notes/XAI3_SHAP.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "31c27dde",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from imblearn.over_sampling import RandomOverSampler\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import f1_score, accuracy_score\n",
    "import shap"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e1018b1",
   "metadata": {},
   "source": [
    "# **Notes from youtube video**: [link to video](https://www.youtube.com/watch?v=9haIOplEIGM)\n",
    "\n",
    "Types of **SHAP** techniques (not really **model-agnostic** anymore but boosts performance of specific model):\n",
    "- **KernelSHAP**\n",
    "- **TreeSHAP**\n",
    "- **DeepSHAP**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d3ca0ed",
   "metadata": {},
   "source": [
    "# SHAP\n",
    "**SHapley Additive exPlanations** <br>\n",
    "SHAP comes from **Cooperative Game Theory**<br> <br>\n",
    "- Imagine a game with $x$ players, all of them forming a **coalition**\n",
    "- When the game is over the gain a certain payout compared with their contributions\n",
    "- What is a fair distribution? $\\rightarrow$ **Shapley values**\n",
    "![alt text](../images/shap_ex.png) <br>\n",
    "- Players $\\rightarrow$ **Features**\n",
    "- Payout $\\rightarrow$ **Model Prediction**\n",
    "- Game $\\rightarrow$ **ML Blackbox model** <br> <br>\n",
    "**The Main idea** : $\\rightarrow$ How the **prediction** (payout) would perform in different **Coalition** variations. (with or without some players) (every possible subset), then \n",
    "1. We calculate each players contribution for each subset (**coalition**)\n",
    "2. **Averaging** over all this contributions that gives us $\\rightarrow$ **Marginal Contribution** of a player to the team <br> <br>\n",
    "---\n",
    "### The paper (A Unified Approach to Interpreting Model Predictions)\n",
    "The Explainable AI technique **SHAP** simply makes use of these **shapley values** <br>\n",
    "- In general, it is a **local explanation technique** \n",
    "- **AIM:** to explain individual predicitons of black box models\n",
    "- However, it is possible to get **global explanations** through aggregating over these individual predictions<br><br>\n",
    "\n",
    "---\n",
    "\n",
    "### Calculating shapley values (SHAP)\n",
    "\n",
    "$\\phi_i \\rightarrow$ Shapley value for feature $i$ **(e.g.: {Age})**<br>\n",
    "$f \\rightarrow$ Black Box Model<br>\n",
    "$x \\rightarrow$ Input data point<br>\n",
    "$z' \\rightarrow$ Subset **(e.g.: {Age, BMI})**<br>\n",
    "$x' \\rightarrow$ Simplified data input<br>\n",
    "Using a **mapping function** we transform $x \\rightarrow x'$ <br>\n",
    "$z'\\subseteq x' \\rightarrow$ Iterate over all possible combinations of features<br>\n",
    "$f_x(z') \\rightarrow$ Black Box Model output for our input **with** the feature(s) we are interested in **(e.g.:{Age, BMI})**<br>\n",
    "$f_x(z' \\backslash i) \\rightarrow$ Black Box Model output for our input **without** the feature(s) we are interested in **(e.g.:{BMI})**<br>\n",
    "$[f_x(z') - f_x(z' \\backslash i)] \\rightarrow$ Tells us how feature $i$ contributed to that subset. Also called the **marginal value**<br>\n",
    "$M \\rightarrow$ Total number of features<br>\n",
    "$\\frac{|z'|!(M - |z'| - 1)!}{M!} \\rightarrow$ Weighting according to how many players are in that correlation<br><br>\n",
    "$$\\phi_i(f,x) = \\sum_{z'\\subseteq x'} \\frac{|z'|!(M - |z'| - 1)!}{M!} [f_x(z') - f_x(z' \\backslash i)]$$\n",
    "\n",
    "\n",
    "---\n",
    "### Calculating shapley values (Game theory)\n",
    "\n",
    "Shapley values are calculated by getting all the possible combinations of groups of players playing in the game, ...\n",
    "\n",
    "\n",
    "---\n",
    "\n",
    "$\\phi_j \\rightarrow$ Shapley value for player $j$ **(e.g.: {Player 1})**<br>\n",
    "$val \\rightarrow$ Funcition that outputs each players contribution given a particular group (coalition) of players<br>\n",
    "$S \\rightarrow$ Subset of players in the game<br>\n",
    "$p \\rightarrow$ Number of Players<br>\n",
    "$|S| \\rightarrow$ Number of present players in coalition<br>\n",
    "$|S| \\rightarrow$ Number of present players in coalition<br>\n",
    "\n",
    "\n",
    "$$\\phi_j(val) = \\sum_{S'\\subseteq \\{1,...,p\\} \\text{\\ } \\{j\\}} \\frac{|S|!(p - |S| - 1)!}{p!} (val(S \\cup \\{j\\}) - val(S))$$\n",
    "<br>\n",
    "\n",
    "<br>\n",
    "\n",
    "---\n",
    "\n",
    "### How do we exclude a feature from a ML model?\n",
    "![alt text](../images/shap_ex2.png)\n",
    "- For the features we want to exclude we just **input random values from the train data set** and do this for all subsets\n",
    "- A random feature has usually no predictive power\n",
    "![alt text](../images/shap_ex3.png)\n",
    "<br>\n",
    "\n",
    "---\n",
    "\n",
    "### Complexity of calculating Shapley Values\n",
    "- Where $n$ is the number of features <br>\n",
    "$$2^n = \\text{total number of subsets of a set}$$\n",
    "- For $10$ features:\n",
    "$$2^{10} = 1024$$ <br>\n",
    "\n",
    "\n",
    "> The basic idea is (as presented in the SHAP paper) that we can **simply approximate** the shapley values instead of calculating all possible combinations\n",
    ">> **KernelSHAP** for instance samples feature subsets and fits a linear regression model based on these samples. <br>\n",
    ">> The Variables in this Linear Regression model are simply: if a feature is present or absent. <br>\n",
    ">> After the training the coefficients $\\beta_n$ can be interpreted as approximated **shapley values**. <br>\n",
    ">> $$Y=x_1\\beta_1+x_2\\beta_2+...$$ <br>\n",
    ">> So we weight our samples based on how much information they contain\n",
    "\n",
    "                                                          "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73862181",
   "metadata": {},
   "source": [
    "# Load and preprocess data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3f5e83a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "path = \"src/healthcare-dataset-stroke-data.csv\"\n",
    "stroke_ds = pd.read_csv(path)\n",
    "#Preprocess\n",
    "categorical_cols = [\"gender\",\n",
    "                    \"ever_married\",\n",
    "                    \"work_type\",\n",
    "                    \"Residence_type\",\n",
    "                    \"smoking_status\"]\n",
    "encoded = pd.get_dummies(stroke_ds[categorical_cols], \n",
    "                        prefix=categorical_cols)\n",
    "\n",
    "# Update data with new columns\n",
    "stroke_ds = pd.concat([encoded, stroke_ds], axis=1)\n",
    "stroke_ds.drop(categorical_cols, axis=1, inplace=True)\n",
    "\n",
    "# Impute missing values of BMI\n",
    "stroke_ds.bmi = stroke_ds.bmi.fillna(0)\n",
    "        \n",
    "# Drop id as it is not relevant\n",
    "stroke_ds.drop([\"id\"], axis=1, inplace=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ac37b2b",
   "metadata": {},
   "source": [
    "# Split the Data for evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9ddc239c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_data_split(ds):\n",
    "    X = ds.iloc[:,:-1]\n",
    "    y = ds.iloc[:,-1]\n",
    "    return train_test_split(X, y, test_size=0.20, random_state=2021)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1e727dc8",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = get_data_split(stroke_ds)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8374909",
   "metadata": {},
   "source": [
    "# Oversample the train data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "383f353f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def oversample(X_train, y_train):\n",
    "        oversample = RandomOverSampler(sampling_strategy='minority')\n",
    "        # Convert to numpy and oversample\n",
    "        x_np = X_train.to_numpy()\n",
    "        y_np = y_train.to_numpy()\n",
    "        x_np, y_np = oversample.fit_resample(x_np, y_np)\n",
    "        # Convert back to pandas\n",
    "        x_over = pd.DataFrame(x_np, columns=X_train.columns)\n",
    "        y_over = pd.Series(y_np, name=y_train.name)\n",
    "        return x_over, y_over"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "30dbe726",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(7778, 21)\n",
      "(1022, 21)\n"
     ]
    }
   ],
   "source": [
    "X_train, y_train = oversample(X_train, y_train)\n",
    "print(X_train.shape)\n",
    "print(X_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d5682e3",
   "metadata": {},
   "source": [
    "# Fit Black box model\n",
    "- Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "293f1f3b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Score 0.5322285353535354\n",
      "Accuracy 0.9432485322896281\n"
     ]
    }
   ],
   "source": [
    "rf = RandomForestClassifier()\n",
    "rf.fit(X_train, y_train)\n",
    "y_pred = rf.predict(X_test)\n",
    "print(f\"F1 Score {f1_score(y_test, y_pred, average='macro')}\")\n",
    "print(f\"Accuracy {accuracy_score(y_test, y_pred)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ae1dfbd",
   "metadata": {},
   "source": [
    "# Create SHAP Explainer\n",
    "- Computational Complexity of **TreeSHAP:**\n",
    "    - O(# Trees $*$ Max (# Leaves) $*$ Max (Depth) ^2 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5c564e63",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gender_Female</th>\n",
       "      <th>gender_Male</th>\n",
       "      <th>gender_Other</th>\n",
       "      <th>ever_married_No</th>\n",
       "      <th>ever_married_Yes</th>\n",
       "      <th>work_type_Govt_job</th>\n",
       "      <th>work_type_Never_worked</th>\n",
       "      <th>work_type_Private</th>\n",
       "      <th>work_type_Self-employed</th>\n",
       "      <th>work_type_children</th>\n",
       "      <th>...</th>\n",
       "      <th>Residence_type_Urban</th>\n",
       "      <th>smoking_status_Unknown</th>\n",
       "      <th>smoking_status_formerly smoked</th>\n",
       "      <th>smoking_status_never smoked</th>\n",
       "      <th>smoking_status_smokes</th>\n",
       "      <th>age</th>\n",
       "      <th>hypertension</th>\n",
       "      <th>heart_disease</th>\n",
       "      <th>avg_glucose_level</th>\n",
       "      <th>bmi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>91.13</td>\n",
       "      <td>33.9</td>\n",
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      "text/plain": [
       "      gender_Female  gender_Male  gender_Other  ever_married_No  \\\n",
       "2813              0            1             0                0   \n",
       "\n",
       "      ever_married_Yes  work_type_Govt_job  work_type_Never_worked  \\\n",
       "2813                 1                   0                       0   \n",
       "\n",
       "      work_type_Private  work_type_Self-employed  work_type_children  ...  \\\n",
       "2813                  1                        0                   0  ...   \n",
       "\n",
       "      Residence_type_Urban  smoking_status_Unknown  \\\n",
       "2813                     1                       0   \n",
       "\n",
       "      smoking_status_formerly smoked  smoking_status_never smoked  \\\n",
       "2813                               0                            1   \n",
       "\n",
       "      smoking_status_smokes   age  hypertension  heart_disease  \\\n",
       "2813                      0  43.0             0              0   \n",
       "\n",
       "      avg_glucose_level   bmi  \n",
       "2813              91.13  33.9  \n",
       "\n",
       "[1 rows x 21 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# computes polinomial time the shapley values,\n",
    "# not exponential anymore\n",
    "explainer = shap.TreeExplainer(rf)\n",
    "# Calculate shapley values for test data\n",
    "start_index = 1\n",
    "end_index = 2\n",
    "shap_values = explainer.shap_values(X_test[start_index:end_index])\n",
    "X_test[start_index:end_index]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff220580",
   "metadata": {},
   "source": [
    "# Inspecting the Shapley values (Classification problem)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a1a7f5ec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 21)\n"
     ]
    },
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       "         -6.16126975e-03, -3.90635326e-03, -6.24913628e-04,\n",
       "         -1.78154016e-05,  2.19216437e-03,  7.65596199e-03,\n",
       "         -1.57619831e-03,  6.49201488e-03,  7.96779323e-03,\n",
       "         -2.18434321e-04,  1.49611483e-02,  1.75175524e-02,\n",
       "          4.91063049e-03,  2.27943091e-01,  1.24926154e-02,\n",
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       "          2.18434321e-04, -1.49611483e-02, -1.75175524e-02,\n",
       "         -4.91063049e-03, -2.27943091e-01, -1.24926154e-02,\n",
       "         -9.83709988e-03, -9.77546473e-02, -8.44876311e-02]])]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# %% Investigating the values (classification problem)\n",
    "# class 0 = contribution to class 1\n",
    "# class 1 = contribution to class 2\n",
    "print(shap_values[0].shape)\n",
    "shap_values\n",
    "# We have an array of size 2 ([class0(no_stroke)], \n",
    "# [class1(stroke)])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f19a52d",
   "metadata": {},
   "source": [
    "# Visualize local predicitons\n",
    "- **Base Value:** that value is calculated as the average model output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "daeaeea6",
   "metadata": {},
   "outputs": [
    {
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n.childContextTypes?u(\"107\",this.getName()||\"ReactCompositeComponent\"):void 0;for(var i in e)i in n.childContextTypes?void 0:u(\"108\",this.getName()||\"ReactCompositeComponent\",i);return c({},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?v.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?u(\"136\",this.getName()||\"ReactCompositeComponent\"):void 0;var a,c=!1;this._context===i?a=o.context:(a=this._processContext(i),c=!0);var s=e.props,l=n.props;e!==n&&(c=!0),c&&o.componentWillReceiveProps&&o.componentWillReceiveProps(l,a);var f=this._processPendingState(l,a),p=!0;this._pendingForceUpdate||(o.shouldComponentUpdate?p=o.shouldComponentUpdate(l,f,a):this._compositeType===_.PureClass&&(p=!m(s,l)||!m(o.state,f))),this._updateBatchNumber=null,p?(this._pendingForceUpdate=!1,this._performComponentUpdate(n,l,f,a,t,i)):(this._currentElement=n,this._context=i,o.props=l,o.state=f,o.context=a)},_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=c({},i?r[0]:n.state),a=i?1:0;a<r.length;a++){var u=r[a];c(o,\"function\"==typeof u?u.call(n,o,t,e):u)}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(y(r,i))v.receiveComponent(n,i,t,this._processChildContext(e));else{var a=v.getHostNode(n);v.unmountComponent(n,!1);var u=d.getType(i);this._renderedNodeType=u;var c=this._instantiateReactComponent(i,u!==d.EMPTY);this._renderedComponent=c;var s=v.mountComponent(c,t,this._hostParent,this._hostContainerInfo,this._processChildContext(e),o);this._replaceNodeWithMarkup(a,s,n)}},_replaceNodeWithMarkup:function(t,e,n){l.replaceNodeWithMarkup(t,e,n)},_renderValidatedComponentWithoutOwnerOrContext:function(){var t,e=this._instance;return t=e.render()},_renderValidatedComponent:function(){var t;if(this._compositeType!==_.StatelessFunctional){f.current=this;try{t=this._renderValidatedComponentWithoutOwnerOrContext()}finally{f.current=null}}else t=this._renderValidatedComponentWithoutOwnerOrContext();return null===t||t===!1||s.isValidElement(t)?void 0:u(\"109\",this.getName()||\"ReactCompositeComponent\"),t},attachRef:function(t,e){var n=this.getPublicInstance();null==n?u(\"110\"):void 0;var r=e.getPublicInstance(),i=n.refs===g?n.refs={}:n.refs;i[t]=r},detachRef:function(t){var e=this.getPublicInstance().refs;delete e[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===_.StatelessFunctional?null:t},_instantiateReactComponent:null};t.exports=x},function(t,e,n){\"use strict\";var r=n(4),i=n(358),o=n(163),a=n(24),u=n(11),c=n(371),s=n(387),l=n(167),f=n(395);n(1);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&&(G[t._tag]&&(null!=e.children||null!=e.dangerouslySetInnerHTML?v(\"137\",t._tag,t._currentElement._owner?\" Check the render method of \"+t._currentElement._owner.getName()+\".\":\"\"):void 0),null!=e.dangerouslySetInnerHTML&&(null!=e.children?v(\"60\"):void 0,\"object\"==typeof e.dangerouslySetInnerHTML&&V in e.dangerouslySetInnerHTML?void 0:v(\"61\")),null!=e.style&&\"object\"!=typeof e.style?v(\"62\",r(t)):void 0)}function o(t,e,n,r){if(!(r instanceof I)){var i=t._hostContainerInfo,o=i._node&&i._node.nodeType===H,u=o?i._node:i._ownerDocument;F(e,u),r.getReactMountReady().enqueue(a,{inst:t,registrationName:e,listener:n})}}function a(){var t=this;C.putListener(t.inst,t.registrationName,t.listener)}function u(){var t=this;S.postMountWrapper(t)}function c(){var t=this;A.postMountWrapper(t)}function s(){var t=this;P.postMountWrapper(t)}function l(){var t=this;t._rootNodeID?void 0:v(\"63\");var e=U(t);switch(e?void 0:v(\"64\"),t._tag){case\"iframe\":case\"object\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topLoad\",\"load\",e)];break;case\"video\":case\"audio\":t._wrapperState.listeners=[];for(var n in q)q.hasOwnProperty(n)&&t._wrapperState.listeners.push(k.trapBubbledEvent(n,q[n],e));break;case\"source\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topError\",\"error\",e)];break;case\"img\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topError\",\"error\",e),k.trapBubbledEvent(\"topLoad\",\"load\",e)];break;case\"form\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topReset\",\"reset\",e),k.trapBubbledEvent(\"topSubmit\",\"submit\",e)];break;case\"input\":case\"select\":case\"textarea\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topInvalid\",\"invalid\",e)]}}function f(){N.postUpdateWrapper(this)}function p(t){Z.call(X,t)||($.test(t)?void 0:v(\"65\",t),X[t]=!0)}function h(t,e){return t.indexOf(\"-\")>=0||null!=e.is}function d(t){var e=t.type;p(e),this._currentElement=t,this._tag=e.toLowerCase(),this._namespaceURI=null,this._renderedChildren=null,this._previousStyle=null,this._previousStyleCopy=null,this._hostNode=null,this._hostParent=null,this._rootNodeID=0,this._domID=0,this._hostContainerInfo=null,this._wrapperState=null,this._topLevelWrapper=null,this._flags=0}var v=n(2),g=n(3),m=n(332),y=n(334),_=n(20),b=n(82),x=n(21),w=n(156),C=n(22),M=n(83),k=n(51),E=n(157),T=n(4),S=n(351),P=n(352),N=n(158),A=n(355),O=(n(9),n(364)),I=n(369),D=(n(8),n(54)),R=(n(0),n(94),n(80),n(96),n(1),E),L=C.deleteListener,U=T.getNodeFromInstance,F=k.listenTo,j=M.registrationNameModules,B={string:!0,number:!0},W=\"style\",V=\"__html\",z={children:null,dangerouslySetInnerHTML:null,suppressContentEditableWarning:null},H=11,q={topAbort:\"abort\",topCanPlay:\"canplay\",topCanPlayThrough:\"canplaythrough\",topDurationChange:\"durationchange\",topEmptied:\"emptied\",topEncrypted:\"encrypted\",topEnded:\"ended\",topError:\"error\",topLoadedData:\"loadeddata\",topLoadedMetadata:\"loadedmetadata\",topLoadStart:\"loadstart\",topPause:\"pause\",topPlay:\"play\",topPlaying:\"playing\",topProgress:\"progress\",topRateChange:\"ratechange\",topSeeked:\"seeked\",topSeeking:\"seeking\",topStalled:\"stalled\",topSuspend:\"suspend\",topTimeUpdate:\"timeupdate\",topVolumeChange:\"volumechange\",topWaiting:\"waiting\"},Y={area:!0,base:!0,br:!0,col:!0,embed:!0,hr:!0,img:!0,input:!0,keygen:!0,link:!0,meta:!0,param:!0,source:!0,track:!0,wbr:!0},K={listing:!0,pre:!0,textarea:!0},G=g({menuitem:!0},Y),$=/^[a-zA-Z][a-zA-Z:_\\.\\-\\d]*$/,X={},Z={}.hasOwnProperty,Q=1;d.displayName=\"ReactDOMComponent\",d.Mixin={mountComponent:function(t,e,n,r){this._rootNodeID=Q++,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(l,this);break;case\"input\":S.mountWrapper(this,o,e),o=S.getHostProps(this,o),t.getReactMountReady().enqueue(l,this);break;case\"option\":P.mountWrapper(this,o,e),o=P.getHostProps(this,o);break;case\"select\":N.mountWrapper(this,o,e),o=N.getHostProps(this,o),t.getReactMountReady().enqueue(l,this);break;case\"textarea\":A.mountWrapper(this,o,e),o=A.getHostProps(this,o),t.getReactMountReady().enqueue(l,this)}i(this,o);var a,f;null!=e?(a=e._namespaceURI,f=e._tag):n._tag&&(a=n._namespaceURI,f=n._tag),(null==a||a===b.svg&&\"foreignobject\"===f)&&(a=b.html),a===b.html&&(\"svg\"===this._tag?a=b.svg:\"math\"===this._tag&&(a=b.mathml)),this._namespaceURI=a;var p;if(t.useCreateElement){var h,d=n._ownerDocument;if(a===b.html)if(\"script\"===this._tag){var v=d.createElement(\"div\"),g=this._currentElement.type;v.innerHTML=\"<\"+g+\"></\"+g+\">\",h=v.removeChild(v.firstChild)}else h=o.is?d.createElement(this._currentElement.type,o.is):d.createElement(this._currentElement.type);else h=d.createElementNS(a,this._currentElement.type);T.precacheNode(this,h),this._flags|=R.hasCachedChildNodes,this._hostParent||w.setAttributeForRoot(h),this._updateDOMProperties(null,o,t);var y=_(h);this._createInitialChildren(t,o,r,y),p=y}else{var x=this._createOpenTagMarkupAndPutListeners(t,o),C=this._createContentMarkup(t,o,r);p=!C&&Y[this._tag]?x+\"/>\":x+\">\"+C+\"</\"+this._currentElement.type+\">\"}switch(this._tag){case\"input\":t.getReactMountReady().enqueue(u,this),o.autoFocus&&t.getReactMountReady().enqueue(m.focusDOMComponent,this);break;case\"textarea\":t.getReactMountReady().enqueue(c,this),o.autoFocus&&t.getReactMountReady().enqueue(m.focusDOMComponent,this);break;case\"select\":o.autoFocus&&t.getReactMountReady().enqueue(m.focusDOMComponent,this);break;case\"button\":o.autoFocus&&t.getReactMountReady().enqueue(m.focusDOMComponent,this);break;case\"option\":t.getReactMountReady().enqueue(s,this)}return p},_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(j.hasOwnProperty(r))i&&o(this,r,i,t);else{r===W&&(i&&(i=this._previousStyleCopy=g({},e.style)),i=y.createMarkupForStyles(i,this));var a=null;null!=this._tag&&h(this._tag,e)?z.hasOwnProperty(r)||(a=w.createMarkupForCustomAttribute(r,i)):a=w.createMarkupForProperty(r,i),a&&(n+=\" \"+a)}}return t.renderToStaticMarkup?n:(this._hostParent||(n+=\" \"+w.createMarkupForRoot()),n+=\" \"+w.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=B[typeof e.children]?e.children:null,a=null!=o?null:e.children;if(null!=o)r=D(o);else if(null!=a){var u=this.mountChildren(a,t,n);r=u.join(\"\")}}return K[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&&_.queueHTML(r,i.__html);else{var o=B[typeof e.children]?e.children:null,a=null!=o?null:e.children;if(null!=o)\"\"!==o&&_.queueText(r,o);else if(null!=a)for(var u=this.mountChildren(a,t,n),c=0;c<u.length;c++)_.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=S.getHostProps(this,o),a=S.getHostProps(this,a);break;case\"option\":o=P.getHostProps(this,o),a=P.getHostProps(this,a);break;case\"select\":o=N.getHostProps(this,o),a=N.getHostProps(this,a);break;case\"textarea\":o=A.getHostProps(this,o),a=A.getHostProps(this,a)}switch(i(this,a),this._updateDOMProperties(o,a,t),this._updateDOMChildren(o,a,t,r),this._tag){case\"input\":S.updateWrapper(this);break;case\"textarea\":A.updateWrapper(this);break;case\"select\":t.getReactMountReady().enqueue(f,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(r===W){var u=this._previousStyleCopy;for(i in u)u.hasOwnProperty(i)&&(a=a||{},a[i]=\"\");this._previousStyleCopy=null}else j.hasOwnProperty(r)?t[r]&&L(this,r):h(this._tag,t)?z.hasOwnProperty(r)||w.deleteValueForAttribute(U(this),r):(x.properties[r]||x.isCustomAttribute(r))&&w.deleteValueForProperty(U(this),r);for(r in e){var c=e[r],s=r===W?this._previousStyleCopy:null!=t?t[r]:void 0;if(e.hasOwnProperty(r)&&c!==s&&(null!=c||null!=s))if(r===W)if(c?c=this._previousStyleCopy=g({},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(j.hasOwnProperty(r))c?o(this,r,c,n):s&&L(this,r);else if(h(this._tag,e))z.hasOwnProperty(r)||w.setValueForAttribute(U(this),r,c);else if(x.properties[r]||x.isCustomAttribute(r)){var l=U(this);null!=c?w.setValueForProperty(l,r,c):w.deleteValueForProperty(l,r)}}a&&y.setValueForStyles(U(this),a,this)},_updateDOMChildren:function(t,e,n,r){var i=B[typeof t.children]?t.children:null,o=B[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 U(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\"html\":case\"head\":case\"body\":v(\"66\",this._tag)}this.unmountChildren(t),T.uncacheNode(this),C.deleteAllListeners(this),this._rootNodeID=0,this._domID=0,this._wrapperState=null},getPublicInstance:function(){return U(this)}},g(d.prototype,d.Mixin,O.Mixin),t.exports=d},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(96),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+\" \";if(t.useCreateElement){var c=n._ownerDocument,s=c.createComment(u);return o.precacheNode(this,s),i(s)}return t.renderToStaticMarkup?\"\":\"<!--\"+u+\"-->\"},receiveComponent:function(){},getHostNode:function(){return o.getNodeFromInstance(this)},unmountComponent:function(){o.uncacheNode(this)}}),t.exports=a},function(t,e,n){\"use strict\";var r={useCreateElement:!0,useFiber:!1};t.exports=r},function(t,e,n){\"use strict\";var r=n(81),i=n(4),o={dangerouslyProcessChildrenUpdates:function(t,e){var n=i.getNodeFromInstance(t);r.processUpdates(n,e)}};t.exports=o},function(t,e,n){\"use strict\";function r(){this._rootNodeID&&f.updateWrapper(this)}function i(t){var e=this._currentElement.props,n=c.executeOnChange(e,t);l.asap(r,this);var i=e.name;if(\"radio\"===e.type&&null!=i){for(var a=s.getNodeFromInstance(this),u=a;u.parentNode;)u=u.parentNode;for(var f=u.querySelectorAll(\"input[name=\"+JSON.stringify(\"\"+i)+'][type=\"radio\"]'),p=0;p<f.length;p++){var h=f[p];if(h!==a&&h.form===a.form){var d=s.getInstanceFromNode(h);d?void 0:o(\"90\"),l.asap(r,d)}}}return n}var o=n(2),a=n(3),u=n(156),c=n(85),s=n(4),l=n(11),f=(n(0),n(1),{getHostProps:function(t,e){var n=c.getValue(e),r=c.getChecked(e),i=a({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});return i},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:i.bind(t)}},updateWrapper:function(t){var e=t._currentElement.props,n=e.checked;null!=n&&u.setValueForProperty(s.getNodeFromInstance(t),\"checked\",n||!1);var r=s.getNodeFromInstance(t),i=c.getValue(e);if(null!=i){var o=\"\"+i;o!==r.value&&(r.value=o)}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=s.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=f},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(158),c=(n(1),!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){var n=a.getNodeFromInstance(t);n.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,a=o+r;return{start:o,end:a}}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(392),l=n(168),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(2),i=n(3),o=n(81),a=n(20),u=n(4),c=n(54),s=(n(0),n(96),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+\" \",s=\" /react-text \";if(this._domID=i,this._hostParent=e,t.useCreateElement){var l=n._ownerDocument,f=l.createComment(o),p=l.createComment(s),h=a(l.createDocumentFragment());return a.queueChild(h,a(f)),this._stringText&&a.queueChild(h,a(l.createTextNode(this._stringText))),a.queueChild(h,a(p)),u.precacheNode(this,f),this._closingComment=p,h}var d=c(this._stringText);return t.renderToStaticMarkup?d:\"<!--\"+o+\"-->\"+d+\"<!--\"+s+\"-->\"},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):void 0,8===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(2),a=n(3),u=n(85),c=n(4),s=n(11),l=(n(0),n(1),{getHostProps:function(t,e){null!=e.dangerouslySetInnerHTML?o(\"91\"):void 0;var n=a({},e,{value:void 0,defaultValue:void 0,children:\"\"+t._wrapperState.initialValue,onChange:t._wrapperState.onChange});return n},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\"):void 0,Array.isArray(c)&&(c.length<=1?void 0: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",
       "n===t._wrapperState.initialValue&&(e.value=n)}});t.exports=l},function(t,e,n){\"use strict\";function r(t,e){\"_hostNode\"in t?void 0:c(\"33\"),\"_hostNode\"in e?void 0: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 t?void 0:c(\"35\"),\"_hostNode\"in e?void 0:c(\"35\");for(;e;){if(e===t)return!0;e=e._hostParent}return!1}function o(t){return\"_hostNode\"in t?void 0:c(\"36\"),t._hostParent}function a(t,e,n){for(var r=[];t;)r.push(t),t=t._hostParent;var i;for(i=r.length;i-- >0;)e(r[i],\"captured\",n);for(i=0;i<r.length;i++)e(r[i],\"bubbled\",n)}function u(t,e,n,i,o){for(var a=t&&e?r(t,e):null,u=[];t&&t!==a;)u.push(t),t=t._hostParent;for(var c=[];e&&e!==a;)c.push(e),e=e._hostParent;var s;for(s=0;s<u.length;s++)n(u[s],\"bubbled\",i);for(s=c.length;s-- 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r(){C||(C=!0,y.EventEmitter.injectReactEventListener(m),y.EventPluginHub.injectEventPluginOrder(u),y.EventPluginUtils.injectComponentTree(p),y.EventPluginUtils.injectTreeTraversal(d),y.EventPluginHub.injectEventPluginsByName({SimpleEventPlugin:w,EnterLeaveEventPlugin:c,ChangeEventPlugin:a,SelectEventPlugin:x,BeforeInputEventPlugin:o}),y.HostComponent.injectGenericComponentClass(f),y.HostComponent.injectTextComponentClass(v),y.DOMProperty.injectDOMPropertyConfig(i),y.DOMProperty.injectDOMPropertyConfig(s),y.DOMProperty.injectDOMPropertyConfig(b),y.EmptyComponent.injectEmptyComponentFactory(function(t){return new h(t)}),y.Updates.injectReconcileTransaction(_),y.Updates.injectBatchingStrategy(g),y.Component.injectEnvironment(l))}var i=n(331),o=n(333),a=n(335),u=n(337),c=n(338),s=n(341),l=n(343),f=n(346),p=n(4),h=n(348),d=n(356),v=n(354),g=n(357),m=n(361),y=n(362),_=n(367),b=n(372),x=n(373),w=n(374),C=!1;t.exports={inject:r}},function(t,e,n){\"use strict\";var r=\"function\"==typeof 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v={_enabled:!0,_handleTopLevel:null,WINDOW_HANDLE:s.canUseDOM?window:null,setHandleTopLevel:function(t){v._handleTopLevel=t},setEnabled:function(t){v._enabled=!!t},isEnabled:function(){return v._enabled},trapBubbledEvent:function(t,e,n){return n?c.listen(n,e,v.dispatchEvent.bind(null,t)):null},trapCapturedEvent:function(t,e,n){return n?c.capture(n,e,v.dispatchEvent.bind(null,t)):null},monitorScrollValue:function(t){var e=a.bind(null,t);c.listen(window,\"scroll\",e)},dispatchEvent:function(t,e){if(v._enabled){var n=i.getPooled(t,e);try{p.batchedUpdates(o,n)}finally{i.release(n)}}}};t.exports=v},function(t,e,n){\"use strict\";var r=n(21),i=n(22),o=n(50),a=n(86),u=n(159),c=n(51),s=n(161),l=n(11),f={Component:a.injection,DOMProperty:r.injection,EmptyComponent:u.injection,EventPluginHub:i.injection,EventPluginUtils:o.injection,EventEmitter:c.injection,HostComponent:s.injection,Updates:l.injection};t.exports=f},function(t,e,n){\"use strict\";var r=n(385),i=/\\/?>/,o=/^<\\!\\-\\-/,a={CHECKSUM_ATTR_NAME:\"data-react-checksum\",addChecksumToMarkup:function(t){var e=r(t);return o.test(t)?t:t.replace(i,\" \"+a.CHECKSUM_ATTR_NAME+'=\"'+e+'\"$&')},canReuseMarkup:function(t,e){var n=e.getAttribute(a.CHECKSUM_ATTR_NAME);n=n&&parseInt(n,10);var i=r(t);return i===n}};t.exports=a},function(t,e,n){\"use strict\";function r(t,e,n){return{type:\"INSERT_MARKUP\",content:t,fromIndex:null,fromNode:null,toIndex:n,afterNode:e}}function i(t,e,n){return{type:\"MOVE_EXISTING\",content:null,fromIndex:t._mountIndex,fromNode:p.getHostNode(t),toIndex:n,afterNode:e}}function o(t,e){return{type:\"REMOVE_NODE\",content:null,fromIndex:t._mountIndex,fromNode:e,toIndex:null,afterNode:null}}function a(t){return{type:\"SET_MARKUP\",content:t,fromIndex:null,fromNode:null,toIndex:null,afterNode:null}}function u(t){return{type:\"TEXT_CONTENT\",content:t,fromIndex:null,fromNode:null,toIndex:null,afterNode:null}}function c(t,e){return e&&(t=t||[],t.push(e)),t}function s(t,e){f.processChildrenUpdates(t,e)}var l=n(2),f=n(86),p=(n(40),n(9),n(15),n(24)),h=n(342),d=(n(8),n(388)),v=(n(0),{Mixin:{_reconcilerInstantiateChildren:function(t,e,n){return h.instantiateChildren(t,e,n)},_reconcilerUpdateChildren:function(t,e,n,r,i,o){var a,u=0;return a=d(e,u),h.updateChildren(t,a,n,r,i,this,this._hostContainerInfo,o,u),a},mountChildren:function(t,e,n){var r=this._reconcilerInstantiateChildren(t,e,n);this._renderedChildren=r;var i=[],o=0;for(var a in r)if(r.hasOwnProperty(a)){var u=r[a],c=0,s=p.mountComponent(u,e,this,this._hostContainerInfo,n,c);u._mountIndex=o++,i.push(s)}return i},updateTextContent:function(t){var e=this._renderedChildren;h.unmountChildren(e,!1);for(var n in e)e.hasOwnProperty(n)&&l(\"118\");var r=[u(t)];s(this,r)},updateMarkup:function(t){var e=this._renderedChildren;h.unmountChildren(e,!1);for(var n in e)e.hasOwnProperty(n)&&l(\"118\");var r=[a(t)];s(this,r)},updateChildren:function(t,e,n){this._updateChildren(t,e,n)},_updateChildren:function(t,e,n){var r=this._renderedChildren,i={},o=[],a=this._reconcilerUpdateChildren(r,t,o,i,e,n);if(a||r){var u,l=null,f=0,h=0,d=0,v=null;for(u in a)if(a.hasOwnProperty(u)){var g=r&&r[u],m=a[u];g===m?(l=c(l,this.moveChild(g,v,f,h)),h=Math.max(g._mountIndex,h),g._mountIndex=f):(g&&(h=Math.max(g._mountIndex,h)),l=c(l,this._mountChildAtIndex(m,o[d],v,f,e,n)),d++),f++,v=p.getHostNode(m)}for(u in i)i.hasOwnProperty(u)&&(l=c(l,this._unmountChild(r[u],i[u])));l&&s(this,l),this._renderedChildren=a}},unmountChildren:function(t){var e=this._renderedChildren;h.unmountChildren(e,t),this._renderedChildren=null},moveChild:function(t,e,n,r){if(t._mountIndex<r)return i(t,e,n)},createChild:function(t,e,n){return r(n,e,t._mountIndex)},removeChild:function(t,e){return o(t,e)},_mountChildAtIndex:function(t,e,n,r,i,o){return t._mountIndex=r,this.createChild(t,n,e)},_unmountChild:function(t,e){var n=this.removeChild(t,e);return t._mountIndex=null,n}}});t.exports=v},function(t,e,n){\"use strict\";function r(t){return!(!t||\"function\"!=typeof t.attachRef||\"function\"!=typeof t.detachRef)}var i=n(2),o=(n(0),{addComponentAsRefTo:function(t,e,n){r(n)?void 0:i(\"119\"),n.attachRef(e,t)},removeComponentAsRefFrom:function(t,e,n){r(n)?void 0:i(\"120\");var o=n.getPublicInstance();o&&o.refs[e]===t.getPublicInstance()&&n.detachRef(e)}});t.exports=o},function(t,e,n){\"use strict\";var r=\"SECRET_DO_NOT_PASS_THIS_OR_YOU_WILL_BE_FIRED\";t.exports=r},function(t,e,n){\"use strict\";function r(t){this.reinitializeTransaction(),this.renderToStaticMarkup=!1,this.reactMountReady=o.getPooled(null),this.useCreateElement=t}var i=n(3),o=n(155),a=n(17),u=n(51),c=n(162),s=(n(9),n(53)),l=n(88),f={initialize:c.getSelectionInformation,close:c.restoreSelection},p={initialize:function(){var t=u.isEnabled();return u.setEnabled(!1),t},close:function(t){u.setEnabled(t)}},h={initialize:function(){this.reactMountReady.reset()},close:function(){this.reactMountReady.notifyAll()}},d=[f,p,h],v={getTransactionWrappers:function(){return d},getReactMountReady:function(){return this.reactMountReady},getUpdateQueue:function(){return l},checkpoint:function(){return this.reactMountReady.checkpoint()},rollback:function(t){this.reactMountReady.rollback(t)},destructor:function(){o.release(this.reactMountReady),this.reactMountReady=null}};i(r.prototype,s,v),a.addPoolingTo(r),t.exports=r},function(t,e,n){\"use strict\";function r(t,e,n){\"function\"==typeof t?t(e.getPublicInstance()):o.addComponentAsRefTo(e,t,n)}function i(t,e,n){\"function\"==typeof t?t(null):o.removeComponentAsRefFrom(e,t,n)}var o=n(365),a={};a.attachRefs=function(t,e){if(null!==e&&\"object\"==typeof e){var n=e.ref;null!=n&&r(n,t,e._owner)}},a.shouldUpdateRefs=function(t,e){var n=null,r=null;null!==t&&\"object\"==typeof 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      "The RF predicted: 0\n"
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       " <script>\n",
       "   if (window.SHAP) SHAP.ReactDom.render(\n",
       "    SHAP.React.createElement(SHAP.AdditiveForceVisualizer, {\"outNames\": [\"f(x)\"], \"baseValue\": 0.4997454358446903, \"outValue\": 2.220446049250313e-16, \"link\": \"identity\", \"featureNames\": [\"gender_Female\", \"gender_Male\", \"gender_Other\", \"ever_married_No\", \"ever_married_Yes\", \"work_type_Govt_job\", \"work_type_Never_worked\", \"work_type_Private\", \"work_type_Self-employed\", \"work_type_children\", \"Residence_type_Rural\", \"Residence_type_Urban\", \"smoking_status_Unknown\", \"smoking_status_formerly smoked\", \"smoking_status_never smoked\", \"smoking_status_smokes\", \"age\", \"hypertension\", \"heart_disease\", \"avg_glucose_level\", \"bmi\"], \"features\": {\"0\": {\"effect\": -0.010714685771803966, \"value\": 0.0}, \"1\": {\"effect\": -0.007323423991171294, \"value\": 1.0}, \"2\": {\"effect\": 3.9367189963834934e-08, \"value\": 0.0}, \"3\": {\"effect\": 0.006161269749982138, \"value\": 0.0}, \"4\": {\"effect\": 0.003906353262224619, \"value\": 1.0}, \"5\": {\"effect\": 0.0006249136281805721, \"value\": 0.0}, \"6\": {\"effect\": 1.7815401638263583e-05, \"value\": 0.0}, \"7\": {\"effect\": -0.0021921643738645447, \"value\": 1.0}, \"8\": {\"effect\": -0.007655961993685074, \"value\": 0.0}, \"9\": {\"effect\": 0.001576198310140129, \"value\": 0.0}, \"10\": {\"effect\": -0.006492014879026001, \"value\": 0.0}, \"11\": {\"effect\": -0.007967793231198351, \"value\": 1.0}, \"12\": {\"effect\": 0.00021843432112011766, \"value\": 0.0}, \"13\": {\"effect\": -0.014961148251079033, \"value\": 0.0}, \"14\": {\"effect\": -0.017517552438975115, \"value\": 1.0}, \"15\": {\"effect\": -0.004910630493996167, \"value\": 0.0}, \"16\": {\"effect\": -0.22794309078339206, \"value\": 43.0}, \"17\": {\"effect\": -0.012492615442682976, \"value\": 0.0}, \"18\": {\"effect\": -0.009837099878931341, \"value\": 0.0}, \"19\": {\"effect\": -0.09775464729638884, \"value\": 91.13}, \"20\": {\"effect\": -0.08448763105897111, \"value\": 33.9}}, \"plot_cmap\": \"RdBu\", \"labelMargin\": 20}),\n",
       "    document.getElementById('i7JONGCO96J8CW4BO64WD')\n",
       "  );\n",
       "</script>"
      ],
      "text/plain": [
       "<shap.plots._force.AdditiveForceVisualizer at 0x7feed048adf0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# %% >> Visualize local predictions\n",
    "shap.initjs()\n",
    "# Force plot: we selected 1 instance\n",
    "prediction = rf.predict(X_test[start_index:end_index])[0]\n",
    "print(f\"The RF predicted: {prediction}\")\n",
    "shap.force_plot(explainer.expected_value[1],\n",
    "                shap_values[1],\n",
    "                X_test[start_index:end_index]) # for values\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "810257ba",
   "metadata": {},
   "source": [
    "# Visualize Global Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5a9cfa55",
   "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": [
    "# Feature summary\n",
    "shap.summary_plot(shap_values, X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0c226c62",
   "metadata": {},
   "outputs": [],
   "source": [
    "from itertools import combinations\n",
    "\n",
    "def get_val(model, background, sample, coalition):\n",
    "    # get coalition feature values and map them to a dictionary\n",
    "    coalition_features = {c: sample[c] for c in coalition} \n",
    "    # assign all coalition values, to whole dataset, predict, \n",
    "    # and get mean()\n",
    "    return model.predict(background.assign(**coalition_features)).mean()\n",
    "\n",
    "\n",
    "import scipy\n",
    "from math import factorial\n",
    "# val(S U i) - val(S)\n",
    "def get_contributions(model, background, sample, feat, coalition):\n",
    "    # val(S U i)\n",
    "    val_s_i = get_val(model, background, sample, coalition + [feat])\n",
    "    val_s = get_val(model, background, sample, coalition)\n",
    "    ## get worth of coalition\n",
    "    val = val_s_i - val_s\n",
    "    ## get number of coalitions\n",
    "    z = len(coalition) # number of present features\n",
    "    M = len(sample.index) # number of total features\n",
    "    num_coalitions = (factorial(z)*factorial(M - z - 1)) / factorial(M)\n",
    "    ## result\n",
    "    return num_coalitions * val  \n",
    "\n",
    "def calculateSHAP(model, background, X):\n",
    "    shap_values = []\n",
    "    #for every sample\n",
    "    for i in range(np.shape(X)[0]):\n",
    "        sample = X.iloc[i]\n",
    "        # 1. calculate coalitions\\\n",
    "        # for every feature\n",
    "        shap_vals_for_x = []\n",
    "        for feat in X.columns:\n",
    "            coalitions = get_coalitions(sample, feat)\n",
    "            print(1)\n",
    "            contributions = []\n",
    "            for coalition in coalitions:\n",
    "                #print(4)\n",
    "                contributions.append(get_contributions(model, \n",
    "                                                    background,\n",
    "                                                     sample, \n",
    "                                                     feat,\n",
    "                                                     coalition))\n",
    "            print(2)\n",
    "            shap_val = np.sum(contributions)\n",
    "            shap_vals_for_x.append(shap_val)\n",
    "        shap_values.append(shap_vals_for_x)\n",
    "        print('feature done ', i)\n",
    "    phi0 = np.average(model.predict(background))\n",
    "    return phi0, shap_values"
   ]
  }
 ],
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