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classification models comparison

_freeze/machinelearning/1-classfication-comparison/execute-results/html.json

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+    "markdown": "---\ntitle: \"classfication models comparison\"\n---\n\n## 1. load package\n\n::: {.cell execution_count=1}\n``` {.julia .cell-code}\n    import MLJ:predict,predict_mode\n    using  MLJ,GLMakie,DataFrames,Random\n    Random.seed!(1222)\n```\n\n::: {.cell-output .cell-output-display execution_count=2}\n```\nTaskLocalRNG()\n```\n:::\n:::\n\n\n## 2. make data \n\n::: {.cell execution_count=2}\n``` {.julia .cell-code}\n    function circle_data()\n    X, y = make_circles(400; noise=0.1, factor=0.3)\n    df = DataFrame(X)\n    df.y = y\n    return df\n    end\n    function moons_data()\n        X, y = make_moons(400; noise=0.1)\n        df = DataFrame(X)\n        df.y = y\n        return df\n    end\n    function blob_data()\n        X, y = make_blobs(400, 2; centers=2, cluster_std=[1.0, 2.0])\n        df = DataFrame(X)\n        df.y = y\n        return df\n    end\n    #cat=df1.y|>levels|>unique\n    colors=[:green, :purple]\n```\n\n::: {.cell-output .cell-output-display execution_count=3}\n```\n2-element Vector{Symbol}:\n :green\n :purple\n```\n:::\n:::\n\n\n## 3. define function \n\n::: {.cell execution_count=3}\n``` {.julia .cell-code}\nfunction plot_origin_data(df)\n    fig=Figure()\n    ax=Axis(fig[1,1])\n    local cat=df.y|>levels|>unique\n    \n    local colors=[:green, :purple]\n    for (i,c) in enumerate(cat)\n        d=df[y.==c,:]\n        scatter!(ax, d[:,1],d[:,2],color=(colors[i],0.6))\n        #@show d\n    end\n    fig\nend\n\nnums=100\nfunction boundary_data(df,;n=nums)\n    n1=n2=n\n    xlow,xhigh=extrema(df[:,:x1])\n    ylow,yhigh=extrema(df[:,:x2])\n    tx = LinRange(xlow,xhigh,n1)\n    ty = LinRange(ylow,yhigh,n2)\n    x_test = mapreduce(collect, hcat, Iterators.product(tx, ty));\n    x_test=MLJ.table(x_test')\n    return tx,ty,x_test\nend\n\nfunction plot_desc_boudary(fig,ytest,i;df=df1,row=1)\n        tx,ty,xs,ys, xtest=boundary_data(df)\n        local ax=Axis(fig[row,i],title=\"$(names[i])\")\n\n        contourf!(ax, tx,ty,ytest,levels=length(cat),colormap=:phase)\n\n        for (i,c) in enumerate(cat)\n            d=df[y.==c,:]\n            scatter!(ax, d[:,1],d[:,2],color=(colors[i],0.6))\n        end\n        hidedecorations!(ax)\nend\n\n```\n\n::: {.cell-output .cell-output-display execution_count=4}\n```\nplot_desc_boudary (generic function with 1 method)\n```\n:::\n:::\n\n\n## 4. define  machine learning models\n\n::: {.cell execution_count=4}\n``` {.julia .cell-code}\n    using CatBoost.MLJCatBoostInterface\n    SVC = @load SVC pkg=LIBSVM   \n    KNNClassifier = @load KNNClassifier pkg=NearestNeighborModels\n    DecisionTreeClassifier = @load DecisionTreeClassifier pkg=DecisionTree\n    RandomForestClassifier = @load RandomForestClassifier pkg=DecisionTree\n    CatBoostClassifier = @load CatBoostClassifier pkg=CatBoost\n    BayesianLDA = @load BayesianLDA pkg=MultivariateStats\n    Booster = @load AdaBoostStumpClassifier pkg=DecisionTree\n    \n    models=[KNNClassifier,DecisionTreeClassifier,RandomForestClassifier,CatBoostClassifier,BayesianLDA,SVC]\n    names=[\"KNN\",\"DecisionTree\",\"RandomForest\",\"CatBoost\",\"BayesianLDA\",\"SVC\"]\n   function _fit(df::DataFrame,m)\n    X,y=df[:,1:2],df[:,3]\n    _,_,xtest=boundary_data(df;n=nums)\n    local predict= m==MLJLIBSVMInterface.SVC  ? MLJ.predict : MLJ.predict_mode \n    model=m()\n   mach = machine(model, X, y)|>fit!\n   yhat=predict(mach, xtest)\n   ytest=yhat|>Array|>d->reshape(d,nums,nums)\n   return  ytest\nend\n\n\n\nfunction plot_desc_boudary(fig,ytest,i;df=df1,row=1)\n    tx,ty,_=boundary_data(df)\n    local y=df.y\n    local ax=Axis(fig[row,i],title=\"$(names[i])\")\n    cat=y|>levels|>unique\n    contourf!(ax, tx,ty,ytest,levels=length(cat),colormap=:redsblues)\n\n    for (i,c) in enumerate(cat)\n        d=df[y.==c,:]\n        scatter!(ax, d[:,1],d[:,2],color=(colors[i],0.6))\n    end\n    hidedecorations!(ax)\n    \n\nend\n\nfunction plot_comparsion(testdata,df;row=1)\n    \n    for (i,data) in enumerate(testdata)\n        plot_desc_boudary(fig,data,i;df=df,row=row)\n    end\n    fig\nend\n```\n\n::: {.cell-output .cell-output-stderr}\n```\n[ Info: For silent loading, specify `verbosity=0`. \n[ Info: For silent loading, specify `verbosity=0`. \n[ Info: For silent loading, specify `verbosity=0`. \n[ Info: For silent loading, specify `verbosity=0`. \n[ Info: For silent loading, specify `verbosity=0`. \n[ Info: For silent loading, specify `verbosity=0`. \n[ Info: For silent loading, specify `verbosity=0`. \n```\n:::\n\n::: {.cell-output .cell-output-stdout}\n```\nimport MLJLIBSVMInterface ✔\nimport NearestNeighborModels ✔\nimport MLJDecisionTreeInterface ✔\nimport MLJDecisionTreeInterface ✔\nimport CatBoost ✔\nimport MLJMultivariateStatsInterface ✔\nimport MLJDecisionTreeInterface ✔\n```\n:::\n\n::: {.cell-output .cell-output-display execution_count=5}\n```\nplot_comparsion (generic function with 1 method)\n```\n:::\n:::\n\n\n::: {.cell execution_count=5}\n``` {.julia .cell-code}\nfig=Figure(resolution=(2100,1000))\nfunction plot_comparsion(testdata,df,row=1)\n    \n    for i in eachindex(testdata)\n        plot_desc_boudary(fig,testdata[i],i;df=df,row=row)\n    end\n    fig\nend\n\n\n\ndf1=circle_data()\n\nytest1=[_fit(df1,m) for (i,m) in enumerate(models)]\n\ndf2=moons_data()\nytest2=[_fit(df2,m) for (i,m) in enumerate(models)]\n\ndf3=blob_data()\nytest3=[_fit(df3,m) for (i,m) in enumerate(models)]\n\ndfs=[df2,df1,df3]\nytests=[ytest2,ytest1,ytest3]\n\nfig=Figure(resolution=(2100,1000))\n\nfor (df, data,i)  in zip(dfs,ytests,[1,2,3])\n    plot_comparsion(data,df;row=i)\nend\n\nfig\n```\n\n::: {.cell-output .cell-output-stderr}\n```\n[ Info: Training machine(KNNClassifier(K = 5, …), …).\n[ Info: Training machine(DecisionTreeClassifier(max_depth = -1, …), …).\n[ Info: Training machine(RandomForestClassifier(max_depth = -1, …), …).\n[ Info: Training machine(CatBoostClassifier(iterations = 1000, …), …).\n[ Info: Training machine(BayesianLDA(method = gevd, …), …).\n[ Info: Training machine(SVC(kernel = RadialBasis, …), …).\n[ Info: Training machine(KNNClassifier(K = 5, …), …).\n[ Info: Training machine(DecisionTreeClassifier(max_depth = -1, …), …).\n[ Info: Training machine(RandomForestClassifier(max_depth = -1, …), …).\n[ Info: Training machine(CatBoostClassifier(iterations = 1000, …), …).\n[ Info: Training machine(BayesianLDA(method = gevd, …), …).\n[ Info: Training machine(SVC(kernel = RadialBasis, …), …).\n[ Info: Training machine(KNNClassifier(K = 5, …), …).\n[ Info: Training machine(DecisionTreeClassifier(max_depth = -1, …), …).\n[ Info: Training machine(RandomForestClassifier(max_depth = -1, …), …).\n[ Info: Training machine(CatBoostClassifier(iterations = 1000, …), …).\n[ Info: Training machine(BayesianLDA(method = gevd, …), …).\n[ Info: Training machine(SVC(kernel = RadialBasis, …), …).\n```\n:::\n\n::: {.cell-output .cell-output-display execution_count=6}\n![](1-classfication-comparison_files/figure-html/cell-6-output-2.png){}\n:::\n:::\n\n\n",
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_quarto.yml

@@ -21,6 +21,8 @@ website:
         contents: math/*
       - section: "Statistics"
         contents: statistics/*
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+        contents: machinelearning/*
       - href: schedule.qmd
         text: Schedule
       - href: materials.qmd

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