From cf5681082615327d265ad5f6408de868bbead00b Mon Sep 17 00:00:00 2001
From: Scott Lundberg <slund1@cs.washington.edu>
Date: Sat, 22 Dec 2018 14:12:07 -0700
Subject: [PATCH] Update LinearExplainer and add sentiment analysis example

---
 ...nt Analysis with Logistic Regression.ipynb | 397 ++++++++++++++++++
 shap/common.py                                |   5 +
 shap/datasets.py                              |  13 +
 shap/explainers/linear.py                     |  57 ++-
 shap/plots/dependence.py                      |  14 +-
 shap/plots/embedding.py                       |  20 +-
 6 files changed, 472 insertions(+), 34 deletions(-)
 create mode 100644 notebooks/linear_explainer/Sentiment Analysis with Logistic Regression.ipynb

diff --git a/notebooks/linear_explainer/Sentiment Analysis with Logistic Regression.ipynb b/notebooks/linear_explainer/Sentiment Analysis with Logistic Regression.ipynb
new file mode 100644
index 000000000..c174a0b71
--- /dev/null
+++ b/notebooks/linear_explainer/Sentiment Analysis with Logistic Regression.ipynb	
@@ -0,0 +1,397 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Sentiment Analysis with Logistic Regression\n",
+    "\n",
+    "This gives a simple example of explaining a linear logistic regression sentiment analysis model using shap. Note that with a linear model the SHAP value for feature i for the prediction $f(x)$ (assuming feature independence) is just $\\phi_i = \\beta_i \\cdot (x_i - E[x_i])$. Since we are explaining a logistic regression model the units of the SHAP values will be in the log-odds space.\n",
+    "\n",
+    "The dataset we use is the classic IMDB dataset from [this paper](http://www.aclweb.org/anthology/P11-1015). It is interesting when explaining the model how the words that are absent from the text are sometimes just as important as those that are present."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       " * NOTE: This will not work correctly for non-generic events such as `change`,\n",
+       " * `reset`, `load`, `error`, and `select`.\n",
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+       " * @return {boolean} True if the event is supported.\n",
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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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+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import sklearn\n",
+    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "import numpy as np\n",
+    "import shap\n",
+    "\n",
+    "shap.initjs()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load the IMDB dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "corpus,y = shap.datasets.imdb()\n",
+    "corpus_train, corpus_test, y_train, y_test = train_test_split(corpus, y, test_size=0.2, random_state=7)\n",
+    "\n",
+    "vectorizer = TfidfVectorizer(min_df=10)\n",
+    "X_train = vectorizer.fit_transform(corpus_train)\n",
+    "X_test = vectorizer.transform(corpus_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Fit a linear logistic regression model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LogisticRegression(C=0.1, class_weight=None, dual=False, fit_intercept=True,\n",
+       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
+       "          penalty='l1', random_state=None, solver='liblinear', tol=0.0001,\n",
+       "          verbose=0, warm_start=False)"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model = sklearn.linear_model.LogisticRegression(penalty=\"l1\", C=0.1)\n",
+    "model.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Explain the linear model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "explainer = shap.LinearExplainer(model, X_train, feature_dependence=\"independent\")\n",
+    "shap_values = explainer.shap_values(X_test)\n",
+    "X_test_array = X_test.toarray() # we need to pass a dense version for the plotting functions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Summarize the effect of all the features"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x684 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 574,
+       "width": 521
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "shap.summary_plot(shap_values, X_test_array, feature_names=vectorizer.get_feature_names())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Explain the first review's sentiment prediction\n",
+    "\n",
+    "Remember that higher means more likely to be negative, so in the plots below the \"red\" features are actually helping raise the chance of a positive review, while the negative features are lowering the chance. It is interesting to see how what is not present in the text (like bad=0 below) is often just as important as what is in the text. Remember the values of the features are TF-IDF values."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<div id='iZGIOF1HLF09NUIH0F160'>\n",
+       "<div style='color: #900; text-align: center;'>\n",
+       "  <b>Visualization omitted, Javascript library not loaded!</b><br>\n",
+       "  Have you run `initjs()` in this notebook? If this notebook was from another\n",
+       "  user you must also trust this notebook (File -> Trust notebook). If you are viewing\n",
+       "  this notebook on github the Javascript has been stripped for security. If you are using\n",
+       "  JupyterLab this error is because a JupyterLab extension has not yet been written.\n",
+       "</div></div>\n",
+       " <script>\n",
+       "   if (window.SHAP) SHAP.ReactDom.render(\n",
+       "    SHAP.React.createElement(SHAP.AdditiveForceVisualizer, {\"outNames\": [\"output value\"], \"baseValue\": -0.09826881834578277, \"outValue\": 1.5979641263575115, \"link\": \"identity\", \"featureNames\": [\"00\", \"000\", \"007\", \"01\", \"02\", \"05\", \"06\", \"10\", \"100\", \"1000\", \"101\", \"102\", \"10th\", \"11\", \"110\", \"11th\", \"12\", \"120\", \"12th\", \"13\", \"13th\", \"14\", \"140\", \"14th\", \"15\", \"150\", \"16\", \"16mm\", \"17\", \"17th\", \"18\", \"180\", \"1800\", \"18th\", \"19\", \"1912\", \"1914\", \"1920\", \"1920s\", \"1922\", \"1924\", \"1927\", \"1928\", \"1929\", \"1930\", \"1930s\", \"1931\", \"1932\", \"1933\", \"1934\", \"1935\", \"1936\", \"1937\", \"1938\", \"1939\", \"1940\", \"1940s\", \"1941\", \"1942\", \"1943\", \"1944\", \"1945\", \"1946\", \"1947\", \"1948\", \"1949\", \"1950\", \"1950s\", \"1951\", \"1952\", \"1953\", \"1954\", \"1955\", \"1956\", \"1957\", \"1958\", \"1959\", \"1960\", \"1960s\", \"1961\", \"1962\", 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\"buses\", \"busey\", \"bush\", \"bushes\", \"business\", \"businesses\", \"businessman\", \"businessmen\", \"bust\", \"busted\", \"buster\", \"busting\", \"busy\", \"but\", \"butch\", \"butcher\", \"butchered\", \"butchering\", \"butler\", \"butt\", \"butter\", \"butterfly\", \"button\", \"buttons\", \"butts\", \"buy\", \"buying\", \"buys\", \"buzz\", \"by\", \"bye\", \"bygone\", \"byrne\", \"byron\", \"ca\", \"caan\", \"cab\", \"cabaret\", \"cabin\", \"cabinet\", \"cable\", \"cad\", \"caesar\", \"cafe\", \"cafeteria\", \"caf\\u00e9\", \"cage\", \"caged\", \"cages\", \"cagney\", \"cain\", \"caine\", \"cake\", \"cal\", \"calamity\", \"calculated\", \"calculating\", \"calendar\", \"caliber\", \"calibre\", \"california\", \"call\", \"callahan\", \"called\", \"calling\", \"callous\", \"calls\", \"calm\", \"calmly\", \"calvin\", \"cam\", \"camaraderie\", \"cambodia\", \"cambodian\", \"camcorder\", \"came\", \"camel\", \"cameo\", \"cameos\", \"camera\", \"cameraman\", \"cameras\", \"camerawork\", \"cameron\", \"camp\", \"campaign\", \"campbell\", \"camper\", \"campers\", \"campfire\", \"campiness\", \"camping\", \"camps\", \"campus\", \"campy\", \"can\", \"canada\", \"canadian\", \"canadians\", \"canal\", \"cancel\", \"canceled\", \"cancelled\", \"cancels\", \"cancer\", \"candid\", \"candidate\", \"candidates\", \"candle\", \"candles\", \"candy\", \"cane\", \"canine\", \"canned\", \"cannes\", \"cannibal\", \"cannibalism\", \"cannibalistic\", \"cannibals\", \"cannon\", \"cannons\", \"cannot\", \"canoe\", \"canon\", \"cans\", \"cant\", \"canvas\", \"canyon\", \"cap\", \"capabilities\", \"capability\", \"capable\", \"capably\", \"capacity\", \"cape\", \"caper\", \"capital\", \"capitalism\", \"capitalist\", \"capitalize\", \"capote\", \"capra\", \"caprica\", \"caprice\", \"capshaw\", \"capsule\", \"capt\", \"captain\", \"captivate\", \"captivated\", \"captivating\", \"captive\", \"captors\", \"capture\", \"captured\", \"captures\", \"capturing\", \"car\", 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\"cassidy\", \"cassie\", \"cast\", \"casted\", \"casting\", \"castle\", \"castles\", \"castro\", \"casts\", \"casual\", \"casually\", \"casualty\", \"cat\", \"catalog\", \"catalogue\", \"catalyst\", \"catastrophe\", \"catastrophic\", \"catch\", \"catches\", \"catching\", \"catchphrase\", \"catchy\", \"cate\", \"categories\", \"categorized\", \"category\", \"cater\", \"catering\", \"catharsis\", \"cathartic\", \"catherine\", \"catholic\", \"cathy\", \"caton\", \"cats\", \"cattle\", \"catwoman\", \"caucasian\", \"caught\", \"cause\", \"caused\", \"causes\", \"causing\", \"caution\", \"cautionary\", \"cavalry\", \"cave\", \"caveman\", \"cavemen\", \"caves\", \"cavorting\", \"cbc\", \"cbs\", \"cd\", \"cease\", \"ceases\", \"cecil\", \"cedric\", \"ceiling\", \"celebrate\", \"celebrated\", \"celebrates\", \"celebrating\", \"celebration\", \"celebrities\", \"celebrity\", \"celeste\", \"celestial\", \"celie\", \"celine\", \"cell\", \"cellar\", \"cells\", \"celluloid\", \"celtic\", \"cement\", 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\"coolness\", \"coop\", \"cooper\", \"cooperate\", \"cooperation\", \"cop\", \"cope\", \"copied\", \"copies\", \"coping\", \"copious\", \"copper\", \"coppola\", \"cops\", \"copy\", \"copying\", \"copyright\", \"corbett\", \"corbin\", \"cord\", \"core\", \"corey\", \"corinne\", \"corky\", \"corleone\", \"corman\", \"corn\", \"cornball\", \"corner\", \"cornered\", \"corners\", \"corny\", \"coronation\", \"coroner\", \"corporate\", \"corporation\", \"corporations\", \"corpse\", \"corpses\", \"correct\", \"corrected\", \"correction\", \"correctly\", \"correctness\", \"corridor\", \"corridors\", \"corrupt\", \"corrupted\", \"corruption\", \"cortez\", \"cos\", \"cosmic\", \"cost\", \"costar\", \"costello\", \"costner\", \"costs\", \"costume\", \"costumed\", \"costumes\", \"costuming\", \"cottage\", \"cotton\", \"couch\", \"could\", \"couldn\", \"council\", \"counseling\", \"counselor\", \"count\", \"countdown\", \"counted\", \"counter\", \"counterpart\", \"counterparts\", \"counterpoint\", 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\"crazier\", \"crazies\", \"craziness\", \"crazy\", \"creaky\", \"cream\", \"create\", \"created\", \"creates\", \"creating\", \"creation\", \"creations\", \"creative\", \"creatively\", \"creativity\", \"creator\", \"creators\", \"creature\", \"creatures\", \"credentials\", \"credibility\", \"credible\", \"credit\", \"credited\", \"credits\", \"credulity\", \"creed\", \"creek\", \"creep\", \"creeped\", \"creepier\", \"creepiest\", \"creepily\", \"creepiness\", \"creeping\", \"creeps\", \"creepshow\", \"creepy\", \"crenna\", \"crew\", \"crews\", \"cricket\", \"cried\", \"cries\", \"crime\", \"crimes\", \"criminal\", \"criminally\", \"criminals\", \"crimson\", \"cringe\", \"cringed\", \"cringing\", \"cripple\", \"crippled\", \"crisis\", \"crisp\", \"crispin\", \"cristina\", \"criteria\", \"criterion\", \"critic\", \"critical\", \"critically\", \"criticise\", \"criticised\", \"criticism\", \"criticisms\", \"criticize\", \"criticized\", \"criticizing\", \"critics\", \"critique\", 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+       "    document.getElementById('iZGIOF1HLF09NUIH0F160')\n",
+       "  );\n",
+       "</script>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ind = 0\n",
+    "shap.force_plot(\n",
+    "    explainer.expected_value, shap_values[ind,:], X_test_array[ind,:],\n",
+    "    feature_names=vectorizer.get_feature_names()\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Positive Review:\n",
+      "\"Twelve Monkeys\" is odd and disturbing, yet being so clever and intelligent at the same time. It cleverly jumps between future and the past, and the story it tells is about a man named James Cole, a convict, who is sent back to the past to gather information about a man-made virus that wiped out 5 billion of the human population on the planet back in 1996. At first Cole is sent back to the year 1990 by accident and by misfortune he is taken to a mental institution where he tries to explain his purpose and where he meets a psychiatrist Dr. Kathryn Railly who tries to help him and a patient named Jeffrey Goines, the insane son of a famous scientist. Being provocative and somehow so sensible, dealing with and between reason and madness, the movie is a definite masterpiece in the history of science-fiction films.<br /><br />The story is just fantastic. It's so original and so entertaining. The screenplay itself written by David and Janet Peoples is inspired by a movie named \"La Jetée\" (1962) which I haven't seen, but I must thank the director and writer of the movie, Chris Marker, for giving such an inspiration for the writers of \"Twelve Monkeys\". I read a little about \"La Jetée\", it's not the same story but it has the same idea, so this is not just a copy of it. David and Janet Peoples have transformed this great deal of inspiration to a modernized story, which tells about this urgent need for people to find a solution for maintaining human existence and it does it in a so beautiful and a realistic way that it's a guaranteed thrill ride from the beginning till the end. The music used in the film is odd and somehow so funny and amusing it doesn't really fit until you really get it and when you do you realise that it's so compelling, composed by Paul Buckmaster.<br /><br />Terry Gilliam, who we remember from Monty Python, as the director of the movie was a real surprise for me, as I really never thought him as a director type of a person. I know he has directed movies before, but I really couldn't believe that he could make something this magnificent. It shouldn't be a surprise though, as he does an amazing job. You can still sense that same weirdness as in the Python's, but for me the directing is pretty much flawless though in its odd way of describing things it also makes some scenes strangely disturbing. Yes, it is indeed odd, weird, bizarre and disturbing, so it also makes the movie a bit heavy too, so the weak minded viewers will probably find it hard to watch the movie all the way through. It's not as heavy as you could imagine, but it just has these certain things which in their own purpose are sometimes pretty severe to watch. Despite that, the movie holds this pure intelligence inside it and through flashbacks, dreams, jumps between the past and the future it mixes up the whole story in a very clever way and it doesn't even make the plot messy in any part, though it does need concentration from the viewer after all.<br /><br />What comes to acting, well the movie doesn't even go wrong there. The role of James Cole is played by the mighty Bruce Willis, who probably does his best role performance yet to date. Now people may disagree with me, as he did some fine job in for example \"The Sixth Sense\" as well, but for me the role of James Cole was so ideal for Willis and he performs it incredibly well. The character is very well written too, yet performed even better. Cole starts to question his own existence and he deals with himself, starting to question his actual time of living, trying to survive and find the crucial missing piece of the puzzle. By hardship he starts to loose his faith, questioning if he can even trust or believe himself. Other role performances worth mentioning are the performances of Madeleine Stow and Brad Pitt. Stow plays the role of Kathryn Railly, the psychiatrist of James Cole, who sees something strangely familiar in Cole and decides to help him to deal with his madness. She somehow starts to believe Cole's story but as a believer of science she tries to find solutions through it and tries to deal with reason when it comes to unbelievable things. Brad Pitt is so good in the role of Jeffrey Goines and he also does one of his best role performances yet to date. The insane yet hilarious personality of the character brought Pitt even an Oscar nomination for it, so I guess I'm not praising the honestly fabulous performance for nothing.<br /><br />All in all, \"Twelve Monkeys\" is a great science-fiction experience and it will surely be a recommendation for everyone, especially for the sci-fi fans. It includes brilliant characters and superb role performances, especially from Willis and Pitt, and an original and an entertaining story which forms a plot that's so intelligent and clever. Yet being that already mentioned weird and disturbing it definitely captures the viewer's attention by making it interesting and witty. It's also an explosive thriller and it has romance in it too, so it's all that in same package and that makes it one of the best sci-fi motion pictures I've ever seen. Through the odd yet terrific vision of Terry Gilliam it manages to keep itself in balance despite the somewhat bumpy yet somehow stable ride. Hard to explain really, but that's how it is, it's mind blowing.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Positive\" if y_test[ind] else \"Negative\", \"Review:\")\n",
+    "print(corpus_test[ind])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Explain the second review's sentiment prediction"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<div id='iFA6ZQAI0YO66OPOXJGUN'>\n",
+       "<div style='color: #900; text-align: center;'>\n",
+       "  <b>Visualization omitted, Javascript library not loaded!</b><br>\n",
+       "  Have you run `initjs()` in this notebook? If this notebook was from another\n",
+       "  user you must also trust this notebook (File -> Trust notebook). If you are viewing\n",
+       "  this notebook on github the Javascript has been stripped for security. If you are using\n",
+       "  JupyterLab this error is because a JupyterLab extension has not yet been written.\n",
+       "</div></div>\n",
+       " <script>\n",
+       "   if (window.SHAP) SHAP.ReactDom.render(\n",
+       "    SHAP.React.createElement(SHAP.AdditiveForceVisualizer, {\"outNames\": [\"output value\"], \"baseValue\": -0.09826881834578277, \"outValue\": -0.5933923868764157, \"link\": \"identity\", \"featureNames\": [\"00\", \"000\", \"007\", \"01\", \"02\", \"05\", \"06\", \"10\", \"100\", \"1000\", \"101\", \"102\", \"10th\", \"11\", \"110\", \"11th\", \"12\", \"120\", \"12th\", \"13\", \"13th\", \"14\", \"140\", \"14th\", \"15\", \"150\", \"16\", \"16mm\", \"17\", \"17th\", \"18\", \"180\", \"1800\", \"18th\", \"19\", \"1912\", \"1914\", \"1920\", \"1920s\", \"1922\", \"1924\", \"1927\", \"1928\", \"1929\", \"1930\", \"1930s\", \"1931\", \"1932\", \"1933\", \"1934\", \"1935\", \"1936\", \"1937\", \"1938\", \"1939\", \"1940\", \"1940s\", \"1941\", \"1942\", \"1943\", \"1944\", \"1945\", \"1946\", \"1947\", \"1948\", \"1949\", \"1950\", \"1950s\", \"1951\", \"1952\", \"1953\", \"1954\", \"1955\", \"1956\", \"1957\", \"1958\", \"1959\", \"1960\", \"1960s\", \"1961\", \"1962\", 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\"kissing\", \"kit\", \"kitamura\", \"kitchen\", \"kitsch\", \"kitschy\", \"kitten\", \"kittens\", \"kitty\", \"klaus\", \"klein\", \"kline\", \"knack\", \"knee\", \"knees\", \"knew\", \"knife\", \"knight\", \"knightley\", \"knightly\", \"knights\", \"knit\", \"knives\", \"knock\", \"knocked\", \"knocking\", \"knockoff\", \"knocks\", \"know\", \"knowing\", \"knowingly\", \"knowledge\", \"knowledgeable\", \"known\", \"knows\", \"knox\", \"knuckle\", \"kolchak\", \"kong\", \"koo\", \"kooky\", \"korda\", \"korea\", \"korean\", \"kothari\", \"kotto\", \"kovacs\", \"kramer\", \"krause\", \"krell\", \"krige\", \"kris\", \"krishna\", \"kristen\", \"kristin\", \"kristofferson\", \"krueger\", \"kruger\", \"kubrick\", \"kudos\", \"kudrow\", \"kumar\", \"kung\", \"kurosawa\", \"kurt\", \"kyle\", \"kylie\", \"la\", \"lab\", \"label\", \"labeled\", \"labels\", \"labeouf\", \"labor\", \"laboratory\", \"labored\", \"labour\", \"labyrinth\", \"laced\", \"lack\", \"lacked\", \"lacking\", 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\"laughs\", \"laughter\", \"launch\", \"launched\", \"launching\", \"laundry\", \"laura\", \"laurel\", \"lauren\", \"laurence\", \"laurie\", \"lava\", \"lavish\", \"law\", \"lawman\", \"lawn\", \"lawrence\", \"laws\", \"lawyer\", \"lawyers\", \"lay\", \"layer\", \"layered\", \"layers\", \"laying\", \"layout\", \"lays\", \"laziness\", \"lazy\", \"lbs\", \"lds\", \"le\", \"lea\", \"leachman\", \"lead\", \"leaden\", \"leader\", \"leaders\", \"leadership\", \"leading\", \"leads\", \"leaf\", \"league\", \"leagues\", \"leak\", \"lean\", \"leaning\", \"leanings\", \"leans\", \"leap\", \"leaping\", \"leaps\", \"learn\", \"learned\", \"learning\", \"learns\", \"learnt\", \"leary\", \"lease\", \"least\", \"leather\", \"leave\", \"leaves\", \"leaving\", \"lecherous\", \"lecture\", \"lectures\", \"led\", \"ledger\", \"lee\", \"leering\", \"left\", \"leftist\", \"leftover\", \"leg\", \"legacy\", \"legal\", \"legally\", \"legend\", \"legendary\", \"legends\", \"legged\", \"legion\", \"legions\", 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\"lifestyles\", \"lifetime\", \"lift\", \"lifted\", \"lifting\", \"lifts\", \"light\", \"lighted\", \"lighten\", \"lighter\", \"lighthearted\", \"lighting\", \"lightly\", \"lightness\", \"lightning\", \"lights\", \"lightweight\", \"likability\", \"likable\", \"like\", \"likeable\", \"liked\", \"likelihood\", \"likely\", \"likes\", \"likewise\", \"liking\", \"lil\", \"lila\", \"lili\", \"lilith\", \"lillian\", \"lilly\", \"lily\", \"limb\", \"limbo\", \"limbs\", \"limelight\", \"limit\", \"limitations\", \"limited\", \"limits\", \"limp\", \"lin\", \"lincoln\", \"linda\", \"linden\", \"lindsay\", \"lindsey\", \"lindy\", \"line\", \"linear\", \"lined\", \"liner\", \"liners\", \"lines\", \"lineup\", \"linger\", \"lingering\", \"lingers\", \"lining\", \"link\", \"linked\", \"linking\", \"linklater\", \"links\", \"lion\", \"lionel\", \"lions\", \"liotta\", \"lip\", \"lips\", \"lipstick\", \"liquid\", \"liquor\", \"lisa\", \"list\", \"listed\", \"listen\", \"listened\", \"listener\", 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\"mae\", \"maestro\", \"mafia\", \"magazine\", \"magazines\", \"maggie\", \"maggots\", \"magic\", \"magical\", \"magically\", \"magician\", \"magnetic\", \"magnetism\", \"magnificence\", \"magnificent\", \"magnificently\", \"magnitude\", \"magnolia\", \"magnum\", \"maguire\", \"mahatma\", \"maher\", \"mahoney\", \"maid\", \"maiden\", \"mail\", \"main\", \"maine\", \"mainland\", \"mainly\", \"mainstream\", \"maintain\", \"maintained\", \"maintaining\", \"maintains\", \"majestic\", \"majesty\", \"major\", \"majority\", \"majors\", \"make\", \"makeover\", \"maker\", \"makers\", \"makes\", \"makeup\", \"making\", \"makings\", \"malcolm\", \"malden\", \"male\", \"males\", \"malevolent\", \"malice\", \"malicious\", \"malik\", \"mall\", \"malone\", \"malta\", \"maltese\", \"maltin\", \"mama\", \"mamet\", \"mamie\", \"mammoth\", \"man\", \"manage\", \"managed\", \"management\", \"manager\", \"manages\", \"managing\", \"manchester\", \"manchu\", \"manchurian\", \"mandatory\", \"mandy\", 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\"marred\", \"marriage\", \"marriages\", \"married\", \"marries\", \"marry\", \"marrying\", \"mars\", \"marsh\", \"marsha\", \"marshal\", \"marshall\", \"mart\", \"martha\", \"martial\", \"martian\", \"martians\", \"martin\", \"martinez\", \"martino\", \"marty\", \"marvel\", \"marvellous\", \"marvelous\", \"marvelously\", \"marvin\", \"marx\", \"marxist\", \"mary\", \"masala\", \"masculine\", \"masculinity\", \"mash\", \"mask\", \"masked\", \"masks\", \"masochist\", \"masochistic\", \"mason\", \"masquerading\", \"mass\", \"massachusetts\", \"massacre\", \"massacred\", \"masses\", \"massey\", \"massive\", \"massively\", \"master\", \"mastered\", \"masterful\", \"masterfully\", \"mastermind\", \"masterpiece\", \"masterpieces\", \"masters\", \"masterson\", \"masterwork\", \"mastery\", \"mastroianni\", \"masturbation\", \"match\", \"matched\", \"matches\", \"matching\", \"mate\", \"material\", \"materials\", \"mates\", \"math\", \"matheson\", \"mathieu\", \"mating\", \"matin\\u00e9e\", 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\"misogynistic\", \"misogyny\", \"misplaced\", \"miss\", \"missed\", \"misses\", \"missile\", \"missiles\", \"missing\", \"mission\", \"missionary\", \"missions\", \"missouri\", \"mist\", \"mistake\", \"mistaken\", \"mistakenly\", \"mistakes\", \"mister\", \"mistreated\", \"mistress\", \"misty\", \"misunderstand\", \"misunderstanding\", \"misunderstandings\", \"misunderstood\", \"misused\", \"mitch\", \"mitchell\", \"mitchum\", \"mix\", \"mixed\", \"mixes\", \"mixing\", \"mixture\", \"miyazaki\", \"mm\", \"mo\", \"moan\", \"moaning\", \"moans\", \"mob\", \"mobile\", \"mobster\", \"mobsters\", \"mock\", \"mocked\", \"mockery\", \"mocking\", \"mockumentary\", \"mode\", \"model\", \"modeled\", \"modeling\", \"models\", \"moderate\", \"moderately\", \"modern\", \"modest\", \"modesty\", \"modicum\", \"modine\", \"moe\", \"mol\", \"mold\", \"mole\", \"molested\", \"molina\", \"moll\", \"molly\", \"mom\", \"moment\", \"momentarily\", \"moments\", \"momentum\", \"mommy\", \"moms\", \"mon\", \"mona\", \"monaghan\", \"monarch\", \"monastery\", \"monday\", \"monetary\", \"money\", \"monica\", \"monitor\", \"monitors\", \"monk\", \"monkey\", \"monkeys\", \"monks\", \"mono\", \"monologue\", \"monologues\", \"mononoke\", \"monotone\", \"monotonous\", \"monotony\", \"monroe\", \"monster\", \"monsters\", \"monstrosity\", \"monstrous\", \"montage\", \"montages\", \"montana\", \"monte\", \"montgomery\", \"month\", \"months\", \"monty\", \"monument\", \"monumental\", \"mood\", \"moodiness\", \"moods\", \"moody\", \"moon\", \"moonlight\", \"moonstruck\", \"moonwalker\", \"moore\", \"moorehead\", \"moral\", \"morale\", \"moralistic\", \"morality\", \"morally\", \"morals\", \"moran\", \"morbid\", \"morbius\", \"more\", \"moreau\", \"moreover\", \"mores\", \"morgan\", \"morgana\", \"morgue\", \"moriarty\", \"morita\", \"mormon\", \"mormons\", \"morning\", \"moron\", \"moronic\", \"morons\", \"morose\", \"morphed\", \"morphs\", \"morricone\", \"morris\", \"morrow\", \"morse\", \"mortal\", \"mortality\", \"mortally\", \"mortensen\", \"mortimer\", \"morton\", \"moscow\", \"moses\", \"moss\", \"most\", \"mostel\", \"mostly\", \"motel\", \"mother\", \"mothers\", \"motif\", \"motifs\", \"motion\", \"motions\", \"motivated\", \"motivation\", \"motivations\", \"motive\", \"motives\", \"motley\", \"motor\", \"motorcycle\", \"moulin\", \"mount\", \"mountain\", \"mountains\", \"mounted\", \"mounting\", \"mourning\", \"mouse\", \"moustache\", \"mouth\", \"mouthed\", \"mouths\", \"move\", \"moved\", \"movement\", \"movements\", \"moves\", \"movie\", \"moviegoer\", \"moviegoers\", \"movies\", \"moving\", \"mower\", \"mpaa\", \"mph\", \"mr\", \"mrs\", \"ms\", \"mst\", \"mst3k\", \"mtv\", \"much\", \"muck\", \"mud\", \"muddled\", \"muddy\", \"mug\", \"mugging\", \"mulder\", \"mulholland\", \"muller\", \"mullet\", \"mulligan\", \"multi\", \"multiple\", \"multiplex\", \"multiply\", \"multitude\", \"mum\", \"mumbai\", \"mumbling\", \"mumbo\", \"mummy\", \"munchies\", \"munching\", \"mundane\", \"muni\", \"munro\", \"muppet\", \"muppets\", \"murder\", \"murdered\", \"murderer\", \"murderers\", \"murdering\", \"murderous\", \"murders\", \"muriel\", \"murky\", \"murphy\", \"murray\", \"muscle\", \"muscles\", \"muscular\", \"muse\", \"museum\", \"mush\", \"music\", \"musical\", \"musically\", \"musicals\", \"musician\", \"musicians\", \"muslim\", \"muslims\", \"mussolini\", \"must\", \"mustache\", \"muster\", \"mutant\", \"mutants\", \"mutated\", \"mute\", \"muted\", \"mutilated\", \"mutilation\", \"mutiny\", \"mutual\", \"my\", \"myers\", \"myles\", \"myra\", \"myriad\", \"myrna\", \"myrtle\", \"myself\", \"mysteries\", \"mysterious\", \"mysteriously\", \"mystery\", \"mystic\", \"mystical\", \"mysticism\", \"mystique\", \"myth\", \"mythic\", \"mythical\", \"mythological\", \"mythology\", \"myths\", \"nada\", \"nadir\", \"nagging\", \"nah\", \"nail\", \"nailed\", \"nails\", \"nair\", \"naive\", \"naivety\", \"naivet\\u00e9\", \"naked\", \"nam\", 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+       "    document.getElementById('iFA6ZQAI0YO66OPOXJGUN')\n",
+       "  );\n",
+       "</script>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ind = 1\n",
+    "shap.force_plot(\n",
+    "    explainer.expected_value, shap_values[ind,:], X_test_array[ind,:],\n",
+    "    feature_names=vectorizer.get_feature_names()\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Negative Review:\n",
+      "I don't understand the positive comments made about this film. It is cheap and nasty on all levels and I cannot understand how it ever got made.<br /><br />Cartoon characters abound - Sue's foul-mouthed, alcoholic, layabout, Irish father being a prime example. None of the characters are remotely sympathetic - except, briefly, for Sue's Asian boyfriend but even he then turns out to be capable of domestic violence! As desperately unattractive as they both are, I've no idea why either Rita and/or Sue would throw themselves at a consummate creep like Bob - but given that they do, why should I be expected to care what happens to them? So many reviews keep carping on about how \"realistic\" it is. If that is true, it is a sad reflection on society but no reason to put it on film.<br /><br />I didn't like the film at all.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Positive\" if y_test[ind] else \"Negative\", \"Review:\")\n",
+    "print(corpus_test[ind])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Explain the third review's sentiment prediction"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<div id='iCZC4P3IAEU08DKAYV2Z9'>\n",
+       "<div style='color: #900; text-align: center;'>\n",
+       "  <b>Visualization omitted, Javascript library not loaded!</b><br>\n",
+       "  Have you run `initjs()` in this notebook? If this notebook was from another\n",
+       "  user you must also trust this notebook (File -> Trust notebook). If you are viewing\n",
+       "  this notebook on github the Javascript has been stripped for security. If you are using\n",
+       "  JupyterLab this error is because a JupyterLab extension has not yet been written.\n",
+       "</div></div>\n",
+       " <script>\n",
+       "   if (window.SHAP) SHAP.ReactDom.render(\n",
+       "    SHAP.React.createElement(SHAP.AdditiveForceVisualizer, {\"outNames\": [\"output value\"], \"baseValue\": -0.09826881834578277, \"outValue\": 0.6319576902821158, \"link\": \"identity\", \"featureNames\": [\"00\", \"000\", \"007\", \"01\", \"02\", \"05\", \"06\", \"10\", \"100\", \"1000\", \"101\", \"102\", \"10th\", \"11\", \"110\", \"11th\", \"12\", \"120\", \"12th\", \"13\", \"13th\", \"14\", \"140\", \"14th\", \"15\", \"150\", \"16\", \"16mm\", \"17\", \"17th\", \"18\", \"180\", \"1800\", \"18th\", \"19\", \"1912\", \"1914\", \"1920\", \"1920s\", \"1922\", \"1924\", \"1927\", \"1928\", \"1929\", \"1930\", \"1930s\", \"1931\", \"1932\", \"1933\", \"1934\", \"1935\", \"1936\", \"1937\", \"1938\", \"1939\", \"1940\", \"1940s\", \"1941\", \"1942\", \"1943\", \"1944\", \"1945\", \"1946\", \"1947\", \"1948\", \"1949\", \"1950\", \"1950s\", \"1951\", \"1952\", \"1953\", \"1954\", \"1955\", \"1956\", \"1957\", \"1958\", \"1959\", \"1960\", \"1960s\", \"1961\", \"1962\", 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\"asians\", \"aside\", \"asides\", \"asin\", \"asinine\", \"ask\", \"asked\", \"askey\", \"asking\", \"asks\", \"asleep\", \"aspect\", \"aspects\", \"aspirations\", \"aspire\", \"aspires\", \"aspiring\", \"ass\", \"assassin\", \"assassinate\", \"assassinated\", \"assassination\", \"assassins\", \"assault\", \"assaulted\", \"assaults\", \"assembled\", \"assembly\", \"assert\", \"assertion\", \"asserts\", \"assessment\", \"asset\", \"assets\", \"assigned\", \"assignment\", \"assignments\", \"assist\", \"assistance\", \"assistant\", \"assistants\", \"assisted\", \"associate\", \"associated\", \"associates\", \"association\", \"assorted\", \"assortment\", \"assume\", \"assumed\", \"assumes\", \"assuming\", \"assumption\", \"assumptions\", \"assurance\", \"assure\", \"assured\", \"astaire\", \"astonished\", \"astonishing\", \"astonishingly\", \"astor\", \"astounding\", \"astoundingly\", \"astray\", \"astronaut\", \"astronauts\", \"astute\", \"asylum\", \"at\", \"ate\", \"atheist\", 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\"bucket\", \"buckets\", \"bucks\", \"bud\", \"budapest\", \"buddhist\", \"buddies\", \"budding\", \"buddy\", \"budget\", \"budgetary\", \"budgeted\", \"budgets\", \"buff\", \"buffalo\", \"buffoon\", \"buffs\", \"buffy\", \"bug\", \"buggy\", \"bugs\", \"build\", \"building\", \"buildings\", \"builds\", \"buildup\", \"built\", \"bulb\", \"bulging\", \"bulk\", \"bull\", \"bulldog\", \"bullet\", \"bullets\", \"bullied\", \"bullies\", \"bullock\", \"bulls\", \"bullshit\", \"bully\", \"bullying\", \"bum\", \"bumbling\", \"bump\", \"bumped\", \"bumping\", \"bumps\", \"bums\", \"bunch\", \"bunker\", \"bunny\", \"bunuel\", \"burden\", \"burdened\", \"bureau\", \"bureaucracy\", \"burgeoning\", \"burgess\", \"burial\", \"buried\", \"buries\", \"burke\", \"burlesque\", \"burn\", \"burned\", \"burnett\", \"burning\", \"burns\", \"burnt\", \"burroughs\", \"burrows\", \"burst\", \"bursting\", \"bursts\", \"burstyn\", \"burt\", \"burton\", \"bury\", \"burying\", \"bus\", \"busby\", \"buscemi\", \"buses\", \"busey\", \"bush\", \"bushes\", \"business\", \"businesses\", \"businessman\", \"businessmen\", \"bust\", \"busted\", \"buster\", \"busting\", \"busy\", \"but\", \"butch\", \"butcher\", \"butchered\", \"butchering\", \"butler\", \"butt\", \"butter\", \"butterfly\", \"button\", \"buttons\", \"butts\", \"buy\", \"buying\", \"buys\", \"buzz\", \"by\", \"bye\", \"bygone\", \"byrne\", \"byron\", \"ca\", \"caan\", \"cab\", \"cabaret\", \"cabin\", \"cabinet\", \"cable\", \"cad\", \"caesar\", \"cafe\", \"cafeteria\", \"caf\\u00e9\", \"cage\", \"caged\", \"cages\", \"cagney\", \"cain\", \"caine\", \"cake\", \"cal\", \"calamity\", \"calculated\", \"calculating\", \"calendar\", \"caliber\", \"calibre\", \"california\", \"call\", \"callahan\", \"called\", \"calling\", \"callous\", \"calls\", \"calm\", \"calmly\", \"calvin\", \"cam\", \"camaraderie\", \"cambodia\", \"cambodian\", \"camcorder\", \"came\", \"camel\", \"cameo\", \"cameos\", \"camera\", \"cameraman\", \"cameras\", \"camerawork\", \"cameron\", \"camp\", \"campaign\", \"campbell\", \"camper\", \"campers\", \"campfire\", \"campiness\", \"camping\", \"camps\", \"campus\", \"campy\", \"can\", \"canada\", \"canadian\", \"canadians\", \"canal\", \"cancel\", \"canceled\", \"cancelled\", \"cancels\", \"cancer\", \"candid\", \"candidate\", \"candidates\", \"candle\", \"candles\", \"candy\", \"cane\", \"canine\", \"canned\", \"cannes\", \"cannibal\", \"cannibalism\", \"cannibalistic\", \"cannibals\", \"cannon\", \"cannons\", \"cannot\", \"canoe\", \"canon\", \"cans\", \"cant\", \"canvas\", \"canyon\", \"cap\", \"capabilities\", \"capability\", \"capable\", \"capably\", \"capacity\", \"cape\", \"caper\", \"capital\", \"capitalism\", \"capitalist\", \"capitalize\", \"capote\", \"capra\", \"caprica\", \"caprice\", \"capshaw\", \"capsule\", \"capt\", \"captain\", \"captivate\", \"captivated\", \"captivating\", \"captive\", \"captors\", \"capture\", \"captured\", \"captures\", \"capturing\", \"car\", \"carbon\", \"card\", \"cardboard\", \"cardinal\", \"cards\", \"care\", \"cared\", \"career\", \"careers\", \"carefree\", \"careful\", \"carefully\", \"careless\", \"carelessly\", \"carell\", \"cares\", \"caretaker\", \"carey\", \"carface\", \"cargo\", \"caribbean\", \"caricature\", \"caricatures\", \"caring\", \"carl\", \"carla\", \"carlisle\", \"carlito\", \"carlo\", \"carlos\", \"carlton\", \"carlyle\", \"carmen\", \"carnage\", \"carnal\", \"carnival\", \"carnosaur\", \"carol\", \"carole\", \"carolina\", \"caroline\", \"caron\", \"carpenter\", \"carpet\", \"carradine\", \"carrey\", \"carriage\", \"carrie\", \"carried\", \"carrier\", \"carries\", \"carroll\", \"carrot\", \"carry\", \"carrying\", \"cars\", \"carson\", \"cart\", \"carter\", \"cartoon\", \"cartoonish\", \"cartoons\", \"cartoony\", \"carved\", \"carving\", \"cary\", \"casablanca\", \"case\", \"cases\", \"casey\", \"cash\", \"cashier\", \"cashing\", \"casino\", \"casper\", \"cassandra\", \"cassavetes\", \"cassel\", \"cassidy\", \"cassie\", \"cast\", \"casted\", \"casting\", \"castle\", \"castles\", \"castro\", \"casts\", \"casual\", \"casually\", \"casualty\", \"cat\", \"catalog\", \"catalogue\", \"catalyst\", \"catastrophe\", \"catastrophic\", \"catch\", \"catches\", \"catching\", \"catchphrase\", \"catchy\", \"cate\", \"categories\", \"categorized\", \"category\", \"cater\", \"catering\", \"catharsis\", \"cathartic\", \"catherine\", \"catholic\", \"cathy\", \"caton\", \"cats\", \"cattle\", \"catwoman\", \"caucasian\", \"caught\", \"cause\", \"caused\", \"causes\", \"causing\", \"caution\", \"cautionary\", \"cavalry\", \"cave\", \"caveman\", \"cavemen\", \"caves\", \"cavorting\", \"cbc\", \"cbs\", \"cd\", \"cease\", \"ceases\", \"cecil\", \"cedric\", \"ceiling\", \"celebrate\", \"celebrated\", \"celebrates\", \"celebrating\", \"celebration\", \"celebrities\", \"celebrity\", \"celeste\", \"celestial\", \"celie\", \"celine\", \"cell\", \"cellar\", \"cells\", \"celluloid\", \"celtic\", \"cement\", \"cemetery\", \"censor\", \"censored\", \"censors\", \"censorship\", \"cent\", \"center\", \"centered\", \"centerpiece\", \"centers\", \"central\", \"centre\", \"centred\", \"centres\", \"cents\", \"centuries\", \"century\", \"ceo\", \"cerebral\", \"ceremonies\", \"ceremony\", \"certain\", \"certainly\", \"certainty\", \"certificate\", \"cesar\", \"cetera\", \"cg\", \"cgi\", \"chad\", \"chagrin\", \"chahine\", \"chain\", \"chained\", \"chains\", \"chainsaw\", \"chair\", \"chairman\", \"chairs\", \"chalk\", \"challenge\", \"challenged\", \"challenger\", \"challenges\", \"challenging\", \"chamber\", \"chamberlain\", \"champagne\", \"champion\", \"champions\", \"championship\", \"chan\", \"chance\", \"chances\", \"chandler\", \"chaney\", \"chang\", \"change\", \"changed\", \"changes\", \"changing\", \"channel\", \"channeling\", \"channels\", \"chant\", \"chanting\", \"chaos\", \"chaotic\", \"chap\", \"chaplin\", \"chapter\", \"chapters\", \"character\", \"characterisation\", \"characterisations\", \"characterised\", \"characteristic\", \"characteristics\", \"characterization\", \"characterizations\", \"characterized\", \"characters\", \"charade\", \"charge\", \"charged\", \"charges\", \"charging\", \"charisma\", \"charismatic\", \"charitable\", \"charity\", \"charles\", \"charley\", \"charlie\", \"charlize\", \"charlotte\", \"charlton\", \"charm\", \"charmed\", \"charmer\", \"charming\", \"charmingly\", \"charms\", \"charts\", \"chase\", \"chased\", \"chases\", \"chasing\", \"chat\", \"chatter\", \"chatting\", \"chauffeur\", \"chavez\", \"che\", \"cheadle\", \"cheap\", \"cheaper\", \"cheapest\", \"cheaply\", \"cheat\", \"cheated\", \"cheating\", \"cheats\", \"check\", \"checked\", \"checking\", \"checkout\", \"checks\", \"cheech\", \"cheek\", \"cheeky\", \"cheer\", \"cheered\", \"cheerful\", \"cheerfully\", \"cheering\", \"cheerleader\", \"cheers\", \"cheery\", \"cheese\", \"cheesiest\", \"cheesiness\", \"cheesy\", \"cheezy\", \"chef\", \"chemical\", \"chemicals\", \"chemistry\", \"chen\", \"cher\", \"cherie\", \"cherish\", \"cherished\", \"cherry\", \"cheryl\", \"chess\", \"chest\", \"chester\", \"cheung\", \"chevy\", \"chew\", \"chewbacca\", \"chewing\", \"chews\", \"chi\", \"chiba\", \"chic\", \"chicago\", \"chick\", \"chicken\", \"chickens\", \"chicks\", \"chico\", \"chief\", \"child\", \"childhood\", \"childish\", \"childishly\", \"childlike\", \"children\", \"chile\", \"chill\", \"chiller\", \"chilling\", \"chillingly\", \"chills\", \"chilly\", \"chimney\", \"chimp\", \"chimps\", \"chin\", \"china\", \"chinatown\", \"chinese\", \"ching\", \"chip\", \"chips\", \"chloe\", \"chock\", \"chocolate\", \"choice\", \"choices\", \"choir\", \"choke\", \"choked\", \"chong\", \"choose\", \"chooses\", \"choosing\", \"chop\", \"chopped\", \"chopper\", \"chopping\", \"choppy\", \"chopra\", \"chops\", \"chord\", \"chore\", \"choreographed\", \"choreographer\", \"choreography\", \"chores\", \"chorus\", \"chose\", \"chosen\", \"chow\", \"chris\", \"chrissy\", \"christ\", \"christensen\", \"christian\", \"christianity\", \"christians\", \"christie\", \"christina\", \"christine\", \"christmas\", \"christopher\", \"christy\", \"chronic\", \"chronicle\", \"chronicles\", \"chronological\", \"chronologically\", \"chronology\", \"chubby\", \"chuck\", \"chuckle\", \"chuckled\", \"chuckles\", \"chuckling\", \"chucky\", \"chunk\", \"chunks\", \"chunky\", \"church\", \"churches\", \"churchill\", \"churn\", \"churned\", \"churning\", \"cia\", \"cigar\", \"cigarette\", \"cigarettes\", \"cigars\", \"cillian\", \"cinderella\", \"cindy\", \"cinema\", \"cinemas\", \"cinematic\", \"cinematically\", \"cinematographer\", \"cinematographic\", \"cinematography\", \"cinemax\", \"circa\", \"circle\", \"circles\", \"circuit\", \"circular\", \"circumstance\", \"circumstances\", \"circus\", \"cited\", \"cities\", \"citizen\", \"citizens\", \"city\", \"civil\", \"civilian\", \"civilians\", \"civilisation\", \"civilization\", \"civilized\", 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\"combo\", \"combs\", \"come\", \"comeback\", \"comedian\", \"comedians\", \"comedic\", \"comedienne\", \"comedies\", \"comedy\", \"comers\", \"comes\", \"comet\", \"comeuppance\", \"comfort\", \"comfortable\", \"comfortably\", \"comforting\", \"comforts\", \"comic\", \"comical\", \"comically\", \"comics\", \"coming\", \"command\", \"commander\", \"commanding\", \"commandments\", \"commando\", \"commandos\", \"commands\", \"commend\", \"commendable\", \"commended\", \"comment\", \"commentaries\", \"commentary\", \"commentator\", \"commentators\", \"commented\", \"commenter\", \"commenters\", \"commenting\", \"comments\", \"commercial\", \"commercially\", \"commercials\", \"commie\", \"commit\", \"commitment\", \"commitments\", \"commits\", \"committed\", \"committee\", \"committing\", \"common\", \"commonly\", \"commonplace\", \"communicate\", \"communicated\", \"communicating\", \"communication\", \"communications\", \"communism\", \"communist\", \"communists\", \"communities\", 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\"confessions\", \"confidant\", \"confidence\", \"confident\", \"confidential\", \"confined\", \"confines\", \"confirm\", \"confirmed\", \"confirms\", \"conflict\", \"conflicted\", \"conflicting\", \"conflicts\", \"confront\", \"confrontation\", \"confronted\", \"confronting\", \"confronts\", \"confuse\", \"confused\", \"confusing\", \"confusion\", \"congratulate\", \"congratulations\", \"congregation\", \"congress\", \"conjure\", \"connect\", \"connected\", \"connecticut\", \"connecting\", \"connection\", \"connections\", \"connects\", \"connell\", \"connelly\", \"connery\", \"connie\", \"conniving\", \"connoisseur\", \"connolly\", \"connor\", \"connors\", \"conquer\", \"conquered\", \"conquers\", \"conquest\", \"conrad\", \"conroy\", \"cons\", \"conscience\", \"conscious\", \"consciously\", \"consciousness\", \"consecutive\", \"consent\", \"consequence\", \"consequences\", \"consequently\", \"conservative\", \"consider\", \"considerable\", \"considerably\", \"consideration\", \"considerations\", \"considered\", \"considering\", \"considers\", \"consist\", \"consisted\", \"consistency\", \"consistent\", \"consistently\", \"consisting\", \"consists\", \"consolation\", \"conspicuous\", \"conspiracies\", \"conspiracy\", \"constance\", \"constant\", \"constantly\", \"constitute\", \"constitutes\", \"constitution\", \"constraints\", \"construct\", \"constructed\", \"construction\", \"constructive\", \"consultant\", \"consumed\", \"consumer\", \"consumers\", \"consuming\", \"consummate\", \"consumption\", \"contact\", \"contacts\", \"contain\", \"contained\", \"containing\", \"contains\", \"contaminated\", \"contemplate\", \"contemplating\", \"contemporaries\", \"contemporary\", \"contempt\", \"contend\", \"contender\", \"content\", \"contention\", \"contents\", \"contest\", \"contestant\", \"contestants\", \"contests\", \"context\", \"continent\", \"continual\", \"continually\", \"continuation\", \"continue\", \"continued\", \"continues\", \"continuing\", 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\"coolness\", \"coop\", \"cooper\", \"cooperate\", \"cooperation\", \"cop\", \"cope\", \"copied\", \"copies\", \"coping\", \"copious\", \"copper\", \"coppola\", \"cops\", \"copy\", \"copying\", \"copyright\", \"corbett\", \"corbin\", \"cord\", \"core\", \"corey\", \"corinne\", \"corky\", \"corleone\", \"corman\", \"corn\", \"cornball\", \"corner\", \"cornered\", \"corners\", \"corny\", \"coronation\", \"coroner\", \"corporate\", \"corporation\", \"corporations\", \"corpse\", \"corpses\", \"correct\", \"corrected\", \"correction\", \"correctly\", \"correctness\", \"corridor\", \"corridors\", \"corrupt\", \"corrupted\", \"corruption\", \"cortez\", \"cos\", \"cosmic\", \"cost\", \"costar\", \"costello\", \"costner\", \"costs\", \"costume\", \"costumed\", \"costumes\", \"costuming\", \"cottage\", \"cotton\", \"couch\", \"could\", \"couldn\", \"council\", \"counseling\", \"counselor\", \"count\", \"countdown\", \"counted\", \"counter\", \"counterpart\", \"counterparts\", \"counterpoint\", \"countess\", \"counting\", \"countless\", \"countries\", \"country\", \"countryside\", \"counts\", \"county\", \"coup\", \"couple\", \"coupled\", \"couples\", \"coupling\", \"courage\", \"courageous\", \"course\", \"court\", \"courtesy\", \"courtney\", \"courtroom\", \"courtyard\", \"cousin\", \"cousins\", \"coven\", \"cover\", \"coverage\", \"covered\", \"covering\", \"covers\", \"cow\", \"coward\", \"cowardly\", \"cowboy\", \"cowboys\", \"cowgirls\", \"cows\", \"cox\", \"coy\", \"coyote\", \"cozy\", \"crack\", \"cracked\", \"cracker\", \"cracking\", \"cracks\", \"cradle\", \"craft\", \"crafted\", \"crafting\", \"crafts\", \"craftsmanship\", \"crafty\", \"craig\", \"cram\", \"crammed\", \"cramped\", \"crane\", \"crank\", \"cranked\", \"cranky\", \"crap\", \"crapfest\", \"crappy\", \"crash\", \"crashed\", \"crashers\", \"crashes\", \"crashing\", \"crass\", \"cratchit\", \"crater\", \"craven\", \"craving\", \"crawford\", \"crawl\", \"crawled\", \"crawling\", \"craze\", \"crazed\", \"crazier\", \"crazies\", \"craziness\", \"crazy\", \"creaky\", \"cream\", \"create\", \"created\", \"creates\", \"creating\", \"creation\", \"creations\", \"creative\", \"creatively\", \"creativity\", \"creator\", \"creators\", \"creature\", \"creatures\", \"credentials\", \"credibility\", \"credible\", \"credit\", \"credited\", \"credits\", \"credulity\", \"creed\", \"creek\", \"creep\", \"creeped\", \"creepier\", \"creepiest\", \"creepily\", \"creepiness\", \"creeping\", \"creeps\", \"creepshow\", \"creepy\", \"crenna\", \"crew\", \"crews\", \"cricket\", \"cried\", \"cries\", \"crime\", \"crimes\", \"criminal\", \"criminally\", \"criminals\", \"crimson\", \"cringe\", \"cringed\", \"cringing\", \"cripple\", \"crippled\", \"crisis\", \"crisp\", \"crispin\", \"cristina\", \"criteria\", \"criterion\", \"critic\", \"critical\", \"critically\", \"criticise\", \"criticised\", \"criticism\", \"criticisms\", \"criticize\", \"criticized\", \"criticizing\", \"critics\", \"critique\", 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\"value\": 0.0}, \"16211\": {\"effect\": -0.021099160122405296, \"value\": 0.0}, \"16240\": {\"effect\": -0.002792889509547814, \"value\": 0.0}, \"16252\": {\"effect\": 0.010196280193499423, \"value\": 0.0}, \"16255\": {\"effect\": 0.10315596019591007, \"value\": 0.0}, \"16260\": {\"effect\": 0.012057091671217095, \"value\": 0.0}}, \"plot_cmap\": \"RdBu\", \"labelMargin\": 20}),\n",
+       "    document.getElementById('iCZC4P3IAEU08DKAYV2Z9')\n",
+       "  );\n",
+       "</script>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ind = 2\n",
+    "shap.force_plot(\n",
+    "    explainer.expected_value, shap_values[ind,:], X_test_array[ind,:],\n",
+    "    feature_names=vectorizer.get_feature_names()\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Positive Review:\n",
+      "I finally saw this film tonight after renting it at Blockbuster (VHS). I have to agree that it is wildly original. Yes, maybe the characters were not fully realized but it isn't one of those movies. Rather, we are treated to the director's eye, his vision of what the story is about. And it does not stop. And to be honest, I didn't want it to. I do believe that Sabu had to have influenced the director's of 'Lock, Stock & Two Smoking Barrels' and 'Run, Lola, Run'. But I absolutely loved the way the three leads SEE the beautiful woman on the street to distract them momentarily. I really need to see this director's other work because this film really intrigued me. If you want insight, culture, sturm und drang, go somewhere else. If you want a laugh, camera movement and criminal hilarity, look here.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Positive\" if y_test[ind] else \"Negative\", \"Review:\")\n",
+    "print(corpus_test[ind])"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/shap/common.py b/shap/common.py
index 80f9084af..b68e87f66 100644
--- a/shap/common.py
+++ b/shap/common.py
@@ -242,6 +242,11 @@ def convert_name(ind, shap_values, feature_names):
             # we allow rank based indexing using the format "rank(int)"
             if ind.startswith("rank("):
                 return np.argsort(-np.abs(shap_values).mean(0))[int(ind[5:-1])]
+
+            # we allow the sum of all the SHAP values to be specified with "sum()"
+            # assuming here that the calling method can deal with this case
+            elif ind == "sum()": 
+                return "sum()"
             else:
                 print("Could not find feature named: " + ind)
                 return None
diff --git a/shap/datasets.py b/shap/datasets.py
index 92161aa4b..785d2c557 100644
--- a/shap/datasets.py
+++ b/shap/datasets.py
@@ -34,6 +34,19 @@ def boston(display=False):
     df = pd.DataFrame(data=d.data, columns=d.feature_names) # pylint: disable=E1101
     return df, d.target # pylint: disable=E1101
 
+def imdb(display=False):
+    """ Return the clssic IMDB sentiment analysis training data in a nice package.
+
+    Full data is at: http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz
+    Paper to cite when using the data is: http://www.aclweb.org/anthology/P11-1015
+    """
+
+    with open(cache(github_data_url + "imdb_train.txt")) as f:
+        data = f.readlines()
+    y = np.ones(25000, dtype=np.bool)
+    y[:12500] = 0
+    return data, y
+
 def communitiesandcrime(display=False):
     """ Predict total number of non-violent crimes per 100K popuation.
 
diff --git a/shap/explainers/linear.py b/shap/explainers/linear.py
index c032eda00..2bd9c3c3c 100644
--- a/shap/explainers/linear.py
+++ b/shap/explainers/linear.py
@@ -26,17 +26,26 @@ class LinearExplainer(Explainer):
     nsamples : int
         Number of samples to use when estimating the transformation matrix used to account for
         feature correlations.
-    feature_dependence : "correlation" (default) or "interventional"
+    feature_dependence : "independent" (default) or "correlation"
         There are two ways we might want to compute SHAP values, either the full conditional SHAP
-        values or the interventional SHAP values. For interventional SHAP values we break any
-        dependence structure in the model and so uncover how the model would behave if we
+        values or the independent SHAP values. For independent SHAP values we break any
+        dependence structure between features in the model and so uncover how the model would behave if we
         intervened and changed some of the inputs. For the full conditional SHAP values we respect
         the correlations among the input features, so if the model depends on one input but that
-        input is correlated with another input, then both get some credit for the model's behavior.
+        input is correlated with another input, then both get some credit for the model's behavior. The
+        independent option stays "true to the model" meaning it will only give credit to features that are
+        actually used by the model, while the correlation option stays "true to the data" in the sense that
+        it only considers how the model would behave when respecting the correlations in the input data. 
     """
 
-    def __init__(self, model, data, nsamples=1000, feature_dependence="correlation"):
+    def __init__(self, model, data, nsamples=1000, feature_dependence=None):
         self.nsamples = nsamples
+        if feature_dependence == "interventional":
+            warnings.warn('The option feature_dependence="interventional" is has been renamed to feature_dependence="independent"!')
+            feature_dependence = "independent"
+        elif feature_dependence is None:
+            warnings.warn('The default value for feature_dependence has been changed to "independent"!')
+            feature_dependence = "independent"
         self.feature_dependence = feature_dependence
 
         # raw coefficents
@@ -64,22 +73,22 @@ def __init__(self, model, data, nsamples=1000, feature_dependence="correlation")
         if type(data) == tuple and len(data) == 2:
             self.mean = data[0]
             self.cov = data[1]
-        elif str(type(data)).endswith("'numpy.ndarray'>"):
-            self.mean = data.mean(0)
-            self.cov = np.cov(data, rowvar=False)
         elif data is None:
             raise Exception("A background data distribution must be provided!")
-
+        else:
+            self.mean = np.array(np.mean(data, 0)).flatten() # assumes it is an array
+            if feature_dependence == "correlation":
+                self.cov = np.cov(data, rowvar=False)
+        #print(self.coef, self.mean.flatten(), self.intercept)
         self.expected_value = np.dot(self.coef, self.mean) + self.intercept
-
         self.M = len(self.mean)
-        self.valid_inds = np.where(np.diag(self.cov) > 1e-8)[0]
-        self.mean = self.mean[self.valid_inds]
-        self.cov = self.cov[:,self.valid_inds][self.valid_inds,:]
-        self.coef = self.coef[self.valid_inds]
 
         # if needed, estimate the transform matrices
         if feature_dependence == "correlation":
+            self.valid_inds = np.where(np.diag(self.cov) > 1e-8)[0]
+            self.mean = self.mean[self.valid_inds]
+            self.cov = self.cov[:,self.valid_inds][self.valid_inds,:]
+            self.coef = self.coef[self.valid_inds]
 
             # group perfectly redundant variables together
             self.avg_proj,sum_proj = duplicate_components(self.cov)
@@ -95,9 +104,9 @@ def __init__(self, model, data, nsamples=1000, feature_dependence="correlation")
             mean_transform, x_transform = self._estimate_transforms(nsamples)
             self.mean_transformed = np.matmul(mean_transform, self.mean)
             self.x_transform = x_transform
-        elif feature_dependence == "interventional":
+        elif feature_dependence == "independent":
             if nsamples != 1000:
-                warnings.warn("Setting nsamples has no effect when feature_dependence = 'interventional'!")
+                warnings.warn("Setting nsamples has no effect when feature_dependence = 'independent'!")
         else:
             raise Exception("Unknown type of feature_dependence provided: " + feature_dependence)
 
@@ -187,18 +196,20 @@ def shap_values(self, X):
         elif str(type(X)).endswith("'pandas.core.frame.DataFrame'>"):
             X = X.values
 
-        assert str(type(X)).endswith("'numpy.ndarray'>"), "Unknown instance type: " + str(type(X))
+        #assert str(type(X)).endswith("'numpy.ndarray'>"), "Unknown instance type: " + str(type(X))
         assert len(X.shape) == 1 or len(X.shape) == 2, "Instance must have 1 or 2 dimensions!"
 
         if self.feature_dependence == "correlation":
             phi = np.matmul(np.matmul(X[:,self.valid_inds], self.avg_proj.T), self.x_transform.T) - self.mean_transformed
             phi = np.matmul(phi, self.avg_proj)
-        elif self.feature_dependence == "interventional":
-            phi = self.coef * (X[:,self.valid_inds] - self.mean)        
-        
-        full_phi = np.zeros(((phi.shape[0], self.M)))
-        full_phi[:,self.valid_inds] = phi
-        return full_phi
+
+            full_phi = np.zeros(((phi.shape[0], self.M)))
+            full_phi[:,self.valid_inds] = phi
+
+            return full_phi
+
+        elif self.feature_dependence == "independent":
+            return np.array(X - self.mean) * self.coef
 
 def duplicate_components(C):
     D = np.diag(1/np.sqrt(np.diag(C)))
diff --git a/shap/plots/dependence.py b/shap/plots/dependence.py
index 6ffb078fb..5f4717850 100644
--- a/shap/plots/dependence.py
+++ b/shap/plots/dependence.py
@@ -224,10 +224,16 @@ def dependence_plot(ind, shap_values, features, feature_names=None, display_feat
     # plot any nan feature values as tick marks along the y-axis
     xv_nans = np.isnan(xv)
     xlim = pl.xlim()
-    pl.scatter(
-        xlim[0] * np.ones(xv_nans.sum()), s[xv_nans], marker=1,
-        linewidth=2, c=cvals[xv_nans], cmap=colors.red_blue, alpha=alpha
-    )
+    if interaction_index is not None:
+        pl.scatter(
+            xlim[0] * np.ones(xv_nans.sum()), s[xv_nans], marker=1,
+            linewidth=2, c=cvals[xv_nans], cmap=colors.red_blue, alpha=alpha
+        )
+    else:
+        pl.scatter(
+            xlim[0] * np.ones(xv_nans.sum()), s[xv_nans], marker=1,
+            linewidth=2, color="#1E88E5", alpha=alpha
+        )
     pl.xlim(*xlim)
 
     # make the plot more readable
diff --git a/shap/plots/embedding.py b/shap/plots/embedding.py
index 8662e21f1..c4ccdda9c 100644
--- a/shap/plots/embedding.py
+++ b/shap/plots/embedding.py
@@ -18,7 +18,8 @@ def embedding_plot(ind, shap_values, feature_names=None, method="pca", alpha=1.0
         If this is an int it is the index of the feature to use to color the embedding.
         If this is a string it is either the name of the feature, or it can have the
         form "rank(int)" to specify the feature with that rank (ordered by mean absolute
-        SHAP value over all the samples).
+        SHAP value over all the samples), or "sum()" to mean the sum of all the SHAP values,
+        which is the model's output (minus it's expected value).
 
     shap_values : numpy.array
         Matrix of SHAP values (# samples x # features).
@@ -40,24 +41,30 @@ def embedding_plot(ind, shap_values, feature_names=None, method="pca", alpha=1.0
         feature_names = [labels['FEATURE'] % str(i) for i in range(shap_values.shape[1])]
     
     ind = convert_name(ind, shap_values, feature_names)
+    if ind == "sum()":
+        cvals = shap_values.sum(1)
+        fname = "sum(SHAP values)"
+    else:
+        cvals = shap_values[:,ind]
+        fname = feature_names[ind]
     
     # see if we need to compute the embedding
-    if method == "pca":
+    if type(method) == str and method == "pca":
         pca = sklearn.decomposition.PCA(2)
         embedding_values = pca.fit_transform(shap_values)
-    elif type(method) == np.array and method.shape[1] == 2:
+    elif hasattr(method, "shape") and method.shape[1] == 2:
         embedding_values = method
     else:
         print("Unsupported embedding method:", method)
 
     pl.scatter(
-        embedding_values[:,0], embedding_values[:,1], c=shap_values[:,ind],
+        embedding_values[:,0], embedding_values[:,1], c=cvals,
         cmap=colors.red_blue_solid, alpha=alpha, linewidth=0
     )
     pl.axis("off")
     #pl.title(feature_names[ind])
     cb = pl.colorbar()
-    cb.set_label("SHAP value for\n"+feature_names[ind], size=13)
+    cb.set_label("SHAP value for\n"+fname, size=13)
     cb.outline.set_visible(False)
     
     
@@ -66,5 +73,4 @@ def embedding_plot(ind, shap_values, feature_names=None, method="pca", alpha=1.0
     cb.ax.set_aspect((bbox.height - 0.7) * 10)
     cb.set_alpha(1)
     if show:
-        pl.show()
-    
\ No newline at end of file
+        pl.show()
\ No newline at end of file