Random seed and concurrency for tests#277

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Merged
Ivanidzo4ka merged 4 commits into from
May 31, 2018
Merged

Random seed and concurrency for tests #277

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May 31, 2018
a31d55a
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May 31, 2018
78f935f
Merge branch 'master' into ivanidze/seed_and_concurrency
May 31, 2018
982c576
specify seed for random.
May 31, 2018
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1 change: 0 additions & 1 deletion src/Microsoft.ML.Sweeper/Algorithms/Grid.cs
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Original file line numberDiff line numberDiff line change
Expand Up@@ -2,7 +2,6 @@
// The .NET Foundation licenses this file to you under the MIT license.
// See the LICENSE file in the project root for more information.


using System.Collections.Generic;
using System.Linq;
using Microsoft.ML.Runtime;
Expand Down
18 changes: 16 additions & 2 deletions src/Microsoft.ML/LearningPipeline.cs
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Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -49,12 +49,27 @@ public ScorerPipelineStep(Var<IDataView> data, Var<ITransformModel> model)
public class LearningPipeline : ICollection<ILearningPipelineItem>
{
private List<ILearningPipelineItem> Items { get; } = new List<ILearningPipelineItem>();
private readonly int? _seed;
private readonly int _conc;

/// <summary>
/// Construct an empty <see cref="LearningPipeline"/> object.
/// </summary>
public LearningPipeline()
{
_seed = null;
_conc = 0;
}

/// <summary>
/// Construct an empty <see cref="LearningPipeline"/> object.
/// </summary>
/// <param name="seed">Specify seed for random generator</param>
/// <param name="conc">Specify concurrency factor (default value - autoselection)</param>
internal LearningPipeline(int? seed=null, int conc=0)
{
_seed = seed;
_conc = conc;
}

/// <summary>
Expand DownExpand Up@@ -137,8 +152,7 @@ public PredictionModel<TInput, TOutput> Train<TInput, TOutput>()
where TInput : class
where TOutput : class, new()
{

using (var environment = new TlcEnvironment())
using (var environment = new TlcEnvironment(seed:_seed, conc:_conc))
{
Experiment experiment = environment.CreateExperiment();
ILearningPipelineStep step = null;
Expand Down
9 changes: 9 additions & 0 deletions src/Microsoft.ML/Properties/AssemblyInfo.cs
View file
Open in desktop
Original file line numberDiff line numberDiff line change
@@ -0,0 +1,9 @@
// Licensed to the .NET Foundation under one or more agreements.
// The .NET Foundation licenses this file to you under the MIT license.
// See the LICENSE file in the project root for more information.

using System.Reflection;
using System.Runtime.CompilerServices;
using System.Runtime.InteropServices;

[assembly: InternalsVisibleTo("Microsoft.ML.Tests, PublicKey=002400000480000094000000060200000024000052534131000400000100010015c01ae1f50e8cc09ba9eac9147cf8fd9fce2cfe9f8dce4f7301c4132ca9fb50ce8cbf1df4dc18dd4d210e4345c744ecb3365ed327efdbc52603faa5e21daa11234c8c4a73e51f03bf192544581ebe107adee3a34928e39d04e524a9ce729d5090bfd7dad9d10c722c0def9ccc08ff0a03790e48bcd1f9b6c476063e1966a1c4")]
41 changes: 24 additions & 17 deletions test/Microsoft.ML.Tests/LearningPipelineTests.cs
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Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -2,7 +2,6 @@
// The .NET Foundation licenses this file to you under the MIT license.
// See the LICENSE file in the project root for more information.

using Microsoft.ML;
using Microsoft.ML.Data;
using Microsoft.ML.Runtime.Api;
using Microsoft.ML.Runtime.Data;
Expand DownExpand Up@@ -34,9 +33,9 @@ public void CanAddAndRemoveFromPipeline()
{
var pipeline = new LearningPipeline()
{
new Transforms.CategoricalOneHotVectorizer("String1", "String2"),
new Transforms.ColumnConcatenator(outputColumn: "Features", "String1", "String2", "Number1", "Number2"),
new Trainers.StochasticDualCoordinateAscentRegressor()
new CategoricalOneHotVectorizer("String1", "String2"),
new ColumnConcatenator(outputColumn: "Features", "String1", "String2", "Number1", "Number2"),
new StochasticDualCoordinateAscentRegressor()
};
Assert.NotNull(pipeline);
Assert.Equal(3, pipeline.Count);
Expand DownExpand Up@@ -66,7 +65,7 @@ private class TransformedData
public void TransformOnlyPipeline()
{
const string _dataPath = @"..\..\Data\breast-cancer.txt";
var pipeline = new LearningPipeline();
var pipeline = new LearningPipeline(seed: 1, conc: 1);
pipeline.Add(new ML.Data.TextLoader(_dataPath).CreateFrom<InputData>(useHeader: false));
pipeline.Add(new CategoricalHashOneHotVectorizer("F1") { HashBits = 10, Seed = 314489979, OutputKind = CategoricalTransformOutputKind.Bag });
var model = pipeline.Train<InputData, TransformedData>();
Expand DownExpand Up@@ -103,9 +102,11 @@ public class Prediction
public void NoTransformPipeline()
{
var data = new Data[1];
data[0] = new Data();
data[0].Features = new float[] { 0.0f, 1.0f };
data[0].Label = 0f;
data[0] = new Data
{
Features = new float[] { 0.0f, 1.0f },
Label = 0f
};
var pipeline = new LearningPipeline();
pipeline.Add(CollectionDataSource.Create(data));
pipeline.Add(new FastForestBinaryClassifier());
Expand All@@ -126,9 +127,11 @@ public class BooleanLabelData
public void BooleanLabelPipeline()
{
var data = new BooleanLabelData[1];
data[0] = new BooleanLabelData();
data[0].Features = new float[] { 0.0f, 1.0f };
data[0].Label = false;
data[0] = new BooleanLabelData
{
Features = new float[] { 0.0f, 1.0f },
Label = false
};
var pipeline = new LearningPipeline();
pipeline.Add(CollectionDataSource.Create(data));
pipeline.Add(new FastForestBinaryClassifier());
Expand All@@ -149,12 +152,16 @@ public class NullableBooleanLabelData
public void NullableBooleanLabelPipeline()
{
var data = new NullableBooleanLabelData[2];
data[0] = new NullableBooleanLabelData();
data[0].Features = new float[] { 0.0f, 1.0f };
data[0].Label = null;
data[1] = new NullableBooleanLabelData();
data[1].Features = new float[] { 1.0f, 0.0f };
data[1].Label = false;
data[0] = new NullableBooleanLabelData
{
Features = new float[] { 0.0f, 1.0f },
Label = null
};
data[1] = new NullableBooleanLabelData
{
Features = new float[] { 1.0f, 0.0f },
Label = false
};
var pipeline = new LearningPipeline();
pipeline.Add(CollectionDataSource.Create(data));
pipeline.Add(new FastForestBinaryClassifier());
Expand Down
12 changes: 6 additions & 6 deletions test/Microsoft.ML.Tests/Scenarios/ClusteringTests.cs
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -16,8 +16,8 @@ public void PredictNewsCluster()
{
string dataPath = GetDataPath(@"external/20newsgroups.txt");

var pipeline = new LearningPipeline();
pipeline.Add(new TextLoader(dataPath).CreateFrom<NewsData>(useHeader: false, allowQuotedStrings:true, supportSparse:false));
var pipeline = new LearningPipeline(seed: 1, conc: 1);
pipeline.Add(new TextLoader(dataPath).CreateFrom<NewsData>(useHeader: false, allowQuotedStrings:true, supportSparse:false));
pipeline.Add(new ColumnConcatenator("AllText", "Subject", "Content"));
pipeline.Add(new TextFeaturizer("Features", "AllText")
{
Expand DownExpand Up@@ -81,8 +81,8 @@ public class ClusteringData
public void PredictClusters()
{
int n = 1000;
int k = 5;
var rand = new Random();
int k = 4;
var rand = new Random(1);

@Ivanidzo4kaIvanidzo4kaMay 31, 2018

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Random(1) [](start = 26, length = 10)

Looks like this is main reason for Random failure.

var clusters = new ClusteringData[k];
var data = new ClusteringData[n];
for (int i = 0; i < k; i++)
Expand All@@ -94,7 +94,7 @@ public void PredictClusters()
for (int i = 0; i < n; i++)
{
var index = rand.Next(0, k);
var shift = (rand.NextDouble() - 0.5) / k;
var shift = (rand.NextDouble() - 0.5) / 10;
data[i] = new ClusteringData
{
Points = new float[2]
Expand All@@ -104,7 +104,7 @@ public void PredictClusters()
}
};
}
var pipeline = new LearningPipeline();
var pipeline = new LearningPipeline(seed: 1, conc: 1);
pipeline.Add(CollectionDataSource.Create(data));
pipeline.Add(new KMeansPlusPlusClusterer() { K = k });
var model = pipeline.Train<ClusteringData, ClusteringPrediction>();
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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Random seed and concurrency for tests#277

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to your account

 Merged

Ivanidzo4ka
merged 4 commits into

from

May 31, 2018
 Merged


Random seed and concurrency for tests
#277
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6113752

first attempt

May 31, 2018
a31d55a

add comments

May 31, 2018
78f935f

Merge branch 'master' into ivanidze/seed_and_concurrency

May 31, 2018
982c576

specify seed for random.

May 31, 2018

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1 change: 0 additions & 1 deletion
src/Microsoft.ML.Sweeper/Algorithms/Grid.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -2,7 +2,6 @@
// The .NET Foundation licenses this file to you under the MIT license.
// See the LICENSE file in the project root for more information.


using System.Collections.Generic;
using System.Linq;
using Microsoft.ML.Runtime;
Expand Down

18 changes: 16 additions & 2 deletions
src/Microsoft.ML/LearningPipeline.cs

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Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -49,12 +49,27 @@ public ScorerPipelineStep(Var<IDataView> data, Var<ITransformModel> model)
 public class LearningPipeline : ICollection<ILearningPipelineItem>
 {
 private List<ILearningPipelineItem> Items { get; } = new List<ILearningPipelineItem>();
 private readonly int? _seed;
 private readonly int _conc;

 /// <summary>
 /// Construct an empty <see cref="LearningPipeline"/> object.
 /// </summary>
 public LearningPipeline()
 {
 _seed = null;
 _conc = 0;
 }

 /// <summary>
 /// Construct an empty <see cref="LearningPipeline"/> object.
 /// </summary>
 /// <param name="seed">Specify seed for random generator</param>
 /// <param name="conc">Specify concurrency factor (default value - autoselection)</param>
 internal LearningPipeline(int? seed=null, int conc=0)
 {
 _seed = seed;
 _conc = conc;
 }

 /// <summary>
Expand DownExpand Up@@ -137,8 +152,7 @@ public PredictionModel<TInput, TOutput> Train<TInput, TOutput>()
 where TInput : class
 where TOutput : class, new()
 {

 using (var environment = new TlcEnvironment())
 using (var environment = new TlcEnvironment(seed:_seed, conc:_conc))
 {
 Experiment experiment = environment.CreateExperiment();
 ILearningPipelineStep step = null;
Expand Down

9 changes: 9 additions & 0 deletions
src/Microsoft.ML/Properties/AssemblyInfo.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -0,0 +1,9 @@
// Licensed to the .NET Foundation under one or more agreements.
// The .NET Foundation licenses this file to you under the MIT license.
// See the LICENSE file in the project root for more information.

using System.Reflection;
using System.Runtime.CompilerServices;
using System.Runtime.InteropServices;

[assembly: InternalsVisibleTo("Microsoft.ML.Tests, PublicKey=002400000480000094000000060200000024000052534131000400000100010015c01ae1f50e8cc09ba9eac9147cf8fd9fce2cfe9f8dce4f7301c4132ca9fb50ce8cbf1df4dc18dd4d210e4345c744ecb3365ed327efdbc52603faa5e21daa11234c8c4a73e51f03bf192544581ebe107adee3a34928e39d04e524a9ce729d5090bfd7dad9d10c722c0def9ccc08ff0a03790e48bcd1f9b6c476063e1966a1c4")]

41 changes: 24 additions & 17 deletions
test/Microsoft.ML.Tests/LearningPipelineTests.cs

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Original file line numberDiff line numberDiff line change
Expand Up@@ -2,7 +2,6 @@
// The .NET Foundation licenses this file to you under the MIT license.
// See the LICENSE file in the project root for more information.

using Microsoft.ML;
using Microsoft.ML.Data;
using Microsoft.ML.Runtime.Api;
using Microsoft.ML.Runtime.Data;
Expand DownExpand Up@@ -34,9 +33,9 @@ public void CanAddAndRemoveFromPipeline()
 {
 var pipeline = new LearningPipeline()
 {
 new Transforms.CategoricalOneHotVectorizer("String1", "String2"),
 new Transforms.ColumnConcatenator(outputColumn: "Features", "String1", "String2", "Number1", "Number2"),
 new Trainers.StochasticDualCoordinateAscentRegressor()
 new CategoricalOneHotVectorizer("String1", "String2"),
 new ColumnConcatenator(outputColumn: "Features", "String1", "String2", "Number1", "Number2"),
 new StochasticDualCoordinateAscentRegressor()
 };
 Assert.NotNull(pipeline);
 Assert.Equal(3, pipeline.Count);
Expand DownExpand Up@@ -66,7 +65,7 @@ private class TransformedData
 public void TransformOnlyPipeline()
 {
 const string _dataPath = @"..\..\Data\breast-cancer.txt";
 var pipeline = new LearningPipeline();
 var pipeline = new LearningPipeline(seed: 1, conc: 1);
 pipeline.Add(new ML.Data.TextLoader(_dataPath).CreateFrom<InputData>(useHeader: false));
 pipeline.Add(new CategoricalHashOneHotVectorizer("F1") { HashBits = 10, Seed = 314489979, OutputKind = CategoricalTransformOutputKind.Bag });
 var model = pipeline.Train<InputData, TransformedData>();
Expand DownExpand Up@@ -103,9 +102,11 @@ public class Prediction
 public void NoTransformPipeline()
 {
 var data = new Data[1];
 data[0] = new Data();
 data[0].Features = new float[] { 0.0f, 1.0f };
 data[0].Label = 0f;
 data[0] = new Data
 {
 Features = new float[] { 0.0f, 1.0f },
 Label = 0f
 };
 var pipeline = new LearningPipeline();
 pipeline.Add(CollectionDataSource.Create(data));
 pipeline.Add(new FastForestBinaryClassifier());
Expand All@@ -126,9 +127,11 @@ public class BooleanLabelData
 public void BooleanLabelPipeline()
 {
 var data = new BooleanLabelData[1];
 data[0] = new BooleanLabelData();
 data[0].Features = new float[] { 0.0f, 1.0f };
 data[0].Label = false;
 data[0] = new BooleanLabelData
 {
 Features = new float[] { 0.0f, 1.0f },
 Label = false
 };
 var pipeline = new LearningPipeline();
 pipeline.Add(CollectionDataSource.Create(data));
 pipeline.Add(new FastForestBinaryClassifier());
Expand All@@ -149,12 +152,16 @@ public class NullableBooleanLabelData
 public void NullableBooleanLabelPipeline()
 {
 var data = new NullableBooleanLabelData[2];
 data[0] = new NullableBooleanLabelData();
 data[0].Features = new float[] { 0.0f, 1.0f };
 data[0].Label = null;
 data[1] = new NullableBooleanLabelData();
 data[1].Features = new float[] { 1.0f, 0.0f };
 data[1].Label = false;
 data[0] = new NullableBooleanLabelData
 {
 Features = new float[] { 0.0f, 1.0f },
 Label = null
 };
 data[1] = new NullableBooleanLabelData
 {
 Features = new float[] { 1.0f, 0.0f },
 Label = false
 };
 var pipeline = new LearningPipeline();
 pipeline.Add(CollectionDataSource.Create(data));
 pipeline.Add(new FastForestBinaryClassifier());
Expand Down

12 changes: 6 additions & 6 deletions
test/Microsoft.ML.Tests/Scenarios/ClusteringTests.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -16,8 +16,8 @@ public void PredictNewsCluster()
 {
 string dataPath = GetDataPath(@"external/20newsgroups.txt");

 var pipeline = new LearningPipeline();
 pipeline.Add(new TextLoader(dataPath).CreateFrom<NewsData>(useHeader: false, allowQuotedStrings:true, supportSparse:false));
 var pipeline = new LearningPipeline(seed: 1, conc: 1);
 pipeline.Add(new TextLoader(dataPath).CreateFrom<NewsData>(useHeader: false, allowQuotedStrings:true, supportSparse:false));
 pipeline.Add(new ColumnConcatenator("AllText", "Subject", "Content"));
 pipeline.Add(new TextFeaturizer("Features", "AllText")
 {
Expand DownExpand Up@@ -81,8 +81,8 @@ public class ClusteringData
 public void PredictClusters()
 {
 int n = 1000;
 int k = 5;
 var rand = new Random();
 int k = 4;
 var rand = new Random(1);
@Ivanidzo4kaIvanidzo4kaMay 31, 2018

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Random(1) [](start = 26, length = 10)
Looks like this is main reason for Random failure.
 var clusters = new ClusteringData[k];
 var data = new ClusteringData[n];
 for (int i = 0; i < k; i++)
Expand All@@ -94,7 +94,7 @@ public void PredictClusters()
 for (int i = 0; i < n; i++)
 {
 var index = rand.Next(0, k);
 var shift = (rand.NextDouble() - 0.5) / k;
 var shift = (rand.NextDouble() - 0.5) / 10;
 data[i] = new ClusteringData
 {
 Points = new float[2]
Expand All@@ -104,7 +104,7 @@ public void PredictClusters()
 }
 };
 }
 var pipeline = new LearningPipeline();
 var pipeline = new LearningPipeline(seed: 1, conc: 1);
 pipeline.Add(CollectionDataSource.Create(data));
 pipeline.Add(new KMeansPlusPlusClusterer() { K = k });
 var model = pipeline.Train<ClusteringData, ClusteringPrediction>();
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
Random seed and concurrency for tests#277

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 Merged

Ivanidzo4ka
merged 4 commits into

from

May 31, 2018
 Merged


Random seed and concurrency for tests
#277
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6113752

first attempt

May 31, 2018
a31d55a

add comments

May 31, 2018
78f935f

Merge branch 'master' into ivanidze/seed_and_concurrency

May 31, 2018
982c576

specify seed for random.

May 31, 2018

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1 change: 0 additions & 1 deletion
src/Microsoft.ML.Sweeper/Algorithms/Grid.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -2,7 +2,6 @@
// The .NET Foundation licenses this file to you under the MIT license.
// See the LICENSE file in the project root for more information.


using System.Collections.Generic;
using System.Linq;
using Microsoft.ML.Runtime;
Expand Down

18 changes: 16 additions & 2 deletions
src/Microsoft.ML/LearningPipeline.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -49,12 +49,27 @@ public ScorerPipelineStep(Var<IDataView> data, Var<ITransformModel> model)
 public class LearningPipeline : ICollection<ILearningPipelineItem>
 {
 private List<ILearningPipelineItem> Items { get; } = new List<ILearningPipelineItem>();
 private readonly int? _seed;
 private readonly int _conc;

 /// <summary>
 /// Construct an empty <see cref="LearningPipeline"/> object.
 /// </summary>
 public LearningPipeline()
 {
 _seed = null;
 _conc = 0;
 }

 /// <summary>
 /// Construct an empty <see cref="LearningPipeline"/> object.
 /// </summary>
 /// <param name="seed">Specify seed for random generator</param>
 /// <param name="conc">Specify concurrency factor (default value - autoselection)</param>
 internal LearningPipeline(int? seed=null, int conc=0)
 {
 _seed = seed;
 _conc = conc;
 }

 /// <summary>
Expand DownExpand Up@@ -137,8 +152,7 @@ public PredictionModel<TInput, TOutput> Train<TInput, TOutput>()
 where TInput : class
 where TOutput : class, new()
 {

 using (var environment = new TlcEnvironment())
 using (var environment = new TlcEnvironment(seed:_seed, conc:_conc))
 {
 Experiment experiment = environment.CreateExperiment();
 ILearningPipelineStep step = null;
Expand Down

9 changes: 9 additions & 0 deletions
src/Microsoft.ML/Properties/AssemblyInfo.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -0,0 +1,9 @@
// Licensed to the .NET Foundation under one or more agreements.
// The .NET Foundation licenses this file to you under the MIT license.
// See the LICENSE file in the project root for more information.

using System.Reflection;
using System.Runtime.CompilerServices;
using System.Runtime.InteropServices;

[assembly: InternalsVisibleTo("Microsoft.ML.Tests, PublicKey=002400000480000094000000060200000024000052534131000400000100010015c01ae1f50e8cc09ba9eac9147cf8fd9fce2cfe9f8dce4f7301c4132ca9fb50ce8cbf1df4dc18dd4d210e4345c744ecb3365ed327efdbc52603faa5e21daa11234c8c4a73e51f03bf192544581ebe107adee3a34928e39d04e524a9ce729d5090bfd7dad9d10c722c0def9ccc08ff0a03790e48bcd1f9b6c476063e1966a1c4")]

41 changes: 24 additions & 17 deletions
test/Microsoft.ML.Tests/LearningPipelineTests.cs

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Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -2,7 +2,6 @@
// The .NET Foundation licenses this file to you under the MIT license.
// See the LICENSE file in the project root for more information.

using Microsoft.ML;
using Microsoft.ML.Data;
using Microsoft.ML.Runtime.Api;
using Microsoft.ML.Runtime.Data;
Expand DownExpand Up@@ -34,9 +33,9 @@ public void CanAddAndRemoveFromPipeline()
 {
 var pipeline = new LearningPipeline()
 {
 new Transforms.CategoricalOneHotVectorizer("String1", "String2"),
 new Transforms.ColumnConcatenator(outputColumn: "Features", "String1", "String2", "Number1", "Number2"),
 new Trainers.StochasticDualCoordinateAscentRegressor()
 new CategoricalOneHotVectorizer("String1", "String2"),
 new ColumnConcatenator(outputColumn: "Features", "String1", "String2", "Number1", "Number2"),
 new StochasticDualCoordinateAscentRegressor()
 };
 Assert.NotNull(pipeline);
 Assert.Equal(3, pipeline.Count);
Expand DownExpand Up@@ -66,7 +65,7 @@ private class TransformedData
 public void TransformOnlyPipeline()
 {
 const string _dataPath = @"..\..\Data\breast-cancer.txt";
 var pipeline = new LearningPipeline();
 var pipeline = new LearningPipeline(seed: 1, conc: 1);
 pipeline.Add(new ML.Data.TextLoader(_dataPath).CreateFrom<InputData>(useHeader: false));
 pipeline.Add(new CategoricalHashOneHotVectorizer("F1") { HashBits = 10, Seed = 314489979, OutputKind = CategoricalTransformOutputKind.Bag });
 var model = pipeline.Train<InputData, TransformedData>();
Expand DownExpand Up@@ -103,9 +102,11 @@ public class Prediction
 public void NoTransformPipeline()
 {
 var data = new Data[1];
 data[0] = new Data();
 data[0].Features = new float[] { 0.0f, 1.0f };
 data[0].Label = 0f;
 data[0] = new Data
 {
 Features = new float[] { 0.0f, 1.0f },
 Label = 0f
 };
 var pipeline = new LearningPipeline();
 pipeline.Add(CollectionDataSource.Create(data));
 pipeline.Add(new FastForestBinaryClassifier());
Expand All@@ -126,9 +127,11 @@ public class BooleanLabelData
 public void BooleanLabelPipeline()
 {
 var data = new BooleanLabelData[1];
 data[0] = new BooleanLabelData();
 data[0].Features = new float[] { 0.0f, 1.0f };
 data[0].Label = false;
 data[0] = new BooleanLabelData
 {
 Features = new float[] { 0.0f, 1.0f },
 Label = false
 };
 var pipeline = new LearningPipeline();
 pipeline.Add(CollectionDataSource.Create(data));
 pipeline.Add(new FastForestBinaryClassifier());
Expand All@@ -149,12 +152,16 @@ public class NullableBooleanLabelData
 public void NullableBooleanLabelPipeline()
 {
 var data = new NullableBooleanLabelData[2];
 data[0] = new NullableBooleanLabelData();
 data[0].Features = new float[] { 0.0f, 1.0f };
 data[0].Label = null;
 data[1] = new NullableBooleanLabelData();
 data[1].Features = new float[] { 1.0f, 0.0f };
 data[1].Label = false;
 data[0] = new NullableBooleanLabelData
 {
 Features = new float[] { 0.0f, 1.0f },
 Label = null
 };
 data[1] = new NullableBooleanLabelData
 {
 Features = new float[] { 1.0f, 0.0f },
 Label = false
 };
 var pipeline = new LearningPipeline();
 pipeline.Add(CollectionDataSource.Create(data));
 pipeline.Add(new FastForestBinaryClassifier());
Expand Down

12 changes: 6 additions & 6 deletions
test/Microsoft.ML.Tests/Scenarios/ClusteringTests.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -16,8 +16,8 @@ public void PredictNewsCluster()
 {
 string dataPath = GetDataPath(@"external/20newsgroups.txt");

 var pipeline = new LearningPipeline();
 pipeline.Add(new TextLoader(dataPath).CreateFrom<NewsData>(useHeader: false, allowQuotedStrings:true, supportSparse:false));
 var pipeline = new LearningPipeline(seed: 1, conc: 1);
 pipeline.Add(new TextLoader(dataPath).CreateFrom<NewsData>(useHeader: false, allowQuotedStrings:true, supportSparse:false));
 pipeline.Add(new ColumnConcatenator("AllText", "Subject", "Content"));
 pipeline.Add(new TextFeaturizer("Features", "AllText")
 {
Expand DownExpand Up@@ -81,8 +81,8 @@ public class ClusteringData
 public void PredictClusters()
 {
 int n = 1000;
 int k = 5;
 var rand = new Random();
 int k = 4;
 var rand = new Random(1);
@Ivanidzo4kaIvanidzo4kaMay 31, 2018

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Choose a reason for hiding this comment

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Random(1) [](start = 26, length = 10)
Looks like this is main reason for Random failure.
 var clusters = new ClusteringData[k];
 var data = new ClusteringData[n];
 for (int i = 0; i < k; i++)
Expand All@@ -94,7 +94,7 @@ public void PredictClusters()
 for (int i = 0; i < n; i++)
 {
 var index = rand.Next(0, k);
 var shift = (rand.NextDouble() - 0.5) / k;
 var shift = (rand.NextDouble() - 0.5) / 10;
 data[i] = new ClusteringData
 {
 Points = new float[2]
Expand All@@ -104,7 +104,7 @@ public void PredictClusters()
 }
 };
 }
 var pipeline = new LearningPipeline();
 var pipeline = new LearningPipeline(seed: 1, conc: 1);
 pipeline.Add(CollectionDataSource.Create(data));
 pipeline.Add(new KMeansPlusPlusClusterer() { K = k });
 var model = pipeline.Train<ClusteringData, ClusteringPrediction>();
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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Random seed and concurrency for tests#277

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 Merged

Ivanidzo4ka
merged 4 commits into

from

May 31, 2018
 Merged


Random seed and concurrency for tests
#277
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6113752

first attempt

May 31, 2018
a31d55a

add comments

May 31, 2018
78f935f

Merge branch 'master' into ivanidze/seed_and_concurrency

May 31, 2018
982c576

specify seed for random.

May 31, 2018

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1 change: 0 additions & 1 deletion
src/Microsoft.ML.Sweeper/Algorithms/Grid.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -2,7 +2,6 @@
// The .NET Foundation licenses this file to you under the MIT license.
// See the LICENSE file in the project root for more information.


using System.Collections.Generic;
using System.Linq;
using Microsoft.ML.Runtime;
Expand Down

18 changes: 16 additions & 2 deletions
src/Microsoft.ML/LearningPipeline.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -49,12 +49,27 @@ public ScorerPipelineStep(Var<IDataView> data, Var<ITransformModel> model)
 public class LearningPipeline : ICollection<ILearningPipelineItem>
 {
 private List<ILearningPipelineItem> Items { get; } = new List<ILearningPipelineItem>();
 private readonly int? _seed;
 private readonly int _conc;

 /// <summary>
 /// Construct an empty <see cref="LearningPipeline"/> object.
 /// </summary>
 public LearningPipeline()
 {
 _seed = null;
 _conc = 0;
 }

 /// <summary>
 /// Construct an empty <see cref="LearningPipeline"/> object.
 /// </summary>
 /// <param name="seed">Specify seed for random generator</param>
 /// <param name="conc">Specify concurrency factor (default value - autoselection)</param>
 internal LearningPipeline(int? seed=null, int conc=0)
 {
 _seed = seed;
 _conc = conc;
 }

 /// <summary>
Expand DownExpand Up@@ -137,8 +152,7 @@ public PredictionModel<TInput, TOutput> Train<TInput, TOutput>()
 where TInput : class
 where TOutput : class, new()
 {

 using (var environment = new TlcEnvironment())
 using (var environment = new TlcEnvironment(seed:_seed, conc:_conc))
 {
 Experiment experiment = environment.CreateExperiment();
 ILearningPipelineStep step = null;
Expand Down

9 changes: 9 additions & 0 deletions
src/Microsoft.ML/Properties/AssemblyInfo.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -0,0 +1,9 @@
// Licensed to the .NET Foundation under one or more agreements.
// The .NET Foundation licenses this file to you under the MIT license.
// See the LICENSE file in the project root for more information.

using System.Reflection;
using System.Runtime.CompilerServices;
using System.Runtime.InteropServices;

[assembly: InternalsVisibleTo("Microsoft.ML.Tests, PublicKey=002400000480000094000000060200000024000052534131000400000100010015c01ae1f50e8cc09ba9eac9147cf8fd9fce2cfe9f8dce4f7301c4132ca9fb50ce8cbf1df4dc18dd4d210e4345c744ecb3365ed327efdbc52603faa5e21daa11234c8c4a73e51f03bf192544581ebe107adee3a34928e39d04e524a9ce729d5090bfd7dad9d10c722c0def9ccc08ff0a03790e48bcd1f9b6c476063e1966a1c4")]

41 changes: 24 additions & 17 deletions
test/Microsoft.ML.Tests/LearningPipelineTests.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -2,7 +2,6 @@
// The .NET Foundation licenses this file to you under the MIT license.
// See the LICENSE file in the project root for more information.

using Microsoft.ML;
using Microsoft.ML.Data;
using Microsoft.ML.Runtime.Api;
using Microsoft.ML.Runtime.Data;
Expand DownExpand Up@@ -34,9 +33,9 @@ public void CanAddAndRemoveFromPipeline()
 {
 var pipeline = new LearningPipeline()
 {
 new Transforms.CategoricalOneHotVectorizer("String1", "String2"),
 new Transforms.ColumnConcatenator(outputColumn: "Features", "String1", "String2", "Number1", "Number2"),
 new Trainers.StochasticDualCoordinateAscentRegressor()
 new CategoricalOneHotVectorizer("String1", "String2"),
 new ColumnConcatenator(outputColumn: "Features", "String1", "String2", "Number1", "Number2"),
 new StochasticDualCoordinateAscentRegressor()
 };
 Assert.NotNull(pipeline);
 Assert.Equal(3, pipeline.Count);
Expand DownExpand Up@@ -66,7 +65,7 @@ private class TransformedData
 public void TransformOnlyPipeline()
 {
 const string _dataPath = @"..\..\Data\breast-cancer.txt";
 var pipeline = new LearningPipeline();
 var pipeline = new LearningPipeline(seed: 1, conc: 1);
 pipeline.Add(new ML.Data.TextLoader(_dataPath).CreateFrom<InputData>(useHeader: false));
 pipeline.Add(new CategoricalHashOneHotVectorizer("F1") { HashBits = 10, Seed = 314489979, OutputKind = CategoricalTransformOutputKind.Bag });
 var model = pipeline.Train<InputData, TransformedData>();
Expand DownExpand Up@@ -103,9 +102,11 @@ public class Prediction
 public void NoTransformPipeline()
 {
 var data = new Data[1];
 data[0] = new Data();
 data[0].Features = new float[] { 0.0f, 1.0f };
 data[0].Label = 0f;
 data[0] = new Data
 {
 Features = new float[] { 0.0f, 1.0f },
 Label = 0f
 };
 var pipeline = new LearningPipeline();
 pipeline.Add(CollectionDataSource.Create(data));
 pipeline.Add(new FastForestBinaryClassifier());
Expand All@@ -126,9 +127,11 @@ public class BooleanLabelData
 public void BooleanLabelPipeline()
 {
 var data = new BooleanLabelData[1];
 data[0] = new BooleanLabelData();
 data[0].Features = new float[] { 0.0f, 1.0f };
 data[0].Label = false;
 data[0] = new BooleanLabelData
 {
 Features = new float[] { 0.0f, 1.0f },
 Label = false
 };
 var pipeline = new LearningPipeline();
 pipeline.Add(CollectionDataSource.Create(data));
 pipeline.Add(new FastForestBinaryClassifier());
Expand All@@ -149,12 +152,16 @@ public class NullableBooleanLabelData
 public void NullableBooleanLabelPipeline()
 {
 var data = new NullableBooleanLabelData[2];
 data[0] = new NullableBooleanLabelData();
 data[0].Features = new float[] { 0.0f, 1.0f };
 data[0].Label = null;
 data[1] = new NullableBooleanLabelData();
 data[1].Features = new float[] { 1.0f, 0.0f };
 data[1].Label = false;
 data[0] = new NullableBooleanLabelData
 {
 Features = new float[] { 0.0f, 1.0f },
 Label = null
 };
 data[1] = new NullableBooleanLabelData
 {
 Features = new float[] { 1.0f, 0.0f },
 Label = false
 };
 var pipeline = new LearningPipeline();
 pipeline.Add(CollectionDataSource.Create(data));
 pipeline.Add(new FastForestBinaryClassifier());
Expand Down

12 changes: 6 additions & 6 deletions
test/Microsoft.ML.Tests/Scenarios/ClusteringTests.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -16,8 +16,8 @@ public void PredictNewsCluster()
 {
 string dataPath = GetDataPath(@"external/20newsgroups.txt");

 var pipeline = new LearningPipeline();
 pipeline.Add(new TextLoader(dataPath).CreateFrom<NewsData>(useHeader: false, allowQuotedStrings:true, supportSparse:false));
 var pipeline = new LearningPipeline(seed: 1, conc: 1);
 pipeline.Add(new TextLoader(dataPath).CreateFrom<NewsData>(useHeader: false, allowQuotedStrings:true, supportSparse:false));
 pipeline.Add(new ColumnConcatenator("AllText", "Subject", "Content"));
 pipeline.Add(new TextFeaturizer("Features", "AllText")
 {
Expand DownExpand Up@@ -81,8 +81,8 @@ public class ClusteringData
 public void PredictClusters()
 {
 int n = 1000;
 int k = 5;
 var rand = new Random();
 int k = 4;
 var rand = new Random(1);
@Ivanidzo4kaIvanidzo4kaMay 31, 2018

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ContributorAuthor
Choose a reason for hiding this comment

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Random(1) [](start = 26, length = 10)
Looks like this is main reason for Random failure.
 var clusters = new ClusteringData[k];
 var data = new ClusteringData[n];
 for (int i = 0; i < k; i++)
Expand All@@ -94,7 +94,7 @@ public void PredictClusters()
 for (int i = 0; i < n; i++)
 {
 var index = rand.Next(0, k);
 var shift = (rand.NextDouble() - 0.5) / k;
 var shift = (rand.NextDouble() - 0.5) / 10;
 data[i] = new ClusteringData
 {
 Points = new float[2]
Expand All@@ -104,7 +104,7 @@ public void PredictClusters()
 }
 };
 }
 var pipeline = new LearningPipeline();
 var pipeline = new LearningPipeline(seed: 1, conc: 1);
 pipeline.Add(CollectionDataSource.Create(data));
 pipeline.Add(new KMeansPlusPlusClusterer() { K = k });
 var model = pipeline.Train<ClusteringData, ClusteringPrediction>();
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" + '

Skip to content
Random seed and concurrency for tests#277

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to your account

 Merged

Ivanidzo4ka
merged 4 commits into

from

May 31, 2018
 Merged


Random seed and concurrency for tests
#277
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6113752

first attempt

May 31, 2018
a31d55a

add comments

May 31, 2018
78f935f

Merge branch 'master' into ivanidze/seed_and_concurrency

May 31, 2018
982c576

specify seed for random.

May 31, 2018

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1 change: 0 additions & 1 deletion
src/Microsoft.ML.Sweeper/Algorithms/Grid.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -2,7 +2,6 @@
// The .NET Foundation licenses this file to you under the MIT license.
// See the LICENSE file in the project root for more information.


using System.Collections.Generic;
using System.Linq;
using Microsoft.ML.Runtime;
Expand Down

18 changes: 16 additions & 2 deletions
src/Microsoft.ML/LearningPipeline.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -49,12 +49,27 @@ public ScorerPipelineStep(Var<IDataView> data, Var<ITransformModel> model)
 public class LearningPipeline : ICollection<ILearningPipelineItem>
 {
 private List<ILearningPipelineItem> Items { get; } = new List<ILearningPipelineItem>();
 private readonly int? _seed;
 private readonly int _conc;

 /// <summary>
 /// Construct an empty <see cref="LearningPipeline"/> object.
 /// </summary>
 public LearningPipeline()
 {
 _seed = null;
 _conc = 0;
 }

 /// <summary>
 /// Construct an empty <see cref="LearningPipeline"/> object.
 /// </summary>
 /// <param name="seed">Specify seed for random generator</param>
 /// <param name="conc">Specify concurrency factor (default value - autoselection)</param>
 internal LearningPipeline(int? seed=null, int conc=0)
 {
 _seed = seed;
 _conc = conc;
 }

 /// <summary>
Expand DownExpand Up@@ -137,8 +152,7 @@ public PredictionModel<TInput, TOutput> Train<TInput, TOutput>()
 where TInput : class
 where TOutput : class, new()
 {

 using (var environment = new TlcEnvironment())
 using (var environment = new TlcEnvironment(seed:_seed, conc:_conc))
 {
 Experiment experiment = environment.CreateExperiment();
 ILearningPipelineStep step = null;
Expand Down

9 changes: 9 additions & 0 deletions
src/Microsoft.ML/Properties/AssemblyInfo.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -0,0 +1,9 @@
// Licensed to the .NET Foundation under one or more agreements.
// The .NET Foundation licenses this file to you under the MIT license.
// See the LICENSE file in the project root for more information.

using System.Reflection;
using System.Runtime.CompilerServices;
using System.Runtime.InteropServices;

[assembly: InternalsVisibleTo("Microsoft.ML.Tests, PublicKey=002400000480000094000000060200000024000052534131000400000100010015c01ae1f50e8cc09ba9eac9147cf8fd9fce2cfe9f8dce4f7301c4132ca9fb50ce8cbf1df4dc18dd4d210e4345c744ecb3365ed327efdbc52603faa5e21daa11234c8c4a73e51f03bf192544581ebe107adee3a34928e39d04e524a9ce729d5090bfd7dad9d10c722c0def9ccc08ff0a03790e48bcd1f9b6c476063e1966a1c4")]

41 changes: 24 additions & 17 deletions
test/Microsoft.ML.Tests/LearningPipelineTests.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -2,7 +2,6 @@
// The .NET Foundation licenses this file to you under the MIT license.
// See the LICENSE file in the project root for more information.

using Microsoft.ML;
using Microsoft.ML.Data;
using Microsoft.ML.Runtime.Api;
using Microsoft.ML.Runtime.Data;
Expand DownExpand Up@@ -34,9 +33,9 @@ public void CanAddAndRemoveFromPipeline()
 {
 var pipeline = new LearningPipeline()
 {
 new Transforms.CategoricalOneHotVectorizer("String1", "String2"),
 new Transforms.ColumnConcatenator(outputColumn: "Features", "String1", "String2", "Number1", "Number2"),
 new Trainers.StochasticDualCoordinateAscentRegressor()
 new CategoricalOneHotVectorizer("String1", "String2"),
 new ColumnConcatenator(outputColumn: "Features", "String1", "String2", "Number1", "Number2"),
 new StochasticDualCoordinateAscentRegressor()
 };
 Assert.NotNull(pipeline);
 Assert.Equal(3, pipeline.Count);
Expand DownExpand Up@@ -66,7 +65,7 @@ private class TransformedData
 public void TransformOnlyPipeline()
 {
 const string _dataPath = @"..\..\Data\breast-cancer.txt";
 var pipeline = new LearningPipeline();
 var pipeline = new LearningPipeline(seed: 1, conc: 1);
 pipeline.Add(new ML.Data.TextLoader(_dataPath).CreateFrom<InputData>(useHeader: false));
 pipeline.Add(new CategoricalHashOneHotVectorizer("F1") { HashBits = 10, Seed = 314489979, OutputKind = CategoricalTransformOutputKind.Bag });
 var model = pipeline.Train<InputData, TransformedData>();
Expand DownExpand Up@@ -103,9 +102,11 @@ public class Prediction
 public void NoTransformPipeline()
 {
 var data = new Data[1];
 data[0] = new Data();
 data[0].Features = new float[] { 0.0f, 1.0f };
 data[0].Label = 0f;
 data[0] = new Data
 {
 Features = new float[] { 0.0f, 1.0f },
 Label = 0f
 };
 var pipeline = new LearningPipeline();
 pipeline.Add(CollectionDataSource.Create(data));
 pipeline.Add(new FastForestBinaryClassifier());
Expand All@@ -126,9 +127,11 @@ public class BooleanLabelData
 public void BooleanLabelPipeline()
 {
 var data = new BooleanLabelData[1];
 data[0] = new BooleanLabelData();
 data[0].Features = new float[] { 0.0f, 1.0f };
 data[0].Label = false;
 data[0] = new BooleanLabelData
 {
 Features = new float[] { 0.0f, 1.0f },
 Label = false
 };
 var pipeline = new LearningPipeline();
 pipeline.Add(CollectionDataSource.Create(data));
 pipeline.Add(new FastForestBinaryClassifier());
Expand All@@ -149,12 +152,16 @@ public class NullableBooleanLabelData
 public void NullableBooleanLabelPipeline()
 {
 var data = new NullableBooleanLabelData[2];
 data[0] = new NullableBooleanLabelData();
 data[0].Features = new float[] { 0.0f, 1.0f };
 data[0].Label = null;
 data[1] = new NullableBooleanLabelData();
 data[1].Features = new float[] { 1.0f, 0.0f };
 data[1].Label = false;
 data[0] = new NullableBooleanLabelData
 {
 Features = new float[] { 0.0f, 1.0f },
 Label = null
 };
 data[1] = new NullableBooleanLabelData
 {
 Features = new float[] { 1.0f, 0.0f },
 Label = false
 };
 var pipeline = new LearningPipeline();
 pipeline.Add(CollectionDataSource.Create(data));
 pipeline.Add(new FastForestBinaryClassifier());
Expand Down

12 changes: 6 additions & 6 deletions
test/Microsoft.ML.Tests/Scenarios/ClusteringTests.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -16,8 +16,8 @@ public void PredictNewsCluster()
 {
 string dataPath = GetDataPath(@"external/20newsgroups.txt");

 var pipeline = new LearningPipeline();
 pipeline.Add(new TextLoader(dataPath).CreateFrom<NewsData>(useHeader: false, allowQuotedStrings:true, supportSparse:false));
 var pipeline = new LearningPipeline(seed: 1, conc: 1);
 pipeline.Add(new TextLoader(dataPath).CreateFrom<NewsData>(useHeader: false, allowQuotedStrings:true, supportSparse:false));
 pipeline.Add(new ColumnConcatenator("AllText", "Subject", "Content"));
 pipeline.Add(new TextFeaturizer("Features", "AllText")
 {
Expand DownExpand Up@@ -81,8 +81,8 @@ public class ClusteringData
 public void PredictClusters()
 {
 int n = 1000;
 int k = 5;
 var rand = new Random();
 int k = 4;
 var rand = new Random(1);
@Ivanidzo4kaIvanidzo4kaMay 31, 2018

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Random(1) [](start = 26, length = 10)
Looks like this is main reason for Random failure.
 var clusters = new ClusteringData[k];
 var data = new ClusteringData[n];
 for (int i = 0; i < k; i++)
Expand All@@ -94,7 +94,7 @@ public void PredictClusters()
 for (int i = 0; i < n; i++)
 {
 var index = rand.Next(0, k);
 var shift = (rand.NextDouble() - 0.5) / k;
 var shift = (rand.NextDouble() - 0.5) / 10;
 data[i] = new ClusteringData
 {
 Points = new float[2]
Expand All@@ -104,7 +104,7 @@ public void PredictClusters()
 }
 };
 }
 var pipeline = new LearningPipeline();
 var pipeline = new LearningPipeline(seed: 1, conc: 1);
 pipeline.Add(CollectionDataSource.Create(data));
 pipeline.Add(new KMeansPlusPlusClusterer() { K = k });
 var model = pipeline.Train<ClusteringData, ClusteringPrediction>();
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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Ivanidzo4ka
merged 4 commits into

from

May 31, 2018
 Merged


Random seed and concurrency for tests
#277
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6113752

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May 31, 2018
a31d55a

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78f935f

Merge branch 'master' into ivanidze/seed_and_concurrency

May 31, 2018
982c576

specify seed for random.

May 31, 2018

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1 change: 0 additions & 1 deletion
src/Microsoft.ML.Sweeper/Algorithms/Grid.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -2,7 +2,6 @@
// The .NET Foundation licenses this file to you under the MIT license.
// See the LICENSE file in the project root for more information.


using System.Collections.Generic;
using System.Linq;
using Microsoft.ML.Runtime;
Expand Down

18 changes: 16 additions & 2 deletions
src/Microsoft.ML/LearningPipeline.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -49,12 +49,27 @@ public ScorerPipelineStep(Var<IDataView> data, Var<ITransformModel> model)
 public class LearningPipeline : ICollection<ILearningPipelineItem>
 {
 private List<ILearningPipelineItem> Items { get; } = new List<ILearningPipelineItem>();
 private readonly int? _seed;
 private readonly int _conc;

 /// <summary>
 /// Construct an empty <see cref="LearningPipeline"/> object.
 /// </summary>
 public LearningPipeline()
 {
 _seed = null;
 _conc = 0;
 }

 /// <summary>
 /// Construct an empty <see cref="LearningPipeline"/> object.
 /// </summary>
 /// <param name="seed">Specify seed for random generator</param>
 /// <param name="conc">Specify concurrency factor (default value - autoselection)</param>
 internal LearningPipeline(int? seed=null, int conc=0)
 {
 _seed = seed;
 _conc = conc;
 }

 /// <summary>
Expand DownExpand Up@@ -137,8 +152,7 @@ public PredictionModel<TInput, TOutput> Train<TInput, TOutput>()
 where TInput : class
 where TOutput : class, new()
 {

 using (var environment = new TlcEnvironment())
 using (var environment = new TlcEnvironment(seed:_seed, conc:_conc))
 {
 Experiment experiment = environment.CreateExperiment();
 ILearningPipelineStep step = null;
Expand Down

9 changes: 9 additions & 0 deletions
src/Microsoft.ML/Properties/AssemblyInfo.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -0,0 +1,9 @@
// Licensed to the .NET Foundation under one or more agreements.
// The .NET Foundation licenses this file to you under the MIT license.
// See the LICENSE file in the project root for more information.

using System.Reflection;
using System.Runtime.CompilerServices;
using System.Runtime.InteropServices;

[assembly: InternalsVisibleTo("Microsoft.ML.Tests, PublicKey=002400000480000094000000060200000024000052534131000400000100010015c01ae1f50e8cc09ba9eac9147cf8fd9fce2cfe9f8dce4f7301c4132ca9fb50ce8cbf1df4dc18dd4d210e4345c744ecb3365ed327efdbc52603faa5e21daa11234c8c4a73e51f03bf192544581ebe107adee3a34928e39d04e524a9ce729d5090bfd7dad9d10c722c0def9ccc08ff0a03790e48bcd1f9b6c476063e1966a1c4")]

41 changes: 24 additions & 17 deletions
test/Microsoft.ML.Tests/LearningPipelineTests.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -2,7 +2,6 @@
// The .NET Foundation licenses this file to you under the MIT license.
// See the LICENSE file in the project root for more information.

using Microsoft.ML;
using Microsoft.ML.Data;
using Microsoft.ML.Runtime.Api;
using Microsoft.ML.Runtime.Data;
Expand DownExpand Up@@ -34,9 +33,9 @@ public void CanAddAndRemoveFromPipeline()
 {
 var pipeline = new LearningPipeline()
 {
 new Transforms.CategoricalOneHotVectorizer("String1", "String2"),
 new Transforms.ColumnConcatenator(outputColumn: "Features", "String1", "String2", "Number1", "Number2"),
 new Trainers.StochasticDualCoordinateAscentRegressor()
 new CategoricalOneHotVectorizer("String1", "String2"),
 new ColumnConcatenator(outputColumn: "Features", "String1", "String2", "Number1", "Number2"),
 new StochasticDualCoordinateAscentRegressor()
 };
 Assert.NotNull(pipeline);
 Assert.Equal(3, pipeline.Count);
Expand DownExpand Up@@ -66,7 +65,7 @@ private class TransformedData
 public void TransformOnlyPipeline()
 {
 const string _dataPath = @"..\..\Data\breast-cancer.txt";
 var pipeline = new LearningPipeline();
 var pipeline = new LearningPipeline(seed: 1, conc: 1);
 pipeline.Add(new ML.Data.TextLoader(_dataPath).CreateFrom<InputData>(useHeader: false));
 pipeline.Add(new CategoricalHashOneHotVectorizer("F1") { HashBits = 10, Seed = 314489979, OutputKind = CategoricalTransformOutputKind.Bag });
 var model = pipeline.Train<InputData, TransformedData>();
Expand DownExpand Up@@ -103,9 +102,11 @@ public class Prediction
 public void NoTransformPipeline()
 {
 var data = new Data[1];
 data[0] = new Data();
 data[0].Features = new float[] { 0.0f, 1.0f };
 data[0].Label = 0f;
 data[0] = new Data
 {
 Features = new float[] { 0.0f, 1.0f },
 Label = 0f
 };
 var pipeline = new LearningPipeline();
 pipeline.Add(CollectionDataSource.Create(data));
 pipeline.Add(new FastForestBinaryClassifier());
Expand All@@ -126,9 +127,11 @@ public class BooleanLabelData
 public void BooleanLabelPipeline()
 {
 var data = new BooleanLabelData[1];
 data[0] = new BooleanLabelData();
 data[0].Features = new float[] { 0.0f, 1.0f };
 data[0].Label = false;
 data[0] = new BooleanLabelData
 {
 Features = new float[] { 0.0f, 1.0f },
 Label = false
 };
 var pipeline = new LearningPipeline();
 pipeline.Add(CollectionDataSource.Create(data));
 pipeline.Add(new FastForestBinaryClassifier());
Expand All@@ -149,12 +152,16 @@ public class NullableBooleanLabelData
 public void NullableBooleanLabelPipeline()
 {
 var data = new NullableBooleanLabelData[2];
 data[0] = new NullableBooleanLabelData();
 data[0].Features = new float[] { 0.0f, 1.0f };
 data[0].Label = null;
 data[1] = new NullableBooleanLabelData();
 data[1].Features = new float[] { 1.0f, 0.0f };
 data[1].Label = false;
 data[0] = new NullableBooleanLabelData
 {
 Features = new float[] { 0.0f, 1.0f },
 Label = null
 };
 data[1] = new NullableBooleanLabelData
 {
 Features = new float[] { 1.0f, 0.0f },
 Label = false
 };
 var pipeline = new LearningPipeline();
 pipeline.Add(CollectionDataSource.Create(data));
 pipeline.Add(new FastForestBinaryClassifier());
Expand Down

12 changes: 6 additions & 6 deletions
test/Microsoft.ML.Tests/Scenarios/ClusteringTests.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -16,8 +16,8 @@ public void PredictNewsCluster()
 {
 string dataPath = GetDataPath(@"external/20newsgroups.txt");

 var pipeline = new LearningPipeline();
 pipeline.Add(new TextLoader(dataPath).CreateFrom<NewsData>(useHeader: false, allowQuotedStrings:true, supportSparse:false));
 var pipeline = new LearningPipeline(seed: 1, conc: 1);
 pipeline.Add(new TextLoader(dataPath).CreateFrom<NewsData>(useHeader: false, allowQuotedStrings:true, supportSparse:false));
 pipeline.Add(new ColumnConcatenator("AllText", "Subject", "Content"));
 pipeline.Add(new TextFeaturizer("Features", "AllText")
 {
Expand DownExpand Up@@ -81,8 +81,8 @@ public class ClusteringData
 public void PredictClusters()
 {
 int n = 1000;
 int k = 5;
 var rand = new Random();
 int k = 4;
 var rand = new Random(1);
@Ivanidzo4kaIvanidzo4kaMay 31, 2018

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ContributorAuthor
Choose a reason for hiding this comment

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Random(1) [](start = 26, length = 10)
Looks like this is main reason for Random failure.
 var clusters = new ClusteringData[k];
 var data = new ClusteringData[n];
 for (int i = 0; i < k; i++)
Expand All@@ -94,7 +94,7 @@ public void PredictClusters()
 for (int i = 0; i < n; i++)
 {
 var index = rand.Next(0, k);
 var shift = (rand.NextDouble() - 0.5) / k;
 var shift = (rand.NextDouble() - 0.5) / 10;
 data[i] = new ClusteringData
 {
 Points = new float[2]
Expand All@@ -104,7 +104,7 @@ public void PredictClusters()
 }
 };
 }
 var pipeline = new LearningPipeline();
 var pipeline = new LearningPipeline(seed: 1, conc: 1);
 pipeline.Add(CollectionDataSource.Create(data));
 pipeline.Add(new KMeansPlusPlusClusterer() { K = k });
 var model = pipeline.Train<ClusteringData, ClusteringPrediction>();
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
Random seed and concurrency for tests#277

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to your account

 Merged

Ivanidzo4ka
merged 4 commits into

from

May 31, 2018
 Merged


Random seed and concurrency for tests
#277
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6113752

first attempt

May 31, 2018
a31d55a

add comments

May 31, 2018
78f935f

Merge branch 'master' into ivanidze/seed_and_concurrency

May 31, 2018
982c576

specify seed for random.

May 31, 2018

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1 change: 0 additions & 1 deletion
src/Microsoft.ML.Sweeper/Algorithms/Grid.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -2,7 +2,6 @@
// The .NET Foundation licenses this file to you under the MIT license.
// See the LICENSE file in the project root for more information.


using System.Collections.Generic;
using System.Linq;
using Microsoft.ML.Runtime;
Expand Down

18 changes: 16 additions & 2 deletions
src/Microsoft.ML/LearningPipeline.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -49,12 +49,27 @@ public ScorerPipelineStep(Var<IDataView> data, Var<ITransformModel> model)
 public class LearningPipeline : ICollection<ILearningPipelineItem>
 {
 private List<ILearningPipelineItem> Items { get; } = new List<ILearningPipelineItem>();
 private readonly int? _seed;
 private readonly int _conc;

 /// <summary>
 /// Construct an empty <see cref="LearningPipeline"/> object.
 /// </summary>
 public LearningPipeline()
 {
 _seed = null;
 _conc = 0;
 }

 /// <summary>
 /// Construct an empty <see cref="LearningPipeline"/> object.
 /// </summary>
 /// <param name="seed">Specify seed for random generator</param>
 /// <param name="conc">Specify concurrency factor (default value - autoselection)</param>
 internal LearningPipeline(int? seed=null, int conc=0)
 {
 _seed = seed;
 _conc = conc;
 }

 /// <summary>
Expand DownExpand Up@@ -137,8 +152,7 @@ public PredictionModel<TInput, TOutput> Train<TInput, TOutput>()
 where TInput : class
 where TOutput : class, new()
 {

 using (var environment = new TlcEnvironment())
 using (var environment = new TlcEnvironment(seed:_seed, conc:_conc))
 {
 Experiment experiment = environment.CreateExperiment();
 ILearningPipelineStep step = null;
Expand Down

9 changes: 9 additions & 0 deletions
src/Microsoft.ML/Properties/AssemblyInfo.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -0,0 +1,9 @@
// Licensed to the .NET Foundation under one or more agreements.
// The .NET Foundation licenses this file to you under the MIT license.
// See the LICENSE file in the project root for more information.

using System.Reflection;
using System.Runtime.CompilerServices;
using System.Runtime.InteropServices;

[assembly: InternalsVisibleTo("Microsoft.ML.Tests, PublicKey=002400000480000094000000060200000024000052534131000400000100010015c01ae1f50e8cc09ba9eac9147cf8fd9fce2cfe9f8dce4f7301c4132ca9fb50ce8cbf1df4dc18dd4d210e4345c744ecb3365ed327efdbc52603faa5e21daa11234c8c4a73e51f03bf192544581ebe107adee3a34928e39d04e524a9ce729d5090bfd7dad9d10c722c0def9ccc08ff0a03790e48bcd1f9b6c476063e1966a1c4")]

41 changes: 24 additions & 17 deletions
test/Microsoft.ML.Tests/LearningPipelineTests.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -2,7 +2,6 @@
// The .NET Foundation licenses this file to you under the MIT license.
// See the LICENSE file in the project root for more information.

using Microsoft.ML;
using Microsoft.ML.Data;
using Microsoft.ML.Runtime.Api;
using Microsoft.ML.Runtime.Data;
Expand DownExpand Up@@ -34,9 +33,9 @@ public void CanAddAndRemoveFromPipeline()
 {
 var pipeline = new LearningPipeline()
 {
 new Transforms.CategoricalOneHotVectorizer("String1", "String2"),
 new Transforms.ColumnConcatenator(outputColumn: "Features", "String1", "String2", "Number1", "Number2"),
 new Trainers.StochasticDualCoordinateAscentRegressor()
 new CategoricalOneHotVectorizer("String1", "String2"),
 new ColumnConcatenator(outputColumn: "Features", "String1", "String2", "Number1", "Number2"),
 new StochasticDualCoordinateAscentRegressor()
 };
 Assert.NotNull(pipeline);
 Assert.Equal(3, pipeline.Count);
Expand DownExpand Up@@ -66,7 +65,7 @@ private class TransformedData
 public void TransformOnlyPipeline()
 {
 const string _dataPath = @"..\..\Data\breast-cancer.txt";
 var pipeline = new LearningPipeline();
 var pipeline = new LearningPipeline(seed: 1, conc: 1);
 pipeline.Add(new ML.Data.TextLoader(_dataPath).CreateFrom<InputData>(useHeader: false));
 pipeline.Add(new CategoricalHashOneHotVectorizer("F1") { HashBits = 10, Seed = 314489979, OutputKind = CategoricalTransformOutputKind.Bag });
 var model = pipeline.Train<InputData, TransformedData>();
Expand DownExpand Up@@ -103,9 +102,11 @@ public class Prediction
 public void NoTransformPipeline()
 {
 var data = new Data[1];
 data[0] = new Data();
 data[0].Features = new float[] { 0.0f, 1.0f };
 data[0].Label = 0f;
 data[0] = new Data
 {
 Features = new float[] { 0.0f, 1.0f },
 Label = 0f
 };
 var pipeline = new LearningPipeline();
 pipeline.Add(CollectionDataSource.Create(data));
 pipeline.Add(new FastForestBinaryClassifier());
Expand All@@ -126,9 +127,11 @@ public class BooleanLabelData
 public void BooleanLabelPipeline()
 {
 var data = new BooleanLabelData[1];
 data[0] = new BooleanLabelData();
 data[0].Features = new float[] { 0.0f, 1.0f };
 data[0].Label = false;
 data[0] = new BooleanLabelData
 {
 Features = new float[] { 0.0f, 1.0f },
 Label = false
 };
 var pipeline = new LearningPipeline();
 pipeline.Add(CollectionDataSource.Create(data));
 pipeline.Add(new FastForestBinaryClassifier());
Expand All@@ -149,12 +152,16 @@ public class NullableBooleanLabelData
 public void NullableBooleanLabelPipeline()
 {
 var data = new NullableBooleanLabelData[2];
 data[0] = new NullableBooleanLabelData();
 data[0].Features = new float[] { 0.0f, 1.0f };
 data[0].Label = null;
 data[1] = new NullableBooleanLabelData();
 data[1].Features = new float[] { 1.0f, 0.0f };
 data[1].Label = false;
 data[0] = new NullableBooleanLabelData
 {
 Features = new float[] { 0.0f, 1.0f },
 Label = null
 };
 data[1] = new NullableBooleanLabelData
 {
 Features = new float[] { 1.0f, 0.0f },
 Label = false
 };
 var pipeline = new LearningPipeline();
 pipeline.Add(CollectionDataSource.Create(data));
 pipeline.Add(new FastForestBinaryClassifier());
Expand Down

12 changes: 6 additions & 6 deletions
test/Microsoft.ML.Tests/Scenarios/ClusteringTests.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -16,8 +16,8 @@ public void PredictNewsCluster()
 {
 string dataPath = GetDataPath(@"external/20newsgroups.txt");

 var pipeline = new LearningPipeline();
 pipeline.Add(new TextLoader(dataPath).CreateFrom<NewsData>(useHeader: false, allowQuotedStrings:true, supportSparse:false));
 var pipeline = new LearningPipeline(seed: 1, conc: 1);
 pipeline.Add(new TextLoader(dataPath).CreateFrom<NewsData>(useHeader: false, allowQuotedStrings:true, supportSparse:false));
 pipeline.Add(new ColumnConcatenator("AllText", "Subject", "Content"));
 pipeline.Add(new TextFeaturizer("Features", "AllText")
 {
Expand DownExpand Up@@ -81,8 +81,8 @@ public class ClusteringData
 public void PredictClusters()
 {
 int n = 1000;
 int k = 5;
 var rand = new Random();
 int k = 4;
 var rand = new Random(1);
@Ivanidzo4kaIvanidzo4kaMay 31, 2018

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Copy Markdown

ContributorAuthor
Choose a reason for hiding this comment

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

Random(1) [](start = 26, length = 10)
Looks like this is main reason for Random failure.
 var clusters = new ClusteringData[k];
 var data = new ClusteringData[n];
 for (int i = 0; i < k; i++)
Expand All@@ -94,7 +94,7 @@ public void PredictClusters()
 for (int i = 0; i < n; i++)
 {
 var index = rand.Next(0, k);
 var shift = (rand.NextDouble() - 0.5) / k;
 var shift = (rand.NextDouble() - 0.5) / 10;
 data[i] = new ClusteringData
 {
 Points = new float[2]
Expand All@@ -104,7 +104,7 @@ public void PredictClusters()
 }
 };
 }
 var pipeline = new LearningPipeline();
 var pipeline = new LearningPipeline(seed: 1, conc: 1);
 pipeline.Add(CollectionDataSource.Create(data));
 pipeline.Add(new KMeansPlusPlusClusterer() { K = k });
 var model = pipeline.Train<ClusteringData, ClusteringPrediction>();
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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Random seed and concurrency for tests#277

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Already on GitHub?
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 Merged

Ivanidzo4ka
merged 4 commits into

from

May 31, 2018
 Merged


Random seed and concurrency for tests
#277
Show file tree
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4 commits


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6113752

first attempt

May 31, 2018
a31d55a

add comments

May 31, 2018
78f935f

Merge branch 'master' into ivanidze/seed_and_concurrency

May 31, 2018
982c576

specify seed for random.

May 31, 2018

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1 change: 0 additions & 1 deletion
src/Microsoft.ML.Sweeper/Algorithms/Grid.cs

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Original file line numberDiff line numberDiff line change
Expand Up@@ -2,7 +2,6 @@
// The .NET Foundation licenses this file to you under the MIT license.
// See the LICENSE file in the project root for more information.


using System.Collections.Generic;
using System.Linq;
using Microsoft.ML.Runtime;
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18 changes: 16 additions & 2 deletions
src/Microsoft.ML/LearningPipeline.cs

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Expand Up@@ -49,12 +49,27 @@ public ScorerPipelineStep(Var<IDataView> data, Var<ITransformModel> model)
 public class LearningPipeline : ICollection<ILearningPipelineItem>
 {
 private List<ILearningPipelineItem> Items { get; } = new List<ILearningPipelineItem>();
 private readonly int? _seed;
 private readonly int _conc;

 /// <summary>
 /// Construct an empty <see cref="LearningPipeline"/> object.
 /// </summary>
 public LearningPipeline()
 {
 _seed = null;
 _conc = 0;
 }

 /// <summary>
 /// Construct an empty <see cref="LearningPipeline"/> object.
 /// </summary>
 /// <param name="seed">Specify seed for random generator</param>
 /// <param name="conc">Specify concurrency factor (default value - autoselection)</param>
 internal LearningPipeline(int? seed=null, int conc=0)
 {
 _seed = seed;
 _conc = conc;
 }

 /// <summary>
Expand DownExpand Up@@ -137,8 +152,7 @@ public PredictionModel<TInput, TOutput> Train<TInput, TOutput>()
 where TInput : class
 where TOutput : class, new()
 {

 using (var environment = new TlcEnvironment())
 using (var environment = new TlcEnvironment(seed:_seed, conc:_conc))
 {
 Experiment experiment = environment.CreateExperiment();
 ILearningPipelineStep step = null;
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9 changes: 9 additions & 0 deletions
src/Microsoft.ML/Properties/AssemblyInfo.cs

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Original file line numberDiff line numberDiff line change
@@ -0,0 +1,9 @@
// Licensed to the .NET Foundation under one or more agreements.
// The .NET Foundation licenses this file to you under the MIT license.
// See the LICENSE file in the project root for more information.

using System.Reflection;
using System.Runtime.CompilerServices;
using System.Runtime.InteropServices;

[assembly: InternalsVisibleTo("Microsoft.ML.Tests, PublicKey=002400000480000094000000060200000024000052534131000400000100010015c01ae1f50e8cc09ba9eac9147cf8fd9fce2cfe9f8dce4f7301c4132ca9fb50ce8cbf1df4dc18dd4d210e4345c744ecb3365ed327efdbc52603faa5e21daa11234c8c4a73e51f03bf192544581ebe107adee3a34928e39d04e524a9ce729d5090bfd7dad9d10c722c0def9ccc08ff0a03790e48bcd1f9b6c476063e1966a1c4")]

41 changes: 24 additions & 17 deletions
test/Microsoft.ML.Tests/LearningPipelineTests.cs

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Original file line numberDiff line numberDiff line change
Expand Up@@ -2,7 +2,6 @@
// The .NET Foundation licenses this file to you under the MIT license.
// See the LICENSE file in the project root for more information.

using Microsoft.ML;
using Microsoft.ML.Data;
using Microsoft.ML.Runtime.Api;
using Microsoft.ML.Runtime.Data;
Expand DownExpand Up@@ -34,9 +33,9 @@ public void CanAddAndRemoveFromPipeline()
 {
 var pipeline = new LearningPipeline()
 {
 new Transforms.CategoricalOneHotVectorizer("String1", "String2"),
 new Transforms.ColumnConcatenator(outputColumn: "Features", "String1", "String2", "Number1", "Number2"),
 new Trainers.StochasticDualCoordinateAscentRegressor()
 new CategoricalOneHotVectorizer("String1", "String2"),
 new ColumnConcatenator(outputColumn: "Features", "String1", "String2", "Number1", "Number2"),
 new StochasticDualCoordinateAscentRegressor()
 };
 Assert.NotNull(pipeline);
 Assert.Equal(3, pipeline.Count);
Expand DownExpand Up@@ -66,7 +65,7 @@ private class TransformedData
 public void TransformOnlyPipeline()
 {
 const string _dataPath = @"..\..\Data\breast-cancer.txt";
 var pipeline = new LearningPipeline();
 var pipeline = new LearningPipeline(seed: 1, conc: 1);
 pipeline.Add(new ML.Data.TextLoader(_dataPath).CreateFrom<InputData>(useHeader: false));
 pipeline.Add(new CategoricalHashOneHotVectorizer("F1") { HashBits = 10, Seed = 314489979, OutputKind = CategoricalTransformOutputKind.Bag });
 var model = pipeline.Train<InputData, TransformedData>();
Expand DownExpand Up@@ -103,9 +102,11 @@ public class Prediction
 public void NoTransformPipeline()
 {
 var data = new Data[1];
 data[0] = new Data();
 data[0].Features = new float[] { 0.0f, 1.0f };
 data[0].Label = 0f;
 data[0] = new Data
 {
 Features = new float[] { 0.0f, 1.0f },
 Label = 0f
 };
 var pipeline = new LearningPipeline();
 pipeline.Add(CollectionDataSource.Create(data));
 pipeline.Add(new FastForestBinaryClassifier());
Expand All@@ -126,9 +127,11 @@ public class BooleanLabelData
 public void BooleanLabelPipeline()
 {
 var data = new BooleanLabelData[1];
 data[0] = new BooleanLabelData();
 data[0].Features = new float[] { 0.0f, 1.0f };
 data[0].Label = false;
 data[0] = new BooleanLabelData
 {
 Features = new float[] { 0.0f, 1.0f },
 Label = false
 };
 var pipeline = new LearningPipeline();
 pipeline.Add(CollectionDataSource.Create(data));
 pipeline.Add(new FastForestBinaryClassifier());
Expand All@@ -149,12 +152,16 @@ public class NullableBooleanLabelData
 public void NullableBooleanLabelPipeline()
 {
 var data = new NullableBooleanLabelData[2];
 data[0] = new NullableBooleanLabelData();
 data[0].Features = new float[] { 0.0f, 1.0f };
 data[0].Label = null;
 data[1] = new NullableBooleanLabelData();
 data[1].Features = new float[] { 1.0f, 0.0f };
 data[1].Label = false;
 data[0] = new NullableBooleanLabelData
 {
 Features = new float[] { 0.0f, 1.0f },
 Label = null
 };
 data[1] = new NullableBooleanLabelData
 {
 Features = new float[] { 1.0f, 0.0f },
 Label = false
 };
 var pipeline = new LearningPipeline();
 pipeline.Add(CollectionDataSource.Create(data));
 pipeline.Add(new FastForestBinaryClassifier());
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12 changes: 6 additions & 6 deletions
test/Microsoft.ML.Tests/Scenarios/ClusteringTests.cs

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Expand Up@@ -16,8 +16,8 @@ public void PredictNewsCluster()
 {
 string dataPath = GetDataPath(@"external/20newsgroups.txt");

 var pipeline = new LearningPipeline();
 pipeline.Add(new TextLoader(dataPath).CreateFrom<NewsData>(useHeader: false, allowQuotedStrings:true, supportSparse:false));
 var pipeline = new LearningPipeline(seed: 1, conc: 1);
 pipeline.Add(new TextLoader(dataPath).CreateFrom<NewsData>(useHeader: false, allowQuotedStrings:true, supportSparse:false));
 pipeline.Add(new ColumnConcatenator("AllText", "Subject", "Content"));
 pipeline.Add(new TextFeaturizer("Features", "AllText")
 {
Expand DownExpand Up@@ -81,8 +81,8 @@ public class ClusteringData
 public void PredictClusters()
 {
 int n = 1000;
 int k = 5;
 var rand = new Random();
 int k = 4;
 var rand = new Random(1);
@Ivanidzo4kaIvanidzo4kaMay 31, 2018

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Random(1) [](start = 26, length = 10)
Looks like this is main reason for Random failure.
 var clusters = new ClusteringData[k];
 var data = new ClusteringData[n];
 for (int i = 0; i < k; i++)
Expand All@@ -94,7 +94,7 @@ public void PredictClusters()
 for (int i = 0; i < n; i++)
 {
 var index = rand.Next(0, k);
 var shift = (rand.NextDouble() - 0.5) / k;
 var shift = (rand.NextDouble() - 0.5) / 10;
 data[i] = new ClusteringData
 {
 Points = new float[2]
Expand All@@ -104,7 +104,7 @@ public void PredictClusters()
 }
 };
 }
 var pipeline = new LearningPipeline();
 var pipeline = new LearningPipeline(seed: 1, conc: 1);
 pipeline.Add(CollectionDataSource.Create(data));
 pipeline.Add(new KMeansPlusPlusClusterer() { K = k });
 var model = pipeline.Train<ClusteringData, ClusteringPrediction>();
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