Scores to Label mapping#239

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Merged
codemzs merged 7 commits into from
May 25, 2018
Merged

Scores to Label mapping #239

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094df6a
Scores to label mapping for multi-class classification problem.
codemzs May 24, 2018
3ea2793
Merge branch 'master' of https://github.com/dotnet/machinelearning in…
codemzs May 24, 2018
f067e0f
update test.
codemzs May 24, 2018
2e11917
Merge branch 'master' of https://github.com/dotnet/machinelearning in…
codemzs May 25, 2018
431117d
PR feedback.
codemzs May 25, 2018
ef46c6c
PR feedback.
codemzs May 25, 2018
6eae0b6
cleanup.
codemzs May 25, 2018
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15 changes: 11 additions & 4 deletions src/Microsoft.ML.Core/Data/ITransformModel.cs
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Original file line numberDiff line numberDiff line change
Expand Up@@ -18,13 +18,20 @@ public interface ITransformModel
/// Note that the schema may have columns that aren't needed by this transform model.
/// If an IDataView exists with this schema, then applying this transform model to it
/// shouldn't fail because of column type issues.
/// REVIEW: Would be nice to be able to trim this to the minimum needed somehow. Note
/// however that doing so may cause issues for composing transform models. For example,
/// if transform model A needs column X and model B needs Y, that is NOT produced by A,
/// then trimming A's input schema would cause composition to fail.
/// </summary>
// REVIEW: Would be nice to be able to trim this to the minimum needed somehow. Note
// however that doing so may cause issues for composing transform models. For example,

@TomFinleyTomFinleyMay 25, 2018•
edited
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Ahh good. I'm half tempted to compose a regular expression to see how many of these beauties had slipped into the XML docs. :P :D #ByDesign

// if transform model A needs column X and model B needs Y, that is NOT produced by A,
// then trimming A's input schema would cause composition to fail.
ISchema InputSchema { get; }

/// <summary>
/// The output schema that this transform model was originally instantiated on. The schema resulting
/// from <see cref="Apply(IHostEnvironment, ITransformModel)"/> may differ from this, similarly to how
/// <see cref="InputSchema"/> may differ from the schema of dataviews we apply this transform model to.
/// </summary>
ISchema OutputSchema { get; }

/// <summary>
/// Apply the transform(s) in the model to the given input data.
/// </summary>
Expand Down
12 changes: 8 additions & 4 deletions src/Microsoft.ML.Data/EntryPoints/TransformModel.cs
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -39,10 +39,14 @@ public sealed class TransformModel : ITransformModel
/// if transform model A needs column X and model B needs Y, that is NOT produced by A,
/// then trimming A's input schema would cause composition to fail.
/// </summary>
public ISchema InputSchema
{
get { return _schemaRoot; }
}
public ISchema InputSchema => _schemaRoot;

/// <summary>
/// The resulting schema once applied to this model. The <see cref="InputSchema"/> might have
/// columns that are not needed by this transform and these columns will be seen in the
/// <see cref="OutputSchema"/> produced by this transform.
/// </summary>
public ISchema OutputSchema => _chain.Schema;

/// <summary>
/// Create a TransformModel containing the transforms from "result" back to "input".
Expand Down
34 changes: 34 additions & 0 deletions src/Microsoft.ML/PredictionModel.cs
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -6,6 +6,7 @@
using Microsoft.ML.Runtime.Api;
using Microsoft.ML.Runtime.Data;
using Microsoft.ML.Runtime.EntryPoints;
using Microsoft.ML.Runtime.Internal.Utilities;
using System;
using System.Collections.Generic;
using System.IO;
Expand All@@ -29,6 +30,39 @@ internal Runtime.EntryPoints.TransformModel PredictorModel
get { return _predictorModel; }
}

/// <summary>
/// Returns labels that correspond to indices of the score array in the case of
/// multi-class classification problem.
/// </summary>
/// <param name="names">Label to score mapping</param>
/// <param name="scoreColumnName">Name of the score column</param>
/// <returns></returns>
public bool TryGetScoreLabelNames(out string[] names, string scoreColumnName = DefaultColumnNames.Score)
{
names = null;
ISchema schema = _predictorModel.OutputSchema;
int colIndex = -1;
if (!schema.TryGetColumnIndex(scoreColumnName, out colIndex))
return false;

int expectedLabelCount = schema.GetColumnType(colIndex).ValueCount;
if (!schema.HasSlotNames(colIndex, expectedLabelCount))
return false;

VBuffer<DvText> labels = default;
schema.GetMetadata(MetadataUtils.Kinds.SlotNames, colIndex, ref labels);

if (labels.Length != expectedLabelCount)
return false;

names = new string[expectedLabelCount];
int index = 0;
foreach(var label in labels.DenseValues())
names[index++] = label.ToString();

return true;
}

/// <summary>
/// Read model from file asynchronously.
/// </summary>
Expand Down
8 changes: 8 additions & 0 deletions test/Microsoft.ML.Tests/Scenarios/IrisPlantClassificationWithStringLabelTests.cs
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -30,6 +30,14 @@ public void TrainAndPredictIrisModelWithStringLabelTest()
pipeline.Add(new StochasticDualCoordinateAscentClassifier());

PredictionModel<IrisDataWithStringLabel, IrisPrediction> model = pipeline.Train<IrisDataWithStringLabel, IrisPrediction>();
string[] scoreLabels;
model.TryGetScoreLabelNames(out scoreLabels);

Assert.NotNull(scoreLabels);
Assert.Equal(3, scoreLabels.Length);
Assert.Equal("Iris-setosa", scoreLabels[0]);
Assert.Equal("Iris-versicolor", scoreLabels[1]);
Assert.Equal("Iris-virginica", scoreLabels[2]);

IrisPrediction prediction = model.Predict(new IrisDataWithStringLabel()
{
Expand Down
, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
 blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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Scores to Label mapping#239

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 Merged

codemzs
merged 7 commits into

from

May 25, 2018
 Merged


Scores to Label mapping
#239
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094df6a

Scores to label mapping for multi-class classification problem.


codemzs May 24, 2018
3ea2793

Merge branch 'master' of https://github.com/dotnet/machinelearning in…


codemzs May 24, 2018
f067e0f

update test.


codemzs May 24, 2018
2e11917

Merge branch 'master' of https://github.com/dotnet/machinelearning in…


codemzs May 25, 2018
431117d

PR feedback.


codemzs May 25, 2018
ef46c6c

PR feedback.


codemzs May 25, 2018
6eae0b6

cleanup.


codemzs May 25, 2018

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15 changes: 11 additions & 4 deletions
src/Microsoft.ML.Core/Data/ITransformModel.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -18,13 +18,20 @@ public interface ITransformModel
 /// Note that the schema may have columns that aren't needed by this transform model.
 /// If an IDataView exists with this schema, then applying this transform model to it
 /// shouldn't fail because of column type issues.
 /// REVIEW: Would be nice to be able to trim this to the minimum needed somehow. Note
 /// however that doing so may cause issues for composing transform models. For example,
 /// if transform model A needs column X and model B needs Y, that is NOT produced by A,
 /// then trimming A's input schema would cause composition to fail.
 /// </summary>
 // REVIEW: Would be nice to be able to trim this to the minimum needed somehow. Note
 // however that doing so may cause issues for composing transform models. For example,
@TomFinleyTomFinleyMay 25, 2018•

edited

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Ahh good. I'm half tempted to compose a regular expression to see how many of these beauties had slipped into the XML docs. :P :D #ByDesign
 // if transform model A needs column X and model B needs Y, that is NOT produced by A,
 // then trimming A's input schema would cause composition to fail.
 ISchema InputSchema { get; }

 /// <summary>
 /// The output schema that this transform model was originally instantiated on. The schema resulting
 /// from <see cref="Apply(IHostEnvironment, ITransformModel)"/> may differ from this, similarly to how
 /// <see cref="InputSchema"/> may differ from the schema of dataviews we apply this transform model to.
 /// </summary>
 ISchema OutputSchema { get; }

 /// <summary>
 /// Apply the transform(s) in the model to the given input data.
 /// </summary>
Expand Down

12 changes: 8 additions & 4 deletions
src/Microsoft.ML.Data/EntryPoints/TransformModel.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -39,10 +39,14 @@ public sealed class TransformModel : ITransformModel
 /// if transform model A needs column X and model B needs Y, that is NOT produced by A,
 /// then trimming A's input schema would cause composition to fail.
 /// </summary>
 public ISchema InputSchema
 {
 get { return _schemaRoot; }
 }
 public ISchema InputSchema => _schemaRoot;

 /// <summary>
 /// The resulting schema once applied to this model. The <see cref="InputSchema"/> might have
 /// columns that are not needed by this transform and these columns will be seen in the 
 /// <see cref="OutputSchema"/> produced by this transform.
 /// </summary>
 public ISchema OutputSchema => _chain.Schema;

 /// <summary>
 /// Create a TransformModel containing the transforms from "result" back to "input".
Expand Down

34 changes: 34 additions & 0 deletions
src/Microsoft.ML/PredictionModel.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -6,6 +6,7 @@
using Microsoft.ML.Runtime.Api;
using Microsoft.ML.Runtime.Data;
using Microsoft.ML.Runtime.EntryPoints;
using Microsoft.ML.Runtime.Internal.Utilities;
using System;
using System.Collections.Generic;
using System.IO;
Expand All@@ -29,6 +30,39 @@ internal Runtime.EntryPoints.TransformModel PredictorModel
 get { return _predictorModel; }
 }

 /// <summary>
 /// Returns labels that correspond to indices of the score array in the case of 
 /// multi-class classification problem.
 /// </summary>
 /// <param name="names">Label to score mapping</param>
 /// <param name="scoreColumnName">Name of the score column</param>
 /// <returns></returns>
 public bool TryGetScoreLabelNames(out string[] names, string scoreColumnName = DefaultColumnNames.Score)
 {
 names = null;
 ISchema schema = _predictorModel.OutputSchema;
 int colIndex = -1;
 if (!schema.TryGetColumnIndex(scoreColumnName, out colIndex))
 return false;

 int expectedLabelCount = schema.GetColumnType(colIndex).ValueCount;
 if (!schema.HasSlotNames(colIndex, expectedLabelCount))
 return false;

 VBuffer<DvText> labels = default;
 schema.GetMetadata(MetadataUtils.Kinds.SlotNames, colIndex, ref labels);

 if (labels.Length != expectedLabelCount)
 return false;

 names = new string[expectedLabelCount];
 int index = 0;
 foreach(var label in labels.DenseValues())
 names[index++] = label.ToString();

 return true;
 }

 /// <summary>
 /// Read model from file asynchronously.
 /// </summary>
Expand Down

8 changes: 8 additions & 0 deletions
test/Microsoft.ML.Tests/Scenarios/IrisPlantClassificationWithStringLabelTests.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -30,6 +30,14 @@ public void TrainAndPredictIrisModelWithStringLabelTest()
 pipeline.Add(new StochasticDualCoordinateAscentClassifier());

 PredictionModel<IrisDataWithStringLabel, IrisPrediction> model = pipeline.Train<IrisDataWithStringLabel, IrisPrediction>();
 string[] scoreLabels;
 model.TryGetScoreLabelNames(out scoreLabels);

 Assert.NotNull(scoreLabels);
 Assert.Equal(3, scoreLabels.Length);
 Assert.Equal("Iris-setosa", scoreLabels[0]);
 Assert.Equal("Iris-versicolor", scoreLabels[1]);
 Assert.Equal("Iris-virginica", scoreLabels[2]);

 IrisPrediction prediction = model.Predict(new IrisDataWithStringLabel()
 {
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('^' + ".*" + '

Skip to content
Scores to Label mapping#239

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 Merged

codemzs
merged 7 commits into

from

May 25, 2018
 Merged


Scores to Label mapping
#239
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094df6a

Scores to label mapping for multi-class classification problem.


codemzs May 24, 2018
3ea2793

Merge branch 'master' of https://github.com/dotnet/machinelearning in…


codemzs May 24, 2018
f067e0f

update test.


codemzs May 24, 2018
2e11917

Merge branch 'master' of https://github.com/dotnet/machinelearning in…


codemzs May 25, 2018
431117d

PR feedback.


codemzs May 25, 2018
ef46c6c

PR feedback.


codemzs May 25, 2018
6eae0b6

cleanup.


codemzs May 25, 2018

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15 changes: 11 additions & 4 deletions
src/Microsoft.ML.Core/Data/ITransformModel.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -18,13 +18,20 @@ public interface ITransformModel
 /// Note that the schema may have columns that aren't needed by this transform model.
 /// If an IDataView exists with this schema, then applying this transform model to it
 /// shouldn't fail because of column type issues.
 /// REVIEW: Would be nice to be able to trim this to the minimum needed somehow. Note
 /// however that doing so may cause issues for composing transform models. For example,
 /// if transform model A needs column X and model B needs Y, that is NOT produced by A,
 /// then trimming A's input schema would cause composition to fail.
 /// </summary>
 // REVIEW: Would be nice to be able to trim this to the minimum needed somehow. Note
 // however that doing so may cause issues for composing transform models. For example,
@TomFinleyTomFinleyMay 25, 2018•

edited

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Choose a reason for hiding this comment

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Ahh good. I'm half tempted to compose a regular expression to see how many of these beauties had slipped into the XML docs. :P :D #ByDesign
 // if transform model A needs column X and model B needs Y, that is NOT produced by A,
 // then trimming A's input schema would cause composition to fail.
 ISchema InputSchema { get; }

 /// <summary>
 /// The output schema that this transform model was originally instantiated on. The schema resulting
 /// from <see cref="Apply(IHostEnvironment, ITransformModel)"/> may differ from this, similarly to how
 /// <see cref="InputSchema"/> may differ from the schema of dataviews we apply this transform model to.
 /// </summary>
 ISchema OutputSchema { get; }

 /// <summary>
 /// Apply the transform(s) in the model to the given input data.
 /// </summary>
Expand Down

12 changes: 8 additions & 4 deletions
src/Microsoft.ML.Data/EntryPoints/TransformModel.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -39,10 +39,14 @@ public sealed class TransformModel : ITransformModel
 /// if transform model A needs column X and model B needs Y, that is NOT produced by A,
 /// then trimming A's input schema would cause composition to fail.
 /// </summary>
 public ISchema InputSchema
 {
 get { return _schemaRoot; }
 }
 public ISchema InputSchema => _schemaRoot;

 /// <summary>
 /// The resulting schema once applied to this model. The <see cref="InputSchema"/> might have
 /// columns that are not needed by this transform and these columns will be seen in the 
 /// <see cref="OutputSchema"/> produced by this transform.
 /// </summary>
 public ISchema OutputSchema => _chain.Schema;

 /// <summary>
 /// Create a TransformModel containing the transforms from "result" back to "input".
Expand Down

34 changes: 34 additions & 0 deletions
src/Microsoft.ML/PredictionModel.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -6,6 +6,7 @@
using Microsoft.ML.Runtime.Api;
using Microsoft.ML.Runtime.Data;
using Microsoft.ML.Runtime.EntryPoints;
using Microsoft.ML.Runtime.Internal.Utilities;
using System;
using System.Collections.Generic;
using System.IO;
Expand All@@ -29,6 +30,39 @@ internal Runtime.EntryPoints.TransformModel PredictorModel
 get { return _predictorModel; }
 }

 /// <summary>
 /// Returns labels that correspond to indices of the score array in the case of 
 /// multi-class classification problem.
 /// </summary>
 /// <param name="names">Label to score mapping</param>
 /// <param name="scoreColumnName">Name of the score column</param>
 /// <returns></returns>
 public bool TryGetScoreLabelNames(out string[] names, string scoreColumnName = DefaultColumnNames.Score)
 {
 names = null;
 ISchema schema = _predictorModel.OutputSchema;
 int colIndex = -1;
 if (!schema.TryGetColumnIndex(scoreColumnName, out colIndex))
 return false;

 int expectedLabelCount = schema.GetColumnType(colIndex).ValueCount;
 if (!schema.HasSlotNames(colIndex, expectedLabelCount))
 return false;

 VBuffer<DvText> labels = default;
 schema.GetMetadata(MetadataUtils.Kinds.SlotNames, colIndex, ref labels);

 if (labels.Length != expectedLabelCount)
 return false;

 names = new string[expectedLabelCount];
 int index = 0;
 foreach(var label in labels.DenseValues())
 names[index++] = label.ToString();

 return true;
 }

 /// <summary>
 /// Read model from file asynchronously.
 /// </summary>
Expand Down

8 changes: 8 additions & 0 deletions
test/Microsoft.ML.Tests/Scenarios/IrisPlantClassificationWithStringLabelTests.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -30,6 +30,14 @@ public void TrainAndPredictIrisModelWithStringLabelTest()
 pipeline.Add(new StochasticDualCoordinateAscentClassifier());

 PredictionModel<IrisDataWithStringLabel, IrisPrediction> model = pipeline.Train<IrisDataWithStringLabel, IrisPrediction>();
 string[] scoreLabels;
 model.TryGetScoreLabelNames(out scoreLabels);

 Assert.NotNull(scoreLabels);
 Assert.Equal(3, scoreLabels.Length);
 Assert.Equal("Iris-setosa", scoreLabels[0]);
 Assert.Equal("Iris-versicolor", scoreLabels[1]);
 Assert.Equal("Iris-virginica", scoreLabels[2]);

 IrisPrediction prediction = model.Predict(new IrisDataWithStringLabel()
 {
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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Scores to Label mapping#239

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 Merged

codemzs
merged 7 commits into

from

May 25, 2018
 Merged


Scores to Label mapping
#239
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094df6a

Scores to label mapping for multi-class classification problem.


codemzs May 24, 2018
3ea2793

Merge branch 'master' of https://github.com/dotnet/machinelearning in…


codemzs May 24, 2018
f067e0f

update test.


codemzs May 24, 2018
2e11917

Merge branch 'master' of https://github.com/dotnet/machinelearning in…


codemzs May 25, 2018
431117d

PR feedback.


codemzs May 25, 2018
ef46c6c

PR feedback.


codemzs May 25, 2018
6eae0b6

cleanup.


codemzs May 25, 2018

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15 changes: 11 additions & 4 deletions
src/Microsoft.ML.Core/Data/ITransformModel.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -18,13 +18,20 @@ public interface ITransformModel
 /// Note that the schema may have columns that aren't needed by this transform model.
 /// If an IDataView exists with this schema, then applying this transform model to it
 /// shouldn't fail because of column type issues.
 /// REVIEW: Would be nice to be able to trim this to the minimum needed somehow. Note
 /// however that doing so may cause issues for composing transform models. For example,
 /// if transform model A needs column X and model B needs Y, that is NOT produced by A,
 /// then trimming A's input schema would cause composition to fail.
 /// </summary>
 // REVIEW: Would be nice to be able to trim this to the minimum needed somehow. Note
 // however that doing so may cause issues for composing transform models. For example,
@TomFinleyTomFinleyMay 25, 2018•

edited

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Ahh good. I'm half tempted to compose a regular expression to see how many of these beauties had slipped into the XML docs. :P :D #ByDesign
 // if transform model A needs column X and model B needs Y, that is NOT produced by A,
 // then trimming A's input schema would cause composition to fail.
 ISchema InputSchema { get; }

 /// <summary>
 /// The output schema that this transform model was originally instantiated on. The schema resulting
 /// from <see cref="Apply(IHostEnvironment, ITransformModel)"/> may differ from this, similarly to how
 /// <see cref="InputSchema"/> may differ from the schema of dataviews we apply this transform model to.
 /// </summary>
 ISchema OutputSchema { get; }

 /// <summary>
 /// Apply the transform(s) in the model to the given input data.
 /// </summary>
Expand Down

12 changes: 8 additions & 4 deletions
src/Microsoft.ML.Data/EntryPoints/TransformModel.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -39,10 +39,14 @@ public sealed class TransformModel : ITransformModel
 /// if transform model A needs column X and model B needs Y, that is NOT produced by A,
 /// then trimming A's input schema would cause composition to fail.
 /// </summary>
 public ISchema InputSchema
 {
 get { return _schemaRoot; }
 }
 public ISchema InputSchema => _schemaRoot;

 /// <summary>
 /// The resulting schema once applied to this model. The <see cref="InputSchema"/> might have
 /// columns that are not needed by this transform and these columns will be seen in the 
 /// <see cref="OutputSchema"/> produced by this transform.
 /// </summary>
 public ISchema OutputSchema => _chain.Schema;

 /// <summary>
 /// Create a TransformModel containing the transforms from "result" back to "input".
Expand Down

34 changes: 34 additions & 0 deletions
src/Microsoft.ML/PredictionModel.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -6,6 +6,7 @@
using Microsoft.ML.Runtime.Api;
using Microsoft.ML.Runtime.Data;
using Microsoft.ML.Runtime.EntryPoints;
using Microsoft.ML.Runtime.Internal.Utilities;
using System;
using System.Collections.Generic;
using System.IO;
Expand All@@ -29,6 +30,39 @@ internal Runtime.EntryPoints.TransformModel PredictorModel
 get { return _predictorModel; }
 }

 /// <summary>
 /// Returns labels that correspond to indices of the score array in the case of 
 /// multi-class classification problem.
 /// </summary>
 /// <param name="names">Label to score mapping</param>
 /// <param name="scoreColumnName">Name of the score column</param>
 /// <returns></returns>
 public bool TryGetScoreLabelNames(out string[] names, string scoreColumnName = DefaultColumnNames.Score)
 {
 names = null;
 ISchema schema = _predictorModel.OutputSchema;
 int colIndex = -1;
 if (!schema.TryGetColumnIndex(scoreColumnName, out colIndex))
 return false;

 int expectedLabelCount = schema.GetColumnType(colIndex).ValueCount;
 if (!schema.HasSlotNames(colIndex, expectedLabelCount))
 return false;

 VBuffer<DvText> labels = default;
 schema.GetMetadata(MetadataUtils.Kinds.SlotNames, colIndex, ref labels);

 if (labels.Length != expectedLabelCount)
 return false;

 names = new string[expectedLabelCount];
 int index = 0;
 foreach(var label in labels.DenseValues())
 names[index++] = label.ToString();

 return true;
 }

 /// <summary>
 /// Read model from file asynchronously.
 /// </summary>
Expand Down

8 changes: 8 additions & 0 deletions
test/Microsoft.ML.Tests/Scenarios/IrisPlantClassificationWithStringLabelTests.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -30,6 +30,14 @@ public void TrainAndPredictIrisModelWithStringLabelTest()
 pipeline.Add(new StochasticDualCoordinateAscentClassifier());

 PredictionModel<IrisDataWithStringLabel, IrisPrediction> model = pipeline.Train<IrisDataWithStringLabel, IrisPrediction>();
 string[] scoreLabels;
 model.TryGetScoreLabelNames(out scoreLabels);

 Assert.NotNull(scoreLabels);
 Assert.Equal(3, scoreLabels.Length);
 Assert.Equal("Iris-setosa", scoreLabels[0]);
 Assert.Equal("Iris-versicolor", scoreLabels[1]);
 Assert.Equal("Iris-virginica", scoreLabels[2]);

 IrisPrediction prediction = model.Predict(new IrisDataWithStringLabel()
 {
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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Scores to Label mapping#239

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 Merged

codemzs
merged 7 commits into

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May 25, 2018
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Scores to Label mapping
#239
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094df6a

Scores to label mapping for multi-class classification problem.


codemzs May 24, 2018
3ea2793

Merge branch 'master' of https://github.com/dotnet/machinelearning in…


codemzs May 24, 2018
f067e0f

update test.


codemzs May 24, 2018
2e11917

Merge branch 'master' of https://github.com/dotnet/machinelearning in…


codemzs May 25, 2018
431117d

PR feedback.


codemzs May 25, 2018
ef46c6c

PR feedback.


codemzs May 25, 2018
6eae0b6

cleanup.


codemzs May 25, 2018

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15 changes: 11 additions & 4 deletions
src/Microsoft.ML.Core/Data/ITransformModel.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -18,13 +18,20 @@ public interface ITransformModel
 /// Note that the schema may have columns that aren't needed by this transform model.
 /// If an IDataView exists with this schema, then applying this transform model to it
 /// shouldn't fail because of column type issues.
 /// REVIEW: Would be nice to be able to trim this to the minimum needed somehow. Note
 /// however that doing so may cause issues for composing transform models. For example,
 /// if transform model A needs column X and model B needs Y, that is NOT produced by A,
 /// then trimming A's input schema would cause composition to fail.
 /// </summary>
 // REVIEW: Would be nice to be able to trim this to the minimum needed somehow. Note
 // however that doing so may cause issues for composing transform models. For example,
@TomFinleyTomFinleyMay 25, 2018•

edited

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Ahh good. I'm half tempted to compose a regular expression to see how many of these beauties had slipped into the XML docs. :P :D #ByDesign
 // if transform model A needs column X and model B needs Y, that is NOT produced by A,
 // then trimming A's input schema would cause composition to fail.
 ISchema InputSchema { get; }

 /// <summary>
 /// The output schema that this transform model was originally instantiated on. The schema resulting
 /// from <see cref="Apply(IHostEnvironment, ITransformModel)"/> may differ from this, similarly to how
 /// <see cref="InputSchema"/> may differ from the schema of dataviews we apply this transform model to.
 /// </summary>
 ISchema OutputSchema { get; }

 /// <summary>
 /// Apply the transform(s) in the model to the given input data.
 /// </summary>
Expand Down

12 changes: 8 additions & 4 deletions
src/Microsoft.ML.Data/EntryPoints/TransformModel.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -39,10 +39,14 @@ public sealed class TransformModel : ITransformModel
 /// if transform model A needs column X and model B needs Y, that is NOT produced by A,
 /// then trimming A's input schema would cause composition to fail.
 /// </summary>
 public ISchema InputSchema
 {
 get { return _schemaRoot; }
 }
 public ISchema InputSchema => _schemaRoot;

 /// <summary>
 /// The resulting schema once applied to this model. The <see cref="InputSchema"/> might have
 /// columns that are not needed by this transform and these columns will be seen in the 
 /// <see cref="OutputSchema"/> produced by this transform.
 /// </summary>
 public ISchema OutputSchema => _chain.Schema;

 /// <summary>
 /// Create a TransformModel containing the transforms from "result" back to "input".
Expand Down

34 changes: 34 additions & 0 deletions
src/Microsoft.ML/PredictionModel.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -6,6 +6,7 @@
using Microsoft.ML.Runtime.Api;
using Microsoft.ML.Runtime.Data;
using Microsoft.ML.Runtime.EntryPoints;
using Microsoft.ML.Runtime.Internal.Utilities;
using System;
using System.Collections.Generic;
using System.IO;
Expand All@@ -29,6 +30,39 @@ internal Runtime.EntryPoints.TransformModel PredictorModel
 get { return _predictorModel; }
 }

 /// <summary>
 /// Returns labels that correspond to indices of the score array in the case of 
 /// multi-class classification problem.
 /// </summary>
 /// <param name="names">Label to score mapping</param>
 /// <param name="scoreColumnName">Name of the score column</param>
 /// <returns></returns>
 public bool TryGetScoreLabelNames(out string[] names, string scoreColumnName = DefaultColumnNames.Score)
 {
 names = null;
 ISchema schema = _predictorModel.OutputSchema;
 int colIndex = -1;
 if (!schema.TryGetColumnIndex(scoreColumnName, out colIndex))
 return false;

 int expectedLabelCount = schema.GetColumnType(colIndex).ValueCount;
 if (!schema.HasSlotNames(colIndex, expectedLabelCount))
 return false;

 VBuffer<DvText> labels = default;
 schema.GetMetadata(MetadataUtils.Kinds.SlotNames, colIndex, ref labels);

 if (labels.Length != expectedLabelCount)
 return false;

 names = new string[expectedLabelCount];
 int index = 0;
 foreach(var label in labels.DenseValues())
 names[index++] = label.ToString();

 return true;
 }

 /// <summary>
 /// Read model from file asynchronously.
 /// </summary>
Expand Down

8 changes: 8 additions & 0 deletions
test/Microsoft.ML.Tests/Scenarios/IrisPlantClassificationWithStringLabelTests.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -30,6 +30,14 @@ public void TrainAndPredictIrisModelWithStringLabelTest()
 pipeline.Add(new StochasticDualCoordinateAscentClassifier());

 PredictionModel<IrisDataWithStringLabel, IrisPrediction> model = pipeline.Train<IrisDataWithStringLabel, IrisPrediction>();
 string[] scoreLabels;
 model.TryGetScoreLabelNames(out scoreLabels);

 Assert.NotNull(scoreLabels);
 Assert.Equal(3, scoreLabels.Length);
 Assert.Equal("Iris-setosa", scoreLabels[0]);
 Assert.Equal("Iris-versicolor", scoreLabels[1]);
 Assert.Equal("Iris-virginica", scoreLabels[2]);

 IrisPrediction prediction = model.Predict(new IrisDataWithStringLabel()
 {
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('^' + ".*" + '

Skip to content
Scores to Label mapping#239

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 Merged

codemzs
merged 7 commits into

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May 25, 2018
 Merged


Scores to Label mapping
#239
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094df6a

Scores to label mapping for multi-class classification problem.


codemzs May 24, 2018
3ea2793

Merge branch 'master' of https://github.com/dotnet/machinelearning in…


codemzs May 24, 2018
f067e0f

update test.


codemzs May 24, 2018
2e11917

Merge branch 'master' of https://github.com/dotnet/machinelearning in…


codemzs May 25, 2018
431117d

PR feedback.


codemzs May 25, 2018
ef46c6c

PR feedback.


codemzs May 25, 2018
6eae0b6

cleanup.


codemzs May 25, 2018

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15 changes: 11 additions & 4 deletions
src/Microsoft.ML.Core/Data/ITransformModel.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -18,13 +18,20 @@ public interface ITransformModel
 /// Note that the schema may have columns that aren't needed by this transform model.
 /// If an IDataView exists with this schema, then applying this transform model to it
 /// shouldn't fail because of column type issues.
 /// REVIEW: Would be nice to be able to trim this to the minimum needed somehow. Note
 /// however that doing so may cause issues for composing transform models. For example,
 /// if transform model A needs column X and model B needs Y, that is NOT produced by A,
 /// then trimming A's input schema would cause composition to fail.
 /// </summary>
 // REVIEW: Would be nice to be able to trim this to the minimum needed somehow. Note
 // however that doing so may cause issues for composing transform models. For example,
@TomFinleyTomFinleyMay 25, 2018•

edited

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Choose a reason for hiding this comment

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Ahh good. I'm half tempted to compose a regular expression to see how many of these beauties had slipped into the XML docs. :P :D #ByDesign
 // if transform model A needs column X and model B needs Y, that is NOT produced by A,
 // then trimming A's input schema would cause composition to fail.
 ISchema InputSchema { get; }

 /// <summary>
 /// The output schema that this transform model was originally instantiated on. The schema resulting
 /// from <see cref="Apply(IHostEnvironment, ITransformModel)"/> may differ from this, similarly to how
 /// <see cref="InputSchema"/> may differ from the schema of dataviews we apply this transform model to.
 /// </summary>
 ISchema OutputSchema { get; }

 /// <summary>
 /// Apply the transform(s) in the model to the given input data.
 /// </summary>
Expand Down

12 changes: 8 additions & 4 deletions
src/Microsoft.ML.Data/EntryPoints/TransformModel.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -39,10 +39,14 @@ public sealed class TransformModel : ITransformModel
 /// if transform model A needs column X and model B needs Y, that is NOT produced by A,
 /// then trimming A's input schema would cause composition to fail.
 /// </summary>
 public ISchema InputSchema
 {
 get { return _schemaRoot; }
 }
 public ISchema InputSchema => _schemaRoot;

 /// <summary>
 /// The resulting schema once applied to this model. The <see cref="InputSchema"/> might have
 /// columns that are not needed by this transform and these columns will be seen in the 
 /// <see cref="OutputSchema"/> produced by this transform.
 /// </summary>
 public ISchema OutputSchema => _chain.Schema;

 /// <summary>
 /// Create a TransformModel containing the transforms from "result" back to "input".
Expand Down

34 changes: 34 additions & 0 deletions
src/Microsoft.ML/PredictionModel.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -6,6 +6,7 @@
using Microsoft.ML.Runtime.Api;
using Microsoft.ML.Runtime.Data;
using Microsoft.ML.Runtime.EntryPoints;
using Microsoft.ML.Runtime.Internal.Utilities;
using System;
using System.Collections.Generic;
using System.IO;
Expand All@@ -29,6 +30,39 @@ internal Runtime.EntryPoints.TransformModel PredictorModel
 get { return _predictorModel; }
 }

 /// <summary>
 /// Returns labels that correspond to indices of the score array in the case of 
 /// multi-class classification problem.
 /// </summary>
 /// <param name="names">Label to score mapping</param>
 /// <param name="scoreColumnName">Name of the score column</param>
 /// <returns></returns>
 public bool TryGetScoreLabelNames(out string[] names, string scoreColumnName = DefaultColumnNames.Score)
 {
 names = null;
 ISchema schema = _predictorModel.OutputSchema;
 int colIndex = -1;
 if (!schema.TryGetColumnIndex(scoreColumnName, out colIndex))
 return false;

 int expectedLabelCount = schema.GetColumnType(colIndex).ValueCount;
 if (!schema.HasSlotNames(colIndex, expectedLabelCount))
 return false;

 VBuffer<DvText> labels = default;
 schema.GetMetadata(MetadataUtils.Kinds.SlotNames, colIndex, ref labels);

 if (labels.Length != expectedLabelCount)
 return false;

 names = new string[expectedLabelCount];
 int index = 0;
 foreach(var label in labels.DenseValues())
 names[index++] = label.ToString();

 return true;
 }

 /// <summary>
 /// Read model from file asynchronously.
 /// </summary>
Expand Down

8 changes: 8 additions & 0 deletions
test/Microsoft.ML.Tests/Scenarios/IrisPlantClassificationWithStringLabelTests.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -30,6 +30,14 @@ public void TrainAndPredictIrisModelWithStringLabelTest()
 pipeline.Add(new StochasticDualCoordinateAscentClassifier());

 PredictionModel<IrisDataWithStringLabel, IrisPrediction> model = pipeline.Train<IrisDataWithStringLabel, IrisPrediction>();
 string[] scoreLabels;
 model.TryGetScoreLabelNames(out scoreLabels);

 Assert.NotNull(scoreLabels);
 Assert.Equal(3, scoreLabels.Length);
 Assert.Equal("Iris-setosa", scoreLabels[0]);
 Assert.Equal("Iris-versicolor", scoreLabels[1]);
 Assert.Equal("Iris-virginica", scoreLabels[2]);

 IrisPrediction prediction = model.Predict(new IrisDataWithStringLabel()
 {
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('^' + ".*" + '

Skip to content
Scores to Label mapping#239

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to your account

 Merged

codemzs
merged 7 commits into

from

May 25, 2018
 Merged


Scores to Label mapping
#239
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094df6a

Scores to label mapping for multi-class classification problem.


codemzs May 24, 2018
3ea2793

Merge branch 'master' of https://github.com/dotnet/machinelearning in…


codemzs May 24, 2018
f067e0f

update test.


codemzs May 24, 2018
2e11917

Merge branch 'master' of https://github.com/dotnet/machinelearning in…


codemzs May 25, 2018
431117d

PR feedback.


codemzs May 25, 2018
ef46c6c

PR feedback.


codemzs May 25, 2018
6eae0b6

cleanup.


codemzs May 25, 2018

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15 changes: 11 additions & 4 deletions
src/Microsoft.ML.Core/Data/ITransformModel.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -18,13 +18,20 @@ public interface ITransformModel
 /// Note that the schema may have columns that aren't needed by this transform model.
 /// If an IDataView exists with this schema, then applying this transform model to it
 /// shouldn't fail because of column type issues.
 /// REVIEW: Would be nice to be able to trim this to the minimum needed somehow. Note
 /// however that doing so may cause issues for composing transform models. For example,
 /// if transform model A needs column X and model B needs Y, that is NOT produced by A,
 /// then trimming A's input schema would cause composition to fail.
 /// </summary>
 // REVIEW: Would be nice to be able to trim this to the minimum needed somehow. Note
 // however that doing so may cause issues for composing transform models. For example,
@TomFinleyTomFinleyMay 25, 2018•

edited

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Contributor
Choose a reason for hiding this comment

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

Ahh good. I'm half tempted to compose a regular expression to see how many of these beauties had slipped into the XML docs. :P :D #ByDesign
 // if transform model A needs column X and model B needs Y, that is NOT produced by A,
 // then trimming A's input schema would cause composition to fail.
 ISchema InputSchema { get; }

 /// <summary>
 /// The output schema that this transform model was originally instantiated on. The schema resulting
 /// from <see cref="Apply(IHostEnvironment, ITransformModel)"/> may differ from this, similarly to how
 /// <see cref="InputSchema"/> may differ from the schema of dataviews we apply this transform model to.
 /// </summary>
 ISchema OutputSchema { get; }

 /// <summary>
 /// Apply the transform(s) in the model to the given input data.
 /// </summary>
Expand Down

12 changes: 8 additions & 4 deletions
src/Microsoft.ML.Data/EntryPoints/TransformModel.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -39,10 +39,14 @@ public sealed class TransformModel : ITransformModel
 /// if transform model A needs column X and model B needs Y, that is NOT produced by A,
 /// then trimming A's input schema would cause composition to fail.
 /// </summary>
 public ISchema InputSchema
 {
 get { return _schemaRoot; }
 }
 public ISchema InputSchema => _schemaRoot;

 /// <summary>
 /// The resulting schema once applied to this model. The <see cref="InputSchema"/> might have
 /// columns that are not needed by this transform and these columns will be seen in the 
 /// <see cref="OutputSchema"/> produced by this transform.
 /// </summary>
 public ISchema OutputSchema => _chain.Schema;

 /// <summary>
 /// Create a TransformModel containing the transforms from "result" back to "input".
Expand Down

34 changes: 34 additions & 0 deletions
src/Microsoft.ML/PredictionModel.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -6,6 +6,7 @@
using Microsoft.ML.Runtime.Api;
using Microsoft.ML.Runtime.Data;
using Microsoft.ML.Runtime.EntryPoints;
using Microsoft.ML.Runtime.Internal.Utilities;
using System;
using System.Collections.Generic;
using System.IO;
Expand All@@ -29,6 +30,39 @@ internal Runtime.EntryPoints.TransformModel PredictorModel
 get { return _predictorModel; }
 }

 /// <summary>
 /// Returns labels that correspond to indices of the score array in the case of 
 /// multi-class classification problem.
 /// </summary>
 /// <param name="names">Label to score mapping</param>
 /// <param name="scoreColumnName">Name of the score column</param>
 /// <returns></returns>
 public bool TryGetScoreLabelNames(out string[] names, string scoreColumnName = DefaultColumnNames.Score)
 {
 names = null;
 ISchema schema = _predictorModel.OutputSchema;
 int colIndex = -1;
 if (!schema.TryGetColumnIndex(scoreColumnName, out colIndex))
 return false;

 int expectedLabelCount = schema.GetColumnType(colIndex).ValueCount;
 if (!schema.HasSlotNames(colIndex, expectedLabelCount))
 return false;

 VBuffer<DvText> labels = default;
 schema.GetMetadata(MetadataUtils.Kinds.SlotNames, colIndex, ref labels);

 if (labels.Length != expectedLabelCount)
 return false;

 names = new string[expectedLabelCount];
 int index = 0;
 foreach(var label in labels.DenseValues())
 names[index++] = label.ToString();

 return true;
 }

 /// <summary>
 /// Read model from file asynchronously.
 /// </summary>
Expand Down

8 changes: 8 additions & 0 deletions
test/Microsoft.ML.Tests/Scenarios/IrisPlantClassificationWithStringLabelTests.cs

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Original file line numberDiff line numberDiff line change
Expand Up@@ -30,6 +30,14 @@ public void TrainAndPredictIrisModelWithStringLabelTest()
 pipeline.Add(new StochasticDualCoordinateAscentClassifier());

 PredictionModel<IrisDataWithStringLabel, IrisPrediction> model = pipeline.Train<IrisDataWithStringLabel, IrisPrediction>();
 string[] scoreLabels;
 model.TryGetScoreLabelNames(out scoreLabels);

 Assert.NotNull(scoreLabels);
 Assert.Equal(3, scoreLabels.Length);
 Assert.Equal("Iris-setosa", scoreLabels[0]);
 Assert.Equal("Iris-versicolor", scoreLabels[1]);
 Assert.Equal("Iris-virginica", scoreLabels[2]);

 IrisPrediction prediction = model.Predict(new IrisDataWithStringLabel()
 {
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, '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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codemzs
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May 25, 2018
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Scores to Label mapping
#239
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094df6a

Scores to label mapping for multi-class classification problem.


codemzs May 24, 2018
3ea2793

Merge branch 'master' of https://github.com/dotnet/machinelearning in…


codemzs May 24, 2018
f067e0f

update test.


codemzs May 24, 2018
2e11917

Merge branch 'master' of https://github.com/dotnet/machinelearning in…


codemzs May 25, 2018
431117d

PR feedback.


codemzs May 25, 2018
ef46c6c

PR feedback.


codemzs May 25, 2018
6eae0b6

cleanup.


codemzs May 25, 2018

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15 changes: 11 additions & 4 deletions
src/Microsoft.ML.Core/Data/ITransformModel.cs

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Original file line numberDiff line numberDiff line change
Expand Up@@ -18,13 +18,20 @@ public interface ITransformModel
 /// Note that the schema may have columns that aren't needed by this transform model.
 /// If an IDataView exists with this schema, then applying this transform model to it
 /// shouldn't fail because of column type issues.
 /// REVIEW: Would be nice to be able to trim this to the minimum needed somehow. Note
 /// however that doing so may cause issues for composing transform models. For example,
 /// if transform model A needs column X and model B needs Y, that is NOT produced by A,
 /// then trimming A's input schema would cause composition to fail.
 /// </summary>
 // REVIEW: Would be nice to be able to trim this to the minimum needed somehow. Note
 // however that doing so may cause issues for composing transform models. For example,
@TomFinleyTomFinleyMay 25, 2018•

edited

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Ahh good. I'm half tempted to compose a regular expression to see how many of these beauties had slipped into the XML docs. :P :D #ByDesign
 // if transform model A needs column X and model B needs Y, that is NOT produced by A,
 // then trimming A's input schema would cause composition to fail.
 ISchema InputSchema { get; }

 /// <summary>
 /// The output schema that this transform model was originally instantiated on. The schema resulting
 /// from <see cref="Apply(IHostEnvironment, ITransformModel)"/> may differ from this, similarly to how
 /// <see cref="InputSchema"/> may differ from the schema of dataviews we apply this transform model to.
 /// </summary>
 ISchema OutputSchema { get; }

 /// <summary>
 /// Apply the transform(s) in the model to the given input data.
 /// </summary>
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12 changes: 8 additions & 4 deletions
src/Microsoft.ML.Data/EntryPoints/TransformModel.cs

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Original file line numberDiff line numberDiff line change
Expand Up@@ -39,10 +39,14 @@ public sealed class TransformModel : ITransformModel
 /// if transform model A needs column X and model B needs Y, that is NOT produced by A,
 /// then trimming A's input schema would cause composition to fail.
 /// </summary>
 public ISchema InputSchema
 {
 get { return _schemaRoot; }
 }
 public ISchema InputSchema => _schemaRoot;

 /// <summary>
 /// The resulting schema once applied to this model. The <see cref="InputSchema"/> might have
 /// columns that are not needed by this transform and these columns will be seen in the 
 /// <see cref="OutputSchema"/> produced by this transform.
 /// </summary>
 public ISchema OutputSchema => _chain.Schema;

 /// <summary>
 /// Create a TransformModel containing the transforms from "result" back to "input".
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34 changes: 34 additions & 0 deletions
src/Microsoft.ML/PredictionModel.cs

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Original file line numberDiff line numberDiff line change
Expand Up@@ -6,6 +6,7 @@
using Microsoft.ML.Runtime.Api;
using Microsoft.ML.Runtime.Data;
using Microsoft.ML.Runtime.EntryPoints;
using Microsoft.ML.Runtime.Internal.Utilities;
using System;
using System.Collections.Generic;
using System.IO;
Expand All@@ -29,6 +30,39 @@ internal Runtime.EntryPoints.TransformModel PredictorModel
 get { return _predictorModel; }
 }

 /// <summary>
 /// Returns labels that correspond to indices of the score array in the case of 
 /// multi-class classification problem.
 /// </summary>
 /// <param name="names">Label to score mapping</param>
 /// <param name="scoreColumnName">Name of the score column</param>
 /// <returns></returns>
 public bool TryGetScoreLabelNames(out string[] names, string scoreColumnName = DefaultColumnNames.Score)
 {
 names = null;
 ISchema schema = _predictorModel.OutputSchema;
 int colIndex = -1;
 if (!schema.TryGetColumnIndex(scoreColumnName, out colIndex))
 return false;

 int expectedLabelCount = schema.GetColumnType(colIndex).ValueCount;
 if (!schema.HasSlotNames(colIndex, expectedLabelCount))
 return false;

 VBuffer<DvText> labels = default;
 schema.GetMetadata(MetadataUtils.Kinds.SlotNames, colIndex, ref labels);

 if (labels.Length != expectedLabelCount)
 return false;

 names = new string[expectedLabelCount];
 int index = 0;
 foreach(var label in labels.DenseValues())
 names[index++] = label.ToString();

 return true;
 }

 /// <summary>
 /// Read model from file asynchronously.
 /// </summary>
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8 changes: 8 additions & 0 deletions
test/Microsoft.ML.Tests/Scenarios/IrisPlantClassificationWithStringLabelTests.cs

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Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -30,6 +30,14 @@ public void TrainAndPredictIrisModelWithStringLabelTest()
 pipeline.Add(new StochasticDualCoordinateAscentClassifier());

 PredictionModel<IrisDataWithStringLabel, IrisPrediction> model = pipeline.Train<IrisDataWithStringLabel, IrisPrediction>();
 string[] scoreLabels;
 model.TryGetScoreLabelNames(out scoreLabels);

 Assert.NotNull(scoreLabels);
 Assert.Equal(3, scoreLabels.Length);
 Assert.Equal("Iris-setosa", scoreLabels[0]);
 Assert.Equal("Iris-versicolor", scoreLabels[1]);
 Assert.Equal("Iris-virginica", scoreLabels[2]);

 IrisPrediction prediction = model.Predict(new IrisDataWithStringLabel()
 {
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