{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# <center>Model Interpretability on Random Forest using SHAP</center>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Table of Contents\n",
"\n",
"1. [Problem Statement](#section1)<br><br>\n",
"2. [Importing Packages](#section2)<br><br>\n",
"3. [Loading Data](#section3)\n",
" - 3.1 [Description of the Dataset](#section301)<br><br>\n",
"4. [Data train/test split](#section4)<br><br>\n",
"5. [Random Forest Model](#section5)\n",
" - 5.1 [Random Forest in scikit-learn](#section501)<br><br> \n",
" - 5.2 [Feature Importances](#section502)<br><br>\n",
" - 5.3 [Using the Model for Prediction](#section503)<br><br>\n",
"6. [Model Evaluation](#section6)\n",
" - 6.1 [R-Squared Value](#section601)<br><br>\n",
"7. [Model Interpretability using SHAP](#section7) \n",
" - 7.1 [Explain predictions](#section701)<br><br>\n",
" - 7.2 [Visualize a single prediction](#section702)<br><br>\n",
" - 7.3 [Visualize many predictions](#section703)<br><br>\n",
" - 7.4 [SHAP Dependence Plots](#section704)<br><br>\n",
" - 7.5 [SHAP Summary Plot](#section705)<br><br>\n",
" - 7.6 [Bar chart of Mean Importance](#section706)<br>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id=section1></a>\n",
"## 1. Problem Statement\n",
"\n",
"\n",
"- We have often found that **Machine Learning (ML)** algorithms capable of capturing **structural non-linearities** in training data - models that are sometimes referred to as **'black box' (e.g. Random Forests, Deep Neural Networks, etc.)** - perform far **better at prediction** than their **linear counterparts (e.g. Generalized Linear Models)**. \n",
"\n",
"\n",
"- They are, however, much **harder to interpret** - in fact, quite often it is **not possible to gain any insight into why a particular prediction has been produced**, when given an **instance of input data (i.e. the model features)**. \n",
"\n",
"\n",
"- Consequently, it has **not been possible to use 'black box' ML algorithms** in situations where clients have sought **cause-and-effect explanations for model predictions**, with end-results being that sub-optimal predictive models have been used in their place, as their explanatory power has been more valuable, in relative terms.\n",
"\n",
"\n",
"- The **problem with model explainability** is that it’s **very hard to define a model’s decision boundary in human understandable manner**. \n",
"\n",
"\n",
"- **SHAP (SHapley Additive exPlanations)** is a unified approach to **explain the output of any machine learning model**.\n",
"\n",
"<br>\n",
"<img src=\"../../images/shap.png\" width=\"700\" height=\"400\"/> <br><br>\n",
"\n",
"\n",
"- We will use **SHAP** to **interpret** our **RandomForest model**."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id=section2></a>\n",
"## 2. Importing Packages"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Install SHAP using the following command.\n",
"\n",
"!pip install shap"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"np.set_printoptions(precision=4) # To display values only upto four decimal places. \n",
"\n",
"import pandas as pd\n",
"pd.set_option('mode.chained_assignment', None) # To suppress pandas warnings.\n",
"pd.set_option('display.max_colwidth', -1) # To display all the data in the columns.\n",
"pd.options.display.max_columns = 40 # To display all the columns. (Set the value to a high number)\n",
"\n",
"import matplotlib.pyplot as plt\n",
"plt.style.use('seaborn-whitegrid') # To apply seaborn whitegrid style to the plots.\n",
"plt.rc('figure', figsize=(10, 8)) # Set the default figure size of plots.\n",
"%matplotlib inline\n",
"\n",
"import warnings\n",
"warnings.filterwarnings('ignore') # To suppress all the warnings in the notebook.\n",
"\n",
"from sklearn.ensemble import RandomForestRegressor\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.metrics import r2_score"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id=section3></a>\n",
"## 3. Loading Data"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
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"<div>\n",
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" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>crim</th>\n",
" <th>zn</th>\n",
" <th>indus</th>\n",
" <th>chas</th>\n",
" <th>nox</th>\n",
" <th>rm</th>\n",
" <th>age</th>\n",
" <th>dis</th>\n",
" <th>rad</th>\n",
" <th>tax</th>\n",
" <th>ptratio</th>\n",
" <th>black</th>\n",
" <th>lstat</th>\n",
" <th>medv</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>0</td>\n",
" <td>0.00632</td>\n",
" <td>18.0</td>\n",
" <td>2.31</td>\n",
" <td>0</td>\n",
" <td>0.538</td>\n",
" <td>6.575</td>\n",
" <td>65.2</td>\n",
" <td>4.0900</td>\n",
" <td>1</td>\n",
" <td>296.0</td>\n",
" <td>15.3</td>\n",
" <td>396.90</td>\n",
" <td>4.98</td>\n",
" <td>24.0</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>0.02731</td>\n",
" <td>0.0</td>\n",
" <td>7.07</td>\n",
" <td>0</td>\n",
" <td>0.469</td>\n",
" <td>6.421</td>\n",
" <td>78.9</td>\n",
" <td>4.9671</td>\n",
" <td>2</td>\n",
" <td>242.0</td>\n",
" <td>17.8</td>\n",
" <td>396.90</td>\n",
" <td>9.14</td>\n",
" <td>21.6</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>0.02729</td>\n",
" <td>0.0</td>\n",
" <td>7.07</td>\n",
" <td>0</td>\n",
" <td>0.469</td>\n",
" <td>7.185</td>\n",
" <td>61.1</td>\n",
" <td>4.9671</td>\n",
" <td>2</td>\n",
" <td>242.0</td>\n",
" <td>17.8</td>\n",
" <td>392.83</td>\n",
" <td>4.03</td>\n",
" <td>34.7</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>0.03237</td>\n",
" <td>0.0</td>\n",
" <td>2.18</td>\n",
" <td>0</td>\n",
" <td>0.458</td>\n",
" <td>6.998</td>\n",
" <td>45.8</td>\n",
" <td>6.0622</td>\n",
" <td>3</td>\n",
" <td>222.0</td>\n",
" <td>18.7</td>\n",
" <td>394.63</td>\n",
" <td>2.94</td>\n",
" <td>33.4</td>\n",
" </tr>\n",
" <tr>\n",
" <td>4</td>\n",
" <td>0.06905</td>\n",
" <td>0.0</td>\n",
" <td>2.18</td>\n",
" <td>0</td>\n",
" <td>0.458</td>\n",
" <td>7.147</td>\n",
" <td>54.2</td>\n",
" <td>6.0622</td>\n",
" <td>3</td>\n",
" <td>222.0</td>\n",
" <td>18.7</td>\n",
" <td>396.90</td>\n",
" <td>5.33</td>\n",
" <td>36.2</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" crim zn indus chas nox rm age dis rad tax \\\n",
"0 0.00632 18.0 2.31 0 0.538 6.575 65.2 4.0900 1 296.0 \n",
"1 0.02731 0.0 7.07 0 0.469 6.421 78.9 4.9671 2 242.0 \n",
"2 0.02729 0.0 7.07 0 0.469 7.185 61.1 4.9671 2 242.0 \n",
"3 0.03237 0.0 2.18 0 0.458 6.998 45.8 6.0622 3 222.0 \n",
"4 0.06905 0.0 2.18 0 0.458 7.147 54.2 6.0622 3 222.0 \n",
"\n",
" ptratio black lstat medv \n",
"0 15.3 396.90 4.98 24.0 \n",
"1 17.8 396.90 9.14 21.6 \n",
"2 17.8 392.83 4.03 34.7 \n",
"3 18.7 394.63 2.94 33.4 \n",
"4 18.7 396.90 5.33 36.2 "
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.read_csv('../../data/Boston.csv')\n",
"df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id=section301></a>\n",
"### 3.1 Description of the Dataset"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- This dataset contains information on **Housing Values in Suburbs of Boston**.\n",
"\n",
"\n",
"- The column **medv** is the **target variable**. It is the **median** value of **owner-occupied homes in $1000s**."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"| Column Name | Description |\n",
"| ---------------------------------|:----------------------------------------------------------------------------------------:| \n",
"| crim | Per capita crime rate by town. |\n",
"| zn | Proportion of residential land zoned for lots over 25,000 sq.ft. |\n",
"| indus | Proportion of non-retail business acres per town. |\n",
"| chas | Charles River dummy variable (= 1 if tract bounds river; 0 otherwise). |\n",
"| nox | Nitrogen oxides concentration (parts per 10 million). |\n",
"| rm | Average number of rooms per dwelling. |\n",
"| age | Proportion of owner-occupied units built prior to 1940. |\n",
"| dis | Weighted mean of distances to five Boston employment centres. |\n",
"| rad | Index of accessibility to radial highways. |\n",
"| tax | Full-value property-tax rate per 10,000 dollars. |\n",
"| ptratio | Pupil-teacher ratio by town. |\n",
"| black | 1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town. |\n",
"| lstat | Lower status of the population (percent). |\n",
"| medv | Target, median value of owner-occupied homes in $1000s. |"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 506 entries, 0 to 505\n",
"Data columns (total 14 columns):\n",
"crim 506 non-null float64\n",
"zn 506 non-null float64\n",
"indus 506 non-null float64\n",
"chas 506 non-null int64\n",
"nox 506 non-null float64\n",
"rm 506 non-null float64\n",
"age 506 non-null float64\n",
"dis 506 non-null float64\n",
"rad 506 non-null int64\n",
"tax 506 non-null float64\n",
"ptratio 506 non-null float64\n",
"black 506 non-null float64\n",
"lstat 506 non-null float64\n",
"medv 506 non-null float64\n",
"dtypes: float64(12), int64(2)\n",
"memory usage: 55.5 KB\n"
]
}
],
"source": [
"df.info()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>crim</th>\n",
" <th>zn</th>\n",
" <th>indus</th>\n",
" <th>chas</th>\n",
" <th>nox</th>\n",
" <th>rm</th>\n",
" <th>age</th>\n",
" <th>dis</th>\n",
" <th>rad</th>\n",
" <th>tax</th>\n",
" <th>ptratio</th>\n",
" <th>black</th>\n",
" <th>lstat</th>\n",
" <th>medv</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>count</td>\n",
" <td>506.000000</td>\n",
" <td>506.000000</td>\n",
" <td>506.000000</td>\n",
" <td>506.000000</td>\n",
" <td>506.000000</td>\n",
" <td>506.000000</td>\n",
" <td>506.000000</td>\n",
" <td>506.000000</td>\n",
" <td>506.000000</td>\n",
" <td>506.000000</td>\n",
" <td>506.000000</td>\n",
" <td>506.000000</td>\n",
" <td>506.000000</td>\n",
" <td>506.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>mean</td>\n",
" <td>3.613524</td>\n",
" <td>11.363636</td>\n",
" <td>11.136779</td>\n",
" <td>0.069170</td>\n",
" <td>0.554695</td>\n",
" <td>6.284634</td>\n",
" <td>68.574901</td>\n",
" <td>3.795043</td>\n",
" <td>9.549407</td>\n",
" <td>408.237154</td>\n",
" <td>18.455534</td>\n",
" <td>356.674032</td>\n",
" <td>12.653063</td>\n",
" <td>22.532806</td>\n",
" </tr>\n",
" <tr>\n",
" <td>std</td>\n",
" <td>8.601545</td>\n",
" <td>23.322453</td>\n",
" <td>6.860353</td>\n",
" <td>0.253994</td>\n",
" <td>0.115878</td>\n",
" <td>0.702617</td>\n",
" <td>28.148861</td>\n",
" <td>2.105710</td>\n",
" <td>8.707259</td>\n",
" <td>168.537116</td>\n",
" <td>2.164946</td>\n",
" <td>91.294864</td>\n",
" <td>7.141062</td>\n",
" <td>9.197104</td>\n",
" </tr>\n",
" <tr>\n",
" <td>min</td>\n",
" <td>0.006320</td>\n",
" <td>0.000000</td>\n",
" <td>0.460000</td>\n",
" <td>0.000000</td>\n",
" <td>0.385000</td>\n",
" <td>3.561000</td>\n",
" <td>2.900000</td>\n",
" <td>1.129600</td>\n",
" <td>1.000000</td>\n",
" <td>187.000000</td>\n",
" <td>12.600000</td>\n",
" <td>0.320000</td>\n",
" <td>1.730000</td>\n",
" <td>5.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>25%</td>\n",
" <td>0.082045</td>\n",
" <td>0.000000</td>\n",
" <td>5.190000</td>\n",
" <td>0.000000</td>\n",
" <td>0.449000</td>\n",
" <td>5.885500</td>\n",
" <td>45.025000</td>\n",
" <td>2.100175</td>\n",
" <td>4.000000</td>\n",
" <td>279.000000</td>\n",
" <td>17.400000</td>\n",
" <td>375.377500</td>\n",
" <td>6.950000</td>\n",
" <td>17.025000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>50%</td>\n",
" <td>0.256510</td>\n",
" <td>0.000000</td>\n",
" <td>9.690000</td>\n",
" <td>0.000000</td>\n",
" <td>0.538000</td>\n",
" <td>6.208500</td>\n",
" <td>77.500000</td>\n",
" <td>3.207450</td>\n",
" <td>5.000000</td>\n",
" <td>330.000000</td>\n",
" <td>19.050000</td>\n",
" <td>391.440000</td>\n",
" <td>11.360000</td>\n",
" <td>21.200000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>75%</td>\n",
" <td>3.677082</td>\n",
" <td>12.500000</td>\n",
" <td>18.100000</td>\n",
" <td>0.000000</td>\n",
" <td>0.624000</td>\n",
" <td>6.623500</td>\n",
" <td>94.075000</td>\n",
" <td>5.188425</td>\n",
" <td>24.000000</td>\n",
" <td>666.000000</td>\n",
" <td>20.200000</td>\n",
" <td>396.225000</td>\n",
" <td>16.955000</td>\n",
" <td>25.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>max</td>\n",
" <td>88.976200</td>\n",
" <td>100.000000</td>\n",
" <td>27.740000</td>\n",
" <td>1.000000</td>\n",
" <td>0.871000</td>\n",
" <td>8.780000</td>\n",
" <td>100.000000</td>\n",
" <td>12.126500</td>\n",
" <td>24.000000</td>\n",
" <td>711.000000</td>\n",
" <td>22.000000</td>\n",
" <td>396.900000</td>\n",
" <td>37.970000</td>\n",
" <td>50.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" crim zn indus chas nox rm \\\n",
"count 506.000000 506.000000 506.000000 506.000000 506.000000 506.000000 \n",
"mean 3.613524 11.363636 11.136779 0.069170 0.554695 6.284634 \n",
"std 8.601545 23.322453 6.860353 0.253994 0.115878 0.702617 \n",
"min 0.006320 0.000000 0.460000 0.000000 0.385000 3.561000 \n",
"25% 0.082045 0.000000 5.190000 0.000000 0.449000 5.885500 \n",
"50% 0.256510 0.000000 9.690000 0.000000 0.538000 6.208500 \n",
"75% 3.677082 12.500000 18.100000 0.000000 0.624000 6.623500 \n",
"max 88.976200 100.000000 27.740000 1.000000 0.871000 8.780000 \n",
"\n",
" age dis rad tax ptratio black \\\n",
"count 506.000000 506.000000 506.000000 506.000000 506.000000 506.000000 \n",
"mean 68.574901 3.795043 9.549407 408.237154 18.455534 356.674032 \n",
"std 28.148861 2.105710 8.707259 168.537116 2.164946 91.294864 \n",
"min 2.900000 1.129600 1.000000 187.000000 12.600000 0.320000 \n",
"25% 45.025000 2.100175 4.000000 279.000000 17.400000 375.377500 \n",
"50% 77.500000 3.207450 5.000000 330.000000 19.050000 391.440000 \n",
"75% 94.075000 5.188425 24.000000 666.000000 20.200000 396.225000 \n",
"max 100.000000 12.126500 24.000000 711.000000 22.000000 396.900000 \n",
"\n",
" lstat medv \n",
"count 506.000000 506.000000 \n",
"mean 12.653063 22.532806 \n",
"std 7.141062 9.197104 \n",
"min 1.730000 5.000000 \n",
"25% 6.950000 17.025000 \n",
"50% 11.360000 21.200000 \n",
"75% 16.955000 25.000000 \n",
"max 37.970000 50.000000 "
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.describe()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id=section4></a>\n",
"## 4. Data train/test split\n",
"\n",
"- Now that the entire **data** is of **numeric datatype**, lets begin our modelling process.\n",
"\n",
"\n",
"- Firstly, **splitting** the complete **dataset** into **training** and **testing** datasets."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>crim</th>\n",
" <th>zn</th>\n",
" <th>indus</th>\n",
" <th>chas</th>\n",
" <th>nox</th>\n",
" <th>rm</th>\n",
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" <th>rad</th>\n",
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" <td>0</td>\n",
" <td>0.00632</td>\n",
" <td>18.0</td>\n",
" <td>2.31</td>\n",
" <td>0</td>\n",
" <td>0.538</td>\n",
" <td>6.575</td>\n",
" <td>65.2</td>\n",
" <td>4.0900</td>\n",
" <td>1</td>\n",
" <td>296.0</td>\n",
" <td>15.3</td>\n",
" <td>396.90</td>\n",
" <td>4.98</td>\n",
" <td>24.0</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>0.02731</td>\n",
" <td>0.0</td>\n",
" <td>7.07</td>\n",
" <td>0</td>\n",
" <td>0.469</td>\n",
" <td>6.421</td>\n",
" <td>78.9</td>\n",
" <td>4.9671</td>\n",
" <td>2</td>\n",
" <td>242.0</td>\n",
" <td>17.8</td>\n",
" <td>396.90</td>\n",
" <td>9.14</td>\n",
" <td>21.6</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>0.02729</td>\n",
" <td>0.0</td>\n",
" <td>7.07</td>\n",
" <td>0</td>\n",
" <td>0.469</td>\n",
" <td>7.185</td>\n",
" <td>61.1</td>\n",
" <td>4.9671</td>\n",
" <td>2</td>\n",
" <td>242.0</td>\n",
" <td>17.8</td>\n",
" <td>392.83</td>\n",
" <td>4.03</td>\n",
" <td>34.7</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>0.03237</td>\n",
" <td>0.0</td>\n",
" <td>2.18</td>\n",
" <td>0</td>\n",
" <td>0.458</td>\n",
" <td>6.998</td>\n",
" <td>45.8</td>\n",
" <td>6.0622</td>\n",
" <td>3</td>\n",
" <td>222.0</td>\n",
" <td>18.7</td>\n",
" <td>394.63</td>\n",
" <td>2.94</td>\n",
" <td>33.4</td>\n",
" </tr>\n",
" <tr>\n",
" <td>4</td>\n",
" <td>0.06905</td>\n",
" <td>0.0</td>\n",
" <td>2.18</td>\n",
" <td>0</td>\n",
" <td>0.458</td>\n",
" <td>7.147</td>\n",
" <td>54.2</td>\n",
" <td>6.0622</td>\n",
" <td>3</td>\n",
" <td>222.0</td>\n",
" <td>18.7</td>\n",
" <td>396.90</td>\n",
" <td>5.33</td>\n",
" <td>36.2</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" crim zn indus chas nox rm age dis rad tax \\\n",
"0 0.00632 18.0 2.31 0 0.538 6.575 65.2 4.0900 1 296.0 \n",
"1 0.02731 0.0 7.07 0 0.469 6.421 78.9 4.9671 2 242.0 \n",
"2 0.02729 0.0 7.07 0 0.469 7.185 61.1 4.9671 2 242.0 \n",
"3 0.03237 0.0 2.18 0 0.458 6.998 45.8 6.0622 3 222.0 \n",
"4 0.06905 0.0 2.18 0 0.458 7.147 54.2 6.0622 3 222.0 \n",
"\n",
" ptratio black lstat medv \n",
"0 15.3 396.90 4.98 24.0 \n",
"1 17.8 396.90 9.14 21.6 \n",
"2 17.8 392.83 4.03 34.7 \n",
"3 18.7 394.63 2.94 33.4 \n",
"4 18.7 396.90 5.33 36.2 "
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
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" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>crim</th>\n",
" <th>zn</th>\n",
" <th>indus</th>\n",
" <th>chas</th>\n",
" <th>nox</th>\n",
" <th>rm</th>\n",
" <th>age</th>\n",
" <th>dis</th>\n",
" <th>rad</th>\n",
" <th>tax</th>\n",
" <th>ptratio</th>\n",
" <th>black</th>\n",
" <th>lstat</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>0</td>\n",
" <td>0.00632</td>\n",
" <td>18.0</td>\n",
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" <td>0</td>\n",
" <td>0.538</td>\n",
" <td>6.575</td>\n",
" <td>65.2</td>\n",
" <td>4.0900</td>\n",
" <td>1</td>\n",
" <td>296.0</td>\n",
" <td>15.3</td>\n",
" <td>396.90</td>\n",
" <td>4.98</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>0.02731</td>\n",
" <td>0.0</td>\n",
" <td>7.07</td>\n",
" <td>0</td>\n",
" <td>0.469</td>\n",
" <td>6.421</td>\n",
" <td>78.9</td>\n",
" <td>4.9671</td>\n",
" <td>2</td>\n",
" <td>242.0</td>\n",
" <td>17.8</td>\n",
" <td>396.90</td>\n",
" <td>9.14</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>0.02729</td>\n",
" <td>0.0</td>\n",
" <td>7.07</td>\n",
" <td>0</td>\n",
" <td>0.469</td>\n",
" <td>7.185</td>\n",
" <td>61.1</td>\n",
" <td>4.9671</td>\n",
" <td>2</td>\n",
" <td>242.0</td>\n",
" <td>17.8</td>\n",
" <td>392.83</td>\n",
" <td>4.03</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>0.03237</td>\n",
" <td>0.0</td>\n",
" <td>2.18</td>\n",
" <td>0</td>\n",
" <td>0.458</td>\n",
" <td>6.998</td>\n",
" <td>45.8</td>\n",
" <td>6.0622</td>\n",
" <td>3</td>\n",
" <td>222.0</td>\n",
" <td>18.7</td>\n",
" <td>394.63</td>\n",
" <td>2.94</td>\n",
" </tr>\n",
" <tr>\n",
" <td>4</td>\n",
" <td>0.06905</td>\n",
" <td>0.0</td>\n",
" <td>2.18</td>\n",
" <td>0</td>\n",
" <td>0.458</td>\n",
" <td>7.147</td>\n",
" <td>54.2</td>\n",
" <td>6.0622</td>\n",
" <td>3</td>\n",
" <td>222.0</td>\n",
" <td>18.7</td>\n",
" <td>396.90</td>\n",
" <td>5.33</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" crim zn indus chas nox rm age dis rad tax \\\n",
"0 0.00632 18.0 2.31 0 0.538 6.575 65.2 4.0900 1 296.0 \n",
"1 0.02731 0.0 7.07 0 0.469 6.421 78.9 4.9671 2 242.0 \n",
"2 0.02729 0.0 7.07 0 0.469 7.185 61.1 4.9671 2 242.0 \n",
"3 0.03237 0.0 2.18 0 0.458 6.998 45.8 6.0622 3 222.0 \n",
"4 0.06905 0.0 2.18 0 0.458 7.147 54.2 6.0622 3 222.0 \n",
"\n",
" ptratio black lstat \n",
"0 15.3 396.90 4.98 \n",
"1 17.8 396.90 9.14 \n",
"2 17.8 392.83 4.03 \n",
"3 18.7 394.63 2.94 \n",
"4 18.7 396.90 5.33 "
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X = df.iloc[:, :-1]\n",
"X.head()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 24.0\n",
"1 21.6\n",
"2 34.7\n",
"3 33.4\n",
"4 36.2\n",
"Name: medv, dtype: float64"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y = df.iloc[:, -1]\n",
"y.head()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"# Using scikit-learn's train_test_split function to split the dataset into train and test sets.\n",
"# 80% of the data will be in the train set and 20% in the test set, as specified by test_size=0.2\n",
"\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(404, 13)\n",
"(404,)\n",
"(102, 13)\n",
"(102,)\n"
]
}
],
"source": [
"# Checking the shapes of all the training and test sets for the dependent and independent features.\n",
"\n",
"print(X_train.shape)\n",
"print(y_train.shape)\n",
"print(X_test.shape)\n",
"print(y_test.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id=section5></a>\n",
"## 5. Random Forest Model\n",
"\n",
"<a id=section501></a>\n",
"### 5.1 Random Forest with Scikit-Learn"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"# Creating a Random Forest Regressor.\n",
"\n",
"regressor_rf = RandomForestRegressor(n_estimators=200, random_state=0, oob_score=True, n_jobs=-1)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
" max_features='auto', max_leaf_nodes=None,\n",
" min_impurity_decrease=0.0, min_impurity_split=None,\n",
" min_samples_leaf=1, min_samples_split=2,\n",
" min_weight_fraction_leaf=0.0, n_estimators=200, n_jobs=-1,\n",
" oob_score=True, random_state=0, verbose=0,\n",
" warm_start=False)"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Fitting the model on the dataset.\n",
"\n",
"regressor_rf.fit(X_train, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.8375610635726134"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"regressor_rf.oob_score_"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- From the **OOB score** we can see how our model's gonna perform against the **test set or new** samples."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id=section502></a>\n",
"### 5.2 Feature Importances"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['crim', 'zn', 'indus', 'chas', 'nox', 'rm', 'age', 'dis', 'rad', 'tax',\n",
" 'ptratio', 'black', 'lstat'],\n",
" dtype='object')"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X_train.columns"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Feature importance of rm : 48.652000069343806 %\n",
"Feature importance of lstat : 32.62519467239989 %\n",
"Feature importance of dis : 5.723002765414628 %\n",
"Feature importance of crim : 3.688078587412392 %\n",
"Feature importance of nox : 1.7565510857613313 %\n",
"Feature importance of ptratio : 1.7390709979839825 %\n",
"Feature importance of tax : 1.662967097458929 %\n",
"Feature importance of age : 1.470551943168394 %\n",
"Feature importance of black : 1.3621235473729538 %\n",
"Feature importance of indus : 0.6664660162044432 %\n",
"Feature importance of rad : 0.3849848939558503 %\n",
"Feature importance of zn : 0.1498360991801689 %\n",
"Feature importance of chas : 0.11917222434323611 %\n"
]
}
],
"source": [
"# Checking the feature importances of various features.\n",
"# Sorting the importances by descending order (lowest importance at the bottom).\n",
"\n",
"for score, name in sorted(zip(regressor_rf.feature_importances_, X_train.columns), reverse=True):\n",
" print('Feature importance of', name, ':', score*100, '%')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id=section503></a>\n",
"### 5.3 Using the Model for Prediction"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([12.511 , 19.9775, 19.77 , 13.258 , 18.5805, 24.453 , 20.89 ,\n",
" 23.869 , 8.595 , 23.5375])"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Making predictions on the training set.\n",
"\n",
"y_pred_train = regressor_rf.predict(X_train)\n",
"y_pred_train[:10]"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([22.57 , 31.5625, 17.109 , 23.243 , 16.7635, 21.303 , 19.24 ,\n",
" 15.6195, 21.429 , 20.964 ])"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Making predictions on test set.\n",
"\n",
"y_pred_test = regressor_rf.predict(X_test)\n",
"y_pred_test[:10]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id=section6></a>\n",
"## 6. Model Evaluation\n",
"\n",
"**Error** is the deviation of the values predicted by the model with the true values."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id=section601></a>\n",
"### 6.1 R-Squared Value"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"R-Squared Value for train data is: 0.9774359941752926\n"
]
}
],
"source": [
"# R-Squared Value on the training set.\n",
"\n",
"print('R-Squared Value for train data is:', r2_score(y_train, y_pred_train))"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"R-Squared Value for test data is: 0.8798665934496468\n"
]
}
],
"source": [
"# R-Squared Value on the test set.\n",
"\n",
"print('R-Squared Value for test data is:', r2_score(y_test, y_pred_test))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- We get an **R-Squared Value** of **97.74%** on our train set and an **R-Squared Value** of **87.98%** on our test set."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id=section7></a>\n",
"## 7. Model Interpretability using SHAP\n",
"\n",
"\n",
"- **SHAP (SHapley Additive exPlanations)** is a unified approach to **explain the output of any machine learning model**. \n",
"\n",
"\n",
"- **SHAP connects game theory with local explanations**, uniting several previous methods and representing the only possible consistent and locally accurate additive feature attribution method based on expectations. "
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"import shap"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
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r={topAbort:null,topAnimationEnd:null,topAnimationIteration:null,topAnimationStart:null,topBlur:null,topCanPlay:null,topCanPlayThrough:null,topChange:null,topClick:null,topCompositionEnd:null,topCompositionStart:null,topCompositionUpdate:null,topContextMenu:null,topCopy:null,topCut:null,topDoubleClick:null,topDrag:null,topDragEnd:null,topDragEnter:null,topDragExit:null,topDragLeave:null,topDragOver:null,topDragStart:null,topDrop:null,topDurationChange:null,topEmptied:null,topEncrypted:null,topEnded:null,topError:null,topFocus:null,topInput:null,topInvalid:null,topKeyDown:null,topKeyPress:null,topKeyUp:null,topLoad:null,topLoadedData:null,topLoadedMetadata:null,topLoadStart:null,topMouseDown:null,topMouseMove:null,topMouseOut:null,topMouseOver:null,topMouseUp:null,topPaste:null,topPause:null,topPlay:null,topPlaying:null,topProgress:null,topRateChange:null,topReset:null,topScroll:null,topSeeked:null,topSeeking:null,topSelectionChange:null,topStalled:null,topSubmit:null,topSuspend:null,topTextInput:null,topTimeUpdate:null,topTouchCancel:null,topTouchEnd:null,topTouchMove:null,topTouchStart:null,topTransitionEnd:null,topVolumeChange:null,topWaiting:null,topWheel:null},i={topLevelTypes:r};t.exports=i},function(t,e,n){\"use 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RegExp(\"^(data|aria)-[\"+r.ATTRIBUTE_NAME_CHAR+\"]*$\")),Properties:{accept:0,acceptCharset:0,accessKey:0,action:0,allowFullScreen:o,allowTransparency:0,alt:0,as:0,async:o,autoComplete:0,autoPlay:o,capture:o,cellPadding:0,cellSpacing:0,charSet:0,challenge:0,checked:i|o,cite:0,classID:0,className:0,cols:u,colSpan:0,content:0,contentEditable:0,contextMenu:0,controls:o,controlsList:0,coords:0,crossOrigin:0,data:0,dateTime:0,default:o,defer:o,dir:0,disabled:o,download:c,draggable:0,encType:0,form:0,formAction:0,formEncType:0,formMethod:0,formNoValidate:o,formTarget:0,frameBorder:0,headers:0,height:0,hidden:o,high:0,href:0,hrefLang:0,htmlFor:0,httpEquiv:0,icon:0,id:0,inputMode:0,integrity:0,is:0,keyParams:0,keyType:0,kind:0,label:0,lang:0,list:0,loop:o,low:0,manifest:0,marginHeight:0,marginWidth:0,max:0,maxLength:0,media:0,mediaGroup:0,method:0,min:0,minLength:0,multiple:i|o,muted:i|o,name:0,nonce:0,noValidate:o,open:o,optimum:0,pattern:0,placeholder:0,playsInline:o,poster:0,preload:0,profile:0,radioGroup:0,readOnly:o,referrerPolicy:0,rel:0,required:o,reversed:o,role:0,rows:u,rowSpan:a,sandbox:0,scope:0,scoped:o,scrolling:0,seamless:o,selected:i|o,shape:0,size:u,sizes:0,span:u,spellCheck:0,src:0,srcDoc:0,srcLang:0,srcSet:0,start:a,step:0,style:0,summary:0,tabIndex:0,target:0,title:0,type:0,useMap:0,value:0,width:0,wmode:0,wrap:0,about:0,datatype:0,inlist:0,prefix:0,property:0,resource:0,typeof:0,vocab:0,autoCapitalize:0,autoCorrect:0,autoSave:0,color:0,itemProp:0,itemScope:o,itemType:0,itemID:0,itemRef:0,results:0,security:0,unselectable:0},DOMAttributeNames:{acceptCharset:\"accept-charset\",className:\"class\",htmlFor:\"for\",httpEquiv:\"http-equiv\"},DOMPropertyNames:{},DOMMutationMethods:{value:function(t,e){if(null==e)return t.removeAttribute(\"value\");\"number\"!==t.type||!1===t.hasAttribute(\"value\")?t.setAttribute(\"value\",\"\"+e):t.validity&&!t.validity.badInput&&t.ownerDocument.activeElement!==t&&t.setAttribute(\"value\",\"\"+e)}}};t.exports=s},function(t,e,n){\"use strict\";(function(e){function r(t,e,n,r){var i=void 0===t[n];null!=e&&i&&(t[n]=o(e,!0))}var i=n(24),o=n(174),a=(n(85),n(96)),u=n(177);n(2);void 0!==e&&e.env;var c={instantiateChildren:function(t,e,n,i){if(null==t)return null;var o={};return u(t,r,o),o},updateChildren:function(t,e,n,r,u,c,s,l,f){if(e||t){var p,h;for(p in e)if(e.hasOwnProperty(p)){h=t&&t[p];var d=h&&h._currentElement,v=e[p];if(null!=h&&a(d,v))i.receiveComponent(h,v,u,l),e[p]=h;else{h&&(r[p]=i.getHostNode(h),i.unmountComponent(h,!1));var g=o(v,!0);e[p]=g;var m=i.mountComponent(g,u,c,s,l,f);n.push(m)}}for(p in t)!t.hasOwnProperty(p)||e&&e.hasOwnProperty(p)||(h=t[p],r[p]=i.getHostNode(h),i.unmountComponent(h,!1))}},unmountChildren:function(t,e){for(var n in t)if(t.hasOwnProperty(n)){var r=t[n];i.unmountComponent(r,e)}}};t.exports=c}).call(e,n(156))},function(t,e,n){\"use strict\";var r=n(82),i=n(364),o={processChildrenUpdates:i.dangerouslyProcessChildrenUpdates,replaceNodeWithMarkup:r.dangerouslyReplaceNodeWithMarkup};t.exports=o},function(t,e,n){\"use strict\";function r(t){}function i(t){return!(!t.prototype||!t.prototype.isReactComponent)}function o(t){return!(!t.prototype||!t.prototype.isPureReactComponent)}var a=n(1),u=n(3),c=n(26),s=n(87),l=n(15),f=n(88),p=n(39),h=(n(9),n(168)),d=n(24),v=n(51),g=(n(0),n(81)),m=n(96),y=(n(2),{ImpureClass:0,PureClass:1,StatelessFunctional:2});r.prototype.render=function(){var t=p.get(this)._currentElement.type,e=t(this.props,this.context,this.updater);return e};var _=1,b={construct:function(t){this._currentElement=t,this._rootNodeID=0,this._compositeType=null,this._instance=null,this._hostParent=null,this._hostContainerInfo=null,this._updateBatchNumber=null,this._pendingElement=null,this._pendingStateQueue=null,this._pendingReplaceState=!1,this._pendingForceUpdate=!1,this._renderedNodeType=null,this._renderedComponent=null,this._context=null,this._mountOrder=0,this._topLevelWrapper=null,this._pendingCallbacks=null,this._calledComponentWillUnmount=!1},mountComponent:function(t,e,n,u){this._context=u,this._mountOrder=_++,this._hostParent=e,this._hostContainerInfo=n;var s,l=this._currentElement.props,f=this._processContext(u),h=this._currentElement.type,d=t.getUpdateQueue(),g=i(h),m=this._constructComponent(g,l,f,d);g||null!=m&&null!=m.render?o(h)?this._compositeType=y.PureClass:this._compositeType=y.ImpureClass:(s=m,null===m||!1===m||c.isValidElement(m)||a(\"105\",h.displayName||h.name||\"Component\"),m=new r(h),this._compositeType=y.StatelessFunctional);m.props=l,m.context=f,m.refs=v,m.updater=d,this._instance=m,p.set(m,this);var b=m.state;void 0===b&&(m.state=b=null),(\"object\"!=typeof b||Array.isArray(b))&&a(\"106\",this.getName()||\"ReactCompositeComponent\"),this._pendingStateQueue=null,this._pendingReplaceState=!1,this._pendingForceUpdate=!1;var x;return x=m.unstable_handleError?this.performInitialMountWithErrorHandling(s,e,n,t,u):this.performInitialMount(s,e,n,t,u),m.componentDidMount&&t.getReactMountReady().enqueue(m.componentDidMount,m),x},_constructComponent:function(t,e,n,r){return this._constructComponentWithoutOwner(t,e,n,r)},_constructComponentWithoutOwner:function(t,e,n,r){var i=this._currentElement.type;return t?new i(e,n,r):i(e,n,r)},performInitialMountWithErrorHandling:function(t,e,n,r,i){var o,a=r.checkpoint();try{o=this.performInitialMount(t,e,n,r,i)}catch(u){r.rollback(a),this._instance.unstable_handleError(u),this._pendingStateQueue&&(this._instance.state=this._processPendingState(this._instance.props,this._instance.context)),a=r.checkpoint(),this._renderedComponent.unmountComponent(!0),r.rollback(a),o=this.performInitialMount(t,e,n,r,i)}return o},performInitialMount:function(t,e,n,r,i){var o=this._instance,a=0;o.componentWillMount&&(o.componentWillMount(),this._pendingStateQueue&&(o.state=this._processPendingState(o.props,o.context))),void 0===t&&(t=this._renderValidatedComponent());var u=h.getType(t);this._renderedNodeType=u;var c=this._instantiateReactComponent(t,u!==h.EMPTY);this._renderedComponent=c;var s=d.mountComponent(c,r,e,n,this._processChildContext(i),a);return s},getHostNode:function(){return d.getHostNode(this._renderedComponent)},unmountComponent:function(t){if(this._renderedComponent){var e=this._instance;if(e.componentWillUnmount&&!e._calledComponentWillUnmount)if(e._calledComponentWillUnmount=!0,t){var n=this.getName()+\".componentWillUnmount()\";f.invokeGuardedCallback(n,e.componentWillUnmount.bind(e))}else e.componentWillUnmount();this._renderedComponent&&(d.unmountComponent(this._renderedComponent,t),this._renderedNodeType=null,this._renderedComponent=null,this._instance=null),this._pendingStateQueue=null,this._pendingReplaceState=!1,this._pendingForceUpdate=!1,this._pendingCallbacks=null,this._pendingElement=null,this._context=null,this._rootNodeID=0,this._topLevelWrapper=null,p.remove(e)}},_maskContext:function(t){var e=this._currentElement.type,n=e.contextTypes;if(!n)return v;var r={};for(var i in n)r[i]=t[i];return r},_processContext:function(t){var e=this._maskContext(t);return e},_processChildContext:function(t){var e,n=this._currentElement.type,r=this._instance;if(r.getChildContext&&(e=r.getChildContext()),e){\"object\"!=typeof n.childContextTypes&&a(\"107\",this.getName()||\"ReactCompositeComponent\");for(var i in e)i in n.childContextTypes||a(\"108\",this.getName()||\"ReactCompositeComponent\",i);return u({},t,e)}return t},_checkContextTypes:function(t,e,n){},receiveComponent:function(t,e,n){var r=this._currentElement,i=this._context;this._pendingElement=null,this.updateComponent(e,r,t,i,n)},performUpdateIfNecessary:function(t){null!=this._pendingElement?d.receiveComponent(this,this._pendingElement,t,this._context):null!==this._pendingStateQueue||this._pendingForceUpdate?this.updateComponent(t,this._currentElement,this._currentElement,this._context,this._context):this._updateBatchNumber=null},updateComponent:function(t,e,n,r,i){var o=this._instance;null==o&&a(\"136\",this.getName()||\"ReactCompositeComponent\");var u,c=!1;this._context===i?u=o.context:(u=this._processContext(i),c=!0);var s=e.props,l=n.props;e!==n&&(c=!0),c&&o.componentWillReceiveProps&&o.componentWillReceiveProps(l,u);var f=this._processPendingState(l,u),p=!0;this._pendingForceUpdate||(o.shouldComponentUpdate?p=o.shouldComponentUpdate(l,f,u):this._compositeType===y.PureClass&&(p=!g(s,l)||!g(o.state,f))),this._updateBatchNumber=null,p?(this._pendingForceUpdate=!1,this._performComponentUpdate(n,l,f,u,t,i)):(this._currentElement=n,this._context=i,o.props=l,o.state=f,o.context=u)},_processPendingState:function(t,e){var n=this._instance,r=this._pendingStateQueue,i=this._pendingReplaceState;if(this._pendingReplaceState=!1,this._pendingStateQueue=null,!r)return n.state;if(i&&1===r.length)return r[0];for(var o=u({},i?r[0]:n.state),a=i?1:0;a<r.length;a++){var c=r[a];u(o,\"function\"==typeof c?c.call(n,o,t,e):c)}return o},_performComponentUpdate:function(t,e,n,r,i,o){var a,u,c,s=this._instance,l=Boolean(s.componentDidUpdate);l&&(a=s.props,u=s.state,c=s.context),s.componentWillUpdate&&s.componentWillUpdate(e,n,r),this._currentElement=t,this._context=o,s.props=e,s.state=n,s.context=r,this._updateRenderedComponent(i,o),l&&i.getReactMountReady().enqueue(s.componentDidUpdate.bind(s,a,u,c),s)},_updateRenderedComponent:function(t,e){var n=this._renderedComponent,r=n._currentElement,i=this._renderValidatedComponent(),o=0;if(m(r,i))d.receiveComponent(n,i,t,this._processChildContext(e));else{var a=d.getHostNode(n);d.unmountComponent(n,!1);var u=h.getType(i);this._renderedNodeType=u;var c=this._instantiateReactComponent(i,u!==h.EMPTY);this._renderedComponent=c;var s=d.mountComponent(c,t,this._hostParent,this._hostContainerInfo,this._processChildContext(e),o);this._replaceNodeWithMarkup(a,s,n)}},_replaceNodeWithMarkup:function(t,e,n){s.replaceNodeWithMarkup(t,e,n)},_renderValidatedComponentWithoutOwnerOrContext:function(){var t=this._instance;return t.render()},_renderValidatedComponent:function(){var t;if(this._compositeType!==y.StatelessFunctional){l.current=this;try{t=this._renderValidatedComponentWithoutOwnerOrContext()}finally{l.current=null}}else t=this._renderValidatedComponentWithoutOwnerOrContext();return null===t||!1===t||c.isValidElement(t)||a(\"109\",this.getName()||\"ReactCompositeComponent\"),t},attachRef:function(t,e){var n=this.getPublicInstance();null==n&&a(\"110\");var r=e.getPublicInstance();(n.refs===v?n.refs={}:n.refs)[t]=r},detachRef:function(t){delete this.getPublicInstance().refs[t]},getName:function(){var t=this._currentElement.type,e=this._instance&&this._instance.constructor;return t.displayName||e&&e.displayName||t.name||e&&e.name||null},getPublicInstance:function(){var t=this._instance;return this._compositeType===y.StatelessFunctional?null:t},_instantiateReactComponent:null};t.exports=b},function(t,e,n){\"use strict\";var r=n(4),i=n(372),o=n(167),a=n(24),u=n(12),c=n(385),s=n(401),l=n(171),f=n(408);n(2);i.inject();var p={findDOMNode:s,render:o.render,unmountComponentAtNode:o.unmountComponentAtNode,version:c,unstable_batchedUpdates:u.batchedUpdates,unstable_renderSubtreeIntoContainer:f};\"undefined\"!=typeof __REACT_DEVTOOLS_GLOBAL_HOOK__&&\"function\"==typeof __REACT_DEVTOOLS_GLOBAL_HOOK__.inject&&__REACT_DEVTOOLS_GLOBAL_HOOK__.inject({ComponentTree:{getClosestInstanceFromNode:r.getClosestInstanceFromNode,getNodeFromInstance:function(t){return t._renderedComponent&&(t=l(t)),t?r.getNodeFromInstance(t):null}},Mount:o,Reconciler:a});t.exports=p},function(t,e,n){\"use strict\";function r(t){if(t){var e=t._currentElement._owner||null;if(e){var n=e.getName();if(n)return\" This DOM node was rendered by `\"+n+\"`.\"}}return\"\"}function i(t,e){e&&($[t._tag]&&(null!=e.children||null!=e.dangerouslySetInnerHTML)&&g(\"137\",t._tag,t._currentElement._owner?\" Check the render method of 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Y)Y.hasOwnProperty(n)&&t._wrapperState.listeners.push(M.trapBubbledEvent(n,Y[n],e));break;case\"source\":t._wrapperState.listeners=[M.trapBubbledEvent(\"topError\",\"error\",e)];break;case\"img\":t._wrapperState.listeners=[M.trapBubbledEvent(\"topError\",\"error\",e),M.trapBubbledEvent(\"topLoad\",\"load\",e)];break;case\"form\":t._wrapperState.listeners=[M.trapBubbledEvent(\"topReset\",\"reset\",e),M.trapBubbledEvent(\"topSubmit\",\"submit\",e)];break;case\"input\":case\"select\":case\"textarea\":t._wrapperState.listeners=[M.trapBubbledEvent(\"topInvalid\",\"invalid\",e)]}}function p(){P.postUpdateWrapper(this)}function h(t){Z.call(Q,t)||(X.test(t)||g(\"65\",t),Q[t]=!0)}function d(t,e){return t.indexOf(\"-\")>=0||null!=e.is}function v(t){var e=t.type;h(e),this._currentElement=t,this._tag=e.toLowerCase(),this._namespaceURI=null,this._renderedChildren=null,this._previousStyle=null,this._previousStyleCopy=null,this._hostNode=null,this._hostParent=null,this._rootNodeID=0,this._domID=0,this._hostContainerInfo=null,this._wrapperState=null,this._topLevelWrapper=null,this._flags=0}var g=n(1),m=n(3),y=n(346),_=n(348),b=n(20),x=n(83),w=n(21),C=n(160),k=n(22),E=n(84),M=n(53),T=n(161),S=n(4),N=n(365),A=n(366),P=n(162),O=n(369),I=(n(9),n(378)),D=n(383),R=(n(11),n(56)),L=(n(0),n(95),n(81),n(173)),U=(n(97),n(2),T),F=k.deleteListener,j=S.getNodeFromInstance,B=M.listenTo,V=E.registrationNameModules,W={string:!0,number:!0},z=\"__html\",H={children:null,dangerouslySetInnerHTML:null,suppressContentEditableWarning:null},q=11,Y={topAbort:\"abort\",topCanPlay:\"canplay\",topCanPlayThrough:\"canplaythrough\",topDurationChange:\"durationchange\",topEmptied:\"emptied\",topEncrypted:\"encrypted\",topEnded:\"ended\",topError:\"error\",topLoadedData:\"loadeddata\",topLoadedMetadata:\"loadedmetadata\",topLoadStart:\"loadstart\",topPause:\"pause\",topPlay:\"play\",topPlaying:\"playing\",topProgress:\"progress\",topRateChange:\"ratechange\",topSeeked:\"seeked\",topSeeking:\"seeking\",topStalled:\"stalled\",topSuspend:\"suspend\",topTimeUpdate:\"timeupdate\",topVolumeChange:\"volumechange\",topWaiting:\"waiting\"},K={area:!0,base:!0,br:!0,col:!0,embed:!0,hr:!0,img:!0,input:!0,keygen:!0,link:!0,meta:!0,param:!0,source:!0,track:!0,wbr:!0},G={listing:!0,pre:!0,textarea:!0},$=m({menuitem:!0},K),X=/^[a-zA-Z][a-zA-Z:_\\.\\-\\d]*$/,Q={},Z={}.hasOwnProperty,J=1;v.displayName=\"ReactDOMComponent\",v.Mixin={mountComponent:function(t,e,n,r){this._rootNodeID=J++,this._domID=n._idCounter++,this._hostParent=e,this._hostContainerInfo=n;var o=this._currentElement.props;switch(this._tag){case\"audio\":case\"form\":case\"iframe\":case\"img\":case\"link\":case\"object\":case\"source\":case\"video\":this._wrapperState={listeners:null},t.getReactMountReady().enqueue(f,this);break;case\"input\":N.mountWrapper(this,o,e),o=N.getHostProps(this,o),t.getReactMountReady().enqueue(l,this),t.getReactMountReady().enqueue(f,this);break;case\"option\":A.mountWrapper(this,o,e),o=A.getHostProps(this,o);break;case\"select\":P.mountWrapper(this,o,e),o=P.getHostProps(this,o),t.getReactMountReady().enqueue(f,this);break;case\"textarea\":O.mountWrapper(this,o,e),o=O.getHostProps(this,o),t.getReactMountReady().enqueue(l,this),t.getReactMountReady().enqueue(f,this)}i(this,o);var a,p;null!=e?(a=e._namespaceURI,p=e._tag):n._tag&&(a=n._namespaceURI,p=n._tag),(null==a||a===x.svg&&\"foreignobject\"===p)&&(a=x.html),a===x.html&&(\"svg\"===this._tag?a=x.svg:\"math\"===this._tag&&(a=x.mathml)),this._namespaceURI=a;var h;if(t.useCreateElement){var d,v=n._ownerDocument;if(a===x.html)if(\"script\"===this._tag){var g=v.createElement(\"div\"),m=this._currentElement.type;g.innerHTML=\"<\"+m+\"></\"+m+\">\",d=g.removeChild(g.firstChild)}else d=o.is?v.createElement(this._currentElement.type,o.is):v.createElement(this._currentElement.type);else d=v.createElementNS(a,this._currentElement.type);S.precacheNode(this,d),this._flags|=U.hasCachedChildNodes,this._hostParent||C.setAttributeForRoot(d),this._updateDOMProperties(null,o,t);var _=b(d);this._createInitialChildren(t,o,r,_),h=_}else{var w=this._createOpenTagMarkupAndPutListeners(t,o),k=this._createContentMarkup(t,o,r);h=!k&&K[this._tag]?w+\"/>\":w+\">\"+k+\"</\"+this._currentElement.type+\">\"}switch(this._tag){case\"input\":t.getReactMountReady().enqueue(u,this),o.autoFocus&&t.getReactMountReady().enqueue(y.focusDOMComponent,this);break;case\"textarea\":t.getReactMountReady().enqueue(c,this),o.autoFocus&&t.getReactMountReady().enqueue(y.focusDOMComponent,this);break;case\"select\":case\"button\":o.autoFocus&&t.getReactMountReady().enqueue(y.focusDOMComponent,this);break;case\"option\":t.getReactMountReady().enqueue(s,this)}return h},_createOpenTagMarkupAndPutListeners:function(t,e){var n=\"<\"+this._currentElement.type;for(var r in e)if(e.hasOwnProperty(r)){var i=e[r];if(null!=i)if(V.hasOwnProperty(r))i&&o(this,r,i,t);else{\"style\"===r&&(i&&(i=this._previousStyleCopy=m({},e.style)),i=_.createMarkupForStyles(i,this));var a=null;null!=this._tag&&d(this._tag,e)?H.hasOwnProperty(r)||(a=C.createMarkupForCustomAttribute(r,i)):a=C.createMarkupForProperty(r,i),a&&(n+=\" \"+a)}}return t.renderToStaticMarkup?n:(this._hostParent||(n+=\" \"+C.createMarkupForRoot()),n+=\" \"+C.createMarkupForID(this._domID))},_createContentMarkup:function(t,e,n){var r=\"\",i=e.dangerouslySetInnerHTML;if(null!=i)null!=i.__html&&(r=i.__html);else{var o=W[typeof e.children]?e.children:null,a=null!=o?null:e.children;if(null!=o)r=R(o);else if(null!=a){var u=this.mountChildren(a,t,n);r=u.join(\"\")}}return G[this._tag]&&\"\\n\"===r.charAt(0)?\"\\n\"+r:r},_createInitialChildren:function(t,e,n,r){var i=e.dangerouslySetInnerHTML;if(null!=i)null!=i.__html&&b.queueHTML(r,i.__html);else{var o=W[typeof e.children]?e.children:null,a=null!=o?null:e.children;if(null!=o)\"\"!==o&&b.queueText(r,o);else if(null!=a)for(var u=this.mountChildren(a,t,n),c=0;c<u.length;c++)b.queueChild(r,u[c])}},receiveComponent:function(t,e,n){var r=this._currentElement;this._currentElement=t,this.updateComponent(e,r,t,n)},updateComponent:function(t,e,n,r){var o=e.props,a=this._currentElement.props;switch(this._tag){case\"input\":o=N.getHostProps(this,o),a=N.getHostProps(this,a);break;case\"option\":o=A.getHostProps(this,o),a=A.getHostProps(this,a);break;case\"select\":o=P.getHostProps(this,o),a=P.getHostProps(this,a);break;case\"textarea\":o=O.getHostProps(this,o),a=O.getHostProps(this,a)}switch(i(this,a),this._updateDOMProperties(o,a,t),this._updateDOMChildren(o,a,t,r),this._tag){case\"input\":N.updateWrapper(this),L.updateValueIfChanged(this);break;case\"textarea\":O.updateWrapper(this);break;case\"select\":t.getReactMountReady().enqueue(p,this)}},_updateDOMProperties:function(t,e,n){var r,i,a;for(r in t)if(!e.hasOwnProperty(r)&&t.hasOwnProperty(r)&&null!=t[r])if(\"style\"===r){var u=this._previousStyleCopy;for(i in u)u.hasOwnProperty(i)&&(a=a||{},a[i]=\"\");this._previousStyleCopy=null}else V.hasOwnProperty(r)?t[r]&&F(this,r):d(this._tag,t)?H.hasOwnProperty(r)||C.deleteValueForAttribute(j(this),r):(w.properties[r]||w.isCustomAttribute(r))&&C.deleteValueForProperty(j(this),r);for(r in e){var c=e[r],s=\"style\"===r?this._previousStyleCopy:null!=t?t[r]:void 0;if(e.hasOwnProperty(r)&&c!==s&&(null!=c||null!=s))if(\"style\"===r)if(c?c=this._previousStyleCopy=m({},c):this._previousStyleCopy=null,s){for(i in s)!s.hasOwnProperty(i)||c&&c.hasOwnProperty(i)||(a=a||{},a[i]=\"\");for(i in c)c.hasOwnProperty(i)&&s[i]!==c[i]&&(a=a||{},a[i]=c[i])}else a=c;else if(V.hasOwnProperty(r))c?o(this,r,c,n):s&&F(this,r);else if(d(this._tag,e))H.hasOwnProperty(r)||C.setValueForAttribute(j(this),r,c);else if(w.properties[r]||w.isCustomAttribute(r)){var l=j(this);null!=c?C.setValueForProperty(l,r,c):C.deleteValueForProperty(l,r)}}a&&_.setValueForStyles(j(this),a,this)},_updateDOMChildren:function(t,e,n,r){var i=W[typeof t.children]?t.children:null,o=W[typeof e.children]?e.children:null,a=t.dangerouslySetInnerHTML&&t.dangerouslySetInnerHTML.__html,u=e.dangerouslySetInnerHTML&&e.dangerouslySetInnerHTML.__html,c=null!=i?null:t.children,s=null!=o?null:e.children,l=null!=i||null!=a,f=null!=o||null!=u;null!=c&&null==s?this.updateChildren(null,n,r):l&&!f&&this.updateTextContent(\"\"),null!=o?i!==o&&this.updateTextContent(\"\"+o):null!=u?a!==u&&this.updateMarkup(\"\"+u):null!=s&&this.updateChildren(s,n,r)},getHostNode:function(){return j(this)},unmountComponent:function(t){switch(this._tag){case\"audio\":case\"form\":case\"iframe\":case\"img\":case\"link\":case\"object\":case\"source\":case\"video\":var e=this._wrapperState.listeners;if(e)for(var n=0;n<e.length;n++)e[n].remove();break;case\"input\":case\"textarea\":L.stopTracking(this);break;case\"html\":case\"head\":case\"body\":g(\"66\",this._tag)}this.unmountChildren(t),S.uncacheNode(this),k.deleteAllListeners(this),this._rootNodeID=0,this._domID=0,this._wrapperState=null},getPublicInstance:function(){return j(this)}},m(v.prototype,v.Mixin,I.Mixin),t.exports=v},function(t,e,n){\"use strict\";function r(t,e){var n={_topLevelWrapper:t,_idCounter:1,_ownerDocument:e?e.nodeType===i?e:e.ownerDocument:null,_node:e,_tag:e?e.nodeName.toLowerCase():null,_namespaceURI:e?e.namespaceURI:null};return n}var i=(n(97),9);t.exports=r},function(t,e,n){\"use strict\";var r=n(3),i=n(20),o=n(4),a=function(t){this._currentElement=null,this._hostNode=null,this._hostParent=null,this._hostContainerInfo=null,this._domID=0};r(a.prototype,{mountComponent:function(t,e,n,r){var a=n._idCounter++;this._domID=a,this._hostParent=e,this._hostContainerInfo=n;var u=\" react-empty: \"+this._domID+\" 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c=u.querySelectorAll(\"input[name=\"+JSON.stringify(\"\"+i)+'][type=\"radio\"]'),p=0;p<c.length;p++){var h=c[p];if(h!==o&&h.form===o.form){var d=l.getInstanceFromNode(h);d||a(\"90\"),f.asap(r,d)}}}return n}var a=n(1),u=n(3),c=n(160),s=n(86),l=n(4),f=n(12),p=(n(0),n(2),{getHostProps:function(t,e){var n=s.getValue(e),r=s.getChecked(e);return u({type:void 0,step:void 0,min:void 0,max:void 0},e,{defaultChecked:void 0,defaultValue:void 0,value:null!=n?n:t._wrapperState.initialValue,checked:null!=r?r:t._wrapperState.initialChecked,onChange:t._wrapperState.onChange})},mountWrapper:function(t,e){var n=e.defaultValue;t._wrapperState={initialChecked:null!=e.checked?e.checked:e.defaultChecked,initialValue:null!=e.value?e.value:n,listeners:null,onChange:o.bind(t),controlled:i(e)}},updateWrapper:function(t){var e=t._currentElement.props,n=e.checked;null!=n&&c.setValueForProperty(l.getNodeFromInstance(t),\"checked\",n||!1);var r=l.getNodeFromInstance(t),i=s.getValue(e);if(null!=i)if(0===i&&\"\"===r.value)r.value=\"0\";else if(\"number\"===e.type){var o=parseFloat(r.value,10)||0;(i!=o||i==o&&r.value!=i)&&(r.value=\"\"+i)}else r.value!==\"\"+i&&(r.value=\"\"+i);else null==e.value&&null!=e.defaultValue&&r.defaultValue!==\"\"+e.defaultValue&&(r.defaultValue=\"\"+e.defaultValue),null==e.checked&&null!=e.defaultChecked&&(r.defaultChecked=!!e.defaultChecked)},postMountWrapper:function(t){var e=t._currentElement.props,n=l.getNodeFromInstance(t);switch(e.type){case\"submit\":case\"reset\":break;case\"color\":case\"date\":case\"datetime\":case\"datetime-local\":case\"month\":case\"time\":case\"week\":n.value=\"\",n.value=n.defaultValue;break;default:n.value=n.value}var r=n.name;\"\"!==r&&(n.name=\"\"),n.defaultChecked=!n.defaultChecked,n.defaultChecked=!n.defaultChecked,\"\"!==r&&(n.name=r)}});t.exports=p},function(t,e,n){\"use strict\";function r(t){var e=\"\";return o.Children.forEach(t,function(t){null!=t&&(\"string\"==typeof t||\"number\"==typeof t?e+=t:c||(c=!0))}),e}var i=n(3),o=n(26),a=n(4),u=n(162),c=(n(2),!1),s={mountWrapper:function(t,e,n){var i=null;if(null!=n){var o=n;\"optgroup\"===o._tag&&(o=o._hostParent),null!=o&&\"select\"===o._tag&&(i=u.getSelectValueContext(o))}var a=null;if(null!=i){var c;if(c=null!=e.value?e.value+\"\":r(e.children),a=!1,Array.isArray(i)){for(var s=0;s<i.length;s++)if(\"\"+i[s]===c){a=!0;break}}else a=\"\"+i===c}t._wrapperState={selected:a}},postMountWrapper:function(t){var e=t._currentElement.props;if(null!=e.value){a.getNodeFromInstance(t).setAttribute(\"value\",e.value)}},getHostProps:function(t,e){var n=i({selected:void 0,children:void 0},e);null!=t._wrapperState.selected&&(n.selected=t._wrapperState.selected);var o=r(e.children);return o&&(n.children=o),n}};t.exports=s},function(t,e,n){\"use strict\";function r(t,e,n,r){return t===n&&e===r}function i(t){var e=document.selection,n=e.createRange(),r=n.text.length,i=n.duplicate();i.moveToElementText(t),i.setEndPoint(\"EndToStart\",n);var o=i.text.length;return{start:o,end:o+r}}function o(t){var e=window.getSelection&&window.getSelection();if(!e||0===e.rangeCount)return null;var n=e.anchorNode,i=e.anchorOffset,o=e.focusNode,a=e.focusOffset,u=e.getRangeAt(0);try{u.startContainer.nodeType,u.endContainer.nodeType}catch(t){return null}var c=r(e.anchorNode,e.anchorOffset,e.focusNode,e.focusOffset),s=c?0:u.toString().length,l=u.cloneRange();l.selectNodeContents(t),l.setEnd(u.startContainer,u.startOffset);var f=r(l.startContainer,l.startOffset,l.endContainer,l.endOffset),p=f?0:l.toString().length,h=p+s,d=document.createRange();d.setStart(n,i),d.setEnd(o,a);var v=d.collapsed;return{start:v?h:p,end:v?p:h}}function a(t,e){var n,r,i=document.selection.createRange().duplicate();void 0===e.end?(n=e.start,r=n):e.start>e.end?(n=e.end,r=e.start):(n=e.start,r=e.end),i.moveToElementText(t),i.moveStart(\"character\",n),i.setEndPoint(\"EndToStart\",i),i.moveEnd(\"character\",r-n),i.select()}function u(t,e){if(window.getSelection){var n=window.getSelection(),r=t[l()].length,i=Math.min(e.start,r),o=void 0===e.end?i:Math.min(e.end,r);if(!n.extend&&i>o){var a=o;o=i,i=a}var u=s(t,i),c=s(t,o);if(u&&c){var f=document.createRange();f.setStart(u.node,u.offset),n.removeAllRanges(),i>o?(n.addRange(f),n.extend(c.node,c.offset)):(f.setEnd(c.node,c.offset),n.addRange(f))}}}var c=n(6),s=n(405),l=n(172),f=c.canUseDOM&&\"selection\"in document&&!(\"getSelection\"in window),p={getOffsets:f?i:o,setOffsets:f?a:u};t.exports=p},function(t,e,n){\"use strict\";var r=n(1),i=n(3),o=n(82),a=n(20),u=n(4),c=n(56),s=(n(0),n(97),function(t){this._currentElement=t,this._stringText=\"\"+t,this._hostNode=null,this._hostParent=null,this._domID=0,this._mountIndex=0,this._closingComment=null,this._commentNodes=null});i(s.prototype,{mountComponent:function(t,e,n,r){var i=n._idCounter++,o=\" react-text: \"+i+\" \";if(this._domID=i,this._hostParent=e,t.useCreateElement){var s=n._ownerDocument,l=s.createComment(o),f=s.createComment(\" /react-text \"),p=a(s.createDocumentFragment());return a.queueChild(p,a(l)),this._stringText&&a.queueChild(p,a(s.createTextNode(this._stringText))),a.queueChild(p,a(f)),u.precacheNode(this,l),this._closingComment=f,p}var h=c(this._stringText);return t.renderToStaticMarkup?h:\"\\x3c!--\"+o+\"--\\x3e\"+h+\"\\x3c!-- /react-text --\\x3e\"},receiveComponent:function(t,e){if(t!==this._currentElement){this._currentElement=t;var n=\"\"+t;if(n!==this._stringText){this._stringText=n;var r=this.getHostNode();o.replaceDelimitedText(r[0],r[1],n)}}},getHostNode:function(){var t=this._commentNodes;if(t)return t;if(!this._closingComment)for(var e=u.getNodeFromInstance(this),n=e.nextSibling;;){if(null==n&&r(\"67\",this._domID),8===n.nodeType&&\" /react-text \"===n.nodeValue){this._closingComment=n;break}n=n.nextSibling}return t=[this._hostNode,this._closingComment],this._commentNodes=t,t},unmountComponent:function(){this._closingComment=null,this._commentNodes=null,u.uncacheNode(this)}}),t.exports=s},function(t,e,n){\"use strict\";function r(){this._rootNodeID&&l.updateWrapper(this)}function i(t){var e=this._currentElement.props,n=u.executeOnChange(e,t);return s.asap(r,this),n}var o=n(1),a=n(3),u=n(86),c=n(4),s=n(12),l=(n(0),n(2),{getHostProps:function(t,e){return null!=e.dangerouslySetInnerHTML&&o(\"91\"),a({},e,{value:void 0,defaultValue:void 0,children:\"\"+t._wrapperState.initialValue,onChange:t._wrapperState.onChange})},mountWrapper:function(t,e){var n=u.getValue(e),r=n;if(null==n){var a=e.defaultValue,c=e.children;null!=c&&(null!=a&&o(\"92\"),Array.isArray(c)&&(c.length<=1||o(\"93\"),c=c[0]),a=\"\"+c),null==a&&(a=\"\"),r=a}t._wrapperState={initialValue:\"\"+r,listeners:null,onChange:i.bind(t)}},updateWrapper:function(t){var e=t._currentElement.props,n=c.getNodeFromInstance(t),r=u.getValue(e);if(null!=r){var i=\"\"+r;i!==n.value&&(n.value=i),null==e.defaultValue&&(n.defaultValue=i)}null!=e.defaultValue&&(n.defaultValue=e.defaultValue)},postMountWrapper:function(t){var e=c.getNodeFromInstance(t),n=e.textContent;n===t._wrapperState.initialValue&&(e.value=n)}});t.exports=l},function(t,e,n){\"use strict\";function r(t,e){\"_hostNode\"in t||c(\"33\"),\"_hostNode\"in e||c(\"33\");for(var n=0,r=t;r;r=r._hostParent)n++;for(var i=0,o=e;o;o=o._hostParent)i++;for(;n-i>0;)t=t._hostParent,n--;for(;i-n>0;)e=e._hostParent,i--;for(var a=n;a--;){if(t===e)return t;t=t._hostParent,e=e._hostParent}return null}function i(t,e){\"_hostNode\"in 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i=n(345),o=n(347),a=n(349),u=n(351),c=n(352),s=n(355),l=n(357),f=n(360),p=n(4),h=n(362),d=n(370),v=n(368),g=n(371),m=n(375),y=n(376),_=n(381),b=n(386),x=n(387),w=n(388),C=!1;t.exports={inject:r}},function(t,e,n){\"use strict\";var r=\"function\"==typeof Symbol&&Symbol.for&&Symbol.for(\"react.element\")||60103;t.exports=r},function(t,e,n){\"use strict\";function r(t){i.enqueueEvents(t),i.processEventQueue(!1)}var i=n(22),o={handleTopLevel:function(t,e,n,o){r(i.extractEvents(t,e,n,o))}};t.exports=o},function(t,e,n){\"use strict\";function r(t){for(;t._hostParent;)t=t._hostParent;var e=f.getNodeFromInstance(t),n=e.parentNode;return f.getClosestInstanceFromNode(n)}function i(t,e){this.topLevelType=t,this.nativeEvent=e,this.ancestors=[]}function o(t){var e=h(t.nativeEvent),n=f.getClosestInstanceFromNode(e),i=n;do{t.ancestors.push(i),i=i&&r(i)}while(i);for(var o=0;o<t.ancestors.length;o++)n=t.ancestors[o],v._handleTopLevel(t.topLevelType,n,t.nativeEvent,h(t.nativeEvent))}function 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"metadata": {},
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"source": [
"# Load JS visualization code to notebook\n",
"\n",
"shap.initjs()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id=section701></a>\n",
"### 7.1 Explain predictions"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"# Explain the model's predictions using SHAP values \n",
"\n",
"explainer = shap.TreeExplainer(regressor_rf)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
"shap_values = explainer.shap_values(X_train.values)"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(404, 13)"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"shap_values.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id=section702></a>\n",
"### 7.2 Visualize a single prediction"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"<div id='i133U4QVHRI8AY0HOZIEX'>\n",
"<div style='color: #900; text-align: center;'>\n",
" <b>Visualization omitted, Javascript library not loaded!</b><br>\n",
" Have you run `initjs()` in this notebook? If this notebook was from another\n",
" user you must also trust this notebook (File -> Trust notebook). If you are viewing\n",
" this notebook on github the Javascript has been stripped for security. If you are using\n",
" JupyterLab this error is because a JupyterLab extension has not yet been written.\n",
"</div></div>\n",
" <script>\n",
" if (window.SHAP) SHAP.ReactDom.render(\n",
" SHAP.React.createElement(SHAP.AdditiveForceVisualizer, {\"outNames\": [\"output value\"], \"baseValue\": 22.8112635910511, \"outValue\": 12.5109998282635, \"link\": \"identity\", \"featureNames\": [\"crim\", \"zn\", \"indus\", \"chas\", \"nox\", \"rm\", \"age\", \"dis\", \"rad\", \"tax\", \"ptratio\", \"black\", \"lstat\"], \"features\": {\"0\": {\"effect\": -0.7862341631628311, \"value\": 15.0234}, \"1\": {\"effect\": 0.0020389613020191473, \"value\": 0.0}, \"2\": {\"effect\": -0.0228669049015685, \"value\": 18.1}, \"3\": {\"effect\": -0.010591592615804626, \"value\": 0.0}, \"4\": {\"effect\": 0.5004854973512266, \"value\": 0.614}, \"5\": {\"effect\": -3.0379193367487023, \"value\": 5.304}, \"6\": {\"effect\": -0.2430492297509486, \"value\": 97.3}, \"7\": {\"effect\": 0.2703917396718358, \"value\": 2.1007}, \"8\": {\"effect\": 0.07545841535484406, \"value\": 24.0}, \"9\": {\"effect\": -0.16150168152001376, \"value\": 666.0}, \"10\": {\"effect\": -0.3957238139068809, \"value\": 20.2}, \"11\": {\"effect\": -0.0799054591439572, \"value\": 349.48}, \"12\": {\"effect\": -6.410846194716821, \"value\": 24.91}}, \"plot_cmap\": \"RdBu\", \"labelMargin\": 20}),\n",
" document.getElementById('i133U4QVHRI8AY0HOZIEX')\n",
" );\n",
"</script>"
],
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"<IPython.core.display.HTML object>"
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"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Visualize the first prediction's explanation\n",
"\n",
"shap.force_plot(explainer.expected_value, shap_values[0, :], X_train.iloc[0, :])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- The above explanation shows **features** each contributing to **push the model output** from the **base value** (the **average model output over the training dataset** we passed) **to the model output**. \n",
"\n",
"\n",
"- **Features pushing the prediction higher** are shown in **red**, those **pushing the prediction lower are in blue**.\n",
"\n",
"\n",
"- The **values** written after each **feature** is their **actual value** in the **data** for this **particular sample (row)**."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id=section703></a>\n",
"### 7.3 Visualize many predictions\n",
"\n",
"- If we take **many explanations** such as the one shown above, **rotate them 90 degrees**, and then **stack them horizontally**, we can see **explanations for an entire dataset**."
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"<div id='iWVJ02OCIPMFRZRQXU0PI'>\n",
"<div style='color: #900; text-align: center;'>\n",
" <b>Visualization omitted, Javascript library not loaded!</b><br>\n",
" Have you run `initjs()` in this notebook? If this notebook was from another\n",
" user you must also trust this notebook (File -> Trust notebook). If you are viewing\n",
" this notebook on github the Javascript has been stripped for security. If you are using\n",
" JupyterLab this error is because a JupyterLab extension has not yet been written.\n",
"</div></div>\n",
" <script>\n",
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" document.getElementById('iWVJ02OCIPMFRZRQXU0PI')\n",
" );\n",
"</script>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Visualize the training set predictions \n",
"\n",
"shap.force_plot(explainer.expected_value, shap_values, X_train)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id=section704></a>\n",
"### 7.4 SHAP Dependence Plots\n",
"\n",
"- **SHAP dependence plots** show the **effect of a single feature across the whole dataset**. \n",
"\n",
"\n",
"- They **plot a feature's value vs. the SHAP value** of that feature **across many samples**.\n",
"\n",
"\n",
"- The **vertical dispersion** of **SHAP values at a single feature value** is driven by **interaction effects**, and **another feature** is chosen for **coloring** to **highlight possible interactions**."
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 540x360 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Create a SHAP dependence plot to show the effect of a single feature across the whole dataset\n",
"\n",
"shap.dependence_plot('rm', shap_values, X_train)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- Since **SHAP values** represent a **feature's responsibility for a change in the model output**, the plot above represents the **change in predicted house price** as **rm** (the average number of rooms per house in an area) **changes**. \n",
"<br><br>\n",
" - **Vertical dispersion** at a single value of **rm** represents **interaction effects** with **other features**. \n",
"<br><br> \n",
" - To help **reveal** these **interactions dependence_plot automatically selects another feature for coloring**. \n",
"<br><br> \n",
" - In this case **coloring** by **rad** (index of accessibility to radial highways) **highlights** that the **average number of rooms per house** has **less impact on home price for areas** with a **high rad** value."
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 540x360 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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2m/OAx5RSZcAhQHLPx7puGxz2a7jxX3DKnfCfmYmOSIi4C9QEeff6uWyeX8HamVv55Pb5iQ5JdJFttH64VTSDNqCUKsBpMTsEuBeYpLX+OJ6BtUdr/SzwbItlpzZ5/i3NGyXtWT4TODjuAbqFXg67ahpffzgfLjgycfEI0QMCtSGCdY0zRdbukMmtRPx1WDJVSk0ClgEXAtfjTBD+mlLq8jjH5jnBkMsabqtRkN+kO/Dx4xMXixA9JKcok/EXDgfAn+FD/Xh0YgMSXealat5oSqZ/wRlB6CWl1E6t9Wql1Ck4/TefjGt0HhEM2Zz/TA0vLwhiFhbz6eAw/XNdUIM+pC98eTe8PgfGD4NTD0l0REL0iCN/fQATLx1JerafrPyMRIcjush2wWk0WtGEOpbGhjo2gNb6c6BfvILymtcXB3l5gdMw2NqRzp8/rU9wRI2q1wYpL8thR5mNHXZZyVmIOModkC2J1ONsv9Hq4VbRlEyXAacBb+xZoJQ6Dlgar6C8JtPf4rVLxn2tX7yd9ae/CsFIg3K/j8JfpM4tYyGEt4VdXK3bUjTJ9FfAG0qp6UCOUmoa8F2clrECONn0M2q/bJZXGfQ2Qlw6yR1Xww1LdzYmUqBh4bYERtPc1qW7WfruJgqH96L0jEGJDkcI4UJequbtMJlqrWcopcYDF+AMjlAOHKa1Xh7v4LzijZWwvNoHBlSRxuMLbe45OtFRQc5Rg0gfmU9g5S6MDD+5F46N2/+lN4b5ZHWYqUN9HD64/SOgaksdL105h0C10+KyvirIxAuHxS02IYQ3ubnBUUtRdY3RWq8F7olzLJ6V3iJ3pPvbXq+n+QuzmfePc/jqxXX0GV/I9UcP7HijLpi7McyUfwZoCEGaL8Snl6ZzxJB9J9QdK6r2JlKATQsqAEmmQvxpTpgP1tgcN9TgN4d5qFgWJ27uV9pSh8lUKRVmH4P1a61dkjYS67SRBhP7wvxtNvnpIa6b6I5q3m/XB7n4tSDhtIFQBsb0Om48MfYTY3+6JkxDJDcGwzB9VbjdZNpv/zx698+ianMdGDDyaGnLJsSLVphfz3Buy3y4xmZEPvxgbGon1GQrmbbsnNgX+AXwVuzD8ab/LArxzYYQZPjYWePjp+8FeeHsxCfUNTvDNG3Au2p7aN8rd8H77+3iq6/SKJxQS5ovg2DYmS/06GHtnwCy8jM476nDWf3ZVvoM70XJQTITnhBrdjd/vXZ32+ulkrB3cmlU90wXtVymlJqHM5TfE/EIymvunxNubNKbbvD2ytgmra46bkw640v8LNgYIjfT4EeHZ8bss2fNquKZZ7YDfrC28uSPBrAuPYOjhvmYOrTjq+lexZkccNbgDtcTIlVcUGrwl69hbSUMzoULSz2USeIk2UqmbekL5MUyEC8b29dA72h8XZDtjh9A70yDmb/IZ/7GIMOL/DEdSGLjxuZDtBXVNXDqpCz69HLHvgvhNSW9DRb+yM/SHTCmEHIz5FhKtnumL9D8nmkGMBV4PV5Bec34fJv02iB9giGq0vyMzLNxvqbEa9gVwL9kNw3Ds2Bs75h97qrMDOoMgyzbpsZn8KfPA6x/ZztjBvr5988L6NMrte/1CNEVuRkGhwxIdBTuYRveyabRlEwXtngdwhlkPvmnL4vSxooQR6zbSZZtEwJ2Z+QmOiQAKncEeOznZVRXBDEMuOC3+1E6uSAmnx3KTuP1wjwKgiGChkFB5P7O0vIQL3xZx9Un5LT/AUII0YFE3zM1TTPDsqyoZkqI5p7p7d0PKbnl1ofJsp3Cux8oDrpjzvF1S6qornBisW1YMrsiZsn0qsMz+O+8BuZt8HFQIeze3LjPeS6p5nabhqDNmgqbwfkG2enyHYnYWrrd5t+LQozqY/DD8cnR0aKn7pmaprnRsqySNt7aBBRG8xn7TKZtVO+2orWWUZCA/Qb7mU3jQMf9+3f1VnRsDRiRQ3qmj0C909x+SAyrefvk+LjxO714cd5OLlR9WLSwlpllDRw+Jp1zDs+K2f+TLCpqbY7+ey3zN4UZWmAw85pshhZIVbiIjW01Nkc8HWB7ZB70zTXwq8O8n1Djec/UNM3hwHOAAfQzTXNOi1VygS3Rfl57Z/2W1btiHwoK0phX0Jv+dQ3sTvczsl/sWs12R+HATK64ZwyLPt9J/+E5HHhsVBdYUXljaZgLXg0Beby8JsT0i3O45ZzYJetk8+LCIPM3ORc1aytsnvgqwO9PdMfvRHhf2XZ7byIFmLkunCTJNH7Z1LKs1aZp3gkUAY8Bj7RYpR74NNrP22cy7ah6Vykll9URby4JsS0znW2Z6QC8v8wdXWMABo3pxaAxvWL+ud9sbqy0sCOvj5FBjPapf+/mJ4UBveXwEbEzvthgSB6si7RdOG0/+X1Fw7KsNwBM01xoWZbuzmdF05p3DHAzMIjGmsx0YDQg7c6AjHQbwrbz7djgd/E0QbFyxhiDe76E6gDkZ8KpTQ7esq1hpq8Mc+hgH5MGyUEN8N3SNO48KYM3yoIcMdTPNYe541aASA4FWQazLknn1aVhRvUxOGlkchx3PdgAaY1pmrfTOs+VWpalovmAaI7oJ4FtwBpgOPAJ8GNaF4lTlhGyYc+0a4ZBIBBuf4MkcGB/H99elc5reh3nHDqEofnO/lvbwkz6ez3VDeD3wYeXZHDMCO9XN8XCzcdmcPOx7ugyJbquut5m1toQIwt9jChqnbSe/jbI0m02F4z3s3+/nktqJbkG1x6SXMdaD3aNeRoowMl1fYFFwPeAv0b7AdEk04MiHz4CeEhrfYdS6gPgYeCOfW2klJoI3IVTgm32i9Jaj4w2QC9YtD4MRuMu7qpKjUm4B+fCuPw6Bja5VfrJqjDVkYbkoTC8tzwsyVQkjap6myMermJBeZjMNHjz8l6cMKbxNPrAl0F++W4AgEe+ClJ2XVarKn4RvR4ctGEqMAQYCtxtWdYVpmm+CPy/aD8gmsum7UAdsAI4AEBrPQvYr4Pt/g5U4MyH+tMWj6Qyup+vsd2zbVOYAl0sy3eHGXNvNSe/PJD976tmR43zBRw22Nds1pypHYzTK4SXfL46yIJyp+apPgjTdPMuiJ+tbWwvsbMWFm1J/lqqeAobRqtHnOy2LGsnsIzIePSWZb0D7B/tB0RTMp0H/BG4HdiolDoLqAGqO9iuFDhSa10fbTBedenhmTxRtuegMrhySnpC4+kJd3xUz9pdTgJdvt3m3k/rueuULCYO9PHJZRm8uzzM5CE+ThktpVKRPEYWOheLgUjONIubXyyePsbPS4udBDooDyYOkIvJ7ujBkuki0zSvxWnVW2WapgJqcQYpiko0yfQXwOM4zYd/DbwEZOHcN23PPJwq3qTvYtN7Ry0XL1jFsIrdbMvJoWhoCUxN7mnFNu4KOyNBRGzY1XgFfsRQP0cMlSTaUm3AZukOmxEFBnmZUvXnRaOL/bx2WQ7TdIDSfj5uOq5596bLDkpjSL7Bsu02Z5h+CnOi/zsHQzY/fqGGD5YGOG50Go+f14u0FGjM2J4evGd6I/A88A5OwfELnPrGO6P9gGiS6Xat9QmR5xuUUkVAhta6o5LpPOBTpdTrwNamb2itb4w2QC/49KXNHLTR6dtbWFPHqncN+FFyJ9PiYAOEfU53Z9tmCIFEh+Rq22tspjwdwNoBA3vDZxenM7JPap8oveqU0nROKd137dPxI/0c34VWIU991cA/ZjsVedPmNDB5WBpXH5HaA6D0VDK1LGtP4Q9glWmaM4DewKpoPyOaZLpJKfUG8C/gXa11AKI6c/ancTD84ibLk27erWxfmKomrzNC7ulnGi+rV9SCEem/ahgsXFwN5Cc0Jjd7eWkYa7sNNpRXwj/nh7jjaOkeIxpVNzRvuFjVkBoNGdsTbTWvaZp5OKXJ0yODMfwTp1HRnkLf7ZZl7XM8+ZbDCVqWtSGyfAfdHU6wiXHABcA9wD+VUv8FntZaf9XBducAv9Fa39t0oVIq6aa8rdmvDyu/3sW6Pr3pv7uG+qFFiQ4p7o4elcZHS2xsw8AfDnPiAY3VXdUNNku2htmv0Oea6egSLc0Ago0nx2BITpSiuR8dmsmzX9cze02ISUP9XHGYjJAVzdi8pmkehnMrckyTxQo4yrKs8na2G04PDScIgNZ6JfAH4A9KqQOB84CXlFLVWuvSdjYNApcqpSYAVzZpiJR0Z9fHdmWz9KgJYBhg22QEQzyV6KDi7JgDe3Pyp+Xs9vnID9tMPsC5qNteYzP5sRqWbbfp18vgs2uyGd1XGmHUB5onT0NyqWghN8tg1i/yqayzyc1KutNkl0RZzXsV8BOcvqKYppmD08XlSdM0B+HMcHa7ZVnNmlb32HCCLSmlSoDjIo8M4NUONmkAjsDJ/DOUUmdqrcvpYPB8L6qtBXyNgzbYSbeHra3fFKQoGGZEfS27srJYXx7gkHFZvLwoyLLtzhewpdrmqa8D3HmSXGFPHODDZzgDZQEcUiIXGKJtkkgbRVMytSzrSgDTNPcsGgBMB64FdgFvAlfglF5bbtvmcIKmaRqWZXXqTN7hEa2U+h+l1Ayc/jeTcQZiGKy1/llH22qtdwOnAbMBrZQ6FKfPalI52ajCH2686Bm7I+lqslsZnhFg7NbtlOyuYuyWbQzPcaZgG1bQ/MefbDOjvL88xLQleSzf3rn+g4cP9nHnsWlM7G/wi8P8nF0qrZ2F6JBhtH50wLKslZZlnWVZVrllWTXAQ8CpHWy23TTNaQCmaZ4KVJqmudY0zUnRhhpNyfRinMZHZ2itK6L9YCLVuVrrMPAzpdQi4AOc8Q6TSv6aSg7FZmuvTPLqAxywtQJI7ta8gS2NU1QYQN0m5xppfD5MrK9lnZFGUSjElMI0kuVP/uz8IBe9FAAKecKqZ8G1WZTkRVeK0BvD3PJJiJAN32wJc8zwEN8zJaEK0Z6uzGdqmuZ4YIxlWS9FFhl03Gj2UWCdaZoGcB/we5xS7SPAodH8vx0WG7TWU7TWf+tkIgVnoIemn/M34Czgy05+jutVVIfJqw+y345qiqsbsMPJX8+736QC/JEJrtOzfIw8xGnJO6+sjoG76zl0VzX7VdXx1fzEjNlRVW/zh+n1/O6D+r2jM3XX+ysaS6M7ap0EGS1dbtO0zdGcDcn/GxGiu2zDaPWIggE8YJpmH9M004Grce6btucgnLETRuMMnfuwZVl/A8ZGG2vc2uZrre9qY9l0nLrspJLhCzNgdyVVGRlkB4OkhYKJDinuSszeXPHIePTHyzjsxDEUDckGYNTQDMI+CNgGfhtGD0/MwO7nP1vL20ucLkrvWEFmX9f9aeiOHeHjqW+cz8zPgoMHRl+FfcIIH3mZsLseMvxw2ujkqv4WIh5so/PHiWVZ803TvAv4HKda7CXLsv7TwWZhIA84E/jSsqwa0zRHAJXR/r/S0S0Gamps0g2DwqCTRKv9yVGt2ZGK3CwW9i+gtFcmezoDNYShHj+2ASED6hLU5XZWkzFSv1ofJhy28XWhyqipSyem0SfLYPrCzVxz9AAG50f/eaMKDb6+MoMZa8OogQbj+0sydSu9PsTlL9dTUT2ARwjy3VI5TSZKZ6p5Lcsa3uT5ozhVt9F6GmegoWLgMtM0J+A0XGrVaGlf5IiOgV6VddiGQZXPR8AwGLQz+RsgzdsYYuJDNfz68yIOfKiGJZEBvVdtCDZrrr1yfdu3KpZuD/PHmUFeLotPtj1z/8YLmu+WpnU7ke5xxlg/1xywi9Lizh86Ly8N888FYZ5dHCacCk2+Peryl+tZsCnMusp0LniujlAK3LZJVaZp5kS60twO/Bw4CXgbZ4KXm4A/RftZ7V5yKaWuwhlBf7rWuqOuMClrxLpyHp88ke3pafQKhbl8+dpEhxR3by4JURsAn21TWW/w3NcNTMkPM6Sfj4JcHxWVYbIyDI46OLvVtpuqbCY/GWBHpA3TX0+Da2I8D+PjZ2dyiumnIQTnTkh8yeLt5WFunO5cOMxcZzM83+Cag6UBkhvVNBl5qD7oTCXol2JHQvTAcIJV7Lu7phF5L6oDdZ9nGaXUb4HrgBnAE0qpEVrr+zsZaEp4Z9woluY4Y2huT4eXJpjcluCY4m18oc3xW3YQSPPjD4X46O0MvqwNkZlhcOu1hZAhrM8AACAASURBVNQEYNTgNAb2bf0Tm7/Z3ptIAT5eHY55MvX5DM6Z4J7q9g2VzY/X9ZVS2nGr+0/L5ILn6qgP2DxwWiYZadLvM2Hi/9WPiNUHtXfJ/iPgKK31UqXUEcDfAEmmbahLb/41BlPg2FtbVsPHQ/oR9hmkhcMcu24bGD7qG2yWrg5wwWm5+9x24gCD4hzYWuO8Pmlk8l/2nz3Wx/1zQpRth5LecPmBUip1q++WprHr1l4sXLyEA8e1N8ibiLd4l0wty1oTq89qL5n21VovjTyfRRIOUB8r47btoAroX13L1uxMJlZWk+xf1+yqNMLZfvAZBEM+yntlMqjGuT86elj7JcJ+vQxmX5HBK0tClPY1UmLO08Jsg6+vSGfFTpth+Qa9M1LgisvD/D6DjOT/WbpeV/qZJkp7yXTvXmitw0opqZfah7RgiMuWrNj7hW3M653QeHpC1qBsp0szgN9gwhF5TLXrmDAmEzWu42mjRvQxuH5y4u9l9qSsNIMDir1zchCdVxewufnDBpZus7lKpXGGtATulh6cz7Tb5C8dA31q65tV7RdU1+5z3WRhhG2a3tAoKfLzPycUJC4gIVzgd9MD3Pe500XuveUhyn7mY7+i5L+NES/JkkyzlVLPN3ndq8VrtNbnxScsb8kIhbHtyKQxgC9J5jOtbbB5fUEDfXIMTiptPvjC4IwQ1AIZPqgPUZIhJwwhVu5sHBUrEIJ1u2z2S/4ZGeMmWZLpnS1eL4pnIF5Wb4eoLywgMxAgkJZGZmXUg2a4Vjhsc/pfd/PZSucq+7cnZ3PLKTl73z//wHQenFlFxS4o7mVw5gHJX7UtREd+PCmdNy2n29jhQ3wcPkQuMrsjKZKp1vr2fb2nlPLjTP4tgMG1u1lTVEhNpFVvoe39kunmSntvIgV4+duGZsm0tL+fRb/K5c05qznz8OH0y5WThkgen60Ls2RzNuZYG18nTujH7edn2S+zWbfL5qCBPjKlW023JEUybYtSqj9wDc6AwPk4c5X2OKXUhcBvccZdfEBr/UiL9ycCT+CMtTgD+LHWOqiUGgo8gzOliwVcpLWu6m48B6xby5aCAdT2zsIfCDFm9cbufmTC9e1tMKzQx5odTrXVIUNa/1SCtjN0YND2zg9eiI789pMAf/jCBkp4Z3uQl87uXH/lQXk+BuXFJ7ZU46VkGlVxQik1WSn1LLAGZ7il24CSeAbWTiyDgD8AU4GJwNVKqf1brPYMcJ3WegxOK5mrIssfBR7VWo8FNHBLLGJKrwowft4qDppjcZBeRu7O6lh8bEKl+w0+/Gkevzw2iztOy+Hh85oPFL9wc5hxj9Tzs5n9GP9IXafn9wRkmDbhSg981fi7fNmyqQ3I7zRRbJ/R6uFW7Y2AlAFchDMK0mBgGlANfF9rvaVHomvbCTjDG+4AUEq9iFPl/PvI62FAttZ6VmT9acDtSqkngKNwZgXYs/xT4NfdDWhFwRAysDGAcJrBhvzkaHEwtI+fu7/X9mwrzy0MUenzQ67BjqDNq2VhfjU1+qreq1+s4R9fBRhV5OPdK3sxolCqiYU7BFtcF0oqTRwvlUzbq+ZdB3wD3Au8rLVuUEpd0jNhtasEKG/yupzmk7e29f5goC+wW2sdbLG8lbKysk4FZKSFmw97lNb5z/CaRZuLIT3SFSbdYGH5DsrKtke17dzNGTw+py8AS7eFueGlzfxhameny02curq6pP/7tpRK+3xk//58uNFpUDc6t541y1ckOKKe052/c2lp7EeLSpZkuhQYg5OoFuCe1rw+ml8sGjhz0XX0fsvltNhur87+KFYGP6KKYgzbBhuK7M2Ulp7fqc/wmqnVYV5pMjn2FLMvpaX9otp2d68gTiWHo19hAaWlA2MdYlxsqbL55JuVnDFuJFnp3jnQu6usrCwuJ0s3enO0za3v1LNx807uu6A/xb1SY7/BfX9nLyXTfdataa2PBE6JvPxEKfUF0Bvo/izL3bMeaHrmHQBsjOL9LUB+pCUykXVi0lIoYOeidn3J+Pp5TK78jLrQvselTRbXTDA4abhBhi/MGfsZXLJ/9D/6w4amcfNxmfTtZXDkCD+/OykzjpFC2Tabo58OoP4RYPrqzt/b3WP2ujCjHqzj/PdLmPJEPdUNUgGYjP72TjVvv7ebb77xc/d/vd/Nzctsw2j1cKt2b1RprZdora8HBgEP4zTaWaSUekUpdW5PBNiGD4HjlVLFSqkc4Gzg3T1vaq3XAHVKqSmRRT8E3tFaB4CZwJ4i4yXAO7EIaFNxOrP7HMGCnAP5rPAolhUl/0hAvTIM3jvHzzenrOC1s/yd7gJw53ey2HpbHjP+pzcD4tStZu6GELPWhrjk9SAz1trM3WTz/ReDXW749DcdpLLeef51uc3Hq7qemIV7vT67bu/z9+fVUycXTQnjpWQaVdcYrXUD8CzwrFJqDHA18BDwQhxj21csG5RSNwMfAxnAE1rrOUqpt4FbtdYap+HU40qpPOBr4C+Rza8FnopML7cWuCAWMT13yME0VNkcu2I9S4r7sLEwkxti8cEesnHxbtbM28WQ8XkMnpCf6HC49cMG7vjYGXg/L8fHnikJd9dDQwiyu5C/hxc0Hsg+A4bmu/fAFl03uiSNzRUNAAwt9pMlkxKIKHR6bN7ITDK/Ukr9Jg7xRBvDszjJvemyU5s8/5bmjZL2LF8DHBPreE6uruSWYaP4YkQJPtvm/IXLY/1fuFr5kkr+/dP5hEM2hg++e2spdSGDklE59B/WenLwnvDo7MDe51W1YdJ7+QiEDX53lI//LgxRUQeXTfTTJzv6E+UNR/h5Z1mIpVsD/PCgTCYMkBbIyejuy/J4/N1qyrfs5IbzBiU6nJTm5pJoS+11jVlFx63CR8Y2HG8aePZwgkuzIWwT9hmkT4yuIU6yWDd/F+GQ81MJ2QbP37+WYMDGn2Zw+T1jGFraeqjBW9+t5W+zGhjbz8fzP+xF/xhX9Y7p6+PLtU417Kgig6+uzaAhBLd/EuS26c4IVdO+DTHvmoyoR7h5YFaIWevDgJ+H5wS58pA0xvWXhJps8nN8/Or7uZSVraekUOZhSyQvjQfTXsn0usi/BvAf4AfxD8ebwgNyYLnh1P0BweGpNfzJ0APz8aUZhIM2oTQ/wUgn91DQxpq9q1UynbM2yB0fOjcft1SFuO29Ov56Tk6rz92XndsDfPz2TjKzDI7/bhFZWa0T2osXZHLrhwEaQnDbcenkZTp/mw+b3Oecv9lmazX0j3JY4RU7GrcNhWF1hc24/lGHLYTopKQomWqt39rzXCkVaPpaNLd90U4wGkuju1dVAcnfCGmPAWYuFz98IGu/2UV6fjqvPLx+b51GvxGtq3nrg+2/bo9t2zzw+7Vs2+RU45avq+fK61t3Fy7J8/HE91u3Ej5hhI8l25yS6YH9DYo70Tb9ioPTeH5RiOoGmNDf4Jjh3iiVflLWwI3PVWEA917Qm6PMjA63EcINkiKZiujl2M1rw/OCgX2smbwGjs1l4NhcvpxdzYa8XHIaAtSlpbG22s+BLdadOsLPFYdm8ORXDYwq8nHLidF3jamrDe9NpADzFtW1ud723SGefLuaYAgu+04vBhY51XUPfCeNQ0oMdtY690w7M4j5lGF+lv4si4+/Wc1Zh48gxyMNU376dCXbq5zf6C+eqeLrOwoTHJEQ0QlLMk0tF59SwMu/W82s4SUM31rBb05J3Sv/hoBNTUYGNRnOd1Bf3/q2u2EYPHFeDn89O5s0f+cOlgafj7U5WQytcZLoity2q4dvfmIX3yx3ku78lQ385xZnxCW/z+CyiV3/2Zfk+Ti4uN4ziRQg2GQSo4agdPPoyOZVNbz94Eoqd9WQ+dNdjDw48a3TU5UzSKs3eKOeyuWyhvbmoIwQ53+1lCPLt1J85IBEh5Qwkw/NYfwBWQAMH5rBySfuewCLziZSgLxMWDOxmHcHFPFGSV/6Tmm7lLWqvLHueM2mEOEUHlT/7vN7k5MBvTIN7jlf5p3tyOv3Lmf94ip2bQjz4u+XYqfwbyfRkqKfqVLqKxpb8+Yqpea0XEdr3ar7SSp67T+bmGHkUjOqCF84zGO/W87v/mImOqyEyMjwcfONA2hoCJOREftrNcMw+OjKHB6bnU5upsG1h7X9Ez5jSjZPv18DwGmTs/C5eLaJWFi+LczizSGmDPdT1Kv59/69gzM54yCnpsBw8cnILeqqGovyDXUhQkGbtB6oibBtm5s+CfPBqjDHDfPxp+N8nboNkYzcnDxbaq++6+Eei8LjVq+ppybLqW4M+3ysqfbODyBeOkqk07+q5Z3PqhkyII1rzs4nsxMnq+JeBrce135V+k/OzOXI8ZkEQnDw6M7NR+k1n60KcuLjNdQFYUiBwdyf9aK4d/PvX5Jo9I6/ciiv3rOcUNDmuMuHkhaHi8K2/HuRzZ9mOS3G520OM77Y4NIJqf13S4pkqrV+qicD8bJTzurLM49WEfY5B92xx6ROS9626E027622mVwCxw1tfSJaUx7grn/sJGzD10sayM70cdX3Y9+daMJ+qXHv+r/fBqiL1Gqvq7D5aHmIH0yUOzhdtf9RRYyaVMCSMosJB/fctM1ba5pXJ2+pkerlZOlnilLqLCBDa/2cUqoQ+AfOhNyvAzc0mc4speUOymFtXoishhC1Ph+5ByV6LoDEWbDVZup/QtSHnA7K75wNJ49ofmLfVhGm6W2orTtDiK4b198POI2t/D4o7SeJtLsysv2kd2J0rFi4ZJyPu78Ms6UGinPgsgnyd/RSa959/rWUUpcDT9A4S8zDwBDgemAscEvco/OIR2cFqEhLZ1NOFruyMrhnZupeY3yx0aY+khtt4JN1ra+ux4/KYNwop9SYk2XwvWNS9+IjFq45PJ0Hz8jk0kPSefWSbA4skVF7vOjjdTZbGoA02NoA09dKyTQpGiABPwXO0lrPiMzO8n3gVK31dKXUAuB94LaeCNLtBuY1/wMXRj+YT9I5cpBBdhrUBp0BoU4Y1vrHn5Fu8Ofri1i9MUjfAh8Fufs++a/eFiIv26Cwl1yl70vYhooam4rqEJV1kki9as1uoEmyWLM7cbG4hZuTZ0vtnaFGaq1nRJ4filPQ+AxAa70cSK0BaNsxfqCfoiqn5Wiv+gYOKkpwQAm0f1+DWRf5+fMxPmb8wM/xw9r+iaX5DXoX+MluZ/qWa/9dxcQ7Ktj/tp28t6ghXiF73sOfNXDbe3W8tijIRc/W8NXa1K0ZiYXP1oUp+XMtk18dwr8X9tx3+YOxBkMiPckG9YYLxnonkcRL2DBaPdyqvZJpSCmVEZl+7RhgTuQ5SqlioLoH4vOErZ+U89h/P6MqI52chgDrK0vh9Jbj/qSOCcUGE4r3/aMPhW3OfqaO1xYH6d/b4MMrsxk3oHmJavX2EM/OccbvrQvAfR/UcvIBqdGgqLOWbWu852zbsHJ7mElDExiQx532rzp2Vzutai95rp4f7O/H3wNdqwblGiy8zI+1A8xC9o4nncq81ACpvZLpDJyp1oYDFwMvNXnv/wGfxjEuT1GFYXZlZbBwUF825ffi4AKZNLo9M1aFeG2xc8W/ucrmnk9blzrzswyymvRo6ddkVpntNTZ/mBnkwdlBGkKdu680d12QMXftot+tFUyLJGuvu2xSBrmRERnNYh8nmTKwWXdU1zUev+GQTVUPTg6el2kwaaAhiTTCxmj1cKv2jrr/Bd4F7gA+Af4KoJRaidMoaWq8g/OKXYeUcP0PjqcyK4O0UIg/TTU4NtFBuVjLOUQL22g12aeXj3/9KJf7PqilX56Pe89xGinZts1xTweYv8U5wc0tt/nXmdH3I732pRqWbXVOllc9X8PZEzLIzXLvARoNNSQN66Y8lm8Lc9AgP73bOBF/vSmMARzkoTlYH58X4u/zQhwy0ODhk9NI66GBN04bm8bri5yLvQMG+slvY1Yi0TPcXK3bUnv9TJcppUYBfbXWW5u8dRPwodZ6R9yj84gnF4SpzHKqIIN+P9NW+fhlgmNys4klfh78biZ/nxOgtJ+P2/cx0P1JB2RwUouq3d317E2kADPXda4WINRk9bANyTJS3MA8HwPz2j7p3zg9yL2znR3/zWQffzzG/SXX6avDXP1OEDDQm2zC4RB/P61n4n7toizeKAuwZNUGfnnysB75P4X3tXvJpbW2WyRStNbPSyJt7rgWU3EdPNA7V1OxFgrbXPJ2iIKHgpz6UmifVWS1QagJQHVD8wTXkfwsgylDGr/fU0d1rtTw4Fk59M81yEqDB8/MJr+H+xImwkNzw20+j9bzi0M8tSyfzVU9d+Xx3oowNKnS6+xFU3d9tzSd00fUtjl+9C+mh+j1QBD1dJANlUlyNeZSXuoaI/UXMdA/x4CGoNNSpj6ImcIDID1v2Ty92GZXPbyzyuahea1PNnM3hPjdm7Vkraxg7jfV3PZh51rqvndhOn8/LY1nz0rjoe90rrQyZUQam24voOaeAq47MqtT23rVqD5Gm8+jccfMIOe/HOSe+UUcMa2BmkDPJI9zSg32Dg1u25zWyYumePl8g82DX9vUBGHuZvj9l9I+Ip68lEzdX9/jAe9YQQg1HvivLw5w09Gp2fK0ZXugtkqdFRUhfrx4OX3rGwgBmwaNBPpH/X/0yjC46uDu9adMpbFqXz8njVtmhDCAO47q3Pf2wcrGP+DKCli502Zcv/h/d5NK/LxxDvx9XojDB/n5zRR39J9tMXUxUi6Nr7CHDlNJpjEwuqj5X3x0oTuuohPhfNPgxaUGb66wObwErjuo9dEwvKaavvVOadQPHF65i84kU9E5IwoMnjmja4f6ccN9zFzndL0Zng8jO1my7Y7Tx/g5fYw7kugeUwcb/PQgg8cX2JQWwq2TU/dY7wluLom2JMk0Bs4/MJ3H5wT4Yk2I/jlBbjshdeeMTPcbvHqmH9u291n6KxqSTVqGQTByP3W/UhlO0K1uO8rPqEKDeSs2c8PxA8lJ987JLV7+cryfvxyf6ChaW77D5tnFYUb1MbjwgORI8mEXd4VpSZJpDGSnG8z8cQ4bd9tsX28xsqjtCatTSXvVqAUDsrjgnv1Z8P5W+g7L5tDvD+zByERnGIbBxeP9HJK2i5LcnptBRXTOthqbI54OstUZiI1N1TbXH+quUn1XSMk0Bfl8BoMLDCrLEx2JNwwdn8fQ8bGfdi0VVdeEuefxHaxY28DRh+Zw9fkp3AIuRZVtt/cmUoAZa22uPzRx8cSKl+6ZJkddgBAp7MX3KtEL69i5O8yrH1ahF9YlOiTRw8YXGwzObXz9nZEeykLtSJaxeUUnfLI6xMtlYUroTWlpoqMRqSQQbN6mtKGHuq8I9yjIMph1aRovW84901P2S45yklTzppiFW8Kc9HSAQBigmJKSEJcc6P37FclEL6yjrj7M4Qdmk5bmnQM0Gt8/MZe5i+pYsyHIEQdnc9iE1Og/257P1tvcOStMYRbcf6yP/r2S62/elkG5Bj9VyXXe8VI1ryTTGPimPEygSQfLz9dKMnWTf7xYwUvvVQEwaXwWt/+sb4Ijiq3CAj+P/W4AgaBNepJdKHRFTcDm9FdC7IrMY1AVCPP6WXI8epGbB7ZvKTnqAhKs5UAFPTVKTLLbuiXAC89t4/13Kwg1+ZIDDWG++XgHZV9WYLfsRd+GGV/V7n3+1QKnhJoI9UGbJdvtuP0+JJE6dtWzN5ECrN0tx6NXyT3TFNNyuqSSXLlG6a76+jB33bmBnTuc2Tu2bglw0SXFADz9uxWs/LYSgCln9eOUKwe3+1mjhqazdYcz8MDgAWlkZfb832dnnc1R/w2xcBsMyYWZP/AzLN+9JwYvG9jb4NIDDJ5aZOM34FeT5Hj0Kjcnz5YkmcbAGaaPc/f38+LiEKV96rlhstyz6q6dO4N7EynAyhVOC9W66tDeRAqw+IuKDpPpDVcU8vJ7ldQ12Jx5Qm6768bLi0ttFm5znq+rhCcWhLljqlQ9xsu0U/z8StnkZ8KQPO+ckEVz0d4zNU0zD/gCON2yrNWmaZ4A3AdkA89ZlvXbuAUZIck0Bvw+g+fPyyActrGs1fTrLf38uqtv33SGDstk7Rqnvu7gQ5xRpTJzfBQPyWLrOie5DjY7Hj0pJ8vHxd/Lj1+wURjYIsyS3nKCj7dxxfIde100IyCZpnkY8DgwJvI6G3gSOBpYB7xlmuYplmW9E8dQJZnGkq+HJi9OBWlpBjfdPIi5uor8Aj/jJzjZyDAMLv/jaGa9sYWMbD9HfK9fpz7Xtm0WzNhJXVWICccWkpXTM6XD0/fzcdeR8MaKMEeUGFw9QX4rQnQkyq4xVwE/AZ6OvD4UWGZZ1ioA0zSfAc4FJJmK1JSd42PqUa1HScotTOfESwd16TPffXw9X7y6BYCv39/G1feP7bGLoJsO83HTYXL/TohYsizrSgDTNPcsKgGajkVXDrR/LygG5MgWKWXp3N17n29YVkPNrmA7awshEilstH5EwUfz2fEMIO5N+CWZipQyfFzjjD7FQ7PIyZPKGSHcqotdY9YDTWfPGABsjEuATciZRKSU068dysCROdRWBVHf6YvPL/cuhXCrLk7BNhswTdMcBawCLsRpkBRXkkxFSvH7DQ49rbhL29YHbX79SZjZa0q4NhTmh+OkYkeIeAp1IZdallVnmuZlwEtAFvA28GJMA2uDJFMhonTXrDAPzg0DOcx+K8SEfgYH9pOSrRDx0plBGyzLGt7k+UfAgXEIaZ/k0lp4Tjhss3RhNWuW13a8cgw1HZbOBtZXyjB1QsRTFxsgJYSUTIXnPPXABuZ96YyCdPqFxZx0Vs8MXH/tQT5eXuoMoH7oQIPjhrr4yBYiCXTxnmlCSMlUeEptTWhvIgX48sOKHvu/1UAfK65J46Wj1zLzIj/Z6d450IXwopBhtHq4lSRT4SmZWT76FDVWqPQflNGj/39RtkFpfgMZ0go4aX22KoD69RYu+HsBT+uGRIeT0qSaN46UUkOBZ4B+gAVcpLWuarFOBvAPQAG1wIVa6yVKqXRgO7CyyeqHaK1DPRK86Dafz+Antw7lg1e2k5nt45Rzk2tuUjdatT3Et1vSGWPa+FNgyMzf3LuFMdVOEn3qr5u46O+D8fmk3JEIIQ9V83oumQKPAo9qrf+rlLoFuAX4dYt1fgZUa61LlVJHAdOAw4EJwJda65N7MmARW/1KMrnoJyWJDiMlvPhtAxc8XU0wXMRTVhVvXtk76RNq39rA3uf9AiG2VYbply/JNBG60jUmUTz1C4mULI+isc/QNJwBjFs6Dfg3gNZ6BlAcKdFOijzXSqlZSqmj4x+1EN718Gf1BCMDsb27JIi1JTETq/ek7H6Ntw7qctLol+/FMkdykMnB46cvsFtrvWdA1X0NYLyvgY5t4FXgLmAc8I5SapzWelvTjcvKyrocYF1dXbe295pU219IrX0u9OcBOQBkp4XZVb6Msp3J3SXoNxfDkx9kU1Vjc8MpVSnzt4bu/bZLS0tjHA2ubnDUkmuTqVLqXOD+FouX0XwAY2h7AOM2BzrWWv+tybJ5SqnZwBTgtaYbd+dHUVZWFpcflVul2v5Cau3ztBE2A9+qpWz9Lm49rS+TRxUlOqS4++OnAR6ocK7XA2v8TPt+zzZySyS3/ba9NA2Fa5Op1voF4IWmy/Y0IFJK+SONhgbS9gDGewY6XhF5PQDYqJT6IfCF1nrPcgMItLG9EALIyzJ45OwcysrWUDpqYMcbJIHHvmpsj/jUNyEe+64t3aASxEslU0/dM9VaB4CZwPmRRZfQ9oSvb0feQyk1FajTWq/FGV7qhshyEzgo8nlCiDaEwjYPzw1x3+Iilid59e4eZt/GE/iwAkMSaQIFjdYPt/JUMo24FrhaKbUYOBL4LYBS6sdKqd9H1nkIyFRKLQL+Avwwsvz3QD+l1EKcRkyXaK0rEZ6zbGuI9RXJ3xgm0X7zaZiffhDmiWV9OOrfQSrrkz+hPntOBtcoP2cMr+L9S1Knild0j2urefdFa70GOKaN5X9t8rwOuLSNdXYD58QzPhF/v3y9lgdmNuAz4O/nZHPFoXLCi5dZGxqTZ3kVrNkN47o26Y5n9Ott8NczMigr28aYvkm+sy4X9FA/Uy+WTEUKq6q3eWCm06E+bMNd0+sTHFFyO3NM48nsgL4wuk8CgxEpJ2C0friV50qmIrVlpUFhjsGOGqfENCjfxUdXErj+UD/jig2+sjbyk2MGkZkm37foOQEPNUCSZCo8Jc1v8NblOdzyXj25mXD/Gdk99n+HbZsH59p8trwf1+aEOX5YalTsnDTCx5C6SgqyvHNiE8nBS10tJJkKzzl8WBofXN3zP90H5trc8EkYyOfNl8MsuNRgTKEkGCHipcZDJdPUuLQWIgYWbG1sjNMQAitFuooIkSi1RuuHW0kyFSJKF5QapEeOmJH5MHWQi49sIZJAA0arh1tJNa8QUTppuI/5lxp8OH8dF00eQh+5hyhEfHnoEJNkKkQnjC0ysAdUSyIVoifIPVMhhBAidUgyFTFl2zY3fBxi9BNBLn07RH1QGukIIbrIMFo/XEqqeUVMvbLM5r65TgJdXmFzYD+b65V7DwAhhIt56NQhyVTE1K76lq+lZCqE6CrvZFOp5hUxdf5YgymDnOelhXDtRPmJCSG6yGjj4VJSMhUxlZNuMPMHfnbWQUEW+Fx8j0MI4XIeOn1IMhUxZxgGhT03ZK4QIml5J5tKHZwQQgjRTVIyFUII4U7eKZhKMhUiFazYHsYARhZJZZTwEu9kUzmyhEhyd35Uz6h7qtjvniru/ri+4w2EcAsPteaVZCpEkvvj9Po2nwvhepJMhRBuMTjf1+ZzIdzPO9lU7pkKkeRevTSbG9+qxzDg3tMyEx2OENFzb+5sRZKpEElu//5+3rw8J9FhCNF5Hhr0Rep8hBBCiG6SkqkQQgh38k7BVJKpEEIIt/JONpVkKoQQwp28k0sl4g9qagAACE1JREFUmQohhHApSaZCCCFEd3knm0prXiGEK909I8C4h2u58IV6qhvsRIcjEsE7YzZIyVQI4T6frQnxmw8DACzaEmJknwB3npCR4KiE2DdJpkII19lRa7d4naBARGK5uCTaklTzCiFc5zuj/Jy4n3N6GpxncP0Rct0fTyt22lz+TpDfzy9mS7VUqXeF/EKFEK6TkWbw3iWZbK6CwmzntYifk18IsqICIJ/Nb4SY/gOXpAYPDSfokm9MCCGaMwyDAbmJjiL5NYTsSCJ1lG2XkmlXSDWvEEKksAy/wWXjGkuAVx/oorQgrXmFEEJ4xZOn+PnReJvydas5/4iRiQ6niY6zp2maHwP9gEBk0TWWZc2OZ1RtkWQqhBApzjAMjhpiUFZVn+hQmusgl5qmaQBjgGGWZQV7IqR9cVF5XgghhOgUM/Lv+6Zpfmua5nWJCkSSqRBCCHfq+J5pH+Aj4CzgeODHpmme2IMR7iXVvEIIITzJsqwvgS/3vDZN8x/AqcAHPR2LJFMhhBDu1EE/U9M0pwKZlmV9tGcLGhsi9ShJpkIIIdyp48a8BcDvTdM8AkgHLgV+HOeo2uS5ZKqUGgo8g9MU2gIu0lpX7WPdE4GbtNbHR14bwL3A6UAYuEpr/XmPBC6EECKmLMt60zTNw4B5gB94JFL12+O82ADpUeBRrfVYQAO3tFxBKeVTSt0A/BfnC97jbKAU2B84E5imlPLcBYUQQqSEKAZtsCzrFsuySi3LGmNZ1oM9G2AjTyVTpVQ6cBTwYmTRNODcNlYtjTyuarH8NOC/Wuuw1nopsBY4Ij7RCiGE6B7vDIHktVJZX2C31npP59xyYHDLlbTWi4ArlVLHtHirJLLNHm1uX1ZW1uUA6+rqurW916Ta/oLsc6qQfe6c0tLSGEeDm3NnK65Npkqpc4H7WyxeBrQchTnciY/1tdjeaGv77vwoysrK4vOjcqlU21+QfU4Vss+iM1ybTLXWLwAvNF0WqebdrpTya61DwP9v7+5DrKjCOI5/zazMsBKDLEwQragwrZ9KlChpipBELyRqhaWZVFCQBdmmfxVSoWFEBlEWUgpKIZZh2juV9RRrkElBJW2vRv9EKYrYH2e2brKrbjPrvffs7wMXdg73Xs7ZZ2afOTOz5xkE/NiFr20rPtPu9C5+3szMjpYmmpk21T3TiNgHvAdML5puAjZ24SteA2ZJ6i1pGGlNx0+q7aWZmfU0DTszPYTbgecltZAeIJoBIGk+cEZELDrEZ9cCY4HPi+05EbG7OztrZmb5a7pkGhE7gQkdtK/ooO3t2vdGxAFgQfEyM7NG5su8ZmZmPUfTzUzNzKyHOMzavI2k14EDB/+nSc8myb8QM7P/ISKaJ/tVzMnUzMysJN8zNTMzK8nJ1MzMrCQ/gFQRSTOBFlJNvccj4sk6d6lbSFoMXF9svhoR90maBCwF+gJrIqKlbh3sJpIeAwZGxGxJI4FngP7Au8D8mvWim56kacBioB+wKSLuyj3Gkm4A7i82N0bEglzjLKk/8AFwZUR811lscx1/d/HMtAKSzgQeAi4DRgLzJJ1X315VrzjoJgOjSOO8WNIM4FngKlKlntGSptavl9WTNJFUdLjdKuDOiDib9J9wB1cnalqShgIrSCUKRwAXFfHMNsaSTgSWA+OBC4Fxxb6eXZwljQXeJ63+hqS+dB7b7MbfnZxMqzEJeDMifo+IP0krLV1X5z51h5+AeyJib7G045ekg/LriPi2OGtdRcdl8ZqSpAGkE6WHi+0hQN+I+Kh4y0oyGi9wNWl20lbEeDrwFxnHmFTz+BjSTLxP8dpHnnG+FbiDf9ckH0MHse0B+3nlfJm3Gh2VdhtTp750m6K0HQCShpMu9z7BEZS1a2JPAw8Ag4vtIyrj18SGAXslrQfOAjYAX5DxmCPiD0kPAjtIJw7vAHvJcMwRMRdAUntTZ/tz7vt55TwzrcYRlXbLhaTzgTeAe4FvyHTskuYC30fElprm3GN9LOlKyxzgEtJa1kPJeMySRgC3AENISWQ/6XZGtmOu0dn+nPt+XjnPTKvRBoyr2c62tJukS4F1wN0RsVrSePItazcdGCSpFRgAnET6A5PreAF+BjZHxC4ASS+TLu/tr3lPbmOeAmyJiF8BJK0krd+dc5zbdVaW0uUqu8gz02psBiZKOq14mOFa4PU696lykgYDrwAzI2J10bwVOEfSMEm9gZl0rSxew4qIKyLigogYCSwC1kfEzcCe4qQC4EYyGW9hAzBF0ilFPKeSngHIMsaFbcAkSf0k9QKmkS715hzndh0ev0VBkZ4w/so4mVYgIn4g3Vd7C2gFXoyIj+vbq26xADgBWCqptZixzS5e64DtpPtOa+vVwaNkFrBM0g7SbHV5nftTmYjYCjxCeuJzO7ATeIqMYxwRm4CXgE9J5Rn7AEvIOM7tImIPncc2+/FXycsJmpmZleSZqZmZWUlOpmZmZiU5mZqZmZXkZGpmZlaSk6mZmVlJTqZmZmYlOZmamZmV5GRqZmZWktfmNWswkhYCC2ua+pCO1bXAL6R6sqNIq9XMi4jPjnonzew/vAKSWQOTdDLwIfACcC6piPME4CvgOeDUiJhStw6aGeDLvGYNq1h4fDXQGhFLiub1EbEtInYDa4Dhdeugmf3Dl3nNGtcyYCBwTU3brpqf9+ETYrOG4GRq1oAk3UaqIzq6mIWaWQNzMjVrMJIuBx4FJkdEW737Y2aH52Rq1nhagOOADZKOB3oV7VuB3+rWKzPrlJ/mNTMzK8kPL5iZmZXkZGpmZlaSk6mZmVlJTqZmZmYlOZmamZmV5GRqZmZWkpOpmZlZSU6mZmZmJf0NY14iBdTexUgAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 540x360 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 540x360 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 540x360 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 540x360 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 540x360 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 540x360 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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lF9ieyLm6qhm2bx5NFS8Bj8XXWKzDXUHj+b4NSQghRHvJvmZoWdY60zR/ChQBfwTub7dLM/BuIufaazLsrnlUKbVPPVGVUrOBKq316n05rpO4PlJK3Q3MB/zAG8AjB3JOIYQQ6cmyrH8DmKa51LIsvb/nSaQ36WTgh8AIWodi+IFJQEkXx50M/ElrPU4p9T/AHYCtlLpGa/3Y/gYMoLV+GHj4QM4hhBAiuXq5A8160zTvpGOuKrUsS3V3cCK1u4eBfGA97jXEd4DxdKyOtvdT4A6llBe4EXdc4FHAjxJ4TSGEEGnOMYwOtyR6HDgVt5J2EG6+Ogd4O5GDE0mGhwBfBn4DoLX+CXB+/EW6MjFeA5xFvDlTa70YGJxIYEIIIdJbLw+6Pwa3D8kPgV2WZV0JXAwcmcjBiSTDStxB7eXAVACt9QJgQnfHKaVm4g57eEtrbSulTgE2JRKYEEKI9GYbRodbEtValrULd0H66QCWZb2KW0vsViKD7j8Ffg7cCWxRSp0HNAIN3Rx3B+4aiA3AHKXUccALwKWJBCaEECK99fL0a8tM07wGt1dpvWmaCmjCnSimW4nUDG/AbeosAv4HeAz4N91c+9Na/x0oBIZqrT/DTarjtNbPJhKYEEKI9NbL1wxvAb4LjMWtvH0AfALcl8jBidQMK7XWc+P3NyulioCA1rrTmqFS6iqt9UNKqWvaPNb2ebTW/5dIcEIIIdJXb85NalnWp7ijHADWmqb5HpADrE3k+ESS4Val1L+BvwKvaa0jQKSL/c/DXdrpwr087wCSDIUQop/bl2ZS0zTzcGtzZ8UH0z+C2ylmd8XrTsuy9jondvvp2CzL2hx/vIoDmY6tjWm4vUl/CTyilHoKeFxrvXAv+/9DKXUZMhBeCCEGtETnJjVN83Dgz8DkNg8r4DjLsiq6OG4svTAdGwBa6zXAz4CfKaUOBi4CnlFKNWitO5sd9tvxfwO4iXQ17hjFEcAU3Jlj/ppIcEIIIdLXPjSTfh24FnesIKZpZuGuSvSwaZojcFdJutOyLLvtQb0yHVt7SqnhwEnxW4C9zAeqtZ4V3/9h4MG21weVUlcBZyb6mkIIIdJXojVDy7KuAjBNc/dDJbiD5a8BanDnpL4St/bY/thOp2MzTdOwLKvLxSba6rY3qVLqW0qp93DHbhwJ3AWM1Fp/p5tDLwAebPfYI8DcTvYVQgjR3xhGx1sCLMtaY1nWeZZlVViW1YjbI/SMbg6rNE3zUQDTNM8A6kzT3GCa5qxEXjORmuFXcJs1z9ZaVydy0rgNuDPV/KPNY1/DbTYVQgjRz+3veoamaU4HJluW9Uz8IYOuO26C2zFzo2maBnAP8GPcWuX9uMv8dSmRa4ZHd7fPXtwAPKuUugXYDIwBRgFn7ef5hBBCpJEDGFphAL81TfNt3EXcr8Yd496VQ3Avw00CxgF/sCyr0TTNXyXygvu0DNO+0Fq/CUzEzcoLgHuByVrrD5P1mkIIIVKHY3g63BJhWdYS3Ety7wPLgc8sy3qym8NsIA93UYgP44lwHO66t91KuAPN/tBab0eGWAghxIC0r82klmWNbXP//9i3MemP4850Nhi43DTNGbgdbzp0uulMUpOhEEIIkUzxYRjgTsH2PrAT+Ax3OtDvs5eRD+11mQyVUl/Hnf37ba11QicUQgghoNemY6vHndmsM0b8OW93J9lrMlRK3Yo7gP494CGl1Dit9b37EagQQoiBqHemJh3XEyfpqmZ4BXCc1nqlUuoo3DGDkgyFEEIkpDdqhpZlre+J83SVDIu11ivj9xcAI3viBYUQQgwM+zvOsC901c+1pRRaa5u9t8kKIYQQHfTyeoYHRHqTCiGESIpUTn7tdZUMM5VSbadSy263jdb6ouSEJYQQIt31l2T403bby5IZiBBCiP6lXyRDrfWde3tOKeXFXZVCCCGE6FS/SIadUUoNBb4BfBPIx11hWAghhOig3yVDpdSRwHXAFwEN3M6eSzP1D//8AKzNcOFRYI7o62iEECKtpdPQiq5moAkAl+LOQjMSeBRoAL4Yn4C7f/nja3DNn9z7P/0X/OMmODuhNSGFEEJ0Ip1qhl2NM9wIfAn4FTBKa/0/QLhXouoL85a23m+OwAW/gvKtfRePEEKkuXQaZ9hVMlwJTMZdIXhS74TTh049ZM/tSBRWbOqbWIQQoh/oF8lQa30scHp8c55S6gMgB8jujcC6o5T6tVLq0R474dfmYN9yXss0O3YwE2fmhB47vRBCDDT9IhkCaK1XaK2/B4wA/oDbeWaZUuo5pdSFvRFgZ5RSc4Cv9vR5w9kl1DGKBoZSFyoh/EZ5T7+EEEIMGP0mGe6mtQ5rrf+utT4RmAmUA/clNbK9UEoNAn4G/LzHT54VwCaDCLk4eDGyAz3+EkIIIVLPPs9NGl/J4ial1A+SEE8iHgR+CIzqaqeysrJ9P/PcQRScPo7Ap9sJnTiKioN8sD/n6UIoFNq/2NJAfy4b9O/ySdnSV0+Wr7S0tEfOs1sq1wTb62poxVq6X6lifM+G0zWl1FXARq31W0qpy7vad78/1Femt9xNxkjDsrKyHv/CpYr+XDbo3+WTsqWvVC6fkz65sMua4bfj/xrAk7jDLPraxcAwpdRnwCAgRyl1r9b6u30clxBCiHb6Rc1Qa/3y7vtKqUjb7b6itT559/14zfAESYRCCJGa+kUyFEIIIQ6ELckw+bTWj+JOESeEECIFOUgyFEIIMcD1i2ZSpdRCWnuT5iqlPm6/j9Z6drICE0IIkd76RTLEnXFGCCGE2C/9IhlqrR/rzUCEEEL0L/1lnCFKqfOAgNb66fg0aH/BnY7tReBGrXW0F2IUQgiRhtKpN+le5yZVSn0NeIjWVSr+gDsF2veAKcBtSY9OCCFE2uovE3VfB5yntX5YKZUFfBG4RWv9HHAt8P96I0AhhBDpqb8kw/Fa6/fi92fj9iydD6C1Xg0MSXJsQggh0phtGB1uqaqrZBhTSu1ew+gE4GOtdRhAKTUYaEhybEIIIdKYY3S8paqukuF7uEs1jQW+AjzT5rn/Bd5NYlxCCCHSnIPR4ZaquupNejPwGvATYB7wAIBSag1up5pjkh2cEEKI9JXKzaLtdTXOcJVSaiJQrLXe0eap7wNvaq2rkh6dEEII0Qu6HGeotXaAHe0e+0dSI0pBTmUdZAcxgv6+DkUIIdJGKvcebU8m6u5KxS7Cs35C42YvGB4yf/cFMq6T1mEhhEhEOiXDrjrQiN+/StNmA/CAA/U3vMztP9nCho3hvo5MCCFSnm10vKUqSYZdyc7AwG7ZbPb6WLE6zB/+tLMPgxJCiPTQXwbdi++dRVbGLgzCbM0p4KHjTgbDoL7B7v5YIYQY4GyMDrdUJcmwK1kZMGMUteQTrIcTFq8gaEe55MLCvo5MCCFSntQM+5G6WUcSIwOA0Tsruff4Bo45Mrubo4QQQqTTNUPpTdoNz/iiPbaDwyURCiFEIvrFoHvhyrtOEVlZRfjjCrLOm0zW6RP6OiQhhEgLqdws2p4kw24YAS/FD57e12EIIUTaSeVm0fYkGQohhEiKVJ6Yuz1JhkIIIZJCrhkKIYQY8CQZCiGEGPD25ZqhaZp5wAfAWZZlrTNNcy5wD5AJPG1Z1q1JCTJOxhkKIYRIikRnoDFN83BgPjA5vp0JPAycA5QCs0zTTGpPRkmGQgghkmIfZqD5OnAtsCW+PRtYZVnWWsuyosATwIXJjFWaSYUQQvQpy7KuAjBNc/dDw4GKNrtUACOTGYMkQyGEEElxAOMMPYDTZtsAkrpCQtolQ6XU7cBF8c2Xtda39GU8QgghOncAvUk3AcPabJfQ2oSaFGl1zVApNRc4BTgEmAkcppQ6r2+jEkII0ZkDWMLpI8A0TXOiaZpe4BLg1aQFSpolQ9x24xu11mGtdQQoA0b3cUxCCCE6ETM63hJhWVYIuBx4BlgOrAD+law4AQzHcbrfKwUppSYB7wNHa61XtXvOefzxx/smsG6EQiGCwWBfh5EU/bls0L/LJ2VLXz1ZvtLS0h4dJX/WVZs6JJiXHhqZkiPx0+6aIYBSairwMnBz+0S4W2lpae8GlaCysrKUje1A9eeyQf8un5QtfaVy+WSi7iRSSh2NW3W+QWv9VF/HI4QQonP7cI2wz6VVMlRKjQKeBy7WWr/d1/EIIYTYu5jMTZo0NwFB4B6l1O7HHtBaP9B3IQkhhOiMNJMmidb6euD6vnjtyvnbWHLNApyIzbR7ZzPktBF9EYYQQqSNWBo1k6bb0Io+s/gbH9JYXkfThgY+vfL9vg5HCCFS3v4OregLaVUz7Et2c6zlvhOO4TgORhq1hwshRG9Lp/UMpWaYoGn3zsab48MT9DLtd4cnlAjf22BzwbMRvvtmlIZweo7nFEKI/RUzjA63VCU1wwSVfGEUp269GBwwPN1/oDsaHc74R5SGCIBDKAp/PE3ebiHEwBHt6wD2gdQME+DsaiR05VM0nfFnYvNWJ3TMljonnghdq3ZJzVAIMbCkU81QkmECQt9+lsjDHxN7bQVNpz2Is3ZHt8dMHWxw4hj3g/d54JuHeJMdphBCpJSo0fGWqqTdLgGRsp3Uk4ePJjIiTThXPoTx9g+6PMbnMfjPxT4WbHEYlm0wcVAKfwuEEGKAk2SYgFAkQDY7yI8vvGwvqEzoOL/X4NhRkgSFEANTVMYZ9i/G8HyyqGrZ9jQ1wa76PoxICCFSX8ToeEtVkgwTUPCbk4ll57Zsh8lg7aVv4kRiXRwlhBADW8QwOtxSlSTDBPinDSG46V7qDzuMSoaxhoOpe3UDtS+u7evQhBAiZUU6uaUquWaYqIJs6s+cw/ZFH7c85MmWt08IIfamMYVrgu3Jr/k+GHzzoYQ+r6Rp0XYKLp5E7mlj+zokIYRIWU3pkwslGe4Lb06Asc+e2ddh7FVoQz1Lz3uLppU1DL+2lAm/mNXXIQkhBrCw9CYVfWHtjz6h/pNKYvVRNv7yc+oW7ezrkIQQA5nRyS1FSc2wP2k345tjyxRwQog+lEbXDKVm2EsaIw5rqh22NHlZsMWhOdrziWrsHYeQPa0QI8PLyO9OJW/W4B5/DSGE6I+kZtgLVlQ6nPiPGFsbwGAcDjGOGAZfm2bwtAXTB8Mvj/MQ8O7fX1HR5hgrX96MN+DhsM/OxbOf5xFCiB6VRjVDSYaJikThmj/Bf5fDWQp+fXm3hzRHHX7+ZoinlsbY2uyDDB9OvNF8QQUsqHBrh29tgEFBh9uO3L8vzqvXazZ+6F4f3PxRJSf95OD9Oo8QQvSo9MmFkgwTdtNj8NCb7n3rRcjNhNsv7vKQW19p4tfzmt0NIwJDc6BlLUSHtt+UjXX712wai9gtiRBg/X+379d5OlXd4P4RMDi/584phBhA0icbyjXDRH1SDrgp7OUpp3L3vGE8+q3FNFSF93rI8m1tpmtzoNBnt3nWYFTVLgDyQs1cXtCwX2HFqsPkZBjgONQ5Ph43J3HlY3VUNjpEVlex7fjHqZj6II3PrNi3E//tXRhyhXv7+b/2KzYhxACXRr1JJRkm6jp3fOGaQWNZMmwaMcNLhVXPh09t7rCrtdPmF+9HmT7Sjzf+DqtRXv52jg9PvMtnZjjM3x59nPfuvY+3f/4AhXe8v0/hrJ+/g4fnvMWjp88jc3UdxZvq+OWZR/DO2OE8vCWDL/+zmapvvkrzexuILN/Jzkufx64JJf4CP/y7WysEuO0pCKfyREpCiJSURslQmkkTddHR8LuXMFbsmVCMjTuAcS3bm2sdjngkQnUIwOCOM7Io3xzBi0OR1+avM9ez9F8hjnrhQ6Zu3UYMD6sZTbB53yb9fvuOz2mqDBNojhLJ8uINOdTkBluaYT/YbuDUNLce0ByjuSHCHZ/4WbbD4avTPVxY2sXfQkW5sN5dxNjJz8LwDczFiZvX19G0tIrsw4f0dShCpKEUzn7tSM1wX9x4DmOrN3LI5s8IRMOMrN7MkX98AGobW3b5dKsdT4SufyyzeXpRM39fFOaMP9Yx/LZPuOjBeYzc2sxqRrPYmIxdkMOYuw+nJuSwpbbza4cbn1rD8js+pWaJu5SU4zh4ojbF2+rIbggTjNlc+GFZy/4Zfg/5PzsBIycAQN7/HsXd5VncvcDm5SJ6C1cAACAASURBVHKHL78QY9mOLq5TPn49HD+VxjEjWRWZyMrBd7PzsSUH8Oalnwa9nWVTn2L1Wa+wfOY/sXc0d3+QECItSc0wQbFVO6m75T1sJnFk+QpOW/lW65M1jZCXBcBhwzwMyoSqJvcpf7S1xlfX7LB1bYSC3Q94DZ4782Aumublw39u4aLnglR7fFx/hJfjRhn86oVazn39M4q31eENRfFHYqx8aCWnLjiLk26fzrs3aDxt8tnMzTt4On7/yoM9ZJ44npE7v4vTFMVTEGTtS9HW8sQc3i6PMqnQR8C3519va9+q4IO7Lby5JzG2bjHT69cBEL3qrzhn/xijMGu/38e6zyppKq+jcM4w/AUZ+32e3lD11GrsBvc9i2xuwHh/FxzXx0EJkU7Sp2IoyTBRTT/8D3a5WyuLkU0zQTIIsTl7DCNGFbfsNyzX4KOvBXjeijF1sEFNDVzxRDO2A2q0l4lTvDjboDork/X5uZz3ymf4XozhA24bv5EbL53D7xbEePOZ7Ry8cRtFFbU4QDTgJeYzqPd6uOf+bUy/bDR3feUEjn1xCWctXoMDnPfNMYw9xktuBpw5wa30Gxk+jAz3Y756pod/rYjR0BTDWxviO/+Exxd4mXdNDlkB91sbi9i89T+fEA25nX3KBw1jetU6AHzRKM6Wmv1OhtufWceyi+aB7ZBl5nPYwi/gy/Xv17l6Q+bUQa0bHgPPuP3/I0CIgSl9sqEkw0RV1e6xudM3mKW5U8kM2wyPxDD8rdfUJg4yuOnI3W+tl6nDvGyqtjl+op91J01j+4XLaN4YoriumWCbmuPMDe6wiEA0yleWrqKwur5DGP6YzbytHm57NoaDD+vkQ1kxagj1JdnUNOVz2poovzu9Y4L5pMJm0RaHF8738Lt5Yf691H184cYYf/+4mePHepk00g+OQyzSWt20C7JaBoHYh4zGMDu/dhaqbGbFTWtYssFi/PljmHXHIR322f73NRCfIq7RqqFu0U4KTxjW6fl62qf3Lmftq5sonlHIUT87FF9G99dAi6+Ygt0QoeHj7RScO46tpdJMKsQ+SZ9cKMkwUVm3nYT91h+J4SNINTWeIvKao4w9LHePRNiZ6cN9TB8OK7bHeP+xeiasbiQDyGi2Cfs8ZETdWtiqkgImb6nk0vnLMbdupyY/iO31YLe5sluTncmW/JyWwfsA5cOLqMgMwC6HP+oYS/6zlXu/lMthh+fj8Xn4aJPNsY9GiNiQ4YUrDvKye5lNA/jdk7U85DjMmRng+5fkcfT3p/LBL5fhzfBy/K8PJavgMOwtNXhPmLTXjjSf319G/VL32umKR1Yzcu5whh0zdI99cmYOYsez6wHwZvvInJi3D5/A/tv07lY+u8+9nlqzuo6C8bnMuGZKQscO+fb0lvtby8q62FMI0YEkw/7Hc7xJ3ktfgZ89gxMrouTwwyiePI7sKxKb7WXRphjH/bGBPzy2aY/HQx4vHhx82WFG2NXc8cr7eGyHzWOLcXwGDmAbXiJeg205OVijShgajbDStol6PHhsh+xwFDIDLed8P6+I458J8+07PkZN9rPg6MlEbPf55hiMG+LjhiN9rFvZyMadDr54RfCtT5vZtGIbv751FFdeMAaP18CI9071TB/eZfmc2J6dcUJlVThHD8FoMx3TmP+dgSfLR9OqWkq+OpHgyOyE3rsDFa7Zc1hIc83ex4burx2fVLLhP5spPKiA8eeM7vHzC5Ge0icbSjLcF6EIfGhhAEG9mthlZ8DJo8FsrQGtmL+Te7XNupwc5pQGuPkotya1/Itv8E9rB9U5mcRqDLy2Q8xr4C2MEHAaWTTSpGhbLYYBMY+BE6+ANWZn0pSVCUBhhoc7Ls/hvmeaOGbzLqqCfsbsqiWnOcL24DBqM/wEYjbZoTC7/D6WDS6kaOk2mravxZg2Gccw8DgOpYEIkb+uoLgxwsE+L/8dOZyGgB8PUN4A1z3VwDdPzeaECT6ammI8/UAF9VURjjutkLEbKtj+z1U8M2gcNSWFnHpaPoFcP85J48h8bxOhtSEK6xupu/oNNv13I6P+egrRUIyq8nryRmQy+sZpvfyhwehThjN0djHbPt5JzqgsSi+b2PUBqyvg0t9CxS64/SK4cq77eE0T28+8n2h1iOxfXUT+UaMAqF1Tx+tffpdYs1vDt5ttJl40NoklEiJNpE8uTM9kqJS6BLgV8AO/1Vrf3ysvrMtb7hq2g/Pofwm/ZJGx4kcYRTn897ktzPlvJhGPH4jw+jqH/IwMZj71GflrdrJ+dAG+5hi+oiChzAx2FWdRPriAYTXV5IRba1YG4Ik52D6DaJtmyXCzjeFAY02UfCPK3BWr8TvucWN37WLBmFEEbZuoYfDGoHxKGkPYHg87A1lM21FD2O9lyq469L31BBrd2pI/GmN8TQ0rBw0iYsAHhbk4Gw3+8ecmfn1IjIrXthGriYDjMPEv8wlU7OCFIw/l09xc2Bjlj3+upM4DFy95g6nBXQxyMqmvdGOqfnwFRT87ihdu+JSq8noy8v2c8+As1jxoUTGvgiFHDOHw387Gm8D1uwPhC3o5/cnjCO1sJqMwgMfXzYii7/wFPl7l3v/GA3DmYVBSSPGXn2DwunVu2eaUEd72fwTyAlSV1bQkQoAdn1ZKMhQC0mqi7rQbZ6iUGgH8DDgGmAlcrZQ6qFde/JxZkOF2TnEwiBFgUySLF776X+b9xuJnH0PE0+YtjcT44P1q1j63li0l+XgjNiPW1pFfGWbIpjrmlwzlviNmcOvJx7Jw5BAa8oLu/DQONGUECIQi5O1qgHjC25oR4K75URaOK2JpXhYepzWBFoTCFIWa8TkQsB2+VL6eCXUNlOdm82ZRARu8PqqjDsGoTWND22nhoCIzg5JQiHqvByf+5XUMg7/OC7Ep4qPJ6yW3sYmxFe4g/IbgnkMijty4gpF11cxYsJDRFR/gwR2O4BuSyabFNVSVux2BmmsifPybMtY+vZbIpkYqHy/ntSFPsfZPVutb1hjlzR98yj8veo8lf1t74J9ZnGEYZA4OdkiEzevrWHbSS3w65Wl2PBFPgE1tmlFjNjS7fzjkbdna8nBBqIqmDXUADFFFBIvc98TwwKiTu25SPlCRxijv/Xwp//7mR6x5e2v3BwghupWONcO5wNta6yoApdS/gAuAHyf9lY8w4dPfEPvVK0QeWcS/p57A6iGjoRH8z20hc/Y48LReB/MAzuvreHrGZM6w1pHRFMUb701pACU73CRhewzemTiGgOFjU34ehZW1DDZsCiIRAs0RHAz+XTKE5Xk5EMkk7BhQkMuqwjym7HJ7uUYNg4jHg98H115aQFFjgPptubz8RrQlwYU8Hqp8XnJKi5i/JQezqYkNBTm8M7aE3EiMGVur8TgOtmFgOA4+A7ZnZ7IjKwixGPUBPznhCMcstSgfNoSYz0t2UwgnlsNLU07GGjKBb73/BFu+NIVlWUP40jfH4Pj3/IqttEIMA4JNMXeMZNRh+Y2acVebACz60ypWvexOcbdzxTJKZhYyZGoBybL2Ox9Q+84WAMqvmEf+3BEE7roUvnAXVNbB/54PY9wetFWHTmbYAnfige1DxlA02Y0ra2gmZ7w4h4r52yg08ymeOajzF+shH92/kqVPux2RNi+s5JIXTiBvpAz7ECkofSqGaZkMhwMVbbYrgNntdypLZs+/m4+Fw8ay+pnWxBcOBDjl0xUsMmewKSebwY1NXP3x5zx03CFsLchlbEU1sxsqsA1aBsqvGFHUcnxhOIKTEaAhM4O6kYOhupbceocmn4+IYbAsP4fR/gjlTuuP3qvjR2IuXErE52N1yRBmjIty4flN+Lxuki0Y4eBfmNmyv+E4FI8yeMMIsGF4Hgvygy3P1QU9LBxdxJX1W1i2M0CG7WB43eZLxzB4fcJwys87mS8v/JxowM8mn5fS+l2M317rJvZdNWzNHsE9c65mod/EH3Z45ckIv7iikoZzhhH6tJ5IMEClnUvmjgaya3e1vLbjOLy/aAWNjpf6tXuuurFySTmVnp79oa/abLN5aYzCEQb5FdWtcUQdVi5ZgXdUJrx7C0YkhhPwQfy7FLr/bBqemoS9K0z4qsOoLF+554lnwI5wIzvKtvVovO1tsVrPb0cdlmmLgrpgF0d0LxQKJff/TB/qz2WDni1faWlpj5ynVfpkw3RMhh6gbddFA7Db79TzH2ob26uJVS/H58siGnU/7KjXi3dTM79a/waDQ5VszRxCgyeba9/9hPos+Paid/BHY3ycMwNiBhvHFTPcZzN5Vx05kSjHVuykKhhsKdBrI4ZwcsUOBntjxA4ZxKm5AW47JZ8nltrc91EMvxd+eGomh3/xEEZMyCQ720t+Xsdrb7d8O8J5f2nEaztEfB4qJ2SzYXUMvLhNgN7WZsOwz8tTBcNpzIaJDU2MqHSn0bGBnblBKkpHM8LwkB2LMb2picKGcMtX3QC8jsOKonEt1zFLdoR4pnw0Pw/HYCoYOJyzpZKMKaNZN7iACsPHtrwslo8fwsZ/54LHw+WHD+f4JR/QVNnM4FnFHHnBIXj9rTFW1cb4zRM17NgV40un5HDSrNZkb22Pcd1zTTRFHX50XICDh3kpHuTl5nu2smxlE5P8MRbGsvHGbGZUVlMcauaLX5kJKz8kVhOm+NvTmHTKoQBU1Du8t8lhxmCD0iK3lGVlZUy8+9JOvxKRzXVsOuVZwssryblgEsOfOhPDm5yrELlfH8wrny8kGrIZrgYx+8yD93iP9kdZWVly/8/0of5cNkjx8qVPLkzLZLgJOLbNdgmwpddefd12mPJtvM1RZg47jYqCEnyxGNsH5TOmchtzdr6Bz4kR9vh5fuQZGLUOl372HwK2ex3tmAbNh4fMYeyMAs743mi2VYT58JVdVJcEWUEWfg+cMzeHono/3tmjWVkV5s2VUSDCB+uibL6jgO8e5SPbbzAkp/tv2hlT/Nx1ThYvlEUpHezhQR0FDDAgEI5SMtjHhgZP/E8KmzqfDzLAyshl8pgAS9dGqMr0Uxv0kxsKkx1rnSQgOjSH8LowAdsmZhhkNoYYE97B2pLBhP1+bAOqI5ATieJzHKoDfvyjswivauD+o2ZgODYeB0I+L4RtyDB4dLWXaTcfyZ+fr6E2I8Cdv6ijzuPl4Fyb+y/J5pHna3l/sTv4/WcPVzN5tJcfP9vEmnrY1OgwZX0NuQ48oAHDoGZkkLcaMyEvk7dtm4nhKF4fLBxSxOkbtrDRE2T+wxfyxwUR6oMBzn2skTvnBDjiSZsmw4MXm6+OidLQYHP0kAw6+83Z2WCz486FeJZXAlD/r1U0vLKW7LPG4wCeHu5EMPLwYr7y8ok07Ghm0MTcA06EQiSNJMOkehO4Qyk1GGgAzgeu7rVXv/Mf0BwlTJDhFY0Mr1gDQFVuNqPrVuMjRpQMGu3hHL9xJSuKh+OxIUQWfkIECHPiLWPIvHgGAGOnw+GnFHV4mevj/06/u3XWk6pGh5qQw7jCffvx+94xfr53jJ91u2LxZOg6dKSXD68Okv/zRmqjhltLbPON8BYHGJ0TYO1m3PUSPV7yh2VQU+HGdMQpg3l3YzHLltdzcvkGd8hJJMLgXdWsnTicoqEBBq/axVG1Ud4dV0KW43DZhcUsfN/PlAW1nLduE4YDr48cyodDi1vWO77/nWZC8U46u7aHyfB5mN8U4Mg/h5jbphEgZsPNd+3A2RWhGNhVkEVVZoDcSIzMSAzHcViyC4j394l4PNT5PBREbSIe9w+CiYfmc+6rBk6mu9Pz5Q40NtFkZLqz8dRGeXgxYBs8vbiQ7yxopKTAy0NneLjh1Qi1jTY1dTGuXWzv8SV8bo3B1+9qxok53H+mn6sO7fhfrbbJ5hu3bcS3uY4JxxRzx7UlCX+mWcVBsoqDhCIOm6ptRuYZeD1p9MsjBoj0+U6mXTLUWm9WSv0QeAcIAA9prT/utQAK3OtXHqIY2DjxDrk5dSHqcVeEr6eEGBn4HIeDdmxhOyMALx6irCoaRFZZrONFzr244bggV/+jAduBSw8LMDR3/2sBjTEPZPogFHWXegq6H//oHFhaBeC406V53MWCzSKDUDO8u2Z3bdCg9P+NZNTOenLyfMycncO8S1cwNRzb4ys/aqiPxWGD2o0RVnoM3po0vKUTz3feshnqBDl101q88cbukzdtY8HQIhwPGD4Dvw9q8eKN2oxrirA9M0hVbpAq4KVmOCwjQqjZYa4KsnR+TcvrDmto5uUpw8BxmL6tlkmV9TRnBWB3+B6D+kw/BXXNHJod4egrx1AZ9eC3Y4Q9rf8Vip0YhuPgRJ3WBnmPAQEfxBy21sNF/4rSGHLcGq0Njx86laM3bWVSXQ11Z5pcVj0MX20TnpjNtU8YnDwmD1/Ay6AgZPrd9+Jbt23kqHfcRZejq7bw4oQAZ5/mdr559z+7WLmyidlH5HDwLHemnletKH/9JMqEQQbvlUfYVONQE/NQFfFwxEiDt76a0TLH7N6EIjaRCORmdfweRWMOPm/P/XjtXoFleF735yyvssnwGozMT58fT5GANPo40y4ZAmit/w78vU9e/NYLYVE5vk/WMHJyM9uahhNZW+uuK2jk8VHubMbW1bTM6mLgkEk9MXyEyabRyGbtX9cw88ZpBBKYpPrKIzI4YaKP6iaHQ0ce2Hi8okzIyPLSHB/XN6nY/aZeMMPP0ndj7g9/Uwz8bi1xXIHBCSMNnl4eY2s9zBpucN4MP9mB1t6S2ble6qqhJiebvPoGsgr82EcNh0Wdr8+4ttphba3NoV4vufHFg2MeD+OyYGemlzuP9XDfp3lU1hkYMZtpa3ZSH2j9mu7MCHD1N4s5ZpjB4AIPly1rJLLLHfqwMys+C49hYA3N5bwhUd43AtRujw+V8BhUZwWozQoytXEXTz3rJtKLcnz8rbgIDA8TnWZuvyCX1Q/U8kkkQG370Ufxqfds23H/o2e5CfL4jTV8MvMgPgFCMT+e5iiemFuLdWyHOY+FKQ/58DgO/29SjEcuzMTY0Drfrc9x+OyDas4+bRCvvLqLlx91O8l8Or+W79zhxSjO5JzHQ0R2v6227X5eThSCPhZs8vD8ihiXzNj7f+lfvtHED96MYACXjojx2HUFGIZBzHb488tZfPLbrYwf4eNX1w+iKL/779rGapvfL4iSnwHfO9q/RyL+9fwIt7zhfr73nOrnhqP2Htf334jwy/lRDOD/zfDwyHkBPFLLFb1MLjbsq6JceO9nUP8kGefPwrulCm9zM1mEyHfqCTQHWJo3jrDhxQb81FHMRoayliwqafRnEMjz4wsmntgmFHs5bJRvj6nN9sfQbINnzvZwwiiDrxxk8NsT3Y+/pN21x+JM+Np0gytneDCLPay+NoB1jZ/3L/eT3a7mcdUto5hQmsXQo4Zy+I+H8K2XjsaeWtQyNjIjGmPuys0EbJsCnwMNbuJ6dtwIdgQzCPl8VA3J55PvZFFzY4CDhnhYUxcf6+j1EMr2MbQ+hBE/38RCOGm8lyGFXgzD4P6flDBidh6xaflsNFuT9PgiD3feMpSgHXMH/hqGW+u1HWyPgb29tfnZWx+luK6ZvIZmVoW9lNU4vPOjQSy+IRO/HXOPi9lkZHrAY1CUCT89yQeZfgj6ITvA8hGD2JKbyfqCbDIaIhgeg2hOBrGgu0RWecjH4IYQM7dW8crCEE8sCpM1IZ9Y/DNt9HkpHJnNHQ9U8efXm1piM4DfvR6ivNJuSYS5kShTGkJMaQyRY9stk58XZ+39++E4Dre9HcExDGzD4OlNHsrK3T8SFnzeTFmZl8JwmO3rQjz5ascJ4tuL2Q4nPhzi1/Mj3PZWhKue33MS89vfieI47tfg9ncinZ5j25oGli2o5u75btJ0gL8ujnHLaz0/XZ7oI7LSff8XeXk5zbe+ikEhAbwtn3F+czPZzTWsYBxZ1HM4rQPHfcEmDDWIubfNwtNHnR7OnODhzAl7PnbloV4+3uzwzjqbUyZ4uP8M3x7Xn7IDBpOL9vwWf7DZwQGOnpzFd34yFnB7tRmGwbkz/Px6/CBmlW1hSGMTc84u5vWvBqgNORzzpwifb3WIFGcy8cixZDVEOPH4vJaesGPzDHweiM9dzonnDmLSrkaqnDATpmZy3CgPBcHWWPJyvfzmWjcJfr7D4eZ3bTwG/Pp49/2dO97LXyrbdDb2eMBxGDoyQF25+yNc7/eyM+BracqtaYrP6lPk5b/fyORvn0aYVOzhlXWNLKzOYM4Yg3MOcrhpfrz674HlJYUsH+7GMXFnLbHs1rliM2sayalv4vIl5fhth4hhsKV8HJdO8/CqzqXJ46U4Fubdj5pp9sXYkJ3BYMPA7zg0+LwESrI4erSHEUTYjJ+RoTC7/5Qa1RwhMMbPhQf7OGXinn9gvbPe5u11NieO8XDSWA8+AyIBL3gMYs1RghlueXduj5Afdd+LDGJUbAhBvMl/b6pDUF7V2qlbb96zQ/fQHIO1u+K9inMMVlY5VIbg8GFuh6LFr2/nxd+swXEg6/BpNHjisTswb23nrQq9zbYdvvV0A88uDqNG+3jqihzyM6X+0F9JMtxPzrY6/NQznG1s4SDa/skzDoss6ggTaFn+CCDvSwdz0C1jGVpa3Nkp+4zPY/CXcxJfV/B778S4d5H7Q3fdIQa/n7Pnj/DofIMF12Xx/qYJzBxqMGmQ+w7kBQ0+/lYmZTtsxhZ6KMzs+Gfi5EFu7fXhpQ6lg+BHR3sIeBNb3WL6YIPXLtgzlt+enUl2wGBTjc1lh/lZudVheomHuWYJb75eQ0NDjM2Ds3n2tQjYDgeVeDlnaut7cfgYH4eP8fHgYpvXtuYC8A/LoWJnDCKOuwxIuya91cV5EGlNDjW5Qc5fsxl/vAbndxwOjoaoKa+nMBKlMD5jT05zmGafjyFNYZ6fPJzsxgjRoiBLzg6yc2kNX1u0ko05mSwaNrQlcfsdhxfOz2DMiD0/v/kbbeY+GcV24Ocf2rxziY/RJX6sWvfHPJbpZ0WTl/FApC66x7GjiztvtWiKOC3XO4uyDE4a7+HtNW45L5y250/Js18K8K1/hzEMOP0gP6WPxLAdOHeiwbPnePjsPzsg3tL8pWVr+PvBE2mKAjGb48amxs/Si5+HefQjt8b79soId73eRMg2iNjwgzlBRhakVmJcuMWmogFOHmu0fE59Lo2mY0uNb10a8p8/A+NGB391I0NZzU7GEsNHiCD1gcGMDK+L72ngzJ6Ccf7hGN89A1av6suwe8SflrTWCP78ucPv53TcZ3iuwYWlHf8jBP0Ghwzvuon47Ikezu5mLu1E5WQY/O6c1rGITG29e9oZrTPbXH10gOoQlOylg1KkXWVFb45BbYRAQQbhjD3/G5VkONQC8elfOWq8j1tPLuapO+ID/A0wZ+TQMNhD2dvuFHeZhX78QzOhHgYH4bGrM8jMy2FojvvHSlOujwzbZmJtA7WZtViFbs1tVA78wTI4F4ejR7S+3x9sdna3nmI77nbIbvN5GAavza9jwZ2bWG8EMTxeHMMgM2hw6pzcdmV3OO/JMC+vtJk6xODNr2ZQkmvwymVBni+LkZ8Bp03e8z140YqxYLMbwPrGKHZ85vnnVztsrIPBY7LY8Lk7nd0kmhnuNLOuye24taHCBg5sEoGe0G4hFp5aHGF9tfvge+URPr+l69pzb/rTpzbfeM39kh45wuDdS734e7Az1EAgyXA/2bZB3eAxDKreSRbVjOBzLGYTxUf1UcdRmGnBZ2vhlIMxHv622zzXT5QOAh2fBGVKcmce6zVBv4eSLirHV0wzeGJxIx9XZnH8KAN22cyrAaeuGY/fg+3xMCxoc8p4Dz880keWD+5bZDM8F759qAePkc9XfzqJNYvrGH9wLuOm58L0XHKKAlRtaGLi0UVcke2nfFOE0cN8DGo3gcLwKTmc/K2xLH5tO2UTSigz3GSxDIeXP4HfL46x6Ctepg12fwAPblmD2QAcZg6Bm470ct3r7jXUbK9D8Pm1VPsDGJkGAdvGAb54ZiFjx+w59+zzZTYvr3RrgMu2O9z7YZRfnuInw2dw8fTOf0Lu+m9rbXN7tQ3ZbnkKgzAoCCd/YwyZuT5qdzZTevpQfvRQa9Pv5xWdnLAPnDM9wAUzAzy/xG0mXbqzNTsu32Zj207KdPR5fGlrS8SHmx1W74KUaIBKjbcnIZIM91PVrzWVqzIJMZkATVQxhIzjx1OghjDkf2fBoAv7OsSkef5cL3d+aOM48KMj+0+S70p2wOCRIzYz2ZyC12OwqzGbu+c1E4k5XHu0l4yAh+E5e/53+sUJeya0iYfmMfHQPZt8xx5WyNjDClu2Z5p7JqK2Zp8/jNnnD/v/7d19kFV1Hcfx97l3lwWWVUARZF1XjTjqmmR88xGfBsEHFDOnUCQjIyV1agqVGkXNnswcrEad1FFzzCwfMh2YEt2SydFh+jqaT3ChRh1RfIpyVVQW9vTH765s6yarnnvO3r2f18wd9lzOznzuw57v+f3O7/x+nPnTzh5zLoWjzcbN8MjLyXvF8MlXt/wfRDzxKpy7fz2HtBZ47KWEaeM2c/Vfu4BOOoY2EEUR9fURkya9f43JukISRhgDdCYM70ePesu2EWvKq5d8YnTE4btHvLIBFu5bYMSQMJLi8K+0vLf/jLYOlj4VmtKn2P9/D7JUV4y4ZW4TSZIQRRHfvnsDVywP3aZzJg+sEa+fGhPx4Nrwfm83DJqbtvILmenfexTH8V+AHehedRzOKJVKKyqVqi8qhh9R92K2HYRT8B1/cwzbnty/1dOrXXNTxLXTK7vs0kDVPbBo1PCIHx+TT1fezAkRv19dnvA9CqMwRzYQWqxlzU3/exDaqby9+l8Jz/w7Yf34eqbNbab95hdoTd5m7OR6Zpy4Kzu3vr8Q3f5PwrVRYOSIhAUfcJtEt7tPHsLC+8JtHJdNryfe/oNPmm47rYl7V3XS1BBxyIT+9tFbpAAAB2JJREFUX7/OQvco7sXHD2dmWz0bN8O0iQPr0Ln4iAI7NMKLbyacPbnINg0DpFD3I0YcxxEwEWgtlUqbtrZ/pQysT7SKjF4wmbpb2hm5dhVdTSMotp2YdySpEb+dWeS2VQnFCHYbCY+/BlN3jtilxw3rJ+1ZYM36hPufTZi6S8TJbQWu802cfk+4bWHxQ508dfYOTDlxLIVixMqVK2ntoxACLH9+S/fgfzoj6vpxHrTHmAL3zO5/C6+uGDGjbcjWd8zZYQOsUHcbWhdx8cFVe4Ial/9dFsfxdsB1pVLpyqxD1EYfVwXUbXiL0WufpkAXdW90EC28Je9IUiPqixGntBU4ac8C+44vMG/vMEFCb4umFFk+p44Lp4SD5NLVW0YBdbwLj77URaEfgyyO2nXLPofsFDG07sO3Ol5+K2H1+mTrO8rg0r/7DEcB7cAJwFRgfhzH0zJK+B61DEVqxOu97n1/Z3P/itq1RxY5tCVhQ2fCqXt9+PPnO1d3MXtpFxs3h4FINxxVtS0YqYBSqfQw8HD3dhzH1wPHAPdlmUMtw4+qdQz85BRobICJO8LlX8o7kcgHmjS+CPUFqCvAkCIt/ZwHtFiIOHWvAvP3KTL8I9y/dumKUAgBbnwy4fkOtRBrRvfsTz0fvcRxPCWO4543aEVsGUiTGbUMP47zjg8PkSpw4ZQia9YnPP1awmmTiuzfnM258PgREbwcCmBjPWw7MAaLShb6d+40ErgkjuMDgXrgy8D8Cqbqk4qhSI0YPSxi6azsB4BcM71AQ3sXr2xIuOCAwsAZ6SgDQqlUWhLH8X7Ao4Rlx68qd51mSsVQRCpqXGPEbTN1nbAm9fO8p1QqLQIWVTTLVqgYiohIhVRPL4CKoYiIVEb11EKNJhUREVHLUEREKkMtQxERkeqhYigiIjVP3aQiIlIZ6iYVERGpHmoZiohIZfQxF+lAFSXJ4Js018wG34sSEcmAu1dPBUvRoCyGIiIiH4auGYqISM1TMRQRkZqnATQZMbOLgC+WN5e6+3l55qkUM7sc2N7d5+adJS1mdhxwEdAILHP3b+YcKTVmNgf4bnnzj+5+Tp550mBm2wAPAce6+7NmdgSwGBgG/M7dL8g14MfUx+s7HfgGkAAOnOHuG/PMWI3UMsxA+Y9xOrAP8GlgspmdkG+q9JnZVMLCnIOGme0G/BL4HLA38BkzOzrfVOkws+HAL4BDgUnAweXvatUys/2AB4GJ5e1hwA3A8cAewGer+fPr4/VNBM4FDiR8PwvAWbkFrGIqhtlYByxw943u3gmsBHbOOVOqzGw08EPgR3lnSdkJhNbE2vJnNwtYkXOmtBQJx4BGwgrj9cDbuSb6+L5GKAYvlrf3Bda4+zPuvgn4NfCFvMKloPfrexc409073D0BnmCQHVuyom7SDLj7U90/m9knCd2lB+WXqCKuAc4HWvIOkrIJwEYzu4dwkFlCzouQpsXd3zCzRcAqYAOwnND9VrXcfR6AmXU/NZ5wMtptHbBTxrFS0/v1uftzwHPl58YAZwNzc4pX1dQyzJCZtQH3Aee6+5q886TFzOYBz7t7e95ZKqAOOAL4KnAAsB+DpCvYzPYGTgNaCUVjM1D11wx7KRCupXWLgK6cslSMmTUD7cD17v5AznGqkophRszsIMKX9TvuflPeeVI2C5huZo8BlwAzzeyKnDOl5SXgfnd/1d3fBu4idL0NBkcC7e7+iru/C/wKOCzXROlbC+zYY3scW7oYBwUz253Qor/J3b+fd55qpW7SDJhZC/AHYJa7/znvPGlz92ndP5vZXOAwd/9WfolStQS4ycxGAm8ARxM+y8Hg78BlZtZI6CY9DvhbvpFStwKIzWwC8AwwmzCgZlAwsyZgGXC+u9+cd55qppZhNs4BhgKLzeyx8mN+3qFk69x9BXAZYQTf04TrMzfmGiol7r4MuBV4BHicMIDm0lxDpczd3yFcQ7uT8PmtAu7IM1PK5gFjgQU9ji2X5B2qGmk6NhERqXlqGYqISM1TMRQRkZqnYigiIjVPxVBERGqeiqGIiNQ8FUMREal5uulepMLMzIErCTP13OXu1+YcSUR6UTEUyYi7V+3SQSKDnYqhSMrKawL+nDAB9u1AQ/n5B4A73P1KM5sNfA/YHvgHYTqtZfkkFhFdMxRJkZmNJUzm/QNgFGHl8b167TOcMKXbSe4+CrgauM7MoozjikiZiqFIumYQFpO91d073f0qQsuvp07CxNinm9kBwM3ALuXFWUUkByqGIukaB7zQ67lne264eycwFRgD/ImwTNTCLMKJSN9UDEXS9SLhWmFPzT03zGwboMndPw9sB8wBLjaz/bOJKCK9qRiKpGsJ0GJmZ5hZnZnNA/botU8jcK+ZHenum4B1hNXY12ecVUTKVAxFUuTurwHHAl8HXicsmPtgr33WEVqDPzOzNwmLBZ/l7qszjisiZVrPUEREap5ahiIiUvNUDEVEpOapGIqISM1TMRQRkZqnYigiIjVPxVBERGqeiqGIiNQ8FUMREal5/wXgjW32OzvfVgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 540x360 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 540x360 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 540x360 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 540x360 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 540x360 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 540x360 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"for name in X_train.columns:\n",
" shap.dependence_plot(name, shap_values, X_train)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- This shows us the method for **Plotting** the **Dependence Plots** for **each feature** in the dataset in a few lines of code."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id=section705></a>\n",
"### 7.5 SHAP Summary Plot\n",
"\n",
"- To get an overview of **which features** are **most important for a model** we can **plot** the **SHAP values** of **every feature** for **every sample**. \n",
"\n",
"\n",
"- We use a **density scatter plot** of **SHAP values** for **each feature to identify** how much **impact each feature has on the model output** for **individuals** in the **validation dataset**. \n",
"\n",
"\n",
"- **Features** are **sorted** by the **sum of the SHAP value magnitudes** across all samples. \n",
"\n",
"\n",
"- Note that when the **scatter points don't fit on a line** they **pile up to show density**, and the **color of each point represents** the **feature value of that individual**."
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x482.4 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Summarize the effects of all the features\n",
"\n",
"shap.summary_plot(shap_values, X_train)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- The **plot** above **sorts features** by the **sum of SHAP value magnitudes over all samples**, and **uses SHAP values** to show the **distribution of the impacts each feature has on the model output**. \n",
"\n",
"\n",
"- The **color represents the feature value (red high, blue low)**. \n",
"\n",
"\n",
"- This **reveals** for example that a **high LSTAT** (% lower status of the population) **lowers the predicted home price**."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id=section706></a>\n",
"### 7.6 Bar chart of Mean Importance\n",
"\n",
"- This takes the **average of the SHAP value magnitudes** across the dataset and **plots** it as a **simple bar chart**."
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x482.4 with 1 Axes>"
]
},
"metadata": {
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},
"output_type": "display_data"
}
],
"source": [
"shap.summary_plot(shap_values, X_train, plot_type=\"bar\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- Here we can see that the **rm** column has the **highest feature importance** followed by **lstat** column.\n",
"\n",
"\n",
"- This implies that the **number of rooms** and **lower status of the population** have the **highest** effect on the **price** of a **house** in **Boston**."
]
}
],
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