{
"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 Preprocessing](#section4)<br><br>\n",
"5. [Data train/test split](#section5)<br><br>\n",
"6. [Random Forest Model](#section6)\n",
" - 6.1 [Random Forest in scikit-learn](#section601)<br><br> \n",
" - 6.2 [Using the Model for Prediction](#section602)<br><br>\n",
"7. [Model Evaluation](#section7)\n",
" - 7.1 [Accuracy Score](#section701)<br><br>\n",
"8. [Model Interpretability using SHAP](#section8) \n",
" - 8.1 [Explain predictions](#section801)<br><br>\n",
" - 8.2 [Visualize a single prediction](#section802)<br><br>\n",
" - 8.3 [Visualize many predictions](#section803)<br><br>\n",
" - 8.4 [SHAP Dependence Plots](#section804)<br><br>\n",
" - 8.5 [SHAP Summary Plot](#section805)<br><br>\n",
" - 8.6 [Bar chart of Mean Importance](#section806)<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. Generalised 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.preprocessing import LabelEncoder\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.ensemble import RandomForestClassifier\n",
"from sklearn.metrics import accuracy_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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" <th></th>\n",
" <th>class</th>\n",
" <th>cap-shape</th>\n",
" <th>cap-surface</th>\n",
" <th>cap-color</th>\n",
" <th>bruises</th>\n",
" <th>odor</th>\n",
" <th>gill-attachment</th>\n",
" <th>gill-spacing</th>\n",
" <th>gill-size</th>\n",
" <th>gill-color</th>\n",
" <th>stalk-shape</th>\n",
" <th>stalk-root</th>\n",
" <th>stalk-surface-above-ring</th>\n",
" <th>stalk-surface-below-ring</th>\n",
" <th>stalk-color-above-ring</th>\n",
" <th>stalk-color-below-ring</th>\n",
" <th>veil-type</th>\n",
" <th>veil-color</th>\n",
" <th>ring-number</th>\n",
" <th>ring-type</th>\n",
" <th>spore-print-color</th>\n",
" <th>population</th>\n",
" <th>habitat</th>\n",
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"text/plain": [
" class cap-shape cap-surface cap-color bruises odor gill-attachment \\\n",
"0 p x s n t p f \n",
"1 e x s y t a f \n",
"2 e b s w t l f \n",
"3 p x y w t p f \n",
"4 e x s g f n f \n",
"\n",
" gill-spacing gill-size gill-color stalk-shape stalk-root \\\n",
"0 c n k e e \n",
"1 c b k e c \n",
"2 c b n e c \n",
"3 c n n e e \n",
"4 w b k t e \n",
"\n",
" stalk-surface-above-ring stalk-surface-below-ring stalk-color-above-ring \\\n",
"0 s s w \n",
"1 s s w \n",
"2 s s w \n",
"3 s s w \n",
"4 s s w \n",
"\n",
" stalk-color-below-ring veil-type veil-color ring-number ring-type \\\n",
"0 w p w o p \n",
"1 w p w o p \n",
"2 w p w o p \n",
"3 w p w o p \n",
"4 w p w o e \n",
"\n",
" spore-print-color population habitat \n",
"0 k s u \n",
"1 n n g \n",
"2 n n m \n",
"3 k s u \n",
"4 n a g "
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.read_csv('../../data/mushrooms.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 includes descriptions** of hypothetical samples corresponding to **23 species** of **gilled mushrooms** in the **Agaricus and Lepiota Family Mushroom** drawn from **The Audubon Society Field Guid**e to **North American Mushrooms (1981)**. \n",
"\n",
"\n",
"- **Each species** is **identified as definitely edible**, **definitely poisonous**, or **of unknown edibility** and **not recommended**. This **latter class was combined with** the **poisonous one**. \n",
"\n",
"\n",
"- The **Guide clearly states** that there is no **simple rule for determining** the **edibility of a mushroom**; no rule like **\"leaflets three, let it be''** for **Poisonous Oak and Ivy**."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['class', 'cap-shape', 'cap-surface', 'cap-color', 'bruises', 'odor',\n",
" 'gill-attachment', 'gill-spacing', 'gill-size', 'gill-color',\n",
" 'stalk-shape', 'stalk-root', 'stalk-surface-above-ring',\n",
" 'stalk-surface-below-ring', 'stalk-color-above-ring',\n",
" 'stalk-color-below-ring', 'veil-type', 'veil-color', 'ring-number',\n",
" 'ring-type', 'spore-print-color', 'population', 'habitat'],\n",
" dtype='object')"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.columns"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"| **Column Name** | **Description** |\n",
"| ---------------------------------|:----------------------------------------------------------------------------------------:| \n",
"| class | classes: edible=e, poisonous=p. |\n",
"| cap-shape | bell=b,conical=c, convex=x, flat=f, knobbed=k, sunken=s. |\n",
"| cap-surface | fibrous=f, grooves=g, scaly=y, smooth=s. |\n",
"| cap-color | brown=n, buff=b, cinnamon=c, gray=g, green=r, pink=p, purple=u, red=e, white=w, yellow=y.|\n",
"| bruises | bruises=t, no=f. |\n",
"| odor | almond=a, anise=l, creosote=c, fishy=y, foul=f, musty=m ,none=n, pungent=p, spicy=s. |\n",
"| gill-attachment | attached=a, descending=d, free=f, notched=n. |\n",
"| gill-spacing | close=c, crowded=w, distant=d. |\n",
"| gill-size | broad=b, narrow=n. |\n",
"| gill-color | black=k, brown=n ,buff=b, chocolate=h, gray=g, green=r, orange=o, pink=p, purple=u, red=e, white=w, yellow=y. |\n",
"| stalk-shape | enlarging=e, tapering=t. |\n",
"| stalk-root | bulbous=b, club=c, cup=u, equal=e, rhizomorphs=z, rooted=r, missing=?. |\n",
"| stalk-surface-above-ring | fibrous=f, scaly=y, silky=k, smooth=s. |\n",
"| stalk-surface-below-ring | fibrous=f, scaly=y, silky=k, smooth=s. |\n",
"| stalk-color-above-ring | brown=n, buff=b, cinnamon=c, gray=g, orange=o, pink=p, red=e, white=w, yellow=y. |\n",
"| stalk-color-below-ring | brown=n, buff=b, cinnamon=c, gray=g, orange=o, pink=p, red=e, white=w, yellow=y. |\n",
"| veil-type | partial=p ,universal=u. |\n",
"| veil-color | brown=n, orange=o, white=w, yellow=y. |\n",
"| ring-number | none=n, one=o, two=t. |\n",
"| ring-type | cobwebby=c, evanescent=e, flaring=f, large=l, none=n, pendant=p, sheathing=s, zone=z. |\n",
"| spore-print-color | black=k, brown=n, buff=b, chocolate=h, green=r, orange=o, purple=u, white=w, yellow=y. |\n",
"| population | abundant=a, clustered=c, numerous=n, scattered=s, several=v, solitary=y. |\n",
"| habitat | grasses=g, leaves=l, meadows=m, paths=p, urban=u, waste=w, woods=d. |"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 8124 entries, 0 to 8123\n",
"Data columns (total 23 columns):\n",
"class 8124 non-null object\n",
"cap-shape 8124 non-null object\n",
"cap-surface 8124 non-null object\n",
"cap-color 8124 non-null object\n",
"bruises 8124 non-null object\n",
"odor 8124 non-null object\n",
"gill-attachment 8124 non-null object\n",
"gill-spacing 8124 non-null object\n",
"gill-size 8124 non-null object\n",
"gill-color 8124 non-null object\n",
"stalk-shape 8124 non-null object\n",
"stalk-root 8124 non-null object\n",
"stalk-surface-above-ring 8124 non-null object\n",
"stalk-surface-below-ring 8124 non-null object\n",
"stalk-color-above-ring 8124 non-null object\n",
"stalk-color-below-ring 8124 non-null object\n",
"veil-type 8124 non-null object\n",
"veil-color 8124 non-null object\n",
"ring-number 8124 non-null object\n",
"ring-type 8124 non-null object\n",
"spore-print-color 8124 non-null object\n",
"population 8124 non-null object\n",
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"dtypes: object(23)\n",
"memory usage: 1.4+ MB\n"
]
}
],
"source": [
"df.info()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
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" class cap-shape cap-surface cap-color bruises odor gill-attachment \\\n",
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"\n",
" gill-spacing gill-size gill-color stalk-shape stalk-root \\\n",
"count 8124 8124 8124 8124 8124 \n",
"unique 2 2 12 2 5 \n",
"top c b b t b \n",
"freq 6812 5612 1728 4608 3776 \n",
"\n",
" stalk-surface-above-ring stalk-surface-below-ring \\\n",
"count 8124 8124 \n",
"unique 4 4 \n",
"top s s \n",
"freq 5176 4936 \n",
"\n",
" stalk-color-above-ring stalk-color-below-ring veil-type veil-color \\\n",
"count 8124 8124 8124 8124 \n",
"unique 9 9 1 4 \n",
"top w w p w \n",
"freq 4464 4384 8124 7924 \n",
"\n",
" ring-number ring-type spore-print-color population habitat \n",
"count 8124 8124 8124 8124 8124 \n",
"unique 3 5 9 6 7 \n",
"top o p w v d \n",
"freq 7488 3968 2388 4040 3148 "
]
},
"execution_count": 5,
"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 Preprocessing"
]
},
{
"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",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>class</th>\n",
" <th>cap-shape</th>\n",
" <th>cap-surface</th>\n",
" <th>cap-color</th>\n",
" <th>bruises</th>\n",
" <th>odor</th>\n",
" <th>gill-attachment</th>\n",
" <th>gill-spacing</th>\n",
" <th>gill-size</th>\n",
" <th>gill-color</th>\n",
" <th>stalk-shape</th>\n",
" <th>stalk-root</th>\n",
" <th>stalk-surface-above-ring</th>\n",
" <th>stalk-surface-below-ring</th>\n",
" <th>stalk-color-above-ring</th>\n",
" <th>stalk-color-below-ring</th>\n",
" <th>veil-type</th>\n",
" <th>veil-color</th>\n",
" <th>ring-number</th>\n",
" <th>ring-type</th>\n",
" <th>spore-print-color</th>\n",
" <th>population</th>\n",
" <th>habitat</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>0</td>\n",
" <td>p</td>\n",
" <td>x</td>\n",
" <td>s</td>\n",
" <td>n</td>\n",
" <td>t</td>\n",
" <td>p</td>\n",
" <td>f</td>\n",
" <td>c</td>\n",
" <td>n</td>\n",
" <td>k</td>\n",
" <td>e</td>\n",
" <td>e</td>\n",
" <td>s</td>\n",
" <td>s</td>\n",
" <td>w</td>\n",
" <td>w</td>\n",
" <td>p</td>\n",
" <td>w</td>\n",
" <td>o</td>\n",
" <td>p</td>\n",
" <td>k</td>\n",
" <td>s</td>\n",
" <td>u</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>e</td>\n",
" <td>x</td>\n",
" <td>s</td>\n",
" <td>y</td>\n",
" <td>t</td>\n",
" <td>a</td>\n",
" <td>f</td>\n",
" <td>c</td>\n",
" <td>b</td>\n",
" <td>k</td>\n",
" <td>e</td>\n",
" <td>c</td>\n",
" <td>s</td>\n",
" <td>s</td>\n",
" <td>w</td>\n",
" <td>w</td>\n",
" <td>p</td>\n",
" <td>w</td>\n",
" <td>o</td>\n",
" <td>p</td>\n",
" <td>n</td>\n",
" <td>n</td>\n",
" <td>g</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>e</td>\n",
" <td>b</td>\n",
" <td>s</td>\n",
" <td>w</td>\n",
" <td>t</td>\n",
" <td>l</td>\n",
" <td>f</td>\n",
" <td>c</td>\n",
" <td>b</td>\n",
" <td>n</td>\n",
" <td>e</td>\n",
" <td>c</td>\n",
" <td>s</td>\n",
" <td>s</td>\n",
" <td>w</td>\n",
" <td>w</td>\n",
" <td>p</td>\n",
" <td>w</td>\n",
" <td>o</td>\n",
" <td>p</td>\n",
" <td>n</td>\n",
" <td>n</td>\n",
" <td>m</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>p</td>\n",
" <td>x</td>\n",
" <td>y</td>\n",
" <td>w</td>\n",
" <td>t</td>\n",
" <td>p</td>\n",
" <td>f</td>\n",
" <td>c</td>\n",
" <td>n</td>\n",
" <td>n</td>\n",
" <td>e</td>\n",
" <td>e</td>\n",
" <td>s</td>\n",
" <td>s</td>\n",
" <td>w</td>\n",
" <td>w</td>\n",
" <td>p</td>\n",
" <td>w</td>\n",
" <td>o</td>\n",
" <td>p</td>\n",
" <td>k</td>\n",
" <td>s</td>\n",
" <td>u</td>\n",
" </tr>\n",
" <tr>\n",
" <td>4</td>\n",
" <td>e</td>\n",
" <td>x</td>\n",
" <td>s</td>\n",
" <td>g</td>\n",
" <td>f</td>\n",
" <td>n</td>\n",
" <td>f</td>\n",
" <td>w</td>\n",
" <td>b</td>\n",
" <td>k</td>\n",
" <td>t</td>\n",
" <td>e</td>\n",
" <td>s</td>\n",
" <td>s</td>\n",
" <td>w</td>\n",
" <td>w</td>\n",
" <td>p</td>\n",
" <td>w</td>\n",
" <td>o</td>\n",
" <td>e</td>\n",
" <td>n</td>\n",
" <td>a</td>\n",
" <td>g</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" class cap-shape cap-surface cap-color bruises odor gill-attachment \\\n",
"0 p x s n t p f \n",
"1 e x s y t a f \n",
"2 e b s w t l f \n",
"3 p x y w t p f \n",
"4 e x s g f n f \n",
"\n",
" gill-spacing gill-size gill-color stalk-shape stalk-root \\\n",
"0 c n k e e \n",
"1 c b k e c \n",
"2 c b n e c \n",
"3 c n n e e \n",
"4 w b k t e \n",
"\n",
" stalk-surface-above-ring stalk-surface-below-ring stalk-color-above-ring \\\n",
"0 s s w \n",
"1 s s w \n",
"2 s s w \n",
"3 s s w \n",
"4 s s w \n",
"\n",
" stalk-color-below-ring veil-type veil-color ring-number ring-type \\\n",
"0 w p w o p \n",
"1 w p w o p \n",
"2 w p w o p \n",
"3 w p w o p \n",
"4 w p w o e \n",
"\n",
" spore-print-color population habitat \n",
"0 k s u \n",
"1 n n g \n",
"2 n n m \n",
"3 k s u \n",
"4 n a g "
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['p', 'e', 'e', ..., 'e', 'p', 'e'], dtype=object)"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Creating labels array from the class column.\n",
"\n",
"labels = df.iloc[:, 0].values\n",
"labels"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"LabelEncoder()"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Creating a LabelEncoder object le and fitting labels array into it.\n",
"\n",
"le = LabelEncoder()\n",
"le.fit(labels)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([1, 0, 0, ..., 0, 1, 0])"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Transforming the labels array to have numerical values.\n",
"\n",
"labels = le.transform(labels)\n",
"labels"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['e', 'p'], dtype=object)"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Storing the different classes found by LabelEncoder in labels array into class_names.\n",
"\n",
"class_names = le.classes_\n",
"class_names"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"# Dropping the class column from the df dataframe.\n",
"\n",
"df.drop(['class'], axis=1, inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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"\n",
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" vertical-align: top;\n",
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"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>cap-shape</th>\n",
" <th>cap-surface</th>\n",
" <th>cap-color</th>\n",
" <th>bruises</th>\n",
" <th>odor</th>\n",
" <th>gill-attachment</th>\n",
" <th>gill-spacing</th>\n",
" <th>gill-size</th>\n",
" <th>gill-color</th>\n",
" <th>stalk-shape</th>\n",
" <th>stalk-root</th>\n",
" <th>stalk-surface-above-ring</th>\n",
" <th>stalk-surface-below-ring</th>\n",
" <th>stalk-color-above-ring</th>\n",
" <th>stalk-color-below-ring</th>\n",
" <th>veil-type</th>\n",
" <th>veil-color</th>\n",
" <th>ring-number</th>\n",
" <th>ring-type</th>\n",
" <th>spore-print-color</th>\n",
" <th>population</th>\n",
" <th>habitat</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>0</td>\n",
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" <td>s</td>\n",
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" <td>1</td>\n",
" <td>x</td>\n",
" <td>s</td>\n",
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" <td>t</td>\n",
" <td>a</td>\n",
" <td>f</td>\n",
" <td>c</td>\n",
" <td>b</td>\n",
" <td>k</td>\n",
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" <td>c</td>\n",
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" <td>s</td>\n",
" <td>w</td>\n",
" <td>w</td>\n",
" <td>p</td>\n",
" <td>w</td>\n",
" <td>o</td>\n",
" <td>p</td>\n",
" <td>n</td>\n",
" <td>n</td>\n",
" <td>g</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>b</td>\n",
" <td>s</td>\n",
" <td>w</td>\n",
" <td>t</td>\n",
" <td>l</td>\n",
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" <td>n</td>\n",
" <td>e</td>\n",
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" <td>s</td>\n",
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" <tr>\n",
" <td>3</td>\n",
" <td>x</td>\n",
" <td>y</td>\n",
" <td>w</td>\n",
" <td>t</td>\n",
" <td>p</td>\n",
" <td>f</td>\n",
" <td>c</td>\n",
" <td>n</td>\n",
" <td>n</td>\n",
" <td>e</td>\n",
" <td>e</td>\n",
" <td>s</td>\n",
" <td>s</td>\n",
" <td>w</td>\n",
" <td>w</td>\n",
" <td>p</td>\n",
" <td>w</td>\n",
" <td>o</td>\n",
" <td>p</td>\n",
" <td>k</td>\n",
" <td>s</td>\n",
" <td>u</td>\n",
" </tr>\n",
" <tr>\n",
" <td>4</td>\n",
" <td>x</td>\n",
" <td>s</td>\n",
" <td>g</td>\n",
" <td>f</td>\n",
" <td>n</td>\n",
" <td>f</td>\n",
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" <td>b</td>\n",
" <td>k</td>\n",
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],
"text/plain": [
" cap-shape cap-surface cap-color bruises odor gill-attachment gill-spacing \\\n",
"0 x s n t p f c \n",
"1 x s y t a f c \n",
"2 b s w t l f c \n",
"3 x y w t p f c \n",
"4 x s g f n f w \n",
"\n",
" gill-size gill-color stalk-shape stalk-root stalk-surface-above-ring \\\n",
"0 n k e e s \n",
"1 b k e c s \n",
"2 b n e c s \n",
"3 n n e e s \n",
"4 b k t e s \n",
"\n",
" stalk-surface-below-ring stalk-color-above-ring stalk-color-below-ring \\\n",
"0 s w w \n",
"1 s w w \n",
"2 s w w \n",
"3 s w w \n",
"4 s w w \n",
"\n",
" veil-type veil-color ring-number ring-type spore-print-color population \\\n",
"0 p w o p k s \n",
"1 p w o p n n \n",
"2 p w o p n n \n",
"3 p w o p k s \n",
"4 p w o e n a \n",
"\n",
" habitat \n",
"0 u \n",
"1 g \n",
"2 m \n",
"3 u \n",
"4 g "
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'bell=b,conical=c,convex=x,flat=f,knobbed=k,sunken=s'"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# We expand the characters into words, using the dataset description provided in the beginning.\n",
"\n",
"categorical_names = '''bell=b,conical=c,convex=x,flat=f,knobbed=k,sunken=s\n",
"fibrous=f,grooves=g,scaly=y,smooth=s\n",
"brown=n,buff=b,cinnamon=c,gray=g,green=r,pink=p,purple=u,red=e,white=w,yellow=y\n",
"bruises=t,no=f\n",
"almond=a,anise=l,creosote=c,fishy=y,foul=f,musty=m,none=n,pungent=p,spicy=s\n",
"attached=a,descending=d,free=f,notched=n\n",
"close=c,crowded=w,distant=d\n",
"broad=b,narrow=n\n",
"black=k,brown=n,buff=b,chocolate=h,gray=g,green=r,orange=o,pink=p,purple=u,red=e,white=w,yellow=y\n",
"enlarging=e,tapering=t\n",
"bulbous=b,club=c,cup=u,equal=e,rhizomorphs=z,rooted=r,missing=?\n",
"fibrous=f,scaly=y,silky=k,smooth=s\n",
"fibrous=f,scaly=y,silky=k,smooth=s\n",
"brown=n,buff=b,cinnamon=c,gray=g,orange=o,pink=p,red=e,white=w,yellow=y\n",
"brown=n,buff=b,cinnamon=c,gray=g,orange=o,pink=p,red=e,white=w,yellow=y\n",
"partial=p,universal=u\n",
"brown=n,orange=o,white=w,yellow=y\n",
"none=n,one=o,two=t\n",
"cobwebby=c,evanescent=e,flaring=f,large=l,none=n,pendant=p,sheathing=s,zone=z\n",
"black=k,brown=n,buff=b,chocolate=h,green=r,orange=o,purple=u,white=w,yellow=y\n",
"abundant=a,clustered=c,numerous=n,scattered=s,several=v,solitary=y\n",
"grasses=g,leaves=l,meadows=m,paths=p,urban=u,waste=w,woods=d'''.split('\\n')\n",
"\n",
"categorical_names[0]"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"for j, names in enumerate(categorical_names):\n",
" values = names.split(',')\n",
" values = dict([(x.split('=')[1], x.split('=')[0]) for x in values])\n",
" df.iloc[:, j] = df.iloc[:, j].map(values)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"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>cap-shape</th>\n",
" <th>cap-surface</th>\n",
" <th>cap-color</th>\n",
" <th>bruises</th>\n",
" <th>odor</th>\n",
" <th>gill-attachment</th>\n",
" <th>gill-spacing</th>\n",
" <th>gill-size</th>\n",
" <th>gill-color</th>\n",
" <th>stalk-shape</th>\n",
" <th>stalk-root</th>\n",
" <th>stalk-surface-above-ring</th>\n",
" <th>stalk-surface-below-ring</th>\n",
" <th>stalk-color-above-ring</th>\n",
" <th>stalk-color-below-ring</th>\n",
" <th>veil-type</th>\n",
" <th>veil-color</th>\n",
" <th>ring-number</th>\n",
" <th>ring-type</th>\n",
" <th>spore-print-color</th>\n",
" <th>population</th>\n",
" <th>habitat</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>0</td>\n",
" <td>convex</td>\n",
" <td>smooth</td>\n",
" <td>brown</td>\n",
" <td>bruises</td>\n",
" <td>pungent</td>\n",
" <td>free</td>\n",
" <td>close</td>\n",
" <td>narrow</td>\n",
" <td>black</td>\n",
" <td>enlarging</td>\n",
" <td>equal</td>\n",
" <td>smooth</td>\n",
" <td>smooth</td>\n",
" <td>white</td>\n",
" <td>white</td>\n",
" <td>partial</td>\n",
" <td>white</td>\n",
" <td>one</td>\n",
" <td>pendant</td>\n",
" <td>black</td>\n",
" <td>scattered</td>\n",
" <td>urban</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>convex</td>\n",
" <td>smooth</td>\n",
" <td>yellow</td>\n",
" <td>bruises</td>\n",
" <td>almond</td>\n",
" <td>free</td>\n",
" <td>close</td>\n",
" <td>broad</td>\n",
" <td>black</td>\n",
" <td>enlarging</td>\n",
" <td>club</td>\n",
" <td>smooth</td>\n",
" <td>smooth</td>\n",
" <td>white</td>\n",
" <td>white</td>\n",
" <td>partial</td>\n",
" <td>white</td>\n",
" <td>one</td>\n",
" <td>pendant</td>\n",
" <td>brown</td>\n",
" <td>numerous</td>\n",
" <td>grasses</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>bell</td>\n",
" <td>smooth</td>\n",
" <td>white</td>\n",
" <td>bruises</td>\n",
" <td>anise</td>\n",
" <td>free</td>\n",
" <td>close</td>\n",
" <td>broad</td>\n",
" <td>brown</td>\n",
" <td>enlarging</td>\n",
" <td>club</td>\n",
" <td>smooth</td>\n",
" <td>smooth</td>\n",
" <td>white</td>\n",
" <td>white</td>\n",
" <td>partial</td>\n",
" <td>white</td>\n",
" <td>one</td>\n",
" <td>pendant</td>\n",
" <td>brown</td>\n",
" <td>numerous</td>\n",
" <td>meadows</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>convex</td>\n",
" <td>scaly</td>\n",
" <td>white</td>\n",
" <td>bruises</td>\n",
" <td>pungent</td>\n",
" <td>free</td>\n",
" <td>close</td>\n",
" <td>narrow</td>\n",
" <td>brown</td>\n",
" <td>enlarging</td>\n",
" <td>equal</td>\n",
" <td>smooth</td>\n",
" <td>smooth</td>\n",
" <td>white</td>\n",
" <td>white</td>\n",
" <td>partial</td>\n",
" <td>white</td>\n",
" <td>one</td>\n",
" <td>pendant</td>\n",
" <td>black</td>\n",
" <td>scattered</td>\n",
" <td>urban</td>\n",
" </tr>\n",
" <tr>\n",
" <td>4</td>\n",
" <td>convex</td>\n",
" <td>smooth</td>\n",
" <td>gray</td>\n",
" <td>no</td>\n",
" <td>none</td>\n",
" <td>free</td>\n",
" <td>crowded</td>\n",
" <td>broad</td>\n",
" <td>black</td>\n",
" <td>tapering</td>\n",
" <td>equal</td>\n",
" <td>smooth</td>\n",
" <td>smooth</td>\n",
" <td>white</td>\n",
" <td>white</td>\n",
" <td>partial</td>\n",
" <td>white</td>\n",
" <td>one</td>\n",
" <td>evanescent</td>\n",
" <td>brown</td>\n",
" <td>abundant</td>\n",
" <td>grasses</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" cap-shape cap-surface cap-color bruises odor gill-attachment \\\n",
"0 convex smooth brown bruises pungent free \n",
"1 convex smooth yellow bruises almond free \n",
"2 bell smooth white bruises anise free \n",
"3 convex scaly white bruises pungent free \n",
"4 convex smooth gray no none free \n",
"\n",
" gill-spacing gill-size gill-color stalk-shape stalk-root \\\n",
"0 close narrow black enlarging equal \n",
"1 close broad black enlarging club \n",
"2 close broad brown enlarging club \n",
"3 close narrow brown enlarging equal \n",
"4 crowded broad black tapering equal \n",
"\n",
" stalk-surface-above-ring stalk-surface-below-ring stalk-color-above-ring \\\n",
"0 smooth smooth white \n",
"1 smooth smooth white \n",
"2 smooth smooth white \n",
"3 smooth smooth white \n",
"4 smooth smooth white \n",
"\n",
" stalk-color-below-ring veil-type veil-color ring-number ring-type \\\n",
"0 white partial white one pendant \n",
"1 white partial white one pendant \n",
"2 white partial white one pendant \n",
"3 white partial white one pendant \n",
"4 white partial white one evanescent \n",
"\n",
" spore-print-color population habitat \n",
"0 black scattered urban \n",
"1 brown numerous grasses \n",
"2 brown numerous meadows \n",
"3 black scattered urban \n",
"4 brown abundant grasses "
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"df_display = df.copy()"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>cap-shape</th>\n",
" <th>cap-surface</th>\n",
" <th>cap-color</th>\n",
" <th>bruises</th>\n",
" <th>odor</th>\n",
" <th>gill-attachment</th>\n",
" <th>gill-spacing</th>\n",
" <th>gill-size</th>\n",
" <th>gill-color</th>\n",
" <th>stalk-shape</th>\n",
" <th>stalk-root</th>\n",
" <th>stalk-surface-above-ring</th>\n",
" <th>stalk-surface-below-ring</th>\n",
" <th>stalk-color-above-ring</th>\n",
" <th>stalk-color-below-ring</th>\n",
" <th>veil-type</th>\n",
" <th>veil-color</th>\n",
" <th>ring-number</th>\n",
" <th>ring-type</th>\n",
" <th>spore-print-color</th>\n",
" <th>population</th>\n",
" <th>habitat</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>0</td>\n",
" <td>convex</td>\n",
" <td>smooth</td>\n",
" <td>brown</td>\n",
" <td>bruises</td>\n",
" <td>pungent</td>\n",
" <td>free</td>\n",
" <td>close</td>\n",
" <td>narrow</td>\n",
" <td>black</td>\n",
" <td>enlarging</td>\n",
" <td>equal</td>\n",
" <td>smooth</td>\n",
" <td>smooth</td>\n",
" <td>white</td>\n",
" <td>white</td>\n",
" <td>partial</td>\n",
" <td>white</td>\n",
" <td>one</td>\n",
" <td>pendant</td>\n",
" <td>black</td>\n",
" <td>scattered</td>\n",
" <td>urban</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>convex</td>\n",
" <td>smooth</td>\n",
" <td>yellow</td>\n",
" <td>bruises</td>\n",
" <td>almond</td>\n",
" <td>free</td>\n",
" <td>close</td>\n",
" <td>broad</td>\n",
" <td>black</td>\n",
" <td>enlarging</td>\n",
" <td>club</td>\n",
" <td>smooth</td>\n",
" <td>smooth</td>\n",
" <td>white</td>\n",
" <td>white</td>\n",
" <td>partial</td>\n",
" <td>white</td>\n",
" <td>one</td>\n",
" <td>pendant</td>\n",
" <td>brown</td>\n",
" <td>numerous</td>\n",
" <td>grasses</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>bell</td>\n",
" <td>smooth</td>\n",
" <td>white</td>\n",
" <td>bruises</td>\n",
" <td>anise</td>\n",
" <td>free</td>\n",
" <td>close</td>\n",
" <td>broad</td>\n",
" <td>brown</td>\n",
" <td>enlarging</td>\n",
" <td>club</td>\n",
" <td>smooth</td>\n",
" <td>smooth</td>\n",
" <td>white</td>\n",
" <td>white</td>\n",
" <td>partial</td>\n",
" <td>white</td>\n",
" <td>one</td>\n",
" <td>pendant</td>\n",
" <td>brown</td>\n",
" <td>numerous</td>\n",
" <td>meadows</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>convex</td>\n",
" <td>scaly</td>\n",
" <td>white</td>\n",
" <td>bruises</td>\n",
" <td>pungent</td>\n",
" <td>free</td>\n",
" <td>close</td>\n",
" <td>narrow</td>\n",
" <td>brown</td>\n",
" <td>enlarging</td>\n",
" <td>equal</td>\n",
" <td>smooth</td>\n",
" <td>smooth</td>\n",
" <td>white</td>\n",
" <td>white</td>\n",
" <td>partial</td>\n",
" <td>white</td>\n",
" <td>one</td>\n",
" <td>pendant</td>\n",
" <td>black</td>\n",
" <td>scattered</td>\n",
" <td>urban</td>\n",
" </tr>\n",
" <tr>\n",
" <td>4</td>\n",
" <td>convex</td>\n",
" <td>smooth</td>\n",
" <td>gray</td>\n",
" <td>no</td>\n",
" <td>none</td>\n",
" <td>free</td>\n",
" <td>crowded</td>\n",
" <td>broad</td>\n",
" <td>black</td>\n",
" <td>tapering</td>\n",
" <td>equal</td>\n",
" <td>smooth</td>\n",
" <td>smooth</td>\n",
" <td>white</td>\n",
" <td>white</td>\n",
" <td>partial</td>\n",
" <td>white</td>\n",
" <td>one</td>\n",
" <td>evanescent</td>\n",
" <td>brown</td>\n",
" <td>abundant</td>\n",
" <td>grasses</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" cap-shape cap-surface cap-color bruises odor gill-attachment \\\n",
"0 convex smooth brown bruises pungent free \n",
"1 convex smooth yellow bruises almond free \n",
"2 bell smooth white bruises anise free \n",
"3 convex scaly white bruises pungent free \n",
"4 convex smooth gray no none free \n",
"\n",
" gill-spacing gill-size gill-color stalk-shape stalk-root \\\n",
"0 close narrow black enlarging equal \n",
"1 close broad black enlarging club \n",
"2 close broad brown enlarging club \n",
"3 close narrow brown enlarging equal \n",
"4 crowded broad black tapering equal \n",
"\n",
" stalk-surface-above-ring stalk-surface-below-ring stalk-color-above-ring \\\n",
"0 smooth smooth white \n",
"1 smooth smooth white \n",
"2 smooth smooth white \n",
"3 smooth smooth white \n",
"4 smooth smooth white \n",
"\n",
" stalk-color-below-ring veil-type veil-color ring-number ring-type \\\n",
"0 white partial white one pendant \n",
"1 white partial white one pendant \n",
"2 white partial white one pendant \n",
"3 white partial white one pendant \n",
"4 white partial white one evanescent \n",
"\n",
" spore-print-color population habitat \n",
"0 black scattered urban \n",
"1 brown numerous grasses \n",
"2 brown numerous meadows \n",
"3 black scattered urban \n",
"4 brown abundant grasses "
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_display.head()"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"range(0, 22)"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Creating a range form 0 upto the number of categorical features. Since all the features in df are categorical using len(). \n",
"\n",
"categorical_features = range(len(df.columns))\n",
"categorical_features"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"# LabelEncoding all the features. Capturing the different class values for each feature in the categorical_names dictionary.\n",
"\n",
"categorical_names = {}\n",
"\n",
"for feature in categorical_features:\n",
" le = LabelEncoder()\n",
" le.fit(df.iloc[:, feature])\n",
" df.iloc[:, feature] = le.transform(df.iloc[:, feature])\n",
" categorical_names[feature] = le.classes_"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['bell', 'conical', 'convex', 'flat', 'knobbed', 'sunken'],\n",
" dtype=object)"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"categorical_names[0]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id=section5></a>\n",
"## 5. 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": 137,
"metadata": {},
"outputs": [
{
"data": {
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" <td>1</td>\n",
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" <td>2</td>\n",
" <td>0</td>\n",
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" <td>2</td>\n",
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" <td>7</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
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" <td>3</td>\n",
" <td>4</td>\n",
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" <tr>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>6</td>\n",
" <td>1</td>\n",
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],
"text/plain": [
" cap-shape cap-surface cap-color bruises odor gill-attachment \\\n",
"0 2 3 0 0 7 1 \n",
"1 2 3 9 0 0 1 \n",
"2 0 3 8 0 1 1 \n",
"3 2 2 8 0 7 1 \n",
"4 2 3 3 1 6 1 \n",
"\n",
" gill-spacing gill-size gill-color stalk-shape stalk-root \\\n",
"0 0 1 0 0 2 \n",
"1 0 0 0 0 1 \n",
"2 0 0 1 0 1 \n",
"3 0 1 1 0 2 \n",
"4 1 0 0 1 2 \n",
"\n",
" stalk-surface-above-ring stalk-surface-below-ring stalk-color-above-ring \\\n",
"0 3 3 7 \n",
"1 3 3 7 \n",
"2 3 3 7 \n",
"3 3 3 7 \n",
"4 3 3 7 \n",
"\n",
" stalk-color-below-ring veil-type veil-color ring-number ring-type \\\n",
"0 7 0 2 1 4 \n",
"1 7 0 2 1 4 \n",
"2 7 0 2 1 4 \n",
"3 7 0 2 1 4 \n",
"4 7 0 2 1 0 \n",
"\n",
" spore-print-color population habitat \n",
"0 0 3 4 \n",
"1 1 2 0 \n",
"2 1 2 2 \n",
"3 0 3 4 \n",
"4 1 0 0 "
]
},
"execution_count": 137,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 139,
"metadata": {},
"outputs": [
{
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" <th>stalk-color-above-ring</th>\n",
" <th>stalk-color-below-ring</th>\n",
" <th>veil-type</th>\n",
" <th>veil-color</th>\n",
" <th>ring-number</th>\n",
" <th>ring-type</th>\n",
" <th>spore-print-color</th>\n",
" <th>population</th>\n",
" <th>habitat</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>0</td>\n",
" <td>2</td>\n",
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" </tr>\n",
" <tr>\n",
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" <td>2</td>\n",
" <td>3</td>\n",
" <td>9</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
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" <td>7</td>\n",
" <td>7</td>\n",
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" <td>2</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
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" <td>3</td>\n",
" <td>8</td>\n",
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" <td>1</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>8</td>\n",
" <td>0</td>\n",
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" <td>4</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>6</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>0</td>\n",
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" <td>1</td>\n",
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"</table>\n",
"</div>"
],
"text/plain": [
" cap-shape cap-surface cap-color bruises odor gill-attachment \\\n",
"0 2 3 0 0 7 1 \n",
"1 2 3 9 0 0 1 \n",
"2 0 3 8 0 1 1 \n",
"3 2 2 8 0 7 1 \n",
"4 2 3 3 1 6 1 \n",
"\n",
" gill-spacing gill-size gill-color stalk-shape stalk-root \\\n",
"0 0 1 0 0 2 \n",
"1 0 0 0 0 1 \n",
"2 0 0 1 0 1 \n",
"3 0 1 1 0 2 \n",
"4 1 0 0 1 2 \n",
"\n",
" stalk-surface-above-ring stalk-surface-below-ring stalk-color-above-ring \\\n",
"0 3 3 7 \n",
"1 3 3 7 \n",
"2 3 3 7 \n",
"3 3 3 7 \n",
"4 3 3 7 \n",
"\n",
" stalk-color-below-ring veil-type veil-color ring-number ring-type \\\n",
"0 7 0 2 1 4 \n",
"1 7 0 2 1 4 \n",
"2 7 0 2 1 4 \n",
"3 7 0 2 1 4 \n",
"4 7 0 2 1 0 \n",
"\n",
" spore-print-color population habitat \n",
"0 0 3 4 \n",
"1 1 2 0 \n",
"2 1 2 2 \n",
"3 0 3 4 \n",
"4 1 0 0 "
]
},
"execution_count": 139,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X = df.iloc[:, :]\n",
"X.head()"
]
},
{
"cell_type": "code",
"execution_count": 140,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([1, 0, 0, 1, 0, 0, 0, 0, 1, 0])"
]
},
"execution_count": 140,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y = labels[:]\n",
"y[:10]"
]
},
{
"cell_type": "code",
"execution_count": 141,
"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": 142,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(6499, 22)\n",
"(6499,)\n",
"(1625, 22)\n",
"(1625,)\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=section6></a>\n",
"## 6. Random Forest Model\n",
"\n",
"<a id=section601></a>\n",
"### 6.1 Random Forest with Scikit-Learn"
]
},
{
"cell_type": "code",
"execution_count": 144,
"metadata": {},
"outputs": [],
"source": [
"# Creating a Random Forest Classifier.\n",
"\n",
"classifier_rf = RandomForestClassifier(n_estimators=200, random_state=0, oob_score=True, n_jobs=-1)"
]
},
{
"cell_type": "code",
"execution_count": 145,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
" max_depth=None, 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",
" n_jobs=-1, oob_score=True, random_state=0, verbose=0,\n",
" warm_start=False)"
]
},
"execution_count": 145,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Fitting the model on the dataset.\n",
"\n",
"classifier_rf.fit(X_train, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 146,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1.0"
]
},
"execution_count": 146,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"classifier_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=section602></a>\n",
"### 6.2 Using the Model for Prediction"
]
},
{
"cell_type": "code",
"execution_count": 147,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([1, 1, 1, 0, 0, 1, 0, 0, 1, 1])"
]
},
"execution_count": 147,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Making predictions on the training set.\n",
"\n",
"y_pred_train = classifier_rf.predict(X_train)\n",
"y_pred_train[:10]"
]
},
{
"cell_type": "code",
"execution_count": 148,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([0, 1, 1, 0, 1, 1, 1, 1, 0, 0])"
]
},
"execution_count": 148,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Making predictions on test set.\n",
"\n",
"y_pred_test = classifier_rf.predict(X_test)\n",
"y_pred_test[:10]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id=section7></a>\n",
"## 7. 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=section701></a>\n",
"### 7.1 Accuracy Score"
]
},
{
"cell_type": "code",
"execution_count": 149,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Accuracy score for train data is: 1.0\n"
]
}
],
"source": [
"# Accuracy score on the training set.\n",
"\n",
"print('Accuracy score for train data is:', accuracy_score(y_train, y_pred_train))"
]
},
{
"cell_type": "code",
"execution_count": 150,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Accuracy score for test data is: 1.0\n"
]
}
],
"source": [
"# Accuracy score on the test set.\n",
"\n",
"print('Accuracy score for test data is:', accuracy_score(y_test, y_pred_test))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- We get an **accuracy** of **100%** on our train set and an **accuracy** of **100%** on our test set.\n",
"\n",
"\n",
"- We can notice that the **accuracy** obtained on the **test set (1.0)** is similar to the one obtained using the **oob_score_ (1.0)**, so we can use the **oob_score_** as a **validation** before testing our model on the **test set**. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id=section8></a>\n",
"## 8. 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": 63,
"metadata": {},
"outputs": [],
"source": [
"import shap"
]
},
{
"cell_type": "code",
"execution_count": 64,
"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=section801></a>\n",
"### 8.1 Explain predictions"
]
},
{
"cell_type": "code",
"execution_count": 151,
"metadata": {},
"outputs": [],
"source": [
"# Explain the model's predictions using SHAP values \n",
"\n",
"explainer = shap.TreeExplainer(classifier_rf)"
]
},
{
"cell_type": "code",
"execution_count": 152,
"metadata": {},
"outputs": [],
"source": [
"# Calculating the SHAP values for the entire dataset. Alternately X_train or X_test can be used instead of df.\n",
"\n",
"shap_values = explainer.shap_values(df)"
]
},
{
"cell_type": "code",
"execution_count": 154,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(8124, 22)"
]
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"execution_count": 154,
"metadata": {},
"output_type": "execute_result"
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"source": [
"# The shap_values consists of SHAP values with respect to both the classes 0 and 1 having an output value 1.00\n",
"# We only need to use SHAP values with respect to one class for visualization.\n",
"# Creating shap_values_1 which is an array of SHAP values where class 1 (i.e. Poisonous) have an output value of 1.00\n",
"# and class 0 (i.e. Edible) have an output value of 0.00\n",
"\n",
"shap_values_1 = np.array(shap_values)[1]\n",
"shap_values_1.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id=section802></a>\n",
"### 8.2 Visualize a single prediction"
]
},
{
"cell_type": "code",
"execution_count": 155,
"metadata": {},
"outputs": [
{
"data": {
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"\n",
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" <b>Visualization omitted, Javascript library not loaded!</b><br>\n",
" Have you run `initjs()` in this notebook? If this notebook was from another\n",
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" 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\": 0.48230881663039327, \"outValue\": 0.9999999883180564, \"link\": \"identity\", \"featureNames\": [\"cap-shape\", \"cap-surface\", \"cap-color\", \"bruises\", \"odor\", \"gill-attachment\", \"gill-spacing\", \"gill-size\", \"gill-color\", \"stalk-shape\", \"stalk-root\", \"stalk-surface-above-ring\", \"stalk-surface-below-ring\", \"stalk-color-above-ring\", \"stalk-color-below-ring\", \"veil-type\", \"veil-color\", \"ring-number\", \"ring-type\", \"spore-print-color\", \"population\", \"habitat\"], \"features\": {\"0\": {\"effect\": 0.0036740428629507605, \"value\": \"convex\"}, \"1\": {\"effect\": 0.03945179551041023, \"value\": \"smooth\"}, \"2\": {\"effect\": 0.004258941512644578, \"value\": \"brown\"}, \"3\": {\"effect\": 0.010097368283762996, \"value\": \"bruises\"}, \"4\": {\"effect\": 0.18476507191756514, \"value\": \"pungent\"}, \"5\": {\"effect\": 0.0018409127835370847, \"value\": \"free\"}, \"6\": {\"effect\": 0.01280860221280068, \"value\": \"close\"}, \"7\": {\"effect\": 0.23882627848463928, \"value\": \"narrow\"}, \"8\": {\"effect\": -0.00609737929429096, \"value\": \"black\"}, \"9\": {\"effect\": 0.013682334800261322, \"value\": \"enlarging\"}, \"10\": {\"effect\": 0.021251167801923997, \"value\": \"equal\"}, \"11\": {\"effect\": -0.013193857801235814, \"value\": \"smooth\"}, \"12\": {\"effect\": 0.005517601353415284, \"value\": \"smooth\"}, \"13\": {\"effect\": 0.0067875721780389095, \"value\": \"white\"}, \"14\": {\"effect\": 0.007722358030588169, \"value\": \"white\"}, \"16\": {\"effect\": 0.0009694069964099276, \"value\": \"white\"}, \"17\": {\"effect\": 0.004342690741317951, \"value\": \"one\"}, \"18\": {\"effect\": -0.009155799582518671, \"value\": \"pendant\"}, \"19\": {\"effect\": -0.03813468401632779, \"value\": \"black\"}, \"20\": {\"effect\": -0.0009189374636955138, \"value\": \"scattered\"}, \"21\": {\"effect\": 0.0291956843754656, \"value\": \"urban\"}}, \"plot_cmap\": \"RdBu\", \"labelMargin\": 20}),\n",
" document.getElementById('iSS97ALXELXU4Y9GR9TL3')\n",
" );\n",
"</script>"
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"execution_count": 155,
"metadata": {},
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}
],
"source": [
"# Visualizing the first row's prediction's explanation\n",
"\n",
"shap.force_plot(explainer.expected_value[1], shap_values_1[0, :], df_display.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",
"- Here **output value = 1** signifies that the **mushrooms are poisonous** and **output value = 0** signifies that the **mushrooms are edible**.\n",
"\n",
"\n",
"- **Features pushing the prediction higher** are shown in **red**, and 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=section803></a>\n",
"### 8.3 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": 156,
"metadata": {},
"outputs": [
{
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"text/html": [
"\n",
"<div id='i30VAE5LLPFGXXPHTMJAI'>\n",
"<div style='color: #900; text-align: center;'>\n",
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" 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",
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" document.getElementById('i30VAE5LLPFGXXPHTMJAI')\n",
" );\n",
"</script>"
],
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"<IPython.core.display.HTML object>"
]
},
"execution_count": 156,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Visualizing the training set predictions, showing only first 500 samples (rows)\n",
"\n",
"shap.force_plot(explainer.expected_value[1], shap_values_1[0:500, :], df_display.iloc[0:500, :])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- In this plot the **samples (rows)** are **sorted** on the **basis of similarity in** the **output value along** the **x-axis**.\n",
"\n",
"\n",
"- The **samples** having **output value = 1 are** to the **left**, while the **samples** having **output value = 0 are** to the **right**.\n",
"\n",
"\n",
"- **On hovering** over the plot, **we get** the **values** of **different features** for that **sample** of data.\n",
"\n",
"\n",
"- **Features pushing** the p**rediction higher** are shown **in red**, and those **pushing** the **prediction lower** are **in blue**."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id=section804></a>\n",
"### 8.4 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": 157,
"metadata": {
"scrolled": false
},
"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('odor', shap_values_1, df)"
]
},
{
"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 class of mushroom** as **odor changes**. \n",
"<br><br>\n",
" - **Vertical dispersion** at a single value of **odor** 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 **stalk-root highlights** that the **probablity** of **mushroom being poisonous** is **higher** when **odor** has a **value 7** (i.e. **odor = pungent**) and the **stalk-root** has a **value 2** (i.e. **stalk-root is equal**)."
]
},
{
"cell_type": "code",
"execution_count": 158,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"10"
]
},
"execution_count": 158,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# To get the key value pairing for each column before and after Label Encoding of the dataset. Use the code below.\n",
"# Getting the index of stalk-root column.\n",
"\n",
"list(df.columns).index('stalk-root')"
]
},
{
"cell_type": "code",
"execution_count": 159,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0 bulbous\n",
"1 club\n",
"2 equal\n",
"3 missing\n",
"4 rooted\n"
]
}
],
"source": [
"# Printing the values after Label Encoding with the original values of stalk-root.\n",
"\n",
"for k, v in enumerate(categorical_names[10]):\n",
" print(k, v)"
]
},
{
"cell_type": "code",
"execution_count": 160,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"4"
]
},
"execution_count": 160,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"list(df.columns).index('odor')"
]
},
{
"cell_type": "code",
"execution_count": 161,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0 almond\n",
"1 anise\n",
"2 creosote\n",
"3 fishy\n",
"4 foul\n",
"5 musty\n",
"6 none\n",
"7 pungent\n",
"8 spicy\n"
]
}
],
"source": [
"for k, v in enumerate(categorical_names[4]):\n",
" print(k, v)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" "
]
},
{
"cell_type": "code",
"execution_count": 162,
"metadata": {
"run_control": {
"marked": false
},
"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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\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": {
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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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I1wH/G0LYHLAY4+z01F8AvwohDAI+EGP8XVp+LXBRb9Sly2RsZg8De7v70l6Ipxz1FP70JP/4Zb2r7u4HtW2b2XeAp0qd19MfqsbGxkx+MLNSa/WF2quz6ts3rS1KxtCat3qPBM4LIbyTpJt6GnBiwfERwC9CCFOA10l6Uf9KkjeuDSFYjPE54NC0fA1wWQjh+Rjj34HD0vKqK6ebeiQwpNqBbIQXgHEF+2OBF7u6yMxGmNkXC4rqgLUVjk1EJDfqi9oxde0S86YvxvgHkteV/gn8HfhJjPG+EMLDIYTxMcZ/A98k6Q19BFgBXBRjXAScAPwhhPAIyUCwr8QYm4GPAz8OITxMkryP6426lNNNfR/wDzO7i6S/fd13y91Pr1ZgnbgTmG5mo4HlwMEk/6hdWQacbmb3uvsDJO+d3Vi9MEVEpNpijGcDZxeVTSnYvpJkQG/xdb8Dflei/K8kA4R7VTkt4yaSUWZrgS2B0QWfXufu84EzgbtJRsXNcvcHzexWM7NOrmsm6XK4wswaSf6xs/hjQkRkk9AKtFDHagbRkrt2cd/SZcvY3T/Vtm1mDWlSy5S7zwJmFZVtMILO3Y8p2p8D7FbV4EREcuJ1xrGC0axlEA2sYgj/ZYusg6pR5Qzg6k8yZdgxwBgzW0wywuwr7r66uuGJiEi1rGUgaxkEQHPBtvS+crqpv0EyecYxwE7A0cCeJK8GiYhITg1mSdH+6xlFIuUM4PoE8D/u3vZubzSzR4F/AKdVLTIREamqoSxmLfNpYjiDWMpQFmUdUs0qJxkPgQ3+XHqN5NUgERHJqVUMYxTzSWaDhJUMY3C2IdWscrqp/whc2TZ1pJltDvyIZD5QERHJqZUM4z/Dtuf+ibvz9LAdWMFmWYdUs8ppGZ9C8i7WK2a2HBhKkog3mEZSRETyY/bkd/L4xB1obmigvrmZHZ+fx+FZB1WjOmwZm1nbhBjvdvd3kqwZ+T5goru/z93/2xsBiohIdTwxZluaGxoAaGloYN6YbbINqIZ11k39XjPbBrjazAaTLLLwKPCamQ0xs01pikwREdlIdS0t0JpO9dHaSl2Lpv3ISmfd1LcBT5NM0rKs6Fjb7OINVYpLRESqrGH12nYrD/Rbo+n6s9Jhy9jdDwUmAiuB7Yo+26b/FRGRnFozaADUpS/G1NUl+5KJTgdwuft8M9vZ3Z/trYBERKR3tNa1f0O1uU5vrGalnLmp/9MbgYiISO+qa2lli4WLGLSiiaYhA3ltpF5tyko5rzaJiEgfNHDVaoYtXQ7AsKUrWDWwf8YR1a5yJv0QEZE+aGDTqqJ9rf2TlbJaxmY2EjiEZEDXd4Hd3f3uagYmIiJV1gotQGt9XfJak15tykyXLWMz2x2YBxwBnApsCdxkZsdWOTYREamiNQP68dLEMbzwxoksnDiWNQPUTZ2VcrqpLwFOdPd9gbXu/gywP/C1agYmIiLVtWrwIFYPGggkrzk1DdV6xlkpJxnvCLRNjdkK4O5/A7aqVlAiIlJ9rUUZoFWL8WWmnGQ8D/hgYYGZ7Qs8UZWIRESkVwxZuoKhS5ZRv7aZIUuWMzQdWS29r5wBXKcBN5vZXcAQM5sJfBg4rKsLzWwacAIwFng3cAFwkrsXT68pIiK9bOCyJoYtXcmWLYtprq9jdX1+W8YhhO8BW8YYj+ng+K7A/THGgUXlmwEPA5+OMf656NivgcdijNOrEXOhLlvG7n4PsDNwP3A18CTwDnfvdD1jM/sK8CXgSmALYCmwNXBpD2MWEZEKaOnfQENLCwANLa209svncgMhhP2AaZ0cHwL8ECg13+elwKgS1xwL7FupGLtS1qtN7v4c8O2NvPdngH3c/Tkzu9jdXzezQ4G4sUGKiEjlDViztt1T4gGrmzOLpbtCCJuT9LpeCOzSwWkXARcDexZd+3GShuKjReVvBI4haUz2ii6TsZm1kA7cKubunf0ZNRR4Od1u+36vAPL33RYR6Ys2+M2ey/eMrwTOJJkHYwMhhI8AQ2KMN4QQCssnAaeQtH5vKyjvR9ILfCJlPI6tlHIGcO0MvLXgsy/we5KWb2dmA1eY2Sig1cz6Ad8E7up+uCIiUimri94rXt1/03rPeN6bRm3wKRRCOA54PsZY8rFpCGEscBbwuaLyepKEe3KMcWXRZdOB38YY51aoGmUpZ6GIx4rLzOyfJA+8Z3Ry6eeBa4FXSFrGy4E5JJOHiIhIxhpamlk5qD8DVq9lTf9+NLTkruPy48C4EMLDwObAsBDCD2KMX0yPf4hkzNI9ba3i9NxpJK/tXp2WvwmYEUI4nmS2yVXpM+Ox6TXLY4zfrWZFurtQxJbA8M5OcPdXgQ+a2RhgErDA3V/o5tcTEZEKG7t0Ca8NHAJAw6o1jFxd3EjctMUY39u2HUI4BtinIBETY5xBQaMxhNAaY5yS7k4sKP8zMD0dTb1jQfn09D5VTcRQ3jPjX9P+QcIAYC+SrurOrvsbcHo6QchLBeVL3L3TRF5GTEeQdD30By5298s6OO9nwF3uPjPdn0TSWt+KZCDZkXrNSkRq1dL+/WgaMpCGNc009+/H8rVNWYdUESGEGcDvY4yd5qkKfr1hMcYe5ZJyWsb/LtpvBmaxflaujrwd+L2Zne7uVxeU9+hFNjObQDJy7m3AKuBeM7vb3ecWnDOe5KH+frR/Rn05cLm7X2dmZwNnA1/pSTwiInn16ogRNLTC8lFDGbiiiUWbb551SN0WY5wJzEy3j+vgnJL5J8a4Twfl08v88nNDCG+OMS4t8/wNlPPM+Nxu3nsl6WAvM9sFOMXdOxyZvRGmkrR2FwOY2Q0kffznFZxzJHATsKitwMz6A+8CPpoWzQT+gpKxiNSolQMH0jQq6ahcNmIYg15bknFEudU2j0Zjd2/QYTIu0T29AXfvdNi3uz9iZu8AfgfcYWaHAWu7E2iB8cCCgv0FJK3wwq/7XQAz26ugeEtgibuvLbhu61JfoLGx2/+eADQ1NfX4HnlSa/WF2quz6ts3LRo1jKEF+4tHDOt2vSdPnlyZoPLpaeChEMI/gYUU5M4YY1mvR3XWMi7unt5YdQDuvtDM3g38FHiA8l6n6kw97f9IqCNZknNjr6Oj63r6Q9XY2FhTP5i1Vl+ovTqrvn3TS8Pnst3rK6CuDlpbWThiaE3Uuwo8/XRbh8m4q+5pM+sqqc4puNcq4AgzOwf4+kZFuKEXgL0L9scCL5Zx3cvACDNrcPdmYFyZ14mI9ElN/RqSRAxQV8eqft19waa2xRjPBQghDCLpvX0lxrhRff7ljKbegWR2kwmsb9X2B7YnfQerFHc/oETZebR/ttsddwLTzWw0ybvLB5MsRtEpd19jZnNI3kubBRxNwawrIiK15tlhg9l81RqWDxzAsKY1PD18cNYh5VK62MSVJDN21QEtIYQ/ANPKTcrl/Bn0E5KJO54FtgH+TDJNWEevEz3t7tuZ2X/peBrNbq+F7O7zzexM4G6S16xmuPuDZnYrcI67d9ZV8FngGjM7C3gOOLy7cYiI5N3jozbj728ct66betDrK7IOKa++D2wG7ESSK7cDvgP8APh0OTcoJxnvSjL4aVvgh+5+vpn9kWSli/NLnH90+t9DygmgO9x9FknrtrCsVEv8mKL9Z4F9qhWXiEieLB8+pF03ddPQQdkGlF8fBCbHGF9P9xtDCEeTrHJYVjIuZzDVIqAJeAp4M4C73w+8sdTJ7v7X9L9/Af6e/vevwBigX7ovIiLSV9QBxQsn1ZPMhVGWcpLxP0mWpqoDXjSzg8zs/STPaztkZkcC89PdbwOXAD9P1zkWEZGMDWxo/ySxX0/fdaldvwV+FUKYEkLYPISwG3AdXU+OtU45//SnALuTTLb9FeAa4GbgnC6uOx34aDrZxgnAgcAeFK2eISIi2VhbtC5ES0sul1DcFJwO/Jfk9d1XgL8Bz7ARk0qV88x4kbtPTbfnm9kWwAB377RlDEx097vN7D3ACnd/AMDMejQvtYiIVMauy17DR26xbgDXTsteB/TceGPFGJcDR6SvNo0EXooxbtRfNuUk44VmdjPwM+B2d18DrCnjuufN7CCSJRNnA5jZp4F5GxOgiIhUx43/+At/frKZP+wS+MC/5rHP2GaS2YSlHCGEDnuI25ZsjDGW9TpvOcn4LSSvAH0b+KmZXQf83N0f6uK6L5G8FvUacKCZTU3v8bFyAhMRkSp7fgl7PbmIvZ58DoCG1pEZB5Q7O6f/HQW8B/gTSff0BOB9JI90y1LOQhFPk6ySdEG64MNhwG/MbLm7dzhvmrvfSbKOMQBmNh8Yl7asRUQkY/133IK1jy1av7/dqAyjyZ8Y46EAIYSbgYMKl2wMIexPhZ8ZA+uWJdw3/QwgWfyh3Gt7vIaxiIhU2FoN2KqQfVi/ImCbO4Hry71Bl6Opzex/zewekme9ewDfBLZ298+XH2fP1jAWEZHKG3nOnuuyQGsdjDhnz2wDyq+5JG8eARBCqAfOBh4u9wbltIyPIhm89RF3f21jIxQRkU1T/eaDqN9iMC3/XQnD+9Nvq6FdXySlnAjcFEI4nWQJxQnAqyQzc5WlnGfGlfhT6cIK3ENERCpoxQ0xScRA3etrWH7dXEaepdbxxoox/jOE8CHgKsCAfwAnxhifKPceFV8vy8x2KlF8U2G5u8+t9NcVEZGN0/TQgnb7K++bj8ZTb7x0sNb1JKOn7yJZy+GeEMLhhYO6OlONxSv/3cXxVjacw1NERHpZ0x1Pt9tf9ZfnMook9y4ADosxrluWN03Q3wayScburtlNRURyoHV10XyYa1qyCST/3gjcUVR2B8n81GXpNBmb2fEkLzXf5e5lvcrUQTd1oVZ3bywzPhERqZZ+9UBBQm7I34svIYTzSJbsbQWujjF+v+j414FjSQZUAVwVY7ysk/IAXEkykcdC4BMxxlfp3BPAh4GbCso+QrKEYlk6TMZmdhZwMnAPMMPMtnX3H5RxT3VTi4jkQMOoQTQvXT8PU/2IfM1LHUJ4N8ncF28F+gNzQwi3xBhjwWlGklDvK7p8g/IQQh1Jt/IXYoy3hxC+BXyVrifvOINkNPUfgWeBbUhm5Cp+97hDnbWMPwW8y92fMLN3kvyl0GUyVje1iEg+jLx0Kos+kqzy1wqM/N57sg1oI8UY/xJCeE+McW0IYQJJTitexMiAM0IIbyBpXJ4WY2wqVQ68GVgeY7w9vfZC6HpMW4zxjyGE3YGPA1sB9wNfjDE+VW5dOkvGW7p727Ds+4Gty7mhmb3P3Web2QEdnePut5YboIiIVMfi429ft10HvPq5Oxl+5FuyC6gbYoxrQgjnkiTTXwPz246FEIYB/wS+TNJlPBM4O4TwzVLlwKPAwhDC1cCuQCNlLvsbY3yMrpcW7lBnyXjdwwN3bzGzcudN+z7J4hKXkfyxVfwQohXYbmOCFBGRymt9aWX7/VebMoqktGe3n9T1SUCM8eshhG+TvFp0PPDjtHwZsK5hGEK4CPhJjPHMUuUkM2ntA7wrxughhPNJctoxFahOp6oxmrrtz6rpJIkXkoTctr0mXcHpfndfVumvLyIitSGEsCMwKMb4cIxxRQjhtyTPj9uOTwKmxhh/khbVAWs6KicZsDUvxuhp+S+BG3qjLp0l48FmVjjJ9dCifdz9sE6uP55kLusXgRdIpgebADwHDAYGmNmH3P3ebkUuIiK1bjvg3BDCXiQNvgNJWrhtVgLfCSHcTbK04UnAjZ2U3wuMDiHsEmN8hGSE9N97oyKdDbb6BvBYwefCov3Hurj348BZ7j7R3fdw90kkffp3uPtYktFn3+/0DiIiIh2IMd4K3ELy/PfvwL0xxutCCLeGECzG+F/gMyTd15GkBXxRJ+UrgYOAq0IIj5GM1P5Sb9Slw5axu5/b0TEzayB5r6szBwJji8ouAV4imVT7xySzk4iISBYa6qC5YDhQ/l4zJsY4neSxaGHZAQXbvwF+U+K6jsofAN5e6Ti7slHPjM1sDMlfEycCI4BfdXL6YmA/YHZB2b7A0nR7EqBVoEREstJSNC5XyxtnpqxkbGZ7kAzv/hjgwNfpetHkrwE3mtkdwPMkyXcq8CkzmwzcTbI2soiIZEHJd5PR2QxcA4AjSWbh2prkPazlwMfc/eWubuzuv4wsVyIAABnjSURBVDWzf5O8BD0eeAj4ors/Y2YTgQPd/YGeV0FERCTfOmsZPw88DHwX+K27rzazozfm5umkIeeXKH8+vX+3mNkRwFkk059d7O6XFR2fAswAhpPMrHKiu681s2nAt0ieWwPc4u5ndjcOEZE8a6H9KF4tE5GdzkZTPwHsQPIge/veCadrZjaBZLmqvYApwAklFqe4FjjZ3XcgGZJwfNvlwKnuPiX9KBGLSM0qWrNJyThDHSZjd98b2D/d/bOZ3QsMA4b2RmCdmEqyitRid19O8kL2upHdZvYGYLC7358WzQQOTbd3B6aZ2b/M7FozG9WLcYuIbFKWDRrQbn9p0b70nk4HcLn748CpZvZVkoR3PPBYOihrlrv/uhdiLDYeWFCwv4D2w9BLHd+6YPt7JC92XwhcSvJcXESk5pz/oX1Y278fez/xLPe/aSItLa3cmHVQNaqs0dTuvhqYBcwysx2AE4AfkkzK3dvqaT8GsI72vSsdHnf3g9oKzew7QMkVNRobe7bcclNTU4/vkSe1Vl+ovTqrvn3TnO0m8sqWI7l5t52gtZUtFi/pdr0nT55c4ehqy0bPTZ0OyjrNzL5WhXjK8QKwd8H+WJIpNwuPjys+bmYjgGML1mSuA9aW+gI9/aFqbGysqR/MWqsv1F6dVd++qbV+MdSlM33U1dFSX18T9d4UdfZq03/o+i20LFZfuhOYbmajSV61OpikpQ6Auz9rZk1mtqe7/w34JHAbsAw43czuTV+pOhnUIyMitWvsq0tYtPlm6xLyVq8uBTSUJgudtYxPTv9bR7JyxSeqH07X3H2+mZ1JMmnIAGCGuz9oZrcC57i7kzwHvsrMhgP/AC5x92YzOwy4wswGk4wW36hXtURE+pLm+npY1bxuGszW+hzOh9lHdDY39S1t22a2pnA/a+4+i+QZdmHZAQXbj1BiblF3nwPsVvUARURyYPDaNTBofRoYtLbkkzvpBRVfz1hERPLhmc1HQUtL0k3d0sozo0ZmHVLNUjIWEalRrw8ZBPXpdBMNdSwdPCjbgGpYZzNwiYhIH1bfrw5a14/TbW3QyhFZ6Ww09UOsH029mZk9WHyOu/f6mo8iIlIZY4fX80LT+kFbWw1ryDCa2tZZN/WlvRaFiIj0uov3beCQW1qTZ8atrVz0HiXjrHQ2mvqa3gxERER618E79WP2oGaub2xh98HzOeqt22QdUs3qdACXmR0EDHD3X5nZ5sDVJCsl/R74krtrHLyISI69d7sG3rtdA42NK7MOpaZ1OIDLzI4lWRO4bZWmS4GJwKnAjsDZVY9ORESkBnQ2mvpzwEHu/hMzGwJ8DDjd3W8ETiKZZlJERER6qLNu6u3c/Z50++0kI6v/CuDuT5rZVtUOTkREpDMhhK8Dh6W7t8QYTy86HoArSSbdXgh8Isb4aghhHEnv73hgBXBkjPGZEIKzPjcOBt4ITIgxvlTNenTWMm42s7aVpvcBHkyXUqRgkQYREZFMhBCmAu8DdiUZz/S2EMJBBcfrSMY4fSvGuAvwT+Cr6eGfAzfHGHdNt78NEGO0GOOUGOMU4AHgnGonYui8ZXwPyVKJs4CjgEsKjp0B/KWagYmIiHRhAfClGONqgBBCIzCp4PhuwPIY4+3p/oXAyBDClsAuwHvT8p8Cfyq8cQhhv/ScT1Uv/PU6S8ZfBm4Hzgf+DPwIwMyeJhnUtVe1gxMREelIjPGxtu0QwvYk3dV7FpzyJmBhCOFqktZzI8l4qO2B54CLQgh7k3Rfn0x75wJnxhibq1eD9Trspnb3eSQVGevu+7V1UZM08Senx0VERKri7xPGbPApJYTwZuCPwJdjjIW5qR/JY9YrYoy7AU8D30/LdwXuijHuDtwEXFN0vy1jjH+oQrVK6vQ9Y3dvBf5bVHZ9VSMSEREpUwhhT+A3wCkxxuuKDi8E5sUYPd3/JXADSY/v0oJkO4v2j2I/CvyqelFvSAtFiIhILoUQJgK/A44okYgB7gVGhxB2Sfc/DPw9xvgU8EIIYf/C8oLr9gDmVCnskrSEooiI5NVpwCDg+8kbTEAyvukjJKOgPR1dfVUIYSjwAuvnyPgYcGUI4bvAEmBawX23S8/tNUrGIiKSSzHGLwBfKHHoRwXnPEAyV0bxtZHkeXKp++5UoRDLpm5qERGRjCkZi4iIZEzJWEREJGNKxiIiIhlTMhYREcmYkrGIiEjGlIxFREQypmQsIiKSMSVjERGRjOVyBi4zOwI4C+gPXOzulxUdnwLMAIaTrMt8oruvNbNJwLXAVkAEjnT3Zb0avIiISJHctYzNbAJwAcl6ylOAE8yseOqya4GT3X0HoA44Pi2/HLjc3XcEHDi70vHd8bl7efScpyt9WxER6cNyl4yBqcBd7r7Y3ZeTLId1SNtBM3sDMNjd70+LZgKHmll/4F3p+evKKxnYjO1v5PnbX2Lpw6uZsf2Nlby1iIj0YXnsph4PLCjYX0D7ScBLHd8a2BJY4u5ri8o30NjYWJFA75tzHyO3HFmRe23KmpqaKvZvlhe1VmfVt+/raZ0nT55cwWhqTx6TcT3QWrBfB7SUcby4nKLr1unuD9XfeLzd/vLHlrPHiXt061550tjYWHP/I9ZanVXfvq8W67wpyWM39QvAuIL9scCLZRx/GRhhZg1p+bii6ypuQP8B1by9iIj0EXlMxncC+5nZaDMbAhwM3N520N2fBZrMbM+06JPAbe6+BpgDfDwtPxq4rZqBhj1C1yeJiEjNy10ydvf5wJnA3cDDwCx3f9DMbjUzS087EviBmT0ODAMuScs/SzL6ei6wN8nrUVVz8+H3VvP2IiLSR+TxmTHuPguYVVR2QMH2I7Qf1NVW/iywT7XjW2dFr30lERHJsdy1jEVERPoaJeNqmpp1ACIikgdKxtXU0PUpIiIiSsbV5FkHICIieZDLAVx5MfB9WUcgItL3hRCGA/cCH4oxPlNQPoVk6uM2o4FXgX2B2QXlI4DRMcZhIYRRwC+ACcAq4IQY48NVrQBKxlXVWnJ+LxERqZQQwjuAq4Adio+lSXRKet4Q4EHgxBjjywXl9cCfSF6ZBTgV+FeM8YAQwoeBS0kWJqoqdVNX0fjxm2UdgohIX3c8cBJdz6j4NeAvMca/FpV/ClgRY2x7XbYBaPvlPRRYWalAO6OWcSW1tkJd3brtJa8tzTYeEZEcu2101wvtxBiPAwih4xkPQwgjgBOAnYvKG0haxAcWFH8PuD+E8CIwHHjvxsbdHWoZV9Ebtp+UdQgiIgJHAb9Lu6cLfQCYF2P8V0HZpcClMcbxJIn4VyGEYdUOUMm4goYuW71ue/CKNYwaNyrDaEREJPVR4Loyyw8EfgIQY7wPeAmo+nJWSsYVNOalFYxZuIwxLy1n/ILltLYUr9goIiK9KYRQB7wNuK/E4T1IFhAq9AhJkiaEsD0wHniimjGCknFFrdysjrHLljJ26RJWDYVhY6resyEiIkVCCLeGENoWDhoNrI4xNpU4dTuSZXcLTQOODSH8m6TVPC3G+Hr1ok1oAFcFTV760rq/brZcvoSBgwdmGo+ISK2IMW5TsH1AwfbLJOval7pmSImyeSTvIfcqtYwrqK5o/7V5xWMFRERENqRkXEF1Rdsj3jQ6q1BERCRHlIyraOTErt+RExERUTKupN23Wr/9hqHU1Rd3XIuIiGxIybiCJs05moHvmUTzrqOY+OgJWYcjIiI5odHUFfTc8ItgdSsNwPOjfsA2zV/NOiQREckBtYwraXXBJB8t0Ly6ObtYREQkN5SMq6ilRWsoiohI15SMq2j+DY9kHYKIiOSAknEFra2r4+bJ2/PbtwRW1dfDfXOzDklERHJAA7gq6KsH7Mtv3pos7nHrjm9ixh23ZByRiIjkgVrGFXTn9tuu2777jW9g/nYZBiMiIrmhlnEFHfrIXA791+P0b27m57vtzCsDNOmHiIh0LXfJ2MwmAdcCWwERONLdlxWdMwC4GjBgJXCEuz9uZv2BRcDTBae/zd0r8g7S1+6+d11Xw1l/+ivz31KJu4qISF+Xx27qy4HL3X1HwIGzS5zzeWC5u08GTgFmpuVvBe5z9ykFn4q9DLzBQhHzKnVnERHpy3KVjNOW7buAG9KimcChJU79IPALAHe/Bxidtqh3T7fdzO43s3dXNeBVVb27iIj0EXnrpt4SWOLua9P9BcDWJc4bnx6j6LxW4HfAN4G3ALeZ2Vvc/ZXCixsbG7sV3KCi/TU9uFeeNDU11UQ9C9VanVXfvq+ndZ48eXIFo6k9m2wyNrNDgR8UFc8jSaiFSk1zVV90Xh3Q4u5XFpT908weAPYEbiq8uLs/VP9pfxu2ALapgR/QxsbGmvsfsdbqrPr2fbVY503JJpuM3f3XwK8Ly9oGYJlZQ/qsdxzwYonLX0iPPZXujwVeNLNPAve6e1t5HUkDtiL+tON2nHb4/jTX1/ONG2azNZFtKnVzERHps3L1zNjd1wBzgI+nRUcDt5U49db0GGa2F9Dk7s8BuwBfSssDsGt6v4o45+CpvD5kMMsGDeSMQ9/P7VtsVqlbi4hIH5arZJz6LHCCmc0F9gbOAjCzE83svPScHwIDzewx4BLgk2n5ecBWZvZvkkFgR7v70koFVt/S2m579fCBlbq1iIj0YZtsN3VH3P1ZYJ8S5T8q2G4CppU4ZwlwSLViu+A3sznjkPezpqGe8357J38b+krXF4mISI+EEI4gaZj1By6OMV5WdHw34EpgAPA8cFSM8bUQwiiSN28mkLz/ckKM8eEQwlDgR8DbSOaqmB5jvLmadchjy3iTtce85/jbhT/mwfN/xPsee5KdFmUdkYhI3xZCmABcAOwFTAFOCCHsVHTa/wHnxBh3IZks6rS0/FTgX2n5+cClafnXgLUkb928D7go/TpVo2RcQcsGr++WXjJ4IE8MyDAYEZHaMBW4K8a4OMa4nOQRZHEPaAMwPN0eQtLabStvG9wztKB8V+CGGGNLjHER8DDw/irFD+Swm3pT1q/gmfFmTat5d6mXrkREpJJKzSvx9qJzTgVmhxAuBpYD70jLvwfcH0J4kSRZvzct/wdwaAjhDmAMySuwD1cn/IRaxhV0147rV236405vZOIOahqLiHTb5oM2/Gyo5LwSbTshhMEkaxVMjTGOI5lS+Wfp4UuBS2OM40kS8a9CCMNIJoZaBfwTuBi4A1hd0bqVqIRUyKjlKzjtsA/w5UPfTyuwxXZVfcQgIiLr55VoM5b280+8BVgZY3ww3b+S9YOADwR+AhBjvA94CZgMjADOiDHuHGM8lKR1/BRVpG7qCnrXvOd497zngOTPtLqjintKRESkwu4EpocQRpN0QR8MnFBw/ElgYgghxBgjSQJ+KD32CPBR4NoQwvYkXd5PkMxTsSNwUgjhrcBu6depGrWMqylsnnUEIiJ9WoxxPnAmcDfJc91ZMcYHQwi3hhAsxvgqcAxwfQjhUeBY4FPp5dOAY0MI/wauA6bFGF8HrgLGhBD+RbIg0WExxorNSVGKWsYVVLyEIq11HZwpIiKVEmOcBcwqKjugYPs2SszWGGOcB+xboryJKs5JUYpaxtW02eCsIxARkRxQMq6mloqtQSEiIn2YknE1vbg86whERCQHlIyraXTJd+JERETaUTKuptkx6whERCQHlIyracnirCMQEZEcUDKuooFnfCTrEEREJAeUjKto3A7juj5JRERqnpJxBY26+gNAMhXmZuftlW0wIiKSG5qBq4JGHDuFEcdOobGxkW0nT846HBERyQm1jEVERDKmZCwiIpIxJWMREZGMKRmLiIhkTMlYREQkY0rGIiIiGVMyFhERyZiSsYiISMZyN+mHmU0CrgW2AiJwpLsv6+Dc9wJfdff90v064LvAh4AW4Hh3/1uvBC4iItKBPLaMLwcud/cdAQfOLj7BzOrN7EvAdUBDwaGDgcnATsBHgZlmlrs/SEREpG/JVTI2s/7Au4Ab0qKZwKElTp2cfo4vKv8gcJ27t7j7E8BzwDurE62IiEh58tYq3BJY4u5r0/0FwNbFJ7n7Y8BxZrZP0aHx6TVtSl7f2NjYoyCbmpp6fI88qbX6Qu3VWfXt+3pa58kZzscfQjgCOAvoD1wcY7ys6PgUYAYwHLgHODHGuDaEsMFjzxjjshDCSOAXwHbAf4HDYowLq1mHTTYZm9mhwA+KiueRLIpUqGUjbltfdH1dqet7+kPV2NiY6Q9mb6u1+kLt1Vn17fvyWucQwgTgAuBtwCrg3hDC3THGuQWnXQscF2O8P4RwNUmv6RWkjz1jjNeFEM4meez5FeAbwJwY4wdDCJ8E/g/4eDXrscl2U7v7r91968IP8D5ghJm1PQceB7y4Ebd9Ib2mzdiNvF5ERDYtU4G7YoyLY4zLSR5jHtJ2MITwBmBwjPH+tGgmcGgIobPHnh8kaRkD/BLYPz2/ajbZlnEp7r7GzOaQ/IUyCzgauG0jbnErcKyZ/RLYFtgBeKj4JDOrQLQiIjWl1d3rKnrD0/qVc79Sjx/f3sXxrUkfe8YYSz32XHdN2p29BBhNFRtvuUrGqc8C15jZWSQDsA4HMLMTgfHufk4n194AvAN4NN3/tLuvLDyh0j9MIiJSVV09fuzoeHE5BdcV54GSjzQrKXfJ2N2fBfYpUf6jEmV/LjzX3VuB09KPiIjk3wvA3gX7xY8fO3o8+TIwIoTQEGNspv1jz/npeS+EEPoBmwGLqhN+YpN9ZiwiIlKGO4H9QgijQwhDSOaTuL3tYIzxWaAphLBnWvRJ4LYY4xqg7bEntH/seWu6T3p8Tnp+1eSuZbwpMbN2w+nd/bKi4xsMpy94LSt3yqjvgcC5JF06/wE+5e6v9nqgFdJVfQvO+yBwqbtv25vxVUMZ3+MAXAmMAhYCn+jL32Mz242kvgOA54Gj3P21Xg+0gsxsOHAv8CF3f6boWO5+Z8UY54cQzgTuJvk+zYgxPhhCuBU4J8bowJHAVSGE4cA/gEvSyz8LXBNCaPfYk2RU9cwQwmPAa+n1VVXX2lrcZS7lMLMJwF8pGE4PHO7ucwvO+TdwnLvfb2ZXA+7uV2QScA91Vd/0f/DHgd3dfb6ZnQeMcPcvZBVzT5Tz/U3PGwP8GRjs7tv0cpgVVcb3uI7ke/wFd7/dzL4F1Ln7V7KKuSfK/H94DnChu99mZhcBK939rEwCrgAzewdwFbAjsEOJZNxnfmfljbqpu28qcJe7L3b3DYbTm9kbSH5BtxtO3+tRVk6n9SVpWZzk7vPT/UeBSb0cYyV1Vd82M0h6A/qCruq8G7Dc3du6AC8ESvYW5EQ53+MGklYiwBBgJfl2PHASJUYF98HfWbmiburu6+5w+rzqtL7uvgi4EcDMBgNfBX7YmwFWWFffX8zs8yRdXvfTN3RV5zcBC9MW065AI/C53guv4rr8HgOnArPN7GJgOcnbGLnl7sdBh69v9rXfWbmilnH3dXc4fV6VVR8zGwHcAjzi7tf0UmzV0Gl9zewtJANFzu/luKqpq+9xP5K3E65w992Ap4Hv91p0ldfV93gwcDUw1d3HkczW9LNejbB39bXfWbmiZNx9Xc3m1ddm++qyPmY2jmR04qPAcb0XWlV0Vd9D0+NOMvJyfPp8Mc+6qvNCYJ67e7r/SzZsSeZJV/V9C8kz4gfT/Ssp8VplH9LXfmflipJx990J7Gdmo81sg+H06fvQTWbWbjh974dZMZ3WN52i9Gbgenc/JX2nO8+6+v5+3d13cPcpwAHAi+6+dwf3yotO60wywGm0me2S7n8Y+Hsvx1hJXdX3SWBiOoIc4EBKzNjXV/TB31m5omTcTelApbbh9A8Ds9z9QTO71dY/kDkS+IGZPQ4MY/1w+twpo74fIRngc4iZPZx+ZmQYco+U+f3tU7qqczpb3UHAVWb2GLAv8KXsIu6ZMur7KnAMcL2ZPQocC3wqs4CrpK/+zsobvdokIiKSMbWMRUREMqZkLCIikjElYxERkYwpGYuIiGRMyVhERCRjSsYivczMHjOzD6TbrelsXu22N+Jee5vZM1UIU0R6keamFull7v7mCt5rDrBNpe4nItlQMhapEjP7GPAtYAvgdyTL1l0FTAdOdvc/bMS9ppLMAz2JZF3db7v7tWa2D3CDu29pZrcBhbOADQIecvc9zGxz4P+A9wMrgB+l99BEAyKbAHVTi1SBme0AXAt8gWSO36eAd/bglj8FznX3kek9r0jXkF7H3fd392HuPgzYC1gKtK01/HOSRQC2JZlf+SiS2aVEZBOgZCxSHZ8AZrv7be6+BvgmPZt0/3XgCDPbF/grMMLdl5Q60czGAr8HvuLu96T7+wNfdPfl6YLy3yVZ21ZENgHqphapjvEk3ckAuHurmT3fyfnrmNkZwBkF1w4jmfv7POA6YDDwYzP7aolrBwE3ATe7+4/T4kkky+E9VTCtdj2weCPrJCJVomQsUh3PU7C8oJnVARPKudDdLwQuLLh2IPBGdz8qvc//kDyDfohkWcNCPwGWk3Rlt1kArAXGuPuq9J6jgM02sk4iUiXqphapjlnAVDN7v5n1A04Btu7mvVqB68ysbY3oF9OyRYUnmdk5JH8AHOrua9vK3f15knWmv21mg9PBXDcAF3QzHhGpMCVjkSpw9/+QLLd3JfAyMBl4DljdjXutJllr97PAEuA+4BJ3/2PRqdOBrYAnzWxZ2yc9djgwBngGmEeS0E/a2FhEpDq0hKJIFZjZJGCYu88tKHsJ+KS7z84uMhHZFOmZsUh1jAduMrP/AZ4FTgAGAvdnGpWIbJLUTS1SBe5+P8mEH38heS3pWODDHb2OJCK1Td3UIiIiGVPLWEREJGNKxiIiIhlTMhYREcmYkrGIiEjGlIxFREQypmQsIiKSsf8Ho+dGTY2PkB4AAAAASUVORK5CYII=\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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\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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\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 df.columns:\n",
" shap.dependence_plot(name, np.array(shap_values)[1, :, :], df)"
]
},
{
"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=section805></a>\n",
"### 8.5 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": 163,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x684 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_1, df)"
]
},
{
"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 for **spore-print-color** having **values \"green\"** and **\"orange\"**; and **odor** having **values \"foul\"** and **\"pungent\"** the **probability** of **mushroom** being **poisonous** is **higher**."
]
},
{
"cell_type": "code",
"execution_count": 164,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"19"
]
},
"execution_count": 164,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"list(df.columns).index('spore-print-color')"
]
},
{
"cell_type": "code",
"execution_count": 165,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0 black\n",
"1 brown\n",
"2 buff\n",
"3 chocolate\n",
"4 green\n",
"5 orange\n",
"6 purple\n",
"7 white\n",
"8 yellow\n"
]
}
],
"source": [
"for k, v in enumerate(categorical_names[19]):\n",
" print(k, v)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id=section806></a>\n",
"### 8.6 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": 166,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x684 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"shap.summary_plot(shap_values_1, df_display, plot_type=\"bar\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- Here we can see that the **spore-print-color** column has the **highest feature importance** followed by **odor** column.\n",
"\n",
"\n",
"- This implies that the **spore print color** and **odor** of the **mushroom** are the **key indicators** in **identifying whether** a **mushroom** is **edible or poisonous**."
]
}
],
"metadata": {
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"kernelspec": {
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"language": "python",
"name": "python3"
},
"language_info": {
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"toc": {
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"title_cell": "Table of Contents",
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},
"varInspector": {
"cols": {
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"lenType": 16,
"lenVar": 40
},
"kernels_config": {
"python": {
"delete_cmd_postfix": "",
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"library": "var_list.py",
"varRefreshCmd": "print(var_dic_list())"
},
"r": {
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"delete_cmd_prefix": "rm(",
"library": "var_list.r",
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},
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