Comments added to LearningPipeline class to make Intellisense more helpful.#13

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Comments added to LearningPipeline class to make Intellisense more helpful. #13

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d80ade9
Merge pull request #1 from dotnet/master
zeahmed May 4, 2018
7856abd
Comments added to LearningPipeline class in accordance with #Bug 2406…
zeahmed May 4, 2018
d090170
Merge branch 'master' into comments_learningpipeline
zeahmed May 4, 2018
77d21ff
Comments added to LearningPipeline class in accordance with #Bug 2406…
zeahmed May 4, 2018
5471ed1
Fixed a typo in namespace
zeahmed May 4, 2018
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90 changes: 90 additions & 0 deletions src/Microsoft.ML/LearningPipeline.cs
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Original file line numberDiff line numberDiff line change
Expand Up@@ -26,25 +26,115 @@ public ScorerPipelineStep(Var<IDataView> data, Var<ITransformModel> model)
public Var<ITransformModel> Model { get; }
}


/// <summary>
/// LearningPipeline class is used to define the steps needed to perform desired machine learning task.<para/>
/// The steps are defined by adding a data loader (e.g. <see cref="TextLoader"/>) followed by zero or more transforms (e.g. <see cref="Microsoft.ML.Transforms.TextFeaturizer"/>)
/// and atmost one trainer/learner (e.g. <see cref="Microsoft.ML.Trainers.FastTreeBinaryClassifier"/>) in the pipeline.
///
/// Data can be analyzed at every step by inspecting the LearningPipeline object in VS.Net debugger.
/// <example>
/// <para/>
/// For example,<para/>
/// <code>
/// var pipeline = new LearningPipeline();
/// pipeline.Add(new TextLoader &lt;SentimentData&gt; (dataPath, separator: ","));
/// pipeline.Add(new TextFeaturizer("Features", "SentimentText"));
/// pipeline.Add(new FastTreeBinaryClassifier());
///
/// var model = pipeline.Train&lt;SentimentData, SentimentPrediction&gt;();
/// </code>
/// </example>
/// </summary>
[DebuggerTypeProxy(typeof(LearningPipelineDebugProxy))]
public class LearningPipeline : ICollection<ILearningPipelineItem>
{
private List<ILearningPipelineItem> Items { get; } = new List<ILearningPipelineItem>();

/// <summary>
/// Construct an empty LearningPipeline object.
/// </summary>
public LearningPipeline()
{
}

/// <summary>
/// Get the count of ML components in the LearningPipeline object
/// </summary>
public int Count => Items.Count;
public bool IsReadOnly => false;

/// <summary>
/// Add a data loader, transform or trainer into the pipeline.
/// Possible data loader(s), transforms and trainers options are
/// <para>
/// Data Loader:
/// <see cref="Microsoft.ML.TextLoader{TInput}" />
/// etc.
/// </para>
/// <para>
/// Transforms:
/// <see cref="Microsoft.ML.Transforms.Dictionarizer"/>,
/// <see cref="Microsoft.ML.Transforms.CategoricalOneHotVectorizer"/>
/// <see cref="Microsoft.ML.Transforms.MinMaxNormalizer"/>,
/// <see cref="Microsoft.ML.Transforms.ColumnCopier"/>,
/// <see cref="Microsoft.ML.Transforms.ColumnConcatenator"/>,
/// <see cref="Microsoft.ML.Transforms.TextFeaturizer"/>,
/// etc.
/// </para>
/// <para>
/// Trainers:
/// <see cref="Microsoft.ML.Trainers.AveragedPerceptronBinaryClassifier"/>,
/// <see cref="Microsoft.ML.Trainers.LogisticRegressor"/>,
/// <see cref="Microsoft.ML.Trainers.StochasticDualCoordinateAscentClassifier"/>,
/// <see cref="Microsoft.ML.Trainers.FastTreeRegressor"/>,
/// etc.
/// </para>
/// For a complete list of transforms and trainers, please see "Microsoft.ML.Transforms" and "Microsoft.ML.Trainers" namespaces.
/// </summary>
/// <param name="item"></param>
public void Add(ILearningPipelineItem item) => Items.Add(item);

/// <summary>
/// Remove all the transforms/trainers from the pipeline.
/// </summary>
public void Clear() => Items.Clear();

/// <summary>
/// Check if a specific loader/transform/trainer is in the pipeline?
/// </summary>
/// <param name="item">Any ML component (data loader, transform or trainer) defined as ILearningPipelineItem.</param>
/// <returns>true/false</returns>
public bool Contains(ILearningPipelineItem item) => Items.Contains(item);

/// <summary>
/// Copy the pipeline items into an array.
/// </summary>
/// <param name="array">Array the items are copied to.</param>
/// <param name="arrayIndex">Index to start copying from.</param>
public void CopyTo(ILearningPipelineItem[] array, int arrayIndex) => Items.CopyTo(array, arrayIndex);
public IEnumerator<ILearningPipelineItem> GetEnumerator() => Items.GetEnumerator();

/// <summary>
/// Remove an item from the pipeline.
/// </summary>
/// <param name="item">ILearningPipelineItem to remove.</param>
/// <returns>true/false</returns>
public bool Remove(ILearningPipelineItem item) => Items.Remove(item);
IEnumerator IEnumerable.GetEnumerator() => GetEnumerator();

/// <summary>
/// Train the model using the ML components in the pipeline.
/// </summary>
/// <typeparam name="TInput">Type of data instances the model will be trained on. It's a custom type defined by the user according to the structure of data.
/// <para/>
/// E.g. please see "Microsoft.ML.Scenarios.ScenarioTests.SentimentData" in "Microsoft.ML.Tests.csproj" for input type definition for sentiment classification task.
/// The type is defined for a .csv file that contains sentiment classification data with Sentiment and SentimentText as two columns in the .csv file.
/// </typeparam>
/// <typeparam name="TOutput">Ouput type. The prediction will be return based on this type.
/// E.g. for sentiment classifcation scenario, the prediction type is defined at "Microsoft.ML.Scenarios.ScenarioTests.SentimentPrediction" in "Microsoft.ML.Tests.csproj".
/// </typeparam>
/// <returns>PredictionModel object. This is the model object used for prediction on new instances. </returns>
public PredictionModel<TInput, TOutput> Train<TInput, TOutput>()
where TInput : class
where TOutput : class, new()
Expand Down
, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
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}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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Comments added to LearningPipeline class to make Intellisense more helpful.#13

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Comments added to LearningPipeline class to make Intellisense more helpful.
#13
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d80ade9

Merge pull request #1 from dotnet/master


zeahmed May 4, 2018
7856abd

Comments added to LearningPipeline class in accordance with #Bug 2406…


zeahmed May 4, 2018
d090170

Merge branch 'master' into comments_learningpipeline


zeahmed May 4, 2018
77d21ff

Comments added to LearningPipeline class in accordance with #Bug 2406…


zeahmed May 4, 2018
5471ed1

Fixed a typo in namespace


zeahmed May 4, 2018

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90 changes: 90 additions & 0 deletions
src/Microsoft.ML/LearningPipeline.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -26,25 +26,115 @@ public ScorerPipelineStep(Var<IDataView> data, Var<ITransformModel> model)
 public Var<ITransformModel> Model { get; }
 }


 /// <summary>
 /// LearningPipeline class is used to define the steps needed to perform desired machine learning task.<para/>
 /// The steps are defined by adding a data loader (e.g. <see cref="TextLoader"/>) followed by zero or more transforms (e.g. <see cref="Microsoft.ML.Transforms.TextFeaturizer"/>) 
 /// and atmost one trainer/learner (e.g. <see cref="Microsoft.ML.Trainers.FastTreeBinaryClassifier"/>) in the pipeline.
 /// 
 /// Data can be analyzed at every step by inspecting the LearningPipeline object in VS.Net debugger.
 /// <example>
 /// <para/>
 /// For example,<para/>
 /// <code>
 /// var pipeline = new LearningPipeline();
 /// pipeline.Add(new TextLoader &lt;SentimentData&gt; (dataPath, separator: ","));
 /// pipeline.Add(new TextFeaturizer("Features", "SentimentText"));
 /// pipeline.Add(new FastTreeBinaryClassifier());
 /// 
 /// var model = pipeline.Train&lt;SentimentData, SentimentPrediction&gt;();
 /// </code>
 /// </example>
 /// </summary>
 [DebuggerTypeProxy(typeof(LearningPipelineDebugProxy))]
 public class LearningPipeline : ICollection<ILearningPipelineItem>
 {
 private List<ILearningPipelineItem> Items { get; } = new List<ILearningPipelineItem>();

 /// <summary>
 /// Construct an empty LearningPipeline object.
 /// </summary>
 public LearningPipeline()
 {
 }

 /// <summary>
 /// Get the count of ML components in the LearningPipeline object
 /// </summary>
 public int Count => Items.Count;
 public bool IsReadOnly => false;

 /// <summary>
 /// Add a data loader, transform or trainer into the pipeline. 
 /// Possible data loader(s), transforms and trainers options are
 /// <para>
 /// Data Loader:
 /// <see cref="Microsoft.ML.TextLoader{TInput}" />
 /// etc.
 /// </para>
 /// <para>
 /// Transforms:
 /// <see cref="Microsoft.ML.Transforms.Dictionarizer"/>,
 /// <see cref="Microsoft.ML.Transforms.CategoricalOneHotVectorizer"/>
 /// <see cref="Microsoft.ML.Transforms.MinMaxNormalizer"/>,
 /// <see cref="Microsoft.ML.Transforms.ColumnCopier"/>,
 /// <see cref="Microsoft.ML.Transforms.ColumnConcatenator"/>,
 /// <see cref="Microsoft.ML.Transforms.TextFeaturizer"/>,
 /// etc.
 /// </para>
 /// <para>
 /// Trainers:
 /// <see cref="Microsoft.ML.Trainers.AveragedPerceptronBinaryClassifier"/>,
 /// <see cref="Microsoft.ML.Trainers.LogisticRegressor"/>,
 /// <see cref="Microsoft.ML.Trainers.StochasticDualCoordinateAscentClassifier"/>,
 /// <see cref="Microsoft.ML.Trainers.FastTreeRegressor"/>,
 /// etc.
 /// </para>
 /// For a complete list of transforms and trainers, please see "Microsoft.ML.Transforms" and "Microsoft.ML.Trainers" namespaces.
 /// </summary>
 /// <param name="item"></param>
 public void Add(ILearningPipelineItem item) => Items.Add(item);

 /// <summary>
 /// Remove all the transforms/trainers from the pipeline.
 /// </summary>
 public void Clear() => Items.Clear();

 /// <summary>
 /// Check if a specific loader/transform/trainer is in the pipeline?
 /// </summary>
 /// <param name="item">Any ML component (data loader, transform or trainer) defined as ILearningPipelineItem.</param>
 /// <returns>true/false</returns>
 public bool Contains(ILearningPipelineItem item) => Items.Contains(item);

 /// <summary>
 /// Copy the pipeline items into an array.
 /// </summary>
 /// <param name="array">Array the items are copied to.</param>
 /// <param name="arrayIndex">Index to start copying from.</param>
 public void CopyTo(ILearningPipelineItem[] array, int arrayIndex) => Items.CopyTo(array, arrayIndex);
 public IEnumerator<ILearningPipelineItem> GetEnumerator() => Items.GetEnumerator();

 /// <summary>
 /// Remove an item from the pipeline.
 /// </summary>
 /// <param name="item">ILearningPipelineItem to remove.</param>
 /// <returns>true/false</returns>
 public bool Remove(ILearningPipelineItem item) => Items.Remove(item);
 IEnumerator IEnumerable.GetEnumerator() => GetEnumerator();

 /// <summary>
 /// Train the model using the ML components in the pipeline.
 /// </summary>
 /// <typeparam name="TInput">Type of data instances the model will be trained on. It's a custom type defined by the user according to the structure of data.
 /// <para/>
 /// E.g. please see "Microsoft.ML.Scenarios.ScenarioTests.SentimentData" in "Microsoft.ML.Tests.csproj" for input type definition for sentiment classification task.
 /// The type is defined for a .csv file that contains sentiment classification data with Sentiment and SentimentText as two columns in the .csv file.
 /// </typeparam>
 /// <typeparam name="TOutput">Ouput type. The prediction will be return based on this type.
 /// E.g. for sentiment classifcation scenario, the prediction type is defined at "Microsoft.ML.Scenarios.ScenarioTests.SentimentPrediction" in "Microsoft.ML.Tests.csproj".
 /// </typeparam>
 /// <returns>PredictionModel object. This is the model object used for prediction on new instances. </returns>
 public PredictionModel<TInput, TOutput> Train<TInput, TOutput>()
 where TInput : class
 where TOutput : class, new()
Expand Down

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

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Comments added to LearningPipeline class to make Intellisense more helpful.#13

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Comments added to LearningPipeline class to make Intellisense more helpful.
#13
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d80ade9

Merge pull request #1 from dotnet/master


zeahmed May 4, 2018
7856abd

Comments added to LearningPipeline class in accordance with #Bug 2406…


zeahmed May 4, 2018
d090170

Merge branch 'master' into comments_learningpipeline


zeahmed May 4, 2018
77d21ff

Comments added to LearningPipeline class in accordance with #Bug 2406…


zeahmed May 4, 2018
5471ed1

Fixed a typo in namespace


zeahmed May 4, 2018

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90 changes: 90 additions & 0 deletions
src/Microsoft.ML/LearningPipeline.cs

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Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -26,25 +26,115 @@ public ScorerPipelineStep(Var<IDataView> data, Var<ITransformModel> model)
 public Var<ITransformModel> Model { get; }
 }


 /// <summary>
 /// LearningPipeline class is used to define the steps needed to perform desired machine learning task.<para/>
 /// The steps are defined by adding a data loader (e.g. <see cref="TextLoader"/>) followed by zero or more transforms (e.g. <see cref="Microsoft.ML.Transforms.TextFeaturizer"/>) 
 /// and atmost one trainer/learner (e.g. <see cref="Microsoft.ML.Trainers.FastTreeBinaryClassifier"/>) in the pipeline.
 /// 
 /// Data can be analyzed at every step by inspecting the LearningPipeline object in VS.Net debugger.
 /// <example>
 /// <para/>
 /// For example,<para/>
 /// <code>
 /// var pipeline = new LearningPipeline();
 /// pipeline.Add(new TextLoader &lt;SentimentData&gt; (dataPath, separator: ","));
 /// pipeline.Add(new TextFeaturizer("Features", "SentimentText"));
 /// pipeline.Add(new FastTreeBinaryClassifier());
 /// 
 /// var model = pipeline.Train&lt;SentimentData, SentimentPrediction&gt;();
 /// </code>
 /// </example>
 /// </summary>
 [DebuggerTypeProxy(typeof(LearningPipelineDebugProxy))]
 public class LearningPipeline : ICollection<ILearningPipelineItem>
 {
 private List<ILearningPipelineItem> Items { get; } = new List<ILearningPipelineItem>();

 /// <summary>
 /// Construct an empty LearningPipeline object.
 /// </summary>
 public LearningPipeline()
 {
 }

 /// <summary>
 /// Get the count of ML components in the LearningPipeline object
 /// </summary>
 public int Count => Items.Count;
 public bool IsReadOnly => false;

 /// <summary>
 /// Add a data loader, transform or trainer into the pipeline. 
 /// Possible data loader(s), transforms and trainers options are
 /// <para>
 /// Data Loader:
 /// <see cref="Microsoft.ML.TextLoader{TInput}" />
 /// etc.
 /// </para>
 /// <para>
 /// Transforms:
 /// <see cref="Microsoft.ML.Transforms.Dictionarizer"/>,
 /// <see cref="Microsoft.ML.Transforms.CategoricalOneHotVectorizer"/>
 /// <see cref="Microsoft.ML.Transforms.MinMaxNormalizer"/>,
 /// <see cref="Microsoft.ML.Transforms.ColumnCopier"/>,
 /// <see cref="Microsoft.ML.Transforms.ColumnConcatenator"/>,
 /// <see cref="Microsoft.ML.Transforms.TextFeaturizer"/>,
 /// etc.
 /// </para>
 /// <para>
 /// Trainers:
 /// <see cref="Microsoft.ML.Trainers.AveragedPerceptronBinaryClassifier"/>,
 /// <see cref="Microsoft.ML.Trainers.LogisticRegressor"/>,
 /// <see cref="Microsoft.ML.Trainers.StochasticDualCoordinateAscentClassifier"/>,
 /// <see cref="Microsoft.ML.Trainers.FastTreeRegressor"/>,
 /// etc.
 /// </para>
 /// For a complete list of transforms and trainers, please see "Microsoft.ML.Transforms" and "Microsoft.ML.Trainers" namespaces.
 /// </summary>
 /// <param name="item"></param>
 public void Add(ILearningPipelineItem item) => Items.Add(item);

 /// <summary>
 /// Remove all the transforms/trainers from the pipeline.
 /// </summary>
 public void Clear() => Items.Clear();

 /// <summary>
 /// Check if a specific loader/transform/trainer is in the pipeline?
 /// </summary>
 /// <param name="item">Any ML component (data loader, transform or trainer) defined as ILearningPipelineItem.</param>
 /// <returns>true/false</returns>
 public bool Contains(ILearningPipelineItem item) => Items.Contains(item);

 /// <summary>
 /// Copy the pipeline items into an array.
 /// </summary>
 /// <param name="array">Array the items are copied to.</param>
 /// <param name="arrayIndex">Index to start copying from.</param>
 public void CopyTo(ILearningPipelineItem[] array, int arrayIndex) => Items.CopyTo(array, arrayIndex);
 public IEnumerator<ILearningPipelineItem> GetEnumerator() => Items.GetEnumerator();

 /// <summary>
 /// Remove an item from the pipeline.
 /// </summary>
 /// <param name="item">ILearningPipelineItem to remove.</param>
 /// <returns>true/false</returns>
 public bool Remove(ILearningPipelineItem item) => Items.Remove(item);
 IEnumerator IEnumerable.GetEnumerator() => GetEnumerator();

 /// <summary>
 /// Train the model using the ML components in the pipeline.
 /// </summary>
 /// <typeparam name="TInput">Type of data instances the model will be trained on. It's a custom type defined by the user according to the structure of data.
 /// <para/>
 /// E.g. please see "Microsoft.ML.Scenarios.ScenarioTests.SentimentData" in "Microsoft.ML.Tests.csproj" for input type definition for sentiment classification task.
 /// The type is defined for a .csv file that contains sentiment classification data with Sentiment and SentimentText as two columns in the .csv file.
 /// </typeparam>
 /// <typeparam name="TOutput">Ouput type. The prediction will be return based on this type.
 /// E.g. for sentiment classifcation scenario, the prediction type is defined at "Microsoft.ML.Scenarios.ScenarioTests.SentimentPrediction" in "Microsoft.ML.Tests.csproj".
 /// </typeparam>
 /// <returns>PredictionModel object. This is the model object used for prediction on new instances. </returns>
 public PredictionModel<TInput, TOutput> Train<TInput, TOutput>()
 where TInput : class
 where TOutput : class, new()
Expand Down

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

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Comments added to LearningPipeline class to make Intellisense more helpful.#13

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Comments added to LearningPipeline class to make Intellisense more helpful.
#13
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d80ade9

Merge pull request #1 from dotnet/master


zeahmed May 4, 2018
7856abd

Comments added to LearningPipeline class in accordance with #Bug 2406…


zeahmed May 4, 2018
d090170

Merge branch 'master' into comments_learningpipeline


zeahmed May 4, 2018
77d21ff

Comments added to LearningPipeline class in accordance with #Bug 2406…


zeahmed May 4, 2018
5471ed1

Fixed a typo in namespace


zeahmed May 4, 2018

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90 changes: 90 additions & 0 deletions
src/Microsoft.ML/LearningPipeline.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -26,25 +26,115 @@ public ScorerPipelineStep(Var<IDataView> data, Var<ITransformModel> model)
 public Var<ITransformModel> Model { get; }
 }


 /// <summary>
 /// LearningPipeline class is used to define the steps needed to perform desired machine learning task.<para/>
 /// The steps are defined by adding a data loader (e.g. <see cref="TextLoader"/>) followed by zero or more transforms (e.g. <see cref="Microsoft.ML.Transforms.TextFeaturizer"/>) 
 /// and atmost one trainer/learner (e.g. <see cref="Microsoft.ML.Trainers.FastTreeBinaryClassifier"/>) in the pipeline.
 /// 
 /// Data can be analyzed at every step by inspecting the LearningPipeline object in VS.Net debugger.
 /// <example>
 /// <para/>
 /// For example,<para/>
 /// <code>
 /// var pipeline = new LearningPipeline();
 /// pipeline.Add(new TextLoader &lt;SentimentData&gt; (dataPath, separator: ","));
 /// pipeline.Add(new TextFeaturizer("Features", "SentimentText"));
 /// pipeline.Add(new FastTreeBinaryClassifier());
 /// 
 /// var model = pipeline.Train&lt;SentimentData, SentimentPrediction&gt;();
 /// </code>
 /// </example>
 /// </summary>
 [DebuggerTypeProxy(typeof(LearningPipelineDebugProxy))]
 public class LearningPipeline : ICollection<ILearningPipelineItem>
 {
 private List<ILearningPipelineItem> Items { get; } = new List<ILearningPipelineItem>();

 /// <summary>
 /// Construct an empty LearningPipeline object.
 /// </summary>
 public LearningPipeline()
 {
 }

 /// <summary>
 /// Get the count of ML components in the LearningPipeline object
 /// </summary>
 public int Count => Items.Count;
 public bool IsReadOnly => false;

 /// <summary>
 /// Add a data loader, transform or trainer into the pipeline. 
 /// Possible data loader(s), transforms and trainers options are
 /// <para>
 /// Data Loader:
 /// <see cref="Microsoft.ML.TextLoader{TInput}" />
 /// etc.
 /// </para>
 /// <para>
 /// Transforms:
 /// <see cref="Microsoft.ML.Transforms.Dictionarizer"/>,
 /// <see cref="Microsoft.ML.Transforms.CategoricalOneHotVectorizer"/>
 /// <see cref="Microsoft.ML.Transforms.MinMaxNormalizer"/>,
 /// <see cref="Microsoft.ML.Transforms.ColumnCopier"/>,
 /// <see cref="Microsoft.ML.Transforms.ColumnConcatenator"/>,
 /// <see cref="Microsoft.ML.Transforms.TextFeaturizer"/>,
 /// etc.
 /// </para>
 /// <para>
 /// Trainers:
 /// <see cref="Microsoft.ML.Trainers.AveragedPerceptronBinaryClassifier"/>,
 /// <see cref="Microsoft.ML.Trainers.LogisticRegressor"/>,
 /// <see cref="Microsoft.ML.Trainers.StochasticDualCoordinateAscentClassifier"/>,
 /// <see cref="Microsoft.ML.Trainers.FastTreeRegressor"/>,
 /// etc.
 /// </para>
 /// For a complete list of transforms and trainers, please see "Microsoft.ML.Transforms" and "Microsoft.ML.Trainers" namespaces.
 /// </summary>
 /// <param name="item"></param>
 public void Add(ILearningPipelineItem item) => Items.Add(item);

 /// <summary>
 /// Remove all the transforms/trainers from the pipeline.
 /// </summary>
 public void Clear() => Items.Clear();

 /// <summary>
 /// Check if a specific loader/transform/trainer is in the pipeline?
 /// </summary>
 /// <param name="item">Any ML component (data loader, transform or trainer) defined as ILearningPipelineItem.</param>
 /// <returns>true/false</returns>
 public bool Contains(ILearningPipelineItem item) => Items.Contains(item);

 /// <summary>
 /// Copy the pipeline items into an array.
 /// </summary>
 /// <param name="array">Array the items are copied to.</param>
 /// <param name="arrayIndex">Index to start copying from.</param>
 public void CopyTo(ILearningPipelineItem[] array, int arrayIndex) => Items.CopyTo(array, arrayIndex);
 public IEnumerator<ILearningPipelineItem> GetEnumerator() => Items.GetEnumerator();

 /// <summary>
 /// Remove an item from the pipeline.
 /// </summary>
 /// <param name="item">ILearningPipelineItem to remove.</param>
 /// <returns>true/false</returns>
 public bool Remove(ILearningPipelineItem item) => Items.Remove(item);
 IEnumerator IEnumerable.GetEnumerator() => GetEnumerator();

 /// <summary>
 /// Train the model using the ML components in the pipeline.
 /// </summary>
 /// <typeparam name="TInput">Type of data instances the model will be trained on. It's a custom type defined by the user according to the structure of data.
 /// <para/>
 /// E.g. please see "Microsoft.ML.Scenarios.ScenarioTests.SentimentData" in "Microsoft.ML.Tests.csproj" for input type definition for sentiment classification task.
 /// The type is defined for a .csv file that contains sentiment classification data with Sentiment and SentimentText as two columns in the .csv file.
 /// </typeparam>
 /// <typeparam name="TOutput">Ouput type. The prediction will be return based on this type.
 /// E.g. for sentiment classifcation scenario, the prediction type is defined at "Microsoft.ML.Scenarios.ScenarioTests.SentimentPrediction" in "Microsoft.ML.Tests.csproj".
 /// </typeparam>
 /// <returns>PredictionModel object. This is the model object used for prediction on new instances. </returns>
 public PredictionModel<TInput, TOutput> Train<TInput, TOutput>()
 where TInput : class
 where TOutput : class, new()
Expand Down

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

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Comments added to LearningPipeline class to make Intellisense more helpful.
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d80ade9

Merge pull request #1 from dotnet/master


zeahmed May 4, 2018
7856abd

Comments added to LearningPipeline class in accordance with #Bug 2406…


zeahmed May 4, 2018
d090170

Merge branch 'master' into comments_learningpipeline


zeahmed May 4, 2018
77d21ff

Comments added to LearningPipeline class in accordance with #Bug 2406…


zeahmed May 4, 2018
5471ed1

Fixed a typo in namespace


zeahmed May 4, 2018

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90 changes: 90 additions & 0 deletions
src/Microsoft.ML/LearningPipeline.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -26,25 +26,115 @@ public ScorerPipelineStep(Var<IDataView> data, Var<ITransformModel> model)
 public Var<ITransformModel> Model { get; }
 }


 /// <summary>
 /// LearningPipeline class is used to define the steps needed to perform desired machine learning task.<para/>
 /// The steps are defined by adding a data loader (e.g. <see cref="TextLoader"/>) followed by zero or more transforms (e.g. <see cref="Microsoft.ML.Transforms.TextFeaturizer"/>) 
 /// and atmost one trainer/learner (e.g. <see cref="Microsoft.ML.Trainers.FastTreeBinaryClassifier"/>) in the pipeline.
 /// 
 /// Data can be analyzed at every step by inspecting the LearningPipeline object in VS.Net debugger.
 /// <example>
 /// <para/>
 /// For example,<para/>
 /// <code>
 /// var pipeline = new LearningPipeline();
 /// pipeline.Add(new TextLoader &lt;SentimentData&gt; (dataPath, separator: ","));
 /// pipeline.Add(new TextFeaturizer("Features", "SentimentText"));
 /// pipeline.Add(new FastTreeBinaryClassifier());
 /// 
 /// var model = pipeline.Train&lt;SentimentData, SentimentPrediction&gt;();
 /// </code>
 /// </example>
 /// </summary>
 [DebuggerTypeProxy(typeof(LearningPipelineDebugProxy))]
 public class LearningPipeline : ICollection<ILearningPipelineItem>
 {
 private List<ILearningPipelineItem> Items { get; } = new List<ILearningPipelineItem>();

 /// <summary>
 /// Construct an empty LearningPipeline object.
 /// </summary>
 public LearningPipeline()
 {
 }

 /// <summary>
 /// Get the count of ML components in the LearningPipeline object
 /// </summary>
 public int Count => Items.Count;
 public bool IsReadOnly => false;

 /// <summary>
 /// Add a data loader, transform or trainer into the pipeline. 
 /// Possible data loader(s), transforms and trainers options are
 /// <para>
 /// Data Loader:
 /// <see cref="Microsoft.ML.TextLoader{TInput}" />
 /// etc.
 /// </para>
 /// <para>
 /// Transforms:
 /// <see cref="Microsoft.ML.Transforms.Dictionarizer"/>,
 /// <see cref="Microsoft.ML.Transforms.CategoricalOneHotVectorizer"/>
 /// <see cref="Microsoft.ML.Transforms.MinMaxNormalizer"/>,
 /// <see cref="Microsoft.ML.Transforms.ColumnCopier"/>,
 /// <see cref="Microsoft.ML.Transforms.ColumnConcatenator"/>,
 /// <see cref="Microsoft.ML.Transforms.TextFeaturizer"/>,
 /// etc.
 /// </para>
 /// <para>
 /// Trainers:
 /// <see cref="Microsoft.ML.Trainers.AveragedPerceptronBinaryClassifier"/>,
 /// <see cref="Microsoft.ML.Trainers.LogisticRegressor"/>,
 /// <see cref="Microsoft.ML.Trainers.StochasticDualCoordinateAscentClassifier"/>,
 /// <see cref="Microsoft.ML.Trainers.FastTreeRegressor"/>,
 /// etc.
 /// </para>
 /// For a complete list of transforms and trainers, please see "Microsoft.ML.Transforms" and "Microsoft.ML.Trainers" namespaces.
 /// </summary>
 /// <param name="item"></param>
 public void Add(ILearningPipelineItem item) => Items.Add(item);

 /// <summary>
 /// Remove all the transforms/trainers from the pipeline.
 /// </summary>
 public void Clear() => Items.Clear();

 /// <summary>
 /// Check if a specific loader/transform/trainer is in the pipeline?
 /// </summary>
 /// <param name="item">Any ML component (data loader, transform or trainer) defined as ILearningPipelineItem.</param>
 /// <returns>true/false</returns>
 public bool Contains(ILearningPipelineItem item) => Items.Contains(item);

 /// <summary>
 /// Copy the pipeline items into an array.
 /// </summary>
 /// <param name="array">Array the items are copied to.</param>
 /// <param name="arrayIndex">Index to start copying from.</param>
 public void CopyTo(ILearningPipelineItem[] array, int arrayIndex) => Items.CopyTo(array, arrayIndex);
 public IEnumerator<ILearningPipelineItem> GetEnumerator() => Items.GetEnumerator();

 /// <summary>
 /// Remove an item from the pipeline.
 /// </summary>
 /// <param name="item">ILearningPipelineItem to remove.</param>
 /// <returns>true/false</returns>
 public bool Remove(ILearningPipelineItem item) => Items.Remove(item);
 IEnumerator IEnumerable.GetEnumerator() => GetEnumerator();

 /// <summary>
 /// Train the model using the ML components in the pipeline.
 /// </summary>
 /// <typeparam name="TInput">Type of data instances the model will be trained on. It's a custom type defined by the user according to the structure of data.
 /// <para/>
 /// E.g. please see "Microsoft.ML.Scenarios.ScenarioTests.SentimentData" in "Microsoft.ML.Tests.csproj" for input type definition for sentiment classification task.
 /// The type is defined for a .csv file that contains sentiment classification data with Sentiment and SentimentText as two columns in the .csv file.
 /// </typeparam>
 /// <typeparam name="TOutput">Ouput type. The prediction will be return based on this type.
 /// E.g. for sentiment classifcation scenario, the prediction type is defined at "Microsoft.ML.Scenarios.ScenarioTests.SentimentPrediction" in "Microsoft.ML.Tests.csproj".
 /// </typeparam>
 /// <returns>PredictionModel object. This is the model object used for prediction on new instances. </returns>
 public PredictionModel<TInput, TOutput> Train<TInput, TOutput>()
 where TInput : class
 where TOutput : class, new()
Expand Down

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

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Comments added to LearningPipeline class to make Intellisense more helpful.#13

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Comments added to LearningPipeline class to make Intellisense more helpful.
#13
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d80ade9

Merge pull request #1 from dotnet/master


zeahmed May 4, 2018
7856abd

Comments added to LearningPipeline class in accordance with #Bug 2406…


zeahmed May 4, 2018
d090170

Merge branch 'master' into comments_learningpipeline


zeahmed May 4, 2018
77d21ff

Comments added to LearningPipeline class in accordance with #Bug 2406…


zeahmed May 4, 2018
5471ed1

Fixed a typo in namespace


zeahmed May 4, 2018

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90 changes: 90 additions & 0 deletions
src/Microsoft.ML/LearningPipeline.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -26,25 +26,115 @@ public ScorerPipelineStep(Var<IDataView> data, Var<ITransformModel> model)
 public Var<ITransformModel> Model { get; }
 }


 /// <summary>
 /// LearningPipeline class is used to define the steps needed to perform desired machine learning task.<para/>
 /// The steps are defined by adding a data loader (e.g. <see cref="TextLoader"/>) followed by zero or more transforms (e.g. <see cref="Microsoft.ML.Transforms.TextFeaturizer"/>) 
 /// and atmost one trainer/learner (e.g. <see cref="Microsoft.ML.Trainers.FastTreeBinaryClassifier"/>) in the pipeline.
 /// 
 /// Data can be analyzed at every step by inspecting the LearningPipeline object in VS.Net debugger.
 /// <example>
 /// <para/>
 /// For example,<para/>
 /// <code>
 /// var pipeline = new LearningPipeline();
 /// pipeline.Add(new TextLoader &lt;SentimentData&gt; (dataPath, separator: ","));
 /// pipeline.Add(new TextFeaturizer("Features", "SentimentText"));
 /// pipeline.Add(new FastTreeBinaryClassifier());
 /// 
 /// var model = pipeline.Train&lt;SentimentData, SentimentPrediction&gt;();
 /// </code>
 /// </example>
 /// </summary>
 [DebuggerTypeProxy(typeof(LearningPipelineDebugProxy))]
 public class LearningPipeline : ICollection<ILearningPipelineItem>
 {
 private List<ILearningPipelineItem> Items { get; } = new List<ILearningPipelineItem>();

 /// <summary>
 /// Construct an empty LearningPipeline object.
 /// </summary>
 public LearningPipeline()
 {
 }

 /// <summary>
 /// Get the count of ML components in the LearningPipeline object
 /// </summary>
 public int Count => Items.Count;
 public bool IsReadOnly => false;

 /// <summary>
 /// Add a data loader, transform or trainer into the pipeline. 
 /// Possible data loader(s), transforms and trainers options are
 /// <para>
 /// Data Loader:
 /// <see cref="Microsoft.ML.TextLoader{TInput}" />
 /// etc.
 /// </para>
 /// <para>
 /// Transforms:
 /// <see cref="Microsoft.ML.Transforms.Dictionarizer"/>,
 /// <see cref="Microsoft.ML.Transforms.CategoricalOneHotVectorizer"/>
 /// <see cref="Microsoft.ML.Transforms.MinMaxNormalizer"/>,
 /// <see cref="Microsoft.ML.Transforms.ColumnCopier"/>,
 /// <see cref="Microsoft.ML.Transforms.ColumnConcatenator"/>,
 /// <see cref="Microsoft.ML.Transforms.TextFeaturizer"/>,
 /// etc.
 /// </para>
 /// <para>
 /// Trainers:
 /// <see cref="Microsoft.ML.Trainers.AveragedPerceptronBinaryClassifier"/>,
 /// <see cref="Microsoft.ML.Trainers.LogisticRegressor"/>,
 /// <see cref="Microsoft.ML.Trainers.StochasticDualCoordinateAscentClassifier"/>,
 /// <see cref="Microsoft.ML.Trainers.FastTreeRegressor"/>,
 /// etc.
 /// </para>
 /// For a complete list of transforms and trainers, please see "Microsoft.ML.Transforms" and "Microsoft.ML.Trainers" namespaces.
 /// </summary>
 /// <param name="item"></param>
 public void Add(ILearningPipelineItem item) => Items.Add(item);

 /// <summary>
 /// Remove all the transforms/trainers from the pipeline.
 /// </summary>
 public void Clear() => Items.Clear();

 /// <summary>
 /// Check if a specific loader/transform/trainer is in the pipeline?
 /// </summary>
 /// <param name="item">Any ML component (data loader, transform or trainer) defined as ILearningPipelineItem.</param>
 /// <returns>true/false</returns>
 public bool Contains(ILearningPipelineItem item) => Items.Contains(item);

 /// <summary>
 /// Copy the pipeline items into an array.
 /// </summary>
 /// <param name="array">Array the items are copied to.</param>
 /// <param name="arrayIndex">Index to start copying from.</param>
 public void CopyTo(ILearningPipelineItem[] array, int arrayIndex) => Items.CopyTo(array, arrayIndex);
 public IEnumerator<ILearningPipelineItem> GetEnumerator() => Items.GetEnumerator();

 /// <summary>
 /// Remove an item from the pipeline.
 /// </summary>
 /// <param name="item">ILearningPipelineItem to remove.</param>
 /// <returns>true/false</returns>
 public bool Remove(ILearningPipelineItem item) => Items.Remove(item);
 IEnumerator IEnumerable.GetEnumerator() => GetEnumerator();

 /// <summary>
 /// Train the model using the ML components in the pipeline.
 /// </summary>
 /// <typeparam name="TInput">Type of data instances the model will be trained on. It's a custom type defined by the user according to the structure of data.
 /// <para/>
 /// E.g. please see "Microsoft.ML.Scenarios.ScenarioTests.SentimentData" in "Microsoft.ML.Tests.csproj" for input type definition for sentiment classification task.
 /// The type is defined for a .csv file that contains sentiment classification data with Sentiment and SentimentText as two columns in the .csv file.
 /// </typeparam>
 /// <typeparam name="TOutput">Ouput type. The prediction will be return based on this type.
 /// E.g. for sentiment classifcation scenario, the prediction type is defined at "Microsoft.ML.Scenarios.ScenarioTests.SentimentPrediction" in "Microsoft.ML.Tests.csproj".
 /// </typeparam>
 /// <returns>PredictionModel object. This is the model object used for prediction on new instances. </returns>
 public PredictionModel<TInput, TOutput> Train<TInput, TOutput>()
 where TInput : class
 where TOutput : class, new()
Expand Down

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

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Comments added to LearningPipeline class to make Intellisense more helpful.
#13
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d80ade9

Merge pull request #1 from dotnet/master


zeahmed May 4, 2018
7856abd

Comments added to LearningPipeline class in accordance with #Bug 2406…


zeahmed May 4, 2018
d090170

Merge branch 'master' into comments_learningpipeline


zeahmed May 4, 2018
77d21ff

Comments added to LearningPipeline class in accordance with #Bug 2406…


zeahmed May 4, 2018
5471ed1

Fixed a typo in namespace


zeahmed May 4, 2018

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90 changes: 90 additions & 0 deletions
src/Microsoft.ML/LearningPipeline.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -26,25 +26,115 @@ public ScorerPipelineStep(Var<IDataView> data, Var<ITransformModel> model)
 public Var<ITransformModel> Model { get; }
 }


 /// <summary>
 /// LearningPipeline class is used to define the steps needed to perform desired machine learning task.<para/>
 /// The steps are defined by adding a data loader (e.g. <see cref="TextLoader"/>) followed by zero or more transforms (e.g. <see cref="Microsoft.ML.Transforms.TextFeaturizer"/>) 
 /// and atmost one trainer/learner (e.g. <see cref="Microsoft.ML.Trainers.FastTreeBinaryClassifier"/>) in the pipeline.
 /// 
 /// Data can be analyzed at every step by inspecting the LearningPipeline object in VS.Net debugger.
 /// <example>
 /// <para/>
 /// For example,<para/>
 /// <code>
 /// var pipeline = new LearningPipeline();
 /// pipeline.Add(new TextLoader &lt;SentimentData&gt; (dataPath, separator: ","));
 /// pipeline.Add(new TextFeaturizer("Features", "SentimentText"));
 /// pipeline.Add(new FastTreeBinaryClassifier());
 /// 
 /// var model = pipeline.Train&lt;SentimentData, SentimentPrediction&gt;();
 /// </code>
 /// </example>
 /// </summary>
 [DebuggerTypeProxy(typeof(LearningPipelineDebugProxy))]
 public class LearningPipeline : ICollection<ILearningPipelineItem>
 {
 private List<ILearningPipelineItem> Items { get; } = new List<ILearningPipelineItem>();

 /// <summary>
 /// Construct an empty LearningPipeline object.
 /// </summary>
 public LearningPipeline()
 {
 }

 /// <summary>
 /// Get the count of ML components in the LearningPipeline object
 /// </summary>
 public int Count => Items.Count;
 public bool IsReadOnly => false;

 /// <summary>
 /// Add a data loader, transform or trainer into the pipeline. 
 /// Possible data loader(s), transforms and trainers options are
 /// <para>
 /// Data Loader:
 /// <see cref="Microsoft.ML.TextLoader{TInput}" />
 /// etc.
 /// </para>
 /// <para>
 /// Transforms:
 /// <see cref="Microsoft.ML.Transforms.Dictionarizer"/>,
 /// <see cref="Microsoft.ML.Transforms.CategoricalOneHotVectorizer"/>
 /// <see cref="Microsoft.ML.Transforms.MinMaxNormalizer"/>,
 /// <see cref="Microsoft.ML.Transforms.ColumnCopier"/>,
 /// <see cref="Microsoft.ML.Transforms.ColumnConcatenator"/>,
 /// <see cref="Microsoft.ML.Transforms.TextFeaturizer"/>,
 /// etc.
 /// </para>
 /// <para>
 /// Trainers:
 /// <see cref="Microsoft.ML.Trainers.AveragedPerceptronBinaryClassifier"/>,
 /// <see cref="Microsoft.ML.Trainers.LogisticRegressor"/>,
 /// <see cref="Microsoft.ML.Trainers.StochasticDualCoordinateAscentClassifier"/>,
 /// <see cref="Microsoft.ML.Trainers.FastTreeRegressor"/>,
 /// etc.
 /// </para>
 /// For a complete list of transforms and trainers, please see "Microsoft.ML.Transforms" and "Microsoft.ML.Trainers" namespaces.
 /// </summary>
 /// <param name="item"></param>
 public void Add(ILearningPipelineItem item) => Items.Add(item);

 /// <summary>
 /// Remove all the transforms/trainers from the pipeline.
 /// </summary>
 public void Clear() => Items.Clear();

 /// <summary>
 /// Check if a specific loader/transform/trainer is in the pipeline?
 /// </summary>
 /// <param name="item">Any ML component (data loader, transform or trainer) defined as ILearningPipelineItem.</param>
 /// <returns>true/false</returns>
 public bool Contains(ILearningPipelineItem item) => Items.Contains(item);

 /// <summary>
 /// Copy the pipeline items into an array.
 /// </summary>
 /// <param name="array">Array the items are copied to.</param>
 /// <param name="arrayIndex">Index to start copying from.</param>
 public void CopyTo(ILearningPipelineItem[] array, int arrayIndex) => Items.CopyTo(array, arrayIndex);
 public IEnumerator<ILearningPipelineItem> GetEnumerator() => Items.GetEnumerator();

 /// <summary>
 /// Remove an item from the pipeline.
 /// </summary>
 /// <param name="item">ILearningPipelineItem to remove.</param>
 /// <returns>true/false</returns>
 public bool Remove(ILearningPipelineItem item) => Items.Remove(item);
 IEnumerator IEnumerable.GetEnumerator() => GetEnumerator();

 /// <summary>
 /// Train the model using the ML components in the pipeline.
 /// </summary>
 /// <typeparam name="TInput">Type of data instances the model will be trained on. It's a custom type defined by the user according to the structure of data.
 /// <para/>
 /// E.g. please see "Microsoft.ML.Scenarios.ScenarioTests.SentimentData" in "Microsoft.ML.Tests.csproj" for input type definition for sentiment classification task.
 /// The type is defined for a .csv file that contains sentiment classification data with Sentiment and SentimentText as two columns in the .csv file.
 /// </typeparam>
 /// <typeparam name="TOutput">Ouput type. The prediction will be return based on this type.
 /// E.g. for sentiment classifcation scenario, the prediction type is defined at "Microsoft.ML.Scenarios.ScenarioTests.SentimentPrediction" in "Microsoft.ML.Tests.csproj".
 /// </typeparam>
 /// <returns>PredictionModel object. This is the model object used for prediction on new instances. </returns>
 public PredictionModel<TInput, TOutput> Train<TInput, TOutput>()
 where TInput : class
 where TOutput : class, new()
Expand Down

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

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Comments added to LearningPipeline class to make Intellisense more helpful.
#13
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d80ade9

Merge pull request #1 from dotnet/master


zeahmed May 4, 2018
7856abd

Comments added to LearningPipeline class in accordance with #Bug 2406…


zeahmed May 4, 2018
d090170

Merge branch 'master' into comments_learningpipeline


zeahmed May 4, 2018
77d21ff

Comments added to LearningPipeline class in accordance with #Bug 2406…


zeahmed May 4, 2018
5471ed1

Fixed a typo in namespace


zeahmed May 4, 2018

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src/Microsoft.ML/LearningPipeline.cs

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Original file line numberDiff line numberDiff line change
Expand Up@@ -26,25 +26,115 @@ public ScorerPipelineStep(Var<IDataView> data, Var<ITransformModel> model)
 public Var<ITransformModel> Model { get; }
 }


 /// <summary>
 /// LearningPipeline class is used to define the steps needed to perform desired machine learning task.<para/>
 /// The steps are defined by adding a data loader (e.g. <see cref="TextLoader"/>) followed by zero or more transforms (e.g. <see cref="Microsoft.ML.Transforms.TextFeaturizer"/>) 
 /// and atmost one trainer/learner (e.g. <see cref="Microsoft.ML.Trainers.FastTreeBinaryClassifier"/>) in the pipeline.
 /// 
 /// Data can be analyzed at every step by inspecting the LearningPipeline object in VS.Net debugger.
 /// <example>
 /// <para/>
 /// For example,<para/>
 /// <code>
 /// var pipeline = new LearningPipeline();
 /// pipeline.Add(new TextLoader &lt;SentimentData&gt; (dataPath, separator: ","));
 /// pipeline.Add(new TextFeaturizer("Features", "SentimentText"));
 /// pipeline.Add(new FastTreeBinaryClassifier());
 /// 
 /// var model = pipeline.Train&lt;SentimentData, SentimentPrediction&gt;();
 /// </code>
 /// </example>
 /// </summary>
 [DebuggerTypeProxy(typeof(LearningPipelineDebugProxy))]
 public class LearningPipeline : ICollection<ILearningPipelineItem>
 {
 private List<ILearningPipelineItem> Items { get; } = new List<ILearningPipelineItem>();

 /// <summary>
 /// Construct an empty LearningPipeline object.
 /// </summary>
 public LearningPipeline()
 {
 }

 /// <summary>
 /// Get the count of ML components in the LearningPipeline object
 /// </summary>
 public int Count => Items.Count;
 public bool IsReadOnly => false;

 /// <summary>
 /// Add a data loader, transform or trainer into the pipeline. 
 /// Possible data loader(s), transforms and trainers options are
 /// <para>
 /// Data Loader:
 /// <see cref="Microsoft.ML.TextLoader{TInput}" />
 /// etc.
 /// </para>
 /// <para>
 /// Transforms:
 /// <see cref="Microsoft.ML.Transforms.Dictionarizer"/>,
 /// <see cref="Microsoft.ML.Transforms.CategoricalOneHotVectorizer"/>
 /// <see cref="Microsoft.ML.Transforms.MinMaxNormalizer"/>,
 /// <see cref="Microsoft.ML.Transforms.ColumnCopier"/>,
 /// <see cref="Microsoft.ML.Transforms.ColumnConcatenator"/>,
 /// <see cref="Microsoft.ML.Transforms.TextFeaturizer"/>,
 /// etc.
 /// </para>
 /// <para>
 /// Trainers:
 /// <see cref="Microsoft.ML.Trainers.AveragedPerceptronBinaryClassifier"/>,
 /// <see cref="Microsoft.ML.Trainers.LogisticRegressor"/>,
 /// <see cref="Microsoft.ML.Trainers.StochasticDualCoordinateAscentClassifier"/>,
 /// <see cref="Microsoft.ML.Trainers.FastTreeRegressor"/>,
 /// etc.
 /// </para>
 /// For a complete list of transforms and trainers, please see "Microsoft.ML.Transforms" and "Microsoft.ML.Trainers" namespaces.
 /// </summary>
 /// <param name="item"></param>
 public void Add(ILearningPipelineItem item) => Items.Add(item);

 /// <summary>
 /// Remove all the transforms/trainers from the pipeline.
 /// </summary>
 public void Clear() => Items.Clear();

 /// <summary>
 /// Check if a specific loader/transform/trainer is in the pipeline?
 /// </summary>
 /// <param name="item">Any ML component (data loader, transform or trainer) defined as ILearningPipelineItem.</param>
 /// <returns>true/false</returns>
 public bool Contains(ILearningPipelineItem item) => Items.Contains(item);

 /// <summary>
 /// Copy the pipeline items into an array.
 /// </summary>
 /// <param name="array">Array the items are copied to.</param>
 /// <param name="arrayIndex">Index to start copying from.</param>
 public void CopyTo(ILearningPipelineItem[] array, int arrayIndex) => Items.CopyTo(array, arrayIndex);
 public IEnumerator<ILearningPipelineItem> GetEnumerator() => Items.GetEnumerator();

 /// <summary>
 /// Remove an item from the pipeline.
 /// </summary>
 /// <param name="item">ILearningPipelineItem to remove.</param>
 /// <returns>true/false</returns>
 public bool Remove(ILearningPipelineItem item) => Items.Remove(item);
 IEnumerator IEnumerable.GetEnumerator() => GetEnumerator();

 /// <summary>
 /// Train the model using the ML components in the pipeline.
 /// </summary>
 /// <typeparam name="TInput">Type of data instances the model will be trained on. It's a custom type defined by the user according to the structure of data.
 /// <para/>
 /// E.g. please see "Microsoft.ML.Scenarios.ScenarioTests.SentimentData" in "Microsoft.ML.Tests.csproj" for input type definition for sentiment classification task.
 /// The type is defined for a .csv file that contains sentiment classification data with Sentiment and SentimentText as two columns in the .csv file.
 /// </typeparam>
 /// <typeparam name="TOutput">Ouput type. The prediction will be return based on this type.
 /// E.g. for sentiment classifcation scenario, the prediction type is defined at "Microsoft.ML.Scenarios.ScenarioTests.SentimentPrediction" in "Microsoft.ML.Tests.csproj".
 /// </typeparam>
 /// <returns>PredictionModel object. This is the model object used for prediction on new instances. </returns>
 public PredictionModel<TInput, TOutput> Train<TInput, TOutput>()
 where TInput : class
 where TOutput : class, new()
Expand Down