Reformatted Ranking samples to width 85#3930

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sayanshaw24 merged 10 commits into from
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Reformatted Ranking samples to width 85 #3930

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Reformatted Ranking samples to width 85
sayanshaw24 Jun 28, 2019
5dc1a0d
Fixed syntax errors
sayanshaw24 Jun 28, 2019
f9a0c57
Removed extra white spaces
sayanshaw24 Jun 28, 2019
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48 changes: 32 additions & 16 deletions docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Ranking/FastTree.cs
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Original file line numberDiff line numberDiff line change
Expand Up@@ -8,19 +8,22 @@ namespace Samples.Dynamic.Trainers.Ranking
{
public static class FastTree
{
// This example requires installation of additional NuGet package
// <a href="https://www.nuget.org/packages/Microsoft.ML.FastTree/">Microsoft.ML.FastTree</a>.
// This example requires installation of additional NuGet package for
// Microsoft.ML.FastTree at
// https://www.nuget.org/packages/Microsoft.ML.FastTree/
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 = GenerateRandomDataPoints(1000);

// 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.
Expand All@@ -29,17 +32,21 @@ public static void Example()
// Train the model.
var model = pipeline.Fit(trainingData);

// Create testing data. Use different random seed to make it different from training data.
var testData = mlContext.Data.LoadFromEnumerable(GenerateRandomDataPoints(500, seed:123));
// Create testing data. Use different random seed to make it different
// from training data.
var testData = mlContext.Data.LoadFromEnumerable(
GenerateRandomDataPoints(500, seed:123));

// Run the model on test data set.
var transformedTestData = model.Transform(testData);

// Take the top 5 rows.
var topTransformedTestData = mlContext.Data.TakeRows(transformedTestData, 5);
var topTransformedTestData = mlContext.Data.TakeRows(
transformedTestData, 5);

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

// Print 5 predictions.
foreach (var p in predictions)
Expand All@@ -61,7 +68,8 @@ public static void Example()
// NDCG: @1:0.99, @2:0.98, @3:0.99
}

private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int seed = 0, int groupSize = 10)
private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count,
int seed = 0, int groupSize = 10)
{
var random = new Random(seed);
float randomFloat() => (float)random.NextDouble();
Expand All@@ -73,13 +81,16 @@ private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int se
Label = (uint)label,
GroupId = (uint)(i / groupSize),
// Create random features that are correlated with the label.
// For data points with larger labels, the feature values are slightly increased by adding a constant.
Features = Enumerable.Repeat(label, 50).Select(x => randomFloat() + x * 0.1f).ToArray()
// For data points with larger labels, the feature values are
// slightly increased by adding a constant.
Features = Enumerable.Repeat(label, 50).Select(
x => randomFloat() + x * 0.1f).ToArray()
};
}
}

// Example with label, groupId, and 50 feature values. A data set is a collection of such examples.
// Example with label, groupId, and 50 feature values. A data set is a
// collection of such examples.
private class DataPoint
{
[KeyType(5)]
Expand All@@ -102,8 +113,13 @@ private class Prediction
// Pretty-print RankerMetrics objects.
public static void PrintMetrics(RankingMetrics metrics)
{
Console.WriteLine($"DCG: {string.Join(", ", metrics.DiscountedCumulativeGains.Select((d, i) => $"@{i + 1}:{d:F2}").ToArray())}");
Console.WriteLine($"NDCG: {string.Join(", ", metrics.NormalizedDiscountedCumulativeGains.Select((d, i) => $"@{i + 1}:{d:F2}").ToArray())}");
Console.WriteLine("DCG: " + string.Join(", ",
metrics.DiscountedCumulativeGains.Select(
(d, i) => (i + 1) + ":" + d + ":F2").ToArray()));

Console.WriteLine("NDCG: " + string.Join(", ",
metrics.NormalizedDiscountedCumulativeGains.Select(
(d, i) => (i + 1) + ":" + d + ":F2").ToArray()));
}
}
}
7 changes: 4 additions & 3 deletions docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Ranking/FastTree.tt
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -6,8 +6,9 @@ string TrainerOptions = null;

string OptionsInclude = "";
string Comments= @"
// This example requires installation of additional NuGet package
// <a href=""https://www.nuget.org/packages/Microsoft.ML.FastTree/"">Microsoft.ML.FastTree</a>.";
// This example requires installation of additional NuGet package for
// Microsoft.ML.FastTree at
// https://www.nuget.org/packages/Microsoft.ML.FastTree/";

string ExpectedOutputPerInstance = @"// Expected output:
// Label: 5, Score: 13.0154
Expand All@@ -19,4 +20,4 @@ string ExpectedOutputPerInstance = @"// Expected output:
string ExpectedOutput = @"// Expected output:
// DCG: @1:41.95, @2:63.33, @3:75.65
// NDCG: @1:0.99, @2:0.98, @3:0.99";
#>
#>
47 changes: 31 additions & 16 deletions docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Ranking/FastTreeWithOptions.cs
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Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -9,19 +9,22 @@ namespace Samples.Dynamic.Trainers.Ranking
{
public static class FastTreeWithOptions
{
// This example requires installation of additional NuGet package
// <a href="https://www.nuget.org/packages/Microsoft.ML.FastTree/">Microsoft.ML.FastTree</a>.
// This example requires installation of additional NuGet package for
// Microsoft.ML.FastTree at
// https://www.nuget.org/packages/Microsoft.ML.FastTree/
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 = GenerateRandomDataPoints(1000);

// 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 trainer options.
Expand All@@ -43,17 +46,21 @@ public static void Example()
// Train the model.
var model = pipeline.Fit(trainingData);

// Create testing data. Use different random seed to make it different from training data.
var testData = mlContext.Data.LoadFromEnumerable(GenerateRandomDataPoints(500, seed:123));
// Create testing data. Use different random seed to make it different
// from training data.
var testData = mlContext.Data.LoadFromEnumerable(
GenerateRandomDataPoints(500, seed:123));

// Run the model on test data set.
var transformedTestData = model.Transform(testData);

// Take the top 5 rows.
var topTransformedTestData = mlContext.Data.TakeRows(transformedTestData, 5);
var topTransformedTestData = mlContext.Data.TakeRows(
transformedTestData, 5);

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

// Print 5 predictions.
foreach (var p in predictions)
Expand All@@ -75,7 +82,8 @@ public static void Example()
// NDCG: @1:0.96, @2:0.95, @3:0.97
}

private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int seed = 0, int groupSize = 10)
private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count,
int seed = 0, int groupSize = 10)
{
var random = new Random(seed);
float randomFloat() => (float)random.NextDouble();
Expand All@@ -87,13 +95,16 @@ private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int se
Label = (uint)label,
GroupId = (uint)(i / groupSize),
// Create random features that are correlated with the label.
// For data points with larger labels, the feature values are slightly increased by adding a constant.
Features = Enumerable.Repeat(label, 50).Select(x => randomFloat() + x * 0.1f).ToArray()
// For data points with larger labels, the feature values are
// slightly increased by adding a constant.
Features = Enumerable.Repeat(label, 50).Select(
x => randomFloat() + x * 0.1f).ToArray()
};
}
}

// Example with label, groupId, and 50 feature values. A data set is a collection of such examples.
// Example with label, groupId, and 50 feature values. A data set is a
// collection of such examples.
private class DataPoint
{
[KeyType(5)]
Expand All@@ -116,8 +127,12 @@ private class Prediction
// Pretty-print RankerMetrics objects.
public static void PrintMetrics(RankingMetrics metrics)
{
Console.WriteLine($"DCG: {string.Join(", ", metrics.DiscountedCumulativeGains.Select((d, i) => $"@{i + 1}:{d:F2}").ToArray())}");
Console.WriteLine($"NDCG: {string.Join(", ", metrics.NormalizedDiscountedCumulativeGains.Select((d, i) => $"@{i + 1}:{d:F2}").ToArray())}");
Console.WriteLine("DCG: " + string.Join(", ",
metrics.DiscountedCumulativeGains.Select(
(d, i) => (i + 1) + ":" + d + ":F2").ToArray()));
Console.WriteLine("NDCG: " + string.Join(", ",
metrics.NormalizedDiscountedCumulativeGains.Select(
(d, i) => (i + 1) + ":" + d + ":F2").ToArray()));
}
}
}
5 changes: 3 additions & 2 deletions docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Ranking/FastTreeWithOptions.tt
View file
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Original file line numberDiff line numberDiff line change
Expand Up@@ -16,8 +16,9 @@ string TrainerOptions = @"FastTreeRankingTrainer.Options

string OptionsInclude = "using Microsoft.ML.Trainers.FastTree;";
string Comments= @"
// This example requires installation of additional NuGet package
// <a href=""https://www.nuget.org/packages/Microsoft.ML.FastTree/"">Microsoft.ML.FastTree</a>.";
// This example requires installation of additional NuGet package for
// Microsoft.ML.FastTree at
// https://www.nuget.org/packages/Microsoft.ML.FastTree/";

string ExpectedOutputPerInstance = @"// Expected output:
// Label: 5, Score: 8.807633
Expand Down
59 changes: 38 additions & 21 deletions docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Ranking/LightGbm.cs
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Original file line numberDiff line numberDiff line change
Expand Up@@ -8,17 +8,22 @@ namespace Samples.Dynamic.Trainers.Ranking
{
public static class LightGbm
{
// This example requires installation of additional NuGet package for
// Microsoft.ML.FastTree at
// https://www.nuget.org/packages/Microsoft.ML.FastTree/
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 = GenerateRandomDataPoints(1000);

// 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.
Expand All@@ -27,39 +32,44 @@ public static void Example()
// Train the model.
var model = pipeline.Fit(trainingData);

// Create testing data. Use different random seed to make it different from training data.
var testData = mlContext.Data.LoadFromEnumerable(GenerateRandomDataPoints(500, seed:123));
// Create testing data. Use different random seed to make it different
// from training data.
var testData = mlContext.Data.LoadFromEnumerable(
GenerateRandomDataPoints(500, seed:123));

// Run the model on test data set.
var transformedTestData = model.Transform(testData);

// Take the top 5 rows.
var topTransformedTestData = mlContext.Data.TakeRows(transformedTestData, 5);
var topTransformedTestData = mlContext.Data.TakeRows(
transformedTestData, 5);

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

// Print 5 predictions.
foreach (var p in predictions)
Console.WriteLine($"Label: {p.Label}, Score: {p.Score}");

// Expected output:
// Label: 5, Score: 2.195333
// Label: 4, Score: 0.2596574
// Label: 4, Score: -2.168355
// Label: 1, Score: -3.074823
// Label: 1, Score: -1.523607
// Label: 5, Score: 2.493263

@natkenatkeJun 28, 2019

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Did these numbers change for the sample?

@sayanshaw24sayanshaw24Jun 29, 2019

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Yeah they must have changed from a more recent pull.

@natkenatkeJun 29, 2019

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Ok, I see that the sample is using random data points. Thanks

// Label: 1, Score: -4.528436
// Label: 3, Score: -3.002865
// Label: 3, Score: -2.151812
// Label: 1, Score: -4.089102

// Evaluate the overall metrics.
var metrics = mlContext.Ranking.Evaluate(transformedTestData);
PrintMetrics(metrics);

// Expected output:
// DCG: @1:26.03, @2:37.57, @3:45.83
// NDCG: @1:0.61, @2:0.57, @3:0.59
// DCG: @1:41.95, @2:63.76, @3:75.97
// NDCG: @1:0.99, @2:0.99, @3:0.99
}

private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int seed = 0, int groupSize = 10)
private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count,
int seed = 0, int groupSize = 10)
{
var random = new Random(seed);
float randomFloat() => (float)random.NextDouble();
Expand All@@ -71,13 +81,16 @@ private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int se
Label = (uint)label,
GroupId = (uint)(i / groupSize),
// Create random features that are correlated with the label.
// For data points with larger labels, the feature values are slightly increased by adding a constant.
Features = Enumerable.Repeat(label, 50).Select(x => randomFloat() + x * 0.1f).ToArray()
// For data points with larger labels, the feature values are
// slightly increased by adding a constant.
Features = Enumerable.Repeat(label, 50).Select(
x => randomFloat() + x * 0.1f).ToArray()
};
}
}

// Example with label, groupId, and 50 feature values. A data set is a collection of such examples.
// Example with label, groupId, and 50 feature values. A data set is a
// collection of such examples.
private class DataPoint
{
[KeyType(5)]
Expand All@@ -100,8 +113,12 @@ private class Prediction
// Pretty-print RankerMetrics objects.
public static void PrintMetrics(RankingMetrics metrics)
{
Console.WriteLine($"DCG: {string.Join(", ", metrics.DiscountedCumulativeGains.Select((d, i) => $"@{i + 1}:{d:F2}").ToArray())}");
Console.WriteLine($"NDCG: {string.Join(", ", metrics.NormalizedDiscountedCumulativeGains.Select((d, i) => $"@{i + 1}:{d:F2}").ToArray())}");
Console.WriteLine("DCG: " + string.Join(", ",
metrics.DiscountedCumulativeGains.Select(
(d, i) => (i + 1) + ":" + d + ":F2").ToArray()));
Console.WriteLine("NDCG: " + string.Join(", ",
metrics.NormalizedDiscountedCumulativeGains.Select(
(d, i) => (i + 1) + ":" + d + ":F2").ToArray()));
}
}
}
5 changes: 3 additions & 2 deletions docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Ranking/LightGbm.tt
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -6,8 +6,9 @@ string TrainerOptions = null;

string OptionsInclude = "";
string Comments= @"
// This example requires installation of additional NuGet package
// <a href=""https://www.nuget.org/packages/Microsoft.ML.LightGbm/"">Microsoft.ML.LightGbm</a>.";
// This example requires installation of additional NuGet package for
// Microsoft.ML.FastTree at
// https://www.nuget.org/packages/Microsoft.ML.FastTree/";

string ExpectedOutputPerInstance = @"// Expected output:
// Label: 5, Score: 2.493263
Expand Down
Loading
, '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(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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Reformatted Ranking samples to width 85#3930

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 Merged

sayanshaw24
merged 10 commits into

from

Jul 2, 2019
 Merged


Reformatted Ranking samples to width 85
#3930
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89b5745

Reformatted Ranking samples to width 85


sayanshaw24 Jun 28, 2019
5dc1a0d

Fixed syntax errors


sayanshaw24 Jun 28, 2019
f9a0c57

Removed extra white spaces


sayanshaw24 Jun 28, 2019
2c32e2d

Spacing changes


sayanshaw24 Jun 28, 2019
6dc4f66

Spacing changes


sayanshaw24 Jun 28, 2019
abd549c

Spacing changes


sayanshaw24 Jun 28, 2019
a0d411d

Spacing changes


sayanshaw24 Jun 28, 2019
e736bd1

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sayanshaw24 Jun 28, 2019
b395322

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sayanshaw24 Jun 28, 2019
8a43dcd

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sayanshaw24 Jul 1, 2019

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48 changes: 32 additions & 16 deletions
docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Ranking/FastTree.cs

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Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -8,19 +8,22 @@ namespace Samples.Dynamic.Trainers.Ranking
{
 public static class FastTree
 {
 // This example requires installation of additional NuGet package
 // <a href="https://www.nuget.org/packages/Microsoft.ML.FastTree/">Microsoft.ML.FastTree</a>.
 // This example requires installation of additional NuGet package for 
 // Microsoft.ML.FastTree at
 // https://www.nuget.org/packages/Microsoft.ML.FastTree/
 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 = GenerateRandomDataPoints(1000);

 // 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.
Expand All@@ -29,17 +32,21 @@ public static void Example()
 // Train the model.
 var model = pipeline.Fit(trainingData);

 // Create testing data. Use different random seed to make it different from training data.
 var testData = mlContext.Data.LoadFromEnumerable(GenerateRandomDataPoints(500, seed:123));
 // Create testing data. Use different random seed to make it different
 // from training data.
 var testData = mlContext.Data.LoadFromEnumerable(
 GenerateRandomDataPoints(500, seed:123));

 // Run the model on test data set.
 var transformedTestData = model.Transform(testData);

 // Take the top 5 rows.
 var topTransformedTestData = mlContext.Data.TakeRows(transformedTestData, 5);
 var topTransformedTestData = mlContext.Data.TakeRows(
 transformedTestData, 5);

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

 // Print 5 predictions.
 foreach (var p in predictions)
Expand All@@ -61,7 +68,8 @@ public static void Example()
 // NDCG: @1:0.99, @2:0.98, @3:0.99
 }

 private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int seed = 0, int groupSize = 10)
 private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count,
 int seed = 0, int groupSize = 10)
 {
 var random = new Random(seed);
 float randomFloat() => (float)random.NextDouble();
Expand All@@ -73,13 +81,16 @@ private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int se
 Label = (uint)label,
 GroupId = (uint)(i / groupSize),
 // Create random features that are correlated with the label.
 // For data points with larger labels, the feature values are slightly increased by adding a constant.
 Features = Enumerable.Repeat(label, 50).Select(x => randomFloat() + x * 0.1f).ToArray()
 // For data points with larger labels, the feature values are
 // slightly increased by adding a constant.
 Features = Enumerable.Repeat(label, 50).Select(
 x => randomFloat() + x * 0.1f).ToArray()
 };
 }
 }

 // Example with label, groupId, and 50 feature values. A data set is a collection of such examples.
 // Example with label, groupId, and 50 feature values. A data set is a
 // collection of such examples.
 private class DataPoint
 {
 [KeyType(5)]
Expand All@@ -102,8 +113,13 @@ private class Prediction
 // Pretty-print RankerMetrics objects.
 public static void PrintMetrics(RankingMetrics metrics)
 {
 Console.WriteLine($"DCG: {string.Join(", ", metrics.DiscountedCumulativeGains.Select((d, i) => $"@{i + 1}:{d:F2}").ToArray())}");
 Console.WriteLine($"NDCG: {string.Join(", ", metrics.NormalizedDiscountedCumulativeGains.Select((d, i) => $"@{i + 1}:{d:F2}").ToArray())}");
 Console.WriteLine("DCG: " + string.Join(", ",
 metrics.DiscountedCumulativeGains.Select(
 (d, i) => (i + 1) + ":" + d + ":F2").ToArray()));

 Console.WriteLine("NDCG: " + string.Join(", ",
 metrics.NormalizedDiscountedCumulativeGains.Select(
 (d, i) => (i + 1) + ":" + d + ":F2").ToArray()));
 }
 }
}

7 changes: 4 additions & 3 deletions
docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Ranking/FastTree.tt

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -6,8 +6,9 @@ string TrainerOptions = null;

string OptionsInclude = "";
string Comments= @"
 // This example requires installation of additional NuGet package
 // <a href=""https://www.nuget.org/packages/Microsoft.ML.FastTree/"">Microsoft.ML.FastTree</a>.";
 // This example requires installation of additional NuGet package for 
 // Microsoft.ML.FastTree at
 // https://www.nuget.org/packages/Microsoft.ML.FastTree/";

string ExpectedOutputPerInstance = @"// Expected output:
 // Label: 5, Score: 13.0154
Expand All@@ -19,4 +20,4 @@ string ExpectedOutputPerInstance = @"// Expected output:
string ExpectedOutput = @"// Expected output:
 // DCG: @1:41.95, @2:63.33, @3:75.65
 // NDCG: @1:0.99, @2:0.98, @3:0.99";
#>
#>

47 changes: 31 additions & 16 deletions
docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Ranking/FastTreeWithOptions.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -9,19 +9,22 @@ namespace Samples.Dynamic.Trainers.Ranking
{
 public static class FastTreeWithOptions
 {
 // This example requires installation of additional NuGet package
 // <a href="https://www.nuget.org/packages/Microsoft.ML.FastTree/">Microsoft.ML.FastTree</a>.
 // This example requires installation of additional NuGet package for 
 // Microsoft.ML.FastTree at
 // https://www.nuget.org/packages/Microsoft.ML.FastTree/
 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 = GenerateRandomDataPoints(1000);

 // 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 trainer options.
Expand All@@ -43,17 +46,21 @@ public static void Example()
 // Train the model.
 var model = pipeline.Fit(trainingData);

 // Create testing data. Use different random seed to make it different from training data.
 var testData = mlContext.Data.LoadFromEnumerable(GenerateRandomDataPoints(500, seed:123));
 // Create testing data. Use different random seed to make it different
 // from training data.
 var testData = mlContext.Data.LoadFromEnumerable(
 GenerateRandomDataPoints(500, seed:123));

 // Run the model on test data set.
 var transformedTestData = model.Transform(testData);

 // Take the top 5 rows.
 var topTransformedTestData = mlContext.Data.TakeRows(transformedTestData, 5);
 var topTransformedTestData = mlContext.Data.TakeRows(
 transformedTestData, 5);

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

 // Print 5 predictions.
 foreach (var p in predictions)
Expand All@@ -75,7 +82,8 @@ public static void Example()
 // NDCG: @1:0.96, @2:0.95, @3:0.97
 }

 private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int seed = 0, int groupSize = 10)
 private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count,
 int seed = 0, int groupSize = 10)
 {
 var random = new Random(seed);
 float randomFloat() => (float)random.NextDouble();
Expand All@@ -87,13 +95,16 @@ private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int se
 Label = (uint)label,
 GroupId = (uint)(i / groupSize),
 // Create random features that are correlated with the label.
 // For data points with larger labels, the feature values are slightly increased by adding a constant.
 Features = Enumerable.Repeat(label, 50).Select(x => randomFloat() + x * 0.1f).ToArray()
 // For data points with larger labels, the feature values are
 // slightly increased by adding a constant.
 Features = Enumerable.Repeat(label, 50).Select(
 x => randomFloat() + x * 0.1f).ToArray()
 };
 }
 }

 // Example with label, groupId, and 50 feature values. A data set is a collection of such examples.
 // Example with label, groupId, and 50 feature values. A data set is a
 // collection of such examples.
 private class DataPoint
 {
 [KeyType(5)]
Expand All@@ -116,8 +127,12 @@ private class Prediction
 // Pretty-print RankerMetrics objects.
 public static void PrintMetrics(RankingMetrics metrics)
 {
 Console.WriteLine($"DCG: {string.Join(", ", metrics.DiscountedCumulativeGains.Select((d, i) => $"@{i + 1}:{d:F2}").ToArray())}");
 Console.WriteLine($"NDCG: {string.Join(", ", metrics.NormalizedDiscountedCumulativeGains.Select((d, i) => $"@{i + 1}:{d:F2}").ToArray())}");
 Console.WriteLine("DCG: " + string.Join(", ",
 metrics.DiscountedCumulativeGains.Select(
 (d, i) => (i + 1) + ":" + d + ":F2").ToArray()));
 Console.WriteLine("NDCG: " + string.Join(", ",
 metrics.NormalizedDiscountedCumulativeGains.Select(
 (d, i) => (i + 1) + ":" + d + ":F2").ToArray()));
 }
 }
}

5 changes: 3 additions & 2 deletions
docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Ranking/FastTreeWithOptions.tt

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -16,8 +16,9 @@ string TrainerOptions = @"FastTreeRankingTrainer.Options

string OptionsInclude = "using Microsoft.ML.Trainers.FastTree;";
string Comments= @"
 // This example requires installation of additional NuGet package
 // <a href=""https://www.nuget.org/packages/Microsoft.ML.FastTree/"">Microsoft.ML.FastTree</a>.";
 // This example requires installation of additional NuGet package for 
 // Microsoft.ML.FastTree at
 // https://www.nuget.org/packages/Microsoft.ML.FastTree/";

string ExpectedOutputPerInstance = @"// Expected output:
 // Label: 5, Score: 8.807633
Expand Down

59 changes: 38 additions & 21 deletions
docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Ranking/LightGbm.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -8,17 +8,22 @@ namespace Samples.Dynamic.Trainers.Ranking
{
 public static class LightGbm
 {
 // This example requires installation of additional NuGet package for 
 // Microsoft.ML.FastTree at
 // https://www.nuget.org/packages/Microsoft.ML.FastTree/
 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 = GenerateRandomDataPoints(1000);

 // 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.
Expand All@@ -27,39 +32,44 @@ public static void Example()
 // Train the model.
 var model = pipeline.Fit(trainingData);

 // Create testing data. Use different random seed to make it different from training data.
 var testData = mlContext.Data.LoadFromEnumerable(GenerateRandomDataPoints(500, seed:123));
 // Create testing data. Use different random seed to make it different
 // from training data.
 var testData = mlContext.Data.LoadFromEnumerable(
 GenerateRandomDataPoints(500, seed:123));

 // Run the model on test data set.
 var transformedTestData = model.Transform(testData);

 // Take the top 5 rows.
 var topTransformedTestData = mlContext.Data.TakeRows(transformedTestData, 5);
 var topTransformedTestData = mlContext.Data.TakeRows(
 transformedTestData, 5);

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

 // Print 5 predictions.
 foreach (var p in predictions)
 Console.WriteLine($"Label: {p.Label}, Score: {p.Score}");

 // Expected output:
 // Label: 5, Score: 2.195333
 // Label: 4, Score: 0.2596574
 // Label: 4, Score: -2.168355
 // Label: 1, Score: -3.074823
 // Label: 1, Score: -1.523607
 // Label: 5, Score: 2.493263
@natkenatkeJun 28, 2019

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Did these numbers change for the sample?
@sayanshaw24sayanshaw24Jun 29, 2019

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Yeah they must have changed from a more recent pull.
@natkenatkeJun 29, 2019

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Ok, I see that the sample is using random data points. Thanks
 // Label: 1, Score: -4.528436
 // Label: 3, Score: -3.002865
 // Label: 3, Score: -2.151812
 // Label: 1, Score: -4.089102

 // Evaluate the overall metrics.
 var metrics = mlContext.Ranking.Evaluate(transformedTestData);
 PrintMetrics(metrics);

 // Expected output:
 // DCG: @1:26.03, @2:37.57, @3:45.83
 // NDCG: @1:0.61, @2:0.57, @3:0.59
 // DCG: @1:41.95, @2:63.76, @3:75.97
 // NDCG: @1:0.99, @2:0.99, @3:0.99
 }

 private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int seed = 0, int groupSize = 10)
 private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count,
 int seed = 0, int groupSize = 10)
 {
 var random = new Random(seed);
 float randomFloat() => (float)random.NextDouble();
Expand All@@ -71,13 +81,16 @@ private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int se
 Label = (uint)label,
 GroupId = (uint)(i / groupSize),
 // Create random features that are correlated with the label.
 // For data points with larger labels, the feature values are slightly increased by adding a constant.
 Features = Enumerable.Repeat(label, 50).Select(x => randomFloat() + x * 0.1f).ToArray()
 // For data points with larger labels, the feature values are
 // slightly increased by adding a constant.
 Features = Enumerable.Repeat(label, 50).Select(
 x => randomFloat() + x * 0.1f).ToArray()
 };
 }
 }

 // Example with label, groupId, and 50 feature values. A data set is a collection of such examples.
 // Example with label, groupId, and 50 feature values. A data set is a
 // collection of such examples.
 private class DataPoint
 {
 [KeyType(5)]
Expand All@@ -100,8 +113,12 @@ private class Prediction
 // Pretty-print RankerMetrics objects.
 public static void PrintMetrics(RankingMetrics metrics)
 {
 Console.WriteLine($"DCG: {string.Join(", ", metrics.DiscountedCumulativeGains.Select((d, i) => $"@{i + 1}:{d:F2}").ToArray())}");
 Console.WriteLine($"NDCG: {string.Join(", ", metrics.NormalizedDiscountedCumulativeGains.Select((d, i) => $"@{i + 1}:{d:F2}").ToArray())}");
 Console.WriteLine("DCG: " + string.Join(", ",
 metrics.DiscountedCumulativeGains.Select(
 (d, i) => (i + 1) + ":" + d + ":F2").ToArray()));
 Console.WriteLine("NDCG: " + string.Join(", ",
 metrics.NormalizedDiscountedCumulativeGains.Select(
 (d, i) => (i + 1) + ":" + d + ":F2").ToArray()));
 }
 }
}

5 changes: 3 additions & 2 deletions
docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Ranking/LightGbm.tt

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -6,8 +6,9 @@ string TrainerOptions = null;

string OptionsInclude = "";
string Comments= @"
 // This example requires installation of additional NuGet package
 // <a href=""https://www.nuget.org/packages/Microsoft.ML.LightGbm/"">Microsoft.ML.LightGbm</a>.";
 // This example requires installation of additional NuGet package for 
 // Microsoft.ML.FastTree at
 // https://www.nuget.org/packages/Microsoft.ML.FastTree/";

string ExpectedOutputPerInstance = @"// Expected output:
 // Label: 5, Score: 2.493263
Expand Down
Loading

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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48 changes: 32 additions & 16 deletions
docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Ranking/FastTree.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -8,19 +8,22 @@ namespace Samples.Dynamic.Trainers.Ranking
{
 public static class FastTree
 {
 // This example requires installation of additional NuGet package
 // <a href="https://www.nuget.org/packages/Microsoft.ML.FastTree/">Microsoft.ML.FastTree</a>.
 // This example requires installation of additional NuGet package for 
 // Microsoft.ML.FastTree at
 // https://www.nuget.org/packages/Microsoft.ML.FastTree/
 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 = GenerateRandomDataPoints(1000);

 // 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.
Expand All@@ -29,17 +32,21 @@ public static void Example()
 // Train the model.
 var model = pipeline.Fit(trainingData);

 // Create testing data. Use different random seed to make it different from training data.
 var testData = mlContext.Data.LoadFromEnumerable(GenerateRandomDataPoints(500, seed:123));
 // Create testing data. Use different random seed to make it different
 // from training data.
 var testData = mlContext.Data.LoadFromEnumerable(
 GenerateRandomDataPoints(500, seed:123));

 // Run the model on test data set.
 var transformedTestData = model.Transform(testData);

 // Take the top 5 rows.
 var topTransformedTestData = mlContext.Data.TakeRows(transformedTestData, 5);
 var topTransformedTestData = mlContext.Data.TakeRows(
 transformedTestData, 5);

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

 // Print 5 predictions.
 foreach (var p in predictions)
Expand All@@ -61,7 +68,8 @@ public static void Example()
 // NDCG: @1:0.99, @2:0.98, @3:0.99
 }

 private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int seed = 0, int groupSize = 10)
 private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count,
 int seed = 0, int groupSize = 10)
 {
 var random = new Random(seed);
 float randomFloat() => (float)random.NextDouble();
Expand All@@ -73,13 +81,16 @@ private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int se
 Label = (uint)label,
 GroupId = (uint)(i / groupSize),
 // Create random features that are correlated with the label.
 // For data points with larger labels, the feature values are slightly increased by adding a constant.
 Features = Enumerable.Repeat(label, 50).Select(x => randomFloat() + x * 0.1f).ToArray()
 // For data points with larger labels, the feature values are
 // slightly increased by adding a constant.
 Features = Enumerable.Repeat(label, 50).Select(
 x => randomFloat() + x * 0.1f).ToArray()
 };
 }
 }

 // Example with label, groupId, and 50 feature values. A data set is a collection of such examples.
 // Example with label, groupId, and 50 feature values. A data set is a
 // collection of such examples.
 private class DataPoint
 {
 [KeyType(5)]
Expand All@@ -102,8 +113,13 @@ private class Prediction
 // Pretty-print RankerMetrics objects.
 public static void PrintMetrics(RankingMetrics metrics)
 {
 Console.WriteLine($"DCG: {string.Join(", ", metrics.DiscountedCumulativeGains.Select((d, i) => $"@{i + 1}:{d:F2}").ToArray())}");
 Console.WriteLine($"NDCG: {string.Join(", ", metrics.NormalizedDiscountedCumulativeGains.Select((d, i) => $"@{i + 1}:{d:F2}").ToArray())}");
 Console.WriteLine("DCG: " + string.Join(", ",
 metrics.DiscountedCumulativeGains.Select(
 (d, i) => (i + 1) + ":" + d + ":F2").ToArray()));

 Console.WriteLine("NDCG: " + string.Join(", ",
 metrics.NormalizedDiscountedCumulativeGains.Select(
 (d, i) => (i + 1) + ":" + d + ":F2").ToArray()));
 }
 }
}

7 changes: 4 additions & 3 deletions
docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Ranking/FastTree.tt

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -6,8 +6,9 @@ string TrainerOptions = null;

string OptionsInclude = "";
string Comments= @"
 // This example requires installation of additional NuGet package
 // <a href=""https://www.nuget.org/packages/Microsoft.ML.FastTree/"">Microsoft.ML.FastTree</a>.";
 // This example requires installation of additional NuGet package for 
 // Microsoft.ML.FastTree at
 // https://www.nuget.org/packages/Microsoft.ML.FastTree/";

string ExpectedOutputPerInstance = @"// Expected output:
 // Label: 5, Score: 13.0154
Expand All@@ -19,4 +20,4 @@ string ExpectedOutputPerInstance = @"// Expected output:
string ExpectedOutput = @"// Expected output:
 // DCG: @1:41.95, @2:63.33, @3:75.65
 // NDCG: @1:0.99, @2:0.98, @3:0.99";
#>
#>

47 changes: 31 additions & 16 deletions
docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Ranking/FastTreeWithOptions.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -9,19 +9,22 @@ namespace Samples.Dynamic.Trainers.Ranking
{
 public static class FastTreeWithOptions
 {
 // This example requires installation of additional NuGet package
 // <a href="https://www.nuget.org/packages/Microsoft.ML.FastTree/">Microsoft.ML.FastTree</a>.
 // This example requires installation of additional NuGet package for 
 // Microsoft.ML.FastTree at
 // https://www.nuget.org/packages/Microsoft.ML.FastTree/
 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 = GenerateRandomDataPoints(1000);

 // 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 trainer options.
Expand All@@ -43,17 +46,21 @@ public static void Example()
 // Train the model.
 var model = pipeline.Fit(trainingData);

 // Create testing data. Use different random seed to make it different from training data.
 var testData = mlContext.Data.LoadFromEnumerable(GenerateRandomDataPoints(500, seed:123));
 // Create testing data. Use different random seed to make it different
 // from training data.
 var testData = mlContext.Data.LoadFromEnumerable(
 GenerateRandomDataPoints(500, seed:123));

 // Run the model on test data set.
 var transformedTestData = model.Transform(testData);

 // Take the top 5 rows.
 var topTransformedTestData = mlContext.Data.TakeRows(transformedTestData, 5);
 var topTransformedTestData = mlContext.Data.TakeRows(
 transformedTestData, 5);

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

 // Print 5 predictions.
 foreach (var p in predictions)
Expand All@@ -75,7 +82,8 @@ public static void Example()
 // NDCG: @1:0.96, @2:0.95, @3:0.97
 }

 private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int seed = 0, int groupSize = 10)
 private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count,
 int seed = 0, int groupSize = 10)
 {
 var random = new Random(seed);
 float randomFloat() => (float)random.NextDouble();
Expand All@@ -87,13 +95,16 @@ private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int se
 Label = (uint)label,
 GroupId = (uint)(i / groupSize),
 // Create random features that are correlated with the label.
 // For data points with larger labels, the feature values are slightly increased by adding a constant.
 Features = Enumerable.Repeat(label, 50).Select(x => randomFloat() + x * 0.1f).ToArray()
 // For data points with larger labels, the feature values are
 // slightly increased by adding a constant.
 Features = Enumerable.Repeat(label, 50).Select(
 x => randomFloat() + x * 0.1f).ToArray()
 };
 }
 }

 // Example with label, groupId, and 50 feature values. A data set is a collection of such examples.
 // Example with label, groupId, and 50 feature values. A data set is a
 // collection of such examples.
 private class DataPoint
 {
 [KeyType(5)]
Expand All@@ -116,8 +127,12 @@ private class Prediction
 // Pretty-print RankerMetrics objects.
 public static void PrintMetrics(RankingMetrics metrics)
 {
 Console.WriteLine($"DCG: {string.Join(", ", metrics.DiscountedCumulativeGains.Select((d, i) => $"@{i + 1}:{d:F2}").ToArray())}");
 Console.WriteLine($"NDCG: {string.Join(", ", metrics.NormalizedDiscountedCumulativeGains.Select((d, i) => $"@{i + 1}:{d:F2}").ToArray())}");
 Console.WriteLine("DCG: " + string.Join(", ",
 metrics.DiscountedCumulativeGains.Select(
 (d, i) => (i + 1) + ":" + d + ":F2").ToArray()));
 Console.WriteLine("NDCG: " + string.Join(", ",
 metrics.NormalizedDiscountedCumulativeGains.Select(
 (d, i) => (i + 1) + ":" + d + ":F2").ToArray()));
 }
 }
}

5 changes: 3 additions & 2 deletions
docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Ranking/FastTreeWithOptions.tt

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -16,8 +16,9 @@ string TrainerOptions = @"FastTreeRankingTrainer.Options

string OptionsInclude = "using Microsoft.ML.Trainers.FastTree;";
string Comments= @"
 // This example requires installation of additional NuGet package
 // <a href=""https://www.nuget.org/packages/Microsoft.ML.FastTree/"">Microsoft.ML.FastTree</a>.";
 // This example requires installation of additional NuGet package for 
 // Microsoft.ML.FastTree at
 // https://www.nuget.org/packages/Microsoft.ML.FastTree/";

string ExpectedOutputPerInstance = @"// Expected output:
 // Label: 5, Score: 8.807633
Expand Down

59 changes: 38 additions & 21 deletions
docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Ranking/LightGbm.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -8,17 +8,22 @@ namespace Samples.Dynamic.Trainers.Ranking
{
 public static class LightGbm
 {
 // This example requires installation of additional NuGet package for 
 // Microsoft.ML.FastTree at
 // https://www.nuget.org/packages/Microsoft.ML.FastTree/
 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 = GenerateRandomDataPoints(1000);

 // 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.
Expand All@@ -27,39 +32,44 @@ public static void Example()
 // Train the model.
 var model = pipeline.Fit(trainingData);

 // Create testing data. Use different random seed to make it different from training data.
 var testData = mlContext.Data.LoadFromEnumerable(GenerateRandomDataPoints(500, seed:123));
 // Create testing data. Use different random seed to make it different
 // from training data.
 var testData = mlContext.Data.LoadFromEnumerable(
 GenerateRandomDataPoints(500, seed:123));

 // Run the model on test data set.
 var transformedTestData = model.Transform(testData);

 // Take the top 5 rows.
 var topTransformedTestData = mlContext.Data.TakeRows(transformedTestData, 5);
 var topTransformedTestData = mlContext.Data.TakeRows(
 transformedTestData, 5);

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

 // Print 5 predictions.
 foreach (var p in predictions)
 Console.WriteLine($"Label: {p.Label}, Score: {p.Score}");

 // Expected output:
 // Label: 5, Score: 2.195333
 // Label: 4, Score: 0.2596574
 // Label: 4, Score: -2.168355
 // Label: 1, Score: -3.074823
 // Label: 1, Score: -1.523607
 // Label: 5, Score: 2.493263
@natkenatkeJun 28, 2019

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Did these numbers change for the sample?
@sayanshaw24sayanshaw24Jun 29, 2019

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Yeah they must have changed from a more recent pull.
@natkenatkeJun 29, 2019

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Ok, I see that the sample is using random data points. Thanks
 // Label: 1, Score: -4.528436
 // Label: 3, Score: -3.002865
 // Label: 3, Score: -2.151812
 // Label: 1, Score: -4.089102

 // Evaluate the overall metrics.
 var metrics = mlContext.Ranking.Evaluate(transformedTestData);
 PrintMetrics(metrics);

 // Expected output:
 // DCG: @1:26.03, @2:37.57, @3:45.83
 // NDCG: @1:0.61, @2:0.57, @3:0.59
 // DCG: @1:41.95, @2:63.76, @3:75.97
 // NDCG: @1:0.99, @2:0.99, @3:0.99
 }

 private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int seed = 0, int groupSize = 10)
 private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count,
 int seed = 0, int groupSize = 10)
 {
 var random = new Random(seed);
 float randomFloat() => (float)random.NextDouble();
Expand All@@ -71,13 +81,16 @@ private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int se
 Label = (uint)label,
 GroupId = (uint)(i / groupSize),
 // Create random features that are correlated with the label.
 // For data points with larger labels, the feature values are slightly increased by adding a constant.
 Features = Enumerable.Repeat(label, 50).Select(x => randomFloat() + x * 0.1f).ToArray()
 // For data points with larger labels, the feature values are
 // slightly increased by adding a constant.
 Features = Enumerable.Repeat(label, 50).Select(
 x => randomFloat() + x * 0.1f).ToArray()
 };
 }
 }

 // Example with label, groupId, and 50 feature values. A data set is a collection of such examples.
 // Example with label, groupId, and 50 feature values. A data set is a
 // collection of such examples.
 private class DataPoint
 {
 [KeyType(5)]
Expand All@@ -100,8 +113,12 @@ private class Prediction
 // Pretty-print RankerMetrics objects.
 public static void PrintMetrics(RankingMetrics metrics)
 {
 Console.WriteLine($"DCG: {string.Join(", ", metrics.DiscountedCumulativeGains.Select((d, i) => $"@{i + 1}:{d:F2}").ToArray())}");
 Console.WriteLine($"NDCG: {string.Join(", ", metrics.NormalizedDiscountedCumulativeGains.Select((d, i) => $"@{i + 1}:{d:F2}").ToArray())}");
 Console.WriteLine("DCG: " + string.Join(", ",
 metrics.DiscountedCumulativeGains.Select(
 (d, i) => (i + 1) + ":" + d + ":F2").ToArray()));
 Console.WriteLine("NDCG: " + string.Join(", ",
 metrics.NormalizedDiscountedCumulativeGains.Select(
 (d, i) => (i + 1) + ":" + d + ":F2").ToArray()));
 }
 }
}

5 changes: 3 additions & 2 deletions
docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Ranking/LightGbm.tt

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -6,8 +6,9 @@ string TrainerOptions = null;

string OptionsInclude = "";
string Comments= @"
 // This example requires installation of additional NuGet package
 // <a href=""https://www.nuget.org/packages/Microsoft.ML.LightGbm/"">Microsoft.ML.LightGbm</a>.";
 // This example requires installation of additional NuGet package for 
 // Microsoft.ML.FastTree at
 // https://www.nuget.org/packages/Microsoft.ML.FastTree/";

string ExpectedOutputPerInstance = @"// Expected output:
 // Label: 5, Score: 2.493263
Expand Down
Loading

, 'i');
if (__m === '*' || __re.test(location.href)) {
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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48 changes: 32 additions & 16 deletions
docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Ranking/FastTree.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -8,19 +8,22 @@ namespace Samples.Dynamic.Trainers.Ranking
{
 public static class FastTree
 {
 // This example requires installation of additional NuGet package
 // <a href="https://www.nuget.org/packages/Microsoft.ML.FastTree/">Microsoft.ML.FastTree</a>.
 // This example requires installation of additional NuGet package for 
 // Microsoft.ML.FastTree at
 // https://www.nuget.org/packages/Microsoft.ML.FastTree/
 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 = GenerateRandomDataPoints(1000);

 // 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.
Expand All@@ -29,17 +32,21 @@ public static void Example()
 // Train the model.
 var model = pipeline.Fit(trainingData);

 // Create testing data. Use different random seed to make it different from training data.
 var testData = mlContext.Data.LoadFromEnumerable(GenerateRandomDataPoints(500, seed:123));
 // Create testing data. Use different random seed to make it different
 // from training data.
 var testData = mlContext.Data.LoadFromEnumerable(
 GenerateRandomDataPoints(500, seed:123));

 // Run the model on test data set.
 var transformedTestData = model.Transform(testData);

 // Take the top 5 rows.
 var topTransformedTestData = mlContext.Data.TakeRows(transformedTestData, 5);
 var topTransformedTestData = mlContext.Data.TakeRows(
 transformedTestData, 5);

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

 // Print 5 predictions.
 foreach (var p in predictions)
Expand All@@ -61,7 +68,8 @@ public static void Example()
 // NDCG: @1:0.99, @2:0.98, @3:0.99
 }

 private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int seed = 0, int groupSize = 10)
 private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count,
 int seed = 0, int groupSize = 10)
 {
 var random = new Random(seed);
 float randomFloat() => (float)random.NextDouble();
Expand All@@ -73,13 +81,16 @@ private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int se
 Label = (uint)label,
 GroupId = (uint)(i / groupSize),
 // Create random features that are correlated with the label.
 // For data points with larger labels, the feature values are slightly increased by adding a constant.
 Features = Enumerable.Repeat(label, 50).Select(x => randomFloat() + x * 0.1f).ToArray()
 // For data points with larger labels, the feature values are
 // slightly increased by adding a constant.
 Features = Enumerable.Repeat(label, 50).Select(
 x => randomFloat() + x * 0.1f).ToArray()
 };
 }
 }

 // Example with label, groupId, and 50 feature values. A data set is a collection of such examples.
 // Example with label, groupId, and 50 feature values. A data set is a
 // collection of such examples.
 private class DataPoint
 {
 [KeyType(5)]
Expand All@@ -102,8 +113,13 @@ private class Prediction
 // Pretty-print RankerMetrics objects.
 public static void PrintMetrics(RankingMetrics metrics)
 {
 Console.WriteLine($"DCG: {string.Join(", ", metrics.DiscountedCumulativeGains.Select((d, i) => $"@{i + 1}:{d:F2}").ToArray())}");
 Console.WriteLine($"NDCG: {string.Join(", ", metrics.NormalizedDiscountedCumulativeGains.Select((d, i) => $"@{i + 1}:{d:F2}").ToArray())}");
 Console.WriteLine("DCG: " + string.Join(", ",
 metrics.DiscountedCumulativeGains.Select(
 (d, i) => (i + 1) + ":" + d + ":F2").ToArray()));

 Console.WriteLine("NDCG: " + string.Join(", ",
 metrics.NormalizedDiscountedCumulativeGains.Select(
 (d, i) => (i + 1) + ":" + d + ":F2").ToArray()));
 }
 }
}

7 changes: 4 additions & 3 deletions
docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Ranking/FastTree.tt

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -6,8 +6,9 @@ string TrainerOptions = null;

string OptionsInclude = "";
string Comments= @"
 // This example requires installation of additional NuGet package
 // <a href=""https://www.nuget.org/packages/Microsoft.ML.FastTree/"">Microsoft.ML.FastTree</a>.";
 // This example requires installation of additional NuGet package for 
 // Microsoft.ML.FastTree at
 // https://www.nuget.org/packages/Microsoft.ML.FastTree/";

string ExpectedOutputPerInstance = @"// Expected output:
 // Label: 5, Score: 13.0154
Expand All@@ -19,4 +20,4 @@ string ExpectedOutputPerInstance = @"// Expected output:
string ExpectedOutput = @"// Expected output:
 // DCG: @1:41.95, @2:63.33, @3:75.65
 // NDCG: @1:0.99, @2:0.98, @3:0.99";
#>
#>

47 changes: 31 additions & 16 deletions
docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Ranking/FastTreeWithOptions.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -9,19 +9,22 @@ namespace Samples.Dynamic.Trainers.Ranking
{
 public static class FastTreeWithOptions
 {
 // This example requires installation of additional NuGet package
 // <a href="https://www.nuget.org/packages/Microsoft.ML.FastTree/">Microsoft.ML.FastTree</a>.
 // This example requires installation of additional NuGet package for 
 // Microsoft.ML.FastTree at
 // https://www.nuget.org/packages/Microsoft.ML.FastTree/
 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 = GenerateRandomDataPoints(1000);

 // 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 trainer options.
Expand All@@ -43,17 +46,21 @@ public static void Example()
 // Train the model.
 var model = pipeline.Fit(trainingData);

 // Create testing data. Use different random seed to make it different from training data.
 var testData = mlContext.Data.LoadFromEnumerable(GenerateRandomDataPoints(500, seed:123));
 // Create testing data. Use different random seed to make it different
 // from training data.
 var testData = mlContext.Data.LoadFromEnumerable(
 GenerateRandomDataPoints(500, seed:123));

 // Run the model on test data set.
 var transformedTestData = model.Transform(testData);

 // Take the top 5 rows.
 var topTransformedTestData = mlContext.Data.TakeRows(transformedTestData, 5);
 var topTransformedTestData = mlContext.Data.TakeRows(
 transformedTestData, 5);

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

 // Print 5 predictions.
 foreach (var p in predictions)
Expand All@@ -75,7 +82,8 @@ public static void Example()
 // NDCG: @1:0.96, @2:0.95, @3:0.97
 }

 private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int seed = 0, int groupSize = 10)
 private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count,
 int seed = 0, int groupSize = 10)
 {
 var random = new Random(seed);
 float randomFloat() => (float)random.NextDouble();
Expand All@@ -87,13 +95,16 @@ private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int se
 Label = (uint)label,
 GroupId = (uint)(i / groupSize),
 // Create random features that are correlated with the label.
 // For data points with larger labels, the feature values are slightly increased by adding a constant.
 Features = Enumerable.Repeat(label, 50).Select(x => randomFloat() + x * 0.1f).ToArray()
 // For data points with larger labels, the feature values are
 // slightly increased by adding a constant.
 Features = Enumerable.Repeat(label, 50).Select(
 x => randomFloat() + x * 0.1f).ToArray()
 };
 }
 }

 // Example with label, groupId, and 50 feature values. A data set is a collection of such examples.
 // Example with label, groupId, and 50 feature values. A data set is a
 // collection of such examples.
 private class DataPoint
 {
 [KeyType(5)]
Expand All@@ -116,8 +127,12 @@ private class Prediction
 // Pretty-print RankerMetrics objects.
 public static void PrintMetrics(RankingMetrics metrics)
 {
 Console.WriteLine($"DCG: {string.Join(", ", metrics.DiscountedCumulativeGains.Select((d, i) => $"@{i + 1}:{d:F2}").ToArray())}");
 Console.WriteLine($"NDCG: {string.Join(", ", metrics.NormalizedDiscountedCumulativeGains.Select((d, i) => $"@{i + 1}:{d:F2}").ToArray())}");
 Console.WriteLine("DCG: " + string.Join(", ",
 metrics.DiscountedCumulativeGains.Select(
 (d, i) => (i + 1) + ":" + d + ":F2").ToArray()));
 Console.WriteLine("NDCG: " + string.Join(", ",
 metrics.NormalizedDiscountedCumulativeGains.Select(
 (d, i) => (i + 1) + ":" + d + ":F2").ToArray()));
 }
 }
}

5 changes: 3 additions & 2 deletions
docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Ranking/FastTreeWithOptions.tt

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -16,8 +16,9 @@ string TrainerOptions = @"FastTreeRankingTrainer.Options

string OptionsInclude = "using Microsoft.ML.Trainers.FastTree;";
string Comments= @"
 // This example requires installation of additional NuGet package
 // <a href=""https://www.nuget.org/packages/Microsoft.ML.FastTree/"">Microsoft.ML.FastTree</a>.";
 // This example requires installation of additional NuGet package for 
 // Microsoft.ML.FastTree at
 // https://www.nuget.org/packages/Microsoft.ML.FastTree/";

string ExpectedOutputPerInstance = @"// Expected output:
 // Label: 5, Score: 8.807633
Expand Down

59 changes: 38 additions & 21 deletions
docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Ranking/LightGbm.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -8,17 +8,22 @@ namespace Samples.Dynamic.Trainers.Ranking
{
 public static class LightGbm
 {
 // This example requires installation of additional NuGet package for 
 // Microsoft.ML.FastTree at
 // https://www.nuget.org/packages/Microsoft.ML.FastTree/
 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 = GenerateRandomDataPoints(1000);

 // 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.
Expand All@@ -27,39 +32,44 @@ public static void Example()
 // Train the model.
 var model = pipeline.Fit(trainingData);

 // Create testing data. Use different random seed to make it different from training data.
 var testData = mlContext.Data.LoadFromEnumerable(GenerateRandomDataPoints(500, seed:123));
 // Create testing data. Use different random seed to make it different
 // from training data.
 var testData = mlContext.Data.LoadFromEnumerable(
 GenerateRandomDataPoints(500, seed:123));

 // Run the model on test data set.
 var transformedTestData = model.Transform(testData);

 // Take the top 5 rows.
 var topTransformedTestData = mlContext.Data.TakeRows(transformedTestData, 5);
 var topTransformedTestData = mlContext.Data.TakeRows(
 transformedTestData, 5);

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

 // Print 5 predictions.
 foreach (var p in predictions)
 Console.WriteLine($"Label: {p.Label}, Score: {p.Score}");

 // Expected output:
 // Label: 5, Score: 2.195333
 // Label: 4, Score: 0.2596574
 // Label: 4, Score: -2.168355
 // Label: 1, Score: -3.074823
 // Label: 1, Score: -1.523607
 // Label: 5, Score: 2.493263
@natkenatkeJun 28, 2019

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Did these numbers change for the sample?
@sayanshaw24sayanshaw24Jun 29, 2019

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Yeah they must have changed from a more recent pull.
@natkenatkeJun 29, 2019

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Ok, I see that the sample is using random data points. Thanks
 // Label: 1, Score: -4.528436
 // Label: 3, Score: -3.002865
 // Label: 3, Score: -2.151812
 // Label: 1, Score: -4.089102

 // Evaluate the overall metrics.
 var metrics = mlContext.Ranking.Evaluate(transformedTestData);
 PrintMetrics(metrics);

 // Expected output:
 // DCG: @1:26.03, @2:37.57, @3:45.83
 // NDCG: @1:0.61, @2:0.57, @3:0.59
 // DCG: @1:41.95, @2:63.76, @3:75.97
 // NDCG: @1:0.99, @2:0.99, @3:0.99
 }

 private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int seed = 0, int groupSize = 10)
 private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count,
 int seed = 0, int groupSize = 10)
 {
 var random = new Random(seed);
 float randomFloat() => (float)random.NextDouble();
Expand All@@ -71,13 +81,16 @@ private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int se
 Label = (uint)label,
 GroupId = (uint)(i / groupSize),
 // Create random features that are correlated with the label.
 // For data points with larger labels, the feature values are slightly increased by adding a constant.
 Features = Enumerable.Repeat(label, 50).Select(x => randomFloat() + x * 0.1f).ToArray()
 // For data points with larger labels, the feature values are
 // slightly increased by adding a constant.
 Features = Enumerable.Repeat(label, 50).Select(
 x => randomFloat() + x * 0.1f).ToArray()
 };
 }
 }

 // Example with label, groupId, and 50 feature values. A data set is a collection of such examples.
 // Example with label, groupId, and 50 feature values. A data set is a
 // collection of such examples.
 private class DataPoint
 {
 [KeyType(5)]
Expand All@@ -100,8 +113,12 @@ private class Prediction
 // Pretty-print RankerMetrics objects.
 public static void PrintMetrics(RankingMetrics metrics)
 {
 Console.WriteLine($"DCG: {string.Join(", ", metrics.DiscountedCumulativeGains.Select((d, i) => $"@{i + 1}:{d:F2}").ToArray())}");
 Console.WriteLine($"NDCG: {string.Join(", ", metrics.NormalizedDiscountedCumulativeGains.Select((d, i) => $"@{i + 1}:{d:F2}").ToArray())}");
 Console.WriteLine("DCG: " + string.Join(", ",
 metrics.DiscountedCumulativeGains.Select(
 (d, i) => (i + 1) + ":" + d + ":F2").ToArray()));
 Console.WriteLine("NDCG: " + string.Join(", ",
 metrics.NormalizedDiscountedCumulativeGains.Select(
 (d, i) => (i + 1) + ":" + d + ":F2").ToArray()));
 }
 }
}

5 changes: 3 additions & 2 deletions
docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Ranking/LightGbm.tt

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -6,8 +6,9 @@ string TrainerOptions = null;

string OptionsInclude = "";
string Comments= @"
 // This example requires installation of additional NuGet package
 // <a href=""https://www.nuget.org/packages/Microsoft.ML.LightGbm/"">Microsoft.ML.LightGbm</a>.";
 // This example requires installation of additional NuGet package for 
 // Microsoft.ML.FastTree at
 // https://www.nuget.org/packages/Microsoft.ML.FastTree/";

string ExpectedOutputPerInstance = @"// Expected output:
 // Label: 5, Score: 2.493263
Expand Down
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 Ranking samples to width 85


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f9a0c57

Removed extra white spaces


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2c32e2d

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48 changes: 32 additions & 16 deletions
docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Ranking/FastTree.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -8,19 +8,22 @@ namespace Samples.Dynamic.Trainers.Ranking
{
 public static class FastTree
 {
 // This example requires installation of additional NuGet package
 // <a href="https://www.nuget.org/packages/Microsoft.ML.FastTree/">Microsoft.ML.FastTree</a>.
 // This example requires installation of additional NuGet package for 
 // Microsoft.ML.FastTree at
 // https://www.nuget.org/packages/Microsoft.ML.FastTree/
 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 = GenerateRandomDataPoints(1000);

 // 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.
Expand All@@ -29,17 +32,21 @@ public static void Example()
 // Train the model.
 var model = pipeline.Fit(trainingData);

 // Create testing data. Use different random seed to make it different from training data.
 var testData = mlContext.Data.LoadFromEnumerable(GenerateRandomDataPoints(500, seed:123));
 // Create testing data. Use different random seed to make it different
 // from training data.
 var testData = mlContext.Data.LoadFromEnumerable(
 GenerateRandomDataPoints(500, seed:123));

 // Run the model on test data set.
 var transformedTestData = model.Transform(testData);

 // Take the top 5 rows.
 var topTransformedTestData = mlContext.Data.TakeRows(transformedTestData, 5);
 var topTransformedTestData = mlContext.Data.TakeRows(
 transformedTestData, 5);

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

 // Print 5 predictions.
 foreach (var p in predictions)
Expand All@@ -61,7 +68,8 @@ public static void Example()
 // NDCG: @1:0.99, @2:0.98, @3:0.99
 }

 private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int seed = 0, int groupSize = 10)
 private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count,
 int seed = 0, int groupSize = 10)
 {
 var random = new Random(seed);
 float randomFloat() => (float)random.NextDouble();
Expand All@@ -73,13 +81,16 @@ private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int se
 Label = (uint)label,
 GroupId = (uint)(i / groupSize),
 // Create random features that are correlated with the label.
 // For data points with larger labels, the feature values are slightly increased by adding a constant.
 Features = Enumerable.Repeat(label, 50).Select(x => randomFloat() + x * 0.1f).ToArray()
 // For data points with larger labels, the feature values are
 // slightly increased by adding a constant.
 Features = Enumerable.Repeat(label, 50).Select(
 x => randomFloat() + x * 0.1f).ToArray()
 };
 }
 }

 // Example with label, groupId, and 50 feature values. A data set is a collection of such examples.
 // Example with label, groupId, and 50 feature values. A data set is a
 // collection of such examples.
 private class DataPoint
 {
 [KeyType(5)]
Expand All@@ -102,8 +113,13 @@ private class Prediction
 // Pretty-print RankerMetrics objects.
 public static void PrintMetrics(RankingMetrics metrics)
 {
 Console.WriteLine($"DCG: {string.Join(", ", metrics.DiscountedCumulativeGains.Select((d, i) => $"@{i + 1}:{d:F2}").ToArray())}");
 Console.WriteLine($"NDCG: {string.Join(", ", metrics.NormalizedDiscountedCumulativeGains.Select((d, i) => $"@{i + 1}:{d:F2}").ToArray())}");
 Console.WriteLine("DCG: " + string.Join(", ",
 metrics.DiscountedCumulativeGains.Select(
 (d, i) => (i + 1) + ":" + d + ":F2").ToArray()));

 Console.WriteLine("NDCG: " + string.Join(", ",
 metrics.NormalizedDiscountedCumulativeGains.Select(
 (d, i) => (i + 1) + ":" + d + ":F2").ToArray()));
 }
 }
}

7 changes: 4 additions & 3 deletions
docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Ranking/FastTree.tt

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -6,8 +6,9 @@ string TrainerOptions = null;

string OptionsInclude = "";
string Comments= @"
 // This example requires installation of additional NuGet package
 // <a href=""https://www.nuget.org/packages/Microsoft.ML.FastTree/"">Microsoft.ML.FastTree</a>.";
 // This example requires installation of additional NuGet package for 
 // Microsoft.ML.FastTree at
 // https://www.nuget.org/packages/Microsoft.ML.FastTree/";

string ExpectedOutputPerInstance = @"// Expected output:
 // Label: 5, Score: 13.0154
Expand All@@ -19,4 +20,4 @@ string ExpectedOutputPerInstance = @"// Expected output:
string ExpectedOutput = @"// Expected output:
 // DCG: @1:41.95, @2:63.33, @3:75.65
 // NDCG: @1:0.99, @2:0.98, @3:0.99";
#>
#>

47 changes: 31 additions & 16 deletions
docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Ranking/FastTreeWithOptions.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -9,19 +9,22 @@ namespace Samples.Dynamic.Trainers.Ranking
{
 public static class FastTreeWithOptions
 {
 // This example requires installation of additional NuGet package
 // <a href="https://www.nuget.org/packages/Microsoft.ML.FastTree/">Microsoft.ML.FastTree</a>.
 // This example requires installation of additional NuGet package for 
 // Microsoft.ML.FastTree at
 // https://www.nuget.org/packages/Microsoft.ML.FastTree/
 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 = GenerateRandomDataPoints(1000);

 // 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 trainer options.
Expand All@@ -43,17 +46,21 @@ public static void Example()
 // Train the model.
 var model = pipeline.Fit(trainingData);

 // Create testing data. Use different random seed to make it different from training data.
 var testData = mlContext.Data.LoadFromEnumerable(GenerateRandomDataPoints(500, seed:123));
 // Create testing data. Use different random seed to make it different
 // from training data.
 var testData = mlContext.Data.LoadFromEnumerable(
 GenerateRandomDataPoints(500, seed:123));

 // Run the model on test data set.
 var transformedTestData = model.Transform(testData);

 // Take the top 5 rows.
 var topTransformedTestData = mlContext.Data.TakeRows(transformedTestData, 5);
 var topTransformedTestData = mlContext.Data.TakeRows(
 transformedTestData, 5);

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

 // Print 5 predictions.
 foreach (var p in predictions)
Expand All@@ -75,7 +82,8 @@ public static void Example()
 // NDCG: @1:0.96, @2:0.95, @3:0.97
 }

 private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int seed = 0, int groupSize = 10)
 private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count,
 int seed = 0, int groupSize = 10)
 {
 var random = new Random(seed);
 float randomFloat() => (float)random.NextDouble();
Expand All@@ -87,13 +95,16 @@ private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int se
 Label = (uint)label,
 GroupId = (uint)(i / groupSize),
 // Create random features that are correlated with the label.
 // For data points with larger labels, the feature values are slightly increased by adding a constant.
 Features = Enumerable.Repeat(label, 50).Select(x => randomFloat() + x * 0.1f).ToArray()
 // For data points with larger labels, the feature values are
 // slightly increased by adding a constant.
 Features = Enumerable.Repeat(label, 50).Select(
 x => randomFloat() + x * 0.1f).ToArray()
 };
 }
 }

 // Example with label, groupId, and 50 feature values. A data set is a collection of such examples.
 // Example with label, groupId, and 50 feature values. A data set is a
 // collection of such examples.
 private class DataPoint
 {
 [KeyType(5)]
Expand All@@ -116,8 +127,12 @@ private class Prediction
 // Pretty-print RankerMetrics objects.
 public static void PrintMetrics(RankingMetrics metrics)
 {
 Console.WriteLine($"DCG: {string.Join(", ", metrics.DiscountedCumulativeGains.Select((d, i) => $"@{i + 1}:{d:F2}").ToArray())}");
 Console.WriteLine($"NDCG: {string.Join(", ", metrics.NormalizedDiscountedCumulativeGains.Select((d, i) => $"@{i + 1}:{d:F2}").ToArray())}");
 Console.WriteLine("DCG: " + string.Join(", ",
 metrics.DiscountedCumulativeGains.Select(
 (d, i) => (i + 1) + ":" + d + ":F2").ToArray()));
 Console.WriteLine("NDCG: " + string.Join(", ",
 metrics.NormalizedDiscountedCumulativeGains.Select(
 (d, i) => (i + 1) + ":" + d + ":F2").ToArray()));
 }
 }
}

5 changes: 3 additions & 2 deletions
docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Ranking/FastTreeWithOptions.tt

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -16,8 +16,9 @@ string TrainerOptions = @"FastTreeRankingTrainer.Options

string OptionsInclude = "using Microsoft.ML.Trainers.FastTree;";
string Comments= @"
 // This example requires installation of additional NuGet package
 // <a href=""https://www.nuget.org/packages/Microsoft.ML.FastTree/"">Microsoft.ML.FastTree</a>.";
 // This example requires installation of additional NuGet package for 
 // Microsoft.ML.FastTree at
 // https://www.nuget.org/packages/Microsoft.ML.FastTree/";

string ExpectedOutputPerInstance = @"// Expected output:
 // Label: 5, Score: 8.807633
Expand Down

59 changes: 38 additions & 21 deletions
docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Ranking/LightGbm.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -8,17 +8,22 @@ namespace Samples.Dynamic.Trainers.Ranking
{
 public static class LightGbm
 {
 // This example requires installation of additional NuGet package for 
 // Microsoft.ML.FastTree at
 // https://www.nuget.org/packages/Microsoft.ML.FastTree/
 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 = GenerateRandomDataPoints(1000);

 // 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.
Expand All@@ -27,39 +32,44 @@ public static void Example()
 // Train the model.
 var model = pipeline.Fit(trainingData);

 // Create testing data. Use different random seed to make it different from training data.
 var testData = mlContext.Data.LoadFromEnumerable(GenerateRandomDataPoints(500, seed:123));
 // Create testing data. Use different random seed to make it different
 // from training data.
 var testData = mlContext.Data.LoadFromEnumerable(
 GenerateRandomDataPoints(500, seed:123));

 // Run the model on test data set.
 var transformedTestData = model.Transform(testData);

 // Take the top 5 rows.
 var topTransformedTestData = mlContext.Data.TakeRows(transformedTestData, 5);
 var topTransformedTestData = mlContext.Data.TakeRows(
 transformedTestData, 5);

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

 // Print 5 predictions.
 foreach (var p in predictions)
 Console.WriteLine($"Label: {p.Label}, Score: {p.Score}");

 // Expected output:
 // Label: 5, Score: 2.195333
 // Label: 4, Score: 0.2596574
 // Label: 4, Score: -2.168355
 // Label: 1, Score: -3.074823
 // Label: 1, Score: -1.523607
 // Label: 5, Score: 2.493263
@natkenatkeJun 28, 2019

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Did these numbers change for the sample?
@sayanshaw24sayanshaw24Jun 29, 2019

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Yeah they must have changed from a more recent pull.
@natkenatkeJun 29, 2019

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Ok, I see that the sample is using random data points. Thanks
 // Label: 1, Score: -4.528436
 // Label: 3, Score: -3.002865
 // Label: 3, Score: -2.151812
 // Label: 1, Score: -4.089102

 // Evaluate the overall metrics.
 var metrics = mlContext.Ranking.Evaluate(transformedTestData);
 PrintMetrics(metrics);

 // Expected output:
 // DCG: @1:26.03, @2:37.57, @3:45.83
 // NDCG: @1:0.61, @2:0.57, @3:0.59
 // DCG: @1:41.95, @2:63.76, @3:75.97
 // NDCG: @1:0.99, @2:0.99, @3:0.99
 }

 private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int seed = 0, int groupSize = 10)
 private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count,
 int seed = 0, int groupSize = 10)
 {
 var random = new Random(seed);
 float randomFloat() => (float)random.NextDouble();
Expand All@@ -71,13 +81,16 @@ private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int se
 Label = (uint)label,
 GroupId = (uint)(i / groupSize),
 // Create random features that are correlated with the label.
 // For data points with larger labels, the feature values are slightly increased by adding a constant.
 Features = Enumerable.Repeat(label, 50).Select(x => randomFloat() + x * 0.1f).ToArray()
 // For data points with larger labels, the feature values are
 // slightly increased by adding a constant.
 Features = Enumerable.Repeat(label, 50).Select(
 x => randomFloat() + x * 0.1f).ToArray()
 };
 }
 }

 // Example with label, groupId, and 50 feature values. A data set is a collection of such examples.
 // Example with label, groupId, and 50 feature values. A data set is a
 // collection of such examples.
 private class DataPoint
 {
 [KeyType(5)]
Expand All@@ -100,8 +113,12 @@ private class Prediction
 // Pretty-print RankerMetrics objects.
 public static void PrintMetrics(RankingMetrics metrics)
 {
 Console.WriteLine($"DCG: {string.Join(", ", metrics.DiscountedCumulativeGains.Select((d, i) => $"@{i + 1}:{d:F2}").ToArray())}");
 Console.WriteLine($"NDCG: {string.Join(", ", metrics.NormalizedDiscountedCumulativeGains.Select((d, i) => $"@{i + 1}:{d:F2}").ToArray())}");
 Console.WriteLine("DCG: " + string.Join(", ",
 metrics.DiscountedCumulativeGains.Select(
 (d, i) => (i + 1) + ":" + d + ":F2").ToArray()));
 Console.WriteLine("NDCG: " + string.Join(", ",
 metrics.NormalizedDiscountedCumulativeGains.Select(
 (d, i) => (i + 1) + ":" + d + ":F2").ToArray()));
 }
 }
}

5 changes: 3 additions & 2 deletions
docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Ranking/LightGbm.tt

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -6,8 +6,9 @@ string TrainerOptions = null;

string OptionsInclude = "";
string Comments= @"
 // This example requires installation of additional NuGet package
 // <a href=""https://www.nuget.org/packages/Microsoft.ML.LightGbm/"">Microsoft.ML.LightGbm</a>.";
 // This example requires installation of additional NuGet package for 
 // Microsoft.ML.FastTree at
 // https://www.nuget.org/packages/Microsoft.ML.FastTree/";

string ExpectedOutputPerInstance = @"// Expected output:
 // Label: 5, Score: 2.493263
Expand Down
Loading

, 'i');
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");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Reformatted Ranking samples to width 85#3930

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 Merged

sayanshaw24
merged 10 commits into

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Jul 2, 2019
 Merged


Reformatted Ranking samples to width 85
#3930
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89b5745

Reformatted Ranking samples to width 85


sayanshaw24 Jun 28, 2019
5dc1a0d

Fixed syntax errors


sayanshaw24 Jun 28, 2019
f9a0c57

Removed extra white spaces


sayanshaw24 Jun 28, 2019
2c32e2d

Spacing changes


sayanshaw24 Jun 28, 2019
6dc4f66

Spacing changes


sayanshaw24 Jun 28, 2019
abd549c

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sayanshaw24 Jun 28, 2019
a0d411d

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sayanshaw24 Jun 28, 2019
e736bd1

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sayanshaw24 Jun 28, 2019
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sayanshaw24 Jul 1, 2019

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48 changes: 32 additions & 16 deletions
docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Ranking/FastTree.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -8,19 +8,22 @@ namespace Samples.Dynamic.Trainers.Ranking
{
 public static class FastTree
 {
 // This example requires installation of additional NuGet package
 // <a href="https://www.nuget.org/packages/Microsoft.ML.FastTree/">Microsoft.ML.FastTree</a>.
 // This example requires installation of additional NuGet package for 
 // Microsoft.ML.FastTree at
 // https://www.nuget.org/packages/Microsoft.ML.FastTree/
 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 = GenerateRandomDataPoints(1000);

 // 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.
Expand All@@ -29,17 +32,21 @@ public static void Example()
 // Train the model.
 var model = pipeline.Fit(trainingData);

 // Create testing data. Use different random seed to make it different from training data.
 var testData = mlContext.Data.LoadFromEnumerable(GenerateRandomDataPoints(500, seed:123));
 // Create testing data. Use different random seed to make it different
 // from training data.
 var testData = mlContext.Data.LoadFromEnumerable(
 GenerateRandomDataPoints(500, seed:123));

 // Run the model on test data set.
 var transformedTestData = model.Transform(testData);

 // Take the top 5 rows.
 var topTransformedTestData = mlContext.Data.TakeRows(transformedTestData, 5);
 var topTransformedTestData = mlContext.Data.TakeRows(
 transformedTestData, 5);

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

 // Print 5 predictions.
 foreach (var p in predictions)
Expand All@@ -61,7 +68,8 @@ public static void Example()
 // NDCG: @1:0.99, @2:0.98, @3:0.99
 }

 private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int seed = 0, int groupSize = 10)
 private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count,
 int seed = 0, int groupSize = 10)
 {
 var random = new Random(seed);
 float randomFloat() => (float)random.NextDouble();
Expand All@@ -73,13 +81,16 @@ private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int se
 Label = (uint)label,
 GroupId = (uint)(i / groupSize),
 // Create random features that are correlated with the label.
 // For data points with larger labels, the feature values are slightly increased by adding a constant.
 Features = Enumerable.Repeat(label, 50).Select(x => randomFloat() + x * 0.1f).ToArray()
 // For data points with larger labels, the feature values are
 // slightly increased by adding a constant.
 Features = Enumerable.Repeat(label, 50).Select(
 x => randomFloat() + x * 0.1f).ToArray()
 };
 }
 }

 // Example with label, groupId, and 50 feature values. A data set is a collection of such examples.
 // Example with label, groupId, and 50 feature values. A data set is a
 // collection of such examples.
 private class DataPoint
 {
 [KeyType(5)]
Expand All@@ -102,8 +113,13 @@ private class Prediction
 // Pretty-print RankerMetrics objects.
 public static void PrintMetrics(RankingMetrics metrics)
 {
 Console.WriteLine($"DCG: {string.Join(", ", metrics.DiscountedCumulativeGains.Select((d, i) => $"@{i + 1}:{d:F2}").ToArray())}");
 Console.WriteLine($"NDCG: {string.Join(", ", metrics.NormalizedDiscountedCumulativeGains.Select((d, i) => $"@{i + 1}:{d:F2}").ToArray())}");
 Console.WriteLine("DCG: " + string.Join(", ",
 metrics.DiscountedCumulativeGains.Select(
 (d, i) => (i + 1) + ":" + d + ":F2").ToArray()));

 Console.WriteLine("NDCG: " + string.Join(", ",
 metrics.NormalizedDiscountedCumulativeGains.Select(
 (d, i) => (i + 1) + ":" + d + ":F2").ToArray()));
 }
 }
}

7 changes: 4 additions & 3 deletions
docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Ranking/FastTree.tt

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -6,8 +6,9 @@ string TrainerOptions = null;

string OptionsInclude = "";
string Comments= @"
 // This example requires installation of additional NuGet package
 // <a href=""https://www.nuget.org/packages/Microsoft.ML.FastTree/"">Microsoft.ML.FastTree</a>.";
 // This example requires installation of additional NuGet package for 
 // Microsoft.ML.FastTree at
 // https://www.nuget.org/packages/Microsoft.ML.FastTree/";

string ExpectedOutputPerInstance = @"// Expected output:
 // Label: 5, Score: 13.0154
Expand All@@ -19,4 +20,4 @@ string ExpectedOutputPerInstance = @"// Expected output:
string ExpectedOutput = @"// Expected output:
 // DCG: @1:41.95, @2:63.33, @3:75.65
 // NDCG: @1:0.99, @2:0.98, @3:0.99";
#>
#>

47 changes: 31 additions & 16 deletions
docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Ranking/FastTreeWithOptions.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -9,19 +9,22 @@ namespace Samples.Dynamic.Trainers.Ranking
{
 public static class FastTreeWithOptions
 {
 // This example requires installation of additional NuGet package
 // <a href="https://www.nuget.org/packages/Microsoft.ML.FastTree/">Microsoft.ML.FastTree</a>.
 // This example requires installation of additional NuGet package for 
 // Microsoft.ML.FastTree at
 // https://www.nuget.org/packages/Microsoft.ML.FastTree/
 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 = GenerateRandomDataPoints(1000);

 // 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 trainer options.
Expand All@@ -43,17 +46,21 @@ public static void Example()
 // Train the model.
 var model = pipeline.Fit(trainingData);

 // Create testing data. Use different random seed to make it different from training data.
 var testData = mlContext.Data.LoadFromEnumerable(GenerateRandomDataPoints(500, seed:123));
 // Create testing data. Use different random seed to make it different
 // from training data.
 var testData = mlContext.Data.LoadFromEnumerable(
 GenerateRandomDataPoints(500, seed:123));

 // Run the model on test data set.
 var transformedTestData = model.Transform(testData);

 // Take the top 5 rows.
 var topTransformedTestData = mlContext.Data.TakeRows(transformedTestData, 5);
 var topTransformedTestData = mlContext.Data.TakeRows(
 transformedTestData, 5);

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

 // Print 5 predictions.
 foreach (var p in predictions)
Expand All@@ -75,7 +82,8 @@ public static void Example()
 // NDCG: @1:0.96, @2:0.95, @3:0.97
 }

 private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int seed = 0, int groupSize = 10)
 private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count,
 int seed = 0, int groupSize = 10)
 {
 var random = new Random(seed);
 float randomFloat() => (float)random.NextDouble();
Expand All@@ -87,13 +95,16 @@ private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int se
 Label = (uint)label,
 GroupId = (uint)(i / groupSize),
 // Create random features that are correlated with the label.
 // For data points with larger labels, the feature values are slightly increased by adding a constant.
 Features = Enumerable.Repeat(label, 50).Select(x => randomFloat() + x * 0.1f).ToArray()
 // For data points with larger labels, the feature values are
 // slightly increased by adding a constant.
 Features = Enumerable.Repeat(label, 50).Select(
 x => randomFloat() + x * 0.1f).ToArray()
 };
 }
 }

 // Example with label, groupId, and 50 feature values. A data set is a collection of such examples.
 // Example with label, groupId, and 50 feature values. A data set is a
 // collection of such examples.
 private class DataPoint
 {
 [KeyType(5)]
Expand All@@ -116,8 +127,12 @@ private class Prediction
 // Pretty-print RankerMetrics objects.
 public static void PrintMetrics(RankingMetrics metrics)
 {
 Console.WriteLine($"DCG: {string.Join(", ", metrics.DiscountedCumulativeGains.Select((d, i) => $"@{i + 1}:{d:F2}").ToArray())}");
 Console.WriteLine($"NDCG: {string.Join(", ", metrics.NormalizedDiscountedCumulativeGains.Select((d, i) => $"@{i + 1}:{d:F2}").ToArray())}");
 Console.WriteLine("DCG: " + string.Join(", ",
 metrics.DiscountedCumulativeGains.Select(
 (d, i) => (i + 1) + ":" + d + ":F2").ToArray()));
 Console.WriteLine("NDCG: " + string.Join(", ",
 metrics.NormalizedDiscountedCumulativeGains.Select(
 (d, i) => (i + 1) + ":" + d + ":F2").ToArray()));
 }
 }
}

5 changes: 3 additions & 2 deletions
docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Ranking/FastTreeWithOptions.tt

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -16,8 +16,9 @@ string TrainerOptions = @"FastTreeRankingTrainer.Options

string OptionsInclude = "using Microsoft.ML.Trainers.FastTree;";
string Comments= @"
 // This example requires installation of additional NuGet package
 // <a href=""https://www.nuget.org/packages/Microsoft.ML.FastTree/"">Microsoft.ML.FastTree</a>.";
 // This example requires installation of additional NuGet package for 
 // Microsoft.ML.FastTree at
 // https://www.nuget.org/packages/Microsoft.ML.FastTree/";

string ExpectedOutputPerInstance = @"// Expected output:
 // Label: 5, Score: 8.807633
Expand Down

59 changes: 38 additions & 21 deletions
docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Ranking/LightGbm.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -8,17 +8,22 @@ namespace Samples.Dynamic.Trainers.Ranking
{
 public static class LightGbm
 {
 // This example requires installation of additional NuGet package for 
 // Microsoft.ML.FastTree at
 // https://www.nuget.org/packages/Microsoft.ML.FastTree/
 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 = GenerateRandomDataPoints(1000);

 // 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.
Expand All@@ -27,39 +32,44 @@ public static void Example()
 // Train the model.
 var model = pipeline.Fit(trainingData);

 // Create testing data. Use different random seed to make it different from training data.
 var testData = mlContext.Data.LoadFromEnumerable(GenerateRandomDataPoints(500, seed:123));
 // Create testing data. Use different random seed to make it different
 // from training data.
 var testData = mlContext.Data.LoadFromEnumerable(
 GenerateRandomDataPoints(500, seed:123));

 // Run the model on test data set.
 var transformedTestData = model.Transform(testData);

 // Take the top 5 rows.
 var topTransformedTestData = mlContext.Data.TakeRows(transformedTestData, 5);
 var topTransformedTestData = mlContext.Data.TakeRows(
 transformedTestData, 5);

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

 // Print 5 predictions.
 foreach (var p in predictions)
 Console.WriteLine($"Label: {p.Label}, Score: {p.Score}");

 // Expected output:
 // Label: 5, Score: 2.195333
 // Label: 4, Score: 0.2596574
 // Label: 4, Score: -2.168355
 // Label: 1, Score: -3.074823
 // Label: 1, Score: -1.523607
 // Label: 5, Score: 2.493263
@natkenatkeJun 28, 2019

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Did these numbers change for the sample?
@sayanshaw24sayanshaw24Jun 29, 2019

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Yeah they must have changed from a more recent pull.
@natkenatkeJun 29, 2019

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Ok, I see that the sample is using random data points. Thanks
 // Label: 1, Score: -4.528436
 // Label: 3, Score: -3.002865
 // Label: 3, Score: -2.151812
 // Label: 1, Score: -4.089102

 // Evaluate the overall metrics.
 var metrics = mlContext.Ranking.Evaluate(transformedTestData);
 PrintMetrics(metrics);

 // Expected output:
 // DCG: @1:26.03, @2:37.57, @3:45.83
 // NDCG: @1:0.61, @2:0.57, @3:0.59
 // DCG: @1:41.95, @2:63.76, @3:75.97
 // NDCG: @1:0.99, @2:0.99, @3:0.99
 }

 private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int seed = 0, int groupSize = 10)
 private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count,
 int seed = 0, int groupSize = 10)
 {
 var random = new Random(seed);
 float randomFloat() => (float)random.NextDouble();
Expand All@@ -71,13 +81,16 @@ private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int se
 Label = (uint)label,
 GroupId = (uint)(i / groupSize),
 // Create random features that are correlated with the label.
 // For data points with larger labels, the feature values are slightly increased by adding a constant.
 Features = Enumerable.Repeat(label, 50).Select(x => randomFloat() + x * 0.1f).ToArray()
 // For data points with larger labels, the feature values are
 // slightly increased by adding a constant.
 Features = Enumerable.Repeat(label, 50).Select(
 x => randomFloat() + x * 0.1f).ToArray()
 };
 }
 }

 // Example with label, groupId, and 50 feature values. A data set is a collection of such examples.
 // Example with label, groupId, and 50 feature values. A data set is a
 // collection of such examples.
 private class DataPoint
 {
 [KeyType(5)]
Expand All@@ -100,8 +113,12 @@ private class Prediction
 // Pretty-print RankerMetrics objects.
 public static void PrintMetrics(RankingMetrics metrics)
 {
 Console.WriteLine($"DCG: {string.Join(", ", metrics.DiscountedCumulativeGains.Select((d, i) => $"@{i + 1}:{d:F2}").ToArray())}");
 Console.WriteLine($"NDCG: {string.Join(", ", metrics.NormalizedDiscountedCumulativeGains.Select((d, i) => $"@{i + 1}:{d:F2}").ToArray())}");
 Console.WriteLine("DCG: " + string.Join(", ",
 metrics.DiscountedCumulativeGains.Select(
 (d, i) => (i + 1) + ":" + d + ":F2").ToArray()));
 Console.WriteLine("NDCG: " + string.Join(", ",
 metrics.NormalizedDiscountedCumulativeGains.Select(
 (d, i) => (i + 1) + ":" + d + ":F2").ToArray()));
 }
 }
}

5 changes: 3 additions & 2 deletions
docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Ranking/LightGbm.tt

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -6,8 +6,9 @@ string TrainerOptions = null;

string OptionsInclude = "";
string Comments= @"
 // This example requires installation of additional NuGet package
 // <a href=""https://www.nuget.org/packages/Microsoft.ML.LightGbm/"">Microsoft.ML.LightGbm</a>.";
 // This example requires installation of additional NuGet package for 
 // Microsoft.ML.FastTree at
 // https://www.nuget.org/packages/Microsoft.ML.FastTree/";

string ExpectedOutputPerInstance = @"// Expected output:
 // Label: 5, Score: 2.493263
Expand Down
Loading

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Reformatted Ranking samples to width 85


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48 changes: 32 additions & 16 deletions
docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Ranking/FastTree.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -8,19 +8,22 @@ namespace Samples.Dynamic.Trainers.Ranking
{
 public static class FastTree
 {
 // This example requires installation of additional NuGet package
 // <a href="https://www.nuget.org/packages/Microsoft.ML.FastTree/">Microsoft.ML.FastTree</a>.
 // This example requires installation of additional NuGet package for 
 // Microsoft.ML.FastTree at
 // https://www.nuget.org/packages/Microsoft.ML.FastTree/
 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 = GenerateRandomDataPoints(1000);

 // 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.
Expand All@@ -29,17 +32,21 @@ public static void Example()
 // Train the model.
 var model = pipeline.Fit(trainingData);

 // Create testing data. Use different random seed to make it different from training data.
 var testData = mlContext.Data.LoadFromEnumerable(GenerateRandomDataPoints(500, seed:123));
 // Create testing data. Use different random seed to make it different
 // from training data.
 var testData = mlContext.Data.LoadFromEnumerable(
 GenerateRandomDataPoints(500, seed:123));

 // Run the model on test data set.
 var transformedTestData = model.Transform(testData);

 // Take the top 5 rows.
 var topTransformedTestData = mlContext.Data.TakeRows(transformedTestData, 5);
 var topTransformedTestData = mlContext.Data.TakeRows(
 transformedTestData, 5);

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

 // Print 5 predictions.
 foreach (var p in predictions)
Expand All@@ -61,7 +68,8 @@ public static void Example()
 // NDCG: @1:0.99, @2:0.98, @3:0.99
 }

 private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int seed = 0, int groupSize = 10)
 private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count,
 int seed = 0, int groupSize = 10)
 {
 var random = new Random(seed);
 float randomFloat() => (float)random.NextDouble();
Expand All@@ -73,13 +81,16 @@ private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int se
 Label = (uint)label,
 GroupId = (uint)(i / groupSize),
 // Create random features that are correlated with the label.
 // For data points with larger labels, the feature values are slightly increased by adding a constant.
 Features = Enumerable.Repeat(label, 50).Select(x => randomFloat() + x * 0.1f).ToArray()
 // For data points with larger labels, the feature values are
 // slightly increased by adding a constant.
 Features = Enumerable.Repeat(label, 50).Select(
 x => randomFloat() + x * 0.1f).ToArray()
 };
 }
 }

 // Example with label, groupId, and 50 feature values. A data set is a collection of such examples.
 // Example with label, groupId, and 50 feature values. A data set is a
 // collection of such examples.
 private class DataPoint
 {
 [KeyType(5)]
Expand All@@ -102,8 +113,13 @@ private class Prediction
 // Pretty-print RankerMetrics objects.
 public static void PrintMetrics(RankingMetrics metrics)
 {
 Console.WriteLine($"DCG: {string.Join(", ", metrics.DiscountedCumulativeGains.Select((d, i) => $"@{i + 1}:{d:F2}").ToArray())}");
 Console.WriteLine($"NDCG: {string.Join(", ", metrics.NormalizedDiscountedCumulativeGains.Select((d, i) => $"@{i + 1}:{d:F2}").ToArray())}");
 Console.WriteLine("DCG: " + string.Join(", ",
 metrics.DiscountedCumulativeGains.Select(
 (d, i) => (i + 1) + ":" + d + ":F2").ToArray()));

 Console.WriteLine("NDCG: " + string.Join(", ",
 metrics.NormalizedDiscountedCumulativeGains.Select(
 (d, i) => (i + 1) + ":" + d + ":F2").ToArray()));
 }
 }
}

7 changes: 4 additions & 3 deletions
docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Ranking/FastTree.tt

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -6,8 +6,9 @@ string TrainerOptions = null;

string OptionsInclude = "";
string Comments= @"
 // This example requires installation of additional NuGet package
 // <a href=""https://www.nuget.org/packages/Microsoft.ML.FastTree/"">Microsoft.ML.FastTree</a>.";
 // This example requires installation of additional NuGet package for 
 // Microsoft.ML.FastTree at
 // https://www.nuget.org/packages/Microsoft.ML.FastTree/";

string ExpectedOutputPerInstance = @"// Expected output:
 // Label: 5, Score: 13.0154
Expand All@@ -19,4 +20,4 @@ string ExpectedOutputPerInstance = @"// Expected output:
string ExpectedOutput = @"// Expected output:
 // DCG: @1:41.95, @2:63.33, @3:75.65
 // NDCG: @1:0.99, @2:0.98, @3:0.99";
#>
#>

47 changes: 31 additions & 16 deletions
docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Ranking/FastTreeWithOptions.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -9,19 +9,22 @@ namespace Samples.Dynamic.Trainers.Ranking
{
 public static class FastTreeWithOptions
 {
 // This example requires installation of additional NuGet package
 // <a href="https://www.nuget.org/packages/Microsoft.ML.FastTree/">Microsoft.ML.FastTree</a>.
 // This example requires installation of additional NuGet package for 
 // Microsoft.ML.FastTree at
 // https://www.nuget.org/packages/Microsoft.ML.FastTree/
 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 = GenerateRandomDataPoints(1000);

 // 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 trainer options.
Expand All@@ -43,17 +46,21 @@ public static void Example()
 // Train the model.
 var model = pipeline.Fit(trainingData);

 // Create testing data. Use different random seed to make it different from training data.
 var testData = mlContext.Data.LoadFromEnumerable(GenerateRandomDataPoints(500, seed:123));
 // Create testing data. Use different random seed to make it different
 // from training data.
 var testData = mlContext.Data.LoadFromEnumerable(
 GenerateRandomDataPoints(500, seed:123));

 // Run the model on test data set.
 var transformedTestData = model.Transform(testData);

 // Take the top 5 rows.
 var topTransformedTestData = mlContext.Data.TakeRows(transformedTestData, 5);
 var topTransformedTestData = mlContext.Data.TakeRows(
 transformedTestData, 5);

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

 // Print 5 predictions.
 foreach (var p in predictions)
Expand All@@ -75,7 +82,8 @@ public static void Example()
 // NDCG: @1:0.96, @2:0.95, @3:0.97
 }

 private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int seed = 0, int groupSize = 10)
 private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count,
 int seed = 0, int groupSize = 10)
 {
 var random = new Random(seed);
 float randomFloat() => (float)random.NextDouble();
Expand All@@ -87,13 +95,16 @@ private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int se
 Label = (uint)label,
 GroupId = (uint)(i / groupSize),
 // Create random features that are correlated with the label.
 // For data points with larger labels, the feature values are slightly increased by adding a constant.
 Features = Enumerable.Repeat(label, 50).Select(x => randomFloat() + x * 0.1f).ToArray()
 // For data points with larger labels, the feature values are
 // slightly increased by adding a constant.
 Features = Enumerable.Repeat(label, 50).Select(
 x => randomFloat() + x * 0.1f).ToArray()
 };
 }
 }

 // Example with label, groupId, and 50 feature values. A data set is a collection of such examples.
 // Example with label, groupId, and 50 feature values. A data set is a
 // collection of such examples.
 private class DataPoint
 {
 [KeyType(5)]
Expand All@@ -116,8 +127,12 @@ private class Prediction
 // Pretty-print RankerMetrics objects.
 public static void PrintMetrics(RankingMetrics metrics)
 {
 Console.WriteLine($"DCG: {string.Join(", ", metrics.DiscountedCumulativeGains.Select((d, i) => $"@{i + 1}:{d:F2}").ToArray())}");
 Console.WriteLine($"NDCG: {string.Join(", ", metrics.NormalizedDiscountedCumulativeGains.Select((d, i) => $"@{i + 1}:{d:F2}").ToArray())}");
 Console.WriteLine("DCG: " + string.Join(", ",
 metrics.DiscountedCumulativeGains.Select(
 (d, i) => (i + 1) + ":" + d + ":F2").ToArray()));
 Console.WriteLine("NDCG: " + string.Join(", ",
 metrics.NormalizedDiscountedCumulativeGains.Select(
 (d, i) => (i + 1) + ":" + d + ":F2").ToArray()));
 }
 }
}

5 changes: 3 additions & 2 deletions
docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Ranking/FastTreeWithOptions.tt

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Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -16,8 +16,9 @@ string TrainerOptions = @"FastTreeRankingTrainer.Options

string OptionsInclude = "using Microsoft.ML.Trainers.FastTree;";
string Comments= @"
 // This example requires installation of additional NuGet package
 // <a href=""https://www.nuget.org/packages/Microsoft.ML.FastTree/"">Microsoft.ML.FastTree</a>.";
 // This example requires installation of additional NuGet package for 
 // Microsoft.ML.FastTree at
 // https://www.nuget.org/packages/Microsoft.ML.FastTree/";

string ExpectedOutputPerInstance = @"// Expected output:
 // Label: 5, Score: 8.807633
Expand Down

59 changes: 38 additions & 21 deletions
docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Ranking/LightGbm.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -8,17 +8,22 @@ namespace Samples.Dynamic.Trainers.Ranking
{
 public static class LightGbm
 {
 // This example requires installation of additional NuGet package for 
 // Microsoft.ML.FastTree at
 // https://www.nuget.org/packages/Microsoft.ML.FastTree/
 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 = GenerateRandomDataPoints(1000);

 // 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.
Expand All@@ -27,39 +32,44 @@ public static void Example()
 // Train the model.
 var model = pipeline.Fit(trainingData);

 // Create testing data. Use different random seed to make it different from training data.
 var testData = mlContext.Data.LoadFromEnumerable(GenerateRandomDataPoints(500, seed:123));
 // Create testing data. Use different random seed to make it different
 // from training data.
 var testData = mlContext.Data.LoadFromEnumerable(
 GenerateRandomDataPoints(500, seed:123));

 // Run the model on test data set.
 var transformedTestData = model.Transform(testData);

 // Take the top 5 rows.
 var topTransformedTestData = mlContext.Data.TakeRows(transformedTestData, 5);
 var topTransformedTestData = mlContext.Data.TakeRows(
 transformedTestData, 5);

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

 // Print 5 predictions.
 foreach (var p in predictions)
 Console.WriteLine($"Label: {p.Label}, Score: {p.Score}");

 // Expected output:
 // Label: 5, Score: 2.195333
 // Label: 4, Score: 0.2596574
 // Label: 4, Score: -2.168355
 // Label: 1, Score: -3.074823
 // Label: 1, Score: -1.523607
 // Label: 5, Score: 2.493263
@natkenatkeJun 28, 2019

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Did these numbers change for the sample?
@sayanshaw24sayanshaw24Jun 29, 2019

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Yeah they must have changed from a more recent pull.
@natkenatkeJun 29, 2019

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Ok, I see that the sample is using random data points. Thanks
 // Label: 1, Score: -4.528436
 // Label: 3, Score: -3.002865
 // Label: 3, Score: -2.151812
 // Label: 1, Score: -4.089102

 // Evaluate the overall metrics.
 var metrics = mlContext.Ranking.Evaluate(transformedTestData);
 PrintMetrics(metrics);

 // Expected output:
 // DCG: @1:26.03, @2:37.57, @3:45.83
 // NDCG: @1:0.61, @2:0.57, @3:0.59
 // DCG: @1:41.95, @2:63.76, @3:75.97
 // NDCG: @1:0.99, @2:0.99, @3:0.99
 }

 private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int seed = 0, int groupSize = 10)
 private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count,
 int seed = 0, int groupSize = 10)
 {
 var random = new Random(seed);
 float randomFloat() => (float)random.NextDouble();
Expand All@@ -71,13 +81,16 @@ private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int se
 Label = (uint)label,
 GroupId = (uint)(i / groupSize),
 // Create random features that are correlated with the label.
 // For data points with larger labels, the feature values are slightly increased by adding a constant.
 Features = Enumerable.Repeat(label, 50).Select(x => randomFloat() + x * 0.1f).ToArray()
 // For data points with larger labels, the feature values are
 // slightly increased by adding a constant.
 Features = Enumerable.Repeat(label, 50).Select(
 x => randomFloat() + x * 0.1f).ToArray()
 };
 }
 }

 // Example with label, groupId, and 50 feature values. A data set is a collection of such examples.
 // Example with label, groupId, and 50 feature values. A data set is a
 // collection of such examples.
 private class DataPoint
 {
 [KeyType(5)]
Expand All@@ -100,8 +113,12 @@ private class Prediction
 // Pretty-print RankerMetrics objects.
 public static void PrintMetrics(RankingMetrics metrics)
 {
 Console.WriteLine($"DCG: {string.Join(", ", metrics.DiscountedCumulativeGains.Select((d, i) => $"@{i + 1}:{d:F2}").ToArray())}");
 Console.WriteLine($"NDCG: {string.Join(", ", metrics.NormalizedDiscountedCumulativeGains.Select((d, i) => $"@{i + 1}:{d:F2}").ToArray())}");
 Console.WriteLine("DCG: " + string.Join(", ",
 metrics.DiscountedCumulativeGains.Select(
 (d, i) => (i + 1) + ":" + d + ":F2").ToArray()));
 Console.WriteLine("NDCG: " + string.Join(", ",
 metrics.NormalizedDiscountedCumulativeGains.Select(
 (d, i) => (i + 1) + ":" + d + ":F2").ToArray()));
 }
 }
}

5 changes: 3 additions & 2 deletions
docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Ranking/LightGbm.tt

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -6,8 +6,9 @@ string TrainerOptions = null;

string OptionsInclude = "";
string Comments= @"
 // This example requires installation of additional NuGet package
 // <a href=""https://www.nuget.org/packages/Microsoft.ML.LightGbm/"">Microsoft.ML.LightGbm</a>.";
 // This example requires installation of additional NuGet package for 
 // Microsoft.ML.FastTree at
 // https://www.nuget.org/packages/Microsoft.ML.FastTree/";

string ExpectedOutputPerInstance = @"// Expected output:
 // Label: 5, Score: 2.493263
Expand Down
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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48 changes: 32 additions & 16 deletions
docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Ranking/FastTree.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -8,19 +8,22 @@ namespace Samples.Dynamic.Trainers.Ranking
{
 public static class FastTree
 {
 // This example requires installation of additional NuGet package
 // <a href="https://www.nuget.org/packages/Microsoft.ML.FastTree/">Microsoft.ML.FastTree</a>.
 // This example requires installation of additional NuGet package for 
 // Microsoft.ML.FastTree at
 // https://www.nuget.org/packages/Microsoft.ML.FastTree/
 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 = GenerateRandomDataPoints(1000);

 // 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.
Expand All@@ -29,17 +32,21 @@ public static void Example()
 // Train the model.
 var model = pipeline.Fit(trainingData);

 // Create testing data. Use different random seed to make it different from training data.
 var testData = mlContext.Data.LoadFromEnumerable(GenerateRandomDataPoints(500, seed:123));
 // Create testing data. Use different random seed to make it different
 // from training data.
 var testData = mlContext.Data.LoadFromEnumerable(
 GenerateRandomDataPoints(500, seed:123));

 // Run the model on test data set.
 var transformedTestData = model.Transform(testData);

 // Take the top 5 rows.
 var topTransformedTestData = mlContext.Data.TakeRows(transformedTestData, 5);
 var topTransformedTestData = mlContext.Data.TakeRows(
 transformedTestData, 5);

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

 // Print 5 predictions.
 foreach (var p in predictions)
Expand All@@ -61,7 +68,8 @@ public static void Example()
 // NDCG: @1:0.99, @2:0.98, @3:0.99
 }

 private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int seed = 0, int groupSize = 10)
 private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count,
 int seed = 0, int groupSize = 10)
 {
 var random = new Random(seed);
 float randomFloat() => (float)random.NextDouble();
Expand All@@ -73,13 +81,16 @@ private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int se
 Label = (uint)label,
 GroupId = (uint)(i / groupSize),
 // Create random features that are correlated with the label.
 // For data points with larger labels, the feature values are slightly increased by adding a constant.
 Features = Enumerable.Repeat(label, 50).Select(x => randomFloat() + x * 0.1f).ToArray()
 // For data points with larger labels, the feature values are
 // slightly increased by adding a constant.
 Features = Enumerable.Repeat(label, 50).Select(
 x => randomFloat() + x * 0.1f).ToArray()
 };
 }
 }

 // Example with label, groupId, and 50 feature values. A data set is a collection of such examples.
 // Example with label, groupId, and 50 feature values. A data set is a
 // collection of such examples.
 private class DataPoint
 {
 [KeyType(5)]
Expand All@@ -102,8 +113,13 @@ private class Prediction
 // Pretty-print RankerMetrics objects.
 public static void PrintMetrics(RankingMetrics metrics)
 {
 Console.WriteLine($"DCG: {string.Join(", ", metrics.DiscountedCumulativeGains.Select((d, i) => $"@{i + 1}:{d:F2}").ToArray())}");
 Console.WriteLine($"NDCG: {string.Join(", ", metrics.NormalizedDiscountedCumulativeGains.Select((d, i) => $"@{i + 1}:{d:F2}").ToArray())}");
 Console.WriteLine("DCG: " + string.Join(", ",
 metrics.DiscountedCumulativeGains.Select(
 (d, i) => (i + 1) + ":" + d + ":F2").ToArray()));

 Console.WriteLine("NDCG: " + string.Join(", ",
 metrics.NormalizedDiscountedCumulativeGains.Select(
 (d, i) => (i + 1) + ":" + d + ":F2").ToArray()));
 }
 }
}

7 changes: 4 additions & 3 deletions
docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Ranking/FastTree.tt

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -6,8 +6,9 @@ string TrainerOptions = null;

string OptionsInclude = "";
string Comments= @"
 // This example requires installation of additional NuGet package
 // <a href=""https://www.nuget.org/packages/Microsoft.ML.FastTree/"">Microsoft.ML.FastTree</a>.";
 // This example requires installation of additional NuGet package for 
 // Microsoft.ML.FastTree at
 // https://www.nuget.org/packages/Microsoft.ML.FastTree/";

string ExpectedOutputPerInstance = @"// Expected output:
 // Label: 5, Score: 13.0154
Expand All@@ -19,4 +20,4 @@ string ExpectedOutputPerInstance = @"// Expected output:
string ExpectedOutput = @"// Expected output:
 // DCG: @1:41.95, @2:63.33, @3:75.65
 // NDCG: @1:0.99, @2:0.98, @3:0.99";
#>
#>

47 changes: 31 additions & 16 deletions
docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Ranking/FastTreeWithOptions.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -9,19 +9,22 @@ namespace Samples.Dynamic.Trainers.Ranking
{
 public static class FastTreeWithOptions
 {
 // This example requires installation of additional NuGet package
 // <a href="https://www.nuget.org/packages/Microsoft.ML.FastTree/">Microsoft.ML.FastTree</a>.
 // This example requires installation of additional NuGet package for 
 // Microsoft.ML.FastTree at
 // https://www.nuget.org/packages/Microsoft.ML.FastTree/
 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 = GenerateRandomDataPoints(1000);

 // 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 trainer options.
Expand All@@ -43,17 +46,21 @@ public static void Example()
 // Train the model.
 var model = pipeline.Fit(trainingData);

 // Create testing data. Use different random seed to make it different from training data.
 var testData = mlContext.Data.LoadFromEnumerable(GenerateRandomDataPoints(500, seed:123));
 // Create testing data. Use different random seed to make it different
 // from training data.
 var testData = mlContext.Data.LoadFromEnumerable(
 GenerateRandomDataPoints(500, seed:123));

 // Run the model on test data set.
 var transformedTestData = model.Transform(testData);

 // Take the top 5 rows.
 var topTransformedTestData = mlContext.Data.TakeRows(transformedTestData, 5);
 var topTransformedTestData = mlContext.Data.TakeRows(
 transformedTestData, 5);

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

 // Print 5 predictions.
 foreach (var p in predictions)
Expand All@@ -75,7 +82,8 @@ public static void Example()
 // NDCG: @1:0.96, @2:0.95, @3:0.97
 }

 private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int seed = 0, int groupSize = 10)
 private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count,
 int seed = 0, int groupSize = 10)
 {
 var random = new Random(seed);
 float randomFloat() => (float)random.NextDouble();
Expand All@@ -87,13 +95,16 @@ private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int se
 Label = (uint)label,
 GroupId = (uint)(i / groupSize),
 // Create random features that are correlated with the label.
 // For data points with larger labels, the feature values are slightly increased by adding a constant.
 Features = Enumerable.Repeat(label, 50).Select(x => randomFloat() + x * 0.1f).ToArray()
 // For data points with larger labels, the feature values are
 // slightly increased by adding a constant.
 Features = Enumerable.Repeat(label, 50).Select(
 x => randomFloat() + x * 0.1f).ToArray()
 };
 }
 }

 // Example with label, groupId, and 50 feature values. A data set is a collection of such examples.
 // Example with label, groupId, and 50 feature values. A data set is a
 // collection of such examples.
 private class DataPoint
 {
 [KeyType(5)]
Expand All@@ -116,8 +127,12 @@ private class Prediction
 // Pretty-print RankerMetrics objects.
 public static void PrintMetrics(RankingMetrics metrics)
 {
 Console.WriteLine($"DCG: {string.Join(", ", metrics.DiscountedCumulativeGains.Select((d, i) => $"@{i + 1}:{d:F2}").ToArray())}");
 Console.WriteLine($"NDCG: {string.Join(", ", metrics.NormalizedDiscountedCumulativeGains.Select((d, i) => $"@{i + 1}:{d:F2}").ToArray())}");
 Console.WriteLine("DCG: " + string.Join(", ",
 metrics.DiscountedCumulativeGains.Select(
 (d, i) => (i + 1) + ":" + d + ":F2").ToArray()));
 Console.WriteLine("NDCG: " + string.Join(", ",
 metrics.NormalizedDiscountedCumulativeGains.Select(
 (d, i) => (i + 1) + ":" + d + ":F2").ToArray()));
 }
 }
}

5 changes: 3 additions & 2 deletions
docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Ranking/FastTreeWithOptions.tt

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Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -16,8 +16,9 @@ string TrainerOptions = @"FastTreeRankingTrainer.Options

string OptionsInclude = "using Microsoft.ML.Trainers.FastTree;";
string Comments= @"
 // This example requires installation of additional NuGet package
 // <a href=""https://www.nuget.org/packages/Microsoft.ML.FastTree/"">Microsoft.ML.FastTree</a>.";
 // This example requires installation of additional NuGet package for 
 // Microsoft.ML.FastTree at
 // https://www.nuget.org/packages/Microsoft.ML.FastTree/";

string ExpectedOutputPerInstance = @"// Expected output:
 // Label: 5, Score: 8.807633
Expand Down

59 changes: 38 additions & 21 deletions
docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Ranking/LightGbm.cs

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Original file line numberDiff line numberDiff line change
Expand Up@@ -8,17 +8,22 @@ namespace Samples.Dynamic.Trainers.Ranking
{
 public static class LightGbm
 {
 // This example requires installation of additional NuGet package for 
 // Microsoft.ML.FastTree at
 // https://www.nuget.org/packages/Microsoft.ML.FastTree/
 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 = GenerateRandomDataPoints(1000);

 // 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.
Expand All@@ -27,39 +32,44 @@ public static void Example()
 // Train the model.
 var model = pipeline.Fit(trainingData);

 // Create testing data. Use different random seed to make it different from training data.
 var testData = mlContext.Data.LoadFromEnumerable(GenerateRandomDataPoints(500, seed:123));
 // Create testing data. Use different random seed to make it different
 // from training data.
 var testData = mlContext.Data.LoadFromEnumerable(
 GenerateRandomDataPoints(500, seed:123));

 // Run the model on test data set.
 var transformedTestData = model.Transform(testData);

 // Take the top 5 rows.
 var topTransformedTestData = mlContext.Data.TakeRows(transformedTestData, 5);
 var topTransformedTestData = mlContext.Data.TakeRows(
 transformedTestData, 5);

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

 // Print 5 predictions.
 foreach (var p in predictions)
 Console.WriteLine($"Label: {p.Label}, Score: {p.Score}");

 // Expected output:
 // Label: 5, Score: 2.195333
 // Label: 4, Score: 0.2596574
 // Label: 4, Score: -2.168355
 // Label: 1, Score: -3.074823
 // Label: 1, Score: -1.523607
 // Label: 5, Score: 2.493263
@natkenatkeJun 28, 2019

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Did these numbers change for the sample?
@sayanshaw24sayanshaw24Jun 29, 2019

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Yeah they must have changed from a more recent pull.
@natkenatkeJun 29, 2019

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Ok, I see that the sample is using random data points. Thanks
 // Label: 1, Score: -4.528436
 // Label: 3, Score: -3.002865
 // Label: 3, Score: -2.151812
 // Label: 1, Score: -4.089102

 // Evaluate the overall metrics.
 var metrics = mlContext.Ranking.Evaluate(transformedTestData);
 PrintMetrics(metrics);

 // Expected output:
 // DCG: @1:26.03, @2:37.57, @3:45.83
 // NDCG: @1:0.61, @2:0.57, @3:0.59
 // DCG: @1:41.95, @2:63.76, @3:75.97
 // NDCG: @1:0.99, @2:0.99, @3:0.99
 }

 private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int seed = 0, int groupSize = 10)
 private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count,
 int seed = 0, int groupSize = 10)
 {
 var random = new Random(seed);
 float randomFloat() => (float)random.NextDouble();
Expand All@@ -71,13 +81,16 @@ private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int se
 Label = (uint)label,
 GroupId = (uint)(i / groupSize),
 // Create random features that are correlated with the label.
 // For data points with larger labels, the feature values are slightly increased by adding a constant.
 Features = Enumerable.Repeat(label, 50).Select(x => randomFloat() + x * 0.1f).ToArray()
 // For data points with larger labels, the feature values are
 // slightly increased by adding a constant.
 Features = Enumerable.Repeat(label, 50).Select(
 x => randomFloat() + x * 0.1f).ToArray()
 };
 }
 }

 // Example with label, groupId, and 50 feature values. A data set is a collection of such examples.
 // Example with label, groupId, and 50 feature values. A data set is a
 // collection of such examples.
 private class DataPoint
 {
 [KeyType(5)]
Expand All@@ -100,8 +113,12 @@ private class Prediction
 // Pretty-print RankerMetrics objects.
 public static void PrintMetrics(RankingMetrics metrics)
 {
 Console.WriteLine($"DCG: {string.Join(", ", metrics.DiscountedCumulativeGains.Select((d, i) => $"@{i + 1}:{d:F2}").ToArray())}");
 Console.WriteLine($"NDCG: {string.Join(", ", metrics.NormalizedDiscountedCumulativeGains.Select((d, i) => $"@{i + 1}:{d:F2}").ToArray())}");
 Console.WriteLine("DCG: " + string.Join(", ",
 metrics.DiscountedCumulativeGains.Select(
 (d, i) => (i + 1) + ":" + d + ":F2").ToArray()));
 Console.WriteLine("NDCG: " + string.Join(", ",
 metrics.NormalizedDiscountedCumulativeGains.Select(
 (d, i) => (i + 1) + ":" + d + ":F2").ToArray()));
 }
 }
}

5 changes: 3 additions & 2 deletions
docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Ranking/LightGbm.tt

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Original file line numberDiff line numberDiff line change
Expand Up@@ -6,8 +6,9 @@ string TrainerOptions = null;

string OptionsInclude = "";
string Comments= @"
 // This example requires installation of additional NuGet package
 // <a href=""https://www.nuget.org/packages/Microsoft.ML.LightGbm/"">Microsoft.ML.LightGbm</a>.";
 // This example requires installation of additional NuGet package for 
 // Microsoft.ML.FastTree at
 // https://www.nuget.org/packages/Microsoft.ML.FastTree/";

string ExpectedOutputPerInstance = @"// Expected output:
 // Label: 5, Score: 2.493263
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