Updated sample for Concatenate API.#3262

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zeahmed merged 5 commits into from
Apr 10, 2019
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Updated sample for Concatenate API. #3262

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bfbb172
Updated sample for Concatenate API.
zeahmed Apr 9, 2019
fe1adfc
Addressed reviewers' comments.
zeahmed Apr 10, 2019
6829e2c
Resolved merge conflicts.
zeahmed Apr 10, 2019
448919b
Addressed reviewers' comments.
zeahmed Apr 10, 2019
2086c38
Merge remote-tracking branch 'upstream/master' into concate_sample
zeahmed Apr 10, 2019
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78 changes: 43 additions & 35 deletions docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Concatenate.cs
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Open in desktop
Original file line numberDiff line numberDiff line change
@@ -1,65 +1,73 @@
using System;
using System.Collections.Generic;
using Microsoft.ML;
using Microsoft.ML.Data;

namespace Samples.Dynamic
{
public static class ConcatTransform
public static class Concatenate
{
public static void Example()
{
// Create a new ML context, for ML.NET operations. It can be used for exception tracking and logging,
// as well as the source of randomness.
var mlContext = new MLContext();

// Get a small dataset as an IEnumerable and them read it as ML.NET's data type.
var data = Microsoft.ML.SamplesUtils.DatasetUtils.GetInfertData();
var trainData = mlContext.Data.LoadFromEnumerable(data);
// Create a small dataset as an IEnumerable.
var samples = new List<InputData>()
{
new InputData(){ Feature1 = 0.1f, Feature2 = new[]{ 1.1f, 2.1f, 3.1f}, Feature3 = 1 },
new InputData(){ Feature1 = 0.2f, Feature2 = new[]{ 1.2f, 2.2f, 3.2f}, Feature3 = 2 },
new InputData(){ Feature1 = 0.3f, Feature2 = new[]{ 1.3f, 2.3f, 3.3f}, Feature3 = 3 },
new InputData(){ Feature1 = 0.4f, Feature2 = new[]{ 1.4f, 2.4f, 3.4f}, Feature3 = 4 },
new InputData(){ Feature1 = 0.5f, Feature2 = new[]{ 1.5f, 2.5f, 3.5f}, Feature3 = 5 },
new InputData(){ Feature1 = 0.6f, Feature2 = new[]{ 1.6f, 2.6f, 3.6f}, Feature3 = 6 },
};

// Preview of the data.
//
// Age Case Education induced parity pooled.stratum row_num ...
// 26.0 1.0 0-5yrs 1.0 6.0 3.0 1.0 ...
// 42.0 1.0 0-5yrs 1.0 1.0 1.0 2.0 ...
// 39.0 1.0 0-5yrs 2.0 6.0 4.0 3.0 ...
// 34.0 1.0 0-5yrs 2.0 4.0 2.0 4.0 ...
// 35.0 1.0 6-11yrs 1.0 3.0 32.0 5.0 ...
// Convert training data to IDataView.
var dataview = mlContext.Data.LoadFromEnumerable(samples);

// A pipeline for concatenating the Age, Parity and Induced columns together into a vector that will be the Features column.
// Concatenation is necessary because learners take **feature vectors** as inputs.
// e.g. var regressionTrainer = mlContext.Regression.Trainers.FastTree(labelColumn: "Label", featureColumn: "Features");
string outputColumnName = "Features";
var pipeline = mlContext.Transforms.Concatenate(outputColumnName, new[] { "Age", "Parity", "Induced" });
// A pipeline for concatenating the "Feature1", "Feature2" and "Feature3" columns together into a vector that will be the Features column.
// Concatenation is necessary because trainers take feature vectors as inputs.
//
// Please note that the "Feature3" column is converted from int32 to float using the ConvertType.
// The Concatenate requires all columns to be of same type.
var pipeline = mlContext.Transforms.Conversion.ConvertType("Feature3", outputKind: DataKind.Single)
.Append(mlContext.Transforms.Concatenate("Features", new[] { "Feature1", "Feature2", "Feature3" }));

// The transformed data.
var transformedData = pipeline.Fit(trainData).Transform(trainData);
var transformedData = pipeline.Fit(dataview).Transform(dataview);

// Now let's take a look at what this concatenation did.
// We can extract the newly created column as an IEnumerable of SampleInfertDataWithFeatures, the class we define above.
var featuresColumn = mlContext.Data.CreateEnumerable<SampleInfertDataWithFeatures>(transformedData, reuseRowObject: false);
// We can extract the newly created column as an IEnumerable of TransformedData.
var featuresColumn = mlContext.Data.CreateEnumerable<TransformedData>(transformedData, reuseRowObject: false);

// And we can write out a few rows
Console.WriteLine($"{outputColumnName} column obtained post-transformation.");
Console.WriteLine($"Features column obtained post-transformation.");
foreach (var featureRow in featuresColumn)
{
foreach (var value in featureRow.Features.GetValues())
Console.Write($"{value} ");
Console.WriteLine("");
}
Console.WriteLine(string.Join(" ", featureRow.Features));

// Expected output:
// Features column obtained post-transformation.
//
// 26 6 1
// 42 1 1
// 39 6 2
// 34 4 2
// 35 3 1
// Features column obtained post-transformation.
// 0.1 1.1 2.1 3.1 1
// 0.2 1.2 2.2 3.2 2
// 0.3 1.3 2.3 3.3 3
// 0.4 1.4 2.4 3.4 4
// 0.5 1.5 2.5 3.5 5
// 0.6 1.6 2.6 3.6 6
}

private class InputData
{
public float Feature1;
[VectorType(3)]
public float[] Feature2;

@Ivanidzo4kaIvanidzo4kaApr 9, 2019•
edited by zeahmed
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I would put VectorType(3) here.
Otherwise you concat scalar+VarVector+Scalar which would be VarVector and you can't use it for trainers.
Or maybe include that in sample, two concats, one on varvector, one on vector.... #Resolved

public int Feature3;
}

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

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Updated sample for Concatenate API.#3262

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 Merged

zeahmed
merged 5 commits into

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Apr 10, 2019
 Merged


Updated sample for Concatenate API.
#3262
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bfbb172

Updated sample for Concatenate API.


zeahmed Apr 9, 2019
fe1adfc

Addressed reviewers' comments.


zeahmed Apr 10, 2019
6829e2c

Resolved merge conflicts.


zeahmed Apr 10, 2019
448919b

Addressed reviewers' comments.


zeahmed Apr 10, 2019
2086c38

Merge remote-tracking branch 'upstream/master' into concate_sample


zeahmed Apr 10, 2019

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78 changes: 43 additions & 35 deletions
docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Concatenate.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -1,65 +1,73 @@
using System;
using System.Collections.Generic;
using Microsoft.ML;
using Microsoft.ML.Data;

namespace Samples.Dynamic
{
 public static class ConcatTransform
 public static class Concatenate
 {
 public static void Example()
 {
 // Create a new ML context, for ML.NET operations. It can be used for exception tracking and logging, 
 // as well as the source of randomness.
 var mlContext = new MLContext();

 // Get a small dataset as an IEnumerable and them read it as ML.NET's data type.
 var data = Microsoft.ML.SamplesUtils.DatasetUtils.GetInfertData();
 var trainData = mlContext.Data.LoadFromEnumerable(data);
 // Create a small dataset as an IEnumerable.
 var samples = new List<InputData>()
 {
 new InputData(){ Feature1 = 0.1f, Feature2 = new[]{ 1.1f, 2.1f, 3.1f}, Feature3 = 1 },
 new InputData(){ Feature1 = 0.2f, Feature2 = new[]{ 1.2f, 2.2f, 3.2f}, Feature3 = 2 },
 new InputData(){ Feature1 = 0.3f, Feature2 = new[]{ 1.3f, 2.3f, 3.3f}, Feature3 = 3 },
 new InputData(){ Feature1 = 0.4f, Feature2 = new[]{ 1.4f, 2.4f, 3.4f}, Feature3 = 4 },
 new InputData(){ Feature1 = 0.5f, Feature2 = new[]{ 1.5f, 2.5f, 3.5f}, Feature3 = 5 },
 new InputData(){ Feature1 = 0.6f, Feature2 = new[]{ 1.6f, 2.6f, 3.6f}, Feature3 = 6 },
 };

 // Preview of the data.
 //
 // Age Case Education induced parity pooled.stratum row_num ...
 // 26.0 1.0 0-5yrs 1.0 6.0 3.0 1.0 ...
 // 42.0 1.0 0-5yrs 1.0 1.0 1.0 2.0 ...
 // 39.0 1.0 0-5yrs 2.0 6.0 4.0 3.0 ...
 // 34.0 1.0 0-5yrs 2.0 4.0 2.0 4.0 ...
 // 35.0 1.0 6-11yrs 1.0 3.0 32.0 5.0 ...
 // Convert training data to IDataView.
 var dataview = mlContext.Data.LoadFromEnumerable(samples);

 // A pipeline for concatenating the Age, Parity and Induced columns together into a vector that will be the Features column.
 // Concatenation is necessary because learners take **feature vectors** as inputs.
 // e.g. var regressionTrainer = mlContext.Regression.Trainers.FastTree(labelColumn: "Label", featureColumn: "Features");
 string outputColumnName = "Features";
 var pipeline = mlContext.Transforms.Concatenate(outputColumnName, new[] { "Age", "Parity", "Induced" });
 // A pipeline for concatenating the "Feature1", "Feature2" and "Feature3" columns together into a vector that will be the Features column.
 // Concatenation is necessary because trainers take feature vectors as inputs.
 //
 // Please note that the "Feature3" column is converted from int32 to float using the ConvertType.
 // The Concatenate requires all columns to be of same type.
 var pipeline = mlContext.Transforms.Conversion.ConvertType("Feature3", outputKind: DataKind.Single)
 .Append(mlContext.Transforms.Concatenate("Features", new[] { "Feature1", "Feature2", "Feature3" }));

 // The transformed data.
 var transformedData = pipeline.Fit(trainData).Transform(trainData);
 var transformedData = pipeline.Fit(dataview).Transform(dataview);

 // Now let's take a look at what this concatenation did.
 // We can extract the newly created column as an IEnumerable of SampleInfertDataWithFeatures, the class we define above.
 var featuresColumn = mlContext.Data.CreateEnumerable<SampleInfertDataWithFeatures>(transformedData, reuseRowObject: false);
 // We can extract the newly created column as an IEnumerable of TransformedData.
 var featuresColumn = mlContext.Data.CreateEnumerable<TransformedData>(transformedData, reuseRowObject: false);

 // And we can write out a few rows
 Console.WriteLine($"{outputColumnName} column obtained post-transformation.");
 Console.WriteLine($"Features column obtained post-transformation.");
 foreach (var featureRow in featuresColumn)
 {
 foreach (var value in featureRow.Features.GetValues())
 Console.Write($"{value} ");
 Console.WriteLine("");
 }
 Console.WriteLine(string.Join(" ", featureRow.Features));

 // Expected output:
 // Features column obtained post-transformation.
 //
 // 26 6 1
 // 42 1 1
 // 39 6 2
 // 34 4 2
 // 35 3 1
 // Features column obtained post-transformation.
 // 0.1 1.1 2.1 3.1 1
 // 0.2 1.2 2.2 3.2 2
 // 0.3 1.3 2.3 3.3 3
 // 0.4 1.4 2.4 3.4 4
 // 0.5 1.5 2.5 3.5 5
 // 0.6 1.6 2.6 3.6 6
 }

 private class InputData
 {
 public float Feature1;
 [VectorType(3)]
 public float[] Feature2;
@Ivanidzo4kaIvanidzo4kaApr 9, 2019•

edited by zeahmed

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Contributor
Choose a reason for hiding this comment

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I would put VectorType(3) here.

Otherwise you concat scalar+VarVector+Scalar which would be VarVector and you can't use it for trainers.

Or maybe include that in sample, two concats, one on varvector, one on vector.... #Resolved
 public int Feature3;
 }

 private class SampleInfertDataWithFeatures
 private sealed class TransformedData
 {
 public VBuffer<float> Features { get; set; }
 public float[] Features { get; set; }
 }
 }
}

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

Skip to content
Updated sample for Concatenate API.#3262

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 Merged

zeahmed
merged 5 commits into

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Apr 10, 2019
 Merged


Updated sample for Concatenate API.
#3262
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5 commits


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bfbb172

Updated sample for Concatenate API.


zeahmed Apr 9, 2019
fe1adfc

Addressed reviewers' comments.


zeahmed Apr 10, 2019
6829e2c

Resolved merge conflicts.


zeahmed Apr 10, 2019
448919b

Addressed reviewers' comments.


zeahmed Apr 10, 2019
2086c38

Merge remote-tracking branch 'upstream/master' into concate_sample


zeahmed Apr 10, 2019

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78 changes: 43 additions & 35 deletions
docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Concatenate.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -1,65 +1,73 @@
using System;
using System.Collections.Generic;
using Microsoft.ML;
using Microsoft.ML.Data;

namespace Samples.Dynamic
{
 public static class ConcatTransform
 public static class Concatenate
 {
 public static void Example()
 {
 // Create a new ML context, for ML.NET operations. It can be used for exception tracking and logging, 
 // as well as the source of randomness.
 var mlContext = new MLContext();

 // Get a small dataset as an IEnumerable and them read it as ML.NET's data type.
 var data = Microsoft.ML.SamplesUtils.DatasetUtils.GetInfertData();
 var trainData = mlContext.Data.LoadFromEnumerable(data);
 // Create a small dataset as an IEnumerable.
 var samples = new List<InputData>()
 {
 new InputData(){ Feature1 = 0.1f, Feature2 = new[]{ 1.1f, 2.1f, 3.1f}, Feature3 = 1 },
 new InputData(){ Feature1 = 0.2f, Feature2 = new[]{ 1.2f, 2.2f, 3.2f}, Feature3 = 2 },
 new InputData(){ Feature1 = 0.3f, Feature2 = new[]{ 1.3f, 2.3f, 3.3f}, Feature3 = 3 },
 new InputData(){ Feature1 = 0.4f, Feature2 = new[]{ 1.4f, 2.4f, 3.4f}, Feature3 = 4 },
 new InputData(){ Feature1 = 0.5f, Feature2 = new[]{ 1.5f, 2.5f, 3.5f}, Feature3 = 5 },
 new InputData(){ Feature1 = 0.6f, Feature2 = new[]{ 1.6f, 2.6f, 3.6f}, Feature3 = 6 },
 };

 // Preview of the data.
 //
 // Age Case Education induced parity pooled.stratum row_num ...
 // 26.0 1.0 0-5yrs 1.0 6.0 3.0 1.0 ...
 // 42.0 1.0 0-5yrs 1.0 1.0 1.0 2.0 ...
 // 39.0 1.0 0-5yrs 2.0 6.0 4.0 3.0 ...
 // 34.0 1.0 0-5yrs 2.0 4.0 2.0 4.0 ...
 // 35.0 1.0 6-11yrs 1.0 3.0 32.0 5.0 ...
 // Convert training data to IDataView.
 var dataview = mlContext.Data.LoadFromEnumerable(samples);

 // A pipeline for concatenating the Age, Parity and Induced columns together into a vector that will be the Features column.
 // Concatenation is necessary because learners take **feature vectors** as inputs.
 // e.g. var regressionTrainer = mlContext.Regression.Trainers.FastTree(labelColumn: "Label", featureColumn: "Features");
 string outputColumnName = "Features";
 var pipeline = mlContext.Transforms.Concatenate(outputColumnName, new[] { "Age", "Parity", "Induced" });
 // A pipeline for concatenating the "Feature1", "Feature2" and "Feature3" columns together into a vector that will be the Features column.
 // Concatenation is necessary because trainers take feature vectors as inputs.
 //
 // Please note that the "Feature3" column is converted from int32 to float using the ConvertType.
 // The Concatenate requires all columns to be of same type.
 var pipeline = mlContext.Transforms.Conversion.ConvertType("Feature3", outputKind: DataKind.Single)
 .Append(mlContext.Transforms.Concatenate("Features", new[] { "Feature1", "Feature2", "Feature3" }));

 // The transformed data.
 var transformedData = pipeline.Fit(trainData).Transform(trainData);
 var transformedData = pipeline.Fit(dataview).Transform(dataview);

 // Now let's take a look at what this concatenation did.
 // We can extract the newly created column as an IEnumerable of SampleInfertDataWithFeatures, the class we define above.
 var featuresColumn = mlContext.Data.CreateEnumerable<SampleInfertDataWithFeatures>(transformedData, reuseRowObject: false);
 // We can extract the newly created column as an IEnumerable of TransformedData.
 var featuresColumn = mlContext.Data.CreateEnumerable<TransformedData>(transformedData, reuseRowObject: false);

 // And we can write out a few rows
 Console.WriteLine($"{outputColumnName} column obtained post-transformation.");
 Console.WriteLine($"Features column obtained post-transformation.");
 foreach (var featureRow in featuresColumn)
 {
 foreach (var value in featureRow.Features.GetValues())
 Console.Write($"{value} ");
 Console.WriteLine("");
 }
 Console.WriteLine(string.Join(" ", featureRow.Features));

 // Expected output:
 // Features column obtained post-transformation.
 //
 // 26 6 1
 // 42 1 1
 // 39 6 2
 // 34 4 2
 // 35 3 1
 // Features column obtained post-transformation.
 // 0.1 1.1 2.1 3.1 1
 // 0.2 1.2 2.2 3.2 2
 // 0.3 1.3 2.3 3.3 3
 // 0.4 1.4 2.4 3.4 4
 // 0.5 1.5 2.5 3.5 5
 // 0.6 1.6 2.6 3.6 6
 }

 private class InputData
 {
 public float Feature1;
 [VectorType(3)]
 public float[] Feature2;
@Ivanidzo4kaIvanidzo4kaApr 9, 2019•

edited by zeahmed

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Choose a reason for hiding this comment

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

I would put VectorType(3) here.

Otherwise you concat scalar+VarVector+Scalar which would be VarVector and you can't use it for trainers.

Or maybe include that in sample, two concats, one on varvector, one on vector.... #Resolved
 public int Feature3;
 }

 private class SampleInfertDataWithFeatures
 private sealed class TransformedData
 {
 public VBuffer<float> Features { get; set; }
 public float[] Features { get; set; }
 }
 }
}

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

Skip to content
Updated sample for Concatenate API.#3262

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 Merged

zeahmed
merged 5 commits into

from

Apr 10, 2019
 Merged


Updated sample for Concatenate API.
#3262
 Changes from all commits
Commits

Show all changes


5 commits


Select commit
Hold shift + click to select a range
bfbb172

Updated sample for Concatenate API.


zeahmed Apr 9, 2019
fe1adfc

Addressed reviewers' comments.


zeahmed Apr 10, 2019
6829e2c

Resolved merge conflicts.


zeahmed Apr 10, 2019
448919b

Addressed reviewers' comments.


zeahmed Apr 10, 2019
2086c38

Merge remote-tracking branch 'upstream/master' into concate_sample


zeahmed Apr 10, 2019

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78 changes: 43 additions & 35 deletions
docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Concatenate.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -1,65 +1,73 @@
using System;
using System.Collections.Generic;
using Microsoft.ML;
using Microsoft.ML.Data;

namespace Samples.Dynamic
{
 public static class ConcatTransform
 public static class Concatenate
 {
 public static void Example()
 {
 // Create a new ML context, for ML.NET operations. It can be used for exception tracking and logging, 
 // as well as the source of randomness.
 var mlContext = new MLContext();

 // Get a small dataset as an IEnumerable and them read it as ML.NET's data type.
 var data = Microsoft.ML.SamplesUtils.DatasetUtils.GetInfertData();
 var trainData = mlContext.Data.LoadFromEnumerable(data);
 // Create a small dataset as an IEnumerable.
 var samples = new List<InputData>()
 {
 new InputData(){ Feature1 = 0.1f, Feature2 = new[]{ 1.1f, 2.1f, 3.1f}, Feature3 = 1 },
 new InputData(){ Feature1 = 0.2f, Feature2 = new[]{ 1.2f, 2.2f, 3.2f}, Feature3 = 2 },
 new InputData(){ Feature1 = 0.3f, Feature2 = new[]{ 1.3f, 2.3f, 3.3f}, Feature3 = 3 },
 new InputData(){ Feature1 = 0.4f, Feature2 = new[]{ 1.4f, 2.4f, 3.4f}, Feature3 = 4 },
 new InputData(){ Feature1 = 0.5f, Feature2 = new[]{ 1.5f, 2.5f, 3.5f}, Feature3 = 5 },
 new InputData(){ Feature1 = 0.6f, Feature2 = new[]{ 1.6f, 2.6f, 3.6f}, Feature3 = 6 },
 };

 // Preview of the data.
 //
 // Age Case Education induced parity pooled.stratum row_num ...
 // 26.0 1.0 0-5yrs 1.0 6.0 3.0 1.0 ...
 // 42.0 1.0 0-5yrs 1.0 1.0 1.0 2.0 ...
 // 39.0 1.0 0-5yrs 2.0 6.0 4.0 3.0 ...
 // 34.0 1.0 0-5yrs 2.0 4.0 2.0 4.0 ...
 // 35.0 1.0 6-11yrs 1.0 3.0 32.0 5.0 ...
 // Convert training data to IDataView.
 var dataview = mlContext.Data.LoadFromEnumerable(samples);

 // A pipeline for concatenating the Age, Parity and Induced columns together into a vector that will be the Features column.
 // Concatenation is necessary because learners take **feature vectors** as inputs.
 // e.g. var regressionTrainer = mlContext.Regression.Trainers.FastTree(labelColumn: "Label", featureColumn: "Features");
 string outputColumnName = "Features";
 var pipeline = mlContext.Transforms.Concatenate(outputColumnName, new[] { "Age", "Parity", "Induced" });
 // A pipeline for concatenating the "Feature1", "Feature2" and "Feature3" columns together into a vector that will be the Features column.
 // Concatenation is necessary because trainers take feature vectors as inputs.
 //
 // Please note that the "Feature3" column is converted from int32 to float using the ConvertType.
 // The Concatenate requires all columns to be of same type.
 var pipeline = mlContext.Transforms.Conversion.ConvertType("Feature3", outputKind: DataKind.Single)
 .Append(mlContext.Transforms.Concatenate("Features", new[] { "Feature1", "Feature2", "Feature3" }));

 // The transformed data.
 var transformedData = pipeline.Fit(trainData).Transform(trainData);
 var transformedData = pipeline.Fit(dataview).Transform(dataview);

 // Now let's take a look at what this concatenation did.
 // We can extract the newly created column as an IEnumerable of SampleInfertDataWithFeatures, the class we define above.
 var featuresColumn = mlContext.Data.CreateEnumerable<SampleInfertDataWithFeatures>(transformedData, reuseRowObject: false);
 // We can extract the newly created column as an IEnumerable of TransformedData.
 var featuresColumn = mlContext.Data.CreateEnumerable<TransformedData>(transformedData, reuseRowObject: false);

 // And we can write out a few rows
 Console.WriteLine($"{outputColumnName} column obtained post-transformation.");
 Console.WriteLine($"Features column obtained post-transformation.");
 foreach (var featureRow in featuresColumn)
 {
 foreach (var value in featureRow.Features.GetValues())
 Console.Write($"{value} ");
 Console.WriteLine("");
 }
 Console.WriteLine(string.Join(" ", featureRow.Features));

 // Expected output:
 // Features column obtained post-transformation.
 //
 // 26 6 1
 // 42 1 1
 // 39 6 2
 // 34 4 2
 // 35 3 1
 // Features column obtained post-transformation.
 // 0.1 1.1 2.1 3.1 1
 // 0.2 1.2 2.2 3.2 2
 // 0.3 1.3 2.3 3.3 3
 // 0.4 1.4 2.4 3.4 4
 // 0.5 1.5 2.5 3.5 5
 // 0.6 1.6 2.6 3.6 6
 }

 private class InputData
 {
 public float Feature1;
 [VectorType(3)]
 public float[] Feature2;
@Ivanidzo4kaIvanidzo4kaApr 9, 2019•

edited by zeahmed

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I would put VectorType(3) here.

Otherwise you concat scalar+VarVector+Scalar which would be VarVector and you can't use it for trainers.

Or maybe include that in sample, two concats, one on varvector, one on vector.... #Resolved
 public int Feature3;
 }

 private class SampleInfertDataWithFeatures
 private sealed class TransformedData
 {
 public VBuffer<float> Features { get; set; }
 public float[] Features { get; set; }
 }
 }
}

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

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Updated sample for Concatenate API.
#3262
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bfbb172

Updated sample for Concatenate API.


zeahmed Apr 9, 2019
fe1adfc

Addressed reviewers' comments.


zeahmed Apr 10, 2019
6829e2c

Resolved merge conflicts.


zeahmed Apr 10, 2019
448919b

Addressed reviewers' comments.


zeahmed Apr 10, 2019
2086c38

Merge remote-tracking branch 'upstream/master' into concate_sample


zeahmed Apr 10, 2019

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78 changes: 43 additions & 35 deletions
docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Concatenate.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -1,65 +1,73 @@
using System;
using System.Collections.Generic;
using Microsoft.ML;
using Microsoft.ML.Data;

namespace Samples.Dynamic
{
 public static class ConcatTransform
 public static class Concatenate
 {
 public static void Example()
 {
 // Create a new ML context, for ML.NET operations. It can be used for exception tracking and logging, 
 // as well as the source of randomness.
 var mlContext = new MLContext();

 // Get a small dataset as an IEnumerable and them read it as ML.NET's data type.
 var data = Microsoft.ML.SamplesUtils.DatasetUtils.GetInfertData();
 var trainData = mlContext.Data.LoadFromEnumerable(data);
 // Create a small dataset as an IEnumerable.
 var samples = new List<InputData>()
 {
 new InputData(){ Feature1 = 0.1f, Feature2 = new[]{ 1.1f, 2.1f, 3.1f}, Feature3 = 1 },
 new InputData(){ Feature1 = 0.2f, Feature2 = new[]{ 1.2f, 2.2f, 3.2f}, Feature3 = 2 },
 new InputData(){ Feature1 = 0.3f, Feature2 = new[]{ 1.3f, 2.3f, 3.3f}, Feature3 = 3 },
 new InputData(){ Feature1 = 0.4f, Feature2 = new[]{ 1.4f, 2.4f, 3.4f}, Feature3 = 4 },
 new InputData(){ Feature1 = 0.5f, Feature2 = new[]{ 1.5f, 2.5f, 3.5f}, Feature3 = 5 },
 new InputData(){ Feature1 = 0.6f, Feature2 = new[]{ 1.6f, 2.6f, 3.6f}, Feature3 = 6 },
 };

 // Preview of the data.
 //
 // Age Case Education induced parity pooled.stratum row_num ...
 // 26.0 1.0 0-5yrs 1.0 6.0 3.0 1.0 ...
 // 42.0 1.0 0-5yrs 1.0 1.0 1.0 2.0 ...
 // 39.0 1.0 0-5yrs 2.0 6.0 4.0 3.0 ...
 // 34.0 1.0 0-5yrs 2.0 4.0 2.0 4.0 ...
 // 35.0 1.0 6-11yrs 1.0 3.0 32.0 5.0 ...
 // Convert training data to IDataView.
 var dataview = mlContext.Data.LoadFromEnumerable(samples);

 // A pipeline for concatenating the Age, Parity and Induced columns together into a vector that will be the Features column.
 // Concatenation is necessary because learners take **feature vectors** as inputs.
 // e.g. var regressionTrainer = mlContext.Regression.Trainers.FastTree(labelColumn: "Label", featureColumn: "Features");
 string outputColumnName = "Features";
 var pipeline = mlContext.Transforms.Concatenate(outputColumnName, new[] { "Age", "Parity", "Induced" });
 // A pipeline for concatenating the "Feature1", "Feature2" and "Feature3" columns together into a vector that will be the Features column.
 // Concatenation is necessary because trainers take feature vectors as inputs.
 //
 // Please note that the "Feature3" column is converted from int32 to float using the ConvertType.
 // The Concatenate requires all columns to be of same type.
 var pipeline = mlContext.Transforms.Conversion.ConvertType("Feature3", outputKind: DataKind.Single)
 .Append(mlContext.Transforms.Concatenate("Features", new[] { "Feature1", "Feature2", "Feature3" }));

 // The transformed data.
 var transformedData = pipeline.Fit(trainData).Transform(trainData);
 var transformedData = pipeline.Fit(dataview).Transform(dataview);

 // Now let's take a look at what this concatenation did.
 // We can extract the newly created column as an IEnumerable of SampleInfertDataWithFeatures, the class we define above.
 var featuresColumn = mlContext.Data.CreateEnumerable<SampleInfertDataWithFeatures>(transformedData, reuseRowObject: false);
 // We can extract the newly created column as an IEnumerable of TransformedData.
 var featuresColumn = mlContext.Data.CreateEnumerable<TransformedData>(transformedData, reuseRowObject: false);

 // And we can write out a few rows
 Console.WriteLine($"{outputColumnName} column obtained post-transformation.");
 Console.WriteLine($"Features column obtained post-transformation.");
 foreach (var featureRow in featuresColumn)
 {
 foreach (var value in featureRow.Features.GetValues())
 Console.Write($"{value} ");
 Console.WriteLine("");
 }
 Console.WriteLine(string.Join(" ", featureRow.Features));

 // Expected output:
 // Features column obtained post-transformation.
 //
 // 26 6 1
 // 42 1 1
 // 39 6 2
 // 34 4 2
 // 35 3 1
 // Features column obtained post-transformation.
 // 0.1 1.1 2.1 3.1 1
 // 0.2 1.2 2.2 3.2 2
 // 0.3 1.3 2.3 3.3 3
 // 0.4 1.4 2.4 3.4 4
 // 0.5 1.5 2.5 3.5 5
 // 0.6 1.6 2.6 3.6 6
 }

 private class InputData
 {
 public float Feature1;
 [VectorType(3)]
 public float[] Feature2;
@Ivanidzo4kaIvanidzo4kaApr 9, 2019•

edited by zeahmed

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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.

I would put VectorType(3) here.

Otherwise you concat scalar+VarVector+Scalar which would be VarVector and you can't use it for trainers.

Or maybe include that in sample, two concats, one on varvector, one on vector.... #Resolved
 public int Feature3;
 }

 private class SampleInfertDataWithFeatures
 private sealed class TransformedData
 {
 public VBuffer<float> Features { get; set; }
 public float[] Features { get; set; }
 }
 }
}

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

Skip to content
Updated sample for Concatenate API.#3262

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 Merged

zeahmed
merged 5 commits into

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Apr 10, 2019
 Merged


Updated sample for Concatenate API.
#3262
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bfbb172

Updated sample for Concatenate API.


zeahmed Apr 9, 2019
fe1adfc

Addressed reviewers' comments.


zeahmed Apr 10, 2019
6829e2c

Resolved merge conflicts.


zeahmed Apr 10, 2019
448919b

Addressed reviewers' comments.


zeahmed Apr 10, 2019
2086c38

Merge remote-tracking branch 'upstream/master' into concate_sample


zeahmed Apr 10, 2019

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78 changes: 43 additions & 35 deletions
docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Concatenate.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -1,65 +1,73 @@
using System;
using System.Collections.Generic;
using Microsoft.ML;
using Microsoft.ML.Data;

namespace Samples.Dynamic
{
 public static class ConcatTransform
 public static class Concatenate
 {
 public static void Example()
 {
 // Create a new ML context, for ML.NET operations. It can be used for exception tracking and logging, 
 // as well as the source of randomness.
 var mlContext = new MLContext();

 // Get a small dataset as an IEnumerable and them read it as ML.NET's data type.
 var data = Microsoft.ML.SamplesUtils.DatasetUtils.GetInfertData();
 var trainData = mlContext.Data.LoadFromEnumerable(data);
 // Create a small dataset as an IEnumerable.
 var samples = new List<InputData>()
 {
 new InputData(){ Feature1 = 0.1f, Feature2 = new[]{ 1.1f, 2.1f, 3.1f}, Feature3 = 1 },
 new InputData(){ Feature1 = 0.2f, Feature2 = new[]{ 1.2f, 2.2f, 3.2f}, Feature3 = 2 },
 new InputData(){ Feature1 = 0.3f, Feature2 = new[]{ 1.3f, 2.3f, 3.3f}, Feature3 = 3 },
 new InputData(){ Feature1 = 0.4f, Feature2 = new[]{ 1.4f, 2.4f, 3.4f}, Feature3 = 4 },
 new InputData(){ Feature1 = 0.5f, Feature2 = new[]{ 1.5f, 2.5f, 3.5f}, Feature3 = 5 },
 new InputData(){ Feature1 = 0.6f, Feature2 = new[]{ 1.6f, 2.6f, 3.6f}, Feature3 = 6 },
 };

 // Preview of the data.
 //
 // Age Case Education induced parity pooled.stratum row_num ...
 // 26.0 1.0 0-5yrs 1.0 6.0 3.0 1.0 ...
 // 42.0 1.0 0-5yrs 1.0 1.0 1.0 2.0 ...
 // 39.0 1.0 0-5yrs 2.0 6.0 4.0 3.0 ...
 // 34.0 1.0 0-5yrs 2.0 4.0 2.0 4.0 ...
 // 35.0 1.0 6-11yrs 1.0 3.0 32.0 5.0 ...
 // Convert training data to IDataView.
 var dataview = mlContext.Data.LoadFromEnumerable(samples);

 // A pipeline for concatenating the Age, Parity and Induced columns together into a vector that will be the Features column.
 // Concatenation is necessary because learners take **feature vectors** as inputs.
 // e.g. var regressionTrainer = mlContext.Regression.Trainers.FastTree(labelColumn: "Label", featureColumn: "Features");
 string outputColumnName = "Features";
 var pipeline = mlContext.Transforms.Concatenate(outputColumnName, new[] { "Age", "Parity", "Induced" });
 // A pipeline for concatenating the "Feature1", "Feature2" and "Feature3" columns together into a vector that will be the Features column.
 // Concatenation is necessary because trainers take feature vectors as inputs.
 //
 // Please note that the "Feature3" column is converted from int32 to float using the ConvertType.
 // The Concatenate requires all columns to be of same type.
 var pipeline = mlContext.Transforms.Conversion.ConvertType("Feature3", outputKind: DataKind.Single)
 .Append(mlContext.Transforms.Concatenate("Features", new[] { "Feature1", "Feature2", "Feature3" }));

 // The transformed data.
 var transformedData = pipeline.Fit(trainData).Transform(trainData);
 var transformedData = pipeline.Fit(dataview).Transform(dataview);

 // Now let's take a look at what this concatenation did.
 // We can extract the newly created column as an IEnumerable of SampleInfertDataWithFeatures, the class we define above.
 var featuresColumn = mlContext.Data.CreateEnumerable<SampleInfertDataWithFeatures>(transformedData, reuseRowObject: false);
 // We can extract the newly created column as an IEnumerable of TransformedData.
 var featuresColumn = mlContext.Data.CreateEnumerable<TransformedData>(transformedData, reuseRowObject: false);

 // And we can write out a few rows
 Console.WriteLine($"{outputColumnName} column obtained post-transformation.");
 Console.WriteLine($"Features column obtained post-transformation.");
 foreach (var featureRow in featuresColumn)
 {
 foreach (var value in featureRow.Features.GetValues())
 Console.Write($"{value} ");
 Console.WriteLine("");
 }
 Console.WriteLine(string.Join(" ", featureRow.Features));

 // Expected output:
 // Features column obtained post-transformation.
 //
 // 26 6 1
 // 42 1 1
 // 39 6 2
 // 34 4 2
 // 35 3 1
 // Features column obtained post-transformation.
 // 0.1 1.1 2.1 3.1 1
 // 0.2 1.2 2.2 3.2 2
 // 0.3 1.3 2.3 3.3 3
 // 0.4 1.4 2.4 3.4 4
 // 0.5 1.5 2.5 3.5 5
 // 0.6 1.6 2.6 3.6 6
 }

 private class InputData
 {
 public float Feature1;
 [VectorType(3)]
 public float[] Feature2;
@Ivanidzo4kaIvanidzo4kaApr 9, 2019•

edited by zeahmed

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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.

I would put VectorType(3) here.

Otherwise you concat scalar+VarVector+Scalar which would be VarVector and you can't use it for trainers.

Or maybe include that in sample, two concats, one on varvector, one on vector.... #Resolved
 public int Feature3;
 }

 private class SampleInfertDataWithFeatures
 private sealed class TransformedData
 {
 public VBuffer<float> Features { get; set; }
 public float[] Features { get; set; }
 }
 }
}

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

Skip to content
Updated sample for Concatenate API.#3262

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 Merged

zeahmed
merged 5 commits into

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Apr 10, 2019
 Merged


Updated sample for Concatenate API.
#3262
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5 commits


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bfbb172

Updated sample for Concatenate API.


zeahmed Apr 9, 2019
fe1adfc

Addressed reviewers' comments.


zeahmed Apr 10, 2019
6829e2c

Resolved merge conflicts.


zeahmed Apr 10, 2019
448919b

Addressed reviewers' comments.


zeahmed Apr 10, 2019
2086c38

Merge remote-tracking branch 'upstream/master' into concate_sample


zeahmed Apr 10, 2019

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78 changes: 43 additions & 35 deletions
docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Concatenate.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -1,65 +1,73 @@
using System;
using System.Collections.Generic;
using Microsoft.ML;
using Microsoft.ML.Data;

namespace Samples.Dynamic
{
 public static class ConcatTransform
 public static class Concatenate
 {
 public static void Example()
 {
 // Create a new ML context, for ML.NET operations. It can be used for exception tracking and logging, 
 // as well as the source of randomness.
 var mlContext = new MLContext();

 // Get a small dataset as an IEnumerable and them read it as ML.NET's data type.
 var data = Microsoft.ML.SamplesUtils.DatasetUtils.GetInfertData();
 var trainData = mlContext.Data.LoadFromEnumerable(data);
 // Create a small dataset as an IEnumerable.
 var samples = new List<InputData>()
 {
 new InputData(){ Feature1 = 0.1f, Feature2 = new[]{ 1.1f, 2.1f, 3.1f}, Feature3 = 1 },
 new InputData(){ Feature1 = 0.2f, Feature2 = new[]{ 1.2f, 2.2f, 3.2f}, Feature3 = 2 },
 new InputData(){ Feature1 = 0.3f, Feature2 = new[]{ 1.3f, 2.3f, 3.3f}, Feature3 = 3 },
 new InputData(){ Feature1 = 0.4f, Feature2 = new[]{ 1.4f, 2.4f, 3.4f}, Feature3 = 4 },
 new InputData(){ Feature1 = 0.5f, Feature2 = new[]{ 1.5f, 2.5f, 3.5f}, Feature3 = 5 },
 new InputData(){ Feature1 = 0.6f, Feature2 = new[]{ 1.6f, 2.6f, 3.6f}, Feature3 = 6 },
 };

 // Preview of the data.
 //
 // Age Case Education induced parity pooled.stratum row_num ...
 // 26.0 1.0 0-5yrs 1.0 6.0 3.0 1.0 ...
 // 42.0 1.0 0-5yrs 1.0 1.0 1.0 2.0 ...
 // 39.0 1.0 0-5yrs 2.0 6.0 4.0 3.0 ...
 // 34.0 1.0 0-5yrs 2.0 4.0 2.0 4.0 ...
 // 35.0 1.0 6-11yrs 1.0 3.0 32.0 5.0 ...
 // Convert training data to IDataView.
 var dataview = mlContext.Data.LoadFromEnumerable(samples);

 // A pipeline for concatenating the Age, Parity and Induced columns together into a vector that will be the Features column.
 // Concatenation is necessary because learners take **feature vectors** as inputs.
 // e.g. var regressionTrainer = mlContext.Regression.Trainers.FastTree(labelColumn: "Label", featureColumn: "Features");
 string outputColumnName = "Features";
 var pipeline = mlContext.Transforms.Concatenate(outputColumnName, new[] { "Age", "Parity", "Induced" });
 // A pipeline for concatenating the "Feature1", "Feature2" and "Feature3" columns together into a vector that will be the Features column.
 // Concatenation is necessary because trainers take feature vectors as inputs.
 //
 // Please note that the "Feature3" column is converted from int32 to float using the ConvertType.
 // The Concatenate requires all columns to be of same type.
 var pipeline = mlContext.Transforms.Conversion.ConvertType("Feature3", outputKind: DataKind.Single)
 .Append(mlContext.Transforms.Concatenate("Features", new[] { "Feature1", "Feature2", "Feature3" }));

 // The transformed data.
 var transformedData = pipeline.Fit(trainData).Transform(trainData);
 var transformedData = pipeline.Fit(dataview).Transform(dataview);

 // Now let's take a look at what this concatenation did.
 // We can extract the newly created column as an IEnumerable of SampleInfertDataWithFeatures, the class we define above.
 var featuresColumn = mlContext.Data.CreateEnumerable<SampleInfertDataWithFeatures>(transformedData, reuseRowObject: false);
 // We can extract the newly created column as an IEnumerable of TransformedData.
 var featuresColumn = mlContext.Data.CreateEnumerable<TransformedData>(transformedData, reuseRowObject: false);

 // And we can write out a few rows
 Console.WriteLine($"{outputColumnName} column obtained post-transformation.");
 Console.WriteLine($"Features column obtained post-transformation.");
 foreach (var featureRow in featuresColumn)
 {
 foreach (var value in featureRow.Features.GetValues())
 Console.Write($"{value} ");
 Console.WriteLine("");
 }
 Console.WriteLine(string.Join(" ", featureRow.Features));

 // Expected output:
 // Features column obtained post-transformation.
 //
 // 26 6 1
 // 42 1 1
 // 39 6 2
 // 34 4 2
 // 35 3 1
 // Features column obtained post-transformation.
 // 0.1 1.1 2.1 3.1 1
 // 0.2 1.2 2.2 3.2 2
 // 0.3 1.3 2.3 3.3 3
 // 0.4 1.4 2.4 3.4 4
 // 0.5 1.5 2.5 3.5 5
 // 0.6 1.6 2.6 3.6 6
 }

 private class InputData
 {
 public float Feature1;
 [VectorType(3)]
 public float[] Feature2;
@Ivanidzo4kaIvanidzo4kaApr 9, 2019•

edited by zeahmed

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I would put VectorType(3) here.

Otherwise you concat scalar+VarVector+Scalar which would be VarVector and you can't use it for trainers.

Or maybe include that in sample, two concats, one on varvector, one on vector.... #Resolved
 public int Feature3;
 }

 private class SampleInfertDataWithFeatures
 private sealed class TransformedData
 {
 public VBuffer<float> Features { get; set; }
 public float[] Features { get; set; }
 }
 }
}

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

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zeahmed
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Apr 10, 2019
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Updated sample for Concatenate API.
#3262
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bfbb172

Updated sample for Concatenate API.


zeahmed Apr 9, 2019
fe1adfc

Addressed reviewers' comments.


zeahmed Apr 10, 2019
6829e2c

Resolved merge conflicts.


zeahmed Apr 10, 2019
448919b

Addressed reviewers' comments.


zeahmed Apr 10, 2019
2086c38

Merge remote-tracking branch 'upstream/master' into concate_sample


zeahmed Apr 10, 2019

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78 changes: 43 additions & 35 deletions
docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Concatenate.cs

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Original file line numberDiff line numberDiff line change
@@ -1,65 +1,73 @@
using System;
using System.Collections.Generic;
using Microsoft.ML;
using Microsoft.ML.Data;

namespace Samples.Dynamic
{
 public static class ConcatTransform
 public static class Concatenate
 {
 public static void Example()
 {
 // Create a new ML context, for ML.NET operations. It can be used for exception tracking and logging, 
 // as well as the source of randomness.
 var mlContext = new MLContext();

 // Get a small dataset as an IEnumerable and them read it as ML.NET's data type.
 var data = Microsoft.ML.SamplesUtils.DatasetUtils.GetInfertData();
 var trainData = mlContext.Data.LoadFromEnumerable(data);
 // Create a small dataset as an IEnumerable.
 var samples = new List<InputData>()
 {
 new InputData(){ Feature1 = 0.1f, Feature2 = new[]{ 1.1f, 2.1f, 3.1f}, Feature3 = 1 },
 new InputData(){ Feature1 = 0.2f, Feature2 = new[]{ 1.2f, 2.2f, 3.2f}, Feature3 = 2 },
 new InputData(){ Feature1 = 0.3f, Feature2 = new[]{ 1.3f, 2.3f, 3.3f}, Feature3 = 3 },
 new InputData(){ Feature1 = 0.4f, Feature2 = new[]{ 1.4f, 2.4f, 3.4f}, Feature3 = 4 },
 new InputData(){ Feature1 = 0.5f, Feature2 = new[]{ 1.5f, 2.5f, 3.5f}, Feature3 = 5 },
 new InputData(){ Feature1 = 0.6f, Feature2 = new[]{ 1.6f, 2.6f, 3.6f}, Feature3 = 6 },
 };

 // Preview of the data.
 //
 // Age Case Education induced parity pooled.stratum row_num ...
 // 26.0 1.0 0-5yrs 1.0 6.0 3.0 1.0 ...
 // 42.0 1.0 0-5yrs 1.0 1.0 1.0 2.0 ...
 // 39.0 1.0 0-5yrs 2.0 6.0 4.0 3.0 ...
 // 34.0 1.0 0-5yrs 2.0 4.0 2.0 4.0 ...
 // 35.0 1.0 6-11yrs 1.0 3.0 32.0 5.0 ...
 // Convert training data to IDataView.
 var dataview = mlContext.Data.LoadFromEnumerable(samples);

 // A pipeline for concatenating the Age, Parity and Induced columns together into a vector that will be the Features column.
 // Concatenation is necessary because learners take **feature vectors** as inputs.
 // e.g. var regressionTrainer = mlContext.Regression.Trainers.FastTree(labelColumn: "Label", featureColumn: "Features");
 string outputColumnName = "Features";
 var pipeline = mlContext.Transforms.Concatenate(outputColumnName, new[] { "Age", "Parity", "Induced" });
 // A pipeline for concatenating the "Feature1", "Feature2" and "Feature3" columns together into a vector that will be the Features column.
 // Concatenation is necessary because trainers take feature vectors as inputs.
 //
 // Please note that the "Feature3" column is converted from int32 to float using the ConvertType.
 // The Concatenate requires all columns to be of same type.
 var pipeline = mlContext.Transforms.Conversion.ConvertType("Feature3", outputKind: DataKind.Single)
 .Append(mlContext.Transforms.Concatenate("Features", new[] { "Feature1", "Feature2", "Feature3" }));

 // The transformed data.
 var transformedData = pipeline.Fit(trainData).Transform(trainData);
 var transformedData = pipeline.Fit(dataview).Transform(dataview);

 // Now let's take a look at what this concatenation did.
 // We can extract the newly created column as an IEnumerable of SampleInfertDataWithFeatures, the class we define above.
 var featuresColumn = mlContext.Data.CreateEnumerable<SampleInfertDataWithFeatures>(transformedData, reuseRowObject: false);
 // We can extract the newly created column as an IEnumerable of TransformedData.
 var featuresColumn = mlContext.Data.CreateEnumerable<TransformedData>(transformedData, reuseRowObject: false);

 // And we can write out a few rows
 Console.WriteLine($"{outputColumnName} column obtained post-transformation.");
 Console.WriteLine($"Features column obtained post-transformation.");
 foreach (var featureRow in featuresColumn)
 {
 foreach (var value in featureRow.Features.GetValues())
 Console.Write($"{value} ");
 Console.WriteLine("");
 }
 Console.WriteLine(string.Join(" ", featureRow.Features));

 // Expected output:
 // Features column obtained post-transformation.
 //
 // 26 6 1
 // 42 1 1
 // 39 6 2
 // 34 4 2
 // 35 3 1
 // Features column obtained post-transformation.
 // 0.1 1.1 2.1 3.1 1
 // 0.2 1.2 2.2 3.2 2
 // 0.3 1.3 2.3 3.3 3
 // 0.4 1.4 2.4 3.4 4
 // 0.5 1.5 2.5 3.5 5
 // 0.6 1.6 2.6 3.6 6
 }

 private class InputData
 {
 public float Feature1;
 [VectorType(3)]
 public float[] Feature2;
@Ivanidzo4kaIvanidzo4kaApr 9, 2019•

edited by zeahmed

Loading

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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.

I would put VectorType(3) here.

Otherwise you concat scalar+VarVector+Scalar which would be VarVector and you can't use it for trainers.

Or maybe include that in sample, two concats, one on varvector, one on vector.... #Resolved
 public int Feature3;
 }

 private class SampleInfertDataWithFeatures
 private sealed class TransformedData
 {
 public VBuffer<float> Features { get; set; }
 public float[] Features { get; set; }
 }
 }
}