ISmLogisticRegression.Probabilities

Probabilities: Array;

Probabilities: System.Array;

Sub UserLogisticR;
Var
    binary: SmLogisticRegression;
    e, s: Array[62] Of Double;
    Coef: IModelCoefficients;
    stat: ISummaryStatistics;
    i, j, res: Integer;
    ROCcurve: IROCcurve;
    FilledDependent, ar, Probabilities, ProbFitted: Array Of Double;
    OneMinusSpecificity, Sensitivity, CutOffPoints: Array Of Double;
    str: String;
Begin
    // Set data for analysis
    // Values corresponding to social status
    s[0] := Double.Nan; s[11] := 0; s[22] := 0; s[33] := Double.Nan; s[44] := 1; s[55] := Double.Nan;
    s[1] := 1; s[12] := 1; s[23] := 1; s[34] := 0; s[45] := 1; s[56] := 1;
    s[2] := Double.Nan; s[13] := 1; s[24] := 0; s[35] := 0; s[46] := 1; s[57] := 1;
    s[3] := 0; s[14] := 0; s[25] := 0; s[36] := 1; s[47] := 0; s[58] := 1;
    s[4] := 0; s[15] := 0; s[26] := 0; s[37] := 0; s[48] := 1; s[59] := 1;
    s[5] := 1; s[16] := 0; s[27] := 0; s[38] := 0; s[49] := 0; s[60] := 0;
    s[6] := 0; s[17] := 0; s[28] := 0; s[39] := 0; s[50] := 0; s[61] := 0;
    s[7] := 1; s[18] := Double.Nan; s[29] := Double.Nan; s[40] := 0; s[51] := Double.Nan;
    s[8] := 1; s[19] := 0; s[30] := 0; s[41] := 0; s[52] := 0;
    s[9] := 1; s[20] := 1; s[31] := 0; s[42] := Double.Nan; s[53] := 0;
    s[10] := 1; s[21] := 0; s[32] := 0; s[43] := 1; s[54] := 0;
    // Values corresponding to age group
    e[0] := 1; e[11] := 0; e[22] := 1; e[33] := 2; e[44] := 2; e[55] := 2;
    e[1] := 1; e[12] := 1; e[23] := 2; e[34] := 1; e[45] := 0; e[56] := 0;
    e[2] := 1; e[13] := 1; e[24] := 1; e[35] := 3; e[46] := 1; e[57] := 1;
    e[3] := 0; e[14] := 1; e[25] := 0; e[36] := 1; e[47] := 1; e[58] := 0;
    e[4] := 0; e[15] := 2; e[26] := 1; e[37] := 1; e[48] := 1; e[59] := 0;
    e[5] := 1; e[16] := 1; e[27] := 0; e[38] := 2; e[49] := 0; e[60] := 2;
    e[6] := 2; e[17] := 0; e[28] := 1; e[39] := 3; e[50] := 1; e[61] := 2;
    e[7] := 0; e[18] := 1; e[29] := 3; e[40] := 1; e[51] := 0;
    e[8] := 3; e[19] := 3; e[30] := 1; e[41] := 0; e[52] := 0;
    e[9] := 1; e[20] := 4; e[31] := 1; e[42] := 4; e[53] := 2;
    e[10] := 2; e[21] := 0; e[32] := 2; e[43] := 1; e[54] := 0;

    // Create a method
    binary := New SmLogisticRegression.Create;
    // Set explained series
    binary.Dependent.Value := s;
    // Set classification attribute
    binary.Explanatories.Add.Value := e;
    // Set accuracy of solution
    binary.Tolerance := 0.005;
    // Set maximum number of iterations
    binary.MaxIteration := 10000;
    // Set ROC curve parameters
    ROCcurve := binary.ROCcurve;
    ROCcurve.ConfidenceLevel := 0.85;
    // Execute calculation and display calculation results
    res := binary.Execute;
    If res <> 0 Then
        Debug.WriteLine(binary.Errors);
    Else
        Debug.WriteLine("Number of iterations required for solution: ");
        Debug.Indent;
        Debug.WriteLine(binary.NumOfIter);
        Debug.Unindent;
        Debug.WriteLine("Before filling; after filling");
        Debug.Indent;
        FilledDependent := binary.FilledDependent.Value;
        For i := 0 To s.Length - 1 Do
            If s[i] = 0 Then
                Debug.Write("Single ");
            Elseif s[i] = 1 Then
                Debug.Write("Married ");
            Else
                Debug.Write(" - ");
            End If;
            If FilledDependent[i] = 0 Then
                Debug.WriteLine("Single");
            Else
                Debug.WriteLine("Married");
            End If;
        End For;

        Debug.Unindent;
        Debug.WriteLine("Estimated coefficient values:");
        Debug.Indent;
        Coef := binary.ModelCoefficients;
        ar := Coef.Coefficients.Estimate;
        For i := 0 To ar.Length - 1 Do
            Debug.WriteLine(ar[i]);
        End For;
        Debug.Unindent;
        Debug.WriteLine("Mean of log-likelihood function: ");
        Debug.Indent;
        Stat := binary.SummaryStatistics;
        Debug.WriteLine(Stat.AvgLogL);
        Debug.Unindent;
        Debug.WriteLine("Probability series");
        Debug.Indent;
        Probabilities := binary.Probabilities;
        For i := 0 To Probabilities.Length - 1 Do
            Debug.WriteLine((i + 1).ToString + ". " + Probabilities[i].ToString);
        End For;
        Debug.Unindent;
        Debug.WriteLine("Probability series for training objects");
        Debug.Indent;
        ProbFitted := binary.ProbFitted;
        For i := 0 To ProbFitted.Length - 1 Do
            Debug.WriteLine((i + 1).ToString + ". " + ProbFitted[i].ToString);
        End For;
        Debug.Unindent;
        // Display ROC curve data
        Debug.WriteLine("ROC curve data:");
        Debug.Indent;
        Debug.WriteLine("Specificity:");
        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;

        Debug.Unindent;
        Debug.WriteLine("Area under curve: " + ROCcurve.Area.ToString);
        Debug.WriteLine("Standard error: " + ROCcurve.StdError.ToString);
        Debug.WriteLine("Asymptotic confidence interval:");
        Debug.Indent;
        Debug.WriteLine("Lower limit: " + ROCcurve.ConfidenceIntervalLower.ToString);
        Debug.WriteLine("Upper limit: " + ROCcurve.ConfidenceIntervalUpper.ToString);
        Debug.Unindent;
        Debug.WriteLine("Cutoff threshold:");
        Debug.Indent;
        CutOffPoints := ROCcurve.CutOffPoints;
        For i := 0 To CutOffPoints.Length - 1 Do
            Debug.WriteLine(CutOffPoints[i]);
        End For;
        Debug.Unindent;
        Debug.Unindent;
        // Output summary results of classification
        Debug.WriteLine("Summary results of classification:");
        Debug.Indent;
        str := "";
        For i := 0 To binary.ClassificationSummary.GetUpperBound(1) Do
            For j := 0 To binary.ClassificationSummary.GetUpperBound(2) Do
                str := str + binary.ClassificationSummary[i, j].ToString + " ";
            End For;
            Debug.WriteLine(str);
            str := "";
        End For;
        Debug.Unindent;
        // Display classification quality criteria      
        Debug.WriteLine("Classification quality criteria");
        Debug.Indent;
        Debug.WriteLine("Overall accuracy: " + binary.RelevanceMeasure.Accuracy.ToString);
        Debug.WriteLine("F - estimate: " + binary.RelevanceMeasure.F1.ToString);
        Debug.WriteLine("Positive result accuracy: " + binary.RelevanceMeasure.Precision.ToString);
        Debug.WriteLine("Completeness of positive result: " + binary.RelevanceMeasure.Recall.ToString);
        Debug.Unindent;
        Debug.Unindent;
    End If;
End Sub UserLogisticR;

Imports Prognoz.Platform.Interop.Stat;
…
Public Shared Sub Main(Params: StartParams);
Var
    binary: SmLogisticRegression;
    e, s: Array[62] Of Double;
    Coef: IModelCoefficients;
    stat: ISummaryStatistics;
    i, j, res: Integer;
    ROCcurve: IROCcurve;
    FilledDependent, ar, Probabilities, ProbFitted: System.Array;
    OneMinusSpecificity, Sensitivity, CutOffPoints: System.Array;
    str: string;
Begin
    // Set data for analysis
    // Values corresponding to social status
    s[0] := Double.Nan; s[11] := 0; s[22] := 0; s[33] := Double.Nan; s[44] := 1; s[55] := Double.Nan;
    s[1] := 1; s[12] := 1; s[23] := 1; s[34] := 0; s[45] := 1; s[56] := 1;
    s[2] := Double.Nan; s[13] := 1; s[24] := 0; s[35] := 0; s[46] := 1; s[57] := 1;
    s[3] := 0; s[14] := 0; s[25] := 0; s[36] := 1; s[47] := 0; s[58] := 1;
    s[4] := 0; s[15] := 0; s[26] := 0; s[37] := 0; s[48] := 1; s[59] := 1;
    s[5] := 1; s[16] := 0; s[27] := 0; s[38] := 0; s[49] := 0; s[60] := 0;
    s[6] := 0; s[17] := 0; s[28] := 0; s[39] := 0; s[50] := 0; s[61] := 0;
    s[7] := 1; s[18] := Double.Nan; s[29] := Double.Nan; s[40] := 0; s[51] := Double.Nan;
    s[8] := 1; s[19] := 0; s[30] := 0; s[41] := 0; s[52] := 0;
    s[9] := 1; s[20] := 1; s[31] := 0; s[42] := Double.Nan; s[53] := 0;
    s[10] := 1; s[21] := 0; s[32] := 0; s[43] := 1; s[54] := 0;
    // Values corresponding to age group
    e[0] := 1; e[11] := 0; e[22] := 1; e[33] := 2; e[44] := 2; e[55] := 2;
    e[1] := 1; e[12] := 1; e[23] := 2; e[34] := 1; e[45] := 0; e[56] := 0;
    e[2] := 1; e[13] := 1; e[24] := 1; e[35] := 3; e[46] := 1; e[57] := 1;
    e[3] := 0; e[14] := 1; e[25] := 0; e[36] := 1; e[47] := 1; e[58] := 0;
    e[4] := 0; e[15] := 2; e[26] := 1; e[37] := 1; e[48] := 1; e[59] := 0;
    e[5] := 1; e[16] := 1; e[27] := 0; e[38] := 2; e[49] := 0; e[60] := 2;
    e[6] := 2; e[17] := 0; e[28] := 1; e[39] := 3; e[50] := 1; e[61] := 2;
    e[7] := 0; e[18] := 1; e[29] := 3; e[40] := 1; e[51] := 0;
    e[8] := 3; e[19] := 3; e[30] := 1; e[41] := 0; e[52] := 0;
    e[9] := 1; e[20] := 4; e[31] := 1; e[42] := 4; e[53] := 2;
    e[10] := 2; e[21] := 0; e[32] := 2; e[43] := 1; e[54] := 0;

    // Create a method
    binary := New SmLogisticRegression.Create();
    // Set explained series
    binary.Dependent.Value := s;
    // Set classification attribute
    binary.Explanatories.Add().Value := e;
    // Set accuracy of solution
    binary.Tolerance := 0.005;
    // Set maximum number of iterations
    binary.MaxIteration := 10000;
    // Set ROC curve parameters
    ROCcurve := binary.ROCcurve;
    ROCcurve.ConfidenceLevel := 0.85;
    // Execute calculation and display calculation results
    res := binary.Execute();
    If res <> 0 Then
        System.Diagnostics.Debug.WriteLine(binary.Errors);
    Else
        System.Diagnostics.Debug.WriteLine("Number of iterations required for solution: ");
        System.Diagnostics.Debug.Indent();
        System.Diagnostics.Debug.WriteLine(binary.NumOfIter);
        System.Diagnostics.Debug.Unindent();
        System.Diagnostics.Debug.WriteLine("Before filling; after filling");
        System.Diagnostics.Debug.Indent();
        FilledDependent := binary.FilledDependent.Value;
        For i := 0 To s.Length - 1 Do
            If s[i] = 0 Then
                System.Diagnostics.Debug.Write("Single ");
            Elseif s[i] = 1 Then
                System.Diagnostics.Debug.Write("Married ");
            Else
                System.Diagnostics.Debug.Write(" - ");
            End If;
            If FilledDependent[i] As double = 0 Then
                System.Diagnostics.Debug.WriteLine("Single");
            Else
                System.Diagnostics.Debug.WriteLine("Married");
            End If;
        End For;
        System.Diagnostics.Debug.Unindent();
        System.Diagnostics.Debug.WriteLine("Estimated coefficient values:");
        System.Diagnostics.Debug.Indent();
        Coef := binary.ModelCoefficients;
        ar := Coef.Coefficients.Estimate;
        For i := 0 To ar.Length - 1 Do
            System.Diagnostics.Debug.WriteLine(ar[i]);
        End For;

        System.Diagnostics.Debug.Unindent();
        System.Diagnostics.Debug.WriteLine("Mean of log-likelihood function: ");
        System.Diagnostics.Debug.Indent();
        Stat := binary.SummaryStatistics;
        System.Diagnostics.Debug.WriteLine(Stat.AvgLogL);
        System.Diagnostics.Debug.Unindent();
        System.Diagnostics.Debug.WriteLine("Probability series");
        System.Diagnostics.Debug.Indent();
        Probabilities := binary.Probabilities;
        For i := 0 To Probabilities.Length - 1 Do
            System.Diagnostics.Debug.WriteLine((i + 1).ToString() + ". " + Probabilities[i].ToString());
        End For;
        System.Diagnostics.Debug.Unindent();
        System.Diagnostics.Debug.WriteLine("Probability series for training objects");
        System.Diagnostics.Debug.Indent();
        ProbFitted := binary.ProbFitted;
        For i := 0 To ProbFitted.Length - 1 Do
            System.Diagnostics.Debug.WriteLine((i + 1).ToString() + ". " + ProbFitted[i].ToString());
        End For;
        System.Diagnostics.Debug.Unindent();
        // Display ROC curve data
        System.Diagnostics.Debug.WriteLine("ROC curve data:");
        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;

        System.Diagnostics.Debug.Unindent();
        System.Diagnostics.Debug.WriteLine("Area under curve: " + ROCcurve.Area.ToString());
        System.Diagnostics.Debug.WriteLine("Standard error: " + ROCcurve.StdError.ToString());
        System.Diagnostics.Debug.WriteLine("Asymptotic confidence interval:");
        System.Diagnostics.Debug.Indent();
        System.Diagnostics.Debug.WriteLine("Lower limit: " + ROCcurve.ConfidenceIntervalLower.ToString());
        System.Diagnostics.Debug.WriteLine("Upper limit: " + ROCcurve.ConfidenceIntervalUpper.ToString();
        System.Diagnostics.Debug.Unindent();
        System.Diagnostics.Debug.WriteLine("Cutoff threshold:");
        System.Diagnostics.Debug.Indent();
        CutOffPoints := ROCcurve.CutOffPoints;
        For i := 0 To CutOffPoints.Length - 1 Do
            System.Diagnostics.Debug.WriteLine(CutOffPoints[i]);
        End For;
        System.Diagnostics.Debug.Unindent();
        System.Diagnostics.Debug.Unindent();
        // Output summary results of classification
        System.Diagnostics.Debug.WriteLine("Summary results of classification:");
        System.Diagnostics.Debug.Indent();
        str := "";
        For i := 0 To binary.ClassificationSummary.GetUpperBound(0) Do
            For j := 0 To binary.ClassificationSummary.GetUpperBound(1) Do
                str := str + binary.ClassificationSummary.GetValue(i, j).ToString() + " ";
            End For;
            System.Diagnostics.Debug.WriteLine(str);
            str := "";
        End For;
        System.Diagnostics.Debug.Unindent();
        // Display classification quality criteria      
        System.Diagnostics.Debug.WriteLine("Classification quality criteria");
        System.Diagnostics.Debug.Indent();
        System.Diagnostics.Debug.WriteLine("Overall accuracy: " + binary.RelevanceMeasure.Accuracy.ToString());
        System.Diagnostics.Debug.WriteLine("F - estimate: " + binary.RelevanceMeasure.F1.ToString());
        System.Diagnostics.Debug.WriteLine("Positive result accuracy: " + binary.RelevanceMeasure.Precision.ToString());
        System.Diagnostics.Debug.WriteLine("Completeness of positive result: " + binary.RelevanceMeasure.Recall.ToString());
        System.Diagnostics.Debug.Unindent();
        System.Diagnostics.Debug.Unindent();
    End If;
End Sub;