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Ivanidzo4kaIvanidzo4ka
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Internalize and cleanup recommender project (#2451)
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‎docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Recommendation/MatrixFactorization.cs‎

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using System;
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using System.Collections.Generic;
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using Microsoft.ML.Data;
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using static Microsoft.ML.SamplesUtils.DatasetUtils;
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namespace Microsoft.ML.Samples.Dynamic
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{
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public partial class MatrixFactorizationExample
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{
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// This example first creates in-memory data and then use it to train a matrix factorization mode with default parameters. Afterward, quality metrics are reported.
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public static void MatrixFactorization()
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{
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// Create a new context for ML.NET operations. It can be used for exception tracking and logging,
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// as a catalog of available operations and as the source of randomness.
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var mlContext = new MLContext(seed: 0, conc: 1);
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// Get a small in-memory dataset.
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var data = GetRecommendationData();
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// Convert the in-memory matrix into an IDataView so that ML.NET components can consume it.
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var dataView = mlContext.Data.ReadFromEnumerable(data);
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// Create a matrix factorization trainer which may consume "Value" as the training label, "MatrixColumnIndex" as the
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// matrix's column index, and "MatrixRowIndex" as the matrix's row index. Here nameof(...) is used to extract field
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// names' in MatrixElement class.
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var pipeline = mlContext.Recommendation().Trainers.MatrixFactorization(nameof(MatrixElement.MatrixColumnIndex),
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nameof(MatrixElement.MatrixRowIndex), nameof(MatrixElement.Value), 10, 0.2, 10);
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// Train a matrix factorization model.
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var model = pipeline.Fit(dataView);
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// Apply the trained model to the training set.
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var prediction = model.Transform(dataView);
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// Calculate regression matrices for the prediction result.
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var metrics = mlContext.Recommendation().Evaluate(prediction,
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label: nameof(MatrixElement.Value), score: nameof(MatrixElementForScore.Score));
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// Print out some metrics for checking the model's quality.
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Console.WriteLine($"L1 - {metrics.L1}"); // 0.17208
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Console.WriteLine($"L2 - {metrics.L2}"); // 0.04766
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Console.WriteLine($"LossFunction - {metrics.LossFn}"); // 0.04766
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Console.WriteLine($"RMS - {metrics.Rms}"); //0.21831
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Console.WriteLine($"RSquared - {metrics.RSquared}"); // 0.97616
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// Create two two entries for making prediction. Of course, the prediction value, Score, is unknown so it can be anything
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// (here we use Score=0 and it will be overwritten by the true prediction). If any of row and column indexes are out-of-range
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// (e.g., MatrixColumnIndex=99999), the prediction value will be NaN.
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var testMatrix = new List<MatrixElementForScore>() {
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new MatrixElementForScore() { MatrixColumnIndex = 1, MatrixRowIndex = 7, Score = 0 },
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new MatrixElementForScore() { MatrixColumnIndex = 3, MatrixRowIndex = 6, Score = 0 } };
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// Again, convert the test data to a format supported by ML.NET.
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var testDataView = mlContext.Data.ReadFromEnumerable(testMatrix);
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// Feed the test data into the model and then iterate through all predictions.
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foreach (var pred in mlContext.CreateEnumerable<MatrixElementForScore>(model.Transform(testDataView), false))
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Console.WriteLine($"Predicted value at row {pred.MatrixRowIndex - 1} and column {pred.MatrixColumnIndex - 1} is {pred.Score}");
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// Predicted value at row 7 and column 1 is 2.876928
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// Predicted value at row 6 and column 3 is 3.587935
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}
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}
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}

‎docs/samples/Microsoft.ML.Samples/Dynamic/MatrixFactorization.cs‎ renamed to ‎docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Recommendation/MatrixFactorizationWithOptions.cs‎

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using System.Collections.Generic;
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using Microsoft.ML.Data;
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using Microsoft.ML.Trainers;
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using static Microsoft.ML.SamplesUtils.DatasetUtils;
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namespace Microsoft.ML.Samples.Dynamic
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{
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public class MatrixFactorizationExample
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public partial class MatrixFactorizationExample
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{
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// The following variables defines the shape of a matrix. Its shape is _synthesizedMatrixRowCount-by-_synthesizedMatrixColumnCount.
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// Because in ML.NET key type's minimal value is zero, the first row index is always zero in C# data structure (e.g., MatrixColumnIndex=0
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// and MatrixRowIndex=0 in MatrixElement below specifies the value at the upper-left corner in the training matrix). If user's row index
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// starts with 1, their row index 1 would be mapped to the 2nd row in matrix factorization module and their first row may contain no values.
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// This behavior is also true to column index.
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const int _synthesizedMatrixFirstColumnIndex = 1;
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const int _synthesizedMatrixFirstRowIndex = 1;
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const int _synthesizedMatrixColumnCount = 60;
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const int _synthesizedMatrixRowCount = 100;
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// A data structure used to encode a single value in matrix
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internal class MatrixElement
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{
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// Matrix column index is at most _synthesizedMatrixColumnCount + _synthesizedMatrixFirstColumnIndex.
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[KeyType(Count = _synthesizedMatrixColumnCount + _synthesizedMatrixFirstColumnIndex)]
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public uint MatrixColumnIndex;
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// Matrix row index is at most _synthesizedMatrixRowCount + _synthesizedMatrixFirstRowIndex.
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[KeyType(Count = _synthesizedMatrixRowCount + _synthesizedMatrixFirstRowIndex)]
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public uint MatrixRowIndex;
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// The value at the column MatrixColumnIndex and row MatrixRowIndex.
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public float Value;
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}
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// A data structure used to encode prediction result. Comparing with MatrixElement, The field Value in MatrixElement is
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// renamed to Score because Score is the default name of matrix factorization's output.
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internal class MatrixElementForScore
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{
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[KeyType(Count = _synthesizedMatrixColumnCount + _synthesizedMatrixFirstColumnIndex)]
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public uint MatrixColumnIndex;
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[KeyType(Count = _synthesizedMatrixRowCount + _synthesizedMatrixFirstRowIndex)]
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public uint MatrixRowIndex;
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public float Score;
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}
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// This example first creates in-memory data and then use it to train a matrix factorization model. Afterward, quality metrics are reported.
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public static void MatrixFactorizationInMemoryData()
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public static void MatrixFactorizationWithOptions()
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{
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// Create an in-memory matrix as a list of tuples (column index, row index, value).
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var dataMatrix = new List<MatrixElement>();
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for (uint i = _synthesizedMatrixFirstColumnIndex; i < _synthesizedMatrixFirstColumnIndex + _synthesizedMatrixColumnCount; ++i)
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for (uint j = _synthesizedMatrixFirstRowIndex; j < _synthesizedMatrixFirstRowIndex + _synthesizedMatrixRowCount; ++j)
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dataMatrix.Add(new MatrixElement() { MatrixColumnIndex = i, MatrixRowIndex = j, Value = (i + j) % 5 });
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// Create a new context for ML.NET operations. It can be used for exception tracking and logging,
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// as a catalog of available operations and as the source of randomness.
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var mlContext = new MLContext(seed: 0, conc: 1);
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// Get a small in-memory dataset.
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var data = GetRecommendationData();
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// Convert the in-memory matrix into an IDataView so that ML.NET components can consume it.
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var dataView = mlContext.Data.ReadFromEnumerable(dataMatrix);
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var dataView = mlContext.Data.ReadFromEnumerable(data);
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// Create a matrix factorization trainer which may consume "Value" as the training label, "MatrixColumnIndex" as the
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// matrix's column index, and "MatrixRowIndex" as the matrix's row index. Here nameof(...) is used to extract field
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// names' in MatrixElement class.
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var options = new MatrixFactorizationTrainer.Options
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{
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MatrixColumnIndexColumnName = nameof(MatrixElement.MatrixColumnIndex),
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MatrixRowIndexColumnName = nameof(MatrixElement.MatrixRowIndex),
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LabelColumnName = nameof(MatrixElement.Value),
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NumIterations = 10,
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NumThreads = 1,
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K = 32,
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ApproximationRank = 32,
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LearningRate = 0.3
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};
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var pipeline = mlContext.Recommendation().Trainers.MatrixFactorization(options);
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label: nameof(MatrixElement.Value), score: nameof(MatrixElementForScore.Score));
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// Print out some metrics for checking the model's quality.
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Console.WriteLine($"L1 - {metrics.L1}");
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Console.WriteLine($"L2 - {metrics.L2}");
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Console.WriteLine($"LossFunction - {metrics.LossFn}");
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Console.WriteLine($"RMS - {metrics.Rms}");
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Console.WriteLine($"RSquared - {metrics.RSquared}");
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Console.WriteLine($"L1 - {metrics.L1}"); // 0.16375
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Console.WriteLine($"L2 - {metrics.L2}"); // 0.04407
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Console.WriteLine($"LossFunction - {metrics.LossFn}"); // 0.04407
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Console.WriteLine($"RMS - {metrics.Rms}"); // 0.2099
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Console.WriteLine($"RSquared - {metrics.RSquared}"); // 0.97797
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// Create two two entries for making prediction. Of course, the prediction value, Score, is unknown so it can be anything
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// (here we use Score=0 and it will be overwritten by the true prediction). If any of row and column indexes are out-of-range
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var testDataView = mlContext.Data.ReadFromEnumerable(testMatrix);
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// Feed the test data into the model and then iterate through all predictions.
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foreach (var pred in mlContext.CreateEnumerable<MatrixElementForScore>(testDataView, false))
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Console.WriteLine($"Predicted value at row {pred.MatrixRowIndex} and column {pred.MatrixColumnIndex} is {pred.Score}");
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foreach (var pred in mlContext.CreateEnumerable<MatrixElementForScore>(model.Transform(testDataView), false))
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Console.WriteLine($"Predicted value at row {pred.MatrixRowIndex-1} and column {pred.MatrixColumnIndex-1} is {pred.Score}");
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// Predicted value at row 7 and column 1 is 2.828761
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// Predicted value at row 6 and column 3 is 3.642226
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}
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}
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}

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