|
using System;
using System.Collections.Generic;
using System.Linq;
using Microsoft.ML;
namespace Samples.Dynamic
{
public static class CalculateFeatureContribution
{
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 a small dataset.
var samples = GenerateData();
// Convert training data to IDataView.
var data = mlContext.Data.LoadFromEnumerable(samples);
// Create a pipeline to concatenate the features into a feature vector
// and normalize it.
var transformPipeline = mlContext.Transforms.Concatenate("Features",
new string[] { nameof(Data.Feature1), nameof(Data.Feature2) })
.Append(mlContext.Transforms.NormalizeMeanVariance("Features"));
// Fit the pipeline.
var transformer = transformPipeline.Fit(data);
// Transform the data.
var transformedData = transformer.Transform(data);
// Define a linear trainer.
var linearTrainer = mlContext.Regression.Trainers.Ols();
// Now we train the model and score it on the transformed data.
var linearModel = linearTrainer.Fit(transformedData);
// Print the model parameters.
Console.WriteLine($"Linear Model Parameters");
Console.WriteLine("Bias: " + linearModel.Model.Bias + " Feature1: " +
linearModel.Model.Weights[0] + " Feature2: " + linearModel.Model
.Weights[1]);
// Define a feature contribution calculator for all the features, and
// don't normalize the contributions.These are "trivial estimators" and
// they don't need to fit to the data, so we can feed a subset.
var simpleScoredDataset = linearModel.Transform(mlContext.Data
.TakeRows(transformedData, 1));
var linearFeatureContributionCalculator = mlContext.Transforms
.CalculateFeatureContribution(linearModel, normalize: false).Fit(
simpleScoredDataset);
// Create a transformer chain to describe the entire pipeline.
var scoringPipeline = transformer.Append(linearModel).Append(
linearFeatureContributionCalculator);
// Create the prediction engine to get the features extracted from the
// text.
var predictionEngine = mlContext.Model.CreatePredictionEngine<Data,
ScoredData>(scoringPipeline);
// Convert the text into numeric features.
var prediction = predictionEngine.Predict(samples.First());
// Write out the prediction, with contributions.
// Note that for the linear model, the feature contributions for a
// feature in an example is the feature-weight*feature-value.
// The total prediction is thus the bias plus the feature contributions.
Console.WriteLine("Label: " + prediction.Label + " Prediction: " +
prediction.Score);
Console.WriteLine("Feature1: " + prediction.Features[0] +
" Feature2: " + prediction.Features[1]);
Console.WriteLine("Feature Contributions: " + prediction
.FeatureContributions[0] + " " + prediction
.FeatureContributions[1]);
// Expected output:
// Linear Model Parameters
// Bias: -0.007505796 Feature1: 1.536963 Feature2: 3.031206
// Label: 1.55184 Prediction: 1.389091
// Feature1: -0.5053467 Feature2: 0.7169741
// Feature Contributions: -0.7766994 2.173296
}
private class Data
{
public float Label { get; set; }
public float Feature1 { get; set; }
public float Feature2 { get; set; }
}
private class ScoredData : Data
{
public float Score { get; set; }
public float[] Features { get; set; }
public float[] FeatureContributions { 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)
{
var rng = new Random(seed);
for (int i = 0; i < nExamples; i++)
{
var data = new Data
{
Feature1 = (float)(rng.Next(10) * (rng.NextDouble() - 0.5)),
Feature2 = (float)(rng.Next(10) * (rng.NextDouble() - 0.5)),
};
// Create a noisy label.
data.Label = (float)(bias + weight1 * data.Feature1 + weight2 *
data.Feature2 + rng.NextDouble() - 0.5);
yield return data;
}
}
}
}
|