public static void Example()
{
// Create a new context for ML.NET operations. It can be used for exception tracking and logging,
// as a catalog of available operations and as the source of randomness.
var mlContext = new MLContext(seed: 999123);
// Step 1: Read the data
var data = PfiHelper.GetHousingRegressionIDataView(mlContext,
out string labelName, out string[] featureNames, binaryPrediction: true);
// Step 2: Pipeline
// Concatenate the features to create a Feature vector.
// Normalize the data set so that for each feature, its maximum value is 1 while its minimum value is 0.
// Then append a logistic regression trainer.
var pipeline = mlContext.Transforms.Concatenate("Features", featureNames)
.Append(mlContext.Transforms.Normalize("Features"))
.Append(mlContext.BinaryClassification.Trainers.LogisticRegression(
labelColumnName: labelName, featureColumnName: "Features"));
var model = pipeline.Fit(data);
// Extract the model from the pipeline
var linearPredictor = model.LastTransformer;
// Linear models for binary classification are wrapped by a calibrator as a generic predictor
// To access it directly, we must extract it out and cast it to the proper class
var weights = PfiHelper.GetLinearModelWeights(linearPredictor.Model.SubModel as LinearBinaryModelParameters);
// Compute the permutation metrics using the properly normalized data.
var transformedData = model.Transform(data);
var permutationMetrics = mlContext.BinaryClassification.PermutationFeatureImportance(
linearPredictor, transformedData, label: labelName, features: "Features", permutationCount: 3);
// Now let's look at which features are most important to the model overall
// Get the feature indices sorted by their impact on AUC
var sortedIndices = permutationMetrics.Select((metrics, index) => new { index, metrics.Auc })
.OrderByDescending(feature => Math.Abs(feature.Auc.Mean))
.Select(feature => feature.index);
// Print out the permutation results, with the model weights, in order of their impact:
// Expected console output (for 100 permutations):
// Feature Model Weight Change in AUC 95% Confidence in the Mean Change in AUC
// PercentPre40s -1.96 -0.06316 0.002377
// RoomsPerDwelling 3.71 -0.04385 0.001245
// EmploymentDistance -1.31 -0.02139 0.0006867
// TeacherRatio -2.46 -0.0203 0.0009566
// PercentNonRetail -1.58 -0.01846 0.001586
// CharlesRiver 0.66 -0.008605 0.0005136
// PercentResidental 0.60 0.002483 0.0004818
// TaxRate -0.95 -0.00221 0.0007394
// NitricOxides -0.32 0.00101 0.0001428
// CrimesPerCapita -0.04 -3.029E-05 1.678E-05
// HighwayDistance 0.00 0 0
// Let's look at these results.
// First, if you look at the weights of the model, they generally correlate with the results of PFI,
// but there are some significant misorderings. See the discussion in the Regression example for an
// explanation of why this happens and how to interpret it.
// Second, the logistic regression learner uses L1 regularization by default. Here, it causes the "HighWay Distance"
// feature to be zeroed out from the model. PFI assigns zero importance to this variable, as expected.
// Third, some features show an *increase* in AUC. This means that the model actually improved
// when these features were shuffled. This is a sign to investigate these features further.
Console.WriteLine("Feature\tModel Weight\tChange in AUC\t95% Confidence in the Mean Change in AUC");
var auc = permutationMetrics.Select(x => x.Auc).ToArray(); // Fetch AUC as an array
foreach (int i in sortedIndices)
{
Console.WriteLine($"{featureNames[i]}\t{weights[i]:0.00}\t{auc[i].Mean:G4}\t{1.96 * auc[i].StandardError:G4}");
}
}
public static void RunExample()
{
// Create a new context for ML.NET operations. It can be used for exception tracking and logging,
// as a catalog of available operations and as the source of randomness.
var mlContext = new MLContext();
// Step 1: Read the data
var data = PfiHelper.GetHousingRegressionIDataView(mlContext, out string labelName, out string[] featureNames);
// Step 2: Pipeline
// Concatenate the features to create a Feature vector.
// Normalize the data set so that for each feature, its maximum value is 1 while its minimum value is 0.
// Then append a linear regression trainer.
var pipeline = mlContext.Transforms.Concatenate("Features", featureNames)
.Append(mlContext.Transforms.Normalize("Features"))
.Append(mlContext.Regression.Trainers.OrdinaryLeastSquares(
labelColumn: labelName, featureColumn: "Features"));
var model = pipeline.Fit(data);
// Extract the model from the pipeline
var linearPredictor = model.LastTransformer;
var weights = PfiHelper.GetLinearModelWeights(linearPredictor.Model);
// Compute the permutation metrics using the properly normalized data.
var transformedData = model.Transform(data);
var permutationMetrics = mlContext.Regression.PermutationFeatureImportance(
linearPredictor, transformedData, label: labelName, features: "Features", permutationCount: 3);
// Now let's look at which features are most important to the model overall
// Get the feature indices sorted by their impact on R-Squared
var sortedIndices = permutationMetrics.Select((metrics, index) => new { index, metrics.RSquared })
.OrderByDescending(feature => Math.Abs(feature.RSquared.Mean))
.Select(feature => feature.index);
// Print out the permutation results, with the model weights, in order of their impact:
// Expected console output for 100 permutations:
// Feature Model Weight Change in R-Squared 95% Confidence Interval of the Mean
// RoomsPerDwelling 53.35 -0.4298 0.005705
// EmploymentDistance -19.21 -0.2609 0.004591
// NitricOxides -19.32 -0.1569 0.003701
// HighwayDistance 6.11 -0.1173 0.0025
// TeacherRatio -21.92 -0.1106 0.002207
// TaxRate -8.68 -0.1008 0.002083
// CrimesPerCapita -16.37 -0.05988 0.00178
// PercentPre40s -4.52 -0.03836 0.001432
// PercentResidental 3.91 -0.02006 0.001079
// CharlesRiver 3.49 -0.01839 0.000841
// PercentNonRetail -1.17 -0.002111 0.0003176
//
// Let's dig into these results a little bit. First, if you look at the weights of the model, they generally correlate
// with the results of PFI, but there are some significant misorderings. For example, "Tax Rate" and "Highway Distance"
// have relatively small model weights, but the permutation analysis shows these feature to have a larger effect
// on the accuracy of the model than higher-weighted features. To understand why the weights don't reflect the same
// feature importance as PFI, we need to go back to the basics of linear models: one of the assumptions of a linear
// model is that the features are uncorrelated. Now, the features in this dataset are clearly correlated: the tax rate
// for a house and the student-to-teacher ratio at the nearest school, for example, are often coupled through school
// levies. The tax rate, distance to a highway, and the crime rate would also seem to be correlated through social
// dynamics. We could draw out similar relationships for all variables in this dataset. The reason why the linear
// model weights don't reflect the same feature importance as PFI is that the solution to the linear model redistributes
// weights between correlated variables in unpredictable ways, so that the weights themselves are no longer a good
// measure of feature importance.
Console.WriteLine("Feature\tModel Weight\tChange in R-Squared\t95% Confidence Interval of the Mean");
var rSquared = permutationMetrics.Select(x => x.RSquared).ToArray(); // Fetch r-squared as an array
foreach (int i in sortedIndices)
{
Console.WriteLine($"{featureNames[i]}\t{weights[i]:0.00}\t{rSquared[i].Mean:G4}\t{1.96 * rSquared[i].StandardError:G4}");
}
}
