search.js

window.pdocSearch = (function(){
/** elasticlunr - http://weixsong.github.io * Copyright (C) 2017 Oliver Nightingale * Copyright (C) 2017 Wei Song * MIT Licensed */!function(){function e(e){if(null===e||"object"!=typeof e)return e;var t=e.constructor();for(var n in e)e.hasOwnProperty(n)&&(t[n]=e[n]);return t}var t=function(e){var n=new t.Index;return n.pipeline.add(t.trimmer,t.stopWordFilter,t.stemmer),e&&e.call(n,n),n};t.version="0.9.5",lunr=t,t.utils={},t.utils.warn=function(e){return function(t){e.console&&console.warn&&console.warn(t)}}(this),t.utils.toString=function(e){return void 0===e||null===e?"":e.toString()},t.EventEmitter=function(){this.events={}},t.EventEmitter.prototype.addListener=function(){var e=Array.prototype.slice.call(arguments),t=e.pop(),n=e;if("function"!=typeof t)throw new TypeError("last argument must be a function");n.forEach(function(e){this.hasHandler(e)||(this.events[e]=[]),this.events[e].push(t)},this)},t.EventEmitter.prototype.removeListener=function(e,t){if(this.hasHandler(e)){var n=this.events[e].indexOf(t);-1!==n&&(this.events[e].splice(n,1),0==this.events[e].length&&delete this.events[e])}},t.EventEmitter.prototype.emit=function(e){if(this.hasHandler(e)){var t=Array.prototype.slice.call(arguments,1);this.events[e].forEach(function(e){e.apply(void 0,t)},this)}},t.EventEmitter.prototype.hasHandler=function(e){return e in this.events},t.tokenizer=function(e){if(!arguments.length||null===e||void 0===e)return[];if(Array.isArray(e)){var n=e.filter(function(e){return null===e||void 0===e?!1:!0});n=n.map(function(e){return t.utils.toString(e).toLowerCase()});var i=[];return n.forEach(function(e){var n=e.split(t.tokenizer.seperator);i=i.concat(n)},this),i}return e.toString().trim().toLowerCase().split(t.tokenizer.seperator)},t.tokenizer.defaultSeperator=/[\s\-]+/,t.tokenizer.seperator=t.tokenizer.defaultSeperator,t.tokenizer.setSeperator=function(e){null!==e&&void 0!==e&&"object"==typeof e&&(t.tokenizer.seperator=e)},t.tokenizer.resetSeperator=function(){t.tokenizer.seperator=t.tokenizer.defaultSeperator},t.tokenizer.getSeperator=function(){return t.tokenizer.seperator},t.Pipeline=function(){this._queue=[]},t.Pipeline.registeredFunctions={},t.Pipeline.registerFunction=function(e,n){n in t.Pipeline.registeredFunctions&&t.utils.warn("Overwriting existing registered function: "+n),e.label=n,t.Pipeline.registeredFunctions[n]=e},t.Pipeline.getRegisteredFunction=function(e){return e in t.Pipeline.registeredFunctions!=!0?null:t.Pipeline.registeredFunctions[e]},t.Pipeline.warnIfFunctionNotRegistered=function(e){var n=e.label&&e.label in this.registeredFunctions;n||t.utils.warn("Function is not registered with pipeline. This may cause problems when serialising the index.\n",e)},t.Pipeline.load=function(e){var n=new t.Pipeline;return e.forEach(function(e){var i=t.Pipeline.getRegisteredFunction(e);if(!i)throw new Error("Cannot load un-registered function: "+e);n.add(i)}),n},t.Pipeline.prototype.add=function(){var e=Array.prototype.slice.call(arguments);e.forEach(function(e){t.Pipeline.warnIfFunctionNotRegistered(e),this._queue.push(e)},this)},t.Pipeline.prototype.after=function(e,n){t.Pipeline.warnIfFunctionNotRegistered(n);var i=this._queue.indexOf(e);if(-1===i)throw new Error("Cannot find existingFn");this._queue.splice(i+1,0,n)},t.Pipeline.prototype.before=function(e,n){t.Pipeline.warnIfFunctionNotRegistered(n);var i=this._queue.indexOf(e);if(-1===i)throw new Error("Cannot find existingFn");this._queue.splice(i,0,n)},t.Pipeline.prototype.remove=function(e){var t=this._queue.indexOf(e);-1!==t&&this._queue.splice(t,1)},t.Pipeline.prototype.run=function(e){for(var t=[],n=e.length,i=this._queue.length,o=0;n>o;o++){for(var r=e[o],s=0;i>s&&(r=this._queue[s](r,o,e),void 0!==r&&null!==r);s++);void 0!==r&&null!==r&&t.push(r)}return t},t.Pipeline.prototype.reset=function(){this._queue=[]},t.Pipeline.prototype.get=function(){return this._queue},t.Pipeline.prototype.toJSON=function(){return this._queue.map(function(e){return t.Pipeline.warnIfFunctionNotRegistered(e),e.label})},t.Index=function(){this._fields=[],this._ref="id",this.pipeline=new t.Pipeline,this.documentStore=new t.DocumentStore,this.index={},this.eventEmitter=new t.EventEmitter,this._idfCache={},this.on("add","remove","update",function(){this._idfCache={}}.bind(this))},t.Index.prototype.on=function(){var e=Array.prototype.slice.call(arguments);return this.eventEmitter.addListener.apply(this.eventEmitter,e)},t.Index.prototype.off=function(e,t){return this.eventEmitter.removeListener(e,t)},t.Index.load=function(e){e.version!==t.version&&t.utils.warn("version mismatch: current "+t.version+" importing "+e.version);var n=new this;n._fields=e.fields,n._ref=e.ref,n.documentStore=t.DocumentStore.load(e.documentStore),n.pipeline=t.Pipeline.load(e.pipeline),n.index={};for(var i in e.index)n.index[i]=t.InvertedIndex.load(e.index[i]);return n},t.Index.prototype.addField=function(e){return this._fields.push(e),this.index[e]=new t.InvertedIndex,this},t.Index.prototype.setRef=function(e){return this._ref=e,this},t.Index.prototype.saveDocument=function(e){return this.documentStore=new t.DocumentStore(e),this},t.Index.prototype.addDoc=function(e,n){if(e){var n=void 0===n?!0:n,i=e[this._ref];this.documentStore.addDoc(i,e),this._fields.forEach(function(n){var o=this.pipeline.run(t.tokenizer(e[n]));this.documentStore.addFieldLength(i,n,o.length);var r={};o.forEach(function(e){e in r?r[e]+=1:r[e]=1},this);for(var s in r){var u=r[s];u=Math.sqrt(u),this.index[n].addToken(s,{ref:i,tf:u})}},this),n&&this.eventEmitter.emit("add",e,this)}},t.Index.prototype.removeDocByRef=function(e){if(e&&this.documentStore.isDocStored()!==!1&&this.documentStore.hasDoc(e)){var t=this.documentStore.getDoc(e);this.removeDoc(t,!1)}},t.Index.prototype.removeDoc=function(e,n){if(e){var n=void 0===n?!0:n,i=e[this._ref];this.documentStore.hasDoc(i)&&(this.documentStore.removeDoc(i),this._fields.forEach(function(n){var o=this.pipeline.run(t.tokenizer(e[n]));o.forEach(function(e){this.index[n].removeToken(e,i)},this)},this),n&&this.eventEmitter.emit("remove",e,this))}},t.Index.prototype.updateDoc=function(e,t){var t=void 0===t?!0:t;this.removeDocByRef(e[this._ref],!1),this.addDoc(e,!1),t&&this.eventEmitter.emit("update",e,this)},t.Index.prototype.idf=function(e,t){var n="@"+t+"/"+e;if(Object.prototype.hasOwnProperty.call(this._idfCache,n))return this._idfCache[n];var i=this.index[t].getDocFreq(e),o=1+Math.log(this.documentStore.length/(i+1));return this._idfCache[n]=o,o},t.Index.prototype.getFields=function(){return this._fields.slice()},t.Index.prototype.search=function(e,n){if(!e)return[];e="string"==typeof e?{any:e}:JSON.parse(JSON.stringify(e));var i=null;null!=n&&(i=JSON.stringify(n));for(var o=new t.Configuration(i,this.getFields()).get(),r={},s=Object.keys(e),u=0;u<s.length;u++){var a=s[u];r[a]=this.pipeline.run(t.tokenizer(e[a]))}var l={};for(var c in o){var d=r[c]||r.any;if(d){var f=this.fieldSearch(d,c,o),h=o[c].boost;for(var p in f)f[p]=f[p]*h;for(var p in f)p in l?l[p]+=f[p]:l[p]=f[p]}}var v,g=[];for(var p in l)v={ref:p,score:l[p]},this.documentStore.hasDoc(p)&&(v.doc=this.documentStore.getDoc(p)),g.push(v);return g.sort(function(e,t){return t.score-e.score}),g},t.Index.prototype.fieldSearch=function(e,t,n){var i=n[t].bool,o=n[t].expand,r=n[t].boost,s=null,u={};return 0!==r?(e.forEach(function(e){var n=[e];1==o&&(n=this.index[t].expandToken(e));var r={};n.forEach(function(n){var o=this.index[t].getDocs(n),a=this.idf(n,t);if(s&&"AND"==i){var l={};for(var c in s)c in o&&(l[c]=o[c]);o=l}n==e&&this.fieldSearchStats(u,n,o);for(var c in o){var d=this.index[t].getTermFrequency(n,c),f=this.documentStore.getFieldLength(c,t),h=1;0!=f&&(h=1/Math.sqrt(f));var p=1;n!=e&&(p=.15*(1-(n.length-e.length)/n.length));var v=d*a*h*p;c in r?r[c]+=v:r[c]=v}},this),s=this.mergeScores(s,r,i)},this),s=this.coordNorm(s,u,e.length)):void 0},t.Index.prototype.mergeScores=function(e,t,n){if(!e)return t;if("AND"==n){var i={};for(var o in t)o in e&&(i[o]=e[o]+t[o]);return i}for(var o in t)o in e?e[o]+=t[o]:e[o]=t[o];return e},t.Index.prototype.fieldSearchStats=function(e,t,n){for(var i in n)i in e?e[i].push(t):e[i]=[t]},t.Index.prototype.coordNorm=function(e,t,n){for(var i in e)if(i in t){var o=t[i].length;e[i]=e[i]*o/n}return e},t.Index.prototype.toJSON=function(){var e={};return this._fields.forEach(function(t){e[t]=this.index[t].toJSON()},this),{version:t.version,fields:this._fields,ref:this._ref,documentStore:this.documentStore.toJSON(),index:e,pipeline:this.pipeline.toJSON()}},t.Index.prototype.use=function(e){var t=Array.prototype.slice.call(arguments,1);t.unshift(this),e.apply(this,t)},t.DocumentStore=function(e){this._save=null===e||void 0===e?!0:e,this.docs={},this.docInfo={},this.length=0},t.DocumentStore.load=function(e){var t=new this;return t.length=e.length,t.docs=e.docs,t.docInfo=e.docInfo,t._save=e.save,t},t.DocumentStore.prototype.isDocStored=function(){return this._save},t.DocumentStore.prototype.addDoc=function(t,n){this.hasDoc(t)||this.length++,this.docs[t]=this._save===!0?e(n):null},t.DocumentStore.prototype.getDoc=function(e){return this.hasDoc(e)===!1?null:this.docs[e]},t.DocumentStore.prototype.hasDoc=function(e){return e in this.docs},t.DocumentStore.prototype.removeDoc=function(e){this.hasDoc(e)&&(delete this.docs[e],delete this.docInfo[e],this.length--)},t.DocumentStore.prototype.addFieldLength=function(e,t,n){null!==e&&void 0!==e&&0!=this.hasDoc(e)&&(this.docInfo[e]||(this.docInfo[e]={}),this.docInfo[e][t]=n)},t.DocumentStore.prototype.updateFieldLength=function(e,t,n){null!==e&&void 0!==e&&0!=this.hasDoc(e)&&this.addFieldLength(e,t,n)},t.DocumentStore.prototype.getFieldLength=function(e,t){return null===e||void 0===e?0:e in this.docs&&t in this.docInfo[e]?this.docInfo[e][t]:0},t.DocumentStore.prototype.toJSON=function(){return{docs:this.docs,docInfo:this.docInfo,length:this.length,save:this._save}},t.stemmer=function(){var e={ational:"ate",tional:"tion",enci:"ence",anci:"ance",izer:"ize",bli:"ble",alli:"al",entli:"ent",eli:"e",ousli:"ous",ization:"ize",ation:"ate",ator:"ate",alism:"al",iveness:"ive",fulness:"ful",ousness:"ous",aliti:"al",iviti:"ive",biliti:"ble",logi:"log"},t={icate:"ic",ative:"",alize:"al",iciti:"ic",ical:"ic",ful:"",ness:""},n="[^aeiou]",i="[aeiouy]",o=n+"[^aeiouy]*",r=i+"[aeiou]*",s="^("+o+")?"+r+o,u="^("+o+")?"+r+o+"("+r+")?$",a="^("+o+")?"+r+o+r+o,l="^("+o+")?"+i,c=new RegExp(s),d=new RegExp(a),f=new RegExp(u),h=new RegExp(l),p=/^(.+?)(ss|i)es$/,v=/^(.+?)([^s])s$/,g=/^(.+?)eed$/,m=/^(.+?)(ed|ing)$/,y=/.$/,S=/(at|bl|iz)$/,x=new RegExp("([^aeiouylsz])\\1$"),w=new RegExp("^"+o+i+"[^aeiouwxy]$"),I=/^(.+?[^aeiou])y$/,b=/^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/,E=/^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/,D=/^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/,F=/^(.+?)(s|t)(ion)$/,_=/^(.+?)e$/,P=/ll$/,k=new RegExp("^"+o+i+"[^aeiouwxy]$"),z=function(n){var i,o,r,s,u,a,l;if(n.length<3)return n;if(r=n.substr(0,1),"y"==r&&(n=r.toUpperCase()+n.substr(1)),s=p,u=v,s.test(n)?n=n.replace(s,"$1$2"):u.test(n)&&(n=n.replace(u,"$1$2")),s=g,u=m,s.test(n)){var z=s.exec(n);s=c,s.test(z[1])&&(s=y,n=n.replace(s,""))}else if(u.test(n)){var z=u.exec(n);i=z[1],u=h,u.test(i)&&(n=i,u=S,a=x,l=w,u.test(n)?n+="e":a.test(n)?(s=y,n=n.replace(s,"")):l.test(n)&&(n+="e"))}if(s=I,s.test(n)){var z=s.exec(n);i=z[1],n=i+"i"}if(s=b,s.test(n)){var z=s.exec(n);i=z[1],o=z[2],s=c,s.test(i)&&(n=i+e[o])}if(s=E,s.test(n)){var z=s.exec(n);i=z[1],o=z[2],s=c,s.test(i)&&(n=i+t[o])}if(s=D,u=F,s.test(n)){var z=s.exec(n);i=z[1],s=d,s.test(i)&&(n=i)}else if(u.test(n)){var z=u.exec(n);i=z[1]+z[2],u=d,u.test(i)&&(n=i)}if(s=_,s.test(n)){var z=s.exec(n);i=z[1],s=d,u=f,a=k,(s.test(i)||u.test(i)&&!a.test(i))&&(n=i)}return s=P,u=d,s.test(n)&&u.test(n)&&(s=y,n=n.replace(s,"")),"y"==r&&(n=r.toLowerCase()+n.substr(1)),n};return z}(),t.Pipeline.registerFunction(t.stemmer,"stemmer"),t.stopWordFilter=function(e){return e&&t.stopWordFilter.stopWords[e]!==!0?e:void 0},t.clearStopWords=function(){t.stopWordFilter.stopWords={}},t.addStopWords=function(e){null!=e&&Array.isArray(e)!==!1&&e.forEach(function(e){t.stopWordFilter.stopWords[e]=!0},this)},t.resetStopWords=function(){t.stopWordFilter.stopWords=t.defaultStopWords},t.defaultStopWords={"":!0,a:!0,able:!0,about:!0,across:!0,after:!0,all:!0,almost:!0,also:!0,am:!0,among:!0,an:!0,and:!0,any:!0,are:!0,as:!0,at:!0,be:!0,because:!0,been:!0,but:!0,by:!0,can:!0,cannot:!0,could:!0,dear:!0,did:!0,"do":!0,does:!0,either:!0,"else":!0,ever:!0,every:!0,"for":!0,from:!0,get:!0,got:!0,had:!0,has:!0,have:!0,he:!0,her:!0,hers:!0,him:!0,his:!0,how:!0,however:!0,i:!0,"if":!0,"in":!0,into:!0,is:!0,it:!0,its:!0,just:!0,least:!0,let:!0,like:!0,likely:!0,may:!0,me:!0,might:!0,most:!0,must:!0,my:!0,neither:!0,no:!0,nor:!0,not:!0,of:!0,off:!0,often:!0,on:!0,only:!0,or:!0,other:!0,our:!0,own:!0,rather:!0,said:!0,say:!0,says:!0,she:!0,should:!0,since:!0,so:!0,some:!0,than:!0,that:!0,the:!0,their:!0,them:!0,then:!0,there:!0,these:!0,they:!0,"this":!0,tis:!0,to:!0,too:!0,twas:!0,us:!0,wants:!0,was:!0,we:!0,were:!0,what:!0,when:!0,where:!0,which:!0,"while":!0,who:!0,whom:!0,why:!0,will:!0,"with":!0,would:!0,yet:!0,you:!0,your:!0},t.stopWordFilter.stopWords=t.defaultStopWords,t.Pipeline.registerFunction(t.stopWordFilter,"stopWordFilter"),t.trimmer=function(e){if(null===e||void 0===e)throw new Error("token should not be undefined");return e.replace(/^\W+/,"").replace(/\W+$/,"")},t.Pipeline.registerFunction(t.trimmer,"trimmer"),t.InvertedIndex=function(){this.root={docs:{},df:0}},t.InvertedIndex.load=function(e){var t=new this;return t.root=e.root,t},t.InvertedIndex.prototype.addToken=function(e,t,n){for(var n=n||this.root,i=0;i<=e.length-1;){var o=e[i];o in n||(n[o]={docs:{},df:0}),i+=1,n=n[o]}var r=t.ref;n.docs[r]?n.docs[r]={tf:t.tf}:(n.docs[r]={tf:t.tf},n.df+=1)},t.InvertedIndex.prototype.hasToken=function(e){if(!e)return!1;for(var t=this.root,n=0;n<e.length;n++){if(!t[e[n]])return!1;t=t[e[n]]}return!0},t.InvertedIndex.prototype.getNode=function(e){if(!e)return null;for(var t=this.root,n=0;n<e.length;n++){if(!t[e[n]])return null;t=t[e[n]]}return t},t.InvertedIndex.prototype.getDocs=function(e){var t=this.getNode(e);return null==t?{}:t.docs},t.InvertedIndex.prototype.getTermFrequency=function(e,t){var n=this.getNode(e);return null==n?0:t in n.docs?n.docs[t].tf:0},t.InvertedIndex.prototype.getDocFreq=function(e){var t=this.getNode(e);return null==t?0:t.df},t.InvertedIndex.prototype.removeToken=function(e,t){if(e){var n=this.getNode(e);null!=n&&t in n.docs&&(delete n.docs[t],n.df-=1)}},t.InvertedIndex.prototype.expandToken=function(e,t,n){if(null==e||""==e)return[];var t=t||[];if(void 0==n&&(n=this.getNode(e),null==n))return t;n.df>0&&t.push(e);for(var i in n)"docs"!==i&&"df"!==i&&this.expandToken(e+i,t,n[i]);return t},t.InvertedIndex.prototype.toJSON=function(){return{root:this.root}},t.Configuration=function(e,n){var e=e||"";if(void 0==n||null==n)throw new Error("fields should not be null");this.config={};var i;try{i=JSON.parse(e),this.buildUserConfig(i,n)}catch(o){t.utils.warn("user configuration parse failed, will use default configuration"),this.buildDefaultConfig(n)}},t.Configuration.prototype.buildDefaultConfig=function(e){this.reset(),e.forEach(function(e){this.config[e]={boost:1,bool:"OR",expand:!1}},this)},t.Configuration.prototype.buildUserConfig=function(e,n){var i="OR",o=!1;if(this.reset(),"bool"in e&&(i=e.bool||i),"expand"in e&&(o=e.expand||o),"fields"in e)for(var r in e.fields)if(n.indexOf(r)>-1){var s=e.fields[r],u=o;void 0!=s.expand&&(u=s.expand),this.config[r]={boost:s.boost||0===s.boost?s.boost:1,bool:s.bool||i,expand:u}}else t.utils.warn("field name in user configuration not found in index instance fields");else this.addAllFields2UserConfig(i,o,n)},t.Configuration.prototype.addAllFields2UserConfig=function(e,t,n){n.forEach(function(n){this.config[n]={boost:1,bool:e,expand:t}},this)},t.Configuration.prototype.get=function(){return 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    /** pdoc search index */const docs = {"version": "0.9.5", "fields": ["qualname", "fullname", "annotation", "default_value", "signature", "bases", "doc"], "ref": "fullname", "documentStore": {"docs": {"KiTE": {"fullname": "KiTE", "modulename": "KiTE", "kind": "module", "doc": "<p>KiTE contains utilities to validate and calidrate supervised machine learning models.</p>\n\n<h2 id=\"main-features\">Main Features</h2>\n\n<p>Here are the major utilities provided by the package:</p>\n\n<ul>\n<li>Metrics to test if local bias is statistically significant within the given model</li>\n<li>Calibration utilities to reduce local bias</li>\n<li>Diffusion Map utilities to transform euclidean distance metrics into a diffusion space</li>\n</ul>\n\n<h2 id=\"example-notebooks\">Example Notebooks</h2>\n\n<p>We created <a href=\"https://github.com/A-Good-System-for-Smart-Cities/KiTE-utils/tree/main/notebooks\">Example Notebooks</a> to showcase basic examples and applications of this library.</p>\n"}, "KiTE.calibrate": {"fullname": "KiTE.calibrate", "modulename": "KiTE.calibrate", "kind": "module", "doc": "<p>Calibration utilities to help reduce the local bias of a given model.</p>\n"}, "KiTE.calibrate.local_bias_estimator": {"fullname": "KiTE.calibrate.local_bias_estimator", "modulename": "KiTE.calibrate", "qualname": "local_bias_estimator", "kind": "function", "doc": "<p>Estimates model bias by calculating bias = Y - model predictions. Assumes model is already close to the oracle model.</p>\n\n<h6 id=\"parameters\">Parameters</h6>\n\n<ul>\n<li><strong>X</strong> (numpy-array):\ndata, of size NxD [N is the number of data points, D is the features dimension]</li>\n<li><strong>Y</strong> (numpy-array):\ncredible error vector, of size Nx1 [N is the number of data points]</li>\n<li><strong>p</strong> (numpy-array):\nprobability vector, of size Nx1 [N is the number of data points]</li>\n<li><strong>X_grid</strong> (numpy-array):\n<ul>\n<li>For EWF, X_grid used to build a kernel. This kernel used to calculate bias</li>\n<li>Else, X_grid used as \"test features\" to predict bias</li>\n</ul></li>\n<li><strong>model</strong> (str):\nIndicates model type. Valid Options include:\n    - \"KRR\": KernelRidge\n    - \"SVR\": SVR\n    - \"EWF\": EWF</li>\n<li><strong>kernel_function</strong> (str):\nIndicates kernel function type.\nRefer to sklearn documentation to see which kernel functions are valid for model chosen.</li>\n<li><strong><em>*kwargs</strong> (</em>*kwargs):\nextra parameters passed to <code>pairwise_kernels()</code> as kernel parameters</li>\n</ul>\n\n<h6 id=\"returns\">Returns</h6>\n\n<ul>\n<li><strong>numpy-array</strong>: estimated bias</li>\n</ul>\n", "signature": "<span class=\"signature pdoc-code condensed\">(<span class=\"param\"><span class=\"n\">X</span>, </span><span class=\"param\"><span class=\"n\">Y</span>, </span><span class=\"param\"><span class=\"n\">p</span>, </span><span class=\"param\"><span class=\"n\">X_grid</span>, </span><span class=\"param\"><span class=\"n\">model</span><span class=\"o\">=</span><span class=\"s1\">&#39;KRR&#39;</span>, </span><span class=\"param\"><span class=\"n\">kernel_function</span><span class=\"o\">=</span><span class=\"s1\">&#39;rbf&#39;</span>, </span><span class=\"param\"><span class=\"o\">**</span><span class=\"n\">kwargs</span></span><span class=\"return-annotation\">):</span></span>", "funcdef": "def"}, "KiTE.calibrate.construct_credible_error_vector": {"fullname": "KiTE.calibrate.construct_credible_error_vector", "modulename": "KiTE.calibrate", "qualname": "construct_credible_error_vector", "kind": "function", "doc": "<p>For a one dimensional output prediction Y, construct the credible error vector.\nUses given lower and upper percentiles. Assumes credible level alpha is fixed.</p>\n\n<h6 id=\"parameters\">Parameters</h6>\n\n<ul>\n<li><strong>Y</strong> (numpy-array):\ndata, of size Nx1 [N is the number of data points]</li>\n<li><strong>Yr_up</strong> (numpy-array):\nupper percentile vector, of size Nx1 [N is the number of data points]</li>\n<li><strong>Yr_down</strong> (numpy-array):\nlower percentile vector, of size Nx1 [N is the number of data points]</li>\n<li><strong>alpha</strong> (float):\nthe theoretical credible level alpha</li>\n</ul>\n\n<h6 id=\"returns\">Returns</h6>\n\n<ul>\n<li><strong>numpy-array</strong>: Credible Error Vector</li>\n</ul>\n", "signature": "<span class=\"signature pdoc-code condensed\">(<span class=\"param\"><span class=\"n\">Y</span>, </span><span class=\"param\"><span class=\"n\">Yr_up</span>, </span><span class=\"param\"><span class=\"n\">Yr_down</span>, </span><span class=\"param\"><span class=\"n\">alpha</span></span><span class=\"return-annotation\">):</span></span>", "funcdef": "def"}, "KiTE.calibrate.calibrate": {"fullname": "KiTE.calibrate.calibrate", "modulename": "KiTE.calibrate", "qualname": "calibrate", "kind": "function", "doc": "<p>A calibration method that takes the predicted probabilties and positive cases and recalibrate the probabilities.</p>\n\n<h6 id=\"parameters\">Parameters</h6>\n\n<ul>\n<li><strong>Xtrain</strong> (array, shape (n_samples_train,)):\nFeatures used for training</li>\n<li><strong>prob_train</strong> (array, shape (n_samples_train,)):\nProbabilities of positive class to train a calibration model</li>\n<li><strong>Ytrain</strong> (array, shape (n_samples_train,)):\nValues used for training</li>\n<li><strong>Xtest</strong> (array, shape (n_samples_test,)):\nFeatures used for testing</li>\n<li><strong>prob_test</strong> (array, shape (n_samples_test,)):\nProbabilities of the positive class to be calibrated (test set). If None it re-calibrate the training set.</li>\n<li><strong>method</strong> (string, 'platt', 'isotonic', 'temperature_scaling', 'beta', 'HB', 'BBG', 'ENIR'):\nThe method to use for calibration. Can be \u2018sigmoid\u2019 which corresponds to Platt\u2019s method\n(i.e. a logistic regression model) or \u2018isotonic\u2019 which is a non-parametric approach.\nIt is not advised to use isotonic calibration with too few calibration samples (&lt;&lt;1000) since it tends to overfit.</li>\n<li><strong>**kwargs</strong>: Additional Args used to fit KRR or EWF</li>\n</ul>\n\n<h6 id=\"returns\">Returns</h6>\n\n<ul>\n<li><strong>array, shape (n_bins,)</strong>: The calibrated error for test set. (p_calibrated)</li>\n</ul>\n", "signature": "<span class=\"signature pdoc-code multiline\">(<span class=\"param\">\t<span class=\"n\">Xtrain</span>,</span><span class=\"param\">\t<span class=\"n\">prob_train</span>,</span><span class=\"param\">\t<span class=\"n\">Ytrain</span>,</span><span class=\"param\">\t<span class=\"n\">Xtest</span><span class=\"o\">=</span><span class=\"kc\">None</span>,</span><span class=\"param\">\t<span class=\"n\">prob_test</span><span class=\"o\">=</span><span class=\"kc\">None</span>,</span><span class=\"param\">\t<span class=\"n\">method</span><span class=\"o\">=</span><span class=\"s1\">&#39;platt&#39;</span>,</span><span class=\"param\">\t<span class=\"o\">**</span><span class=\"n\">kwargs</span></span><span class=\"return-annotation\">):</span></span>", "funcdef": "def"}, "KiTE.calibrate.calibration_error": {"fullname": "KiTE.calibrate.calibration_error", "modulename": "KiTE.calibrate", "qualname": "calibration_error", "kind": "function", "doc": "<p>Compute calibration error given true targets and predicted probabilities.\n Calibration curves may also be referred to as reliability diagrams.</p>\n\n<h6 id=\"parameters\">Parameters</h6>\n\n<ul>\n<li><strong>y_true</strong> (array, shape (n_samples,)):\nTrue targets.</li>\n<li><strong>y_prob</strong> (array, shape (n_samples,)):\nProbabilities of the positive class.</li>\n<li><strong>method</strong> (string, default='ECE', {'ECE', 'MCE', 'BS'}):\nWhich method to be used to compute calibration error.</li>\n<li><strong>n_bins</strong> (int):\nNumber of bins. Note that a bigger number requires more data.</li>\n</ul>\n\n<h6 id=\"returns\">Returns</h6>\n\n<ul>\n<li><strong>float</strong>: calibration error score</li>\n</ul>\n", "signature": "<span class=\"signature pdoc-code condensed\">(<span class=\"param\"><span class=\"n\">y_true</span>, </span><span class=\"param\"><span class=\"n\">y_prob</span>, </span><span class=\"param\"><span class=\"n\">n_bins</span><span class=\"o\">=</span><span class=\"mi\">10</span>, </span><span class=\"param\"><span class=\"n\">method</span><span class=\"o\">=</span><span class=\"s1\">&#39;ECE&#39;</span></span><span class=\"return-annotation\">):</span></span>", "funcdef": "def"}, "KiTE.calibration_models": {"fullname": "KiTE.calibration_models", "modulename": "KiTE.calibration_models", "kind": "module", "doc": "<p>Additional Callibration Models</p>\n"}, "KiTE.calibration_models.KRR_calibration": {"fullname": "KiTE.calibration_models.KRR_calibration", "modulename": "KiTE.calibration_models", "qualname": "KRR_calibration", "kind": "class", "doc": "<p></p>\n"}, "KiTE.calibration_models.KRR_calibration.__init__": {"fullname": "KiTE.calibration_models.KRR_calibration.__init__", "modulename": "KiTE.calibration_models", "qualname": "KRR_calibration.__init__", "kind": "function", "doc": "<p></p>\n", "signature": "<span class=\"signature pdoc-code condensed\">()</span>"}, "KiTE.calibration_models.KRR_calibration.fit": {"fullname": "KiTE.calibration_models.KRR_calibration.fit", "modulename": "KiTE.calibration_models", "qualname": "KRR_calibration.fit", "kind": "function", "doc": "<p></p>\n", "signature": "<span class=\"signature pdoc-code condensed\">(<span class=\"param\"><span class=\"bp\">self</span>, </span><span class=\"param\"><span class=\"n\">X</span>, </span><span class=\"param\"><span class=\"n\">p</span>, </span><span class=\"param\"><span class=\"n\">Y</span>, </span><span class=\"param\"><span class=\"n\">use_y</span><span class=\"o\">=</span><span class=\"kc\">True</span>, </span><span class=\"param\"><span class=\"n\">kernel_function</span><span class=\"o\">=</span><span class=\"s1\">&#39;rbf&#39;</span>, </span><span class=\"param\"><span class=\"o\">**</span><span class=\"n\">kwargs</span></span><span class=\"return-annotation\">):</span></span>", "funcdef": "def"}, "KiTE.calibration_models.KRR_calibration.predict": {"fullname": "KiTE.calibration_models.KRR_calibration.predict", "modulename": "KiTE.calibration_models", "qualname": "KRR_calibration.predict", "kind": "function", "doc": "<p></p>\n", "signature": "<span class=\"signature pdoc-code condensed\">(<span class=\"param\"><span class=\"bp\">self</span>, </span><span class=\"param\"><span class=\"n\">X</span>, </span><span class=\"param\"><span class=\"n\">p</span>, </span><span class=\"param\"><span class=\"n\">mode</span><span class=\"o\">=</span><span class=\"s1\">&#39;prob&#39;</span></span><span class=\"return-annotation\">):</span></span>", "funcdef": "def"}, "KiTE.calibration_models.EWF_calibration": {"fullname": "KiTE.calibration_models.EWF_calibration", "modulename": "KiTE.calibration_models", "qualname": "EWF_calibration", "kind": "class", "doc": "<p></p>\n"}, "KiTE.calibration_models.EWF_calibration.__init__": {"fullname": "KiTE.calibration_models.EWF_calibration.__init__", "modulename": "KiTE.calibration_models", "qualname": "EWF_calibration.__init__", "kind": "function", "doc": "<p></p>\n", "signature": "<span class=\"signature pdoc-code condensed\">()</span>"}, "KiTE.calibration_models.EWF_calibration.fit": {"fullname": "KiTE.calibration_models.EWF_calibration.fit", "modulename": "KiTE.calibration_models", "qualname": "EWF_calibration.fit", "kind": "function", "doc": "<p></p>\n", "signature": "<span class=\"signature pdoc-code condensed\">(<span class=\"param\"><span class=\"bp\">self</span>, </span><span class=\"param\"><span class=\"n\">X</span>, </span><span class=\"param\"><span class=\"n\">p</span>, </span><span class=\"param\"><span class=\"n\">Y</span>, </span><span class=\"param\"><span class=\"n\">kernel_function</span><span class=\"o\">=</span><span class=\"s1\">&#39;rbf&#39;</span>, </span><span class=\"param\"><span class=\"o\">**</span><span class=\"n\">kwargs</span></span><span class=\"return-annotation\">):</span></span>", "funcdef": "def"}, "KiTE.calibration_models.EWF_calibration.predict": {"fullname": "KiTE.calibration_models.EWF_calibration.predict", "modulename": "KiTE.calibration_models", "qualname": "EWF_calibration.predict", "kind": "function", "doc": "<p></p>\n", "signature": "<span class=\"signature pdoc-code condensed\">(<span class=\"param\"><span class=\"bp\">self</span>, </span><span class=\"param\"><span class=\"n\">Xtest</span>, </span><span class=\"param\"><span class=\"n\">ptest</span>, </span><span class=\"param\"><span class=\"n\">mode</span><span class=\"o\">=</span><span class=\"s1\">&#39;prob&#39;</span></span><span class=\"return-annotation\">):</span></span>", "funcdef": "def"}, "KiTE.diffusion_maps": {"fullname": "KiTE.diffusion_maps", "modulename": "KiTE.diffusion_maps", "kind": "module", "doc": "<p>Utilities to transform coordinates and distances into a diffusion space.</p>\n"}, "KiTE.diffusion_maps.calculate_kernel_matrix": {"fullname": "KiTE.diffusion_maps.calculate_kernel_matrix", "modulename": "KiTE.diffusion_maps", "qualname": "calculate_kernel_matrix", "kind": "function", "doc": "<p>Calculate Kernel Matrix ... an indication of local geometry\nNOTE: K is symmetric (k(x,y)=k(y,x)) and positivity preserving (k(x,y) &gt;= 0 forall x,y)</p>\n\n<h6 id=\"parameters\">Parameters</h6>\n\n<ul>\n<li><strong>X</strong> (numpy-array):\nInput data</li>\n<li><strong>epsilon</strong> (float):\nMetric for kernel width</li>\n</ul>\n\n<h6 id=\"returns\">Returns</h6>\n\n<ul>\n<li><strong>numpy-array</strong>: Kernel Matrix</li>\n</ul>\n", "signature": "<span class=\"signature pdoc-code condensed\">(<span class=\"param\"><span class=\"n\">X</span>, </span><span class=\"param\"><span class=\"n\">epsilon</span></span><span class=\"return-annotation\">):</span></span>", "funcdef": "def"}, "KiTE.diffusion_maps.get_connectivity_matrix": {"fullname": "KiTE.diffusion_maps.get_connectivity_matrix", "modulename": "KiTE.diffusion_maps", "qualname": "get_connectivity_matrix", "kind": "function", "doc": "<p>Calculate connectivity matrix (as normalization of Kernel Matrix Rows).\nEach value in connectivity matrix is probability of stepping to another cell in 1 timestep!\nMultiply connectivity matrices to see how probabilities change over 2 timesteps</p>\n\n<p>NOTES:\n    - p is positivity preserving (p(x,y) &gt;= 0 forall x,y) &amp; sum of P in each row = 1</p>\n\n<p>P_i = collection of all connectivities leading to point xi\n    = Diffusion Space of X</p>\n\n<h6 id=\"parameters\">Parameters</h6>\n\n<ul>\n<li><strong>K</strong> (numpy-array):\nkernel matrix</li>\n</ul>\n\n<h6 id=\"returns\">Returns</h6>\n\n<ul>\n<li><strong>numpy-array</strong>: Full Connectivity Matrix</li>\n</ul>\n", "signature": "<span class=\"signature pdoc-code condensed\">(<span class=\"param\"><span class=\"n\">K</span></span><span class=\"return-annotation\">):</span></span>", "funcdef": "def"}, "KiTE.diffusion_maps.transform_into_diffusion_space": {"fullname": "KiTE.diffusion_maps.transform_into_diffusion_space", "modulename": "KiTE.diffusion_maps", "qualname": "transform_into_diffusion_space", "kind": "function", "doc": "<p>Given Kernel, calculates connectivity matrix (as normalization of Kernel Matrix Rows).\nPerforms Eigendecomposition to transform kernel coordinates into a diffusion space</p>\n\n<h6 id=\"parameters\">Parameters</h6>\n\n<ul>\n<li><strong>K</strong> (numpy-array):\nkernel matrix</li>\n<li><strong>num_timesteps</strong> (int):\nNumber of timesteps for diffusion calculation</li>\n<li><strong>num_eigenvectors</strong> (int):\nNumber of eigenvectors (used in descending order of magnitude) used to calculate diffusion coordinates</li>\n</ul>\n\n<h6 id=\"returns\">Returns</h6>\n\n<ul>\n<li><strong>numpy-array</strong>: Diffusion Coordinates/Map</li>\n</ul>\n", "signature": "<span class=\"signature pdoc-code condensed\">(<span class=\"param\"><span class=\"n\">K</span><span class=\"o\">=</span><span class=\"kc\">None</span>, </span><span class=\"param\"><span class=\"n\">num_timesteps</span><span class=\"o\">=</span><span class=\"mi\">1</span>, </span><span class=\"param\"><span class=\"n\">num_eigenvectors</span><span class=\"o\">=</span><span class=\"mi\">10</span></span><span class=\"return-annotation\">):</span></span>", "funcdef": "def"}, "KiTE.diffusion_maps.calculate_diffusion_distance_matrix": {"fullname": "KiTE.diffusion_maps.calculate_diffusion_distance_matrix", "modulename": "KiTE.diffusion_maps", "qualname": "calculate_diffusion_distance_matrix", "kind": "function", "doc": "<p>New Distance based on pairwise distance between 2 points' connectivity</p>\n\n<p>D^2 = sum_u [ (P^t)_iu - (P^t)_ju ]^2 ... D = dist(p_iu, p_ij)</p>\n\n<p>D(xi, xj) ^ 2 = (Pi - Pj)^2</p>\n\n<h6 id=\"parameters\">Parameters</h6>\n\n<ul>\n<li><strong>diffy_map</strong> (numpy-array):\nDiffusion Coordinates/Map</li>\n</ul>\n\n<h6 id=\"returns\">Returns</h6>\n\n<ul>\n<li><strong>numpy-array</strong>: Diffusion Distance Matrix</li>\n</ul>\n", "signature": "<span class=\"signature pdoc-code condensed\">(<span class=\"param\"><span class=\"n\">diffy_map</span></span><span class=\"return-annotation\">):</span></span>", "funcdef": "def"}, "KiTE.metrics": {"fullname": "KiTE.metrics", "modulename": "KiTE.metrics", "kind": "module", "doc": "<p>Metrics utilities to help test if a model is locally biased.</p>\n"}, "KiTE.metrics.ELCE2_estimator": {"fullname": "KiTE.metrics.ELCE2_estimator", "modulename": "KiTE.metrics", "qualname": "ELCE2_estimator", "kind": "function", "doc": "<p>The estimator ELCE^2</p>\n\n<h6 id=\"parameters\">Parameters</h6>\n\n<ul>\n<li><strong>K_xx</strong> (numpy-array):\nevaluated kernel function</li>\n<li><strong>err</strong> (numpy-array):\none-dimensional error vector</li>\n</ul>\n\n<h6 id=\"returns\">Returns</h6>\n\n<ul>\n<li><strong>numpy-array</strong>: estimated ELCE^2</li>\n</ul>\n", "signature": "<span class=\"signature pdoc-code condensed\">(<span class=\"param\"><span class=\"n\">K_xx</span>, </span><span class=\"param\"><span class=\"n\">err</span></span><span class=\"return-annotation\">):</span></span>", "funcdef": "def"}, "KiTE.metrics.ELCE2_normalization": {"fullname": "KiTE.metrics.ELCE2_normalization", "modulename": "KiTE.metrics", "qualname": "ELCE2_normalization", "kind": "function", "doc": "<p>The normalization of estimator ELCE^2</p>\n\n<h6 id=\"parameters\">Parameters</h6>\n\n<ul>\n<li><strong>K</strong> (numpy-array):\nevaluated kernel function</li>\n</ul>\n\n<h6 id=\"returns\">Returns</h6>\n\n<ul>\n<li><strong>float</strong>: estimated normalization of ELCE^2</li>\n</ul>\n", "signature": "<span class=\"signature pdoc-code condensed\">(<span class=\"param\"><span class=\"n\">K</span></span><span class=\"return-annotation\">):</span></span>", "funcdef": "def"}, "KiTE.metrics.ELCE2_null_estimator": {"fullname": "KiTE.metrics.ELCE2_null_estimator", "modulename": "KiTE.metrics", "qualname": "ELCE2_null_estimator", "kind": "function", "doc": "<p>Compute the ELCE^2_u for one bootstrap realization</p>\n\n<h6 id=\"parameters\">Parameters</h6>\n\n<ul>\n<li><strong>err</strong> (numpy-array):\none-dimensional error vector</li>\n<li><strong>K</strong> (numpy-array):\nevaluated kernel function</li>\n<li><strong>rng</strong> (type(np.random.RandomState())):\nnumpy random function</li>\n</ul>\n\n<h6 id=\"returns\">Returns</h6>\n\n<ul>\n<li><strong>float</strong>: unbiased estimate of ELCE^2_u</li>\n</ul>\n", "signature": "<span class=\"signature pdoc-code condensed\">(<span class=\"param\"><span class=\"n\">err</span>, </span><span class=\"param\"><span class=\"n\">K</span>, </span><span class=\"param\"><span class=\"n\">rng</span></span><span class=\"return-annotation\">):</span></span>", "funcdef": "def"}, "KiTE.metrics.compute_null_distribution": {"fullname": "KiTE.metrics.compute_null_distribution", "modulename": "KiTE.metrics", "qualname": "compute_null_distribution", "kind": "function", "doc": "<p>Compute the null-distribution of test statistics via a bootstrap procedure</p>\n\n<h6 id=\"parameters\">Parameters</h6>\n\n<ul>\n<li><strong>p_err</strong> (numpy-array):\none-dimensional probability error vector</li>\n<li><strong>K</strong> (numpy-array):\nevaluated kernel function</li>\n<li><strong>iterations</strong> (int):\ncontrols the number of bootstrap realizations</li>\n<li><strong>verbose</strong> (bool):\ncontrols the verbosity of the model's output</li>\n<li><strong>random_state</strong> (type(np.random.RandomState()) or None):\ndefines the initial random state</li>\n</ul>\n\n<h6 id=\"returns\">Returns</h6>\n\n<ul>\n<li><strong>numpy-array</strong>: boostrap samples of the test null distribution</li>\n</ul>\n", "signature": "<span class=\"signature pdoc-code multiline\">(<span class=\"param\">\t<span class=\"n\">p_err</span>,</span><span class=\"param\">\t<span class=\"n\">K</span>,</span><span class=\"param\">\t<span class=\"n\">iterations</span><span class=\"o\">=</span><span class=\"mi\">1000</span>,</span><span class=\"param\">\t<span class=\"n\">n_jobs</span><span class=\"o\">=</span><span class=\"mi\">1</span>,</span><span class=\"param\">\t<span class=\"n\">verbose</span><span class=\"o\">=</span><span class=\"kc\">False</span>,</span><span class=\"param\">\t<span class=\"n\">random_state</span><span class=\"o\">=</span><span class=\"kc\">None</span></span><span class=\"return-annotation\">):</span></span>", "funcdef": "def"}, "KiTE.metrics.ELCE2": {"fullname": "KiTE.metrics.ELCE2", "modulename": "KiTE.metrics", "qualname": "ELCE2", "kind": "function", "doc": "<p>This function estimate ELCE^2_u employing a kernel trick. ELCE^2_u tests if a proposed posterior credible interval\nis calibrated employing a randomly drawn calibration test. The null hypothesis is that the posteriors are\nproperly calibrated. This function perform a bootstrap algorithm to estimate the null distribution,\nand corresponding p-value.</p>\n\n<h6 id=\"parameters\">Parameters</h6>\n\n<ul>\n<li><strong>X</strong> (numpy-array):\ndata, of size NxD [N is the number of data points, D is the features dimension]</li>\n<li><strong>Y</strong> (numpy-array):\ncredible error vector, of size Nx1 [N is the number of data points]</li>\n<li><strong>p</strong> (numpy-array):\nprobability vector, of size Nx1 [N is the number of data points]</li>\n<li><strong>kernel_function</strong> (string):\ndefines the kernel function. For the list of implemented kernels, please consult with <a href=\"https://scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise.kernel_metrics.html#sklearn.metrics.pairwise.kernel_metrics\">sklearn</a></li>\n<li><strong>prob_kernel_width</strong> (float):\nWidth of the probably kernel function</li>\n<li><strong>iterations</strong> (int):\ncontrols the number of bootstrap realizations</li>\n<li><strong>use_diffusion_distance</strong> (bool):\nUse diffusion instead of eucliden distances\nTransforms X into a diffusion space (see diffusion_maps.py)</li>\n<li><strong>verbose</strong> (bool):\ncontrols the verbosity of the model's output</li>\n<li><strong>random_state</strong> (type(np.random.RandomState()) or None):\ndefines the initial random state</li>\n<li><strong>n_jobs</strong> (int):\nnumber of jobs to run in parallel</li>\n<li><strong><em>*kwargs</strong> (</em>*kwargs):\nextra parameters, these are passed to <code>pairwise_kernels()</code> as kernel parameters\nE.g., if <code>kernel_two_sample_test(..., kernel_function='rbf', gamma=0.1)</code></li>\n</ul>\n\n<h6 id=\"returns\">Returns</h6>\n\n<ul>\n<li><strong>tuple</strong>: - SIZE = 1 if iterations=<code>None</code> else 3 (float, numpy-array, float)\n<ul>\n<li>first element is the test value,</li>\n<li>second element is samples from the null distribution via a bootstraps algorithm,</li>\n<li>third element is the estimated p-value.</li>\n</ul></li>\n</ul>\n", "signature": "<span class=\"signature pdoc-code multiline\">(<span class=\"param\">\t<span class=\"n\">X</span>,</span><span class=\"param\">\t<span class=\"n\">Y</span>,</span><span class=\"param\">\t<span class=\"n\">p</span>,</span><span class=\"param\">\t<span class=\"n\">kernel_function</span><span class=\"o\">=</span><span class=\"s1\">&#39;rbf&#39;</span>,</span><span class=\"param\">\t<span class=\"n\">prob_kernel_width</span><span class=\"o\">=</span><span class=\"mf\">0.1</span>,</span><span class=\"param\">\t<span class=\"n\">iterations</span><span class=\"o\">=</span><span class=\"kc\">None</span>,</span><span class=\"param\">\t<span class=\"n\">use_diffusion_distance</span><span class=\"o\">=</span><span class=\"kc\">False</span>,</span><span class=\"param\">\t<span class=\"n\">verbose</span><span class=\"o\">=</span><span class=\"kc\">True</span>,</span><span class=\"param\">\t<span class=\"n\">random_state</span><span class=\"o\">=</span><span class=\"kc\">None</span>,</span><span class=\"param\">\t<span class=\"n\">n_jobs</span><span class=\"o\">=</span><span class=\"mi\">1</span>,</span><span class=\"param\">\t<span class=\"o\">**</span><span class=\"n\">kwargs</span></span><span class=\"return-annotation\">):</span></span>", "funcdef": "def"}, "KiTE.plots": {"fullname": "KiTE.plots", "modulename": "KiTE.plots", "kind": "module", "doc": "<p></p>\n"}, "KiTE.plots.plot_probability_frequency": {"fullname": "KiTE.plots.plot_probability_frequency", "modulename": "KiTE.plots", "qualname": "plot_probability_frequency", "kind": "function", "doc": "<p>Utility to plot histogram of X-Test, prob_pos</p>\n\n<h6 id=\"parameters\">Parameters</h6>\n\n<ul>\n<li><p><strong>prob_pos</strong> (numpy-array):</p></li>\n<li><p><strong>ELCE2_</strong> (tuple or int):</p></li>\n<li><p><strong>name</strong> (string):\nModel Name</p></li>\n</ul>\n\n<h6 id=\"returns\">Returns</h6>\n\n<ul>\n<li><strong>fig</strong> (Histogram Plotly Figure):</li>\n</ul>\n", "signature": "<span class=\"signature pdoc-code condensed\">(<span class=\"param\"><span class=\"n\">prob_pos</span>, </span><span class=\"param\"><span class=\"n\">ELCE2_</span>, </span><span class=\"param\"><span class=\"n\">name</span><span class=\"o\">=</span><span class=\"s1\">&#39;Name_of_model&#39;</span></span><span class=\"return-annotation\">):</span></span>", "funcdef": "def"}, "KiTE.plots.plot_ELCE2_number_line": {"fullname": "KiTE.plots.plot_ELCE2_number_line", "modulename": "KiTE.plots", "qualname": "plot_ELCE2_number_line", "kind": "function", "doc": "<p></p>\n", "signature": "<span class=\"signature pdoc-code condensed\">(<span class=\"param\"><span class=\"n\">ELCE2_</span></span><span class=\"return-annotation\">):</span></span>", "funcdef": "def"}, "KiTE.tests": {"fullname": "KiTE.tests", "modulename": "KiTE.tests", "kind": "module", "doc": "<p></p>\n"}, "KiTE.tests.metrics_test": {"fullname": "KiTE.tests.metrics_test", "modulename": "KiTE.tests.metrics_test", "kind": "module", "doc": "<p></p>\n"}, "KiTE.tests.validation_test": {"fullname": "KiTE.tests.validation_test", "modulename": "KiTE.tests.validation_test", "kind": "module", "doc": "<p></p>\n"}, "KiTE.tests.validation_test.Test_check_attributes": {"fullname": "KiTE.tests.validation_test.Test_check_attributes", "modulename": "KiTE.tests.validation_test", "qualname": "Test_check_attributes", "kind": "class", "doc": "<p></p>\n"}, "KiTE.tests.validation_test.Test_check_attributes.__init__": {"fullname": "KiTE.tests.validation_test.Test_check_attributes.__init__", "modulename": "KiTE.tests.validation_test", "qualname": "Test_check_attributes.__init__", "kind": "function", "doc": "<p></p>\n", "signature": "<span class=\"signature pdoc-code condensed\">()</span>"}, "KiTE.tests.validation_test.Test_check_attributes.test_None_inputs": {"fullname": "KiTE.tests.validation_test.Test_check_attributes.test_None_inputs", "modulename": "KiTE.tests.validation_test", "qualname": "Test_check_attributes.test_None_inputs", "kind": "function", "doc": "<p></p>\n", "signature": "<span class=\"signature pdoc-code condensed\">(<span class=\"param\"><span class=\"bp\">self</span></span><span class=\"return-annotation\">):</span></span>", "funcdef": "def"}, "KiTE.tests.validation_test.Test_check_attributes.test_incompatible_dims": {"fullname": "KiTE.tests.validation_test.Test_check_attributes.test_incompatible_dims", "modulename": "KiTE.tests.validation_test", "qualname": 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"Test_check_attributes.test_invalid_n_jobs", "kind": "function", "doc": "<p></p>\n", "signature": "<span class=\"signature pdoc-code condensed\">(<span class=\"param\"><span class=\"bp\">self</span></span><span class=\"return-annotation\">):</span></span>", "funcdef": "def"}, "KiTE.validation": {"fullname": "KiTE.validation", "modulename": "KiTE.validation", "kind": "module", "doc": "<p></p>\n"}, "KiTE.validation.check_attributes": {"fullname": "KiTE.validation.check_attributes", "modulename": "KiTE.validation", "qualname": "check_attributes", "kind": "function", "doc": "<p>Check whether the input attributes are in proper format. If not exit with an Error message.</p>\n", "signature": "<span class=\"signature pdoc-code condensed\">(<span class=\"param\"><span class=\"n\">X</span>, </span><span class=\"param\"><span class=\"n\">e</span>, </span><span class=\"param\"><span class=\"n\">iterations</span><span class=\"o\">=</span><span class=\"mi\">1000</span>, </span><span class=\"param\"><span class=\"n\">n_jobs</span><span class=\"o\">=</span><span class=\"mi\">1</span></span><span class=\"return-annotation\">):</span></span>", "funcdef": "def"}, "KiTE.validation.check_credible_vector_args": {"fullname": "KiTE.validation.check_credible_vector_args", "modulename": "KiTE.validation", "qualname": "check_credible_vector_args", "kind": "function", "doc": "<p>Check arguments of construct_credible_error_vector()</p>\n", "signature": "<span class=\"signature pdoc-code condensed\">(<span class=\"param\"><span class=\"n\">Y</span>, </span><span class=\"param\"><span class=\"n\">Yr_up</span>, </span><span 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1.4142135623730951}, "KiTE.plots.plot_probability_frequency": {"tf": 1}}, "df": 5, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {"KiTE.calibrate.calibrate": {"tf": 1}}, "df": 1}}}}, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {"KiTE.calibrate.calibrate": {"tf": 1}}, "df": 1}}}}, "i": {"docs": {"KiTE.diffusion_maps.get_connectivity_matrix": {"tf": 1}, "KiTE.diffusion_maps.calculate_diffusion_distance_matrix": {"tf": 1}}, "df": 2}, "j": {"docs": {"KiTE.diffusion_maps.calculate_diffusion_distance_matrix": {"tf": 1}}, "df": 1}, "x": {"docs": {"KiTE.metrics.ELCE2_estimator": {"tf": 1}}, "df": 1}}, "j": {"docs": {}, "df": 0, "u": {"docs": {"KiTE.diffusion_maps.calculate_diffusion_distance_matrix": {"tf": 1}}, "df": 1}, "o": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "s": {"docs": {"KiTE.metrics.ELCE2": {"tf": 1.4142135623730951}}, "df": 1}}}}}}}, "pipeline": ["trimmer"], "_isPrebuiltIndex": true};

    // mirrored in build-search-index.js (part 1)
    // Also split on html tags. this is a cheap heuristic, but good enough.
    elasticlunr.tokenizer.setSeperator(/[\s\-.;&_'"=,()]+|<[^>]*>/);

    let searchIndex;
    if (docs._isPrebuiltIndex) {
        console.info("using precompiled search index");
        searchIndex = elasticlunr.Index.load(docs);
    } else {
        console.time("building search index");
        // mirrored in build-search-index.js (part 2)
        searchIndex = elasticlunr(function () {
            this.pipeline.remove(elasticlunr.stemmer);
            this.pipeline.remove(elasticlunr.stopWordFilter);
            this.addField("qualname");
            this.addField("fullname");
            this.addField("annotation");
            this.addField("default_value");
            this.addField("signature");
            this.addField("bases");
            this.addField("doc");
            this.setRef("fullname");
        });
        for (let doc of docs) {
            searchIndex.addDoc(doc);
        }
        console.timeEnd("building search index");
    }

    return (term) => searchIndex.search(term, {
        fields: {
            qualname: {boost: 4},
            fullname: {boost: 2},
            annotation: {boost: 2},
            default_value: {boost: 2},
            signature: {boost: 2},
            bases: {boost: 2},
            doc: {boost: 1},
        },
        expand: true
    });
})();