From b33c4df23f007a8a7eb6721c049b80f6d8a20815 Mon Sep 17 00:00:00 2001 From: "Documenter.jl" Date: Fri, 22 Mar 2024 03:10:24 +0000 Subject: [PATCH] build based on 7bd4676 --- dev/404.html | 12 +++---- dev/API.html | 20 ++++++------ dev/architecture.pptx | Bin 47840 -> 0 bytes dev/assets/API.md.CfF3NmNB.lean.js | 1 - ...{API.md.CfF3NmNB.js => API.md.DTVXxwSH.js} | 4 +-- dev/assets/API.md.DTVXxwSH.lean.js | 1 + dev/assets/app.DGNdeVFL.js | 7 ---- dev/assets/app.DvvBbGJ8.js | 7 ++++ .../chunks/@localSearchIndexroot.D4YodZar.js | 1 + .../chunks/@localSearchIndexroot.D7cbWRs2.js | 1 - ...4VtkPM.js => VPLocalSearchBox.-AZ4MAmB.js} | 14 ++++---- dev/assets/chunks/framework.BResyDFY.js | 17 ---------- dev/assets/chunks/framework.CUwTDK2x.js | 17 ++++++++++ dev/assets/chunks/theme.5Q-JEVsf.js | 7 ---- dev/assets/chunks/theme.Ddw-j1ke.js | 7 ++++ .../decision-tree.Dqh78YdA.png} | Bin dev/assets/design.md.0tBnC0I_.js | 30 ++++++++++++++++++ dev/assets/design.md.0tBnC0I_.lean.js | 1 + dev/assets/design.md.CXyLal5T.js | 30 ------------------ dev/assets/design.md.CXyLal5T.lean.js | 1 - 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- + + - + - + -
NeuroTreeModels

Appearance

404

PAGE NOT FOUND

But if you don't change your direction, and if you keep looking, you may end up where you are heading.
Take me home
- +
NeuroTreeModels

Appearance

404

PAGE NOT FOUND

But if you don't change your direction, and if you keep looking, you may end up where you are heading.
Take me home
+ \ No newline at end of file diff --git a/dev/API.html b/dev/API.html index f7059a8..8fd5eaa 100644 --- a/dev/API.html +++ b/dev/API.html @@ -5,20 +5,20 @@ API | NeuroTreeModels - - + + - + - - - - + + + + -
NeuroTreeModels

Appearance

API ​

Training ​

# NeuroTreeModels.fit — Function.
julia
function fit(
+    
NeuroTreeModels
Appearance
API ​
Training ​
# NeuroTreeModels.fit — Function. 
julia
function fit(
     config::NeuroTreeRegressor,
     dtrain;
     feature_names,
@@ -31,8 +31,8 @@
     early_stopping_rounds=9999,
     verbosity=1,
     return_logger=false
-)
Training function of NeuroTreeModels' internal API.
Arguments
  • config::NeuroTreeRegressor
  • dtrain: Must be a AbstractDataFrame
Keyword arguments
  • feature_names: Required kwarg, a Vector{Symbol} or Vector{String} of the feature names.
  • target_name Required kwarg, a Symbol or String indicating the name of the target variable.
  • weight_name=nothing
  • offset_name=nothing
  • deval=nothing Data for tracking evaluation metric and perform early stopping.
  • metric=nothing: evaluation metric tracked on deval. Can be one of:
    • :mse
    • :mae
    • :logloss
    • :mlogloss
    • :gaussian_mle
  • print_every_n=9999
  • early_stopping_rounds=9999
  • verbosity=1
  • return_logger=false
source

Inference ​
# NeuroTreeModels.infer — Function. 
infer(m::NeuroTreeModel, data)
Return the inference of a NeuroTreeModel over data, where data is AbstractDataFrame.
source


-    
+)

Training function of NeuroTreeModels' internal API.

Arguments

  • config::NeuroTreeRegressor

  • dtrain: Must be a AbstractDataFrame

Keyword arguments

  • feature_names: Required kwarg, a Vector{Symbol} or Vector{String} of the feature names.

  • target_name Required kwarg, a Symbol or String indicating the name of the target variable.

  • weight_name=nothing

  • offset_name=nothing

  • deval=nothing Data for tracking evaluation metric and perform early stopping.

  • metric=nothing: evaluation metric tracked on deval. Can be one of:

    • :mse

    • :mae

    • :logloss

    • :mlogloss

    • :gaussian_mle

  • print_every_n=9999

  • early_stopping_rounds=9999

  • verbosity=1

  • return_logger=false

source


Inference ​

# NeuroTreeModels.infer — Function.

infer(m::NeuroTreeModel, data)

Return the inference of a NeuroTreeModel over data, where data is AbstractDataFrame.

source


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Training ​

# NeuroTreeModels.fit — Function.
julia
function fit(
+import{_ as i,c as s,o as e,a7 as a}from"./chunks/framework.CUwTDK2x.js";const E=JSON.parse('{"title":"API","description":"","frontmatter":{},"headers":[],"relativePath":"API.md","filePath":"API.md","lastUpdated":null}'),n={name:"API.md"},l=a(`
API ​
Training ​
# NeuroTreeModels.fit — Function. 
julia
function fit(
     config::NeuroTreeRegressor,
     dtrain;
     feature_names,
@@ -11,4 +11,4 @@ import{_ as i,c as s,o as e,a7 as a}from"./chunks/framework.BResyDFY.js";const E
     early_stopping_rounds=9999,
     verbosity=1,
     return_logger=false
-)
Training function of NeuroTreeModels' internal API.
Arguments
  • config::NeuroTreeRegressor
  • dtrain: Must be a AbstractDataFrame
Keyword arguments
  • feature_names: Required kwarg, a Vector{Symbol} or Vector{String} of the feature names.
  • target_name Required kwarg, a Symbol or String indicating the name of the target variable.
  • weight_name=nothing
  • offset_name=nothing
  • deval=nothing Data for tracking evaluation metric and perform early stopping.
  • metric=nothing: evaluation metric tracked on deval. Can be one of:
    • :mse
    • :mae
    • :logloss
    • :mlogloss
    • :gaussian_mle
  • print_every_n=9999
  • early_stopping_rounds=9999
  • verbosity=1
  • return_logger=false
source

Inference ​
# NeuroTreeModels.infer — Function. 
infer(m::NeuroTreeModel, data)
Return the inference of a NeuroTreeModel over data, where data is AbstractDataFrame.
source

`,7),t=[l];function r(o,p,d,h,k,c){return e(),s("div",null,t)}const u=i(n,[["render",r]]);export{E as __pageData,u as default};
+)

Training function of NeuroTreeModels' internal API.

Arguments

  • config::NeuroTreeRegressor

  • dtrain: Must be a AbstractDataFrame

Keyword arguments

  • feature_names: Required kwarg, a Vector{Symbol} or Vector{String} of the feature names.

  • target_name Required kwarg, a Symbol or String indicating the name of the target variable.

  • weight_name=nothing

  • offset_name=nothing

  • deval=nothing Data for tracking evaluation metric and perform early stopping.

  • metric=nothing: evaluation metric tracked on deval. Can be one of:

    • :mse

    • :mae

    • :logloss

    • :mlogloss

    • :gaussian_mle

  • print_every_n=9999

  • early_stopping_rounds=9999

  • verbosity=1

  • return_logger=false

source


Inference ​

# NeuroTreeModels.infer — Function.

infer(m::NeuroTreeModel, data)

Return the inference of a NeuroTreeModel over data, where data is AbstractDataFrame.

source


`,7),t=[l];function r(o,p,d,h,c,k){return e(),s("div",null,t)}const u=i(n,[["render",r]]);export{E as __pageData,u as default}; diff --git a/dev/assets/API.md.DTVXxwSH.lean.js b/dev/assets/API.md.DTVXxwSH.lean.js new file mode 100644 index 0000000..82ab133 --- /dev/null +++ b/dev/assets/API.md.DTVXxwSH.lean.js @@ -0,0 +1 @@ +import{_ as i,c as s,o as e,a7 as a}from"./chunks/framework.CUwTDK2x.js";const E=JSON.parse('{"title":"API","description":"","frontmatter":{},"headers":[],"relativePath":"API.md","filePath":"API.md","lastUpdated":null}'),n={name:"API.md"},l=a("",7),t=[l];function r(o,p,d,h,c,k){return e(),s("div",null,t)}const u=i(n,[["render",r]]);export{E as __pageData,u as default}; diff --git a/dev/assets/app.DGNdeVFL.js b/dev/assets/app.DGNdeVFL.js deleted file mode 100644 index 9b2bcf9..0000000 --- a/dev/assets/app.DGNdeVFL.js +++ /dev/null @@ -1,7 +0,0 @@ -import{j as o,aa as p,ab as u,ac as l,ad as c,ae as f,af as d,ag as m,ah as h,ai as A,aj as g,Y as v,d as P,u as w,l as 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n=g(t),r=null;return n&&(e&&(a=n),(e||a===n)&&(n=n.replace(/\.js$/,".lean.js")),r=v(()=>import(n),__vite__mapDeps([]))),o&&(e=!1),r},s.NotFound)}o&&T().then(({app:e,router:a,data:t})=>{a.go().then(()=>{p(a.route,t.site),e.mount("#app")})});export{T as createApp}; -function __vite__mapDeps(indexes) { - if (!__vite__mapDeps.viteFileDeps) { - __vite__mapDeps.viteFileDeps = [] - } - return indexes.map((i) => __vite__mapDeps.viteFileDeps[i]) -} diff --git a/dev/assets/app.DvvBbGJ8.js b/dev/assets/app.DvvBbGJ8.js new file mode 100644 index 0000000..5005c32 --- /dev/null +++ b/dev/assets/app.DvvBbGJ8.js @@ -0,0 +1,7 @@ +function __vite__mapDeps(indexes) { + if (!__vite__mapDeps.viteFileDeps) { + __vite__mapDeps.viteFileDeps = [] + } + return indexes.map((i) => __vite__mapDeps.viteFileDeps[i]) +} +import{j as o,a8 as p,a9 as u,aa as l,ab as c,ac as f,ad as d,ae as m,af as h,ag as g,ah as A,Y as P,d as _,u as v,l as R,z as w,ai as y,aj as C,ak as E,a6 as b}from"./chunks/framework.CUwTDK2x.js";import{R as T}from"./chunks/theme.Ddw-j1ke.js";function i(e){if(e.extends){const a=i(e.extends);return{...a,...e,async enhanceApp(t){a.enhanceApp&&await a.enhanceApp(t),e.enhanceApp&&await e.enhanceApp(t)}}}return e}const s=i(T),S=_({name:"VitePressApp",setup(){const{site:e,lang:a,dir:t}=v();return R(()=>{w(()=>{document.documentElement.lang=a.value,document.documentElement.dir=t.value})}),e.value.router.prefetchLinks&&y(),C(),E(),s.setup&&s.setup(),()=>b(s.Layout)}});async function j(){globalThis.__VITEPRESS__=!0;const e=L(),a=D();a.provide(u,e);const t=l(e.route);return a.provide(c,t),a.component("Content",f),a.component("ClientOnly",d),Object.defineProperties(a.config.globalProperties,{$frontmatter:{get(){return t.frontmatter.value}},$params:{get(){return t.page.value.params}}}),s.enhanceApp&&await s.enhanceApp({app:a,router:e,siteData:m}),{app:a,router:e,data:t}}function D(){return h(S)}function L(){let e=o,a;return g(t=>{let 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t,r,n=this._options.autoVacuum;try{this._options.autoVacuum=!1;try{for(var a=D(e),i=a.next();!i.done;i=a.next()){var s=i.value;this.discard(s)}}catch(u){t={error:u}}finally{try{i&&!i.done&&(r=a.return)&&r.call(a)}finally{if(t)throw t.error}}}finally{this._options.autoVacuum=n}this.maybeAutoVacuum()},o.prototype.replace=function(e){var t=this._options,r=t.idField,n=t.extractField,a=n(e,r);this.discard(a),this.add(e)},o.prototype.vacuum=function(e){return e===void 0&&(e={}),this.conditionalVacuum(e)},o.prototype.conditionalVacuum=function(e,t){var r=this;return this._currentVacuum?(this._enqueuedVacuumConditions=this._enqueuedVacuumConditions&&t,this._enqueuedVacuum!=null?this._enqueuedVacuum:(this._enqueuedVacuum=this._currentVacuum.then(function(){var n=r._enqueuedVacuumConditions;return 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string");this._size=void 0;var r=ze(this._tree,e);return r.set(G,t(r.get(G))),this},o.prototype.fetch=function(e,t){if(typeof e!="string")throw new Error("key must be a string");this._size=void 0;var r=ze(this._tree,e),n=r.get(G);return n===void 0&&r.set(G,n=t()),n},o.prototype.values=function(){return new Me(this,St)},o.prototype[Symbol.iterator]=function(){return this.entries()},o.from=function(e){var t,r,n=new o;try{for(var a=D(e),i=a.next();!i.done;i=a.next()){var s=J(i.value,2),u=s[0],l=s[1];n.set(u,l)}}catch(h){t={error:h}}finally{try{i&&!i.done&&(r=a.return)&&r.call(a)}finally{if(t)throw t.error}}return n},o.fromObject=function(e){return o.from(Object.entries(e))},o}(),ke=function(o,e,t){var r,n;if(t===void 0&&(t=[]),e.length===0||o==null)return[o,t];try{for(var a=D(o.keys()),i=a.next();!i.done;i=a.next()){var s=i.value;if(s!==G&&e.startsWith(s))return t.push([o,s]),ke(o.get(s),e.slice(s.length),t)}}catch(u){r={error:u}}finally{try{i&&!i.done&&(n=a.return)&&n.call(a)}finally{if(r)throw r.error}}return t.push([o,e]),ke(void 0,"",t)},Ke=function(o,e){var t,r;if(e.length===0||o==null)return o;try{for(var n=D(o.keys()),a=n.next();!a.done;a=n.next()){var i=a.value;if(i!==G&&e.startsWith(i))return Ke(o.get(i),e.slice(i.length))}}catch(s){t={error:s}}finally{try{a&&!a.done&&(r=n.return)&&r.call(n)}finally{if(t)throw t.error}}},ze=function(o,e){var t,r,n=e.length;e:for(var a=0;o&&a0)throw new Error("Expected documents to be present. Omit the argument to remove all documents.");this._index=new Le,this._documentCount=0,this._documentIds=new Map,this._idToShortId=new Map,this._fieldLength=new Map,this._avgFieldLength=[],this._storedFields=new Map,this._nextId=0}},o.prototype.discard=function(e){var t=this,r=this._idToShortId.get(e);if(r==null)throw new Error("MiniSearch: cannot discard document with ID ".concat(e,": it is not in the index"));this._idToShortId.delete(e),this._documentIds.delete(r),this._storedFields.delete(r),(this._fieldLength.get(r)||[]).forEach(function(n,a){t.removeFieldLength(r,a,t._documentCount,n)}),this._fieldLength.delete(r),this._documentCount-=1,this._dirtCount+=1,this.maybeAutoVacuum()},o.prototype.maybeAutoVacuum=function(){if(this._options.autoVacuum!==!1){var e=this._options.autoVacuum,t=e.minDirtFactor,r=e.minDirtCount,n=e.batchSize,a=e.batchWait;this.conditionalVacuum({batchSize:n,batchWait:a},{minDirtCount:r,minDirtFactor:t})}},o.prototype.discardAll=function(e){var t,r,n=this._options.autoVacuum;try{this._options.autoVacuum=!1;try{for(var a=D(e),i=a.next();!i.done;i=a.next()){var s=i.value;this.discard(s)}}catch(u){t={error:u}}finally{try{i&&!i.done&&(r=a.return)&&r.call(a)}finally{if(t)throw t.error}}}finally{this._options.autoVacuum=n}this.maybeAutoVacuum()},o.prototype.replace=function(e){var t=this._options,r=t.idField,n=t.extractField,a=n(e,r);this.discard(a),this.add(e)},o.prototype.vacuum=function(e){return e===void 0&&(e={}),this.conditionalVacuum(e)},o.prototype.conditionalVacuum=function(e,t){var r=this;return this._currentVacuum?(this._enqueuedVacuumConditions=this._enqueuedVacuumConditions&&t,this._enqueuedVacuum!=null?this._enqueuedVacuum:(this._enqueuedVacuum=this._currentVacuum.then(function(){var n=r._enqueuedVacuumConditions;return r._enqueuedVacuumConditions=Ue,r.performVacuuming(e,n)}),this._enqueuedVacuum)):this.vacuumConditionsMet(t)===!1?Promise.resolve():(this._currentVacuum=this.performVacuuming(e),this._currentVacuum)},o.prototype.performVacuuming=function(e,t){return Rr(this,void 0,void 0,function(){var r,n,a,i,s,u,l,h,d,v,y,b,E,g,S,T,F,L,R,B,N,_,A,O,w;return Mr(this,function(c){switch(c.label){case 0:if(r=this._dirtCount,!this.vacuumConditionsMet(t))return[3,10];n=e.batchSize||Je.batchSize,a=e.batchWait||Je.batchWait,i=1,c.label=1;case 1:c.trys.push([1,7,8,9]),s=D(this._index),u=s.next(),c.label=2;case 2:if(u.done)return[3,6];l=J(u.value,2),h=l[0],d=l[1];try{for(v=(_=void 0,D(d)),y=v.next();!y.done;y=v.next()){b=J(y.value,2),E=b[0],g=b[1];try{for(S=(O=void 0,D(g)),T=S.next();!T.done;T=S.next())F=J(T.value,1),L=F[0],!this._documentIds.has(L)&&(g.size<=1?d.delete(E):g.delete(L))}catch(f){O={error:f}}finally{try{T&&!T.done&&(w=S.return)&&w.call(S)}finally{if(O)throw O.error}}}}catch(f){_={error:f}}finally{try{y&&!y.done&&(A=v.return)&&A.call(v)}finally{if(_)throw _.error}}return this._index.get(h).size===0&&this._index.delete(h),i%n!==0?[3,4]:[4,new Promise(function(f){return setTimeout(f,a)})];case 3:c.sent(),c.label=4;case 4:i+=1,c.label=5;case 5:return u=s.next(),[3,2];case 6:return[3,9];case 7:return R=c.sent(),B={error:R},[3,9];case 8:try{u&&!u.done&&(N=s.return)&&N.call(s)}finally{if(B)throw B.error}return[7];case 9:this._dirtCount-=r,c.label=10;case 10:return[4,null];case 11:return c.sent(),this._currentVacuum=this._enqueuedVacuum,this._enqueuedVacuum=null,[2]}})})},o.prototype.vacuumConditionsMet=function(e){if(e==null)return!0;var t=e.minDirtCount,r=e.minDirtFactor;return t=t||Be.minDirtCount,r=r||Be.minDirtFactor,this.dirtCount>=t&&this.dirtFactor>=r},Object.defineProperty(o.prototype,"isVacuuming",{get:function(){return this._currentVacuum!=null},enumerable:!1,configurable:!0}),Object.defineProperty(o.prototype,"dirtCount",{get:function(){return this._dirtCount},enumerable:!1,configurable:!0}),Object.defineProperty(o.prototype,"dirtFactor",{get:function(){return this._dirtCount/(1+this._documentCount+this._dirtCount)},enumerable:!1,configurable:!0}),o.prototype.has=function(e){return this._idToShortId.has(e)},o.prototype.getStoredFields=function(e){var t=this._idToShortId.get(e);if(t!=null)return this._storedFields.get(t)},o.prototype.search=function(e,t){var r,n;t===void 0&&(t={});var a=this.executeQuery(e,t),i=[];try{for(var s=D(a),u=s.next();!u.done;u=s.next()){var l=J(u.value,2),h=l[0],d=l[1],v=d.score,y=d.terms,b=d.match,E=y.length||1,g={id:this._documentIds.get(h),score:v*E,terms:Object.keys(b),queryTerms:y,match:b};Object.assign(g,this._storedFields.get(h)),(t.filter==null||t.filter(g))&&i.push(g)}}catch(S){r={error:S}}finally{try{u&&!u.done&&(n=s.return)&&n.call(s)}finally{if(r)throw r.error}}return e===o.wildcard&&t.boostDocument==null&&this._options.searchOptions.boostDocument==null||i.sort(vt),i},o.prototype.autoSuggest=function(e,t){var r,n,a,i;t===void 0&&(t={}),t=W(W({},this._options.autoSuggestOptions),t);var s=new Map;try{for(var u=D(this.search(e,t)),l=u.next();!l.done;l=u.next()){var h=l.value,d=h.score,v=h.terms,y=v.join(" "),b=s.get(y);b!=null?(b.score+=d,b.count+=1):s.set(y,{score:d,terms:v,count:1})}}catch(R){r={error:R}}finally{try{l&&!l.done&&(n=u.return)&&n.call(u)}finally{if(r)throw r.error}}var E=[];try{for(var g=D(s),S=g.next();!S.done;S=g.next()){var T=J(S.value,2),b=T[0],F=T[1],d=F.score,v=F.terms,L=F.count;E.push({suggestion:b,terms:v,score:d/L})}}catch(R){a={error:R}}finally{try{S&&!S.done&&(i=g.return)&&i.call(g)}finally{if(a)throw a.error}}return E.sort(vt),E},Object.defineProperty(o.prototype,"documentCount",{get:function(){return this._documentCount},enumerable:!1,configurable:!0}),Object.defineProperty(o.prototype,"termCount",{get:function(){return this._index.size},enumerable:!1,configurable:!0}),o.loadJSON=function(e,t){if(t==null)throw new Error("MiniSearch: loadJSON should be given the same options used when serializing the index");return this.loadJS(JSON.parse(e),t)},o.getDefault=function(e){if(Ve.hasOwnProperty(e))return Pe(Ve,e);throw new Error('MiniSearch: unknown option "'.concat(e,'"'))},o.loadJS=function(e,t){var r,n,a,i,s,u,l=e.index,h=e.documentCount,d=e.nextId,v=e.documentIds,y=e.fieldIds,b=e.fieldLength,E=e.averageFieldLength,g=e.storedFields,S=e.dirtCount,T=e.serializationVersion;if(T!==1&&T!==2)throw new Error("MiniSearch: cannot deserialize an index created with an incompatible version");var F=new o(t);F._documentCount=h,F._nextId=d,F._documentIds=Te(v),F._idToShortId=new Map,F._fieldIds=y,F._fieldLength=Te(b),F._avgFieldLength=E,F._storedFields=Te(g),F._dirtCount=S||0,F._index=new Le;try{for(var L=D(F._documentIds),R=L.next();!R.done;R=L.next()){var B=J(R.value,2),N=B[0],_=B[1];F._idToShortId.set(_,N)}}catch(P){r={error:P}}finally{try{R&&!R.done&&(n=L.return)&&n.call(L)}finally{if(r)throw r.error}}try{for(var A=D(l),O=A.next();!O.done;O=A.next()){var w=J(O.value,2),c=w[0],f=w[1],p=new Map;try{for(var C=(s=void 0,D(Object.keys(f))),I=C.next();!I.done;I=C.next()){var M=I.value,z=f[M];T===1&&(z=z.ds),p.set(parseInt(M,10),Te(z))}}catch(P){s={error:P}}finally{try{I&&!I.done&&(u=C.return)&&u.call(C)}finally{if(s)throw s.error}}F._index.set(c,p)}}catch(P){a={error:P}}finally{try{O&&!O.done&&(i=A.return)&&i.call(A)}finally{if(a)throw a.error}}return F},o.prototype.executeQuery=function(e,t){var r=this;if(t===void 0&&(t={}),e===o.wildcard)return this.executeWildcardQuery(t);if(typeof e!="string"){var n=W(W(W({},t),e),{queries:void 0}),a=e.queries.map(function(g){return r.executeQuery(g,n)});return this.combineResults(a,n.combineWith)}var i=this._options,s=i.tokenize,u=i.processTerm,l=i.searchOptions,h=W(W({tokenize:s,processTerm:u},l),t),d=h.tokenize,v=h.processTerm,y=d(e).flatMap(function(g){return v(g)}).filter(function(g){return!!g}),b=y.map(Jr(h)),E=b.map(function(g){return r.executeQuerySpec(g,h)});return this.combineResults(E,h.combineWith)},o.prototype.executeQuerySpec=function(e,t){var r,n,a,i,s=W(W({},this._options.searchOptions),t),u=(s.fields||this._options.fields).reduce(function(M,z){var P;return W(W({},M),(P={},P[z]=Pe(s.boost,z)||1,P))},{}),l=s.boostDocument,h=s.weights,d=s.maxFuzzy,v=s.bm25,y=W(W({},ht.weights),h),b=y.fuzzy,E=y.prefix,g=this._index.get(e.term),S=this.termResults(e.term,e.term,1,g,u,l,v),T,F;if(e.prefix&&(T=this._index.atPrefix(e.term)),e.fuzzy){var L=e.fuzzy===!0?.2:e.fuzzy,R=L<1?Math.min(d,Math.round(e.term.length*L)):L;R&&(F=this._index.fuzzyGet(e.term,R))}if(T)try{for(var B=D(T),N=B.next();!N.done;N=B.next()){var _=J(N.value,2),A=_[0],O=_[1],w=A.length-e.term.length;if(w){F==null||F.delete(A);var c=E*A.length/(A.length+.3*w);this.termResults(e.term,A,c,O,u,l,v,S)}}}catch(M){r={error:M}}finally{try{N&&!N.done&&(n=B.return)&&n.call(B)}finally{if(r)throw r.error}}if(F)try{for(var f=D(F.keys()),p=f.next();!p.done;p=f.next()){var A=p.value,C=J(F.get(A),2),I=C[0],w=C[1];if(w){var c=b*A.length/(A.length+w);this.termResults(e.term,A,c,I,u,l,v,S)}}}catch(M){a={error:M}}finally{try{p&&!p.done&&(i=f.return)&&i.call(f)}finally{if(a)throw a.error}}return S},o.prototype.executeWildcardQuery=function(e){var t,r,n=new Map,a=W(W({},this._options.searchOptions),e);try{for(var i=D(this._documentIds),s=i.next();!s.done;s=i.next()){var u=J(s.value,2),l=u[0],h=u[1],d=a.boostDocument?a.boostDocument(h,"",this._storedFields.get(l)):1;n.set(l,{score:d,terms:[],match:{}})}}catch(v){t={error:v}}finally{try{s&&!s.done&&(r=i.return)&&r.call(i)}finally{if(t)throw t.error}}return n},o.prototype.combineResults=function(e,t){if(t===void 0&&(t=Ge),e.length===0)return new Map;var r=t.toLowerCase();return e.reduce($r[r])||new Map},o.prototype.toJSON=function(){var e,t,r,n,a=[];try{for(var i=D(this._index),s=i.next();!s.done;s=i.next()){var u=J(s.value,2),l=u[0],h=u[1],d={};try{for(var v=(r=void 0,D(h)),y=v.next();!y.done;y=v.next()){var b=J(y.value,2),E=b[0],g=b[1];d[E]=Object.fromEntries(g)}}catch(S){r={error:S}}finally{try{y&&!y.done&&(n=v.return)&&n.call(v)}finally{if(r)throw r.error}}a.push([l,d])}}catch(S){e={error:S}}finally{try{s&&!s.done&&(t=i.return)&&t.call(i)}finally{if(e)throw e.error}}return{documentCount:this._documentCount,nextId:this._nextId,documentIds:Object.fromEntries(this._documentIds),fieldIds:this._fieldIds,fieldLength:Object.fromEntries(this._fieldLength),averageFieldLength:this._avgFieldLength,storedFields:Object.fromEntries(this._storedFields),dirtCount:this._dirtCount,index:a,serializationVersion:2}},o.prototype.termResults=function(e,t,r,n,a,i,s,u){var l,h,d,v,y;if(u===void 0&&(u=new Map),n==null)return u;try{for(var b=D(Object.keys(a)),E=b.next();!E.done;E=b.next()){var g=E.value,S=a[g],T=this._fieldIds[g],F=n.get(T);if(F!=null){var L=F.size,R=this._avgFieldLength[T];try{for(var B=(d=void 0,D(F.keys())),N=B.next();!N.done;N=B.next()){var _=N.value;if(!this._documentIds.has(_)){this.removeTerm(T,_,t),L-=1;continue}var A=i?i(this._documentIds.get(_),t,this._storedFields.get(_)):1;if(A){var O=F.get(_),w=this._fieldLength.get(_)[T],c=Kr(O,L,this._documentCount,w,R,s),f=r*S*A*c,p=u.get(_);if(p){p.score+=f,jr(p.terms,e);var C=Pe(p.match,t);C?C.push(g):p.match[t]=[g]}else u.set(_,{score:f,terms:[e],match:(y={},y[t]=[g],y)})}}}catch(I){d={error:I}}finally{try{N&&!N.done&&(v=B.return)&&v.call(B)}finally{if(d)throw d.error}}}}}catch(I){l={error:I}}finally{try{E&&!E.done&&(h=b.return)&&h.call(b)}finally{if(l)throw l.error}}return u},o.prototype.addTerm=function(e,t,r){var n=this._index.fetch(r,pt),a=n.get(e);if(a==null)a=new Map,a.set(t,1),n.set(e,a);else{var i=a.get(t);a.set(t,(i||0)+1)}},o.prototype.removeTerm=function(e,t,r){if(!this._index.has(r)){this.warnDocumentChanged(t,e,r);return}var n=this._index.fetch(r,pt),a=n.get(e);a==null||a.get(t)==null?this.warnDocumentChanged(t,e,r):a.get(t)<=1?a.size<=1?n.delete(e):a.delete(t):a.set(t,a.get(t)-1),this._index.get(r).size===0&&this._index.delete(r)},o.prototype.warnDocumentChanged=function(e,t,r){var n,a;try{for(var i=D(Object.keys(this._fieldIds)),s=i.next();!s.done;s=i.next()){var u=s.value;if(this._fieldIds[u]===t){this._options.logger("warn","MiniSearch: document with ID ".concat(this._documentIds.get(e),' has changed before removal: term "').concat(r,'" was not present in field "').concat(u,'". 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NeuroTree - A differentiable tree operator for tabular data ​

Overview ​

This work introduces NeuroTree a differentiable binary tree operator adapted for the treatment of tabular data.

  • Address the shortcoming of traditional trees greediness: all node and leaves are learned simultaneously. It provides the ability to learn an optimal configuration across all the tree levels. The notion extent also to the collection of trees that are simultaneously learned.

  • Extend the notion of forest/bagging and boosting.

    • Although the predictions from the all the trees forming a NeuroTree operator are averaged, each of the tree prediction is tuned simultaneously. This is different from boosting (ex XGBoost) where each tree is learned sequentially and over the residual from previous trees. Also, unlike random forest and bagging, trees aren't learned in isolation but tuned collaboratively, resulting in predictions that account for all of the other tree predictions.

  • General operator compatible for composition.

    • Allows integration within Flux's Chain like other standard operators from NNLib. Composition is also illustrated through the built-in StackTree layer, a residual composition of multiple NeuroTree building blocks.

  • Compatible with general purpose machine learning framework.

    • MLJ integration

Architecture ​

A NeuroTree operator acts as collection of complete binary trees, ie. trees without any pruned node. To be differentiable, hence trainable using first-order gradient based methods (ex. Adam optimiser), each tree path implements a soft decision rather than a hard one like in traditional decision tree.

To introduce the implementation of a NeuroTree, we first get back to the architecture of a basic decision tree.

The above is a binary decision tree of depth 2.

Highlighted in green is the decision path taken for a given sample. It goes into depth number of binary decisions, resulting in the path node1 → node3 → leaf3.

One way to view the role of the decision nodes (gray background) is to provide an index of the leaf prediction to fetch (index 3 in the figure). Such indexing view is applicable given that node routing relies on hard conditions: either true or false.

An alternative perspective that we adopt here is that tree nodes collectively provide weights associated to each leaf. A tree prediction becomes the weighted sum of the leaf's values and the leaf's weights. In regular decision trees, since all conditions are binary, leaf weights take the form of a mask. In the above example, the mask is [0, 0, 1, 0].

By relaxing these hard conditions into soft ones, the mask takes the form of a probability vector associated to each leaf, where ∑(leaf_weights) = 1 and where each each leaf_weight element is [0, 1]. A tree prediction can be obtained with the dot product: leaf_values' * leaf_weights.

The following illustrate how a basic decision tree is represented as a single differentiable tree within NeuroTree:

Node weights ​

To illustrate how a NeuroTree derives the soft decision probability (referred to NW1 - NW3 in the above figure), we first break down how a traditional tree split condition is derived from 2 underlying decisions:

  1. Selection of the feature on which to perform the condition.

Such selection can be represented as the application of a binary mask where all elements are set to false except for that single selected feature where it's set to true.

  1. Selection of the condition's threshold value.

For a given observation, if the selected feature's value is below that threshold, then the node decision is set to false (pointing to left child), and true otherwise (pooinnting to right child).

In NeuroTree, these 2 hard steps are translated into soft, differentiable ones.

Leaf weights ​

Computing the leaf weights consists of accumulating the weights through each tree branch. It's the technically more challenging part as such computation cannot be represented as a form of matrix multiplication, unlike other common operators like Dense, Conv or MultiHeadAttention / Transformer. Performing probability accumulation though a tree index naturally leads to in-place element wise operations, which are notoriously not friendly for auto-differentiation engines. Since NeuroTree was intended to integrate with the Flux.jl ecosystem, Zygote.jl acts as the underlying AD, the approach used was to manually implement backward / adjoint of the terminal leaf function and instruct the AD to use that custom rule rather than attempt to differentiate a non-AD compliant function.

Below are the algo and actual implementation of the forward and backward function that compute the leaf weights. For brevity, the loops over each observation of the batch and each tree are omitted. Parallelism, both on CPU and GPU, is obtained through parallelization over the tree and batch dimensions.

Forward

julia
function leaf_weights!(nw)
+    cw = ones(eltype(nw), 2 * size(nw, 1) + 1)
+
+    for i = 2:2:size(cw, 1)
+        cw[i] = cw[i>>1] * nw[i>>1]
+        cw[i+1, tree, batch] = cw[i>>1] * (1 - nw[i>>1])
+    end
+    
+    lw = cw[size(nw, 1)+1:size(cw, 1)]
+    return (cw, lw)
+end

Backward

julia
function Δ_leaf_weights!(Δnw, ȳ, cw, nw, max_depth, node_offset)
+    
+    for i in axes(nw, 1)        
+        depth = floor(Int, log2(i)) # current depth level - starting at 0
+        step = 2^(max_depth - depth) # iteration length
+        leaf_offset = step * (i - 2^depth) # offset on the leaf row
+
+        for j = (1+leaf_offset):(step÷2+leaf_offset)
+            k = j + node_offset # move from leaf position to full tree position 
+            Δnw[i] += ȳ[j] * cw[k] / nw[i]
+        end
+    
+        for j = (1+leaf_offset+step÷2):(step+leaf_offset)
+            k = j + node_offset
+            Δnw[i] -= ȳ[j] * cw[k] / (1 - nw[i])
+        end
+    end
+
+    return nothing
+end

Tree prediction ​

Composability ​

  • StackTree

  • General operator: Chain NeuroTree with MLP

Benchmarks ​

For each dataset and algo, the following methodology is followed:

  • Data is split in three parts: train, eval and test

  • A random grid of 16 hyper-parameters is generated

  • For each parameter configuration, a model is trained on train data until the evaluation metric tracked against the eval stops improving (early stopping)

  • The trained model is evaluated against the test data

  • The metric presented in below are the ones obtained on the test for the model that generated the best eval metric.

Source code available at MLBenchmarks.jl.

For performance assessment, benchmarks is run on the following selection of common Tabular datasets:

  • Year: min squared error regression. 515,345 observations, 90 features.

  • MSRank: ranking problem with min squared error regression. 1,200,192 observations, 136 features.

  • YahooRank: ranking problem with min squared error regression. 709,877 observations, 519 features.

  • Higgs: 2-level classification with logistic regression. 11,000,000 observations, 28 features.

  • Boston Housing: min squared error regression.

  • Titanic: 2-level classification with logistic regression. 891 observations, 7 features.

Comparison is performed against the following algos (implementation in link) considered as state of the art on classification tasks:

Boston ​

model_typetrain_timemsegini
neurotrees12.818.90.947
evotrees0.20619.70.927
xgboost0.064819.40.935
lightgbm0.86525.40.926
catboost0.051113.90.946

Titanic ​

model_typetrain_timeloglossaccuracy
neurotrees7.580.4070.828
evotrees0.6730.3820.828
xgboost0.03790.3750.821
lightgbm0.6150.3900.836
catboost0.03260.3880.836

Year ​

model_typetrain_timemsegini
neurotrees280.076.40.652
evotrees18.680.10.627
xgboost17.280.20.626
lightgbm8.1180.30.624
catboost80.079.20.635

MSRank ​

model_typetrain_timemsendcg
neurotrees39.10.5780.462
evotrees37.00.5540.504
xgboost12.50.5540.503
lightgbm37.50.5530.503
catboost15.10.5580.497

Yahoo ​

model_typetrain_timemsendcg
neurotrees417.00.5840.781
evotrees687.00.5450.797
xgboost120.00.5470.798
lightgbm244.00.5400.796
catboost161.00.5610.794

Higgs ​

model_typetrain_timeloglossaccuracy
neurotrees12300.00.4520.781
evotrees2620.00.4640.776
xgboost1390.00.4620.776
lightgbm1330.00.4610.779
catboost7180.00.4640.775

Discussion ​

NeuroTreeModels can achieve top tier performance on both small (Boston) and large (Higgs) datasets. Its performance trailed on the two ranking regression problems (MSRank and Yahoo). Although the large number of features is a distinguishing characteristic of the Yahoo dataset, the 136 features of MSRank are not materially different for the YEAR dataset (90 features), and on which NeuroTreeMoels outperform all other algos. Considering that no sparsity mechanism is present in the feature selection for the node conditions, datasets with a very large number of features may present a challenge. Substituting the default tanh activation with a sparsity inducing one such as hardsigmoid or EntrOpt has not resulted in improvement from the experiments.

Another potential weakness may stem from the soft nature of the decision criteria. Traditional trees can isolate the effect of a specific feature value. This can be notably meaningful in a situation where a numeric feature taking a value of 0 may carry a particular meaning (ex. missing, unknown value). Such stump the effect of a feature should be harder to pick with NeuroTree's soft condition.

References ​

`,57),h=[l];function o(d,p,g,k,c,y){return i(),e("div",null,h)}const u=t(r,[["render",o]]);export{f as __pageData,u as default}; diff --git a/dev/assets/design.md.0tBnC0I_.lean.js b/dev/assets/design.md.0tBnC0I_.lean.js new file mode 100644 index 0000000..2b2c0de --- /dev/null +++ b/dev/assets/design.md.0tBnC0I_.lean.js @@ -0,0 +1 @@ +import{_ as t,c as e,o as i,a7 as s}from"./chunks/framework.CUwTDK2x.js";const a="/NeuroTreeModels.jl/dev/assets/decision-tree.Dqh78YdA.png",n="/NeuroTreeModels.jl/dev/assets/neurotree.vCO5vhkM.png",f=JSON.parse('{"title":"NeuroTree - A differentiable tree operator for tabular data","description":"","frontmatter":{},"headers":[],"relativePath":"design.md","filePath":"design.md","lastUpdated":null}'),r={name:"design.md"},l=s("",57),h=[l];function o(d,p,g,k,c,y){return i(),e("div",null,h)}const u=t(r,[["render",o]]);export{f as __pageData,u as default}; diff --git a/dev/assets/design.md.CXyLal5T.js b/dev/assets/design.md.CXyLal5T.js deleted file mode 100644 index 33d9e1b..0000000 --- a/dev/assets/design.md.CXyLal5T.js +++ /dev/null @@ -1,30 +0,0 @@ -import{_ as t,c as e,o as i,a7 as s,a8 as a,a9 as n}from"./chunks/framework.BResyDFY.js";const f=JSON.parse('{"title":"NeuroTree - A differentiable tree operator for tabular data","description":"","frontmatter":{},"headers":[],"relativePath":"design.md","filePath":"design.md","lastUpdated":null}'),r={name:"design.md"},l=s('

NeuroTree - A differentiable tree operator for tabular data ​

Overview ​

This work introduces NeuroTree a differentiable binary tree operator adapted for the treatment of tabular data.

  • Address the shortcoming of traditional trees greediness: all node and leaves are learned simultaneously. It provides the ability to learn an optimal configuration across all of the tree levels.

The notion extent also to the collection of trees that are simultaneously learned.

  • Extend the notion of forest/bagging and boosting.

    • Although the predictions from the all of of the trees forming a NeuroTree operator are averaged, each of the tree prediction tuned simultaneously. This is different from boosting (ex XGBoost) where each tree is learned sequentially and over the residual from previous trees. Also, unlike random forest and bagging, trees aren't learned in isolation but tuned collaboratively, resulting in predictions that account for all of the other trees predictions.

  • General operator compatible for composition.

    • Allows integration within Flux's Chain like other standard operators from NNLib. Composition is also illustrated through the built-in StackTree layer, a residual composition of multiple NeuroTree building blocks.

  • Compatible with general purpose machine learning framework.

    • MLJ integration

Architecture ​

To introduce the implementation of a NeuroTree, we first get back to the architecture of a basic decision tree.

decision-tree

The above is a binary decision tree of depth 2.

Highlighted in green is the decision path taken for a given sample. It goes into depth number of binary decisions, resulting in the path node1 → node3 → leaf3.

One way to view the role of the decision nodes is to provide an index of the leaf prediction to fetch (index 3 in the figure). Such indexing view is applicable given that node routing relies a hard conditions: it's either true or false.

An alternative perspective that we will adopt here is that tree nodes collectively provide weights associated to each leaf. A tree prediction is the weighted sum of the leaf's value and those leaf weights. In regular decision trees, since all conditions are binary, leaf weights take the form of a mask. In the above example, the mask is [0, 0, 1, 0].

By relaxing these these hard condition into soft ones, the mask takes the form of a probability vector associated to each leaf, where ∑(leaf_weights) = 1 and where each each leaf_weight element is [0, 1].

The following illustrate how a basic decision tree is represented as a single differentiable tree within NeuroTree:

decision-tree

Node weights ​

To derive how a NeuroTree performs those soft decision, we first break down the structure of how the traditional hard decisions are taken. A nodes's split actually relies on 2 binary conditions:

1. Selection of the feature on which to perform the condition ​

The selection of a feature out of the selected ones In NeuroTree, these hard decisions are translated into soft, differentiable ones: 1.

2. Selection of the condition's threshold value ​

A NeuroTree operator acts as collection of complete binary trees, ie. trees without any pruned node. In order to be differentiable, hence trainable using gradient based methods such as Adam, each tree path implements a soft decision rather than a hard one like in traditional decision tree.

Leaf weights ​

Computing the leaf weights consists of accumulating the weights through each tree branch. It's the technically more challenging part as such computation cannot be represented as a form of matrix multiplication, unlike other common operators like Dense, Conv or MultiHeadAttention / Transformer. Performing probability accumulation though a tree index naturally leads to in-place element wise operations, which are notoriously not friendly for auto-differentiation engines. Since NeuroTree was intended to integrate with the Flux.jl ecosystem, Zygote.jl acts as the underlying AD, the approach used was to to manually implement backward / adjoint of the terminal leaf function and instruct the AD to use that custom rule rather than attempt to differentiate a non-AD compliant function.

Below are the algo and actual implementation of the forward and backward function that compute the leaf weights. For brevity, the loops over each observation of the batch and each tree are omitted. Parallelism, both on CPU and GPU, is obtained through parallelization over the tree and batch dimensions.

Forward

julia
function leaf_weights!(nw)
-    cw = ones(eltype(nw), 2 * size(nw, 1) + 1)
-
-    for i = 2:2:size(cw, 1)
-        cw[i] = cw[i>>1] * nw[i>>1]
-        cw[i+1, tree, batch] = cw[i>>1] * (1 - nw[i>>1])
-    end
-    
-    lw = cw[size(nw, 1)+1:size(cw, 1)]
-    return (cw, lw)
-end

Backward

julia
function Δ_leaf_weights!(Δnw, ȳ, cw, nw, max_depth, node_offset)
-    
-    for i in axes(nw, 1)        
-        depth = floor(Int, log2(i)) # current depth level - starting at 0
-        step = 2^(max_depth - depth) # iteration length
-        leaf_offset = step * (i - 2^depth) # offset on the leaf row
-
-        for j = (1+leaf_offset):(step÷2+leaf_offset)
-            k = j + node_offset # move from leaf position to full tree position 
-            Δnw[i] += ȳ[j] * cw[k] / nw[i]
-        end
-    
-        for j = (1+leaf_offset+step÷2):(step+leaf_offset)
-            k = j + node_offset
-            Δnw[i] -= ȳ[j] * cw[k] / (1 - nw[i])
-        end
-    end
-
-    return nothing
-end

Tree prediction ​

Composability ​

  • StackTree

  • General operator: Chain neurotree with MLP

Benchmarks ​

For each dataset and algo, the following methodology is followed:

  • Data is split in three parts: train, eval and test

  • A random grid of 16 hyper-parameters is generated

  • For each parameter configuration, a model is trained on train data until the evaluation metric tracked against the eval stops improving (early stopping)

  • The trained model is evaluated against the test data

  • The metric presented in below are the ones obtained on the test for the model that generated the best eval metric.

Source code available at MLBenchmarks.jl.

For performance assessment, benchmarks is run on the following selection of common Tabular datasets:

  • Year: min squared error regression

  • MSRank: ranking problem with min squared error regression

  • YahooRank: ranking problem with min squared error regression

  • Higgs: 2-level classification with logistic regression

  • Boston Housing: min squared error regression

  • Titanic: 2-level classification with logistic regression

Comparison is performed against the following algos (implementation in link) considered as state of the art on classification tasks:

Boston ​

model_typetrain_timemsegini
neurotrees12.818.90.947
evotrees0.20619.70.927
xgboost0.064819.40.935
lightgbm0.86525.40.926
catboost0.051113.90.946

Titanic ​

model_typetrain_timeloglossaccuracy
neurotrees7.580.4070.828
evotrees0.6730.3820.828
xgboost0.03790.3750.821
lightgbm0.6150.3900.836
catboost0.03260.3880.836

Year ​

model_typetrain_timemsegini
neurotrees280.076.40.652
evotrees18.680.10.627
xgboost17.280.20.626
lightgbm8.1180.30.624
catboost80.079.20.635

MSRank ​

model_typetrain_timemsendcg
neurotrees39.10.5780.462
evotrees37.00.5540.504
xgboost12.50.5540.503
lightgbm37.50.5530.503
catboost15.10.5580.497

Yahoo ​

model_typetrain_timemsendcg
neurotrees417.00.5840.781
evotrees687.00.5450.797
xgboost120.00.5470.798
lightgbm244.00.5400.796
catboost161.00.5610.794

Higgs ​

model_typetrain_timeloglossaccuracy
neurotrees12300.00.4520.781
evotrees2620.00.4640.776
xgboost1390.00.4620.776
lightgbm1330.00.4610.779
catboost7180.00.4640.775

References ​

`,54),h=[l];function o(d,p,k,g,c,y){return i(),e("div",null,h)}const u=t(r,[["render",o]]);export{f as __pageData,u as default}; diff --git a/dev/assets/design.md.CXyLal5T.lean.js b/dev/assets/design.md.CXyLal5T.lean.js deleted file mode 100644 index 090b4bf..0000000 --- a/dev/assets/design.md.CXyLal5T.lean.js +++ /dev/null @@ -1 +0,0 @@ -import{_ as t,c as e,o as i,a7 as s,a8 as a,a9 as n}from"./chunks/framework.BResyDFY.js";const f=JSON.parse('{"title":"NeuroTree - A differentiable tree operator for tabular data","description":"","frontmatter":{},"headers":[],"relativePath":"design.md","filePath":"design.md","lastUpdated":null}'),r={name:"design.md"},l=s("",54),h=[l];function o(d,p,k,g,c,y){return i(),e("div",null,h)}const u=t(r,[["render",o]]);export{f as __pageData,u as default}; diff --git a/dev/assets/index.md.CTJC3X05.js b/dev/assets/index.md.6G8I0lEX.js similarity index 91% rename from dev/assets/index.md.CTJC3X05.js rename to dev/assets/index.md.6G8I0lEX.js index 2130f7a..8e94f84 100644 --- a/dev/assets/index.md.CTJC3X05.js +++ b/dev/assets/index.md.6G8I0lEX.js @@ -1 +1 @@ -import{_ as e,c as t,o as a}from"./chunks/framework.BResyDFY.js";const p=JSON.parse('{"title":"","description":"","frontmatter":{"layout":"home","hero":{"text":"NeuroTreeModels.jl","tagline":"Differentiable tree-based models for tabular data","image":{"src":"/evo-chevron.png","alt":"Evovest"},"actions":[{"theme":"brand","text":"Quick start","link":"/quick-start"},{"theme":"alt","text":"Design","link":"/design"},{"theme":"alt","text":"Models","link":"/models"},{"theme":"alt","text":"Source code","link":"https://github.com/Evovest/NeuroTreeModels.jl"}]}},"headers":[],"relativePath":"index.md","filePath":"index.md","lastUpdated":null}'),o={name:"index.md"};function r(n,s,l,i,d,c){return a(),t("div")}const h=e(o,[["render",r]]);export{p as __pageData,h as default}; 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+import{_ as e,c as t,o as a}from"./chunks/framework.CUwTDK2x.js";const p=JSON.parse('{"title":"","description":"","frontmatter":{"layout":"home","hero":{"text":"NeuroTreeModels.jl","tagline":"Differentiable tree-based models for tabular data","image":{"src":"/evo-chevron.png","alt":"Evovest"},"actions":[{"theme":"brand","text":"Quick start","link":"/quick-start"},{"theme":"alt","text":"Design","link":"/design"},{"theme":"alt","text":"Models","link":"/models"},{"theme":"alt","text":"Source code","link":"https://github.com/Evovest/NeuroTreeModels.jl"}]}},"headers":[],"relativePath":"index.md","filePath":"index.md","lastUpdated":null}'),o={name:"index.md"};function r(n,s,l,i,d,c){return a(),t("div")}const h=e(o,[["render",r]]);export{p as __pageData,h as default}; diff --git a/dev/assets/models.md.BfCHGEUK.js b/dev/assets/models.md.CuUA2Mbx.js similarity index 97% rename from dev/assets/models.md.BfCHGEUK.js rename to dev/assets/models.md.CuUA2Mbx.js index 4c6c214..e20cba2 100644 --- a/dev/assets/models.md.BfCHGEUK.js +++ b/dev/assets/models.md.CuUA2Mbx.js @@ -1,4 +1,4 @@ -import{_ as e,c as s,o as i,a7 as a}from"./chunks/framework.BResyDFY.js";const u=JSON.parse('{"title":"Models","description":"","frontmatter":{},"headers":[],"relativePath":"models.md","filePath":"models.md","lastUpdated":null}'),t={name:"models.md"},n=a(`

Models ​

NeuroTreeRegressor ​

# NeuroTreeModels.NeuroTreeRegressor — Type.

NeuroTreeRegressor(;kwargs...)

A model type for constructing a NeuroTreeRegressor, based on NeuroTreeModels.jl, and implementing both an internal API and the MLJ model interface.

Hyper-parameters

  • loss=:mse: Loss to be be minimized during training. One of:

    • :mse

    • :mae

    • :logloss

    • :mlogloss

    • :gaussian_mle

  • nrounds=10: Max number of rounds (epochs).

  • lr=1.0f-2: Learning rate. Must be > 0. A lower eta results in slower learning, typically requiring a higher nrounds.

  • wd=0.f0: Weight decay applied to the gradients by the optimizer.

  • batchsize=2048: Batch size.

  • actA=:tanh: Activation function applied to each of input variable for determination of split node weight. Can be one of:

    • :tanh

    • :identity

  • outsize=1: Number of predictions returned by the model. Typically only used for classification tasks and set to the number of target levels / classes.

  • depth=6: Depth of a tree. Must be >= 1. A tree of depth 1 has 2 prediction leaf nodes. A complete tree of depth N contains 2^N terminal leaves and 2^N - 1 split nodes. Compute cost is proportional to 2^depth. Typical optimal values are in the 3 to 5 range.

  • ntrees=64: Number of trees (per stack).

  • hidden_size=16: Size of hidden layers. Applicable only when stack_size > 1.

  • stack_size=1: Number of stacked NeuroTree blocks.

  • init_scale=1.0: Scaling factor applied to the predictions weights. Values in the ]0, 1] short result in best performance.

  • MLE_tree_split=false: Whether independent models are buillt for each of the 2 parameters (mu, sigma) of the the gaussian_mle loss.

  • rng=123: Either an integer used as a seed to the random number generator or an actual random number generator (::Random.AbstractRNG).

  • device=:cpu: Device to use. Either :cpu or :gpu (recommended as it improves significantly the training speed).

  • gpuID=0: ID of the GPU to use for training.

Internal API

Do config = NeuroTreeRegressor() to construct an instance with default hyper-parameters. Provide keyword arguments to override hyper-parameter defaults, as in NeuroTreeRegressor(loss=...).

Training model

A model is trained using fit:

julia
m = fit(config, dtrain; feature_names, target_name, kwargs...)

Inference

Models act as a functor. returning predictions when called as a function with features as argument:

julia
m(data)

MLJ Interface

From MLJ, the type can be imported using:

julia
NeuroTreeRegressor = @load NeuroTreeRegressor pkg=NeuroTreeModels

Do model = NeuroTreeRegressor() to construct an instance with default hyper-parameters. Provide keyword arguments to override hyper-parameter defaults, as in NeuroTreeRegressor(loss=...).

Training model

In MLJ or MLJBase, bind an instance model to data with mach = machine(model, X, y) where

  • X: any table of input features (eg, a DataFrame) whose columns each have one of the following element scitypes: Continuous, Count, or <:OrderedFactor; check column scitypes with schema(X)

  • y: is the target, which can be any AbstractVector whose element scitype is <:Continuous; check the scitype with scitype(y)

Train the machine using fit!(mach, rows=...).

Operations

  • predict(mach, Xnew): return predictions of the target given features Xnew having the same scitype as X above.

Fitted parameters

The fields of fitted_params(mach) are:

  • :fitresult: The NeuroTreeModel object.

Report

The fields of report(mach) are:

  • :features: The names of the features encountered in training.

Examples

Internal API

julia
using NeuroTreeModels, DataFrames
+import{_ as e,c as s,o as i,a7 as a}from"./chunks/framework.CUwTDK2x.js";const u=JSON.parse('{"title":"Models","description":"","frontmatter":{},"headers":[],"relativePath":"models.md","filePath":"models.md","lastUpdated":null}'),t={name:"models.md"},n=a(`
Models ​
NeuroTreeRegressor ​
# NeuroTreeModels.NeuroTreeRegressor — Type. 
NeuroTreeRegressor(;kwargs...)
A model type for constructing a NeuroTreeRegressor, based on NeuroTreeModels.jl, and implementing both an internal API and the MLJ model interface.
Hyper-parameters
  • loss=:mse: Loss to be be minimized during training. One of:
    • :mse
    • :mae
    • :logloss
    • :mlogloss
    • :gaussian_mle
  • nrounds=10: Max number of rounds (epochs).
  • lr=1.0f-2: Learning rate. Must be > 0. A lower eta results in slower learning, typically requiring a higher nrounds.
  • wd=0.f0: Weight decay applied to the gradients by the optimizer.
  • batchsize=2048: Batch size.
  • actA=:tanh: Activation function applied to each of input variable for determination of split node weight. Can be one of:
    • :tanh
    • :identity
  • outsize=1: Number of predictions returned by the model. Typically only used for classification tasks and set to the number of target levels / classes.
  • depth=6: Depth of a tree. Must be >= 1. A tree of depth 1 has 2 prediction leaf nodes. A complete tree of depth N contains 2^N terminal leaves and 2^N - 1 split nodes. Compute cost is proportional to 2^depth. Typical optimal values are in the 3 to 5 range.
  • ntrees=64: Number of trees (per stack).
  • hidden_size=16: Size of hidden layers. Applicable only when stack_size > 1.
  • stack_size=1: Number of stacked NeuroTree blocks.
  • init_scale=1.0: Scaling factor applied to the predictions weights. Values in the ]0, 1] short result in best performance.
  • MLE_tree_split=false: Whether independent models are buillt for each of the 2 parameters (mu, sigma) of the the gaussian_mle loss.
  • rng=123: Either an integer used as a seed to the random number generator or an actual random number generator (::Random.AbstractRNG).
  • device=:cpu: Device to use. Either :cpu or :gpu (recommended as it improves significantly the training speed).
  • gpuID=0: ID of the GPU to use for training.
Internal API
Do config = NeuroTreeRegressor() to construct an instance with default hyper-parameters. Provide keyword arguments to override hyper-parameter defaults, as in NeuroTreeRegressor(loss=...).
Training model
A model is trained using fit:
julia
m = fit(config, dtrain; feature_names, target_name, kwargs...)
Inference
Models act as a functor. returning predictions when called as a function with features as argument:
julia
m(data)
MLJ Interface
From MLJ, the type can be imported using:
julia
NeuroTreeRegressor = @load NeuroTreeRegressor pkg=NeuroTreeModels
Do model = NeuroTreeRegressor() to construct an instance with default hyper-parameters. Provide keyword arguments to override hyper-parameter defaults, as in NeuroTreeRegressor(loss=...).
Training model
In MLJ or MLJBase, bind an instance model to data with mach = machine(model, X, y) where
  • X: any table of input features (eg, a DataFrame) whose columns each have one of the following element scitypes: Continuous, Count, or <:OrderedFactor; check column scitypes with schema(X)
  • y: is the target, which can be any AbstractVector whose element scitype is <:Continuous; check the scitype with scitype(y)
Train the machine using fit!(mach, rows=...).
Operations
  • predict(mach, Xnew): return predictions of the target given features Xnew having the same scitype as X above.
Fitted parameters
The fields of fitted_params(mach) are:
  • :fitresult: The NeuroTreeModel object.
Report
The fields of report(mach) are:
  • :features: The names of the features encountered in training.
Examples
Internal API
julia
using NeuroTreeModels, DataFrames
 config = NeuroTreeRegressor(depth=5, nrounds=10)
 nobs, nfeats = 1_000, 5
 dtrain = DataFrame(randn(nobs, nfeats), :auto)
@@ -9,4 +9,4 @@ import{_ as e,c as s,o as i,a7 as a}from"./chunks/framework.BResyDFY.js";const u
 m = NeuroTreeRegressor(depth=5, nrounds=10)
 X, y = @load_boston
 mach = machine(m, X, y) |> fit!
-p = predict(mach, X)
source

NeuroTreeModel ​
# NeuroTreeModels.NeuroTreeModel — Type. 
julia
NeuroTreeModel
A NeuroTreeModel is made of a collection of Tree, either regular NeuroTree or StackTree. Prediction is the sum of all the trees composing a NeuroTreeModel.
source

`,7),o=[n];function r(l,p,d,h,c,k){return i(),s("div",null,o)}const E=e(t,[["render",r]]);export{u as __pageData,E as default};
+p = predict(mach, X)

source


NeuroTreeModel ​

# NeuroTreeModels.NeuroTreeModel — Type.
julia
NeuroTreeModel

A NeuroTreeModel is made of a collection of Tree, either regular NeuroTree or StackTree. Prediction is the sum of all the trees composing a NeuroTreeModel.

source


`,7),o=[n];function r(l,p,d,h,c,k){return i(),s("div",null,o)}const E=e(t,[["render",r]]);export{u as __pageData,E as default}; diff --git a/dev/assets/models.md.BfCHGEUK.lean.js b/dev/assets/models.md.CuUA2Mbx.lean.js similarity index 66% rename from dev/assets/models.md.BfCHGEUK.lean.js rename to dev/assets/models.md.CuUA2Mbx.lean.js index e0cbdc1..de3524c 100644 --- a/dev/assets/models.md.BfCHGEUK.lean.js +++ b/dev/assets/models.md.CuUA2Mbx.lean.js @@ -1 +1 @@ -import{_ as e,c as s,o as i,a7 as a}from"./chunks/framework.BResyDFY.js";const u=JSON.parse('{"title":"Models","description":"","frontmatter":{},"headers":[],"relativePath":"models.md","filePath":"models.md","lastUpdated":null}'),t={name:"models.md"},n=a("",7),o=[n];function r(l,p,d,h,c,k){return i(),s("div",null,o)}const E=e(t,[["render",r]]);export{u as __pageData,E as default}; +import{_ as e,c as s,o as i,a7 as a}from"./chunks/framework.CUwTDK2x.js";const u=JSON.parse('{"title":"Models","description":"","frontmatter":{},"headers":[],"relativePath":"models.md","filePath":"models.md","lastUpdated":null}'),t={name:"models.md"},n=a("",7),o=[n];function r(l,p,d,h,c,k){return i(),s("div",null,o)}const E=e(t,[["render",r]]);export{u as __pageData,E as default}; diff --git a/dev/neurotree.png b/dev/assets/neurotree.vCO5vhkM.png similarity index 100% rename from dev/neurotree.png rename to dev/assets/neurotree.vCO5vhkM.png diff --git a/dev/assets/quick-start.md.BP9XKzHk.js b/dev/assets/quick-start.md.BAS8qdd6.js similarity index 89% rename from dev/assets/quick-start.md.BP9XKzHk.js rename to dev/assets/quick-start.md.BAS8qdd6.js index 13fda20..e10c665 100644 --- a/dev/assets/quick-start.md.BP9XKzHk.js +++ b/dev/assets/quick-start.md.BAS8qdd6.js @@ -1,11 +1,11 @@ -import{_ as s,c as i,o as a,a7 as n}from"./chunks/framework.BResyDFY.js";const c=JSON.parse('{"title":"Getting started with NeuroTreeModels.jl","description":"","frontmatter":{},"headers":[],"relativePath":"quick-start.md","filePath":"quick-start.md","lastUpdated":null}'),e={name:"quick-start.md"},t=n(`

Getting started with NeuroTreeModels.jl ​

Installation ​

julia
] add NeuroTreeModels

Configuring a model ​

A model configuration is defined with the NeuroTreeRegressor constructor:

julia
using NeuroTreeModels, DataFrames
+import{_ as s,c as i,o as a,a7 as e}from"./chunks/framework.CUwTDK2x.js";const c=JSON.parse('{"title":"Getting started with NeuroTreeModels.jl","description":"","frontmatter":{},"headers":[],"relativePath":"quick-start.md","filePath":"quick-start.md","lastUpdated":null}'),n={name:"quick-start.md"},t=e(`
Getting started with NeuroTreeModels.jl ​
Installation ​
julia
] add NeuroTreeModels
Configuring a model ​
A model configuration is defined with the NeuroTreeRegressor constructor:
julia
using NeuroTreeModels, DataFrames
 
 config = NeuroTreeRegressor(
     loss = :mse,
     nrounds = 10,
     num_trees = 16,
     depth = 5,
-)
Training ​
Building and training a model according to the above config is done with NeuroTreeModels.fit. See the docs for additional features, notably early stopping support through the tracking of an evaluation metric.
julia
nobs, nfeats = 1_000, 5
+)
Training ​
Building and training a model according to the above config is done with NeuroTreeModels.fit. See the docs for additional features, notably early stopping support through the tracking of an evaluation metric.
julia
nobs, nfeats = 1_000, 5
 dtrain = DataFrame(randn(nobs, nfeats), :auto)
 dtrain.y = rand(nobs)
 feature_names, target_name = names(dtrain, r"x"), "y"
@@ -14,4 +14,4 @@ import{_ as s,c as i,o as a,a7 as n}from"./chunks/framework.BResyDFY.js";const c
 m = NeuroTreeRegressor(depth=5, nrounds=10)
 X, y = @load_boston
 mach = machine(m, X, y) |> fit!
-p = predict(mach, X)
Benchmarks ​
Benchmarking against prominent ML libraries for tabular is performed at MLBenchmarks.jl.
`,16),h=[t];function l(k,p,r,d,o,E){return a(),i("div",null,h)}const u=s(e,[["render",l]]);export{c as __pageData,u as default};
+p = predict(mach, X)

Benchmarks ​

Benchmarking against prominent ML libraries for tabular is performed at MLBenchmarks.jl.

`,16),h=[t];function l(k,p,r,d,o,E){return a(),i("div",null,h)}const u=s(n,[["render",l]]);export{c as __pageData,u as default}; diff --git a/dev/assets/quick-start.md.BAS8qdd6.lean.js b/dev/assets/quick-start.md.BAS8qdd6.lean.js new file mode 100644 index 0000000..114500a --- /dev/null +++ b/dev/assets/quick-start.md.BAS8qdd6.lean.js @@ -0,0 +1 @@ +import{_ as s,c as i,o as a,a7 as e}from"./chunks/framework.CUwTDK2x.js";const c=JSON.parse('{"title":"Getting started with NeuroTreeModels.jl","description":"","frontmatter":{},"headers":[],"relativePath":"quick-start.md","filePath":"quick-start.md","lastUpdated":null}'),n={name:"quick-start.md"},t=e("",16),h=[t];function l(k,p,r,d,o,E){return a(),i("div",null,h)}const u=s(n,[["render",l]]);export{c as __pageData,u as default}; diff --git a/dev/assets/quick-start.md.BP9XKzHk.lean.js b/dev/assets/quick-start.md.BP9XKzHk.lean.js deleted file mode 100644 index 6543d89..0000000 --- a/dev/assets/quick-start.md.BP9XKzHk.lean.js +++ /dev/null @@ -1 +0,0 @@ -import{_ as s,c as i,o as a,a7 as n}from"./chunks/framework.BResyDFY.js";const c=JSON.parse('{"title":"Getting started with NeuroTreeModels.jl","description":"","frontmatter":{},"headers":[],"relativePath":"quick-start.md","filePath":"quick-start.md","lastUpdated":null}'),e={name:"quick-start.md"},t=n("",16),h=[t];function l(k,p,r,d,o,E){return a(),i("div",null,h)}const u=s(e,[["render",l]]);export{c as __pageData,u as default}; diff --git a/dev/assets/style.te4yWCQZ.css b/dev/assets/style.X8RF7YHI.css similarity index 62% rename from dev/assets/style.te4yWCQZ.css rename to dev/assets/style.X8RF7YHI.css index a7b046a..2df2c6f 100644 --- a/dev/assets/style.te4yWCQZ.css +++ b/dev/assets/style.X8RF7YHI.css @@ -1 +1 @@ -@import"https://fonts.googleapis.com/css?family=Space+Mono:regular,italic,700,700italic";@import"https://fonts.googleapis.com/css?family=Space+Grotesk:regular,italic,700,700italic";@font-face{font-family:Inter var;font-weight:100 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a/dev/assets/tutorials-classification-iris.md.D9s1vaUF.js +++ b/dev/assets/tutorials-classification-iris.md.B2d12MNu.js @@ -1,4 +1,4 @@ -import{_ as s,c as i,o as a,a7 as n}from"./chunks/framework.BResyDFY.js";const y=JSON.parse('{"title":"Classification on Iris dataset","description":"","frontmatter":{},"headers":[],"relativePath":"tutorials-classification-iris.md","filePath":"tutorials-classification-iris.md","lastUpdated":null}'),t={name:"tutorials-classification-iris.md"},h=n(`

Classification on Iris dataset ​

We will use the iris dataset, which is included in the MLDatasets package. This dataset consists of measurements of the sepal length, sepal width, petal length, and petal width for three different types of iris flowers: Setosa, Versicolor, and Virginica.

Getting started ​

To begin, we will load the required packages and the dataset:

julia
using NeuroTreeModels
+import{_ as s,c as i,o as a,a7 as n}from"./chunks/framework.CUwTDK2x.js";const y=JSON.parse('{"title":"Classification on Iris dataset","description":"","frontmatter":{},"headers":[],"relativePath":"tutorials-classification-iris.md","filePath":"tutorials-classification-iris.md","lastUpdated":null}'),t={name:"tutorials-classification-iris.md"},h=n(`
Classification on Iris dataset ​
We will use the iris dataset, which is included in the MLDatasets package. This dataset consists of measurements of the sepal length, sepal width, petal length, and petal width for three different types of iris flowers: Setosa, Versicolor, and Virginica.
Getting started ​
To begin, we will load the required packages and the dataset:
julia
using NeuroTreeModels
 using MLDatasets
 using DataFrames
 using Statistics: mean
@@ -15,7 +15,7 @@ import{_ as s,c as i,o as a,a7 as n}from"./chunks/framework.BResyDFY.js";const y
 train_indices = randperm(nrow(df))[1:Int(train_ratio * nrow(df))]
 
 dtrain = df[train_indices, :]
-deval = df[setdiff(1:nrow(df), train_indices), :]
Training ​
Now we are ready to train our model. We first define a model configuration using the NeuroTreeRegressor model constructor. Then, we use NeuroTreeModels.fit to train a boosted tree model. We pass the optional deval argument to enable the usage of early stopping.
julia
config = NeuroTreeRegressor(
+deval = df[setdiff(1:nrow(df), train_indices), :]
Training ​
Now we are ready to train our model. We first define a model configuration using the NeuroTreeRegressor model constructor. Then, we use NeuroTreeModels.fit to train a boosted tree model. We pass the optional deval argument to enable the usage of early stopping.
julia
config = NeuroTreeRegressor(
     device=:cpu,
     loss=:mlogloss,
     nrounds=400,
diff --git a/dev/assets/tutorials-classification-iris.md.D9s1vaUF.lean.js b/dev/assets/tutorials-classification-iris.md.B2d12MNu.lean.js
similarity index 73%
rename from dev/assets/tutorials-classification-iris.md.D9s1vaUF.lean.js
rename to dev/assets/tutorials-classification-iris.md.B2d12MNu.lean.js
index 6374b7f..0704cae 100644
--- a/dev/assets/tutorials-classification-iris.md.D9s1vaUF.lean.js
+++ b/dev/assets/tutorials-classification-iris.md.B2d12MNu.lean.js
@@ -1 +1 @@
-import{_ as s,c as i,o as a,a7 as n}from"./chunks/framework.BResyDFY.js";const y=JSON.parse('{"title":"Classification on Iris dataset","description":"","frontmatter":{},"headers":[],"relativePath":"tutorials-classification-iris.md","filePath":"tutorials-classification-iris.md","lastUpdated":null}'),t={name:"tutorials-classification-iris.md"},h=n("",15),l=[h];function e(k,p,r,E,d,g){return a(),i("div",null,l)}const c=s(t,[["render",e]]);export{y as __pageData,c as default};
+import{_ as s,c as i,o as a,a7 as n}from"./chunks/framework.CUwTDK2x.js";const y=JSON.parse('{"title":"Classification on Iris dataset","description":"","frontmatter":{},"headers":[],"relativePath":"tutorials-classification-iris.md","filePath":"tutorials-classification-iris.md","lastUpdated":null}'),t={name:"tutorials-classification-iris.md"},h=n("",15),l=[h];function e(k,p,r,E,d,g){return a(),i("div",null,l)}const c=s(t,[["render",e]]);export{y as __pageData,c as default};
diff --git a/dev/assets/tutorials-logistic-titanic.md.CGZ87Zr8.js b/dev/assets/tutorials-logistic-titanic.md.C6NYXUQN.js
similarity index 95%
rename from dev/assets/tutorials-logistic-titanic.md.CGZ87Zr8.js
rename to dev/assets/tutorials-logistic-titanic.md.C6NYXUQN.js
index 447e6e2..bc0f1e8 100644
--- a/dev/assets/tutorials-logistic-titanic.md.CGZ87Zr8.js
+++ b/dev/assets/tutorials-logistic-titanic.md.C6NYXUQN.js
@@ -1,4 +1,4 @@
-import{_ as s,c as i,o as a,a7 as n}from"./chunks/framework.BResyDFY.js";const c=JSON.parse('{"title":"Logistic Regression on Titanic Dataset","description":"","frontmatter":{},"headers":[],"relativePath":"tutorials-logistic-titanic.md","filePath":"tutorials-logistic-titanic.md","lastUpdated":null}'),t={name:"tutorials-logistic-titanic.md"},e=n(`
Logistic Regression on Titanic Dataset ​
We will use the Titanic dataset, which is included in the MLDatasets package. It describes the survival status of individual passengers on the Titanic. The model will be approached as a logistic regression problem, although a Classifier model could also have been used (see the Classification - Iris tutorial).
Getting started ​
To begin, we will load the required packages and the dataset:
julia
using NeuroTreeModels
+import{_ as s,c as i,o as a,a7 as n}from"./chunks/framework.CUwTDK2x.js";const c=JSON.parse('{"title":"Logistic Regression on Titanic Dataset","description":"","frontmatter":{},"headers":[],"relativePath":"tutorials-logistic-titanic.md","filePath":"tutorials-logistic-titanic.md","lastUpdated":null}'),t={name:"tutorials-logistic-titanic.md"},e=n(`
Logistic Regression on Titanic Dataset ​
We will use the Titanic dataset, which is included in the MLDatasets package. It describes the survival status of individual passengers on the Titanic. The model will be approached as a logistic regression problem, although a Classifier model could also have been used (see the Classification - Iris tutorial).
Getting started ​
To begin, we will load the required packages and the dataset:
julia
using NeuroTreeModels
 using MLDatasets
 using DataFrames
 using Statistics: mean
@@ -23,7 +23,7 @@ import{_ as s,c as i,o as a,a7 as n}from"./chunks/framework.BResyDFY.js";const c
 deval = df[setdiff(1:nrow(df), train_indices), :]
 
 target_name = "Survived"
-feature_names = setdiff(names(df), [target_name])
Training ​
Now we are ready to train our model. We first define a model configuration using the NeuroTreeRegressor model constructor. Then, we use NeuroTreeModels.fit to train a boosted tree model. We pass the optional deval argument to enable the usage of early stopping.
julia
config = NeuroTreeRegressor(
+feature_names = setdiff(names(df), [target_name])
Training ​
Now we are ready to train our model. We first define a model configuration using the NeuroTreeRegressor model constructor. Then, we use NeuroTreeModels.fit to train a boosted tree model. We pass the optional deval argument to enable the usage of early stopping.
julia
config = NeuroTreeRegressor(
     device=:cpu,
     loss=:logloss,
     nrounds=400,
diff --git a/dev/assets/tutorials-logistic-titanic.md.CGZ87Zr8.lean.js b/dev/assets/tutorials-logistic-titanic.md.C6NYXUQN.lean.js
similarity index 73%
rename from dev/assets/tutorials-logistic-titanic.md.CGZ87Zr8.lean.js
rename to dev/assets/tutorials-logistic-titanic.md.C6NYXUQN.lean.js
index 7d3c025..5d93f19 100644
--- a/dev/assets/tutorials-logistic-titanic.md.CGZ87Zr8.lean.js
+++ b/dev/assets/tutorials-logistic-titanic.md.C6NYXUQN.lean.js
@@ -1 +1 @@
-import{_ as s,c as i,o as a,a7 as n}from"./chunks/framework.BResyDFY.js";const c=JSON.parse('{"title":"Logistic Regression on Titanic Dataset","description":"","frontmatter":{},"headers":[],"relativePath":"tutorials-logistic-titanic.md","filePath":"tutorials-logistic-titanic.md","lastUpdated":null}'),t={name:"tutorials-logistic-titanic.md"},e=n("",19),h=[e];function l(k,p,r,d,E,g){return a(),i("div",null,h)}const y=s(t,[["render",l]]);export{c as __pageData,y as default};
+import{_ as s,c as i,o as a,a7 as n}from"./chunks/framework.CUwTDK2x.js";const c=JSON.parse('{"title":"Logistic Regression on Titanic Dataset","description":"","frontmatter":{},"headers":[],"relativePath":"tutorials-logistic-titanic.md","filePath":"tutorials-logistic-titanic.md","lastUpdated":null}'),t={name:"tutorials-logistic-titanic.md"},e=n("",19),h=[e];function l(k,p,r,d,E,g){return a(),i("div",null,h)}const y=s(t,[["render",l]]);export{c as __pageData,y as default};
diff --git a/dev/assets/tutorials-regression-boston.md.CrsWgv-f.lean.js b/dev/assets/tutorials-regression-boston.md.CrsWgv-f.lean.js
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+++ /dev/null
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-import{_ as s,c as i,o as a,a7 as n}from"./chunks/framework.BResyDFY.js";const y=JSON.parse('{"title":"Regression on Boston Housing Dataset","description":"","frontmatter":{},"headers":[],"relativePath":"tutorials-regression-boston.md","filePath":"tutorials-regression-boston.md","lastUpdated":null}'),t={name:"tutorials-regression-boston.md"},h=n("",15),l=[h];function e(k,p,r,E,d,g){return a(),i("div",null,l)}const c=s(t,[["render",e]]);export{y as __pageData,c as default};
diff --git a/dev/assets/tutorials-regression-boston.md.CrsWgv-f.js b/dev/assets/tutorials-regression-boston.md.nTriyBwB.js
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rename to dev/assets/tutorials-regression-boston.md.nTriyBwB.js
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-import{_ as s,c as i,o as a,a7 as n}from"./chunks/framework.BResyDFY.js";const y=JSON.parse('{"title":"Regression on Boston Housing Dataset","description":"","frontmatter":{},"headers":[],"relativePath":"tutorials-regression-boston.md","filePath":"tutorials-regression-boston.md","lastUpdated":null}'),t={name:"tutorials-regression-boston.md"},h=n(`
Regression on Boston Housing Dataset ​
We will use the Boston Housing dataset, which is included in the MLDatasets package. It's derived from information collected by the U.S. Census Service concerning housing in the area of Boston. Target variable represents the median housing value.
Getting started ​
To begin, we will load the required packages and the dataset:
julia
using NeuroTreeModels
+import{_ as s,c as i,o as a,a7 as n}from"./chunks/framework.CUwTDK2x.js";const y=JSON.parse('{"title":"Regression on Boston Housing Dataset","description":"","frontmatter":{},"headers":[],"relativePath":"tutorials-regression-boston.md","filePath":"tutorials-regression-boston.md","lastUpdated":null}'),t={name:"tutorials-regression-boston.md"},h=n(`
Regression on Boston Housing Dataset ​
We will use the Boston Housing dataset, which is included in the MLDatasets package. It's derived from information collected by the U.S. Census Service concerning housing in the area of Boston. Target variable represents the median housing value.
Getting started ​
To begin, we will load the required packages and the dataset:
julia
using NeuroTreeModels
 using MLDatasets
 using DataFrames
 using Statistics: mean, std
@@ -17,7 +17,7 @@ import{_ as s,c as i,o as a,a7 as n}from"./chunks/framework.BResyDFY.js";const y
 transform!(dtrain, :MEDV => (x -> (x .- _mean) ./ _std) => "target")
 transform!(deval, :MEDV => (x -> (x .- _mean) ./ _std) => "target")
 
-target_name = "target"
Training ​
Now we are ready to train our model. We first define a model configuration using the NeuroTreeRegressor model constructor. Then, we use NeuroTreeModels.fit to train a boosted tree model. We pass the optional deval argument to enable the usage of early stopping.
julia
config = NeuroTreeRegressor(
+target_name = "target"
Training ​
Now we are ready to train our model. We first define a model configuration using the NeuroTreeRegressor model constructor. Then, we use NeuroTreeModels.fit to train a boosted tree model. We pass the optional deval argument to enable the usage of early stopping.
julia
config = NeuroTreeRegressor(
     device=:cpu,
     loss=:mse,
     nrounds=400,
@@ -39,4 +39,4 @@ import{_ as s,c as i,o as a,a7 as n}from"./chunks/framework.BResyDFY.js";const y
 0.8985784079860025
 
 julia> mean(abs.(p_eval .- deval[!, "MEDV"]))
-2.3287859731914597
`,15),l=[h];function e(k,p,r,E,d,g){return a(),i("div",null,l)}const c=s(t,[["render",e]]);export{y as __pageData,c as default};
+2.3287859731914597
`,15),l=[h];function k(e,p,r,E,d,g){return a(),i("div",null,l)}const c=s(t,[["render",k]]);export{y as __pageData,c as default};
diff --git a/dev/assets/tutorials-regression-boston.md.nTriyBwB.lean.js b/dev/assets/tutorials-regression-boston.md.nTriyBwB.lean.js
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+import{_ as s,c as i,o as a,a7 as n}from"./chunks/framework.CUwTDK2x.js";const y=JSON.parse('{"title":"Regression on Boston Housing Dataset","description":"","frontmatter":{},"headers":[],"relativePath":"tutorials-regression-boston.md","filePath":"tutorials-regression-boston.md","lastUpdated":null}'),t={name:"tutorials-regression-boston.md"},h=n("",15),l=[h];function k(e,p,r,E,d,g){return a(),i("div",null,l)}const c=s(t,[["render",k]]);export{y as __pageData,c as default};
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     NeuroTree - A differentiable tree operator for tabular data | NeuroTreeModels
     
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NeuroTreeModels
Appearance
NeuroTree - A differentiable tree operator for tabular data ​
Overview ​
This work introduces NeuroTree a differentiable binary tree operator adapted for the treatment of tabular data.
  • Address the shortcoming of traditional trees greediness: all node and leaves are learned simultaneously. It provides the ability to learn an optimal configuration across all of the tree levels.
The notion extent also to the collection of trees that are simultaneously learned.
  • Extend the notion of forest/bagging and boosting.
    • Although the predictions from the all of of the trees forming a NeuroTree operator are averaged, each of the tree prediction tuned simultaneously. This is different from boosting (ex XGBoost) where each tree is learned sequentially and over the residual from previous trees. Also, unlike random forest and bagging, trees aren't learned in isolation but tuned collaboratively, resulting in predictions that account for all of the other trees predictions.
  • General operator compatible for composition.
    • Allows integration within Flux's Chain like other standard operators from NNLib. Composition is also illustrated through the built-in StackTree layer, a residual composition of multiple NeuroTree building blocks.
  • Compatible with general purpose machine learning framework.
    • MLJ integration
Architecture ​
To introduce the implementation of a NeuroTree, we first get back to the architecture of a basic decision tree.
decision-tree
The above is a binary decision tree of depth 2.
Highlighted in green is the decision path taken for a given sample. It goes into depth number of binary decisions, resulting in the path node1 → node3 → leaf3.
One way to view the role of the decision nodes is to provide an index of the leaf prediction to fetch (index 3 in the figure). Such indexing view is applicable given that node routing relies a hard conditions: it's either true or false.
An alternative perspective that we will adopt here is that tree nodes collectively provide weights associated to each leaf. A tree prediction is the weighted sum of the leaf's value and those leaf weights. In regular decision trees, since all conditions are binary, leaf weights take the form of a mask. In the above example, the mask is [0, 0, 1, 0].
By relaxing these these hard condition into soft ones, the mask takes the form of a probability vector associated to each leaf, where ∑(leaf_weights) = 1 and where each each leaf_weight element is [0, 1].
The following illustrate how a basic decision tree is represented as a single differentiable tree within NeuroTree:
decision-tree
Node weights ​
To derive how a NeuroTree performs those soft decision, we first break down the structure of how the traditional hard decisions are taken. A nodes's split actually relies on 2 binary conditions:
1. Selection of the feature on which to perform the condition ​
The selection of a feature out of the selected ones In NeuroTree, these hard decisions are translated into soft, differentiable ones: 1.
2. Selection of the condition's threshold value ​
A NeuroTree operator acts as collection of complete binary trees, ie. trees without any pruned node. In order to be differentiable, hence trainable using gradient based methods such as Adam, each tree path implements a soft decision rather than a hard one like in traditional decision tree.
Leaf weights ​
Computing the leaf weights consists of accumulating the weights through each tree branch. It's the technically more challenging part as such computation cannot be represented as a form of matrix multiplication, unlike other common operators like Dense, Conv or MultiHeadAttention / Transformer. Performing probability accumulation though a tree index naturally leads to in-place element wise operations, which are notoriously not friendly for auto-differentiation engines. Since NeuroTree was intended to integrate with the Flux.jl ecosystem, Zygote.jl acts as the underlying AD, the approach used was to to manually implement backward / adjoint of the terminal leaf function and instruct the AD to use that custom rule rather than attempt to differentiate a non-AD compliant function.
Below are the algo and actual implementation of the forward and backward function that compute the leaf weights. For brevity, the loops over each observation of the batch and each tree are omitted. Parallelism, both on CPU and GPU, is obtained through parallelization over the tree and batch dimensions.
Forward
julia
function leaf_weights!(nw)
+    
NeuroTreeModels
Appearance
NeuroTree - A differentiable tree operator for tabular data ​
Overview ​
This work introduces NeuroTree a differentiable binary tree operator adapted for the treatment of tabular data.
  • Address the shortcoming of traditional trees greediness: all node and leaves are learned simultaneously. It provides the ability to learn an optimal configuration across all the tree levels. The notion extent also to the collection of trees that are simultaneously learned.
  • Extend the notion of forest/bagging and boosting.
    • Although the predictions from the all the trees forming a NeuroTree operator are averaged, each of the tree prediction is tuned simultaneously. This is different from boosting (ex XGBoost) where each tree is learned sequentially and over the residual from previous trees. Also, unlike random forest and bagging, trees aren't learned in isolation but tuned collaboratively, resulting in predictions that account for all of the other tree predictions.
  • General operator compatible for composition.
    • Allows integration within Flux's Chain like other standard operators from NNLib. Composition is also illustrated through the built-in StackTree layer, a residual composition of multiple NeuroTree building blocks.
  • Compatible with general purpose machine learning framework.
    • MLJ integration
Architecture ​
A NeuroTree operator acts as collection of complete binary trees, ie. trees without any pruned node. To be differentiable, hence trainable using first-order gradient based methods (ex. Adam optimiser), each tree path implements a soft decision rather than a hard one like in traditional decision tree.
To introduce the implementation of a NeuroTree, we first get back to the architecture of a basic decision tree.
The above is a binary decision tree of depth 2.
Highlighted in green is the decision path taken for a given sample. It goes into depth number of binary decisions, resulting in the path node1 → node3 → leaf3.
One way to view the role of the decision nodes (gray background) is to provide an index of the leaf prediction to fetch (index 3 in the figure). Such indexing view is applicable given that node routing relies on hard conditions: either true or false.
An alternative perspective that we adopt here is that tree nodes collectively provide weights associated to each leaf. A tree prediction becomes the weighted sum of the leaf's values and the leaf's weights. In regular decision trees, since all conditions are binary, leaf weights take the form of a mask. In the above example, the mask is [0, 0, 1, 0].
By relaxing these hard conditions into soft ones, the mask takes the form of a probability vector associated to each leaf, where ∑(leaf_weights) = 1 and where each each leaf_weight element is [0, 1]. A tree prediction can be obtained with the dot product: leaf_values' * leaf_weights.
The following illustrate how a basic decision tree is represented as a single differentiable tree within NeuroTree:
Node weights ​
To illustrate how a NeuroTree derives the soft decision probability (referred to NW1 - NW3 in the above figure), we first break down how a traditional tree split condition is derived from 2 underlying decisions:
  1. Selection of the feature on which to perform the condition.
Such selection can be represented as the application of a binary mask where all elements are set to false except for that single selected feature where it's set to true.
  1. Selection of the condition's threshold value.
For a given observation, if the selected feature's value is below that threshold, then the node decision is set to false (pointing to left child), and true otherwise (pooinnting to right child).
In NeuroTree, these 2 hard steps are translated into soft, differentiable ones.
Leaf weights ​
Computing the leaf weights consists of accumulating the weights through each tree branch. It's the technically more challenging part as such computation cannot be represented as a form of matrix multiplication, unlike other common operators like Dense, Conv or MultiHeadAttention / Transformer. Performing probability accumulation though a tree index naturally leads to in-place element wise operations, which are notoriously not friendly for auto-differentiation engines. Since NeuroTree was intended to integrate with the Flux.jl ecosystem, Zygote.jl acts as the underlying AD, the approach used was to manually implement backward / adjoint of the terminal leaf function and instruct the AD to use that custom rule rather than attempt to differentiate a non-AD compliant function.
Below are the algo and actual implementation of the forward and backward function that compute the leaf weights. For brevity, the loops over each observation of the batch and each tree are omitted. Parallelism, both on CPU and GPU, is obtained through parallelization over the tree and batch dimensions.
Forward
julia
function leaf_weights!(nw)
     cw = ones(eltype(nw), 2 * size(nw, 1) + 1)
 
     for i = 2:2:size(cw, 1)
@@ -47,8 +47,8 @@
     end
 
     return nothing
-end
Tree prediction ​
Composability ​
  • StackTree
  • General operator: Chain neurotree with MLP
Benchmarks ​
For each dataset and algo, the following methodology is followed:
  • Data is split in three parts: train, eval and test
  • A random grid of 16 hyper-parameters is generated
  • For each parameter configuration, a model is trained on train data until the evaluation metric tracked against the eval stops improving (early stopping)
  • The trained model is evaluated against the test data
  • The metric presented in below are the ones obtained on the test for the model that generated the best eval metric.
Source code available at MLBenchmarks.jl.
For performance assessment, benchmarks is run on the following selection of common Tabular datasets:
  • Year: min squared error regression
  • MSRank: ranking problem with min squared error regression
  • YahooRank: ranking problem with min squared error regression
  • Higgs: 2-level classification with logistic regression
  • Boston Housing: min squared error regression
  • Titanic: 2-level classification with logistic regression
Comparison is performed against the following algos (implementation in link) considered as state of the art on classification tasks:
Boston ​
model_typetrain_timemsegini
neurotrees12.818.90.947
evotrees0.20619.70.927
xgboost0.064819.40.935
lightgbm0.86525.40.926
catboost0.051113.90.946
Titanic ​
model_typetrain_timeloglossaccuracy
neurotrees7.580.4070.828
evotrees0.6730.3820.828
xgboost0.03790.3750.821
lightgbm0.6150.3900.836
catboost0.03260.3880.836
Year ​
model_typetrain_timemsegini
neurotrees280.076.40.652
evotrees18.680.10.627
xgboost17.280.20.626
lightgbm8.1180.30.624
catboost80.079.20.635
MSRank ​
model_typetrain_timemsendcg
neurotrees39.10.5780.462
evotrees37.00.5540.504
xgboost12.50.5540.503
lightgbm37.50.5530.503
catboost15.10.5580.497
Yahoo ​
model_typetrain_timemsendcg
neurotrees417.00.5840.781
evotrees687.00.5450.797
xgboost120.00.5470.798
lightgbm244.00.5400.796
catboost161.00.5610.794
Higgs ​
model_typetrain_timeloglossaccuracy
neurotrees12300.00.4520.781
evotrees2620.00.4640.776
xgboost1390.00.4620.776
lightgbm1330.00.4610.779
catboost7180.00.4640.775
References ​

-    
+end
Tree prediction ​
Composability ​
  • StackTree
  • General operator: Chain NeuroTree with MLP
Benchmarks ​
For each dataset and algo, the following methodology is followed:
  • Data is split in three parts: train, eval and test
  • A random grid of 16 hyper-parameters is generated
  • For each parameter configuration, a model is trained on train data until the evaluation metric tracked against the eval stops improving (early stopping)
  • The trained model is evaluated against the test data
  • The metric presented in below are the ones obtained on the test for the model that generated the best eval metric.
Source code available at MLBenchmarks.jl.
For performance assessment, benchmarks is run on the following selection of common Tabular datasets:
  • Year: min squared error regression. 515,345 observations, 90 features.
  • MSRank: ranking problem with min squared error regression. 1,200,192 observations, 136 features.
  • YahooRank: ranking problem with min squared error regression. 709,877 observations, 519 features.
  • Higgs: 2-level classification with logistic regression. 11,000,000 observations, 28 features.
  • Boston Housing: min squared error regression.
  • Titanic: 2-level classification with logistic regression. 891 observations, 7 features.
Comparison is performed against the following algos (implementation in link) considered as state of the art on classification tasks:
Boston ​
model_typetrain_timemsegini
neurotrees12.818.90.947
evotrees0.20619.70.927
xgboost0.064819.40.935
lightgbm0.86525.40.926
catboost0.051113.90.946
Titanic ​
model_typetrain_timeloglossaccuracy
neurotrees7.580.4070.828
evotrees0.6730.3820.828
xgboost0.03790.3750.821
lightgbm0.6150.3900.836
catboost0.03260.3880.836
Year ​
model_typetrain_timemsegini
neurotrees280.076.40.652
evotrees18.680.10.627
xgboost17.280.20.626
lightgbm8.1180.30.624
catboost80.079.20.635
MSRank ​
model_typetrain_timemsendcg
neurotrees39.10.5780.462
evotrees37.00.5540.504
xgboost12.50.5540.503
lightgbm37.50.5530.503
catboost15.10.5580.497
Yahoo ​
model_typetrain_timemsendcg
neurotrees417.00.5840.781
evotrees687.00.5450.797
xgboost120.00.5470.798
lightgbm244.00.5400.796
catboost161.00.5610.794
Higgs ​
model_typetrain_timeloglossaccuracy
neurotrees12300.00.4520.781
evotrees2620.00.4640.776
xgboost1390.00.4620.776
lightgbm1330.00.4610.779
catboost7180.00.4640.775
Discussion ​
NeuroTreeModels can achieve top tier performance on both small (Boston) and large (Higgs) datasets. Its performance trailed on the two ranking regression problems (MSRank and Yahoo). Although the large number of features is a distinguishing characteristic of the Yahoo dataset, the 136 features of MSRank are not materially different for the YEAR dataset (90 features), and on which NeuroTreeMoels outperform all other algos. Considering that no sparsity mechanism is present in the feature selection for the node conditions, datasets with a very large number of features may present a challenge. Substituting the default tanh activation with a sparsity inducing one such as hardsigmoid or EntrOpt has not resulted in improvement from the experiments.
Another potential weakness may stem from the soft nature of the decision criteria. Traditional trees can isolate the effect of a specific feature value. This can be notably meaningful in a situation where a numeric feature taking a value of 0 may carry a particular meaning (ex. missing, unknown value). Such stump the effect of a feature should be harder to pick with NeuroTree's soft condition.
References ​

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+{"models.md":"CuUA2Mbx","tutorials-classification-iris.md":"B2d12MNu","quick-start.md":"BAS8qdd6","design.md":"0tBnC0I_","api.md":"DTVXxwSH","tutorials-logistic-titanic.md":"C6NYXUQN","index.md":"6G8I0lEX","tutorials-regression-boston.md":"nTriyBwB"}
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NeuroTreeModels.jl
Differentiable tree-based models for tabular data
Quick start
Design
Models
Source code
Evovest

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NeuroTreeModels.jl
Differentiable tree-based models for tabular data
Quick start
Design
Models
Source code
Evovest

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     Models | NeuroTreeModels
     
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Models ​
NeuroTreeRegressor ​
# NeuroTreeModels.NeuroTreeRegressor — Type. 
NeuroTreeRegressor(;kwargs...)
A model type for constructing a NeuroTreeRegressor, based on NeuroTreeModels.jl, and implementing both an internal API and the MLJ model interface.
Hyper-parameters
  • loss=:mse: Loss to be be minimized during training. One of:
    • :mse
    • :mae
    • :logloss
    • :mlogloss
    • :gaussian_mle
  • nrounds=10: Max number of rounds (epochs).
  • lr=1.0f-2: Learning rate. Must be > 0. A lower eta results in slower learning, typically requiring a higher nrounds.
  • wd=0.f0: Weight decay applied to the gradients by the optimizer.
  • batchsize=2048: Batch size.
  • actA=:tanh: Activation function applied to each of input variable for determination of split node weight. Can be one of:
    • :tanh
    • :identity
  • outsize=1: Number of predictions returned by the model. Typically only used for classification tasks and set to the number of target levels / classes.
  • depth=6: Depth of a tree. Must be >= 1. A tree of depth 1 has 2 prediction leaf nodes. A complete tree of depth N contains 2^N terminal leaves and 2^N - 1 split nodes. Compute cost is proportional to 2^depth. Typical optimal values are in the 3 to 5 range.
  • ntrees=64: Number of trees (per stack).
  • hidden_size=16: Size of hidden layers. Applicable only when stack_size > 1.
  • stack_size=1: Number of stacked NeuroTree blocks.
  • init_scale=1.0: Scaling factor applied to the predictions weights. Values in the ]0, 1] short result in best performance.
  • MLE_tree_split=false: Whether independent models are buillt for each of the 2 parameters (mu, sigma) of the the gaussian_mle loss.
  • rng=123: Either an integer used as a seed to the random number generator or an actual random number generator (::Random.AbstractRNG).
  • device=:cpu: Device to use. Either :cpu or :gpu (recommended as it improves significantly the training speed).
  • gpuID=0: ID of the GPU to use for training.
Internal API
Do config = NeuroTreeRegressor() to construct an instance with default hyper-parameters. Provide keyword arguments to override hyper-parameter defaults, as in NeuroTreeRegressor(loss=...).
Training model
A model is trained using fit:
julia
m = fit(config, dtrain; feature_names, target_name, kwargs...)
Inference
Models act as a functor. returning predictions when called as a function with features as argument:
julia
m(data)
MLJ Interface
From MLJ, the type can be imported using:
julia
NeuroTreeRegressor = @load NeuroTreeRegressor pkg=NeuroTreeModels
Do model = NeuroTreeRegressor() to construct an instance with default hyper-parameters. Provide keyword arguments to override hyper-parameter defaults, as in NeuroTreeRegressor(loss=...).
Training model
In MLJ or MLJBase, bind an instance model to data with mach = machine(model, X, y) where
  • X: any table of input features (eg, a DataFrame) whose columns each have one of the following element scitypes: Continuous, Count, or <:OrderedFactor; check column scitypes with schema(X)
  • y: is the target, which can be any AbstractVector whose element scitype is <:Continuous; check the scitype with scitype(y)
Train the machine using fit!(mach, rows=...).
Operations
  • predict(mach, Xnew): return predictions of the target given features Xnew having the same scitype as X above.
Fitted parameters
The fields of fitted_params(mach) are:
  • :fitresult: The NeuroTreeModel object.
Report
The fields of report(mach) are:
  • :features: The names of the features encountered in training.
Examples
Internal API
julia
using NeuroTreeModels, DataFrames
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NeuroTreeModels
Appearance
Models ​
NeuroTreeRegressor ​
# NeuroTreeModels.NeuroTreeRegressor — Type. 
NeuroTreeRegressor(;kwargs...)
A model type for constructing a NeuroTreeRegressor, based on NeuroTreeModels.jl, and implementing both an internal API and the MLJ model interface.
Hyper-parameters
  • loss=:mse: Loss to be be minimized during training. One of:
    • :mse
    • :mae
    • :logloss
    • :mlogloss
    • :gaussian_mle
  • nrounds=10: Max number of rounds (epochs).
  • lr=1.0f-2: Learning rate. Must be > 0. A lower eta results in slower learning, typically requiring a higher nrounds.
  • wd=0.f0: Weight decay applied to the gradients by the optimizer.
  • batchsize=2048: Batch size.
  • actA=:tanh: Activation function applied to each of input variable for determination of split node weight. Can be one of:
    • :tanh
    • :identity
  • outsize=1: Number of predictions returned by the model. Typically only used for classification tasks and set to the number of target levels / classes.
  • depth=6: Depth of a tree. Must be >= 1. A tree of depth 1 has 2 prediction leaf nodes. A complete tree of depth N contains 2^N terminal leaves and 2^N - 1 split nodes. Compute cost is proportional to 2^depth. Typical optimal values are in the 3 to 5 range.
  • ntrees=64: Number of trees (per stack).
  • hidden_size=16: Size of hidden layers. Applicable only when stack_size > 1.
  • stack_size=1: Number of stacked NeuroTree blocks.
  • init_scale=1.0: Scaling factor applied to the predictions weights. Values in the ]0, 1] short result in best performance.
  • MLE_tree_split=false: Whether independent models are buillt for each of the 2 parameters (mu, sigma) of the the gaussian_mle loss.
  • rng=123: Either an integer used as a seed to the random number generator or an actual random number generator (::Random.AbstractRNG).
  • device=:cpu: Device to use. Either :cpu or :gpu (recommended as it improves significantly the training speed).
  • gpuID=0: ID of the GPU to use for training.
Internal API
Do config = NeuroTreeRegressor() to construct an instance with default hyper-parameters. Provide keyword arguments to override hyper-parameter defaults, as in NeuroTreeRegressor(loss=...).
Training model
A model is trained using fit:
julia
m = fit(config, dtrain; feature_names, target_name, kwargs...)
Inference
Models act as a functor. returning predictions when called as a function with features as argument:
julia
m(data)
MLJ Interface
From MLJ, the type can be imported using:
julia
NeuroTreeRegressor = @load NeuroTreeRegressor pkg=NeuroTreeModels
Do model = NeuroTreeRegressor() to construct an instance with default hyper-parameters. Provide keyword arguments to override hyper-parameter defaults, as in NeuroTreeRegressor(loss=...).
Training model
In MLJ or MLJBase, bind an instance model to data with mach = machine(model, X, y) where
  • X: any table of input features (eg, a DataFrame) whose columns each have one of the following element scitypes: Continuous, Count, or <:OrderedFactor; check column scitypes with schema(X)
  • y: is the target, which can be any AbstractVector whose element scitype is <:Continuous; check the scitype with scitype(y)
Train the machine using fit!(mach, rows=...).
Operations
  • predict(mach, Xnew): return predictions of the target given features Xnew having the same scitype as X above.
Fitted parameters
The fields of fitted_params(mach) are:
  • :fitresult: The NeuroTreeModel object.
Report
The fields of report(mach) are:
  • :features: The names of the features encountered in training.
Examples
Internal API
julia
using NeuroTreeModels, DataFrames
 config = NeuroTreeRegressor(depth=5, nrounds=10)
 nobs, nfeats = 1_000, 5
 dtrain = DataFrame(randn(nobs, nfeats), :auto)
@@ -29,8 +29,8 @@
 m = NeuroTreeRegressor(depth=5, nrounds=10)
 X, y = @load_boston
 mach = machine(m, X, y) |> fit!
-p = predict(mach, X)
source

NeuroTreeModel ​
# NeuroTreeModels.NeuroTreeModel — Type. 
julia
NeuroTreeModel
A NeuroTreeModel is made of a collection of Tree, either regular NeuroTree or StackTree. Prediction is the sum of all the trees composing a NeuroTreeModel.
source


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+p = predict(mach, X)
source

NeuroTreeModel ​
# NeuroTreeModels.NeuroTreeModel — Type. 
julia
NeuroTreeModel
A NeuroTreeModel is made of a collection of Tree, either regular NeuroTree or StackTree. Prediction is the sum of all the trees composing a NeuroTreeModel.
source


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     Getting started with NeuroTreeModels.jl | NeuroTreeModels
     
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NeuroTreeModels
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Getting started with NeuroTreeModels.jl ​
Installation ​
julia
] add NeuroTreeModels
Configuring a model ​
A model configuration is defined with the NeuroTreeRegressor constructor:
julia
using NeuroTreeModels, DataFrames
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NeuroTreeModels
Appearance
Getting started with NeuroTreeModels.jl ​
Installation ​
julia
] add NeuroTreeModels
Configuring a model ​
A model configuration is defined with the NeuroTreeRegressor constructor:
julia
using NeuroTreeModels, DataFrames
 
 config = NeuroTreeRegressor(
     loss = :mse,
     nrounds = 10,
     num_trees = 16,
     depth = 5,
-)
Training ​
Building and training a model according to the above config is done with NeuroTreeModels.fit. See the docs for additional features, notably early stopping support through the tracking of an evaluation metric.
julia
nobs, nfeats = 1_000, 5
+)
Training ​
Building and training a model according to the above config is done with NeuroTreeModels.fit. See the docs for additional features, notably early stopping support through the tracking of an evaluation metric.
julia
nobs, nfeats = 1_000, 5
 dtrain = DataFrame(randn(nobs, nfeats), :auto)
 dtrain.y = rand(nobs)
 feature_names, target_name = names(dtrain, r"x"), "y"
@@ -34,8 +34,8 @@
 m = NeuroTreeRegressor(depth=5, nrounds=10)
 X, y = @load_boston
 mach = machine(m, X, y) |> fit!
-p = predict(mach, X)
Benchmarks ​
Benchmarking against prominent ML libraries for tabular is performed at MLBenchmarks.jl.

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+p = predict(mach, X)
Benchmarks ​
Benchmarking against prominent ML libraries for tabular is performed at MLBenchmarks.jl.

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diff --git a/dev/tutorials-classification-iris.html b/dev/tutorials-classification-iris.html
index 561c738..bdf7993 100644
--- a/dev/tutorials-classification-iris.html
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     Classification on Iris dataset | NeuroTreeModels
     
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Classification on Iris dataset ​
We will use the iris dataset, which is included in the MLDatasets package. This dataset consists of measurements of the sepal length, sepal width, petal length, and petal width for three different types of iris flowers: Setosa, Versicolor, and Virginica.
Getting started ​
To begin, we will load the required packages and the dataset:
julia
using NeuroTreeModels
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NeuroTreeModels
Appearance
Classification on Iris dataset ​
We will use the iris dataset, which is included in the MLDatasets package. This dataset consists of measurements of the sepal length, sepal width, petal length, and petal width for three different types of iris flowers: Setosa, Versicolor, and Virginica.
Getting started ​
To begin, we will load the required packages and the dataset:
julia
using NeuroTreeModels
 using MLDatasets
 using DataFrames
 using Statistics: mean
@@ -35,7 +35,7 @@
 train_indices = randperm(nrow(df))[1:Int(train_ratio * nrow(df))]
 
 dtrain = df[train_indices, :]
-deval = df[setdiff(1:nrow(df), train_indices), :]
Training ​
Now we are ready to train our model. We first define a model configuration using the NeuroTreeRegressor model constructor. Then, we use NeuroTreeModels.fit to train a boosted tree model. We pass the optional deval argument to enable the usage of early stopping.
julia
config = NeuroTreeRegressor(
+deval = df[setdiff(1:nrow(df), train_indices), :]
Training ​
Now we are ready to train our model. We first define a model configuration using the NeuroTreeRegressor model constructor. Then, we use NeuroTreeModels.fit to train a boosted tree model. We pass the optional deval argument to enable the usage of early stopping.
julia
config = NeuroTreeRegressor(
     device=:cpu,
     loss=:mlogloss,
     nrounds=400,
@@ -58,8 +58,8 @@
 0.9833333333333333
 
 julia> mean(deval[!, target_name] .== NeuroTreeModels.onecold(p_eval'))
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diff --git a/dev/tutorials-logistic-titanic.html b/dev/tutorials-logistic-titanic.html
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--- a/dev/tutorials-logistic-titanic.html
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     Logistic Regression on Titanic Dataset | NeuroTreeModels
     
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Logistic Regression on Titanic Dataset ​
We will use the Titanic dataset, which is included in the MLDatasets package. It describes the survival status of individual passengers on the Titanic. The model will be approached as a logistic regression problem, although a Classifier model could also have been used (see the Classification - Iris tutorial).
Getting started ​
To begin, we will load the required packages and the dataset:
julia
using NeuroTreeModels
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NeuroTreeModels
Appearance
Logistic Regression on Titanic Dataset ​
We will use the Titanic dataset, which is included in the MLDatasets package. It describes the survival status of individual passengers on the Titanic. The model will be approached as a logistic regression problem, although a Classifier model could also have been used (see the Classification - Iris tutorial).
Getting started ​
To begin, we will load the required packages and the dataset:
julia
using NeuroTreeModels
 using MLDatasets
 using DataFrames
 using Statistics: mean
@@ -43,7 +43,7 @@
 deval = df[setdiff(1:nrow(df), train_indices), :]
 
 target_name = "Survived"
-feature_names = setdiff(names(df), [target_name])
Training ​
Now we are ready to train our model. We first define a model configuration using the NeuroTreeRegressor model constructor. Then, we use NeuroTreeModels.fit to train a boosted tree model. We pass the optional deval argument to enable the usage of early stopping.
julia
config = NeuroTreeRegressor(
+feature_names = setdiff(names(df), [target_name])
Training ​
Now we are ready to train our model. We first define a model configuration using the NeuroTreeRegressor model constructor. Then, we use NeuroTreeModels.fit to train a boosted tree model. We pass the optional deval argument to enable the usage of early stopping.
julia
config = NeuroTreeRegressor(
     device=:cpu,
     loss=:logloss,
     nrounds=400,
@@ -65,8 +65,8 @@
 0.8527349228611
 
 julia> mean((p_eval .> 0.5) .== deval[!, target_name])
-0.8426966292134831

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diff --git a/dev/tutorials-regression-boston.html b/dev/tutorials-regression-boston.html
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--- a/dev/tutorials-regression-boston.html
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     Regression on Boston Housing Dataset | NeuroTreeModels
     
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Regression on Boston Housing Dataset ​
We will use the Boston Housing dataset, which is included in the MLDatasets package. It's derived from information collected by the U.S. Census Service concerning housing in the area of Boston. Target variable represents the median housing value.
Getting started ​
To begin, we will load the required packages and the dataset:
julia
using NeuroTreeModels
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NeuroTreeModels
Appearance
Regression on Boston Housing Dataset ​
We will use the Boston Housing dataset, which is included in the MLDatasets package. It's derived from information collected by the U.S. Census Service concerning housing in the area of Boston. Target variable represents the median housing value.
Getting started ​
To begin, we will load the required packages and the dataset:
julia
using NeuroTreeModels
 using MLDatasets
 using DataFrames
 using Statistics: mean, std
@@ -37,7 +37,7 @@
 transform!(dtrain, :MEDV => (x -> (x .- _mean) ./ _std) => "target")
 transform!(deval, :MEDV => (x -> (x .- _mean) ./ _std) => "target")
 
-target_name = "target"
Training ​
Now we are ready to train our model. We first define a model configuration using the NeuroTreeRegressor model constructor. Then, we use NeuroTreeModels.fit to train a boosted tree model. We pass the optional deval argument to enable the usage of early stopping.
julia
config = NeuroTreeRegressor(
+target_name = "target"
Training ​
Now we are ready to train our model. We first define a model configuration using the NeuroTreeRegressor model constructor. Then, we use NeuroTreeModels.fit to train a boosted tree model. We pass the optional deval argument to enable the usage of early stopping.
julia
config = NeuroTreeRegressor(
     device=:cpu,
     loss=:mse,
     nrounds=400,
@@ -59,8 +59,8 @@
 0.8985784079860025
 
 julia> mean(abs.(p_eval .- deval[!, "MEDV"]))
-2.3287859731914597

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