public void DoStepTest()
{
double[][] inputs = Matrix.Expand(
new double[][] {
new double[] { 0, 0, 0 },
new double[] { 1, 0, 0 },
new double[] { 0, 1, 0 },
new double[] { 1, 1, 0 },
new double[] { 0, 0, 1 },
new double[] { 1, 0, 1 },
new double[] { 0, 1, 1 },
new double[] { 1, 1, 1 },
}, new int[] { 60, 17, 8, 2, 187, 85, 51, 23 });
double[] outputs = Matrix.Expand(
new double[] { 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0 },
new int[] { 5, 60 - 5, 2, 17 - 2, 1, 8 - 1, 0, 2 - 0, 35, 187 - 35, 13, 85 - 13, 15, 51 - 15, 8, 23 - 8 });
var target2 = new LogisticRegressionAnalysis(inputs, outputs);
target2.Compute();
Assert.AreEqual(target2.CoefficientValues[0], -2.377661, 0.0001);
Assert.AreEqual(target2.CoefficientValues[1], -0.067775, 0.0001);
Assert.AreEqual(target2.CoefficientValues[2], 0.69531, 0.0001);
Assert.AreEqual(target2.CoefficientValues[3], 0.871939, 0.0001);
var target = new StepwiseLogisticRegressionAnalysis(
inputs, outputs,
new string[] { "x1", "x2", "x3" }, "Y"
);
target.Threshold = 0.15;
int actual;
actual = target.DoStep();
Assert.AreEqual(0, actual);
actual = target.DoStep();
Assert.AreEqual(-1, actual);
}
protected override void EndProcessing()
{
double[][] inputs;
double[] outputs;
string[] inputNames;
string outputName;
if (ParameterSetName == "XY")
{
inputs = Converter.ToDoubleJaggedArray(X);
outputs = Converter.ToDoubleArray(Y);
inputNames = Accord.Math.Vector.Range(0, inputs[0].Length, 1).Select(x => "x" + x).ToArray();
outputName = "y";
}
else
{
outputs = _data.GetColumn(OutputName).ToDoubleArray();
_data.RemoveColumn(OutputName);
inputs = _data.ToDoubleJaggedArray();
inputNames = _data.ColumnNames.ToArray();
outputName = OutputName;
}
double[] w = null;
if (Weights != null)
{
w = Converter.ToDoubleArray(Weights);
}
var model = new StepwiseLogisticRegressionAnalysis(inputs, outputs, inputNames, outputName);
#pragma warning disable CS0618
model.Compute();
#pragma warning restore CS0618
WriteObject(model);
}
public void ComputeTest()
{
// Suppose we have the following data about some patients.
// The first variable is continuous and represent patient
// age. The second variable is dichotomic and give whether
// they smoke or not (this is completely fictional data).
double[][] inputs =
{
// Age Smoking
new double[] { 55, 0 }, // 1
new double[] { 28, 0 }, // 2
new double[] { 65, 1 }, // 3
new double[] { 46, 0 }, // 4
new double[] { 86, 1 }, // 5
new double[] { 56, 1 }, // 6
new double[] { 85, 0 }, // 7
new double[] { 33, 0 }, // 8
new double[] { 21, 1 }, // 9
new double[] { 42, 1 }, // 10
new double[] { 33, 0 }, // 11
new double[] { 20, 1 }, // 12
new double[] { 43, 1 }, // 13
new double[] { 31, 1 }, // 14
new double[] { 22, 1 }, // 15
new double[] { 43, 1 }, // 16
new double[] { 46, 0 }, // 17
new double[] { 86, 1 }, // 18
new double[] { 56, 1 }, // 19
new double[] { 55, 0 }, // 20
};
// Additionally, we also have information about whether
// or not they those patients had lung cancer. The array
// below gives 0 for those who did not, and 1 for those
// who did.
double[] output =
{
0, 0, 0, 1, 1, 1, 0, 0, 0, 1,
0, 1, 1, 1, 1, 1, 0, 1, 1, 0
};
// Create a Stepwise Logistic Regression analysis
var regression = new StepwiseLogisticRegressionAnalysis(inputs, output,
new[] { "Age", "Smoking" }, "Cancer");
regression.Compute(); // compute the analysis.
// The full model will be stored in the complete property:
StepwiseLogisticRegressionModel full = regression.Complete;
// The best model will be stored in the current property:
StepwiseLogisticRegressionModel best = regression.Current;
// Let's check the full model results
// DataGridBox.Show(full.Coefficients);
// We can see only the Smoking variable is statistically significant.
// This is an indication the Age variable could be discarded from
// the model.
// And check the best inner model result
// DataGridBox.Show(best.Coefficients);
// This is the best nested model found. This model only has the
// Smoking variable, which is still significant. Since no other
// variables can be dropped, this is the best final model.
// The variables used in the current best model are
string[] inputVariableNames = best.Inputs; // Smoking
// The best model likelihood ratio p-value is
ChiSquareTest test = best.ChiSquare; // {0.816990081334823}
// so the model is distinguishable from a null model. We can also
// query the other nested models by checking the Nested property:
// DataGridBox.Show(regression.Nested);
// Finally, we can also use the analysis to classify a new patient
double y = regression.Current.Regression.Compute(new double[] { 1 });
// For a smoking person, the answer probability is approximately 83%.
Assert.AreEqual(3, full.Coefficients.Count);
Assert.AreEqual("Intercept", full.Coefficients[0].Name);
Assert.AreEqual("Age", full.Coefficients[1].Name);
Assert.AreEqual("Smoking", full.Coefficients[2].Name);
Assert.AreEqual(0.10115178966846869, full.Coefficients[0].OddsRatio, 1e-10);
Assert.AreEqual(1.0071560349008841, full.Coefficients[1].OddsRatio, 1e-10);
Assert.AreEqual(35.498643454320685, full.Coefficients[2].OddsRatio, 1e-10);
Assert.IsFalse(full.Coefficients.Apply(p => p.OddsRatio).HasNaN());
Assert.AreEqual(1.8621025559858235, full.Coefficients[0].StandardError, 1e-10);
Assert.AreEqual(0.030965622111482096, full.Coefficients[1].StandardError, 1e-10);
Assert.AreEqual(1.3272612173685281, full.Coefficients[2].StandardError, 1e-10);
Assert.IsFalse(full.Coefficients.Apply(p => p.StandardError).HasNaN());
Assert.AreEqual(2, best.Coefficients.Count);
Assert.AreEqual("Intercept", best.Coefficients[0].Name);
Assert.AreEqual("Smoking", best.Coefficients[1].Name);
Assert.AreEqual(0.14285724083908749, best.Coefficients[0].OddsRatio);
Assert.AreEqual(34.999975694637072, best.Coefficients[1].OddsRatio);
Assert.AreEqual(1.0685028815195794, best.Coefficients[0].StandardError, 1e-10);
Assert.AreEqual(1.3197099261438616, best.Coefficients[1].StandardError, 1e-10);
Assert.IsFalse(best.Coefficients.Apply(p => p.StandardError).HasNaN());
Assert.AreEqual(2, regression.Nested.Count);
Assert.AreEqual(best, regression.Nested[0]);
Assert.AreEqual("Age", regression.Nested[1].Names);
Assert.AreEqual(0.83333333214363825, y);
int[] finalVars = regression.Current.Variables;
double[][] finalData = inputs.Submatrix(null, finalVars);
double[] expectedOutput = regression.Current.Regression.Compute(finalData);
Assert.IsTrue(regression.Result.IsEqual(expectedOutput));
}
public void ComputeTest()
{
// Suppose we have the following data about some patients.
// The first variable is continuous and represent patient
// age. The second variable is dichotomic and give whether
// they smoke or not (this is completely fictional data).
double[][] inputs =
{
// Age Smoking
new double[] { 55, 0 }, // 1
new double[] { 28, 0 }, // 2
new double[] { 65, 1 }, // 3
new double[] { 46, 0 }, // 4
new double[] { 86, 1 }, // 5
new double[] { 56, 1 }, // 6
new double[] { 85, 0 }, // 7
new double[] { 33, 0 }, // 8
new double[] { 21, 1 }, // 9
new double[] { 42, 1 }, // 10
new double[] { 33, 0 }, // 11
new double[] { 20, 1 }, // 12
new double[] { 43, 1 }, // 13
new double[] { 31, 1 }, // 14
new double[] { 22, 1 }, // 15
new double[] { 43, 1 }, // 16
new double[] { 46, 0 }, // 17
new double[] { 86, 1 }, // 18
new double[] { 56, 1 }, // 19
new double[] { 55, 0 }, // 20
};
// Additionally, we also have information about whether
// or not they those patients had lung cancer. The array
// below gives 0 for those who did not, and 1 for those
// who did.
double[] output =
{
0, 0, 0, 1, 1, 1, 0, 0, 0, 1,
0, 1, 1, 1, 1, 1, 0, 1, 1, 0
};
// Create a Stepwise Logistic Regression analysis
var regression = new StepwiseLogisticRegressionAnalysis(inputs, output,
new[] { "Age", "Smoking" }, "Cancer");
regression.Compute(); // compute the analysis.
// The full model will be stored in the complete property:
StepwiseLogisticRegressionModel full = regression.Complete;
// The best model will be stored in the current property:
StepwiseLogisticRegressionModel best = regression.Current;
// Let's check the full model results
// DataGridBox.Show(full.Coefficients);
// We can see only the Smoking variable is statistically significant.
// This is an indication the Age variable could be discarded from
// the model.
// And check the best inner model result
// DataGridBox.Show(best.Coefficients);
// This is the best nested model found. This model only has the
// Smoking variable, which is still significant. Since no other
// variables can be dropped, this is the best final model.
// The variables used in the current best model are
string[] inputVariableNames = best.Inputs; // Smoking
// The best model likelihood ratio p-value is
ChiSquareTest test = best.ChiSquare; // {0.816990081334823}
// so the model is distinguishable from a null model. We can also
// query the other nested models by checking the Nested property:
// DataGridBox.Show(regression.Nested);
// Finally, we can also use the analysis to classify a new patient
double y = regression.Current.Regression.Compute(new double[] { 1 });
// For a smoking person, the answer probability is approximately 83%.
Assert.AreEqual(3, full.Coefficients.Count);
Assert.AreEqual("Intercept", full.Coefficients[0].Name);
Assert.AreEqual("Age", full.Coefficients[1].Name);
Assert.AreEqual("Smoking", full.Coefficients[2].Name);
Assert.AreEqual(0.10115178966846869, full.Coefficients[0].OddsRatio, 1e-8);
Assert.AreEqual(1.0071560349008841, full.Coefficients[1].OddsRatio, 1e-8);
Assert.AreEqual(35.498643454320685, full.Coefficients[2].OddsRatio, 1e-6);
Assert.IsFalse(full.Coefficients.Apply(p => p.OddsRatio).HasNaN());
Assert.AreEqual(1.8621025559858235, full.Coefficients[0].StandardError, 1e-8);
Assert.AreEqual(0.030965622111482096, full.Coefficients[1].StandardError, 1e-8);
Assert.AreEqual(1.3272612173685281, full.Coefficients[2].StandardError, 1e-8);
Assert.IsFalse(full.Coefficients.Apply(p => p.StandardError).HasNaN());
Assert.AreEqual(2, best.Coefficients.Count);
Assert.AreEqual("Intercept", best.Coefficients[0].Name);
Assert.AreEqual("Smoking", best.Coefficients[1].Name);
Assert.AreEqual(0.14285724083908749, best.Coefficients[0].OddsRatio, 1e-8);
Assert.AreEqual(34.999975694637072, best.Coefficients[1].OddsRatio, 1e-6);
Assert.AreEqual(1.0685028815195794, best.Coefficients[0].StandardError, 1e-6);
Assert.AreEqual(1.3197099261438616, best.Coefficients[1].StandardError, 1e-6);
Assert.IsFalse(best.Coefficients.Apply(p => p.StandardError).HasNaN());
Assert.AreEqual(2, regression.Nested.Count);
Assert.AreEqual(best, regression.Nested[0]);
Assert.AreEqual("Age", regression.Nested[1].Names);
Assert.AreEqual(0.83333333214363825, y, 1e-8);
int[] finalVars = regression.Current.Variables;
double[][] finalData = inputs.Submatrix(null, finalVars);
double[] expectedOutput = regression.Current.Regression.Compute(finalData);
Assert.IsTrue(regression.Result.IsEqual(expectedOutput));
}
public void DoStepTest()
{
double[][] inputs = Matrix.Expand(
new double[][] {
new double[] {0, 0, 0},
new double[] {1, 0, 0},
new double[] {0, 1, 0},
new double[] {1, 1, 0},
new double[] {0, 0, 1},
new double[] {1, 0, 1},
new double[] {0, 1, 1},
new double[] {1, 1, 1},
}, new int[] { 60, 17, 8, 2, 187, 85, 51, 23 });
double[] outputs = Matrix.Expand(
new double[] { 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0 },
new int[] { 5, 60 - 5, 2, 17 - 2, 1, 8 - 1, 0, 2 - 0, 35, 187 - 35, 13, 85 - 13, 15, 51 - 15, 8, 23 - 8 });
var target2 = new LogisticRegressionAnalysis(inputs, outputs);
target2.Compute();
Assert.AreEqual(target2.CoefficientValues[0], -2.377661, 0.0001);
Assert.AreEqual(target2.CoefficientValues[1], -0.067775, 0.0001);
Assert.AreEqual(target2.CoefficientValues[2], 0.69531, 0.0001);
Assert.AreEqual(target2.CoefficientValues[3], 0.871939, 0.0001);
var target = new StepwiseLogisticRegressionAnalysis(
inputs, outputs,
new string[] { "x1", "x2", "x3" }, "Y"
);
target.Threshold = 0.15;
int actual;
actual = target.DoStep();
Assert.AreEqual(0, actual);
actual = target.DoStep();
Assert.AreEqual(-1, actual);
}
