diff --git "a/AdultNoteBook/Kernels/ExtraTrees/12-compare-all-the-classification-models.ipynb" "b/AdultNoteBook/Kernels/ExtraTrees/12-compare-all-the-classification-models.ipynb" new file mode 100644--- /dev/null +++ "b/AdultNoteBook/Kernels/ExtraTrees/12-compare-all-the-classification-models.ipynb" @@ -0,0 +1,4109 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 1. Introduction\n", + "A census is the procedure of systematically acquiring and recording information about the members of a given population.\n", + "The census is a special, wide-range activity, which takes place once a decade in the entire country. The purpose is to gather information about the general population, in order to present a full and reliable picture of the population in the country - its housing conditions and demographic, social and economic characteristics. The information collected includes data on age, gender, country of origin, marital status, housing conditions, marriage, education, employment, etc." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1.1 Data description\n", + "This data was extracted from the 1994 Census bureau database by Ronny Kohavi and Barry Becker (Data Mining and Visualization, Silicon Graphics). The prediction task is to determine whether a person makes over $50K a year." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1.2 Features Description\n", + "**1. Categorical Attributes**\n", + " * **workclass**: Private, Self-emp-not-inc, Self-emp-inc, Federal-gov, Local-gov, State-gov, Without-pay, Never-worked.\n", + " - Individual work category \n", + " * **education**: Bachelors, Some-college, 11th, HS-grad, Prof-school, Assoc-acdm, Assoc-voc, 9th, 7th-8th, 12th, Masters, 1st-4th, 10th, Doctorate, 5th-6th, Preschool.\n", + " - Individual's highest education degree \n", + " * **marital-status**: Married-civ-spouse, Divorced, Never-married, Separated, Widowed, Married-spouse-absent, Married-AF-spouse.\n", + " - Individual marital status \n", + " * **occupation**: Tech-support, Craft-repair, Other-service, Sales, Exec-managerial, Prof-specialty, Handlers-cleaners, Machine-op-inspct, Adm-clerical, Farming-fishing, Transport-moving, Priv-house-serv, Protective-serv, Armed-Forces.\n", + " - Individual's occupation \n", + " * **relationship**: Wife, Own-child, Husband, Not-in-family, Other-relative, Unmarried.\n", + " - Individual's relation in a family \n", + " * **race**: White, Asian-Pac-Islander, Amer-Indian-Eskimo, Other, Black.\n", + " - Race of Individual \n", + " * **sex**: Female, Male.\n", + " * **native-country**: United-States, Cambodia, England, Puerto-Rico, Canada, Germany, Outlying-US(Guam-USVI-etc), India, Japan, Greece, South, China, Cuba, Iran, Honduras, Philippines, Italy, Poland, Jamaica, Vietnam, Mexico, Portugal, Ireland, France, Dominican-Republic, Laos, Ecuador, Taiwan, Haiti, Columbia, Hungary, Guatemala, Nicaragua, Scotland, Thailand, Yugoslavia, El-Salvador, Trinadad&Tobago, Peru, Hong, Holand-Netherlands.\n", + " - Individual's native country \n", + " \n", + "**2. Continuous Attributes**\n", + " * **age**: continuous.\n", + " - Age of an individual \n", + " * **fnlwgt**: final weight, continuous. \n", + " * The weights on the CPS files are controlled to independent estimates of the civilian noninstitutional population of the US. These are prepared monthly for us by Population Division here at the Census Bureau.\n", + " * **capital-gain**: continuous.\n", + " * **capital-loss**: continuous.\n", + " * **hours-per-week**: continuous.\n", + " - Individual's working hour per week " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1.3 Objective of this project\n", + "The goal of this machine learning project is to predict whether a person makes over 50K a year or not given their demographic variation. This is a classification problem." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2. Import packages" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np \n", + "import pandas as pd \n", + "import seaborn as sns\n", + "import warnings\n", + "warnings.filterwarnings(\"ignore\")\n", + "import plotly.offline as py\n", + "import plotly.graph_objs as go\n", + "import plotly.tools as tls\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.model_selection import train_test_split,cross_val_score,GridSearchCV\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.tree import DecisionTreeClassifier\n", + "from sklearn.svm import SVC\n", + "from sklearn.neighbors import KNeighborsClassifier\n", + "from sklearn.ensemble import RandomForestClassifier,AdaBoostClassifier,BaggingClassifier,ExtraTreesClassifier\n", + "from sklearn.naive_bayes import GaussianNB\n", + "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n", + "from sklearn.ensemble import AdaBoostClassifier\n", + "from sklearn.ensemble import GradientBoostingClassifier\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "from sklearn.ensemble import ExtraTreesClassifier\n", + "from sklearn.metrics import accuracy_score,confusion_matrix,roc_auc_score\n", + "from sklearn import metrics\n", + "from datetime import datetime\n", + "from sklearn.feature_selection import RFE\n", + "from sklearn.model_selection import StratifiedKFold" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "from aif360.datasets import StandardDataset\n", + "from aif360.metrics import BinaryLabelDatasetMetric, ClassificationMetric\n", + "import matplotlib.patches as patches\n", + "from aif360.algorithms.preprocessing import Reweighing\n", + "#from packages import *\n", + "#from ml_fairness import *\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "\n", + "\n", + "from IPython.display import Markdown, display" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load Data" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
ageworkclassfnlwgteducationeducation.nummarital.statusoccupationrelationshipracesexcapital.gaincapital.losshours.per.weeknative.countryincome
090?77053HS-grad9Widowed?Not-in-familyWhiteFemale0435640United-States<=50K
182Private132870HS-grad9WidowedExec-managerialNot-in-familyWhiteFemale0435618United-States<=50K
266?186061Some-college10Widowed?UnmarriedBlackFemale0435640United-States<=50K
354Private1403597th-8th4DivorcedMachine-op-inspctUnmarriedWhiteFemale0390040United-States<=50K
441Private264663Some-college10SeparatedProf-specialtyOwn-childWhiteFemale0390040United-States<=50K
\n", + "
" + ], + "text/plain": [ + " age workclass fnlwgt education education.num marital.status \\\n", + "0 90 ? 77053 HS-grad 9 Widowed \n", + "1 82 Private 132870 HS-grad 9 Widowed \n", + "2 66 ? 186061 Some-college 10 Widowed \n", + "3 54 Private 140359 7th-8th 4 Divorced \n", + "4 41 Private 264663 Some-college 10 Separated \n", + "\n", + " occupation relationship race sex capital.gain \\\n", + "0 ? Not-in-family White Female 0 \n", + "1 Exec-managerial Not-in-family White Female 0 \n", + "2 ? Unmarried Black Female 0 \n", + "3 Machine-op-inspct Unmarried White Female 0 \n", + "4 Prof-specialty Own-child White Female 0 \n", + "\n", + " capital.loss hours.per.week native.country income \n", + "0 4356 40 United-States <=50K \n", + "1 4356 18 United-States <=50K \n", + "2 4356 40 United-States <=50K \n", + "3 3900 40 United-States <=50K \n", + "4 3900 40 United-States <=50K " + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = pd.read_csv(\"../../Data/adult.csv\")\n", + "data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(32561, 15)" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 3. Data Cleaning" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Fixing the common nan values\n", + "\n", + " Nan values were as ? in data. Hence we fix this with most frequent element(mode) in the entire dataset. It generalizes well, as we will see with the accuracy of our classifiers" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "attrib, counts = np.unique(data['workclass'], return_counts = True)\n", + "most_freq_attrib = attrib[np.argmax(counts, axis = 0)]\n", + "data['workclass'][data['workclass'] == '?'] = most_freq_attrib \n", + "\n", + "attrib, counts = np.unique(data['occupation'], return_counts = True)\n", + "most_freq_attrib = attrib[np.argmax(counts, axis = 0)]\n", + "data['occupation'][data['occupation'] == '?'] = most_freq_attrib \n", + "\n", + "attrib, counts = np.unique(data['native.country'], return_counts = True)\n", + "most_freq_attrib = attrib[np.argmax(counts, axis = 0)]\n", + "data['native.country'][data['native.country'] == '?'] = most_freq_attrib " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Lets look the data it again :" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
ageworkclassfnlwgteducationeducation.nummarital.statusoccupationrelationshipracesexcapital.gaincapital.losshours.per.weeknative.countryincome
090Private77053HS-grad9WidowedProf-specialtyNot-in-familyWhiteFemale0435640United-States<=50K
182Private132870HS-grad9WidowedExec-managerialNot-in-familyWhiteFemale0435618United-States<=50K
266Private186061Some-college10WidowedProf-specialtyUnmarriedBlackFemale0435640United-States<=50K
354Private1403597th-8th4DivorcedMachine-op-inspctUnmarriedWhiteFemale0390040United-States<=50K
441Private264663Some-college10SeparatedProf-specialtyOwn-childWhiteFemale0390040United-States<=50K
534Private216864HS-grad9DivorcedOther-serviceUnmarriedWhiteFemale0377045United-States<=50K
638Private15060110th6SeparatedAdm-clericalUnmarriedWhiteMale0377040United-States<=50K
774State-gov88638Doctorate16Never-marriedProf-specialtyOther-relativeWhiteFemale0368320United-States>50K
868Federal-gov422013HS-grad9DivorcedProf-specialtyNot-in-familyWhiteFemale0368340United-States<=50K
941Private70037Some-college10Never-marriedCraft-repairUnmarriedWhiteMale0300460United-States>50K
\n", + "
" + ], + "text/plain": [ + " age workclass fnlwgt education education.num marital.status \\\n", + "0 90 Private 77053 HS-grad 9 Widowed \n", + "1 82 Private 132870 HS-grad 9 Widowed \n", + "2 66 Private 186061 Some-college 10 Widowed \n", + "3 54 Private 140359 7th-8th 4 Divorced \n", + "4 41 Private 264663 Some-college 10 Separated \n", + "5 34 Private 216864 HS-grad 9 Divorced \n", + "6 38 Private 150601 10th 6 Separated \n", + "7 74 State-gov 88638 Doctorate 16 Never-married \n", + "8 68 Federal-gov 422013 HS-grad 9 Divorced \n", + "9 41 Private 70037 Some-college 10 Never-married \n", + "\n", + " occupation relationship race sex capital.gain \\\n", + "0 Prof-specialty Not-in-family White Female 0 \n", + "1 Exec-managerial Not-in-family White Female 0 \n", + "2 Prof-specialty Unmarried Black Female 0 \n", + "3 Machine-op-inspct Unmarried White Female 0 \n", + "4 Prof-specialty Own-child White Female 0 \n", + "5 Other-service Unmarried White Female 0 \n", + "6 Adm-clerical Unmarried White Male 0 \n", + "7 Prof-specialty Other-relative White Female 0 \n", + "8 Prof-specialty Not-in-family White Female 0 \n", + "9 Craft-repair Unmarried White Male 0 \n", + "\n", + " capital.loss hours.per.week native.country income \n", + "0 4356 40 United-States <=50K \n", + "1 4356 18 United-States <=50K \n", + "2 4356 40 United-States <=50K \n", + "3 3900 40 United-States <=50K \n", + "4 3900 40 United-States <=50K \n", + "5 3770 45 United-States <=50K \n", + "6 3770 40 United-States <=50K \n", + "7 3683 20 United-States >50K \n", + "8 3683 40 United-States <=50K \n", + "9 3004 60 United-States >50K " + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# for later use\n", + "data_num = data.copy()\n", + "data1 = data.copy()\n", + "data.head(10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 4. Feature Engineering" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Education\n", + "\n", + " 9th, 10th, 11th, 12th comes under HighSchool Grad but it has mentioned separately\n", + " Create Elementary object for 1st-4th, 5th-6th, 7th-8th\n", + "\n", + "Marital Status\n", + "\n", + " \n", + " Married-civ-spouse,Married-spouse-absent,Married-AF-spouse comes under category Married\n", + " Divorced, separated again comes under category separated.\n", + "\n", + "Workclass\n", + "\n", + " Self-emp-not-inc, Self-emp-inc comes under category self employed\n", + " Local-gov,State-gov,Federal-gov comes under category goverment emloyees\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "HS-grad 13556\n", + "Some-college 7291\n", + "Bachelors 5355\n", + "Masters 1723\n", + "Assoc-voc 1382\n", + "elementary_school 1147\n", + "Assoc-acdm 1067\n", + "Prof-school 576\n", + "Doctorate 413\n", + "Preschool 51\n", + "Name: education, dtype: int64" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "hs_grad = ['HS-grad','11th','10th','9th','12th']\n", + "elementary = ['1st-4th','5th-6th','7th-8th']\n", + "\n", + "# replace elements in list.\n", + "data1['education'].replace(to_replace = hs_grad,value = 'HS-grad',inplace = True)\n", + "data1['education'].replace(to_replace = elementary,value = 'elementary_school',inplace = True)\n", + "\n", + "data1['education'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Married 15417\n", + "Never-married 10683\n", + "Separated 5468\n", + "Widowed 993\n", + "Name: marital.status, dtype: int64" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "married= ['Married-spouse-absent','Married-civ-spouse','Married-AF-spouse']\n", + "separated = ['Separated','Divorced']\n", + "\n", + "#replace elements in list.\n", + "data1['marital.status'].replace(to_replace = married ,value = 'Married',inplace = True)\n", + "data1['marital.status'].replace(to_replace = separated,value = 'Separated',inplace = True)\n", + "\n", + "data1['marital.status'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Private 24532\n", + "Govt_employees 4351\n", + "Self_employed 3657\n", + "Without-pay 14\n", + "Never-worked 7\n", + "Name: workclass, dtype: int64" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "self_employed = ['Self-emp-not-inc','Self-emp-inc']\n", + "govt_employees = ['Local-gov','State-gov','Federal-gov']\n", + "\n", + "#replace elements in list.\n", + "data1['workclass'].replace(to_replace = self_employed ,value = 'Self_employed',inplace = True)\n", + "data1['workclass'].replace(to_replace = govt_employees,value = 'Govt_employees',inplace = True)\n", + "\n", + "data1['workclass'].value_counts()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Deleting the unuseful features and observations" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "del_cols = ['education.num']\n", + "data1.drop(labels = del_cols,axis = 1,inplace = True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Updating the columns" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "num_col_new = ['age','capital.gain', 'capital.loss',\n", + " 'hours.per.week','fnlwgt']\n", + "cat_col_new = ['workclass', 'education', 'marital.status', 'occupation','relationship',\n", + " 'race', 'sex', 'income']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 5. Pipeline" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
agecapital.gaincapital.losshours.per.weekfnlwgt
01.0000000.01.0000000.3979590.043987
10.8904110.01.0000000.1734690.081896
20.6712330.01.0000000.3979590.118021
30.5068490.00.8953170.3979590.086982
40.3287670.00.8953170.3979590.171404
\n", + "
" + ], + "text/plain": [ + " age capital.gain capital.loss hours.per.week fnlwgt\n", + "0 1.000000 0.0 1.000000 0.397959 0.043987\n", + "1 0.890411 0.0 1.000000 0.173469 0.081896\n", + "2 0.671233 0.0 1.000000 0.397959 0.118021\n", + "3 0.506849 0.0 0.895317 0.397959 0.086982\n", + "4 0.328767 0.0 0.895317 0.397959 0.171404" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.pipeline import Pipeline\n", + "from sklearn.base import TransformerMixin\n", + "from sklearn.preprocessing import MinMaxScaler,StandardScaler\n", + "\n", + "scaler = MinMaxScaler()\n", + "pd.DataFrame(scaler.fit_transform(data1[num_col_new]),columns = num_col_new).head(5)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "class DataFrameSelector(TransformerMixin):\n", + " def __init__(self,attribute_names):\n", + " self.attribute_names = attribute_names\n", + " \n", + " def fit(self,X,y = None):\n", + " return self\n", + " \n", + " def transform(self,X):\n", + " return X[self.attribute_names]\n", + " \n", + " \n", + "class num_trans(TransformerMixin):\n", + " def __init__(self):\n", + " pass\n", + " \n", + " def fit(self,X,y=None):\n", + " return self\n", + " \n", + " def transform(self,X):\n", + " df = pd.DataFrame(X)\n", + " df.columns = num_col_new \n", + " return df\n", + " \n", + " \n", + " \n", + "pipeline = Pipeline([('selector',DataFrameSelector(num_col_new)), \n", + " ('scaler',MinMaxScaler()),\n", + " ('transform',num_trans())])" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(32561, 5)" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "num_df = pipeline.fit_transform(data1)\n", + "num_df.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "# columns which I don't need after creating dummy variables dataframe\n", + "cols = ['workclass_Govt_employess','education_Some-college',\n", + " 'marital-status_Never-married','occupation_Other-service',\n", + " 'race_Black','sex_Male','income_>50K']" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(32561, 43)" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "class dummies(TransformerMixin):\n", + " def __init__(self,cols):\n", + " self.cols = cols\n", + " \n", + " def fit(self,X,y = None):\n", + " return self\n", + " \n", + " def transform(self,X):\n", + " df = pd.get_dummies(X)\n", + " df_new = df[df.columns.difference(cols)] \n", + "#difference returns the original columns, with the columns passed as argument removed.\n", + " return df_new\n", + "\n", + "pipeline_cat=Pipeline([('selector',DataFrameSelector(cat_col_new)),\n", + " ('dummies',dummies(cols))])\n", + "cat_df = pipeline_cat.fit_transform(data1)\n", + "cat_df.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "cat_df['id'] = pd.Series(range(cat_df.shape[0]))\n", + "num_df['id'] = pd.Series(range(num_df.shape[0]))" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of observations in final dataset: (32561, 49)\n" + ] + } + ], + "source": [ + "final_df = pd.merge(cat_df,num_df,how = 'inner', on = 'id')\n", + "print(f\"Number of observations in final dataset: {final_df.shape}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5.2 Split the dataset¶" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "y = final_df['income_<=50K']\n", + "final_df.drop(labels = ['id','income_<=50K','fnlwgt'],axis = 1,inplace = True)\n", + "X = final_df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5.3 Take a look to Income class distribution" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "sns.countplot(x=\"income\", data= data)\n", + "plt.show()\n", + "data[\"income\"].value_counts()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Implementating model on imbalanced data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "X_train1,X_test1,y_train1,y_test1 = train_test_split(X,y,test_size =0.15,random_state = 42)\n", + "#fitting the model\n", + "lr=LogisticRegression()\n", + "lr.fit(X_train1,y_train1)\n", + "# predict \n", + "y_pred4=lr.predict(X_test1)\n", + "print(\"Accuracy:\",metrics.accuracy_score(y_test1, y_pred4))\n", + "print(\"Precision:\",metrics.precision_score(y_test1, y_pred4))\n", + "print(\"Recall:\",metrics.recall_score(y_test1, y_pred4))\n", + "print(\"F1 score:\",metrics.f1_score(y_test1, y_pred4))\n", + "print(\"AUC :\",metrics.roc_auc_score(y_test1, y_pred4))" + ] + }, + { + "attachments": { + "resampling.png": { + "image/png": "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" + } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5.4 Resampling\n", + "\n", + "The main idea of sampling classes is to either increasing the samples of the minority class or decreasing the samples of the majority class. This is done in order to obtain a fair balance in the number of instances for both the classes.\n", + "\n", + "There can be two main types of sampling:\n", + "\n", + " You can add copies of instances from the minority class which is called over-sampling (or more formally sampling with replacement), or\n", + " You can delete instances from the majority class, which is called under-sampling.\n", + "\n", + "\n", + "A widely adopted technique for dealing with highly unbalanced datasets is called resampling. It consists of removing samples from the majority class (under-sampling) and / or adding more examples from the minority class (over-sampling).\n", + "\n", + "Despite the advantage of balancing classes, these techniques also have their weaknesses (there is no free lunch). The simplest implementation of over-sampling is to duplicate random records from the minority class, which can cause overfitting. In under-sampling, the simplest technique involves removing random records from the majority class, which can cause loss of information.\n", + "\n", + "**Under-sampling**\n", + "\n", + "Advantages of this approach:\n", + "\n", + " It can help improve the runtime of the model and solve the memory problems by reducing the number of training data samples when the training data set is enormous.\n", + "![resampling.png](attachment:resampling.png)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from imblearn.under_sampling import RandomUnderSampler\n", + "\n", + "rus = RandomUnderSampler()\n", + "X_rus, y_rus = rus.fit_sample(X, y)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "X_rus = pd.DataFrame(X_rus)\n", + "X_rus.columns = ['education_Assoc-acdm', 'education_Assoc-voc', 'education_Bachelors',\n", + " 'education_Doctorate', 'education_HS-grad', 'education_Masters',\n", + " 'education_Preschool', 'education_Prof-school',\n", + " 'education_elementary_school', 'gender_Female',\n", + " 'marital-status_Married', 'marital-status_Separated',\n", + " 'marital-status_Widowed', 'occupation_Adm-clerical',\n", + " 'occupation_Armed-Forces', 'occupation_Craft-repair',\n", + " 'occupation_Exec-managerial', 'occupation_Farming-fishing',\n", + " 'occupation_Handlers-cleaners', 'occupation_Machine-op-inspct',\n", + " 'occupation_Priv-house-serv', 'occupation_Prof-specialty',\n", + " 'occupation_Protective-serv', 'occupation_Sales',\n", + " 'occupation_Tech-support', 'occupation_Transport-moving',\n", + " 'race_Amer-Indian-Eskimo', 'race_Asian-Pac-Islander', 'race_Other',\n", + " 'race_White', 'relationship_Husband', 'relationship_Not-in-family',\n", + " 'relationship_Other-relative', 'relationship_Own-child',\n", + " 'relationship_Unmarried', 'relationship_Wife',\n", + " 'workclass_Govt_employees', 'workclass_Never-worked',\n", + " 'workclass_Private', 'workclass_Self_employed', 'workclass_Without-pay',\n", + " 'age', 'capital-gain', 'capital-loss', 'hours-per-week']\n", + "y_rus = pd.DataFrame(y_rus)\n", + "y_rus.columns = [\"income\"]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "sns.countplot(x=y_rus[\"income\"])\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "X_train,X_test,y_train,y_test = train_test_split(X_rus,y_rus,test_size =0.15,random_state = 42)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5.5 Baseline models" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Spot-Check Algorithms\n", + "def GetBasedModel():\n", + " basedModels = []\n", + " basedModels.append(('LR' , LogisticRegression()))\n", + " basedModels.append(('LDA' , LinearDiscriminantAnalysis()))\n", + " basedModels.append(('KNN' , KNeighborsClassifier()))\n", + " basedModels.append(('CART' , DecisionTreeClassifier()))\n", + " basedModels.append(('NB' , GaussianNB()))\n", + " basedModels.append(('AB' , AdaBoostClassifier()))\n", + " basedModels.append(('GBM' , GradientBoostingClassifier()))\n", + " basedModels.append(('RF' , RandomForestClassifier()))\n", + " basedModels.append(('ET' , ExtraTreesClassifier()))\n", + "\n", + " \n", + " return basedModels" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def BasedLine2(X_train, y_train,models):\n", + " # Test options and evaluation metric\n", + " num_folds = 3\n", + " scoring = 'accuracy'\n", + "\n", + " results = []\n", + " names = []\n", + " for name, model in models:\n", + " kfold = StratifiedKFold(n_splits=num_folds, random_state=10)\n", + " cv_results = cross_val_score(model, X_train, y_train, cv=kfold, scoring=scoring)\n", + " results.append(cv_results)\n", + " names.append(name)\n", + " msg = \"%s: %f (%f)\" % (name, cv_results.mean(), cv_results.std())\n", + " print(msg)\n", + " \n", + " return names,results" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "models = GetBasedModel()\n", + "names,results = BasedLine2(X_train, y_train,models)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5.6 Models Scores" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def ScoreDataFrame(names,results):\n", + " def floatingDecimals(f_val, dec=3):\n", + " prc = \"{:.\"+str(dec)+\"f}\" \n", + " \n", + " return float(prc.format(f_val))\n", + "\n", + " scores = []\n", + " for r in results:\n", + " scores.append(floatingDecimals(r.mean(),4))\n", + "\n", + " scoreDataFrame = pd.DataFrame({'Model':names, 'Score': scores})\n", + " return scoreDataFrame\n", + "basedLineScore = ScoreDataFrame(names,results)\n", + "basedLineScore.sort_values(by='Score', ascending=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 6. Tuning Machine Learning Models" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 6.1 Logistic Regression" + ] + }, + { + "attachments": { + "logistic.png": { + "image/png": "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" + }, + "sfd.png": { + "image/png": "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" + } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Logistic Regression is used when the dependent variable(target) is categorical.**\n", + "\n", + "**Model**\n", + "\n", + "Output = 0 or 1\n", + "\n", + "Hypothesis => Z = WX + B\n", + "\n", + "hΘ(x) = sigmoid (Z)\n", + "\n", + "**Sigmoid Function**\n", + "![logistic.png](attachment:logistic.png)\n", + "\n", + "If ‘Z’ goes to infinity, Y(predicted) will become 1 and if ‘Z’ goes to negative infinity, Y(predicted) will become 0.\n", + "\n", + "**Cost Function**\n", + "![sfd.png](attachment:sfd.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Hyperparameter Tuning \n", + "A hyperparameter is a parameter whose value is set before the learning process begins.\n", + "Tuning Strategies\n", + "\n", + "We will explore two different methods for optimizing hyperparameters:\n", + "\n", + " Grid Search\n", + " Random Search" + ] + }, + { + "attachments": { + "HPO1.png": { + "image/png": "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" + } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Grid Search vs Random search\n", + "\n", + "**Grid search** is a traditional way to perform hyperparameter optimization. It works by searching exhaustively through a specified subset of hyperparameters.\n", + "\n", + "**Random search** differs from grid search mainly in that it searches the specified subset of hyperparameters randomly instead of exhaustively. The major benefit being decreased processing time.\n", + "\n", + "* There is a tradeoff to decreased processing time, however. We aren’t guaranteed to find the optimal combination of hyperparameters.\n", + "\n", + "![HPO1.png](attachment:HPO1.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Grid Search" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%time\n", + "from sklearn.model_selection import GridSearchCV\n", + "lr = LogisticRegression(class_weight='balanced',random_state=42)\n", + "param_grid = { \n", + " 'C': [0.1,0.2,0.3,0.4],\n", + " 'penalty': ['l1', 'l2'],\n", + " 'class_weight':[{0: 1, 1: 1},{ 0:0.67, 1:0.33 },{ 0:0.75, 1:0.25 },{ 0:0.8, 1:0.2 }]}\n", + "CV_rfc = GridSearchCV(estimator=lr, param_grid=param_grid, cv= 5)\n", + "CV_rfc.fit(X_train, y_train)\n", + "print(CV_rfc.best_params_)" + ] + }, + { + "attachments": { + "ewr.png": { + "image/png": "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" + }, + "tre.png": { + "image/png": "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" + } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**C : Inverse of regularization strength**\n", + "\n", + "we use paramter C as our regularization parameter. Parameter C = 1/λ.\n", + "\n", + "Lambda (λ) controls the trade-off between allowing the model to increase it's complexity as much as it wants with trying to keep it simple. For example, if λ is very low or 0, the model will have enough power to increase it's complexity (overfit) by assigning big values to the weights for each parameter. If, in the other hand, we increase the value of λ, the model will tend to underfit, as the model will become too simple.\n", + "\n", + "* Parameter C will work the other way around. For small values of C, we increase the regularization strength which will create simple models which underfit the data. For big values of C, we low the power of regularization which imples the model is allowed to increase it's complexity, and therefore, overfit the data.\n", + "\n", + "**L2 Regularization or Ridge Regularization**\n", + "\n", + "* Ridge regression adds “squared magnitude” of coefficient as penalty term to the loss function. Here the highlighted part represents L2 regularization element.\n", + "![ewr.png](attachment:ewr.png)\n", + "\n", + "**L1 Regularization or Lasso**\n", + "\n", + "* Lasso Regression (Least Absolute Shrinkage and Selection Operator) adds “absolute value of magnitude” of coefficient as penalty term to the loss function.\n", + "![tre.png](attachment:tre.png)\n", + "\n", + "The key difference between these techniques is that Lasso shrinks the less important feature’s coefficient to zero thus, removing some feature altogether. So, this works well for feature selection in case we have a huge number of features.\n", + "\n", + "**Class weight**\n", + "\n", + "* If we have highly imbalanced classes and have no addressed it during preprocessing, we have the option of using the class_weight parameter to weight the classes to make certain we have a balanced mix of each class. Class weights will be given by n_samples / (n_classes * np.bincount(y))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%time\n", + "#fitting the model\n", + "lr1=LogisticRegression(C=0.4, random_state=4 ,penalty='l1', class_weight={0:1,1:1})\n", + "lr1.fit(X_train,y_train)\n", + "# predict \n", + "y_pred1=lr1.predict(X_test)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Evaluation of logistic regression(Grid Search)\n", + "**Confusion Matrix**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "cf_matrix = confusion_matrix(y_test, y_pred1)\n", + "sns.heatmap(pd.DataFrame(cf_matrix), annot=True, cmap=\"YlGnBu\" ,fmt='g')\n", + "plt.tight_layout()\n", + "plt.title('Confusion matrix', y=1.1)\n", + "plt.ylabel('Actual label')\n", + "plt.xlabel('Predicted label')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(\"Accuracy:\",metrics.accuracy_score(y_test, y_pred1))\n", + "print(\"Precision:\",metrics.precision_score(y_test, y_pred1))\n", + "print(\"Recall:\",metrics.recall_score(y_test, y_pred1))\n", + "print(\"F1 score:\",metrics.f1_score(y_test, y_pred1))\n", + "print(\"AUC :\",metrics.roc_auc_score(y_test, y_pred1))" + ] + }, + { + "attachments": { + "ret.png": { + "image/png": "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" + }, + "uyt.png": { + "image/png": "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" + }, + "ytu.png": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPYAAABPCAIAAABXta8iAAAJgklEQVR42uybe0hTbx/AH9Pf1CleZ/rajKZNzTQ0rWxqWUh5QSzQUuoPFQrTIiXUDMSiki20f3K9WWFBUJSVhlGSsl7tYtbUspuh4IWZsuYVdcvbecGHHg5n09+0rXR+P3+dfc/ZfHzO5zzn+9xMKIpCAGC4rIAqAEBxAADFAQAUBwBQHABAcQAAxQEAFAdAcQAAxQEAFAcAUBwAQHEAAMUBABQHQHEAAMUBABQHAFAcAEBxAADFAQAUBwBQHADFAWCJYwJVsAgRCoWTk5OznU1MTORyuTdu3Pj+/TuO2Nraurq6hoeHQ9VpgAIWHx4eHnPcMolEQlHUhg0bGHEej/f8+XOoPQaQqCxGPn36NPELT09PhJBcLh//xY4dO8iVONLS0hITE9Pe3n7w4EGoPcjFl0L6SANHWCzWP7+gX4kjHh4e5eXl8fHx3d3dT548gQoExZcSxsbGWl65adMmhFBbWxtUGihumIpjud3d3aHSlvyIyv9m8PX13bNnD0KotrZWIpEYGxvn5uYaYCO0Qqtm6P3791euXHFwcPD39wetdaB4eXn558+fyUc7OzsulxsdHf1nCn3o0KG2trbg4GCseHFx8e3btx0dHQ1S8blb8eTkZDab3dra+uzZM4RQUVGRg4MDaK0DxRMSElQqFSPo6en54sULDoej1xIPDg7iN3JAQACONDQ0IIT8/PyWYaJSUVHB4XB4PF5aWlpGRoabmxs4rQPFGxsbsd88Hi80NFSpVJaWlk5NTbW0tOTl5YnFYr2WWCqV4gP8Rh4cHPz27RtCaOPGjctQ8R8/foDEuu9u4lYTIZSVlVVSUnLnzp1r164xThE6OjoaGxsHBgY0/lR/f39dXZ1MJmPEJyYmXr9+TV4UfX199KQTH/j6+uLnDX9cnq04oF/FBQIBPli3bh0+WLVqFbksPz/fzs6Ox+P5+/vb2dnFx8dPT0/Ts3lvb297e3uBQODi4hIVFYXjAwMDhw8fZrFYQUFB5ubm6enpBw4c4HA4ZFKjvr4eIWRpaent7U0vjEbFe3p6ujXR09MDii8XFjAjSpJgEtm/fz+OlJaW4gjpekZERGzZsgUfp6am4rPFxcXqJRkZGaEoKiQkRGM5CwoK8HfXrl2LEAoODqb/aXt7e/VyDg0NzeaHQCBYKvPPeC5zcHCQEffx8YH1F9ow7zoiy4PMzMzS0tLi4+PNzc1xJCMjA1+TkZGBI2KxGEdwixsaGkpRFE6dEUKBgYGNjY0URVVXVycmJlIUlZ+fj09lZmaOjY09evTIyckJR/DCDJLwpKen41/GHaywsDD1on78+JE7C3l5eUvlDoWFhYHif1RxnCcwMDMzIzbL5XLcdiYkJAwNDclksqSkJHzZiRMnKIpKS0sjHUf6L09PT2Oht27dSoLp6elkIIWiqKqqKvzx1q1bFEWRHD0rK8uAb5JSqZxXHKBjsuBEPDIy0svLq6SkpL+/X6VSOTs743h1dfXU1BRC6M4M5IuxsbEFBQUIocePH+MhP8YkRU1NTW9vL0KIpD0IITz6zufzra2t1fuacyfi+uP06dP4gMvlkmUkGA6HY2lpSY9YWloyBlJNTEy4XK72f87MzGxeceC3Bg3JmN2lS5dcXV3DwsLwMuWbN2/iiZi6ujp8QVRU1MqVKx0cHDgcTkxMDJ5Y7u3t7ezspHdV6WOR+IDk7p2dnbjZJgOCjL4mKYzGEcOQkJCvX78yOh7qQzQL4MyZMzq/Ewt+Wuhd/D/54Bm44sbGxq6urgih3bt3czgchULx6tUrfAFuia2srHBrzYCs4mfcA/JFPFeKD86dO8cwGLfiuAknhbG2tlZfmDE+Pv7y5UuN/8LmzZt/s9by8vLwQXd3N2PvgkKhGBkZYXSjFQoFPTI5Oak+Tqoe6ejo+Ot+6PDBI6++Ra34z58/m5ub8YAGCa5fv76mpkahULS2tvL5/NWrVyOEhoeHGxoaSCpSW1vL5/P/MwOOVFZWZmdns9nsqqoqPFdqY2ODT4lEorNnzxYWFl6/fp2uuPq8JlZcY5bCYrHkcrnG/wLnPDpJVHSITCbT1dOC35OM5oMxGz04Az2iUqlIK6OPB29pKE4SA7xukwyK19TU4MyYz+eHhIQUFhbSRcQ0NTVhxQMCAqRS6ZcvXxwdHUmajhDavn07/lgyA0Jo586dEomETGQy5jVVKlVXV9cc85pLa7WGepKwZs2av14qdaEX/LQsjUSFSEZvOL28vPABTnxjYmKEQmFhYSF9bnnfvn0kuygoKMjMzHz37h0ZOjx+/DhCKCgo6OTJk0KhEMdTUlLc3d0lEgmfz7e1tcV7YfBLMDAwECHU0tKyaOc1//rmy7dv3+JuDOl+YDuLiooQQg8ePPjw4YOzs3NKSgr9W83Nzffv3/fx8YmLi5vtMVsMD55+p36UM2gT7OzslEqlra2tExMT6r/T1dX15s2b9vZ2Rnx4eFgqlSoUCvyxr69vjsKMjY1p/HHYfJmTk6P+d/GgLUVRu3btYkxcYO7du0efZVum4+KANkzQUN98ia/BitM3X+IVEDpUvKysbJwGOUsU53K5U1NThq047PrRT/6nt82XTU1NRkZGFRUVWq5v+YcG4+zRo0dlMplIJDLsewGK6x19bL5UKpW/X7Dc3FwnJyehUDg0NASKA79RxSu0rWTtN1/O0ZfVHlNT01OnTg0PD5MuvmG+UUHBRdKK62Pz5dOnTxUKBUmWIiMj7e3t6RccO3ZMLBaLRKLU1FQXFxcjIyNQHNCx4lpuvgwMDFQqlWNjYzhFycnJOX/+vIWFhYmJycOHD8l6TAb/nYF8LCsrw4ss6GRnZycnJ4tEIjyeCIoDOlZcy82XNjY2VlZWo6OjIyMj3d3dbDbb1NSUxWKx2WwLC4vZfvzq1avbtm0jxcBL7RkkJSVdvnxZLBYfOXIEFAd0r7iWmy8rKyvxQV1dnUAgyM/P37t3779+y83Nbe4RetKQx8XFXbx4MSIiArqbgB5HVObRMpnosm2KjY0NDQ0tKSlZPLPuoPhyV1znZGdnI4Tu3r0LigPzr+JZBg3pq0e0R0/bIMLDw6Ojo2dbfgy5OPDnWnE/Pz9tno0FPD8XLlyor6+Xy+VkM65hYLSwtgSYFyqVSmPrO1tcr38Ur/sfHR0lW08MG1AcMPREEaoAAMUBABQHAFAcAEBxAADFAQAUBwBQHADFAQAUBwBQHABAcQAAxQEAFAcAUBwAxQEAFAcAUBwAFhn/DwAA//9MLoCKxatjPQAAAABJRU5ErkJggg==" + } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Precision:**\n", + "Precision is about being precise, i.e., how accurate your model is. In other words, you can say, when a model makes a prediction, how often it is correct.\n", + "Precision can be thought of as a measure of a classifier's exactness.\n", + "![ret.png](attachment:ret.png)\n", + "\n", + "**Recall:**\n", + "Out of all the positive classes, how much we predicted correctly. It should be high as possible.\n", + "Recall can be thought of as a measure of a classifier's completeness.\n", + "![ytu.png](attachment:ytu.png)\n", + "\n", + "**F1 score**\n", + "It is difficult to compare two models with low precision and high recall or vice versa. So to make them comparable, we use F-Score.\n", + "\n", + "F-score helps to measure Recall and Precision at the same time. It uses Harmonic Mean in place of Arithmetic Mean by punishing the extreme values more.\n", + "![uyt.png](attachment:uyt.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### ROC Curve" + ] + }, + { + "attachments": { + "gfh.png": { + "image/png": "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" + } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "An ROC curve (receiver operating characteristic curve) is a graph showing the performance of a classification model at all classification thresholds. This curve plots two parameters:\n", + "\n", + " True Positive Rate\n", + " False Positive Rate\n", + "![gfh.png](attachment:gfh.png)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fpr, tpr, _ = metrics.roc_curve(y_test, y_pred1)\n", + "auc = metrics.roc_auc_score(y_test, y_pred1)\n", + "plt.plot(fpr,tpr,label=\"data 1, auc=\"+str(auc))\n", + "plt.legend(loc=4)\n", + "plt.plot([0, 1], [0, 1],'r--')\n", + "plt.xlabel('False Positive Rate')\n", + "plt.ylabel('True Positive Rate')\n", + "plt.title('Receiver operating characteristic')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Random search" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%time\n", + "from sklearn.model_selection import RandomizedSearchCV\n", + "lr = LogisticRegression(class_weight='balanced',random_state=42)\n", + "param_grid = { \n", + " 'C': [0.1,0.2,0.3,0.4],\n", + " 'penalty': ['l1', 'l2'],\n", + " 'class_weight':[{0: 1, 1: 1},{ 0:0.67, 1:0.33 },{ 0:0.75, 1:0.25 },{ 0:0.8, 1:0.2 }]}\n", + "CV_rfc = RandomizedSearchCV(estimator=lr, param_distributions=param_grid, cv= 5,random_state=1)\n", + "CV_rfc.fit(X_train, y_train)\n", + "print(CV_rfc.best_params_)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%time\n", + "#fitting the model\n", + "lr2=LogisticRegression(C=0.3, random_state=4 ,penalty='l2', class_weight={0:1,1:1})\n", + "lr2.fit(X_train,y_train)\n", + "# predict \n", + "y_pred2=lr2.predict(X_test)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Evaluation of logistic regression(Random Search)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Confusion Matrix" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "cf_matrix = confusion_matrix(y_test, y_pred2)\n", + "sns.heatmap(pd.DataFrame(cf_matrix), annot=True, cmap=\"YlGnBu\" ,fmt='g')\n", + "plt.tight_layout()\n", + "plt.title('Confusion matrix', y=1.1)\n", + "plt.ylabel('Actual label')\n", + "plt.xlabel('Predicted label')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(\"Accuracy:\",metrics.accuracy_score(y_test, y_pred2))\n", + "print(\"Precision:\",metrics.precision_score(y_test, y_pred2))\n", + "print(\"Recall:\",metrics.recall_score(y_test, y_pred2))\n", + "print(\"F1 score:\",metrics.f1_score(y_test, y_pred2))\n", + "print(\"AUC :\",metrics.roc_auc_score(y_test, y_pred2))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fpr, tpr, _ = metrics.roc_curve(y_test, y_pred2)\n", + "auc = metrics.roc_auc_score(y_test, y_pred2)\n", + "plt.plot(fpr,tpr,label=\"data 1, auc=\"+str(auc))\n", + "plt.legend(loc=4)\n", + "plt.plot([0, 1], [0, 1],'r--')\n", + "plt.xlabel('False Positive Rate')\n", + "plt.ylabel('True Positive Rate')\n", + "plt.title('Receiver operating characteristic')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Recursive Feature Elimination" + ] + }, + { + "attachments": { + "vnb.png": { + "image/png": "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" + } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* The Recursive Feature Elimination (or RFE) works by recursively removing attributes and building a model on those attributes that remain. \n", + "* It uses the model accuracy to identify which attributes (and combination of attributes) contribute the most to predicting the target attribute.\n", + "* As the name suggests, this method eliminates worst performing features on a particular model one after the other until the best subset of features are known.\n", + "![vnb.png](attachment:vnb.png)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%time\n", + "from sklearn.feature_selection import RFE\n", + "\n", + "# feature extraction\n", + "lr = LogisticRegression()\n", + "rfe = RFE(lr, 15)\n", + "lr3 = rfe.fit(X_train, y_train)\n", + "\n", + "print(\"Num Features: \", lr3.n_features_)\n", + "print(\"Selected Features: \", lr3.support_)\n", + "print(\"Feature Ranking: \", lr3.ranking_)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Features sorted by their rank:**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "feature = list(X_train.columns.values) \n", + "print(sorted(zip(map(lambda x: round(x, 4), lr3.ranking_), feature)))\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "X_train_f = X_train[['age','capital-gain','capital-loss','education_Bachelors','education_Doctorate','education_Masters',\n", + " 'education_Preschool','education_Prof-school','education_elementary_school',\n", + " 'hours-per-week','occupation_Priv-house-serv','relationship_Not-in-family','relationship_Other-relative'\n", + " ,'relationship_Own-child','relationship_Unmarried']]\n", + "\n", + "X_test_f = X_test[['age','capital-gain','capital-loss','education_Bachelors','education_Doctorate','education_Masters',\n", + " 'education_Preschool','education_Prof-school','education_elementary_school',\n", + " 'hours-per-week','occupation_Priv-house-serv','relationship_Not-in-family','relationship_Other-relative'\n", + " ,'relationship_Own-child','relationship_Unmarried']]\n", + "\n", + "lr4=LogisticRegression(C=0.4, random_state=4 ,penalty='l2', class_weight={0:1,1:1})\n", + "%time lr4.fit(X_train_f,y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# predict \n", + "y_pred4=lr4.predict(X_test_f)\n", + "cf_matrix = confusion_matrix(y_test, y_pred4)\n", + "sns.heatmap(pd.DataFrame(cf_matrix), annot=True, cmap=\"YlGnBu\" ,fmt='g')\n", + "plt.tight_layout()\n", + "plt.title('Confusion matrix', y=1.1)\n", + "plt.ylabel('Actual label')\n", + "plt.xlabel('Predicted label')\n", + "plt.show()\n", + "\n", + "print(\"Accuracy:\",metrics.accuracy_score(y_test, y_pred4))\n", + "print(\"Precision:\",metrics.precision_score(y_test, y_pred4))\n", + "print(\"Recall:\",metrics.recall_score(y_test, y_pred4))\n", + "print(\"F1 score:\",metrics.f1_score(y_test, y_pred4))\n", + "print(\"AUC :\",metrics.roc_auc_score(y_test, y_pred4))\n", + "\n", + "fpr, tpr, _ = metrics.roc_curve(y_test, y_pred4)\n", + "auc = metrics.roc_auc_score(y_test, y_pred4)\n", + "plt.plot(fpr,tpr,label=\"data 1, auc=\"+str(auc))\n", + "plt.legend(loc=4)\n", + "plt.plot([0, 1], [0, 1],'r--')\n", + "plt.xlabel('False Positive Rate')\n", + "plt.ylabel('True Positive Rate')\n", + "plt.title('Receiver operating characteristic')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## PCA analysis\n", + "Principal Component Analysis (or PCA) uses linear algebra to transform the dataset into a compressed form.\n", + "\n", + "Generally this is called a data reduction technique. A property of PCA is that you can choose the number of dimensions or principal component in the transformed result.\n", + "\n", + "In this case we will use it to analyse the feature importanace" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.preprocessing import StandardScaler\n", + "X_std = StandardScaler().fit_transform(X_train)\n", + "\n", + "\n", + "from sklearn.decomposition import PCA as sklearnPCA\n", + "\n", + "sklearn_pca = sklearnPCA(n_components=39)\n", + "Y_sklearn = sklearn_pca.fit_transform(X_std)\n", + "\n", + "cum_sum = sklearn_pca.explained_variance_ratio_.cumsum()\n", + "\n", + "sklearn_pca.explained_variance_ratio_[:10].sum()\n", + "\n", + "cum_sum = cum_sum*100\n", + "\n", + "fig, ax = plt.subplots(figsize=(8,8))\n", + "plt.bar(range(39), cum_sum, label='Cumulative _Sum_of_Explained _Varaince', color = 'b',alpha=0.5)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#Cumulative explained variance\n", + "from sklearn.decomposition import PCA\n", + "pca = PCA(39)\n", + "pca_full = pca.fit(X)\n", + "\n", + "plt.plot(np.cumsum(pca_full.explained_variance_ratio_))\n", + "plt.xlabel('# of components')\n", + "plt.ylabel('Cumulative explained variance')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%time\n", + "# 26 Principal Components seems good \n", + "pca = PCA(n_components=26)\n", + "X_transformed = pca.fit_transform(X_train)\n", + "\n", + "X_train_pca, X_test_pca, y_train_pca, y_test_pca = train_test_split( \n", + " X_transformed, y_train, test_size=0.2, random_state=13)\n", + "\n", + "lr5=LogisticRegression(C=0.4, random_state=4 ,penalty='l1', class_weight={0:1,1:1})\n", + "lr5.fit(X_train_pca, y_train_pca)\n", + "\n", + "# predict \n", + "y_pred =lr5.predict(X_test_pca)\n", + "\n", + "cf_matrix = confusion_matrix(y_test_pca, y_pred)\n", + "sns.heatmap(pd.DataFrame(cf_matrix), annot=True, cmap=\"YlGnBu\" ,fmt='g')\n", + "plt.tight_layout()\n", + "plt.title('Confusion matrix', y=1.1)\n", + "plt.ylabel('Actual label')\n", + "plt.xlabel('Predicted label')\n", + "plt.show()\n", + "\n", + "print(\"Accuracy:\",metrics.accuracy_score(y_test_pca, y_pred))\n", + "print(\"Precision:\",metrics.precision_score(y_test_pca, y_pred))\n", + "print(\"Recall:\",metrics.recall_score(y_test_pca, y_pred))\n", + "print(\"F1 score:\",metrics.f1_score(y_test_pca, y_pred))\n", + "print(\"AUC :\",metrics.roc_auc_score(y_test_pca, y_pred))\n", + "\n", + "fpr, tpr, _ = metrics.roc_curve(y_test_pca, y_pred)\n", + "auc = metrics.roc_auc_score(y_test_pca, y_pred)\n", + "plt.plot(fpr,tpr,label=\"data 1, auc=\"+str(auc))\n", + "plt.legend(loc=4)\n", + "plt.plot([0, 1], [0, 1],'r--')\n", + "plt.xlabel('False Positive Rate')\n", + "plt.ylabel('True Positive Rate')\n", + "plt.title('Receiver operating characteristic')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Feature Importance" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "data1 = pd.DataFrame(X,y)\n", + "\n", + "clf = ExtraTreesClassifier(n_estimators=250,\n", + " random_state=2)\n", + "\n", + "clf.fit(X_train, y_train)\n", + "\n", + "\n", + "# Plot feature importance\n", + "feature_importance = clf.feature_importances_\n", + "# make importances relative to max importance\n", + "feature_importance = 100.0 * (feature_importance / feature_importance.max())\n", + "sorted_idx = np.argsort(feature_importance)\n", + "pos = np.arange(sorted_idx.shape[0]) + .5\n", + "plt.figure(figsize=(10,10))\n", + "plt.barh(pos, feature_importance[sorted_idx], align='center')\n", + "plt.yticks(pos, data1.columns[sorted_idx])\n", + "plt.xlabel('Relative Importance')\n", + "plt.title('Variable Importance')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "X_train_ef = X_train[['race_Asian-Pac-Islander','education_Assoc-voc', 'workclass_Govt_employees','occupation_Craft-repair',\n", + " 'education_Doctorate','workclass_Self_employed', 'occupation_Adm-clerical','occupation_Sales', 'education_Prof-school',\n", + " 'workclass_Private','race_White', 'occupation_Prof-specialty', 'relationship_Unmarried','marital-status_Separated', 'education_elementary_school',\n", + " 'education_Masters', 'relationship_Wife', 'education_Bachelors','capital-loss', 'occupation_Exec-managerial', 'gender_Female',\n", + " 'relationship_Not-in-family', 'education_HS-grad','relationship_Own-child', 'capital-gain', 'relationship_Husband',\n", + " 'marital-status_Married', 'hours-per-week', 'age']]\n", + " \n", + "X_test_ef = X_test[['race_Asian-Pac-Islander','education_Assoc-voc', 'workclass_Govt_employees',\n", + " 'occupation_Craft-repair', 'education_Doctorate','workclass_Self_employed', 'occupation_Adm-clerical',\n", + " 'occupation_Sales', 'education_Prof-school', 'workclass_Private','race_White', 'occupation_Prof-specialty', 'relationship_Unmarried',\n", + " 'marital-status_Separated', 'education_elementary_school','education_Masters', 'relationship_Wife', 'education_Bachelors',\n", + " 'capital-loss', 'occupation_Exec-managerial', 'gender_Female','relationship_Not-in-family', 'education_HS-grad',\n", + " 'relationship_Own-child', 'capital-gain', 'relationship_Husband','marital-status_Married', 'hours-per-week', 'age']]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%time\n", + "lr5=LogisticRegression(C=0.4, random_state=4 ,penalty='l1', class_weight={0:1,1:1})\n", + "lr5.fit(X_train_ef, y_train)\n", + "\n", + "# predict \n", + "y_pred =lr5.predict(X_test_ef)\n", + "\n", + "cf_matrix = confusion_matrix(y_test, y_pred)\n", + "sns.heatmap(pd.DataFrame(cf_matrix), annot=True, cmap=\"YlGnBu\" ,fmt='g')\n", + "plt.tight_layout()\n", + "plt.title('Confusion matrix', y=1.1)\n", + "plt.ylabel('Actual label')\n", + "plt.xlabel('Predicted label')\n", + "plt.show()\n", + "\n", + "print(\"Accuracy:\",metrics.accuracy_score(y_test, y_pred))\n", + "print(\"Precision:\",metrics.precision_score(y_test, y_pred))\n", + "print(\"Recall:\",metrics.recall_score(y_test, y_pred))\n", + "print(\"F1 score:\",metrics.f1_score(y_test, y_pred))\n", + "print(\"AUC :\",metrics.roc_auc_score(y_test, y_pred))\n", + "\n", + "fpr, tpr, _ = metrics.roc_curve(y_test, y_pred)\n", + "auc = metrics.roc_auc_score(y_test, y_pred)\n", + "plt.plot(fpr,tpr,label=\"data 1, auc=\"+str(auc))\n", + "plt.legend(loc=4)\n", + "plt.plot([0, 1], [0, 1],'r--')\n", + "plt.xlabel('False Positive Rate')\n", + "plt.ylabel('True Positive Rate')\n", + "plt.title('Receiver operating characteristic')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Grid Search" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "class GridSearch(object):\n", + " \n", + " def __init__(self,X_train,y_train,model,hyperparameters):\n", + " \n", + " self.X_train = X_train\n", + " self.y_train = y_train\n", + " self.model = model\n", + " self.hyperparameters = hyperparameters\n", + " \n", + " def GridSearch(self):\n", + " # Create randomized search 10-fold cross validation and 100 iterations\n", + " cv = 10\n", + " clf = GridSearchCV(self.model,\n", + " self.hyperparameters,\n", + " cv=cv,\n", + " verbose=0,\n", + " n_jobs=-1\n", + " )\n", + " # Fit randomized search\n", + " best_model = clf.fit(self.X_train, self.y_train)\n", + " message = (best_model.best_score_, best_model.best_params_)\n", + " print(\"Best: %f using %s\" % (message))\n", + "\n", + " return best_model,best_model.best_params_\n", + " \n", + " def Best_Model_Predict(self,X_test):\n", + " \n", + " best_model,_ = self.GridSearch()\n", + " pred = best_model.predict(X_test)\n", + " return pred" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Random Search" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from scipy.stats import uniform\n", + "\n", + "class RandomSearch(object):\n", + " \n", + " def __init__(self,X_train,y_train,model,hyperparameters):\n", + " \n", + " self.X_train = X_train\n", + " self.y_train = y_train\n", + " self.model = model\n", + " self.hyperparameters = hyperparameters\n", + " \n", + " def RandomSearch(self):\n", + " # Create randomized search 10-fold cross validation and 100 iterations\n", + " cv = 10\n", + " clf = RandomizedSearchCV(self.model,\n", + " self.hyperparameters,\n", + " random_state=1,\n", + " n_iter=100,\n", + " cv=cv,\n", + " verbose=0,\n", + " n_jobs=-1\n", + " )\n", + " # Fit randomized search\n", + " best_model = clf.fit(self.X_train, self.y_train)\n", + " message = (best_model.best_score_, best_model.best_params_)\n", + " print(\"Best: %f using %s\" % (message))\n", + "\n", + " return best_model,best_model.best_params_\n", + " \n", + " def Best_Model_Predict(self,X_test):\n", + " \n", + " best_model,_ = self.RandomSearch()\n", + " pred = best_model.predict(X_test)\n", + " return pred" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 6.2 KNN" + ] + }, + { + "attachments": { + "gh.jpeg": { + "image/jpeg": "/9j/2wCEAAMCAgMCAgMDAwMEAwMEBQgFBQQEBQoHBwYIDAoMDAsKCwsNDhIQDQ4RDgsLEBYQERMUFRUVDA8XGBYUGBIUFRQBAwQEBQQFCQUFCRQNCw0UFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFP/AABEIANMCvAMBIgACEQEDEQH/xAGiAAABBQEBAQEBAQAAAAAAAAAAAQIDBAUGBwgJCgsQAAIBAwMCBAMFBQQEAAABfQECAwAEEQUSITFBBhNRYQcicRQygZGhCCNCscEVUtHwJDNicoIJChYXGBkaJSYnKCkqNDU2Nzg5OkNERUZHSElKU1RVVldYWVpjZGVmZ2hpanN0dXZ3eHl6g4SFhoeIiYqSk5SVlpeYmZqio6Slpqeoqaqys7S1tre4ubrCw8TFxsfIycrS09TV1tfY2drh4uPk5ebn6Onq8fLz9PX29/j5+gEAAwEBAQEBAQEBAQAAAAAAAAECAwQFBgcICQoLEQACAQIEBAMEBwUEBAABAncAAQIDEQQFITEGEkFRB2FxEyIygQgUQpGhscEJIzNS8BVictEKFiQ04SXxFxgZGiYnKCkqNTY3ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1dnd4eXqCg4SFhoeIiYqSk5SVlpeYmZqio6Slpqeoqaqys7S1tre4ubrCw8TFxsfIycrS09TV1tfY2dri4+Tl5ufo6ery8/T19vf4+fr/2gAMAwEAAhEDEQA/AP1TooooAKKKKACiiigDI8U+LNF8EaBd634h1ay0PRrRQ1xqGoXCQQQgsFBd2ICgkgcnqQO9c3oHxy+HXirSf7U0bx14c1XTvtkWn/arLVIJYxcyECOHcrkeYxIAXqc8CuI/bS8B678UP2Y/G/hjwzYyaprl/Farb2kTKryFLqGRsFyFBCox5PavnXwP8DfiTbeIPE2vWfh7xNptnqvirwtdL/wkt3YNq8lvZzSG7aT7KfKEKo67V5YlnznigD74VwGwSB2607ep7ivz5l+Cvxo8NeD9PurCz8S6jq+oeG9UttegfXJHdrn+0omtvLBnXDrbmUqI2TI+UsM8dD8FPgZ8Tp/GPh218bN4otPCem6trd7HFPqxjbyWNm9gkoincsm5bgiIu4XBUsR1APuMsN2AecZrmJ/ij4PtfBzeK5vFGkReGFJVtZe+iW0Uh/LOZd2wfOCvXrx1r5z/AGHbXxRql/4y1PXtdudc0bQbj/hEdBupLx5kvbe2mlke7YkkPI3mxxl+TmAjNeIeGv2aPi5B8N/EnhLxLoNxr+maxBatploLpUg0yJNaaW6spYhJsZpYWWcTYyQWU4wBQB98az8VvBvh2/ay1TxVo2nXiy20Bgur6KNxJcFhbrhmBzIVbYP4trYzg1r6d4n0nWLrVbWw1K0vbnSpxbX0ME6u1rKUVwkgB+RtrK2GxwwPQ18Ba3+x9491nxZfeGNd0yTxP4Hh8Q+HLGy1CS4UTXGg2xvt3mtu3GSBLpIi3VvKVxkk1678Bvgf8R9I+Hvxy8O+L73ytf8AEGoTW+meIQ4Jv4Rp8NrDdsFOVdggL553hiBQB7jpX7Qnwx1lddaw+IPhm7TQk8zUzBq0DCyTdtLSEPhV3fLuPGeM5r0JJkZQQ4IPQ5/GvgXX/hP478a/AaXwTbfA238N6voXg2TQ21a6ntfOuLrMKiGwMch8yJ/LaRnl2fwgDdnL9V+G3xzvvjL451O20rxNp9heJr1nE1lq8YgliktgunvbyS3DKr7lBAMESxMcFjkmgD753j1FcrqvxT8HaJd31tqHirRrG5sbu3sbqG5v4o2guLgBreJwWBV5AQVU8sDwDXwRJ8Jfjg/wgs7M6R4zRbTXZ7iGJLrNzdRmzjRDc2h1Heied5mDFdHaw8zy8ELXbRfAL4leL/iHpep+LtDumtrXxZ4V1TYNU86OKO30gxXrBwwLBbjAJIyTyBySQD7Y8O+JtK8XaPbavomp2mr6Vc5MF7YzLLDKAxU7XUkNggg4PUGrGqavY6Np899qF7b2NlApea5upVjjjUdSzMQAPc18FfD/AOEfxg0K2+Htt4n8OeKtalh0m2t7QaZ4jSwi0e+Gqzy3M14wdtym1eHGEm3BCm1ScjnNb+BHxo8bQ/Fu31bw/wCIDp+u+GtTjTTZtSDW8+ppqkUtqIWN05YGAPtcrEpGU24HzAH6H6L4p0fxDPqUOmapZ6hLptybO8S2nWQ204RXMbgE7W2srbTg4YHoRWpvU45HNfBmofC/4nQyX11eeGvF2peCZPFt5ey+G9I1ZbbVJrOTSLSKyYSeenyRXKTb035DNuwwFV4PgR8chpen6jq17rt34t0iw8KLbzWusMsDTx3bnUQyLIFlYQMquzDDkEjJNAH214t8feHPBC276/run6Mk6TSxG+uUiDpDEZZWG4j5URSzHooGTgVuWt1Dd2sM8MqSwyosiSIwKspGQQe4INfIn7VvhHWfEvxX1SwtLV73/hIfhX4h0Lw/BkbX1QyRSSRKSQqvJCFIJIysT8/LXNeHPhR8f9I8Aw6FE2qT+J7HVriS+1yLxUulQ6lbvFF9naNGtroKsahojEBGEKZ+beTQB9p6l4m0rSblbW81G1truSGW4jtpJlEskcYBkZUzlgoIyQOMipNE1qw8RaVZarpd7BqOm3sKXFtd2sokimiYZV0YHBUgggjgg18z+G/g54g0f40/Cfx1rWmMNR0zw1qVl4k1eXUxeFJCIfJV5dkYk+VX+ZYk6c+/Y/sWafdWPwB0V5opbfT7u+1G+0q3lQo0OnzXk0lquDyB5TIQPQigD3eiiigAooooAKKKKACiiigArgvGvx2+HPw31yLR/Ffjzw54a1WaITx2Wr6pBbTNGSQHCu4O0kEA9Mg13h6V8LftefBHxp43+OPiHVtG8M+LdW0vVvBEWhW9z4ZudPiT7WJ7lilz9qOREBKmSgzyfSgD7R0rxbout6nf6dp+r2V9f2Cwvd2tvcLJJbrKpeIuoOVDqCyk/eAJGRWvvX1FfDWmfAb4pz+M7Ww1W3vrPw5Je+FYr7+xNRa3t/s9vpFxFfrEVkDiIXBiBHBPB5xmuF1T4TftBJoXhSxt9O8UHUdDRGsb+PVhK21NZeRkuGa6QKRZ+WB+7naQErlMZoA/R/eoGcjHrWbrPiTStAksE1LUrXT3v7hbS0W5mWM3EzAlY0yRuYgEhRzwa+F/iV4Z8e/Bz4Ky+Nl1bVYPiI/jq5Nrpt/rEkkeo2txc3NvbW0abigAhnjmCAZBhHfp6j8dvgT4+1n4K/C3wl4J1yaLxToN/a+Z4kvj9oe3eO1mja6k3nLEu3XkgtnBxigD6JvfH/hvTtZvtIute0y31WxsTqd1Yy3SLNDagkGdkJyIwQRvIxweazo/jF4GmstWu4/GOhPa6TbQ3moTDUIitnBMm+KWU7vkR1O5WbAYcgkV8Q6v+zd8UPE9nd+L9M8M3WgeN9L8CWOmWVnqmoC4+33G68i1OxlkMjF1mSRJEdm4fyTkbWrI8U/sf/E2fwN4x1TQdHNp4tudH0bQWspZ026rpv8AZltBeQfewHhniEiMehjcDIegD798a/Evwn8N9Ej1jxT4j0rw9pUjLHHd6leRwRu7ZwqliASQCcD0qfQfiH4W8UXFlFo/iLS9VlvbMajbLZXkcpnti2wTJtJ3Ju+XcOM8da8Q+OHgjxHZfEH4S+PrLwjceO9O8KwX1rqGgWDQfala5hjSO5hSZ0R2jKMpG4HEpI6EV434t+EXxOGmX2s6B8No/Clxe+Gfsn9heG74IbHzdbS4lRGSWLdN9nDO6RuisxZQw7gH3rvX+8Koa5r+m+G9Iu9V1W/ttN0uziae5vLqURQwRqMs7uxAVQOpJwK/OvWPg/8AHyLwb4XtodP8X3V7pt3qq2dm2oR+Ttku1e1FwY71Xh2xDAlElwqDKFCeTu+K/hT8b7/xx8VXs9L8STw6zpetJBJe6kht43lhC2aWsiXOyRNwG1Gt43i5LO2c0AfbQ+LHgtvESaCPFuinW5HhjXThfxfaGeWNpIlEe7dl0R3UYyVUkZArq1dSoO4Y9c18O2H7PnxB0X4u3/iHS9NvLT+0PGWmahNeyXgcNbpoLwySOpc5Vbt8EdeOOBmub074afFyH4UDS18FeNodUmu9Hg8Q3V74oF3LfSI05vbu0t1uI/k3GHrPEriTJQeV8wB90+IfHnhvwotw2ta9pmkrbwC6mN7eRw+VCXEYkbcRhN5C7jxuIHWtxJUOOQM8CvzYu/2evjJJoOnX95oevX/jW4+Hr6C+of2iheC7i1kSokredj5rUfK2WB9c5au81X4XfGe5+Jfiae3svEq6k+oeIbmTX/7ZX+zL3SZ7KZNMs7eDzfknSVoB/q1KGNmLtmgD7raRSBhgen69K53SviL4Y1rXJNGsdf0271ZGuEayhuUaXdA6pONoOf3bOit/dLAHBIrwj9lz4Y+Ovhz4l19Ndl1caXfeF9AZX1XUmvVGrrDML8rvdivzeVnGFJHHrXzfovwg+J2veG/hvqnhOzvJJPCtnrGneKtOhnFvd6hcf21FLdWayl1aOSUCSUPkBgOTh6AP0kutQt9OtJrq6njtraCNpZZpnCJGijLMxPAAAJJPSuV0H4weB/Ft3ZW2h+MdC1qW9lkgtl07UYbjzpY4xLJGuxjlljIcqOQpB6V8xeKPhX8bvEOt+MI7qz1bUfBup2+qwRaUPHUMZmtp4JkhiWBtOxE53ooDSsIz3bbzx2heCPGOg23gDRZNG1bQhcfEvSrzw1pmtNbPqVpZWtk32+SZrf5TEEEyDcSSHUdHAoA/QBTkClpqDC+1OoAKKKKACiiigAooooASub8d/Enwp8MdKi1Pxd4j0rwzp0sot0u9XvI7aJ5CCQgZyAWIVjjrwfSukPavm/8AbY+HniPx74d8AN4e0fWdYbSPFEOpXkXh+W2jvo4Ft50LxG5Ij3bpFGG9fagD2Pw/8VvBniuPSZND8W6LrCat5v8AZ72OoRTLd+V/rfLKsd+z+LGdvfFdWJF5+Ycdfavz08N/s/8Axd0DwBrMWmaNrGmXF/8A8Jpf2kU17brqQe9sY0sWumgbyhcNMrYMfCgL05q74q+C3xe0XTbvR9M07xNqWg391oFxctDrLz3MUi2EwvXjD3URdTceSHUyKg+9tbGKAPv/AHr6iqmr6xYaFpV7qepXkFhp1lA9zc3dzII4oYkUs7uxICqFBJJ4ABNfDXg/4HfEa98IeJLr4ia/4g8NXVh8O7C0stTuNeeOC21RI7sXE8nlybSy/uMu2Qckj1rsvhRpvjj4ufsdfEvxLrcNzceK/iPo+pXGn6KbjK20LWJtbSCPf8q+Z5fm54GZsnvQB9O33xA8M6Zb6FPeeINMtINenjttKknu40W/lkXdGkJJ/eMy8gLkkdKrQ/FPwfc61b6RF4p0eTVbi7uLCKxF9GZpLiAK08KpuyZIw6FlAyoYZAyK+HNO/ZY+JHjDwt4G8H+P9OvNRbTPE8F7d+IIrwKkNk2iyxQmGPefJa1nKR4UfMyLJzuOKnhn9lH4t+I9Z8MXvim0TTfE2neI/FWtL4gtpVEMF5LHYmwugobdsllt3yuPulwQMigD75sfHfh3U/DEviS113TbjQIhMZNVjukNsgidkl3SA7RsZGVueCpBwQawfDnx3+HXi/S7bUtF8c+H9UsbnUI9KhuLTU4ZEku5M+XbqQ3MjYJVPvHsDXzNovwA+IWrf8E/4fAuoaFBbeOV1K41a48OzXKLBeAazJem1MgJUJJGQAScZK5xzVnxh4I8ZfFHWvDviPTPg6vghLPxr4evZBdzW0epXUFp9oMstwkTtGIYzIqphi5BY7RgCgD7MEi4+8PXrQZFxkMPz/Ovzc0L4S/HuTQfF4v9O8aadDqenadK9rbX8c0gvY9QZrryg96WlQwlQf3kDSoDtCcA6N98L/j6dI+Fk8eieI31nTofJaybWCbMH+02dXuZBdiW3f7Ns5b7Uu3EeMgmgD7e1D40+AdKVGvfGugWgea7t18/U4UJktQTdKMt1hCsZB/AFO7GK66yv7e/tIbq3njntp0WSKaNtySKRkMp6EEEEEV8Da5+zd8RtW1/xJrkmj6g+rLpvjuLSLk34DJJd3YbTFUCQAb42cgEYA+9t2jG7pvw8+Lem+I9be58MeKNY8TbHl0rWU8Tiy0mGw/stI4rR4w7FpvtIdSuwHLCTzcCgD7W1HWbDSmt1vb62tDcyiCEXEqp5shBIRcn5mODwOeDUXh7xNpXivSYtT0XUbXVtOlZ0ju7KZZYmKOUcBlJBKsrKfQgjtX58eDfgP8AFKYaddeJPDnim/0jTfGPhzWrGyvL4C5ihS3njvpEj+1ylcS+UxVpSWA3bQWKie9+DfxutfB9tYXmkeKdT1iXSLy10GbRtaS2j0bVW1O6kW5vB5yB42he3YHDjahTaCcUAfolvGOorm9a+Inhfw5rdro+qa/punapdeX5Fnc3SRyyGR/LjAUkEl3O1R3PAya+X/Bvwe+J2jfFfw54ovptYnuh441L+1ZW1NzaNo72riMrAXKCNpghUbQwPoBXL/G/4Y+N/HXxr+JOk6BI1p4ivtW8H6xoepSxrMtnp1t5iTzBGIDCG5MkjIDz5qHvQB91CRcBgQwPQ/XpXNf8LU8Gf2jrGn/8JboX2/RojPqdr/aUPm2MY6vMu7Maj1bAr5FsPhv+0E3hDwjbada6zpN7YacLTV1fx2lsbi9SVxJcKHsrkusvDqxccMF2LtrF1D4KeI/GXijxe2r/AAj17T7a203VbDRLLRrixtobxbpo2vLie+eRpJLm4MfyI0IjQdeTuUA+zvD/AMV/BnjG7toNA8V6Nrkt0JfIGm38VwJDEEMoXYxyVEsZIHQOueorrYyGQEdD39a+FfAPhDxppHjH4W6fqNhqOl58d6hq2kWOrfZzqVtoSaYYpftbQfIS0rKACSQGjBJwMfdMX+rXOMnk49aAH0UUUAFFFFABRRRQAUUUUAMdwnYn6Uu4YrkPHbXc2q+FrC21C401L/UHinmtNgkZFtZ5AoLKcDcinp2p58FXC4z4t10f9tLf/wCM1Lkoq7A6ogHuMjoT2pCg7HFcr/whlx/0Nuu/9/Lf/wCNUn/CGTn/AJm3Xf8Av7b/APxqp9pBaXHZnVeWMYLA/UUhhU5w2M9/6/WuW/4Qy4/6G3XP+/lv/wDGqP8AhDJ/+ht13/v5b/8Axqj2kO6CzOmgs4bSHyoI44IwSQkahVBPXgfU/nUhjBGMgduBXKnwbP8A9Ddrn/f23/8AjVQjwwxneAeMtaMyLuMYmt8gev8Aqql1qSteS18w5X2Ov8oA8ED680uwZ+9+dcr/AMIbP/0N2uf9/Lf/AONUDwbcf9Ddrn/f23/+NVXtIdwszqvLHQtke9HljPXr1HNcr/whlx/0N2u/9/Lf/wCNUf8ACGXH/Q3a5/38t/8A41R7SHdBZnVbPRgPoKTyxzgjJOepFcv/AMIZP/0N2uf9/bf/AONUf8IZP/0N2u/9/Lf/AONU+ePcLM6op6ED680hjHqOmBmuV/4Q24/6G3XP+/tv/wDGqX/hDJ/+ht13/v5b/wDxql7SHcLM6kIOcnJ9aUop78e/Oa5M+Dbgf8zdrn/fyD/41UUfhkyKSvjPWGwSp2z25AYcEf6rsapSUnaLuFmdY9qkrKzbWKHKlgDg+v8AT8TUgQDuCa5IeEpD/wAzhrf/AH+t/wD41QfCcg/5nHWv+/1v/wDGquzCzOskgSVWV1V1YbSrDII7g0sUYiCKNoVRjgY9K5H/AIRV/wDocda/7/W//wAapf8AhE5D/wAzjrX/AH+t/wD41RZhZnYNIFGevtQsgbGK5FPB00pITxdrjEdhLB/8aqLwNLeweJvFWl3OpXWpwWM1t5D3ewyKHgDMMqq55yenekLY7aiiigAooooAazbR0zQHBz2+tEgyprzrw/puoeJtT8TSyeItVs47XVpbWGC0MKxxxrHGQADGT/Ee9AHo24etMIBzyK5T/hC7j/obdd/7+W//AMao/wCEMuP+ht13/v5b/wDxqs/aQ7jszqfLx0I/Gl2DPUY6cVyn/CGz/wDQ3a5/38t//jVH/CGz/wDQ3a5/38t//jVHtIdwszpLiwhutnnRxy+W29N67tjf3hnoalEYGRnj+lcv/wAIZP8A9Dbrv/fy3/8AjVRy+EpYULv4v1xFUEkmW34H/fqk6tOKu5KwWZ1pTIAYgj6YoKA4JIJFcjD4UkuIlli8Ya28bDKsstuQR/36qT/hDJ/+hu1z/v5b/wDxqhVackpKSsws+x1bIGBBIweaTZz94HPXNcqPBtx/0Nuuf9/bf/41S/8ACGXH/Q267/38t/8A41T9pDugszqtgBzkUmwHqa5b/hC7j/obdd/7+W//AMapP+ENn/6G3XP+/tv/APGqPaw/mQWZ1PlD+9296XyxnJIYehrlv+EMuP8Aobdd/wC/tv8A/GqP+EMn/wChu13/AL+W/wD8aodSC6oLM6jyh685zmnbR6iuV/4Qyf8A6G7Xf+/lv/8AGqQeDLj/AKG3XP8Av5b/APxqj2kO4WZ1TRgjr+pqNLSKIuUVELnc5VcFmxjJ9eAB+Armj4Mn/wChu13/AL+W/wD8apj+D7hRx4t1w/8AbW36/wDfmj2kF9pBZnWBBg81G1qjSK5CMyghWI5GcdD26VyS+GmeSSNfGesNLHgMgntsgkZGR5WRkc/Sn/8ACKOP+Zx1ofWa3/8AjVaLXYLM7IMAOtBcAZ61xw8JyH/mcNb/AO/1v/8AGqyvGfh+/wBE8F69qlp4t1triz0+4uYSzwFd6Rsy5/dcjIFOzCzR6Kkm84AP1p9QWvMSHsVBwPpU9IQUUUUAFITgUtct8Sb+7sPCF1LZXD2lwZreJZ4wCyB7iNGIyCM4Y9qAOmEik4zQ2G71yn/CE3EYx/wluvAdOZYD/OKm/wDCGXHbxbrp/wC2tv8A/GqhzjHdjszqjGOzY/Wk8oHjIwex6Vy3/CGXH/Q267/38t//AI1S/wDCGT/9Ddrv/fy3/wDjVL2sO6CzOlmtI7mN45gksbgqyMMgj0IPWnRwJEiooVFUABVGAPwrmP8AhDJ/+ht13/v5b/8Axqg+DJwM/wDCXa5/39t//jVCqQfULM6nZjo3PbNDJuI5GPTJrj38MPFNHE/jHWlkkzsUzW+W+n7qpR4NnOf+Ku1zj/prb/8AxqpVelJ2UkPlZ1iqAMZGO/pSeWOec/X09K5b/hDLj/obdd/7+W//AMao/wCEMuP+hu13/v5b/wDxqq9pDuKzOp2DAGR+ApWUMOTn9K5X/hDJ/wDobtd/7+W//wAao/4Qyf8A6G7Xf+/tv/8AGqfPHuFmdUUHZvwpAmT8xB+mRXK/8IZcd/F2uf8Af23/APjVH/CGT4/5G7XPwlt//jVT7WHcLM6pow3f9aPLHqCffmuV/wCEMuP+ht13/v5b/wDxql/4Qyf/AKG3Xf8Av5b/APxqn7SHcLM6nYOmRjtnJ/rzTPs0fnCXC+YBt345x6Z69h+Vcz/whk//AENuu/8Afy3/APjVIPBlx/0N2uf9/Lf/AONUe0h3QWZ1Xlg9x6jHal2LXKnwZOP+Zu13/v5b/wDxqgeDJz/zNuu/9/bf/wCNUvaw/mQWZ0z2yM4fahkClQxHOPTPYcCp1OFAJya49/B9wnP/AAluu4Ayf3lv/wDGqjh8LvcSPGnjHWnkRijKs1uSp64P7rg4wce9XGSl8LuFmdmJASQAeKfXB6faXuifEKw099av9SsrnS7q5aK98ptsiTW6qylUXHEjj8a7yqEFFFFABRRRQAUUUUAcl4wH/FV+Bv8AsKT/APpDc1sapxKg9jWP4v8A+Rr8Df8AYUn/APSG5rX1b/Wp9DXm5hpQdjal8RTPINNAxz0pT1H0o7V8a5O97no2tsOA4o2g0Cj1rWTaQCbQKYLaJZmlCKJWADOByQOnNSY5owKn4kuZf1924DCtCrhhxTj1pB1qOZ31Yx1NfJIp/emgcnNW20tdxKwhyEJAyR0HTJqGyknmtkaeIQTHqgbcB+NWMc0oGKnkd+bmfoFyPbjmlUZ5pCeDQDgHHXHFJS1dmN7GD8QfF1t4B8E6z4hvI5ZrbTrZp2jgALseihckDJJA5OK82+G3ha58J+C9MsdSKza28YutWuBJ5n2i/kVXuZt3fdIWPQcY4HQT/FS6PjD4keG/Bifv9M09B4g1mPy8gAMy2COx4KyTRTkqMnEGfkHJ6Jgob+Hsc+vH/wCuvv8AIsNy05VZbyJWupJGo/uCmzJwflH6U9QpH8FEm3Z0SvpXbog0KZQZPy9zVlIxj7neq2RvP3epqzGRgY25+tW7dirHS+CFC6nPhdv7k/8AoS1N4T/5KB44/wCu1l/6TCofBH/ITm+7/qT0/wB5am8J/wDI/wDjf/rrZf8ApMteNif4rPNrfGdrRRRXKYhRRRQAjdK4n4en/SfGH/YeuP8A0VFXav8AdNcV8PP+Pjxh/wBh64/9FxVMvhY1ua56mkxR3NLXwcr8zPVWxFg0qDnNKKVOgrJNt2LFpkiK6lWAKkYKkcGpKTANVLVa6iSSGQQxwRLHGgRFGAq8ACnEGlwM0MOBTWkbLb8vQQwLz0p+KZ3p46ZqE31GwIqpdSzxSwCGESoz4kJbGxcdR61bByaMCpqJyVoyYk7AATzwM01+M04YFNfvWrei1ATHSn4PamMQikkgADJJOBXk03xc1rxj5o8C6ZZ3Wkbnij8R6tMVtJyoIZoIkG+ZQwADgqjc7WYYNdGFo1cRLlpolux6H4n8V6Z4P0efVNXu47SzhxlmPLEkKqquCWZmO0AZJPABJrzXUvHHizx/JHa+HLS88FaPuLSa/qltE11Mo4CwWj5KZbOXmAwq/KhLAito3w00zTdUj1vVZ5vFPiZfm/trWZPPlibBz9nUkrbRks2EiCjB5LYBHVKQSSdufX8K+xwmSU4WqYj3mG6M7wv4VsPC2mtbWUcrtM5nubu6lMtxdSsPmllkPLsc9eB2AUAKNKVAG+5j8BUy4OMbf8ioJCMjO3/Ir6eMYxVkgSQscYB+53rpfFX/ACRnxD/2Bbv/ANEvXNRgZ/h6103ir/kjPiL/ALAt3/6JeuPF2UUzCulY7W0/1Ef+4v8AKp6gtP8AUR/7i/yqevLOEKKKKACuQ+KX/Im3H/X3Z/8ApVFXX1yHxS/5Ey4/6+7P/wBKoqAN/VP9SP8Af/oazFHXitLU/wDVL/v/ANDWeOpr4/Mm/rG/RHoUfhDFNYYp3emP1Nee27G6QYp+KZ6VJUxba3BoryW0bzJI0atImdrEZIz6VKgxTmpF71KjGMrxVhsXFNIpwFIeDWkmydBQvFIRgE06k7Uot23GVoZbiSWdZoViRWxEwbO9cdT6VOtIfvGlzisqd4aSk35v8th2HU0jJp3OKAMjmt5yFYMcUEZ60UUuZpANI7HpSAHovU0kxIRiuC3UAnGe+K53xR4rtvAXhW41bVJHlEeFWFfnluJnOI4IwBlmZmVVUDJ4PPNRBuU1FdRN2Of+Jnj65tZLjwp4XAu/GN5akxvn91pcbqQLqc4OAOSiAFpGAUADcyr8LfCtn4Sn0uxtQ80gYvc3kvzTXk5jJknkYkks7ZY5JxnAOAK534feHbzTLTUtW1sJL4l166a/1J95cRk58m2U8DZBGRCDgZ2FsZZs914Z/wCQ9YncpJdicc/8s2r9LwWBjhMPK/xNamcl7rbN69/5Krov/YFvv/Si0rsK4++/5Krov/YFvv8A0otK7Cuc80KKKKACiiigAooooA5Lxf8A8jX4G/7Ck/8A6Q3NbGq/6xf901j+L/8Aka/A3/YUn/8ASG5rY1X/AFyfQ15uYfwGbUfiKJ6j6UoNIe30oBwa+Le6PSHClpjSLHyzBQeMk4pwOa1k1tf+vQA70tFFCAYxAPJxSBu4rO1bTp9QMax3b20QOXEYG5vQA9hUFxNcaTp8MYmFxcvKsYedTyMjg49q8mrjJ4ec5VKMlCKve8XfbRJSv/wz8r6KKaVnqzRhu5JbueI28kax4xKw+WTPp9KtbgOvFVLO/jup54VJMsBAkyDgEjPB71Zlj8xGXJGQRkHkcdR713x/htxnz6vW1u+nyIejHZGetIWxUFjafYraOHzHl2DG+Q5Y/WrBGRW1OTcU2rP9SXZMo28ss9zO5liktwQqbOWBHDZP1qe5uYbO1mnuJktreJC8k0rbVRQMliewABNJb2kVohSKNY0LFiqjAyeteX/FzxP/AMJJY6v8P9DhW/1rUbN7a9uHOLbSoJkIMkzDkuVJKQr8zHGSqncM8Dh6lWUaf2m9fQcn0RhfDN5vEs/iHxzeRyCbxFd7rEztmSHTIhi1hIHCjLTy4XP+v5JPTtmxu6n8qj0+yGnadbWaszrbxJAHfqwVAoP6H161latPria7pSafaWk2ku0v2+4mmKzQgIfLMa4wxLYBz0FfslCj7OCgug4xujdT6n8qJcbTyfypEIz16k4/nSy8p+dW9CCrzuPzdz2qwn3Rz+lVzwxPue/tViPp+lU9CjpPBP8AyE5uc/uT2/2lqXwn/wAj/wCN/wDrrZf+ky1D4I/5CU3/AFx/9mWpvCf/ACP/AI3/AOutl/6TLXjYn+Kzza3xna0UUVymIUUUUANf7privh5/x8eMP+w9cf8AouKu1f7privh7/x8eMP+w9cf+i4qmXwsa3NbuaWkPU0tfBy3bPVWxGDTl6U0daVJFY7QwLDqueRWCajLV2uWx9JS0h6irbtuIKQkEdaPfHvWMukXYvWuX1GQkv8AcUAJs/u1y161Wko+zpOV/NKy76tDik92aN3M1tBJIkLzOqkiNBksfQVLbyvJErOrRkrkq3Ue1ZI1jybu/MhH2S0A3kKxcMe2MYPB7VrxyCaJXXowBBIrLC141qrdOV91bSzs7XWr7aDkrKw/gHP4UtVLuz+0z28hllTyWLbUOA+ex9qtDgV180nJprYhrQgvZmigfZJHHMRiMy9N3YGnRF/JTzCpkwNxXpnvSXFrFdhRKiyKrBhkZwR0qVhQoT9pKT2toO6MfxgM+Etc/wCwfcf+iXrzD4OH/iz/AICJYj/inNM7f9OcNet6vFbXGlX0N6xSykglSdg2CIyhDnPbjNee+EbbTLTwpolvosnnaNBYW0VjKsm8NbLCgiYNjkbAmCevXvX2fDqd5pGbV3Y0pDkZz+YqJDycn9Pasvw/c65cR3x12zs7OQXciWq2czSCSAY2O2eQ55yO2K1E9fb+lfbtNLUu1lZlhen3j27e1QS/e6nv2qwnT8v5VBL94/jUIEJH16nr6V03ir/kjPiL/sC3f/ol65mPr+NdN4q/5Ix4i/7At3/6JeuLGfAvU56/wna2n+oj/wBxf5VPUFp/qI/9xf5VPXlnCwooooEFch8Uv+RMuP8Ar7s//SqKuvrkPil/yJlx/wBfdn/6VRU1uBv6kcQj/f8A6Gs4d60NTGYR/v8A9DWetfG5l/vD9F+p6FH4RwIFNPU0oFMdwgyTgV50rKN2bodnJo9KjV1YAqcg9COalpQacbpjB2GOtNBApW6VR1O2nu7Vo7e4+zOxAL4ycd8f41lWlKnBzhFyfRK136XaX3jSvoXs8VUlu5VvkgW2kZWRnM4HyqR0B96pIsmhabdyS3cl0qDcnnDcV9Accnmp7LU9xghuNounh81ljB2++D+NccsU5uFKt+7nvZ2e7sk2rq71trrZ9iuW12tUaIOBRkAUmDVe1svsrXDebLL5shk/eNnbn+Ee1eheXNFJaPcgsNx3xVMyySXyCKSIwoh81ergn7vHbvV1ulQQ2sUM0sqIFklxvYDlsdM/ma56sZy5eXa+vy2/EE1rcnBzyaM8470HpRkA11y1fqJBnJFNlkWFC7sI1UZ3McAe59q81+LHxZufBzR6T4Z04eJfF0him/seMMwjt2kw0srLxECM7S5AJBxnBrnb3QNe+I1y934yu2sdEZwYfB1o0bW5VSdrXk4XdO5OHMSkRLwMSEF29jCZZWxcY1Ir3X+gtbG34j+L82pX82i+B7P+19Shm8q51W7hkGlWO3BkDTLjzpBnaIoiWDH5igBNZOgeBLaPXV8Sa7JFr/i3nGrTwjNovI8q2U58mIAtgLyc5YscGt60sbXS7KCzsreK0tIECRW8KBEjX0AHA7+n49rNvwxr7bCZZRwabiryfUEiw33eTg+gFXfDX/IwWP8Avv8A+i2qm3Srnhn/AJD9j/vt/wCi2r0anwS9CZ/Czevf+Sq6J/2BL3/0otK7CuPvv+Sq6J/2Bb7/ANKLSuwrwTywooooAKKKKACiiigDkvF//I1+Bv8AsKT/APpDc1r6t/rk+hrI8X/8jX4G/wCwpP8A+kNzWxqnM6fQ15uYfwGbUviKR6fhSdaU9h7UDrXxj3R6RS1hbNrPN8E+zowYl+gOeP1q4pz7+hokjWUFXVXX0YZFOx1/OlyctRztuF+gU0yAOF4yRnGead3NZVzFHDrMN1PNGAV8mFGXDbj1wazrVZUoxaV7tLfb8H/TQJXbLMjXJugoCNb7SXJY7g3bA9KhkuDqVvNHY3KLMj7N5GQrAjIx9Ksx2KQzzygtvmILZY9hjj0qWOFEY7VVdzbjgYya5XRqVFKL2k3fW7tbSz6bddddyroa0qWsRd2AVRlm6D3pun6hHqdstxEGEbZxvGD+VR3WmJeyqZHZotjI0BOUcHuRUwRLWMLGoVAOFHAAA/wrobrKu+ZKNNX63k9fi8la+mrb6ifK15ku7bUFrfLdS3CIjqYHKNvXGcenrWd9v0vWJLSNrhPPLCWGGV/LkJHfYfm9e2P6bHGMjoefSnB1KiU4yTg1f1/ytvsxabdSnZ3Ut1bh5Ld7aQ5zGxyR+VeO+GpUj+KXxKtGwlw1/a3KREYLQtZwqrqO6lkcAjjMbA8jFew6msz2siWsiQztwryLlQM815R8TtK1Tw9480vxVb6VqHiDTr7SzoWqw6XbGa4tghlmhnVFO5laSRo2AB27lbjBr18jxKwuN5Kre1ru1r/Ld/JIJW0k9jqjJvHygtnJGMHP09aiZsZx7+nGOefSuD8IaJ4yu9JttL8I6DD4N8O2qusN54wWWe6m5xtFmjq6DJY7pJOgHy85Glr/AIZ8XfDyxGu/23d+N7OGMvqunSW8MMyIBl57TYF3FMcwuSWQHDbgA36DLNcJCq6cZ31sK6TdjYvNW1G08RaXYQ6RNcabcpK91qXmKq2pVcqpXqSxwOPWtaX7nv71W0rUrXWdNs9QsZVuLG7gS5tpkztkikVXRhkA4KlSMgH1o1TUbXS7Qz3lxDawghTJLIEUE4AGT3JPAr19JWsv+CPR2UUIWyTx3NWI24PHeqxHznHTNWYzx9eaT1A6TwQf+JnN/wBcf/Zlqbwn/wAj/wCN/wDrrZf+ky1D4IP/ABM5v+uP/sy1N4T/AOR/8b/9dbL/ANJlrxsT/FZ5tb4ztaKKK5TEKKKKAGv901xXw8/4+PGH/YeuP/RcVdq/3TXFfDz/AI+PGH/YeuP/AEXFUy+FjW5rdzRR3NLXwct2estiJgMHPSqdv9kXVptm37a6BpcfeKjgcdhV489KasKh9+0B8Y3Y5x6ZrjlFymn2Zd9CWkP8hS1HPu8ptp2tg4JGcHHFbTfLd2v/AF8yRVlWRNwwy+3NUft7WNiJ9QMcBDEEoflwTgVW0u0hbTJtOaZZJBlJzBlDvPNaggT7OkRUMgAGG56VwU51asVVjp7q63Sd9mrdLffcppJ2ZVjSf7W0nnJ9l2cR7ed3rn6U+61WCxnhhlJEkzbVUDJPv7D3qwMZ6jPpVWz0qK3aN3ZridQyrNLy4DHpn0qZU6sEoUN27tvVK+6S3/RDum/eLvUZxg88VXvr9LG3kldHZY+oQZb8qLu+is3t43Lbpm2JtUnnB6ntRbWENtcTzIu2WYguck5x0rqrVJSvCi1daO+rTtdWS39L7ELuxs95LD5Pl2zzCRwGIONgPc1YZs8c+3vTiRnGPw9ao7Lo6mWE0f2Py8eUB8271zTk3T95tyu0ktElpq+9tbu/lbqNWexPPFFcwvBcRLLDKrRyRSJkOCMMuD94Y7d68R+EanRdM1rwnOJY7nwzqt3ZQW077zDp5leTT1WQk71FqYVBJLAJtblePWNd8H6b4i1DR76+jmkuNIuTd2vlzMgEhUqcgEBhgng8V5r8RYjpHxV0VvDTeZrmpRq+t2chAtX06MFftDtnckqlgse0HdyGXb8y/VZNWhQrqKd3K91Z6a6fhuZysjqZDwCcqM8np2rG8M6tqOr2M82p6RLotwlzLEtvNIHZ41JCycdAw5xW3KTwc9M9+cY/Tv61GuCSNoAzjjjHHX/PrX6KmuWzRXMkrWJkfjp6fyqGU8k/WmxajayXklms8TXkUaySW6yAyIpHBZeoHBp0o5P9Klqw7NaMI+v4103ir/kjHiL/ALAt3/6JeuZj+9+NdN4q/wCSM+Iv+wLd/wDol64MZ8C9Tmr/AAna2n+oj/3F/lU9QWn+oj/3F/lU9eWcLCiiigQVyHxS/wCRMuP+vuz/APSqKuvrkPil/wAiZcf9fdn/AOlUVNbgb+pf6of7/wDQ1nDvWhqZxEn++P5Gs5e/1r43Mv8AeH6L9T0KPwj16VVvVikgmSUKYipDg+mOc1aXpTJVB6/TFeZUV6bRutyppj25s4fsxX7PtAjIPBWrlMjiWJQEAVQMBRwAPpTzwBSoR5YKL6De9xkkgQDcQuSByar3TXCtGYFjPzgSeYcYXvj9Kq65bpIttNNJHHa28nmyCQZB6gH9atLapNcx3WWLhCind8pBwc4/KuGdSpVnKklbVa3s7Pd2t09dSkkkmH2yOSaS0jlT7UqbtuOVznBI79KW1hmt7dRcOss23Dui7Q3+FWdiiQuFAcjBbHJH1qrfWzXQj2zNDscOdhHzAdjx05racZR/ePV6pWdtLrvpfre3pYStstg0/VYdRe4WIHED+WzEcFvY96tk4FQWlpFZxBIo1jXJO1RgZquZLbWIby0IdkVjFICCufp/jU051aWHUa8k5tPurvst9PMHZt8uxL9vVrw2wRw6oJN2Plxn19aLW5lnluEe2eJYmAV2IxIPUVPFEsUaqvCqMAZ7YxTHH7timN2OCeQOKbjNSVSpPRPZJWatdK7u3bysF1tYkD7ug7flTHlCb2PCqMnNZN/rSeFPCd/qusT74NOtJb26khQk7I1MjYHchV/HgfTwvw94A1LxAusavrGpX2kWfie4XUbrwzY3P7uNWRcRyTYMhcrgP5bInLKARyfey/BSzGDknytJb26t79tF59RJXk4o7zw54k03xvDc69pdg9raahcEpPJCEe8Ef7tZz3IKodpb5goGQBil8SatqGlWtrJpmjyaxPLdQwyxRyKvlRscNKc9do5wKvWVnb6faxW1rDHbW8KCOKKFQqIoGAqgcADAwPYVZzlRxkc/yr9Mw8I0Kaha6W3mCtF2kVZT9QM4AIp8H3qZdSx26PLKyxwxruZnbaqKBkknsABSaZeQahbRXFvKk8EihkljYMrgjOQR1FdMttECvvYuHvV3w1x4hsR/tt/6A1Um71e8N/8AIxWR/wBtv/QGrnn8EvQzn8Ju3v8AyVXRP+wJe/8ApRaV2Fcfff8AJVdE/wCwLff+lFpXYV4R5YUUUUAFFFFABRSZoyPWgDkvGB/4qzwL/wBhOf8A9Ibmr+pw3J1NZRcZt/LK+RsH3s9c9aoeMf8AkbfAn/YTn/8ASG5rX1MjzE+hryMzgpUU3fRp6O33+RvRdplQ9qOjUpFIetfItWlbtoegthfejIqO4jeaB445DE7KQsgGSp9cUlvE8UEaSSGV1UKXYAFj64HrVSlJzUeXTv0DQl+nSmtEjkFlDEHIyOhqrdata2MjJNcJEypvIY9B61a81Vj3EhVPQk1jGrRqJ0+ZN9V6Ds1qxJCQhPcA1zq6rqUl+ypC20w7xbOVDKd+Cc/StH+0jfWNxJZbTKjNGBOCo3A459qZay/bLeSeOJY73DRBpUwdwPTPXbk14uKk8XUpxw9Rp7praSWl49HurJ6PRvY3guRNtEtjZ3dgs5nunvs5aOMqAQQOgIHevNPif4h1Dw9408A6qdQaw0/UZLrSLiwmlMccUjW73CTls7Sy/ZjHhgf9ZkYwc+sW4k8hPOKtIFG4r0z7e1ZHirR9J8Q2CabrGiWuvafPIA9re2kdxCCOQzI4IJHbivbwU6eX+zqpv2aWsZau7fVyu9NrbXehjJub21PnO28MS+MfG8sFoml63rq+JbPWLnxDZRkrp1pFcRTiCS4YsfOaOMoIoz0bLBVOT7Z4k+LeneG9VOlSWGo6lrG0znTdKt/tM6W/RZ5ApAjRmDKpYjcVbAO040tN8NaTbeGf7M0PSLPQLB3JNna2iW8YG75yEjAGTgHP0rwLVdP0LTvj3ITfFYHv2vL7F4VjttVkhSOG3Mi8TPJDGrfZ2y0SruyqyhT7lHE/27mkISThTcdOWKad07rb3UlbVW3LUbu8tz2bwx498JeM9PuPsniJLgx3HmTQXcv2e4tnOGEbxOFZcdMEdjzkV02oavbRfZSNYsbRSRKfNkQiRCOxJHBxwQa8K8d3Xhe81q/Pi3wHaay9oIbTS7m90q3v59SlkUkW1orqzFg24EZ2rhmbaFY1j6R8PfC1rPrWlX3wi8PW/iu2sv7Th025gtr6KeGTIjWKd0Kxr5iMnlKFVSpKgq2T3PhihRqrD0qvK5WtrrZa77k2lflbPdNC+IngvxRqF1NpfiTT7y408+TMFvAAoOQCckBgSrgOOCQ2CcYHD+IfFVz8Yw2naJczWfgVtwvNZhYxyasozugtm6rCeQ8wILL8qHkuLM3gTw74s0jSf+Ej8G6Dcz21ssaWd9p1vdrYgqu6GMuhCqMDhcDjpUPxHvda0rwhcDwxZSXWrSGO2t1t0Rvs4Zgvm7DgFUXnHoPQV7OD4eoUq/NLXr8+77kqDlKzZ0+n2cGnWVvaWsKW1rbxLDDBEu1I41UKqKOwCgD8Kr6/oeneI9Oay1Szgv7QsrmG4QOu5TlTg9wRU2lx3EFhbR3ky3N2kSLNMibFkcKNzBewJycdulWZT8p5r6fWErp6rsDvCVkVTxnHHJ/lVhBx+NVc/N+JqzGaAOk8ED/iZzf9cf8A2Zam8J/8j/43/wCutl/6TLUPgj/kJTf9cf8A2Zam8J/8j/43/wCutl/6TCvHxP8AFZ5tb4ztaKKK5TEKKKTNACP0rifh+GM3jEKcN/b1xg/9soq7Z+RXFfD04ufGH/YeuP8A0VFWdRc0XHuilurF+xhngtlS5m+0TgkNJsC7ufQVYpAACQBgUtfBKKguVN6d9Wete+ow8UK4OBTX4UnsKha1kN5FMtwyxIGDRAAh/TJ6jFc7k07xTbbV7dEPS2papCuetJK4jUkkAAZyewqpb6va3cvlQzpLJt37VOTt9aqpVhTkoTkk3shWb1RaSFUZiqhSxyxAxk1V1K5mt0RYonkMjbS64/d8H5jntxTrzVILFVMjN8zqgCrk5PT8Kq6ley2dwpMYksn/AHcm0ZcMTgcelcmKxFKNOcYTs09WktG9v66dCop81zNsZNT1exhT7Q1tIED/AGtQjB27jb27Vt+dcQTW0PltOrgh5hgBMDOSPc8fjUcULwzQx2whisE3BowMHPbb7Zq/kBN3oOvpXLgcLUppKVSXtPdTk3dO1naz0s9nJ+89bu5dSSk9EYXjS08QXehsnhm+s9P1bzois9/C0sQjDgyDaOclcgH1NaV5eS2kIKQm4lJX92vHU8nntUMmrtHcmI2txxMIQ+zg8Z3D27Zqvd2VyuqfboIkuGZUiUBtrKuTuOTwe3HtXRiMfzUYxw0b8srNpXav+Lt0tddBKO3NsW9WMQs8zXv2CJWBMzSBO/QkjHr+FOhtoRM93Gxbz0U5DkqV6gjt+Ir5V8F6T4U8d6R408Sz2X/CwL2HXdVu4NEuLg3lqE+0SraPDaSsYt0kCoySFCdr/KQpArv9D8G6npmi2Ufh3xJrPgrTXiEq6BFFazw2JYb2jj82NzGqk4Eedo7AV9tU4ZcoqvGSk3bfe9u3TclqUUeqa/4ktPBekanrGv6lDbaZbq0oaQBNqhc7B03OTwAOSeB1FedfDWxu5NGOuatpsWna3q7vdSxNH+/htmctawSnnLxxMiPjguHPcmsK3+HttpnjTQ7mbS7vxU0cUsra74h1V7+TTpwEw0EUzMsTOerQhSNuBgDFekr8qnGBjpx0444/pXs5XkcMt/ezfNLVJvVq7el+wpcy1YSc9SfT61EvU/n+lcp9t8S3XxOmgEL2XhSysQfMmhVhfTvkgo4OV8sKMg9d1dWvU5x1PT6f/rr6ucXT67jceVIrxaDptvq8+qx2VvHqdxEsMt2IwJXQdFLdcDJ/OrUv3u3fpUynp/ntUEh5H+e1Z3b3E23uEfX8a6bxV/yRjxF/2Bbv/wBEvXMx9fxrpvFX/JGPEX/YFu//AES9cOM+Bepy1/hO1tP9RH/uL/Kp6gtP9RH/ALi/yqevLOFhRRSZoELXH/FQ48GXH/X3Z/8ApXFXX5rj/ir/AMiXcf8AX3Z/+lcVNO2oGtrsNxI1uYrgxIku50CA7xg8c9KgTp+taOqY8oDqd39DWcoGa+JzCmoYmTV9bbu/3dj0aTvBD16UxzgnNL3pr9Ca427K9rmyFDZNLVSyglgTbNOZ2LMwdgAQOwwKdd38FoYxLIqb87Qx64BJ/QGueNRRp+0qPl839w7XdkTSxrIhV1DqeqsMilXgY6D9BUUF3FdWyzxSB4XB2uOhqG31GO4nuIEJMkIBbIIGCDjB79KUq1FSjK6XNs+r0bsu91sHLJmZqOrXsV/EkUTxqJSm1yoE3yEjBPTkVZ0+3vnuBdTXTrHIgLWhAYI2OxosLgXUjJfWyfarZt28JlBngbGPt1q7aJceWxuWjZ95GY8429vxrxsPSnXqe2qVJTi5NpJ2UWkl71tVs7R+F3b5bm82oxskkLZTzXAmMsJg2OUUP/EB0P41k2lr4ii8YX9xc3tk/hl7aJbS0WErcpNk+Yzv0IIxgDpWteXLWdq8qxPM6jPlxj5j+FVxevfpeRLE8Ei/IhmTCsSOMY6jPWvapYtYVexb5ptP7Kv30e1+1jFJv3ktCRbuSS6khETCJVBE2Rhic9MfhTfsHmzXTPNI6ToEMW7ATGc4I5FcZ430HRbjwPqOn+KLptI8N2yxTzahDetb42OGJLDlRuABHetPUvGp0nX9Ks/7NuptEurGa8k16N1NtbrGoIVznJLKSwPfHGecLDYaWLw8alVNyu21ayi0nfXTmSXNbe6LkraROI+LGmHxh4u0fwPNd3UPhwaVPf38EM7RC7w6RRxyuhDtHhpSVDAHA3ZHynfS1SGC2hCCNYERURBtVNqgAAdgBxj9B0rk9G11/id4807xvYaXeaToCaLJZ29xqaiKa+E0kM6SpFkssQWPIZ8El8BcAmtzw3fapqNg8ur6WNHvPPmjS3E4lBjViI3JAGNy4ODzzX6TlWWw+rc1WGrSi4vayba93b526LUzi21c2UOTTwOBVDU7qax0u8uLa1kvrmKFnitYmAaVwpwgyetZXw8bXZPB2nS+JZA+szJ50yeSImi35ZY2UEgFFIUnPJUmvpeS0edO1iuVtOXY2L62hvraa3uI0mgmRopI5BlXUgggjuCCRTNE0210eyt7GxgitbO3QRxQQrtRFA4AHap5T6Gi3Pz9f84NNtpWuLmdrFpu/wDntVzw0c+ILH/fb/0W1Uj0P+ewq74a/wCQ/Y/77f8AotqwqfBL0M5/Czevv+SraJ/2Bb7/ANKLSuwrj77/AJKton/YFvv/AEotK7CvBPKCiiigApsgyvBwfWnU2RSyEA4J7igDwf4xfFTx3pXxO0rwV4As/DT6o+kSa3IniW5liGoBJRGLO2KEbZDkkyMGCAplTmr/AMH/AI+RfF3xvdWWnRJDpUWg2l/JBOpS9s7x55oZ7W4XOFeNodpGPvA4yCDU37S32nRPBdl4h0fTrW/8WaXfwvosMui/2k81wxK+SoGGh3glTMGXZnceBg8r+ztf654g+JPjvVvGPhOw8CeKbhLZJdFtNNG6SAbgk8moABbssSVwAPLxg8mgD17xZ/yNfgP/ALCc/wD6Q3NbeqffT/dNYnivnxV4CP8A1E5+f+3G5ra1T/WR/Q15uYfwGbUfiKh7Uh6UMP5ULwa+N0vqekQyWaSXcdwd3mRqyrhiFOfUVPwmfTPWlzSP0z1pqEKV3FbhuRvbRSSmRo0Z9u3LKDx6fSs/WLFdQktYXtDcwl8u27aEwOCR3qKP7aPEUzLv+wmMbvN6Z/2P61sk8V5kI08fRqQlCyUnF3S1tZ3XRpu97l602mmZUwGpzyQ3FvNEtvKGjcttEmOQcjqKW4EV3qVqssDSBMzLPn5FbHFXpo0uo3Xkq4KnBxx0PIrx34+aF4n1nwnaaB4Gup9N1Dzk33EV01vL5K/ORC/3S5KKp34G1m71z1afLOCm1yzlDV21vNNJ6Wsl89FY0h72i7Ho/ijWb7SdJvNR06yXVhBaSyRWVvKqzXMwB2RozEICTxlq+avgz8aLnwdew+C/F2uX0Mj6bBfx6nr4ELtcJGf7QjRpTl18wB1+Y4DEDgKtal1+0Rb+HWGnXSf2uLfQ7q+1DTbq0ax1OC8tzCEZlbC7JGlKghSu6M7WcHA5C/8Ag/Z/CDwZpl5caG3i3XreJhf61d5ubbSZGjAlnSzyFaMM8j4CeYFLfNgYP3VPJZ1auJw3OvZ+64ybbl1va3RJLzabVrhTUHfudnqf7Q2r6v4mtZtM0W01W0u4lNj4dgkmi1mS3mAEVwZMGFGfJbyJFVlTa7umCo3PiHoc3gXxzPrmkeGbjxPpmp2y3F7pVtCssljqUYYRXgy2AZlYRSMAcfZ42z8xFeeeHdWvNBl1bwzo3iTTpfE3ii5tCq+G3jmEMRZX1G+FyBiOSW3LOsY3BXKMMlsDr7H4N63b6V4jtLbxXrHhy3v5ZY4baw1CS8huIQoEbSveedNHJywcwSR7gAeCM114bLasassXlajTadry5vf2jfZPpZ6bJ9hNWlZvbYk+Fmrx+Ofibaa5cWU1u8vhcx30UyuF0u4MiDyondFKO4Zg2MZ8sAgban1dNcvfif4yk0++gtda09NPtreK6gL+ZpKr5zbFGCS8r3ChyTgjbxjIyW8H+IPhpoDaj4a1RdS1S30wR6rpV/Jfalb3snyMssUcl1mNgQwHIBB55FdDp1hc+EdPvfGutan4i8W6zJpUEDx3FtbLPHCpMnlRwwRoud8rsdxcgALnC8+1Qy7Fxzd4+cI8trLVv7k/l1XULSvzJXVyl4I8a2nh7+ztB1Ky8QaXJqoa4tLrxEwkd55Sxa2BXoyAZx0AP59B4U1abSrpPC+u6ydc8SxWzX0t0tiYI3iLkLjGVyOmPxrrFEd7bwXD2zA7RKomT51LLnGT0ODjI55OaV1G89SemcYyOK+plWjO/u2v/W/by6jnUhJvSzMzUtK1G81/Rru11g2NhaNKbyxWEML3cgEeWP3dpyePWteUDaeQPwrI1nStU1HUtGnsNam0u3s7lpr22SFZBfRbcCMk/dAbnIrXlBCdCOvWsm9FqYzeiK7Bdx+YdT29qsxgBSc+/T2qqW+Y9ep7e1W0YYxyefT2qJ6ak30udD4JAGpznP8AyxPb/aWpvCX/ACUDxx/11sv/AEmFc/oFxry+MdMi060tZ9Iljm/tG4nlKzRABfL2KBhsnOc4rf8ACI/4r/xx6+bZf+ky15OKjy1N73PPrxtLc7aiiiuMwGv2x1r5W+HX7T/iq9sNG8U+MLPw5ZeBvFfmw6Vc6XcStPol38xgtNSLnbulVSu5du2UCMgllJ+qJBlT9K+P/jHrvi/wr4q8deH/AAN8OdJ8a+GNSto7vXFn8PiOGxuH2iR3AwNTZkzIYlAkBQDd8wAAPo34NeL734gfC3wr4l1CKGC+1bTYLyeO3B8tXdAxC5JOASetHw9/4+PGH/Yen/8ARUVSfCO3s7X4ceGItPu0vbKPTIFhuY7IWSypsGGEGB5Q/wCmYxt6VH8Pf+Pjxh/2Hp//AEVFUy+FjW5q9zQTwaO5ox1r4OXxM9ZbEbDnNRWNolnAY4yxXcW+Zix596mpykYrlUIuak+isVfQUgMCDyDxUK2sUfMcaoQMfKuOPSm6juNlME37ihx5X3unb3qp4f8AtY06IXpzNjA9dvbd/tetZVK0frUaLp3dm07XS1V16vdegWfK3cqxxtYvqF6unObktsCo+WmUdCPT6VftoYIpJboB0mucM6u3Tjpj1q4doxu4ycfjVLULG3aSK7lR3kttzR7SeCevHeuP6r7CN6bT5LuztHVtty0TfNbuXzcxFpCRGKaZLdrR52LusmdxPr+IqpqfiYeGvDl/qGpobiWyheaS309PMlcDO1UQ8lj0A7msrTNLvdQ8S2eq2GvXcGkwJPHdaNLACJ5mI2uXPzLsAOAOua8f+JnibVPiTJ448J2HhrXNL1ewuI7O11fTNRijTcNskUs5zmOEghio3MVBXhq7cswk68MO8JKMlrda21bu7JK76vl0d3ZlPkVTlqXsekeA/jb4b8c6leJZ61F532RrpdMk4njijwJWIHUqWAYDocCt/wAI6toviLQrPxFo13JLpU6SSxvl0VgT8xKtyCCDivn+z+GviKDxB4a1jR7G18N3XhJJLFPtFxKo1qFQGzI0TZ8p3aVirhsMdxzyDxXhH4l+M57i20vw3q//AAkF34pF7a3s98ztYwzKwKzWibVK+XBuWRIj5SsYejk5+hq8ORjU9jBqUk7qUm/du9k7JWvbrtG2urVzhJNrRJ/1956DY6Xr0Hxi1fTfhrotroPhvUy899qOq2TNbLcozCSeCFXjbY5Kp9/aSGZRt5bqbCTX/CXxFTwrrWqDxLFdaa+qQ6nHZizFud21oGTJV1PWMozOoDCQY2ucrxV8H7rXtO8D6YfEc9novh63S3v5dzLPfQrDHG6NIGGFljQq/OQJWIOcV2M2t+T4n07SoNJumtpLJp01KJF+zRBcBYt2SQcdMccgHg19XSy7HTjRVZxlGHTXW17N3v7y8m1a2pFne67HH+NoNbXxBEbgapqGjSXMVxZw+Hk8qa3eCJmdZ3J+dJCFUDHX0rSsfF8vj/TrO/0XVG8PnSbpW1vT7m082dFWPLWzdArcE7hkiuhHiJV8VRaIbO8Mklm139sWEfZwofbsLZzv749K24oUi3FIxGZCC21QCTtAzkdTx3zX10qijCKlDVde/wDXoW5qKSlEzfD/AIhsPFei2er6eWlsbpDJGXiaNiBkEFWwR0xyP51W8MaXqOlw3y6nrH9sSTXcs8MhhEfkxN92LA67cHnvmttzgdwMdAMDNYfh/S9S0q51iTUdbl1SC5u3ntY3hWMWcJH+qBH3gDk5NYXXI7aeX/BOdPSRvrj1Hbt7VDLt3dfWpgcdc549D2qKQEt0PX0rFakLewkYGeo6+ldL4qI/4Ux4gProl3/6JeubjGWAJIBOMgc1YNxr118EvFzeILK1sZ1sdRS2S0mMitbiJ/KdsjhiOo7Vy4uPuJ36mNdXhdM9StP9TH/uL/Kp6gtP9RH/ALi/yqevIOBhXkP7QXxJ17wBa+FrDwz/AGLBrXibWBpMGpeI5HXT7HFvNO0kgQhnYi3MaIGXc7rzwQfXq4n4u6VpmrfDvxHDq0ds1mlnJOZLrTl1BYmRSyyfZ2BEpVgGCdyOKBHl/hL9oTWNV8ZeEPB+s6dp+l+LpNWutK8RadFI8ixeXZS3EVzbFtpMMwSNkYg8Myk7lIr1b4qf8iTP/wBfVl/6VRV84/CfxV4y8d/GjwVffETwNpvgu5t9OlGlap/YpludXYxuCguDk6cBH+8+zOS7biA2FOfo74qf8iVOP+nqz/8ASqKmtwOh1L/Ur/v/ANDWaO9aOpnEC/7/APQ1nA5zXxuZf7w/RfqehR+EXFVryxS8VA5YbZA42MVOR6+v0q0vSmOe3evMqQjOHLLqbpu+g1Rt5/GiWKKYqXQEg8FgCQaUGsfVxenVLA2bSKuW8zdgxbf9rvn0rkxFdYamqjhzq8VZbvVfLzLjFylvYsa1ERprwx2v2lWIXyUITIJ5waiMCAjSBaSLZmHAlVvlHfGeua1CMrnr3GTTcnnH3weRmoq4NSq3va9k9tYrePdJ316Apu1v6v3M6+WFrKKyNs91btIsJCEkqAOp+mKnvnnt44TamHargymTsnt71malDY6VCkLfaoVnmErzQEnDdsnsCcU3wjoOp6VoLWWua1J4iumnlkN3LCsZ8t3LLHtXjCqduevesKNP20ayclGcVFJJ69WlzWWi8++nU0eiTexT13xuth4h0/w/ZWN9d3+oW0txFc28Ia3hCAcO+cKTnIHfFTa5rukaLP4e0zWrxhf6nOILT5G/ezIu45KjA6HrxWJqPhm48DX+reJ9BttV8R6jPBbWS6CL0JboiycuitwrYJJ555A61ha78Q7r4V/D46x44vLTXNXubi6OkrZ24UTttdraBcBsswAXeARl8mvap4FV4Qq1bScoKLguZrmbu9078rkk3omo3SWtqtG37vp97f8AwD0TU7XSoNLm0/UvKurOZHaSC9USLIg+dgQew6814P4M8G2nxBbUvEni3Q5ZHvNQnk02z1KVnFpZEBYkSLO2MFFORgEgkHPSkXw18VvFAuf7b8bRLous6Usd3pz6YivZPOuZ0tmXbJGY0IRGkeTJyzAnGO68NRanpdvNb6ze2t7cmaV7RbWHyyLYfcQLn5ioOCe/Ga+yyrJsVg3OFVQlFrRa6d1r5Xv12t1EkmvMvanaXX/CP3lvos0FpqHkMLOaeIyRxybSUyoI+UEgYHYD6DzKw8YnwLql9qPiS012OK4nW0vdTulH9nwPDCqiaKMElI5mYgc9RmvR/C2sy654di1FtKvtJbMg+w3cQWUBGIyAOOcZH1pnh7WIvGHh631A6fc20N0H/wBE1GELJgMQNynPpkfUV9hgoPC0fYygrLR2vZPV2V9el9S4ztzJrRmVa6zJ4b8QiHWdb+3Q+IbwJo1sliVFuoTLIzrnOcg7mx0xW34o0zUNX0pINK1Y6HcrPFIbkQiXMasC8e0/3lBXPatVNueM9cgnn8fY9Ky/FmmahrWhz2mlavLoV67RmO9SJZDGAwLDDcHIBH41rzqU1Jaf12MOZSadrF6XB6Y/75p1uBnr+lRjKIod2dwoDOR9445OO1SwH5h15OOn1rOXcl7kzYx1/Srvhkj/AISGx5BAdv8A0BqpE5B6/l0qldT69Bcac/hyztb6/a7jDRXkxijEBB8x8gE7gpOPWsZR5k1e2hEldNXO4vf+Sq6J/wBgW+/9KLSuwrj7wY+KmiD/AKgl7/6UWddhXgnkhRRRQAUUUUAMeMOecfjTRCB0qWigDj/F67fFngTuf7Un/wDSG5ra1P8A1sf0NY/jH/kbfAf/AGE5/wD0hua2NSGZY/oa83MP4DNqXxFM9vpSZxS4zSDk18Y9z0h3rSMu6lxRWstUBXvLeae3dIJzbzH7soGSv4VMAVQKTnA5p2cVm3OrlWnjhgeeWFlDqFxgHPOT2HtXHUq08NetVdm1brt6L8xpOWhj3fizQfBOjXc+q3sei6bZu6tLfyhAcHkrk5bPYDk8cc1Wh8f+GT4e1HxdBr0N9oNtA1zLNbSCVYUQHdhRyG4I2nDZGMV8t2ni+bXtag8RaJpSeIvEuteInutNXW4pcvpU8b3Fp5EjZW3xbxeaSq7lUDKl/lPU+OPDHhbwl4L8T3mseNtOn+IGsGyuLqOXyo4HuLS4N5Fbm2Q7kiZsozysX2v8zjpXsyyfD4KNCjXqfvNJJJW5Yva9n/LovMc+ZLm7nXaVot14zfSfFfjy5BvbgpNZaDMYktbJmwYU4BaaRcAjc7ASAsFBVcMsvGvh/wCKXiv7Fo2samH8O3AvZZrCVorach2QxFv4wDncmACcnkGr1l4k8K/EpLfT75IE1mLZeDSNSiMVzbzAMQ6I+CwVg3zLxweTivM9X0TXvDvww8P2Engq1sp/BNlb2l74kj1j7Pc3UUGxZprJYwCzOiCYG4IAfKMjk8/okalPCUofV6XNe6una2m/n96+YQcIrSN5M9Rtfg3qxOpXEerzaDpkt0fsGk+FYba2VIWCB3dzBuErvvYlD2U5yM1n+IvDml+A/iP4ZsPB93qtxqku/wDtnS7nVLq8hWwZMfaZ2mlYQsjoPL/ikZ2ABAYr2Oq+PdH+HXwng1Cz1Y6tcPp5n0qPVrgm61KV13RJtCqxZmkRdqqNuQMDtx8PhfUfDnw+1u7h1yPTfEupMdW1PW721Dj7QUTexiHARFTYq5O1VHckn5rLI4jMMY67qv2ell0/K9+6u0rLq2Qveai+hftodMsfiVqfkaFeQ6tf2EU93rQU+RMqMVSPfnAcbicAVZ8dz/2foservrd5oVlpc3268exjEjTxLy0RGCcH/Z5rV0jVbXXdItNSsrlbuyu4RPDOilVkRhkMAfXr0zXKfGHxNJ4b8JraW8N1LqGszjTLYWNzHbzpI4I3Iz5Bwcdu9fokE6lZQ/r/AIB1RvOaj2O8tL2DUbSOe3YPFKgkVuQdrKCvB6ZGOvNZPizW7vw/o0t9Y6Pda7cLIiiys2XzGDMFJ57AHJ+lUPAXheLSrd9ZurCbT9f1SGD+0op7s3DCRFKgEj5cjnJUAGruiTa9LqGsLrNtYW9olzjTWtJGZ3hx1lBGA2fTiudxUJS5dkZSS5pNdDWvTdLY3P2NYmuwj+Us8mI2fB2hiM8Zrn/Bek67p2n3Vx4g1I3mpXsguJLRH3W9mSgBihOAdmcnnmtzU8LpV5l5kQQOS1qP3gAU52cfe9PeuP8Ah34gW507TdIgtPEE9rHpqXSatrUIVptxK+XIeCJRtJOR6VcW5U5SiUnLkZ2AGH+Zflyc/NVLwlqOqappHn6vpLaLfCaWP7GZ1kOxWIR8jA+YAH8au5BbHB5PWrMRwAPx/H1rPmVrGd7qxs/DXWLHWr26msLyC9ijDwO9vKJAsisu5CR0IyMj3rR8J/8AJQPHH/XWy/8AScVnfDbSLHRr26isLOCyikV5njgjCKzsylmIHc9zWj4T/wCSgeOP+utl/wCkwrw8Vb2rsebXtzux2tFFFchziMNykeoxUXkEHO45xjnJ/wA//qqaigCNIthJ6k9T6/5xXGfD3/j48Yf9h64/9FRV2x6VxPw9/wCPnxh/2Hrj/wBFRVMvhY1uax6mlpD1NLXwUvtHqrYj7GlTtR2oToKyg7SuWxWG6qxt5vt3m+fmApt8naMbvXNWhUF7dpZW8k8m7ZGpY7Rk4Ht3qKsYaOX2Xe97f0vz2Er7Iq60sSwxzyxSz+S6uqQ53Z6cAdatXMZubd0VmjLrgSKRuX1I96yNY8WW2haXqWq6hHLBplham8kuFQsTGq7mIUDOQB0GTXjlprvjb4nahd3ra7eeFPCUkrzaQumWaQahdRbFw8zTLIEjGSVUIHYtk7VCht8FgnjYzxMH7kuWLvdc0r2331/JFqMmvQ9G+J3jVPh/4Lu5IpFutZlVLPT7UzKstxcyt5ceMgngkuSAcLG5xxivO9M1Pw/8Evh7a2+r6jC1xZpGmozwgyXN9qEiAzSsOXaWZw7ZcjOR8wHTD8QfB6y0jVdD8TaEkH9uaVdh7i/13UJ3Els4xMZHJJZwpYoDtQF2OBkivR7LQtBh1fUbu3tLNtRuTFPdSKqtJJsA8p2GT2HB9uM1+i5TldPLaclJX7b99ru9rd7NhDkb5pnDar4RtvGvgTUr+HXNQ0+0124t9Ukg1xiYY412lraSHeu2KQDa67hncecHBoaZ4a0W0+IGk2+u+Kp9S8XGWfV7WwiuXitGQoUVILQs6RxxqWC7SNxLlixzXW/EzwTB4/hsLE6yumX1lP8Aa4kkhjuo3BGw+ZbSHbL1JRm+44UjONp1NA8IaT4b0/TLOztEZdNtFsbWecb50iVAuPMI3dueldWIh9Yr8ylyuPK3azbX8rTV1przJJ/IUnvdbmjrWnW2p6HqNjfWpvLK4t5YprVWz5iFSCgGc89PrisrwPcW114M0aSx0m60OxEAjh06+UxzQoowqsuT2X+VQ3ervpfxEsbW7162jttTs3js9E+znzZZo/nkkEmem3jGPxrpbjqTncSD05J4zjOM17TvGHqU/dVl1OQ07UY9H8c6jpl5rl3e3GqRfb7TT5osQ2cMfysEcDGSWz8xzj6V2gbIJ4x14Oc8Z49a8Z1G/n+InxP1LQBpl/deHtPik0zVZItSjjtsyoH3GNR5m8MhX7wx9K9H1u21XR/CBtfCltZz6jaxRQWcGoyssOwbVIdh833AfckD1ratSScXf3rL8TatBXjG+pNFrF5N4ov9KfR7mGyt7aOZNVZ1MEzsWBjXnOVwD0xz+eR430vxHrZtLDRb6PSrGcuL7UI5SLuAAKUMIwVJJBB3dj711a+YYIjNsWYoN6ryAcDOM84zmuL8bXMNr4h8MyPNrQmWWd4rfS490E5ELHbPxjHHy5I5NRSfvq3n+RnSd56dDtokKIqFi5UAFieTx1+p6/jWL4n1LVdNisW0vSDrDTXsUNwguBGYIWzumOeoX071c8Oas2vaHY6i9jdaY11GJTaX0YSaHI+64BPP49MVbk/h6cc4I6cVgm4yvJamfNJSvIq6hq9jo0KTX93BZxPKsKNPMqBnbhVGepNdb4rOfg34hJH/ADBbvkn/AKYPXI32kWOswpDf2kN7EkqyqlwgcB15VgD0IPeuw8VLj4MeIf8AsCXY/wDIL1w4xx5I27nLXtyo7a0/1Ef+4v8AKp6gtP8AUR/7i/yqevIOBhUUkJfPzYz6fpUtFAiFLfyzweMY/wA/57muV+Kn/Il3H/X3Z/8ApXFXYVx3xV/5Eu4/6+7P/wBK4qa3A6DVP9Qv+/8A0NZy9TWjqfMK/wC//Q1njvXxuZf7w/RfqehR+EUHFMbljTu9Mfgk15z+E6EHpSyIXU4OGI6+/rR1xTugpJXi4vZ77g9NStawzQ2qJNMbiUDDSsAC1VLIRQardRRwTCWQLM8hJKHtgE8VNe6mLaYQhGlnMbSKijhsDON3TNch428Xaivk+HdAeKz8Y6nZy3Wn/wBo20klqhjI3CVk6fe4Fc1KlDFV4Yei/hu3u7JbtstJ2OzkszNewzea6qqlfKz8rEjPPrilvL63061kubmaO3t4xukllcKiD1LHgDHv/OuN8QfFO08Lw2Nrc2N9qmu3kbvHpel2+ZpFjwJJMMQEjDEDcxUfMB3rz6H4fy+MIZNR+IH/ABONTuy5OmSzeZZ6dEWZkt4lVVVmVSoaRgSzqSCF2ge9l2WfXJOpHSEt331t1302WiJV2+VnPeP/AIq6f8YvibbfDvRtU12z0e2S5XVNQ0dfKE9ziMwok4JIiA8/ccBWbaFLDNdp4b+FfhTwdcJdaPodvbXMEbJAxLOtvn5n8hGJWDewDN5QUM3JzW5pOk2OjW6QafaQWMCqqbIIwgCqOAcY4Hv71eguYbyMSW8sc8R6PGwZTx2IJBr72GGoYajHD0/ddtG7N8z3aTVk7dFfzujR8qs4rb8yLczIGaPYx/hLZI79e9cp4ji0y28aeFNQn0G81LVXkmsbW/tVLpZq6gu0nOArbRzz0rrLwSSKyxuI5CDtcruAOODjIzjGce1cz8OdWOq+G41l1u38Q3tnK9neXttEYkeZDhhtPTHFetRi401Jvmt1ej1TXRJfgEU7uZt+JNOuNZ0O+srTUZ9Hu54iI762AaWD/aAPBOB+tUvB/iSx8U+HrG9sLuW/hZTF9ouEMcjtGfLdipAP3lPbH4U/xv4qg8EeENU164QzR6fCZjEHWMsRwAGbgH6964v4VeFp7mysdd1ywvrTWbYTw2k19qAupHtpW8wE+WFQ4ztGQTx1ropU06DnU01Vvu1/QqME6XNJ9T0e7nltbC4nhtZLuSKJnWCEjdIQMhRnuelQ6BqM+saJYX11YTaVPcwrJJZXLDzYSRypx37ZqnFLrx8XvE1tYDwx9kDJcCRvtX2nP3Sv3dm3vnOa3B0wCOpPoB1/SueS5Uk11MmlF2aOL8P6V4lfxFqGqa7fxxQfPb2mmWcu63MIbdHM2QCJccHtXWxEj0/FvY1574Y16LT9dvLC3t/EuprfaxcRS3V/B+6s2VFJ2k4PkEn5TznmvQ7cEMck/hxW9dtNNoure92Zul6jqlzretW15o32CwtHjFjemdW+1goSx2jlcYxzmtrwnq9jc+Mo7GG6glvbUhp7dJVMkSsjFSyg5AIBxSFQMnHeneE9IsbbxfFfxWkEd7cnZPcLGBJKqxttDN1IFctVpwlfsYytyts6m9/5Kron/YFvv/Si0rsK4++/5Kron/YFvv8A0otK7CvnTyAooooAKKKKACiiigDkfGP/ACNvgP8A7Cc//pDc1s6l/rI/oaxvGP8AyNvgP/sJz/8ApDc1san95f8AdNebmH8Bm1L4innFIPvUHt9KVRg18b1PSHUnrS45GOf8a868TfG/w/ot8dM0xbvxZrqjH9laBGtxKByCzuWWKMfKeXcdR1JArpjSnUfJBXZNz0TFcx8TvD2p+K/h/wCING0W+Ol6rfWkkFtdq7RmNiOTuX5l4yNw5GeKwdO8SeKb7wu/259P0zXrhHdDbxG5hs8kFUOXXzio4JG0EgkADFYrD4jdD400gjn/AJlw/wDyVXs0soxsbTjHX5CdzhvG3hv7Fpeg2XjXQx4U8NWE6RWWpeG9WdYbHMbQLBNIsaPBC8bGHeCB82N6Fgaz9I+H/n/Es+E47DSYtB0hoPENxb6fbBLu5iM5+y200zbnc/abZ55C7ZcKgOfmrtbiw+Ilyb6O88ZaReWVzF5UlnP4cWSJlwVYFPtHIYcEMSCO1cz8NdC8X/BWTWNG0PRrPxXFqtyl6mr6hqX2KC1jWFYzbOAksg27V8pVEg2FsspT5vdxMMxeEnSSvOSspRs7X9dvXoaaw03PQf2mNPtH+DXiPVntohqul2wu7G82ATWsyyIweN+qHJ7HnnPU0z4gWOlaj4R1q31mwbVdJWGSS4sowzNKqHcqgKQScqMAHt9ay7rwvrXjOSIeN9eW7tpNrHw9pkZt7A7W3bZGYGSdQcckoCFGUweewZyG3ZwSSc+/Q/5967Mpw1XCQftr3bvpe5MLxfMeXeAPAcNmdR8W6VZIs2q2ML6VZa3C5uNP2xkCPzZGd0UlhlEIHtmuw0vUvEbX1jYavotv9nfTfOvNStrgGBLnIBiVG+YrjnJ65q34l0251zRbqxsNXl0W+mVSl9boryR4dCcK3ByFI+mai8T+E7bxFa2X2ie7SfT5DcwPaz+U5lCFQWxw3XODkZAr6GPsl7tlG/ltZWW1l0101erOlSU3d6XKXiHxtonhayuHd/PS0mitZrXTY/NktzIQqBkXlFHU9ABntViw8IQprd/qF9dSastxPHcWtreojxWLogXdCNuVJxkn1FYvwq0l9N8KWt3f6TNpev3US/2g108b3dw8eVWSWReGYgD6E+tdD/wjcUniiPXGu7zzo7R7P7IszC2cFslzH0L9txrefLTm4Qf/AAV5DbjCVo6EvhXxTbeMdFGpWkF1bwNLJAI72BopMoxU/Kf4T29a0bhZGilEJRZmVgjOMqGxwSPTOOB6VYZyxJbk9Mn0rlviN4pl8F+DtT1y3tYr6azRWEE1wtujAuqkl24XAJP4VyxTnPlivT+vMxiuaVoq4/4eXHiW40D/AIqu3s4NWimkjMlk2YrhV+7KBnKgjsSau+LtK1DWNNWDTNZfRLpZ45WukhSZmjDAshDjGGAxnqO1W9F1a213SrPUbSWOe0uoUmikjO5WVgCMHuP8KtTN8o545PSqbkqvO0k79thym+dsr5G447ZqxHj9arE/Oee57e1WkI2/j6Vm9DM6PwR/yEpv+uP/ALMtTeE/+R/8b/8AXWy/9JlqLwOc6lP/ANcT2/2lqXwn/wAlA8cf9drL/wBJhXjYn+Kzzqy99na0UUVymAUUUUAIelcT8Pf+Pnxh/wBh64/9FRV2x6VxXw9/4+PGH/Yen/8ARUVTL4WNbmqeppaTuaWvg5btHqrYYKVOgpBSp0FYw+JlMdTSATzTqp6rqFtpOn3N9dzLbWttE80sznhEUEkn8Aapx5ny9xN2POfj9rMi+GLTwlp7sur+LLgaZHtx8lrkNfS7m4Xba+dtOCd7RgDJFclqA8W6r4s1Pwj4Kn0XSYND0fTp1OqW8szulxJcxBQQ42+WlpnkNuLHOOtJ4dh1P4gW2oeO55m03WdasZLfQ/NtlP8AZ+ns5ktS0eSfMkBjldWIILKhA2Veu/h213cJfzeI9WstWn0+DTdSudJlW3W+SEuyZBUshBnmwUKt859Aa/QMDgsRh8JenbnbTu9dhx+HmvqQ/Ei/02PWdN8Ga3a6frtjrxNhe2jXK713RPIN0Gd+wiPk5GCVPPWun0rwro2k38t9YaXZ2t/LbRWj3EMQEjwxjbFET1KoOAOgHSszw/4E8O+FpBLpmjWVvdksz3piEl3IzfeLzvulcngEs56e1dJFKksZ2FWwSpI5wR1H1r18NRrUqbeInzNtvba/Qb97W2pwXjaGyj8bWSafp93B4t1fT57O18RQW7TRWMacjzAW28sc8jnAraZfElld6JDE9jqNskLrqc8uYppZAg2tEq/KNzbs54FdMz/LtJ49KwI9Juj4lm1UaxO+nS2YtxpQjQxK+cmUNjJJ6Y6Yr1IOHxOKvtfv899tjojNyVmtBND1mWbwzb6z4msLfw5eRpI80VxcRyC1AyCfN6DK/TryKparrNn4zn1vwzZXl/p94tlFMdTtIiESOYEK0UjAqxAUdPWuf8XeBFXxJYSW1jqetadreoP/AG3aNeK9mIWh2B5Uk6xqcEInIOT2FeivDHFbJBFGIYETZHHHhRGoXGB6YBxx6VbdKDVRPV3+Vn8xS5ItS69Dmb690zwEtiXsri4utUu4bOW6tbQPNNKUwJZ2UDsuSx9a7DK7TnoRg+/FYXhbQY/Cui2+mQ3d3eRwBgJryYyyvk7jlj161u7hg8jA9vasarTlpq9TGo4t23scn47uPFdp/ZE/hmCyvkS6C6haXTbHkiOATG+QFK8sc56V0q4OQeM9CDxjHX88/lj68rdfEBLT4n2/hG5ht4Rc2H2u1uXul8yWQMQ0Xlfe4AJz0rqQw56DqDkc9O9azjJRjFrzXoaS5opJoz7HSdRt/EmoX8+syXWm3UUawaY0SKts6j52Dj5m3E9DnHatGRhxwMY9Paplbp68fyqBzjb0HHTHtXO25O73MXq7ioRn8a6bxV/yRjxF/wBgW7/9EvXMoefx9Paum8Vf8kY8Rf8AYFu//RL1wYz4F6nNX+E7W0/1Ef8AuL/Kp6gtP9RH/uL/ACqevLOFhRRRQIK4/wCKn/Il3H/X3Z/+lcVdhXHfFX/kS7j/AK+7P/0riprcDodSH7lf9/8Aoazh3rR1H/VD/f8A6Gs4d6+NzL/eH6L9T0KPwjgM0xwOaevSmnqa857G6EwAKVTkUzPIp7MEUkkKB3JwBRDoxsY46dPp615R4y8ZeINZ8bah4T8K6ha6M2j2treahqdxa/ayzXBl8q3SPIABEJZ2JztZdvJLLD4x+MEviNzoXw+Fxql+9zFBd6/bwpJYaZGZcTlpGYK8qxh9iIJPn2hgBmqGh+CpPDmp6edL1ARaUBPLqUM8JluNRupNuLiSc8lvlIJ+gGAAK+ryrKXWqe1qwtHe2u44rmItK8NeLJPiC/irxFrWnaug0prK1tdOsXtxFmVZCUzI5YsVTJLH7oC7ec9grbkDFPLJ/hz09OlWM57g7hlsDgnFQ3EyRDLuqAkDLcZJ6CvsKWH5ZxjTsoJW5UtN07+v57gveVkg8tJ1eJ13JIpVh6gjBH5V5/8ADOCKeKxfw3FdeGPCOktc6e+h3llsa5dXwJlkdi20Hdj1zzmvQlxzn6dKL4NcwSwrcNbyzKY0mGCyEqfmAPBIznFdcbWcLX/R2te3+WyNYScU4pbnIJqvi+0ltIr3R7G+NxqUkMsun3exbWzHKSuH5ZsdQta9/r+leHRPHJJEtwlvLffYYAPPlROXZYxyx/D0qi/gw6j4FHhzWdVvNUaS3EM+oK/2eeXndnKfdPIBx2rH+H/h57XXtcu9R0y6W7tLuSDTtU1KSOWaS0bB2xEfMkQK4Afnqa7LUpxb7dFp6f1Yu0Gm30NiDTLTxx/ZfiBru7k0a607a2h3sS/Z5g5Vw8sTrneu3GKuaP4jtNT1jVtIt7W6t30to4XeSBo4W3xhl8tjwQBgHHTGKsa54ei1y80qeW7u7c6dci7VLWcospCkbZAv30IPQ8cVot2U849eawlKLWvVfdqZynzJpj0ZcbgO36YrmvDFz4sXxNr9rrtvYvpKSCXS720bazRn/lm8ZJO5RjLcAk8DnA6C5ma2tJ50USPGjyKjHaGIUkAnt061z/w08cRfEPwdZ6yiQQSyZS4tredZ0glUkFN68Ejr+NEU1FzSutCU3ytxWmhq6zZT32mXdrZ3TWF1NC8cN2iB2gcqQr7W4Yr15pfD1tcWOlWlvd3p1K6ijWOW8KqhmcDliq8AnrxViVgw5wfwp8Lbm5OalyfLyt3E5SceW5ZYjFXPDXOv2P8Avt/6LaqRPyn/AAq74Yb/AIqGy/3n/wDRbVzVPgl6GM/hN6+/5Kton/YFvv8A0otK7CuPvv8Akquif9gW+/8ASi0rsK8E8sKKKKACiiigAooooA5Hxj/yNvgP/sJz/wDpDc1samCZEx/dNY/jH/kbfAf/AGE5/wD0huay/ib8TtD+Hpso9Re5utRvd6WWladbPc3d2y4yEjQcDLKC7lUUkbmFcGOjKdFqKubUviOgYgdSB+NcH4l+MvhvQNRk0uC4l13XE4OlaPEbm4Rs4xIVO2I9f9YV6H0rk72f4geMVK6lrMfgqzON1poBWe+zg/K1zKhRcEjISI/d4YVo+HfDWmeE9Oi0/SbGOxtIx0TJdz/edyS0jHJJZiWJzknJrlwmQ1KtpYh2XY9HVmVd2XjH4i5m8QavN4X0GbLR+HdEPl3TRnAC3V5uO4kByUhWMDco8xtpZug0rRNO8O2K2Gl2Ntp1kmNtvaxiOMYAA+VQB0A5OT6k4ydNR39z/Tj8KikGXNfY4bDUsMrUo2BIEHXljxUnb+LvSRjA/Cnnqfxrqe+oirNyx69TTY1P+19fSnTJ8x69TRCvzd6LIoz9Y8P2N3qNnrUti13qmlRzGyKuQy71AKgZ2ndtHJ6ZrM8H6fdT/aNfvf7TsbvV4YXk0e9mV1sGVMFFxxn1rrMgAt6DqOo965rw9o+i+Dri80uxvG+03txNqbW9zdmWU7yCzKGOQmeAOgrenOTg4o0jK8HEvQ+GrGHxHPryQuNTmtRaPKJGwYlbdt252jk9QM1qkccAkDPB78U1eAegPTPTB9yKzfDVxq95o4l13T4dL1Ayyr9mgm81QoPytu9SKytKXvNk26speGdAuPDelmzudXvNac3Esv2u92+YA7Ftvy8YHQexrYe4hsLeW4uZFhghRnkkc4VFUEkk+wBoYfN36ms/SI9VlvNXh1SGyOmBwtkI2ZnkjKYcTBhgktn1yK0+P3pPb+tAspfEXtI1mx8QaXa6jp11HeWN1GJYJ4mysinoR7VkeIL2KfVrDQrnRLnU7PUEkaS48lZLaHywGAlz0zxj3qhrVzpFh4k8K2cXiYaGIrmeCHRbRkEd84jUmJlA+UoMHHHWt/W9X03w/p0t/q15b2FjBgTT3LhETJAAJPTnA/GqjFRanFb/ANL7ilBRacSv4i0C71Pw9/Zui6tJ4amRojFdWkSt5aI6lkVTwFZQV/GteRWWMZ3NgEE468daWJgwyCGyM5ByDWRDql7c+ItT0+bR57ewtoIpYNSd1Mdyzk7kUdQVwM5rJNy0IV3oXyTv79TVmP7veq/Q9O5qzGMj9Kl6iOj8EcanPyf9Sf8A0Jal8J/8lA8cf9drL/0mFQ+CVxqcx/6Y/wDsy1N4T/5H/wAb/wDXWy/9JlrxsT/FZ51Z++ztaKKK5TAKKKKAEPSuK+Hv/Hx4w/7D0/8A6KirtT0rifh7/wAfPjD/ALD1x/6KiqZfCxrc1u5paTuaWvg5fEz1lsR9qVe1HahSAuT0HJrGHxMpj68f+Od0nibVPDPgJGeVNTn/ALR1W2TA3afB8x3E8gNMYBgYLBWGcbq6b4rfEF/B+jix0lFvfF+pq8WkaaRkyyKMtLIMjbDHwzuxCgYGSzKDyXgPwBpngPTTDaq11qNwkf8AaGqXMjS3N/Ioxvld2LH5tzbQdoLNgZzX0uU5e8RNVpr3UZ3uM8J+BLjw74q8T6/d+INS1u51iZXjtrlwlvYwIPkgijXjgkkvgE+nGT1cwUDAJI5A+lPQbcDJPHU96ZMOh+tffxSTuNEHAbOSPrXI3/hxNGsf+Ec0XS9RtrHXpbqS+1PT7kK9jJIpcy/Pk5ZjgAdzXXd+emTVDxN4UsPGOkxWGotciCO5hulNvO0TGSNwy5Yc7d2AQeMV0U5KMtv66fcbU5csi5FpyR6MmnyyTXUaQC3aSdyXlG0KSxGOSB1HOTVfQdDsvDWkWelafG0VlaRiGFGcuQo6DLcn8au2ep2msWiXdjcw3ts+ds1vIHjPODgjjqD+VZfiC41i2Nh/ZFhDfM90kdz58/leTAc7pF9SOOPepSk7xWgld3HeI/D8/iCLT1t9YvdJ+y3kd07WRXNwi9YXz/C3etacknnIPJ/z+dSxgFTg5GOKyvEn9ppo142jR20mqBD9nS7JWIvjHzlecfSnHmk1Htf8SUnJ8pHeeIdM0zU9P066vooL7UHdbS3dvmmKruYKO+AQfxrXUk9OTnjn24rJeK1WG3vtTSzS4tRkTzBcQyMADsZuVzwOOvArL8A3VnN/b8dr4ml8SNDqkySrMyk2DjGbbgDhckc+vWnKEXHmS23KcFy80UTaBNbeIpJNYn0CfTNRt5JLNJNQt0SdkQkBlYZOw5JB96ms9I1C38WX+pvrM8+lzW0cMWkGFRHDIvLSh+pLA4wfSr7azpzazJpK31udTjgW5e0EgMoiJKhyuc7cjGaNTuJ7LT7u4tbR7+5hieSK1jIDTMFyFBPAJOOtNyk9ErIblKT9S+owo9wOn0/yaikJz371Dod3PqGj2F1dWb6ddTQRyy2cpBeFigJRiOCRntUsq84zWGzaM+thUPzd+tdN4q/5Iz4i/wCwLd/+iXrmY+v4103ir/kjHiL/ALAt3/6JeuHGfAvU5q/wna2n+oj/ANxf5VPUFp/qI/8AcX+VT15ZwsKKKKBBXHfFX/kS7j/r7s//AErirsa4/wCKn/Il3H/X3Z/+lcVNbgdBqX+pB7b/AOhrOFaWp/8AHsP97+hrNHQV8dmX+8P0X6noUfhHA4FRvyxA5PpTt4UHPAHc9K8x8XfEjUNQ8Q3HhvwhFaz3lmwXUdWvgzWlixG4Q7VKmWbGCVVgFBXc2WVTy0aFTEvkpK7Nr22Oj8a/EXRvAttEb+4Et9cHFnpttiS7vH6BYogcvzxngLgkkAGuA1DSNe+JlwJfGESaVoUY/ceGbK9aQTEjmS8mTbvOMBYl+UclmkyAs3hnwba6Jc3OpXUp1jxFetvvdavIgbic4xtU8+VEoJCxIQqjsSWLdXtGeOv/ANavucFk1PDJSray/IGitFaQ2NnBa26LDbQIscMMahEjQDACqOFHGMDgADrQME96mlHH6VAq5NfRoaJ14A5NZHiTw3p3ii0itdTt/tVvFcxXaIXZcSRnchyp7Ht0NbCL8tQzD5jk017ruh7bHL6PaXmveKDrV3Fquj/2c1zYRWLzqbe8jJDC4KjnPZc+9beq+HNP1jVNM1C7hZ7rTZXktn81lCMybDkA4PBPWs6LSNF8PeKLzW5Lw22oaz9ntClzdYikZAxRI4ycbsZPy9a6XPPXH6VrVk3JOOmhrUk+a6IGPYH247Vi6NoVxpuuaxfyate3sOoNE0VlPt8qz2qQRHjn5iec/wB0VLp9zq82r6tDf2MFtpsTx/YLmKfe9ypTLFl/hwTj6Vpx43d6TbguUm3KrdywMnHWsrSPEmmeIhdvpl9Fepa3DWszQtkJKv3kPuKJH1keJYVSO0/sL7KxklLsLkT7vlCjG3bjOc81zfxCbR9G8OXit4gj8F/6RDczXtkI45SzOANy45Eh+Xdj8aqNJN2e7HGEWb3ifW30PT4Zl0q91gT3EVs0FjGrsqvkF2B/hHOfarB0SCx0K60/SRHowkikSGS1hVBA7KQHCjgkEg/hV4ywxRNM8irEqlzKTxgDO4n0xz6VDpOq2Wu6Zb3+nXcF9Y3Cb4riBw6OpyMgjryDWblJR+HRCd7WWxS0PTrrSdFsrG/1KXVr2CFY5r6ZAjzsBy5A4BPoK0bcYfjJrJ1DUr638Q6dZQ6VPPY3KyvcaikiiO1KgYDA8ktk1r2q85+7nBx6cU53erFLuTk4Hervhr/kYLH13t/6A1UmGM1e8N/8jFZH/bb/ANAauefwS9DGfwm7ff8AJVdE/wCwLff+lFpXYVx97/yVXRP+wJe/+lFpXYV4J5YUUUUAFFFFABRRRQByPjH/AJG3wH/2E5//AEhuaqeNYwmowSAASGFl3gANtLcrnrg+nSrfjH/kbfAf/YTn/wDSG5qr44wL226f6pv5104dJ1EmbUviOYlXpgHgYGKh5z0NSyAZ/h/Oodo9F/Ovb31Z6RcVSPzNQyZDnipgAR/D1Peq8qgt/D+dStwJY8kdO1PIOScetRR8DHy9PWpMAqThe/ek9ySvN948Hqe9EIww602YDfjAPJ7nilhA3Dhfzq2tCi3g4zzxzWZPodg+rjU2s4jqSwtbLeFB5ojJBKBuuMgVo4G0/d6HvUUoG8/d6+tTFtPQlaMehJJ4PzHJ/wAPpTmQCNsLjPb6VFGB/s/nUjACI8L370rWViiq4JYjGDk1NCD7j3zUBA3H7vU96khPPRfzq+gGfYeDNE0e7vLu00u2iubu6a9ml8sFnmKhTJk5w2ABkY4FWtT0201i1ltL61ivLSXG+G4QOjYIIyCOeRV1+R/D+dQFRn+H86OZt3bG22TRrjgZA7CiWP5TgHvRGB/s/nRKBt6L371DepGxWIwTwepq1HwM+p/pVQgFiPl6nv7VYj+50X86qWhR03gof8TKb/rif/QhUnhP/koHjgf9NrL/ANJhUXgc51Kfp/qT0P8AtLUvhP8A5KD45/662X/pMK8bE/xWefW+Nna0UUVynOFFFFACHpXE/D3/AI+fGH/YeuP/AEVFXbHpXE/D3/j58Yf9h64/9FRVMvhY1ua3c0tIeppCTjI/LFfBy3Z6q2GkHBxyfSuG8ffEeTQJo9C8P2kes+Lrtd0FnJJsgtYycfabpxkxxL6AF3PyoCckUfHXxIvRqtx4W8Ii3uvEqKv2y7uEMlrpCuu5WmAI3ylcMsIIJBBJVTuNHwl4UsvC9jLHbyTXt1cv5t7qV6++6vZiMGWVgAMnnCgBFGFUAAAe7lmVTxD9tVVofixvUTwx4RTQHu7+8updX8Q34UahqtygSWYDlY1XnyolJbbGpwCSTk5J2skEcVI3K4+XGc8cVCyjI+7+dfoFOEYRUYqyQl2JgTnpTZicdPWmoAf7v50kwwOi+3J5qyiLBzjHc1aRNyYIyCMEGqgA3YwvX1q2gwo+7+dDE2VNM0Sx8P6fHY6ZZw2FlGWKW9ugRFycnAHHXP51IVyemfrU0nK/w/mag2jP8P507t6jRaU5Hf8AE5qCbgnr36VIgG3ov51FOBn+Hv3pLcVjN17w7p3irSp9M1azS/sJseZBLnDYwR0x3ANadrp9tZeZ9ntordZXLyLDGqb2PUkgcn3qNQAT93r6+1WsDb/D19faqbdlG+hV3a3QpPpdp/aD3/2SEX7xiFroRjzCg5C7uuAcnGamUZJwCP8A9VLIB/s/nTEAyeF/P2NJtslllVxgBeOn6VDJnI49alUDj7v5+1QSYyOF/P2qUJCxg56d66bxV/yRjxF/2Bbv/wBEvXMRgbv4evrXT+Kv+SM+Iv8AsC3f/ol64sZ8C9TCv8J2tp/qI/8AcX+VT1Baf6iP/cX+VT15ZwsKKKKBBXH/ABV/5Eu4/wCvuz/9K4q7CuP+Kn/Il3H/AF92f/pXFTW4HQal/qAPV/6Gs3dggVd1i6jt0gV2AMsuxBzy21jj8ga4n4jeNIvA/hqW+8trnUZWFtp1nGjM93dup8mIAerDk5GACcjGa+Qx8ZSxPLFXbS/U76XwmR8WPHl94Xs7DTNDSKfxPrfm2mmQzfKkcgjLNcyt/DDEoLMNpZztRRlhWL4O8LQ+C/C9ho8DtN5StJPcNgG4uJCXmnOO7yPI/wDwLHTiq3grwzdWm7XteuV1PxfqMKG9vsBUtwVB+zWygkRwRk7QASzEbnZmOa6KTBI4Ue2T6V9tleA+p07y+Jm6BM579f6VaAOenp/KqaAZ/h9etW8DH8P5+1e0xsZNkjoRzUKZB6GpJQMfw9u9QgDPRc/U0IEWo/u9KhlBZjxUkeNvRR+NQyAFv4fzprcZWvdEsNWezkvbKG7ezm+0W7TIGMMmMb19GxxmtPBB71AgA/u/nUxA9F6jvSbbVhN30ZBMMEAA+p9zj/61NjUk9KJlBb+H86IgB/d/OnfS42WduRyKwbvwhot1rk2rT6VbXGoyQpbvPNGHLRq25VweOG5zjNbpA4+7+dV5FBc/d/OiEnD4WEW07od5SyRsjoHRgVKsAQQRjBostOttMsYrOzt0tLWJdkcEChEQegA4HPNKgH+z+dS4H+z+dK7SsO5WkTLZwc0+3zuxz+P40yUAE/d/Oltsbj9386fQRZYHB4q74byfEFkcfxt/6A1UXAx/D+dXPDQH/CQWPT77d/8AYasqnwS9DOfwm/ff8lW0X/sC33/pRaV2Fcfe/wDJVdE/7At9/wClFpXYV4J5YUUUUAFFFFABRRRQBxvjWUReKvAzMQoGqTcn/rwufwrR1fTbLWJY5JLoIUBUBWXofrV7W/D+m+IrdYNTsLfUIFfzFjuIg4DYIyM9DgkfQmscfC7wlj/kXdN/8Bl/wqlJxd07DTa2IX8Kac3/AC/Ef8CSmjwhpuf+P4/mtWf+FX+E/wDoXdO/8Bl/wo/4Vf4T/wChd07/AMBl/wAK0Vep0kae0n3I18LaeOPtx/FkpreEtOY5+3Y/4ElTf8Kv8J/9C7p3/gMv+FH/AAq/wn/0Lunf+Ay/4U/bVf5g9pPuQr4S05f+X7/x5Kd/wiun/wDP9/48lSf8Kv8ACf8A0Lunf+Ay/wCFH/Cr/Cf/AELunf8AgMv+FHtqv8wc8+5z3iT4drrX9nGw8SzaQLe7SeYRCNvtEYzuiYMOA3HI5rYi8JacpGb/ACfqtWP+FXeE/wDoXdO/8Bl/wo/4Vf4T/wChd07/AMBl/wAKf1mq1a+g/a1LWuMPhXTyP+P7/wAeSmnwnp5PN8f++kqb/hWHhIf8y7pv426/4VmTeFPh7btMJNP0GIwTJbS7hEPLlfGyNueGbcuFPJyPWl7ar/MT7SfcvDwppw/5ff8Ax5aU+FdPIx9u4/3kriPC2r/Cjxr4/wDEvhDQ9P0zUdZ8OwW8upJFaAx25meVVjL4wXzC+QOgx613f/CsPCbc/wDCO6dz/wBOy/4Ue3q/zD9pPuVz4O00/wDL8c/7yUqeENOTpfH/AL6Sp/8AhV/hP/oXdO/8Bl/wo/4Vf4T/AOhd07/wGX/Cj29X+YPaT7kR8Kaf/wA/x/76Sm/8Ilp3/P6f++kqf/hV/hP/AKF3Tv8AwGX/AAo/4Vf4T/6F3Tv/AAGX/Cj29X+YPaT7kS+FNPA4vf8Ax5KR/CmnsMfbj/30lTf8Kv8ACf8A0Lunf+Ay/wCFH/Cr/Cf/AELunf8AgMv+FHtqv8wc8+5U/wCEQ04n/j/Ofqv+FSr4U05Rj7af++kqX/hV3hL/AKFzTv8AwGX/AApf+FX+E/8AoXdO/wDAZf8ACj29X+YPaT7ljStKstHneWO8Viy7PmZfUH+lZPg6RZPH3jhlYOvnWQ3Dpn7MO/Srv/CsPCY6eHdO/wDAda2ND8O6Z4dhki0zT7fT45G3OtvEE3HGMnHXispScneRDk3qzToooqSQooooAZJnHBxXEfD+4iju/F6yOsZ/t6c4Y4OPKiruG6etc9f/AA+8N6rezXd3odhcXUx3STPbqWc9Mk45PA5pNJppgXDFaEn/AEhf++hWR4n0GPxBpM1jb6/d6JLIyk3mnPEJ0UEEhS6sBnpnaevFP/4Vf4T/AOhd07/wGX/Cj/hWHhPGP+Ee07HXH2Zf8K5Fg6Cd+W5pzy7mH4c+FXhzwrpEWm6bMYLZGeRi8iu8srnc8sjtlnkY8l2JJ7mtdfCOnKc/bsn1JSpR8LvCQ/5l3Tv/AAGX/Cl/4Vf4T/6F3Tv/AAGX/CvSVWotmP2k+5EfCun/APP8f++kpP8AhE9OP/L8f++kqb/hV/hP/oXdO/8AAZf8KP8AhV/hP/oXdO/8Bl/wp+2q/wAwc8+5EvhPTgf+P78NyVh6V8PFsNa1q7ufEs9/Z3sqvb2cojC2agYZUIGSGPPzZ9q6H/hV/hP/AKF3Tv8AwGX/AApP+FXeE/8AoXdO/wDAZf8ACmsRVj1GqtRXsysPCOmn/l+OfqtSr4V08DH24/8AfSVIfhd4T/6F3Tv/AAGX/Cq+oeAPBGkWU15faNpFnaQrulnuIkSNB6sx4A+tL21X+YXtJ23JG8KaeR/x/H/vpab/AMIlpv8Az+n/AL6Sue8aL8LPh9oWq6vrtvoVhZaZEst27xoTCrZ2blAzlsHaMc9qu+CvDngTx54T0jxFpfh+xfTdUtIr22eSzCM0ciB1JU8g4I4o9vV/mD2k+5rL4U08D/j9/Nkpr+EdOc83x/76Sp/+FX+E/wDoXdO/8Bl/wo/4Vf4T/wChd07/AMBl/wAKPbVf5g9pPuQDwhpwP/H8f++kp/8Awiun/wDP9/48tSf8Kv8ACf8A0Lunf+Ay/wCFH/Cr/Cf/AELunf8AgMv+FHt6v8we0n3IW8Jacw/4/v8Ax5KQeEdOX/l+J/4ElT/8Kv8ACf8A0Lunf+Ay/wCFH/Cr/Cf/AELunf8AgMv+FHt6v8we0n3Ix4V08D/j+/8AHkpjeENOY5+3H/vpKn/4Vf4T/wChd07/AMBl/wAKP+FX+E/+hd07/wABl/wo9tV/mDnn3IB4S00f8vpH/Alqv8QGtrH4V+JraOcOqaNeKCWGT+5er/8Awq/wn/0Lunf+Ay/4Uh+FvhIj/kXNNPs1shB/SolOc/idyZSk92dFZkmKMdPkHB69Ks0xBgAU+syAooooAK4/4q8eC7j0+1WfPPH+lRV2FVdS0211axltL23juraUAPFMgdW5yMg+4FAFe5mtp1w1wgweMOB7VxuufDjR/Efim01q/wBTnujYx7bKweVPs1vKch51T+KUqQoZs7RnaFLNnYX4X+EhjHh3TRgYH+jL/hS/8Kv8J4x/wjunY/69l/wrJUoKaqW95GinJbEY8Laeeft2cnOdyn/PpTT4Q04n/j+P/fSVP/wq/wAJ/wDQu6d/4DL/AIUf8Kv8J/8AQu6d/wCAy/4V2e3q/wAw+efcrjwfpw/5fj/30lSf8Irp/wDz/f8AjyVJ/wAKv8J/9C7p3/gMv+FH/Cr/AAn/ANC7p3/gMv8AhR7ar/MHPPuRN4U05hzfEf8AAkrI8S+AIdW0K9srDX5dJu54ykd7CI2eFuzAMCD+Nbn/AAq/wmP+Zd04f9uy/wCFH/Cr/Cf/AEL2nf8AgMv+FNYiquoKrNbMp2HhCzgs4YptUa5ljRUaZigMhAALEdMnGeOKlPhDTif+P4/99JUw+F3hIf8AMu6d/wCAy/4Uv/Cr/Cf/AELunf8AgMv+FHt6r15hupNu9yEeE9OH/L8f++kpx8Laf/z/AH/jyVVuPBXgO0uZLefS9EhnjgN08UiRqywg4MhB6KDxu6Vxt74g+Edn8R9A8DC00i58SazDJc21pbW6yfuVj3+YxAwFK4we9L21X+Yn2k+53DeEtObk3x/76WhfCWnDpfH81qx/wrDwkR/yLunf+A6/4Uf8Kv8ACf8A0Lunf+Ay/wCFHt6v8w/aT7kf/CLaef8Al+P/AH0lMPhHTiSftx/76Sp/+FX+E/8AoXdO/wDAZf8ACj/hV/hP/oXdO/8AAZf8KPbVf5g9pPuQjwnpw/5fj/30lO/4RXT/APn+/wDHkqT/AIVf4T/6F3Tv/AZf8KP+FX+E/wDoXdO/8Bl/wo9tV/mD2k+5Xfwjpzf8vxH4rSR+EdOTOL4/mtWf+FX+E/8AoXdO/wDAZf8ACj/hV3hL/oXdO/8AAZf8KPb1f5g9pPuRjwrp+Ob4n/gSVLY+HbCwvYrpLzLxkkAsmORj+ppP+FX+E/8AoXdO/wDAZf8ACj/hV3hP/oXdO/8AAZf8Kl1pvRyFzyKtzOk3xU0Xy3V8aLfZxz/y8WddpWHo3g7RPD1w9xpmkWdhO6lGlggVWZSQSMjnGQPyHpW5WRmFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQBHMMqPl3c9M18seN/hL4n1b9qHTvH1t4QFx4U026s7S804Xiq2pz+W6x6sIt4QmzEpRQ/zsGdl5ijz9UsM4o20AeUeG/BGp6b+0b428TNYiHRNR8O6TZW90jLiWeGe9eZdoOc4mjO4+vHevWF4AoAxS0AFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFcn8Ubdrr4c+JLdfDv/CXGawmiGhEoq3+5SvksXIUK2cEk4xmuspCMigD5h/Z5+C3ir4b/AAt8ceD/ABNpyajr9xEXj8QfazcDVVe22QwlpDvU2wVYBuABVVYcs2PYfgH4a1Hwd8FfA2havbfY9U03RLK0ubfcG8uVIEV1yCQcEEcHFd4BzyKWgBaKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAPl/8Aa5+EHib4s694dPhvw5HdnRrW4vL6+lvvsw1S2JUPoo2sCVuNoZi+EXykIOWNd54p8FaprvxO+EGv2OimzsdIN6+oITGrWiyWeyNCAecMdvykgY9Oa9i2+9G2gBFGD+FPoooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigD/2Q==" + } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**kNN is non-parametric, instance based, lazy algorithm and used in the supervised setting.**\n", + "\n", + "* Non-parametric :\n", + " \n", + " It means that algorithm has no pre assumptions about the functional form of the model, to avoid mismodeling .\n", + "\n", + "* Instance based :\n", + " \n", + " It means that our algorithm does not explicitly learn a model.\n", + " Instead, it memorize the training instances which are subsequently used as “knowledge” for the prediction.\n", + "\n", + "* Lazy algorithm :\n", + "\n", + " It means that it does not use the training data for the Generalization i.e. these algorithm has no explicit training phase or it is minimal. Training is very fast.\n", + " \n", + "**kNN Algorithm for Classification**\n", + "\n", + "Training element {xi, yi} , Testing point(x)\n", + "\n", + " Compute the Distance D(x,xi) to every training element xi.\n", + " Select k closest instance xi1,xi2,…….., xik and their labels yi1, yi2 …, yik.\n", + " Output the class y* which is most frequent in yi1,yi2 ……yik.\n", + " \n", + "\n", + "\n", + "**Significant of “k”**\n", + "\n", + " Value of k has strong effect on kNN performance.\n", + " k act as controller to decide the shape of decision boundary.\n", + " Large value of k has following properties:\n", + "\n", + " 1. Smoother decision boundary\n", + " 2. It provide more voters for prediction, it implies less affect from outliers.\n", + " 3. As a result has Lower Variance and High Bias.\n", + " \n", + " ![gh.jpeg](attachment:gh.jpeg)\n", + " \n", + "**How to Select k**\n", + "\n", + " The simplest solution is Cross Validation.\n", + " Best method is to try many k values and use Cross-Validation to see which k value is giving the best result.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%time\n", + "k_range = list(range(2,15))\n", + "d_metric = ['euclidean','minkowski']\n", + "\n", + "param_grid = dict(n_neighbors = k_range, metric =d_metric)\n", + "\n", + "knn = KNeighborsClassifier()\n", + "\n", + "KNN_GridSearch = GridSearch(X_train_f, y_train, knn ,param_grid)\n", + "y_pred = KNN_GridSearch.Best_Model_Predict(X_test_f)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "cf_matrix = confusion_matrix(y_test, y_pred)\n", + "sns.heatmap(pd.DataFrame(cf_matrix), annot=True, cmap=\"YlGnBu\" ,fmt='g')\n", + "plt.tight_layout()\n", + "plt.title('Confusion matrix', y=1.1)\n", + "plt.ylabel('Actual label')\n", + "plt.xlabel('Predicted label')\n", + "plt.show()\n", + "\n", + "print(\"Accuracy:\",metrics.accuracy_score(y_test, y_pred))\n", + "print(\"Precision:\",metrics.precision_score(y_test, y_pred))\n", + "print(\"Recall:\",metrics.recall_score(y_test, y_pred))\n", + "print(\"F1 score:\",metrics.f1_score(y_test, y_pred))\n", + "print(\"AUC :\",metrics.roc_auc_score(y_test, y_pred))\n", + "\n", + "fpr, tpr, _ = metrics.roc_curve(y_test, y_pred)\n", + "auc = metrics.roc_auc_score(y_test, y_pred)\n", + "plt.plot(fpr,tpr,label=\"data 1, auc=\"+str(auc))\n", + "plt.legend(loc=4)\n", + "plt.plot([0, 1], [0, 1],'r--')\n", + "plt.xlabel('False Positive Rate')\n", + "plt.ylabel('True Positive Rate')\n", + "plt.title('Receiver operating characteristic')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 6.3 SVM (Support Vector Machine)" + ] + }, + { + "attachments": { + "Margin.png": { + "image/png": "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" + } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**What is a Support Vector Machine**\n", + "A Support Vector Machine is a supervised machine learning algorithm which can be used for both classification and regression problems. It follows a technique called the kernel trick to transform the data and based on these transformations, it finds an optimal boundary between the possible outputs.\n", + "\n", + "**How does it work?**\n", + "The main idea is to identify the optimal separating hyperplane which maximizes the margin of the training data. \n", + "\n", + "The goal of SVMs is to find the optimal hyperplane because it not only classifies the existing dataset but also helps predict the class of the unseen data. And the optimal hyperplane is the one which has the biggest margin.\n", + "\n", + "![Margin.png](attachment:Margin.png)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%time\n", + "param_grid = [{'gamma': [ 0.1, 1, 10],'C': [ 0.10, 10, 100]}]\n", + "\n", + "svm = SVC()\n", + "\n", + "svm_GridSearch = GridSearch(X_train_f, y_train, svm,param_grid )\n", + "y_pred = svm_GridSearch.Best_Model_Predict(X_test_f)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Kernel**\n", + "\n", + "kernel parameters selects the type of hyperplane used to separate the data. Using ‘linear’ will use a linear hyperplane (a line in the case of 2D data). ‘rbf’ and ‘poly’ uses a non linear hyper-plane.\n", + "\n", + "**gamma**\n", + "\n", + "gamma is a parameter for non linear hyperplanes. The higher the gamma value it tries to exactly fit the training data set. Increasing gamma leads to overfitting as the classifier tries to perfectly fit the training data.\n", + "\n", + "**C**\n", + "\n", + "C is the penalty parameter of the error term. It controls the trade off between smooth decision boundary and classifying the training points correctly.\n", + "A smaller C value leads to a wider street but more margin violations" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "cf_matrix = confusion_matrix(y_test, y_pred)\n", + "sns.heatmap(pd.DataFrame(cf_matrix), annot=True, cmap=\"YlGnBu\" ,fmt='g')\n", + "plt.tight_layout()\n", + "plt.title('Confusion matrix', y=1.1)\n", + "plt.ylabel('Actual label')\n", + "plt.xlabel('Predicted label')\n", + "plt.show()\n", + "\n", + "print(\"Accuracy:\",metrics.accuracy_score(y_test, y_pred))\n", + "print(\"Precision:\",metrics.precision_score(y_test, y_pred))\n", + "print(\"Recall:\",metrics.recall_score(y_test, y_pred))\n", + "print(\"F1 score:\",metrics.f1_score(y_test, y_pred))\n", + "print(\"AUC :\",metrics.roc_auc_score(y_test, y_pred))\n", + "\n", + "fpr, tpr, _ = metrics.roc_curve(y_test, y_pred)\n", + "auc = metrics.roc_auc_score(y_test, y_pred)\n", + "plt.plot(fpr,tpr,label=\"data 1, auc=\"+str(auc))\n", + "plt.legend(loc=4)\n", + "plt.plot([0, 1], [0, 1],'r--')\n", + "plt.xlabel('False Positive Rate')\n", + "plt.ylabel('True Positive Rate')\n", + "plt.title('Receiver operating characteristic')\n", + "plt.show()" + ] + }, + { + "attachments": { + "sdfg.jpg": { + "image/jpeg": "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" + } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 6.4 LDA\n", + "\n", + "* Linear Discriminant Analysis (LDA) is most commonly used as dimensionality reduction technique in the pre-processing step for pattern-classification and machine learning applications. \n", + "\n", + "* The goal is to project a dataset onto a lower-dimensional space with good class-separability in order avoid overfitting (“curse of dimensionality”) and also reduce computational costs.\n", + "\n", + "In general, dimensionality reduction does not only help reducing computational costs for a given classification task, but it can also be helpful to avoid overfitting by minimizing the error in parameter estimation (“curse of dimensionality”).\n", + "\n", + "![sdfg.jpg](attachment:sdfg.jpg)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%time\n", + "param_grid = [{'n_components': [1,2,3,4]}]\n", + "\n", + "lda = LinearDiscriminantAnalysis()\n", + "\n", + "lda_GridSearch = GridSearch(X_train, y_train, lda , param_grid )\n", + "y_pred = lda_GridSearch.Best_Model_Predict(X_test)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "cf_matrix = confusion_matrix(y_test, y_pred)\n", + "sns.heatmap(pd.DataFrame(cf_matrix), annot=True, cmap=\"YlGnBu\" ,fmt='g')\n", + "plt.tight_layout()\n", + "plt.title('Confusion matrix', y=1.1)\n", + "plt.ylabel('Actual label')\n", + "plt.xlabel('Predicted label')\n", + "plt.show()\n", + "\n", + "print(\"Accuracy:\",metrics.accuracy_score(y_test, y_pred))\n", + "print(\"Precision:\",metrics.precision_score(y_test, y_pred))\n", + "print(\"Recall:\",metrics.recall_score(y_test, y_pred))\n", + "print(\"F1 score:\",metrics.f1_score(y_test, y_pred))\n", + "print(\"AUC :\",metrics.roc_auc_score(y_test, y_pred))\n", + "\n", + "fpr, tpr, _ = metrics.roc_curve(y_test, y_pred)\n", + "auc = metrics.roc_auc_score(y_test, y_pred)\n", + "plt.plot(fpr,tpr,label=\"data 1, auc=\"+str(auc))\n", + "plt.legend(loc=4)\n", + "plt.plot([0, 1], [0, 1],'r--')\n", + "plt.xlabel('False Positive Rate')\n", + "plt.ylabel('True Positive Rate')\n", + "plt.title('Receiver operating characteristic')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 6.5 Decision Tree" + ] + }, + { + "attachments": { + "asdwq.png": { + "image/png": "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" + } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Introduction to Decision Trees**\n", + "\n", + "* A decision tree is a decision support tool that uses a tree-like graph or model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility. It is one way to display an algorithm that only contains conditional control statements.\n", + "\n", + "**How does Decision Tree works ?**\n", + "\n", + "* Decision tree is a type of supervised learning algorithm (having a pre-defined target variable) that is mostly used in classification problems. It works for both categorical and continuous input and output variables. In this technique, we split the population or sample into two or more homogeneous sets (or sub-populations) based on most significant splitter / differentiator in input variables.\n", + "\n", + "![asdwq.png](attachment:asdwq.png)\n", + "\n", + "The core algorithm for building decision trees called ID3 by J. R. Quinlan which employs a top-down, greedy search through the space of possible branches with no backtracking. ID3 uses Entropy and Information Gain to construct a decision tree. \n", + "\n", + "**The popular attribute selection measures:**\n", + "\n", + " Information gain\n", + " Gini index\n", + "\n", + "\n", + "**Advantages of CART**\n", + "\n", + " Simple to understand, interpret, visualize.\n", + " Decision trees implicitly perform variable screening or feature selection.\n", + " Can handle both numerical and categorical data. Can also handle multi-output problems.\n", + " Decision trees require relatively little effort from users for data preparation.\n", + " Nonlinear relationships between parameters do not affect tree performance.\n", + " \n", + "**Disadvantages of CART**\n", + "\n", + " Decision-tree learners can create over-complex trees that do not generalize the data well.This is called overfitting.\n", + " Decision trees can be unstable because small variations in the data might result in a completely different tree being generated. This is called variance, which needs to be lowered by methods like bagging and boosting." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Implemention of Decision Tree" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%time\n", + "from scipy.stats import randint\n", + "max_depth_value = [4, 5,6,7,8,9,10,11,12,13]\n", + "max_features_value = randint(1, 7)\n", + "min_samples_leaf_value = randint(1, 4)\n", + "criterion_value = [\"gini\", \"entropy\"]\n", + "\n", + "param_grid = dict(max_depth = max_depth_value,\n", + " max_features = max_features_value,\n", + " min_samples_leaf = min_samples_leaf_value,\n", + " criterion = criterion_value)\n", + "\n", + "CART = DecisionTreeClassifier(random_state=1)\n", + "\n", + "CART_RandSearch = RandomSearch(X_train_f, y_train, CART, param_grid)\n", + "Prediction_CART = CART_RandSearch.Best_Model_Predict(X_test_f)\n" + ] + }, + { + "attachments": { + "dt.PNG": { + "image/png": "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" + }, + "et.PNG": { + "image/png": "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" + } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### How does a tree decide where to split?\n", + "\n", + "Decision tree splits the nodes on all available variables and then selects the split which results in most homogeneous sub-nodes.\n", + "\n", + "Let’s look at the two most commonly used algorithms in decision tree:\n", + "\n", + "#### Gini:\n", + "\n", + "Gini says, if we select two items from a population at random then they must be of same class and probability for this is 1 if population is pure.\n", + "Steps to Calculate Gini for a split\n", + "\n", + " Calculate Gini for sub-nodes, using formula sum of square of probability for success and failure (p^2+q^2).\n", + " Calculate Gini for split using weighted Gini score of each node of that split\n", + "\n", + "Example: – Referring to example used above, where we want to segregate the students based on target variable ( playing cricket or not ). \n", + "\n", + "![dt.PNG](attachment:dt.PNG)\n", + "Split on Gender:\n", + "\n", + " Calculate, Gini for sub-node Female = (0.2)*(0.2)+(0.8)*(0.8)=0.68\n", + " Gini for sub-node Male = (0.65)*(0.65)+(0.35)*(0.35)=0.55\n", + " Calculate weighted Gini for Split Gender = (10/30)*0.68+(20/30)*0.55 = 0.59\n", + "\n", + "Similar for Split on Class:\n", + "\n", + " Gini for sub-node Class IX = (0.43)*(0.43)+(0.57)*(0.57)=0.51\n", + " Gini for sub-node Class X = (0.56)*(0.56)+(0.44)*(0.44)=0.51\n", + " Calculate weighted Gini for Split Class = (14/30)*0.51+(16/30)*0.51 = 0.51\n", + "\n", + "Above, you can see that Gini score for Split on Gender is higher than Split on Class, hence, the node split will take place on Gender.\n", + "\n", + "#### Information Gain:\n", + "\n", + "Information gain can be understood as decrease in “uncertainty” of the result.\n", + " \n", + "Information theory is a measure to define this degree of disorganization in a system known as Entropy. If the sample is completely homogeneous, then the entropy is zero and if the sample is an equally divided (50% – 50%), it has entropy of one.\n", + "\n", + "Entropy can be calculated using formula:-\n", + "![et.PNG](attachment:et.PNG)\n", + "\n", + "Here p and q is probability of success and failure respectively in that node. Entropy is also used with categorical target variable. It chooses the split which has lowest entropy compared to parent node and other splits. The lesser the entropy, the better it is.\n", + "\n", + "Steps to calculate entropy for a split:\n", + "\n", + " Calculate entropy of parent node\n", + " Calculate entropy of each individual node of split and calculate weighted average of all sub-nodes available in split.\n", + "\n", + "Example: Let’s use this method to identify best split for student example.\n", + "\n", + " Entropy for parent node = -(15/30) log2 (15/30) – (15/30) log2 (15/30) = 1. Here 1 shows that it is a impure node.\n", + " Entropy for Female node = -(2/10) log2 (2/10) – (8/10) log2 (8/10) = 0.72 and for male node, -(13/20) log2 (13/20) – (7/20) log2 (7/20) = 0.93\n", + " Entropy for split Gender = Weighted entropy of sub-nodes = (10/30)*0.72 + (20/30)*0.93 = 0.86\n", + " Entropy for Class IX node, -(6/14) log2 (6/14) – (8/14) log2 (8/14) = 0.99 and for Class X node, -(9/16) log2 (9/16) – (7/16) log2 (7/16) = 0.99.\n", + " Entropy for split Class = (14/30)*0.99 + (16/30)*0.99 = 0.99\n", + "\n", + "Above, you can see that entropy for Split on Gender is the lowest among all, so the tree will split on Gender. We can derive information gain from entropy as 1- Entropy." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Visualize Decision Tree" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Visualize Decision Tree\n", + "from sklearn import tree\n", + "from sklearn.tree import export_graphviz\n", + "from IPython.display import Image \n", + "\n", + "feature_names = [i for i in X_train_f.columns]\n", + "\n", + "y_train_str = y_train.astype('str')\n", + "y_train_str[y_train_str == 1] = \"1\"\n", + "y_train_str[y_train_str == 0] =\"0\"\n", + "y_train_str = y_train_str.values\n", + "\n", + "model_1 = tree.DecisionTreeClassifier(criterion='gini', max_depth= 13, max_features= 2, min_samples_leaf= 2) \n", + "model_1.fit(X_train_f, y_train)\n", + "\n", + "from sklearn.externals.six import StringIO\n", + "# Let's give dot_data some space so it will not feel nervous any more\n", + "dot_data = StringIO()\n", + "tree.export_graphviz(model_1, out_file=dot_data,filled=True)\n", + "import pydotplus\n", + "\n", + "graph = pydotplus.graphviz.graph_from_dot_data(dot_data.getvalue())\n", + "# make sure you have graphviz installed and set in path\n", + "Image(graph.create_png())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "cf_matrix = confusion_matrix(y_test, Prediction_CART)\n", + "sns.heatmap(pd.DataFrame(cf_matrix), annot=True, cmap=\"YlGnBu\" ,fmt='g')\n", + "plt.tight_layout()\n", + "plt.title('Confusion matrix', y=1.1)\n", + "plt.ylabel('Actual label')\n", + "plt.xlabel('Predicted label')\n", + "plt.show()\n", + "\n", + "print(\"Accuracy:\",metrics.accuracy_score(y_test, Prediction_CART))\n", + "print(\"Precision:\",metrics.precision_score(y_test, Prediction_CART))\n", + "print(\"Recall:\",metrics.recall_score(y_test, Prediction_CART))\n", + "print(\"F1 score:\",metrics.f1_score(y_test, Prediction_CART))\n", + "print(\"AUC :\",metrics.roc_auc_score(y_test, Prediction_CART))\n", + "\n", + "fpr, tpr, _ = metrics.roc_curve(y_test, Prediction_CART)\n", + "auc = metrics.roc_auc_score(y_test, Prediction_CART)\n", + "plt.plot(fpr,tpr,label=\"data 1, auc=\"+str(auc))\n", + "plt.legend(loc=4)\n", + "plt.plot([0, 1], [0, 1],'r--')\n", + "plt.xlabel('False Positive Rate')\n", + "plt.ylabel('True Positive Rate')\n", + "plt.title('Receiver operating characteristic')\n", + "plt.show()" + ] + }, + { + "attachments": { + "boosting.png": { + "image/png": "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" + }, + "dfg.png": { + "image/png": "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" + } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 6.6 Ensemble methods\n", + "\n", + "**What is an ensemble method?**\n", + "\n", + "Ensemble is a Machine Learning concept in which the idea is to train multiple models using the same learning algorithm. The ensembles take part in a bigger group of methods, called multiclassifiers, where a set of hundreds or thousands of learners with a common objective are fused together to solve the problem.\n", + "When we try to predict the target variable using any machine learning technique, the main causes of difference in actual and predicted values are noise, variance, and bias. Ensemble helps to reduce these factors (except noise, which is irreducible error).\n", + "\n", + "**Techniques to perform ensemble decision trees:**\n", + "\n", + "**1. Bagging**\n", + "\n", + "Bagging is used when the goal is to reduce the variance of a decision tree classifier. Here the objective is to create several subsets of data from training sample chosen randomly with replacement. Each collection of subset data is used to train their decision trees. As a result, we get an ensemble of different models. Average of all the predictions from different trees are used which is more robust than a single decision tree classifier.\n", + "\n", + "Bagging Steps:\n", + "\n", + " Suppose there are N observations and M features in training data set. A sample from training data set is taken randomly with replacement.\n", + " A subset of M features are selected randomly and whichever feature gives the best split is used to split the node iteratively.\n", + " The tree is grown to the largest.\n", + " Above steps are repeated n times and prediction is given based on the aggregation of predictions from n number of trees.\n", + "\n", + "Advantages:\n", + "\n", + " Reduces over-fitting of the model.\n", + " Handles higher dimensionality data very well.\n", + " Maintains accuracy for missing data.\n", + "\n", + "Disadvantages:\n", + "\n", + " Since final prediction is based on the mean predictions from subset trees, it won’t give precise values for the classification and regression model.\n", + " \n", + " ![dfg.png](attachment:dfg.png)\n", + "\n", + "\n", + "**2. Boosting**\n", + "\n", + "Boosting is used to create a collection of predictors. In this technique, learners are learned sequentially with early learners fitting simple models to the data and then analysing data for errors. Consecutive trees (random sample) are fit and at every step, the goal is to improve the accuracy from the prior tree. When an input is misclassified by a hypothesis, its weight is increased so that next hypothesis is more likely to classify it correctly. This process converts weak learners into better performing model.\n", + "\n", + "Boosting Steps:\n", + "\n", + " Draw a random subset of training samples d1 without replacement from the training set D to train a weak learner C1\n", + " Draw second random training subset d2 without replacement from the training set and add 50 percent of the samples that were previously falsely classified/misclassified to train a weak learner C2\n", + " Find the training samples d3 in the training set D on which C1 and C2 disagree to train a third weak learner C3\n", + " Combine all the weak learners via majority voting.\n", + "\n", + "Advantages:\n", + "\n", + " Supports different loss function (we have used ‘binary:logistic’ for this example).\n", + " Works well with interactions.\n", + "\n", + "Disadvantages:\n", + "\n", + " Prone to over-fitting.\n", + " Requires careful tuning of different hyper-parameters.\n", + "\n", + "* Bagging to decrease the model’s variance;\n", + "* Boosting to decreasing the model’s bias, and;\n", + "* Stacking to increasing the predictive force of the classifier.\n", + "\n", + "![boosting.png](attachment:boosting.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 6.6.1 Bagging" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 6.6.1.1 Random Forest\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%time\n", + "param_grid = [\n", + "{'n_estimators': [10, 25,30], 'max_features': ['auto', 'sqrt', 'log2', None], \n", + " 'max_depth': [10, 20, None], 'bootstrap': [True, False]}\n", + "]\n", + "\n", + "rf = RandomForestClassifier()\n", + "\n", + "rf_GridSearch = GridSearch(X_train, y_train, rf ,param_grid )\n", + "y_pred = rf_GridSearch.Best_Model_Predict(X_test)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "cf_matrix = confusion_matrix(y_test, y_pred)\n", + "sns.heatmap(pd.DataFrame(cf_matrix), annot=True, cmap=\"YlGnBu\" ,fmt='g')\n", + "plt.tight_layout()\n", + "plt.title('Confusion matrix', y=1.1)\n", + "plt.ylabel('Actual label')\n", + "plt.xlabel('Predicted label')\n", + "plt.show()\n", + "\n", + "print(\"Accuracy:\",metrics.accuracy_score(y_test, y_pred))\n", + "print(\"Precision:\",metrics.precision_score(y_test, y_pred))\n", + "print(\"Recall:\",metrics.recall_score(y_test, y_pred))\n", + "print(\"F1 score:\",metrics.f1_score(y_test, y_pred))\n", + "print(\"AUC :\",metrics.roc_auc_score(y_test, y_pred))\n", + "\n", + "fpr, tpr, _ = metrics.roc_curve(y_test, y_pred)\n", + "auc = metrics.roc_auc_score(y_test, y_pred)\n", + "plt.plot(fpr,tpr,label=\"data 1, auc=\"+str(auc))\n", + "plt.legend(loc=4)\n", + "plt.plot([0, 1], [0, 1],'r--')\n", + "plt.xlabel('False Positive Rate')\n", + "plt.ylabel('True Positive Rate')\n", + "plt.title('Receiver operating characteristic')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Set of hyperparameters:**\n", + "\n", + "* n_estimators = number of trees in the foreset\n", + "* max_features = max number of features considered for splitting a node\n", + "* max_depth = max number of levels in each decision tree\n", + "* bootstrap = method for sampling data points (with or without replacement)\n", + "\n", + "\n", + "**max_features:** \n", + "\n", + "These are the maximum number of features Random Forest is allowed to try in individual tree. \n", + "\n", + "1)Auto : This will simply take all the features which make sense in every tree.Here we simply do not put any restrictions on the individual tree.\n", + "\n", + "2)sqrt : This option will take square root of the total number of features in individual run. For instance, if the total number of variables are 100, we can only take 10 of them in individual tree.\n", + "\n", + "3)log2:It is another option which takes log to the base 2 of the features input.\n", + "\n", + "Increasing max_features generally improves the performance of the model as at each node now we have a higher number of options to be considered.But, for sure, you decrease the speed of algorithm by increasing the max_features. Hence, you need to strike the right balance and choose the optimal max_features.\n", + "\n", + "**n_estimators :** \n", + "\n", + "This is the number of trees you want to build before taking the maximum voting or averages of predictions. Higher number of trees give you better performance but makes your code slower. You should choose as high value as your processor can handle because this makes your predictions stronger and more stable.\n", + "\n", + "**min_sample_leaf:** \n", + "\n", + "Leaf is the end node of a decision tree. A smaller leaf makes the model more prone to capturing noise in train data. Hence it is important to try different values to get good estimate." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%time\n", + "param_grid = [\n", + "{'n_estimators': [10, 25,30], 'max_features': ['auto', 'sqrt', 'log2', None], \n", + " 'max_depth': [10, 20, None], 'bootstrap': [True, False]}\n", + "]\n", + "\n", + "rf = RandomForestClassifier()\n", + "\n", + "rf_GridSearch = GridSearch(X_train_ef, y_train, rf ,param_grid )\n", + "y_pred = rf_GridSearch.Best_Model_Predict(X_test_ef)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "cf_matrix = confusion_matrix(y_test, y_pred)\n", + "sns.heatmap(pd.DataFrame(cf_matrix), annot=True, cmap=\"YlGnBu\" ,fmt='g')\n", + "plt.tight_layout()\n", + "plt.title('Confusion matrix', y=1.1)\n", + "plt.ylabel('Actual label')\n", + "plt.xlabel('Predicted label')\n", + "plt.show()\n", + "\n", + "print(\"Accuracy:\",metrics.accuracy_score(y_test, y_pred))\n", + "print(\"Precision:\",metrics.precision_score(y_test, y_pred))\n", + "print(\"Recall:\",metrics.recall_score(y_test, y_pred))\n", + "print(\"F1 score:\",metrics.f1_score(y_test, y_pred))\n", + "print(\"AUC :\",metrics.roc_auc_score(y_test, y_pred))\n", + "\n", + "fpr, tpr, _ = metrics.roc_curve(y_test, y_pred)\n", + "auc = metrics.roc_auc_score(y_test, y_pred)\n", + "plt.plot(fpr,tpr,label=\"data 1, auc=\"+str(auc))\n", + "plt.legend(loc=4)\n", + "plt.plot([0, 1], [0, 1],'r--')\n", + "plt.xlabel('False Positive Rate')\n", + "plt.ylabel('True Positive Rate')\n", + "plt.title('Receiver operating characteristic')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 6.6.2 Boosting" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 6.6.2.1 GradientBoosting\n", + "\n", + "A special case of boosting where errors are minimized by gradient descent algorithm e.g. the strategy consulting firms leverage by using case interviews to weed out less qualified candidates." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%time\n", + "learning_rate_ = [.01,.05,.1,.5,1]\n", + "n_estimators_ = [50,100,150,200,250,300]\n", + "\n", + "param_grid = dict(learning_rate=learning_rate_, n_estimators=n_estimators_)\n", + "\n", + "GB = GradientBoostingClassifier()\n", + "\n", + "GB_GridSearch = RandomSearch(X_train, y_train, GB, param_grid)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Prediction_GB = GB_GridSearch.Best_Model_Predict(X_test)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from xgboost import XGBClassifier\n", + "%%time\n", + "learning_rate_ = [.01,.05,.1,.5,1]\n", + "n_estimators_ = [50,100,150,200,250,300]\n", + "\n", + "param_grid = dict(learning_rate=learning_rate_, n_estimators=n_estimators_,n_jobs=-1)\n", + "\n", + "GB = XGBClassifier()\n", + "\n", + "GB_GridSearch = RandomSearch(X_train, y_train, GB, param_grid)\n", + "Prediction_GB = GB_GridSearch.Best_Model_Predict(X_test)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "cf_matrix = confusion_matrix(y_test, Prediction_GB)\n", + "sns.heatmap(pd.DataFrame(cf_matrix), annot=True, cmap=\"YlGnBu\" ,fmt='g')\n", + "plt.tight_layout()\n", + "plt.title('Confusion matrix', y=1.1)\n", + "plt.ylabel('Actual label')\n", + "plt.xlabel('Predicted label')\n", + "plt.show()\n", + "\n", + "print(\"Accuracy:\",metrics.accuracy_score(y_test, Prediction_GB))\n", + "print(\"Precision:\",metrics.precision_score(y_test, Prediction_GB))\n", + "print(\"Recall:\",metrics.recall_score(y_test, Prediction_GB))\n", + "print(\"F1 score:\",metrics.f1_score(y_test, Prediction_GB))\n", + "print(\"AUC :\",metrics.roc_auc_score(y_test, Prediction_GB))\n", + "\n", + "\n", + "fpr, tpr, _ = metrics.roc_curve(y_test, Prediction_GB)\n", + "auc = metrics.roc_auc_score(y_test, Prediction_GB)\n", + "plt.plot(fpr,tpr,label=\"data 1, auc=\"+str(auc))\n", + "plt.legend(loc=4)\n", + "plt.plot([0, 1], [0, 1],'r--')\n", + "plt.xlabel('False Positive Rate')\n", + "plt.ylabel('True Positive Rate')\n", + "plt.title('Receiver operating characteristic')\n", + "plt.show()\n", + "#.712" + ] + }, + { + "attachments": { + "nn_diagram_1.png": { + "image/png": "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" + } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 7. Neural Network\n", + "\n", + "![nn_diagram_1.png](attachment:nn_diagram_1.png)\n", + "\n", + "**The Sequential model is a linear stack of layers.**\n", + "\n", + "**Specifying the input shape**\n", + "\n", + " The model needs to know what input shape it should expect. For this reason, the first layer in a Sequential model (and only the first, because following layers can do automatic shape inference) needs to receive information about its input shape.\n", + " \n", + "**Compilation**\n", + "\n", + "Before training a model, we need to configure the learning process, which is done via the compile method. It receives three arguments:\n", + "\n", + " An optimizer. \n", + " This could be the string identifier of an existing optimizer (such as rmsprop , adam or adagrad).\n", + " \n", + " A loss function. \n", + " This is the objective that the model will try to minimize. It can be the string identifier of an existing loss function (such as categorical_crossentropy or mse), or it can be an objective function. \n", + " \n", + " A list of metrics.\n", + " For any classification problem you will want to set this to metrics=['accuracy']. A metric could be the string identifier of an existing metric or a custom metric function.\n", + "\n", + "**Training**\n", + "\n", + " Keras models are trained on Numpy arrays of input data and labels. For training a model,we will use the fit function." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%time\n", + "import keras\n", + "from keras.models import Sequential\n", + "from keras.layers import Dense\n", + "\n", + "model = Sequential()\n", + "\n", + "# Adding the input layer and the first hidden layer\n", + "model.add(Dense(output_dim = 16, activation = 'relu', input_dim = 45))\n", + "# Adding the second hidden layer\n", + "model.add(Dense(output_dim = 8, activation = 'relu'))\n", + "\n", + "# Adding the output layer\n", + "model.add(Dense(output_dim = 1, activation = 'sigmoid'))\n", + "\n", + "model.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])\n", + "\n", + "model.fit(X_train, y_train, batch_size = 10, nb_epoch = 100)\n", + "\n", + "y_pred1 = model.predict(X_test)\n", + "y_pred1 = (y_pred1 > 0.5)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "cf_matrix = confusion_matrix(y_test, y_pred1)\n", + "sns.heatmap(pd.DataFrame(cf_matrix), annot=True, cmap=\"YlGnBu\" ,fmt='g')\n", + "plt.tight_layout()\n", + "plt.title('Confusion matrix', y=1.1)\n", + "plt.ylabel('Actual label')\n", + "plt.xlabel('Predicted label')\n", + "plt.show()\n", + "\n", + "print(\"Accuracy:\",metrics.accuracy_score(y_test, y_pred1))\n", + "print(\"Precision:\",metrics.precision_score(y_test, y_pred1))\n", + "print(\"Recall:\",metrics.recall_score(y_test, y_pred1))\n", + "print(\"F1 score:\",metrics.f1_score(y_test, y_pred1))\n", + "print(\"AUC :\",metrics.roc_auc_score(y_test, y_pred1))\n", + "\n", + "\n", + "fpr, tpr, _ = metrics.roc_curve(y_test, y_pred1)\n", + "auc = metrics.roc_auc_score(y_test, y_pred1)\n", + "plt.plot(fpr,tpr,label=\"data 1, auc=\"+str(auc))\n", + "plt.legend(loc=4)\n", + "plt.plot([0, 1], [0, 1],'r--')\n", + "plt.xlabel('False Positive Rate')\n", + "plt.ylabel('True Positive Rate')\n", + "plt.title('Receiver operating characteristic')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "m = { \n", + "\"GradientBoosting\" : {\n", + " \"Accuracy\": 0.8280,\n", + " \"Precision\": 0.84150,\n", + " \"Recall\": 0.8063,\n", + " \"F1 score\": 0.82352,\n", + " \"AUC\" : 0.82795,\n", + " \"RunTime(sec)\" : 481\n", + " },\n", + "\"Random Forest\" : {\n", + " \"Accuracy\": 0.82064,\n", + " \"Precision\": 0.86139,\n", + " \"Recall\": 0.76217,\n", + " \"F1 score\": 0.80875,\n", + " \"AUC\" : 0.8203,\n", + " \"RunTime(sec)\" : 59.4\n", + " },\n", + "\"LDA\" : {\n", + " \"Accuracy\": 0.8001,\n", + " \"Precision\": 0.8307,\n", + " \"Recall\": 0.7512,\n", + " \"F1 score\": 0.7890,\n", + " \"AUC\" : 0.7998,\n", + " \"RunTime(sec)\" : 1.2\n", + " },\n", + "\"Decision Tree\" : {\n", + " \"Accuracy\": 0.7992,\n", + " \"Precision\": 0.8607,\n", + " \"Recall\": 0.7117,\n", + " \"F1 score\": 0.7791,\n", + " \"AUC\" : 0.7988,\n", + " \"RunTime(sec)\" : 2.1\n", + " },\n", + "\"SVM\" : {\n", + " \"Accuracy\": 0.8066,\n", + " \"Precision\": 0.8592,\n", + " \"Recall\": 0.7312,\n", + " \"F1 score\": 0.7900,\n", + " \"AUC\" : 0.8063,\n", + " \"RunTime(sec)\" : 500.4\n", + " },\n", + "\"KNN\" : {\n", + " \"Accuracy\": 0.7924,\n", + " \"Precision\": 0.8212,\n", + " \"Recall\": 0.74498,\n", + " \"F1 score\": 0.7812,\n", + " \"AUC\" : 0.7921,\n", + " \"RunTime(sec)\" : 144.1\n", + " },\n", + "\"LR(ET)\" : {\n", + " \"Accuracy\": 0.8069,\n", + " \"Precision\": 0.8156,\n", + " \"Recall\": 0.7908,\n", + " \"F1 score\": 0.8030,\n", + " \"AUC\" : 0.80687,\n", + " \"RunTime(sec)\" : .688\n", + " },\n", + "\"LR(PCA)\" : {\n", + " \"Accuracy\": 0.7866,\n", + " \"Precision\": 0.8162,\n", + " \"Recall\": 0.7492,\n", + " \"F1 score\": 0.7813,\n", + " \"AUC\" : 0.7872,\n", + " \"RunTime(sec)\" : .433\n", + " },\n", + "\"LR(REF)\" : {\n", + " \"Accuracy\": 0.7984,\n", + " \"Precision\": 0.8409,\n", + " \"Recall\": 0.7335,\n", + " \"F1 score\":0.7835,\n", + " \"AUC\" : 0.7980,\n", + " \"RunTime(sec)\" : 1.7\n", + " },\n", + "\"LR(GridSearch)\" : {\n", + " \"Accuracy\": 0.81636,\n", + " \"Precision\": 0.8270,\n", + " \"Recall\": 0.7977,\n", + " \"F1 score\": 0.8121,\n", + " \"AUC\" : 0.8162,\n", + " \"RunTime(sec)\" : 23.8\n", + " },\n", + "\"LR(RandomSearch)\":{\n", + " \"Accuracy\": 0.8120,\n", + " \"Precision\": 0.8302,\n", + " \"Recall\": 0.7822,\n", + " \"F1 score\": 0.8055,\n", + " \"AUC\" : 0.8119,\n", + " \"RunTime(sec)\" : 4.9\n", + " },\n", + "\"Neural Network\" : {\n", + " \"Accuracy\": 0.8157,\n", + " \"Precision\": 0.8466,\n", + " \"Recall\": 0.76905,\n", + " \"F1 score\": 0.7707,\n", + " \"AUC\" : 0.8155,\n", + " \"RunTime(sec)\" : 120\n", + " },\n", + "}\n", + "df = pd.DataFrame(m)\n", + "df.T\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Summary\n", + "\n", + "I have choosen two performance criterion for models:\n", + "1. Model Accuracy\n", + "2. Run time\n", + "\n", + "#### 1. Model Accuracy\n", + "\n", + "**GradientBoosting** outperform all the models if we take accuracy under consideration.It has 82.7% accuracy. But it takes nearly 481 sec (8 min) to pick the hyperparameters, train the model and predict the output.\n", + "\n", + "There is another way to reduce the runtime by a factor of 8 without losing much accuracy if we use Random Forest for modelling.\n", + "**Random Forest** has accuracy 82% but it has nearly 1 min runtime.\n", + "\n", + "#### 2. Run time\n", + "\n", + "In case if our focus is on runtime then **Logistic Regression** has runtime 4.9 sec with accuracy 81.1%.\n", + "\n", + "But if we want lowest runtime, we have to diminishing the accuracy. In that case **LDA** is the best choice with nearly 80% accuracy and 1.2 sec of runtime." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**The results obtained above can be used as a standard point of reference for other comparative studies done in the field of predicting values from census data. This comparative study can further be used as a basis for improving the present classifiers and techniques resulting in making better technologies for accurately predicting income level of an individual.**" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "{", + "\"tags\":", + "[", + "\"hide_input\"", + "]", + "}" + ] + }, + "source": [ + "## Fairness" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "# This DataFrame is created to stock differents models and fair metrics that we produce in this notebook\n", + "algo_metrics = pd.DataFrame(columns=['model', 'fair_metrics', 'prediction', 'probs'])\n", + "\n", + "def add_to_df_algo_metrics(algo_metrics, model, fair_metrics, preds, probs, name):\n", + " return algo_metrics.append(pd.DataFrame(data=[[model, fair_metrics, preds, probs]], columns=['model', 'fair_metrics', 'prediction', 'probs'], index=[name]))" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "def fair_metrics(dataset, pred, pred_is_dataset=False):\n", + " if pred_is_dataset:\n", + " dataset_pred = pred\n", + " else:\n", + " dataset_pred = dataset.copy()\n", + " dataset_pred.labels = pred\n", + " \n", + " cols = ['statistical_parity_difference', 'equal_opportunity_difference', 'average_abs_odds_difference', 'disparate_impact', 'theil_index']\n", + " obj_fairness = [[0,0,0,1,0]]\n", + " \n", + " fair_metrics = pd.DataFrame(data=obj_fairness, index=['objective'], columns=cols)\n", + " \n", + " for attr in dataset_pred.protected_attribute_names:\n", + " idx = dataset_pred.protected_attribute_names.index(attr)\n", + " privileged_groups = [{attr:dataset_pred.privileged_protected_attributes[idx][0]}] \n", + " unprivileged_groups = [{attr:dataset_pred.unprivileged_protected_attributes[idx][0]}] \n", + " \n", + " classified_metric = ClassificationMetric(dataset, \n", + " dataset_pred,\n", + " unprivileged_groups=unprivileged_groups,\n", + " privileged_groups=privileged_groups)\n", + "\n", + " metric_pred = BinaryLabelDatasetMetric(dataset_pred,\n", + " unprivileged_groups=unprivileged_groups,\n", + " privileged_groups=privileged_groups)\n", + "\n", + " acc = classified_metric.accuracy()\n", + "\n", + " row = pd.DataFrame([[metric_pred.mean_difference(),\n", + " classified_metric.equal_opportunity_difference(),\n", + " classified_metric.average_abs_odds_difference(),\n", + " metric_pred.disparate_impact(),\n", + " classified_metric.theil_index()]],\n", + " columns = cols,\n", + " index = [attr]\n", + " )\n", + " fair_metrics = fair_metrics.append(row) \n", + " \n", + " fair_metrics = fair_metrics.replace([-np.inf, np.inf], 2)\n", + " \n", + " return fair_metrics\n", + "\n", + "def plot_fair_metrics(fair_metrics):\n", + " fig, ax = plt.subplots(figsize=(20,4), ncols=5, nrows=1)\n", + "\n", + " plt.subplots_adjust(\n", + " left = 0.125, \n", + " bottom = 0.1, \n", + " right = 0.9, \n", + " top = 0.9, \n", + " wspace = .5, \n", + " hspace = 1.1\n", + " )\n", + "\n", + " y_title_margin = 1.2\n", + "\n", + " plt.suptitle(\"Fairness metrics\", y = 1.09, fontsize=20)\n", + " sns.set(style=\"dark\")\n", + "\n", + " cols = fair_metrics.columns.values\n", + " obj = fair_metrics.loc['objective']\n", + " size_rect = [0.2,0.2,0.2,0.4,0.25]\n", + " rect = [-0.1,-0.1,-0.1,0.8,0]\n", + " bottom = [-1,-1,-1,0,0]\n", + " top = [1,1,1,2,1]\n", + " bound = [[-0.1,0.1],[-0.1,0.1],[-0.1,0.1],[0.8,1.2],[0,0.25]]\n", + "\n", + " display(Markdown(\"### Check bias metrics :\"))\n", + " display(Markdown(\"A model can be considered bias if just one of these five metrics show that this model is biased.\"))\n", + " for attr in fair_metrics.index[1:len(fair_metrics)].values:\n", + " display(Markdown(\"#### For the %s attribute :\"%attr))\n", + " check = [bound[i][0] < fair_metrics.loc[attr][i] < bound[i][1] for i in range(0,5)]\n", + " display(Markdown(\"With default thresholds, bias against unprivileged group detected in **%d** out of 5 metrics\"%(5 - sum(check))))\n", + "\n", + " for i in range(0,5):\n", + " plt.subplot(1, 5, i+1)\n", + " ax = sns.barplot(x=fair_metrics.index[1:len(fair_metrics)], y=fair_metrics.iloc[1:len(fair_metrics)][cols[i]])\n", + " \n", + " for j in range(0,len(fair_metrics)-1):\n", + " a, val = ax.patches[j], fair_metrics.iloc[j+1][cols[i]]\n", + " marg = -0.2 if val < 0 else 0.1\n", + " ax.text(a.get_x()+a.get_width()/5, a.get_y()+a.get_height()+marg, round(val, 3), fontsize=15,color='black')\n", + "\n", + " plt.ylim(bottom[i], top[i])\n", + " plt.setp(ax.patches, linewidth=0)\n", + " ax.add_patch(patches.Rectangle((-5,rect[i]), 10, size_rect[i], alpha=0.3, facecolor=\"green\", linewidth=1, linestyle='solid'))\n", + " plt.axhline(obj[i], color='black', alpha=0.3)\n", + " plt.title(cols[i])\n", + " ax.set_ylabel('') \n", + " ax.set_xlabel('')" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "def get_fair_metrics_and_plot(data, model, plot=False, model_aif=False):\n", + " pred = model.predict(data).labels if model_aif else model.predict(data.features)\n", + " # fair_metrics function available in the metrics.py file\n", + " fair = fair_metrics(data, pred)\n", + "\n", + " if plot:\n", + " # plot_fair_metrics function available in the visualisations.py file\n", + " # The visualisation of this function is inspired by the dashboard on the demo of IBM aif360 \n", + " plot_fair_metrics(fair)\n", + " display(fair)\n", + " \n", + " return fair" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
education_Assoc-acdmeducation_Assoc-voceducation_Bachelorseducation_Doctorateeducation_HS-gradeducation_Masterseducation_Preschooleducation_Prof-schooleducation_elementary_schoolincome_<=50K...workclass_Never-workedworkclass_Privateworkclass_Self_employedworkclass_Without-payidagecapital.gaincapital.losshours.per.weekfnlwgt
00000100001...010001.0000000.01.0000000.3979590.043987
10000100001...010010.8904110.01.0000000.1734690.081896
20000000001...010020.6712330.01.0000000.3979590.118021
30000000011...010030.5068490.00.8953170.3979590.086982
40000000001...010040.3287670.00.8953170.3979590.171404
..................................................................
325560000000001...0100325560.0684930.00.0000000.3979590.202298
325571000000001...0100325570.1369860.00.0000000.3775510.166404
325580000100000...0100325580.3150680.00.0000000.3979590.096500
325590000100001...0100325590.5616440.00.0000000.3979590.094827
325600000100001...0100325600.0684930.00.0000000.1938780.128499
\n", + "

32561 rows × 49 columns

\n", + "
" + ], + "text/plain": [ + " education_Assoc-acdm education_Assoc-voc education_Bachelors \\\n", + "0 0 0 0 \n", + "1 0 0 0 \n", + "2 0 0 0 \n", + "3 0 0 0 \n", + "4 0 0 0 \n", + "... ... ... ... \n", + "32556 0 0 0 \n", + "32557 1 0 0 \n", + "32558 0 0 0 \n", + "32559 0 0 0 \n", + "32560 0 0 0 \n", + "\n", + " education_Doctorate education_HS-grad education_Masters \\\n", + "0 0 1 0 \n", + "1 0 1 0 \n", + "2 0 0 0 \n", + "3 0 0 0 \n", + "4 0 0 0 \n", + "... ... ... ... \n", + "32556 0 0 0 \n", + "32557 0 0 0 \n", + "32558 0 1 0 \n", + "32559 0 1 0 \n", + "32560 0 1 0 \n", + "\n", + " education_Preschool education_Prof-school \\\n", + "0 0 0 \n", + "1 0 0 \n", + "2 0 0 \n", + "3 0 0 \n", + "4 0 0 \n", + "... ... ... \n", + "32556 0 0 \n", + "32557 0 0 \n", + "32558 0 0 \n", + "32559 0 0 \n", + "32560 0 0 \n", + "\n", + " education_elementary_school income_<=50K ... workclass_Never-worked \\\n", + "0 0 1 ... 0 \n", + "1 0 1 ... 0 \n", + "2 0 1 ... 0 \n", + "3 1 1 ... 0 \n", + "4 0 1 ... 0 \n", + "... ... ... ... ... \n", + "32556 0 1 ... 0 \n", + "32557 0 1 ... 0 \n", + "32558 0 0 ... 0 \n", + "32559 0 1 ... 0 \n", + "32560 0 1 ... 0 \n", + "\n", + " workclass_Private workclass_Self_employed workclass_Without-pay \\\n", + "0 1 0 0 \n", + "1 1 0 0 \n", + "2 1 0 0 \n", + "3 1 0 0 \n", + "4 1 0 0 \n", + "... ... ... ... \n", + "32556 1 0 0 \n", + "32557 1 0 0 \n", + "32558 1 0 0 \n", + "32559 1 0 0 \n", + "32560 1 0 0 \n", + "\n", + " id age capital.gain capital.loss hours.per.week fnlwgt \n", + "0 0 1.000000 0.0 1.000000 0.397959 0.043987 \n", + "1 1 0.890411 0.0 1.000000 0.173469 0.081896 \n", + "2 2 0.671233 0.0 1.000000 0.397959 0.118021 \n", + "3 3 0.506849 0.0 0.895317 0.397959 0.086982 \n", + "4 4 0.328767 0.0 0.895317 0.397959 0.171404 \n", + "... ... ... ... ... ... ... \n", + "32556 32556 0.068493 0.0 0.000000 0.397959 0.202298 \n", + "32557 32557 0.136986 0.0 0.000000 0.377551 0.166404 \n", + "32558 32558 0.315068 0.0 0.000000 0.397959 0.096500 \n", + "32559 32559 0.561644 0.0 0.000000 0.397959 0.094827 \n", + "32560 32560 0.068493 0.0 0.000000 0.193878 0.128499 \n", + "\n", + "[32561 rows x 49 columns]" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "final_df" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "privileged_groups = [{'sex_Female': 1}]\n", + "unprivileged_groups = [{'sex_Female': 0}]\n", + "dataset_orig = StandardDataset(final_df,\n", + " label_name='income_<=50K',\n", + " protected_attribute_names=['sex_Female'],\n", + " favorable_classes=[1],\n", + " privileged_classes=[[1]])\n" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "#### Original training dataset" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Difference in mean outcomes between unprivileged and privileged groups = -0.196276\n" + ] + } + ], + "source": [ + "metric_orig_train = BinaryLabelDatasetMetric(dataset_orig, \n", + " unprivileged_groups=unprivileged_groups,\n", + " privileged_groups=privileged_groups)\n", + "display(Markdown(\"#### Original training dataset\"))\n", + "print(\"Difference in mean outcomes between unprivileged and privileged groups = %f\" % metric_orig_train.mean_difference())" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "import ipynbname\n", + "nb_fname = ipynbname.name()\n", + "nb_path = ipynbname.path()\n", + "\n", + "from xgboost import XGBClassifier\n", + "import pickle\n", + "\n", + "data_orig_train, data_orig_test = dataset_orig.split([0.7], shuffle=True)\n", + "X_train = data_orig_train.features\n", + "y_train = data_orig_train.labels.ravel()\n", + "\n", + "X_test = data_orig_test.features\n", + "y_test = data_orig_test.labels.ravel()\n", + "num_estimators = 250\n", + "\n", + "model = ExtraTreesClassifier(n_estimators=250,\n", + " random_state=2)\n", + "\n", + "\n", + "mdl = model.fit(X_train, y_train)\n", + "with open('../../Results/ExtraTrees/' + nb_fname + '.pkl', 'wb') as f:\n", + " pickle.dump(mdl, f)\n", + "\n", + "with open('../../Results/ExtraTrees/' + nb_fname + '_Train' + '.pkl', 'wb') as f:\n", + " pickle.dump(data_orig_train, f) \n", + " \n", + "with open('../../Results/ExtraTrees/' + nb_fname + '_Test' + '.pkl', 'wb') as f:\n", + " pickle.dump(data_orig_test, f) " + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "from csv import writer\n", + "from sklearn.metrics import accuracy_score, f1_score\n", + "\n", + "final_metrics = []\n", + "accuracy = []\n", + "f1= []\n", + "\n", + "for i in range(1,num_estimators+1):\n", + " \n", + " model = ExtraTreesClassifier(n_estimators=i,random_state=2)\n", + "\n", + " \n", + " mdl = model.fit(X_train, y_train)\n", + " yy = mdl.predict(X_test)\n", + " accuracy.append(accuracy_score(y_test, yy))\n", + " f1.append(f1_score(y_test, yy))\n", + " fair = get_fair_metrics_and_plot(data_orig_test, mdl) \n", + " fair_list = fair.iloc[1].tolist()\n", + " fair_list.insert(0, i)\n", + " final_metrics.append(fair_list)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 0 1 2 3 4 5\n", + "0 1 -0.193706 -0.107867 0.102835 0.780925 0.140455\n", + "1 2 -0.250336 -0.159807 0.122705 0.698025 0.194922\n", + "2 3 -0.189530 -0.096166 0.093642 0.789723 0.116978\n", + "3 4 -0.232338 -0.136963 0.109096 0.733989 0.147484\n", + "4 5 -0.195195 -0.098474 0.097431 0.784574 0.112486\n", + ".. ... ... ... ... ... ...\n", + "245 246 -0.190732 -0.084208 0.095555 0.792765 0.092379\n", + "246 247 -0.189553 -0.083239 0.095068 0.794188 0.091774\n", + "247 248 -0.189977 -0.083987 0.094488 0.793586 0.092367\n", + "248 249 -0.189236 -0.084123 0.093121 0.794462 0.092293\n", + "249 250 -0.190294 -0.085007 0.094280 0.793313 0.092664\n", + "\n", + "[250 rows x 6 columns]\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
classifierT0T1T2T3T4T5T6T7T8...T240T241T242T243T244T245T246T247T248T249
accuracy0.8411300.7955780.7783810.8169720.8087830.8230120.8212710.8289490.8299720.833657...0.8416420.8414370.8417440.8416420.8418470.8419490.8419490.8416420.8412320.841130
f10.8969730.8640110.8427860.8798390.8695900.8838740.8799170.8883990.8868450.891457...0.8973390.8971520.8974260.8972840.8974850.8974630.8975580.8972980.8970870.896973
statistical_parity_difference-0.190294-0.193706-0.250336-0.189530-0.232338-0.195195-0.216088-0.185311-0.207259-0.186550...-0.189538-0.190279-0.189085-0.190279-0.189236-0.190732-0.189553-0.189977-0.189236-0.190294
equal_opportunity_difference-0.085007-0.107867-0.159807-0.096166-0.136963-0.098474-0.119843-0.086831-0.104340-0.084859...-0.083325-0.083852-0.082883-0.083631-0.082883-0.084208-0.083239-0.083987-0.084123-0.085007
average_abs_odds_difference0.0942800.1028350.1227050.0936420.1090960.0974310.0892970.0868290.0918070.086567...0.0951110.0960920.0946510.0962200.0948900.0955550.0950680.0944880.0931210.094280
disparate_impact-0.231538-0.247276-0.359500-0.236073-0.309261-0.242614-0.280111-0.228267-0.264166-0.229802...-0.230504-0.231607-0.229884-0.231607-0.230090-0.232228-0.230435-0.231193-0.230090-0.231538
theil_index0.0926640.1404550.1949220.1169780.1474840.1124860.1290780.1047760.1169410.101702...0.0921080.0925900.0919100.0924530.0918850.0923790.0917740.0923670.0922930.092664
\n", + "

7 rows × 251 columns

\n", + "
" + ], + "text/plain": [ + " classifier T0 T1 T2 \\\n", + "accuracy 0.841130 0.795578 0.778381 0.816972 \n", + "f1 0.896973 0.864011 0.842786 0.879839 \n", + "statistical_parity_difference -0.190294 -0.193706 -0.250336 -0.189530 \n", + "equal_opportunity_difference -0.085007 -0.107867 -0.159807 -0.096166 \n", + "average_abs_odds_difference 0.094280 0.102835 0.122705 0.093642 \n", + "disparate_impact -0.231538 -0.247276 -0.359500 -0.236073 \n", + "theil_index 0.092664 0.140455 0.194922 0.116978 \n", + "\n", + " T3 T4 T5 T6 \\\n", + "accuracy 0.808783 0.823012 0.821271 0.828949 \n", + "f1 0.869590 0.883874 0.879917 0.888399 \n", + "statistical_parity_difference -0.232338 -0.195195 -0.216088 -0.185311 \n", + "equal_opportunity_difference -0.136963 -0.098474 -0.119843 -0.086831 \n", + "average_abs_odds_difference 0.109096 0.097431 0.089297 0.086829 \n", + "disparate_impact -0.309261 -0.242614 -0.280111 -0.228267 \n", + "theil_index 0.147484 0.112486 0.129078 0.104776 \n", + "\n", + " T7 T8 ... T240 T241 \\\n", + "accuracy 0.829972 0.833657 ... 0.841642 0.841437 \n", + "f1 0.886845 0.891457 ... 0.897339 0.897152 \n", + "statistical_parity_difference -0.207259 -0.186550 ... -0.189538 -0.190279 \n", + "equal_opportunity_difference -0.104340 -0.084859 ... -0.083325 -0.083852 \n", + "average_abs_odds_difference 0.091807 0.086567 ... 0.095111 0.096092 \n", + "disparate_impact -0.264166 -0.229802 ... -0.230504 -0.231607 \n", + "theil_index 0.116941 0.101702 ... 0.092108 0.092590 \n", + "\n", + " T242 T243 T244 T245 \\\n", + "accuracy 0.841744 0.841642 0.841847 0.841949 \n", + "f1 0.897426 0.897284 0.897485 0.897463 \n", + "statistical_parity_difference -0.189085 -0.190279 -0.189236 -0.190732 \n", + "equal_opportunity_difference -0.082883 -0.083631 -0.082883 -0.084208 \n", + "average_abs_odds_difference 0.094651 0.096220 0.094890 0.095555 \n", + "disparate_impact -0.229884 -0.231607 -0.230090 -0.232228 \n", + "theil_index 0.091910 0.092453 0.091885 0.092379 \n", + "\n", + " T246 T247 T248 T249 \n", + "accuracy 0.841949 0.841642 0.841232 0.841130 \n", + "f1 0.897558 0.897298 0.897087 0.896973 \n", + "statistical_parity_difference -0.189553 -0.189977 -0.189236 -0.190294 \n", + "equal_opportunity_difference -0.083239 -0.083987 -0.084123 -0.085007 \n", + "average_abs_odds_difference 0.095068 0.094488 0.093121 0.094280 \n", + "disparate_impact -0.230435 -0.231193 -0.230090 -0.231538 \n", + "theil_index 0.091774 0.092367 0.092293 0.092664 \n", + "\n", + "[7 rows x 251 columns]" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np\n", + "final_result = pd.DataFrame(final_metrics)\n", + "print(final_result)\n", + "final_result[4] = np.log(final_result[4])\n", + "final_result = final_result.transpose()\n", + "final_result.loc[0] = f1 # add f1 and acc to df\n", + "acc = pd.DataFrame(accuracy).transpose()\n", + "acc = acc.rename(index={0: 'accuracy'})\n", + "final_result = pd.concat([acc,final_result])\n", + "final_result = final_result.rename(index={0: 'f1', 1: 'statistical_parity_difference', 2: 'equal_opportunity_difference', 3: 'average_abs_odds_difference', 4: 'disparate_impact', 5: 'theil_index'})\n", + "final_result.columns = ['T' + str(col) for col in final_result.columns]\n", + "final_result.insert(0, \"classifier\", final_result['T' + str(num_estimators - 1)]) ##Add final metrics add the beginning of the df\n", + "final_result.to_csv('../../Results/ExtraTrees/' + nb_fname + '.csv')\n", + "final_result" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +}