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using System;
using System.Collections.Generic;
using System.Linq;
using Microsoft.ML;
namespace Samples.Dynamic.Trainers.Ranking
{
public static class PermutationFeatureImportance
{
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: 1);
// Create sample data.
var samples = GenerateData();
// Load the sample data as an IDataView.
var data = mlContext.Data.LoadFromEnumerable(samples);
// Define a training pipeline that concatenates features into a vector,
// normalizes them, and then trains a linear model.
var featureColumns = new string[] { nameof(Data.Feature1), nameof(
Data.Feature2) };
var pipeline = mlContext.Transforms.Concatenate("Features",
featureColumns)
.Append(mlContext.Transforms.Conversion.MapValueToKey("Label"))
.Append(mlContext.Transforms.Conversion.MapValueToKey(
"GroupId"))
.Append(mlContext.Transforms.NormalizeMinMax("Features"))
.Append(mlContext.Ranking.Trainers.FastTree());
// Fit the pipeline to the data.
var model = pipeline.Fit(data);
// Transform the dataset.
var transformedData = model.Transform(data);
// Extract the predictor.
var linearPredictor = model.LastTransformer;
// Compute the permutation metrics for the linear model using the
// normalized data.
var permutationMetrics = mlContext.Ranking.PermutationFeatureImportance(
linearPredictor, transformedData, permutationCount: 30);
// Now let's look at which features are most important to the model
// overall. Get the feature indices sorted by their impact on NDCG@1.
var sortedIndices = permutationMetrics.Select((metrics, index) => new
{
index,
metrics.NormalizedDiscountedCumulativeGains
})
.OrderByDescending(feature => Math.Abs(
feature.NormalizedDiscountedCumulativeGains[0].Mean))
.Select(feature => feature.index);
Console.WriteLine("Feature\tChange in NDCG@1\t95% Confidence in the" +
"Mean Change in NDCG@1");
var ndcg = permutationMetrics.Select(
x => x.NormalizedDiscountedCumulativeGains).ToArray();
foreach (int i in sortedIndices)
{
Console.WriteLine("{0}\t{1:G4}\t{2:G4}",
featureColumns[i],
ndcg[i][0].Mean,
1.96 * ndcg[i][0].StandardError);
}
// Expected output:
// Feature Change in NDCG@1 95% Confidence in the Mean Change in NDCG@1
// Feature2 -0.2421 0.001748
// Feature1 -0.0513 0.001184
}
private class Data
{
public float Label { get; set; }
public int GroupId { get; set; }
public float Feature1 { get; set; }
public float Feature2 { get; set; }
}
/// <summary>
/// Generate an enumerable of Data objects, creating the label as a simple
/// linear combination of the features.
/// </summary>
///
/// <param name="nExamples">The number of examples.</param>
///
/// <param name="bias">The bias, or offset, in the calculation of the label.
/// </param>
///
/// <param name="weight1">The weight to multiply the first feature with to
/// compute the label.</param>
///
/// <param name="weight2">The weight to multiply the second feature with to
/// compute the label.</param>
///
/// <param name="seed">The seed for generating feature values and label
/// noise.</param>
///
/// <returns>An enumerable of Data objects.</returns>
private static IEnumerable<Data> GenerateData(int nExamples = 10000,
double bias = 0, double weight1 = 1, double weight2 = 2, int seed = 1,
int groupSize = 5)
{
var rng = new Random(seed);
var max = bias + 4.5 * weight1 + 4.5 * weight2 + 0.5;
for (int i = 0; i < nExamples; i++)
{
var data = new Data
{
GroupId = i / groupSize,
Feature1 = (float)(rng.Next(10) * (rng.NextDouble() - 0.5)),
Feature2 = (float)(rng.Next(10) * (rng.NextDouble() - 0.5)),
};
// Create a noisy label.
var value = (float)(bias + weight1 * data.Feature1 + weight2 *
data.Feature2 + rng.NextDouble() - 0.5);
if (value < max / 3)
data.Label = 0;
else if (value < 2 * max / 3)
data.Label = 1;
else
data.Label = 2;
yield return data;
}
}
}
}
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