ISmCART.ROCcurve

Fore Fore.NET

Sub UserProc;
Var
    res, i: Integer;
    CART: SmCART;
    ROCcurve: IROCcurve;
    y: Array[16] Of Integer;
    x1: Array[16] Of Integer;
    x2: Array[16] Of Integer;
    x3dbl: Array[16] Of Double;
    x4cat: Array[16] Of Integer;
    OneMinusSpecificity, Sensitivity: Array Of Double;
Begin
    // Create a method
    CART := New SmCART.Create;
    // Set initial data
    // Set values corresponding to age group
    y[0] := 1000; y[1] := 1000; y[2] := 1000; y[3] := 1000;
    y[4] := 1000; y[5] := 1000; y[6] := 1000; y[7] := 1000;
    y[8] := 1000; y[9] := 5002; y[10] := 5002; y[11] := 5002;
    y[12] := 5002; y[13] := 5002; y[14] := 5002; y[15] := 5002;
    // Set values, corresponding to social status
    x1[0] := 0; x1[1] := 0; x1[2] := 0; x1[3] := 0;
    x1[4] := 0; x1[5] := 0; x1[6] := 0; x1[7] := 0;
    x1[8] := 0; x1[9] := 1; x1[10] := 1; x1[11] := 1;
    x1[12] := 1; x1[13] := 1; x1[14] := 1; x1[15] := 1;
    // Set values, corresponding to social status
    x2[0] := 10; x2[1] := 10; x2[2] := 10; x2[3] := 10;
    x2[4] := 10; x2[5] := 20; x2[6] := 20; x2[7] := 20;
    x2[8] := 20; x2[9] := 10; x2[10] := 10; x2[11] := 20;
    x2[12] := 20; x2[13] := 20; x2[14] := 20; x2[15] := 20;

// Set values of explanatory quantitative series
 x3dbl[0] := 1; x3dbl[1] := 2; x3dbl[2] := 3; x3dbl[3] := 5;
 x3dbl[4] := 4; x3dbl[5] := 6; x3dbl[6] := 7; x3dbl[7] := 8;
 x3dbl[8] := 9; x3dbl[9] := 9; x3dbl[10] := 10; x3dbl[11] := 10;
 x3dbl[12] := 11; x3dbl[13] := 12; x3dbl[14] := 13; x3dbl[15] := 14;
 // Set values of explanatory categorical series
 x4cat[0] := 1; x4cat[1] := 1; x4cat[2] := 1; x4cat[3] := 1;
 x4cat[4] := 1; x4cat[5] := 1; x4cat[6] := 1; x4cat[7] := 2;
 x4cat[8] := 2; x4cat[9] := 2; x4cat[10] := 2; x4cat[11] := 2;
 x4cat[12] := 3; x4cat[13] := 3; x4cat[14] := 3; x4cat[15] := 3;
 // Determine maximum number of levels in tree
 CART.TreeSizeSpecification.MaximumNumberOfLevels := 3;
 // Determine minimum number of observations that can be in tree node
 CART.TreeSizeSpecification.MinimumNumberOfCases := 2;
 // Consider each category as an attribute
 CART.TreeSizeSpecification.ReduceCategories := True;
 // Set explained series
 CART.Dependent.Value := y;
 // Set explanatory sequence series
 CART.ExplanatoriesOrdered.Add.Value := x1;
 CART.ExplanatoriesOrdered. Add.Value := x2;
 CART.ExplanatoriesContinuous.Add.Value := x3dbl;
 CART.ExplanatoriesCategorical.Add.Value := x4cat;
 // Execute calculation and display values to the console window
 res := CART.Execute;
 ROCcurve := CART.ROCcurve;
 Debug.WriteLine(CART.Errors);
 If res <> 0 Then
 Debug.WriteLine("Failed");
 Else
 If ROCcurve <> Null Then
 Debug.WriteLine("ROC curve data:");
 Debug.Indent;
 Debug.WriteLine("Specifity:");
 Debug.Indent;
 OneMinusSpecificity := ROCcurve.OneMinusSpecificity;
 For i := 0 To OneMinusSpecificity.Length - 1 Do
 Debug.WriteLine(OneMinusSpecificity[i]);
 End For;
 Debug.Unindent;
 Debug.WriteLine("Sensitivity:");
 Debug.Indent;
 Sensitivity := ROCcurve.Sensitivity;
 For i := 0 To Sensitivity.Length - 1 Do
 Debug.WriteLine(Sensitivity[i]);
 End For;
 End If;
 // Display classification quality criteria
 Debug.Unindent;
 Debug.Unindent;
 Debug.WriteLine(" == Qualification criteria of classification == ");
 Debug.WriteLine("Overall accuracy: " + CART.RelevanceMeasure.Accuracy.ToString);
 Debug.WriteLine("F - estimate: " + CART.RelevanceMeasure.F1.ToString);
 Debug.WriteLine("Number of truly positive values: " + CART.RelevanceMeasure.TruePositive.ToString);
 Debug.WriteLine("Number of truly negative values: " + CART.RelevanceMeasure.TrueNegative.ToString);
 End If;
End Sub UserProc;

Imports Prognoz.Platform.Interop.Stat;
…
Public Shared Sub Main(Params: StartParams);
Var
 res, i: integer;
 CART: SmCART;
 ROCcurve: IROCcurve;
 y: Array[16] Of integer;
 x1: Array[16] Of integer;
 x2: Array[16] Of integer;
 x3dbl: Array[16] Of double;
 x4cat: Array[16] Of integer;
 OneMinusSpecificity, Sensitivity: System.Array;
Begin
 // Create a method
 CART := New SmCART.Create();
 // Set initial data
 // Set values corresponding to age group
 y[0] := 1000; y[1] := 1000; y[2] := 1000; y[3] := 1000;
 y[4] := 1000; y[5] := 1000; y[6] := 1000; y[7] := 1000;
 y[8] := 1000; y[9] := 5002; y[10] := 5002; y[11] := 5002;
 y[12] := 5002; y[13] := 5002; y[14] := 5002; y[15] := 5002;
 // Set values, corresponding to social status
 x1[0] := 0; x1[1] := 0; x1[2] := 0; x1[3] := 0;
 x1[4] := 0; x1[5] := 0; x1[6] := 0; x1[7] := 0;
 x1[8] := 0; x1[9] := 1; x1[10] := 1; x1[11] := 1;
 x1[12] := 1; x1[13] := 1; x1[14] := 1; x1[15] := 1;
 // Set values, corresponding to social status
 x2[0] := 10; x2[1] := 10; x2[2] := 10; x2[3] := 10;
 x2[4] := 10; x2[5] := 20; x2[6] := 20; x2[7] := 20;
 x2[8] := 20; x2[9] := 10; x2[10] := 10; x2[11] := 20;
    x2[12] := 20; x2[13] := 20; x2[14] := 20; x2[15] := 20;     // Set values of explanatory quantitative series     x3dbl[0] := 1; x3dbl[1] := 2; x3dbl[2] := 3; x3dbl[3] := 5;     x3dbl[4] := 4; x3dbl[5] := 6; x3dbl[6] := 7; x3dbl[7] := 8;     x3dbl[8] := 9; x3dbl[9] := 9; x3dbl[10] := 10; x3dbl[11] := 10;     x3dbl[12] := 11; x3dbl[13] := 12; x3dbl[14] := 13; x3dbl[15] := 14;     // Set values of explanatory categorical series     x4cat[0] := 1; x4cat[1] := 1; x4cat[2] := 1; x4cat[3] := 1;     x4cat[4] := 1; x4cat[5] := 1; x4cat[6] := 1; x4cat[7] := 2;     x4cat[8] := 2; x4cat[9] := 2; x4cat[10] := 2; x4cat[11] := 2;     x4cat[12] := 3; x4cat[13] := 3; x4cat[14] := 3; x4cat[15] := 3;     // Determine maximum number of levels in tree     CART.TreeSizeSpecification.MaximumNumberOfLevels := 3;     // Determine minimum number of observations that can be in a tree node     CART.TreeSizeSpecification.MinimumNumberOfCases := 2;     // Consider each category as a separate attribute     CART.TreeSizeSpecification.ReduceCategories := True;     // Set explained series     CART.Dependent.Value := y;     // Set explanatory ordinal     CART.ExplanatoriesOrdered.Add().Value := x1;     CART.ExplanatoriesOrdered.Add().Value := x2;     CART.ExplanatoriesContinuous.Add().Value := x3dbl;     CART.ExplanatoriesCategorical.Add().Value := x4cat;     // Calculate and display values to console window     res := CART.Execute();     ROCcurve := CART.ROCcurve;     System.Diagnostics.Debug.WriteLine(CART.Errors);     If res <> 0 Then         System.Diagnostics.Debug.WriteLine("Error occurred");     Else         If ROCcurve <> Null Then             System.Diagnostics.Debug.WriteLine("Data of ROC curve:");             System.Diagnostics.Debug.Indent();             System.Diagnostics.Debug.WriteLine("Specificity:");             System.Diagnostics.Debug.Indent();             OneMinusSpecificity := ROCcurve.OneMinusSpecificity;             For i := 0 To OneMinusSpecificity.Length - 1 Do                 System.Diagnostics.Debug.WriteLine(OneMinusSpecificity[i]);             End For;             System.Diagnostics.Debug.Unindent();             System.Diagnostics.Debug.WriteLine("Sensitivity:");             System.Diagnostics.Debug.Indent();             Sensitivity := ROCcurve.Sensitivity;             For i := 0 To Sensitivity.Length - 1 Do                 System.Diagnostics.Debug.WriteLine(Sensitivity[i]);             End For;         End If;         // Display criteria of classification quality         System.Diagnostics.Debug.Unindent();         System.Diagnostics.Debug.Unindent();         System.Diagnostics.Debug.WriteLine(" == Criteria of classification quality == ");         System.Diagnostics.Debug.WriteLine("Overall accuracy: " + CART.RelevanceMeasure.Accuracy.ToString());         System.Diagnostics.Debug.WriteLine("F - estimate: " + CART.RelevanceMeasure.F1.ToString());         System.Diagnostics.Debug.WriteLine("Number of truly positive values: " + CART.RelevanceMeasure.TruePositive.ToString());         System.Diagnostics.Debug.WriteLine("Number of truly negative values: " + CART.RelevanceMeasure.TrueNegative.ToString());     End If; End Sub;

ISmCART.ROCcurve

Syntax

Description

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Example