ab73NWzY0HKyQ2xsLC1atKBZs2Zs27aN3NxcfvvtN3Jzc/Hx8bFK3Tt27LAc8+fu7o6Liwsmk4lmzZqxZcsWS13NmzfHMAyef/55AgICmDx5Ms7OzjdsLzU11Wq1ioiIiHVYJeT5+fkRHR19w3JnZ2c2bNhAhQoVSElJITc3Fzc3t3zHijVp0oSDBw9ao6xie+AB6NUrmd69H+TIkSMAuLq6Mnr0aPr3709YWBjBwcFUr16dY8eOMXHiRABGjRpFdHQ0oaGhXL16lY4dO9KoUSNatGhBaGgoQ4cOtZypeyvDhg0jMTGxWPXOmjWL/fv307JlS3JzcwkLCyMyMpLIyEhq167Nc889xxdffEFYWBh79+6ld+/efPvtt+zatYutW7cSFRVFVFQUe/fuzbfduLg4WrduXaxaRERExL5MhmEY1tjwmTNnGD58+E0vMfL1118zefJk2rVrx+TJkxk/fjxPPPEE7dq1A6B9+/Z8++23uLi4FDjGvn37cL924JyVZGRk4OHhwYIFC+jYsSP+/v5WHe+amJgYQkJC8HCAm+bOnj2byMhIqlevbu9SLK7NizgOzYnj0Zw4Js2L47HlnAQGBtpkHICCU5SVPPHEE3To0IHRo0fz6aef3nC8Wm5ubqEBD/J2RVr7y4qPjycwMJAJEyYwZ84cOnfubNXxrhkyZAj33HOPTcYqyOHDh2nUqBHt27e3dyn5XJsXcRyaE8ejOXFMmhfHY6s5iY+Pt/oY17Pp2bVms5nevXuTlZWFk5MTnp6eODk50axZM2JjY4G87lz9+vVtWVaRVKlShSlTpthsPEcIeAANGjTgpZdesncZIiIiUkw26eStX7+eK1euEBoaSteuXYmMjMTFxYWAgACefvppTCYT33//PWFhYRiGwbRp02xRloiIiEipZbWQV6tWLcvxeF27drUsDw0NJTQ09Ib1J0+ebK1SRERERMocXQxZREREpBRSyBMREREphRTyREREREohhTwRERGRUkghT0RERKQUUsgTERERKYUU8kRERERKIYU8ERERkVJIIU9ERESkFDIZhmHYu4jbtW/fPtzd3e1dhoiIiEihMjMzadKkic3GK9EhT0RERERuTrtrRUREREohhTwRERGRUkghT0RERKQUUsgTERERKYUU8kRERERKIYW8/8rNzWX8+PGEhoYSFRVFQkJCvtdXrVpFjx496NWrF5s3b7ZTlWVLYXOyZMkSQkJCCAkJYf78+XaqsmwpbE6urfPss8+yfPlyO1RYNhU2L1u2bKFXr16EhIQwceJEdFEF6ytsThYvXkyPHj0IDg7mm2++sVOVZVdcXBxRUVE3LN+0aRPBwcGEhoayatUqO1R2lxliGIZhfPXVV8aoUaMMwzCMvXv3GoMHD7a8dv78eaNLly5GZmamkZqaanks1lXQnJw6dcp45plnjOzsbCM3N9cIDQ014uPj7VVqmVHQnFzz1ltvGSEhIcayZctsXV6ZVdC8XL582ejcubNx8eJFwzAMY+HChZbHYj0FzcmlS5eMdu3aGZmZmUZKSorRvn17e5VZJi1cuNDo0qWLERISkm95VlaW0aFDByMlJcXIzMw0evToYSQmJtqpyrtDnbz/2rNnD0FBQQA0adKEgwcPWl7bv38/TZs2xc3NjQoVKuDn58fhw4ftVWqZUdCc1KhRg/fffx9nZ2dMJhPZ2dm6MLYNFDQnAF9++SUmk8myjthGQfOyd+9e6tevz8yZM4mIiMDX1xcfHx97lVpmFDQnnp6e3HPPPaSnp5Oeno7JZLJXmWWSn58f0dHRNyw/fvw4fn5+VKpUCTc3N5o3b86PP/5ohwrvHhd7F+AozGYzXl5elufOzs5kZ2fj4uKC2WymQoUKltfKly+P2Wy2R5llSkFz4urqio+PD4ZhMGvWLBo2bEjdunXtWG3ZUNCcHD16lM8//5x58+bx9ttv27HKsqegeUlOTmbnzp18+umnlCtXjsjISJo0aaK/L1ZW0JwA1KxZk86dO5OTk8OgQYPsVWaZ1LFjR86cOXPD8tL4b71C3n95eXmRlpZmeZ6bm2v5y/jn19LS0vL9Ioh1FDQnkHd7mFdffZXy5cszYcIEe5RY5hQ0J59++innzp2jb9++nD17FldXV+69917atm1rr3LLjILmxdvbm7/85S9UrVoVgBYtWhAfH6+QZ2UFzUlsbCznz5/nu+++A6B///40a9aMxo0b26VWyVMa/63X7tr/atasGbGxsUDePXHr169vea1x48bs2bOHzMxMLl++zPHjx/O9LtZR0JwYhsHzzz9PQEAAkydPxtnZ2V5llikFzcnIkSNZvXo1MTExPPPMM/z9739XwLORgublgQce4OjRoyQlJZGdnU1cXBz/7//9P3uVWmYUNCeVKlXCw8MDNzc33N3dqVChAqmpqfYqVf6rXr16JCQkkJKSQlZWFrt376Zp06b2LuuOqJP3X3/729/4/vvvCQsLwzAMpk2bxocffoifnx+PP/44UVFRREREYBgGw4YN0/FfNlDQnOTm5rJr1y6ysrLYunUrAMOHDy/xfyEdXWF/T8Q+CpuXESNG8OyzzwLw5JNP6n9SbaCwOdm+fTu9evXCycmJZs2a0aZNG3uXXGatX7+eK1euEBoayujRo+nfvz+GYRAcHEz16tXtXd4dMRmGzqUXERERKW20u1ZERESkFFLIExERESmFFPJERERESiGFPBEREZFSSCFPREREpBRSyBORUmfRokU88sgjZGZmAhAVFcXx48dvuf7tXL4iMTGRiRMnAhAQEHBbdYqIWJNCnoiUOuvWreOpp57iiy++sNoYVatWtYS8hg0bWm0cEZHbpZAnIqXKzp078fPzIywsjI8//jjfa9HR0QwbNow+ffrQvXt3du/eDUBWVhYjRowgPDyc5557jqtXr/LHH38wePBg/vGPf9ClSxe+/fbbfNs6c+YMvXr1AvI6hwBz5swhLCyMnj17snDhQht8WhGRW9MdL0SkVFm9ejUhISH4+/vj5uZGXFxcvtc9PDz46KOP+OWXXxgxYgTr1q3jypUrDBs2jFq1ahEVFUV8fDxms5l//OMftGrVip9++ono6Gg6dOhw0zF9fX2BvCvnf/TRR1SrVo21a9da/bOKiBREIU9ESo1Lly4RGxtLUlISMTExmM1mli5dmm+dhx9+GID777+fCxcuAHn3Eq1VqxaQF9jS09OpWrUqCxYsYM2aNZhMJrKzswsd/4033uCtt97iwoULBAUF3eVPJyJSPAp5IlJqrFu3juDgYEaNGgVAeno6jz/+OJUrV7asc+jQIbp168bRo0ct96U0mUw3bOtf//oXISEhtGvXjk8++YT/+7//K3DsrKwsvvzyS2bPng3AU089RefOnbn33nvv1scTESkWhTwRKTVWr17NrFmzLM89PT154oknWLNmjWVZfHw8ffv2JT09nddff/2W23ryySeZNWsWCxcupEaNGiQnJxc4tpubG5UqVaJXr154eHjQpk0b7rnnnjv/UCIit8lkGIZh7yJERGwhOjoaX19fwsPD7V2KiIjV6exaERERkVJInTwRERGRUkidPBEREZFSSCFPREREpBRSyBMREREphRTyREREREohhTwRERGRUkghT0RERKQU+v9n3zp3Vk5ElgAAAABJRU5ErkJggg==",
"text/plain": [
"<Figure size 720x432 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(figsize = (10,6))\n",
"ax.plot(alpha, cv_log_error_array,c='blue')\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.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": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"For values of best alpha = 0.001 The train log loss is: 0.49219141662711363\n",
"For values of best alpha = 0.001 The cross validation log loss is: 1.3016277903025404\n",
"For values of best alpha = 0.001 The test log loss is: 1.1225495964534915\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))"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"### Testing the model with best hyper param"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Log loss : 1.3016277903025404\n",
"Number of mis-classified points : 0.39285714285714285\n",
"-------------------- Confusion matrix --------------------\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 1440x504 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"-------------------- Precision matrix (Columm Sum=1) --------------------\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 1440x504 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"-------------------- Recall matrix (Row sum=1) --------------------\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 1440x504 with 2 Axes>"
]
},
"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)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Testing query point and interpretability"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Predicted Class : 2\n",
"Predicted Class Probabilities: [[5.800e-03 9.497e-01 4.300e-03 5.400e-03 1.360e-02 4.600e-03 1.180e-02\n",
" 3.900e-03 9.000e-04]]\n",
"Actual Class : 2\n",
"--------------------------------------------------\n",
"123 Text feature [e255k] present in test data point [True]\n",
"129 Text feature [y253h] present in test data point [True]\n",
"167 Text feature [ccyr] present in test data point [True]\n",
"179 Text feature [t315i] present in test data point [True]\n",
"235 Text feature [gimema] present in test data point [True]\n",
"312 Text feature [v299l] present in test data point [True]\n",
"343 Text feature [f317l] present in test data point [True]\n",
"356 Text feature [y253f] present in test data point [True]\n",
"374 Text feature [interindividual] present in test data point [True]\n",
"384 Text feature [leukemianet] present in test data point [True]\n",
"405 Text feature [advise] present in test data point [True]\n",
"454 Text feature [abstracts] present in test data point [True]\n",
"460 Text feature [pcyr] present in test data point [True]\n",
"Out of the top 500 features 13 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)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## Linear Support Vector Machines"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"For C = 1e-05\n",
"Log Loss : 1.4850274747038281\n",
"For C = 0.0001\n",
"Log Loss : 1.3728846641871968\n",
"For C = 0.001\n",
"Log Loss : 1.2879888004320357\n",
"For C = 0.01\n",
"Log Loss : 1.27862784839991\n",
"For C = 0.1\n",
"Log Loss : 1.4391520022148308\n",
"For C = 1\n",
"Log Loss : 1.759293069317317\n",
"For C = 10\n",
"Log Loss : 1.758207983428532\n",
"For C = 100\n",
"Log Loss : 1.7582088852549378\n"
]
},
{
"data": {
"image/png": 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mHTt2aO/evWX24YzJnzVr1qhp06ZaunSpevTooffff79YX48//rh9+7IQ/BzYvn27GjVqZH/fpk0b3XDDDZe1ba1atSRJzZs3V+PGjR22f++99/Tcc8/Z3/fs2VOxsbH617/+pW3btunrr79WixYttHHjRknSxo0blZubW6wvSapfv762bdtWbljdtm2bOnXqJEm6/fbbyw1Yffr0Ue/evSVJDRo0KDb7VxrDMDR58mTFxsbK19dXe/bs0enTp2U2mzV06FAdOnRIUuG/UDIzM2WxWGQYhkwmU7G+evXqpU8++cTh9wYAwOXKyclxOKFT2u/IrKwsWSwWNWjQQCaTSR07dtTmzZvL7McZkz9NmzZVdna2pMKJKT8/v2J9Va1aVZUqVdK+ffvK7Oe6eGSbK6WlpRULVc5y4MABVa1aVQ0bNpRUGJ4GDRqkkJAQSVKXLl20d+9ePfnkk5o6daoGDBigLl262A/xFmW1WlWjRg37+zZt2lzSJisrS8HBwfb3vr6+KigosP8lKiooKMj+umvXrkpOTi73s2zcuFFNmjSx/0cUFhamJ598Ut27d9fWrVv1yiuvaPXq1WrSpIkiIiJUuXJlPfjgg6patWqx/dx8881u/dgbAMD1Z//+/ZdM6JRU2u/IksuqVKlS7mlfRSd/Lsd7772n2bNn29/37NlTMTExCg4O1nPPPaevv/5aN9xwg77//nv16NFDGRkZWrJkSbG+pMJJlS1btpTZLzN+5Undppo6rPOn90unv5VSvpfOJkrntkr5WVLmf6WMfVLmASnrsJR9XLpwSspNkfJSpfzzUkGOZLVINqtkGGV2tXnzZvshXKnwL12vXr2UnZ0twzCUmJioVq1aaevWrXr00Ue1ZMkSNWzYUK1bt7607NRUh2E1ODjY/q8GqfB8htJC39VYu3at+vfvb3/fqlUr3X///ZKktm3b6syZM9q3b5+++eYbbdiwQRs3blRqaqo+++yzYvvx9fWVr6+vbDbbNakLAIDz589f1e/Iksuys7MvmbC4WmVN/oSGhiogIMA++TNv3jwNGTJE69ev1/vvv6/hw4dfsq+wsDClp6eX2RczfmXJTZE+b6vbLCa9tb2+tOFfxden1pMSPpX25isly1fTPg/T24/8Vv4+D9eRvvmHdCpfMvlq8KJa+uegLAX4++nwhiB1aGpIa6dIJj+F+PhpxH3SwD5tFeBnUvtmAepi+UVHU6RRM9IlmVS7ur+mPX6j9N0WyeQn+fhJJl+dObpdrRqFSD8OLt7/+X3SjtellCC1Dj6rr1evV4/QVdp+IFNNa+dd2r4sKblS1sEy2+/etlWto85LP5okSfOWH1H1YD8N7VlP+45l64aQHIUcmKRK1jMK3DFcvn4+CrUc0vnt+6QaK+37MQxDAUamfLYMuby6UCFuSE+XMqq7ugyUwLi4H8bEHZkUVqmZjp4/X26r1q1b6+uvv1aPHj20fft2NW3aVMHBwfL399exY8dUv359bdq0qdjpWX9EWZM/69evV1BQkBITExUREaETJ07YjwTWrFmzWBC9KCMjQzVr1iyzL4JfWfIzJElVWo9Vq72ntLdeR7VscqNkFEi2AsV3KpAMq2QrUI0Ci+oc/o90V5fCZf9rI6Og2Ps3Wv2+jYwCfTDp9/UTnrm4ze/t+95boL5diiwzCtQwrEAfjaj6ex/WY9L537c58JtVt97go/8ezZF+O1b8M1kCpLM/SD6GHrxJ+v4nf0WNOyVDJk171CL9dkrzv/ZT8xtt6tysnFm2VFPhvn77SpK07hdf5VikyHZWpWZJwX6BMp3eYG/+5J3SK8sD9O1Ph+TrI03vm6+bClIU2cZXMRO+l7+foQahhvrdky/99vu5hvtPmXR7PV97P3APVfLzpVx/V5eBEhgX98OYuCGTj25t2FJL1u4vt9mDDz6o77//XlFRUTIMQ9OmTZNUeMu0l19+WVarVR07dtRtt92mlJQUTZs2TW+//fYVlTJ48GD985//VEBAgA4fPmy/mleSQkJCNGLECA0cOFABAQFq3769unTpoubNm2vs2LFaunSpCgoKNHny5Ev2u3PnTo0YMaLsjo3rwN69eyu+n/Q9hrFEhnFkuXH27Fnj9ddfL3M7i8VinDlzpgIqdGz8+PHG6dOnjXHjxhl79uy54u2/+uorY/PmzU6o7MrNmDHDWLVqlavLQAkV9d8jrgzj4n4YE/e0d+/eq/4dWZr8/Hxj+vTpl/ThCmlpacZTTz1VbhvO8SuLzVL40ydANWvW1JQpU8ps6u/vr7CwsAoqrHwTJ05U7dq19cILL9jvjXclWrRoofbt2zuhsiuTkpKirKws+yXqAABcK1f7O7I0hmHoiSeeuCb7+qMWLVpU/myfuLijbNbfg9/1yFFYLcuNN97ohGquXFhYGDdwBgA4xdX+jiyNO03+vPjii8VuHF0agl9ZjPzCn77XZ/ADAAAoieBXFtv1PeMHAABQEsGvLNf5oV4AAICSCH5lsc/4cSk+AADwDAS/snCoFwAAeBiCX1kIfgAAwMMQ/MpC8AMAAB6G4FcWgh8AAPAwBL+yEPwAAICHIfiVheAHAAA8DMGvLAQ/AADgYQh+ZeEGzgAAwMMQ/Mpi5EsmH8nH19WVAAAAXBMEv7LYLMz2AQAAj0LwK4uV4AcAADwLwa8sNgvP6QUAAB6F4FcWDvUCAAAPQ/ArC8EPAAB4GIJfWQh+AADAwxD8ykLwAwAAHobgVxaCHwAA8DAEv7IQ/AAAgIch+JWF4AcAADwMwa8sBD8AAOBhCH5lseUT/AAAgEch+JXFZpF8CX4AAMBzEPzKwqFeAADgYQh+ZbFaJBPP6gUAAJ6D4FcWDvUCAAAP47Tgt2PHDpnN5mLLUlJSZDab7X/atm2rZcuWOauEP4ZDvQAAwMP4OWOnCxYs0Nq1a1W5cuViy8PCwhQfHy9J+uWXX/T222+rf//+zijhjyP4AQAAD+OUGb8GDRooLi6uzPWGYWjy5MmKjY2Vr6+vM0r44wh+AADAwzhlxq9r165KTk4uc/3GjRvVpEkThYeHX9b+8vLylJSUdK3KK1Nubq69n2bWPKWmnVdKBfSLshUdE7gHxsQ9MS7uhzFxT94+Lk4Jfo6sXbtWAwcOvOz2gYGBatGihRMrKpSUlFTYj2FIv+SrVtiNqlUB/aJs9jGB22BM3BPj4n4YE/dUEePizsHSJVf17t69W61bt3ZF15fHKCj8yaFeAADgQSok+K1bt07Lly+XJKWmpio4OFgmk6kiur46NkvhT4IfAADwIE471FuvXj2tWLFCktS7d2/78tDQUP373/92VrfXBsEPAAB4IG7gXBpbfuFPbuAMAAA8CMGvNMz4AQAAD0TwK83F4MezegEAgAch+JXGyowfAADwPAS/0lyc8eMcPwAA4EEIfqXhHD8AAOCBCH6lIfgBAAAPRPArDcEPAAB4IIJfaQh+AADAAxH8SkPwAwAAHojgVxqCHwAA8EAEv9JwHz8AAOCBCH6lMXhWLwAA8DwEv9JwqBcAAHgggl9peFYvAADwQAS/0lh5ZBsAAPA8BL/ScKgXAAB4IIJfaQh+AADAAxH8SmM/x8/PtXUAAABcQwS/0tgshbN9JpOrKwEAALhmCH6luRj8AAAAPAjBrzQEPwAA4IEIfqUh+AEAAA9E8CsNwQ8AAHgggl9prAQ/AADgeQh+pTHyeWoHAADwOAS/0tgsPKcXAAB4HIJfaTjUCwAAPBDBrzQ2C4d6AQCAxyH4lYaregEAgAci+JWG4AcAADwQwa80BD8AAOCBCH6lIfgBAAAPRPArDcEPAAB4IIJfaQh+AADAAxH8SkPwAwAAHojgVxqCHwAA8EAEv9LY8gl+AADA4xD8SmOzSD48qxcAAHgWgl9pONQLAAA8EMGvJJtVMmwEPwAA4HEIfiXZLIU/fQl+AADAsxD8SroY/JjxAwAAHobgVxLBDwAAeCiCX0kEPwAA4KEIfiUR/AAAgIci+JVE8AMAAB6K4FcSwQ8AAHgogl9JBD8AAOChCH4l2fILfxL8AACAhyH4lWSf8eNZvQAAwLMQ/EriUC8AAPBQBL+SrAQ/AADgmQh+JfGsXgAA4KGcFvx27Nghs9l8yfKdO3cqJiZG0dHRev7555WXl+esEq4Oh3oBAICH8nPGThcsWKC1a9eqcuXKxZYbhqFx48Zp7ty5atiwoVauXKkTJ04oPDzcGWVcHYIfAADwUE6Z8WvQoIHi4uIuWX748GFVr15dixYt0mOPPab09HT3Cn0SwQ8AAHgsp8z4de3aVcnJyZcsT0tL0y+//KLx48erQYMGGjZsmFq1aqX27duXu7+8vDwlJSU5o9RicnNzdersUd0g6deDR2T1z3Z6nyhfbm5uhYw9Lh9j4p4YF/fDmLgnbx8XpwS/slSvXl0NGzZU48aNJUmdOnXS7t27HQa/wMBAtWjRwun1JSUl6YagmtJxqWmzVlJgqNP7RPmSkpIqZOxx+RgT98S4uB/GxD1VxLi4c7Cs0Kt669evr+zsbB09elSStHXrVjVp0qQiS3CMQ70AAMBDVciM37p165STk6PIyEhNnTpVI0eOlGEYuuOOO3TPPfdURAmXj+AHAAA8lNOCX7169bRixQpJUu/eve3L27dvr1WrVjmr2z/OyiPbAACAZ+IGziUZ+ZLJTzKZXF0JAADANUXwK8lm4TAvAADwSAS/kqwEPwAA4JkIfiXZLDynFwAAeCSCX0kc6gUAAB6K4FcSwQ8AAHgogl9JBD8AAOChCH4lEfwAAICHIviVRPADAAAeiuBXEsEPAAB4KIJfSQQ/AADgoQh+JXEDZwAA4KEIfiUZ+ZKPv6urAAAAuOYIfiVxqBcAAHgoP0cNTp8+rTfffFOpqanq1q2bmjVrpttuu60ianMNgh8AAPBQDmf8xo0bp4iICOXn56tt27aaOnVqRdTlOpzjBwAAPJTD4Jebm6v27dvLZDIpPDxcgYGBFVGX69gski/BDwAAeB6HwS8wMFDfffedbDabtm/froAADw9FHOoFAAAeymHwmzx5stasWaO0tDR98MEHio2NrYCyXIjgBwAAPJTDizsWLVqkt99+uyJqcQ8EPwAA4KEczvgdOHBA58+fr4ha3APBDwAAeCiHM34HDx5Uu3btFBoaKpPJJEnatGmT0wtzCcNa+IfgBwAAPJDD4Pf1119XRB1uwWQUFL4g+AEAAA/kMPi99tprlyybPn26U4pxNZORX/iC4AcAADyQw+DXo0cPSZJhGNq7d6/OnDnj9KJc5ffgx7N6AQCA53EY/Dp16mR/3blzZw0ePNipBbkSh3oBAIAncxj8il7IkZKSorNnzzq1IJcyLIU/CX4AAMADOQx+n376qf11YGCgpk2b5tSCXMlk4xw/AADguRwGv+nTp8tqtcowDG3fvl1NmzatiLpcwn6OH8/qBQAAHshh8Js6daoaN26skydPas+ePapVq5ZmzJhREbVVOK7qBQAAnszhkzt27dqlqKgo/fLLL3r//ff122+/VURdLkHwAwAAnsxh8LPZbNq9e7fq1asni8Wi7OzsiqjLJQh+AADAkzkMfn369NHEiRM1ePBgvfnmm4qMjKyIulyC4AcAADyZw3P8BgwYoAEDBkiSXn/9deXn5zu9KFfhql4AAODJHAa/jz76SAsXLlRBQYEMw5C/v7+++OKLiqitwjHjBwAAPJnDQ71LlixRfHy8OnfurOnTp6tx48YVUZdLEPwAAIAncxj8ateurdq1ays7O1vt2rVTZmZmRdTlEjyrFwAAeDKHwS8kJERfffWVTCaTPvroI6Wnp1dAWa7Bs3oBAIAncxj8pkyZoptuukkvvfSSjhw5orFjx1ZEXS7BoV4AAODJHF7cUblyZe3evVsnT57UvffeqyZNmlREXS5B8AMAAJ7M4Yzf+PHjdfLkSW3evFnZ2dkaNWpURdTlEjyrFwAAeDKHwe/YsWN64YUXFBgYqPvuu8+zL+7gPn4AAMCDOQx+VqtVqampkqSsrCz5+Djc5Lpln/EzcVUvAADwPA7P8XvxxRcVHR2tlJQURUZGasyYMRVRl0uYjHzJ5Cv5+Lq6FAAAgGvOYfC766679MUXXyg1NVWhoaEVUZPLmIx8DvMCAACPdVmPbFu+fLny8vLsy9avX+/UolyF4AcAADyZw+D34Ycfav78+apWrVpF1ONSBD8AAODJHAa/Zs2a6YYbbpCvr+ef90bwAwAAnsxh8Lv77rv1wAMPqH79+jIMQyaTSR9++GFF1FbxbPk8pxcAAHgsh8Fv+fLlmjNnjkJCQiqiHpcyGQXM+AEAAI/lMPjVqVNHt9xyi0ffv+8iDvUCAABP5jD4WSwW9enTR02aNJHJZJIkzZo1y+mFuYLJyOdxbQAAwGM5DH5PPfVURdThFpjxAwAAnuyybuDsLQpn/Cq5ugwAAACncNqJezt27JDZbL5k+aJFi9SzZ0+ZzWaZzWYdOnTIWSVcMWb8AACAJ3M44/fjjz/q7rvvvqKdLliwQGvXrlXlypUvWbd7927NmDFDrVq1uqJ9VgSCHwAA8GQmwzCM8hoMGDBAS5YsuaKdfvHFF2rWrJleffVVrVixoti67t27q0mTJkpJSdE999xzWecQbt++XYGBgVdUw9X4094+KqjUQMnhcU7vC5cnNzdXlSpx+N2dMCbuiXFxP4yJe6qocWnRooXT+7gaDmf8TCaTnn32WTVq1Mh+S5eXXnqp3G26du2q5OTkUtf17NlTMTExCg4O1nPPPaevv/5a9957b7n7CwwMrJAvMG+vVSHVarrtYHmjpKQkxsPNMCbuiXFxP4yJe6qIcUlKSnLq/v8Ih8EvIiLimnVmGIYGDRpkvxl0ly5dtHfvXofBr6JwqBcAAHgyhxd39O7dWzk5Odq5c6fOnz+vnj17XnVnWVlZ6tWrl7Kzs2UYhhITE93qXD+Tka+0HJPGjx9vX3bhwgVFRUXp4MGDl72f3NxcDR8+XDExMRo6dKhSU1MlXf6FLcnJybrvvvuuqIYtW7aoS5cu9vdr165Vv379FBERoaVLl9qX9+vXz97/a6+9JqnwecySNHfuXB04cOCyPycAALi+OJzxGz9+vKpWraoOHTpoy5YtGjt2rGbOnHlFnaxbt045OTmKjIzUiBEjNHDgQAUEBKh9+/bFwoqrmYx8zVl9XDEvjJEk7dq1SxMmTNDp06evaD/Lli1T06ZNNXz4cH366ad69913NXbs2Mu+sCU0NFR169a97BpOnTqlhQsXqqCgwL5s5syZ+uSTTxQUFKSePXuqZ8+eqlSpkgzDUHx8fLHtW7ZsKUl6/PHHNXLkSC1YsOCKPi8AALg+OJzxO3r0qEaPHq0HHnhAY8aM0bFjxy5rx/Xq1bNf2NG7d29FRkZKkvr27avVq1dr2bJlev755/9A6dde9oUC7TqcpebNm0sqfGrJO++8o/DwcHub/Px8jRkzRgMGDFB0dLQSExMv2c+2bdvUqVMnSVLnzp31ww8/SJL27Nmj+fPnKzo6Wu+9916ZdQQFBWnu3Lll1lBUXl6eJkyYoNjY2GLLmzVrpszMTFksFhmGIZPJpH379unChQsaPHiwBg4cqO3bt0uSPehVrVpVlSpV0r59+y7j2wIAANcbhzN+eXl5unDhgipXrqzc3FxZrdaKqMsldh43qdGNIfb3bdq0uaTNypUrVaNGDU2bNk1paWl67LHH9OmnnxZrk5WVZT+PsUqVKsrMzJR0ZRe21KpVq8waipo0aZIGDx6sOnXqFFvepEkTRUREqHLlynrwwQftoe6JJ57Qo48+qiNHjmjo0KH6/PPP7X1JhYFxy5Yt9vALAAA8h8PgN2jQIPuzeg8cOOB2s3TXUlq2oVrVL733YFG//vqrtm3bpp07d0qSCgoKtGvXLvvh74ceekjBwcHKzs6WJGVnZ6tq1apOubDl9OnT2rp1q44dO6Z33nlHGRkZGjFihJ566il988032rBhg4KCgvTKK6/os88+0/3336+GDRvKZDKpUaNGql69ulJSUnTDDTfY9xkWFnbFh7YBAMD1wWHwCwsL04oVK3T8+HHVq1dPNWrUqIi6Kp5hqFaVfJ1PK39GMzw8XHXr1tWwYcOUm5urf/zjH/rzn/9c7Ly5zMxMffvtt7r11luVkJCgNm3a2C9sWb9+vYKCgpSYmPiHr5iuU6eOvvjiC/v7Dh066O2339aJEydUqVIlBQYGytfXV6GhoTp//rxWrVqlX3/9VbGxsTp9+rSysrIUFhZWbJ8ZGRmqWbPmH6oLAAC4J4fn+MXFxal69eq65ZZbPDf0XTglfeSn2+pd0P6jGeU2jYqK0qFDh/TYY48pKipKN910k/3+hhdFR0frv//9r6Kjo7V8+XI999xzCgkJsV/YEhMTo5tvvlldunRRUlKSpk6dekXlpqen67nnnitz/U033aTIyEjFxMQoOjpamZmZ6tevnx555BFlZmYqOjpaI0aM0LRp0+TnVzz779y584qf1AIAAK4PTrmB83Un97Rk2FQlQGrVpLb27t1rv9JVUrHZvICAAIdXNVeuXNl+cUZRffv2Vd++fYsta9iwoYKCghyWWLSG6tWra968eZe0+f777+2vo6OjFR0dfUmbWbNmldlHenq6CgoK1LhxY4f1AACA64/D4Ne3b1/5+vpWRC1u4YUB7fX20qWaMmVKhfRntVo1dOjQCunLkUWLFmnEiBGuLgMAADiJw+C3fv16ffDBBxVRi1uoGVpdU6aMrrD+Ll7s4Q5efPFFV5cAAACcyGHwq1q1qjZs2KA//elP9kO9jRo1cnphLsMj2wAAgIdyGPzOnTunRYsW2d+bTCZ9+OGHzqzJtQh+AADAQzkMfiUf75WXl+e0YtwCwQ8AAHioMm/nUvR8r6Ln+LnLhQhO4+Pv6goAAACcoszgd+7cOfvrb775xv7aMAynFuRyzPgBAAAP5fAGzlLxsGcymZxWjFsg+AEAAA9VZvArGvA8PuwVRfADAAAeqsyLOw4cOKCRI0fKMIxirw8ePFiR9VU8gh8AAPBQZQa/OXPm2F9HRUWV+toj+RL8AACAZyoz+N11110VWYf7YMYPAAB4qMu6uMOrEPwAAICHIviVRPADAAAeiuBXEsEPAAB4KIJfSQQ/AADgoQh+JRH8AACAhyL4lcSzegEAgIci+JXEjB8AAPBQBL+STL6urgAAAMApCH4ledNziQEAgFch+JWQlpam8ePHS5I2btyoiIgIRUZGasWKFWVuM23aNC1btqzc/ZrNZiUmJtrfL1q0SG+99VaZ7S9cuKA+ffooISFBknTmzBkNGjRIMTExevrpp5WVlSVJ+vjjj9W7d2/FxMRo5cqVkqTRo0drzZo12r9/v+bNm3d5HxwAAHg8gl8Jc+bMUUxMjPLz8zV9+nR98MEHio+P1/Lly3X27NlibVNTUzVkyBBt3LjR4X5r1aql2rVrKzc3VyNHjtTSpUvLbT9p0iSZisw+LliwQP369dPSpUvVsmVLrVq1SqmpqZo7d67i4+O1ePFirVu3TsnJyfa+mjVrpqNHj+rYsWNX92UAAACPQvArIivPR7t27VLz5s118OBBNWjQQNWqVVNAQIDatGmjn376qVj77OxsDR8+XH369HG473HjxulPf/qT8vLy1K9fPw0bNqzMtu+//77uuOMONW/e3L5szJgxeuihh2Sz2XTq1CmFhIQoOTlZzZo1U/Xq1eXj46NbbrlFO3bs0JNPPml/1nL37t21ZMmSq/xGAACAJyH4FbE9uZIaNWokScrKylJISIh9XZUqVeyHVy+qX7++brvttsvad2hoqEwmk6pVq6aOHTuW2e6HH37Q0aNH1b9//2LLTSaTrFarevXqpcTERN19991q2LChDhw4oLNnz+rChQv64YcflJOTo6pVqyogoPDq5GbNmmnLli2XVSMAAPBsfq4uwJ2k5fiqVq1akqTg4GBlZ2fb12VnZxcLgs6yatUqnThxQmazWYcOHdKePXsUFhamFi1ayN/fX+vXr9fmzZs1atQoLV68WK+99pqGDx+u6tWr689//rNq1KhRbH9hYWFKT093et0AAMD9EfyKqFnFqvPnz0uSGjdurKNHjyo9PV1BQUHaunWrnnjiCafXMGvWLPvr0aNHq0ePHmrRooViY2PVrVs33X333apSpYpMJpMKCgq0d+9eLV26VPn5+frb3/6mESNGFNvf+fPnFRoa6vS6AQCA++NQbxG31bug/fv3S5L8/f01evRoPfHEE4qKilJERITq1KmjAwcOKDY2ttz9jBgxQikpKVfU98yZM7Vz584y15vNZr3zzjsym82aPXu2YmNj5edXmNv79esns9kss9l8ScjbsWOH2rdvf0W1AAAAz2QyDMNwdRGOJCUlqUWLFs7rIG279NkdkqTx+8YpKipKLVu2vOrdzZ49W8OGDVNQUNA1KvDqjRw5Ui+++KLq16/v6lKuitPHHleMMXFPjIv7YUzcU0WMizuPPTN+JbzwwgsOb7XiSFRUlFuEvn379qlBgwbXbegDAADXFuf4lVCzZk1NmTLlD+3jxhtvvEbV/DHNmzcvdksYAADg3ZjxAwAA8BIEPwAAAC9B8AMAAPASBD8AAAAvQfADAADwEgQ/AAAAL0HwAwAA8BIEPwAAAC9B8AMAAPASBD8AAAAvQfADAADwEgQ/AAAAL0HwAwAA8BIEPwAAAC9B8AMAAPASBD8AAAAvQfADAADwEk4Lfjt27JDZbC5z/bhx4/TWW285q3sAAACU4JTgt2DBAo0dO1Z5eXmlrv/oo4/066+/OqNrAAAAlMEpwa9BgwaKi4srdd3PP/+sHTt2KDIy0hldAwAAoAx+zthp165dlZycfMnyM2fO6J133tG8efP02WefXfb+8vLylJSUdC1LLCYw55DC//famf3gyuXm5jImboYxcU+Mi/thTNyTt4+LU4JfWT7//HOlpaXpySefVEpKinJzcxUeHq6HH3643O0CAwPVokUL5xWWliftL3zp1H5wxZKSkhgTN8OYuCfGxf0wJu6pIsbFnYNlhQa/gQMHauDAgZKkNWvW6NChQw5DHwAAAK6NCrmdy7p167R8+fKK6AoAAABlcNqMX7169bRixQpJUu/evS9Zz0wfAABAxeIGzgAAAF6C4FeEIZOrSwAAAHAagl8xBD8AAOC5CH7F8HUAAADPRdIpigk/AADgwQh+RRgyKS0tTePHj5ckbdy4UREREYqMjLRfoVzU0aNHFR0drZiYGE2YMEE2m02SNG/ePD3yyCOKiorSzp07y217cV1pVz4XlZiYKLPZbH+fmpqqrl27lvo85AMHDig6OlpRUVEaPXq0CgoKlJSUJLPZbP9zyy23KCEhQWfOnNGgQYMUExOjp59+WllZWUpOTtZ9990nSZo4caLOnj17hd8kAABwRwS/YkyaM2eOYmJilJ+fr+nTp+uDDz5QfHy8li9ffkkAmj59ul588UUtXbpUhmFow4YN2rNnj7Zs2aKVK1dq9uzZmjhxYpltJenjjz/WiBEjlJqaWm5ltWrVUu3atSVJ3333nQYPHqyUlJRS286ePVsvvfSSPvroI0nS119/rRYtWig+Pl7x8fGKiYnRX//6V3Xu3FkLFixQv379tHTpUrVs2VKrVq1SaGio6tatK0kym82aNWvW1X+lAADAbRD8isjK9dGuXbvUvHlzHTx4UA0aNFC1atUUEBCgNm3a6KeffirWfs+ePbrrrrskSZ07d9bmzZu1bds2dezYUSaTSTfeeKOsVqtSU1NLbStJ1apV0+LFix3WFh4ertdff12S5OPjo4ULF6p69eqlto2Li9Odd94pi8WilJQUBQcH29fl5OQoLi7Ovq8xY8booYceks1m06lTpxQSEqKgoCDNnTvX3u+hQ4eUlpZ2Bd8kAABwRwS/InYkV1KjRo0kSVlZWQoJCbGvq1KlirKysoq1NwxDJpPJvj4zM1NZWVnFgtbF5aW1laR7771XQUFBDmszmUwKDQ2VJHXo0EE1atQos62vr69OnDihXr16KS0tTc2bN7evW7Vqlbp162bfl8lkktVqVa9evZSYmKi7775bUuEM40Xh4eH6+eefHdYIAADcG8GviLQLvvbAExwcrOzsbPu67OzsYkFQKpx5K7q+atWqZW5XWltnuummm/Tll18qOjpab7zxhn35unXr9OijjxZr6+/vr/Xr12vy5MkaNWrUJfsKCwtTenq6U+sFAADOR/ArokYVm86fPy9Jaty4sY4ePar09HRZLBZt3bpVd9xxR7H2LVu2VGJioiQpISFBbdu2VevWrbVp0ybZbDadPHlSNptNoaGhpbZ1lmHDhunIkSOSCmcXL4bOzMxMWSwW3XDDDfa2sbGx+vHHH+1tL85KFpWRkaGaNWs6rV4AAFAxCH5F3FYvX/v375dUOAs2evRoPfHEE4qKilJERITq1KmjAwcOKDY2VpI0atQoxcXFKTIyUvn5+eratatatWqltm3bKjIyUsOHD7dfIVxa27IkJCRo/vz5V1R70bqefPJJjR49Wmaz2X7xiCQdPnxYN910U7HtzGaz3nnnHZnNZs2ePdu+j6KSkpKcGlQBAEDFMBmGYbi6CEeSkpLUokUL53WQtl367A5ZfYI1ce8IRUVFqWXLls7rz4Fz585p5cqVGjZsmMtquOjAgQNauHChpk6d6pL+nT72uGKMiXtiXNwPY+KeKmJc3HnsmfErymTSCy+8oKVLl7q0DMMwNHjwYJfWcFF8fLxeeOEFV5cBAACuAT9XF+BeTKpZs6amTJni0iqKXlHrahfvQwgAAK5/zPgVYfDMNgAA4MEIfsUQ/AAAgOci+BVVyq1MAAAAPAXBrxiCHwAA8FwEv2IIfgAAwHMR/Ioh+AEAAM9F8CuCq3oBAIAnI/gVxcUdAADAgxH8iiH4AQAAz0XwK4bgBwAAPBfBrxiCHwAA8FwEvyIMzvEDAAAejOBXDMEPAAB4LoJfMQQ/AADguQh+xRD8AACA5yL4AQAAeAmCXxGGia8DAAB4LpJOMRzqBQAAnovgVwzBDwAAeC6CXzEEPwAA4LkIfkWR+wAAgAcj+BVh8HUAAAAPRtIphik/AADguQh+xRD8AACA5yL4SSLwAQAAb0DwkyQZ//tJAAQAAJ6L4CdJxv+CH7kPAAB4MIKfpN9n/Pg6AACA5yLpSJJhK/zBlB8AAPBgBD9JnOMHAAC8AcFPKnKOH8EPAAB4LoKfJGb8AACANyD4SSL4AQAAb0Dwk7i4AwAAeAWCnyT7jB/n+AEAAA9G8JN+v7iDGT8AAODBCH6SOMcPAAB4A4KfpN+DHwAAgOci+En2Q72Gia8DAAB4LqclnR07dshsNl+y/IsvvlBERIQeeeQR/etf/3JW91fI9r+fHOoFAACey88ZO12wYIHWrl2rypUrF1tutVo1a9YsrV69WkFBQerRo4d69+6t0NBQZ5Rx+bi4AwAAeAGnzPg1aNBAcXFxlyz39fXV+vXrFRISovT0dNlsNgUEBDijhCtE8AMAAJ7PKTN+Xbt2VXJycukd+vnpyy+/1KRJk9SlS5dLZgVLk5eXp6SkpGtdpl3Q+SNqKMlmszm1H1y53NxcxsTNMCbuiXFxP4yJe/L2cXFK8HPkr3/9qx544AGNHj1aH3/8sSIiIsptHxgYqBYtWjivoFPJ0kHJ5OununXr6u2339akSZO0ceNGvfPOO/Lz81NERIT69+9fbLOjR49q9OjRMplMatKkiSZMmCAfHx/NmzdP33zzjfz8/DRmzBjdeuut9m2mTZumRo0aKTo6usxyzGaznnvuObVr106StGjRIp09e1Yvv/zyJW1nzJihn3/+WQUFBYqMjFT//v118uRJvfrqqzIMQ9WqVdOsWbNUuXJlrV27VgsXLpSPj48iIiIUExNjn5mNjo7Wu+++q/Hjx1+Lb/SaSUpKcu7Y44oxJu6JcXE/jIl7qohxcedgWaGXsWZlZemxxx6TxWKRj4+PKleuLB8fN7iS1vj94o45c+YoJiZG+fn5mj59uj744APFx8dr+fLlOnv2bLHNpk+frhdffFFLly6VYRjasGGD9uzZoy1btmjlypWaPXu2Jk6cKElKTU3VkCFDtHHjRofl1KpVS7Vr11Zubq5GjhyppUuXltruxx9/1LFjx7R8+XItW7ZMCxYsUEZGhhYtWqTu3btryZIlatKkiVatWiVJmjlzphYuXKhly5Zp4cKFysjIsPdVq1YtValSRVu2bLn67xEAALi1CpnxW7dunXJychQZGanevXtrwIAB8vPzU7NmzfTQQw9VRAkOFJ7jl5VraNeuXZo4caL27dunBg0aqFq1apKkNm3a6KefflL37t3tW+3Zs0d33XWXJKlz5876/vvv1ahRI3Xs2FEmk0k33nijrFarUlNTlZ2dreHDhyshIcFhNePGjVONGjV0/vx59evXTx06dNChQ4cuaXfHHXcU+1eL1WqVn5+fWrRood9++63wM2VlqW7dupKkZs2aKTMzU35+fjIMQyaTSX369LFv36tXL8XFxdk/EwAA8CxOC3716tXTihUrJEm9e/e2L4+MjFRkZKSzur1KhcFv19ECNWrUSFJhYAoJCbG3qFKlirKysopv9b/wdHF9ZmamsrKyVL169WLbZWZmqmHDhqpfv/5lBb+LVzlXq1ZNHTt21Jo1a0ptFxgYqMDAQOXn52v06NGKjIxUlSpVVLduXc2aNUuffPKJLBaLnnvuOUlSkyZNFBERocqVK+vBBx9U1apVi+3v5ptv1rZt2xzWBwAArk9ucJzVDfzvdi7p2YZq1aolSQoODlZ2dra9SXZ2drEgKKnYYers7GxVrVr1sra7ljIyMjRkyBA1btxYTz31lKTCQ7rTp0/Xp59+qtdff12jRo3Svn379M0332jDhg3auHGjUlNT9dlnnxXbl6+vr/z8/GSz2UrrCgAAXOcIfpIuzviFhvjo/PnzkqTGjRvr6NGjSk9Pl8Vi0datW3XHHXcU26ply5ZKTEyUJCUkJKht27Zq3bq1Nm3aJJvNppMnT8pmszntPoW5ubl6/PHHFRERoWeffda+vGrVqvawWbt2bZ0/f14hISGqVKmSAgMD5evrq9DQUPtntX8LhiE/Pz/3OO8SAABcc/yGL+KWhv7as2ePxo8fL39/f/Xo0UP33HOP7rrrLjVq1Eh16tTRgQMHFBsbK6nw6ttnnnlGrVu31pYtW/Tggw+qVatWatu2rfr27atu3bqVe5VsYmKi2rdvr5SUFEmFF4B07dpVeXl5l7Q9d+6cYmJiZDabdd999ykhIUF///vftX//fk2aNElt27ZV8+bNtXr1ag0dOlRDhgxRmzZt9MQTT2jUqFFKTk5WTk6OoqOj1b59e6Wnp6tfv37F+ti/f79uv/32a/Z9AgAA90LwKyIo0EcFBQW6++67lZ+fr08//VTffvuttmzZoiNHjujs2bO6+eab7cEvPj5e7777rn7++Wfddddd+uabbyRJ9evXl5+fn0JCQtS2bdtifQwfPtx+K5datWopNDRUVapU0XfffafBgwfbQ2BRDz/8sI4fP65x48YpPj5eTz75pL7//nuNGjVKe/fu1datWzV58mT17NlTERERWrFihSZMmKBt27bppZde0g8//KBatWrplltu0erVqzVz5kz96U9/uuTm2WvXrtWAAQOu/RcLAADcAsFP0sUndmRdsMrf31+bN2/WwYMH7Vf1BgQE2K/qLarkVb2bN2+WVHhRxuLFix32Gh4erjfffFNBQUHy8fHRwoULi10YUtTs2bPtV/BarVYFBgba1+Xk5CguLk6vv/66JOnAgQPq3LmzJKl169batm2bwsPD7ev/8pe/6LPPPit2Ll9KSoqysrIuCaoAAMBzEPwk6X9X5u46mq+bb75ZU6ZMueqreiXp3nvvVVBQ0GV0a1LLli0lSR06dFCNGjXKbFu7dm1J0s8//6zFixfr8ccft69btWqVunXrZj+XsEWLFvb7BW7YsEEXLlyQyWSyr794jt+vv/5q30dYWJgmTZrksGYAAHD9IvhJujjjl55t+8NX9TrT+vXrNWHCBM2fP7/YBSPr1q3To48+an8/atQobdy4UWazWSaTqdRAWbt2baWnpzu1XgAA4F4IfpJkKvwaQoN9//BVvc7y73//W4sXL1Z8fLzq169vX56ZmSmLxaIbbrjBvmzz5s0aMWKE4uPj5evrq7/85S+X7C8jI0M1a9Z0Wr0AAMD9EPwkqc59UpNnFdZxovbv3y9J8vf31+jRo/XEE08oKipKERERl1zVO2rUKMXFxSkyMlL5+fnq2rVrmV0kJCRo/vz5V1TWxb6sVqumTp1qf/qH2WzW3LlzJUmHDx/WTTfdVGy7Ro0a6eWXX1ZUVJQOHz58yTOGbTabTp8+rZtvvvmK6gEAANc3k2H87+7FbqyiHnSdlJSkZcuWKSoqyn7u3bVy7tw5rVy5UsOGDbum+70a3377rfbs2aNnnnnG1aU4xEPO3Q9j4p4YF/fDmLinihgXdx57ZvxKeOGFF7R06dJrvl/DMDR48OBrvt+rqWPdunXFLg4BAADewWnP6r1e1axZU1OmTLnm+7140YirmUwmvfXWW64uAwAAuAAzfgAAAF6C4AcAAOAlCH4AAABeguAHAADgJQh+AAAAXoLgBwAA4CUIfgAAAF6C4AcAAOAlCH4AAABeguAHAADgJQh+AAAAXsJkGIbh6iIc2b59uwIDA11dBgAAgEN5eXm6/fbbXV1Gqa6L4AcAAIA/jkO9AAAAXoLgBwAA4CUIfgAAAF6C4AcAAOAlCH4AAABews/VBbiazWZTbGys9u/fr4CAAE2ZMkUNGzZ0dVleKT8/X2PGjNGJEydksVj09NNP6+abb9bo0aNlMpnUpEkTTZgwQT4+/Hulop07d04PP/ywPvjgA/n5+TEmbuC9997Txo0blZ+fr+joaN11112Miwvl5+dr9OjROnHihHx8fDR58mT+W3GxHTt26K233lJ8fLyOHj1a6ljMmzdP33zzjfz8/DRmzBjdeuutri7b6bz+b+BXX30li8Wi5cuXa+TIkXrjjTdcXZLXWrt2rapXr66lS5fq//7v/zR58mRNnz5dL774opYuXSrDMLRhwwZXl+l18vPzNX78eFWqVEmSGBM3kJiYqF9++UXLli1TfHy8fvvtN8bFxb799lsVFBToo48+0rPPPqs5c+YwJi60YMECjR07Vnl5eZJK///Wnj17tGXLFq1cuVKzZ8/WxIkTXVx1xfD64Ldt2zZ16tRJknT77bdr9+7dLq7Ie3Xr1k0vvPCCJMkwDPn6+mrPnj266667JEmdO3fW5s2bXVmiV5oxY4aioqJUu3ZtSWJM3MCmTZvUtGlTPfvssxo2bJjuuecexsXFGjVqJKvVKpvNpqysLPn5+TEmLtSgQQPFxcXZ35c2Ftu2bVPHjh1lMpl04403ymq1KjU11VUlVxivD35ZWVkKDg62v/f19VVBQYELK/JeVapUUXBwsLKysvT888/rxRdflGEYMplM9vWZmZkurtK7rFmzRqGhofZ/HEliTNxAWlqadu/erb///e+aOHGiXn75ZcbFxYKCgnTixAl1795d48aNk9lsZkxcqGvXrvLz+/1sttLGouTvf28ZI68/xy84OFjZ2dn29zabrdhfFlSsU6dO6dlnn1VMTIx69+6tN998074uOztbVatWdWF13mf16tUymUz64YcflJSUpFGjRhX7FzFj4hrVq1dXeHi4AgICFB4ersDAQP3222/29YxLxVu0aJE6duyokSNH6tSpUxo0aJDy8/Pt6xkT1yp6buXFsSj5+z87O1shISGuKK9Cef2MX+vWrZWQkCCp8JnATZs2dXFF3uvs2bMaPHiwXnnlFT3yyCOSpJYtWyoxMVGSlJCQoLZt27qyRK+zZMkSLV68WPHx8WrRooVmzJihzp07MyYu1qZNG3333XcyDEOnT5/WhQsX1L59e8bFhapWrWoPDdWqVVNBQQH//3IjpY1F69attWnTJtlsNp08eVI2m02hoaEurtT5vP5ZvRev6v31119lGIamTZumxo0bu7osrzRlyhR99tlnCg8Pty97/fXXNWXKFOXn5ys8PFxTpkyRr6+vC6v0XmazWbGxsfLx8dG4ceMYExebOXOmEhMTZRiGRowYoXr16jEuLpSdna0xY8YoJSVF+fn5GjhwoFq1asWYuFBycrJeeuklrVixQocPHy51LOLi4pSQkCCbzabXXnvNK8K51wc/AAAAb+H1h3oBAAC8BcEPAADASxD8AAAAvATBDwAAwEsQ/AAAALwEwQ/AdWvBggXq2LGj/XmcZrNZBw8eLLN9hw4drriPlJQUxcbGSpKaNWt2VXUCgLsg+AG4bq1du1Y9evTQp59+6rQ+wsLC7MGvZcuWTusHACoCwQ/AdSkxMVENGjRQVFSUlixZUmxdXFycRowYoYEDB6pv377aunWrJMlisWjkyJGKjo7W008/rfz8fP32228aNmyY/va3v6lXr1766quviu0rOTlZ/fv3l1Q4wyhJb7/9tqKiovTII49o/vz5FfBpAeDa4KG0AK5LK1eu1KOPPmp/Zu2OHTuKra9UqZI+/PBD/fe//9XIkSO1du1a5eTk2J9yYTablZSUpKysLP3tb39Tu3bt9PPPPysuLk4PPPBAqX3WqlVLkrRu3Tp9+OGHql27ttasWeP0zwoA1wrBD8B1JyMjQwkJCUpNTVV8fLyysrK0ePHiYm3uvvtuSVKTJk109uxZSYXPUK1Xr56kwhB34cIFhYWF6R//+IdWrVolk8mkgoICh/2/+eabmjVrls6ePatOnTpd408HAM5D8ANw3Vm7dq0iIiI0atQoSdKFCxd0//33q0aNGvY2e/bsUZ8+ffTrr7+qTp06kiSTyXTJvv7+97/r0UcfVZcuXbR69Wr9v//3/8rt22Kx6PPPP9fs2bMlST169FDPnj110003XauPBwBOQ/ADcN1ZuXKlZs6caX9fuXJl/fWvf9WqVavsy5KSkjRo0CBduHBBkydPLnNf3bp108yZMzV//nzVrVtXaWlp5fYdEBCgatWqqX///qpUqZI6dOigG2+88Y9/KACoACbDMAxXFwEA11JcXJxq1aql6OhoV5cCAG6Fq3oBAAC8BDN+AAAAXoIZPwAAAC9B8AMAAPASBD8AAAAvQfADAADwEgQ/AAAAL0HwAwAA8BL/HyMH5jKuPJA4AAAAAElFTkSuQmCC",
"text/plain": [
"<Figure size 720x432 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"For values of best alpha = 0.01 The train log loss is: 0.7178527678032585\n",
"For values of best alpha = 0.01 The cross validation log loss is: 1.27862784839991\n",
"For values of best alpha = 0.01 The test log loss is: 1.155955461983535\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(figsize = (10,6))\n",
"ax.plot(alpha, cv_log_error_array,c='orange')\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.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))"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"### Querying some correctly classified point"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Predicted Class : 2\n",
"Predicted Class Probabilities: [[0.0385 0.8102 0.0092 0.032 0.0308 0.0165 0.0556 0.0041 0.003 ]]\n",
"Actual Class : 2\n",
"--------------------------------------------------\n",
"13 Text feature [y253h] present in test data point [True]\n",
"14 Text feature [ccyr] present in test data point [True]\n",
"33 Text feature [e255k] present in test data point [True]\n",
"40 Text feature [gimema] present in test data point [True]\n",
"42 Text feature [v299l] present in test data point [True]\n",
"59 Text feature [leukemianet] present in test data point [True]\n",
"67 Text feature [t315i] present in test data point [True]\n",
"106 Text feature [f317l] present in test data point [True]\n",
"129 Text feature [pcyr] present in test data point [True]\n",
"134 Text feature [m351t] present in test data point [True]\n",
"155 Text feature [y253f] present in test data point [True]\n",
"168 Text feature [interindividual] present in test data point [True]\n",
"170 Text feature [f486s] present in test data point [True]\n",
"186 Text feature [cml] present in test data point [True]\n",
"192 Text feature [g250e] present in test data point [True]\n",
"210 Text feature [cytogenetic] present in test data point [True]\n",
"229 Text feature [sound] present in test data point [True]\n",
"270 Text feature [tailoring] present in test data point [True]\n",
"317 Text feature [detection] present in test data point [True]\n",
"321 Text feature [e355g] present in test data point [True]\n",
"345 Text feature [e255v] present in test data point [True]\n",
"346 Text feature [warned] present in test data point [True]\n",
"347 Text feature [worthless] present in test data point [True]\n",
"348 Text feature [advise] present in test data point [True]\n",
"390 Text feature [limit] present in test data point [True]\n",
"440 Text feature [ie] present in test data point [True]\n",
"442 Text feature [prescribed] present in test data point [True]\n",
"445 Text feature [outcompete] present in test data point [True]\n",
"463 Text feature [achieved] present in test data point [True]\n",
"Out of the top 500 features 29 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)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## Random Forest Classifier"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"### Model with One hot encoder"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"for n_estimators = 100 and max depth = 5\n",
"Log Loss : 1.3028777773449207\n",
"for n_estimators = 100 and max depth = 10\n",
"Log Loss : 1.216754120740516\n",
"for n_estimators = 200 and max depth = 5\n",
"Log Loss : 1.2940075344150932\n",
"for n_estimators = 200 and max depth = 10\n",
"Log Loss : 1.2135943365753081\n",
"for n_estimators = 500 and max depth = 5\n",
"Log Loss : 1.288516346869715\n",
"for n_estimators = 500 and max depth = 10\n",
"Log Loss : 1.2117036509997654\n",
"for n_estimators = 1000 and max depth = 5\n",
"Log Loss : 1.2883983110975077\n",
"for n_estimators = 1000 and max depth = 10\n",
"Log Loss : 1.2080410815694964\n",
"for n_estimators = 2000 and max depth = 5\n",
"Log Loss : 1.2876977456616903\n",
"for n_estimators = 2000 and max depth = 10\n",
"Log Loss : 1.2062540755731626\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": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"For values of best estimator = 2000 The train log loss is: 0.6833646071546681\n",
"For values of best estimator = 2000 The cross validation log loss is: 1.2062540755731626\n",
"For values of best estimator = 2000 The test log loss is: 1.1354907044699905\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))"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"### Testing on test data using best hyper param"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Log loss : 1.2062540755731626\n",
"Number of mis-classified points : 0.3890977443609023\n",
"-------------------- Confusion matrix --------------------\n"
]
},
{
"data": {
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"text/plain": [
"<Figure size 1440x504 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"-------------------- Precision matrix (Columm Sum=1) --------------------\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 1440x504 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"-------------------- Recall matrix (Row sum=1) --------------------\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 1440x504 with 2 Axes>"
]
},
"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": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Predicted Class : 2\n",
"Predicted Class Probabilities: [[0.0369 0.8112 0.0136 0.0265 0.0354 0.0274 0.0418 0.0036 0.0036]]\n",
"Actual Class : 2\n",
"--------------------------------------------------\n",
"1 Text feature [kinase] present in test data point [True]\n",
"2 Text feature [inhibitors] present in test data point [True]\n",
"3 Text feature [activation] present in test data point [True]\n",
"5 Text feature [tyrosine] present in test data point [True]\n",
"9 Text feature [inhibitor] present in test data point [True]\n",
"10 Text feature [treatment] present in test data point [True]\n",
"24 Text feature [therapy] present in test data point [True]\n",
"28 Text feature [loss] present in test data point [True]\n",
"33 Text feature [therapeutic] present in test data point [True]\n",
"35 Text feature [cell] present in test data point [True]\n",
"37 Text feature [trials] present in test data point [True]\n",
"39 Text feature [inhibition] present in test data point [True]\n",
"42 Text feature [efficacy] present in test data point [True]\n",
"43 Text feature [cells] present in test data point [True]\n",
"50 Text feature [advanced] present in test data point [True]\n",
"51 Text feature [imatinib] present in test data point [True]\n",
"54 Text feature [resistance] present in test data point [True]\n",
"56 Text feature [patients] present in test data point [True]\n",
"57 Text feature [months] present in test data point [True]\n",
"61 Text feature [dose] present in test data point [True]\n",
"68 Text feature [drug] present in test data point [True]\n",
"70 Text feature [resistant] present in test data point [True]\n",
"72 Text feature [tkis] present in test data point [True]\n",
"77 Text feature [ic50] present in test data point [True]\n",
"78 Text feature [treated] present in test data point [True]\n",
"83 Text feature [tki] present in test data point [True]\n",
"84 Text feature [chronic] present in test data point [True]\n",
"88 Text feature [clinical] present in test data point [True]\n",
"93 Text feature [proteins] present in test data point [True]\n",
"94 Text feature [factor] present in test data point [True]\n",
"98 Text feature [wild] present in test data point [True]\n",
"99 Text feature [response] present in test data point [True]\n",
"Out of the top 100 features 32 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)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"### Random Forest with Response Coding"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"for n_estimators = 10 and max depth = 2\n",
"Log Loss : 2.2305945944280983\n",
"for n_estimators = 10 and max depth = 3\n",
"Log Loss : 1.5025762006917014\n",
"for n_estimators = 10 and max depth = 5\n",
"Log Loss : 1.4920367694325738\n",
"for n_estimators = 10 and max depth = 10\n",
"Log Loss : 1.6273461353911929\n",
"for n_estimators = 50 and max depth = 2\n",
"Log Loss : 1.690206969664186\n",
"for n_estimators = 50 and max depth = 3\n",
"Log Loss : 1.4078999586725418\n",
"for n_estimators = 50 and max depth = 5\n",
"Log Loss : 1.5403585416410555\n",
"for n_estimators = 50 and max depth = 10\n",
"Log Loss : 1.6553617693579217\n",
"for n_estimators = 100 and max depth = 2\n",
"Log Loss : 1.533141953830446\n",
"for n_estimators = 100 and max depth = 3\n",
"Log Loss : 1.4981511174424322\n",
"for n_estimators = 100 and max depth = 5\n",
"Log Loss : 1.449574332382478\n",
"for n_estimators = 100 and max depth = 10\n",
"Log Loss : 1.6680417032087973\n",
"for n_estimators = 200 and max depth = 2\n",
"Log Loss : 1.5874145449688761\n",
"for n_estimators = 200 and max depth = 3\n",
"Log Loss : 1.5377680801207638\n",
"for n_estimators = 200 and max depth = 5\n",
"Log Loss : 1.4595835596912765\n",
"for n_estimators = 200 and max depth = 10\n",
"Log Loss : 1.6636291873342717\n",
"for n_estimators = 500 and max depth = 2\n",
"Log Loss : 1.6840321884621672\n",
"for n_estimators = 500 and max depth = 3\n",
"Log Loss : 1.5850813610725507\n",
"for n_estimators = 500 and max depth = 5\n",
"Log Loss : 1.4405000199430753\n",
"for n_estimators = 500 and max depth = 10\n",
"Log Loss : 1.6946239844355908\n",
"for n_estimators = 1000 and max depth = 2\n",
"Log Loss : 1.6577105154754188\n",
"for n_estimators = 1000 and max depth = 3\n",
"Log Loss : 1.5907263652260182\n",
"for n_estimators = 1000 and max depth = 5\n",
"Log Loss : 1.4316755307926046\n",
"for n_estimators = 1000 and max depth = 10\n",
"Log Loss : 1.6955753866976022\n",
"For values of best alpha = 50 The train log loss is: 0.17187311680255057\n",
"For values of best alpha = 50 The cross validation log loss is: 1.4078999586725418\n",
"For values of best alpha = 50 The test log loss is: 1.3644695916484806\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))"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"### Testing model with best hyper param"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Log loss : 1.4078999586725418\n",
"Number of mis-classified points : 0.48872180451127817\n",
"-------------------- Confusion matrix --------------------\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 1440x504 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"-------------------- Precision matrix (Columm Sum=1) --------------------\n"
]
},
{
"data": {
"image/png": 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"text/plain": [
"<Figure size 1440x504 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"-------------------- Recall matrix (Row sum=1) --------------------\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 1440x504 with 2 Axes>"
]
},
"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)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Query the classified point"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Predicted Class : 2\n",
"Predicted Class Probabilities: [[0.0282 0.5118 0.0835 0.0201 0.0323 0.0326 0.0226 0.075 0.1938]]\n",
"Actual Class : 2\n",
"--------------------------------------------------\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",
"Text is important feature\n",
"Text is important feature\n",
"Variation is important feature\n",
"Variation is important feature\n",
"Gene 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",
"Variation is important feature\n",
"Text is important feature\n",
"Variation is important feature\n",
"Gene is important feature\n",
"Text is important feature\n",
"Variation is important feature\n",
"Gene is important feature\n",
"Gene is important feature\n",
"Gene is important feature\n",
"Text is important feature\n",
"Text 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": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Log loss (train) on the stacking classifier : 0.4852213271277535\n",
"Log loss (CV) on the stacking classifier : 1.2994424334112118\n",
"Log loss (test) on the stacking classifier : 1.1741437395930845\n",
"Number of misclassified point : 0.35789473684210527\n",
"-------------------- Confusion matrix --------------------\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 1440x504 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"-------------------- Precision matrix (Columm Sum=1) --------------------\n"
]
},
{
"data": {
"image/png": 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ut/N47w7cdvuNHDp0hOGvzAaHg4AAP4aP7sX1BXeMAPi/2wby8Rfji+zdClz80c5lnHcr2HkQHDByzGP8mLiFKmFlC/oi/24FDoeDx57owG23N+G3Q0cY/soHOCjoi1GPFuqLmW9/TJkyJYr03QoCfG71dAjiVDTfv6/Fz1JQvKcslq872GXH+mP7RJcd61JcnhwAmDVrFuHh4bRv3/6K9y2qyYFrUVFNDoh4WlFMDlyrimJy4FpVVJMD16Kimhy4Vik5UJTo/bvoUHLgcrkzOXBV1hx4/PHHr8ZhRURERERERIo471z3XwsSioiIiIiIiLiIu9cKcBXvjFpEREREREREXEYjB0RERERERERcxFtHDig5ICIiIiIiIuIiBi8doK/kgIiIiIiIiIiLeOvIAe+MWkRERERERERcRiMHRERERERERFzEYDB4OoR/RckBERERERERERfRtAIRERERERER8UoaOSAiIiIiIiLiIrpbgYiIiIiIiMg1TtMKRERERERERMQraeSAiIiIiIiIiIt468gBJQdEREREREREXMRb1xzwzqhFRERERERExGU0ckBERERERETEVTStwDX2jAnzdAhSoHr3JE+HIGfZs6ixp0OQAnVKVvd0CFKgbkmTp0OQAtl5xzwdghQ4ZDnu6RDkLDVLeDoCEXE3b11zwDujFhERERERERGXKXIjB0RERERERES8lcFg8HQI/4qSAyIiIiIiIiIuorsViIiIiIiIiIhX0sgBERERERERERfx1gUJlRwQERERERERcRUvXXPAO1MaIiIiIiIiIuIyGjkgIiIiIiIi4ipe+hO8kgMiIiIiIiIirqJpBSIiIiIiIiLijTRyQERERERERMRVvHTkgJIDIiIiIiIiIq7ipePzvTRsEREREREREXEVjRwQERERERERcRGHphWIiIiIiIiIXOO8Mzeg5ICIiIiIiIiIt7Hb7YwYMYK0tDTMZjNjxowhPDwcgB07djBu3Dhn3eTkZN5++21atmx5weMpOXCZ7HY7o0a+T1rqfsxmX0aN6Ud4eAVn+dKEb0iI/waTj5G+fR+kdZsmzrL58z7n6NETPD8wliNHTjDo+dedZamp+3huYA+6dr3Dre3xZgYDjHq0CbXDSmHLyWPorJ858GeGs7xVowo83ak+BiBl3wmGz91AgJ+J15+6lRJBZnJy7bzwzjr+PJHl3KffvXWpHVaSAdPXeqBF3iv/gjSTtLR9mM2+jBnzNOHhFZ3lCQlfERe3Ah8fE/36daFNm5s4fvwUgwZNJjvbRtmypRk/fgABAf7nrSuXz263M3LEu6Sm7cds9mHMmP6FrlEJCV8TH/cVPj4m+vbrTJs2N3Li+GkGDpqCtaAvxo1/hoAAPz744GM+/zwRo8FIn74P0r79zR5smXfSuVF02O12xo6az860g5jNPgwf1Yuw8HLO8uVLf2BZwg+YTEae6HsvrVpHcupkBvfe9RI1alYCoO1tjekeezsAx4+f5uHuY1n28Wj8/MweaZO3stvtzJj4Ift2HcbX7MMzL3ehYpUyheqcOpHBC4+/xfTFgzD7+eJwOHi4wygqVrkegNoNwnnkqbv57ssNfLjwBwKD/Lmtw43cfl9TTzRJ5D/T+0UxZXTP0IGVK1dis9mIj48nOTmZCRMmMHPmTADq1KnDggULAPjf//5H2bJlL5oYACUHLtu3K3/GZrWxJH48m5N3MmniPN6e8RIAR46cYOGCL1m6fBJWq40e3V7h1maNsNvtvPrKTLZu3cXtt+d/uL7++lLMWzAKgORNabwxbTGdO9/msXZ5o/aNK+Pna6LziG+IrHEdQ7pH0XfqagCC/H146aFIuo35lhMZNnp3qEPpED/ubVaVlH3Hmf7RNh5oWY3eHeowesFGID+Z0CaqIr8fy/Rks7zSypXrCi5Ik0lOTmXChA+YOfMVIP+8WLDgM5Yvfx2r1Ua3boNp1iyKGTPi6NChFZ063cZ77y0lPn4Fd9/d6rx1zWZfD7fQe6xcuR6rzUZ8/ESSk9OYOGEOM2YOBf7ui89ZvnxKwd93CM2aRfL2jHg6dGhJp07teO+95cTHf0WnTm1ZMP9zvvp6JllZVu7v+JySA/+Czo2i47tvN2Kz5bBgyats2bybKZPieOPtAQAcPXKSxQtXsmTpcKzWHB7pMY5bbq3Hju0HuPOupgx5JbbQsdb8uJU3pi7l2NFTnmiK11u3KoUcWw5TPniG1K0HmP3Gp7w6uZezPOmnVOa9/QUnjqc7n/v90DGq16rM8KmPOZ87dTKDhe+u4I35zxMU4s8rT71LoxtrUq5iabe2R8QV9H5RTLlwzYH4+Hji4+Od2zExMcTExACQlJREixYtAIiMjCQlJeWc/TMzM3nrrbdYuHDhJV/LLXcrsNls7niZq2pj0g6at4gCoFFkBNtS9jjLtm7dTVR0bcxmX0JCgggLL09a2gGs1hw6dmxNnz4PnHM8h8PB2DGzGTa8NyaTyW3tKA6a1LqexM2/A5C8+xgNqp35MBBdswxpB08xtHs0ca+24+ipbI6nW5m7Io0ZH28HoMJ1gZzOzP8/GV4umIfa1mDasq3ub0gxkJS0nRYtGgMQGVmblJRdzrItW3YSFVXnzHkRVoHU1H2F9mnZsglr126+YF25fElJO2jRIhqAyMhapKTsdpZt3bKL6LP+vuFhFUhL3c/Gs/Zp2TKan9ZuJiDAn4oVrycry0pWVjYGL11Qx9N0bhQdmzbu4tbmDQBo2KgG27ad+fulbN1HZFSNgr9vIFXCyrIz7SDbt+9nx/YD9Oo5nkHPTufIkZMAGI0G3vvgRUqUCPJEU7zetuR9RN9SG8gfAbBrx8FC5UajgTHT+xISGuh8bnfqQY4fOcWQfjMY/uz7HDrwF3/8dpxqNSsSUiIQo9FIzbpVSE054Na2iLiK3i/kUmJiYvjwww+dj78TAwAZGRkEBwc7t00mE7m5uYX2X7ZsGXfeeSelS186gerS5MB3331HmzZtaN++PV9++aXz+ccff9yVL+MRGZYsgkPOvFkZTUZyc/PyyzIyCTmrLCgogPR0CyVKBNOseeR5j/f99xuoUaMK1W6odFXjLo6CA3xJz8pxbtvtDkwFQ3dKhfhxc92yTIxLptekVTxyZy2qlg/Jr+dwsHBoW3reHsHXGw4R6OfDiEea8PLsX8izOzzSFm+XkZFJcPCZ//umc86LMx+gg4ICyMjIJCMjy3m+/H2uXKiuXD5LRiYhF+2Lf1yjMjILPZ/fF/l/8/IVytDh7qfpdP9AYnt2cGMrig+dG0WHJSOr8LlhPLsvCr+3BwX5k5GeRbVqFXiy//18MH8IbdpFM2Fs/q8tt9xan5Ilg5F/J8uSTVCwv3PbZDSSV9AXAFFNaxFasnDipfR1oXR+uB3jZz5Jl0faMWXYYipVKcOve//gxLF0srNtbP5lF9Ys7/8hSq5Ner8opgwufFxEcHAwFovFuW232/HxKTw54LPPPqNz586XFbZLpxW88847fPzxx9jtdgYMGIDVauX+++/H4fD+L17BQQFYLGfmqDvsdnx88n/xDw4OLFRmsWQRGnLxXxU++3SVPnT/SxlZOQT5n/mvazAanF/uT2bY2LL3OEdPZQPwS+pf1A0vyf4/8oco9hj3HTdUCGHWC62YsDiZ60v48+bTtxIaZKZcyQD63FOHdz/b4f5Geal//t+32x3/OC/OvClZLFmEhAQRHJx/Lvn7++WfK6FBF6wrly/okn1R+BqV3xeBhfoiJDSI1YkbOfLXCVZ++y4Ajz02kujo2jRsGOHeBnk5nRtFR1BwABZLtnPb7ji7LwLIPKvMYskmJDSQBo1uwN/fD8hfb2DG9I/cG3QxFRDkT5bF6ty2OxyYfC4+erJG3SqYTPm/ZdWLvIFjR08RFBLA48/dx/iX5hJSIojqtSufk1QQ8RZ6vyim3LTmQHR0NN9//z133XUXycnJREQU/ryWnp6OzWajQoUKFzhCYS4dOeDr60uJEiUoVaoUM2bMYOHChaxbt65YDEuNiq7N6lX5c9Q3J++kZkS4s6xBgxokbdiB1WojPd3C3j2HqBkRdtHjbUvZQ1RUrasac3GVtPMIrSPzF2qJrHEdOw+edJZt23+ciMolKBVsxmQ0EFnjOnb/dpq+99alY/OqAGRac7HbHXy94RAdhq6g+9jvGLNgIz9t/1OJgSsUHV2HxMQNACQnpxJx1nnRsGEESUnbnefFnj0HiYgIJzq6LqtW5e+TmLiBxo3rXbCuXL7o6NqsSkwCIDk5rdDfr0HDmmwo9Pc9REREGFHRtVm1Kn+fxMSNNGlcl9ASQfj7mzGbffHzMxMaEsTp05bzvqZcmM6NoiMqqgY/rt4MwJbNu6lZs7KzrH6DamxM2lnw981k397D1KhZiRGvzmHl1/l9sX7ddurWreqJ0Iuduo2qsWFt/vts6tYDVK1+6Q+rS97/mk+WJAKwd+dhri9XEnuenT2ph5j4Xn9eGteTQ/v/ok6jqlczdJGrRu8X8l+0b98es9lM165dGT9+PEOGDGHOnDl8++23AOzbt49KlS5/pLrB4cKf9V988UVKlSrFgAEDCAwM5Pfff+exxx7j9OnT/Pjjj5d1jDzHuYsoFAV/361gZ9qB/PUCxj9F4qqNhIVXoG3bG1ma8A1LE77BbnfQu08nbr/jFue+H334Hfv2/cbzA/MXNjp+/BSP9RrFRx9P8VRzLktEj6I5D995t4IqJcFgYPC762gdWZEDf2bw7cbf6HBzGE90qAPAF+t+5b3Pd3BdqD+T+96M2deIyWjgtfjNJO086jxm0zpl6dauRpG+W8GeRY09HcI5/l5hd+fO/TgcDsaNG0BiYhJhYRVo164pCQlfER+/AofDQZ8+nbnjjmYcPXqCwYOnYbFkUapUKFOmDCIw0P+8dYsqB3mXruRmf9+tIG3nfhwOGD/uaVYlJhEeVoG27W4iIeFrEuK/xu6w06fPg9xxx60cPXqSlwa/4eyLyVOeJzDQnzffXMKPqzdiMBppHF2HF158uMgmeQ0UzTVbrsVzIzvvmKdDOK+/71awa+chHA4Ho8Y+xurELYSFlaN12yiWL/2B5UtXYbfbebx3B267/UYOHTrC8Fdmg8NBQIAfw0f34vrrSzqP+X+3DeTjL8YX2bsVHMwoun0xY+KH7N99GIcDnh0Ww4Y1qVSsch1NW9Z31ut13xjeSRiM2c+XjNOZTB6+mOxMK0aTkX4vdqJK1XIsfv8r1q1Kwdfsy/3dW9G8XSMPtuziapbQSNGiY6enAzjHtfh+ka94j0is2X62y46165vHLl3JRVyaHMjNzeXTTz/l//7v/wgICADg6NGjvPvuu7z88suXdYyimhy4FhXV5MC1qigmB65VRTE5cK0qqsmBa1FRTQ5ci4pqcuBapeRAUVL0kgPXrmKeHLjdhcmBr92XHHDpmgM+Pj506tSp0HNlypS57MSAiIiIiIiIiLifS5MDIiIiIiIiItc0Ny1I6GpKDoiIiIiIiIi4infmBlx7twIRERERERER8T4aOSAiIiIiIiLiIo4iepenS1FyQERERERERMRVvHTNAU0rEBEREREREbnGaeSAiIiIiIiIiKt458ABJQdEREREREREXMZL1xzQtAIRERERERGRa5xGDoiIiIiIiIi4ipcuSKjkgIiIiIiIiIireGduQNMKRERERERERK51GjkgIiIiIiIi4ipeuiChkgMiIiIiIiIiruKlyQFNKxARERERERG5xmnkgIiIiIiIiIireOlP8EUuOWAymD0dghTYs6ixp0OQs9S49ydPhyAFdnzc0NMhSAFfY5CnQ5AC/qbrPB2CFKga4u/pEERErm1eOq2gyCUHRERERERERLyWd+YGvHXAg4iIiIiIiIi4ikYOiIiIiIiIiLiIw+idQweUHBARERERERFxFS9dc0DTCkRERERERESucRo5ICIiIiIiIuIq3jlwQMkBEREREREREZfx0jUHNK1ARERERERE5BqnkQMiIiIiIiIiruKlCxIqOSAiIiIiIiLiKt6ZG9C0AhEREREREZFrnUYOiIiIiIiIiLiKly5IqOSAiIiIiIiIiKt4aXJA0wpERERERERErnEaOSAiIiIiIiLiIg7vHDig5ICIiIiIiIiIy3jptAIlBy6T3W5nxIiZpKXtw2z2ZcyYpwkPr+gsT0j4iri4Ffj4mOjXrwtt2tzE8eOnGDRoMtnZNsqWLc348QMICPA/b125fOqLosNggJF9b6ZOtVLYcuwMnb6WA7+nO8tbRlfimYcaAbBtzzGGv7Meo9HA0F5NaFCzDGYfI28u2cz3Gw6xaOwdzv1uqFyCD7/dzWvzN7q9Td7KbrczetQH7Ew9gK/Zh1Gj+xAWXt5ZvizhWxISvsXHZKR33/tp3aYxR/46wUuDp5OTk0uJEsFMmNSfoKAAALKyrDzx2FhGjenDDTdU8lSzvJauU0WH+qLo0HVK5Fy6RklRojUHLtPKleuw2WzEx09m4MCHmTDhA2fZkSMnWLDgM+LiJjF79kimTp2PzZbDjBlxdOjQisWLJ1K37g3Ex6+4YF25fOqLoqP9zWH4mU10fvF/vDY/iSG9mjjLggJ8eOnRxjwx6lsefOFLDv2VQelQPzq2vgFfHyMxg/9H37HfE14xBIDuL39F95e/4qU31/DHUQtvJ2zxVLO80rcrN2Cz2lgUN5rnnu/Ga5MWOMuOHjnJooUrWLh4JO/OGsobr8dhs+Uwe9an3HtfK+YvHEntOlVZvvQ7AFJS9vBw7EgOHvzTU83xerpOFR3qi6JD1ymRc+kaVUwZDK57uJGSA5cpKWk7LVo0BiAysjYpKbucZVu27CQqqg5msy8hIUGEhVUgNXVfoX1atmzC2rWbL1hXLp/6ouhoUqcsiRt/AyA57Sj1a5RxlkXXLkvagZMMeawJS8bfydGT2Rw/baVFdCX+OJbJ+6+2Y2z/W/ju50OFjvnK4zcxaV4Smdm5bm2Lt9u0MZVmzSMBaBRZk20pe51lW7fuJjK6VsH/9UCqhJUnLe1XBg/pyT33Nsdut/PH78cICQ0CwGbL5Y23nqdatYrneym5DLpOFR3qi6JD1ymRc+kaVUwZDa57XITdbmfYsGHExMQQGxvLgQMHCpWvWrWKLl260LlzZ0aMGIHD4bh42P+54ZeQnZ2NzWa72i9z1WVkZBIcHOjcNpmM5ObmOctCQoKcZUFBAWRkZJKRkUVISKDzufR0ywXryuVTXxQdwYG+pFvOZKXtdjumgotYqVA/bm5Qnklzk3hs5EoevbcOVSuGUirUj/AKoTwx+lveW57ChAHNnPvXqlqK4EBfftryh9vb4u3y/48HOLeNhc6LLEKCz5QFBfmTkZ6JwWAgL89Ox3tf4Jeft9O0aT0AoqNrUaFCGeTf03Wq6FBfFB26TomcS9co+S9WrlxZMPIknoEDBzJhwgRnWUZGBq+99hrvvPMOS5cupVKlSpw4ceKix3N5cmD37t08+eSTDBkyhLVr13LXXXdx11138f3337v6pdwqODgQiyXLuW23O/DxMZ1Vdubks1iyCAkJIjg4wLmPxZJFaGjQBevK5VNfFB0ZmTkEB5xZusRoMJBnz89Inky3snXXUY6ezCYzO5efU/6kbrVSnDxt5ftfDgLw87Y/qVYx1Ln/fa1vIP7rne5tRDGR/38827ntKHReFC6zWLIJCc3/UOHr68Onn09h+MgnGPrSDPcGXYzpOlV0qC+KDl2nRM6la1QxZXTh4yKSkpJo0aIFAJGRkaSkpDjLNm3aREREBBMnTqRbt26UKVOG0qVLXzJslxo+fDiPPPIIN910E8888wxLly7l448/5t1333X1S7lVdHQdEhM3AJCcnEpERLizrGHDCJKStmO12khPt7Bnz0EiIsKJjq7LqlX5+yQmbqBx43oXrCuXT31RdCTt+ItWTSoDEFmrDGkHzmQjU/Ycp2Z4SUqF+GEyGoisdT27Dp4iacdftC7Yp3bVUhw+YnHuc2vDCiRuPOzeRhQTUdG1WJ24CYDNybuoGVHFWdagQQ02JqUW/F/PZN/e36hZswqjR87m5/XbgPxf6QxeurJuUaTrVNGhvig6dJ0SOZeuUcWUC9cciI+Pp1OnTs5HfHy882UyMjIIDg52bptMJnJz86fmnjhxgvXr1zNo0CDef/995s2bx759F59q4vK7Fdjtdm66KX9lzPXr13Pdddflv5CPd98YoX37W1izJpmuXV/A4XAwbtwA5sz5mLCwCrRr15TY2Hvo1m0wDoeD556Lxc/PTL9+XRg8eBoJCV9TqlQoU6YMIjDQ/7x15fKpL4qOr9f9SrPIiiRM/D8MBhj8xhp63VeXA7+n8+3PB5k8fyNzRrYH4Msf97Pr15McOHyakU/ezLLX7gJg2IyfnMcrUyqAk+lWj7TF27W77UbWrt1K94deBQeMHteXeXO/ICysHG3aNqF7jzvp2WMEDruDZ56Nwc/PTPfYOxk1YhYzZyzHaDTwyrDHPN2MYkPXqaJDfVF06Dolci5do+RSYmJiiImJOW9ZcHAwFsuZH9rsdrvze3fJkiVp0KAB119/PQBNmjRhx44dVKtW7YKvZXBcalWCKzR06FAMBgOjR4/GaMwfmPDee++xfft2pk2bdhlH0JBikfOpce9Pl64kbrHj44aeDkEK+Bo1ZFLkn3LslktXErfxNUZ5OgRx0veMoiPC0wFcVTc887HLjrX3zY4XLPvqq6/4/vvvmTBhAsnJyUyfPp1Zs2YBcOzYMTp37syyZcsIDQ3loYceYuzYsUREXPhv7/Kf88eMGcN3333nTAwAlCtXjtjYWFe/lIiIiIiIiEiR4nDTLQjbt2/PmjVr6Nq1a8HIk3HMmTOHsLAw2rVrx8CBA3n88ccBuPPOOy+aGICrMHLgv1NGT+R8NHKg6NDIgaJDIwdEzqWRA0WLRg4UJfqeUXQU75ED1QZ84rJj7XvjPpcd61K8eyEAERERERERkaLE5cv+u4eSAyIiIiIiIiKu4qV3VvHSnIaIiIiIiIiIuIpGDoiIiIiIiIi4ipsWJHQ1JQdEREREREREXEXTCkRERERERETEG2nkgIiIiIiIiIireOfAASUHRERERERERFzFoWkFIiIiIiIiIuKNNHJARERERERExFW8dOSAkgMiIiIiIiIiruKltzLUtAIRERERERGRa5xGDoiIiIiIiIi4ipf+BK/kgIiIiIiIiIiraFqBiIiIiIiIiHijIjdyYPUfuzwdghRoXKakp0OQs+z+9BZPhyAFajT5n6dDkAJrVlfzdAhSoFxAbU+HIAUc2D0dgojItU13KxARERERERG5xnlpckDTCkRERERERESucRo5ICIiIiIiIuIiDi9dkFDJARERERERERFX8dLx+V4atoiIiIiIiIi4ikYOiIiIiIiIiLiKphWIiIiIiIiIXON0twIRERERERER8UYaOSAiIiIiIiLiKl46ckDJARERERERERFX8c7cgKYViIiIiIiIiFzrNHJARERERERExEUcmlYgIiIiIiIico3TrQxFRERERERErnFeOnJAaw6IiIiIiIiIXOM0ckBERERERETEVbxz4ICSAyIiIiIiIiKuYvTS8flKDlwmu93OoteXc3D3YXzMPjz8QhfKVb7eWf51wip++W4TAA1ursO9j9zhLNuYuIUNP2ym97BYACYNeNtZ9sevf3HrnTfyYJ8ObmqJ97Pb7YwbvZCdaQcxm30YNvIRwsLLOcs/XLqKZUt/wMdk4vE+HWjZOpJTJzPoePdQqtesBEDbdtF0i23Pj6u38N6MT3E4HNSpW5Uhr/bA4KULiHiC3W5nxIiZpKXtw2z2ZcyYpwkPr+gsT0j4iri4Ffj4mOjXrwtt2tzE8eOnGDRoMtnZNsqWLc348QMICPA/b125fAYDjHypLXVqlsGWk8fQ0Ss5cOgUAHUiyvDKwFbOupH1y9Nv0Ofs2HmEKaPvxNfXyKnT2Qx89SssmTnOemOGtuXUaSuvTV/j9vZ4O7vdztRxH7Fn52F8fX14cXhnKoeVKVTn5PEMnnzkbeYsfR4/P18ADv16lJefn8e8ZQMB+PP3E4x5JQ6Hw0FoaCDDxnfDP8Ds9vZ4M12nig673c6YUXNISz2A2ezLyNFPEBZe3lm+LOE7liZ8i4/JRO++HWnVJpojf51gyOAZ5OTkEloimAmTniQoKID5c7/kw2XfU6p0KADDRj5GtWoVL/TSIkWWrlFSlCg5cJk2/ZhCji2XoTMHsGfbfpbO+JT+4x4D4MjhY6xfmcTLM5/FYDQwof9bRLVoQJXqFVny5kds+yWNKjXOnOQvvvGUc793RsyjQ2x7j7TJW33/7SZs1hzmL36ZLZv3MPW1eKZNfwaAo0dOsWTRShYlDMNqzaFX7HhuvrUeO3Yc4I67mvLSy92dx7FYspg2eSnvz32RUqVCmDv7f5w4kU7pgg8acmkrV67DZrMRHz+Z5ORUJkz4gJkzXwHgyJETLFjwGcuXv47VaqNbt8E0axbFjBlxdOjQik6dbuO995YSH7+Cu+9udd66ZrOvh1voPdq3ro6f2UTnXglE1i/PkOda0Hfg5wDs2HmU7n2WA/B/7Wrwx18ZJP50gJefb8mHX2zn4y9SeaZ3U7p0rM+cxflJzq6d6lOrRhl+3vibx9rkzVZ/vw2bNYeZ859m25YDvD31M8ZPe9RZ/vPaNN5940uOH0t3PvfV50ksW/wjp05kOJ9LWLiatrc34v6YW3n/rf/xxcc/88BDzd3aFm+n61TR8d3KDVitOSyKG8Xm5F28NmkRb72dnwg7euQkixZ+RfyyMVitOfTsPpJbmjXgg1mfce99Lbi3Y0tmTF/G8qXf0/ORu9i+fR9jJ/ajXr0bPNwqkf9G16jiyVt/a7yqAx6OHTt2NQ/vVru37KP+TbUBqF6vKvvTDjrLSpUtybOTemM0GTEYDOTl5uFrzs+7VK9flR7PPXDeY8a99TEP9umAf6Df1W9AMbJp4y5ubV4fgIaNqrN9235nWcrWvTSKqonZ7EtISCBVwsqyK+0QO7YdYMf2/Tz28AReeG4GR46cZPOmPdSoWYmpk+LpFTue0teFKjFwhZKSttOiRWMAIiNrk5Kyy1m2ZctOoqLqFPRFEGFhFUhN3Vdon5Ytm7B27eYL1pXL1ySyIok/HQAgOeUP6tcpd06dAH8fBvS5mTGTVwEwdmoin3yZisEAFcqFcDrdCkBUwwo0ql+eJR9udV8Dipmtm/bRtFn+e0a9huGkbTtUqNxgMDD13d6EhgY6nwsJDeDN2f0K1atRqyLpp7MAsFis+PiYrnLkxY+uU0XHxo1pNG/eEIBGkTXZnrLXWbZ16x6ioiOc799hYeXYmfYrLw6JpcO9zbHb7fzx+3FCCs6Z7dv2Mfu9T+nZfQSz3vvEI+0RcQVdo4ong8F1D3dy6ciBffsK/wccPHgwEydOBKBatWqufCm3y8rMJiDI37ltNBrJy83D5GPCx8dESMlgHA4HS2d+RljNSpSvUhaAm9pGkbpp9znHO7jnMFmZ2dRpHOG2NhQXFksWwSEBzm2T0Uhubh4+PiYslmxCgs+UBQb5k56RSdUbKtCvXjg331KPLz//iYljF9H2tmg2/JxK3PKRBAb60avnBBpFVie8avnzvaycR0ZGJsHBZ77cmExn+iIjI5OQkCBnWVBQABkZmWRkZBESEuh8Lj3dcsG6cvmCg8ykZ1id23a7A5PJQF6ew/lc5/vq8b+VuzlxKtv5nMlo5PMl3TCbfXjr/fVcf10gzzzRlH6DPueu9jXd2obixGKxEhR81nvGWecGwI23nHvtv7Vl3XOeK1uuBO+++SUr/7eJnJxcHu2rkWZXSteposOSkUVwyJm+MBbqi6xC/RQU5E96eiYGg4Hc3DwevH8IVquNvk/eD8D/3XULXbu1JzgokAFPT2XV9xtp1Sba7W0S+a90jZKixKXJgUcffRR/f3/Kli2Lw+Fg3759DBs2DIPBwPz58135Um4XEOhPduaZD94OhwPTWb/g5FhzmDMxDv9AP3o89+Alj7fu6yRadrj5qsRa3AUFBZBpOfPlxu5wOD9wBwX5YzmrLNOSTUhIIA0a3oC/f/4IjTbtopk5/WPuf6Al9epXo8z1JQCIbhJBWuqvSg5cgeDgQCyWLOe23X6mL/LLzrwpWSxZhIQEERwcgMWShb+/HxZLFqGhQResK5cvw2IjOPDMXHSjgUKJAYB7/682Tw/+otBzuXl27uyykFtvqsLkkbez4rvdlCrpz+w376PMdYEE+PuyZ/9xPvx8h1vaUVwEBfmRaTnrPeOsc+NKzHj9C4aOiuGmW2vxU+IOxr4Sx6Tpj7ky1GJP16miIyg4oNB7dOG+CCDzrH6yWLIJDc3/+/r6+vDJ56/x09qtDH1pJnPmv0qPnv/n/HLUslUUO3bsV3JAvJKuUcWTt65h5tJpBcuXL6dGjRr06dOHBQsWULt2bRYsWOD1iQGAGg2qsnV9/ofjPdv2U6laBWeZw+Fg+ssfUKVGRXoO6oLRdOk/646Nu5zTFOTKREbV4MfE/OHOWzbnTw34W/0GN7Bp406s1hzS0zPZt/d3atSszKhhc/n2mw0A/LxuB3XqhlOnbji7d//GiRPp5ObmsXXzHm6orsWMrkR0dB0SE/P/rsnJqUREhDvLGjaMIClpO1arjfR0C3v2HCQiIpzo6LqsWpW/T2LiBho3rnfBunL5kjb/TqtmVYH8BQfTdhee1hUcZMbsa+L3P8/MZx85uA03N64MgMViw+5wMD9+Mx1j4+jeZznvzt3AZyvSlBj4F+pHVmXdj/l/t21bDnBDzX+XdAwJDXCOQLiubKhzioFcPl2nio6o6FqsTkwGYHPyLmpGVHGWNWhQnaSktIK/byZ79/5GjZqVGTPyA35evw3I/3HAaDSSkZHF/fe+SKYlG4fDwc/rt1G3nnePUJVrl65RxZO7phXY7XaGDRtGTEwMsbGxHDhwoFD5mDFj6NSpE7GxscTGxpKenn6BI+Vz6ciB6667jmnTpjFx4kS2bi1ec1WjWjRg+4adjH/yTRwOB4++1JWv43+gbOUy2PPspG3eQ05OLlvXpwLwwBN3U71+1Qse7/Tx0wSXUDbv32h7WzTrftrOw93H4nDAyDG9WDD3K6qElaV12yge6n4bvWLH43A4eOqZTvj5+fLMcw8y4pU5JMR9T0CAH8NGPULp60J5+tkHeKr3VADa33EjNWpW9nDrvEv79rewZk0yXbu+gMPhYNy4AcyZ8zFhYRVo164psbH30K3bYBwOB889F4ufn5l+/bowePA0EhK+plSpUKZMGURgoP9568rl+/r73TRrGkbC7M4YDAYGj/yGXt2jOHDwJN8m7qNaeCl++/10oX3mxSUzemhb+jtuwm6H4RO+91D0xU/LtvXZsG4X/XpOBxy8NDKG+AWrqFSlDM1b17vs4zw7uCOvT/gIu92Rf24Mvf/qBV1M6TpVdLS7rQk/rd1Kj4eG43A4GD2uD/PmfkFYWHnatG1M9x538HCPUdjtdp55NgY/PzPdYu9g9IgPeGfGRxiNBl4Z9ighIYEMeDaGXo+MwWz2oenN9WnZKsrTzRP5V3SNkv9i5cqVBQtaxpOcnMyECROYOXOms3zbtm3MmjWL0qVLX9bxDA6Hw3Hpalfuww8/5MMPP2ThwoVXtN/qP764dCVxi8ZlSno6BDlLoM/1l64kblGjyf88HYIUWLNavxYWFeUCNBquqLDZL/7LkLiX2djY0yGI005PByBOxXvdtZrvJrrsWGNK/k58fLxzOyYmhpiYGADGjx9Pw4YNufvuuwFo0aIFq1evBvJHFTRv3pzo6GiOHj3Kgw8+yIMPXnz6+1W7lWGnTp3o1KnT1Tq8iIiIiIiISJFjcOHk/bOTAf+UkZFBcHCwc9tkMpGbm4uPjw+ZmZn06NGDRx99lLy8PHr27En9+vWpXfvCyfyreitDEREREREREXG94OBgLBaLc9tut+Pjk//7f0BAAD179iQgIIDg4GBuvvlmUlNTL3o8JQdEREREREREXMRdCxJGR0eTmJg/hSE5OZmIiDPTNfbv389DDz1EXl4eOTk5bNy4kXr1Lr7u0VWbViAiIiIiIiJyrTG66U6G7du3Z82aNXTt2rVgQctxzJkzh7CwMNq1a8d9991Hly5d8PX15b777qNmzZoXPZ6SAyIiIiIiIiJexmg0MmrUqELPVa9e3fnvxx9/nMcff/yyj6fkgIiIiIiIiIiLXGo6QFGl5ICIiIiIiIiIi3hrckALEoqIiIiIiIhc4y4rOWC328nLy2PDhg3YbLarHZOIiIiIiIiIVzIYDC57uNMlpxWMHTuW6tWrc/jwYbZt20aZMmWYOHGiO2ITERERERER8SoGLx2ff8mwt27dSteuXdm0aROzZ8/mjz/+cEdcIiIiIiIiIuImlxw5YLfbSUlJoXLlythsNiwWizviEhEREREREfE6xXZBwvvuu4+RI0fSq1cvXnvtNWJiYtwRl4iIiIiIiIjXMRhc93CnS44c6N69O927dwegV69eVKhQ4aoHJSIiIiIiIiLuc8nkwKxZswgNDeX06dN8+OGHtGjRgiFDhrgjNhERERERERGvUmynFXz99dd07NiRxMREvvzyS7Zv3+6OuERERERERES8jtHguoc7XXLkgNFo5OjRo5QpUwYAq9V6VQNqUNpL7/tQDAX6XO/pEOQsR7JTPR2CFEj9ubWnQ5ACdd4/5ekQpMD/HkrzdAhSICxYU0BFROTKXfKbeNOmTYmNjaVHjx6MGzeOVq1auSMuEREREREREa9TbBckfO6553juuecAaNCgAb6+vlc9KBERERERERFv5K1rDlwyOfDtt9+yePFicnJycDgcnDx5ks8++8wdsYmIiIiIiIiIG1xyWsG0adPo378/FSpU4P7776dWrVruiEtERERERETE6xiMBpc93OmSyYGyZcsSFRUFQKdOnfjzzz+velAiIiIiIiIi3shb1xy4ZHLA19eXX375hdzcXFavXs2JEyfcEZeIiIiIiIiIuMklkwMjR44kNzeXfv36kZCQQL9+/dwRl4iIiIiIiIjX8daRAxdckHDfvn3Of5cvXx6A559//upHJCIiIiIiIuKlit3dCoYNG3be5w0GA/Pnz79qAYmIiIiIiIiIe10wObBgwQLy8vIwmUwAZGRk4O/vj4/PJe9+KCIiIiIiInJNcvNNBlzmgmsO7Ny5kzvvvJNTp04BsG7dOu688052797ttuBEREREREREvEmxW3Ng7NixTJ06lRIlSgBw2223Ubp0acaMGcPcuXPdFZ+IiIiIiIiIXGUXTA7Y7XYaNGhQ6Lno6GhycnKuelAiIiIiIiIi3shwyXsCFk0XTQ6cT25u7lULRkRERERERMSbeevdCi6Y02jZsiUTJ04kPT0dAIvFwsSJE7n55pvdFpyIiIiIiIiIXH0XHDnQu3dv3n//fe6//36ys7MpUaIEHTt25LHHHnNnfCIiIiIiIiJew+ClQwcumBwwGAz07t2b3r17uzMeEREREREREa/lpbmBCycHpDC73c6kMcvYlfYbZrMPQ0d2pUrY9c7yj5f9xEdL12LyMdKr9+00b1WPw4eOMfLlRTiAChVKMWR4DP4BZpbM/4FvVmwE4NYWdXm8350eapV3stvtjBgxk7S0fZjNvowZ8zTh4RWd5QkJXxEXtwIfHxP9+nWhTZubOH78FIMGTSY720bZsqUZP34AAQH+560rV8ZutzNl7Efs3nkYX7MPLw3vTOWwMoXqnDieQb+H32besufx8/MF4NCvRxn63DzmLx/orLdty6/MfOMLps/u59Y2FAd2u53Ro2aRlnoAs9mXkaP7Eh5e3lm+NGElSxNWYjKZ6NO3E63bNObw4aO8+vJM8vLycDgcjBjVh2rVKvL11+uY9f4nGAzQoUMLYnve5cGWeScDMLJFDWpfF4wtz87QVTv59XS2s/yVW6vTuHwolpw8APp+tQ1fo4Gpbevg52Pkr0wrL/2wk+xcO11ql6dr3Qrk2R3M2Pgr3/963EOt8k52u50ZEz9k367f8fU18cwrXahYpfA16tSJDAY9Pp23Fw/E7OeLw+Hg4btHO+vVblCVR/qfOQ/eHLuUkNBAHn36bre2xdvZ7XbGjJpDWuqvBdepxwk76zq1LOE7liZ8h4/JSO++HWnVJpojf51gyOCZ5OTkEloiiAmTniQoKIDPPlnNnA++ICQkkPs6tqTTg6091zCR/0Cfa6UoUXLgMq36bis2aw6zFz3H1s37eeO1T5j81uMAHDt6moRFicyNH4jNmkPvnm9y0y21eHPqp3Tq0ow77m7MJ8t/YvH8H7jjrmhWfJHEB4ufw2g00Lvnm7Rq25CatSpeIgL528qV67DZbMTHTyY5OZUJEz5g5sxXADhy5AQLFnzG8uWvY7Xa6NZtMM2aRTFjRhwdOrSiU6fbeO+9pcTHr+Duu1udt67Z7OvhFnqX1d9tw2bL4d0FT5Oy5QDTp3zGhDcedZavX5PGO298yfFj6c7nVnyWxNLFP3LyRIbzuUVzvuerzzfiH2B2a/zFxbcrf8FqzWFx3Fg2J+/ktUnzmf72iwAcOXKSRQv/R8KyCVitOcR2f5VbmzXkrTfj6Nb9DtrddhM//pjMtKmLmTrteV6fspiEZRMIDPTn3g7P0eGe5pQqFerhFnqX9tWuw89kpMvHyUSWDWHILTfQ76vtzvJ6ZYLp9eVWTmSfWeT31Vur89nuv/hw55/0jqxC1zoV+Hz3X/SsX4lOH27E7GMk7t5I1hw6gc3u8ESzvNJPP2zDZs1lygdPk7r1ALOmfcawKWeuUUk/pTF3+hecOOsa9fuhY1SvVYnhr587lfJ/H/7EgT1/UD/qBrfEX5x8tzIJqzWHRXEj2Zy8i9cmLeKtt/MTxEePnGTRwq+IXzYGqzWHnt1HckuzBnww63Puva8F93ZswYzpy1m+9Afuua85099cRsLysYSEBvJEr/E0vaUelSpdf4kIRIoefa4tnrx15MAFFyS02WwXfFwuu93On3/+ecE7H3iTzRv3cnPzOgA0aFSV1O0HnWXbtv5Kw6hqmM0+BIcEUDmsDLt3Hmbfnj+4pWCfhlHV2LxpL+XKl+KNd/pgMhkxGAzk5ubh56cczZVIStpOixaNAYiMrE1Kyi5n2ZYtO4mKqoPZ7EtISBBhYRVITd1XaJ+WLZuwdu3mC9aVK7Nl0z6a3lobgPoNw0nddqhQudFoYNp7vQktEeh8LiQ04JzRAZWqXMfYqT2vfsDF1MaNqTRvHglAo8gItqXscZZt3bqbqOhaBf/XAwkLK09a2gFeHNyTlq2iAcjLtWP288VkMvLZF68TEhLIyZPp5Nnt+PrqGnWlGpcvQeLBEwAk/5VO/etDnGUGoGqJAMa0jCDuvkY8WKtcwT6hJB7MHxWQ+OtxmlUqScOyIWz88xQ2u4MMWx4HTmdR67ogt7fHm23fvI/Gt9YCoHaDcHbvOFio3Gg0MPbtPoSEnrlG7d5xiGNHTvNS35kMHzCLQ/v/KjjWftJSfuXO+7U487+xcWMazZs3AqBRZE22p5x5z926dQ9R0RGFrlM7037lxSE96HBvM+x2O3/8foyQ0EAOHfyLiNphlCgZjNFopF79G9iSvNtTzRL5T/S5tngyGFz3cKcLfuK78847MRgMOByFf50wGAx8++23Fzzg0KFDGTduHJs3b2bQoEGULFkSi8XCuHHjiIyMdFng7maxWAkO9nduG435X+x9fExYMrILlQUG+ZGRnkVErUqs/iGFu++7idXfp5CVZcPH10TJUsE4HA7enPIpEbUrEVa1rCea5LUyMjIJDj7zIc5kMjr7IiMjk5CQMx+cg4ICyMjIJCMji5CQQOdz6emWC9aVK2OxWAkKOevcOKs/AG68JeKcfZq1qnvOc61va8jvv2m49L9lOev/OBTuB8s/zpmgoAAy0jOdowH27TvM5NcW8Ob0FwDw8THxzdfrGTN6Nq1aRRMQ4I9cmWBfE+m2M6MC7HYHJgPkOSDQ18SClMN8sPUQJoOBBfc0ZOuRDILNPqTb8qcZWHLyCDb7FHru7+dDzErWXIlMSzZBQWe/fxvJy83DVHCNimp67jWqdJkQOj/Slha3NWJb8j4mD1vMsKm9WDLra15+7RFWf7PZbfEXJ5aMLIJDApzbxkLv31n/uE75k56e5fwh5cH7h2C15tD3yfsJCg5kz+7fOHr0FEFB/qxft42qVSt4okki/5k+10pRcsFPGN99992/OuChQ/m/Gr7++uu8//77VK1alT///JOBAweycOHCfxdlERAU5Eemxercttsdzi8/QcH+ZGaeKcu0WAkJDWDAC/fx2rjlfP7xem5tUZeSJfNPWKs1hzGvLiEwyI8XX+ns3oYUA8HBgVgsWc7ts/siv+zMhdBiySIkJIjg4AAsliz8/f2wWLIIDQ26YF25Mv88Nxxn9Ye4T1DB//G/OQpdowKxWM7Md7dYsggJzf+/vn59CmNGzWb8xP5Uq3ZmelP725vS7rYbeXnIDD79ZBX3d2rjppYUDxk5eQT5njkPjAYDeQW59qzcPOal/EZ2bv6ounW/naT2dUFk2HIJ8jVhzbMTVJBc+Pu5vwX5mjhtzUUuX2CQP1lnvUfbHQ5nYuBCatStgsmUP7iyXmQ1jh89zepvNnPqpIURA2Zx4lg61uwcKlctS/t7bryq8Rcn+depM9ciu91+1vt3AJmFrlPZhBaM5vD19eGTz1/jp7UpDH3pHeYueJUXX+rB8wOmUaJkMHXqVqVkqWD3NkbERfS5tngyFrdpBX/79ttveeyxx+jZsyexsbHcc889l3Vgk8lE1apVAShXrpzXTy1oGHUDa1fnzxfdunk/NWqeyVDXaxBGctJerNYcMtKz2L/3T26oUYGff0rjyWfuZuacpzGajNx0Sy0cDgcvPDOLmrUqMmR4jPPDh1y+6Og6JCZuACA5OZWIiHBnWcOGESQlbcdqtZGebmHPnoNERIQTHV2XVavy90lM3EDjxvUuWFeuTIOoqqz7cQcAKVsOcEPN8pfYQ66GqOhaJCZuAmBz8k5qRoQ5yxo0qMHGpB0F/9cz2bv3N2rWrML69SlMGDeXd98bSv361YH8XzAejh2OzZaD0WgkIMAPg7e+w3nQxj9O0zqsNACRZUNIO25xllUrEUDcfY0wGsDHaKBx+VC2Hc0g6c8z+7QMK82G30+x5a90mpQvgdlkINhsonrJQHaesJz3NeX86jaqyi9rUgFI3XqAqtUvfY1a/P7XfLJkNQB7dx6mTLmS3PdQC95c8BwT3n2SBx9uS6s7opQYuEJR0RGsTkwGYHPyLmpGVHGWNWhQnaSk1ELXqRo1KzNm5Bx+Xr8NyB9N8PfIzR3b9zFv4TCmvP4M+/YeJiq6lieaJPKf6XNt8WQ0uO7hTpccmzht2jRGjRpFXFwcTZs2Zc2aNRetn5GRQadOncjMzGTp0qXce++9TJgwgYoVvXvBvdbtGvDzT2k83mMaDoeDV0d3Y/G876kcdj0t29SnS/eW9Hn4Tex2B32fuRs/P1/CqpZl2EsLMZt9qFa9PC++/CCrvtvKpg17yLHl8lPBF6onB3SgQWQ1D7fQe7Rvfwtr1iTTtesLOBwOxo0bwJw5HxMWVoF27ZoSG3sP3boNxuFw8Nxzsfj5menXrwuDB08jIeFrSpUKZcqUQQQG+p+3rlyZlm3r88tPu+jbczoOh4Oho2KIm7+KymFlaN66nqfDu2bcdttN/LR2C90fegWHw8GYcU8yd+7nhIWVp23bJnTv8X/E9hiOw27nmWe74udnZsL4eeTk5DJ0yNsAVK1WkREje3P3PS3oGTscHx8TERHh3HNPSw+3zvt8ve8ozSqXJP6+RhgMBl76IY1HG1TiwOksvjtwnE92/cXSjlHk2u18vPMvdp/IZMbGX5nUphZd6pTnRHYOz3+bSlaunfkpv7Hk3kiMBnj9l/3Y8rQY4ZW4pXV9Nq3fycBebwHw7LAYPlq0igqVy3Bzq/Nfozo/3JbJwxbzy487MJmMPDe8qztDLrba3daEn9ZupcdDI3A4HIwe14d5c78kLKwcbdo2pnuPO3i4x2jsdjvPPNsFPz8z3WLvYPSID3hnxkcYjUZeGfao81fVLg+8jNnsy8OP3kWpUiGXeHWRokmfa6UoMTj+uajAPzz22GPMnj2bwYMHM3HiRGJjY1mwYMFFD2qz2UhNTcXf35+qVauyfPlyHnzwQXx9L71a5knb/66sBXLVlDRX93QIcpYj2ameDkEKlDIrE19U1Hn/lKdDkAL/e0h9UVSEBWv+fVFiNjbxdAjitNPTAYjTueu9FCd3fPWjy4711R3NXXasS7nkyAFfX19++eUXcnNzWb16NSdOnLjkQc1mMw0bNnRuP/TQQ/8tShEREREREREv4K0zMi854X3kyJHk5ubSr18/EhIS6Nev36V2EREREREREbkmGV34uBi73c6wYcOIiYkhNjaWAwcOnLfO448/zpIlSy4Z9yVHDpQrVw4fHx+sVitDhgy55AFFRERERERE5OpauXIlNpuN+Ph4kpOTmTBhAjNnzixUZ9q0aZw+ffqyjnfJ5MCIESNITEykbNmyOBwODAYDcXFx/y56ERERERERkWLMaHDd4sHx8fHEx8c7t2NiYoiJiQEgKSmJFi1aABAZGUlKSkqhfVesWIHBYHDWuZRLJge2bNnCypUrMRp1yz0RERERERGRi3HlmgNnJwP+KSMjg+DgYOe2yWQiNzcXHx8fdu7cyeeff86bb77J22+/fVmvdcnkQHh4OFarlYCAgMsMX0RERERERESupuDgYCwWi3Pbbrfj45P/Ff/jjz/mzz//5OGHH+a3337D19eXSpUq0bLlhW9RfcnkwO+//06bNm0ID8+/dZemFYiIiIiIiIicn7vG3EdHR/P9999z1113kZycTETEmVtEvvjii85/v/XWW5QpU+aiiQG4jOTAlClT/kO4IiIiIiIiItcOd93KsH379qxZs4auXbvicDgYN24cc+bMISwsjHbt2l3x8S6YHFi6dCmdO3cmLi4Og6Fw655//vkrj1xEREREREREXMJoNDJq1KhCz1WvXv2cek8//fRlHe+CyYHy5csDcMMNN1xJfCIiIiIiIiLXLIML71bgThecDvH37Q4MBkOhh6+vLxs2bHBbgCIiIiIiIiLewmhw3cOdLrnmwBdffEF2djaRkZFs2bIFq9WKyWSiXr16DB061B0xioiIiIiIiMhVdMnkQG5uLvPmzcNoNGK323niiSeYPXs2Xbt2dUd8IiIiIiIiIl7DXXcrcLVLJgdOnjxJbm4uZrOZ3NxcTp06BYDNZrvqwYmIiIiIiIh4E6OXrjlwyeRAt27duOeee6hZsyZ79+7l8ccf55133nGuSSAiIiIiIiIi3u2SyYHOnTtz22238euvvxIWFkapUqXIy8vDZDK5Iz4RERERERERr+HuhQRd5YLJgRkzZvDkk0/y/PPPYzAUbt2UKVOuemAiIiIiIiIi3qbYrTnQtm1bAO666y5CQ0PdFpDDkee215KLs+ad8nQIIkWS0XDJQVfiJtufCPF0CFKgXrNtng5BCmxbU9nTIYiIiBe64Cfc2rVrAzB79myWLFnitoBEREREREREvFWxm1bwtxIlSjBv3jyqVauG0Zg/QKJ58+ZXPTARERERERERb1Ns71ZQqlQpUlNTSU1NdT6n5ICIiIiIiIhI8XHJ5ED//v05fPgwFSpUoHJlzWETERERERERuZBiN63AYrEwcOBATp48SaVKlThw4AClS5dm6tSpBAcHuzNGEREREREREa9Q7O5WMGXKFO688046duzofG7p0qVMmjSJUaNGuSM2EREREREREXGDCyY1UlNTCyUGADp37kxaWtrVjklERERERETEKxkNDpc93OmCIwd8fM5fZDKZrlowIiIiIiIiIt7MW9ccuODIgZIlS7J169ZCz23dupUSJUpc9aBERERERERExH0uOHLgxRdfpF+/fjRt2pQqVapw6NAhfvrpJ2bOnOnO+ERERERERES8RrEbOVC5cmWWLVvGjTfeSE5ODg0bNiQhIYEqVaq4Mz4RERERERERr2F04cOdLjhyAMDPz4877rjDXbGIiIiIiIiIiAdcNDkgIiIiIiIiIpfP3XcZcBUlB0RERERERERcpNitOSAiIiIiIiIi1waNHBARERERERFxEW/9BV7Jgctkt9t5beyH7Eo7jK/Zh6EjulAlrEyhOieOZ9D74bdYuGwQfn6+ZGfnMGLIIk4czyAwyI9hYx6iVOlgVv+wjQ/e/QaTyUiHjjfR8cGbPdQq72S32xk7ai5pab9iNvswYtTjhIWXd5YvW/o9yxK+w2Qy0rtvR1q1jnKWbfhlB0MGz+Sb794E4Msv1rJw/lf4+BipWbMKLw97BKPRW09nz7Db7UwZ+xG7d+afGy8N70zl85wb/R5+m3nLnsfPzxeAQ78eZehz85i/fKCz3rYtvzLzjS+YPrufW9tQHNjtdkaNfI/U1P2Yzb6MHvMk4eEVnOUJCd+QEP81Jh8jfft2pk2bJs6yefM+4+jRkwwcGAvA1q27mDhhLg6HgzJlSjLptWfx8zO7vU3ezG63M3rUB+xMPYCv2YdRo/sUvk4lfEtCwrf4mIz07ns/rds05vfDR3n15XfIzcsDBwwf9QTVqlXk008SmfPB54SEBHBfx1Y88GBbD7bM+xgMMOKFltSucR22nDxeHv8Dvx46DUCdmtcx9NlmzrqR9crx5EsrWL3uIAAPxzTk+tIBTJ65HoB7bq9Jr26NsOc5WPZ5Kks+2ub+Bnmxf3NeHPnrBC8Nnk5OTi4lSgQzYVJ/goICAMjKsvLEY2MZNaYPN9xQyVPNEvlP7HY7I0bMJC1tH2azL2PGPE14eEVneULCV8TFrcDHx0S/fl1o0+Ymjh8/xaBBk8nOtlG2bGnGjx9AQID/eeuKZ2haQTG36rsUrNYcZi18hqcG3M2bkz8tVL5uTSoD+r7LsaPpzuc+TFhL9ZoVeHdef+66pwlz3ltJbk4eb7z2CW+825uZc57kk+XrOHYs/Z8vJxfx3bdJWG05LFwyggHPd2XypMXOsqNHTrJ44VfMXzSMd94fzBuvx2Oz5QDwx+/HmD/3f+Tm5AGQnW1j+pvLmD13KPMXDSc9I5NVP2zySJu82ervtmGz5fDugqfpO+Aupk/5rFD5+jVpPN/3fY6f9f98xWdJDB+8iJMnMpzPLZrzPRNHLsVmzXVb7MXJypU/Y7XmEBc/gecH9mDSxLnOsiNHTrBwwRcsXjKOWbOG8frUhdhsOWRnW3lh0OssXrzCWdfhcDDs1ZmMHdefRYvH0bxFFId/O+KBFnm3b1duwGa1sShuNM89343XJi1wlh09cpJFC1ewcPFI3p01lDdej8Nmy+GtNxN4qPsdzJ0/nCf6dGTa1CWcOHGa6W8mMHfeMObOH84Xn6/ht9/+8mDLvE/7ltXwM5uI6f0Rk2es56Wnb3WW7dh1jNinPiX2qU9ZtDyFr37Yy+p1B/HzMzF5RDu6P1Cv0LEGP30LjzzzGV37fESvbo0IDVHS7Er8m/Ni9qxPufe+VsxfOJLadaqyfOl3AKSk7OHh2JEcPPinp5oj4hIrV67DZrMRHz+ZgQMfZsKED5xlR46cYMGCz4iLm8Ts2SOZOnU+NlsOM2bE0aFDKxYvnkjdujcQH7/ignVFroSSA5dp86Z93NKsNgD1G4WTuv1goXKD0cBb7/UltETgefe5pXltflm/k337/qRylTKEhgbi6+tDo6hqJCftdV9DioFNG9No1rwhAI0a1WD7tn3Osq1b9xAVFYHZ7EtISCBhYeXYmfYrVquN0SM/4OVhjzjrms0+LFg0nIAAPwDycu36dfRf2LJpH01vLTg3GoaTuu1QoXKj0cC093oXOjdCQgPOGR1Qqcp1jJ3a8+oHXExtTNpB8xb5o2QiI2uRkrLHWbZ16y6io2sXnBdBhIWXJy1tP1ZrDh07tqFvnwecdffvO0zJkiHMm/cZsT1e4dSpDKrpF7krtmljKs2aRwLQKLIm21LOXOe3bt1NZHQt53WqSlh50tJ+5YXBPWjZKr8Pc3Pz8PPz5dDBv6hVO5wSJYMxGo3Ur38Dm5N3e6JJXqtxowrOkQCbt/1JgzrXn1MnwN+HZx6/kbGvrwHAz2zioy/TmDl3Y6F6abuPERJkxmw2YQAc3rkYtcf8m/Ni8JCe3HNvc+x2O3/8foyQ0CAAbLZc3njreapVq3i+lxLxGklJ22nRojEAkZG1SUnZ5SzbsmUnUVF1zrx/h1UgNXVfoX1atmzC2rWbL1hXPMNgcLjs4U5XPTlw/PhxHMXg3dOSkU1QsL9z22g0kpub59xuekstSpQMOnefkPx9AoP8yEjPLvTcmeezrnL0xUtGRhbBwWe+aJ7dF5aMLIJDzpQFBgWQkZ7FuDHzePjRuylXrnSh/a4rUwKAxQu/JjMzm1ture+mVhQfFou10P9po6nwuXHjLRHnnBvNWtUlILBwIqb1bQ3x8TFd3WCLsQxLJiFn/d83ndUPGf84L4KCAkhPz6REiWDnB/W/nTh5mk2b0uje/S4+mDOCdT9tZd26rW5pQ3GSkZFFSEiAc9v4j/4ICT5TFhTkT0Z6JqVKheLr68O+fYeZ/NpC+j31IGHh5dm9+xBHj54kK8vKunXbyMrKdnt7vFlwkJn0DJtzOy/PgclUeLzng/fUYcV3ezlxKv9vezrdxpqfCyc6AXbtPc6Hcx/ky8Vd+X7NgULHlUv7N+eFwWAgL89Ox3tf4Jeft9O0af5ojujoWlSoUHgKm4g3ysjILPS5tvD7dyYhIWc+QwUFBZCRkVlwLgU6n0tPt1ywrniG0eC6h1vjdvUBly9fzvTp09m2bRt33nknjz76KHfeeSdr16519Uu5VVCwP5mZVue23e645BeZoGB/Mi35+2RarISEBBR67uzn5fIFBweQaTmTULE77M6+CAoOwHJWWaYlCx+zDxuT0nhnxof0engMp05l8OLA6fn72u1MnrSYn37aytQ3BmAweOkEIQ8KCvIr9H/acRnnhrhecFBgof/7dvuZ8yL4H+eFxZJFaEjQOccAKFkyhLCw8lSvXhlfXx+at4giJUW/VF+p/L/5mS/xZ58X/yyzWLIJCc3/kPfz+m08038y4yc+RbVqFSlRIpjBL/XkuQGv8+KgN6lbtyqlSoW6tzFeLsNiIyjI17ltNBrIyyv8o8W9d9Qk4dPtFz1Oreqlad0snLadFtGm00KuKxXAnW1vuCoxF1f/9rzw9fXh08+nMHzkEwx9aYZ7gxa5yoKD//n+ffZ5EYjFcuYLvsWSRUhIUKH3dYsli9DQoAvWFbkSLk8OLF68mF69ejFp0iRmzpzJJ598wvz585kyZYqrX8qtGkZWY+3qHQCkbD5A9ZoVLrEHNIys6tznpx9TaRRdjWrVynHw16OcOpVJTk4um5L2Ur9R1asZerETGRXB6tWbAdi8eTc1a1ZxljVoUJ2NSWlYrTbS0zPZu/cwDRrcwGdfTuaDea/wwbxXKFEimElT+gMwasQH2Gw5vPHWc87pBXJlGkRVZd2PBefGlgPcULP8JfaQqyE6ujaJq/KHQCcnpxEREe4sa9CgJkkbdhScFxb27vmNmhFh5z1O5crlyMzM5sCB34H84Y41alQ5b125sKjoWqxOzF/DZHPyLmpGnH2dqsHGpFTndWrf3t+oWbMKP6/fxvhx83j3vSHUr18dyJ9esH37PuYvHMGU159l397DREVHeKRN3ippyx+0uiX//3ujeuXYued4ofLgIDNmXxN//GW56HHSLTayrblYrbnY7Q6OnciiRIjeN67EvzkvRo+czc/r8xd+DAryx+Ctq3yJXEB0dB0SEzcAkJycWuj9u2HDCJKStjvfv/fsOUhERDjR0XVZtSp/n8TEDTRuXO+CdcUzjC58uJPL71bg6+tLYGAgQUFBVKmSf9EvV66c1/8i27pdfX5Zt5MnYt/E4YBXRseweP4qKle5jpZtzj8U/YEutzLqlSX0fvgtfH18GDWxOz6+JgYMupdn+76H3e7gnvtvpGy5Em5ujXdrd1sT1q1NIbbbSBwOB6PH9mb+3C+pElaONm0b063HHTwSOxq73cHTAzpfcB2B7dv38dHyVUQ3rsXjj44DoHvsHbS77UZ3NsfrtWxbn19+2kXfntNxOBwMHRVD3PxVVA4rQ/PW9S59AHGJ29o3Ze3azTzUdQgOh4Nx4/szd86nhIWXp23bm+gRezc9ur+M3e7g2We7XfC8MJt9GTP2KV4Y9DoOh4OoqNq0bt3kvHXlwtrddiNr126l+0OvggNGj+vLvLlfEBZWjjZtm9C9x5307DECh93BM8/G4OdnZsL4eeTm5DJ0SP4vo9WqVWT4yCcA6PzAEPzMvjz86N0aOXCFvlm1l2Y3VSbuvfsxAEPGfs+jXRty4NBpvvtxP9XCSvDb75deGPjwHxnEfbydJe92JCfHzq+/nebDL9KufgOKkX9zXnSPvZNRI2Yxc8ZyjEYDrwx7zNPNEHGp9u1vYc2aZLp2fSH//XvcAObM+ZiwsAq0a9eU2Nh76NZtMA6Hg+eei8XPz0y/fl0YPHgaCQlfU6pUKFOmDCIw0P+8dcUzjG5eK8BVDA4XLwjw3nvvsWnTJiIiIkhJSaFFixasXr2aOnXqMGjQoEvuf8L6uSvDkf8g0Kecp0OQs5zO+d3TIUiB6/yqezoEKZDn0JzvoqJes3WeDkEKbFujWyQXJb7GqEtXEjfZ6ekAxKl4j4J7NWmly441uvFtLjvWpbh85EDv3r35+eef+fHHH6lYsSLHjh0jNjaW1q1bu/qlRERERERERIoUb50B5fLkAMBNN93ETTfddDUOLSIiIiIiIlJkeWtywN1rHIiIiIiIiIhIEXNVRg6IiIiIiIiIXIu89abeSg6IiIiIiIiIuIi77lZgt9sZMWIEaWlpmM1mxowZQ3j4mVtYLlq0iA8//BCDwUCvXr246667Lno8JQdEREREREREvMzKlSux2WzEx8eTnJzMhAkTmDlzJgDHjx9nyZIlfPTRR1itVu6++27+7//+D4PhwgsiKDkgIiIiIiIi4iLuWpAwKSmJFi1aABAZGUlKSoqzrHTp0nz88cf4+Pjw22+/4efnd9HEACg5ICIiIiIiIuIyrkwOxMfHEx8f79yOiYkhJiYGgIyMDIKDg51lJpOJ3NxcfHzyv+b7+PiwcOFC3nrrLWJjYy/5WkoOiIiIiIiIiBRBZycD/ik4OBiLxeLcttvtzsTA33r06EGXLl144oknWLduHTfffPMFX0u3MhQRERERERFxEZPBdY+LiY6OJjExEYDk5GQiIiKcZXv37qV///44HA58fX0xm80YjRf/+q+RAyIiIiIiIiIu4q41B9q3b8+aNWvo2rUrDoeDcePGMWfOHMLCwmjXrh21a9cmJiYGg8FAixYtuOmmmy56PCUHRERERERERLyM0Whk1KhRhZ6rXr2689/9+/enf//+l308JQdEREREREREXMRocHg6hH9FyQERERERERERF3HXtAJXU3JARERERERExEVMng7gX9LdCkRERERERESucRo5ICIiIiIiIuIimlbgIiX9ql+6krjF0r0HPB2CnKVNRU9HIFL0HLce9HQIUmD7mls9HYIUqNVju6dDkLPsWRzl6RBExM28dUFCTSsQERERERERucYVuZEDIiIiIiIiIt7KpGkFIiIiIiIiItc2b11zQNMKRERERERERK5xGjkgIiIiIiIi4iLeOnJAyQERERERERERF/HW5ICmFYiIiIiIiIhc4zRyQERERERERMRFTAaHp0P4V5QcEBEREREREXERbx2e761xi4iIiIiIiIiLaOSAiIiIiIiIiIt464KESg6IiIiIiIiIuIi3Jgc0rUBERERERETkGqeRAyIiIiIiIiIuorsViIiIiIiIiFzjNK1ARERERERERLySRg6IiIiIiIiIuIi3jhxQcuAy2e12Ro54l9S0/ZjNPowZ05/w8ArO8oSEr4mP+wofHxN9+3WmTZsbOXH8NAMHTcGabaNs2dKMG/8MAQF+fPDBx3z+eSJGg5E+fR+kffubPdgy72O32/n87aX8sfcwJl8fOj7blesqXu8sX/vR92xdtQmAmjfWoW33/8PhcPBa7HBnvSp1qnL7o/c49/nkjTgCQgK5vde97m1MMWC325ky9iN27zyMr9mHl4Z3pnJYmUJ1ThzPoN/DbzNv2fP4+fkCcOjXowx9bh7zlw901tu25VdmvvEF02f3c2sbigO73c6oke+Rmrofs9mX0WOe/Mc16hsS4r/G5GOkb9/OtGnTxFk2b95nHD16koEDYwHYunUXEyfMxeFwUKZMSSa99ix+fma3t8mb2e12po77iD07D+Pr68OL5zkvTh7P4MlH3mbO0sLnxcvPz2Pesvzz4s/fTzDmlTgcDgehoYEMG98N/wD1xZWw2+2MHjWLtNQDmM2+jBzdl/Dw8s7ypQkrWZqwEpPJRJ++nWjdpjGHDx/l1ZdnkpeXh8PhYMSoPlSrVpGtW3czaeJ857kxcdLTOjeugMEAox5tQu3wUthy8hj6/s8c+DPDWd6qUQWe7lQfgwFS9p1g+JwNBPiZeP2pWykRZCYnz84LM9fx54kswssFM/qxG/E1GbHl2hnw1hpOZtg82DqRf8dutzNixEzS0vZhNvsyZszThIdXdJYnJHxFXNwKfHxM9OvXhTZtbuL48VMMGjSZ7ILvGOPHDyAgwP+8dcUzvDU5oGkFl2nlyvVYbTbi4ycycGBPJk6Y4yw7cuQECxZ8zpK4CcyaPZypUxdgs+Xw9ox4OnRoyaLF46lT9wbi47/i9OkMFsz/nLi4icz+YATjx832YKu8046ftpJry6X3689x+6P3sOL9j51lx38/yubvk3hiyrP0fv059mxM4499v3H896NUrF6ZxyY9zWOTni6UGPjlyzX8uf93D7SkeFj93TZsthzeXfA0fQfcxfQpnxUqX78mjef7vs/xY+nO51Z8lsTwwYs4eeLMh8JFc75n4sil2Ky5bou9OFm58mes1hzi4ifw/MAeTJo411l25MgJFi74gsVLxjFr1jBen7oQmy2H7GwrLwx6ncWLVzjrOhwOhr06k7Hj+rNo8Tiat4ji8G9HPNAi77b6+23YrDnMnP80fQbcxdtTC58XP69NY2C/wufFV58nMfKlRZw667xIWLiatrc3YvoHT1Ktejm++Phnt7WhuPh25S9YrTksjhvLc89347VJ851lR46cZNHC/7Fw8Wjem/Uy015fjM2Ww1tvxtGt+x3MnT+CJ/rcz7Spi3E4HAwf9i5jxj7JwkWjad48ksOHj3quYV6ofZPK+Pma6Dz8G16L28yQ7lHOsiB/H17qFskTk1fxwLBv+O2IhdIhfsS0qUHKvuM8NPpbPvlxP73vqQPA2MdvYmrCFh4a/S2LV+6iWoUQTzVL5D9ZuXIdNpuN+PjJDBz4MBMmfOAsy/+O8RlxcZOYPXskU6fOx2bLYcaMODp0aMXixROpW/cG4uNXXLCuyJVweXIgIyPj0pW8UFLSDlq0iAYgMrIWKSm7nWVbt+wiOqoOZrMvISFBhIdVIC11PxvP2qdly2h+WruZgAB/Kla8nqwsK1lZ2RgMXppW8qBft+2lRuP8DwdV6lTlt10HnWUlri9Fz9F9MZqMGAwG8nLz8PH15fCug5w+dooPBr/F/Fff4cihP/OPtX0fh1IP0OSuWz3SluJgy6Z9NL21NgD1G4aTuu1QoXKj0cC093oTWiLQ+VxIaMA5owMqVbmOsVN7Xv2Ai6mNSTto3iL/g3b+NWqPs2zr1l1ER9d2XqPCwsuTlrYfqzWHjh3b0LfPA866+/cdpmTJEObN+4zYHq9w6lQG1W6o5Pb2eLutm/bRtFn+eVGvYThp/zgvDAYDU9/tTWho4fPizX+cFzVqVST9dBYAFosVHx/TVY68+Nm4MZXmzSMBaBQZwbZC58ZuoqJrFZwbgYSFlSct7QAvDu5Jy1b57995uXbMfr7s3/87JUuGMH/e5zwcOzz/3KhW8XwvKRfQpNb1JG7JT8Yn7z5GgxtKO8uiI8qQdvAUQ7tHEzesHUdPZXM83crcFWnM+Hg7ABWuC+S0xYafr4nrQv1oF12JRa+0JbpmGTbvPu6RNon8V0lJ22nRojEAkZG1SUnZ5SzbsmUnUWd9xwgLq0Bq6r5C+7Rs2YS1azdfsK54hsnguoc7uTw50KxZM5YuXerqw3qcJSOTkOAzH+JMJiO5uXkAZGRkEhJypiwoKID0jMxCzwcFBZCenglA+Qpl6HD303S6fyCxPTu4sRXFgzUzG/8gf+e20WggLy+/L0w+JoJKBONwOFjx/sdUqF6ZMpXLElI6lJYxt9Fr4tO0imnP8kkLSD9+iu8XreDuJx/0VFOKBYvFSlDIWf1x1rkBcOMtEZQoGVRon2at6hIQWHgobuvbGuqLz3+QYSl8HSp8jcoi+J/XqPRMSpQIplnBl6a/nTh5mk2b0uje/S4+mDOCdT9tZd26rW5pQ3FisVgJCr6y8+LWlnUJ+MeUgbLlSvBh/Bp6dprM+jWptG7f8OoGXgxZMrIKnRtn94UlI5Pg4MLnRkZ6JqVKheLr68O+fYeZ/NoCnnyqMydOnCZ5Uxrdut/JrA9eZd26FNatS3F7e7xZcIAv6Zlnfsm02x2YCsbelgrx4+a6ZZm4JJleE1fxyP/Vomr5/NEAdoeDhS+3pecdEXy94RAlg81EVCnJmpQ/6D7mO0KDzHRqWc0jbRL5rzL+cR069zvGmfeKoKAAMjIyyTjrupb/nm65YF3xDKPB4bKHW+N29QFr167Njh076NmzJz//XHyGPwYFB2KxZDm37XaH84tM8D/KLJYsQkKCCj1vsWQREhrE6sSNHPnrBCu/fZfvf3iflSvXs2XLTvc2xsv5BfpjzbI6tx12BybTmS+VObYclk6ajzXLyj1PdQagYs0wat/cAIDw+tU5ffw0KYmbyDydwYJh77I6YSVbfkhi4zfr3duYYiAoyI9MS+H+0Jd89wsO+uc1yn7WNSrgnGtUaEjQOccAKFkyhLCw8lSvXhlfXx+at4gqNFJKLo+rzosZr3/B0FExzP9wEM+8cB9jX4lzZZjXhKB//P8/uy/y39uznWV/v1cDrF+fwjP9X2P8xP5Uq1bx3HOjeaNCoxDk0jKycgjyP7PclcFgIM+e/8H3ZLqNLXuPc/RUNpnWXH5J/Yu64SWddXuM/Y6uo1by9rPNOZlhIz0zh3Xb/wLg+02HC41CEPEm//wece53jDNf8M98xwgo9B0jNDTognVFroTLkwN+fn4MGzaMF154gQULFnDPPfcwduxY5s+ff+mdi7Do6NqsSkwCIDk5jYiIcGdZg4Y12ZC0HavVRnq6hT17DhEREUZUdG1WrcrfJzFxI00a1yW0RBD+/mbMZl/8/MyEhgRx+rTFI23yVmF1q7Hrl/whhgd37KfcWcM6HQ4Hi0fOony1Stz3TAxGU/5/8e8XreCnj1cB8Pve3yhRpiS3dGxNv7de4LFJT9Oiy200bN2Y6PZN3d8gL9cgqirrftwBQMqWA9xQs/wl9pCrITq6NomrNgLnuUY1qEnShh3Oa9TePb9RMyLsvMepXLkcmZnZHDiQP/Q3KWk7NWpUufoNKGbqR545L7b9h/MiJDTAOQLhurKhzikGcvmiomuRmJi/SO3m5J2F/u83aFCDjUl/nxuZ7N37GzVrVmH9+hQmjJvLu+8NpX796sDZ58YfAGxMStW5cYWS0o7QOjL/PTuyxnXsPHjSWbZt/3EiKpegVIgZk9FAZI3r2P3bafreW5eOzasCkJmdi93uwJqTx/4/TtOkVv4iwzfWvp5dh065uzkiLhEdXYfExA0AJCenFnr/btgwgqRC3zEOEhERTnR0XVatyt8nMXEDjRvXu2Bd8QyjCx/u5PK7FTgc+RngBg0a8NZbb5Gens4vv/zCvn3ePeelffubWbtmM127DsbhgPHjnmbOnE8ID6tA23Y3ERvbge7dhmJ32Hn2ue74+Znp168LLw1+g6UJ31CqVCiTpzxPYKA/a9duIabLixiMRhpH16FZs0hPN8+r1Lm1IXs2pfHe86+DA+5/vhtrPvye0hXL4Mizs3/rbnJzctm1If+DeftHO9Cyy20se20BO3/ehtFkpNPA7h5uRfHRsm19fvlpF317TsfhcDB0VAxx81dROawMzVvX83R414zb2jdl7drNPNR1CA6Hg3Hj+zN3zqeEhZenbdub6BF7Nz26v4zd7uDZZ7tdcIV1s9mXMWOf4oVBr+NwOIiKqk3r1k3OW1curGXb+mxYt4t+PacDDl4aGUP8glVUqnJl58Wzgzvy+oSPsNsdOBwOnht6/9ULupi67bab+GntFro/9AoOh4Mx455k7tzPCQsrT9u2Teje4/+I7TEch93OM892xc/PzITx88jJyWXokLcBqFqtIiNG9mbUmH68+MIbOBwQFRVBq9bRHm6dd/l6wyGaNyjP0hG3gcHA4HfX0euuWhz4I4NvN/7G5PjNzH2pDQBfrPuVnYdOcey0lcn9bqZz6xswGQ0Mfjd/hN9L7/3MyEebYDIaOHTEwqQlmz3ZNJF/rX37W1izJpmuXV/If/8eN4A5cz4mLKwC7do1JTb2Hrp1G5z/HvBcrPM7xuDB00hI+JpSpUKZMmUQgYH+560rnuGtdyswOP7+Nu8iH330Efff/+8/vDjY4cJo5L9YuveAp0OQs7SpqFs0FRXX+VX3dAhS4Ei2hnUXFdf56ReqoqJWj+2eDkHOsmfxQ54OQZw0lbfoiPB0AFfVd4e/dNmx2la8y2XHuhSXjxz4L4kBEREREREREW/m7rsMuIrLkwMiIiIiIiIi1yp332XAVdy9xoGIiIiIiIiIFDEaOSAiIiIiIiLiIt66IKGSAyIiIiIiIiIu4q3JAU0rEBEREREREbnGaeSAiIiIiIiIiIt46y/wSg6IiIiIiIiIuIjBTdMK7HY7I0aMIC0tDbPZzJgxYwgPD3eWz507ly+++AKAVq1a0b9//4sez1uTGiIiIiIiIiLXrJUrV2Kz2YiPj2fgwIFMmDDBWXbw4EE+/fRT4uLiSEhI4McffyQ1NfWix9PIAREREREREREXceXAgfj4eOLj453bMTExxMTEAJCUlESLFi0AiIyMJCUlxVmvfPnyzJo1C5PJBEBubi5+fn4XfS0lB0RERERERERcxJXTCs5OBvxTRkYGwcHBzm2TyURubi4+Pj74+vpSunRpHA4HkyZNom7dulSrVu2ir6VpBSIiIiIiIiJeJjg4GIvF4ty22+34+Jz5/d9qtTJo0CAsFgvDhw+/5PGUHBARERERERFxEaMLHxcTHR1NYmIiAMnJyURERDjLHA4HTz75JLVq1WLUqFHO6QUXo2kFIiIiIiIiIi5iMDjc8jrt27dnzZo1dO3aFYfDwbhx45gzZw5hYWHY7XZ+/vlnbDYbq1evBuD5558nKirqgsdTckBERERERETEyxiNRkaNGlXouerVqzv/vXXr1is6npIDIiIiIiIiIi7iyrsVuFORSw4YuPRcCHGPLjfc4OkQREQuqlxAbU+HIFLk7FrUyNMhiBRJO07u9HQIUqBOyYhLV/JirrxbgTtpQUIRERERERGRa1yRGzkgIiIiIiIi4q28dOCAkgMiIiIiIiIirmL00uyAphWIiIiIiIiIXOM0ckBERERERETERbx04ICSAyIiIiIiIiKuorsViIiIiIiIiIhX0sgBERERERERERfx0oEDSg6IiIiIiIiIuIqSAyIiIiIiIiLXON3KUERERERERES8kkYOiIiIiIiIiLiIlw4cUHJARERERERExFUMBoenQ/hXNK1ARERERERE5BqnkQMiIiIiIiIiLqJpBSIiIiIiIiLXOIOXZgc0rUBERERERETkGqeRA5fJbrczYsRM0tL2YTb7MmbM04SHV3SWJyR8RVzcCnx8TPTr14U2bW7i+PFTDBo0mexsG2XLlmb8+AEEBPift65cPvVF0aG+KDrUF0WL+qPoUF8UHXa7nVEj3yU1dT9msy+jxzxFeHgFZ3lCwtckxH+NycdE374P0qbNjc6yefM+4+jREwwc2BOALz5fzfz5n2EymYiICGPY8D4YjfrNS7yP3W7n3Ukfsn/XYXzMPvQf2oUKVcoUqnPqRAZDnniLaYsGYfbzJS/Pzpxpn7I79SA5tly6PnEHNzavy8v9Zjj3+W3/X7Tt0ISeT3Vwd5ME7/0FXsmBy7Ry5TpsNhvx8ZNJTk5lwoQPmDnzFQCOHDnBggWfsXz561itNrp1G0yzZlHMmBFHhw6t6NTpNt57bynx8Su4++5W561rNvt6uIXeQ31RdKgvig71RdGi/ig61BdFx8qV67Fac4iLn0hychqTJs7h7RlDgfy+WLjgC5Ytn4zVaqN7t6E0axaJ3W7n1VdmsGXrLm6//WYAsrOtvPHGIj759A0CAvwY+PwUfvhhA23bKlkj3mf9qhRsthwmzn6GtK0HmPPGpwyd3MtZvmldKvPf/oITx9Kdz/3wvw3k5uUx4f2nOfbXKdZ8uxmAsTOfBOCP347x2tD5dH60vXsbI06aVnABNpuN7Ozsq/0yV11S0nZatGgMQGRkbVJSdjnLtmzZSVRUHcxmX0JCgggLq0Bq6r5C+7Rs2YS1azdfsK5cPvVF0aG+KDrUF0WL+qPoUF8UHRuTdtC8RRQAkZG1SEnZ4yzbunUX0dG1z/x9wyuQlrYfqzWHjh3b0LfPg866ZrMvi5dMICDAD4C8vDz8zGb3NkbERXZs3kf0zbUBqNUgnN2pBwuVGwwGRk7vS3CJQOdzyevSuO76Eox+bhZvj0vgxhZ1C+0z+/WP6dn/bgIC/a5+A6RYcXlyYN++fTzzzDMMHDiQ5ORk7rnnHu6++26+/PJLV7+UW2VkZBIcfOakNJmM5ObmOctCQoKcZUFBAWRkZJKRkUVISKDzufR0ywXryuVTXxQd6ouiQ31RtKg/ig71RdGRYTnzd4Vz+yL4H3/f9PRMSpQIplnzyELHMRqNlClTEoCFC74gMzObW5s1uurxi1wNmZZsAoP9ndtGo5G8gvMCILJpLUJLBBXa5/QpC78fPMorUx+jU8+2vDU63lm2f9dhMi1WGt0YcfWDlwsyuPDhTi6fVvDqq6/y5JNPkp6eTp8+ffj0008JCQnh0Ucf5a677nL1y7lNcHAgFkuWc9tud+DjYzqr7MwHBIsli5CQIIKDA7BYsvD398NiySI0NOiCdeXyqS+KDvVF0aG+KFrUH0WH+qLoCA4KwGI5M5r03L44008WSxahF/n72u12Jr82n/37D/PGm4MxeOsYXrnmBQb5k5VpdW477A5MBefFhYSEBtGkeV0MBgP1o6tz+NcjzrIfViRx+31Nr1q8cnm89ZLk8pEDubm53Hrrrdx+++2ULFmScuXKERgYiI+Pdy9vEB1dh8TEDQAkJ6cSERHuLGvYMIKkpO1YrTbS0y3s2XOQiIhwoqPrsmpV/j6JiRto3LjeBevK5VNfFB3qi6JDfVG0qD+KDvVF0REdXYfEVUkAJCenERER5ixr0KAmSRvO/H337jlEzbPK/2n48JlYrTamv/2Sc3qBiDeq3bAaSWt3AJC29QDhNSpcYg+oE3lmn307D3N9+ZLOsi2/7CbqltpXJVYp/gwOh8PhygMOHDgQu91OXl4ehw4dokWLFgQHB7Nt2zamTZt2GUfY6cpwXObv1Y537tyPw+Fg3LgBJCYmERZWgXbtmpKQ8BXx8StwOBz06dOZO+5oxtGjJxg8eBoWSxalSoUyZcogAgP9z1tXLp/6ouhQXxQd6ouiRf1RdFyLfWF35Ho6hPP6+24FaWkH8vti/NMkrkoiLLwCbdveRELC1yxN+Aa73U6fPg9y+x23OPf96MPv2LvvEAMH9mTbtj10fvAFGjeu4xwxENuzA+3b3+yppl2U0VD30pXELXac/NzTIZzDebeC3YfBAU+/GkPS2lQqVL6Om1rWd9Z7ouMY3o4fjNnPlxxbLu9MXMbBfX/iAPq++ADVa1cGoFeHkXzw+XAPteby1SlZvO+icMjymcuOVTnoHpcd61JcnhzIzc1l1apVVK1alaCgIObOnUuJEiV4+OGHCQwMvPQBimhyQERERMQbFNXkwLVKyYGioygmB65VxT05cDjTdcmBioHuSw64fKy/j48P7dq1c26/9NJLrn4JEREREREREXEh714IQERERERERKQI8dL1CJUcEBEREREREXEVg8GlM/fdxuV3KxARERERERER76KRAyIiIiIiIiIuomkFIiIiIiIiItc4g5dmBzStQEREREREROQap5EDIiIiIiIiIi7ipQMHlBwQERERERERcRVvHZ7vrXGLiIiIiIiIiIto5ICIiIiIiIiIi2hBQhEREREREZFrnsGFjwuz2+0MGzaMmJgYYmNjOXDgwDl1jh8/zh133IHVar1k1EoOiIiIiIiIiHiZlStXYrPZiI+PZ+DAgUyYMKFQ+erVq+nVqxdHjhy5rONpWoGIiIiIiIiIixhceL+C+Ph44uPjndsxMTHExMQAkJSURIsWLQCIjIwkJSWl0L5Go5E5c+bwwAMPXNZrKTkgIiIiIiIi4iIGg+sG6J+dDPinjIwMgoODndsmk4nc3Fx8fPK/5jdr1uyKXkvTCkRERERERES8THBwMBaLxbltt9udiYF/Q8kBEREREREREZdxz4KE0dHRJCYmApCcnExERMR/ilrTCuSC7I4cT4cgZzEafD0dgoiIeAGDweTpEESKpOiGizwdghTI+rWDp0O4qly55sDFtG/fnjVr1tC1a1ccDgfjxo1jzpw5hIWF0a5duys+nsHhcDiuQpz/wU5PByAFlBwoWpQcEBGRy+GgiH20u8YZqOXpEKRAQNhDng5BCmT9usTTIVxVp2wrXHasEuY7XXasS9HIARERERERERGXcc/IAVdTckBERERERETERVx5twJ38s6oRURERERERMRlNHJARERERERExGU0rUBERERERETkmuauuxW4mqYViIiIiIiIiFzjNHJARERERERExEW8deSAkgMiIiIiIiIiLuOdA/S9M2oRERERERERcRmNHBARERERERFxEYNB0wpERERERERErnHemRzQtAIRERERERGRa5xGDoiIiIiIiIi4iO5WICIiIiIiInLN884B+t4ZtYiIiIiIiIi4jEYOXCa73c6IETNJS9uH2ezLmDFPEx5e0VmekPAVcXEr8PEx0a9fF9q0uYnjx08xaNBksrNtlC1bmvHjBxAQ4H/eunL57HY7o0a+R2rqfsxmX0aPeZLw8ArO8oSEb0iI/xqTj5G+fTvTpk0TZ9m8eZ9x9OhJBg6MLXTMYa/OpETJ4HOel4vTeVF0qC+KFvVH0aG+KDrsdjsjR7xDqrMv+p/TF/FxX+HjY6RvvxjatLmRE8dPM3DQZKwFfTFu/AACAvycx+vTexTt2jWl60P/56lmifwnBoOBN8b2omGdMKy2XPq9+B57D/zpLB/wxN106XgrDruDSdM/5tOvNhAY4Mfct/pTqkQQtpxcnnhuJof/PEGzm2ox/uUeOBwOVq/fwSvjl3iwZdc2b51WoJEDl2nlynXYbDbi4yczcODDTJjwgbPsyJETLFjwGXFxk5g9eyRTp87HZsthxow4OnRoxeLFE6lb9wbi41dcsK5cvpUrf8ZqzSEufgLPD+zBpIlznWVHjpxg4YIvWLxkHLNmDeP1qQux2XLIzrbywqDXWbx4xTnHi4/7ip07D7ixBcWHzouiQ31RtKg/ig71RdGxcuU6rDYb8fGvMXBgTyae0xefsyRuIrPO+vu+XdAXixZPoE5BX/xt2rSFnD6d4YmmiLjMvXc0wd/Pl9b3D+fVCUuY8GoPZ1mJ0ECe6nUnrTsOo0OP8bw2vCcAvbq1ZdPWfbTvPIolH/7I8/3uAWDS8J7E9n+TVh2H0aRRdRrVq+qJJgn5SR9XPdzpqiYHHA7H1Ty8WyUlbadFi8YAREbWJiVll7Nsy5adREXVwWz2JSQkiLCwCqSm7iu0T8uWTVi7dvMF68rl25i0g+YtogCIjKxFSsoeZ9nWrbuIjq595u8bXp60tP1YrTl07NiGvn0eKHSsTRtT2bJlFzExt7u1DcWFzouiQ31RtKg/ig71RdGRlLSDFi2igb/7YrezbOuWnUSf9fcND6tAWup+NiZtd+7TsmVjflq7GYAVK9ZgNPx/e/ceF3Wd73H8NUAgDDfZMndPYmDScipC3FZZ0bOope1WrnhBTVwvDzOz4y0NNeTouktalmXmBfOykSKYaD7a0ux4ocsuq6Spj6Px8IL3A6gozkDcZs4f5hSpm4/OxG/g934+HvPH/L6/+c779/syM8xnvr/fz4uEb9pEmqrfPHQv23Ze/bv+594jdIyJdLXZK6s5eeY81oAWWP39cDiufrdatOJD5r2xEYA2/3Y7lyoqAej2xExOnCrDGuBHcFAANvvXjbw10tS5vThw8uRJRo0aRWJiIvfffz8DBw7kueeeo6yszN1P1ahstkoCAwNc9729vairq3e1BQVZXW1Wqz82WyU2WxVBQQGuZVeu2G+6rtw6m73StV/h+2NRReB32q7u90pCQgLpkhDboJ/S0ou8+WYuaTNHN0ru5kivC8+hsfAsGg/PobHwHHZbJUGB3+7D68fie5/fNnuD5dfGoqjoBH97fxfjJwxp3A0Q+QkEBfpz+cq37yX19Q68vb/9inb67AW++O+X+fyDDBav+nbmjMPh5MPsNMYO78XmLbtdj/11h3so3PYyJWWXOHPuQuNtiHyPxY23xuP24sDs2bNJS0tjx44drFmzhk6dOjFixAheeOEFdz9VowoMDMBur3Lddzic+Ph4f6ft2xe13V5FUJCVwEB/12Ps9iqCg603XVduXaD1+2Ph+M5Y+Ddos9urCL7J/t265e+Ul1cw5qk/s3z5Rv72/idszNv+04ZvZvS68BwaC8+i8fAcGgvPYf3BsWj4+X11LAIajEVQsJX3Nm2npOQif/xjGhs3bmfV6vf4JL+wcTdGxE2u2KoICvR33ffyslBf7wCgV2IsrVuFEt1lAlHx/8njvR7iVw+2c6376OA/07P/bLKXTXIt++feI/yyy3j2HSxmyrg+jbch0oAFL7fdGpPbn81msxEREQFAbGwsX3zxBffffz8VFRXufqpGFRcXTX7+HgD27TtMVFRbV1tMTBSFhf9DdXUNV67YOXr0FFFRbYmL+3d27br6mPz8PXTseN9N15VbFxf3S/J3fQHAvn1fNdh/DzzQnsI9h1z799jRM7SPCr9hPynDfs+GvPm8nTWH0aP78vvHutI3qXujbENzodeF59BYeBaNh+fQWHiOuLhodt1kLB6IiWLPDfZvh7hodu26+sU/P7+QX3W8j6nPjyB3/XyysjLo27c7I4b3oWu3joZsk8j/19/3FNErMRaAX3e4h4OHT7naLl22UfV1DdXVtVRX13Kpwk5IcABTxvVhcFICAHb7165iwsfv/hehIVeLljZ7leswBDFC05w5YHG6+cQAzz33HFarlW7durFz506sVivx8fH89a9/ZdWqVbfQQ5E747jNtbMdFxUV43Q6yciYQH5+IeHhP6dHj05Xz7CbswWn08mYMQPo1asL58+Xk5r6GnZ7FS1bBvPKK1MICGhxw3U9kcPpmSdauna1gq++OnF1LF58lvxdXxDetjXdu/+a3NxtrM/9CIfDyZgx/XikV7zrsRvztnPs+Jnrrkpws+WexMtym9ERrmPG14Wn0lh4Fo2H5zDjWDjxzC8E165W8NU3Y/FixgR25e+hbfjP6f7NWOTmbMXh2r+/4fz5cqZ9ZyzmfzMW17zxxlruuL2lR1+twMK9RkeQb/iHDzY6wnWuXa3ggV+GY7HAU1OW0TsxlqMnSvjbtkLSJvfnkf94EIfDwee7v2JGxlpa3R7C8lfH0sLvNry9vZg5N5u/7ynisYc7MnVcH6pr6vjf0nLGPp+JvbLa6E28oaqTzftKCtX1u93Wl5/3Q27r64e4vThQU1PD+vXrOXLkCNHR0fTr148DBw7Qtm1bWrZseQs9eGZxwIw8tThgVp5YHBAREc/jqcUBs1JxwHN4YnHArJp7caDGscdtffl6/eqHV3ITH3d36Ovry5NPPtlgWWxsrLufRkRERERERMQDNe7hAO7SuGc4EBERERERERGP4/aZAyIiIiIiIiJm1dhXGXAXFQdERERERERE3EaHFYiIiIiIiIhIE6SZAyIiIiIiIiJuYmmiMwdUHBARERERERFxE4ulaRYHdFiBiIiIiIiIiMlp5oCIiIiIiIiI2zTN3+BVHBARERERERFxk6Z6zoGmWdIQEREREREREbfRzAERERERERERt2maMwdUHBARERERERFxE12tQERERERERESaJBUHRERERERERNzGy423m3M4HKSnp5OcnExKSgonTpxo0J6bm0tSUhIDBw5kx44dP5hahxWIiIiIiIiIuEljXa3g448/pqamhpycHPbt28fcuXNZsmQJAGVlZWRlZbFhwwaqq6sZMmQIXbp0wdfX96b9aeaAiIiIiIiISBNTWFhI165dAYiNjeXgwYOutv3799OhQwd8fX0JCgoiPDycw4cP/8v+PHDmQJTRAeQbXk3zPBoiIiKmpo9vkRurOpltdAQxDfd9p83JySEnJ8d1Pzk5meTkZABsNhuBgYGuNm9vb+rq6vDx8cFmsxEUFORqs1qt2Gy2f/lcHlgcEBEREREREZHvFgO+LzAwELvd7rrvcDjw8fG5YZvdbm9QLLgRHVYgIiIiIiIi0sTExcWRn58PwL59+4iK+nbGQkxMDIWFhVRXV3PlyhWOHj3aoP1GLE6n0/mTJhYRERERERERt3I4HMyaNYuioiKcTicZGRnk5+cTHh5Ojx49yM3NJScnB6fTyZgxY+jVq9e/7E/FARERERERERGT02EFIiIiIiIiIian4oCIiIiIiIiIyak4ICIiIiIiImJyupThT+DLL79k/vz5ZGVlGR3FtGpra5kxYwZnzpyhpqaGsWPH0qNHD6NjmVZ9fT1paWkcP34ci8XC7Nmzf/BsqfLTunDhAklJSaxcuZJ27doZHce0+vbt67o+8V133cWLL75ocCLzWrZsGdu3b6e2tpbBgwczYMAAoyOZVl5eHhs3bgSgurqaQ4cO8dlnnxEcHGxwMvOpra1l2rRpnDlzBi8vL+bMmaPPDIPU1NQwffp0Tp06RWBgIOnp6dx9991Gx5JmRsUBN1u+fDmbN2/G39/f6CimtnnzZkJDQ3n55Ze5dOkSf/jDH1QcMNCOHTsAWLduHQUFBSxYsIAlS5YYnMq8amtrSU9Pp0WLFkZHMbXq6mqcTqcKyR6goKCAvXv3kp2dTVVVFStXrjQ6kqklJSWRlJQEwOzZs+nXr58KAwbZtWsXdXV1rFu3js8++4zXXnuNN954w+hYppSbm0tAQAC5ubkcO3aMOXPmsGLFCqNjSTOjwwrcLDw8XG+aHqB3795MmDABAKfTibe3t8GJzK1nz57MmTMHgLNnz+qfPIPNmzePQYMG0apVK6OjmNrhw4epqqpi5MiRDBs2jH379hkdybQ+/fRToqKiGDduHE8//TS//e1vjY4kwIEDBzhy5AjJyclGRzGtiIgI6uvrcTgc2Gw2fHz0u6JRjhw5Qrdu3QCIjIzk6NGjBieS5kivcDfr1asXp0+fNjqG6VmtVgBsNhvjx49n4sSJxgYSfHx8SE1NZdu2bSxcuNDoOKaVl5dHWFgYXbt2JTMz0+g4ptaiRQtGjRrFgAEDKC4uZvTo0WzZskX/fBugvLycs2fPsnTpUk6fPs3YsWPZsmULFovF6GimtmzZMsaNG2d0DFMLCAjgzJkzPProo5SXl7N06VKjI5lWdHQ0O3bsoGfPnnz55ZeUlJRQX1+vH8DErTRzQJqtc+fOMWzYMPr06cPjjz9udBzh6i/WW7duZebMmVRWVhodx5Q2bNjA559/TkpKCocOHSI1NZWysjKjY5lSREQETzzxBBaLhYiICEJDQzUWBgkNDSUhIQFfX18iIyPx8/Pj4sWLRscytYqKCo4fP07nzp2NjmJqq1evJiEhga1bt/Lee+8xbdo0qqurjY5lSv369SMwMJAhQ4awbds27rvvPhUGxO1UHJBm6fz584wcOZKpU6fSv39/o+OY3qZNm1i2bBkA/v7+WCwWvLz09mOENWvW8M4775CVlUV0dDTz5s3jjjvuMDqWKb377rvMnTsXgJKSEmw2m8bCIB07duSTTz7B6XRSUlJCVVUVoaGhRscytd27dxMfH290DNMLDg4mKCgIgJCQEOrq6qivrzc4lTkdOHCA+Ph4srOz6d27N23atDE6kjRDmrsozdLSpUupqKhg8eLFLF68GLh6skidgM0YjzzyCNOnT+fJJ5+krq6OGTNmaCzE9Pr378/06dMZPHgwFouFjIwMHVJgkMTERHbv3k3//v1xOp2kp6frFzmDHT9+nLvuusvoGKY3fPhwZsyYwZAhQ6itrWXSpEkEBAQYHcuU2rZty+uvv87SpUsJCgriL3/5i9GRpBmyOJ1Op9EhRERERERERMQ4mtcrIiIiIiIiYnIqDoiIiIiIiIiYnIoDIiIiIiIiIian4oCIiIiIiIiIyak4ICIiIiIiImJyKg6IiIjpFBQUEB8fT0pKCikpKQwcOJCsrKwf1df8+fPJy8vj0KFDLFq06Kbrbdu2jZKSklvqMz8/n2nTpl23/Ny5c0yYMIGUlBQGDBjArFmzqKmp4fTp0wwcOPBH5RcREREBFQdERMSkOnfuTFZWFllZWbzzzjusWrWKioqKH91fdHQ0zz777E3b3377bWw224/uv76+nmeeeYaRI0eSlZXF+vXr8fHxYeHChT+6TxEREZFrfIwOICIiYjSbzYaXlxfe3t6kpKQQFhbG5cuXyczMZNasWZw4cQKHw8HEiRPp1KkTW7duZcmSJYSFhVFbW0tkZCQFBQWsW7eOBQsWsH79erKzs3E4HHTv3p2YmBgOHTpEamoqa9euJScnh/fffx+LxcLvfvc7hg0bxtGjR5kxYwb+/v74+/sTEhLSIGNhYSGtW7fmwQcfdC2bOnUqDoeDCxcuuJZt2bKFNWvWUFdXh8Vicc1mmDhxIk6nk+rqambPnk1kZCQTJkzAZrNRVVXFpEmTSEhIaJwdLiIiIh5HxQERETGlf/zjH6SkpGCxWLjtttuYOXMmVqsVgMcee4yHH36YtWvX0rJlSzIyMigvL2fo0KFs2rSJuXPnkpeXR2hoKE899VSDfi9cuMDy5cvZvHkzfn5+vPLKKzz00ENER0cza9YsTp48yQcffMDatWsBGDFiBAkJCbz00kuMHz+eLl26kJmZybFjxxr0W1paSps2bRos8/Pzu267iouLyczMxN/fn/T0dD799FOCg4MJDQ3lpZde4siRI1RWVnLy5EkuXbrEW2+9xYULFyguLnbj3hUREZGmRsUBERExpc6dO7NgwYIbtkVERABQVFREYWEh+/fvB6Curo6ysjJCQkJo2bIlAB06dGjw2FOnTtG+fXtatGgBwJQpUxq0FxUVcfbsWYYPHw7A5cuXOXHiBMXFxcTExAAQFxd3XXHgF7/4BR999FGDZeXl5ezdu5eoqCjXsp/97GekpqZitVo5duwYsbGxdOvWjeLiYp555hl8fHwYO3Ys7du3Jzk5mcmTJ1NXV0dKSsot7zsRERFpflQcEBER+R6LxQJAZGQkrVu35umnn+brr79myZIl3H777VRUVHDx4kXCwsI4cOAArVu3dj02PDycY8eOUVNTg6+vL+PHj+eFF17AYrHgdDqJjIzknnvu4a233sJisbB69Wruvfde2rVrx969e+nWrRsHDx68LlNsbCynT59m//79xMTE4HQ6WbRoEX5+fq7iwJUrV1i4cCE7d+4Ers5KcDqdFBQU0KpVK1auXMnevXt59dVXSUtLw263k5mZSWlpKYMGDSIxMfGn37kiIiLikVQcEBERuYlBgwaRlpbG0KFDsdlsDBkyBF9fX9LT0xk1ahQhISH4+DT8KA0LC2P06NEMHToUi8VCYmIid955Jx06dOD5559n5cqVxMfHM3jwYGpqaoiJieHOO+9k2rRppKamsmLFCsLCwq47ZMDLy4vXX3+dP/3pT1RVVVFZWUlsbCwTJ06ktLQUgMDAQOLi4khOTsbHx4fg4GBKS0vp3r07kydPJjs7m7q6OsaNG8fdd9/Nm2++yYcffojD4WD8+PGNtl9FRETE81icTqfT6BAiIiIiIiIiYhxdylBERERERETE5FQcEBERERERETE5FQdERERERERETE7FARERERERERGTU3FARERERERExORUHBARERERERExORUHREREREREREzu/wDJj8Z8bTrCoAAAAABJRU5ErkJggg==",
"text/plain": [
"<Figure size 1440x504 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"-------------------- Recall matrix (Row sum=1) --------------------\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 1440x504 with 2 Axes>"
]
},
"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 misclassified 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))"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.9"
}
},
"nbformat": 4,
"nbformat_minor": 4
}