Reformatting misc. samples to width 85#3949

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
sierralee51 merged 1 commit into from
Jul 2, 2019
Merged

Reformatting misc. samples to width 85 #3949

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
84 changes: 61 additions & 23 deletions docs/samples/Microsoft.ML.Samples/Dynamic/NgramExtraction.cs
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -9,15 +9,21 @@ public static partial class TransformSamples
{
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 to IDataView.
var data = new List<SampleSentimentData>() {
new SampleSentimentData { Sentiment = true, SentimentText = "Best game I've ever played." },
new SampleSentimentData { Sentiment = false, SentimentText = "==RUDE== Dude, 2" },
new SampleSentimentData { Sentiment = true, SentimentText = "Until the next game, this is the best Xbox game!" } };
new SampleSentimentData { Sentiment = true,
SentimentText = "Best game I've ever played." },

new SampleSentimentData { Sentiment = false,
SentimentText = "==RUDE== Dude, 2" },

new SampleSentimentData { Sentiment = true,
SentimentText = "Until the next game," +
"this is the best Xbox game!" } };

// Convert IEnumerable to IDataView.
var trainData = ml.Data.LoadFromEnumerable(data);
Expand All@@ -29,23 +35,42 @@ public static void Example()
// false ==RUDE== Dude, 2.
// true Until the next game, this is the best Xbox game!

// A pipeline to tokenize text as characters and then combine them together into n-grams
// The pipeline uses the default settings to featurize.
// A pipeline to tokenize text as characters and then combine them
// together into n-grams. The pipeline uses the default settings to
// featurize.

var charsPipeline = ml.Transforms.Text
.TokenizeIntoCharactersAsKeys("Chars", "SentimentText",
useMarkerCharacters: false);

var ngramOnePipeline = ml.Transforms.Text
.ProduceNgrams("CharsUnigrams", "Chars", ngramLength: 1);

var charsPipeline = ml.Transforms.Text.TokenizeIntoCharactersAsKeys("Chars", "SentimentText", useMarkerCharacters: false);
var ngramOnePipeline = ml.Transforms.Text.ProduceNgrams("CharsUnigrams", "Chars", ngramLength: 1);
var ngramTwpPipeline = ml.Transforms.Text.ProduceNgrams("CharsTwograms", "Chars");
var oneCharsPipeline = charsPipeline.Append(ngramOnePipeline);
var twoCharsPipeline = charsPipeline.Append(ngramTwpPipeline);
var ngramTwpPipeline = ml.Transforms.Text
.ProduceNgrams("CharsTwograms", "Chars");

var oneCharsPipeline = charsPipeline
.Append(ngramOnePipeline);

var twoCharsPipeline = charsPipeline
.Append(ngramTwpPipeline);

// The transformed data for pipelines.
var transformedData_onechars = oneCharsPipeline.Fit(trainData).Transform(trainData);
var transformedData_twochars = twoCharsPipeline.Fit(trainData).Transform(trainData);
var transformedData_onechars = oneCharsPipeline.Fit(trainData)
.Transform(trainData);

var transformedData_twochars = twoCharsPipeline.Fit(trainData)
.Transform(trainData);

// Small helper to print the text inside the columns, in the console.
Action<string, IEnumerable<VBuffer<float>>, VBuffer<ReadOnlyMemory<char>>> printHelper = (columnName, column, names) =>
Action<string, IEnumerable<VBuffer<float>>,
VBuffer<ReadOnlyMemory<char>>>
printHelper = (columnName, column, names) =>

{
Console.WriteLine($"{columnName} column obtained post-transformation.");
Console.WriteLine(
$"{columnName} column obtained post-transformation.");

var slots = names.GetValues();
foreach (var featureRow in column)
{
Expand All@@ -54,21 +79,33 @@ public static void Example()
Console.WriteLine("");
}

Console.WriteLine("===================================================");
Console.WriteLine(
"===================================================");
};
// Preview of the CharsUnigrams column obtained after processing the input.
// Preview of the CharsUnigrams column obtained after processing the
// input.
VBuffer<ReadOnlyMemory<char>> slotNames = default;
transformedData_onechars.Schema["CharsUnigrams"].GetSlotNames(ref slotNames);
var charsOneGramColumn = transformedData_onechars.GetColumn<VBuffer<float>>(transformedData_onechars.Schema["CharsUnigrams"]);
transformedData_onechars.Schema["CharsUnigrams"]
.GetSlotNames(ref slotNames);

var charsOneGramColumn = transformedData_onechars
.GetColumn<VBuffer<float>>(transformedData_onechars
.Schema["CharsUnigrams"]);

printHelper("CharsUnigrams", charsOneGramColumn, slotNames);

// CharsUnigrams column obtained post-transformation.
// 'B' - 1 'e' - 6 's' - 1 't' - 1 '<?>' - 4 'g' - 1 'a' - 2 'm' - 1 'I' - 1 ''' - 1 'v' - 2 ...
// 'e' - 1 '<?>' - 2 'd' - 1 '=' - 4 'R' - 1 'U' - 1 'D' - 2 'E' - 1 'u' - 1 ',' - 1 '2' - 1
// 'B' - 0 'e' - 6 's' - 3 't' - 6 '<?>' - 9 'g' - 2 'a' - 2 'm' - 2 'I' - 0 ''' - 0 'v' - 0 ...
// Preview of the CharsTwoGrams column obtained after processing the input.
var charsTwoGramColumn = transformedData_twochars.GetColumn<VBuffer<float>>(transformedData_twochars.Schema["CharsTwograms"]);
transformedData_twochars.Schema["CharsTwograms"].GetSlotNames(ref slotNames);
var charsTwoGramColumn = transformedData_twochars
.GetColumn<VBuffer<float>>(transformedData_twochars
.Schema["CharsTwograms"]);

transformedData_twochars.Schema["CharsTwograms"]
.GetSlotNames(ref slotNames);

printHelper("CharsTwograms", charsTwoGramColumn, slotNames);

// CharsTwograms column obtained post-transformation.
Expand All@@ -78,7 +115,8 @@ public static void Example()
}

/// <summary>
/// A dataset that contains a tweet and the sentiment assigned to that tweet: 0 - negative and 1 - positive sentiment.
/// A dataset that contains a tweet and the sentiment assigned to that
/// tweet: 0 - negative and 1 - positive sentiment.
/// </summary>
public class SampleSentimentData
{
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" + '

Skip to content
Reformatting misc. samples to width 85#3949

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

 Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and
privacy statement. We’ll occasionally send you account related emails.

Already on GitHub?
Sign in
to your account

 Merged

sierralee51
merged 1 commit into

from

Jul 2, 2019
 Merged


Reformatting misc. samples to width 85
#3949
Show file tree
Hide file tree
 Changes from all commits
Commits

Show all changes


1 commit


Select commit


File filter

Filter by extension
Filter by extension
Conversations

Failed to load comments. 
Loading
Jump to

Jump to file


Failed to load files. 
Loading
 Diff view

 Diff view


84 changes: 61 additions & 23 deletions
docs/samples/Microsoft.ML.Samples/Dynamic/NgramExtraction.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -9,15 +9,21 @@ public static partial class TransformSamples
 {
 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 to IDataView.
 var data = new List<SampleSentimentData>() {
 new SampleSentimentData { Sentiment = true, SentimentText = "Best game I've ever played." },
 new SampleSentimentData { Sentiment = false, SentimentText = "==RUDE== Dude, 2" },
 new SampleSentimentData { Sentiment = true, SentimentText = "Until the next game, this is the best Xbox game!" } };
 new SampleSentimentData { Sentiment = true,
 SentimentText = "Best game I've ever played." },

 new SampleSentimentData { Sentiment = false,
 SentimentText = "==RUDE== Dude, 2" },

 new SampleSentimentData { Sentiment = true,
 SentimentText = "Until the next game," +
 "this is the best Xbox game!" } };

 // Convert IEnumerable to IDataView.
 var trainData = ml.Data.LoadFromEnumerable(data);
Expand All@@ -29,23 +35,42 @@ public static void Example()
 // false ==RUDE== Dude, 2.
 // true Until the next game, this is the best Xbox game!

 // A pipeline to tokenize text as characters and then combine them together into n-grams
 // The pipeline uses the default settings to featurize.
 // A pipeline to tokenize text as characters and then combine them
 // together into n-grams. The pipeline uses the default settings to
 // featurize.

 var charsPipeline = ml.Transforms.Text
 .TokenizeIntoCharactersAsKeys("Chars", "SentimentText",
 useMarkerCharacters: false);

 var ngramOnePipeline = ml.Transforms.Text
 .ProduceNgrams("CharsUnigrams", "Chars", ngramLength: 1);

 var charsPipeline = ml.Transforms.Text.TokenizeIntoCharactersAsKeys("Chars", "SentimentText", useMarkerCharacters: false);
 var ngramOnePipeline = ml.Transforms.Text.ProduceNgrams("CharsUnigrams", "Chars", ngramLength: 1);
 var ngramTwpPipeline = ml.Transforms.Text.ProduceNgrams("CharsTwograms", "Chars");
 var oneCharsPipeline = charsPipeline.Append(ngramOnePipeline);
 var twoCharsPipeline = charsPipeline.Append(ngramTwpPipeline);
 var ngramTwpPipeline = ml.Transforms.Text
 .ProduceNgrams("CharsTwograms", "Chars");

 var oneCharsPipeline = charsPipeline
 .Append(ngramOnePipeline);

 var twoCharsPipeline = charsPipeline
 .Append(ngramTwpPipeline);

 // The transformed data for pipelines.
 var transformedData_onechars = oneCharsPipeline.Fit(trainData).Transform(trainData);
 var transformedData_twochars = twoCharsPipeline.Fit(trainData).Transform(trainData);
 var transformedData_onechars = oneCharsPipeline.Fit(trainData)
 .Transform(trainData);

 var transformedData_twochars = twoCharsPipeline.Fit(trainData)
 .Transform(trainData);

 // Small helper to print the text inside the columns, in the console. 
 Action<string, IEnumerable<VBuffer<float>>, VBuffer<ReadOnlyMemory<char>>> printHelper = (columnName, column, names) =>
 Action<string, IEnumerable<VBuffer<float>>,
 VBuffer<ReadOnlyMemory<char>>>
 printHelper = (columnName, column, names) =>

 {
 Console.WriteLine($"{columnName} column obtained post-transformation.");
 Console.WriteLine(
 $"{columnName} column obtained post-transformation.");

 var slots = names.GetValues();
 foreach (var featureRow in column)
 {
Expand All@@ -54,21 +79,33 @@ public static void Example()
 Console.WriteLine("");
 }

 Console.WriteLine("===================================================");
 Console.WriteLine(
 "===================================================");
 };
 // Preview of the CharsUnigrams column obtained after processing the input.
 // Preview of the CharsUnigrams column obtained after processing the
 // input.
 VBuffer<ReadOnlyMemory<char>> slotNames = default;
 transformedData_onechars.Schema["CharsUnigrams"].GetSlotNames(ref slotNames);
 var charsOneGramColumn = transformedData_onechars.GetColumn<VBuffer<float>>(transformedData_onechars.Schema["CharsUnigrams"]);
 transformedData_onechars.Schema["CharsUnigrams"]
 .GetSlotNames(ref slotNames);

 var charsOneGramColumn = transformedData_onechars
 .GetColumn<VBuffer<float>>(transformedData_onechars
 .Schema["CharsUnigrams"]);

 printHelper("CharsUnigrams", charsOneGramColumn, slotNames);

 // CharsUnigrams column obtained post-transformation.
 // 'B' - 1 'e' - 6 's' - 1 't' - 1 '<?>' - 4 'g' - 1 'a' - 2 'm' - 1 'I' - 1 ''' - 1 'v' - 2 ...
 // 'e' - 1 '<?>' - 2 'd' - 1 '=' - 4 'R' - 1 'U' - 1 'D' - 2 'E' - 1 'u' - 1 ',' - 1 '2' - 1
 // 'B' - 0 'e' - 6 's' - 3 't' - 6 '<?>' - 9 'g' - 2 'a' - 2 'm' - 2 'I' - 0 ''' - 0 'v' - 0 ...
 // Preview of the CharsTwoGrams column obtained after processing the input.
 var charsTwoGramColumn = transformedData_twochars.GetColumn<VBuffer<float>>(transformedData_twochars.Schema["CharsTwograms"]);
 transformedData_twochars.Schema["CharsTwograms"].GetSlotNames(ref slotNames);
 var charsTwoGramColumn = transformedData_twochars
 .GetColumn<VBuffer<float>>(transformedData_twochars
 .Schema["CharsTwograms"]);

 transformedData_twochars.Schema["CharsTwograms"]
 .GetSlotNames(ref slotNames);

 printHelper("CharsTwograms", charsTwoGramColumn, slotNames);

 // CharsTwograms column obtained post-transformation.
Expand All@@ -78,7 +115,8 @@ public static void Example()
 }

 /// <summary>
 /// A dataset that contains a tweet and the sentiment assigned to that tweet: 0 - negative and 1 - positive sentiment.
 /// A dataset that contains a tweet and the sentiment assigned to that
 /// tweet: 0 - negative and 1 - positive sentiment.
 /// </summary>
 public class SampleSentimentData
 {
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('^' + ".*" + '

Skip to content
Reformatting misc. samples to width 85#3949

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

 Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and
privacy statement. We’ll occasionally send you account related emails.

Already on GitHub?
Sign in
to your account

 Merged

sierralee51
merged 1 commit into

from

Jul 2, 2019
 Merged


Reformatting misc. samples to width 85
#3949
Show file tree
Hide file tree
 Changes from all commits
Commits

Show all changes


1 commit


Select commit


File filter

Filter by extension
Filter by extension
Conversations

Failed to load comments. 
Loading
Jump to

Jump to file


Failed to load files. 
Loading
 Diff view

 Diff view


84 changes: 61 additions & 23 deletions
docs/samples/Microsoft.ML.Samples/Dynamic/NgramExtraction.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -9,15 +9,21 @@ public static partial class TransformSamples
 {
 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 to IDataView.
 var data = new List<SampleSentimentData>() {
 new SampleSentimentData { Sentiment = true, SentimentText = "Best game I've ever played." },
 new SampleSentimentData { Sentiment = false, SentimentText = "==RUDE== Dude, 2" },
 new SampleSentimentData { Sentiment = true, SentimentText = "Until the next game, this is the best Xbox game!" } };
 new SampleSentimentData { Sentiment = true,
 SentimentText = "Best game I've ever played." },

 new SampleSentimentData { Sentiment = false,
 SentimentText = "==RUDE== Dude, 2" },

 new SampleSentimentData { Sentiment = true,
 SentimentText = "Until the next game," +
 "this is the best Xbox game!" } };

 // Convert IEnumerable to IDataView.
 var trainData = ml.Data.LoadFromEnumerable(data);
Expand All@@ -29,23 +35,42 @@ public static void Example()
 // false ==RUDE== Dude, 2.
 // true Until the next game, this is the best Xbox game!

 // A pipeline to tokenize text as characters and then combine them together into n-grams
 // The pipeline uses the default settings to featurize.
 // A pipeline to tokenize text as characters and then combine them
 // together into n-grams. The pipeline uses the default settings to
 // featurize.

 var charsPipeline = ml.Transforms.Text
 .TokenizeIntoCharactersAsKeys("Chars", "SentimentText",
 useMarkerCharacters: false);

 var ngramOnePipeline = ml.Transforms.Text
 .ProduceNgrams("CharsUnigrams", "Chars", ngramLength: 1);

 var charsPipeline = ml.Transforms.Text.TokenizeIntoCharactersAsKeys("Chars", "SentimentText", useMarkerCharacters: false);
 var ngramOnePipeline = ml.Transforms.Text.ProduceNgrams("CharsUnigrams", "Chars", ngramLength: 1);
 var ngramTwpPipeline = ml.Transforms.Text.ProduceNgrams("CharsTwograms", "Chars");
 var oneCharsPipeline = charsPipeline.Append(ngramOnePipeline);
 var twoCharsPipeline = charsPipeline.Append(ngramTwpPipeline);
 var ngramTwpPipeline = ml.Transforms.Text
 .ProduceNgrams("CharsTwograms", "Chars");

 var oneCharsPipeline = charsPipeline
 .Append(ngramOnePipeline);

 var twoCharsPipeline = charsPipeline
 .Append(ngramTwpPipeline);

 // The transformed data for pipelines.
 var transformedData_onechars = oneCharsPipeline.Fit(trainData).Transform(trainData);
 var transformedData_twochars = twoCharsPipeline.Fit(trainData).Transform(trainData);
 var transformedData_onechars = oneCharsPipeline.Fit(trainData)
 .Transform(trainData);

 var transformedData_twochars = twoCharsPipeline.Fit(trainData)
 .Transform(trainData);

 // Small helper to print the text inside the columns, in the console. 
 Action<string, IEnumerable<VBuffer<float>>, VBuffer<ReadOnlyMemory<char>>> printHelper = (columnName, column, names) =>
 Action<string, IEnumerable<VBuffer<float>>,
 VBuffer<ReadOnlyMemory<char>>>
 printHelper = (columnName, column, names) =>

 {
 Console.WriteLine($"{columnName} column obtained post-transformation.");
 Console.WriteLine(
 $"{columnName} column obtained post-transformation.");

 var slots = names.GetValues();
 foreach (var featureRow in column)
 {
Expand All@@ -54,21 +79,33 @@ public static void Example()
 Console.WriteLine("");
 }

 Console.WriteLine("===================================================");
 Console.WriteLine(
 "===================================================");
 };
 // Preview of the CharsUnigrams column obtained after processing the input.
 // Preview of the CharsUnigrams column obtained after processing the
 // input.
 VBuffer<ReadOnlyMemory<char>> slotNames = default;
 transformedData_onechars.Schema["CharsUnigrams"].GetSlotNames(ref slotNames);
 var charsOneGramColumn = transformedData_onechars.GetColumn<VBuffer<float>>(transformedData_onechars.Schema["CharsUnigrams"]);
 transformedData_onechars.Schema["CharsUnigrams"]
 .GetSlotNames(ref slotNames);

 var charsOneGramColumn = transformedData_onechars
 .GetColumn<VBuffer<float>>(transformedData_onechars
 .Schema["CharsUnigrams"]);

 printHelper("CharsUnigrams", charsOneGramColumn, slotNames);

 // CharsUnigrams column obtained post-transformation.
 // 'B' - 1 'e' - 6 's' - 1 't' - 1 '<?>' - 4 'g' - 1 'a' - 2 'm' - 1 'I' - 1 ''' - 1 'v' - 2 ...
 // 'e' - 1 '<?>' - 2 'd' - 1 '=' - 4 'R' - 1 'U' - 1 'D' - 2 'E' - 1 'u' - 1 ',' - 1 '2' - 1
 // 'B' - 0 'e' - 6 's' - 3 't' - 6 '<?>' - 9 'g' - 2 'a' - 2 'm' - 2 'I' - 0 ''' - 0 'v' - 0 ...
 // Preview of the CharsTwoGrams column obtained after processing the input.
 var charsTwoGramColumn = transformedData_twochars.GetColumn<VBuffer<float>>(transformedData_twochars.Schema["CharsTwograms"]);
 transformedData_twochars.Schema["CharsTwograms"].GetSlotNames(ref slotNames);
 var charsTwoGramColumn = transformedData_twochars
 .GetColumn<VBuffer<float>>(transformedData_twochars
 .Schema["CharsTwograms"]);

 transformedData_twochars.Schema["CharsTwograms"]
 .GetSlotNames(ref slotNames);

 printHelper("CharsTwograms", charsTwoGramColumn, slotNames);

 // CharsTwograms column obtained post-transformation.
Expand All@@ -78,7 +115,8 @@ public static void Example()
 }

 /// <summary>
 /// A dataset that contains a tweet and the sentiment assigned to that tweet: 0 - negative and 1 - positive sentiment.
 /// A dataset that contains a tweet and the sentiment assigned to that
 /// tweet: 0 - negative and 1 - positive sentiment.
 /// </summary>
 public class SampleSentimentData
 {
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 misc. samples to width 85#3949

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

 Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and
privacy statement. We’ll occasionally send you account related emails.

Already on GitHub?
Sign in
to your account

 Merged

sierralee51
merged 1 commit into

from

Jul 2, 2019
 Merged


Reformatting misc. samples to width 85
#3949
Show file tree
Hide file tree
 Changes from all commits
Commits

Show all changes


1 commit


Select commit


File filter

Filter by extension
Filter by extension
Conversations

Failed to load comments. 
Loading
Jump to

Jump to file


Failed to load files. 
Loading
 Diff view

 Diff view


84 changes: 61 additions & 23 deletions
docs/samples/Microsoft.ML.Samples/Dynamic/NgramExtraction.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -9,15 +9,21 @@ public static partial class TransformSamples
 {
 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 to IDataView.
 var data = new List<SampleSentimentData>() {
 new SampleSentimentData { Sentiment = true, SentimentText = "Best game I've ever played." },
 new SampleSentimentData { Sentiment = false, SentimentText = "==RUDE== Dude, 2" },
 new SampleSentimentData { Sentiment = true, SentimentText = "Until the next game, this is the best Xbox game!" } };
 new SampleSentimentData { Sentiment = true,
 SentimentText = "Best game I've ever played." },

 new SampleSentimentData { Sentiment = false,
 SentimentText = "==RUDE== Dude, 2" },

 new SampleSentimentData { Sentiment = true,
 SentimentText = "Until the next game," +
 "this is the best Xbox game!" } };

 // Convert IEnumerable to IDataView.
 var trainData = ml.Data.LoadFromEnumerable(data);
Expand All@@ -29,23 +35,42 @@ public static void Example()
 // false ==RUDE== Dude, 2.
 // true Until the next game, this is the best Xbox game!

 // A pipeline to tokenize text as characters and then combine them together into n-grams
 // The pipeline uses the default settings to featurize.
 // A pipeline to tokenize text as characters and then combine them
 // together into n-grams. The pipeline uses the default settings to
 // featurize.

 var charsPipeline = ml.Transforms.Text
 .TokenizeIntoCharactersAsKeys("Chars", "SentimentText",
 useMarkerCharacters: false);

 var ngramOnePipeline = ml.Transforms.Text
 .ProduceNgrams("CharsUnigrams", "Chars", ngramLength: 1);

 var charsPipeline = ml.Transforms.Text.TokenizeIntoCharactersAsKeys("Chars", "SentimentText", useMarkerCharacters: false);
 var ngramOnePipeline = ml.Transforms.Text.ProduceNgrams("CharsUnigrams", "Chars", ngramLength: 1);
 var ngramTwpPipeline = ml.Transforms.Text.ProduceNgrams("CharsTwograms", "Chars");
 var oneCharsPipeline = charsPipeline.Append(ngramOnePipeline);
 var twoCharsPipeline = charsPipeline.Append(ngramTwpPipeline);
 var ngramTwpPipeline = ml.Transforms.Text
 .ProduceNgrams("CharsTwograms", "Chars");

 var oneCharsPipeline = charsPipeline
 .Append(ngramOnePipeline);

 var twoCharsPipeline = charsPipeline
 .Append(ngramTwpPipeline);

 // The transformed data for pipelines.
 var transformedData_onechars = oneCharsPipeline.Fit(trainData).Transform(trainData);
 var transformedData_twochars = twoCharsPipeline.Fit(trainData).Transform(trainData);
 var transformedData_onechars = oneCharsPipeline.Fit(trainData)
 .Transform(trainData);

 var transformedData_twochars = twoCharsPipeline.Fit(trainData)
 .Transform(trainData);

 // Small helper to print the text inside the columns, in the console. 
 Action<string, IEnumerable<VBuffer<float>>, VBuffer<ReadOnlyMemory<char>>> printHelper = (columnName, column, names) =>
 Action<string, IEnumerable<VBuffer<float>>,
 VBuffer<ReadOnlyMemory<char>>>
 printHelper = (columnName, column, names) =>

 {
 Console.WriteLine($"{columnName} column obtained post-transformation.");
 Console.WriteLine(
 $"{columnName} column obtained post-transformation.");

 var slots = names.GetValues();
 foreach (var featureRow in column)
 {
Expand All@@ -54,21 +79,33 @@ public static void Example()
 Console.WriteLine("");
 }

 Console.WriteLine("===================================================");
 Console.WriteLine(
 "===================================================");
 };
 // Preview of the CharsUnigrams column obtained after processing the input.
 // Preview of the CharsUnigrams column obtained after processing the
 // input.
 VBuffer<ReadOnlyMemory<char>> slotNames = default;
 transformedData_onechars.Schema["CharsUnigrams"].GetSlotNames(ref slotNames);
 var charsOneGramColumn = transformedData_onechars.GetColumn<VBuffer<float>>(transformedData_onechars.Schema["CharsUnigrams"]);
 transformedData_onechars.Schema["CharsUnigrams"]
 .GetSlotNames(ref slotNames);

 var charsOneGramColumn = transformedData_onechars
 .GetColumn<VBuffer<float>>(transformedData_onechars
 .Schema["CharsUnigrams"]);

 printHelper("CharsUnigrams", charsOneGramColumn, slotNames);

 // CharsUnigrams column obtained post-transformation.
 // 'B' - 1 'e' - 6 's' - 1 't' - 1 '<?>' - 4 'g' - 1 'a' - 2 'm' - 1 'I' - 1 ''' - 1 'v' - 2 ...
 // 'e' - 1 '<?>' - 2 'd' - 1 '=' - 4 'R' - 1 'U' - 1 'D' - 2 'E' - 1 'u' - 1 ',' - 1 '2' - 1
 // 'B' - 0 'e' - 6 's' - 3 't' - 6 '<?>' - 9 'g' - 2 'a' - 2 'm' - 2 'I' - 0 ''' - 0 'v' - 0 ...
 // Preview of the CharsTwoGrams column obtained after processing the input.
 var charsTwoGramColumn = transformedData_twochars.GetColumn<VBuffer<float>>(transformedData_twochars.Schema["CharsTwograms"]);
 transformedData_twochars.Schema["CharsTwograms"].GetSlotNames(ref slotNames);
 var charsTwoGramColumn = transformedData_twochars
 .GetColumn<VBuffer<float>>(transformedData_twochars
 .Schema["CharsTwograms"]);

 transformedData_twochars.Schema["CharsTwograms"]
 .GetSlotNames(ref slotNames);

 printHelper("CharsTwograms", charsTwoGramColumn, slotNames);

 // CharsTwograms column obtained post-transformation.
Expand All@@ -78,7 +115,8 @@ public static void Example()
 }

 /// <summary>
 /// A dataset that contains a tweet and the sentiment assigned to that tweet: 0 - negative and 1 - positive sentiment.
 /// A dataset that contains a tweet and the sentiment assigned to that
 /// tweet: 0 - negative and 1 - positive sentiment.
 /// </summary>
 public class SampleSentimentData
 {
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" + '

Skip to content
Reformatting misc. samples to width 85#3949

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

 Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and
privacy statement. We’ll occasionally send you account related emails.

Already on GitHub?
Sign in
to your account

 Merged

sierralee51
merged 1 commit into

from

Jul 2, 2019
 Merged


Reformatting misc. samples to width 85
#3949
Show file tree
Hide file tree
 Changes from all commits
Commits

Show all changes


1 commit


Select commit


File filter

Filter by extension
Filter by extension
Conversations

Failed to load comments. 
Loading
Jump to

Jump to file


Failed to load files. 
Loading
 Diff view

 Diff view


84 changes: 61 additions & 23 deletions
docs/samples/Microsoft.ML.Samples/Dynamic/NgramExtraction.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -9,15 +9,21 @@ public static partial class TransformSamples
 {
 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 to IDataView.
 var data = new List<SampleSentimentData>() {
 new SampleSentimentData { Sentiment = true, SentimentText = "Best game I've ever played." },
 new SampleSentimentData { Sentiment = false, SentimentText = "==RUDE== Dude, 2" },
 new SampleSentimentData { Sentiment = true, SentimentText = "Until the next game, this is the best Xbox game!" } };
 new SampleSentimentData { Sentiment = true,
 SentimentText = "Best game I've ever played." },

 new SampleSentimentData { Sentiment = false,
 SentimentText = "==RUDE== Dude, 2" },

 new SampleSentimentData { Sentiment = true,
 SentimentText = "Until the next game," +
 "this is the best Xbox game!" } };

 // Convert IEnumerable to IDataView.
 var trainData = ml.Data.LoadFromEnumerable(data);
Expand All@@ -29,23 +35,42 @@ public static void Example()
 // false ==RUDE== Dude, 2.
 // true Until the next game, this is the best Xbox game!

 // A pipeline to tokenize text as characters and then combine them together into n-grams
 // The pipeline uses the default settings to featurize.
 // A pipeline to tokenize text as characters and then combine them
 // together into n-grams. The pipeline uses the default settings to
 // featurize.

 var charsPipeline = ml.Transforms.Text
 .TokenizeIntoCharactersAsKeys("Chars", "SentimentText",
 useMarkerCharacters: false);

 var ngramOnePipeline = ml.Transforms.Text
 .ProduceNgrams("CharsUnigrams", "Chars", ngramLength: 1);

 var charsPipeline = ml.Transforms.Text.TokenizeIntoCharactersAsKeys("Chars", "SentimentText", useMarkerCharacters: false);
 var ngramOnePipeline = ml.Transforms.Text.ProduceNgrams("CharsUnigrams", "Chars", ngramLength: 1);
 var ngramTwpPipeline = ml.Transforms.Text.ProduceNgrams("CharsTwograms", "Chars");
 var oneCharsPipeline = charsPipeline.Append(ngramOnePipeline);
 var twoCharsPipeline = charsPipeline.Append(ngramTwpPipeline);
 var ngramTwpPipeline = ml.Transforms.Text
 .ProduceNgrams("CharsTwograms", "Chars");

 var oneCharsPipeline = charsPipeline
 .Append(ngramOnePipeline);

 var twoCharsPipeline = charsPipeline
 .Append(ngramTwpPipeline);

 // The transformed data for pipelines.
 var transformedData_onechars = oneCharsPipeline.Fit(trainData).Transform(trainData);
 var transformedData_twochars = twoCharsPipeline.Fit(trainData).Transform(trainData);
 var transformedData_onechars = oneCharsPipeline.Fit(trainData)
 .Transform(trainData);

 var transformedData_twochars = twoCharsPipeline.Fit(trainData)
 .Transform(trainData);

 // Small helper to print the text inside the columns, in the console. 
 Action<string, IEnumerable<VBuffer<float>>, VBuffer<ReadOnlyMemory<char>>> printHelper = (columnName, column, names) =>
 Action<string, IEnumerable<VBuffer<float>>,
 VBuffer<ReadOnlyMemory<char>>>
 printHelper = (columnName, column, names) =>

 {
 Console.WriteLine($"{columnName} column obtained post-transformation.");
 Console.WriteLine(
 $"{columnName} column obtained post-transformation.");

 var slots = names.GetValues();
 foreach (var featureRow in column)
 {
Expand All@@ -54,21 +79,33 @@ public static void Example()
 Console.WriteLine("");
 }

 Console.WriteLine("===================================================");
 Console.WriteLine(
 "===================================================");
 };
 // Preview of the CharsUnigrams column obtained after processing the input.
 // Preview of the CharsUnigrams column obtained after processing the
 // input.
 VBuffer<ReadOnlyMemory<char>> slotNames = default;
 transformedData_onechars.Schema["CharsUnigrams"].GetSlotNames(ref slotNames);
 var charsOneGramColumn = transformedData_onechars.GetColumn<VBuffer<float>>(transformedData_onechars.Schema["CharsUnigrams"]);
 transformedData_onechars.Schema["CharsUnigrams"]
 .GetSlotNames(ref slotNames);

 var charsOneGramColumn = transformedData_onechars
 .GetColumn<VBuffer<float>>(transformedData_onechars
 .Schema["CharsUnigrams"]);

 printHelper("CharsUnigrams", charsOneGramColumn, slotNames);

 // CharsUnigrams column obtained post-transformation.
 // 'B' - 1 'e' - 6 's' - 1 't' - 1 '<?>' - 4 'g' - 1 'a' - 2 'm' - 1 'I' - 1 ''' - 1 'v' - 2 ...
 // 'e' - 1 '<?>' - 2 'd' - 1 '=' - 4 'R' - 1 'U' - 1 'D' - 2 'E' - 1 'u' - 1 ',' - 1 '2' - 1
 // 'B' - 0 'e' - 6 's' - 3 't' - 6 '<?>' - 9 'g' - 2 'a' - 2 'm' - 2 'I' - 0 ''' - 0 'v' - 0 ...
 // Preview of the CharsTwoGrams column obtained after processing the input.
 var charsTwoGramColumn = transformedData_twochars.GetColumn<VBuffer<float>>(transformedData_twochars.Schema["CharsTwograms"]);
 transformedData_twochars.Schema["CharsTwograms"].GetSlotNames(ref slotNames);
 var charsTwoGramColumn = transformedData_twochars
 .GetColumn<VBuffer<float>>(transformedData_twochars
 .Schema["CharsTwograms"]);

 transformedData_twochars.Schema["CharsTwograms"]
 .GetSlotNames(ref slotNames);

 printHelper("CharsTwograms", charsTwoGramColumn, slotNames);

 // CharsTwograms column obtained post-transformation.
Expand All@@ -78,7 +115,8 @@ public static void Example()
 }

 /// <summary>
 /// A dataset that contains a tweet and the sentiment assigned to that tweet: 0 - negative and 1 - positive sentiment.
 /// A dataset that contains a tweet and the sentiment assigned to that
 /// tweet: 0 - negative and 1 - positive sentiment.
 /// </summary>
 public class SampleSentimentData
 {
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('^' + ".*" + '

Skip to content
Reformatting misc. samples to width 85#3949

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

 Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and
privacy statement. We’ll occasionally send you account related emails.

Already on GitHub?
Sign in
to your account

 Merged

sierralee51
merged 1 commit into

from

Jul 2, 2019
 Merged


Reformatting misc. samples to width 85
#3949
Show file tree
Hide file tree
 Changes from all commits
Commits

Show all changes


1 commit


Select commit


File filter

Filter by extension
Filter by extension
Conversations

Failed to load comments. 
Loading
Jump to

Jump to file


Failed to load files. 
Loading
 Diff view

 Diff view


84 changes: 61 additions & 23 deletions
docs/samples/Microsoft.ML.Samples/Dynamic/NgramExtraction.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -9,15 +9,21 @@ public static partial class TransformSamples
 {
 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 to IDataView.
 var data = new List<SampleSentimentData>() {
 new SampleSentimentData { Sentiment = true, SentimentText = "Best game I've ever played." },
 new SampleSentimentData { Sentiment = false, SentimentText = "==RUDE== Dude, 2" },
 new SampleSentimentData { Sentiment = true, SentimentText = "Until the next game, this is the best Xbox game!" } };
 new SampleSentimentData { Sentiment = true,
 SentimentText = "Best game I've ever played." },

 new SampleSentimentData { Sentiment = false,
 SentimentText = "==RUDE== Dude, 2" },

 new SampleSentimentData { Sentiment = true,
 SentimentText = "Until the next game," +
 "this is the best Xbox game!" } };

 // Convert IEnumerable to IDataView.
 var trainData = ml.Data.LoadFromEnumerable(data);
Expand All@@ -29,23 +35,42 @@ public static void Example()
 // false ==RUDE== Dude, 2.
 // true Until the next game, this is the best Xbox game!

 // A pipeline to tokenize text as characters and then combine them together into n-grams
 // The pipeline uses the default settings to featurize.
 // A pipeline to tokenize text as characters and then combine them
 // together into n-grams. The pipeline uses the default settings to
 // featurize.

 var charsPipeline = ml.Transforms.Text
 .TokenizeIntoCharactersAsKeys("Chars", "SentimentText",
 useMarkerCharacters: false);

 var ngramOnePipeline = ml.Transforms.Text
 .ProduceNgrams("CharsUnigrams", "Chars", ngramLength: 1);

 var charsPipeline = ml.Transforms.Text.TokenizeIntoCharactersAsKeys("Chars", "SentimentText", useMarkerCharacters: false);
 var ngramOnePipeline = ml.Transforms.Text.ProduceNgrams("CharsUnigrams", "Chars", ngramLength: 1);
 var ngramTwpPipeline = ml.Transforms.Text.ProduceNgrams("CharsTwograms", "Chars");
 var oneCharsPipeline = charsPipeline.Append(ngramOnePipeline);
 var twoCharsPipeline = charsPipeline.Append(ngramTwpPipeline);
 var ngramTwpPipeline = ml.Transforms.Text
 .ProduceNgrams("CharsTwograms", "Chars");

 var oneCharsPipeline = charsPipeline
 .Append(ngramOnePipeline);

 var twoCharsPipeline = charsPipeline
 .Append(ngramTwpPipeline);

 // The transformed data for pipelines.
 var transformedData_onechars = oneCharsPipeline.Fit(trainData).Transform(trainData);
 var transformedData_twochars = twoCharsPipeline.Fit(trainData).Transform(trainData);
 var transformedData_onechars = oneCharsPipeline.Fit(trainData)
 .Transform(trainData);

 var transformedData_twochars = twoCharsPipeline.Fit(trainData)
 .Transform(trainData);

 // Small helper to print the text inside the columns, in the console. 
 Action<string, IEnumerable<VBuffer<float>>, VBuffer<ReadOnlyMemory<char>>> printHelper = (columnName, column, names) =>
 Action<string, IEnumerable<VBuffer<float>>,
 VBuffer<ReadOnlyMemory<char>>>
 printHelper = (columnName, column, names) =>

 {
 Console.WriteLine($"{columnName} column obtained post-transformation.");
 Console.WriteLine(
 $"{columnName} column obtained post-transformation.");

 var slots = names.GetValues();
 foreach (var featureRow in column)
 {
Expand All@@ -54,21 +79,33 @@ public static void Example()
 Console.WriteLine("");
 }

 Console.WriteLine("===================================================");
 Console.WriteLine(
 "===================================================");
 };
 // Preview of the CharsUnigrams column obtained after processing the input.
 // Preview of the CharsUnigrams column obtained after processing the
 // input.
 VBuffer<ReadOnlyMemory<char>> slotNames = default;
 transformedData_onechars.Schema["CharsUnigrams"].GetSlotNames(ref slotNames);
 var charsOneGramColumn = transformedData_onechars.GetColumn<VBuffer<float>>(transformedData_onechars.Schema["CharsUnigrams"]);
 transformedData_onechars.Schema["CharsUnigrams"]
 .GetSlotNames(ref slotNames);

 var charsOneGramColumn = transformedData_onechars
 .GetColumn<VBuffer<float>>(transformedData_onechars
 .Schema["CharsUnigrams"]);

 printHelper("CharsUnigrams", charsOneGramColumn, slotNames);

 // CharsUnigrams column obtained post-transformation.
 // 'B' - 1 'e' - 6 's' - 1 't' - 1 '<?>' - 4 'g' - 1 'a' - 2 'm' - 1 'I' - 1 ''' - 1 'v' - 2 ...
 // 'e' - 1 '<?>' - 2 'd' - 1 '=' - 4 'R' - 1 'U' - 1 'D' - 2 'E' - 1 'u' - 1 ',' - 1 '2' - 1
 // 'B' - 0 'e' - 6 's' - 3 't' - 6 '<?>' - 9 'g' - 2 'a' - 2 'm' - 2 'I' - 0 ''' - 0 'v' - 0 ...
 // Preview of the CharsTwoGrams column obtained after processing the input.
 var charsTwoGramColumn = transformedData_twochars.GetColumn<VBuffer<float>>(transformedData_twochars.Schema["CharsTwograms"]);
 transformedData_twochars.Schema["CharsTwograms"].GetSlotNames(ref slotNames);
 var charsTwoGramColumn = transformedData_twochars
 .GetColumn<VBuffer<float>>(transformedData_twochars
 .Schema["CharsTwograms"]);

 transformedData_twochars.Schema["CharsTwograms"]
 .GetSlotNames(ref slotNames);

 printHelper("CharsTwograms", charsTwoGramColumn, slotNames);

 // CharsTwograms column obtained post-transformation.
Expand All@@ -78,7 +115,8 @@ public static void Example()
 }

 /// <summary>
 /// A dataset that contains a tweet and the sentiment assigned to that tweet: 0 - negative and 1 - positive sentiment.
 /// A dataset that contains a tweet and the sentiment assigned to that
 /// tweet: 0 - negative and 1 - positive sentiment.
 /// </summary>
 public class SampleSentimentData
 {
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 misc. samples to width 85#3949

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

 Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and
privacy statement. We’ll occasionally send you account related emails.

Already on GitHub?
Sign in
to your account

 Merged

sierralee51
merged 1 commit into

from

Jul 2, 2019
 Merged


Reformatting misc. samples to width 85
#3949
Show file tree
Hide file tree
 Changes from all commits
Commits

Show all changes


1 commit


Select commit


File filter

Filter by extension
Filter by extension
Conversations

Failed to load comments. 
Loading
Jump to

Jump to file


Failed to load files. 
Loading
 Diff view

 Diff view


84 changes: 61 additions & 23 deletions
docs/samples/Microsoft.ML.Samples/Dynamic/NgramExtraction.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -9,15 +9,21 @@ public static partial class TransformSamples
 {
 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 to IDataView.
 var data = new List<SampleSentimentData>() {
 new SampleSentimentData { Sentiment = true, SentimentText = "Best game I've ever played." },
 new SampleSentimentData { Sentiment = false, SentimentText = "==RUDE== Dude, 2" },
 new SampleSentimentData { Sentiment = true, SentimentText = "Until the next game, this is the best Xbox game!" } };
 new SampleSentimentData { Sentiment = true,
 SentimentText = "Best game I've ever played." },

 new SampleSentimentData { Sentiment = false,
 SentimentText = "==RUDE== Dude, 2" },

 new SampleSentimentData { Sentiment = true,
 SentimentText = "Until the next game," +
 "this is the best Xbox game!" } };

 // Convert IEnumerable to IDataView.
 var trainData = ml.Data.LoadFromEnumerable(data);
Expand All@@ -29,23 +35,42 @@ public static void Example()
 // false ==RUDE== Dude, 2.
 // true Until the next game, this is the best Xbox game!

 // A pipeline to tokenize text as characters and then combine them together into n-grams
 // The pipeline uses the default settings to featurize.
 // A pipeline to tokenize text as characters and then combine them
 // together into n-grams. The pipeline uses the default settings to
 // featurize.

 var charsPipeline = ml.Transforms.Text
 .TokenizeIntoCharactersAsKeys("Chars", "SentimentText",
 useMarkerCharacters: false);

 var ngramOnePipeline = ml.Transforms.Text
 .ProduceNgrams("CharsUnigrams", "Chars", ngramLength: 1);

 var charsPipeline = ml.Transforms.Text.TokenizeIntoCharactersAsKeys("Chars", "SentimentText", useMarkerCharacters: false);
 var ngramOnePipeline = ml.Transforms.Text.ProduceNgrams("CharsUnigrams", "Chars", ngramLength: 1);
 var ngramTwpPipeline = ml.Transforms.Text.ProduceNgrams("CharsTwograms", "Chars");
 var oneCharsPipeline = charsPipeline.Append(ngramOnePipeline);
 var twoCharsPipeline = charsPipeline.Append(ngramTwpPipeline);
 var ngramTwpPipeline = ml.Transforms.Text
 .ProduceNgrams("CharsTwograms", "Chars");

 var oneCharsPipeline = charsPipeline
 .Append(ngramOnePipeline);

 var twoCharsPipeline = charsPipeline
 .Append(ngramTwpPipeline);

 // The transformed data for pipelines.
 var transformedData_onechars = oneCharsPipeline.Fit(trainData).Transform(trainData);
 var transformedData_twochars = twoCharsPipeline.Fit(trainData).Transform(trainData);
 var transformedData_onechars = oneCharsPipeline.Fit(trainData)
 .Transform(trainData);

 var transformedData_twochars = twoCharsPipeline.Fit(trainData)
 .Transform(trainData);

 // Small helper to print the text inside the columns, in the console. 
 Action<string, IEnumerable<VBuffer<float>>, VBuffer<ReadOnlyMemory<char>>> printHelper = (columnName, column, names) =>
 Action<string, IEnumerable<VBuffer<float>>,
 VBuffer<ReadOnlyMemory<char>>>
 printHelper = (columnName, column, names) =>

 {
 Console.WriteLine($"{columnName} column obtained post-transformation.");
 Console.WriteLine(
 $"{columnName} column obtained post-transformation.");

 var slots = names.GetValues();
 foreach (var featureRow in column)
 {
Expand All@@ -54,21 +79,33 @@ public static void Example()
 Console.WriteLine("");
 }

 Console.WriteLine("===================================================");
 Console.WriteLine(
 "===================================================");
 };
 // Preview of the CharsUnigrams column obtained after processing the input.
 // Preview of the CharsUnigrams column obtained after processing the
 // input.
 VBuffer<ReadOnlyMemory<char>> slotNames = default;
 transformedData_onechars.Schema["CharsUnigrams"].GetSlotNames(ref slotNames);
 var charsOneGramColumn = transformedData_onechars.GetColumn<VBuffer<float>>(transformedData_onechars.Schema["CharsUnigrams"]);
 transformedData_onechars.Schema["CharsUnigrams"]
 .GetSlotNames(ref slotNames);

 var charsOneGramColumn = transformedData_onechars
 .GetColumn<VBuffer<float>>(transformedData_onechars
 .Schema["CharsUnigrams"]);

 printHelper("CharsUnigrams", charsOneGramColumn, slotNames);

 // CharsUnigrams column obtained post-transformation.
 // 'B' - 1 'e' - 6 's' - 1 't' - 1 '<?>' - 4 'g' - 1 'a' - 2 'm' - 1 'I' - 1 ''' - 1 'v' - 2 ...
 // 'e' - 1 '<?>' - 2 'd' - 1 '=' - 4 'R' - 1 'U' - 1 'D' - 2 'E' - 1 'u' - 1 ',' - 1 '2' - 1
 // 'B' - 0 'e' - 6 's' - 3 't' - 6 '<?>' - 9 'g' - 2 'a' - 2 'm' - 2 'I' - 0 ''' - 0 'v' - 0 ...
 // Preview of the CharsTwoGrams column obtained after processing the input.
 var charsTwoGramColumn = transformedData_twochars.GetColumn<VBuffer<float>>(transformedData_twochars.Schema["CharsTwograms"]);
 transformedData_twochars.Schema["CharsTwograms"].GetSlotNames(ref slotNames);
 var charsTwoGramColumn = transformedData_twochars
 .GetColumn<VBuffer<float>>(transformedData_twochars
 .Schema["CharsTwograms"]);

 transformedData_twochars.Schema["CharsTwograms"]
 .GetSlotNames(ref slotNames);

 printHelper("CharsTwograms", charsTwoGramColumn, slotNames);

 // CharsTwograms column obtained post-transformation.
Expand All@@ -78,7 +115,8 @@ public static void Example()
 }

 /// <summary>
 /// A dataset that contains a tweet and the sentiment assigned to that tweet: 0 - negative and 1 - positive sentiment.
 /// A dataset that contains a tweet and the sentiment assigned to that
 /// tweet: 0 - negative and 1 - positive sentiment.
 /// </summary>
 public class SampleSentimentData
 {
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); }
})();
})();

Skip to content
Reformatting misc. samples to width 85#3949

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

 Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and
privacy statement. We’ll occasionally send you account related emails.

Already on GitHub?
Sign in
to your account

 Merged

sierralee51
merged 1 commit into

from

Jul 2, 2019
 Merged


Reformatting misc. samples to width 85
#3949
Show file tree
Hide file tree
 Changes from all commits
Commits

Show all changes


1 commit


Select commit


File filter

Filter by extension
Filter by extension
Conversations

Failed to load comments. 
Loading
Jump to

Jump to file


Failed to load files. 
Loading
 Diff view

 Diff view


84 changes: 61 additions & 23 deletions
docs/samples/Microsoft.ML.Samples/Dynamic/NgramExtraction.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -9,15 +9,21 @@ public static partial class TransformSamples
 {
 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 to IDataView.
 var data = new List<SampleSentimentData>() {
 new SampleSentimentData { Sentiment = true, SentimentText = "Best game I've ever played." },
 new SampleSentimentData { Sentiment = false, SentimentText = "==RUDE== Dude, 2" },
 new SampleSentimentData { Sentiment = true, SentimentText = "Until the next game, this is the best Xbox game!" } };
 new SampleSentimentData { Sentiment = true,
 SentimentText = "Best game I've ever played." },

 new SampleSentimentData { Sentiment = false,
 SentimentText = "==RUDE== Dude, 2" },

 new SampleSentimentData { Sentiment = true,
 SentimentText = "Until the next game," +
 "this is the best Xbox game!" } };

 // Convert IEnumerable to IDataView.
 var trainData = ml.Data.LoadFromEnumerable(data);
Expand All@@ -29,23 +35,42 @@ public static void Example()
 // false ==RUDE== Dude, 2.
 // true Until the next game, this is the best Xbox game!

 // A pipeline to tokenize text as characters and then combine them together into n-grams
 // The pipeline uses the default settings to featurize.
 // A pipeline to tokenize text as characters and then combine them
 // together into n-grams. The pipeline uses the default settings to
 // featurize.

 var charsPipeline = ml.Transforms.Text
 .TokenizeIntoCharactersAsKeys("Chars", "SentimentText",
 useMarkerCharacters: false);

 var ngramOnePipeline = ml.Transforms.Text
 .ProduceNgrams("CharsUnigrams", "Chars", ngramLength: 1);

 var charsPipeline = ml.Transforms.Text.TokenizeIntoCharactersAsKeys("Chars", "SentimentText", useMarkerCharacters: false);
 var ngramOnePipeline = ml.Transforms.Text.ProduceNgrams("CharsUnigrams", "Chars", ngramLength: 1);
 var ngramTwpPipeline = ml.Transforms.Text.ProduceNgrams("CharsTwograms", "Chars");
 var oneCharsPipeline = charsPipeline.Append(ngramOnePipeline);
 var twoCharsPipeline = charsPipeline.Append(ngramTwpPipeline);
 var ngramTwpPipeline = ml.Transforms.Text
 .ProduceNgrams("CharsTwograms", "Chars");

 var oneCharsPipeline = charsPipeline
 .Append(ngramOnePipeline);

 var twoCharsPipeline = charsPipeline
 .Append(ngramTwpPipeline);

 // The transformed data for pipelines.
 var transformedData_onechars = oneCharsPipeline.Fit(trainData).Transform(trainData);
 var transformedData_twochars = twoCharsPipeline.Fit(trainData).Transform(trainData);
 var transformedData_onechars = oneCharsPipeline.Fit(trainData)
 .Transform(trainData);

 var transformedData_twochars = twoCharsPipeline.Fit(trainData)
 .Transform(trainData);

 // Small helper to print the text inside the columns, in the console. 
 Action<string, IEnumerable<VBuffer<float>>, VBuffer<ReadOnlyMemory<char>>> printHelper = (columnName, column, names) =>
 Action<string, IEnumerable<VBuffer<float>>,
 VBuffer<ReadOnlyMemory<char>>>
 printHelper = (columnName, column, names) =>

 {
 Console.WriteLine($"{columnName} column obtained post-transformation.");
 Console.WriteLine(
 $"{columnName} column obtained post-transformation.");

 var slots = names.GetValues();
 foreach (var featureRow in column)
 {
Expand All@@ -54,21 +79,33 @@ public static void Example()
 Console.WriteLine("");
 }

 Console.WriteLine("===================================================");
 Console.WriteLine(
 "===================================================");
 };
 // Preview of the CharsUnigrams column obtained after processing the input.
 // Preview of the CharsUnigrams column obtained after processing the
 // input.
 VBuffer<ReadOnlyMemory<char>> slotNames = default;
 transformedData_onechars.Schema["CharsUnigrams"].GetSlotNames(ref slotNames);
 var charsOneGramColumn = transformedData_onechars.GetColumn<VBuffer<float>>(transformedData_onechars.Schema["CharsUnigrams"]);
 transformedData_onechars.Schema["CharsUnigrams"]
 .GetSlotNames(ref slotNames);

 var charsOneGramColumn = transformedData_onechars
 .GetColumn<VBuffer<float>>(transformedData_onechars
 .Schema["CharsUnigrams"]);

 printHelper("CharsUnigrams", charsOneGramColumn, slotNames);

 // CharsUnigrams column obtained post-transformation.
 // 'B' - 1 'e' - 6 's' - 1 't' - 1 '<?>' - 4 'g' - 1 'a' - 2 'm' - 1 'I' - 1 ''' - 1 'v' - 2 ...
 // 'e' - 1 '<?>' - 2 'd' - 1 '=' - 4 'R' - 1 'U' - 1 'D' - 2 'E' - 1 'u' - 1 ',' - 1 '2' - 1
 // 'B' - 0 'e' - 6 's' - 3 't' - 6 '<?>' - 9 'g' - 2 'a' - 2 'm' - 2 'I' - 0 ''' - 0 'v' - 0 ...
 // Preview of the CharsTwoGrams column obtained after processing the input.
 var charsTwoGramColumn = transformedData_twochars.GetColumn<VBuffer<float>>(transformedData_twochars.Schema["CharsTwograms"]);
 transformedData_twochars.Schema["CharsTwograms"].GetSlotNames(ref slotNames);
 var charsTwoGramColumn = transformedData_twochars
 .GetColumn<VBuffer<float>>(transformedData_twochars
 .Schema["CharsTwograms"]);

 transformedData_twochars.Schema["CharsTwograms"]
 .GetSlotNames(ref slotNames);

 printHelper("CharsTwograms", charsTwoGramColumn, slotNames);

 // CharsTwograms column obtained post-transformation.
Expand All@@ -78,7 +115,8 @@ public static void Example()
 }

 /// <summary>
 /// A dataset that contains a tweet and the sentiment assigned to that tweet: 0 - negative and 1 - positive sentiment.
 /// A dataset that contains a tweet and the sentiment assigned to that
 /// tweet: 0 - negative and 1 - positive sentiment.
 /// </summary>
 public class SampleSentimentData
 {
Expand Down
Loading