Created samples for 'ProduceWordBags' and 'ProduceHashedWordBags' API.#3183

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zeahmed merged 6 commits into from
Apr 4, 2019
Merged

Created samples for 'ProduceWordBags' and 'ProduceHashedWordBags' API. #3183

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9471b5d
Created samples for 'ProduceWordBags' and 'ProduceHashedWordBags' API.
zeahmed Apr 2, 2019
1b2daca
Updated comments!
zeahmed Apr 3, 2019
202f051
Addressed reviewers' comments.
zeahmed Apr 3, 2019
d288792
Addressed reviewers' comments.
zeahmed Apr 3, 2019
bdca2a5
Changed input/output classes to private.
zeahmed Apr 4, 2019
bc60f09
Addressed reviewers' comments.
zeahmed Apr 4, 2019
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89 changes: 89 additions & 0 deletions docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Text/ProduceHashedWordBags.cs
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using System;
using System.Collections.Generic;
using System.Text;
using Microsoft.ML.Data;
using Microsoft.ML.Transforms.Text;

namespace Microsoft.ML.Samples.Dynamic
{
public static class ProduceHashedWordBags
{
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.
var mlContext = new MLContext();

// Create a small dataset as an IEnumerable.
var samples = new List<TextData>()
{
new TextData(){ Text = "This is an example to compute bag-of-word features using hashing." },
new TextData(){ Text = "ML.NET's ProduceHashedWordBags API produces count of n-grams and hashes it as an index into a vector of given bit length." },
new TextData(){ Text = "It does so by first tokenizing text/string into words/tokens then " },
new TextData(){ Text = "computing n-grams and hash them to the index given by hash value." },
new TextData(){ Text = "The hashing reduces the size of the output feature vector" },
new TextData(){ Text = "which is useful in case when number of n-grams is very large." },
};

// Convert training data to IDataView.
var dataview = mlContext.Data.LoadFromEnumerable(samples);

// A pipeline for converting text into numeric bag-of-word features using hashing.
// The following call to 'ProduceHashedWordBags' implicitly tokenizes the text/string into words/tokens.
// Please note that the length of the output feature vector depends on the 'numberOfBits' settings.
var textPipeline = mlContext.Transforms.Text.ProduceHashedWordBags("BagOfWordFeatures", "Text",
numberOfBits: 5,
ngramLength: 3,
useAllLengths: false,
maximumNumberOfInverts: 1);

// Fit to data.
var textTransformer = textPipeline.Fit(dataview);
var transformedDataView = textTransformer.Transform(dataview);

// Create the prediction engine to get the bag-of-word features extracted from the text.
var predictionEngine = mlContext.Model.CreatePredictionEngine<TextData, TransformedTextData>(textTransformer);

// Convert the text into numeric features.
var prediction = predictionEngine.Predict(samples[0]);

// Print the length of the feature vector.
Console.WriteLine($"Number of Features: {prediction.BagOfWordFeatures.Length}");

// Preview of the produced n-grams.
// Get the slot names from the column's metadata.
// The slot names for a vector column corresponds to the names associated with each position in the vector.
VBuffer<ReadOnlyMemory<char>> slotNames = default;
transformedDataView.Schema["BagOfWordFeatures"].GetSlotNames(ref slotNames);
var BagOfWordFeaturesColumn = transformedDataView.GetColumn<VBuffer<float>>(transformedDataView.Schema["BagOfWordFeatures"]);
var slots = slotNames.GetValues();
Console.Write("N-grams: ");
foreach (var featureRow in BagOfWordFeaturesColumn)
{
foreach (var item in featureRow.Items())
Console.Write($"{slots[item.Key]} ");
Console.WriteLine();
}

// Print the first 10 feature values.
Console.Write("Features: ");
for (int i = 0; i < 10; i++)
Console.Write($"{prediction.BagOfWordFeatures[i]:F4} ");

// Expected output:
// Number of Features: 32
// N-grams: an|example|to is|an|example example|to|compute This|is|an compute|bag-of-word|features bag-of-word|features|using to|compute|bag-of-word ML.NET's|ProduceHashedWordBags|API as|an|index API|produces|count ...
// Features: 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 1.0000 2.0000 0.0000 0.0000 ...
}

private class TextData
{
public string Text { get; set; }
}

private class TransformedTextData : TextData
{
public float[] BagOfWordFeatures { get; set; }
}
}
}
89 changes: 89 additions & 0 deletions docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Text/ProduceWordBags.cs
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using System;
using System.Collections.Generic;
using System.Text;
using Microsoft.ML.Data;
using Microsoft.ML.Transforms.Text;

namespace Microsoft.ML.Samples.Dynamic
{
public static class ProduceWordBags
{
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.
var mlContext = new MLContext();

// Create a small dataset as an IEnumerable.
var samples = new List<TextData>()
{
new TextData(){ Text = "This is an example to compute bag-of-word features." },
new TextData(){ Text = "ML.NET's ProduceWordBags API produces bag-of-word features from input text." },
new TextData(){ Text = "It does so by first tokenizing text/string into words/tokens then " },
new TextData(){ Text = "computing n-grams and their neumeric values." },
new TextData(){ Text = "Each position in the output vector corresponds to a particular n-gram." },
new TextData(){ Text = "The value at each position corresponds to," },
new TextData(){ Text = "the number of times n-gram occured in the data (Tf), or" },
new TextData(){ Text = "the inverse of the number of documents contain the n-gram (Idf)," },
new TextData(){ Text = "or compute both and multipy together (Tf-Idf)." },
};

// Convert training data to IDataView.
var dataview = mlContext.Data.LoadFromEnumerable(samples);

// A pipeline for converting text into numeric bag-of-word features.
// The following call to 'ProduceWordBags' implicitly tokenizes the text/string into words/tokens.
// Please note that the length of the output feature vector depends on the n-gram settings.
var textPipeline = mlContext.Transforms.Text.ProduceWordBags("BagOfWordFeatures", "Text",
ngramLength: 3, useAllLengths: false, weighting: NgramExtractingEstimator.WeightingCriteria.Tf);

// Fit to data.
var textTransformer = textPipeline.Fit(dataview);
var transformedDataView = textTransformer.Transform(dataview);

// Create the prediction engine to get the bag-of-word features extracted from the text.
var predictionEngine = mlContext.Model.CreatePredictionEngine<TextData, TransformedTextData>(textTransformer);

@rogancarrrogancarrApr 4, 2019

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Why predictionEngine rather than TakeRows and ConvertToEnumerable? I would use the one we would recommend people to use to inspect data in practice.


// Convert the text into numeric features.
var prediction = predictionEngine.Predict(samples[0]);

// Print the length of the feature vector.
Console.WriteLine($"Number of Features: {prediction.BagOfWordFeatures.Length}");

// Preview of the produced n-grams.
// Get the slot names from the column's metadata.
// The slot names for a vector column corresponds to the names associated with each position in the vector.
VBuffer<ReadOnlyMemory<char>> slotNames = default;
transformedDataView.Schema["BagOfWordFeatures"].GetSlotNames(ref slotNames);
var BagOfWordFeaturesColumn = transformedDataView.GetColumn<VBuffer<float>>(transformedDataView.Schema["BagOfWordFeatures"]);
var slots = slotNames.GetValues();
Console.Write("N-grams: ");
foreach (var featureRow in BagOfWordFeaturesColumn)
{
foreach (var item in featureRow.Items())
Console.Write($"{slots[item.Key]} ");
Console.WriteLine();
}

// Print the first 10 feature values.
Console.Write("Features: ");
for (int i = 0; i < 10; i++)
Console.Write($"{prediction.BagOfWordFeatures[i]:F4} ");

// Expected output:
// Number of Features: 62
// N-grams: This|is|an is|an|example an|example|to example|to|compute to|compute|bag-of-word compute|bag-of-word|features. ML.NET's|ProduceWordBags|API ProduceWordBags|API|produces API|produces|bag-of-word produces|bag-of-word|features ...
// Features: 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.0000 0.0000 0.0000 0.0000 ...
}

private class TextData
{
public string Text { get; set; }
}

private class TransformedTextData : TextData
{
public float[] BagOfWordFeatures { get; set; }
}
}
}
, '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" + '

Skip to content
Created samples for 'ProduceWordBags' and 'ProduceHashedWordBags' API.#3183

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to your account

 Merged

zeahmed
merged 6 commits into

from

Apr 4, 2019
 Merged


Created samples for 'ProduceWordBags' and 'ProduceHashedWordBags' API.
#3183
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9471b5d

Created samples for 'ProduceWordBags' and 'ProduceHashedWordBags' API.


zeahmed Apr 2, 2019
1b2daca

Updated comments!


zeahmed Apr 3, 2019
202f051

Addressed reviewers' comments.


zeahmed Apr 3, 2019
d288792

Addressed reviewers' comments.


zeahmed Apr 3, 2019
bdca2a5

Changed input/output classes to private.


zeahmed Apr 4, 2019
bc60f09

Addressed reviewers' comments.


zeahmed Apr 4, 2019

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89 changes: 89 additions & 0 deletions
docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Text/ProduceHashedWordBags.cs

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Original file line numberDiff line numberDiff line change
@@ -0,0 +1,89 @@
using System;
using System.Collections.Generic;
using System.Text;
using Microsoft.ML.Data;
using Microsoft.ML.Transforms.Text;

namespace Microsoft.ML.Samples.Dynamic
{
 public static class ProduceHashedWordBags
 {
 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.
 var mlContext = new MLContext();

 // Create a small dataset as an IEnumerable.
 var samples = new List<TextData>()
 {
 new TextData(){ Text = "This is an example to compute bag-of-word features using hashing." },
 new TextData(){ Text = "ML.NET's ProduceHashedWordBags API produces count of n-grams and hashes it as an index into a vector of given bit length." },
 new TextData(){ Text = "It does so by first tokenizing text/string into words/tokens then " },
 new TextData(){ Text = "computing n-grams and hash them to the index given by hash value." },
 new TextData(){ Text = "The hashing reduces the size of the output feature vector" },
 new TextData(){ Text = "which is useful in case when number of n-grams is very large." },
 };

 // Convert training data to IDataView.
 var dataview = mlContext.Data.LoadFromEnumerable(samples);

 // A pipeline for converting text into numeric bag-of-word features using hashing.
 // The following call to 'ProduceHashedWordBags' implicitly tokenizes the text/string into words/tokens.
 // Please note that the length of the output feature vector depends on the 'numberOfBits' settings.
 var textPipeline = mlContext.Transforms.Text.ProduceHashedWordBags("BagOfWordFeatures", "Text",
 numberOfBits: 5,
 ngramLength: 3,
 useAllLengths: false,
 maximumNumberOfInverts: 1);

 // Fit to data.
 var textTransformer = textPipeline.Fit(dataview);
 var transformedDataView = textTransformer.Transform(dataview);

 // Create the prediction engine to get the bag-of-word features extracted from the text.
 var predictionEngine = mlContext.Model.CreatePredictionEngine<TextData, TransformedTextData>(textTransformer);

 // Convert the text into numeric features.
 var prediction = predictionEngine.Predict(samples[0]);

 // Print the length of the feature vector.
 Console.WriteLine($"Number of Features: {prediction.BagOfWordFeatures.Length}");

 // Preview of the produced n-grams.
 // Get the slot names from the column's metadata.
 // The slot names for a vector column corresponds to the names associated with each position in the vector.
 VBuffer<ReadOnlyMemory<char>> slotNames = default;
 transformedDataView.Schema["BagOfWordFeatures"].GetSlotNames(ref slotNames);
 var BagOfWordFeaturesColumn = transformedDataView.GetColumn<VBuffer<float>>(transformedDataView.Schema["BagOfWordFeatures"]);
 var slots = slotNames.GetValues();
 Console.Write("N-grams: ");
 foreach (var featureRow in BagOfWordFeaturesColumn)
 {
 foreach (var item in featureRow.Items())
 Console.Write($"{slots[item.Key]} ");
 Console.WriteLine();
 }

 // Print the first 10 feature values.
 Console.Write("Features: ");
 for (int i = 0; i < 10; i++)
 Console.Write($"{prediction.BagOfWordFeatures[i]:F4} ");

 // Expected output:
 // Number of Features: 32
 // N-grams: an|example|to is|an|example example|to|compute This|is|an compute|bag-of-word|features bag-of-word|features|using to|compute|bag-of-word ML.NET's|ProduceHashedWordBags|API as|an|index API|produces|count ...
 // Features: 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 1.0000 2.0000 0.0000 0.0000 ...
 }

 private class TextData
 {
 public string Text { get; set; }
 }

 private class TransformedTextData : TextData
 {
 public float[] BagOfWordFeatures { get; set; }
 }
 }
}

89 changes: 89 additions & 0 deletions
docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Text/ProduceWordBags.cs

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Open in desktop

Original file line numberDiff line numberDiff line change
@@ -0,0 +1,89 @@
using System;
using System.Collections.Generic;
using System.Text;
using Microsoft.ML.Data;
using Microsoft.ML.Transforms.Text;

namespace Microsoft.ML.Samples.Dynamic
{
 public static class ProduceWordBags
 {
 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.
 var mlContext = new MLContext();

 // Create a small dataset as an IEnumerable.
 var samples = new List<TextData>()
 {
 new TextData(){ Text = "This is an example to compute bag-of-word features." },
 new TextData(){ Text = "ML.NET's ProduceWordBags API produces bag-of-word features from input text." },
 new TextData(){ Text = "It does so by first tokenizing text/string into words/tokens then " },
 new TextData(){ Text = "computing n-grams and their neumeric values." },
 new TextData(){ Text = "Each position in the output vector corresponds to a particular n-gram." },
 new TextData(){ Text = "The value at each position corresponds to," },
 new TextData(){ Text = "the number of times n-gram occured in the data (Tf), or" },
 new TextData(){ Text = "the inverse of the number of documents contain the n-gram (Idf)," },
 new TextData(){ Text = "or compute both and multipy together (Tf-Idf)." },
 };

 // Convert training data to IDataView.
 var dataview = mlContext.Data.LoadFromEnumerable(samples);

 // A pipeline for converting text into numeric bag-of-word features.
 // The following call to 'ProduceWordBags' implicitly tokenizes the text/string into words/tokens.
 // Please note that the length of the output feature vector depends on the n-gram settings.
 var textPipeline = mlContext.Transforms.Text.ProduceWordBags("BagOfWordFeatures", "Text",
 ngramLength: 3, useAllLengths: false, weighting: NgramExtractingEstimator.WeightingCriteria.Tf);

 // Fit to data.
 var textTransformer = textPipeline.Fit(dataview);
 var transformedDataView = textTransformer.Transform(dataview);

 // Create the prediction engine to get the bag-of-word features extracted from the text.
 var predictionEngine = mlContext.Model.CreatePredictionEngine<TextData, TransformedTextData>(textTransformer);
@rogancarrrogancarrApr 4, 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.

Why predictionEngine rather than TakeRows and ConvertToEnumerable? I would use the one we would recommend people to use to inspect data in practice.

 // Convert the text into numeric features.
 var prediction = predictionEngine.Predict(samples[0]);

 // Print the length of the feature vector.
 Console.WriteLine($"Number of Features: {prediction.BagOfWordFeatures.Length}");

 // Preview of the produced n-grams.
 // Get the slot names from the column's metadata.
 // The slot names for a vector column corresponds to the names associated with each position in the vector.
 VBuffer<ReadOnlyMemory<char>> slotNames = default;
 transformedDataView.Schema["BagOfWordFeatures"].GetSlotNames(ref slotNames);
 var BagOfWordFeaturesColumn = transformedDataView.GetColumn<VBuffer<float>>(transformedDataView.Schema["BagOfWordFeatures"]);
 var slots = slotNames.GetValues();
 Console.Write("N-grams: ");
 foreach (var featureRow in BagOfWordFeaturesColumn)
 {
 foreach (var item in featureRow.Items())
 Console.Write($"{slots[item.Key]} ");
 Console.WriteLine();
 }

 // Print the first 10 feature values.
 Console.Write("Features: ");
 for (int i = 0; i < 10; i++)
 Console.Write($"{prediction.BagOfWordFeatures[i]:F4} ");

 // Expected output:
 // Number of Features: 62
 // N-grams: This|is|an is|an|example an|example|to example|to|compute to|compute|bag-of-word compute|bag-of-word|features. ML.NET's|ProduceWordBags|API ProduceWordBags|API|produces API|produces|bag-of-word produces|bag-of-word|features ...
 // Features: 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.0000 0.0000 0.0000 0.0000 ...
 }

 private class TextData
 {
 public string Text { get; set; }
 }

 private class TransformedTextData : TextData
 {
 public float[] BagOfWordFeatures { get; set; }
 }
 }
}

, '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('^' + ".*" + '

Skip to content
Created samples for 'ProduceWordBags' and 'ProduceHashedWordBags' API.#3183

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privacy statement. We’ll occasionally send you account related emails.

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to your account

 Merged

zeahmed
merged 6 commits into

from

Apr 4, 2019
 Merged


Created samples for 'ProduceWordBags' and 'ProduceHashedWordBags' API.
#3183
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9471b5d

Created samples for 'ProduceWordBags' and 'ProduceHashedWordBags' API.


zeahmed Apr 2, 2019
1b2daca

Updated comments!


zeahmed Apr 3, 2019
202f051

Addressed reviewers' comments.


zeahmed Apr 3, 2019
d288792

Addressed reviewers' comments.


zeahmed Apr 3, 2019
bdca2a5

Changed input/output classes to private.


zeahmed Apr 4, 2019
bc60f09

Addressed reviewers' comments.


zeahmed Apr 4, 2019

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89 changes: 89 additions & 0 deletions
docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Text/ProduceHashedWordBags.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -0,0 +1,89 @@
using System;
using System.Collections.Generic;
using System.Text;
using Microsoft.ML.Data;
using Microsoft.ML.Transforms.Text;

namespace Microsoft.ML.Samples.Dynamic
{
 public static class ProduceHashedWordBags
 {
 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.
 var mlContext = new MLContext();

 // Create a small dataset as an IEnumerable.
 var samples = new List<TextData>()
 {
 new TextData(){ Text = "This is an example to compute bag-of-word features using hashing." },
 new TextData(){ Text = "ML.NET's ProduceHashedWordBags API produces count of n-grams and hashes it as an index into a vector of given bit length." },
 new TextData(){ Text = "It does so by first tokenizing text/string into words/tokens then " },
 new TextData(){ Text = "computing n-grams and hash them to the index given by hash value." },
 new TextData(){ Text = "The hashing reduces the size of the output feature vector" },
 new TextData(){ Text = "which is useful in case when number of n-grams is very large." },
 };

 // Convert training data to IDataView.
 var dataview = mlContext.Data.LoadFromEnumerable(samples);

 // A pipeline for converting text into numeric bag-of-word features using hashing.
 // The following call to 'ProduceHashedWordBags' implicitly tokenizes the text/string into words/tokens.
 // Please note that the length of the output feature vector depends on the 'numberOfBits' settings.
 var textPipeline = mlContext.Transforms.Text.ProduceHashedWordBags("BagOfWordFeatures", "Text",
 numberOfBits: 5,
 ngramLength: 3,
 useAllLengths: false,
 maximumNumberOfInverts: 1);

 // Fit to data.
 var textTransformer = textPipeline.Fit(dataview);
 var transformedDataView = textTransformer.Transform(dataview);

 // Create the prediction engine to get the bag-of-word features extracted from the text.
 var predictionEngine = mlContext.Model.CreatePredictionEngine<TextData, TransformedTextData>(textTransformer);

 // Convert the text into numeric features.
 var prediction = predictionEngine.Predict(samples[0]);

 // Print the length of the feature vector.
 Console.WriteLine($"Number of Features: {prediction.BagOfWordFeatures.Length}");

 // Preview of the produced n-grams.
 // Get the slot names from the column's metadata.
 // The slot names for a vector column corresponds to the names associated with each position in the vector.
 VBuffer<ReadOnlyMemory<char>> slotNames = default;
 transformedDataView.Schema["BagOfWordFeatures"].GetSlotNames(ref slotNames);
 var BagOfWordFeaturesColumn = transformedDataView.GetColumn<VBuffer<float>>(transformedDataView.Schema["BagOfWordFeatures"]);
 var slots = slotNames.GetValues();
 Console.Write("N-grams: ");
 foreach (var featureRow in BagOfWordFeaturesColumn)
 {
 foreach (var item in featureRow.Items())
 Console.Write($"{slots[item.Key]} ");
 Console.WriteLine();
 }

 // Print the first 10 feature values.
 Console.Write("Features: ");
 for (int i = 0; i < 10; i++)
 Console.Write($"{prediction.BagOfWordFeatures[i]:F4} ");

 // Expected output:
 // Number of Features: 32
 // N-grams: an|example|to is|an|example example|to|compute This|is|an compute|bag-of-word|features bag-of-word|features|using to|compute|bag-of-word ML.NET's|ProduceHashedWordBags|API as|an|index API|produces|count ...
 // Features: 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 1.0000 2.0000 0.0000 0.0000 ...
 }

 private class TextData
 {
 public string Text { get; set; }
 }

 private class TransformedTextData : TextData
 {
 public float[] BagOfWordFeatures { get; set; }
 }
 }
}

89 changes: 89 additions & 0 deletions
docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Text/ProduceWordBags.cs

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using System;
using System.Collections.Generic;
using System.Text;
using Microsoft.ML.Data;
using Microsoft.ML.Transforms.Text;

namespace Microsoft.ML.Samples.Dynamic
{
 public static class ProduceWordBags
 {
 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.
 var mlContext = new MLContext();

 // Create a small dataset as an IEnumerable.
 var samples = new List<TextData>()
 {
 new TextData(){ Text = "This is an example to compute bag-of-word features." },
 new TextData(){ Text = "ML.NET's ProduceWordBags API produces bag-of-word features from input text." },
 new TextData(){ Text = "It does so by first tokenizing text/string into words/tokens then " },
 new TextData(){ Text = "computing n-grams and their neumeric values." },
 new TextData(){ Text = "Each position in the output vector corresponds to a particular n-gram." },
 new TextData(){ Text = "The value at each position corresponds to," },
 new TextData(){ Text = "the number of times n-gram occured in the data (Tf), or" },
 new TextData(){ Text = "the inverse of the number of documents contain the n-gram (Idf)," },
 new TextData(){ Text = "or compute both and multipy together (Tf-Idf)." },
 };

 // Convert training data to IDataView.
 var dataview = mlContext.Data.LoadFromEnumerable(samples);

 // A pipeline for converting text into numeric bag-of-word features.
 // The following call to 'ProduceWordBags' implicitly tokenizes the text/string into words/tokens.
 // Please note that the length of the output feature vector depends on the n-gram settings.
 var textPipeline = mlContext.Transforms.Text.ProduceWordBags("BagOfWordFeatures", "Text",
 ngramLength: 3, useAllLengths: false, weighting: NgramExtractingEstimator.WeightingCriteria.Tf);

 // Fit to data.
 var textTransformer = textPipeline.Fit(dataview);
 var transformedDataView = textTransformer.Transform(dataview);

 // Create the prediction engine to get the bag-of-word features extracted from the text.
 var predictionEngine = mlContext.Model.CreatePredictionEngine<TextData, TransformedTextData>(textTransformer);
@rogancarrrogancarrApr 4, 2019

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Why predictionEngine rather than TakeRows and ConvertToEnumerable? I would use the one we would recommend people to use to inspect data in practice.

 // Convert the text into numeric features.
 var prediction = predictionEngine.Predict(samples[0]);

 // Print the length of the feature vector.
 Console.WriteLine($"Number of Features: {prediction.BagOfWordFeatures.Length}");

 // Preview of the produced n-grams.
 // Get the slot names from the column's metadata.
 // The slot names for a vector column corresponds to the names associated with each position in the vector.
 VBuffer<ReadOnlyMemory<char>> slotNames = default;
 transformedDataView.Schema["BagOfWordFeatures"].GetSlotNames(ref slotNames);
 var BagOfWordFeaturesColumn = transformedDataView.GetColumn<VBuffer<float>>(transformedDataView.Schema["BagOfWordFeatures"]);
 var slots = slotNames.GetValues();
 Console.Write("N-grams: ");
 foreach (var featureRow in BagOfWordFeaturesColumn)
 {
 foreach (var item in featureRow.Items())
 Console.Write($"{slots[item.Key]} ");
 Console.WriteLine();
 }

 // Print the first 10 feature values.
 Console.Write("Features: ");
 for (int i = 0; i < 10; i++)
 Console.Write($"{prediction.BagOfWordFeatures[i]:F4} ");

 // Expected output:
 // Number of Features: 62
 // N-grams: This|is|an is|an|example an|example|to example|to|compute to|compute|bag-of-word compute|bag-of-word|features. ML.NET's|ProduceWordBags|API ProduceWordBags|API|produces API|produces|bag-of-word produces|bag-of-word|features ...
 // Features: 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.0000 0.0000 0.0000 0.0000 ...
 }

 private class TextData
 {
 public string Text { get; set; }
 }

 private class TransformedTextData : TextData
 {
 public float[] BagOfWordFeatures { get; set; }
 }
 }
}

, '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
Created samples for 'ProduceWordBags' and 'ProduceHashedWordBags' API.#3183

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 Merged

zeahmed
merged 6 commits into

from

Apr 4, 2019
 Merged


Created samples for 'ProduceWordBags' and 'ProduceHashedWordBags' API.
#3183
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9471b5d

Created samples for 'ProduceWordBags' and 'ProduceHashedWordBags' API.


zeahmed Apr 2, 2019
1b2daca

Updated comments!


zeahmed Apr 3, 2019
202f051

Addressed reviewers' comments.


zeahmed Apr 3, 2019
d288792

Addressed reviewers' comments.


zeahmed Apr 3, 2019
bdca2a5

Changed input/output classes to private.


zeahmed Apr 4, 2019
bc60f09

Addressed reviewers' comments.


zeahmed Apr 4, 2019

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docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Text/ProduceHashedWordBags.cs

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Original file line numberDiff line numberDiff line change
@@ -0,0 +1,89 @@
using System;
using System.Collections.Generic;
using System.Text;
using Microsoft.ML.Data;
using Microsoft.ML.Transforms.Text;

namespace Microsoft.ML.Samples.Dynamic
{
 public static class ProduceHashedWordBags
 {
 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.
 var mlContext = new MLContext();

 // Create a small dataset as an IEnumerable.
 var samples = new List<TextData>()
 {
 new TextData(){ Text = "This is an example to compute bag-of-word features using hashing." },
 new TextData(){ Text = "ML.NET's ProduceHashedWordBags API produces count of n-grams and hashes it as an index into a vector of given bit length." },
 new TextData(){ Text = "It does so by first tokenizing text/string into words/tokens then " },
 new TextData(){ Text = "computing n-grams and hash them to the index given by hash value." },
 new TextData(){ Text = "The hashing reduces the size of the output feature vector" },
 new TextData(){ Text = "which is useful in case when number of n-grams is very large." },
 };

 // Convert training data to IDataView.
 var dataview = mlContext.Data.LoadFromEnumerable(samples);

 // A pipeline for converting text into numeric bag-of-word features using hashing.
 // The following call to 'ProduceHashedWordBags' implicitly tokenizes the text/string into words/tokens.
 // Please note that the length of the output feature vector depends on the 'numberOfBits' settings.
 var textPipeline = mlContext.Transforms.Text.ProduceHashedWordBags("BagOfWordFeatures", "Text",
 numberOfBits: 5,
 ngramLength: 3,
 useAllLengths: false,
 maximumNumberOfInverts: 1);

 // Fit to data.
 var textTransformer = textPipeline.Fit(dataview);
 var transformedDataView = textTransformer.Transform(dataview);

 // Create the prediction engine to get the bag-of-word features extracted from the text.
 var predictionEngine = mlContext.Model.CreatePredictionEngine<TextData, TransformedTextData>(textTransformer);

 // Convert the text into numeric features.
 var prediction = predictionEngine.Predict(samples[0]);

 // Print the length of the feature vector.
 Console.WriteLine($"Number of Features: {prediction.BagOfWordFeatures.Length}");

 // Preview of the produced n-grams.
 // Get the slot names from the column's metadata.
 // The slot names for a vector column corresponds to the names associated with each position in the vector.
 VBuffer<ReadOnlyMemory<char>> slotNames = default;
 transformedDataView.Schema["BagOfWordFeatures"].GetSlotNames(ref slotNames);
 var BagOfWordFeaturesColumn = transformedDataView.GetColumn<VBuffer<float>>(transformedDataView.Schema["BagOfWordFeatures"]);
 var slots = slotNames.GetValues();
 Console.Write("N-grams: ");
 foreach (var featureRow in BagOfWordFeaturesColumn)
 {
 foreach (var item in featureRow.Items())
 Console.Write($"{slots[item.Key]} ");
 Console.WriteLine();
 }

 // Print the first 10 feature values.
 Console.Write("Features: ");
 for (int i = 0; i < 10; i++)
 Console.Write($"{prediction.BagOfWordFeatures[i]:F4} ");

 // Expected output:
 // Number of Features: 32
 // N-grams: an|example|to is|an|example example|to|compute This|is|an compute|bag-of-word|features bag-of-word|features|using to|compute|bag-of-word ML.NET's|ProduceHashedWordBags|API as|an|index API|produces|count ...
 // Features: 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 1.0000 2.0000 0.0000 0.0000 ...
 }

 private class TextData
 {
 public string Text { get; set; }
 }

 private class TransformedTextData : TextData
 {
 public float[] BagOfWordFeatures { get; set; }
 }
 }
}

89 changes: 89 additions & 0 deletions
docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Text/ProduceWordBags.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -0,0 +1,89 @@
using System;
using System.Collections.Generic;
using System.Text;
using Microsoft.ML.Data;
using Microsoft.ML.Transforms.Text;

namespace Microsoft.ML.Samples.Dynamic
{
 public static class ProduceWordBags
 {
 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.
 var mlContext = new MLContext();

 // Create a small dataset as an IEnumerable.
 var samples = new List<TextData>()
 {
 new TextData(){ Text = "This is an example to compute bag-of-word features." },
 new TextData(){ Text = "ML.NET's ProduceWordBags API produces bag-of-word features from input text." },
 new TextData(){ Text = "It does so by first tokenizing text/string into words/tokens then " },
 new TextData(){ Text = "computing n-grams and their neumeric values." },
 new TextData(){ Text = "Each position in the output vector corresponds to a particular n-gram." },
 new TextData(){ Text = "The value at each position corresponds to," },
 new TextData(){ Text = "the number of times n-gram occured in the data (Tf), or" },
 new TextData(){ Text = "the inverse of the number of documents contain the n-gram (Idf)," },
 new TextData(){ Text = "or compute both and multipy together (Tf-Idf)." },
 };

 // Convert training data to IDataView.
 var dataview = mlContext.Data.LoadFromEnumerable(samples);

 // A pipeline for converting text into numeric bag-of-word features.
 // The following call to 'ProduceWordBags' implicitly tokenizes the text/string into words/tokens.
 // Please note that the length of the output feature vector depends on the n-gram settings.
 var textPipeline = mlContext.Transforms.Text.ProduceWordBags("BagOfWordFeatures", "Text",
 ngramLength: 3, useAllLengths: false, weighting: NgramExtractingEstimator.WeightingCriteria.Tf);

 // Fit to data.
 var textTransformer = textPipeline.Fit(dataview);
 var transformedDataView = textTransformer.Transform(dataview);

 // Create the prediction engine to get the bag-of-word features extracted from the text.
 var predictionEngine = mlContext.Model.CreatePredictionEngine<TextData, TransformedTextData>(textTransformer);
@rogancarrrogancarrApr 4, 2019

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Contributor
Choose a reason for hiding this comment

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

Why predictionEngine rather than TakeRows and ConvertToEnumerable? I would use the one we would recommend people to use to inspect data in practice.

 // Convert the text into numeric features.
 var prediction = predictionEngine.Predict(samples[0]);

 // Print the length of the feature vector.
 Console.WriteLine($"Number of Features: {prediction.BagOfWordFeatures.Length}");

 // Preview of the produced n-grams.
 // Get the slot names from the column's metadata.
 // The slot names for a vector column corresponds to the names associated with each position in the vector.
 VBuffer<ReadOnlyMemory<char>> slotNames = default;
 transformedDataView.Schema["BagOfWordFeatures"].GetSlotNames(ref slotNames);
 var BagOfWordFeaturesColumn = transformedDataView.GetColumn<VBuffer<float>>(transformedDataView.Schema["BagOfWordFeatures"]);
 var slots = slotNames.GetValues();
 Console.Write("N-grams: ");
 foreach (var featureRow in BagOfWordFeaturesColumn)
 {
 foreach (var item in featureRow.Items())
 Console.Write($"{slots[item.Key]} ");
 Console.WriteLine();
 }

 // Print the first 10 feature values.
 Console.Write("Features: ");
 for (int i = 0; i < 10; i++)
 Console.Write($"{prediction.BagOfWordFeatures[i]:F4} ");

 // Expected output:
 // Number of Features: 62
 // N-grams: This|is|an is|an|example an|example|to example|to|compute to|compute|bag-of-word compute|bag-of-word|features. ML.NET's|ProduceWordBags|API ProduceWordBags|API|produces API|produces|bag-of-word produces|bag-of-word|features ...
 // Features: 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.0000 0.0000 0.0000 0.0000 ...
 }

 private class TextData
 {
 public string Text { get; set; }
 }

 private class TransformedTextData : TextData
 {
 public float[] BagOfWordFeatures { get; set; }
 }
 }
}

, '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" + '

Skip to content
Created samples for 'ProduceWordBags' and 'ProduceHashedWordBags' API.#3183

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to your account

 Merged

zeahmed
merged 6 commits into

from

Apr 4, 2019
 Merged


Created samples for 'ProduceWordBags' and 'ProduceHashedWordBags' API.
#3183
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9471b5d

Created samples for 'ProduceWordBags' and 'ProduceHashedWordBags' API.


zeahmed Apr 2, 2019
1b2daca

Updated comments!


zeahmed Apr 3, 2019
202f051

Addressed reviewers' comments.


zeahmed Apr 3, 2019
d288792

Addressed reviewers' comments.


zeahmed Apr 3, 2019
bdca2a5

Changed input/output classes to private.


zeahmed Apr 4, 2019
bc60f09

Addressed reviewers' comments.


zeahmed Apr 4, 2019

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89 changes: 89 additions & 0 deletions
docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Text/ProduceHashedWordBags.cs

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Open in desktop

Original file line numberDiff line numberDiff line change
@@ -0,0 +1,89 @@
using System;
using System.Collections.Generic;
using System.Text;
using Microsoft.ML.Data;
using Microsoft.ML.Transforms.Text;

namespace Microsoft.ML.Samples.Dynamic
{
 public static class ProduceHashedWordBags
 {
 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.
 var mlContext = new MLContext();

 // Create a small dataset as an IEnumerable.
 var samples = new List<TextData>()
 {
 new TextData(){ Text = "This is an example to compute bag-of-word features using hashing." },
 new TextData(){ Text = "ML.NET's ProduceHashedWordBags API produces count of n-grams and hashes it as an index into a vector of given bit length." },
 new TextData(){ Text = "It does so by first tokenizing text/string into words/tokens then " },
 new TextData(){ Text = "computing n-grams and hash them to the index given by hash value." },
 new TextData(){ Text = "The hashing reduces the size of the output feature vector" },
 new TextData(){ Text = "which is useful in case when number of n-grams is very large." },
 };

 // Convert training data to IDataView.
 var dataview = mlContext.Data.LoadFromEnumerable(samples);

 // A pipeline for converting text into numeric bag-of-word features using hashing.
 // The following call to 'ProduceHashedWordBags' implicitly tokenizes the text/string into words/tokens.
 // Please note that the length of the output feature vector depends on the 'numberOfBits' settings.
 var textPipeline = mlContext.Transforms.Text.ProduceHashedWordBags("BagOfWordFeatures", "Text",
 numberOfBits: 5,
 ngramLength: 3,
 useAllLengths: false,
 maximumNumberOfInverts: 1);

 // Fit to data.
 var textTransformer = textPipeline.Fit(dataview);
 var transformedDataView = textTransformer.Transform(dataview);

 // Create the prediction engine to get the bag-of-word features extracted from the text.
 var predictionEngine = mlContext.Model.CreatePredictionEngine<TextData, TransformedTextData>(textTransformer);

 // Convert the text into numeric features.
 var prediction = predictionEngine.Predict(samples[0]);

 // Print the length of the feature vector.
 Console.WriteLine($"Number of Features: {prediction.BagOfWordFeatures.Length}");

 // Preview of the produced n-grams.
 // Get the slot names from the column's metadata.
 // The slot names for a vector column corresponds to the names associated with each position in the vector.
 VBuffer<ReadOnlyMemory<char>> slotNames = default;
 transformedDataView.Schema["BagOfWordFeatures"].GetSlotNames(ref slotNames);
 var BagOfWordFeaturesColumn = transformedDataView.GetColumn<VBuffer<float>>(transformedDataView.Schema["BagOfWordFeatures"]);
 var slots = slotNames.GetValues();
 Console.Write("N-grams: ");
 foreach (var featureRow in BagOfWordFeaturesColumn)
 {
 foreach (var item in featureRow.Items())
 Console.Write($"{slots[item.Key]} ");
 Console.WriteLine();
 }

 // Print the first 10 feature values.
 Console.Write("Features: ");
 for (int i = 0; i < 10; i++)
 Console.Write($"{prediction.BagOfWordFeatures[i]:F4} ");

 // Expected output:
 // Number of Features: 32
 // N-grams: an|example|to is|an|example example|to|compute This|is|an compute|bag-of-word|features bag-of-word|features|using to|compute|bag-of-word ML.NET's|ProduceHashedWordBags|API as|an|index API|produces|count ...
 // Features: 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 1.0000 2.0000 0.0000 0.0000 ...
 }

 private class TextData
 {
 public string Text { get; set; }
 }

 private class TransformedTextData : TextData
 {
 public float[] BagOfWordFeatures { get; set; }
 }
 }
}

89 changes: 89 additions & 0 deletions
docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Text/ProduceWordBags.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -0,0 +1,89 @@
using System;
using System.Collections.Generic;
using System.Text;
using Microsoft.ML.Data;
using Microsoft.ML.Transforms.Text;

namespace Microsoft.ML.Samples.Dynamic
{
 public static class ProduceWordBags
 {
 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.
 var mlContext = new MLContext();

 // Create a small dataset as an IEnumerable.
 var samples = new List<TextData>()
 {
 new TextData(){ Text = "This is an example to compute bag-of-word features." },
 new TextData(){ Text = "ML.NET's ProduceWordBags API produces bag-of-word features from input text." },
 new TextData(){ Text = "It does so by first tokenizing text/string into words/tokens then " },
 new TextData(){ Text = "computing n-grams and their neumeric values." },
 new TextData(){ Text = "Each position in the output vector corresponds to a particular n-gram." },
 new TextData(){ Text = "The value at each position corresponds to," },
 new TextData(){ Text = "the number of times n-gram occured in the data (Tf), or" },
 new TextData(){ Text = "the inverse of the number of documents contain the n-gram (Idf)," },
 new TextData(){ Text = "or compute both and multipy together (Tf-Idf)." },
 };

 // Convert training data to IDataView.
 var dataview = mlContext.Data.LoadFromEnumerable(samples);

 // A pipeline for converting text into numeric bag-of-word features.
 // The following call to 'ProduceWordBags' implicitly tokenizes the text/string into words/tokens.
 // Please note that the length of the output feature vector depends on the n-gram settings.
 var textPipeline = mlContext.Transforms.Text.ProduceWordBags("BagOfWordFeatures", "Text",
 ngramLength: 3, useAllLengths: false, weighting: NgramExtractingEstimator.WeightingCriteria.Tf);

 // Fit to data.
 var textTransformer = textPipeline.Fit(dataview);
 var transformedDataView = textTransformer.Transform(dataview);

 // Create the prediction engine to get the bag-of-word features extracted from the text.
 var predictionEngine = mlContext.Model.CreatePredictionEngine<TextData, TransformedTextData>(textTransformer);
@rogancarrrogancarrApr 4, 2019

Copy link


Copy Markdown

Contributor
Choose a reason for hiding this comment

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

Why predictionEngine rather than TakeRows and ConvertToEnumerable? I would use the one we would recommend people to use to inspect data in practice.

 // Convert the text into numeric features.
 var prediction = predictionEngine.Predict(samples[0]);

 // Print the length of the feature vector.
 Console.WriteLine($"Number of Features: {prediction.BagOfWordFeatures.Length}");

 // Preview of the produced n-grams.
 // Get the slot names from the column's metadata.
 // The slot names for a vector column corresponds to the names associated with each position in the vector.
 VBuffer<ReadOnlyMemory<char>> slotNames = default;
 transformedDataView.Schema["BagOfWordFeatures"].GetSlotNames(ref slotNames);
 var BagOfWordFeaturesColumn = transformedDataView.GetColumn<VBuffer<float>>(transformedDataView.Schema["BagOfWordFeatures"]);
 var slots = slotNames.GetValues();
 Console.Write("N-grams: ");
 foreach (var featureRow in BagOfWordFeaturesColumn)
 {
 foreach (var item in featureRow.Items())
 Console.Write($"{slots[item.Key]} ");
 Console.WriteLine();
 }

 // Print the first 10 feature values.
 Console.Write("Features: ");
 for (int i = 0; i < 10; i++)
 Console.Write($"{prediction.BagOfWordFeatures[i]:F4} ");

 // Expected output:
 // Number of Features: 62
 // N-grams: This|is|an is|an|example an|example|to example|to|compute to|compute|bag-of-word compute|bag-of-word|features. ML.NET's|ProduceWordBags|API ProduceWordBags|API|produces API|produces|bag-of-word produces|bag-of-word|features ...
 // Features: 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.0000 0.0000 0.0000 0.0000 ...
 }

 private class TextData
 {
 public string Text { get; set; }
 }

 private class TransformedTextData : TextData
 {
 public float[] BagOfWordFeatures { get; set; }
 }
 }
}

, '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('^' + ".*" + '

Skip to content
Created samples for 'ProduceWordBags' and 'ProduceHashedWordBags' API.#3183

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 Merged

zeahmed
merged 6 commits into

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Apr 4, 2019
 Merged


Created samples for 'ProduceWordBags' and 'ProduceHashedWordBags' API.
#3183
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9471b5d

Created samples for 'ProduceWordBags' and 'ProduceHashedWordBags' API.


zeahmed Apr 2, 2019
1b2daca

Updated comments!


zeahmed Apr 3, 2019
202f051

Addressed reviewers' comments.


zeahmed Apr 3, 2019
d288792

Addressed reviewers' comments.


zeahmed Apr 3, 2019
bdca2a5

Changed input/output classes to private.


zeahmed Apr 4, 2019
bc60f09

Addressed reviewers' comments.


zeahmed Apr 4, 2019

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docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Text/ProduceHashedWordBags.cs

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Original file line numberDiff line numberDiff line change
@@ -0,0 +1,89 @@
using System;
using System.Collections.Generic;
using System.Text;
using Microsoft.ML.Data;
using Microsoft.ML.Transforms.Text;

namespace Microsoft.ML.Samples.Dynamic
{
 public static class ProduceHashedWordBags
 {
 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.
 var mlContext = new MLContext();

 // Create a small dataset as an IEnumerable.
 var samples = new List<TextData>()
 {
 new TextData(){ Text = "This is an example to compute bag-of-word features using hashing." },
 new TextData(){ Text = "ML.NET's ProduceHashedWordBags API produces count of n-grams and hashes it as an index into a vector of given bit length." },
 new TextData(){ Text = "It does so by first tokenizing text/string into words/tokens then " },
 new TextData(){ Text = "computing n-grams and hash them to the index given by hash value." },
 new TextData(){ Text = "The hashing reduces the size of the output feature vector" },
 new TextData(){ Text = "which is useful in case when number of n-grams is very large." },
 };

 // Convert training data to IDataView.
 var dataview = mlContext.Data.LoadFromEnumerable(samples);

 // A pipeline for converting text into numeric bag-of-word features using hashing.
 // The following call to 'ProduceHashedWordBags' implicitly tokenizes the text/string into words/tokens.
 // Please note that the length of the output feature vector depends on the 'numberOfBits' settings.
 var textPipeline = mlContext.Transforms.Text.ProduceHashedWordBags("BagOfWordFeatures", "Text",
 numberOfBits: 5,
 ngramLength: 3,
 useAllLengths: false,
 maximumNumberOfInverts: 1);

 // Fit to data.
 var textTransformer = textPipeline.Fit(dataview);
 var transformedDataView = textTransformer.Transform(dataview);

 // Create the prediction engine to get the bag-of-word features extracted from the text.
 var predictionEngine = mlContext.Model.CreatePredictionEngine<TextData, TransformedTextData>(textTransformer);

 // Convert the text into numeric features.
 var prediction = predictionEngine.Predict(samples[0]);

 // Print the length of the feature vector.
 Console.WriteLine($"Number of Features: {prediction.BagOfWordFeatures.Length}");

 // Preview of the produced n-grams.
 // Get the slot names from the column's metadata.
 // The slot names for a vector column corresponds to the names associated with each position in the vector.
 VBuffer<ReadOnlyMemory<char>> slotNames = default;
 transformedDataView.Schema["BagOfWordFeatures"].GetSlotNames(ref slotNames);
 var BagOfWordFeaturesColumn = transformedDataView.GetColumn<VBuffer<float>>(transformedDataView.Schema["BagOfWordFeatures"]);
 var slots = slotNames.GetValues();
 Console.Write("N-grams: ");
 foreach (var featureRow in BagOfWordFeaturesColumn)
 {
 foreach (var item in featureRow.Items())
 Console.Write($"{slots[item.Key]} ");
 Console.WriteLine();
 }

 // Print the first 10 feature values.
 Console.Write("Features: ");
 for (int i = 0; i < 10; i++)
 Console.Write($"{prediction.BagOfWordFeatures[i]:F4} ");

 // Expected output:
 // Number of Features: 32
 // N-grams: an|example|to is|an|example example|to|compute This|is|an compute|bag-of-word|features bag-of-word|features|using to|compute|bag-of-word ML.NET's|ProduceHashedWordBags|API as|an|index API|produces|count ...
 // Features: 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 1.0000 2.0000 0.0000 0.0000 ...
 }

 private class TextData
 {
 public string Text { get; set; }
 }

 private class TransformedTextData : TextData
 {
 public float[] BagOfWordFeatures { get; set; }
 }
 }
}

89 changes: 89 additions & 0 deletions
docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Text/ProduceWordBags.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -0,0 +1,89 @@
using System;
using System.Collections.Generic;
using System.Text;
using Microsoft.ML.Data;
using Microsoft.ML.Transforms.Text;

namespace Microsoft.ML.Samples.Dynamic
{
 public static class ProduceWordBags
 {
 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.
 var mlContext = new MLContext();

 // Create a small dataset as an IEnumerable.
 var samples = new List<TextData>()
 {
 new TextData(){ Text = "This is an example to compute bag-of-word features." },
 new TextData(){ Text = "ML.NET's ProduceWordBags API produces bag-of-word features from input text." },
 new TextData(){ Text = "It does so by first tokenizing text/string into words/tokens then " },
 new TextData(){ Text = "computing n-grams and their neumeric values." },
 new TextData(){ Text = "Each position in the output vector corresponds to a particular n-gram." },
 new TextData(){ Text = "The value at each position corresponds to," },
 new TextData(){ Text = "the number of times n-gram occured in the data (Tf), or" },
 new TextData(){ Text = "the inverse of the number of documents contain the n-gram (Idf)," },
 new TextData(){ Text = "or compute both and multipy together (Tf-Idf)." },
 };

 // Convert training data to IDataView.
 var dataview = mlContext.Data.LoadFromEnumerable(samples);

 // A pipeline for converting text into numeric bag-of-word features.
 // The following call to 'ProduceWordBags' implicitly tokenizes the text/string into words/tokens.
 // Please note that the length of the output feature vector depends on the n-gram settings.
 var textPipeline = mlContext.Transforms.Text.ProduceWordBags("BagOfWordFeatures", "Text",
 ngramLength: 3, useAllLengths: false, weighting: NgramExtractingEstimator.WeightingCriteria.Tf);

 // Fit to data.
 var textTransformer = textPipeline.Fit(dataview);
 var transformedDataView = textTransformer.Transform(dataview);

 // Create the prediction engine to get the bag-of-word features extracted from the text.
 var predictionEngine = mlContext.Model.CreatePredictionEngine<TextData, TransformedTextData>(textTransformer);
@rogancarrrogancarrApr 4, 2019

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Choose a reason for hiding this comment

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Why predictionEngine rather than TakeRows and ConvertToEnumerable? I would use the one we would recommend people to use to inspect data in practice.

 // Convert the text into numeric features.
 var prediction = predictionEngine.Predict(samples[0]);

 // Print the length of the feature vector.
 Console.WriteLine($"Number of Features: {prediction.BagOfWordFeatures.Length}");

 // Preview of the produced n-grams.
 // Get the slot names from the column's metadata.
 // The slot names for a vector column corresponds to the names associated with each position in the vector.
 VBuffer<ReadOnlyMemory<char>> slotNames = default;
 transformedDataView.Schema["BagOfWordFeatures"].GetSlotNames(ref slotNames);
 var BagOfWordFeaturesColumn = transformedDataView.GetColumn<VBuffer<float>>(transformedDataView.Schema["BagOfWordFeatures"]);
 var slots = slotNames.GetValues();
 Console.Write("N-grams: ");
 foreach (var featureRow in BagOfWordFeaturesColumn)
 {
 foreach (var item in featureRow.Items())
 Console.Write($"{slots[item.Key]} ");
 Console.WriteLine();
 }

 // Print the first 10 feature values.
 Console.Write("Features: ");
 for (int i = 0; i < 10; i++)
 Console.Write($"{prediction.BagOfWordFeatures[i]:F4} ");

 // Expected output:
 // Number of Features: 62
 // N-grams: This|is|an is|an|example an|example|to example|to|compute to|compute|bag-of-word compute|bag-of-word|features. ML.NET's|ProduceWordBags|API ProduceWordBags|API|produces API|produces|bag-of-word produces|bag-of-word|features ...
 // Features: 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.0000 0.0000 0.0000 0.0000 ...
 }

 private class TextData
 {
 public string Text { get; set; }
 }

 private class TransformedTextData : TextData
 {
 public float[] BagOfWordFeatures { get; set; }
 }
 }
}

, '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
Created samples for 'ProduceWordBags' and 'ProduceHashedWordBags' API.#3183

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to your account

 Merged

zeahmed
merged 6 commits into

from

Apr 4, 2019
 Merged


Created samples for 'ProduceWordBags' and 'ProduceHashedWordBags' API.
#3183
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9471b5d

Created samples for 'ProduceWordBags' and 'ProduceHashedWordBags' API.


zeahmed Apr 2, 2019
1b2daca

Updated comments!


zeahmed Apr 3, 2019
202f051

Addressed reviewers' comments.


zeahmed Apr 3, 2019
d288792

Addressed reviewers' comments.


zeahmed Apr 3, 2019
bdca2a5

Changed input/output classes to private.


zeahmed Apr 4, 2019
bc60f09

Addressed reviewers' comments.


zeahmed Apr 4, 2019

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89 changes: 89 additions & 0 deletions
docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Text/ProduceHashedWordBags.cs

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Open in desktop

Original file line numberDiff line numberDiff line change
@@ -0,0 +1,89 @@
using System;
using System.Collections.Generic;
using System.Text;
using Microsoft.ML.Data;
using Microsoft.ML.Transforms.Text;

namespace Microsoft.ML.Samples.Dynamic
{
 public static class ProduceHashedWordBags
 {
 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.
 var mlContext = new MLContext();

 // Create a small dataset as an IEnumerable.
 var samples = new List<TextData>()
 {
 new TextData(){ Text = "This is an example to compute bag-of-word features using hashing." },
 new TextData(){ Text = "ML.NET's ProduceHashedWordBags API produces count of n-grams and hashes it as an index into a vector of given bit length." },
 new TextData(){ Text = "It does so by first tokenizing text/string into words/tokens then " },
 new TextData(){ Text = "computing n-grams and hash them to the index given by hash value." },
 new TextData(){ Text = "The hashing reduces the size of the output feature vector" },
 new TextData(){ Text = "which is useful in case when number of n-grams is very large." },
 };

 // Convert training data to IDataView.
 var dataview = mlContext.Data.LoadFromEnumerable(samples);

 // A pipeline for converting text into numeric bag-of-word features using hashing.
 // The following call to 'ProduceHashedWordBags' implicitly tokenizes the text/string into words/tokens.
 // Please note that the length of the output feature vector depends on the 'numberOfBits' settings.
 var textPipeline = mlContext.Transforms.Text.ProduceHashedWordBags("BagOfWordFeatures", "Text",
 numberOfBits: 5,
 ngramLength: 3,
 useAllLengths: false,
 maximumNumberOfInverts: 1);

 // Fit to data.
 var textTransformer = textPipeline.Fit(dataview);
 var transformedDataView = textTransformer.Transform(dataview);

 // Create the prediction engine to get the bag-of-word features extracted from the text.
 var predictionEngine = mlContext.Model.CreatePredictionEngine<TextData, TransformedTextData>(textTransformer);

 // Convert the text into numeric features.
 var prediction = predictionEngine.Predict(samples[0]);

 // Print the length of the feature vector.
 Console.WriteLine($"Number of Features: {prediction.BagOfWordFeatures.Length}");

 // Preview of the produced n-grams.
 // Get the slot names from the column's metadata.
 // The slot names for a vector column corresponds to the names associated with each position in the vector.
 VBuffer<ReadOnlyMemory<char>> slotNames = default;
 transformedDataView.Schema["BagOfWordFeatures"].GetSlotNames(ref slotNames);
 var BagOfWordFeaturesColumn = transformedDataView.GetColumn<VBuffer<float>>(transformedDataView.Schema["BagOfWordFeatures"]);
 var slots = slotNames.GetValues();
 Console.Write("N-grams: ");
 foreach (var featureRow in BagOfWordFeaturesColumn)
 {
 foreach (var item in featureRow.Items())
 Console.Write($"{slots[item.Key]} ");
 Console.WriteLine();
 }

 // Print the first 10 feature values.
 Console.Write("Features: ");
 for (int i = 0; i < 10; i++)
 Console.Write($"{prediction.BagOfWordFeatures[i]:F4} ");

 // Expected output:
 // Number of Features: 32
 // N-grams: an|example|to is|an|example example|to|compute This|is|an compute|bag-of-word|features bag-of-word|features|using to|compute|bag-of-word ML.NET's|ProduceHashedWordBags|API as|an|index API|produces|count ...
 // Features: 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 1.0000 2.0000 0.0000 0.0000 ...
 }

 private class TextData
 {
 public string Text { get; set; }
 }

 private class TransformedTextData : TextData
 {
 public float[] BagOfWordFeatures { get; set; }
 }
 }
}

89 changes: 89 additions & 0 deletions
docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Text/ProduceWordBags.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -0,0 +1,89 @@
using System;
using System.Collections.Generic;
using System.Text;
using Microsoft.ML.Data;
using Microsoft.ML.Transforms.Text;

namespace Microsoft.ML.Samples.Dynamic
{
 public static class ProduceWordBags
 {
 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.
 var mlContext = new MLContext();

 // Create a small dataset as an IEnumerable.
 var samples = new List<TextData>()
 {
 new TextData(){ Text = "This is an example to compute bag-of-word features." },
 new TextData(){ Text = "ML.NET's ProduceWordBags API produces bag-of-word features from input text." },
 new TextData(){ Text = "It does so by first tokenizing text/string into words/tokens then " },
 new TextData(){ Text = "computing n-grams and their neumeric values." },
 new TextData(){ Text = "Each position in the output vector corresponds to a particular n-gram." },
 new TextData(){ Text = "The value at each position corresponds to," },
 new TextData(){ Text = "the number of times n-gram occured in the data (Tf), or" },
 new TextData(){ Text = "the inverse of the number of documents contain the n-gram (Idf)," },
 new TextData(){ Text = "or compute both and multipy together (Tf-Idf)." },
 };

 // Convert training data to IDataView.
 var dataview = mlContext.Data.LoadFromEnumerable(samples);

 // A pipeline for converting text into numeric bag-of-word features.
 // The following call to 'ProduceWordBags' implicitly tokenizes the text/string into words/tokens.
 // Please note that the length of the output feature vector depends on the n-gram settings.
 var textPipeline = mlContext.Transforms.Text.ProduceWordBags("BagOfWordFeatures", "Text",
 ngramLength: 3, useAllLengths: false, weighting: NgramExtractingEstimator.WeightingCriteria.Tf);

 // Fit to data.
 var textTransformer = textPipeline.Fit(dataview);
 var transformedDataView = textTransformer.Transform(dataview);

 // Create the prediction engine to get the bag-of-word features extracted from the text.
 var predictionEngine = mlContext.Model.CreatePredictionEngine<TextData, TransformedTextData>(textTransformer);
@rogancarrrogancarrApr 4, 2019

Copy link


Copy Markdown

Contributor
Choose a reason for hiding this comment

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

Why predictionEngine rather than TakeRows and ConvertToEnumerable? I would use the one we would recommend people to use to inspect data in practice.

 // Convert the text into numeric features.
 var prediction = predictionEngine.Predict(samples[0]);

 // Print the length of the feature vector.
 Console.WriteLine($"Number of Features: {prediction.BagOfWordFeatures.Length}");

 // Preview of the produced n-grams.
 // Get the slot names from the column's metadata.
 // The slot names for a vector column corresponds to the names associated with each position in the vector.
 VBuffer<ReadOnlyMemory<char>> slotNames = default;
 transformedDataView.Schema["BagOfWordFeatures"].GetSlotNames(ref slotNames);
 var BagOfWordFeaturesColumn = transformedDataView.GetColumn<VBuffer<float>>(transformedDataView.Schema["BagOfWordFeatures"]);
 var slots = slotNames.GetValues();
 Console.Write("N-grams: ");
 foreach (var featureRow in BagOfWordFeaturesColumn)
 {
 foreach (var item in featureRow.Items())
 Console.Write($"{slots[item.Key]} ");
 Console.WriteLine();
 }

 // Print the first 10 feature values.
 Console.Write("Features: ");
 for (int i = 0; i < 10; i++)
 Console.Write($"{prediction.BagOfWordFeatures[i]:F4} ");

 // Expected output:
 // Number of Features: 62
 // N-grams: This|is|an is|an|example an|example|to example|to|compute to|compute|bag-of-word compute|bag-of-word|features. ML.NET's|ProduceWordBags|API ProduceWordBags|API|produces API|produces|bag-of-word produces|bag-of-word|features ...
 // Features: 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.0000 0.0000 0.0000 0.0000 ...
 }

 private class TextData
 {
 public string Text { get; set; }
 }

 private class TransformedTextData : TextData
 {
 public float[] BagOfWordFeatures { get; set; }
 }
 }
}

, '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); }
})();
})();

Skip to content
Created samples for 'ProduceWordBags' and 'ProduceHashedWordBags' API.#3183

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privacy statement. We’ll occasionally send you account related emails.

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to your account

 Merged

zeahmed
merged 6 commits into

from

Apr 4, 2019
 Merged


Created samples for 'ProduceWordBags' and 'ProduceHashedWordBags' API.
#3183
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9471b5d

Created samples for 'ProduceWordBags' and 'ProduceHashedWordBags' API.


zeahmed Apr 2, 2019
1b2daca

Updated comments!


zeahmed Apr 3, 2019
202f051

Addressed reviewers' comments.


zeahmed Apr 3, 2019
d288792

Addressed reviewers' comments.


zeahmed Apr 3, 2019
bdca2a5

Changed input/output classes to private.


zeahmed Apr 4, 2019
bc60f09

Addressed reviewers' comments.


zeahmed Apr 4, 2019

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89 changes: 89 additions & 0 deletions
docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Text/ProduceHashedWordBags.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -0,0 +1,89 @@
using System;
using System.Collections.Generic;
using System.Text;
using Microsoft.ML.Data;
using Microsoft.ML.Transforms.Text;

namespace Microsoft.ML.Samples.Dynamic
{
 public static class ProduceHashedWordBags
 {
 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.
 var mlContext = new MLContext();

 // Create a small dataset as an IEnumerable.
 var samples = new List<TextData>()
 {
 new TextData(){ Text = "This is an example to compute bag-of-word features using hashing." },
 new TextData(){ Text = "ML.NET's ProduceHashedWordBags API produces count of n-grams and hashes it as an index into a vector of given bit length." },
 new TextData(){ Text = "It does so by first tokenizing text/string into words/tokens then " },
 new TextData(){ Text = "computing n-grams and hash them to the index given by hash value." },
 new TextData(){ Text = "The hashing reduces the size of the output feature vector" },
 new TextData(){ Text = "which is useful in case when number of n-grams is very large." },
 };

 // Convert training data to IDataView.
 var dataview = mlContext.Data.LoadFromEnumerable(samples);

 // A pipeline for converting text into numeric bag-of-word features using hashing.
 // The following call to 'ProduceHashedWordBags' implicitly tokenizes the text/string into words/tokens.
 // Please note that the length of the output feature vector depends on the 'numberOfBits' settings.
 var textPipeline = mlContext.Transforms.Text.ProduceHashedWordBags("BagOfWordFeatures", "Text",
 numberOfBits: 5,
 ngramLength: 3,
 useAllLengths: false,
 maximumNumberOfInverts: 1);

 // Fit to data.
 var textTransformer = textPipeline.Fit(dataview);
 var transformedDataView = textTransformer.Transform(dataview);

 // Create the prediction engine to get the bag-of-word features extracted from the text.
 var predictionEngine = mlContext.Model.CreatePredictionEngine<TextData, TransformedTextData>(textTransformer);

 // Convert the text into numeric features.
 var prediction = predictionEngine.Predict(samples[0]);

 // Print the length of the feature vector.
 Console.WriteLine($"Number of Features: {prediction.BagOfWordFeatures.Length}");

 // Preview of the produced n-grams.
 // Get the slot names from the column's metadata.
 // The slot names for a vector column corresponds to the names associated with each position in the vector.
 VBuffer<ReadOnlyMemory<char>> slotNames = default;
 transformedDataView.Schema["BagOfWordFeatures"].GetSlotNames(ref slotNames);
 var BagOfWordFeaturesColumn = transformedDataView.GetColumn<VBuffer<float>>(transformedDataView.Schema["BagOfWordFeatures"]);
 var slots = slotNames.GetValues();
 Console.Write("N-grams: ");
 foreach (var featureRow in BagOfWordFeaturesColumn)
 {
 foreach (var item in featureRow.Items())
 Console.Write($"{slots[item.Key]} ");
 Console.WriteLine();
 }

 // Print the first 10 feature values.
 Console.Write("Features: ");
 for (int i = 0; i < 10; i++)
 Console.Write($"{prediction.BagOfWordFeatures[i]:F4} ");

 // Expected output:
 // Number of Features: 32
 // N-grams: an|example|to is|an|example example|to|compute This|is|an compute|bag-of-word|features bag-of-word|features|using to|compute|bag-of-word ML.NET's|ProduceHashedWordBags|API as|an|index API|produces|count ...
 // Features: 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 1.0000 2.0000 0.0000 0.0000 ...
 }

 private class TextData
 {
 public string Text { get; set; }
 }

 private class TransformedTextData : TextData
 {
 public float[] BagOfWordFeatures { get; set; }
 }
 }
}

89 changes: 89 additions & 0 deletions
docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Text/ProduceWordBags.cs

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using System;
using System.Collections.Generic;
using System.Text;
using Microsoft.ML.Data;
using Microsoft.ML.Transforms.Text;

namespace Microsoft.ML.Samples.Dynamic
{
 public static class ProduceWordBags
 {
 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.
 var mlContext = new MLContext();

 // Create a small dataset as an IEnumerable.
 var samples = new List<TextData>()
 {
 new TextData(){ Text = "This is an example to compute bag-of-word features." },
 new TextData(){ Text = "ML.NET's ProduceWordBags API produces bag-of-word features from input text." },
 new TextData(){ Text = "It does so by first tokenizing text/string into words/tokens then " },
 new TextData(){ Text = "computing n-grams and their neumeric values." },
 new TextData(){ Text = "Each position in the output vector corresponds to a particular n-gram." },
 new TextData(){ Text = "The value at each position corresponds to," },
 new TextData(){ Text = "the number of times n-gram occured in the data (Tf), or" },
 new TextData(){ Text = "the inverse of the number of documents contain the n-gram (Idf)," },
 new TextData(){ Text = "or compute both and multipy together (Tf-Idf)." },
 };

 // Convert training data to IDataView.
 var dataview = mlContext.Data.LoadFromEnumerable(samples);

 // A pipeline for converting text into numeric bag-of-word features.
 // The following call to 'ProduceWordBags' implicitly tokenizes the text/string into words/tokens.
 // Please note that the length of the output feature vector depends on the n-gram settings.
 var textPipeline = mlContext.Transforms.Text.ProduceWordBags("BagOfWordFeatures", "Text",
 ngramLength: 3, useAllLengths: false, weighting: NgramExtractingEstimator.WeightingCriteria.Tf);

 // Fit to data.
 var textTransformer = textPipeline.Fit(dataview);
 var transformedDataView = textTransformer.Transform(dataview);

 // Create the prediction engine to get the bag-of-word features extracted from the text.
 var predictionEngine = mlContext.Model.CreatePredictionEngine<TextData, TransformedTextData>(textTransformer);
@rogancarrrogancarrApr 4, 2019

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Why predictionEngine rather than TakeRows and ConvertToEnumerable? I would use the one we would recommend people to use to inspect data in practice.

 // Convert the text into numeric features.
 var prediction = predictionEngine.Predict(samples[0]);

 // Print the length of the feature vector.
 Console.WriteLine($"Number of Features: {prediction.BagOfWordFeatures.Length}");

 // Preview of the produced n-grams.
 // Get the slot names from the column's metadata.
 // The slot names for a vector column corresponds to the names associated with each position in the vector.
 VBuffer<ReadOnlyMemory<char>> slotNames = default;
 transformedDataView.Schema["BagOfWordFeatures"].GetSlotNames(ref slotNames);
 var BagOfWordFeaturesColumn = transformedDataView.GetColumn<VBuffer<float>>(transformedDataView.Schema["BagOfWordFeatures"]);
 var slots = slotNames.GetValues();
 Console.Write("N-grams: ");
 foreach (var featureRow in BagOfWordFeaturesColumn)
 {
 foreach (var item in featureRow.Items())
 Console.Write($"{slots[item.Key]} ");
 Console.WriteLine();
 }

 // Print the first 10 feature values.
 Console.Write("Features: ");
 for (int i = 0; i < 10; i++)
 Console.Write($"{prediction.BagOfWordFeatures[i]:F4} ");

 // Expected output:
 // Number of Features: 62
 // N-grams: This|is|an is|an|example an|example|to example|to|compute to|compute|bag-of-word compute|bag-of-word|features. ML.NET's|ProduceWordBags|API ProduceWordBags|API|produces API|produces|bag-of-word produces|bag-of-word|features ...
 // Features: 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.0000 0.0000 0.0000 0.0000 ...
 }

 private class TextData
 {
 public string Text { get; set; }
 }

 private class TransformedTextData : TextData
 {
 public float[] BagOfWordFeatures { get; set; }
 }
 }
}