public void NAReplaceWorkout()
{
var data = new[] {
new TestClass()
{
A = 1, B = 3, C = new float[2] {
1, 2
}, D = new double[2] {
3, 4
}
},
new TestClass()
{
A = float.NaN, B = double.NaN, C = new float[2] {
float.NaN, float.NaN
}, D = new double[2] {
double.NaN, double.NaN
}
},
new TestClass()
{
A = float.NegativeInfinity, B = double.NegativeInfinity, C = new float[2] {
float.NegativeInfinity, float.NegativeInfinity
}, D = new double[2] {
double.NegativeInfinity, double.NegativeInfinity
}
},
new TestClass()
{
A = float.PositiveInfinity, B = double.PositiveInfinity, C = new float[2] {
float.PositiveInfinity, float.PositiveInfinity,
}, D = new double[2] {
double.PositiveInfinity, double.PositiveInfinity
}
},
new TestClass()
{
A = 2, B = 1, C = new float[2] {
3, 4
}, D = new double[2] {
5, 6
}
},
};
var dataView = ComponentCreation.CreateDataView(Env, data);
var pipe = new MissingValueReplacingEstimator(Env,
new MissingValueReplacingTransformer.ColumnInfo("A", "NAA", MissingValueReplacingTransformer.ColumnInfo.ReplacementMode.Mean),
new MissingValueReplacingTransformer.ColumnInfo("B", "NAB", MissingValueReplacingTransformer.ColumnInfo.ReplacementMode.Mean),
new MissingValueReplacingTransformer.ColumnInfo("C", "NAC", MissingValueReplacingTransformer.ColumnInfo.ReplacementMode.Mean),
new MissingValueReplacingTransformer.ColumnInfo("D", "NAD", MissingValueReplacingTransformer.ColumnInfo.ReplacementMode.Mean));
TestEstimatorCore(pipe, dataView);
Done();
}
public void TestOldSavingAndLoading()
{
var data = new[] {
new TestClass()
{
A = 1, B = 3, C = new float[2] {
1, 2
}, D = new double[2] {
3, 4
}
},
new TestClass()
{
A = float.NaN, B = double.NaN, C = new float[2] {
float.NaN, float.NaN
}, D = new double[2] {
double.NaN, double.NaN
}
},
new TestClass()
{
A = float.NegativeInfinity, B = double.NegativeInfinity, C = new float[2] {
float.NegativeInfinity, float.NegativeInfinity
}, D = new double[2] {
double.NegativeInfinity, double.NegativeInfinity
}
},
new TestClass()
{
A = float.PositiveInfinity, B = double.PositiveInfinity, C = new float[2] {
float.PositiveInfinity, float.PositiveInfinity,
}, D = new double[2] {
double.PositiveInfinity, double.PositiveInfinity
}
},
new TestClass()
{
A = 2, B = 1, C = new float[2] {
3, 4
}, D = new double[2] {
5, 6
}
},
};
var dataView = ComponentCreation.CreateDataView(Env, data);
var pipe = new MissingValueReplacingEstimator(Env,
new MissingValueReplacingTransformer.ColumnInfo("A", "NAA", MissingValueReplacingTransformer.ColumnInfo.ReplacementMode.Mean),
new MissingValueReplacingTransformer.ColumnInfo("B", "NAB", MissingValueReplacingTransformer.ColumnInfo.ReplacementMode.Mean),
new MissingValueReplacingTransformer.ColumnInfo("C", "NAC", MissingValueReplacingTransformer.ColumnInfo.ReplacementMode.Mean),
new MissingValueReplacingTransformer.ColumnInfo("D", "NAD", MissingValueReplacingTransformer.ColumnInfo.ReplacementMode.Mean));
var result = pipe.Fit(dataView).Transform(dataView);
var resultRoles = new RoleMappedData(result);
using (var ms = new MemoryStream())
{
TrainUtils.SaveModel(Env, Env.Start("saving"), ms, null, resultRoles);
ms.Position = 0;
var loadedView = ModelFileUtils.LoadTransforms(Env, dataView, ms);
}
}
