public void TestFeatureImportance()
{
// Setup synthetic dataset.
const int numberOfInstances = 1000;
var rand = new Random(10);
float[] yArray = new float[numberOfInstances],
x1Array = new float[numberOfInstances],
x2Array = new float[numberOfInstances],
x3Array = new float[numberOfInstances],
x4RandArray = new float[numberOfInstances];
for (var i = 0; i < numberOfInstances; i++)
{
var x1 = rand.Next(1000);
x1Array[i] = x1;
var x2Important = rand.Next(10000);
x2Array[i] = x2Important;
var x3 = rand.Next(5000);
x3Array[i] = x3;
var x4Rand = rand.Next(1000);
x4RandArray[i] = x4Rand;
var noise = rand.Next(50);
yArray[i] = (float)(10 * x1 + 20 * x2Important + 5.5 * x3 + noise);
}
// Create data view.
var bldr = new ArrayDataViewBuilder(Env);
bldr.AddColumn("X1", NumberType.Float, x1Array);
bldr.AddColumn("X2Important", NumberType.Float, x2Array);
bldr.AddColumn("X3", NumberType.Float, x3Array);
bldr.AddColumn("X4Rand", NumberType.Float, x4RandArray);
bldr.AddColumn("Label", NumberType.Float, yArray);
var srcDV = bldr.GetDataView();
var pipeline = ML.Transforms.Concatenate("Features", "X1", "X2Important", "X3", "X4Rand")
.AppendCacheCheckpoint(ML)
.Append(ML.Transforms.Normalize("Features"));
var data = pipeline.Fit(srcDV).Transform(srcDV);
var model = ML.Regression.Trainers.OrdinaryLeastSquares().Fit(data);
var args = new FeatureContributionCalculationTransform.Arguments()
{
Bottom = 10,
Top = 10
};
var output = FeatureContributionCalculationTransform.Create(Env, args, data, model.Model, model.FeatureColumn);
// Get prediction scores and contributions
var enumerator = output.AsEnumerable <ScoreAndContribution>(Env, true).GetEnumerator();
ScoreAndContribution row = null;
var expectedValues = new List <float[]>();
expectedValues.Add(new float[4] {
0.06319684F, 1, 0.1386623F, 4.46209469E-06F
});
expectedValues.Add(new float[4] {
0.03841561F, 1, 0.1633037F, 2.68303256E-06F
});
expectedValues.Add(new float[4] {
0.12006103F, 1, 0.254072F, 1.18671605E-05F
});
expectedValues.Add(new float[4] {
0.20861618F, 0.99999994F, 0.407312155F, 6.963478E-05F
});
expectedValues.Add(new float[4] {
0.024050576F, 0.99999994F, 0.31106182F, 8.456762E-06F
});
int index = 0;
while (enumerator.MoveNext() && index < expectedValues.Count)
{
row = enumerator.Current;
// We set predicion to 6 because the limit of floating-point numbers is 7.
Assert.Equal(expectedValues[index][0], row.FeatureContributions[0], 6);
Assert.Equal(expectedValues[index][1], row.FeatureContributions[1], 6);
Assert.Equal(expectedValues[index][2], row.FeatureContributions[2], 6);
Assert.Equal(expectedValues[index++][3], row.FeatureContributions[3], 6);
}
Done();
}
/// <summary>
/// Features: x1, x2, x3, xRand; y = 10*x1 + 20x2 + 5.5x3 + e, xRand- random, Label y is dependant on xRand.
/// Test verifies that feature contribution scores are outputted along with a score for predicted data.
/// </summary>
private void TestFeatureContribution(
ITrainerEstimator <ISingleFeaturePredictionTransformer <IPredictor>, IPredictor> trainer,
List <float[]> expectedValues,
int precision = 6)
{
// Setup synthetic dataset.
const int numInstances = 1000;
const int numFeatures = 4;
var rand = new Random(10);
float[] yArray = new float[numInstances];
float[][] xArray = new float[numFeatures][];
int[] xRangeArray = new[] { 1000, 10000, 5000, 1000 };
float[] xWeightArray = new[] {
10,
20, // Most important feature with high weight. Should have the highest contribution.
5.5f,
0, // Least important feature. Should have the least contribution.
};
for (var instanceIndex = 0; instanceIndex < numInstances; instanceIndex++)
{
for (int featureIndex = 0; featureIndex < numFeatures; featureIndex++)
{
if (xArray[featureIndex] == null)
{
xArray[featureIndex] = new float[numInstances];
}
xArray[featureIndex][instanceIndex] = rand.Next(xRangeArray[featureIndex]);
yArray[instanceIndex] += xArray[featureIndex][instanceIndex] * xWeightArray[featureIndex];
}
var noise = rand.Next(50);
yArray[instanceIndex] += noise;
}
// Create data view.
var bldr = new ArrayDataViewBuilder(Env);
bldr.AddColumn("X1", NumberType.Float, xArray[0]);
bldr.AddColumn("X2Important", NumberType.Float, xArray[1]);
bldr.AddColumn("X3", NumberType.Float, xArray[2]);
bldr.AddColumn("X4Rand", NumberType.Float, xArray[3]);
bldr.AddColumn("Label", NumberType.Float, yArray);
var srcDV = bldr.GetDataView();
var pipeline = ML.Transforms.Concatenate("Features", "X1", "X2Important", "X3", "X4Rand")
.AppendCacheCheckpoint(ML)
.Append(ML.Transforms.Normalize("Features"));
var data = pipeline.Fit(srcDV).Transform(srcDV);
var model = trainer.Fit(data);
var args = new FeatureContributionCalculationTransform.Arguments()
{
Bottom = 10,
Top = 10
};
var output = FeatureContributionCalculationTransform.Create(Env, args, data, model.Model, model.FeatureColumn);
var transformedOutput = output.AsEnumerable <ScoreAndContribution>(Env, true);
int rowIndex = 0;
foreach (var row in transformedOutput.Take(expectedValues.Count))
{
var expectedValue = expectedValues[rowIndex++];
for (int i = 0; i < numFeatures; i++)
{
Assert.Equal(expectedValue[i], row.FeatureContributions[i], precision);
}
}
Done();
}
public void TestFeatureImportance()
{
// Setup synthetic dataset.
const int numberOfInstances = 1000;
var rand = new Random(10);
float[] yArray = new float[numberOfInstances],
x1Array = new float[numberOfInstances],
x2Array = new float[numberOfInstances],
x3Array = new float[numberOfInstances],
x4RandArray = new float[numberOfInstances];
for (var i = 0; i < numberOfInstances; i++)
{
var x1 = rand.Next(1000);
x1Array[i] = x1;
var x2Important = rand.Next(10000);
x2Array[i] = x2Important;
var x3 = rand.Next(5000);
x3Array[i] = x3;
var x4Rand = rand.Next(1000);
x4RandArray[i] = x4Rand;
var noise = rand.Next(50);
yArray[i] = (float)(10 * x1 + 20 * x2Important + 5.5 * x3 + noise);
}
// Create data view.
var bldr = new ArrayDataViewBuilder(Env);
bldr.AddColumn("X1", NumberType.Float, x1Array);
bldr.AddColumn("X2Important", NumberType.Float, x2Array);
bldr.AddColumn("X3", NumberType.Float, x3Array);
bldr.AddColumn("X4Rand", NumberType.Float, x4RandArray);
bldr.AddColumn("Label", NumberType.Float, yArray);
var srcDV = bldr.GetDataView();
var pipeline = ML.Transforms.Concatenate("Features", "X1", "X2Important", "X3", "X4Rand")
.Append(ML.Transforms.Normalize("Features"));
var data = pipeline.Fit(srcDV).Transform(srcDV);
var model = ML.Regression.Trainers.OnlineGradientDescent().Fit(data);
var args = new FeatureContributionCalculationTransform.Arguments()
{
Bottom = 10,
Top = 10
};
var output = FeatureContributionCalculationTransform.Create(Env, args, data, model.Model, model.FeatureColumn);
// Get prediction scores and contributions
var enumerator = output.AsEnumerable <ScoreAndContribution>(Env, true).GetEnumerator();
ScoreAndContribution row = null;
var expectedValues = new List <float[]>();
expectedValues.Add(new float[4] {
0.15640761F, 1, 0.155862764F, 0.07276783F
});
expectedValues.Add(new float[4] {
0.09507586F, 1, 0.1835608F, 0.0437548943F
});
expectedValues.Add(new float[4] {
0.297142357F, 1, 0.2855884F, 0.193529665F
});
expectedValues.Add(new float[4] {
0.45465675F, 0.8805887F, 0.4031663F, 1
});
expectedValues.Add(new float[4] {
0.0595234372F, 0.99999994F, 0.349647522F, 0.137912869F
});
int index = 0;
while (enumerator.MoveNext() && index < expectedValues.Count)
{
row = enumerator.Current;
Assert.True(row.FeatureContributions[0] == expectedValues[index][0]);
Assert.True(row.FeatureContributions[1] == expectedValues[index][1]);
Assert.True(row.FeatureContributions[2] == expectedValues[index][2]);
Assert.True(row.FeatureContributions[3] == expectedValues[index++][3]);
}
Done();
}
