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Reformatted Recommendation samples to width 85 #3941

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Reformatted Recommendation samples
sayanshaw24 Jul 1, 2019
3bbb568
Fix for tab spacing errors
sayanshaw24 Jul 1, 2019
4715016
Removed precision for reformatting in Console.WriteLine and fixed Key…
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80 changes: 53 additions & 27 deletions docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Recommendation/MatrixFactorization.cs
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Expand Up@@ -9,25 +9,32 @@ namespace Samples.Dynamic.Trainers.Recommendation
public static class MatrixFactorization
{

// This example requires installation of additional nuget package <a href="https://www.nuget.org/packages/Microsoft.ML.Recommender/">Microsoft.ML.Recommender</a>.
// In this example we will create in-memory data and then use it to train
// a matrix factorization model with default parameters. Afterward, quality metrics are reported.
// This example requires installation of additional nuget package at
// for Microsoft.ML.Recommender at
// https://www.nuget.org/packages/Microsoft.ML.Recommender/
// In this example we will create in-memory data and then use it to train
// a matrix factorization model with default parameters. Afterward, quality
// metrics are reported.
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.
// Setting the seed to a fixed number in this example to make outputs deterministic.
// 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. Setting the seed to a fixed number
// in this example to make outputs deterministic.
var mlContext = new MLContext(seed: 0);

// Create a list of training data points.
var dataPoints = GenerateMatrix();

// Convert the list of data points to an IDataView object, which is consumable by ML.NET API.
// Convert the list of data points to an IDataView object, which is
// consumable by ML.NET API.
var trainingData = mlContext.Data.LoadFromEnumerable(dataPoints);

// Define the trainer.
var pipeline = mlContext.Recommendation().Trainers.MatrixFactorization(nameof(MatrixElement.Value), nameof(MatrixElement.MatrixColumnIndex),
nameof(MatrixElement.MatrixRowIndex), 10, 0.2, 1);
var pipeline = mlContext.Recommendation().Trainers.
MatrixFactorization(nameof(MatrixElement.Value),
nameof(MatrixElement.MatrixColumnIndex),

@sharwellsharwellJul 2, 2019

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📝 This is incorrectly indented

nameof(MatrixElement.MatrixRowIndex), 10, 0.2, 1);

// Train the model.
var model = pipeline.Fit(trainingData);
Expand All@@ -36,11 +43,15 @@ public static void Example()
var transformedData = model.Transform(trainingData);

// Convert IDataView object to a list.
var predictions = mlContext.Data.CreateEnumerable<MatrixElement>(transformedData, reuseRowObject: false).Take(5).ToList();
var predictions = mlContext.Data
.CreateEnumerable<MatrixElement>(transformedData,
reuseRowObject: false).Take(5).ToList();

@sharwellsharwellJul 2, 2019•
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📝 This layout is very difficult to read. The following would be preferable:

varpredictions=mlContext.Data.CreateEnumerable<MatrixElement>(transformedData,reuseRowObject:false).Take(5).ToList();


// Look at 5 predictions for the Label, side by side with the actual Label for comparison.
// Look at 5 predictions for the Label, side by side with the actual
// Label for comparison.
foreach (var p in predictions)
Console.WriteLine($"Actual value: {p.Value:F3}, Predicted score: {p.Score:F3}");
Console.WriteLine($"Actual value: {p.Value:F3}," +
$"Predicted score: {p.Score:F3}");

// Expected output:
// Actual value: 0.000, Predicted score: 1.234
Expand All@@ -50,7 +61,10 @@ public static void Example()
// Actual value: 4.000, Predicted score: 2.362

// Evaluate the overall metrics
var metrics = mlContext.Regression.Evaluate(transformedData, labelColumnName: nameof(MatrixElement.Value), scoreColumnName: nameof(MatrixElement.Score));
var metrics = mlContext.Regression.Evaluate(transformedData,
labelColumnName: nameof(MatrixElement.Value),
scoreColumnName: nameof(MatrixElement.Score));

PrintMetrics(metrics);

// Expected output:
Expand All@@ -60,11 +74,15 @@ public static void Example()
// RSquared: 0.61 (closer to 1 is better. The worest case is 0)
}

// The following variables are used to define the shape of the example matrix. Its shape is MatrixRowCount-by-MatrixColumnCount.
// 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
// and MatrixRowIndex=0 in MatrixElement below specifies the value at the upper-left corner in the training matrix). If user's row index
// 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.
// This behavior is also true to column index.
// The following variables are used to define the shape of the example
// matrix. Its shape is MatrixRowCount-by-MatrixColumnCount. 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 and MatrixRowIndex=0
// in MatrixElement below specifies the value at the upper-left corner in
// the training matrix). If user's row index 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. This behavior is also true to column
// index.
private const uint MatrixColumnCount = 60;
private const uint MatrixRowCount = 100;

Expand All@@ -74,32 +92,40 @@ private static List<MatrixElement> GenerateMatrix()
var dataMatrix = new List<MatrixElement>();
for (uint i = 0; i < MatrixColumnCount; ++i)
for (uint j = 0; j < MatrixRowCount; ++j)
dataMatrix.Add(new MatrixElement() { MatrixColumnIndex = i, MatrixRowIndex = j, Value = (i + j) % 5 });
dataMatrix.Add(new MatrixElement() { MatrixColumnIndex = i,
MatrixRowIndex = j, Value = (i + j) % 5 });

@sharwellsharwellJul 2, 2019

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📝 Incorrect line wrapping and indentation


@wschinwschinJul 1, 2019

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Empty line. Is it produced by an auto-formatting tool?

@sayanshaw24sayanshaw24Jul 2, 2019

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Actually, Zeeshan asked us to do that, so that's what we've been doing as a norm.

return dataMatrix;
}

// A class used to define a matrix element and capture its prediction result.
// A class used to define a matrix element and capture its prediction
// result.
private class MatrixElement
{
// Matrix column index. Its allowed range is from 0 to MatrixColumnCount - 1.
// Matrix column index. Its allowed range is from 0 to
// MatrixColumnCount - 1.
[KeyType(MatrixColumnCount)]
public uint MatrixColumnIndex { get; set; }
// Matrix row index. Its allowed range is from 0 to MatrixRowCount - 1.
[KeyType(MatrixRowCount)]
public uint MatrixRowIndex { get; set; }
// The actual value at the MatrixColumnIndex-th column and the MatrixRowIndex-th row.
// The actual value at the MatrixColumnIndex-th column and the
// MatrixRowIndex-th row.
public float Value { get; set; }
// The predicted value at the MatrixColumnIndex-th column and the MatrixRowIndex-th row.
// The predicted value at the MatrixColumnIndex-th column and the
// MatrixRowIndex-th row.
public float Score { get; set; }
}

// Print some evaluation metrics to regression problems.
private static void PrintMetrics(RegressionMetrics metrics)
{
Console.WriteLine($"Mean Absolute Error: {metrics.MeanAbsoluteError:F2}");
Console.WriteLine($"Mean Squared Error: {metrics.MeanSquaredError:F2}");
Console.WriteLine($"Root Mean Squared Error: {metrics.RootMeanSquaredError:F2}");
Console.WriteLine($"RSquared: {metrics.RSquared:F2}");
Console.WriteLine("Mean Absolute Error: " + metrics.MeanAbsoluteError);
Console.WriteLine("Mean Squared Error: " + metrics.MeanSquaredError);
Console.WriteLine("Root Mean Squared Error: " +
metrics.RootMeanSquaredError);

Console.WriteLine("RSquared: " + metrics.RSquared);
}
}
}
Expand Down
17 changes: 11 additions & 6 deletions docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Recommendation/MatrixFactorization.tt
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Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -2,13 +2,18 @@

<#+
string ClassHeader = @"
// This example requires installation of additional nuget package <a href=""https://www.nuget.org/packages/Microsoft.ML.Recommender/"">Microsoft.ML.Recommender</a>.
// In this example we will create in-memory data and then use it to train
// a matrix factorization model with default parameters. Afterward, quality metrics are reported.";
// This example requires installation of additional nuget package at
// for Microsoft.ML.Recommender at
// https://www.nuget.org/packages/Microsoft.ML.Recommender/
// In this example we will create in-memory data and then use it to train
// a matrix factorization model with default parameters. Afterward, quality
// metrics are reported.";
string ClassName="MatrixFactorization";
string ExtraUsing = null;
string Trainer = @"MatrixFactorization(nameof(MatrixElement.Value), nameof(MatrixElement.MatrixColumnIndex),
nameof(MatrixElement.MatrixRowIndex), 10, 0.2, 1)";
string Trainer = @"
MatrixFactorization(nameof(MatrixElement.Value),
nameof(MatrixElement.MatrixColumnIndex),
nameof(MatrixElement.MatrixRowIndex), 10, 0.2, 1)";
string TrainerOptions = null;

string ExpectedOutputPerInstance= @"// Expected output:
Expand All@@ -23,4 +28,4 @@ string ExpectedOutput = @"// Expected output:
// Mean Squared Error: 0.79
// Root Mean Squared Error: 0.89
// RSquared: 0.61 (closer to 1 is better. The worest case is 0)";
#>
#>
68 changes: 45 additions & 23 deletions ...icrosoft.ML.Samples/Dynamic/Trainers/Recommendation/MatrixFactorizationTemplate.ttinclude
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -16,15 +16,17 @@ namespace Samples.Dynamic.Trainers.Recommendation
<# } #>
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.
// Setting the seed to a fixed number in this example to make outputs deterministic.
// 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. Setting the seed to a fixed number
// in this example to make outputs deterministic.
var mlContext = new MLContext(seed: 0);

// Create a list of training data points.
var dataPoints = GenerateMatrix();

// Convert the list of data points to an IDataView object, which is consumable by ML.NET API.
// Convert the list of data points to an IDataView object, which is
// consumable by ML.NET API.
var trainingData = mlContext.Data.LoadFromEnumerable(dataPoints);

<# if (TrainerOptions == null) { #>
Expand All@@ -35,7 +37,8 @@ namespace Samples.Dynamic.Trainers.Recommendation
var options = new <#=TrainerOptions#>;

// Define the trainer.
var pipeline = mlContext.Recommendation().Trainers.<#=Trainer#>(options);
var pipeline = mlContext.Recommendation().Trainers.<#=Trainer#>(
options);
<# } #>

// Train the model.
Expand All@@ -45,26 +48,37 @@ namespace Samples.Dynamic.Trainers.Recommendation
var transformedData = model.Transform(trainingData);

// Convert IDataView object to a list.
var predictions = mlContext.Data.CreateEnumerable<MatrixElement>(transformedData, reuseRowObject: false).Take(5).ToList();
var predictions = mlContext.Data
.CreateEnumerable<MatrixElement>(transformedData,
reuseRowObject: false).Take(5).ToList();

// Look at 5 predictions for the Label, side by side with the actual Label for comparison.
// Look at 5 predictions for the Label, side by side with the actual
// Label for comparison.
foreach (var p in predictions)
Console.WriteLine($"Actual value: {p.Value:F3}, Predicted score: {p.Score:F3}");
Console.WriteLine($"Actual value: {p.Value:F3}," +
$"Predicted score: {p.Score:F3}");

<#=ExpectedOutputPerInstance#>

// Evaluate the overall metrics
var metrics = mlContext.Regression.Evaluate(transformedData, labelColumnName: nameof(MatrixElement.Value), scoreColumnName: nameof(MatrixElement.Score));
var metrics = mlContext.Regression.Evaluate(transformedData,
labelColumnName: nameof(MatrixElement.Value),
scoreColumnName: nameof(MatrixElement.Score));

PrintMetrics(metrics);

<#=ExpectedOutput#>
}

// The following variables are used to define the shape of the example matrix. Its shape is MatrixRowCount-by-MatrixColumnCount.
// 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
// and MatrixRowIndex=0 in MatrixElement below specifies the value at the upper-left corner in the training matrix). If user's row index
// 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.
// This behavior is also true to column index.
// The following variables are used to define the shape of the example
// matrix. Its shape is MatrixRowCount-by-MatrixColumnCount. 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 and MatrixRowIndex=0
// in MatrixElement below specifies the value at the upper-left corner in
// the training matrix). If user's row index 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. This behavior is also true to column
// index.
private const uint MatrixColumnCount = 60;
private const uint MatrixRowCount = 100;

Expand All@@ -74,32 +88,40 @@ namespace Samples.Dynamic.Trainers.Recommendation
var dataMatrix = new List<MatrixElement>();
for (uint i = 0; i < MatrixColumnCount; ++i)
for (uint j = 0; j < MatrixRowCount; ++j)
dataMatrix.Add(new MatrixElement() { MatrixColumnIndex = i, MatrixRowIndex = j, Value = (i + j) % 5 });
dataMatrix.Add(new MatrixElement() { MatrixColumnIndex = i,
MatrixRowIndex = j, Value = (i + j) % 5 });

return dataMatrix;
}

// A class used to define a matrix element and capture its prediction result.
// A class used to define a matrix element and capture its prediction
// result.
private class MatrixElement
{
// Matrix column index. Its allowed range is from 0 to MatrixColumnCount - 1.
// Matrix column index. Its allowed range is from 0 to
// MatrixColumnCount - 1.
[KeyType(MatrixColumnCount)]
public uint MatrixColumnIndex { get; set; }
// Matrix row index. Its allowed range is from 0 to MatrixRowCount - 1.
[KeyType(MatrixRowCount)]
public uint MatrixRowIndex { get; set; }
// The actual value at the MatrixColumnIndex-th column and the MatrixRowIndex-th row.
// The actual value at the MatrixColumnIndex-th column and the
// MatrixRowIndex-th row.
public float Value { get; set; }
// The predicted value at the MatrixColumnIndex-th column and the MatrixRowIndex-th row.
// The predicted value at the MatrixColumnIndex-th column and the
// MatrixRowIndex-th row.
public float Score { get; set; }
}

// Print some evaluation metrics to regression problems.
private static void PrintMetrics(RegressionMetrics metrics)
{
Console.WriteLine($"Mean Absolute Error: {metrics.MeanAbsoluteError:F2}");
Console.WriteLine($"Mean Squared Error: {metrics.MeanSquaredError:F2}");
Console.WriteLine($"Root Mean Squared Error: {metrics.RootMeanSquaredError:F2}");
Console.WriteLine($"RSquared: {metrics.RSquared:F2}");
Console.WriteLine("Mean Absolute Error: " + metrics.MeanAbsoluteError);
Console.WriteLine("Mean Squared Error: " + metrics.MeanSquaredError);
Console.WriteLine("Root Mean Squared Error: " +
metrics.RootMeanSquaredError);

Console.WriteLine("RSquared: " + metrics.RSquared);
}
}
}
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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
 blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
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var __re = new RegExp('^' + "github\\.com" + '

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 Merged

sayanshaw24
merged 3 commits into

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Jul 2, 2019
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Reformatted Recommendation samples to width 85
#3941
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13b798f

Reformatted Recommendation samples


sayanshaw24 Jul 1, 2019
3bbb568

Fix for tab spacing errors


sayanshaw24 Jul 1, 2019
4715016

Removed precision for reformatting in Console.WriteLine and fixed Key…


sayanshaw24 Jul 2, 2019

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80 changes: 53 additions & 27 deletions
docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Recommendation/MatrixFactorization.cs

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Original file line numberDiff line numberDiff line change
Expand Up@@ -9,25 +9,32 @@ namespace Samples.Dynamic.Trainers.Recommendation
 public static class MatrixFactorization
 {

 // This example requires installation of additional nuget package <a href="https://www.nuget.org/packages/Microsoft.ML.Recommender/">Microsoft.ML.Recommender</a>.
 // In this example we will create in-memory data and then use it to train 
 // a matrix factorization model with default parameters. Afterward, quality metrics are reported.
 // This example requires installation of additional nuget package at
 // for Microsoft.ML.Recommender at
 // https://www.nuget.org/packages/Microsoft.ML.Recommender/
 // In this example we will create in-memory data and then use it to train
 // a matrix factorization model with default parameters. Afterward, quality
 // metrics are reported.
 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.
 // Setting the seed to a fixed number in this example to make outputs deterministic.
 // 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. Setting the seed to a fixed number
 // in this example to make outputs deterministic.
 var mlContext = new MLContext(seed: 0);

 // Create a list of training data points.
 var dataPoints = GenerateMatrix();

 // Convert the list of data points to an IDataView object, which is consumable by ML.NET API.
 // Convert the list of data points to an IDataView object, which is
 // consumable by ML.NET API.
 var trainingData = mlContext.Data.LoadFromEnumerable(dataPoints);

 // Define the trainer.
 var pipeline = mlContext.Recommendation().Trainers.MatrixFactorization(nameof(MatrixElement.Value), nameof(MatrixElement.MatrixColumnIndex),
 nameof(MatrixElement.MatrixRowIndex), 10, 0.2, 1);
 var pipeline = mlContext.Recommendation().Trainers.
 MatrixFactorization(nameof(MatrixElement.Value),
 nameof(MatrixElement.MatrixColumnIndex),
@sharwellsharwellJul 2, 2019

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📝 This is incorrectly indented
 nameof(MatrixElement.MatrixRowIndex), 10, 0.2, 1);

 // Train the model.
 var model = pipeline.Fit(trainingData);
Expand All@@ -36,11 +43,15 @@ public static void Example()
 var transformedData = model.Transform(trainingData);

 // Convert IDataView object to a list.
 var predictions = mlContext.Data.CreateEnumerable<MatrixElement>(transformedData, reuseRowObject: false).Take(5).ToList();
 var predictions = mlContext.Data
 .CreateEnumerable<MatrixElement>(transformedData,
 reuseRowObject: false).Take(5).ToList();
@sharwellsharwellJul 2, 2019•

edited

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📝 This layout is very difficult to read. The following would be preferable:
varpredictions=mlContext.Data.CreateEnumerable<MatrixElement>(transformedData,reuseRowObject:false).Take(5).ToList();

 // Look at 5 predictions for the Label, side by side with the actual Label for comparison.
 // Look at 5 predictions for the Label, side by side with the actual
 // Label for comparison.
 foreach (var p in predictions)
 Console.WriteLine($"Actual value: {p.Value:F3}, Predicted score: {p.Score:F3}");
 Console.WriteLine($"Actual value: {p.Value:F3}," +
 $"Predicted score: {p.Score:F3}");

 // Expected output:
 // Actual value: 0.000, Predicted score: 1.234
Expand All@@ -50,7 +61,10 @@ public static void Example()
 // Actual value: 4.000, Predicted score: 2.362

 // Evaluate the overall metrics
 var metrics = mlContext.Regression.Evaluate(transformedData, labelColumnName: nameof(MatrixElement.Value), scoreColumnName: nameof(MatrixElement.Score));
 var metrics = mlContext.Regression.Evaluate(transformedData,
 labelColumnName: nameof(MatrixElement.Value),
 scoreColumnName: nameof(MatrixElement.Score));

 PrintMetrics(metrics);

 // Expected output:
Expand All@@ -60,11 +74,15 @@ public static void Example()
 // RSquared: 0.61 (closer to 1 is better. The worest case is 0)
 }

 // The following variables are used to define the shape of the example matrix. Its shape is MatrixRowCount-by-MatrixColumnCount.
 // 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
 // and MatrixRowIndex=0 in MatrixElement below specifies the value at the upper-left corner in the training matrix). If user's row index
 // 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.
 // This behavior is also true to column index.
 // The following variables are used to define the shape of the example
 // matrix. Its shape is MatrixRowCount-by-MatrixColumnCount. 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 and MatrixRowIndex=0
 // in MatrixElement below specifies the value at the upper-left corner in
 // the training matrix). If user's row index 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. This behavior is also true to column
 // index.
 private const uint MatrixColumnCount = 60;
 private const uint MatrixRowCount = 100;

Expand All@@ -74,32 +92,40 @@ private static List<MatrixElement> GenerateMatrix()
 var dataMatrix = new List<MatrixElement>();
 for (uint i = 0; i < MatrixColumnCount; ++i)
 for (uint j = 0; j < MatrixRowCount; ++j)
 dataMatrix.Add(new MatrixElement() { MatrixColumnIndex = i, MatrixRowIndex = j, Value = (i + j) % 5 });
 dataMatrix.Add(new MatrixElement() { MatrixColumnIndex = i,
 MatrixRowIndex = j, Value = (i + j) % 5 });
@sharwellsharwellJul 2, 2019

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📝 Incorrect line wrapping and indentation

@wschinwschinJul 1, 2019

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Empty line. Is it produced by an auto-formatting tool?
@sayanshaw24sayanshaw24Jul 2, 2019

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Actually, Zeeshan asked us to do that, so that's what we've been doing as a norm.
 return dataMatrix;
 }

 // A class used to define a matrix element and capture its prediction result.
 // A class used to define a matrix element and capture its prediction
 // result.
 private class MatrixElement
 {
 // Matrix column index. Its allowed range is from 0 to MatrixColumnCount - 1.
 // Matrix column index. Its allowed range is from 0 to
 // MatrixColumnCount - 1.
 [KeyType(MatrixColumnCount)]
 public uint MatrixColumnIndex { get; set; }
 // Matrix row index. Its allowed range is from 0 to MatrixRowCount - 1.
 [KeyType(MatrixRowCount)]
 public uint MatrixRowIndex { get; set; }
 // The actual value at the MatrixColumnIndex-th column and the MatrixRowIndex-th row.
 // The actual value at the MatrixColumnIndex-th column and the
 // MatrixRowIndex-th row.
 public float Value { get; set; }
 // The predicted value at the MatrixColumnIndex-th column and the MatrixRowIndex-th row.
 // The predicted value at the MatrixColumnIndex-th column and the
 // MatrixRowIndex-th row.
 public float Score { get; set; }
 }

 // Print some evaluation metrics to regression problems.
 private static void PrintMetrics(RegressionMetrics metrics)
 {
 Console.WriteLine($"Mean Absolute Error: {metrics.MeanAbsoluteError:F2}");
 Console.WriteLine($"Mean Squared Error: {metrics.MeanSquaredError:F2}");
 Console.WriteLine($"Root Mean Squared Error: {metrics.RootMeanSquaredError:F2}");
 Console.WriteLine($"RSquared: {metrics.RSquared:F2}");
 Console.WriteLine("Mean Absolute Error: " + metrics.MeanAbsoluteError);
 Console.WriteLine("Mean Squared Error: " + metrics.MeanSquaredError);
 Console.WriteLine("Root Mean Squared Error: " +
 metrics.RootMeanSquaredError);

 Console.WriteLine("RSquared: " + metrics.RSquared);
 }
 }
}
Expand Down

17 changes: 11 additions & 6 deletions
docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Recommendation/MatrixFactorization.tt

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Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -2,13 +2,18 @@

<#+
string ClassHeader = @"
 // This example requires installation of additional nuget package <a href=""https://www.nuget.org/packages/Microsoft.ML.Recommender/"">Microsoft.ML.Recommender</a>.
 // In this example we will create in-memory data and then use it to train 
 // a matrix factorization model with default parameters. Afterward, quality metrics are reported.";
 // This example requires installation of additional nuget package at
 // for Microsoft.ML.Recommender at
 // https://www.nuget.org/packages/Microsoft.ML.Recommender/
 // In this example we will create in-memory data and then use it to train
 // a matrix factorization model with default parameters. Afterward, quality
 // metrics are reported.";
string ClassName="MatrixFactorization";
string ExtraUsing = null;
string Trainer = @"MatrixFactorization(nameof(MatrixElement.Value), nameof(MatrixElement.MatrixColumnIndex),
 nameof(MatrixElement.MatrixRowIndex), 10, 0.2, 1)";
string Trainer = @"
 MatrixFactorization(nameof(MatrixElement.Value),
 nameof(MatrixElement.MatrixColumnIndex),
 nameof(MatrixElement.MatrixRowIndex), 10, 0.2, 1)";
string TrainerOptions = null;

string ExpectedOutputPerInstance= @"// Expected output:
Expand All@@ -23,4 +28,4 @@ string ExpectedOutput = @"// Expected output:
 // Mean Squared Error: 0.79
 // Root Mean Squared Error: 0.89
 // RSquared: 0.61 (closer to 1 is better. The worest case is 0)";
#>
#>

68 changes: 45 additions & 23 deletions
...icrosoft.ML.Samples/Dynamic/Trainers/Recommendation/MatrixFactorizationTemplate.ttinclude

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -16,15 +16,17 @@ namespace Samples.Dynamic.Trainers.Recommendation
<# } #>
 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.
 // Setting the seed to a fixed number in this example to make outputs deterministic.
 // 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. Setting the seed to a fixed number
 // in this example to make outputs deterministic.
 var mlContext = new MLContext(seed: 0);

 // Create a list of training data points.
 var dataPoints = GenerateMatrix();

 // Convert the list of data points to an IDataView object, which is consumable by ML.NET API.
 // Convert the list of data points to an IDataView object, which is
 // consumable by ML.NET API.
 var trainingData = mlContext.Data.LoadFromEnumerable(dataPoints);

<# if (TrainerOptions == null) { #>
Expand All@@ -35,7 +37,8 @@ namespace Samples.Dynamic.Trainers.Recommendation
 var options = new <#=TrainerOptions#>;

 // Define the trainer.
 var pipeline = mlContext.Recommendation().Trainers.<#=Trainer#>(options);
 var pipeline = mlContext.Recommendation().Trainers.<#=Trainer#>(
 options);
<# } #>

 // Train the model.
Expand All@@ -45,26 +48,37 @@ namespace Samples.Dynamic.Trainers.Recommendation
 var transformedData = model.Transform(trainingData);

 // Convert IDataView object to a list.
 var predictions = mlContext.Data.CreateEnumerable<MatrixElement>(transformedData, reuseRowObject: false).Take(5).ToList();
 var predictions = mlContext.Data
 .CreateEnumerable<MatrixElement>(transformedData,
 reuseRowObject: false).Take(5).ToList();

 // Look at 5 predictions for the Label, side by side with the actual Label for comparison.
 // Look at 5 predictions for the Label, side by side with the actual
 // Label for comparison.
 foreach (var p in predictions)
 Console.WriteLine($"Actual value: {p.Value:F3}, Predicted score: {p.Score:F3}");
 Console.WriteLine($"Actual value: {p.Value:F3}," +
 $"Predicted score: {p.Score:F3}");

 <#=ExpectedOutputPerInstance#>

 // Evaluate the overall metrics
 var metrics = mlContext.Regression.Evaluate(transformedData, labelColumnName: nameof(MatrixElement.Value), scoreColumnName: nameof(MatrixElement.Score));
 var metrics = mlContext.Regression.Evaluate(transformedData,
 labelColumnName: nameof(MatrixElement.Value),
 scoreColumnName: nameof(MatrixElement.Score));

 PrintMetrics(metrics);

 <#=ExpectedOutput#>
 }

 // The following variables are used to define the shape of the example matrix. Its shape is MatrixRowCount-by-MatrixColumnCount.
 // 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
 // and MatrixRowIndex=0 in MatrixElement below specifies the value at the upper-left corner in the training matrix). If user's row index
 // 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.
 // This behavior is also true to column index.
 // The following variables are used to define the shape of the example
 // matrix. Its shape is MatrixRowCount-by-MatrixColumnCount. 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 and MatrixRowIndex=0
 // in MatrixElement below specifies the value at the upper-left corner in
 // the training matrix). If user's row index 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. This behavior is also true to column
 // index.
 private const uint MatrixColumnCount = 60;
 private const uint MatrixRowCount = 100;

Expand All@@ -74,32 +88,40 @@ namespace Samples.Dynamic.Trainers.Recommendation
 var dataMatrix = new List<MatrixElement>();
 for (uint i = 0; i < MatrixColumnCount; ++i)
 for (uint j = 0; j < MatrixRowCount; ++j)
 dataMatrix.Add(new MatrixElement() { MatrixColumnIndex = i, MatrixRowIndex = j, Value = (i + j) % 5 });
 dataMatrix.Add(new MatrixElement() { MatrixColumnIndex = i,
 MatrixRowIndex = j, Value = (i + j) % 5 });

 return dataMatrix;
 }

 // A class used to define a matrix element and capture its prediction result.
 // A class used to define a matrix element and capture its prediction
 // result.
 private class MatrixElement
 {
 // Matrix column index. Its allowed range is from 0 to MatrixColumnCount - 1.
 // Matrix column index. Its allowed range is from 0 to
 // MatrixColumnCount - 1.
 [KeyType(MatrixColumnCount)]
 public uint MatrixColumnIndex { get; set; }
 // Matrix row index. Its allowed range is from 0 to MatrixRowCount - 1.
 [KeyType(MatrixRowCount)]
 public uint MatrixRowIndex { get; set; }
 // The actual value at the MatrixColumnIndex-th column and the MatrixRowIndex-th row.
 // The actual value at the MatrixColumnIndex-th column and the
 // MatrixRowIndex-th row.
 public float Value { get; set; }
 // The predicted value at the MatrixColumnIndex-th column and the MatrixRowIndex-th row.
 // The predicted value at the MatrixColumnIndex-th column and the
 // MatrixRowIndex-th row.
 public float Score { get; set; }
 }

 // Print some evaluation metrics to regression problems.
 private static void PrintMetrics(RegressionMetrics metrics)
 {
 Console.WriteLine($"Mean Absolute Error: {metrics.MeanAbsoluteError:F2}");
 Console.WriteLine($"Mean Squared Error: {metrics.MeanSquaredError:F2}");
 Console.WriteLine($"Root Mean Squared Error: {metrics.RootMeanSquaredError:F2}");
 Console.WriteLine($"RSquared: {metrics.RSquared:F2}");
 Console.WriteLine("Mean Absolute Error: " + metrics.MeanAbsoluteError);
 Console.WriteLine("Mean Squared Error: " + metrics.MeanSquaredError);
 Console.WriteLine("Root Mean Squared Error: " +
 metrics.RootMeanSquaredError);

 Console.WriteLine("RSquared: " + metrics.RSquared);
 }
 }
}
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13b798f

Reformatted Recommendation samples


sayanshaw24 Jul 1, 2019
3bbb568

Fix for tab spacing errors


sayanshaw24 Jul 1, 2019
4715016

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80 changes: 53 additions & 27 deletions
docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Recommendation/MatrixFactorization.cs

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Original file line numberDiff line numberDiff line change
Expand Up@@ -9,25 +9,32 @@ namespace Samples.Dynamic.Trainers.Recommendation
 public static class MatrixFactorization
 {

 // This example requires installation of additional nuget package <a href="https://www.nuget.org/packages/Microsoft.ML.Recommender/">Microsoft.ML.Recommender</a>.
 // In this example we will create in-memory data and then use it to train 
 // a matrix factorization model with default parameters. Afterward, quality metrics are reported.
 // This example requires installation of additional nuget package at
 // for Microsoft.ML.Recommender at
 // https://www.nuget.org/packages/Microsoft.ML.Recommender/
 // In this example we will create in-memory data and then use it to train
 // a matrix factorization model with default parameters. Afterward, quality
 // metrics are reported.
 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.
 // Setting the seed to a fixed number in this example to make outputs deterministic.
 // 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. Setting the seed to a fixed number
 // in this example to make outputs deterministic.
 var mlContext = new MLContext(seed: 0);

 // Create a list of training data points.
 var dataPoints = GenerateMatrix();

 // Convert the list of data points to an IDataView object, which is consumable by ML.NET API.
 // Convert the list of data points to an IDataView object, which is
 // consumable by ML.NET API.
 var trainingData = mlContext.Data.LoadFromEnumerable(dataPoints);

 // Define the trainer.
 var pipeline = mlContext.Recommendation().Trainers.MatrixFactorization(nameof(MatrixElement.Value), nameof(MatrixElement.MatrixColumnIndex),
 nameof(MatrixElement.MatrixRowIndex), 10, 0.2, 1);
 var pipeline = mlContext.Recommendation().Trainers.
 MatrixFactorization(nameof(MatrixElement.Value),
 nameof(MatrixElement.MatrixColumnIndex),
@sharwellsharwellJul 2, 2019

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📝 This is incorrectly indented
 nameof(MatrixElement.MatrixRowIndex), 10, 0.2, 1);

 // Train the model.
 var model = pipeline.Fit(trainingData);
Expand All@@ -36,11 +43,15 @@ public static void Example()
 var transformedData = model.Transform(trainingData);

 // Convert IDataView object to a list.
 var predictions = mlContext.Data.CreateEnumerable<MatrixElement>(transformedData, reuseRowObject: false).Take(5).ToList();
 var predictions = mlContext.Data
 .CreateEnumerable<MatrixElement>(transformedData,
 reuseRowObject: false).Take(5).ToList();
@sharwellsharwellJul 2, 2019•

edited

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📝 This layout is very difficult to read. The following would be preferable:
varpredictions=mlContext.Data.CreateEnumerable<MatrixElement>(transformedData,reuseRowObject:false).Take(5).ToList();

 // Look at 5 predictions for the Label, side by side with the actual Label for comparison.
 // Look at 5 predictions for the Label, side by side with the actual
 // Label for comparison.
 foreach (var p in predictions)
 Console.WriteLine($"Actual value: {p.Value:F3}, Predicted score: {p.Score:F3}");
 Console.WriteLine($"Actual value: {p.Value:F3}," +
 $"Predicted score: {p.Score:F3}");

 // Expected output:
 // Actual value: 0.000, Predicted score: 1.234
Expand All@@ -50,7 +61,10 @@ public static void Example()
 // Actual value: 4.000, Predicted score: 2.362

 // Evaluate the overall metrics
 var metrics = mlContext.Regression.Evaluate(transformedData, labelColumnName: nameof(MatrixElement.Value), scoreColumnName: nameof(MatrixElement.Score));
 var metrics = mlContext.Regression.Evaluate(transformedData,
 labelColumnName: nameof(MatrixElement.Value),
 scoreColumnName: nameof(MatrixElement.Score));

 PrintMetrics(metrics);

 // Expected output:
Expand All@@ -60,11 +74,15 @@ public static void Example()
 // RSquared: 0.61 (closer to 1 is better. The worest case is 0)
 }

 // The following variables are used to define the shape of the example matrix. Its shape is MatrixRowCount-by-MatrixColumnCount.
 // 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
 // and MatrixRowIndex=0 in MatrixElement below specifies the value at the upper-left corner in the training matrix). If user's row index
 // 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.
 // This behavior is also true to column index.
 // The following variables are used to define the shape of the example
 // matrix. Its shape is MatrixRowCount-by-MatrixColumnCount. 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 and MatrixRowIndex=0
 // in MatrixElement below specifies the value at the upper-left corner in
 // the training matrix). If user's row index 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. This behavior is also true to column
 // index.
 private const uint MatrixColumnCount = 60;
 private const uint MatrixRowCount = 100;

Expand All@@ -74,32 +92,40 @@ private static List<MatrixElement> GenerateMatrix()
 var dataMatrix = new List<MatrixElement>();
 for (uint i = 0; i < MatrixColumnCount; ++i)
 for (uint j = 0; j < MatrixRowCount; ++j)
 dataMatrix.Add(new MatrixElement() { MatrixColumnIndex = i, MatrixRowIndex = j, Value = (i + j) % 5 });
 dataMatrix.Add(new MatrixElement() { MatrixColumnIndex = i,
 MatrixRowIndex = j, Value = (i + j) % 5 });
@sharwellsharwellJul 2, 2019

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📝 Incorrect line wrapping and indentation

@wschinwschinJul 1, 2019

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Empty line. Is it produced by an auto-formatting tool?
@sayanshaw24sayanshaw24Jul 2, 2019

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Actually, Zeeshan asked us to do that, so that's what we've been doing as a norm.
 return dataMatrix;
 }

 // A class used to define a matrix element and capture its prediction result.
 // A class used to define a matrix element and capture its prediction
 // result.
 private class MatrixElement
 {
 // Matrix column index. Its allowed range is from 0 to MatrixColumnCount - 1.
 // Matrix column index. Its allowed range is from 0 to
 // MatrixColumnCount - 1.
 [KeyType(MatrixColumnCount)]
 public uint MatrixColumnIndex { get; set; }
 // Matrix row index. Its allowed range is from 0 to MatrixRowCount - 1.
 [KeyType(MatrixRowCount)]
 public uint MatrixRowIndex { get; set; }
 // The actual value at the MatrixColumnIndex-th column and the MatrixRowIndex-th row.
 // The actual value at the MatrixColumnIndex-th column and the
 // MatrixRowIndex-th row.
 public float Value { get; set; }
 // The predicted value at the MatrixColumnIndex-th column and the MatrixRowIndex-th row.
 // The predicted value at the MatrixColumnIndex-th column and the
 // MatrixRowIndex-th row.
 public float Score { get; set; }
 }

 // Print some evaluation metrics to regression problems.
 private static void PrintMetrics(RegressionMetrics metrics)
 {
 Console.WriteLine($"Mean Absolute Error: {metrics.MeanAbsoluteError:F2}");
 Console.WriteLine($"Mean Squared Error: {metrics.MeanSquaredError:F2}");
 Console.WriteLine($"Root Mean Squared Error: {metrics.RootMeanSquaredError:F2}");
 Console.WriteLine($"RSquared: {metrics.RSquared:F2}");
 Console.WriteLine("Mean Absolute Error: " + metrics.MeanAbsoluteError);
 Console.WriteLine("Mean Squared Error: " + metrics.MeanSquaredError);
 Console.WriteLine("Root Mean Squared Error: " +
 metrics.RootMeanSquaredError);

 Console.WriteLine("RSquared: " + metrics.RSquared);
 }
 }
}
Expand Down

17 changes: 11 additions & 6 deletions
docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Recommendation/MatrixFactorization.tt

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -2,13 +2,18 @@

<#+
string ClassHeader = @"
 // This example requires installation of additional nuget package <a href=""https://www.nuget.org/packages/Microsoft.ML.Recommender/"">Microsoft.ML.Recommender</a>.
 // In this example we will create in-memory data and then use it to train 
 // a matrix factorization model with default parameters. Afterward, quality metrics are reported.";
 // This example requires installation of additional nuget package at
 // for Microsoft.ML.Recommender at
 // https://www.nuget.org/packages/Microsoft.ML.Recommender/
 // In this example we will create in-memory data and then use it to train
 // a matrix factorization model with default parameters. Afterward, quality
 // metrics are reported.";
string ClassName="MatrixFactorization";
string ExtraUsing = null;
string Trainer = @"MatrixFactorization(nameof(MatrixElement.Value), nameof(MatrixElement.MatrixColumnIndex),
 nameof(MatrixElement.MatrixRowIndex), 10, 0.2, 1)";
string Trainer = @"
 MatrixFactorization(nameof(MatrixElement.Value),
 nameof(MatrixElement.MatrixColumnIndex),
 nameof(MatrixElement.MatrixRowIndex), 10, 0.2, 1)";
string TrainerOptions = null;

string ExpectedOutputPerInstance= @"// Expected output:
Expand All@@ -23,4 +28,4 @@ string ExpectedOutput = @"// Expected output:
 // Mean Squared Error: 0.79
 // Root Mean Squared Error: 0.89
 // RSquared: 0.61 (closer to 1 is better. The worest case is 0)";
#>
#>

68 changes: 45 additions & 23 deletions
...icrosoft.ML.Samples/Dynamic/Trainers/Recommendation/MatrixFactorizationTemplate.ttinclude

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -16,15 +16,17 @@ namespace Samples.Dynamic.Trainers.Recommendation
<# } #>
 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.
 // Setting the seed to a fixed number in this example to make outputs deterministic.
 // 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. Setting the seed to a fixed number
 // in this example to make outputs deterministic.
 var mlContext = new MLContext(seed: 0);

 // Create a list of training data points.
 var dataPoints = GenerateMatrix();

 // Convert the list of data points to an IDataView object, which is consumable by ML.NET API.
 // Convert the list of data points to an IDataView object, which is
 // consumable by ML.NET API.
 var trainingData = mlContext.Data.LoadFromEnumerable(dataPoints);

<# if (TrainerOptions == null) { #>
Expand All@@ -35,7 +37,8 @@ namespace Samples.Dynamic.Trainers.Recommendation
 var options = new <#=TrainerOptions#>;

 // Define the trainer.
 var pipeline = mlContext.Recommendation().Trainers.<#=Trainer#>(options);
 var pipeline = mlContext.Recommendation().Trainers.<#=Trainer#>(
 options);
<# } #>

 // Train the model.
Expand All@@ -45,26 +48,37 @@ namespace Samples.Dynamic.Trainers.Recommendation
 var transformedData = model.Transform(trainingData);

 // Convert IDataView object to a list.
 var predictions = mlContext.Data.CreateEnumerable<MatrixElement>(transformedData, reuseRowObject: false).Take(5).ToList();
 var predictions = mlContext.Data
 .CreateEnumerable<MatrixElement>(transformedData,
 reuseRowObject: false).Take(5).ToList();

 // Look at 5 predictions for the Label, side by side with the actual Label for comparison.
 // Look at 5 predictions for the Label, side by side with the actual
 // Label for comparison.
 foreach (var p in predictions)
 Console.WriteLine($"Actual value: {p.Value:F3}, Predicted score: {p.Score:F3}");
 Console.WriteLine($"Actual value: {p.Value:F3}," +
 $"Predicted score: {p.Score:F3}");

 <#=ExpectedOutputPerInstance#>

 // Evaluate the overall metrics
 var metrics = mlContext.Regression.Evaluate(transformedData, labelColumnName: nameof(MatrixElement.Value), scoreColumnName: nameof(MatrixElement.Score));
 var metrics = mlContext.Regression.Evaluate(transformedData,
 labelColumnName: nameof(MatrixElement.Value),
 scoreColumnName: nameof(MatrixElement.Score));

 PrintMetrics(metrics);

 <#=ExpectedOutput#>
 }

 // The following variables are used to define the shape of the example matrix. Its shape is MatrixRowCount-by-MatrixColumnCount.
 // 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
 // and MatrixRowIndex=0 in MatrixElement below specifies the value at the upper-left corner in the training matrix). If user's row index
 // 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.
 // This behavior is also true to column index.
 // The following variables are used to define the shape of the example
 // matrix. Its shape is MatrixRowCount-by-MatrixColumnCount. 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 and MatrixRowIndex=0
 // in MatrixElement below specifies the value at the upper-left corner in
 // the training matrix). If user's row index 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. This behavior is also true to column
 // index.
 private const uint MatrixColumnCount = 60;
 private const uint MatrixRowCount = 100;

Expand All@@ -74,32 +88,40 @@ namespace Samples.Dynamic.Trainers.Recommendation
 var dataMatrix = new List<MatrixElement>();
 for (uint i = 0; i < MatrixColumnCount; ++i)
 for (uint j = 0; j < MatrixRowCount; ++j)
 dataMatrix.Add(new MatrixElement() { MatrixColumnIndex = i, MatrixRowIndex = j, Value = (i + j) % 5 });
 dataMatrix.Add(new MatrixElement() { MatrixColumnIndex = i,
 MatrixRowIndex = j, Value = (i + j) % 5 });

 return dataMatrix;
 }

 // A class used to define a matrix element and capture its prediction result.
 // A class used to define a matrix element and capture its prediction
 // result.
 private class MatrixElement
 {
 // Matrix column index. Its allowed range is from 0 to MatrixColumnCount - 1.
 // Matrix column index. Its allowed range is from 0 to
 // MatrixColumnCount - 1.
 [KeyType(MatrixColumnCount)]
 public uint MatrixColumnIndex { get; set; }
 // Matrix row index. Its allowed range is from 0 to MatrixRowCount - 1.
 [KeyType(MatrixRowCount)]
 public uint MatrixRowIndex { get; set; }
 // The actual value at the MatrixColumnIndex-th column and the MatrixRowIndex-th row.
 // The actual value at the MatrixColumnIndex-th column and the
 // MatrixRowIndex-th row.
 public float Value { get; set; }
 // The predicted value at the MatrixColumnIndex-th column and the MatrixRowIndex-th row.
 // The predicted value at the MatrixColumnIndex-th column and the
 // MatrixRowIndex-th row.
 public float Score { get; set; }
 }

 // Print some evaluation metrics to regression problems.
 private static void PrintMetrics(RegressionMetrics metrics)
 {
 Console.WriteLine($"Mean Absolute Error: {metrics.MeanAbsoluteError:F2}");
 Console.WriteLine($"Mean Squared Error: {metrics.MeanSquaredError:F2}");
 Console.WriteLine($"Root Mean Squared Error: {metrics.RootMeanSquaredError:F2}");
 Console.WriteLine($"RSquared: {metrics.RSquared:F2}");
 Console.WriteLine("Mean Absolute Error: " + metrics.MeanAbsoluteError);
 Console.WriteLine("Mean Squared Error: " + metrics.MeanSquaredError);
 Console.WriteLine("Root Mean Squared Error: " +
 metrics.RootMeanSquaredError);

 Console.WriteLine("RSquared: " + metrics.RSquared);
 }
 }
}
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injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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80 changes: 53 additions & 27 deletions
docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Recommendation/MatrixFactorization.cs

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Original file line numberDiff line numberDiff line change
Expand Up@@ -9,25 +9,32 @@ namespace Samples.Dynamic.Trainers.Recommendation
 public static class MatrixFactorization
 {

 // This example requires installation of additional nuget package <a href="https://www.nuget.org/packages/Microsoft.ML.Recommender/">Microsoft.ML.Recommender</a>.
 // In this example we will create in-memory data and then use it to train 
 // a matrix factorization model with default parameters. Afterward, quality metrics are reported.
 // This example requires installation of additional nuget package at
 // for Microsoft.ML.Recommender at
 // https://www.nuget.org/packages/Microsoft.ML.Recommender/
 // In this example we will create in-memory data and then use it to train
 // a matrix factorization model with default parameters. Afterward, quality
 // metrics are reported.
 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.
 // Setting the seed to a fixed number in this example to make outputs deterministic.
 // 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. Setting the seed to a fixed number
 // in this example to make outputs deterministic.
 var mlContext = new MLContext(seed: 0);

 // Create a list of training data points.
 var dataPoints = GenerateMatrix();

 // Convert the list of data points to an IDataView object, which is consumable by ML.NET API.
 // Convert the list of data points to an IDataView object, which is
 // consumable by ML.NET API.
 var trainingData = mlContext.Data.LoadFromEnumerable(dataPoints);

 // Define the trainer.
 var pipeline = mlContext.Recommendation().Trainers.MatrixFactorization(nameof(MatrixElement.Value), nameof(MatrixElement.MatrixColumnIndex),
 nameof(MatrixElement.MatrixRowIndex), 10, 0.2, 1);
 var pipeline = mlContext.Recommendation().Trainers.
 MatrixFactorization(nameof(MatrixElement.Value),
 nameof(MatrixElement.MatrixColumnIndex),
@sharwellsharwellJul 2, 2019

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📝 This is incorrectly indented
 nameof(MatrixElement.MatrixRowIndex), 10, 0.2, 1);

 // Train the model.
 var model = pipeline.Fit(trainingData);
Expand All@@ -36,11 +43,15 @@ public static void Example()
 var transformedData = model.Transform(trainingData);

 // Convert IDataView object to a list.
 var predictions = mlContext.Data.CreateEnumerable<MatrixElement>(transformedData, reuseRowObject: false).Take(5).ToList();
 var predictions = mlContext.Data
 .CreateEnumerable<MatrixElement>(transformedData,
 reuseRowObject: false).Take(5).ToList();
@sharwellsharwellJul 2, 2019•

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📝 This layout is very difficult to read. The following would be preferable:
varpredictions=mlContext.Data.CreateEnumerable<MatrixElement>(transformedData,reuseRowObject:false).Take(5).ToList();

 // Look at 5 predictions for the Label, side by side with the actual Label for comparison.
 // Look at 5 predictions for the Label, side by side with the actual
 // Label for comparison.
 foreach (var p in predictions)
 Console.WriteLine($"Actual value: {p.Value:F3}, Predicted score: {p.Score:F3}");
 Console.WriteLine($"Actual value: {p.Value:F3}," +
 $"Predicted score: {p.Score:F3}");

 // Expected output:
 // Actual value: 0.000, Predicted score: 1.234
Expand All@@ -50,7 +61,10 @@ public static void Example()
 // Actual value: 4.000, Predicted score: 2.362

 // Evaluate the overall metrics
 var metrics = mlContext.Regression.Evaluate(transformedData, labelColumnName: nameof(MatrixElement.Value), scoreColumnName: nameof(MatrixElement.Score));
 var metrics = mlContext.Regression.Evaluate(transformedData,
 labelColumnName: nameof(MatrixElement.Value),
 scoreColumnName: nameof(MatrixElement.Score));

 PrintMetrics(metrics);

 // Expected output:
Expand All@@ -60,11 +74,15 @@ public static void Example()
 // RSquared: 0.61 (closer to 1 is better. The worest case is 0)
 }

 // The following variables are used to define the shape of the example matrix. Its shape is MatrixRowCount-by-MatrixColumnCount.
 // 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
 // and MatrixRowIndex=0 in MatrixElement below specifies the value at the upper-left corner in the training matrix). If user's row index
 // 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.
 // This behavior is also true to column index.
 // The following variables are used to define the shape of the example
 // matrix. Its shape is MatrixRowCount-by-MatrixColumnCount. 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 and MatrixRowIndex=0
 // in MatrixElement below specifies the value at the upper-left corner in
 // the training matrix). If user's row index 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. This behavior is also true to column
 // index.
 private const uint MatrixColumnCount = 60;
 private const uint MatrixRowCount = 100;

Expand All@@ -74,32 +92,40 @@ private static List<MatrixElement> GenerateMatrix()
 var dataMatrix = new List<MatrixElement>();
 for (uint i = 0; i < MatrixColumnCount; ++i)
 for (uint j = 0; j < MatrixRowCount; ++j)
 dataMatrix.Add(new MatrixElement() { MatrixColumnIndex = i, MatrixRowIndex = j, Value = (i + j) % 5 });
 dataMatrix.Add(new MatrixElement() { MatrixColumnIndex = i,
 MatrixRowIndex = j, Value = (i + j) % 5 });
@sharwellsharwellJul 2, 2019

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📝 Incorrect line wrapping and indentation

@wschinwschinJul 1, 2019

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Empty line. Is it produced by an auto-formatting tool?
@sayanshaw24sayanshaw24Jul 2, 2019

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Actually, Zeeshan asked us to do that, so that's what we've been doing as a norm.
 return dataMatrix;
 }

 // A class used to define a matrix element and capture its prediction result.
 // A class used to define a matrix element and capture its prediction
 // result.
 private class MatrixElement
 {
 // Matrix column index. Its allowed range is from 0 to MatrixColumnCount - 1.
 // Matrix column index. Its allowed range is from 0 to
 // MatrixColumnCount - 1.
 [KeyType(MatrixColumnCount)]
 public uint MatrixColumnIndex { get; set; }
 // Matrix row index. Its allowed range is from 0 to MatrixRowCount - 1.
 [KeyType(MatrixRowCount)]
 public uint MatrixRowIndex { get; set; }
 // The actual value at the MatrixColumnIndex-th column and the MatrixRowIndex-th row.
 // The actual value at the MatrixColumnIndex-th column and the
 // MatrixRowIndex-th row.
 public float Value { get; set; }
 // The predicted value at the MatrixColumnIndex-th column and the MatrixRowIndex-th row.
 // The predicted value at the MatrixColumnIndex-th column and the
 // MatrixRowIndex-th row.
 public float Score { get; set; }
 }

 // Print some evaluation metrics to regression problems.
 private static void PrintMetrics(RegressionMetrics metrics)
 {
 Console.WriteLine($"Mean Absolute Error: {metrics.MeanAbsoluteError:F2}");
 Console.WriteLine($"Mean Squared Error: {metrics.MeanSquaredError:F2}");
 Console.WriteLine($"Root Mean Squared Error: {metrics.RootMeanSquaredError:F2}");
 Console.WriteLine($"RSquared: {metrics.RSquared:F2}");
 Console.WriteLine("Mean Absolute Error: " + metrics.MeanAbsoluteError);
 Console.WriteLine("Mean Squared Error: " + metrics.MeanSquaredError);
 Console.WriteLine("Root Mean Squared Error: " +
 metrics.RootMeanSquaredError);

 Console.WriteLine("RSquared: " + metrics.RSquared);
 }
 }
}
Expand Down

17 changes: 11 additions & 6 deletions
docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Recommendation/MatrixFactorization.tt

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -2,13 +2,18 @@

<#+
string ClassHeader = @"
 // This example requires installation of additional nuget package <a href=""https://www.nuget.org/packages/Microsoft.ML.Recommender/"">Microsoft.ML.Recommender</a>.
 // In this example we will create in-memory data and then use it to train 
 // a matrix factorization model with default parameters. Afterward, quality metrics are reported.";
 // This example requires installation of additional nuget package at
 // for Microsoft.ML.Recommender at
 // https://www.nuget.org/packages/Microsoft.ML.Recommender/
 // In this example we will create in-memory data and then use it to train
 // a matrix factorization model with default parameters. Afterward, quality
 // metrics are reported.";
string ClassName="MatrixFactorization";
string ExtraUsing = null;
string Trainer = @"MatrixFactorization(nameof(MatrixElement.Value), nameof(MatrixElement.MatrixColumnIndex),
 nameof(MatrixElement.MatrixRowIndex), 10, 0.2, 1)";
string Trainer = @"
 MatrixFactorization(nameof(MatrixElement.Value),
 nameof(MatrixElement.MatrixColumnIndex),
 nameof(MatrixElement.MatrixRowIndex), 10, 0.2, 1)";
string TrainerOptions = null;

string ExpectedOutputPerInstance= @"// Expected output:
Expand All@@ -23,4 +28,4 @@ string ExpectedOutput = @"// Expected output:
 // Mean Squared Error: 0.79
 // Root Mean Squared Error: 0.89
 // RSquared: 0.61 (closer to 1 is better. The worest case is 0)";
#>
#>

68 changes: 45 additions & 23 deletions
...icrosoft.ML.Samples/Dynamic/Trainers/Recommendation/MatrixFactorizationTemplate.ttinclude

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Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -16,15 +16,17 @@ namespace Samples.Dynamic.Trainers.Recommendation
<# } #>
 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.
 // Setting the seed to a fixed number in this example to make outputs deterministic.
 // 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. Setting the seed to a fixed number
 // in this example to make outputs deterministic.
 var mlContext = new MLContext(seed: 0);

 // Create a list of training data points.
 var dataPoints = GenerateMatrix();

 // Convert the list of data points to an IDataView object, which is consumable by ML.NET API.
 // Convert the list of data points to an IDataView object, which is
 // consumable by ML.NET API.
 var trainingData = mlContext.Data.LoadFromEnumerable(dataPoints);

<# if (TrainerOptions == null) { #>
Expand All@@ -35,7 +37,8 @@ namespace Samples.Dynamic.Trainers.Recommendation
 var options = new <#=TrainerOptions#>;

 // Define the trainer.
 var pipeline = mlContext.Recommendation().Trainers.<#=Trainer#>(options);
 var pipeline = mlContext.Recommendation().Trainers.<#=Trainer#>(
 options);
<# } #>

 // Train the model.
Expand All@@ -45,26 +48,37 @@ namespace Samples.Dynamic.Trainers.Recommendation
 var transformedData = model.Transform(trainingData);

 // Convert IDataView object to a list.
 var predictions = mlContext.Data.CreateEnumerable<MatrixElement>(transformedData, reuseRowObject: false).Take(5).ToList();
 var predictions = mlContext.Data
 .CreateEnumerable<MatrixElement>(transformedData,
 reuseRowObject: false).Take(5).ToList();

 // Look at 5 predictions for the Label, side by side with the actual Label for comparison.
 // Look at 5 predictions for the Label, side by side with the actual
 // Label for comparison.
 foreach (var p in predictions)
 Console.WriteLine($"Actual value: {p.Value:F3}, Predicted score: {p.Score:F3}");
 Console.WriteLine($"Actual value: {p.Value:F3}," +
 $"Predicted score: {p.Score:F3}");

 <#=ExpectedOutputPerInstance#>

 // Evaluate the overall metrics
 var metrics = mlContext.Regression.Evaluate(transformedData, labelColumnName: nameof(MatrixElement.Value), scoreColumnName: nameof(MatrixElement.Score));
 var metrics = mlContext.Regression.Evaluate(transformedData,
 labelColumnName: nameof(MatrixElement.Value),
 scoreColumnName: nameof(MatrixElement.Score));

 PrintMetrics(metrics);

 <#=ExpectedOutput#>
 }

 // The following variables are used to define the shape of the example matrix. Its shape is MatrixRowCount-by-MatrixColumnCount.
 // 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
 // and MatrixRowIndex=0 in MatrixElement below specifies the value at the upper-left corner in the training matrix). If user's row index
 // 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.
 // This behavior is also true to column index.
 // The following variables are used to define the shape of the example
 // matrix. Its shape is MatrixRowCount-by-MatrixColumnCount. 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 and MatrixRowIndex=0
 // in MatrixElement below specifies the value at the upper-left corner in
 // the training matrix). If user's row index 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. This behavior is also true to column
 // index.
 private const uint MatrixColumnCount = 60;
 private const uint MatrixRowCount = 100;

Expand All@@ -74,32 +88,40 @@ namespace Samples.Dynamic.Trainers.Recommendation
 var dataMatrix = new List<MatrixElement>();
 for (uint i = 0; i < MatrixColumnCount; ++i)
 for (uint j = 0; j < MatrixRowCount; ++j)
 dataMatrix.Add(new MatrixElement() { MatrixColumnIndex = i, MatrixRowIndex = j, Value = (i + j) % 5 });
 dataMatrix.Add(new MatrixElement() { MatrixColumnIndex = i,
 MatrixRowIndex = j, Value = (i + j) % 5 });

 return dataMatrix;
 }

 // A class used to define a matrix element and capture its prediction result.
 // A class used to define a matrix element and capture its prediction
 // result.
 private class MatrixElement
 {
 // Matrix column index. Its allowed range is from 0 to MatrixColumnCount - 1.
 // Matrix column index. Its allowed range is from 0 to
 // MatrixColumnCount - 1.
 [KeyType(MatrixColumnCount)]
 public uint MatrixColumnIndex { get; set; }
 // Matrix row index. Its allowed range is from 0 to MatrixRowCount - 1.
 [KeyType(MatrixRowCount)]
 public uint MatrixRowIndex { get; set; }
 // The actual value at the MatrixColumnIndex-th column and the MatrixRowIndex-th row.
 // The actual value at the MatrixColumnIndex-th column and the
 // MatrixRowIndex-th row.
 public float Value { get; set; }
 // The predicted value at the MatrixColumnIndex-th column and the MatrixRowIndex-th row.
 // The predicted value at the MatrixColumnIndex-th column and the
 // MatrixRowIndex-th row.
 public float Score { get; set; }
 }

 // Print some evaluation metrics to regression problems.
 private static void PrintMetrics(RegressionMetrics metrics)
 {
 Console.WriteLine($"Mean Absolute Error: {metrics.MeanAbsoluteError:F2}");
 Console.WriteLine($"Mean Squared Error: {metrics.MeanSquaredError:F2}");
 Console.WriteLine($"Root Mean Squared Error: {metrics.RootMeanSquaredError:F2}");
 Console.WriteLine($"RSquared: {metrics.RSquared:F2}");
 Console.WriteLine("Mean Absolute Error: " + metrics.MeanAbsoluteError);
 Console.WriteLine("Mean Squared Error: " + metrics.MeanSquaredError);
 Console.WriteLine("Root Mean Squared Error: " +
 metrics.RootMeanSquaredError);

 Console.WriteLine("RSquared: " + metrics.RSquared);
 }
 }
}
Loading

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

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Reformatted Recommendation samples to width 85
#3941
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13b798f

Reformatted Recommendation samples


sayanshaw24 Jul 1, 2019
3bbb568

Fix for tab spacing errors


sayanshaw24 Jul 1, 2019
4715016

Removed precision for reformatting in Console.WriteLine and fixed Key…


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80 changes: 53 additions & 27 deletions
docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Recommendation/MatrixFactorization.cs

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Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -9,25 +9,32 @@ namespace Samples.Dynamic.Trainers.Recommendation
 public static class MatrixFactorization
 {

 // This example requires installation of additional nuget package <a href="https://www.nuget.org/packages/Microsoft.ML.Recommender/">Microsoft.ML.Recommender</a>.
 // In this example we will create in-memory data and then use it to train 
 // a matrix factorization model with default parameters. Afterward, quality metrics are reported.
 // This example requires installation of additional nuget package at
 // for Microsoft.ML.Recommender at
 // https://www.nuget.org/packages/Microsoft.ML.Recommender/
 // In this example we will create in-memory data and then use it to train
 // a matrix factorization model with default parameters. Afterward, quality
 // metrics are reported.
 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.
 // Setting the seed to a fixed number in this example to make outputs deterministic.
 // 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. Setting the seed to a fixed number
 // in this example to make outputs deterministic.
 var mlContext = new MLContext(seed: 0);

 // Create a list of training data points.
 var dataPoints = GenerateMatrix();

 // Convert the list of data points to an IDataView object, which is consumable by ML.NET API.
 // Convert the list of data points to an IDataView object, which is
 // consumable by ML.NET API.
 var trainingData = mlContext.Data.LoadFromEnumerable(dataPoints);

 // Define the trainer.
 var pipeline = mlContext.Recommendation().Trainers.MatrixFactorization(nameof(MatrixElement.Value), nameof(MatrixElement.MatrixColumnIndex),
 nameof(MatrixElement.MatrixRowIndex), 10, 0.2, 1);
 var pipeline = mlContext.Recommendation().Trainers.
 MatrixFactorization(nameof(MatrixElement.Value),
 nameof(MatrixElement.MatrixColumnIndex),
@sharwellsharwellJul 2, 2019

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📝 This is incorrectly indented
 nameof(MatrixElement.MatrixRowIndex), 10, 0.2, 1);

 // Train the model.
 var model = pipeline.Fit(trainingData);
Expand All@@ -36,11 +43,15 @@ public static void Example()
 var transformedData = model.Transform(trainingData);

 // Convert IDataView object to a list.
 var predictions = mlContext.Data.CreateEnumerable<MatrixElement>(transformedData, reuseRowObject: false).Take(5).ToList();
 var predictions = mlContext.Data
 .CreateEnumerable<MatrixElement>(transformedData,
 reuseRowObject: false).Take(5).ToList();
@sharwellsharwellJul 2, 2019•

edited

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📝 This layout is very difficult to read. The following would be preferable:
varpredictions=mlContext.Data.CreateEnumerable<MatrixElement>(transformedData,reuseRowObject:false).Take(5).ToList();

 // Look at 5 predictions for the Label, side by side with the actual Label for comparison.
 // Look at 5 predictions for the Label, side by side with the actual
 // Label for comparison.
 foreach (var p in predictions)
 Console.WriteLine($"Actual value: {p.Value:F3}, Predicted score: {p.Score:F3}");
 Console.WriteLine($"Actual value: {p.Value:F3}," +
 $"Predicted score: {p.Score:F3}");

 // Expected output:
 // Actual value: 0.000, Predicted score: 1.234
Expand All@@ -50,7 +61,10 @@ public static void Example()
 // Actual value: 4.000, Predicted score: 2.362

 // Evaluate the overall metrics
 var metrics = mlContext.Regression.Evaluate(transformedData, labelColumnName: nameof(MatrixElement.Value), scoreColumnName: nameof(MatrixElement.Score));
 var metrics = mlContext.Regression.Evaluate(transformedData,
 labelColumnName: nameof(MatrixElement.Value),
 scoreColumnName: nameof(MatrixElement.Score));

 PrintMetrics(metrics);

 // Expected output:
Expand All@@ -60,11 +74,15 @@ public static void Example()
 // RSquared: 0.61 (closer to 1 is better. The worest case is 0)
 }

 // The following variables are used to define the shape of the example matrix. Its shape is MatrixRowCount-by-MatrixColumnCount.
 // 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
 // and MatrixRowIndex=0 in MatrixElement below specifies the value at the upper-left corner in the training matrix). If user's row index
 // 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.
 // This behavior is also true to column index.
 // The following variables are used to define the shape of the example
 // matrix. Its shape is MatrixRowCount-by-MatrixColumnCount. 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 and MatrixRowIndex=0
 // in MatrixElement below specifies the value at the upper-left corner in
 // the training matrix). If user's row index 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. This behavior is also true to column
 // index.
 private const uint MatrixColumnCount = 60;
 private const uint MatrixRowCount = 100;

Expand All@@ -74,32 +92,40 @@ private static List<MatrixElement> GenerateMatrix()
 var dataMatrix = new List<MatrixElement>();
 for (uint i = 0; i < MatrixColumnCount; ++i)
 for (uint j = 0; j < MatrixRowCount; ++j)
 dataMatrix.Add(new MatrixElement() { MatrixColumnIndex = i, MatrixRowIndex = j, Value = (i + j) % 5 });
 dataMatrix.Add(new MatrixElement() { MatrixColumnIndex = i,
 MatrixRowIndex = j, Value = (i + j) % 5 });
@sharwellsharwellJul 2, 2019

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📝 Incorrect line wrapping and indentation

@wschinwschinJul 1, 2019

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Empty line. Is it produced by an auto-formatting tool?
@sayanshaw24sayanshaw24Jul 2, 2019

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Actually, Zeeshan asked us to do that, so that's what we've been doing as a norm.
 return dataMatrix;
 }

 // A class used to define a matrix element and capture its prediction result.
 // A class used to define a matrix element and capture its prediction
 // result.
 private class MatrixElement
 {
 // Matrix column index. Its allowed range is from 0 to MatrixColumnCount - 1.
 // Matrix column index. Its allowed range is from 0 to
 // MatrixColumnCount - 1.
 [KeyType(MatrixColumnCount)]
 public uint MatrixColumnIndex { get; set; }
 // Matrix row index. Its allowed range is from 0 to MatrixRowCount - 1.
 [KeyType(MatrixRowCount)]
 public uint MatrixRowIndex { get; set; }
 // The actual value at the MatrixColumnIndex-th column and the MatrixRowIndex-th row.
 // The actual value at the MatrixColumnIndex-th column and the
 // MatrixRowIndex-th row.
 public float Value { get; set; }
 // The predicted value at the MatrixColumnIndex-th column and the MatrixRowIndex-th row.
 // The predicted value at the MatrixColumnIndex-th column and the
 // MatrixRowIndex-th row.
 public float Score { get; set; }
 }

 // Print some evaluation metrics to regression problems.
 private static void PrintMetrics(RegressionMetrics metrics)
 {
 Console.WriteLine($"Mean Absolute Error: {metrics.MeanAbsoluteError:F2}");
 Console.WriteLine($"Mean Squared Error: {metrics.MeanSquaredError:F2}");
 Console.WriteLine($"Root Mean Squared Error: {metrics.RootMeanSquaredError:F2}");
 Console.WriteLine($"RSquared: {metrics.RSquared:F2}");
 Console.WriteLine("Mean Absolute Error: " + metrics.MeanAbsoluteError);
 Console.WriteLine("Mean Squared Error: " + metrics.MeanSquaredError);
 Console.WriteLine("Root Mean Squared Error: " +
 metrics.RootMeanSquaredError);

 Console.WriteLine("RSquared: " + metrics.RSquared);
 }
 }
}
Expand Down

17 changes: 11 additions & 6 deletions
docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Recommendation/MatrixFactorization.tt

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -2,13 +2,18 @@

<#+
string ClassHeader = @"
 // This example requires installation of additional nuget package <a href=""https://www.nuget.org/packages/Microsoft.ML.Recommender/"">Microsoft.ML.Recommender</a>.
 // In this example we will create in-memory data and then use it to train 
 // a matrix factorization model with default parameters. Afterward, quality metrics are reported.";
 // This example requires installation of additional nuget package at
 // for Microsoft.ML.Recommender at
 // https://www.nuget.org/packages/Microsoft.ML.Recommender/
 // In this example we will create in-memory data and then use it to train
 // a matrix factorization model with default parameters. Afterward, quality
 // metrics are reported.";
string ClassName="MatrixFactorization";
string ExtraUsing = null;
string Trainer = @"MatrixFactorization(nameof(MatrixElement.Value), nameof(MatrixElement.MatrixColumnIndex),
 nameof(MatrixElement.MatrixRowIndex), 10, 0.2, 1)";
string Trainer = @"
 MatrixFactorization(nameof(MatrixElement.Value),
 nameof(MatrixElement.MatrixColumnIndex),
 nameof(MatrixElement.MatrixRowIndex), 10, 0.2, 1)";
string TrainerOptions = null;

string ExpectedOutputPerInstance= @"// Expected output:
Expand All@@ -23,4 +28,4 @@ string ExpectedOutput = @"// Expected output:
 // Mean Squared Error: 0.79
 // Root Mean Squared Error: 0.89
 // RSquared: 0.61 (closer to 1 is better. The worest case is 0)";
#>
#>

68 changes: 45 additions & 23 deletions
...icrosoft.ML.Samples/Dynamic/Trainers/Recommendation/MatrixFactorizationTemplate.ttinclude

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -16,15 +16,17 @@ namespace Samples.Dynamic.Trainers.Recommendation
<# } #>
 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.
 // Setting the seed to a fixed number in this example to make outputs deterministic.
 // 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. Setting the seed to a fixed number
 // in this example to make outputs deterministic.
 var mlContext = new MLContext(seed: 0);

 // Create a list of training data points.
 var dataPoints = GenerateMatrix();

 // Convert the list of data points to an IDataView object, which is consumable by ML.NET API.
 // Convert the list of data points to an IDataView object, which is
 // consumable by ML.NET API.
 var trainingData = mlContext.Data.LoadFromEnumerable(dataPoints);

<# if (TrainerOptions == null) { #>
Expand All@@ -35,7 +37,8 @@ namespace Samples.Dynamic.Trainers.Recommendation
 var options = new <#=TrainerOptions#>;

 // Define the trainer.
 var pipeline = mlContext.Recommendation().Trainers.<#=Trainer#>(options);
 var pipeline = mlContext.Recommendation().Trainers.<#=Trainer#>(
 options);
<# } #>

 // Train the model.
Expand All@@ -45,26 +48,37 @@ namespace Samples.Dynamic.Trainers.Recommendation
 var transformedData = model.Transform(trainingData);

 // Convert IDataView object to a list.
 var predictions = mlContext.Data.CreateEnumerable<MatrixElement>(transformedData, reuseRowObject: false).Take(5).ToList();
 var predictions = mlContext.Data
 .CreateEnumerable<MatrixElement>(transformedData,
 reuseRowObject: false).Take(5).ToList();

 // Look at 5 predictions for the Label, side by side with the actual Label for comparison.
 // Look at 5 predictions for the Label, side by side with the actual
 // Label for comparison.
 foreach (var p in predictions)
 Console.WriteLine($"Actual value: {p.Value:F3}, Predicted score: {p.Score:F3}");
 Console.WriteLine($"Actual value: {p.Value:F3}," +
 $"Predicted score: {p.Score:F3}");

 <#=ExpectedOutputPerInstance#>

 // Evaluate the overall metrics
 var metrics = mlContext.Regression.Evaluate(transformedData, labelColumnName: nameof(MatrixElement.Value), scoreColumnName: nameof(MatrixElement.Score));
 var metrics = mlContext.Regression.Evaluate(transformedData,
 labelColumnName: nameof(MatrixElement.Value),
 scoreColumnName: nameof(MatrixElement.Score));

 PrintMetrics(metrics);

 <#=ExpectedOutput#>
 }

 // The following variables are used to define the shape of the example matrix. Its shape is MatrixRowCount-by-MatrixColumnCount.
 // 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
 // and MatrixRowIndex=0 in MatrixElement below specifies the value at the upper-left corner in the training matrix). If user's row index
 // 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.
 // This behavior is also true to column index.
 // The following variables are used to define the shape of the example
 // matrix. Its shape is MatrixRowCount-by-MatrixColumnCount. 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 and MatrixRowIndex=0
 // in MatrixElement below specifies the value at the upper-left corner in
 // the training matrix). If user's row index 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. This behavior is also true to column
 // index.
 private const uint MatrixColumnCount = 60;
 private const uint MatrixRowCount = 100;

Expand All@@ -74,32 +88,40 @@ namespace Samples.Dynamic.Trainers.Recommendation
 var dataMatrix = new List<MatrixElement>();
 for (uint i = 0; i < MatrixColumnCount; ++i)
 for (uint j = 0; j < MatrixRowCount; ++j)
 dataMatrix.Add(new MatrixElement() { MatrixColumnIndex = i, MatrixRowIndex = j, Value = (i + j) % 5 });
 dataMatrix.Add(new MatrixElement() { MatrixColumnIndex = i,
 MatrixRowIndex = j, Value = (i + j) % 5 });

 return dataMatrix;
 }

 // A class used to define a matrix element and capture its prediction result.
 // A class used to define a matrix element and capture its prediction
 // result.
 private class MatrixElement
 {
 // Matrix column index. Its allowed range is from 0 to MatrixColumnCount - 1.
 // Matrix column index. Its allowed range is from 0 to
 // MatrixColumnCount - 1.
 [KeyType(MatrixColumnCount)]
 public uint MatrixColumnIndex { get; set; }
 // Matrix row index. Its allowed range is from 0 to MatrixRowCount - 1.
 [KeyType(MatrixRowCount)]
 public uint MatrixRowIndex { get; set; }
 // The actual value at the MatrixColumnIndex-th column and the MatrixRowIndex-th row.
 // The actual value at the MatrixColumnIndex-th column and the
 // MatrixRowIndex-th row.
 public float Value { get; set; }
 // The predicted value at the MatrixColumnIndex-th column and the MatrixRowIndex-th row.
 // The predicted value at the MatrixColumnIndex-th column and the
 // MatrixRowIndex-th row.
 public float Score { get; set; }
 }

 // Print some evaluation metrics to regression problems.
 private static void PrintMetrics(RegressionMetrics metrics)
 {
 Console.WriteLine($"Mean Absolute Error: {metrics.MeanAbsoluteError:F2}");
 Console.WriteLine($"Mean Squared Error: {metrics.MeanSquaredError:F2}");
 Console.WriteLine($"Root Mean Squared Error: {metrics.RootMeanSquaredError:F2}");
 Console.WriteLine($"RSquared: {metrics.RSquared:F2}");
 Console.WriteLine("Mean Absolute Error: " + metrics.MeanAbsoluteError);
 Console.WriteLine("Mean Squared Error: " + metrics.MeanSquaredError);
 Console.WriteLine("Root Mean Squared Error: " +
 metrics.RootMeanSquaredError);

 Console.WriteLine("RSquared: " + metrics.RSquared);
 }
 }
}
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if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
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Reformatted Recommendation samples


sayanshaw24 Jul 1, 2019
3bbb568

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sayanshaw24 Jul 1, 2019
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80 changes: 53 additions & 27 deletions
docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Recommendation/MatrixFactorization.cs

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Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -9,25 +9,32 @@ namespace Samples.Dynamic.Trainers.Recommendation
 public static class MatrixFactorization
 {

 // This example requires installation of additional nuget package <a href="https://www.nuget.org/packages/Microsoft.ML.Recommender/">Microsoft.ML.Recommender</a>.
 // In this example we will create in-memory data and then use it to train 
 // a matrix factorization model with default parameters. Afterward, quality metrics are reported.
 // This example requires installation of additional nuget package at
 // for Microsoft.ML.Recommender at
 // https://www.nuget.org/packages/Microsoft.ML.Recommender/
 // In this example we will create in-memory data and then use it to train
 // a matrix factorization model with default parameters. Afterward, quality
 // metrics are reported.
 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.
 // Setting the seed to a fixed number in this example to make outputs deterministic.
 // 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. Setting the seed to a fixed number
 // in this example to make outputs deterministic.
 var mlContext = new MLContext(seed: 0);

 // Create a list of training data points.
 var dataPoints = GenerateMatrix();

 // Convert the list of data points to an IDataView object, which is consumable by ML.NET API.
 // Convert the list of data points to an IDataView object, which is
 // consumable by ML.NET API.
 var trainingData = mlContext.Data.LoadFromEnumerable(dataPoints);

 // Define the trainer.
 var pipeline = mlContext.Recommendation().Trainers.MatrixFactorization(nameof(MatrixElement.Value), nameof(MatrixElement.MatrixColumnIndex),
 nameof(MatrixElement.MatrixRowIndex), 10, 0.2, 1);
 var pipeline = mlContext.Recommendation().Trainers.
 MatrixFactorization(nameof(MatrixElement.Value),
 nameof(MatrixElement.MatrixColumnIndex),
@sharwellsharwellJul 2, 2019

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📝 This is incorrectly indented
 nameof(MatrixElement.MatrixRowIndex), 10, 0.2, 1);

 // Train the model.
 var model = pipeline.Fit(trainingData);
Expand All@@ -36,11 +43,15 @@ public static void Example()
 var transformedData = model.Transform(trainingData);

 // Convert IDataView object to a list.
 var predictions = mlContext.Data.CreateEnumerable<MatrixElement>(transformedData, reuseRowObject: false).Take(5).ToList();
 var predictions = mlContext.Data
 .CreateEnumerable<MatrixElement>(transformedData,
 reuseRowObject: false).Take(5).ToList();
@sharwellsharwellJul 2, 2019•

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📝 This layout is very difficult to read. The following would be preferable:
varpredictions=mlContext.Data.CreateEnumerable<MatrixElement>(transformedData,reuseRowObject:false).Take(5).ToList();

 // Look at 5 predictions for the Label, side by side with the actual Label for comparison.
 // Look at 5 predictions for the Label, side by side with the actual
 // Label for comparison.
 foreach (var p in predictions)
 Console.WriteLine($"Actual value: {p.Value:F3}, Predicted score: {p.Score:F3}");
 Console.WriteLine($"Actual value: {p.Value:F3}," +
 $"Predicted score: {p.Score:F3}");

 // Expected output:
 // Actual value: 0.000, Predicted score: 1.234
Expand All@@ -50,7 +61,10 @@ public static void Example()
 // Actual value: 4.000, Predicted score: 2.362

 // Evaluate the overall metrics
 var metrics = mlContext.Regression.Evaluate(transformedData, labelColumnName: nameof(MatrixElement.Value), scoreColumnName: nameof(MatrixElement.Score));
 var metrics = mlContext.Regression.Evaluate(transformedData,
 labelColumnName: nameof(MatrixElement.Value),
 scoreColumnName: nameof(MatrixElement.Score));

 PrintMetrics(metrics);

 // Expected output:
Expand All@@ -60,11 +74,15 @@ public static void Example()
 // RSquared: 0.61 (closer to 1 is better. The worest case is 0)
 }

 // The following variables are used to define the shape of the example matrix. Its shape is MatrixRowCount-by-MatrixColumnCount.
 // 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
 // and MatrixRowIndex=0 in MatrixElement below specifies the value at the upper-left corner in the training matrix). If user's row index
 // 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.
 // This behavior is also true to column index.
 // The following variables are used to define the shape of the example
 // matrix. Its shape is MatrixRowCount-by-MatrixColumnCount. 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 and MatrixRowIndex=0
 // in MatrixElement below specifies the value at the upper-left corner in
 // the training matrix). If user's row index 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. This behavior is also true to column
 // index.
 private const uint MatrixColumnCount = 60;
 private const uint MatrixRowCount = 100;

Expand All@@ -74,32 +92,40 @@ private static List<MatrixElement> GenerateMatrix()
 var dataMatrix = new List<MatrixElement>();
 for (uint i = 0; i < MatrixColumnCount; ++i)
 for (uint j = 0; j < MatrixRowCount; ++j)
 dataMatrix.Add(new MatrixElement() { MatrixColumnIndex = i, MatrixRowIndex = j, Value = (i + j) % 5 });
 dataMatrix.Add(new MatrixElement() { MatrixColumnIndex = i,
 MatrixRowIndex = j, Value = (i + j) % 5 });
@sharwellsharwellJul 2, 2019

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📝 Incorrect line wrapping and indentation

@wschinwschinJul 1, 2019

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Empty line. Is it produced by an auto-formatting tool?
@sayanshaw24sayanshaw24Jul 2, 2019

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Actually, Zeeshan asked us to do that, so that's what we've been doing as a norm.
 return dataMatrix;
 }

 // A class used to define a matrix element and capture its prediction result.
 // A class used to define a matrix element and capture its prediction
 // result.
 private class MatrixElement
 {
 // Matrix column index. Its allowed range is from 0 to MatrixColumnCount - 1.
 // Matrix column index. Its allowed range is from 0 to
 // MatrixColumnCount - 1.
 [KeyType(MatrixColumnCount)]
 public uint MatrixColumnIndex { get; set; }
 // Matrix row index. Its allowed range is from 0 to MatrixRowCount - 1.
 [KeyType(MatrixRowCount)]
 public uint MatrixRowIndex { get; set; }
 // The actual value at the MatrixColumnIndex-th column and the MatrixRowIndex-th row.
 // The actual value at the MatrixColumnIndex-th column and the
 // MatrixRowIndex-th row.
 public float Value { get; set; }
 // The predicted value at the MatrixColumnIndex-th column and the MatrixRowIndex-th row.
 // The predicted value at the MatrixColumnIndex-th column and the
 // MatrixRowIndex-th row.
 public float Score { get; set; }
 }

 // Print some evaluation metrics to regression problems.
 private static void PrintMetrics(RegressionMetrics metrics)
 {
 Console.WriteLine($"Mean Absolute Error: {metrics.MeanAbsoluteError:F2}");
 Console.WriteLine($"Mean Squared Error: {metrics.MeanSquaredError:F2}");
 Console.WriteLine($"Root Mean Squared Error: {metrics.RootMeanSquaredError:F2}");
 Console.WriteLine($"RSquared: {metrics.RSquared:F2}");
 Console.WriteLine("Mean Absolute Error: " + metrics.MeanAbsoluteError);
 Console.WriteLine("Mean Squared Error: " + metrics.MeanSquaredError);
 Console.WriteLine("Root Mean Squared Error: " +
 metrics.RootMeanSquaredError);

 Console.WriteLine("RSquared: " + metrics.RSquared);
 }
 }
}
Expand Down

17 changes: 11 additions & 6 deletions
docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Recommendation/MatrixFactorization.tt

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -2,13 +2,18 @@

<#+
string ClassHeader = @"
 // This example requires installation of additional nuget package <a href=""https://www.nuget.org/packages/Microsoft.ML.Recommender/"">Microsoft.ML.Recommender</a>.
 // In this example we will create in-memory data and then use it to train 
 // a matrix factorization model with default parameters. Afterward, quality metrics are reported.";
 // This example requires installation of additional nuget package at
 // for Microsoft.ML.Recommender at
 // https://www.nuget.org/packages/Microsoft.ML.Recommender/
 // In this example we will create in-memory data and then use it to train
 // a matrix factorization model with default parameters. Afterward, quality
 // metrics are reported.";
string ClassName="MatrixFactorization";
string ExtraUsing = null;
string Trainer = @"MatrixFactorization(nameof(MatrixElement.Value), nameof(MatrixElement.MatrixColumnIndex),
 nameof(MatrixElement.MatrixRowIndex), 10, 0.2, 1)";
string Trainer = @"
 MatrixFactorization(nameof(MatrixElement.Value),
 nameof(MatrixElement.MatrixColumnIndex),
 nameof(MatrixElement.MatrixRowIndex), 10, 0.2, 1)";
string TrainerOptions = null;

string ExpectedOutputPerInstance= @"// Expected output:
Expand All@@ -23,4 +28,4 @@ string ExpectedOutput = @"// Expected output:
 // Mean Squared Error: 0.79
 // Root Mean Squared Error: 0.89
 // RSquared: 0.61 (closer to 1 is better. The worest case is 0)";
#>
#>

68 changes: 45 additions & 23 deletions
...icrosoft.ML.Samples/Dynamic/Trainers/Recommendation/MatrixFactorizationTemplate.ttinclude

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -16,15 +16,17 @@ namespace Samples.Dynamic.Trainers.Recommendation
<# } #>
 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.
 // Setting the seed to a fixed number in this example to make outputs deterministic.
 // 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. Setting the seed to a fixed number
 // in this example to make outputs deterministic.
 var mlContext = new MLContext(seed: 0);

 // Create a list of training data points.
 var dataPoints = GenerateMatrix();

 // Convert the list of data points to an IDataView object, which is consumable by ML.NET API.
 // Convert the list of data points to an IDataView object, which is
 // consumable by ML.NET API.
 var trainingData = mlContext.Data.LoadFromEnumerable(dataPoints);

<# if (TrainerOptions == null) { #>
Expand All@@ -35,7 +37,8 @@ namespace Samples.Dynamic.Trainers.Recommendation
 var options = new <#=TrainerOptions#>;

 // Define the trainer.
 var pipeline = mlContext.Recommendation().Trainers.<#=Trainer#>(options);
 var pipeline = mlContext.Recommendation().Trainers.<#=Trainer#>(
 options);
<# } #>

 // Train the model.
Expand All@@ -45,26 +48,37 @@ namespace Samples.Dynamic.Trainers.Recommendation
 var transformedData = model.Transform(trainingData);

 // Convert IDataView object to a list.
 var predictions = mlContext.Data.CreateEnumerable<MatrixElement>(transformedData, reuseRowObject: false).Take(5).ToList();
 var predictions = mlContext.Data
 .CreateEnumerable<MatrixElement>(transformedData,
 reuseRowObject: false).Take(5).ToList();

 // Look at 5 predictions for the Label, side by side with the actual Label for comparison.
 // Look at 5 predictions for the Label, side by side with the actual
 // Label for comparison.
 foreach (var p in predictions)
 Console.WriteLine($"Actual value: {p.Value:F3}, Predicted score: {p.Score:F3}");
 Console.WriteLine($"Actual value: {p.Value:F3}," +
 $"Predicted score: {p.Score:F3}");

 <#=ExpectedOutputPerInstance#>

 // Evaluate the overall metrics
 var metrics = mlContext.Regression.Evaluate(transformedData, labelColumnName: nameof(MatrixElement.Value), scoreColumnName: nameof(MatrixElement.Score));
 var metrics = mlContext.Regression.Evaluate(transformedData,
 labelColumnName: nameof(MatrixElement.Value),
 scoreColumnName: nameof(MatrixElement.Score));

 PrintMetrics(metrics);

 <#=ExpectedOutput#>
 }

 // The following variables are used to define the shape of the example matrix. Its shape is MatrixRowCount-by-MatrixColumnCount.
 // 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
 // and MatrixRowIndex=0 in MatrixElement below specifies the value at the upper-left corner in the training matrix). If user's row index
 // 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.
 // This behavior is also true to column index.
 // The following variables are used to define the shape of the example
 // matrix. Its shape is MatrixRowCount-by-MatrixColumnCount. 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 and MatrixRowIndex=0
 // in MatrixElement below specifies the value at the upper-left corner in
 // the training matrix). If user's row index 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. This behavior is also true to column
 // index.
 private const uint MatrixColumnCount = 60;
 private const uint MatrixRowCount = 100;

Expand All@@ -74,32 +88,40 @@ namespace Samples.Dynamic.Trainers.Recommendation
 var dataMatrix = new List<MatrixElement>();
 for (uint i = 0; i < MatrixColumnCount; ++i)
 for (uint j = 0; j < MatrixRowCount; ++j)
 dataMatrix.Add(new MatrixElement() { MatrixColumnIndex = i, MatrixRowIndex = j, Value = (i + j) % 5 });
 dataMatrix.Add(new MatrixElement() { MatrixColumnIndex = i,
 MatrixRowIndex = j, Value = (i + j) % 5 });

 return dataMatrix;
 }

 // A class used to define a matrix element and capture its prediction result.
 // A class used to define a matrix element and capture its prediction
 // result.
 private class MatrixElement
 {
 // Matrix column index. Its allowed range is from 0 to MatrixColumnCount - 1.
 // Matrix column index. Its allowed range is from 0 to
 // MatrixColumnCount - 1.
 [KeyType(MatrixColumnCount)]
 public uint MatrixColumnIndex { get; set; }
 // Matrix row index. Its allowed range is from 0 to MatrixRowCount - 1.
 [KeyType(MatrixRowCount)]
 public uint MatrixRowIndex { get; set; }
 // The actual value at the MatrixColumnIndex-th column and the MatrixRowIndex-th row.
 // The actual value at the MatrixColumnIndex-th column and the
 // MatrixRowIndex-th row.
 public float Value { get; set; }
 // The predicted value at the MatrixColumnIndex-th column and the MatrixRowIndex-th row.
 // The predicted value at the MatrixColumnIndex-th column and the
 // MatrixRowIndex-th row.
 public float Score { get; set; }
 }

 // Print some evaluation metrics to regression problems.
 private static void PrintMetrics(RegressionMetrics metrics)
 {
 Console.WriteLine($"Mean Absolute Error: {metrics.MeanAbsoluteError:F2}");
 Console.WriteLine($"Mean Squared Error: {metrics.MeanSquaredError:F2}");
 Console.WriteLine($"Root Mean Squared Error: {metrics.RootMeanSquaredError:F2}");
 Console.WriteLine($"RSquared: {metrics.RSquared:F2}");
 Console.WriteLine("Mean Absolute Error: " + metrics.MeanAbsoluteError);
 Console.WriteLine("Mean Squared Error: " + metrics.MeanSquaredError);
 Console.WriteLine("Root Mean Squared Error: " +
 metrics.RootMeanSquaredError);

 Console.WriteLine("RSquared: " + metrics.RSquared);
 }
 }
}
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var __m = "*";
var __re = new RegExp('^' + ".*" + '

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80 changes: 53 additions & 27 deletions
docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Recommendation/MatrixFactorization.cs

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Original file line numberDiff line numberDiff line change
Expand Up@@ -9,25 +9,32 @@ namespace Samples.Dynamic.Trainers.Recommendation
 public static class MatrixFactorization
 {

 // This example requires installation of additional nuget package <a href="https://www.nuget.org/packages/Microsoft.ML.Recommender/">Microsoft.ML.Recommender</a>.
 // In this example we will create in-memory data and then use it to train 
 // a matrix factorization model with default parameters. Afterward, quality metrics are reported.
 // This example requires installation of additional nuget package at
 // for Microsoft.ML.Recommender at
 // https://www.nuget.org/packages/Microsoft.ML.Recommender/
 // In this example we will create in-memory data and then use it to train
 // a matrix factorization model with default parameters. Afterward, quality
 // metrics are reported.
 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.
 // Setting the seed to a fixed number in this example to make outputs deterministic.
 // 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. Setting the seed to a fixed number
 // in this example to make outputs deterministic.
 var mlContext = new MLContext(seed: 0);

 // Create a list of training data points.
 var dataPoints = GenerateMatrix();

 // Convert the list of data points to an IDataView object, which is consumable by ML.NET API.
 // Convert the list of data points to an IDataView object, which is
 // consumable by ML.NET API.
 var trainingData = mlContext.Data.LoadFromEnumerable(dataPoints);

 // Define the trainer.
 var pipeline = mlContext.Recommendation().Trainers.MatrixFactorization(nameof(MatrixElement.Value), nameof(MatrixElement.MatrixColumnIndex),
 nameof(MatrixElement.MatrixRowIndex), 10, 0.2, 1);
 var pipeline = mlContext.Recommendation().Trainers.
 MatrixFactorization(nameof(MatrixElement.Value),
 nameof(MatrixElement.MatrixColumnIndex),
@sharwellsharwellJul 2, 2019

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📝 This is incorrectly indented
 nameof(MatrixElement.MatrixRowIndex), 10, 0.2, 1);

 // Train the model.
 var model = pipeline.Fit(trainingData);
Expand All@@ -36,11 +43,15 @@ public static void Example()
 var transformedData = model.Transform(trainingData);

 // Convert IDataView object to a list.
 var predictions = mlContext.Data.CreateEnumerable<MatrixElement>(transformedData, reuseRowObject: false).Take(5).ToList();
 var predictions = mlContext.Data
 .CreateEnumerable<MatrixElement>(transformedData,
 reuseRowObject: false).Take(5).ToList();
@sharwellsharwellJul 2, 2019•

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📝 This layout is very difficult to read. The following would be preferable:
varpredictions=mlContext.Data.CreateEnumerable<MatrixElement>(transformedData,reuseRowObject:false).Take(5).ToList();

 // Look at 5 predictions for the Label, side by side with the actual Label for comparison.
 // Look at 5 predictions for the Label, side by side with the actual
 // Label for comparison.
 foreach (var p in predictions)
 Console.WriteLine($"Actual value: {p.Value:F3}, Predicted score: {p.Score:F3}");
 Console.WriteLine($"Actual value: {p.Value:F3}," +
 $"Predicted score: {p.Score:F3}");

 // Expected output:
 // Actual value: 0.000, Predicted score: 1.234
Expand All@@ -50,7 +61,10 @@ public static void Example()
 // Actual value: 4.000, Predicted score: 2.362

 // Evaluate the overall metrics
 var metrics = mlContext.Regression.Evaluate(transformedData, labelColumnName: nameof(MatrixElement.Value), scoreColumnName: nameof(MatrixElement.Score));
 var metrics = mlContext.Regression.Evaluate(transformedData,
 labelColumnName: nameof(MatrixElement.Value),
 scoreColumnName: nameof(MatrixElement.Score));

 PrintMetrics(metrics);

 // Expected output:
Expand All@@ -60,11 +74,15 @@ public static void Example()
 // RSquared: 0.61 (closer to 1 is better. The worest case is 0)
 }

 // The following variables are used to define the shape of the example matrix. Its shape is MatrixRowCount-by-MatrixColumnCount.
 // 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
 // and MatrixRowIndex=0 in MatrixElement below specifies the value at the upper-left corner in the training matrix). If user's row index
 // 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.
 // This behavior is also true to column index.
 // The following variables are used to define the shape of the example
 // matrix. Its shape is MatrixRowCount-by-MatrixColumnCount. 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 and MatrixRowIndex=0
 // in MatrixElement below specifies the value at the upper-left corner in
 // the training matrix). If user's row index 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. This behavior is also true to column
 // index.
 private const uint MatrixColumnCount = 60;
 private const uint MatrixRowCount = 100;

Expand All@@ -74,32 +92,40 @@ private static List<MatrixElement> GenerateMatrix()
 var dataMatrix = new List<MatrixElement>();
 for (uint i = 0; i < MatrixColumnCount; ++i)
 for (uint j = 0; j < MatrixRowCount; ++j)
 dataMatrix.Add(new MatrixElement() { MatrixColumnIndex = i, MatrixRowIndex = j, Value = (i + j) % 5 });
 dataMatrix.Add(new MatrixElement() { MatrixColumnIndex = i,
 MatrixRowIndex = j, Value = (i + j) % 5 });
@sharwellsharwellJul 2, 2019

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📝 Incorrect line wrapping and indentation

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Empty line. Is it produced by an auto-formatting tool?
@sayanshaw24sayanshaw24Jul 2, 2019

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Actually, Zeeshan asked us to do that, so that's what we've been doing as a norm.
 return dataMatrix;
 }

 // A class used to define a matrix element and capture its prediction result.
 // A class used to define a matrix element and capture its prediction
 // result.
 private class MatrixElement
 {
 // Matrix column index. Its allowed range is from 0 to MatrixColumnCount - 1.
 // Matrix column index. Its allowed range is from 0 to
 // MatrixColumnCount - 1.
 [KeyType(MatrixColumnCount)]
 public uint MatrixColumnIndex { get; set; }
 // Matrix row index. Its allowed range is from 0 to MatrixRowCount - 1.
 [KeyType(MatrixRowCount)]
 public uint MatrixRowIndex { get; set; }
 // The actual value at the MatrixColumnIndex-th column and the MatrixRowIndex-th row.
 // The actual value at the MatrixColumnIndex-th column and the
 // MatrixRowIndex-th row.
 public float Value { get; set; }
 // The predicted value at the MatrixColumnIndex-th column and the MatrixRowIndex-th row.
 // The predicted value at the MatrixColumnIndex-th column and the
 // MatrixRowIndex-th row.
 public float Score { get; set; }
 }

 // Print some evaluation metrics to regression problems.
 private static void PrintMetrics(RegressionMetrics metrics)
 {
 Console.WriteLine($"Mean Absolute Error: {metrics.MeanAbsoluteError:F2}");
 Console.WriteLine($"Mean Squared Error: {metrics.MeanSquaredError:F2}");
 Console.WriteLine($"Root Mean Squared Error: {metrics.RootMeanSquaredError:F2}");
 Console.WriteLine($"RSquared: {metrics.RSquared:F2}");
 Console.WriteLine("Mean Absolute Error: " + metrics.MeanAbsoluteError);
 Console.WriteLine("Mean Squared Error: " + metrics.MeanSquaredError);
 Console.WriteLine("Root Mean Squared Error: " +
 metrics.RootMeanSquaredError);

 Console.WriteLine("RSquared: " + metrics.RSquared);
 }
 }
}
Expand Down

17 changes: 11 additions & 6 deletions
docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Recommendation/MatrixFactorization.tt

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -2,13 +2,18 @@

<#+
string ClassHeader = @"
 // This example requires installation of additional nuget package <a href=""https://www.nuget.org/packages/Microsoft.ML.Recommender/"">Microsoft.ML.Recommender</a>.
 // In this example we will create in-memory data and then use it to train 
 // a matrix factorization model with default parameters. Afterward, quality metrics are reported.";
 // This example requires installation of additional nuget package at
 // for Microsoft.ML.Recommender at
 // https://www.nuget.org/packages/Microsoft.ML.Recommender/
 // In this example we will create in-memory data and then use it to train
 // a matrix factorization model with default parameters. Afterward, quality
 // metrics are reported.";
string ClassName="MatrixFactorization";
string ExtraUsing = null;
string Trainer = @"MatrixFactorization(nameof(MatrixElement.Value), nameof(MatrixElement.MatrixColumnIndex),
 nameof(MatrixElement.MatrixRowIndex), 10, 0.2, 1)";
string Trainer = @"
 MatrixFactorization(nameof(MatrixElement.Value),
 nameof(MatrixElement.MatrixColumnIndex),
 nameof(MatrixElement.MatrixRowIndex), 10, 0.2, 1)";
string TrainerOptions = null;

string ExpectedOutputPerInstance= @"// Expected output:
Expand All@@ -23,4 +28,4 @@ string ExpectedOutput = @"// Expected output:
 // Mean Squared Error: 0.79
 // Root Mean Squared Error: 0.89
 // RSquared: 0.61 (closer to 1 is better. The worest case is 0)";
#>
#>

68 changes: 45 additions & 23 deletions
...icrosoft.ML.Samples/Dynamic/Trainers/Recommendation/MatrixFactorizationTemplate.ttinclude

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Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -16,15 +16,17 @@ namespace Samples.Dynamic.Trainers.Recommendation
<# } #>
 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.
 // Setting the seed to a fixed number in this example to make outputs deterministic.
 // 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. Setting the seed to a fixed number
 // in this example to make outputs deterministic.
 var mlContext = new MLContext(seed: 0);

 // Create a list of training data points.
 var dataPoints = GenerateMatrix();

 // Convert the list of data points to an IDataView object, which is consumable by ML.NET API.
 // Convert the list of data points to an IDataView object, which is
 // consumable by ML.NET API.
 var trainingData = mlContext.Data.LoadFromEnumerable(dataPoints);

<# if (TrainerOptions == null) { #>
Expand All@@ -35,7 +37,8 @@ namespace Samples.Dynamic.Trainers.Recommendation
 var options = new <#=TrainerOptions#>;

 // Define the trainer.
 var pipeline = mlContext.Recommendation().Trainers.<#=Trainer#>(options);
 var pipeline = mlContext.Recommendation().Trainers.<#=Trainer#>(
 options);
<# } #>

 // Train the model.
Expand All@@ -45,26 +48,37 @@ namespace Samples.Dynamic.Trainers.Recommendation
 var transformedData = model.Transform(trainingData);

 // Convert IDataView object to a list.
 var predictions = mlContext.Data.CreateEnumerable<MatrixElement>(transformedData, reuseRowObject: false).Take(5).ToList();
 var predictions = mlContext.Data
 .CreateEnumerable<MatrixElement>(transformedData,
 reuseRowObject: false).Take(5).ToList();

 // Look at 5 predictions for the Label, side by side with the actual Label for comparison.
 // Look at 5 predictions for the Label, side by side with the actual
 // Label for comparison.
 foreach (var p in predictions)
 Console.WriteLine($"Actual value: {p.Value:F3}, Predicted score: {p.Score:F3}");
 Console.WriteLine($"Actual value: {p.Value:F3}," +
 $"Predicted score: {p.Score:F3}");

 <#=ExpectedOutputPerInstance#>

 // Evaluate the overall metrics
 var metrics = mlContext.Regression.Evaluate(transformedData, labelColumnName: nameof(MatrixElement.Value), scoreColumnName: nameof(MatrixElement.Score));
 var metrics = mlContext.Regression.Evaluate(transformedData,
 labelColumnName: nameof(MatrixElement.Value),
 scoreColumnName: nameof(MatrixElement.Score));

 PrintMetrics(metrics);

 <#=ExpectedOutput#>
 }

 // The following variables are used to define the shape of the example matrix. Its shape is MatrixRowCount-by-MatrixColumnCount.
 // 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
 // and MatrixRowIndex=0 in MatrixElement below specifies the value at the upper-left corner in the training matrix). If user's row index
 // 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.
 // This behavior is also true to column index.
 // The following variables are used to define the shape of the example
 // matrix. Its shape is MatrixRowCount-by-MatrixColumnCount. 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 and MatrixRowIndex=0
 // in MatrixElement below specifies the value at the upper-left corner in
 // the training matrix). If user's row index 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. This behavior is also true to column
 // index.
 private const uint MatrixColumnCount = 60;
 private const uint MatrixRowCount = 100;

Expand All@@ -74,32 +88,40 @@ namespace Samples.Dynamic.Trainers.Recommendation
 var dataMatrix = new List<MatrixElement>();
 for (uint i = 0; i < MatrixColumnCount; ++i)
 for (uint j = 0; j < MatrixRowCount; ++j)
 dataMatrix.Add(new MatrixElement() { MatrixColumnIndex = i, MatrixRowIndex = j, Value = (i + j) % 5 });
 dataMatrix.Add(new MatrixElement() { MatrixColumnIndex = i,
 MatrixRowIndex = j, Value = (i + j) % 5 });

 return dataMatrix;
 }

 // A class used to define a matrix element and capture its prediction result.
 // A class used to define a matrix element and capture its prediction
 // result.
 private class MatrixElement
 {
 // Matrix column index. Its allowed range is from 0 to MatrixColumnCount - 1.
 // Matrix column index. Its allowed range is from 0 to
 // MatrixColumnCount - 1.
 [KeyType(MatrixColumnCount)]
 public uint MatrixColumnIndex { get; set; }
 // Matrix row index. Its allowed range is from 0 to MatrixRowCount - 1.
 [KeyType(MatrixRowCount)]
 public uint MatrixRowIndex { get; set; }
 // The actual value at the MatrixColumnIndex-th column and the MatrixRowIndex-th row.
 // The actual value at the MatrixColumnIndex-th column and the
 // MatrixRowIndex-th row.
 public float Value { get; set; }
 // The predicted value at the MatrixColumnIndex-th column and the MatrixRowIndex-th row.
 // The predicted value at the MatrixColumnIndex-th column and the
 // MatrixRowIndex-th row.
 public float Score { get; set; }
 }

 // Print some evaluation metrics to regression problems.
 private static void PrintMetrics(RegressionMetrics metrics)
 {
 Console.WriteLine($"Mean Absolute Error: {metrics.MeanAbsoluteError:F2}");
 Console.WriteLine($"Mean Squared Error: {metrics.MeanSquaredError:F2}");
 Console.WriteLine($"Root Mean Squared Error: {metrics.RootMeanSquaredError:F2}");
 Console.WriteLine($"RSquared: {metrics.RSquared:F2}");
 Console.WriteLine("Mean Absolute Error: " + metrics.MeanAbsoluteError);
 Console.WriteLine("Mean Squared Error: " + metrics.MeanSquaredError);
 Console.WriteLine("Root Mean Squared Error: " +
 metrics.RootMeanSquaredError);

 Console.WriteLine("RSquared: " + metrics.RSquared);
 }
 }
}
Loading

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

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Reformatted Recommendation samples to width 85
#3941
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13b798f

Reformatted Recommendation samples


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Fix for tab spacing errors


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4715016

Removed precision for reformatting in Console.WriteLine and fixed Key…


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80 changes: 53 additions & 27 deletions
docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Recommendation/MatrixFactorization.cs

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Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -9,25 +9,32 @@ namespace Samples.Dynamic.Trainers.Recommendation
 public static class MatrixFactorization
 {

 // This example requires installation of additional nuget package <a href="https://www.nuget.org/packages/Microsoft.ML.Recommender/">Microsoft.ML.Recommender</a>.
 // In this example we will create in-memory data and then use it to train 
 // a matrix factorization model with default parameters. Afterward, quality metrics are reported.
 // This example requires installation of additional nuget package at
 // for Microsoft.ML.Recommender at
 // https://www.nuget.org/packages/Microsoft.ML.Recommender/
 // In this example we will create in-memory data and then use it to train
 // a matrix factorization model with default parameters. Afterward, quality
 // metrics are reported.
 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.
 // Setting the seed to a fixed number in this example to make outputs deterministic.
 // 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. Setting the seed to a fixed number
 // in this example to make outputs deterministic.
 var mlContext = new MLContext(seed: 0);

 // Create a list of training data points.
 var dataPoints = GenerateMatrix();

 // Convert the list of data points to an IDataView object, which is consumable by ML.NET API.
 // Convert the list of data points to an IDataView object, which is
 // consumable by ML.NET API.
 var trainingData = mlContext.Data.LoadFromEnumerable(dataPoints);

 // Define the trainer.
 var pipeline = mlContext.Recommendation().Trainers.MatrixFactorization(nameof(MatrixElement.Value), nameof(MatrixElement.MatrixColumnIndex),
 nameof(MatrixElement.MatrixRowIndex), 10, 0.2, 1);
 var pipeline = mlContext.Recommendation().Trainers.
 MatrixFactorization(nameof(MatrixElement.Value),
 nameof(MatrixElement.MatrixColumnIndex),
@sharwellsharwellJul 2, 2019

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📝 This is incorrectly indented
 nameof(MatrixElement.MatrixRowIndex), 10, 0.2, 1);

 // Train the model.
 var model = pipeline.Fit(trainingData);
Expand All@@ -36,11 +43,15 @@ public static void Example()
 var transformedData = model.Transform(trainingData);

 // Convert IDataView object to a list.
 var predictions = mlContext.Data.CreateEnumerable<MatrixElement>(transformedData, reuseRowObject: false).Take(5).ToList();
 var predictions = mlContext.Data
 .CreateEnumerable<MatrixElement>(transformedData,
 reuseRowObject: false).Take(5).ToList();
@sharwellsharwellJul 2, 2019•

edited

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📝 This layout is very difficult to read. The following would be preferable:
varpredictions=mlContext.Data.CreateEnumerable<MatrixElement>(transformedData,reuseRowObject:false).Take(5).ToList();

 // Look at 5 predictions for the Label, side by side with the actual Label for comparison.
 // Look at 5 predictions for the Label, side by side with the actual
 // Label for comparison.
 foreach (var p in predictions)
 Console.WriteLine($"Actual value: {p.Value:F3}, Predicted score: {p.Score:F3}");
 Console.WriteLine($"Actual value: {p.Value:F3}," +
 $"Predicted score: {p.Score:F3}");

 // Expected output:
 // Actual value: 0.000, Predicted score: 1.234
Expand All@@ -50,7 +61,10 @@ public static void Example()
 // Actual value: 4.000, Predicted score: 2.362

 // Evaluate the overall metrics
 var metrics = mlContext.Regression.Evaluate(transformedData, labelColumnName: nameof(MatrixElement.Value), scoreColumnName: nameof(MatrixElement.Score));
 var metrics = mlContext.Regression.Evaluate(transformedData,
 labelColumnName: nameof(MatrixElement.Value),
 scoreColumnName: nameof(MatrixElement.Score));

 PrintMetrics(metrics);

 // Expected output:
Expand All@@ -60,11 +74,15 @@ public static void Example()
 // RSquared: 0.61 (closer to 1 is better. The worest case is 0)
 }

 // The following variables are used to define the shape of the example matrix. Its shape is MatrixRowCount-by-MatrixColumnCount.
 // 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
 // and MatrixRowIndex=0 in MatrixElement below specifies the value at the upper-left corner in the training matrix). If user's row index
 // 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.
 // This behavior is also true to column index.
 // The following variables are used to define the shape of the example
 // matrix. Its shape is MatrixRowCount-by-MatrixColumnCount. 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 and MatrixRowIndex=0
 // in MatrixElement below specifies the value at the upper-left corner in
 // the training matrix). If user's row index 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. This behavior is also true to column
 // index.
 private const uint MatrixColumnCount = 60;
 private const uint MatrixRowCount = 100;

Expand All@@ -74,32 +92,40 @@ private static List<MatrixElement> GenerateMatrix()
 var dataMatrix = new List<MatrixElement>();
 for (uint i = 0; i < MatrixColumnCount; ++i)
 for (uint j = 0; j < MatrixRowCount; ++j)
 dataMatrix.Add(new MatrixElement() { MatrixColumnIndex = i, MatrixRowIndex = j, Value = (i + j) % 5 });
 dataMatrix.Add(new MatrixElement() { MatrixColumnIndex = i,
 MatrixRowIndex = j, Value = (i + j) % 5 });
@sharwellsharwellJul 2, 2019

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📝 Incorrect line wrapping and indentation

@wschinwschinJul 1, 2019

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Empty line. Is it produced by an auto-formatting tool?
@sayanshaw24sayanshaw24Jul 2, 2019

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Actually, Zeeshan asked us to do that, so that's what we've been doing as a norm.
 return dataMatrix;
 }

 // A class used to define a matrix element and capture its prediction result.
 // A class used to define a matrix element and capture its prediction
 // result.
 private class MatrixElement
 {
 // Matrix column index. Its allowed range is from 0 to MatrixColumnCount - 1.
 // Matrix column index. Its allowed range is from 0 to
 // MatrixColumnCount - 1.
 [KeyType(MatrixColumnCount)]
 public uint MatrixColumnIndex { get; set; }
 // Matrix row index. Its allowed range is from 0 to MatrixRowCount - 1.
 [KeyType(MatrixRowCount)]
 public uint MatrixRowIndex { get; set; }
 // The actual value at the MatrixColumnIndex-th column and the MatrixRowIndex-th row.
 // The actual value at the MatrixColumnIndex-th column and the
 // MatrixRowIndex-th row.
 public float Value { get; set; }
 // The predicted value at the MatrixColumnIndex-th column and the MatrixRowIndex-th row.
 // The predicted value at the MatrixColumnIndex-th column and the
 // MatrixRowIndex-th row.
 public float Score { get; set; }
 }

 // Print some evaluation metrics to regression problems.
 private static void PrintMetrics(RegressionMetrics metrics)
 {
 Console.WriteLine($"Mean Absolute Error: {metrics.MeanAbsoluteError:F2}");
 Console.WriteLine($"Mean Squared Error: {metrics.MeanSquaredError:F2}");
 Console.WriteLine($"Root Mean Squared Error: {metrics.RootMeanSquaredError:F2}");
 Console.WriteLine($"RSquared: {metrics.RSquared:F2}");
 Console.WriteLine("Mean Absolute Error: " + metrics.MeanAbsoluteError);
 Console.WriteLine("Mean Squared Error: " + metrics.MeanSquaredError);
 Console.WriteLine("Root Mean Squared Error: " +
 metrics.RootMeanSquaredError);

 Console.WriteLine("RSquared: " + metrics.RSquared);
 }
 }
}
Expand Down

17 changes: 11 additions & 6 deletions
docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Recommendation/MatrixFactorization.tt

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Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -2,13 +2,18 @@

<#+
string ClassHeader = @"
 // This example requires installation of additional nuget package <a href=""https://www.nuget.org/packages/Microsoft.ML.Recommender/"">Microsoft.ML.Recommender</a>.
 // In this example we will create in-memory data and then use it to train 
 // a matrix factorization model with default parameters. Afterward, quality metrics are reported.";
 // This example requires installation of additional nuget package at
 // for Microsoft.ML.Recommender at
 // https://www.nuget.org/packages/Microsoft.ML.Recommender/
 // In this example we will create in-memory data and then use it to train
 // a matrix factorization model with default parameters. Afterward, quality
 // metrics are reported.";
string ClassName="MatrixFactorization";
string ExtraUsing = null;
string Trainer = @"MatrixFactorization(nameof(MatrixElement.Value), nameof(MatrixElement.MatrixColumnIndex),
 nameof(MatrixElement.MatrixRowIndex), 10, 0.2, 1)";
string Trainer = @"
 MatrixFactorization(nameof(MatrixElement.Value),
 nameof(MatrixElement.MatrixColumnIndex),
 nameof(MatrixElement.MatrixRowIndex), 10, 0.2, 1)";
string TrainerOptions = null;

string ExpectedOutputPerInstance= @"// Expected output:
Expand All@@ -23,4 +28,4 @@ string ExpectedOutput = @"// Expected output:
 // Mean Squared Error: 0.79
 // Root Mean Squared Error: 0.89
 // RSquared: 0.61 (closer to 1 is better. The worest case is 0)";
#>
#>

68 changes: 45 additions & 23 deletions
...icrosoft.ML.Samples/Dynamic/Trainers/Recommendation/MatrixFactorizationTemplate.ttinclude

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Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -16,15 +16,17 @@ namespace Samples.Dynamic.Trainers.Recommendation
<# } #>
 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.
 // Setting the seed to a fixed number in this example to make outputs deterministic.
 // 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. Setting the seed to a fixed number
 // in this example to make outputs deterministic.
 var mlContext = new MLContext(seed: 0);

 // Create a list of training data points.
 var dataPoints = GenerateMatrix();

 // Convert the list of data points to an IDataView object, which is consumable by ML.NET API.
 // Convert the list of data points to an IDataView object, which is
 // consumable by ML.NET API.
 var trainingData = mlContext.Data.LoadFromEnumerable(dataPoints);

<# if (TrainerOptions == null) { #>
Expand All@@ -35,7 +37,8 @@ namespace Samples.Dynamic.Trainers.Recommendation
 var options = new <#=TrainerOptions#>;

 // Define the trainer.
 var pipeline = mlContext.Recommendation().Trainers.<#=Trainer#>(options);
 var pipeline = mlContext.Recommendation().Trainers.<#=Trainer#>(
 options);
<# } #>

 // Train the model.
Expand All@@ -45,26 +48,37 @@ namespace Samples.Dynamic.Trainers.Recommendation
 var transformedData = model.Transform(trainingData);

 // Convert IDataView object to a list.
 var predictions = mlContext.Data.CreateEnumerable<MatrixElement>(transformedData, reuseRowObject: false).Take(5).ToList();
 var predictions = mlContext.Data
 .CreateEnumerable<MatrixElement>(transformedData,
 reuseRowObject: false).Take(5).ToList();

 // Look at 5 predictions for the Label, side by side with the actual Label for comparison.
 // Look at 5 predictions for the Label, side by side with the actual
 // Label for comparison.
 foreach (var p in predictions)
 Console.WriteLine($"Actual value: {p.Value:F3}, Predicted score: {p.Score:F3}");
 Console.WriteLine($"Actual value: {p.Value:F3}," +
 $"Predicted score: {p.Score:F3}");

 <#=ExpectedOutputPerInstance#>

 // Evaluate the overall metrics
 var metrics = mlContext.Regression.Evaluate(transformedData, labelColumnName: nameof(MatrixElement.Value), scoreColumnName: nameof(MatrixElement.Score));
 var metrics = mlContext.Regression.Evaluate(transformedData,
 labelColumnName: nameof(MatrixElement.Value),
 scoreColumnName: nameof(MatrixElement.Score));

 PrintMetrics(metrics);

 <#=ExpectedOutput#>
 }

 // The following variables are used to define the shape of the example matrix. Its shape is MatrixRowCount-by-MatrixColumnCount.
 // 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
 // and MatrixRowIndex=0 in MatrixElement below specifies the value at the upper-left corner in the training matrix). If user's row index
 // 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.
 // This behavior is also true to column index.
 // The following variables are used to define the shape of the example
 // matrix. Its shape is MatrixRowCount-by-MatrixColumnCount. 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 and MatrixRowIndex=0
 // in MatrixElement below specifies the value at the upper-left corner in
 // the training matrix). If user's row index 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. This behavior is also true to column
 // index.
 private const uint MatrixColumnCount = 60;
 private const uint MatrixRowCount = 100;

Expand All@@ -74,32 +88,40 @@ namespace Samples.Dynamic.Trainers.Recommendation
 var dataMatrix = new List<MatrixElement>();
 for (uint i = 0; i < MatrixColumnCount; ++i)
 for (uint j = 0; j < MatrixRowCount; ++j)
 dataMatrix.Add(new MatrixElement() { MatrixColumnIndex = i, MatrixRowIndex = j, Value = (i + j) % 5 });
 dataMatrix.Add(new MatrixElement() { MatrixColumnIndex = i,
 MatrixRowIndex = j, Value = (i + j) % 5 });

 return dataMatrix;
 }

 // A class used to define a matrix element and capture its prediction result.
 // A class used to define a matrix element and capture its prediction
 // result.
 private class MatrixElement
 {
 // Matrix column index. Its allowed range is from 0 to MatrixColumnCount - 1.
 // Matrix column index. Its allowed range is from 0 to
 // MatrixColumnCount - 1.
 [KeyType(MatrixColumnCount)]
 public uint MatrixColumnIndex { get; set; }
 // Matrix row index. Its allowed range is from 0 to MatrixRowCount - 1.
 [KeyType(MatrixRowCount)]
 public uint MatrixRowIndex { get; set; }
 // The actual value at the MatrixColumnIndex-th column and the MatrixRowIndex-th row.
 // The actual value at the MatrixColumnIndex-th column and the
 // MatrixRowIndex-th row.
 public float Value { get; set; }
 // The predicted value at the MatrixColumnIndex-th column and the MatrixRowIndex-th row.
 // The predicted value at the MatrixColumnIndex-th column and the
 // MatrixRowIndex-th row.
 public float Score { get; set; }
 }

 // Print some evaluation metrics to regression problems.
 private static void PrintMetrics(RegressionMetrics metrics)
 {
 Console.WriteLine($"Mean Absolute Error: {metrics.MeanAbsoluteError:F2}");
 Console.WriteLine($"Mean Squared Error: {metrics.MeanSquaredError:F2}");
 Console.WriteLine($"Root Mean Squared Error: {metrics.RootMeanSquaredError:F2}");
 Console.WriteLine($"RSquared: {metrics.RSquared:F2}");
 Console.WriteLine("Mean Absolute Error: " + metrics.MeanAbsoluteError);
 Console.WriteLine("Mean Squared Error: " + metrics.MeanSquaredError);
 Console.WriteLine("Root Mean Squared Error: " +
 metrics.RootMeanSquaredError);

 Console.WriteLine("RSquared: " + metrics.RSquared);
 }
 }
}
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