Reformatting Test, Projection and TimeSeries of Transform to Width 85#3947

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rayankrish merged 3 commits into from
Jul 3, 2019
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Reformatting Test, Projection and TimeSeries of Transform to Width 85 #3947

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9c6325f
transform/text and projection formatted to 85 char
Jul 2, 2019
da95c60
transforms/timeseries formatted to 85 char
Jul 2, 2019
e6aca2a
minor tab and spacing fixes
Jul 2, 2019
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41 changes: 28 additions & 13 deletions docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Projection/VectorWhiten.cs
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Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -9,11 +9,12 @@ namespace Samples.Dynamic
public sealed class VectorWhiten
{

/// This example requires installation of additional nuget package <a href="https://www.nuget.org/packages/Microsoft.ML.Mkl.Components/">Microsoft.ML.Mkl.Components</a>.
/// This example requires installation of additional nuget package
/// <a href="https://www.nuget.org/packages/Microsoft.ML.Mkl.Components/">Microsoft.ML.Mkl.Components</a>.
public static void Example()
{
// Create a new ML context, for ML.NET operations. It can be used for exception tracking and logging,
// as well as the source of randomness.
// Create a new ML context, for ML.NET operations. It can be used for
// exception tracking and logging, as well as the source of randomness.
var ml = new MLContext();

// Get a small dataset as an IEnumerable and convert it to an IDataView.
Expand All@@ -32,20 +33,29 @@ public static void Example()
// 6 7 8 9 0 1 2 3 4 5

// A small printing utility.
Action<string, IEnumerable<VBuffer<float>>> printHelper = (colName, column) =>
Action<string, IEnumerable<VBuffer<float>>> printHelper = (colName,
column) =>
{
Console.WriteLine($"{colName} column obtained post-transformation.");
Console.WriteLine($"{colName} column obtained " +
$"post-transformation.");

foreach (var row in column)
Console.WriteLine($"{string.Join(" ", row.DenseValues().Select(x => x.ToString("f3")))} ");
Console.WriteLine(string.Join(" ", row.DenseValues().Select(x =>
x.ToString("f3")))+" ");

@codemzscodemzsJul 2, 2019

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ctrl+k+d is your friend.

};

// A pipeline to project Features column into white noise vector.
var whiteningPipeline = ml.Transforms.VectorWhiten(nameof(SampleVectorOfNumbersData.Features),
kind: Microsoft.ML.Transforms.WhiteningKind.ZeroPhaseComponentAnalysis);
var whiteningPipeline = ml.Transforms.VectorWhiten(nameof(
SampleVectorOfNumbersData.Features), kind: Microsoft.ML.Transforms
.WhiteningKind.ZeroPhaseComponentAnalysis);

// The transformed (projected) data.
var transformedData = whiteningPipeline.Fit(trainData).Transform(trainData);
var transformedData = whiteningPipeline.Fit(trainData).Transform(
trainData);

// Getting the data of the newly created column, so we can preview it.
var whitening = transformedData.GetColumn<VBuffer<float>>(transformedData.Schema[nameof(SampleVectorOfNumbersData.Features)]);
var whitening = transformedData.GetColumn<VBuffer<float>>(
transformedData.Schema[nameof(SampleVectorOfNumbersData.Features)]);

printHelper(nameof(SampleVectorOfNumbersData.Features), whitening);

Expand All@@ -68,11 +78,16 @@ private class SampleVectorOfNumbersData
/// <summary>
/// Returns a few rows of the infertility dataset.
/// </summary>
private static IEnumerable<SampleVectorOfNumbersData> GetVectorOfNumbersData()
private static IEnumerable<SampleVectorOfNumbersData>
GetVectorOfNumbersData()
{
var data = new List<SampleVectorOfNumbersData>();
data.Add(new SampleVectorOfNumbersData { Features = new float[10] { 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 } });
data.Add(new SampleVectorOfNumbersData { Features = new float[10] { 1, 2, 3, 4, 5, 6, 7, 8, 9, 0 } });
data.Add(new SampleVectorOfNumbersData { Features = new float[10] { 0,
1, 2, 3, 4, 5, 6, 7, 8, 9 } });

data.Add(new SampleVectorOfNumbersData { Features = new float[10] { 1,
2, 3, 4, 5, 6, 7, 8, 9, 0 } });

data.Add(new SampleVectorOfNumbersData
{
Features = new float[10] { 2, 3, 4, 5, 6, 7, 8, 9, 0, 1 }
Expand Down
40 changes: 28 additions & 12 deletions docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Projection/VectorWhitenWithOptions.cs
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -8,11 +8,12 @@ namespace Samples.Dynamic
{
public sealed class VectorWhitenWithOptions
{
/// This example requires installation of additional nuget package <a href="https://www.nuget.org/packages/Microsoft.ML.Mkl.Components/">Microsoft.ML.Mkl.Components</a>.
/// This example requires installation of additional nuget package
/// <a href="https://www.nuget.org/packages/Microsoft.ML.Mkl.Components/">Microsoft.ML.Mkl.Components</a>.
public static void Example()
{
// Create a new ML context, for ML.NET operations. It can be used for exception tracking and logging,
// as well as the source of randomness.
// Create a new ML context, for ML.NET operations. It can be used for
// exception tracking and logging, as well as the source of randomness.
var ml = new MLContext();

// Get a small dataset as an IEnumerable and convert it to an IDataView.
Expand All@@ -31,20 +32,30 @@ public static void Example()
// 6 7 8 9 0 1 2 3 4 5

// A small printing utility.
Action<string, IEnumerable<VBuffer<float>>> printHelper = (colName, column) =>
Action<string, IEnumerable<VBuffer<float>>> printHelper = (colName,
column) =>
{
Console.WriteLine($"{colName} column obtained post-transformation.");
Console.WriteLine($"{colName} column obtained" +
$"post-transformation.");

foreach (var row in column)
Console.WriteLine($"{string.Join(" ", row.DenseValues().Select(x => x.ToString("f3")))} ");
Console.WriteLine(string.Join(" ", row.DenseValues().Select(x =>
x.ToString("f3")))+" ");
};


// A pipeline to project Features column into white noise vector.
var whiteningPipeline = ml.Transforms.VectorWhiten(nameof(SampleVectorOfNumbersData.Features), kind: Microsoft.ML.Transforms.WhiteningKind.PrincipalComponentAnalysis, rank: 4);
var whiteningPipeline = ml.Transforms.VectorWhiten(nameof(
SampleVectorOfNumbersData.Features), kind: Microsoft.ML.Transforms
.WhiteningKind.PrincipalComponentAnalysis, rank: 4);

// The transformed (projected) data.
var transformedData = whiteningPipeline.Fit(trainData).Transform(trainData);
var transformedData = whiteningPipeline.Fit(trainData).Transform(
trainData);

// Getting the data of the newly created column, so we can preview it.
var whitening = transformedData.GetColumn<VBuffer<float>>(transformedData.Schema[nameof(SampleVectorOfNumbersData.Features)]);
var whitening = transformedData.GetColumn<VBuffer<float>>(
transformedData.Schema[nameof(SampleVectorOfNumbersData.Features)]);

printHelper(nameof(SampleVectorOfNumbersData.Features), whitening);

Expand All@@ -66,11 +77,16 @@ private class SampleVectorOfNumbersData
/// <summary>
/// Returns a few rows of the infertility dataset.
/// </summary>
private static IEnumerable<SampleVectorOfNumbersData> GetVectorOfNumbersData()
private static IEnumerable<SampleVectorOfNumbersData>
GetVectorOfNumbersData()
{
var data = new List<SampleVectorOfNumbersData>();
data.Add(new SampleVectorOfNumbersData { Features = new float[10] { 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 } });
data.Add(new SampleVectorOfNumbersData { Features = new float[10] { 1, 2, 3, 4, 5, 6, 7, 8, 9, 0 } });
data.Add(new SampleVectorOfNumbersData { Features = new float[10] { 0,
1, 2, 3, 4, 5, 6, 7, 8, 9 } });

data.Add(new SampleVectorOfNumbersData { Features = new float[10] { 1,
2, 3, 4, 5, 6, 7, 8, 9, 0 } });

data.Add(new SampleVectorOfNumbersData
{
Features = new float[10] { 2, 3, 4, 5, 6, 7, 8, 9, 0, 1 }
Expand Down
37 changes: 23 additions & 14 deletions docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Text/ApplyCustomWordEmbedding.cs
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -9,12 +9,13 @@ public static class ApplyCustomWordEmbedding
{
public static void Example()
{
// Create a new ML context, for ML.NET operations. It can be used for exception tracking and logging,
// as well as the source of randomness.
// Create a new ML context, for ML.NET operations. It can be used for
// exception tracking and logging, as well as the source of randomness.
var mlContext = new MLContext();

// Create an empty list as the dataset. The 'ApplyWordEmbedding' does not require training data as
// the estimator ('WordEmbeddingEstimator') created by 'ApplyWordEmbedding' API is not a trainable estimator.
// Create an empty list as the dataset. The 'ApplyWordEmbedding' does
// not require training data as the estimator ('WordEmbeddingEstimator')
// created by 'ApplyWordEmbedding' API is not a trainable estimator.
// The empty list is only needed to pass input schema to the pipeline.
var emptySamples = new List<TextData>();

Expand All@@ -33,25 +34,33 @@ public static void Example()
file.WriteLine("buy 0 0 20");
}

// A pipeline for converting text into a 9-dimension word embedding vector using the custom word embedding model.
// The 'ApplyWordEmbedding' computes the minimum, average and maximum values for each token's embedding vector.
// Tokens in 'custommodel.txt' model are represented as 3-dimension vector.
// Therefore, the output is of 9-dimension [min, avg, max].
// A pipeline for converting text into a 9-dimension word embedding
// vector using the custom word embedding model. The
// 'ApplyWordEmbedding' computes the minimum, average and maximum values
// for each token's embedding vector. Tokens in 'custommodel.txt' model
// are represented as 3-dimension vector. Therefore, the output is of
// 9 -dimension [min, avg, max].
//
// The 'ApplyWordEmbedding' API requires vector of text as input.
// The pipeline first normalizes and tokenizes text then applies word embedding transformation.
// The pipeline first normalizes and tokenizes text then applies word
// embedding transformation.
var textPipeline = mlContext.Transforms.Text.NormalizeText("Text")
.Append(mlContext.Transforms.Text.TokenizeIntoWords("Tokens", "Text"))
.Append(mlContext.Transforms.Text.ApplyWordEmbedding("Features", pathToCustomModel, "Tokens"));
.Append(mlContext.Transforms.Text.TokenizeIntoWords("Tokens",
"Text"))
.Append(mlContext.Transforms.Text.ApplyWordEmbedding("Features",
pathToCustomModel, "Tokens"));

// Fit to data.
var textTransformer = textPipeline.Fit(emptyDataView);

// Create the prediction engine to get the embedding vector from the input text/string.
var predictionEngine = mlContext.Model.CreatePredictionEngine<TextData, TransformedTextData>(textTransformer);
// Create the prediction engine to get the embedding vector from the
// input text/string.
var predictionEngine = mlContext.Model.CreatePredictionEngine<TextData,
TransformedTextData>(textTransformer);

// Call the prediction API to convert the text into embedding vector.
var data = new TextData() { Text = "This is a great product. I would like to buy it again." };
var data = new TextData() { Text = "This is a great product. I would " +
"like to buy it again." };
var prediction = predictionEngine.Predict(data);

// Print the length of the embedding vector.
Expand Down
40 changes: 25 additions & 15 deletions docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Text/ApplyWordEmbedding.cs
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -9,38 +9,48 @@ public static class ApplyWordEmbedding
{
public static void Example()
{
// Create a new ML context, for ML.NET operations. It can be used for exception tracking and logging,
// as well as the source of randomness.
// Create a new ML context, for ML.NET operations. It can be used for
// exception tracking and logging, as well as the source of randomness.
var mlContext = new MLContext();

// Create an empty list as the dataset. The 'ApplyWordEmbedding' does not require training data as
// the estimator ('WordEmbeddingEstimator') created by 'ApplyWordEmbedding' API is not a trainable estimator.
// Create an empty list as the dataset. The 'ApplyWordEmbedding' does
// not require training data as the estimator ('WordEmbeddingEstimator')
// created by 'ApplyWordEmbedding' API is not a trainable estimator.
// The empty list is only needed to pass input schema to the pipeline.
var emptySamples = new List<TextData>();

// Convert sample list to an empty IDataView.
var emptyDataView = mlContext.Data.LoadFromEnumerable(emptySamples);

// A pipeline for converting text into a 150-dimension embedding vector using pretrained 'SentimentSpecificWordEmbedding' model.
// The 'ApplyWordEmbedding' computes the minimum, average and maximum values for each token's embedding vector.
// Tokens in 'SentimentSpecificWordEmbedding' model are represented as 50-dimension vector.
// Therefore, the output is of 150-dimension [min, avg, max].
// A pipeline for converting text into a 150-dimension embedding vector
// using pretrained 'SentimentSpecificWordEmbedding' model. The
// 'ApplyWordEmbedding' computes the minimum, average and maximum values
// for each token's embedding vector. Tokens in
// 'SentimentSpecificWordEmbedding' model are represented as
// 50 -dimension vector. Therefore, the output is of 150-dimension [min,
// avg, max].
//
// The 'ApplyWordEmbedding' API requires vector of text as input.
// The pipeline first normalizes and tokenizes text then applies word embedding transformation.
// The pipeline first normalizes and tokenizes text then applies word
// embedding transformation.
var textPipeline = mlContext.Transforms.Text.NormalizeText("Text")
.Append(mlContext.Transforms.Text.TokenizeIntoWords("Tokens", "Text"))
.Append(mlContext.Transforms.Text.ApplyWordEmbedding("Features", "Tokens",
WordEmbeddingEstimator.PretrainedModelKind.SentimentSpecificWordEmbedding));
.Append(mlContext.Transforms.Text.TokenizeIntoWords("Tokens",
"Text"))
.Append(mlContext.Transforms.Text.ApplyWordEmbedding("Features",
"Tokens", WordEmbeddingEstimator.PretrainedModelKind
.SentimentSpecificWordEmbedding));

// Fit to data.
var textTransformer = textPipeline.Fit(emptyDataView);

// Create the prediction engine to get the embedding vector from the input text/string.
var predictionEngine = mlContext.Model.CreatePredictionEngine<TextData, TransformedTextData>(textTransformer);
// Create the prediction engine to get the embedding vector from the
// input text/string.
var predictionEngine = mlContext.Model.CreatePredictionEngine<TextData,
TransformedTextData>(textTransformer);

// Call the prediction API to convert the text into embedding vector.
var data = new TextData() { Text = "This is a great product. I would like to buy it again." };
var data = new TextData() { Text = "This is a great product. I would " +
"like to buy it again." };
var prediction = predictionEngine.Predict(data);

// Print the length of the embedding vector.
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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 Merged

rayankrish
merged 3 commits into

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Jul 3, 2019
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Reformatting Test, Projection and TimeSeries of Transform to Width 85
#3947
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9c6325f

transform/text and projection formatted to 85 char

Jul 2, 2019
da95c60

transforms/timeseries formatted to 85 char

Jul 2, 2019
e6aca2a

minor tab and spacing fixes

Jul 2, 2019

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41 changes: 28 additions & 13 deletions
docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Projection/VectorWhiten.cs

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Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -9,11 +9,12 @@ namespace Samples.Dynamic
 public sealed class VectorWhiten
 {

 /// This example requires installation of additional nuget package <a href="https://www.nuget.org/packages/Microsoft.ML.Mkl.Components/">Microsoft.ML.Mkl.Components</a>.
 /// This example requires installation of additional nuget package 
 /// <a href="https://www.nuget.org/packages/Microsoft.ML.Mkl.Components/">Microsoft.ML.Mkl.Components</a>.
 public static void Example()
 {
 // Create a new ML context, for ML.NET operations. It can be used for exception tracking and logging, 
 // as well as the source of randomness.
 // Create a new ML context, for ML.NET operations. It can be used for
 // exception tracking and logging, as well as the source of randomness.
 var ml = new MLContext();

 // Get a small dataset as an IEnumerable and convert it to an IDataView.
Expand All@@ -32,20 +33,29 @@ public static void Example()
 // 6 7 8 9 0 1 2 3 4 5

 // A small printing utility.
 Action<string, IEnumerable<VBuffer<float>>> printHelper = (colName, column) =>
 Action<string, IEnumerable<VBuffer<float>>> printHelper = (colName,
 column) =>
 {
 Console.WriteLine($"{colName} column obtained post-transformation.");
 Console.WriteLine($"{colName} column obtained " +
 $"post-transformation.");

 foreach (var row in column)
 Console.WriteLine($"{string.Join(" ", row.DenseValues().Select(x => x.ToString("f3")))} ");
 Console.WriteLine(string.Join(" ", row.DenseValues().Select(x =>
 x.ToString("f3")))+" ");
@codemzscodemzsJul 2, 2019

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ctrl+k+d is your friend.
 };

 // A pipeline to project Features column into white noise vector.
 var whiteningPipeline = ml.Transforms.VectorWhiten(nameof(SampleVectorOfNumbersData.Features),
 kind: Microsoft.ML.Transforms.WhiteningKind.ZeroPhaseComponentAnalysis);
 var whiteningPipeline = ml.Transforms.VectorWhiten(nameof(
 SampleVectorOfNumbersData.Features), kind: Microsoft.ML.Transforms
 .WhiteningKind.ZeroPhaseComponentAnalysis);

 // The transformed (projected) data.
 var transformedData = whiteningPipeline.Fit(trainData).Transform(trainData);
 var transformedData = whiteningPipeline.Fit(trainData).Transform(
 trainData);

 // Getting the data of the newly created column, so we can preview it.
 var whitening = transformedData.GetColumn<VBuffer<float>>(transformedData.Schema[nameof(SampleVectorOfNumbersData.Features)]);
 var whitening = transformedData.GetColumn<VBuffer<float>>(
 transformedData.Schema[nameof(SampleVectorOfNumbersData.Features)]);

 printHelper(nameof(SampleVectorOfNumbersData.Features), whitening);

Expand All@@ -68,11 +78,16 @@ private class SampleVectorOfNumbersData
 /// <summary>
 /// Returns a few rows of the infertility dataset.
 /// </summary>
 private static IEnumerable<SampleVectorOfNumbersData> GetVectorOfNumbersData()
 private static IEnumerable<SampleVectorOfNumbersData>
 GetVectorOfNumbersData()
 {
 var data = new List<SampleVectorOfNumbersData>();
 data.Add(new SampleVectorOfNumbersData { Features = new float[10] { 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 } });
 data.Add(new SampleVectorOfNumbersData { Features = new float[10] { 1, 2, 3, 4, 5, 6, 7, 8, 9, 0 } });
 data.Add(new SampleVectorOfNumbersData { Features = new float[10] { 0,
 1, 2, 3, 4, 5, 6, 7, 8, 9 } });

 data.Add(new SampleVectorOfNumbersData { Features = new float[10] { 1,
 2, 3, 4, 5, 6, 7, 8, 9, 0 } });

 data.Add(new SampleVectorOfNumbersData
 {
 Features = new float[10] { 2, 3, 4, 5, 6, 7, 8, 9, 0, 1 }
Expand Down

40 changes: 28 additions & 12 deletions
docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Projection/VectorWhitenWithOptions.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -8,11 +8,12 @@ namespace Samples.Dynamic
{
 public sealed class VectorWhitenWithOptions
 {
 /// This example requires installation of additional nuget package <a href="https://www.nuget.org/packages/Microsoft.ML.Mkl.Components/">Microsoft.ML.Mkl.Components</a>.
 /// This example requires installation of additional nuget package
 /// <a href="https://www.nuget.org/packages/Microsoft.ML.Mkl.Components/">Microsoft.ML.Mkl.Components</a>.
 public static void Example()
 {
 // Create a new ML context, for ML.NET operations. It can be used for exception tracking and logging, 
 // as well as the source of randomness.
 // Create a new ML context, for ML.NET operations. It can be used for
 // exception tracking and logging, as well as the source of randomness.
 var ml = new MLContext();

 // Get a small dataset as an IEnumerable and convert it to an IDataView.
Expand All@@ -31,20 +32,30 @@ public static void Example()
 // 6 7 8 9 0 1 2 3 4 5

 // A small printing utility.
 Action<string, IEnumerable<VBuffer<float>>> printHelper = (colName, column) =>
 Action<string, IEnumerable<VBuffer<float>>> printHelper = (colName,
 column) =>
 {
 Console.WriteLine($"{colName} column obtained post-transformation.");
 Console.WriteLine($"{colName} column obtained" +
 $"post-transformation.");

 foreach (var row in column)
 Console.WriteLine($"{string.Join(" ", row.DenseValues().Select(x => x.ToString("f3")))} ");
 Console.WriteLine(string.Join(" ", row.DenseValues().Select(x =>
 x.ToString("f3")))+" ");
 };


 // A pipeline to project Features column into white noise vector.
 var whiteningPipeline = ml.Transforms.VectorWhiten(nameof(SampleVectorOfNumbersData.Features), kind: Microsoft.ML.Transforms.WhiteningKind.PrincipalComponentAnalysis, rank: 4);
 var whiteningPipeline = ml.Transforms.VectorWhiten(nameof(
 SampleVectorOfNumbersData.Features), kind: Microsoft.ML.Transforms
 .WhiteningKind.PrincipalComponentAnalysis, rank: 4);

 // The transformed (projected) data.
 var transformedData = whiteningPipeline.Fit(trainData).Transform(trainData);
 var transformedData = whiteningPipeline.Fit(trainData).Transform(
 trainData);

 // Getting the data of the newly created column, so we can preview it.
 var whitening = transformedData.GetColumn<VBuffer<float>>(transformedData.Schema[nameof(SampleVectorOfNumbersData.Features)]);
 var whitening = transformedData.GetColumn<VBuffer<float>>(
 transformedData.Schema[nameof(SampleVectorOfNumbersData.Features)]);

 printHelper(nameof(SampleVectorOfNumbersData.Features), whitening);

Expand All@@ -66,11 +77,16 @@ private class SampleVectorOfNumbersData
 /// <summary>
 /// Returns a few rows of the infertility dataset.
 /// </summary>
 private static IEnumerable<SampleVectorOfNumbersData> GetVectorOfNumbersData()
 private static IEnumerable<SampleVectorOfNumbersData> 
 GetVectorOfNumbersData()
 {
 var data = new List<SampleVectorOfNumbersData>();
 data.Add(new SampleVectorOfNumbersData { Features = new float[10] { 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 } });
 data.Add(new SampleVectorOfNumbersData { Features = new float[10] { 1, 2, 3, 4, 5, 6, 7, 8, 9, 0 } });
 data.Add(new SampleVectorOfNumbersData { Features = new float[10] { 0,
 1, 2, 3, 4, 5, 6, 7, 8, 9 } });

 data.Add(new SampleVectorOfNumbersData { Features = new float[10] { 1,
 2, 3, 4, 5, 6, 7, 8, 9, 0 } });

 data.Add(new SampleVectorOfNumbersData
 {
 Features = new float[10] { 2, 3, 4, 5, 6, 7, 8, 9, 0, 1 }
Expand Down

37 changes: 23 additions & 14 deletions
docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Text/ApplyCustomWordEmbedding.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -9,12 +9,13 @@ public static class ApplyCustomWordEmbedding
 {
 public static void Example()
 {
 // Create a new ML context, for ML.NET operations. It can be used for exception tracking and logging, 
 // as well as the source of randomness.
 // Create a new ML context, for ML.NET operations. It can be used for
 // exception tracking and logging, as well as the source of randomness.
 var mlContext = new MLContext();

 // Create an empty list as the dataset. The 'ApplyWordEmbedding' does not require training data as
 // the estimator ('WordEmbeddingEstimator') created by 'ApplyWordEmbedding' API is not a trainable estimator.
 // Create an empty list as the dataset. The 'ApplyWordEmbedding' does
 // not require training data as the estimator ('WordEmbeddingEstimator')
 // created by 'ApplyWordEmbedding' API is not a trainable estimator.
 // The empty list is only needed to pass input schema to the pipeline.
 var emptySamples = new List<TextData>();

Expand All@@ -33,25 +34,33 @@ public static void Example()
 file.WriteLine("buy 0 0 20");
 }

 // A pipeline for converting text into a 9-dimension word embedding vector using the custom word embedding model.
 // The 'ApplyWordEmbedding' computes the minimum, average and maximum values for each token's embedding vector.
 // Tokens in 'custommodel.txt' model are represented as 3-dimension vector.
 // Therefore, the output is of 9-dimension [min, avg, max].
 // A pipeline for converting text into a 9-dimension word embedding
 // vector using the custom word embedding model. The 
 // 'ApplyWordEmbedding' computes the minimum, average and maximum values
 // for each token's embedding vector. Tokens in 'custommodel.txt' model
 // are represented as 3-dimension vector. Therefore, the output is of
 // 9 -dimension [min, avg, max].
 //
 // The 'ApplyWordEmbedding' API requires vector of text as input.
 // The pipeline first normalizes and tokenizes text then applies word embedding transformation.
 // The pipeline first normalizes and tokenizes text then applies word
 // embedding transformation.
 var textPipeline = mlContext.Transforms.Text.NormalizeText("Text")
 .Append(mlContext.Transforms.Text.TokenizeIntoWords("Tokens", "Text"))
 .Append(mlContext.Transforms.Text.ApplyWordEmbedding("Features", pathToCustomModel, "Tokens"));
 .Append(mlContext.Transforms.Text.TokenizeIntoWords("Tokens",
 "Text"))
 .Append(mlContext.Transforms.Text.ApplyWordEmbedding("Features",
 pathToCustomModel, "Tokens"));

 // Fit to data.
 var textTransformer = textPipeline.Fit(emptyDataView);

 // Create the prediction engine to get the embedding vector from the input text/string.
 var predictionEngine = mlContext.Model.CreatePredictionEngine<TextData, TransformedTextData>(textTransformer);
 // Create the prediction engine to get the embedding vector from the
 // input text/string.
 var predictionEngine = mlContext.Model.CreatePredictionEngine<TextData,
 TransformedTextData>(textTransformer);

 // Call the prediction API to convert the text into embedding vector.
 var data = new TextData() { Text = "This is a great product. I would like to buy it again." };
 var data = new TextData() { Text = "This is a great product. I would " +
 "like to buy it again." };
 var prediction = predictionEngine.Predict(data);

 // Print the length of the embedding vector.
Expand Down

40 changes: 25 additions & 15 deletions
docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Text/ApplyWordEmbedding.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -9,38 +9,48 @@ public static class ApplyWordEmbedding
 {
 public static void Example()
 {
 // Create a new ML context, for ML.NET operations. It can be used for exception tracking and logging, 
 // as well as the source of randomness.
 // Create a new ML context, for ML.NET operations. It can be used for
 // exception tracking and logging, as well as the source of randomness.
 var mlContext = new MLContext();

 // Create an empty list as the dataset. The 'ApplyWordEmbedding' does not require training data as
 // the estimator ('WordEmbeddingEstimator') created by 'ApplyWordEmbedding' API is not a trainable estimator.
 // Create an empty list as the dataset. The 'ApplyWordEmbedding' does
 // not require training data as the estimator ('WordEmbeddingEstimator')
 // created by 'ApplyWordEmbedding' API is not a trainable estimator.
 // The empty list is only needed to pass input schema to the pipeline.
 var emptySamples = new List<TextData>();

 // Convert sample list to an empty IDataView.
 var emptyDataView = mlContext.Data.LoadFromEnumerable(emptySamples);

 // A pipeline for converting text into a 150-dimension embedding vector using pretrained 'SentimentSpecificWordEmbedding' model.
 // The 'ApplyWordEmbedding' computes the minimum, average and maximum values for each token's embedding vector.
 // Tokens in 'SentimentSpecificWordEmbedding' model are represented as 50-dimension vector.
 // Therefore, the output is of 150-dimension [min, avg, max].
 // A pipeline for converting text into a 150-dimension embedding vector
 // using pretrained 'SentimentSpecificWordEmbedding' model. The
 // 'ApplyWordEmbedding' computes the minimum, average and maximum values
 // for each token's embedding vector. Tokens in 
 // 'SentimentSpecificWordEmbedding' model are represented as
 // 50 -dimension vector. Therefore, the output is of 150-dimension [min,
 // avg, max].
 //
 // The 'ApplyWordEmbedding' API requires vector of text as input.
 // The pipeline first normalizes and tokenizes text then applies word embedding transformation.
 // The pipeline first normalizes and tokenizes text then applies word
 // embedding transformation.
 var textPipeline = mlContext.Transforms.Text.NormalizeText("Text")
 .Append(mlContext.Transforms.Text.TokenizeIntoWords("Tokens", "Text"))
 .Append(mlContext.Transforms.Text.ApplyWordEmbedding("Features", "Tokens",
 WordEmbeddingEstimator.PretrainedModelKind.SentimentSpecificWordEmbedding));
 .Append(mlContext.Transforms.Text.TokenizeIntoWords("Tokens",
 "Text"))
 .Append(mlContext.Transforms.Text.ApplyWordEmbedding("Features",
 "Tokens", WordEmbeddingEstimator.PretrainedModelKind
 .SentimentSpecificWordEmbedding));

 // Fit to data.
 var textTransformer = textPipeline.Fit(emptyDataView);

 // Create the prediction engine to get the embedding vector from the input text/string.
 var predictionEngine = mlContext.Model.CreatePredictionEngine<TextData, TransformedTextData>(textTransformer);
 // Create the prediction engine to get the embedding vector from the
 // input text/string.
 var predictionEngine = mlContext.Model.CreatePredictionEngine<TextData,
 TransformedTextData>(textTransformer);

 // Call the prediction API to convert the text into embedding vector.
 var data = new TextData() { Text = "This is a great product. I would like to buy it again." };
 var data = new TextData() { Text = "This is a great product. I would " +
 "like to buy it again." };
 var prediction = predictionEngine.Predict(data);

 // Print the length of the embedding vector.
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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merged 3 commits into

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Jul 3, 2019
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Reformatting Test, Projection and TimeSeries of Transform to Width 85
#3947
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transform/text and projection formatted to 85 char

Jul 2, 2019
da95c60

transforms/timeseries formatted to 85 char

Jul 2, 2019
e6aca2a

minor tab and spacing fixes

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41 changes: 28 additions & 13 deletions
docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Projection/VectorWhiten.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -9,11 +9,12 @@ namespace Samples.Dynamic
 public sealed class VectorWhiten
 {

 /// This example requires installation of additional nuget package <a href="https://www.nuget.org/packages/Microsoft.ML.Mkl.Components/">Microsoft.ML.Mkl.Components</a>.
 /// This example requires installation of additional nuget package 
 /// <a href="https://www.nuget.org/packages/Microsoft.ML.Mkl.Components/">Microsoft.ML.Mkl.Components</a>.
 public static void Example()
 {
 // Create a new ML context, for ML.NET operations. It can be used for exception tracking and logging, 
 // as well as the source of randomness.
 // Create a new ML context, for ML.NET operations. It can be used for
 // exception tracking and logging, as well as the source of randomness.
 var ml = new MLContext();

 // Get a small dataset as an IEnumerable and convert it to an IDataView.
Expand All@@ -32,20 +33,29 @@ public static void Example()
 // 6 7 8 9 0 1 2 3 4 5

 // A small printing utility.
 Action<string, IEnumerable<VBuffer<float>>> printHelper = (colName, column) =>
 Action<string, IEnumerable<VBuffer<float>>> printHelper = (colName,
 column) =>
 {
 Console.WriteLine($"{colName} column obtained post-transformation.");
 Console.WriteLine($"{colName} column obtained " +
 $"post-transformation.");

 foreach (var row in column)
 Console.WriteLine($"{string.Join(" ", row.DenseValues().Select(x => x.ToString("f3")))} ");
 Console.WriteLine(string.Join(" ", row.DenseValues().Select(x =>
 x.ToString("f3")))+" ");
@codemzscodemzsJul 2, 2019

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ctrl+k+d is your friend.
 };

 // A pipeline to project Features column into white noise vector.
 var whiteningPipeline = ml.Transforms.VectorWhiten(nameof(SampleVectorOfNumbersData.Features),
 kind: Microsoft.ML.Transforms.WhiteningKind.ZeroPhaseComponentAnalysis);
 var whiteningPipeline = ml.Transforms.VectorWhiten(nameof(
 SampleVectorOfNumbersData.Features), kind: Microsoft.ML.Transforms
 .WhiteningKind.ZeroPhaseComponentAnalysis);

 // The transformed (projected) data.
 var transformedData = whiteningPipeline.Fit(trainData).Transform(trainData);
 var transformedData = whiteningPipeline.Fit(trainData).Transform(
 trainData);

 // Getting the data of the newly created column, so we can preview it.
 var whitening = transformedData.GetColumn<VBuffer<float>>(transformedData.Schema[nameof(SampleVectorOfNumbersData.Features)]);
 var whitening = transformedData.GetColumn<VBuffer<float>>(
 transformedData.Schema[nameof(SampleVectorOfNumbersData.Features)]);

 printHelper(nameof(SampleVectorOfNumbersData.Features), whitening);

Expand All@@ -68,11 +78,16 @@ private class SampleVectorOfNumbersData
 /// <summary>
 /// Returns a few rows of the infertility dataset.
 /// </summary>
 private static IEnumerable<SampleVectorOfNumbersData> GetVectorOfNumbersData()
 private static IEnumerable<SampleVectorOfNumbersData>
 GetVectorOfNumbersData()
 {
 var data = new List<SampleVectorOfNumbersData>();
 data.Add(new SampleVectorOfNumbersData { Features = new float[10] { 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 } });
 data.Add(new SampleVectorOfNumbersData { Features = new float[10] { 1, 2, 3, 4, 5, 6, 7, 8, 9, 0 } });
 data.Add(new SampleVectorOfNumbersData { Features = new float[10] { 0,
 1, 2, 3, 4, 5, 6, 7, 8, 9 } });

 data.Add(new SampleVectorOfNumbersData { Features = new float[10] { 1,
 2, 3, 4, 5, 6, 7, 8, 9, 0 } });

 data.Add(new SampleVectorOfNumbersData
 {
 Features = new float[10] { 2, 3, 4, 5, 6, 7, 8, 9, 0, 1 }
Expand Down

40 changes: 28 additions & 12 deletions
docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Projection/VectorWhitenWithOptions.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -8,11 +8,12 @@ namespace Samples.Dynamic
{
 public sealed class VectorWhitenWithOptions
 {
 /// This example requires installation of additional nuget package <a href="https://www.nuget.org/packages/Microsoft.ML.Mkl.Components/">Microsoft.ML.Mkl.Components</a>.
 /// This example requires installation of additional nuget package
 /// <a href="https://www.nuget.org/packages/Microsoft.ML.Mkl.Components/">Microsoft.ML.Mkl.Components</a>.
 public static void Example()
 {
 // Create a new ML context, for ML.NET operations. It can be used for exception tracking and logging, 
 // as well as the source of randomness.
 // Create a new ML context, for ML.NET operations. It can be used for
 // exception tracking and logging, as well as the source of randomness.
 var ml = new MLContext();

 // Get a small dataset as an IEnumerable and convert it to an IDataView.
Expand All@@ -31,20 +32,30 @@ public static void Example()
 // 6 7 8 9 0 1 2 3 4 5

 // A small printing utility.
 Action<string, IEnumerable<VBuffer<float>>> printHelper = (colName, column) =>
 Action<string, IEnumerable<VBuffer<float>>> printHelper = (colName,
 column) =>
 {
 Console.WriteLine($"{colName} column obtained post-transformation.");
 Console.WriteLine($"{colName} column obtained" +
 $"post-transformation.");

 foreach (var row in column)
 Console.WriteLine($"{string.Join(" ", row.DenseValues().Select(x => x.ToString("f3")))} ");
 Console.WriteLine(string.Join(" ", row.DenseValues().Select(x =>
 x.ToString("f3")))+" ");
 };


 // A pipeline to project Features column into white noise vector.
 var whiteningPipeline = ml.Transforms.VectorWhiten(nameof(SampleVectorOfNumbersData.Features), kind: Microsoft.ML.Transforms.WhiteningKind.PrincipalComponentAnalysis, rank: 4);
 var whiteningPipeline = ml.Transforms.VectorWhiten(nameof(
 SampleVectorOfNumbersData.Features), kind: Microsoft.ML.Transforms
 .WhiteningKind.PrincipalComponentAnalysis, rank: 4);

 // The transformed (projected) data.
 var transformedData = whiteningPipeline.Fit(trainData).Transform(trainData);
 var transformedData = whiteningPipeline.Fit(trainData).Transform(
 trainData);

 // Getting the data of the newly created column, so we can preview it.
 var whitening = transformedData.GetColumn<VBuffer<float>>(transformedData.Schema[nameof(SampleVectorOfNumbersData.Features)]);
 var whitening = transformedData.GetColumn<VBuffer<float>>(
 transformedData.Schema[nameof(SampleVectorOfNumbersData.Features)]);

 printHelper(nameof(SampleVectorOfNumbersData.Features), whitening);

Expand All@@ -66,11 +77,16 @@ private class SampleVectorOfNumbersData
 /// <summary>
 /// Returns a few rows of the infertility dataset.
 /// </summary>
 private static IEnumerable<SampleVectorOfNumbersData> GetVectorOfNumbersData()
 private static IEnumerable<SampleVectorOfNumbersData> 
 GetVectorOfNumbersData()
 {
 var data = new List<SampleVectorOfNumbersData>();
 data.Add(new SampleVectorOfNumbersData { Features = new float[10] { 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 } });
 data.Add(new SampleVectorOfNumbersData { Features = new float[10] { 1, 2, 3, 4, 5, 6, 7, 8, 9, 0 } });
 data.Add(new SampleVectorOfNumbersData { Features = new float[10] { 0,
 1, 2, 3, 4, 5, 6, 7, 8, 9 } });

 data.Add(new SampleVectorOfNumbersData { Features = new float[10] { 1,
 2, 3, 4, 5, 6, 7, 8, 9, 0 } });

 data.Add(new SampleVectorOfNumbersData
 {
 Features = new float[10] { 2, 3, 4, 5, 6, 7, 8, 9, 0, 1 }
Expand Down

37 changes: 23 additions & 14 deletions
docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Text/ApplyCustomWordEmbedding.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -9,12 +9,13 @@ public static class ApplyCustomWordEmbedding
 {
 public static void Example()
 {
 // Create a new ML context, for ML.NET operations. It can be used for exception tracking and logging, 
 // as well as the source of randomness.
 // Create a new ML context, for ML.NET operations. It can be used for
 // exception tracking and logging, as well as the source of randomness.
 var mlContext = new MLContext();

 // Create an empty list as the dataset. The 'ApplyWordEmbedding' does not require training data as
 // the estimator ('WordEmbeddingEstimator') created by 'ApplyWordEmbedding' API is not a trainable estimator.
 // Create an empty list as the dataset. The 'ApplyWordEmbedding' does
 // not require training data as the estimator ('WordEmbeddingEstimator')
 // created by 'ApplyWordEmbedding' API is not a trainable estimator.
 // The empty list is only needed to pass input schema to the pipeline.
 var emptySamples = new List<TextData>();

Expand All@@ -33,25 +34,33 @@ public static void Example()
 file.WriteLine("buy 0 0 20");
 }

 // A pipeline for converting text into a 9-dimension word embedding vector using the custom word embedding model.
 // The 'ApplyWordEmbedding' computes the minimum, average and maximum values for each token's embedding vector.
 // Tokens in 'custommodel.txt' model are represented as 3-dimension vector.
 // Therefore, the output is of 9-dimension [min, avg, max].
 // A pipeline for converting text into a 9-dimension word embedding
 // vector using the custom word embedding model. The 
 // 'ApplyWordEmbedding' computes the minimum, average and maximum values
 // for each token's embedding vector. Tokens in 'custommodel.txt' model
 // are represented as 3-dimension vector. Therefore, the output is of
 // 9 -dimension [min, avg, max].
 //
 // The 'ApplyWordEmbedding' API requires vector of text as input.
 // The pipeline first normalizes and tokenizes text then applies word embedding transformation.
 // The pipeline first normalizes and tokenizes text then applies word
 // embedding transformation.
 var textPipeline = mlContext.Transforms.Text.NormalizeText("Text")
 .Append(mlContext.Transforms.Text.TokenizeIntoWords("Tokens", "Text"))
 .Append(mlContext.Transforms.Text.ApplyWordEmbedding("Features", pathToCustomModel, "Tokens"));
 .Append(mlContext.Transforms.Text.TokenizeIntoWords("Tokens",
 "Text"))
 .Append(mlContext.Transforms.Text.ApplyWordEmbedding("Features",
 pathToCustomModel, "Tokens"));

 // Fit to data.
 var textTransformer = textPipeline.Fit(emptyDataView);

 // Create the prediction engine to get the embedding vector from the input text/string.
 var predictionEngine = mlContext.Model.CreatePredictionEngine<TextData, TransformedTextData>(textTransformer);
 // Create the prediction engine to get the embedding vector from the
 // input text/string.
 var predictionEngine = mlContext.Model.CreatePredictionEngine<TextData,
 TransformedTextData>(textTransformer);

 // Call the prediction API to convert the text into embedding vector.
 var data = new TextData() { Text = "This is a great product. I would like to buy it again." };
 var data = new TextData() { Text = "This is a great product. I would " +
 "like to buy it again." };
 var prediction = predictionEngine.Predict(data);

 // Print the length of the embedding vector.
Expand Down

40 changes: 25 additions & 15 deletions
docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Text/ApplyWordEmbedding.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -9,38 +9,48 @@ public static class ApplyWordEmbedding
 {
 public static void Example()
 {
 // Create a new ML context, for ML.NET operations. It can be used for exception tracking and logging, 
 // as well as the source of randomness.
 // Create a new ML context, for ML.NET operations. It can be used for
 // exception tracking and logging, as well as the source of randomness.
 var mlContext = new MLContext();

 // Create an empty list as the dataset. The 'ApplyWordEmbedding' does not require training data as
 // the estimator ('WordEmbeddingEstimator') created by 'ApplyWordEmbedding' API is not a trainable estimator.
 // Create an empty list as the dataset. The 'ApplyWordEmbedding' does
 // not require training data as the estimator ('WordEmbeddingEstimator')
 // created by 'ApplyWordEmbedding' API is not a trainable estimator.
 // The empty list is only needed to pass input schema to the pipeline.
 var emptySamples = new List<TextData>();

 // Convert sample list to an empty IDataView.
 var emptyDataView = mlContext.Data.LoadFromEnumerable(emptySamples);

 // A pipeline for converting text into a 150-dimension embedding vector using pretrained 'SentimentSpecificWordEmbedding' model.
 // The 'ApplyWordEmbedding' computes the minimum, average and maximum values for each token's embedding vector.
 // Tokens in 'SentimentSpecificWordEmbedding' model are represented as 50-dimension vector.
 // Therefore, the output is of 150-dimension [min, avg, max].
 // A pipeline for converting text into a 150-dimension embedding vector
 // using pretrained 'SentimentSpecificWordEmbedding' model. The
 // 'ApplyWordEmbedding' computes the minimum, average and maximum values
 // for each token's embedding vector. Tokens in 
 // 'SentimentSpecificWordEmbedding' model are represented as
 // 50 -dimension vector. Therefore, the output is of 150-dimension [min,
 // avg, max].
 //
 // The 'ApplyWordEmbedding' API requires vector of text as input.
 // The pipeline first normalizes and tokenizes text then applies word embedding transformation.
 // The pipeline first normalizes and tokenizes text then applies word
 // embedding transformation.
 var textPipeline = mlContext.Transforms.Text.NormalizeText("Text")
 .Append(mlContext.Transforms.Text.TokenizeIntoWords("Tokens", "Text"))
 .Append(mlContext.Transforms.Text.ApplyWordEmbedding("Features", "Tokens",
 WordEmbeddingEstimator.PretrainedModelKind.SentimentSpecificWordEmbedding));
 .Append(mlContext.Transforms.Text.TokenizeIntoWords("Tokens",
 "Text"))
 .Append(mlContext.Transforms.Text.ApplyWordEmbedding("Features",
 "Tokens", WordEmbeddingEstimator.PretrainedModelKind
 .SentimentSpecificWordEmbedding));

 // Fit to data.
 var textTransformer = textPipeline.Fit(emptyDataView);

 // Create the prediction engine to get the embedding vector from the input text/string.
 var predictionEngine = mlContext.Model.CreatePredictionEngine<TextData, TransformedTextData>(textTransformer);
 // Create the prediction engine to get the embedding vector from the
 // input text/string.
 var predictionEngine = mlContext.Model.CreatePredictionEngine<TextData,
 TransformedTextData>(textTransformer);

 // Call the prediction API to convert the text into embedding vector.
 var data = new TextData() { Text = "This is a great product. I would like to buy it again." };
 var data = new TextData() { Text = "This is a great product. I would " +
 "like to buy it again." };
 var prediction = predictionEngine.Predict(data);

 // Print the length of the embedding vector.
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('^' + ".*" + '

Skip to content
Reformatting Test, Projection and TimeSeries of Transform to Width 85#3947

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 Merged

rayankrish
merged 3 commits into

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Jul 3, 2019
 Merged


Reformatting Test, Projection and TimeSeries of Transform to Width 85
#3947
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9c6325f

transform/text and projection formatted to 85 char

Jul 2, 2019
da95c60

transforms/timeseries formatted to 85 char

Jul 2, 2019
e6aca2a

minor tab and spacing fixes

Jul 2, 2019

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41 changes: 28 additions & 13 deletions
docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Projection/VectorWhiten.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -9,11 +9,12 @@ namespace Samples.Dynamic
 public sealed class VectorWhiten
 {

 /// This example requires installation of additional nuget package <a href="https://www.nuget.org/packages/Microsoft.ML.Mkl.Components/">Microsoft.ML.Mkl.Components</a>.
 /// This example requires installation of additional nuget package 
 /// <a href="https://www.nuget.org/packages/Microsoft.ML.Mkl.Components/">Microsoft.ML.Mkl.Components</a>.
 public static void Example()
 {
 // Create a new ML context, for ML.NET operations. It can be used for exception tracking and logging, 
 // as well as the source of randomness.
 // Create a new ML context, for ML.NET operations. It can be used for
 // exception tracking and logging, as well as the source of randomness.
 var ml = new MLContext();

 // Get a small dataset as an IEnumerable and convert it to an IDataView.
Expand All@@ -32,20 +33,29 @@ public static void Example()
 // 6 7 8 9 0 1 2 3 4 5

 // A small printing utility.
 Action<string, IEnumerable<VBuffer<float>>> printHelper = (colName, column) =>
 Action<string, IEnumerable<VBuffer<float>>> printHelper = (colName,
 column) =>
 {
 Console.WriteLine($"{colName} column obtained post-transformation.");
 Console.WriteLine($"{colName} column obtained " +
 $"post-transformation.");

 foreach (var row in column)
 Console.WriteLine($"{string.Join(" ", row.DenseValues().Select(x => x.ToString("f3")))} ");
 Console.WriteLine(string.Join(" ", row.DenseValues().Select(x =>
 x.ToString("f3")))+" ");
@codemzscodemzsJul 2, 2019

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Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

ctrl+k+d is your friend.
 };

 // A pipeline to project Features column into white noise vector.
 var whiteningPipeline = ml.Transforms.VectorWhiten(nameof(SampleVectorOfNumbersData.Features),
 kind: Microsoft.ML.Transforms.WhiteningKind.ZeroPhaseComponentAnalysis);
 var whiteningPipeline = ml.Transforms.VectorWhiten(nameof(
 SampleVectorOfNumbersData.Features), kind: Microsoft.ML.Transforms
 .WhiteningKind.ZeroPhaseComponentAnalysis);

 // The transformed (projected) data.
 var transformedData = whiteningPipeline.Fit(trainData).Transform(trainData);
 var transformedData = whiteningPipeline.Fit(trainData).Transform(
 trainData);

 // Getting the data of the newly created column, so we can preview it.
 var whitening = transformedData.GetColumn<VBuffer<float>>(transformedData.Schema[nameof(SampleVectorOfNumbersData.Features)]);
 var whitening = transformedData.GetColumn<VBuffer<float>>(
 transformedData.Schema[nameof(SampleVectorOfNumbersData.Features)]);

 printHelper(nameof(SampleVectorOfNumbersData.Features), whitening);

Expand All@@ -68,11 +78,16 @@ private class SampleVectorOfNumbersData
 /// <summary>
 /// Returns a few rows of the infertility dataset.
 /// </summary>
 private static IEnumerable<SampleVectorOfNumbersData> GetVectorOfNumbersData()
 private static IEnumerable<SampleVectorOfNumbersData>
 GetVectorOfNumbersData()
 {
 var data = new List<SampleVectorOfNumbersData>();
 data.Add(new SampleVectorOfNumbersData { Features = new float[10] { 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 } });
 data.Add(new SampleVectorOfNumbersData { Features = new float[10] { 1, 2, 3, 4, 5, 6, 7, 8, 9, 0 } });
 data.Add(new SampleVectorOfNumbersData { Features = new float[10] { 0,
 1, 2, 3, 4, 5, 6, 7, 8, 9 } });

 data.Add(new SampleVectorOfNumbersData { Features = new float[10] { 1,
 2, 3, 4, 5, 6, 7, 8, 9, 0 } });

 data.Add(new SampleVectorOfNumbersData
 {
 Features = new float[10] { 2, 3, 4, 5, 6, 7, 8, 9, 0, 1 }
Expand Down

40 changes: 28 additions & 12 deletions
docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Projection/VectorWhitenWithOptions.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -8,11 +8,12 @@ namespace Samples.Dynamic
{
 public sealed class VectorWhitenWithOptions
 {
 /// This example requires installation of additional nuget package <a href="https://www.nuget.org/packages/Microsoft.ML.Mkl.Components/">Microsoft.ML.Mkl.Components</a>.
 /// This example requires installation of additional nuget package
 /// <a href="https://www.nuget.org/packages/Microsoft.ML.Mkl.Components/">Microsoft.ML.Mkl.Components</a>.
 public static void Example()
 {
 // Create a new ML context, for ML.NET operations. It can be used for exception tracking and logging, 
 // as well as the source of randomness.
 // Create a new ML context, for ML.NET operations. It can be used for
 // exception tracking and logging, as well as the source of randomness.
 var ml = new MLContext();

 // Get a small dataset as an IEnumerable and convert it to an IDataView.
Expand All@@ -31,20 +32,30 @@ public static void Example()
 // 6 7 8 9 0 1 2 3 4 5

 // A small printing utility.
 Action<string, IEnumerable<VBuffer<float>>> printHelper = (colName, column) =>
 Action<string, IEnumerable<VBuffer<float>>> printHelper = (colName,
 column) =>
 {
 Console.WriteLine($"{colName} column obtained post-transformation.");
 Console.WriteLine($"{colName} column obtained" +
 $"post-transformation.");

 foreach (var row in column)
 Console.WriteLine($"{string.Join(" ", row.DenseValues().Select(x => x.ToString("f3")))} ");
 Console.WriteLine(string.Join(" ", row.DenseValues().Select(x =>
 x.ToString("f3")))+" ");
 };


 // A pipeline to project Features column into white noise vector.
 var whiteningPipeline = ml.Transforms.VectorWhiten(nameof(SampleVectorOfNumbersData.Features), kind: Microsoft.ML.Transforms.WhiteningKind.PrincipalComponentAnalysis, rank: 4);
 var whiteningPipeline = ml.Transforms.VectorWhiten(nameof(
 SampleVectorOfNumbersData.Features), kind: Microsoft.ML.Transforms
 .WhiteningKind.PrincipalComponentAnalysis, rank: 4);

 // The transformed (projected) data.
 var transformedData = whiteningPipeline.Fit(trainData).Transform(trainData);
 var transformedData = whiteningPipeline.Fit(trainData).Transform(
 trainData);

 // Getting the data of the newly created column, so we can preview it.
 var whitening = transformedData.GetColumn<VBuffer<float>>(transformedData.Schema[nameof(SampleVectorOfNumbersData.Features)]);
 var whitening = transformedData.GetColumn<VBuffer<float>>(
 transformedData.Schema[nameof(SampleVectorOfNumbersData.Features)]);

 printHelper(nameof(SampleVectorOfNumbersData.Features), whitening);

Expand All@@ -66,11 +77,16 @@ private class SampleVectorOfNumbersData
 /// <summary>
 /// Returns a few rows of the infertility dataset.
 /// </summary>
 private static IEnumerable<SampleVectorOfNumbersData> GetVectorOfNumbersData()
 private static IEnumerable<SampleVectorOfNumbersData> 
 GetVectorOfNumbersData()
 {
 var data = new List<SampleVectorOfNumbersData>();
 data.Add(new SampleVectorOfNumbersData { Features = new float[10] { 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 } });
 data.Add(new SampleVectorOfNumbersData { Features = new float[10] { 1, 2, 3, 4, 5, 6, 7, 8, 9, 0 } });
 data.Add(new SampleVectorOfNumbersData { Features = new float[10] { 0,
 1, 2, 3, 4, 5, 6, 7, 8, 9 } });

 data.Add(new SampleVectorOfNumbersData { Features = new float[10] { 1,
 2, 3, 4, 5, 6, 7, 8, 9, 0 } });

 data.Add(new SampleVectorOfNumbersData
 {
 Features = new float[10] { 2, 3, 4, 5, 6, 7, 8, 9, 0, 1 }
Expand Down

37 changes: 23 additions & 14 deletions
docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Text/ApplyCustomWordEmbedding.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -9,12 +9,13 @@ public static class ApplyCustomWordEmbedding
 {
 public static void Example()
 {
 // Create a new ML context, for ML.NET operations. It can be used for exception tracking and logging, 
 // as well as the source of randomness.
 // Create a new ML context, for ML.NET operations. It can be used for
 // exception tracking and logging, as well as the source of randomness.
 var mlContext = new MLContext();

 // Create an empty list as the dataset. The 'ApplyWordEmbedding' does not require training data as
 // the estimator ('WordEmbeddingEstimator') created by 'ApplyWordEmbedding' API is not a trainable estimator.
 // Create an empty list as the dataset. The 'ApplyWordEmbedding' does
 // not require training data as the estimator ('WordEmbeddingEstimator')
 // created by 'ApplyWordEmbedding' API is not a trainable estimator.
 // The empty list is only needed to pass input schema to the pipeline.
 var emptySamples = new List<TextData>();

Expand All@@ -33,25 +34,33 @@ public static void Example()
 file.WriteLine("buy 0 0 20");
 }

 // A pipeline for converting text into a 9-dimension word embedding vector using the custom word embedding model.
 // The 'ApplyWordEmbedding' computes the minimum, average and maximum values for each token's embedding vector.
 // Tokens in 'custommodel.txt' model are represented as 3-dimension vector.
 // Therefore, the output is of 9-dimension [min, avg, max].
 // A pipeline for converting text into a 9-dimension word embedding
 // vector using the custom word embedding model. The 
 // 'ApplyWordEmbedding' computes the minimum, average and maximum values
 // for each token's embedding vector. Tokens in 'custommodel.txt' model
 // are represented as 3-dimension vector. Therefore, the output is of
 // 9 -dimension [min, avg, max].
 //
 // The 'ApplyWordEmbedding' API requires vector of text as input.
 // The pipeline first normalizes and tokenizes text then applies word embedding transformation.
 // The pipeline first normalizes and tokenizes text then applies word
 // embedding transformation.
 var textPipeline = mlContext.Transforms.Text.NormalizeText("Text")
 .Append(mlContext.Transforms.Text.TokenizeIntoWords("Tokens", "Text"))
 .Append(mlContext.Transforms.Text.ApplyWordEmbedding("Features", pathToCustomModel, "Tokens"));
 .Append(mlContext.Transforms.Text.TokenizeIntoWords("Tokens",
 "Text"))
 .Append(mlContext.Transforms.Text.ApplyWordEmbedding("Features",
 pathToCustomModel, "Tokens"));

 // Fit to data.
 var textTransformer = textPipeline.Fit(emptyDataView);

 // Create the prediction engine to get the embedding vector from the input text/string.
 var predictionEngine = mlContext.Model.CreatePredictionEngine<TextData, TransformedTextData>(textTransformer);
 // Create the prediction engine to get the embedding vector from the
 // input text/string.
 var predictionEngine = mlContext.Model.CreatePredictionEngine<TextData,
 TransformedTextData>(textTransformer);

 // Call the prediction API to convert the text into embedding vector.
 var data = new TextData() { Text = "This is a great product. I would like to buy it again." };
 var data = new TextData() { Text = "This is a great product. I would " +
 "like to buy it again." };
 var prediction = predictionEngine.Predict(data);

 // Print the length of the embedding vector.
Expand Down

40 changes: 25 additions & 15 deletions
docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Text/ApplyWordEmbedding.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -9,38 +9,48 @@ public static class ApplyWordEmbedding
 {
 public static void Example()
 {
 // Create a new ML context, for ML.NET operations. It can be used for exception tracking and logging, 
 // as well as the source of randomness.
 // Create a new ML context, for ML.NET operations. It can be used for
 // exception tracking and logging, as well as the source of randomness.
 var mlContext = new MLContext();

 // Create an empty list as the dataset. The 'ApplyWordEmbedding' does not require training data as
 // the estimator ('WordEmbeddingEstimator') created by 'ApplyWordEmbedding' API is not a trainable estimator.
 // Create an empty list as the dataset. The 'ApplyWordEmbedding' does
 // not require training data as the estimator ('WordEmbeddingEstimator')
 // created by 'ApplyWordEmbedding' API is not a trainable estimator.
 // The empty list is only needed to pass input schema to the pipeline.
 var emptySamples = new List<TextData>();

 // Convert sample list to an empty IDataView.
 var emptyDataView = mlContext.Data.LoadFromEnumerable(emptySamples);

 // A pipeline for converting text into a 150-dimension embedding vector using pretrained 'SentimentSpecificWordEmbedding' model.
 // The 'ApplyWordEmbedding' computes the minimum, average and maximum values for each token's embedding vector.
 // Tokens in 'SentimentSpecificWordEmbedding' model are represented as 50-dimension vector.
 // Therefore, the output is of 150-dimension [min, avg, max].
 // A pipeline for converting text into a 150-dimension embedding vector
 // using pretrained 'SentimentSpecificWordEmbedding' model. The
 // 'ApplyWordEmbedding' computes the minimum, average and maximum values
 // for each token's embedding vector. Tokens in 
 // 'SentimentSpecificWordEmbedding' model are represented as
 // 50 -dimension vector. Therefore, the output is of 150-dimension [min,
 // avg, max].
 //
 // The 'ApplyWordEmbedding' API requires vector of text as input.
 // The pipeline first normalizes and tokenizes text then applies word embedding transformation.
 // The pipeline first normalizes and tokenizes text then applies word
 // embedding transformation.
 var textPipeline = mlContext.Transforms.Text.NormalizeText("Text")
 .Append(mlContext.Transforms.Text.TokenizeIntoWords("Tokens", "Text"))
 .Append(mlContext.Transforms.Text.ApplyWordEmbedding("Features", "Tokens",
 WordEmbeddingEstimator.PretrainedModelKind.SentimentSpecificWordEmbedding));
 .Append(mlContext.Transforms.Text.TokenizeIntoWords("Tokens",
 "Text"))
 .Append(mlContext.Transforms.Text.ApplyWordEmbedding("Features",
 "Tokens", WordEmbeddingEstimator.PretrainedModelKind
 .SentimentSpecificWordEmbedding));

 // Fit to data.
 var textTransformer = textPipeline.Fit(emptyDataView);

 // Create the prediction engine to get the embedding vector from the input text/string.
 var predictionEngine = mlContext.Model.CreatePredictionEngine<TextData, TransformedTextData>(textTransformer);
 // Create the prediction engine to get the embedding vector from the
 // input text/string.
 var predictionEngine = mlContext.Model.CreatePredictionEngine<TextData,
 TransformedTextData>(textTransformer);

 // Call the prediction API to convert the text into embedding vector.
 var data = new TextData() { Text = "This is a great product. I would like to buy it again." };
 var data = new TextData() { Text = "This is a great product. I would " +
 "like to buy it again." };
 var prediction = predictionEngine.Predict(data);

 // Print the length of the embedding vector.
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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Reformatting Test, Projection and TimeSeries of Transform to Width 85
#3947
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9c6325f

transform/text and projection formatted to 85 char

Jul 2, 2019
da95c60

transforms/timeseries formatted to 85 char

Jul 2, 2019
e6aca2a

minor tab and spacing fixes

Jul 2, 2019

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41 changes: 28 additions & 13 deletions
docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Projection/VectorWhiten.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -9,11 +9,12 @@ namespace Samples.Dynamic
 public sealed class VectorWhiten
 {

 /// This example requires installation of additional nuget package <a href="https://www.nuget.org/packages/Microsoft.ML.Mkl.Components/">Microsoft.ML.Mkl.Components</a>.
 /// This example requires installation of additional nuget package 
 /// <a href="https://www.nuget.org/packages/Microsoft.ML.Mkl.Components/">Microsoft.ML.Mkl.Components</a>.
 public static void Example()
 {
 // Create a new ML context, for ML.NET operations. It can be used for exception tracking and logging, 
 // as well as the source of randomness.
 // Create a new ML context, for ML.NET operations. It can be used for
 // exception tracking and logging, as well as the source of randomness.
 var ml = new MLContext();

 // Get a small dataset as an IEnumerable and convert it to an IDataView.
Expand All@@ -32,20 +33,29 @@ public static void Example()
 // 6 7 8 9 0 1 2 3 4 5

 // A small printing utility.
 Action<string, IEnumerable<VBuffer<float>>> printHelper = (colName, column) =>
 Action<string, IEnumerable<VBuffer<float>>> printHelper = (colName,
 column) =>
 {
 Console.WriteLine($"{colName} column obtained post-transformation.");
 Console.WriteLine($"{colName} column obtained " +
 $"post-transformation.");

 foreach (var row in column)
 Console.WriteLine($"{string.Join(" ", row.DenseValues().Select(x => x.ToString("f3")))} ");
 Console.WriteLine(string.Join(" ", row.DenseValues().Select(x =>
 x.ToString("f3")))+" ");
@codemzscodemzsJul 2, 2019

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ctrl+k+d is your friend.
 };

 // A pipeline to project Features column into white noise vector.
 var whiteningPipeline = ml.Transforms.VectorWhiten(nameof(SampleVectorOfNumbersData.Features),
 kind: Microsoft.ML.Transforms.WhiteningKind.ZeroPhaseComponentAnalysis);
 var whiteningPipeline = ml.Transforms.VectorWhiten(nameof(
 SampleVectorOfNumbersData.Features), kind: Microsoft.ML.Transforms
 .WhiteningKind.ZeroPhaseComponentAnalysis);

 // The transformed (projected) data.
 var transformedData = whiteningPipeline.Fit(trainData).Transform(trainData);
 var transformedData = whiteningPipeline.Fit(trainData).Transform(
 trainData);

 // Getting the data of the newly created column, so we can preview it.
 var whitening = transformedData.GetColumn<VBuffer<float>>(transformedData.Schema[nameof(SampleVectorOfNumbersData.Features)]);
 var whitening = transformedData.GetColumn<VBuffer<float>>(
 transformedData.Schema[nameof(SampleVectorOfNumbersData.Features)]);

 printHelper(nameof(SampleVectorOfNumbersData.Features), whitening);

Expand All@@ -68,11 +78,16 @@ private class SampleVectorOfNumbersData
 /// <summary>
 /// Returns a few rows of the infertility dataset.
 /// </summary>
 private static IEnumerable<SampleVectorOfNumbersData> GetVectorOfNumbersData()
 private static IEnumerable<SampleVectorOfNumbersData>
 GetVectorOfNumbersData()
 {
 var data = new List<SampleVectorOfNumbersData>();
 data.Add(new SampleVectorOfNumbersData { Features = new float[10] { 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 } });
 data.Add(new SampleVectorOfNumbersData { Features = new float[10] { 1, 2, 3, 4, 5, 6, 7, 8, 9, 0 } });
 data.Add(new SampleVectorOfNumbersData { Features = new float[10] { 0,
 1, 2, 3, 4, 5, 6, 7, 8, 9 } });

 data.Add(new SampleVectorOfNumbersData { Features = new float[10] { 1,
 2, 3, 4, 5, 6, 7, 8, 9, 0 } });

 data.Add(new SampleVectorOfNumbersData
 {
 Features = new float[10] { 2, 3, 4, 5, 6, 7, 8, 9, 0, 1 }
Expand Down

40 changes: 28 additions & 12 deletions
docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Projection/VectorWhitenWithOptions.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -8,11 +8,12 @@ namespace Samples.Dynamic
{
 public sealed class VectorWhitenWithOptions
 {
 /// This example requires installation of additional nuget package <a href="https://www.nuget.org/packages/Microsoft.ML.Mkl.Components/">Microsoft.ML.Mkl.Components</a>.
 /// This example requires installation of additional nuget package
 /// <a href="https://www.nuget.org/packages/Microsoft.ML.Mkl.Components/">Microsoft.ML.Mkl.Components</a>.
 public static void Example()
 {
 // Create a new ML context, for ML.NET operations. It can be used for exception tracking and logging, 
 // as well as the source of randomness.
 // Create a new ML context, for ML.NET operations. It can be used for
 // exception tracking and logging, as well as the source of randomness.
 var ml = new MLContext();

 // Get a small dataset as an IEnumerable and convert it to an IDataView.
Expand All@@ -31,20 +32,30 @@ public static void Example()
 // 6 7 8 9 0 1 2 3 4 5

 // A small printing utility.
 Action<string, IEnumerable<VBuffer<float>>> printHelper = (colName, column) =>
 Action<string, IEnumerable<VBuffer<float>>> printHelper = (colName,
 column) =>
 {
 Console.WriteLine($"{colName} column obtained post-transformation.");
 Console.WriteLine($"{colName} column obtained" +
 $"post-transformation.");

 foreach (var row in column)
 Console.WriteLine($"{string.Join(" ", row.DenseValues().Select(x => x.ToString("f3")))} ");
 Console.WriteLine(string.Join(" ", row.DenseValues().Select(x =>
 x.ToString("f3")))+" ");
 };


 // A pipeline to project Features column into white noise vector.
 var whiteningPipeline = ml.Transforms.VectorWhiten(nameof(SampleVectorOfNumbersData.Features), kind: Microsoft.ML.Transforms.WhiteningKind.PrincipalComponentAnalysis, rank: 4);
 var whiteningPipeline = ml.Transforms.VectorWhiten(nameof(
 SampleVectorOfNumbersData.Features), kind: Microsoft.ML.Transforms
 .WhiteningKind.PrincipalComponentAnalysis, rank: 4);

 // The transformed (projected) data.
 var transformedData = whiteningPipeline.Fit(trainData).Transform(trainData);
 var transformedData = whiteningPipeline.Fit(trainData).Transform(
 trainData);

 // Getting the data of the newly created column, so we can preview it.
 var whitening = transformedData.GetColumn<VBuffer<float>>(transformedData.Schema[nameof(SampleVectorOfNumbersData.Features)]);
 var whitening = transformedData.GetColumn<VBuffer<float>>(
 transformedData.Schema[nameof(SampleVectorOfNumbersData.Features)]);

 printHelper(nameof(SampleVectorOfNumbersData.Features), whitening);

Expand All@@ -66,11 +77,16 @@ private class SampleVectorOfNumbersData
 /// <summary>
 /// Returns a few rows of the infertility dataset.
 /// </summary>
 private static IEnumerable<SampleVectorOfNumbersData> GetVectorOfNumbersData()
 private static IEnumerable<SampleVectorOfNumbersData> 
 GetVectorOfNumbersData()
 {
 var data = new List<SampleVectorOfNumbersData>();
 data.Add(new SampleVectorOfNumbersData { Features = new float[10] { 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 } });
 data.Add(new SampleVectorOfNumbersData { Features = new float[10] { 1, 2, 3, 4, 5, 6, 7, 8, 9, 0 } });
 data.Add(new SampleVectorOfNumbersData { Features = new float[10] { 0,
 1, 2, 3, 4, 5, 6, 7, 8, 9 } });

 data.Add(new SampleVectorOfNumbersData { Features = new float[10] { 1,
 2, 3, 4, 5, 6, 7, 8, 9, 0 } });

 data.Add(new SampleVectorOfNumbersData
 {
 Features = new float[10] { 2, 3, 4, 5, 6, 7, 8, 9, 0, 1 }
Expand Down

37 changes: 23 additions & 14 deletions
docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Text/ApplyCustomWordEmbedding.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -9,12 +9,13 @@ public static class ApplyCustomWordEmbedding
 {
 public static void Example()
 {
 // Create a new ML context, for ML.NET operations. It can be used for exception tracking and logging, 
 // as well as the source of randomness.
 // Create a new ML context, for ML.NET operations. It can be used for
 // exception tracking and logging, as well as the source of randomness.
 var mlContext = new MLContext();

 // Create an empty list as the dataset. The 'ApplyWordEmbedding' does not require training data as
 // the estimator ('WordEmbeddingEstimator') created by 'ApplyWordEmbedding' API is not a trainable estimator.
 // Create an empty list as the dataset. The 'ApplyWordEmbedding' does
 // not require training data as the estimator ('WordEmbeddingEstimator')
 // created by 'ApplyWordEmbedding' API is not a trainable estimator.
 // The empty list is only needed to pass input schema to the pipeline.
 var emptySamples = new List<TextData>();

Expand All@@ -33,25 +34,33 @@ public static void Example()
 file.WriteLine("buy 0 0 20");
 }

 // A pipeline for converting text into a 9-dimension word embedding vector using the custom word embedding model.
 // The 'ApplyWordEmbedding' computes the minimum, average and maximum values for each token's embedding vector.
 // Tokens in 'custommodel.txt' model are represented as 3-dimension vector.
 // Therefore, the output is of 9-dimension [min, avg, max].
 // A pipeline for converting text into a 9-dimension word embedding
 // vector using the custom word embedding model. The 
 // 'ApplyWordEmbedding' computes the minimum, average and maximum values
 // for each token's embedding vector. Tokens in 'custommodel.txt' model
 // are represented as 3-dimension vector. Therefore, the output is of
 // 9 -dimension [min, avg, max].
 //
 // The 'ApplyWordEmbedding' API requires vector of text as input.
 // The pipeline first normalizes and tokenizes text then applies word embedding transformation.
 // The pipeline first normalizes and tokenizes text then applies word
 // embedding transformation.
 var textPipeline = mlContext.Transforms.Text.NormalizeText("Text")
 .Append(mlContext.Transforms.Text.TokenizeIntoWords("Tokens", "Text"))
 .Append(mlContext.Transforms.Text.ApplyWordEmbedding("Features", pathToCustomModel, "Tokens"));
 .Append(mlContext.Transforms.Text.TokenizeIntoWords("Tokens",
 "Text"))
 .Append(mlContext.Transforms.Text.ApplyWordEmbedding("Features",
 pathToCustomModel, "Tokens"));

 // Fit to data.
 var textTransformer = textPipeline.Fit(emptyDataView);

 // Create the prediction engine to get the embedding vector from the input text/string.
 var predictionEngine = mlContext.Model.CreatePredictionEngine<TextData, TransformedTextData>(textTransformer);
 // Create the prediction engine to get the embedding vector from the
 // input text/string.
 var predictionEngine = mlContext.Model.CreatePredictionEngine<TextData,
 TransformedTextData>(textTransformer);

 // Call the prediction API to convert the text into embedding vector.
 var data = new TextData() { Text = "This is a great product. I would like to buy it again." };
 var data = new TextData() { Text = "This is a great product. I would " +
 "like to buy it again." };
 var prediction = predictionEngine.Predict(data);

 // Print the length of the embedding vector.
Expand Down

40 changes: 25 additions & 15 deletions
docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Text/ApplyWordEmbedding.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -9,38 +9,48 @@ public static class ApplyWordEmbedding
 {
 public static void Example()
 {
 // Create a new ML context, for ML.NET operations. It can be used for exception tracking and logging, 
 // as well as the source of randomness.
 // Create a new ML context, for ML.NET operations. It can be used for
 // exception tracking and logging, as well as the source of randomness.
 var mlContext = new MLContext();

 // Create an empty list as the dataset. The 'ApplyWordEmbedding' does not require training data as
 // the estimator ('WordEmbeddingEstimator') created by 'ApplyWordEmbedding' API is not a trainable estimator.
 // Create an empty list as the dataset. The 'ApplyWordEmbedding' does
 // not require training data as the estimator ('WordEmbeddingEstimator')
 // created by 'ApplyWordEmbedding' API is not a trainable estimator.
 // The empty list is only needed to pass input schema to the pipeline.
 var emptySamples = new List<TextData>();

 // Convert sample list to an empty IDataView.
 var emptyDataView = mlContext.Data.LoadFromEnumerable(emptySamples);

 // A pipeline for converting text into a 150-dimension embedding vector using pretrained 'SentimentSpecificWordEmbedding' model.
 // The 'ApplyWordEmbedding' computes the minimum, average and maximum values for each token's embedding vector.
 // Tokens in 'SentimentSpecificWordEmbedding' model are represented as 50-dimension vector.
 // Therefore, the output is of 150-dimension [min, avg, max].
 // A pipeline for converting text into a 150-dimension embedding vector
 // using pretrained 'SentimentSpecificWordEmbedding' model. The
 // 'ApplyWordEmbedding' computes the minimum, average and maximum values
 // for each token's embedding vector. Tokens in 
 // 'SentimentSpecificWordEmbedding' model are represented as
 // 50 -dimension vector. Therefore, the output is of 150-dimension [min,
 // avg, max].
 //
 // The 'ApplyWordEmbedding' API requires vector of text as input.
 // The pipeline first normalizes and tokenizes text then applies word embedding transformation.
 // The pipeline first normalizes and tokenizes text then applies word
 // embedding transformation.
 var textPipeline = mlContext.Transforms.Text.NormalizeText("Text")
 .Append(mlContext.Transforms.Text.TokenizeIntoWords("Tokens", "Text"))
 .Append(mlContext.Transforms.Text.ApplyWordEmbedding("Features", "Tokens",
 WordEmbeddingEstimator.PretrainedModelKind.SentimentSpecificWordEmbedding));
 .Append(mlContext.Transforms.Text.TokenizeIntoWords("Tokens",
 "Text"))
 .Append(mlContext.Transforms.Text.ApplyWordEmbedding("Features",
 "Tokens", WordEmbeddingEstimator.PretrainedModelKind
 .SentimentSpecificWordEmbedding));

 // Fit to data.
 var textTransformer = textPipeline.Fit(emptyDataView);

 // Create the prediction engine to get the embedding vector from the input text/string.
 var predictionEngine = mlContext.Model.CreatePredictionEngine<TextData, TransformedTextData>(textTransformer);
 // Create the prediction engine to get the embedding vector from the
 // input text/string.
 var predictionEngine = mlContext.Model.CreatePredictionEngine<TextData,
 TransformedTextData>(textTransformer);

 // Call the prediction API to convert the text into embedding vector.
 var data = new TextData() { Text = "This is a great product. I would like to buy it again." };
 var data = new TextData() { Text = "This is a great product. I would " +
 "like to buy it again." };
 var prediction = predictionEngine.Predict(data);

 // Print the length of the embedding vector.
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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Reformatting Test, Projection and TimeSeries of Transform to Width 85#3947

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Reformatting Test, Projection and TimeSeries of Transform to Width 85
#3947
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9c6325f

transform/text and projection formatted to 85 char

Jul 2, 2019
da95c60

transforms/timeseries formatted to 85 char

Jul 2, 2019
e6aca2a

minor tab and spacing fixes

Jul 2, 2019

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41 changes: 28 additions & 13 deletions
docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Projection/VectorWhiten.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -9,11 +9,12 @@ namespace Samples.Dynamic
 public sealed class VectorWhiten
 {

 /// This example requires installation of additional nuget package <a href="https://www.nuget.org/packages/Microsoft.ML.Mkl.Components/">Microsoft.ML.Mkl.Components</a>.
 /// This example requires installation of additional nuget package 
 /// <a href="https://www.nuget.org/packages/Microsoft.ML.Mkl.Components/">Microsoft.ML.Mkl.Components</a>.
 public static void Example()
 {
 // Create a new ML context, for ML.NET operations. It can be used for exception tracking and logging, 
 // as well as the source of randomness.
 // Create a new ML context, for ML.NET operations. It can be used for
 // exception tracking and logging, as well as the source of randomness.
 var ml = new MLContext();

 // Get a small dataset as an IEnumerable and convert it to an IDataView.
Expand All@@ -32,20 +33,29 @@ public static void Example()
 // 6 7 8 9 0 1 2 3 4 5

 // A small printing utility.
 Action<string, IEnumerable<VBuffer<float>>> printHelper = (colName, column) =>
 Action<string, IEnumerable<VBuffer<float>>> printHelper = (colName,
 column) =>
 {
 Console.WriteLine($"{colName} column obtained post-transformation.");
 Console.WriteLine($"{colName} column obtained " +
 $"post-transformation.");

 foreach (var row in column)
 Console.WriteLine($"{string.Join(" ", row.DenseValues().Select(x => x.ToString("f3")))} ");
 Console.WriteLine(string.Join(" ", row.DenseValues().Select(x =>
 x.ToString("f3")))+" ");
@codemzscodemzsJul 2, 2019

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ctrl+k+d is your friend.
 };

 // A pipeline to project Features column into white noise vector.
 var whiteningPipeline = ml.Transforms.VectorWhiten(nameof(SampleVectorOfNumbersData.Features),
 kind: Microsoft.ML.Transforms.WhiteningKind.ZeroPhaseComponentAnalysis);
 var whiteningPipeline = ml.Transforms.VectorWhiten(nameof(
 SampleVectorOfNumbersData.Features), kind: Microsoft.ML.Transforms
 .WhiteningKind.ZeroPhaseComponentAnalysis);

 // The transformed (projected) data.
 var transformedData = whiteningPipeline.Fit(trainData).Transform(trainData);
 var transformedData = whiteningPipeline.Fit(trainData).Transform(
 trainData);

 // Getting the data of the newly created column, so we can preview it.
 var whitening = transformedData.GetColumn<VBuffer<float>>(transformedData.Schema[nameof(SampleVectorOfNumbersData.Features)]);
 var whitening = transformedData.GetColumn<VBuffer<float>>(
 transformedData.Schema[nameof(SampleVectorOfNumbersData.Features)]);

 printHelper(nameof(SampleVectorOfNumbersData.Features), whitening);

Expand All@@ -68,11 +78,16 @@ private class SampleVectorOfNumbersData
 /// <summary>
 /// Returns a few rows of the infertility dataset.
 /// </summary>
 private static IEnumerable<SampleVectorOfNumbersData> GetVectorOfNumbersData()
 private static IEnumerable<SampleVectorOfNumbersData>
 GetVectorOfNumbersData()
 {
 var data = new List<SampleVectorOfNumbersData>();
 data.Add(new SampleVectorOfNumbersData { Features = new float[10] { 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 } });
 data.Add(new SampleVectorOfNumbersData { Features = new float[10] { 1, 2, 3, 4, 5, 6, 7, 8, 9, 0 } });
 data.Add(new SampleVectorOfNumbersData { Features = new float[10] { 0,
 1, 2, 3, 4, 5, 6, 7, 8, 9 } });

 data.Add(new SampleVectorOfNumbersData { Features = new float[10] { 1,
 2, 3, 4, 5, 6, 7, 8, 9, 0 } });

 data.Add(new SampleVectorOfNumbersData
 {
 Features = new float[10] { 2, 3, 4, 5, 6, 7, 8, 9, 0, 1 }
Expand Down

40 changes: 28 additions & 12 deletions
docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Projection/VectorWhitenWithOptions.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -8,11 +8,12 @@ namespace Samples.Dynamic
{
 public sealed class VectorWhitenWithOptions
 {
 /// This example requires installation of additional nuget package <a href="https://www.nuget.org/packages/Microsoft.ML.Mkl.Components/">Microsoft.ML.Mkl.Components</a>.
 /// This example requires installation of additional nuget package
 /// <a href="https://www.nuget.org/packages/Microsoft.ML.Mkl.Components/">Microsoft.ML.Mkl.Components</a>.
 public static void Example()
 {
 // Create a new ML context, for ML.NET operations. It can be used for exception tracking and logging, 
 // as well as the source of randomness.
 // Create a new ML context, for ML.NET operations. It can be used for
 // exception tracking and logging, as well as the source of randomness.
 var ml = new MLContext();

 // Get a small dataset as an IEnumerable and convert it to an IDataView.
Expand All@@ -31,20 +32,30 @@ public static void Example()
 // 6 7 8 9 0 1 2 3 4 5

 // A small printing utility.
 Action<string, IEnumerable<VBuffer<float>>> printHelper = (colName, column) =>
 Action<string, IEnumerable<VBuffer<float>>> printHelper = (colName,
 column) =>
 {
 Console.WriteLine($"{colName} column obtained post-transformation.");
 Console.WriteLine($"{colName} column obtained" +
 $"post-transformation.");

 foreach (var row in column)
 Console.WriteLine($"{string.Join(" ", row.DenseValues().Select(x => x.ToString("f3")))} ");
 Console.WriteLine(string.Join(" ", row.DenseValues().Select(x =>
 x.ToString("f3")))+" ");
 };


 // A pipeline to project Features column into white noise vector.
 var whiteningPipeline = ml.Transforms.VectorWhiten(nameof(SampleVectorOfNumbersData.Features), kind: Microsoft.ML.Transforms.WhiteningKind.PrincipalComponentAnalysis, rank: 4);
 var whiteningPipeline = ml.Transforms.VectorWhiten(nameof(
 SampleVectorOfNumbersData.Features), kind: Microsoft.ML.Transforms
 .WhiteningKind.PrincipalComponentAnalysis, rank: 4);

 // The transformed (projected) data.
 var transformedData = whiteningPipeline.Fit(trainData).Transform(trainData);
 var transformedData = whiteningPipeline.Fit(trainData).Transform(
 trainData);

 // Getting the data of the newly created column, so we can preview it.
 var whitening = transformedData.GetColumn<VBuffer<float>>(transformedData.Schema[nameof(SampleVectorOfNumbersData.Features)]);
 var whitening = transformedData.GetColumn<VBuffer<float>>(
 transformedData.Schema[nameof(SampleVectorOfNumbersData.Features)]);

 printHelper(nameof(SampleVectorOfNumbersData.Features), whitening);

Expand All@@ -66,11 +77,16 @@ private class SampleVectorOfNumbersData
 /// <summary>
 /// Returns a few rows of the infertility dataset.
 /// </summary>
 private static IEnumerable<SampleVectorOfNumbersData> GetVectorOfNumbersData()
 private static IEnumerable<SampleVectorOfNumbersData> 
 GetVectorOfNumbersData()
 {
 var data = new List<SampleVectorOfNumbersData>();
 data.Add(new SampleVectorOfNumbersData { Features = new float[10] { 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 } });
 data.Add(new SampleVectorOfNumbersData { Features = new float[10] { 1, 2, 3, 4, 5, 6, 7, 8, 9, 0 } });
 data.Add(new SampleVectorOfNumbersData { Features = new float[10] { 0,
 1, 2, 3, 4, 5, 6, 7, 8, 9 } });

 data.Add(new SampleVectorOfNumbersData { Features = new float[10] { 1,
 2, 3, 4, 5, 6, 7, 8, 9, 0 } });

 data.Add(new SampleVectorOfNumbersData
 {
 Features = new float[10] { 2, 3, 4, 5, 6, 7, 8, 9, 0, 1 }
Expand Down

37 changes: 23 additions & 14 deletions
docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Text/ApplyCustomWordEmbedding.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -9,12 +9,13 @@ public static class ApplyCustomWordEmbedding
 {
 public static void Example()
 {
 // Create a new ML context, for ML.NET operations. It can be used for exception tracking and logging, 
 // as well as the source of randomness.
 // Create a new ML context, for ML.NET operations. It can be used for
 // exception tracking and logging, as well as the source of randomness.
 var mlContext = new MLContext();

 // Create an empty list as the dataset. The 'ApplyWordEmbedding' does not require training data as
 // the estimator ('WordEmbeddingEstimator') created by 'ApplyWordEmbedding' API is not a trainable estimator.
 // Create an empty list as the dataset. The 'ApplyWordEmbedding' does
 // not require training data as the estimator ('WordEmbeddingEstimator')
 // created by 'ApplyWordEmbedding' API is not a trainable estimator.
 // The empty list is only needed to pass input schema to the pipeline.
 var emptySamples = new List<TextData>();

Expand All@@ -33,25 +34,33 @@ public static void Example()
 file.WriteLine("buy 0 0 20");
 }

 // A pipeline for converting text into a 9-dimension word embedding vector using the custom word embedding model.
 // The 'ApplyWordEmbedding' computes the minimum, average and maximum values for each token's embedding vector.
 // Tokens in 'custommodel.txt' model are represented as 3-dimension vector.
 // Therefore, the output is of 9-dimension [min, avg, max].
 // A pipeline for converting text into a 9-dimension word embedding
 // vector using the custom word embedding model. The 
 // 'ApplyWordEmbedding' computes the minimum, average and maximum values
 // for each token's embedding vector. Tokens in 'custommodel.txt' model
 // are represented as 3-dimension vector. Therefore, the output is of
 // 9 -dimension [min, avg, max].
 //
 // The 'ApplyWordEmbedding' API requires vector of text as input.
 // The pipeline first normalizes and tokenizes text then applies word embedding transformation.
 // The pipeline first normalizes and tokenizes text then applies word
 // embedding transformation.
 var textPipeline = mlContext.Transforms.Text.NormalizeText("Text")
 .Append(mlContext.Transforms.Text.TokenizeIntoWords("Tokens", "Text"))
 .Append(mlContext.Transforms.Text.ApplyWordEmbedding("Features", pathToCustomModel, "Tokens"));
 .Append(mlContext.Transforms.Text.TokenizeIntoWords("Tokens",
 "Text"))
 .Append(mlContext.Transforms.Text.ApplyWordEmbedding("Features",
 pathToCustomModel, "Tokens"));

 // Fit to data.
 var textTransformer = textPipeline.Fit(emptyDataView);

 // Create the prediction engine to get the embedding vector from the input text/string.
 var predictionEngine = mlContext.Model.CreatePredictionEngine<TextData, TransformedTextData>(textTransformer);
 // Create the prediction engine to get the embedding vector from the
 // input text/string.
 var predictionEngine = mlContext.Model.CreatePredictionEngine<TextData,
 TransformedTextData>(textTransformer);

 // Call the prediction API to convert the text into embedding vector.
 var data = new TextData() { Text = "This is a great product. I would like to buy it again." };
 var data = new TextData() { Text = "This is a great product. I would " +
 "like to buy it again." };
 var prediction = predictionEngine.Predict(data);

 // Print the length of the embedding vector.
Expand Down

40 changes: 25 additions & 15 deletions
docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Text/ApplyWordEmbedding.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -9,38 +9,48 @@ public static class ApplyWordEmbedding
 {
 public static void Example()
 {
 // Create a new ML context, for ML.NET operations. It can be used for exception tracking and logging, 
 // as well as the source of randomness.
 // Create a new ML context, for ML.NET operations. It can be used for
 // exception tracking and logging, as well as the source of randomness.
 var mlContext = new MLContext();

 // Create an empty list as the dataset. The 'ApplyWordEmbedding' does not require training data as
 // the estimator ('WordEmbeddingEstimator') created by 'ApplyWordEmbedding' API is not a trainable estimator.
 // Create an empty list as the dataset. The 'ApplyWordEmbedding' does
 // not require training data as the estimator ('WordEmbeddingEstimator')
 // created by 'ApplyWordEmbedding' API is not a trainable estimator.
 // The empty list is only needed to pass input schema to the pipeline.
 var emptySamples = new List<TextData>();

 // Convert sample list to an empty IDataView.
 var emptyDataView = mlContext.Data.LoadFromEnumerable(emptySamples);

 // A pipeline for converting text into a 150-dimension embedding vector using pretrained 'SentimentSpecificWordEmbedding' model.
 // The 'ApplyWordEmbedding' computes the minimum, average and maximum values for each token's embedding vector.
 // Tokens in 'SentimentSpecificWordEmbedding' model are represented as 50-dimension vector.
 // Therefore, the output is of 150-dimension [min, avg, max].
 // A pipeline for converting text into a 150-dimension embedding vector
 // using pretrained 'SentimentSpecificWordEmbedding' model. The
 // 'ApplyWordEmbedding' computes the minimum, average and maximum values
 // for each token's embedding vector. Tokens in 
 // 'SentimentSpecificWordEmbedding' model are represented as
 // 50 -dimension vector. Therefore, the output is of 150-dimension [min,
 // avg, max].
 //
 // The 'ApplyWordEmbedding' API requires vector of text as input.
 // The pipeline first normalizes and tokenizes text then applies word embedding transformation.
 // The pipeline first normalizes and tokenizes text then applies word
 // embedding transformation.
 var textPipeline = mlContext.Transforms.Text.NormalizeText("Text")
 .Append(mlContext.Transforms.Text.TokenizeIntoWords("Tokens", "Text"))
 .Append(mlContext.Transforms.Text.ApplyWordEmbedding("Features", "Tokens",
 WordEmbeddingEstimator.PretrainedModelKind.SentimentSpecificWordEmbedding));
 .Append(mlContext.Transforms.Text.TokenizeIntoWords("Tokens",
 "Text"))
 .Append(mlContext.Transforms.Text.ApplyWordEmbedding("Features",
 "Tokens", WordEmbeddingEstimator.PretrainedModelKind
 .SentimentSpecificWordEmbedding));

 // Fit to data.
 var textTransformer = textPipeline.Fit(emptyDataView);

 // Create the prediction engine to get the embedding vector from the input text/string.
 var predictionEngine = mlContext.Model.CreatePredictionEngine<TextData, TransformedTextData>(textTransformer);
 // Create the prediction engine to get the embedding vector from the
 // input text/string.
 var predictionEngine = mlContext.Model.CreatePredictionEngine<TextData,
 TransformedTextData>(textTransformer);

 // Call the prediction API to convert the text into embedding vector.
 var data = new TextData() { Text = "This is a great product. I would like to buy it again." };
 var data = new TextData() { Text = "This is a great product. I would " +
 "like to buy it again." };
 var prediction = predictionEngine.Predict(data);

 // Print the length of the embedding vector.
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('^' + ".*" + '

Skip to content
Reformatting Test, Projection and TimeSeries of Transform to Width 85#3947

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 Merged

rayankrish
merged 3 commits into

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Jul 3, 2019
 Merged


Reformatting Test, Projection and TimeSeries of Transform to Width 85
#3947
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9c6325f

transform/text and projection formatted to 85 char

Jul 2, 2019
da95c60

transforms/timeseries formatted to 85 char

Jul 2, 2019
e6aca2a

minor tab and spacing fixes

Jul 2, 2019

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41 changes: 28 additions & 13 deletions
docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Projection/VectorWhiten.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -9,11 +9,12 @@ namespace Samples.Dynamic
 public sealed class VectorWhiten
 {

 /// This example requires installation of additional nuget package <a href="https://www.nuget.org/packages/Microsoft.ML.Mkl.Components/">Microsoft.ML.Mkl.Components</a>.
 /// This example requires installation of additional nuget package 
 /// <a href="https://www.nuget.org/packages/Microsoft.ML.Mkl.Components/">Microsoft.ML.Mkl.Components</a>.
 public static void Example()
 {
 // Create a new ML context, for ML.NET operations. It can be used for exception tracking and logging, 
 // as well as the source of randomness.
 // Create a new ML context, for ML.NET operations. It can be used for
 // exception tracking and logging, as well as the source of randomness.
 var ml = new MLContext();

 // Get a small dataset as an IEnumerable and convert it to an IDataView.
Expand All@@ -32,20 +33,29 @@ public static void Example()
 // 6 7 8 9 0 1 2 3 4 5

 // A small printing utility.
 Action<string, IEnumerable<VBuffer<float>>> printHelper = (colName, column) =>
 Action<string, IEnumerable<VBuffer<float>>> printHelper = (colName,
 column) =>
 {
 Console.WriteLine($"{colName} column obtained post-transformation.");
 Console.WriteLine($"{colName} column obtained " +
 $"post-transformation.");

 foreach (var row in column)
 Console.WriteLine($"{string.Join(" ", row.DenseValues().Select(x => x.ToString("f3")))} ");
 Console.WriteLine(string.Join(" ", row.DenseValues().Select(x =>
 x.ToString("f3")))+" ");
@codemzscodemzsJul 2, 2019

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ctrl+k+d is your friend.
 };

 // A pipeline to project Features column into white noise vector.
 var whiteningPipeline = ml.Transforms.VectorWhiten(nameof(SampleVectorOfNumbersData.Features),
 kind: Microsoft.ML.Transforms.WhiteningKind.ZeroPhaseComponentAnalysis);
 var whiteningPipeline = ml.Transforms.VectorWhiten(nameof(
 SampleVectorOfNumbersData.Features), kind: Microsoft.ML.Transforms
 .WhiteningKind.ZeroPhaseComponentAnalysis);

 // The transformed (projected) data.
 var transformedData = whiteningPipeline.Fit(trainData).Transform(trainData);
 var transformedData = whiteningPipeline.Fit(trainData).Transform(
 trainData);

 // Getting the data of the newly created column, so we can preview it.
 var whitening = transformedData.GetColumn<VBuffer<float>>(transformedData.Schema[nameof(SampleVectorOfNumbersData.Features)]);
 var whitening = transformedData.GetColumn<VBuffer<float>>(
 transformedData.Schema[nameof(SampleVectorOfNumbersData.Features)]);

 printHelper(nameof(SampleVectorOfNumbersData.Features), whitening);

Expand All@@ -68,11 +78,16 @@ private class SampleVectorOfNumbersData
 /// <summary>
 /// Returns a few rows of the infertility dataset.
 /// </summary>
 private static IEnumerable<SampleVectorOfNumbersData> GetVectorOfNumbersData()
 private static IEnumerable<SampleVectorOfNumbersData>
 GetVectorOfNumbersData()
 {
 var data = new List<SampleVectorOfNumbersData>();
 data.Add(new SampleVectorOfNumbersData { Features = new float[10] { 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 } });
 data.Add(new SampleVectorOfNumbersData { Features = new float[10] { 1, 2, 3, 4, 5, 6, 7, 8, 9, 0 } });
 data.Add(new SampleVectorOfNumbersData { Features = new float[10] { 0,
 1, 2, 3, 4, 5, 6, 7, 8, 9 } });

 data.Add(new SampleVectorOfNumbersData { Features = new float[10] { 1,
 2, 3, 4, 5, 6, 7, 8, 9, 0 } });

 data.Add(new SampleVectorOfNumbersData
 {
 Features = new float[10] { 2, 3, 4, 5, 6, 7, 8, 9, 0, 1 }
Expand Down

40 changes: 28 additions & 12 deletions
docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Projection/VectorWhitenWithOptions.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -8,11 +8,12 @@ namespace Samples.Dynamic
{
 public sealed class VectorWhitenWithOptions
 {
 /// This example requires installation of additional nuget package <a href="https://www.nuget.org/packages/Microsoft.ML.Mkl.Components/">Microsoft.ML.Mkl.Components</a>.
 /// This example requires installation of additional nuget package
 /// <a href="https://www.nuget.org/packages/Microsoft.ML.Mkl.Components/">Microsoft.ML.Mkl.Components</a>.
 public static void Example()
 {
 // Create a new ML context, for ML.NET operations. It can be used for exception tracking and logging, 
 // as well as the source of randomness.
 // Create a new ML context, for ML.NET operations. It can be used for
 // exception tracking and logging, as well as the source of randomness.
 var ml = new MLContext();

 // Get a small dataset as an IEnumerable and convert it to an IDataView.
Expand All@@ -31,20 +32,30 @@ public static void Example()
 // 6 7 8 9 0 1 2 3 4 5

 // A small printing utility.
 Action<string, IEnumerable<VBuffer<float>>> printHelper = (colName, column) =>
 Action<string, IEnumerable<VBuffer<float>>> printHelper = (colName,
 column) =>
 {
 Console.WriteLine($"{colName} column obtained post-transformation.");
 Console.WriteLine($"{colName} column obtained" +
 $"post-transformation.");

 foreach (var row in column)
 Console.WriteLine($"{string.Join(" ", row.DenseValues().Select(x => x.ToString("f3")))} ");
 Console.WriteLine(string.Join(" ", row.DenseValues().Select(x =>
 x.ToString("f3")))+" ");
 };


 // A pipeline to project Features column into white noise vector.
 var whiteningPipeline = ml.Transforms.VectorWhiten(nameof(SampleVectorOfNumbersData.Features), kind: Microsoft.ML.Transforms.WhiteningKind.PrincipalComponentAnalysis, rank: 4);
 var whiteningPipeline = ml.Transforms.VectorWhiten(nameof(
 SampleVectorOfNumbersData.Features), kind: Microsoft.ML.Transforms
 .WhiteningKind.PrincipalComponentAnalysis, rank: 4);

 // The transformed (projected) data.
 var transformedData = whiteningPipeline.Fit(trainData).Transform(trainData);
 var transformedData = whiteningPipeline.Fit(trainData).Transform(
 trainData);

 // Getting the data of the newly created column, so we can preview it.
 var whitening = transformedData.GetColumn<VBuffer<float>>(transformedData.Schema[nameof(SampleVectorOfNumbersData.Features)]);
 var whitening = transformedData.GetColumn<VBuffer<float>>(
 transformedData.Schema[nameof(SampleVectorOfNumbersData.Features)]);

 printHelper(nameof(SampleVectorOfNumbersData.Features), whitening);

Expand All@@ -66,11 +77,16 @@ private class SampleVectorOfNumbersData
 /// <summary>
 /// Returns a few rows of the infertility dataset.
 /// </summary>
 private static IEnumerable<SampleVectorOfNumbersData> GetVectorOfNumbersData()
 private static IEnumerable<SampleVectorOfNumbersData> 
 GetVectorOfNumbersData()
 {
 var data = new List<SampleVectorOfNumbersData>();
 data.Add(new SampleVectorOfNumbersData { Features = new float[10] { 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 } });
 data.Add(new SampleVectorOfNumbersData { Features = new float[10] { 1, 2, 3, 4, 5, 6, 7, 8, 9, 0 } });
 data.Add(new SampleVectorOfNumbersData { Features = new float[10] { 0,
 1, 2, 3, 4, 5, 6, 7, 8, 9 } });

 data.Add(new SampleVectorOfNumbersData { Features = new float[10] { 1,
 2, 3, 4, 5, 6, 7, 8, 9, 0 } });

 data.Add(new SampleVectorOfNumbersData
 {
 Features = new float[10] { 2, 3, 4, 5, 6, 7, 8, 9, 0, 1 }
Expand Down

37 changes: 23 additions & 14 deletions
docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Text/ApplyCustomWordEmbedding.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -9,12 +9,13 @@ public static class ApplyCustomWordEmbedding
 {
 public static void Example()
 {
 // Create a new ML context, for ML.NET operations. It can be used for exception tracking and logging, 
 // as well as the source of randomness.
 // Create a new ML context, for ML.NET operations. It can be used for
 // exception tracking and logging, as well as the source of randomness.
 var mlContext = new MLContext();

 // Create an empty list as the dataset. The 'ApplyWordEmbedding' does not require training data as
 // the estimator ('WordEmbeddingEstimator') created by 'ApplyWordEmbedding' API is not a trainable estimator.
 // Create an empty list as the dataset. The 'ApplyWordEmbedding' does
 // not require training data as the estimator ('WordEmbeddingEstimator')
 // created by 'ApplyWordEmbedding' API is not a trainable estimator.
 // The empty list is only needed to pass input schema to the pipeline.
 var emptySamples = new List<TextData>();

Expand All@@ -33,25 +34,33 @@ public static void Example()
 file.WriteLine("buy 0 0 20");
 }

 // A pipeline for converting text into a 9-dimension word embedding vector using the custom word embedding model.
 // The 'ApplyWordEmbedding' computes the minimum, average and maximum values for each token's embedding vector.
 // Tokens in 'custommodel.txt' model are represented as 3-dimension vector.
 // Therefore, the output is of 9-dimension [min, avg, max].
 // A pipeline for converting text into a 9-dimension word embedding
 // vector using the custom word embedding model. The 
 // 'ApplyWordEmbedding' computes the minimum, average and maximum values
 // for each token's embedding vector. Tokens in 'custommodel.txt' model
 // are represented as 3-dimension vector. Therefore, the output is of
 // 9 -dimension [min, avg, max].
 //
 // The 'ApplyWordEmbedding' API requires vector of text as input.
 // The pipeline first normalizes and tokenizes text then applies word embedding transformation.
 // The pipeline first normalizes and tokenizes text then applies word
 // embedding transformation.
 var textPipeline = mlContext.Transforms.Text.NormalizeText("Text")
 .Append(mlContext.Transforms.Text.TokenizeIntoWords("Tokens", "Text"))
 .Append(mlContext.Transforms.Text.ApplyWordEmbedding("Features", pathToCustomModel, "Tokens"));
 .Append(mlContext.Transforms.Text.TokenizeIntoWords("Tokens",
 "Text"))
 .Append(mlContext.Transforms.Text.ApplyWordEmbedding("Features",
 pathToCustomModel, "Tokens"));

 // Fit to data.
 var textTransformer = textPipeline.Fit(emptyDataView);

 // Create the prediction engine to get the embedding vector from the input text/string.
 var predictionEngine = mlContext.Model.CreatePredictionEngine<TextData, TransformedTextData>(textTransformer);
 // Create the prediction engine to get the embedding vector from the
 // input text/string.
 var predictionEngine = mlContext.Model.CreatePredictionEngine<TextData,
 TransformedTextData>(textTransformer);

 // Call the prediction API to convert the text into embedding vector.
 var data = new TextData() { Text = "This is a great product. I would like to buy it again." };
 var data = new TextData() { Text = "This is a great product. I would " +
 "like to buy it again." };
 var prediction = predictionEngine.Predict(data);

 // Print the length of the embedding vector.
Expand Down

40 changes: 25 additions & 15 deletions
docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Text/ApplyWordEmbedding.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -9,38 +9,48 @@ public static class ApplyWordEmbedding
 {
 public static void Example()
 {
 // Create a new ML context, for ML.NET operations. It can be used for exception tracking and logging, 
 // as well as the source of randomness.
 // Create a new ML context, for ML.NET operations. It can be used for
 // exception tracking and logging, as well as the source of randomness.
 var mlContext = new MLContext();

 // Create an empty list as the dataset. The 'ApplyWordEmbedding' does not require training data as
 // the estimator ('WordEmbeddingEstimator') created by 'ApplyWordEmbedding' API is not a trainable estimator.
 // Create an empty list as the dataset. The 'ApplyWordEmbedding' does
 // not require training data as the estimator ('WordEmbeddingEstimator')
 // created by 'ApplyWordEmbedding' API is not a trainable estimator.
 // The empty list is only needed to pass input schema to the pipeline.
 var emptySamples = new List<TextData>();

 // Convert sample list to an empty IDataView.
 var emptyDataView = mlContext.Data.LoadFromEnumerable(emptySamples);

 // A pipeline for converting text into a 150-dimension embedding vector using pretrained 'SentimentSpecificWordEmbedding' model.
 // The 'ApplyWordEmbedding' computes the minimum, average and maximum values for each token's embedding vector.
 // Tokens in 'SentimentSpecificWordEmbedding' model are represented as 50-dimension vector.
 // Therefore, the output is of 150-dimension [min, avg, max].
 // A pipeline for converting text into a 150-dimension embedding vector
 // using pretrained 'SentimentSpecificWordEmbedding' model. The
 // 'ApplyWordEmbedding' computes the minimum, average and maximum values
 // for each token's embedding vector. Tokens in 
 // 'SentimentSpecificWordEmbedding' model are represented as
 // 50 -dimension vector. Therefore, the output is of 150-dimension [min,
 // avg, max].
 //
 // The 'ApplyWordEmbedding' API requires vector of text as input.
 // The pipeline first normalizes and tokenizes text then applies word embedding transformation.
 // The pipeline first normalizes and tokenizes text then applies word
 // embedding transformation.
 var textPipeline = mlContext.Transforms.Text.NormalizeText("Text")
 .Append(mlContext.Transforms.Text.TokenizeIntoWords("Tokens", "Text"))
 .Append(mlContext.Transforms.Text.ApplyWordEmbedding("Features", "Tokens",
 WordEmbeddingEstimator.PretrainedModelKind.SentimentSpecificWordEmbedding));
 .Append(mlContext.Transforms.Text.TokenizeIntoWords("Tokens",
 "Text"))
 .Append(mlContext.Transforms.Text.ApplyWordEmbedding("Features",
 "Tokens", WordEmbeddingEstimator.PretrainedModelKind
 .SentimentSpecificWordEmbedding));

 // Fit to data.
 var textTransformer = textPipeline.Fit(emptyDataView);

 // Create the prediction engine to get the embedding vector from the input text/string.
 var predictionEngine = mlContext.Model.CreatePredictionEngine<TextData, TransformedTextData>(textTransformer);
 // Create the prediction engine to get the embedding vector from the
 // input text/string.
 var predictionEngine = mlContext.Model.CreatePredictionEngine<TextData,
 TransformedTextData>(textTransformer);

 // Call the prediction API to convert the text into embedding vector.
 var data = new TextData() { Text = "This is a great product. I would like to buy it again." };
 var data = new TextData() { Text = "This is a great product. I would " +
 "like to buy it again." };
 var prediction = predictionEngine.Predict(data);

 // Print the length of the embedding vector.
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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Reformatting Test, Projection and TimeSeries of Transform to Width 85
#3947
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9c6325f

transform/text and projection formatted to 85 char

Jul 2, 2019
da95c60

transforms/timeseries formatted to 85 char

Jul 2, 2019
e6aca2a

minor tab and spacing fixes

Jul 2, 2019

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41 changes: 28 additions & 13 deletions
docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Projection/VectorWhiten.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -9,11 +9,12 @@ namespace Samples.Dynamic
 public sealed class VectorWhiten
 {

 /// This example requires installation of additional nuget package <a href="https://www.nuget.org/packages/Microsoft.ML.Mkl.Components/">Microsoft.ML.Mkl.Components</a>.
 /// This example requires installation of additional nuget package 
 /// <a href="https://www.nuget.org/packages/Microsoft.ML.Mkl.Components/">Microsoft.ML.Mkl.Components</a>.
 public static void Example()
 {
 // Create a new ML context, for ML.NET operations. It can be used for exception tracking and logging, 
 // as well as the source of randomness.
 // Create a new ML context, for ML.NET operations. It can be used for
 // exception tracking and logging, as well as the source of randomness.
 var ml = new MLContext();

 // Get a small dataset as an IEnumerable and convert it to an IDataView.
Expand All@@ -32,20 +33,29 @@ public static void Example()
 // 6 7 8 9 0 1 2 3 4 5

 // A small printing utility.
 Action<string, IEnumerable<VBuffer<float>>> printHelper = (colName, column) =>
 Action<string, IEnumerable<VBuffer<float>>> printHelper = (colName,
 column) =>
 {
 Console.WriteLine($"{colName} column obtained post-transformation.");
 Console.WriteLine($"{colName} column obtained " +
 $"post-transformation.");

 foreach (var row in column)
 Console.WriteLine($"{string.Join(" ", row.DenseValues().Select(x => x.ToString("f3")))} ");
 Console.WriteLine(string.Join(" ", row.DenseValues().Select(x =>
 x.ToString("f3")))+" ");
@codemzscodemzsJul 2, 2019

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ctrl+k+d is your friend.
 };

 // A pipeline to project Features column into white noise vector.
 var whiteningPipeline = ml.Transforms.VectorWhiten(nameof(SampleVectorOfNumbersData.Features),
 kind: Microsoft.ML.Transforms.WhiteningKind.ZeroPhaseComponentAnalysis);
 var whiteningPipeline = ml.Transforms.VectorWhiten(nameof(
 SampleVectorOfNumbersData.Features), kind: Microsoft.ML.Transforms
 .WhiteningKind.ZeroPhaseComponentAnalysis);

 // The transformed (projected) data.
 var transformedData = whiteningPipeline.Fit(trainData).Transform(trainData);
 var transformedData = whiteningPipeline.Fit(trainData).Transform(
 trainData);

 // Getting the data of the newly created column, so we can preview it.
 var whitening = transformedData.GetColumn<VBuffer<float>>(transformedData.Schema[nameof(SampleVectorOfNumbersData.Features)]);
 var whitening = transformedData.GetColumn<VBuffer<float>>(
 transformedData.Schema[nameof(SampleVectorOfNumbersData.Features)]);

 printHelper(nameof(SampleVectorOfNumbersData.Features), whitening);

Expand All@@ -68,11 +78,16 @@ private class SampleVectorOfNumbersData
 /// <summary>
 /// Returns a few rows of the infertility dataset.
 /// </summary>
 private static IEnumerable<SampleVectorOfNumbersData> GetVectorOfNumbersData()
 private static IEnumerable<SampleVectorOfNumbersData>
 GetVectorOfNumbersData()
 {
 var data = new List<SampleVectorOfNumbersData>();
 data.Add(new SampleVectorOfNumbersData { Features = new float[10] { 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 } });
 data.Add(new SampleVectorOfNumbersData { Features = new float[10] { 1, 2, 3, 4, 5, 6, 7, 8, 9, 0 } });
 data.Add(new SampleVectorOfNumbersData { Features = new float[10] { 0,
 1, 2, 3, 4, 5, 6, 7, 8, 9 } });

 data.Add(new SampleVectorOfNumbersData { Features = new float[10] { 1,
 2, 3, 4, 5, 6, 7, 8, 9, 0 } });

 data.Add(new SampleVectorOfNumbersData
 {
 Features = new float[10] { 2, 3, 4, 5, 6, 7, 8, 9, 0, 1 }
Expand Down

40 changes: 28 additions & 12 deletions
docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Projection/VectorWhitenWithOptions.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -8,11 +8,12 @@ namespace Samples.Dynamic
{
 public sealed class VectorWhitenWithOptions
 {
 /// This example requires installation of additional nuget package <a href="https://www.nuget.org/packages/Microsoft.ML.Mkl.Components/">Microsoft.ML.Mkl.Components</a>.
 /// This example requires installation of additional nuget package
 /// <a href="https://www.nuget.org/packages/Microsoft.ML.Mkl.Components/">Microsoft.ML.Mkl.Components</a>.
 public static void Example()
 {
 // Create a new ML context, for ML.NET operations. It can be used for exception tracking and logging, 
 // as well as the source of randomness.
 // Create a new ML context, for ML.NET operations. It can be used for
 // exception tracking and logging, as well as the source of randomness.
 var ml = new MLContext();

 // Get a small dataset as an IEnumerable and convert it to an IDataView.
Expand All@@ -31,20 +32,30 @@ public static void Example()
 // 6 7 8 9 0 1 2 3 4 5

 // A small printing utility.
 Action<string, IEnumerable<VBuffer<float>>> printHelper = (colName, column) =>
 Action<string, IEnumerable<VBuffer<float>>> printHelper = (colName,
 column) =>
 {
 Console.WriteLine($"{colName} column obtained post-transformation.");
 Console.WriteLine($"{colName} column obtained" +
 $"post-transformation.");

 foreach (var row in column)
 Console.WriteLine($"{string.Join(" ", row.DenseValues().Select(x => x.ToString("f3")))} ");
 Console.WriteLine(string.Join(" ", row.DenseValues().Select(x =>
 x.ToString("f3")))+" ");
 };


 // A pipeline to project Features column into white noise vector.
 var whiteningPipeline = ml.Transforms.VectorWhiten(nameof(SampleVectorOfNumbersData.Features), kind: Microsoft.ML.Transforms.WhiteningKind.PrincipalComponentAnalysis, rank: 4);
 var whiteningPipeline = ml.Transforms.VectorWhiten(nameof(
 SampleVectorOfNumbersData.Features), kind: Microsoft.ML.Transforms
 .WhiteningKind.PrincipalComponentAnalysis, rank: 4);

 // The transformed (projected) data.
 var transformedData = whiteningPipeline.Fit(trainData).Transform(trainData);
 var transformedData = whiteningPipeline.Fit(trainData).Transform(
 trainData);

 // Getting the data of the newly created column, so we can preview it.
 var whitening = transformedData.GetColumn<VBuffer<float>>(transformedData.Schema[nameof(SampleVectorOfNumbersData.Features)]);
 var whitening = transformedData.GetColumn<VBuffer<float>>(
 transformedData.Schema[nameof(SampleVectorOfNumbersData.Features)]);

 printHelper(nameof(SampleVectorOfNumbersData.Features), whitening);

Expand All@@ -66,11 +77,16 @@ private class SampleVectorOfNumbersData
 /// <summary>
 /// Returns a few rows of the infertility dataset.
 /// </summary>
 private static IEnumerable<SampleVectorOfNumbersData> GetVectorOfNumbersData()
 private static IEnumerable<SampleVectorOfNumbersData> 
 GetVectorOfNumbersData()
 {
 var data = new List<SampleVectorOfNumbersData>();
 data.Add(new SampleVectorOfNumbersData { Features = new float[10] { 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 } });
 data.Add(new SampleVectorOfNumbersData { Features = new float[10] { 1, 2, 3, 4, 5, 6, 7, 8, 9, 0 } });
 data.Add(new SampleVectorOfNumbersData { Features = new float[10] { 0,
 1, 2, 3, 4, 5, 6, 7, 8, 9 } });

 data.Add(new SampleVectorOfNumbersData { Features = new float[10] { 1,
 2, 3, 4, 5, 6, 7, 8, 9, 0 } });

 data.Add(new SampleVectorOfNumbersData
 {
 Features = new float[10] { 2, 3, 4, 5, 6, 7, 8, 9, 0, 1 }
Expand Down

37 changes: 23 additions & 14 deletions
docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Text/ApplyCustomWordEmbedding.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -9,12 +9,13 @@ public static class ApplyCustomWordEmbedding
 {
 public static void Example()
 {
 // Create a new ML context, for ML.NET operations. It can be used for exception tracking and logging, 
 // as well as the source of randomness.
 // Create a new ML context, for ML.NET operations. It can be used for
 // exception tracking and logging, as well as the source of randomness.
 var mlContext = new MLContext();

 // Create an empty list as the dataset. The 'ApplyWordEmbedding' does not require training data as
 // the estimator ('WordEmbeddingEstimator') created by 'ApplyWordEmbedding' API is not a trainable estimator.
 // Create an empty list as the dataset. The 'ApplyWordEmbedding' does
 // not require training data as the estimator ('WordEmbeddingEstimator')
 // created by 'ApplyWordEmbedding' API is not a trainable estimator.
 // The empty list is only needed to pass input schema to the pipeline.
 var emptySamples = new List<TextData>();

Expand All@@ -33,25 +34,33 @@ public static void Example()
 file.WriteLine("buy 0 0 20");
 }

 // A pipeline for converting text into a 9-dimension word embedding vector using the custom word embedding model.
 // The 'ApplyWordEmbedding' computes the minimum, average and maximum values for each token's embedding vector.
 // Tokens in 'custommodel.txt' model are represented as 3-dimension vector.
 // Therefore, the output is of 9-dimension [min, avg, max].
 // A pipeline for converting text into a 9-dimension word embedding
 // vector using the custom word embedding model. The 
 // 'ApplyWordEmbedding' computes the minimum, average and maximum values
 // for each token's embedding vector. Tokens in 'custommodel.txt' model
 // are represented as 3-dimension vector. Therefore, the output is of
 // 9 -dimension [min, avg, max].
 //
 // The 'ApplyWordEmbedding' API requires vector of text as input.
 // The pipeline first normalizes and tokenizes text then applies word embedding transformation.
 // The pipeline first normalizes and tokenizes text then applies word
 // embedding transformation.
 var textPipeline = mlContext.Transforms.Text.NormalizeText("Text")
 .Append(mlContext.Transforms.Text.TokenizeIntoWords("Tokens", "Text"))
 .Append(mlContext.Transforms.Text.ApplyWordEmbedding("Features", pathToCustomModel, "Tokens"));
 .Append(mlContext.Transforms.Text.TokenizeIntoWords("Tokens",
 "Text"))
 .Append(mlContext.Transforms.Text.ApplyWordEmbedding("Features",
 pathToCustomModel, "Tokens"));

 // Fit to data.
 var textTransformer = textPipeline.Fit(emptyDataView);

 // Create the prediction engine to get the embedding vector from the input text/string.
 var predictionEngine = mlContext.Model.CreatePredictionEngine<TextData, TransformedTextData>(textTransformer);
 // Create the prediction engine to get the embedding vector from the
 // input text/string.
 var predictionEngine = mlContext.Model.CreatePredictionEngine<TextData,
 TransformedTextData>(textTransformer);

 // Call the prediction API to convert the text into embedding vector.
 var data = new TextData() { Text = "This is a great product. I would like to buy it again." };
 var data = new TextData() { Text = "This is a great product. I would " +
 "like to buy it again." };
 var prediction = predictionEngine.Predict(data);

 // Print the length of the embedding vector.
Expand Down

40 changes: 25 additions & 15 deletions
docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Text/ApplyWordEmbedding.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -9,38 +9,48 @@ public static class ApplyWordEmbedding
 {
 public static void Example()
 {
 // Create a new ML context, for ML.NET operations. It can be used for exception tracking and logging, 
 // as well as the source of randomness.
 // Create a new ML context, for ML.NET operations. It can be used for
 // exception tracking and logging, as well as the source of randomness.
 var mlContext = new MLContext();

 // Create an empty list as the dataset. The 'ApplyWordEmbedding' does not require training data as
 // the estimator ('WordEmbeddingEstimator') created by 'ApplyWordEmbedding' API is not a trainable estimator.
 // Create an empty list as the dataset. The 'ApplyWordEmbedding' does
 // not require training data as the estimator ('WordEmbeddingEstimator')
 // created by 'ApplyWordEmbedding' API is not a trainable estimator.
 // The empty list is only needed to pass input schema to the pipeline.
 var emptySamples = new List<TextData>();

 // Convert sample list to an empty IDataView.
 var emptyDataView = mlContext.Data.LoadFromEnumerable(emptySamples);

 // A pipeline for converting text into a 150-dimension embedding vector using pretrained 'SentimentSpecificWordEmbedding' model.
 // The 'ApplyWordEmbedding' computes the minimum, average and maximum values for each token's embedding vector.
 // Tokens in 'SentimentSpecificWordEmbedding' model are represented as 50-dimension vector.
 // Therefore, the output is of 150-dimension [min, avg, max].
 // A pipeline for converting text into a 150-dimension embedding vector
 // using pretrained 'SentimentSpecificWordEmbedding' model. The
 // 'ApplyWordEmbedding' computes the minimum, average and maximum values
 // for each token's embedding vector. Tokens in 
 // 'SentimentSpecificWordEmbedding' model are represented as
 // 50 -dimension vector. Therefore, the output is of 150-dimension [min,
 // avg, max].
 //
 // The 'ApplyWordEmbedding' API requires vector of text as input.
 // The pipeline first normalizes and tokenizes text then applies word embedding transformation.
 // The pipeline first normalizes and tokenizes text then applies word
 // embedding transformation.
 var textPipeline = mlContext.Transforms.Text.NormalizeText("Text")
 .Append(mlContext.Transforms.Text.TokenizeIntoWords("Tokens", "Text"))
 .Append(mlContext.Transforms.Text.ApplyWordEmbedding("Features", "Tokens",
 WordEmbeddingEstimator.PretrainedModelKind.SentimentSpecificWordEmbedding));
 .Append(mlContext.Transforms.Text.TokenizeIntoWords("Tokens",
 "Text"))
 .Append(mlContext.Transforms.Text.ApplyWordEmbedding("Features",
 "Tokens", WordEmbeddingEstimator.PretrainedModelKind
 .SentimentSpecificWordEmbedding));

 // Fit to data.
 var textTransformer = textPipeline.Fit(emptyDataView);

 // Create the prediction engine to get the embedding vector from the input text/string.
 var predictionEngine = mlContext.Model.CreatePredictionEngine<TextData, TransformedTextData>(textTransformer);
 // Create the prediction engine to get the embedding vector from the
 // input text/string.
 var predictionEngine = mlContext.Model.CreatePredictionEngine<TextData,
 TransformedTextData>(textTransformer);

 // Call the prediction API to convert the text into embedding vector.
 var data = new TextData() { Text = "This is a great product. I would like to buy it again." };
 var data = new TextData() { Text = "This is a great product. I would " +
 "like to buy it again." };
 var prediction = predictionEngine.Predict(data);

 // Print the length of the embedding vector.
Expand Down
Loading