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Extend Gam Samples #3281

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Adding Regression GAM samples.
Apr 10, 2019
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Adding GAM Binary Classification samples.
Apr 10, 2019
73d8270
Update docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClass…
wschin Apr 11, 2019
4469a85
Update docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClass…
wschin Apr 11, 2019
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Update docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClass…
wschin Apr 11, 2019
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Update docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClass…
wschin Apr 11, 2019
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Addressing PR comments; rewriting comments.
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Addressing PR comments.
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154 changes: 154 additions & 0 deletions docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/Gam.cs
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using System;
using System.Collections.Generic;
using Microsoft.ML;
using Microsoft.ML.Data;

namespace Samples.Dynamic.Trainers.BinaryClassification
{
public static class Gam
{
// This example requires installation of additional NuGet package
// <a href="https://www.nuget.org/packages/Microsoft.ML.FastTree/">Microsoft.ML.FastTree</a>.
public static void Example()
{
// Create a new context for ML.NET operations. It can be used for exception tracking and logging,
// as a catalog of available operations and as the source of randomness.
var mlContext = new MLContext();

// Create the dataset.
var samples = GenerateData();

// Convert the dataset to an IDataView.
var data = mlContext.Data.LoadFromEnumerable(samples);

// Create training and validation sets.
var dataSets = mlContext.Data.TrainTestSplit(data);
var trainSet = dataSets.TrainSet;
var validSet = dataSets.TestSet;

// Create a GAM trainer.
// Use a small number of bins for this example. The setting below means for each feature,
// we divide its range into 16 discrete regions for the training process. Note that these
// regions are not evenly spaced, and that the final model may contain fewer bins, as
// neighboring bins with identical values will be combined. In general, we recommend using
// at least the default number of bins, as a small number of bins limits the capacity of
// the model.
var trainer = mlContext.BinaryClassification.Trainers.Gam(maximumBinCountPerFeature: 16);

// Fit the model using both of training and validation sets. GAM can use a technique called
// pruning to tune the model to the validation set after training to improve generalization.
var model = trainer.Fit(trainSet, validSet);

// Extract the model parameters.
var gam = model.Model.SubModel;

// Now we can inspect the parameters of the Generalized Additive Model to understand the fit
// and potentially learn about our dataset.
// First, we will look at the bias; the bias represents the average prediction for the training data.
Console.WriteLine($"Average prediction: {gam.Bias:0.00}");

// Now look at the shape functions that the model has learned. Similar to a linear model, we have
// one response per feature, and they are independent. Unlike a linear model, this response is a
// generic function instead of a line. Because we have included a bias term, each feature response
// represents the deviation from the average prediction as a function of the feature value.
for (int i = 0; i < gam.NumberOfShapeFunctions; i++)
{
// Break a line.
Console.WriteLine();

// Get the bin upper bounds for the feature.
var binUpperBounds = gam.GetBinUpperBounds(i);

// Get the bin effects; these are the function values for each bin.
var binEffects = gam.GetBinEffects(i);

// Now, write the function to the console. The function is a set of bins, and the corresponding
// function values. You can think of GAMs as building a bar-chart or lookup table for each feature.
Console.WriteLine($"Feature{i}");
for (int j = 0; j < binUpperBounds.Count; j++)
Console.WriteLine($"x < {binUpperBounds[j]:0.00} => {binEffects[j]:0.000}");
}

// Expected output:
// Average prediction: 0.82
//
// Feature0
// x < -0.44 => 0.286
// x < -0.38 => 0.225
// x < -0.32 => 0.048
// x < -0.26 => -0.110
// x < -0.20 => -0.116
// x < 0.18 => -0.143
// x < 0.25 => -0.115
// x < 0.31 => -0.005
// x < 0.37 => 0.097
// x < 0.44 => 0.263
// x < ∞ => 0.284
//
// Feature1
// x < 0.00 => -0.350
// x < 0.24 => 0.875
// x < 0.31 => -0.138
// x < ∞ => -0.188

// Let's consider this output. To score a given example, we look up the first bin where the inequality
// is satisfied for the feature value. We can look at the whole function to get a sense for how the
// model responds to the variable on a global level.
// The model can be seen to reconstruct the parabolic and step-wise function, shifted with respect to the average
// expected output over the training set. Very few bins are used to model the second feature because the GAM model
// discards unchanged bins to create smaller models.
// One last thing to notice is that these feature functions can be noisy. While we know that Feature1 should be
// symmetric, this is not captured in the model. This is due to noise in the data. Common practice is to use
// resampling methods to estimate a confidence interval at each bin. This will help to determine if the effect is
// real or just sampling noise. See for example:
// Tan, Caruana, Hooker, and Lou. "Distill-and-Compare: Auditing Black-Box Models Using Transparent Model
// Distillation." <a href='https://arxiv.org/abs/1710.06169'>arXiv:1710.06169</a>."
}

private class Data
{
public bool Label { get; set; }

[VectorType(2)]
public float[] Features { get; set; }
}

/// <summary>
/// Creates a dataset, an IEnumerable of Data objects, for a GAM sample. Feature1 is a parabola centered around 0,
/// while Feature2 is a simple piecewise function.
/// </summary>
/// <param name="numExamples">The number of examples to generate.</param>
/// <param name="seed">The seed for the random number generator used to produce data.</param>
/// <returns></returns>
private static IEnumerable<Data> GenerateData(int numExamples = 25000, int seed = 1)
{
var rng = new Random(seed);
float centeredFloat() => (float)(rng.NextDouble() - 0.5);
for (int i = 0; i < numExamples; i++)
{
// Generate random, uncoupled features.
var data = new Data {
Features = new float[2] { centeredFloat(), centeredFloat() }
};
// Compute the label from the shape functions and add noise.
data.Label = Sigmoid(Parabola(data.Features[0]) + SimplePiecewise(data.Features[1]) + centeredFloat()) > 0.5;

yield return data;
}
}

private static float Parabola(float x) => x * x;

private static float SimplePiecewise(float x)
{
if (x < 0)
return 0;
else if (x < 0.25)
return 1;
else
return 0;

@shmoradimsshmoradimsApr 12, 2019•
edited by rogancarr
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0 [](start = 23, length = 1)

is this the same as x < 0 value? #Resolved

@rogancarrrogancarrApr 12, 2019

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That is correct.

In reply to: 274927257 [](ancestors = 274927257)

}

private static double Sigmoid(double x) => 1.0 / (1.0 + Math.Exp(-1 * x));
}
}
163 changes: 163 additions & 0 deletions docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/GamWithOptions.cs
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using System;

@wschinwschinApr 11, 2019•
edited by rogancarr
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Similar comments for Gam without Options apply here (and other sample files) #Resolved

using System.Collections.Generic;
using Microsoft.ML;
using Microsoft.ML.Data;
using Microsoft.ML.Trainers.FastTree;

namespace Samples.Dynamic.Trainers.BinaryClassification
{
public static class GamWithOptions
{
// This example requires installation of additional NuGet package
// <a href="https://www.nuget.org/packages/Microsoft.ML.FastTree/">Microsoft.ML.FastTree</a>.
public static void Example()
{
// Create a new context for ML.NET operations. It can be used for exception tracking and logging,
// as a catalog of available operations and as the source of randomness.
var mlContext = new MLContext();

// Create the dataset.
var samples = GenerateData();

// Convert the dataset to an IDataView.
var data = mlContext.Data.LoadFromEnumerable(samples);

// Create training and validation datasets.
var dataSets = mlContext.Data.TrainTestSplit(data);
var trainSet = dataSets.TrainSet;
var validSet = dataSets.TestSet;

// Create a GAM trainer.
// Use a small number of bins for this example. The setting below means for each feature,
// we divide its range into 16 discrete regions for the training process. Note that these
// regions are not evenly spaced, and that the final model may contain fewer bins, as
// neighboring bins with identical values will be combined. In general, we recommend using
// at least the default number of bins, as a small number of bins limits the capacity of
// the model.
// Also, set the learning rate to half the default to slow down the gradient descent, and
// double the number of iterations to compensate.
var trainer = mlContext.BinaryClassification.Trainers.Gam(
new GamBinaryTrainer.Options {
NumberOfIterations = 19000,
MaximumBinCountPerFeature = 16,
LearningRate = 0.001
});

// Fit the model using both of training and validation sets. GAM can use a technique called
// pruning to tune the model to the validation set after training to improve generalization.
var model = trainer.Fit(trainSet, validSet);

// Extract the model parameters.
var gam = model.Model.SubModel;

// Now we can inspect the parameters of the Generalized Additive Model to understand the fit
// and potentially learn about our dataset.
// First, we will look at the bias; the bias represents the average prediction for the training data.
Console.WriteLine($"Average prediction: {gam.Bias:0.00}");

// Now look at the shape functions that the model has learned. Similar to a linear model, we have
// one response per feature, and they are independent. Unlike a linear model, this response is a
// generic function instead of a line. Because we have included a bias term, each feature response
// represents the deviation from the average prediction as a function of the feature value.
for (int i = 0; i < gam.NumberOfShapeFunctions; i++)
{
// Break a line.
Console.WriteLine();

// Get the bin upper bounds for the feature.
var binUpperBounds = gam.GetBinUpperBounds(i);

// Get the bin effects; these are the function values for each bin.
var binEffects = gam.GetBinEffects(i);

// Now, write the function to the console. The function is a set of bins, and the corresponding
// function values. You can think of GAMs as building a bar-chart or lookup table for each feature.
Console.WriteLine($"Feature{i}");
for (int j = 0; j < binUpperBounds.Count; j++)
Console.WriteLine($"x < {binUpperBounds[j]:0.00} => {binEffects[j]:0.000}");
}

// Expected output:
// Average prediction: 0.82
//
// Feature0
// x < -0.44 => 0.286
// x < -0.38 => 0.225
// x < -0.32 => 0.048
// x < -0.26 => -0.110
// x < -0.20 => -0.116
// x < 0.18 => -0.143
// x < 0.25 => -0.115
// x < 0.31 => -0.005
// x < 0.37 => 0.097
// x < 0.44 => 0.263
// x < ∞ => 0.284
//
// Feature1
// x < 0.00 => -0.350
// x < 0.24 => 0.875
// x < 0.31 => -0.138
// x < ∞ => -0.188

// Let's consider this output. To score a given example, we look up the first bin where the inequality
// is satisfied for the feature value. We can look at the whole function to get a sense for how the
// model responds to the variable on a global level.
// The model can be seen to reconstruct the parabolic and step-wise function, shifted with respect to the average
// expected output over the training set. Very few bins are used to model the second feature because the GAM model
// discards unchanged bins to create smaller models.
// One last thing to notice is that these feature functions can be noisy. While we know that Feature1 should be
// symmetric, this is not captured in the model. This is due to noise in the data. Common practice is to use
// resampling methods to estimate a confidence interval at each bin. This will help to determine if the effect is
// real or just sampling noise. See for example:
// Tan, Caruana, Hooker, and Lou. "Distill-and-Compare: Auditing Black-Box Models Using Transparent Model
// Distillation." <a href='https://arxiv.org/abs/1710.06169'>arXiv:1710.06169</a>."
}

private class Data
{
public bool Label { get; set; }

[VectorType(2)]
public float[] Features { get; set; }
}

/// <summary>
/// Creates a dataset, an IEnumerable of Data objects, for a GAM sample. Feature1 is a parabola centered around 0,
/// while Feature2 is a simple piecewise function.
/// </summary>
/// <param name="numExamples">The number of examples to generate.</param>
/// <param name="seed">The seed for the random number generator used to produce data.</param>
/// <returns></returns>
private static IEnumerable<Data> GenerateData(int numExamples = 25000, int seed = 1)
{
var rng = new Random(seed);
float centeredFloat() => (float)(rng.NextDouble() - 0.5);
for (int i = 0; i < numExamples; i++)
{
// Generate random, uncoupled features.
var data = new Data
{
Features = new float[2] { centeredFloat(), centeredFloat() }
};
// Compute the label from the shape functions and add noise.
data.Label = Sigmoid(Parabola(data.Features[0]) + SimplePiecewise(data.Features[1]) + centeredFloat()) > 0.5;

yield return data;
}
}

private static float Parabola(float x) => x * x;

private static float SimplePiecewise(float x)
{
if (x < 0)
return 0;
else if (x < 0.25)
return 1;
else
return 0;
}

private static double Sigmoid(double x) => 1.0 / (1.0 + Math.Exp(-1 * x));
}
}
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 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");
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Extend Gam Samples
#3281
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fce8ef4

Adding Regression GAM samples.

Apr 10, 2019
7d335d0

Adding GAM Binary Classification samples.

Apr 10, 2019
73d8270

Update docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClass…


wschin Apr 11, 2019
4469a85

Update docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClass…


wschin Apr 11, 2019
091fafd

Update docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClass…


wschin Apr 11, 2019
7f7c991

Update docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClass…


wschin Apr 11, 2019
1c99277

Addressing PR comments; rewriting comments.

Apr 11, 2019
a717ac5

Addressing PR comments.

Apr 12, 2019

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154 changes: 154 additions & 0 deletions
docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/Gam.cs

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Original file line numberDiff line numberDiff line change
@@ -0,0 +1,154 @@
using System;
using System.Collections.Generic;
using Microsoft.ML;
using Microsoft.ML.Data;

namespace Samples.Dynamic.Trainers.BinaryClassification
{
 public static class Gam
 {
 // This example requires installation of additional NuGet package
 // <a href="https://www.nuget.org/packages/Microsoft.ML.FastTree/">Microsoft.ML.FastTree</a>.
 public static void Example()
 {
 // Create a new context for ML.NET operations. It can be used for exception tracking and logging, 
 // as a catalog of available operations and as the source of randomness.
 var mlContext = new MLContext();

 // Create the dataset.
 var samples = GenerateData();

 // Convert the dataset to an IDataView.
 var data = mlContext.Data.LoadFromEnumerable(samples);

 // Create training and validation sets.
 var dataSets = mlContext.Data.TrainTestSplit(data);
 var trainSet = dataSets.TrainSet;
 var validSet = dataSets.TestSet;

 // Create a GAM trainer.
 // Use a small number of bins for this example. The setting below means for each feature, 
 // we divide its range into 16 discrete regions for the training process. Note that these
 // regions are not evenly spaced, and that the final model may contain fewer bins, as
 // neighboring bins with identical values will be combined. In general, we recommend using
 // at least the default number of bins, as a small number of bins limits the capacity of
 // the model.
 var trainer = mlContext.BinaryClassification.Trainers.Gam(maximumBinCountPerFeature: 16);

 // Fit the model using both of training and validation sets. GAM can use a technique called 
 // pruning to tune the model to the validation set after training to improve generalization.
 var model = trainer.Fit(trainSet, validSet);

 // Extract the model parameters.
 var gam = model.Model.SubModel;

 // Now we can inspect the parameters of the Generalized Additive Model to understand the fit
 // and potentially learn about our dataset.
 // First, we will look at the bias; the bias represents the average prediction for the training data.
 Console.WriteLine($"Average prediction: {gam.Bias:0.00}");

 // Now look at the shape functions that the model has learned. Similar to a linear model, we have
 // one response per feature, and they are independent. Unlike a linear model, this response is a 
 // generic function instead of a line. Because we have included a bias term, each feature response 
 // represents the deviation from the average prediction as a function of the feature value.
 for (int i = 0; i < gam.NumberOfShapeFunctions; i++)
 {
 // Break a line.
 Console.WriteLine();

 // Get the bin upper bounds for the feature.
 var binUpperBounds = gam.GetBinUpperBounds(i);

 // Get the bin effects; these are the function values for each bin.
 var binEffects = gam.GetBinEffects(i);

 // Now, write the function to the console. The function is a set of bins, and the corresponding
 // function values. You can think of GAMs as building a bar-chart or lookup table for each feature.
 Console.WriteLine($"Feature{i}");
 for (int j = 0; j < binUpperBounds.Count; j++)
 Console.WriteLine($"x < {binUpperBounds[j]:0.00} => {binEffects[j]:0.000}");
 }

 // Expected output:
 // Average prediction: 0.82
 //
 // Feature0
 // x < -0.44 => 0.286
 // x < -0.38 => 0.225
 // x < -0.32 => 0.048
 // x < -0.26 => -0.110
 // x < -0.20 => -0.116
 // x < 0.18 => -0.143
 // x < 0.25 => -0.115
 // x < 0.31 => -0.005
 // x < 0.37 => 0.097
 // x < 0.44 => 0.263
 // x < ∞ => 0.284
 //
 // Feature1
 // x < 0.00 => -0.350
 // x < 0.24 => 0.875
 // x < 0.31 => -0.138
 // x < ∞ => -0.188

 // Let's consider this output. To score a given example, we look up the first bin where the inequality
 // is satisfied for the feature value. We can look at the whole function to get a sense for how the
 // model responds to the variable on a global level.
 // The model can be seen to reconstruct the parabolic and step-wise function, shifted with respect to the average
 // expected output over the training set. Very few bins are used to model the second feature because the GAM model
 // discards unchanged bins to create smaller models.
 // One last thing to notice is that these feature functions can be noisy. While we know that Feature1 should be
 // symmetric, this is not captured in the model. This is due to noise in the data. Common practice is to use 
 // resampling methods to estimate a confidence interval at each bin. This will help to determine if the effect is 
 // real or just sampling noise. See for example:
 // Tan, Caruana, Hooker, and Lou. "Distill-and-Compare: Auditing Black-Box Models Using Transparent Model 
 // Distillation." <a href='https://arxiv.org/abs/1710.06169'>arXiv:1710.06169</a>."
 }

 private class Data
 {
 public bool Label { get; set; }

 [VectorType(2)]
 public float[] Features { get; set; }
 }

 /// <summary>
 /// Creates a dataset, an IEnumerable of Data objects, for a GAM sample. Feature1 is a parabola centered around 0,
 /// while Feature2 is a simple piecewise function.
 /// </summary>
 /// <param name="numExamples">The number of examples to generate.</param>
 /// <param name="seed">The seed for the random number generator used to produce data.</param>
 /// <returns></returns>
 private static IEnumerable<Data> GenerateData(int numExamples = 25000, int seed = 1)
 {
 var rng = new Random(seed);
 float centeredFloat() => (float)(rng.NextDouble() - 0.5);
 for (int i = 0; i < numExamples; i++)
 {
 // Generate random, uncoupled features.
 var data = new Data {
 Features = new float[2] { centeredFloat(), centeredFloat() }
 };
 // Compute the label from the shape functions and add noise.
 data.Label = Sigmoid(Parabola(data.Features[0]) + SimplePiecewise(data.Features[1]) + centeredFloat()) > 0.5;

 yield return data;
 }
 }

 private static float Parabola(float x) => x * x;

 private static float SimplePiecewise(float x)
 {
 if (x < 0)
 return 0;
 else if (x < 0.25)
 return 1;
 else
 return 0;
@shmoradimsshmoradimsApr 12, 2019•

edited by rogancarr

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0 [](start = 23, length = 1)
is this the same as x < 0 value? #Resolved
@rogancarrrogancarrApr 12, 2019

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That is correct.
In reply to: 274927257 [](ancestors = 274927257)
 }

 private static double Sigmoid(double x) => 1.0 / (1.0 + Math.Exp(-1 * x));
 }
}

163 changes: 163 additions & 0 deletions
docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/GamWithOptions.cs

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using System;
@wschinwschinApr 11, 2019•

edited by rogancarr

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Similar comments for Gam without Options apply here (and other sample files) #Resolved
using System.Collections.Generic;
using Microsoft.ML;
using Microsoft.ML.Data;
using Microsoft.ML.Trainers.FastTree;

namespace Samples.Dynamic.Trainers.BinaryClassification
{
 public static class GamWithOptions
 {
 // This example requires installation of additional NuGet package
 // <a href="https://www.nuget.org/packages/Microsoft.ML.FastTree/">Microsoft.ML.FastTree</a>.
 public static void Example()
 {
 // Create a new context for ML.NET operations. It can be used for exception tracking and logging, 
 // as a catalog of available operations and as the source of randomness.
 var mlContext = new MLContext();

 // Create the dataset.
 var samples = GenerateData();

 // Convert the dataset to an IDataView.
 var data = mlContext.Data.LoadFromEnumerable(samples);

 // Create training and validation datasets.
 var dataSets = mlContext.Data.TrainTestSplit(data);
 var trainSet = dataSets.TrainSet;
 var validSet = dataSets.TestSet;

 // Create a GAM trainer.
 // Use a small number of bins for this example. The setting below means for each feature, 
 // we divide its range into 16 discrete regions for the training process. Note that these
 // regions are not evenly spaced, and that the final model may contain fewer bins, as
 // neighboring bins with identical values will be combined. In general, we recommend using
 // at least the default number of bins, as a small number of bins limits the capacity of
 // the model.
 // Also, set the learning rate to half the default to slow down the gradient descent, and
 // double the number of iterations to compensate.
 var trainer = mlContext.BinaryClassification.Trainers.Gam(
 new GamBinaryTrainer.Options {
 NumberOfIterations = 19000,
 MaximumBinCountPerFeature = 16,
 LearningRate = 0.001
 });

 // Fit the model using both of training and validation sets. GAM can use a technique called 
 // pruning to tune the model to the validation set after training to improve generalization.
 var model = trainer.Fit(trainSet, validSet);

 // Extract the model parameters.
 var gam = model.Model.SubModel;

 // Now we can inspect the parameters of the Generalized Additive Model to understand the fit
 // and potentially learn about our dataset.
 // First, we will look at the bias; the bias represents the average prediction for the training data.
 Console.WriteLine($"Average prediction: {gam.Bias:0.00}");

 // Now look at the shape functions that the model has learned. Similar to a linear model, we have
 // one response per feature, and they are independent. Unlike a linear model, this response is a 
 // generic function instead of a line. Because we have included a bias term, each feature response 
 // represents the deviation from the average prediction as a function of the feature value.
 for (int i = 0; i < gam.NumberOfShapeFunctions; i++)
 {
 // Break a line.
 Console.WriteLine();

 // Get the bin upper bounds for the feature.
 var binUpperBounds = gam.GetBinUpperBounds(i);

 // Get the bin effects; these are the function values for each bin.
 var binEffects = gam.GetBinEffects(i);

 // Now, write the function to the console. The function is a set of bins, and the corresponding
 // function values. You can think of GAMs as building a bar-chart or lookup table for each feature.
 Console.WriteLine($"Feature{i}");
 for (int j = 0; j < binUpperBounds.Count; j++)
 Console.WriteLine($"x < {binUpperBounds[j]:0.00} => {binEffects[j]:0.000}");
 }

 // Expected output:
 // Average prediction: 0.82
 //
 // Feature0
 // x < -0.44 => 0.286
 // x < -0.38 => 0.225
 // x < -0.32 => 0.048
 // x < -0.26 => -0.110
 // x < -0.20 => -0.116
 // x < 0.18 => -0.143
 // x < 0.25 => -0.115
 // x < 0.31 => -0.005
 // x < 0.37 => 0.097
 // x < 0.44 => 0.263
 // x < ∞ => 0.284
 //
 // Feature1
 // x < 0.00 => -0.350
 // x < 0.24 => 0.875
 // x < 0.31 => -0.138
 // x < ∞ => -0.188

 // Let's consider this output. To score a given example, we look up the first bin where the inequality
 // is satisfied for the feature value. We can look at the whole function to get a sense for how the
 // model responds to the variable on a global level.
 // The model can be seen to reconstruct the parabolic and step-wise function, shifted with respect to the average
 // expected output over the training set. Very few bins are used to model the second feature because the GAM model
 // discards unchanged bins to create smaller models.
 // One last thing to notice is that these feature functions can be noisy. While we know that Feature1 should be
 // symmetric, this is not captured in the model. This is due to noise in the data. Common practice is to use 
 // resampling methods to estimate a confidence interval at each bin. This will help to determine if the effect is 
 // real or just sampling noise. See for example:
 // Tan, Caruana, Hooker, and Lou. "Distill-and-Compare: Auditing Black-Box Models Using Transparent Model 
 // Distillation." <a href='https://arxiv.org/abs/1710.06169'>arXiv:1710.06169</a>."
 }

 private class Data
 {
 public bool Label { get; set; }

 [VectorType(2)]
 public float[] Features { get; set; }
 }

 /// <summary>
 /// Creates a dataset, an IEnumerable of Data objects, for a GAM sample. Feature1 is a parabola centered around 0,
 /// while Feature2 is a simple piecewise function.
 /// </summary>
 /// <param name="numExamples">The number of examples to generate.</param>
 /// <param name="seed">The seed for the random number generator used to produce data.</param>
 /// <returns></returns>
 private static IEnumerable<Data> GenerateData(int numExamples = 25000, int seed = 1)
 {
 var rng = new Random(seed);
 float centeredFloat() => (float)(rng.NextDouble() - 0.5);
 for (int i = 0; i < numExamples; i++)
 {
 // Generate random, uncoupled features.
 var data = new Data
 {
 Features = new float[2] { centeredFloat(), centeredFloat() }
 };
 // Compute the label from the shape functions and add noise.
 data.Label = Sigmoid(Parabola(data.Features[0]) + SimplePiecewise(data.Features[1]) + centeredFloat()) > 0.5;

 yield return data;
 }
 }

 private static float Parabola(float x) => x * x;

 private static float SimplePiecewise(float x)
 {
 if (x < 0)
 return 0;
 else if (x < 0.25)
 return 1;
 else
 return 0;
 }

 private static double Sigmoid(double x) => 1.0 / (1.0 + Math.Exp(-1 * x));
 }
}
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Adding Regression GAM samples.

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Adding GAM Binary Classification samples.

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docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/Gam.cs

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using System;
using System.Collections.Generic;
using Microsoft.ML;
using Microsoft.ML.Data;

namespace Samples.Dynamic.Trainers.BinaryClassification
{
 public static class Gam
 {
 // This example requires installation of additional NuGet package
 // <a href="https://www.nuget.org/packages/Microsoft.ML.FastTree/">Microsoft.ML.FastTree</a>.
 public static void Example()
 {
 // Create a new context for ML.NET operations. It can be used for exception tracking and logging, 
 // as a catalog of available operations and as the source of randomness.
 var mlContext = new MLContext();

 // Create the dataset.
 var samples = GenerateData();

 // Convert the dataset to an IDataView.
 var data = mlContext.Data.LoadFromEnumerable(samples);

 // Create training and validation sets.
 var dataSets = mlContext.Data.TrainTestSplit(data);
 var trainSet = dataSets.TrainSet;
 var validSet = dataSets.TestSet;

 // Create a GAM trainer.
 // Use a small number of bins for this example. The setting below means for each feature, 
 // we divide its range into 16 discrete regions for the training process. Note that these
 // regions are not evenly spaced, and that the final model may contain fewer bins, as
 // neighboring bins with identical values will be combined. In general, we recommend using
 // at least the default number of bins, as a small number of bins limits the capacity of
 // the model.
 var trainer = mlContext.BinaryClassification.Trainers.Gam(maximumBinCountPerFeature: 16);

 // Fit the model using both of training and validation sets. GAM can use a technique called 
 // pruning to tune the model to the validation set after training to improve generalization.
 var model = trainer.Fit(trainSet, validSet);

 // Extract the model parameters.
 var gam = model.Model.SubModel;

 // Now we can inspect the parameters of the Generalized Additive Model to understand the fit
 // and potentially learn about our dataset.
 // First, we will look at the bias; the bias represents the average prediction for the training data.
 Console.WriteLine($"Average prediction: {gam.Bias:0.00}");

 // Now look at the shape functions that the model has learned. Similar to a linear model, we have
 // one response per feature, and they are independent. Unlike a linear model, this response is a 
 // generic function instead of a line. Because we have included a bias term, each feature response 
 // represents the deviation from the average prediction as a function of the feature value.
 for (int i = 0; i < gam.NumberOfShapeFunctions; i++)
 {
 // Break a line.
 Console.WriteLine();

 // Get the bin upper bounds for the feature.
 var binUpperBounds = gam.GetBinUpperBounds(i);

 // Get the bin effects; these are the function values for each bin.
 var binEffects = gam.GetBinEffects(i);

 // Now, write the function to the console. The function is a set of bins, and the corresponding
 // function values. You can think of GAMs as building a bar-chart or lookup table for each feature.
 Console.WriteLine($"Feature{i}");
 for (int j = 0; j < binUpperBounds.Count; j++)
 Console.WriteLine($"x < {binUpperBounds[j]:0.00} => {binEffects[j]:0.000}");
 }

 // Expected output:
 // Average prediction: 0.82
 //
 // Feature0
 // x < -0.44 => 0.286
 // x < -0.38 => 0.225
 // x < -0.32 => 0.048
 // x < -0.26 => -0.110
 // x < -0.20 => -0.116
 // x < 0.18 => -0.143
 // x < 0.25 => -0.115
 // x < 0.31 => -0.005
 // x < 0.37 => 0.097
 // x < 0.44 => 0.263
 // x < ∞ => 0.284
 //
 // Feature1
 // x < 0.00 => -0.350
 // x < 0.24 => 0.875
 // x < 0.31 => -0.138
 // x < ∞ => -0.188

 // Let's consider this output. To score a given example, we look up the first bin where the inequality
 // is satisfied for the feature value. We can look at the whole function to get a sense for how the
 // model responds to the variable on a global level.
 // The model can be seen to reconstruct the parabolic and step-wise function, shifted with respect to the average
 // expected output over the training set. Very few bins are used to model the second feature because the GAM model
 // discards unchanged bins to create smaller models.
 // One last thing to notice is that these feature functions can be noisy. While we know that Feature1 should be
 // symmetric, this is not captured in the model. This is due to noise in the data. Common practice is to use 
 // resampling methods to estimate a confidence interval at each bin. This will help to determine if the effect is 
 // real or just sampling noise. See for example:
 // Tan, Caruana, Hooker, and Lou. "Distill-and-Compare: Auditing Black-Box Models Using Transparent Model 
 // Distillation." <a href='https://arxiv.org/abs/1710.06169'>arXiv:1710.06169</a>."
 }

 private class Data
 {
 public bool Label { get; set; }

 [VectorType(2)]
 public float[] Features { get; set; }
 }

 /// <summary>
 /// Creates a dataset, an IEnumerable of Data objects, for a GAM sample. Feature1 is a parabola centered around 0,
 /// while Feature2 is a simple piecewise function.
 /// </summary>
 /// <param name="numExamples">The number of examples to generate.</param>
 /// <param name="seed">The seed for the random number generator used to produce data.</param>
 /// <returns></returns>
 private static IEnumerable<Data> GenerateData(int numExamples = 25000, int seed = 1)
 {
 var rng = new Random(seed);
 float centeredFloat() => (float)(rng.NextDouble() - 0.5);
 for (int i = 0; i < numExamples; i++)
 {
 // Generate random, uncoupled features.
 var data = new Data {
 Features = new float[2] { centeredFloat(), centeredFloat() }
 };
 // Compute the label from the shape functions and add noise.
 data.Label = Sigmoid(Parabola(data.Features[0]) + SimplePiecewise(data.Features[1]) + centeredFloat()) > 0.5;

 yield return data;
 }
 }

 private static float Parabola(float x) => x * x;

 private static float SimplePiecewise(float x)
 {
 if (x < 0)
 return 0;
 else if (x < 0.25)
 return 1;
 else
 return 0;
@shmoradimsshmoradimsApr 12, 2019•

edited by rogancarr

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0 [](start = 23, length = 1)
is this the same as x < 0 value? #Resolved
@rogancarrrogancarrApr 12, 2019

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That is correct.
In reply to: 274927257 [](ancestors = 274927257)
 }

 private static double Sigmoid(double x) => 1.0 / (1.0 + Math.Exp(-1 * x));
 }
}

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docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/GamWithOptions.cs

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using System;
@wschinwschinApr 11, 2019•

edited by rogancarr

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Similar comments for Gam without Options apply here (and other sample files) #Resolved
using System.Collections.Generic;
using Microsoft.ML;
using Microsoft.ML.Data;
using Microsoft.ML.Trainers.FastTree;

namespace Samples.Dynamic.Trainers.BinaryClassification
{
 public static class GamWithOptions
 {
 // This example requires installation of additional NuGet package
 // <a href="https://www.nuget.org/packages/Microsoft.ML.FastTree/">Microsoft.ML.FastTree</a>.
 public static void Example()
 {
 // Create a new context for ML.NET operations. It can be used for exception tracking and logging, 
 // as a catalog of available operations and as the source of randomness.
 var mlContext = new MLContext();

 // Create the dataset.
 var samples = GenerateData();

 // Convert the dataset to an IDataView.
 var data = mlContext.Data.LoadFromEnumerable(samples);

 // Create training and validation datasets.
 var dataSets = mlContext.Data.TrainTestSplit(data);
 var trainSet = dataSets.TrainSet;
 var validSet = dataSets.TestSet;

 // Create a GAM trainer.
 // Use a small number of bins for this example. The setting below means for each feature, 
 // we divide its range into 16 discrete regions for the training process. Note that these
 // regions are not evenly spaced, and that the final model may contain fewer bins, as
 // neighboring bins with identical values will be combined. In general, we recommend using
 // at least the default number of bins, as a small number of bins limits the capacity of
 // the model.
 // Also, set the learning rate to half the default to slow down the gradient descent, and
 // double the number of iterations to compensate.
 var trainer = mlContext.BinaryClassification.Trainers.Gam(
 new GamBinaryTrainer.Options {
 NumberOfIterations = 19000,
 MaximumBinCountPerFeature = 16,
 LearningRate = 0.001
 });

 // Fit the model using both of training and validation sets. GAM can use a technique called 
 // pruning to tune the model to the validation set after training to improve generalization.
 var model = trainer.Fit(trainSet, validSet);

 // Extract the model parameters.
 var gam = model.Model.SubModel;

 // Now we can inspect the parameters of the Generalized Additive Model to understand the fit
 // and potentially learn about our dataset.
 // First, we will look at the bias; the bias represents the average prediction for the training data.
 Console.WriteLine($"Average prediction: {gam.Bias:0.00}");

 // Now look at the shape functions that the model has learned. Similar to a linear model, we have
 // one response per feature, and they are independent. Unlike a linear model, this response is a 
 // generic function instead of a line. Because we have included a bias term, each feature response 
 // represents the deviation from the average prediction as a function of the feature value.
 for (int i = 0; i < gam.NumberOfShapeFunctions; i++)
 {
 // Break a line.
 Console.WriteLine();

 // Get the bin upper bounds for the feature.
 var binUpperBounds = gam.GetBinUpperBounds(i);

 // Get the bin effects; these are the function values for each bin.
 var binEffects = gam.GetBinEffects(i);

 // Now, write the function to the console. The function is a set of bins, and the corresponding
 // function values. You can think of GAMs as building a bar-chart or lookup table for each feature.
 Console.WriteLine($"Feature{i}");
 for (int j = 0; j < binUpperBounds.Count; j++)
 Console.WriteLine($"x < {binUpperBounds[j]:0.00} => {binEffects[j]:0.000}");
 }

 // Expected output:
 // Average prediction: 0.82
 //
 // Feature0
 // x < -0.44 => 0.286
 // x < -0.38 => 0.225
 // x < -0.32 => 0.048
 // x < -0.26 => -0.110
 // x < -0.20 => -0.116
 // x < 0.18 => -0.143
 // x < 0.25 => -0.115
 // x < 0.31 => -0.005
 // x < 0.37 => 0.097
 // x < 0.44 => 0.263
 // x < ∞ => 0.284
 //
 // Feature1
 // x < 0.00 => -0.350
 // x < 0.24 => 0.875
 // x < 0.31 => -0.138
 // x < ∞ => -0.188

 // Let's consider this output. To score a given example, we look up the first bin where the inequality
 // is satisfied for the feature value. We can look at the whole function to get a sense for how the
 // model responds to the variable on a global level.
 // The model can be seen to reconstruct the parabolic and step-wise function, shifted with respect to the average
 // expected output over the training set. Very few bins are used to model the second feature because the GAM model
 // discards unchanged bins to create smaller models.
 // One last thing to notice is that these feature functions can be noisy. While we know that Feature1 should be
 // symmetric, this is not captured in the model. This is due to noise in the data. Common practice is to use 
 // resampling methods to estimate a confidence interval at each bin. This will help to determine if the effect is 
 // real or just sampling noise. See for example:
 // Tan, Caruana, Hooker, and Lou. "Distill-and-Compare: Auditing Black-Box Models Using Transparent Model 
 // Distillation." <a href='https://arxiv.org/abs/1710.06169'>arXiv:1710.06169</a>."
 }

 private class Data
 {
 public bool Label { get; set; }

 [VectorType(2)]
 public float[] Features { get; set; }
 }

 /// <summary>
 /// Creates a dataset, an IEnumerable of Data objects, for a GAM sample. Feature1 is a parabola centered around 0,
 /// while Feature2 is a simple piecewise function.
 /// </summary>
 /// <param name="numExamples">The number of examples to generate.</param>
 /// <param name="seed">The seed for the random number generator used to produce data.</param>
 /// <returns></returns>
 private static IEnumerable<Data> GenerateData(int numExamples = 25000, int seed = 1)
 {
 var rng = new Random(seed);
 float centeredFloat() => (float)(rng.NextDouble() - 0.5);
 for (int i = 0; i < numExamples; i++)
 {
 // Generate random, uncoupled features.
 var data = new Data
 {
 Features = new float[2] { centeredFloat(), centeredFloat() }
 };
 // Compute the label from the shape functions and add noise.
 data.Label = Sigmoid(Parabola(data.Features[0]) + SimplePiecewise(data.Features[1]) + centeredFloat()) > 0.5;

 yield return data;
 }
 }

 private static float Parabola(float x) => x * x;

 private static float SimplePiecewise(float x)
 {
 if (x < 0)
 return 0;
 else if (x < 0.25)
 return 1;
 else
 return 0;
 }

 private static double Sigmoid(double x) => 1.0 / (1.0 + Math.Exp(-1 * x));
 }
}
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, '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); }
})();
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var __m = "*";
var __re = new RegExp('^' + ".*" + '

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fce8ef4

Adding Regression GAM samples.

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Adding GAM Binary Classification samples.

Apr 10, 2019
73d8270

Update docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClass…


wschin Apr 11, 2019
4469a85

Update docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClass…


wschin Apr 11, 2019
091fafd

Update docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClass…


wschin Apr 11, 2019
7f7c991

Update docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClass…


wschin Apr 11, 2019
1c99277

Addressing PR comments; rewriting comments.

Apr 11, 2019
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Addressing PR comments.

Apr 12, 2019

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docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/Gam.cs

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Original file line numberDiff line numberDiff line change
@@ -0,0 +1,154 @@
using System;
using System.Collections.Generic;
using Microsoft.ML;
using Microsoft.ML.Data;

namespace Samples.Dynamic.Trainers.BinaryClassification
{
 public static class Gam
 {
 // This example requires installation of additional NuGet package
 // <a href="https://www.nuget.org/packages/Microsoft.ML.FastTree/">Microsoft.ML.FastTree</a>.
 public static void Example()
 {
 // Create a new context for ML.NET operations. It can be used for exception tracking and logging, 
 // as a catalog of available operations and as the source of randomness.
 var mlContext = new MLContext();

 // Create the dataset.
 var samples = GenerateData();

 // Convert the dataset to an IDataView.
 var data = mlContext.Data.LoadFromEnumerable(samples);

 // Create training and validation sets.
 var dataSets = mlContext.Data.TrainTestSplit(data);
 var trainSet = dataSets.TrainSet;
 var validSet = dataSets.TestSet;

 // Create a GAM trainer.
 // Use a small number of bins for this example. The setting below means for each feature, 
 // we divide its range into 16 discrete regions for the training process. Note that these
 // regions are not evenly spaced, and that the final model may contain fewer bins, as
 // neighboring bins with identical values will be combined. In general, we recommend using
 // at least the default number of bins, as a small number of bins limits the capacity of
 // the model.
 var trainer = mlContext.BinaryClassification.Trainers.Gam(maximumBinCountPerFeature: 16);

 // Fit the model using both of training and validation sets. GAM can use a technique called 
 // pruning to tune the model to the validation set after training to improve generalization.
 var model = trainer.Fit(trainSet, validSet);

 // Extract the model parameters.
 var gam = model.Model.SubModel;

 // Now we can inspect the parameters of the Generalized Additive Model to understand the fit
 // and potentially learn about our dataset.
 // First, we will look at the bias; the bias represents the average prediction for the training data.
 Console.WriteLine($"Average prediction: {gam.Bias:0.00}");

 // Now look at the shape functions that the model has learned. Similar to a linear model, we have
 // one response per feature, and they are independent. Unlike a linear model, this response is a 
 // generic function instead of a line. Because we have included a bias term, each feature response 
 // represents the deviation from the average prediction as a function of the feature value.
 for (int i = 0; i < gam.NumberOfShapeFunctions; i++)
 {
 // Break a line.
 Console.WriteLine();

 // Get the bin upper bounds for the feature.
 var binUpperBounds = gam.GetBinUpperBounds(i);

 // Get the bin effects; these are the function values for each bin.
 var binEffects = gam.GetBinEffects(i);

 // Now, write the function to the console. The function is a set of bins, and the corresponding
 // function values. You can think of GAMs as building a bar-chart or lookup table for each feature.
 Console.WriteLine($"Feature{i}");
 for (int j = 0; j < binUpperBounds.Count; j++)
 Console.WriteLine($"x < {binUpperBounds[j]:0.00} => {binEffects[j]:0.000}");
 }

 // Expected output:
 // Average prediction: 0.82
 //
 // Feature0
 // x < -0.44 => 0.286
 // x < -0.38 => 0.225
 // x < -0.32 => 0.048
 // x < -0.26 => -0.110
 // x < -0.20 => -0.116
 // x < 0.18 => -0.143
 // x < 0.25 => -0.115
 // x < 0.31 => -0.005
 // x < 0.37 => 0.097
 // x < 0.44 => 0.263
 // x < ∞ => 0.284
 //
 // Feature1
 // x < 0.00 => -0.350
 // x < 0.24 => 0.875
 // x < 0.31 => -0.138
 // x < ∞ => -0.188

 // Let's consider this output. To score a given example, we look up the first bin where the inequality
 // is satisfied for the feature value. We can look at the whole function to get a sense for how the
 // model responds to the variable on a global level.
 // The model can be seen to reconstruct the parabolic and step-wise function, shifted with respect to the average
 // expected output over the training set. Very few bins are used to model the second feature because the GAM model
 // discards unchanged bins to create smaller models.
 // One last thing to notice is that these feature functions can be noisy. While we know that Feature1 should be
 // symmetric, this is not captured in the model. This is due to noise in the data. Common practice is to use 
 // resampling methods to estimate a confidence interval at each bin. This will help to determine if the effect is 
 // real or just sampling noise. See for example:
 // Tan, Caruana, Hooker, and Lou. "Distill-and-Compare: Auditing Black-Box Models Using Transparent Model 
 // Distillation." <a href='https://arxiv.org/abs/1710.06169'>arXiv:1710.06169</a>."
 }

 private class Data
 {
 public bool Label { get; set; }

 [VectorType(2)]
 public float[] Features { get; set; }
 }

 /// <summary>
 /// Creates a dataset, an IEnumerable of Data objects, for a GAM sample. Feature1 is a parabola centered around 0,
 /// while Feature2 is a simple piecewise function.
 /// </summary>
 /// <param name="numExamples">The number of examples to generate.</param>
 /// <param name="seed">The seed for the random number generator used to produce data.</param>
 /// <returns></returns>
 private static IEnumerable<Data> GenerateData(int numExamples = 25000, int seed = 1)
 {
 var rng = new Random(seed);
 float centeredFloat() => (float)(rng.NextDouble() - 0.5);
 for (int i = 0; i < numExamples; i++)
 {
 // Generate random, uncoupled features.
 var data = new Data {
 Features = new float[2] { centeredFloat(), centeredFloat() }
 };
 // Compute the label from the shape functions and add noise.
 data.Label = Sigmoid(Parabola(data.Features[0]) + SimplePiecewise(data.Features[1]) + centeredFloat()) > 0.5;

 yield return data;
 }
 }

 private static float Parabola(float x) => x * x;

 private static float SimplePiecewise(float x)
 {
 if (x < 0)
 return 0;
 else if (x < 0.25)
 return 1;
 else
 return 0;
@shmoradimsshmoradimsApr 12, 2019•

edited by rogancarr

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0 [](start = 23, length = 1)
is this the same as x < 0 value? #Resolved
@rogancarrrogancarrApr 12, 2019

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That is correct.
In reply to: 274927257 [](ancestors = 274927257)
 }

 private static double Sigmoid(double x) => 1.0 / (1.0 + Math.Exp(-1 * x));
 }
}

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docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/GamWithOptions.cs

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using System;
@wschinwschinApr 11, 2019•

edited by rogancarr

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Similar comments for Gam without Options apply here (and other sample files) #Resolved
using System.Collections.Generic;
using Microsoft.ML;
using Microsoft.ML.Data;
using Microsoft.ML.Trainers.FastTree;

namespace Samples.Dynamic.Trainers.BinaryClassification
{
 public static class GamWithOptions
 {
 // This example requires installation of additional NuGet package
 // <a href="https://www.nuget.org/packages/Microsoft.ML.FastTree/">Microsoft.ML.FastTree</a>.
 public static void Example()
 {
 // Create a new context for ML.NET operations. It can be used for exception tracking and logging, 
 // as a catalog of available operations and as the source of randomness.
 var mlContext = new MLContext();

 // Create the dataset.
 var samples = GenerateData();

 // Convert the dataset to an IDataView.
 var data = mlContext.Data.LoadFromEnumerable(samples);

 // Create training and validation datasets.
 var dataSets = mlContext.Data.TrainTestSplit(data);
 var trainSet = dataSets.TrainSet;
 var validSet = dataSets.TestSet;

 // Create a GAM trainer.
 // Use a small number of bins for this example. The setting below means for each feature, 
 // we divide its range into 16 discrete regions for the training process. Note that these
 // regions are not evenly spaced, and that the final model may contain fewer bins, as
 // neighboring bins with identical values will be combined. In general, we recommend using
 // at least the default number of bins, as a small number of bins limits the capacity of
 // the model.
 // Also, set the learning rate to half the default to slow down the gradient descent, and
 // double the number of iterations to compensate.
 var trainer = mlContext.BinaryClassification.Trainers.Gam(
 new GamBinaryTrainer.Options {
 NumberOfIterations = 19000,
 MaximumBinCountPerFeature = 16,
 LearningRate = 0.001
 });

 // Fit the model using both of training and validation sets. GAM can use a technique called 
 // pruning to tune the model to the validation set after training to improve generalization.
 var model = trainer.Fit(trainSet, validSet);

 // Extract the model parameters.
 var gam = model.Model.SubModel;

 // Now we can inspect the parameters of the Generalized Additive Model to understand the fit
 // and potentially learn about our dataset.
 // First, we will look at the bias; the bias represents the average prediction for the training data.
 Console.WriteLine($"Average prediction: {gam.Bias:0.00}");

 // Now look at the shape functions that the model has learned. Similar to a linear model, we have
 // one response per feature, and they are independent. Unlike a linear model, this response is a 
 // generic function instead of a line. Because we have included a bias term, each feature response 
 // represents the deviation from the average prediction as a function of the feature value.
 for (int i = 0; i < gam.NumberOfShapeFunctions; i++)
 {
 // Break a line.
 Console.WriteLine();

 // Get the bin upper bounds for the feature.
 var binUpperBounds = gam.GetBinUpperBounds(i);

 // Get the bin effects; these are the function values for each bin.
 var binEffects = gam.GetBinEffects(i);

 // Now, write the function to the console. The function is a set of bins, and the corresponding
 // function values. You can think of GAMs as building a bar-chart or lookup table for each feature.
 Console.WriteLine($"Feature{i}");
 for (int j = 0; j < binUpperBounds.Count; j++)
 Console.WriteLine($"x < {binUpperBounds[j]:0.00} => {binEffects[j]:0.000}");
 }

 // Expected output:
 // Average prediction: 0.82
 //
 // Feature0
 // x < -0.44 => 0.286
 // x < -0.38 => 0.225
 // x < -0.32 => 0.048
 // x < -0.26 => -0.110
 // x < -0.20 => -0.116
 // x < 0.18 => -0.143
 // x < 0.25 => -0.115
 // x < 0.31 => -0.005
 // x < 0.37 => 0.097
 // x < 0.44 => 0.263
 // x < ∞ => 0.284
 //
 // Feature1
 // x < 0.00 => -0.350
 // x < 0.24 => 0.875
 // x < 0.31 => -0.138
 // x < ∞ => -0.188

 // Let's consider this output. To score a given example, we look up the first bin where the inequality
 // is satisfied for the feature value. We can look at the whole function to get a sense for how the
 // model responds to the variable on a global level.
 // The model can be seen to reconstruct the parabolic and step-wise function, shifted with respect to the average
 // expected output over the training set. Very few bins are used to model the second feature because the GAM model
 // discards unchanged bins to create smaller models.
 // One last thing to notice is that these feature functions can be noisy. While we know that Feature1 should be
 // symmetric, this is not captured in the model. This is due to noise in the data. Common practice is to use 
 // resampling methods to estimate a confidence interval at each bin. This will help to determine if the effect is 
 // real or just sampling noise. See for example:
 // Tan, Caruana, Hooker, and Lou. "Distill-and-Compare: Auditing Black-Box Models Using Transparent Model 
 // Distillation." <a href='https://arxiv.org/abs/1710.06169'>arXiv:1710.06169</a>."
 }

 private class Data
 {
 public bool Label { get; set; }

 [VectorType(2)]
 public float[] Features { get; set; }
 }

 /// <summary>
 /// Creates a dataset, an IEnumerable of Data objects, for a GAM sample. Feature1 is a parabola centered around 0,
 /// while Feature2 is a simple piecewise function.
 /// </summary>
 /// <param name="numExamples">The number of examples to generate.</param>
 /// <param name="seed">The seed for the random number generator used to produce data.</param>
 /// <returns></returns>
 private static IEnumerable<Data> GenerateData(int numExamples = 25000, int seed = 1)
 {
 var rng = new Random(seed);
 float centeredFloat() => (float)(rng.NextDouble() - 0.5);
 for (int i = 0; i < numExamples; i++)
 {
 // Generate random, uncoupled features.
 var data = new Data
 {
 Features = new float[2] { centeredFloat(), centeredFloat() }
 };
 // Compute the label from the shape functions and add noise.
 data.Label = Sigmoid(Parabola(data.Features[0]) + SimplePiecewise(data.Features[1]) + centeredFloat()) > 0.5;

 yield return data;
 }
 }

 private static float Parabola(float x) => x * x;

 private static float SimplePiecewise(float x)
 {
 if (x < 0)
 return 0;
 else if (x < 0.25)
 return 1;
 else
 return 0;
 }

 private static double Sigmoid(double x) => 1.0 / (1.0 + Math.Exp(-1 * x));
 }
}
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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");
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Adding Regression GAM samples.

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Adding GAM Binary Classification samples.

Apr 10, 2019
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Update docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClass…


wschin Apr 11, 2019
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Update docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClass…


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Update docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClass…


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Update docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClass…


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docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/Gam.cs

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@@ -0,0 +1,154 @@
using System;
using System.Collections.Generic;
using Microsoft.ML;
using Microsoft.ML.Data;

namespace Samples.Dynamic.Trainers.BinaryClassification
{
 public static class Gam
 {
 // This example requires installation of additional NuGet package
 // <a href="https://www.nuget.org/packages/Microsoft.ML.FastTree/">Microsoft.ML.FastTree</a>.
 public static void Example()
 {
 // Create a new context for ML.NET operations. It can be used for exception tracking and logging, 
 // as a catalog of available operations and as the source of randomness.
 var mlContext = new MLContext();

 // Create the dataset.
 var samples = GenerateData();

 // Convert the dataset to an IDataView.
 var data = mlContext.Data.LoadFromEnumerable(samples);

 // Create training and validation sets.
 var dataSets = mlContext.Data.TrainTestSplit(data);
 var trainSet = dataSets.TrainSet;
 var validSet = dataSets.TestSet;

 // Create a GAM trainer.
 // Use a small number of bins for this example. The setting below means for each feature, 
 // we divide its range into 16 discrete regions for the training process. Note that these
 // regions are not evenly spaced, and that the final model may contain fewer bins, as
 // neighboring bins with identical values will be combined. In general, we recommend using
 // at least the default number of bins, as a small number of bins limits the capacity of
 // the model.
 var trainer = mlContext.BinaryClassification.Trainers.Gam(maximumBinCountPerFeature: 16);

 // Fit the model using both of training and validation sets. GAM can use a technique called 
 // pruning to tune the model to the validation set after training to improve generalization.
 var model = trainer.Fit(trainSet, validSet);

 // Extract the model parameters.
 var gam = model.Model.SubModel;

 // Now we can inspect the parameters of the Generalized Additive Model to understand the fit
 // and potentially learn about our dataset.
 // First, we will look at the bias; the bias represents the average prediction for the training data.
 Console.WriteLine($"Average prediction: {gam.Bias:0.00}");

 // Now look at the shape functions that the model has learned. Similar to a linear model, we have
 // one response per feature, and they are independent. Unlike a linear model, this response is a 
 // generic function instead of a line. Because we have included a bias term, each feature response 
 // represents the deviation from the average prediction as a function of the feature value.
 for (int i = 0; i < gam.NumberOfShapeFunctions; i++)
 {
 // Break a line.
 Console.WriteLine();

 // Get the bin upper bounds for the feature.
 var binUpperBounds = gam.GetBinUpperBounds(i);

 // Get the bin effects; these are the function values for each bin.
 var binEffects = gam.GetBinEffects(i);

 // Now, write the function to the console. The function is a set of bins, and the corresponding
 // function values. You can think of GAMs as building a bar-chart or lookup table for each feature.
 Console.WriteLine($"Feature{i}");
 for (int j = 0; j < binUpperBounds.Count; j++)
 Console.WriteLine($"x < {binUpperBounds[j]:0.00} => {binEffects[j]:0.000}");
 }

 // Expected output:
 // Average prediction: 0.82
 //
 // Feature0
 // x < -0.44 => 0.286
 // x < -0.38 => 0.225
 // x < -0.32 => 0.048
 // x < -0.26 => -0.110
 // x < -0.20 => -0.116
 // x < 0.18 => -0.143
 // x < 0.25 => -0.115
 // x < 0.31 => -0.005
 // x < 0.37 => 0.097
 // x < 0.44 => 0.263
 // x < ∞ => 0.284
 //
 // Feature1
 // x < 0.00 => -0.350
 // x < 0.24 => 0.875
 // x < 0.31 => -0.138
 // x < ∞ => -0.188

 // Let's consider this output. To score a given example, we look up the first bin where the inequality
 // is satisfied for the feature value. We can look at the whole function to get a sense for how the
 // model responds to the variable on a global level.
 // The model can be seen to reconstruct the parabolic and step-wise function, shifted with respect to the average
 // expected output over the training set. Very few bins are used to model the second feature because the GAM model
 // discards unchanged bins to create smaller models.
 // One last thing to notice is that these feature functions can be noisy. While we know that Feature1 should be
 // symmetric, this is not captured in the model. This is due to noise in the data. Common practice is to use 
 // resampling methods to estimate a confidence interval at each bin. This will help to determine if the effect is 
 // real or just sampling noise. See for example:
 // Tan, Caruana, Hooker, and Lou. "Distill-and-Compare: Auditing Black-Box Models Using Transparent Model 
 // Distillation." <a href='https://arxiv.org/abs/1710.06169'>arXiv:1710.06169</a>."
 }

 private class Data
 {
 public bool Label { get; set; }

 [VectorType(2)]
 public float[] Features { get; set; }
 }

 /// <summary>
 /// Creates a dataset, an IEnumerable of Data objects, for a GAM sample. Feature1 is a parabola centered around 0,
 /// while Feature2 is a simple piecewise function.
 /// </summary>
 /// <param name="numExamples">The number of examples to generate.</param>
 /// <param name="seed">The seed for the random number generator used to produce data.</param>
 /// <returns></returns>
 private static IEnumerable<Data> GenerateData(int numExamples = 25000, int seed = 1)
 {
 var rng = new Random(seed);
 float centeredFloat() => (float)(rng.NextDouble() - 0.5);
 for (int i = 0; i < numExamples; i++)
 {
 // Generate random, uncoupled features.
 var data = new Data {
 Features = new float[2] { centeredFloat(), centeredFloat() }
 };
 // Compute the label from the shape functions and add noise.
 data.Label = Sigmoid(Parabola(data.Features[0]) + SimplePiecewise(data.Features[1]) + centeredFloat()) > 0.5;

 yield return data;
 }
 }

 private static float Parabola(float x) => x * x;

 private static float SimplePiecewise(float x)
 {
 if (x < 0)
 return 0;
 else if (x < 0.25)
 return 1;
 else
 return 0;
@shmoradimsshmoradimsApr 12, 2019•

edited by rogancarr

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0 [](start = 23, length = 1)
is this the same as x < 0 value? #Resolved
@rogancarrrogancarrApr 12, 2019

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That is correct.
In reply to: 274927257 [](ancestors = 274927257)
 }

 private static double Sigmoid(double x) => 1.0 / (1.0 + Math.Exp(-1 * x));
 }
}

163 changes: 163 additions & 0 deletions
docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/GamWithOptions.cs

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using System;
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edited by rogancarr

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Similar comments for Gam without Options apply here (and other sample files) #Resolved
using System.Collections.Generic;
using Microsoft.ML;
using Microsoft.ML.Data;
using Microsoft.ML.Trainers.FastTree;

namespace Samples.Dynamic.Trainers.BinaryClassification
{
 public static class GamWithOptions
 {
 // This example requires installation of additional NuGet package
 // <a href="https://www.nuget.org/packages/Microsoft.ML.FastTree/">Microsoft.ML.FastTree</a>.
 public static void Example()
 {
 // Create a new context for ML.NET operations. It can be used for exception tracking and logging, 
 // as a catalog of available operations and as the source of randomness.
 var mlContext = new MLContext();

 // Create the dataset.
 var samples = GenerateData();

 // Convert the dataset to an IDataView.
 var data = mlContext.Data.LoadFromEnumerable(samples);

 // Create training and validation datasets.
 var dataSets = mlContext.Data.TrainTestSplit(data);
 var trainSet = dataSets.TrainSet;
 var validSet = dataSets.TestSet;

 // Create a GAM trainer.
 // Use a small number of bins for this example. The setting below means for each feature, 
 // we divide its range into 16 discrete regions for the training process. Note that these
 // regions are not evenly spaced, and that the final model may contain fewer bins, as
 // neighboring bins with identical values will be combined. In general, we recommend using
 // at least the default number of bins, as a small number of bins limits the capacity of
 // the model.
 // Also, set the learning rate to half the default to slow down the gradient descent, and
 // double the number of iterations to compensate.
 var trainer = mlContext.BinaryClassification.Trainers.Gam(
 new GamBinaryTrainer.Options {
 NumberOfIterations = 19000,
 MaximumBinCountPerFeature = 16,
 LearningRate = 0.001
 });

 // Fit the model using both of training and validation sets. GAM can use a technique called 
 // pruning to tune the model to the validation set after training to improve generalization.
 var model = trainer.Fit(trainSet, validSet);

 // Extract the model parameters.
 var gam = model.Model.SubModel;

 // Now we can inspect the parameters of the Generalized Additive Model to understand the fit
 // and potentially learn about our dataset.
 // First, we will look at the bias; the bias represents the average prediction for the training data.
 Console.WriteLine($"Average prediction: {gam.Bias:0.00}");

 // Now look at the shape functions that the model has learned. Similar to a linear model, we have
 // one response per feature, and they are independent. Unlike a linear model, this response is a 
 // generic function instead of a line. Because we have included a bias term, each feature response 
 // represents the deviation from the average prediction as a function of the feature value.
 for (int i = 0; i < gam.NumberOfShapeFunctions; i++)
 {
 // Break a line.
 Console.WriteLine();

 // Get the bin upper bounds for the feature.
 var binUpperBounds = gam.GetBinUpperBounds(i);

 // Get the bin effects; these are the function values for each bin.
 var binEffects = gam.GetBinEffects(i);

 // Now, write the function to the console. The function is a set of bins, and the corresponding
 // function values. You can think of GAMs as building a bar-chart or lookup table for each feature.
 Console.WriteLine($"Feature{i}");
 for (int j = 0; j < binUpperBounds.Count; j++)
 Console.WriteLine($"x < {binUpperBounds[j]:0.00} => {binEffects[j]:0.000}");
 }

 // Expected output:
 // Average prediction: 0.82
 //
 // Feature0
 // x < -0.44 => 0.286
 // x < -0.38 => 0.225
 // x < -0.32 => 0.048
 // x < -0.26 => -0.110
 // x < -0.20 => -0.116
 // x < 0.18 => -0.143
 // x < 0.25 => -0.115
 // x < 0.31 => -0.005
 // x < 0.37 => 0.097
 // x < 0.44 => 0.263
 // x < ∞ => 0.284
 //
 // Feature1
 // x < 0.00 => -0.350
 // x < 0.24 => 0.875
 // x < 0.31 => -0.138
 // x < ∞ => -0.188

 // Let's consider this output. To score a given example, we look up the first bin where the inequality
 // is satisfied for the feature value. We can look at the whole function to get a sense for how the
 // model responds to the variable on a global level.
 // The model can be seen to reconstruct the parabolic and step-wise function, shifted with respect to the average
 // expected output over the training set. Very few bins are used to model the second feature because the GAM model
 // discards unchanged bins to create smaller models.
 // One last thing to notice is that these feature functions can be noisy. While we know that Feature1 should be
 // symmetric, this is not captured in the model. This is due to noise in the data. Common practice is to use 
 // resampling methods to estimate a confidence interval at each bin. This will help to determine if the effect is 
 // real or just sampling noise. See for example:
 // Tan, Caruana, Hooker, and Lou. "Distill-and-Compare: Auditing Black-Box Models Using Transparent Model 
 // Distillation." <a href='https://arxiv.org/abs/1710.06169'>arXiv:1710.06169</a>."
 }

 private class Data
 {
 public bool Label { get; set; }

 [VectorType(2)]
 public float[] Features { get; set; }
 }

 /// <summary>
 /// Creates a dataset, an IEnumerable of Data objects, for a GAM sample. Feature1 is a parabola centered around 0,
 /// while Feature2 is a simple piecewise function.
 /// </summary>
 /// <param name="numExamples">The number of examples to generate.</param>
 /// <param name="seed">The seed for the random number generator used to produce data.</param>
 /// <returns></returns>
 private static IEnumerable<Data> GenerateData(int numExamples = 25000, int seed = 1)
 {
 var rng = new Random(seed);
 float centeredFloat() => (float)(rng.NextDouble() - 0.5);
 for (int i = 0; i < numExamples; i++)
 {
 // Generate random, uncoupled features.
 var data = new Data
 {
 Features = new float[2] { centeredFloat(), centeredFloat() }
 };
 // Compute the label from the shape functions and add noise.
 data.Label = Sigmoid(Parabola(data.Features[0]) + SimplePiecewise(data.Features[1]) + centeredFloat()) > 0.5;

 yield return data;
 }
 }

 private static float Parabola(float x) => x * x;

 private static float SimplePiecewise(float x)
 {
 if (x < 0)
 return 0;
 else if (x < 0.25)
 return 1;
 else
 return 0;
 }

 private static double Sigmoid(double x) => 1.0 / (1.0 + Math.Exp(-1 * x));
 }
}
Loading

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

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Adding GAM Binary Classification samples.

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Update docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClass…


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Update docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClass…


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docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/Gam.cs

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using System;
using System.Collections.Generic;
using Microsoft.ML;
using Microsoft.ML.Data;

namespace Samples.Dynamic.Trainers.BinaryClassification
{
 public static class Gam
 {
 // This example requires installation of additional NuGet package
 // <a href="https://www.nuget.org/packages/Microsoft.ML.FastTree/">Microsoft.ML.FastTree</a>.
 public static void Example()
 {
 // Create a new context for ML.NET operations. It can be used for exception tracking and logging, 
 // as a catalog of available operations and as the source of randomness.
 var mlContext = new MLContext();

 // Create the dataset.
 var samples = GenerateData();

 // Convert the dataset to an IDataView.
 var data = mlContext.Data.LoadFromEnumerable(samples);

 // Create training and validation sets.
 var dataSets = mlContext.Data.TrainTestSplit(data);
 var trainSet = dataSets.TrainSet;
 var validSet = dataSets.TestSet;

 // Create a GAM trainer.
 // Use a small number of bins for this example. The setting below means for each feature, 
 // we divide its range into 16 discrete regions for the training process. Note that these
 // regions are not evenly spaced, and that the final model may contain fewer bins, as
 // neighboring bins with identical values will be combined. In general, we recommend using
 // at least the default number of bins, as a small number of bins limits the capacity of
 // the model.
 var trainer = mlContext.BinaryClassification.Trainers.Gam(maximumBinCountPerFeature: 16);

 // Fit the model using both of training and validation sets. GAM can use a technique called 
 // pruning to tune the model to the validation set after training to improve generalization.
 var model = trainer.Fit(trainSet, validSet);

 // Extract the model parameters.
 var gam = model.Model.SubModel;

 // Now we can inspect the parameters of the Generalized Additive Model to understand the fit
 // and potentially learn about our dataset.
 // First, we will look at the bias; the bias represents the average prediction for the training data.
 Console.WriteLine($"Average prediction: {gam.Bias:0.00}");

 // Now look at the shape functions that the model has learned. Similar to a linear model, we have
 // one response per feature, and they are independent. Unlike a linear model, this response is a 
 // generic function instead of a line. Because we have included a bias term, each feature response 
 // represents the deviation from the average prediction as a function of the feature value.
 for (int i = 0; i < gam.NumberOfShapeFunctions; i++)
 {
 // Break a line.
 Console.WriteLine();

 // Get the bin upper bounds for the feature.
 var binUpperBounds = gam.GetBinUpperBounds(i);

 // Get the bin effects; these are the function values for each bin.
 var binEffects = gam.GetBinEffects(i);

 // Now, write the function to the console. The function is a set of bins, and the corresponding
 // function values. You can think of GAMs as building a bar-chart or lookup table for each feature.
 Console.WriteLine($"Feature{i}");
 for (int j = 0; j < binUpperBounds.Count; j++)
 Console.WriteLine($"x < {binUpperBounds[j]:0.00} => {binEffects[j]:0.000}");
 }

 // Expected output:
 // Average prediction: 0.82
 //
 // Feature0
 // x < -0.44 => 0.286
 // x < -0.38 => 0.225
 // x < -0.32 => 0.048
 // x < -0.26 => -0.110
 // x < -0.20 => -0.116
 // x < 0.18 => -0.143
 // x < 0.25 => -0.115
 // x < 0.31 => -0.005
 // x < 0.37 => 0.097
 // x < 0.44 => 0.263
 // x < ∞ => 0.284
 //
 // Feature1
 // x < 0.00 => -0.350
 // x < 0.24 => 0.875
 // x < 0.31 => -0.138
 // x < ∞ => -0.188

 // Let's consider this output. To score a given example, we look up the first bin where the inequality
 // is satisfied for the feature value. We can look at the whole function to get a sense for how the
 // model responds to the variable on a global level.
 // The model can be seen to reconstruct the parabolic and step-wise function, shifted with respect to the average
 // expected output over the training set. Very few bins are used to model the second feature because the GAM model
 // discards unchanged bins to create smaller models.
 // One last thing to notice is that these feature functions can be noisy. While we know that Feature1 should be
 // symmetric, this is not captured in the model. This is due to noise in the data. Common practice is to use 
 // resampling methods to estimate a confidence interval at each bin. This will help to determine if the effect is 
 // real or just sampling noise. See for example:
 // Tan, Caruana, Hooker, and Lou. "Distill-and-Compare: Auditing Black-Box Models Using Transparent Model 
 // Distillation." <a href='https://arxiv.org/abs/1710.06169'>arXiv:1710.06169</a>."
 }

 private class Data
 {
 public bool Label { get; set; }

 [VectorType(2)]
 public float[] Features { get; set; }
 }

 /// <summary>
 /// Creates a dataset, an IEnumerable of Data objects, for a GAM sample. Feature1 is a parabola centered around 0,
 /// while Feature2 is a simple piecewise function.
 /// </summary>
 /// <param name="numExamples">The number of examples to generate.</param>
 /// <param name="seed">The seed for the random number generator used to produce data.</param>
 /// <returns></returns>
 private static IEnumerable<Data> GenerateData(int numExamples = 25000, int seed = 1)
 {
 var rng = new Random(seed);
 float centeredFloat() => (float)(rng.NextDouble() - 0.5);
 for (int i = 0; i < numExamples; i++)
 {
 // Generate random, uncoupled features.
 var data = new Data {
 Features = new float[2] { centeredFloat(), centeredFloat() }
 };
 // Compute the label from the shape functions and add noise.
 data.Label = Sigmoid(Parabola(data.Features[0]) + SimplePiecewise(data.Features[1]) + centeredFloat()) > 0.5;

 yield return data;
 }
 }

 private static float Parabola(float x) => x * x;

 private static float SimplePiecewise(float x)
 {
 if (x < 0)
 return 0;
 else if (x < 0.25)
 return 1;
 else
 return 0;
@shmoradimsshmoradimsApr 12, 2019•

edited by rogancarr

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0 [](start = 23, length = 1)
is this the same as x < 0 value? #Resolved
@rogancarrrogancarrApr 12, 2019

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That is correct.
In reply to: 274927257 [](ancestors = 274927257)
 }

 private static double Sigmoid(double x) => 1.0 / (1.0 + Math.Exp(-1 * x));
 }
}

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docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/GamWithOptions.cs

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using System;
@wschinwschinApr 11, 2019•

edited by rogancarr

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Similar comments for Gam without Options apply here (and other sample files) #Resolved
using System.Collections.Generic;
using Microsoft.ML;
using Microsoft.ML.Data;
using Microsoft.ML.Trainers.FastTree;

namespace Samples.Dynamic.Trainers.BinaryClassification
{
 public static class GamWithOptions
 {
 // This example requires installation of additional NuGet package
 // <a href="https://www.nuget.org/packages/Microsoft.ML.FastTree/">Microsoft.ML.FastTree</a>.
 public static void Example()
 {
 // Create a new context for ML.NET operations. It can be used for exception tracking and logging, 
 // as a catalog of available operations and as the source of randomness.
 var mlContext = new MLContext();

 // Create the dataset.
 var samples = GenerateData();

 // Convert the dataset to an IDataView.
 var data = mlContext.Data.LoadFromEnumerable(samples);

 // Create training and validation datasets.
 var dataSets = mlContext.Data.TrainTestSplit(data);
 var trainSet = dataSets.TrainSet;
 var validSet = dataSets.TestSet;

 // Create a GAM trainer.
 // Use a small number of bins for this example. The setting below means for each feature, 
 // we divide its range into 16 discrete regions for the training process. Note that these
 // regions are not evenly spaced, and that the final model may contain fewer bins, as
 // neighboring bins with identical values will be combined. In general, we recommend using
 // at least the default number of bins, as a small number of bins limits the capacity of
 // the model.
 // Also, set the learning rate to half the default to slow down the gradient descent, and
 // double the number of iterations to compensate.
 var trainer = mlContext.BinaryClassification.Trainers.Gam(
 new GamBinaryTrainer.Options {
 NumberOfIterations = 19000,
 MaximumBinCountPerFeature = 16,
 LearningRate = 0.001
 });

 // Fit the model using both of training and validation sets. GAM can use a technique called 
 // pruning to tune the model to the validation set after training to improve generalization.
 var model = trainer.Fit(trainSet, validSet);

 // Extract the model parameters.
 var gam = model.Model.SubModel;

 // Now we can inspect the parameters of the Generalized Additive Model to understand the fit
 // and potentially learn about our dataset.
 // First, we will look at the bias; the bias represents the average prediction for the training data.
 Console.WriteLine($"Average prediction: {gam.Bias:0.00}");

 // Now look at the shape functions that the model has learned. Similar to a linear model, we have
 // one response per feature, and they are independent. Unlike a linear model, this response is a 
 // generic function instead of a line. Because we have included a bias term, each feature response 
 // represents the deviation from the average prediction as a function of the feature value.
 for (int i = 0; i < gam.NumberOfShapeFunctions; i++)
 {
 // Break a line.
 Console.WriteLine();

 // Get the bin upper bounds for the feature.
 var binUpperBounds = gam.GetBinUpperBounds(i);

 // Get the bin effects; these are the function values for each bin.
 var binEffects = gam.GetBinEffects(i);

 // Now, write the function to the console. The function is a set of bins, and the corresponding
 // function values. You can think of GAMs as building a bar-chart or lookup table for each feature.
 Console.WriteLine($"Feature{i}");
 for (int j = 0; j < binUpperBounds.Count; j++)
 Console.WriteLine($"x < {binUpperBounds[j]:0.00} => {binEffects[j]:0.000}");
 }

 // Expected output:
 // Average prediction: 0.82
 //
 // Feature0
 // x < -0.44 => 0.286
 // x < -0.38 => 0.225
 // x < -0.32 => 0.048
 // x < -0.26 => -0.110
 // x < -0.20 => -0.116
 // x < 0.18 => -0.143
 // x < 0.25 => -0.115
 // x < 0.31 => -0.005
 // x < 0.37 => 0.097
 // x < 0.44 => 0.263
 // x < ∞ => 0.284
 //
 // Feature1
 // x < 0.00 => -0.350
 // x < 0.24 => 0.875
 // x < 0.31 => -0.138
 // x < ∞ => -0.188

 // Let's consider this output. To score a given example, we look up the first bin where the inequality
 // is satisfied for the feature value. We can look at the whole function to get a sense for how the
 // model responds to the variable on a global level.
 // The model can be seen to reconstruct the parabolic and step-wise function, shifted with respect to the average
 // expected output over the training set. Very few bins are used to model the second feature because the GAM model
 // discards unchanged bins to create smaller models.
 // One last thing to notice is that these feature functions can be noisy. While we know that Feature1 should be
 // symmetric, this is not captured in the model. This is due to noise in the data. Common practice is to use 
 // resampling methods to estimate a confidence interval at each bin. This will help to determine if the effect is 
 // real or just sampling noise. See for example:
 // Tan, Caruana, Hooker, and Lou. "Distill-and-Compare: Auditing Black-Box Models Using Transparent Model 
 // Distillation." <a href='https://arxiv.org/abs/1710.06169'>arXiv:1710.06169</a>."
 }

 private class Data
 {
 public bool Label { get; set; }

 [VectorType(2)]
 public float[] Features { get; set; }
 }

 /// <summary>
 /// Creates a dataset, an IEnumerable of Data objects, for a GAM sample. Feature1 is a parabola centered around 0,
 /// while Feature2 is a simple piecewise function.
 /// </summary>
 /// <param name="numExamples">The number of examples to generate.</param>
 /// <param name="seed">The seed for the random number generator used to produce data.</param>
 /// <returns></returns>
 private static IEnumerable<Data> GenerateData(int numExamples = 25000, int seed = 1)
 {
 var rng = new Random(seed);
 float centeredFloat() => (float)(rng.NextDouble() - 0.5);
 for (int i = 0; i < numExamples; i++)
 {
 // Generate random, uncoupled features.
 var data = new Data
 {
 Features = new float[2] { centeredFloat(), centeredFloat() }
 };
 // Compute the label from the shape functions and add noise.
 data.Label = Sigmoid(Parabola(data.Features[0]) + SimplePiecewise(data.Features[1]) + centeredFloat()) > 0.5;

 yield return data;
 }
 }

 private static float Parabola(float x) => x * x;

 private static float SimplePiecewise(float x)
 {
 if (x < 0)
 return 0;
 else if (x < 0.25)
 return 1;
 else
 return 0;
 }

 private static double Sigmoid(double x) => 1.0 / (1.0 + Math.Exp(-1 * x));
 }
}
Loading

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

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 Merged

rogancarr
merged 8 commits into

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Apr 12, 2019
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Extend Gam Samples
#3281
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fce8ef4

Adding Regression GAM samples.

Apr 10, 2019
7d335d0

Adding GAM Binary Classification samples.

Apr 10, 2019
73d8270

Update docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClass…


wschin Apr 11, 2019
4469a85

Update docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClass…


wschin Apr 11, 2019
091fafd

Update docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClass…


wschin Apr 11, 2019
7f7c991

Update docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClass…


wschin Apr 11, 2019
1c99277

Addressing PR comments; rewriting comments.

Apr 11, 2019
a717ac5

Addressing PR comments.

Apr 12, 2019

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docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/Gam.cs

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@@ -0,0 +1,154 @@
using System;
using System.Collections.Generic;
using Microsoft.ML;
using Microsoft.ML.Data;

namespace Samples.Dynamic.Trainers.BinaryClassification
{
 public static class Gam
 {
 // This example requires installation of additional NuGet package
 // <a href="https://www.nuget.org/packages/Microsoft.ML.FastTree/">Microsoft.ML.FastTree</a>.
 public static void Example()
 {
 // Create a new context for ML.NET operations. It can be used for exception tracking and logging, 
 // as a catalog of available operations and as the source of randomness.
 var mlContext = new MLContext();

 // Create the dataset.
 var samples = GenerateData();

 // Convert the dataset to an IDataView.
 var data = mlContext.Data.LoadFromEnumerable(samples);

 // Create training and validation sets.
 var dataSets = mlContext.Data.TrainTestSplit(data);
 var trainSet = dataSets.TrainSet;
 var validSet = dataSets.TestSet;

 // Create a GAM trainer.
 // Use a small number of bins for this example. The setting below means for each feature, 
 // we divide its range into 16 discrete regions for the training process. Note that these
 // regions are not evenly spaced, and that the final model may contain fewer bins, as
 // neighboring bins with identical values will be combined. In general, we recommend using
 // at least the default number of bins, as a small number of bins limits the capacity of
 // the model.
 var trainer = mlContext.BinaryClassification.Trainers.Gam(maximumBinCountPerFeature: 16);

 // Fit the model using both of training and validation sets. GAM can use a technique called 
 // pruning to tune the model to the validation set after training to improve generalization.
 var model = trainer.Fit(trainSet, validSet);

 // Extract the model parameters.
 var gam = model.Model.SubModel;

 // Now we can inspect the parameters of the Generalized Additive Model to understand the fit
 // and potentially learn about our dataset.
 // First, we will look at the bias; the bias represents the average prediction for the training data.
 Console.WriteLine($"Average prediction: {gam.Bias:0.00}");

 // Now look at the shape functions that the model has learned. Similar to a linear model, we have
 // one response per feature, and they are independent. Unlike a linear model, this response is a 
 // generic function instead of a line. Because we have included a bias term, each feature response 
 // represents the deviation from the average prediction as a function of the feature value.
 for (int i = 0; i < gam.NumberOfShapeFunctions; i++)
 {
 // Break a line.
 Console.WriteLine();

 // Get the bin upper bounds for the feature.
 var binUpperBounds = gam.GetBinUpperBounds(i);

 // Get the bin effects; these are the function values for each bin.
 var binEffects = gam.GetBinEffects(i);

 // Now, write the function to the console. The function is a set of bins, and the corresponding
 // function values. You can think of GAMs as building a bar-chart or lookup table for each feature.
 Console.WriteLine($"Feature{i}");
 for (int j = 0; j < binUpperBounds.Count; j++)
 Console.WriteLine($"x < {binUpperBounds[j]:0.00} => {binEffects[j]:0.000}");
 }

 // Expected output:
 // Average prediction: 0.82
 //
 // Feature0
 // x < -0.44 => 0.286
 // x < -0.38 => 0.225
 // x < -0.32 => 0.048
 // x < -0.26 => -0.110
 // x < -0.20 => -0.116
 // x < 0.18 => -0.143
 // x < 0.25 => -0.115
 // x < 0.31 => -0.005
 // x < 0.37 => 0.097
 // x < 0.44 => 0.263
 // x < ∞ => 0.284
 //
 // Feature1
 // x < 0.00 => -0.350
 // x < 0.24 => 0.875
 // x < 0.31 => -0.138
 // x < ∞ => -0.188

 // Let's consider this output. To score a given example, we look up the first bin where the inequality
 // is satisfied for the feature value. We can look at the whole function to get a sense for how the
 // model responds to the variable on a global level.
 // The model can be seen to reconstruct the parabolic and step-wise function, shifted with respect to the average
 // expected output over the training set. Very few bins are used to model the second feature because the GAM model
 // discards unchanged bins to create smaller models.
 // One last thing to notice is that these feature functions can be noisy. While we know that Feature1 should be
 // symmetric, this is not captured in the model. This is due to noise in the data. Common practice is to use 
 // resampling methods to estimate a confidence interval at each bin. This will help to determine if the effect is 
 // real or just sampling noise. See for example:
 // Tan, Caruana, Hooker, and Lou. "Distill-and-Compare: Auditing Black-Box Models Using Transparent Model 
 // Distillation." <a href='https://arxiv.org/abs/1710.06169'>arXiv:1710.06169</a>."
 }

 private class Data
 {
 public bool Label { get; set; }

 [VectorType(2)]
 public float[] Features { get; set; }
 }

 /// <summary>
 /// Creates a dataset, an IEnumerable of Data objects, for a GAM sample. Feature1 is a parabola centered around 0,
 /// while Feature2 is a simple piecewise function.
 /// </summary>
 /// <param name="numExamples">The number of examples to generate.</param>
 /// <param name="seed">The seed for the random number generator used to produce data.</param>
 /// <returns></returns>
 private static IEnumerable<Data> GenerateData(int numExamples = 25000, int seed = 1)
 {
 var rng = new Random(seed);
 float centeredFloat() => (float)(rng.NextDouble() - 0.5);
 for (int i = 0; i < numExamples; i++)
 {
 // Generate random, uncoupled features.
 var data = new Data {
 Features = new float[2] { centeredFloat(), centeredFloat() }
 };
 // Compute the label from the shape functions and add noise.
 data.Label = Sigmoid(Parabola(data.Features[0]) + SimplePiecewise(data.Features[1]) + centeredFloat()) > 0.5;

 yield return data;
 }
 }

 private static float Parabola(float x) => x * x;

 private static float SimplePiecewise(float x)
 {
 if (x < 0)
 return 0;
 else if (x < 0.25)
 return 1;
 else
 return 0;
@shmoradimsshmoradimsApr 12, 2019•

edited by rogancarr

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0 [](start = 23, length = 1)
is this the same as x < 0 value? #Resolved
@rogancarrrogancarrApr 12, 2019

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That is correct.
In reply to: 274927257 [](ancestors = 274927257)
 }

 private static double Sigmoid(double x) => 1.0 / (1.0 + Math.Exp(-1 * x));
 }
}

163 changes: 163 additions & 0 deletions
docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/GamWithOptions.cs

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using System;
@wschinwschinApr 11, 2019•

edited by rogancarr

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Similar comments for Gam without Options apply here (and other sample files) #Resolved
using System.Collections.Generic;
using Microsoft.ML;
using Microsoft.ML.Data;
using Microsoft.ML.Trainers.FastTree;

namespace Samples.Dynamic.Trainers.BinaryClassification
{
 public static class GamWithOptions
 {
 // This example requires installation of additional NuGet package
 // <a href="https://www.nuget.org/packages/Microsoft.ML.FastTree/">Microsoft.ML.FastTree</a>.
 public static void Example()
 {
 // Create a new context for ML.NET operations. It can be used for exception tracking and logging, 
 // as a catalog of available operations and as the source of randomness.
 var mlContext = new MLContext();

 // Create the dataset.
 var samples = GenerateData();

 // Convert the dataset to an IDataView.
 var data = mlContext.Data.LoadFromEnumerable(samples);

 // Create training and validation datasets.
 var dataSets = mlContext.Data.TrainTestSplit(data);
 var trainSet = dataSets.TrainSet;
 var validSet = dataSets.TestSet;

 // Create a GAM trainer.
 // Use a small number of bins for this example. The setting below means for each feature, 
 // we divide its range into 16 discrete regions for the training process. Note that these
 // regions are not evenly spaced, and that the final model may contain fewer bins, as
 // neighboring bins with identical values will be combined. In general, we recommend using
 // at least the default number of bins, as a small number of bins limits the capacity of
 // the model.
 // Also, set the learning rate to half the default to slow down the gradient descent, and
 // double the number of iterations to compensate.
 var trainer = mlContext.BinaryClassification.Trainers.Gam(
 new GamBinaryTrainer.Options {
 NumberOfIterations = 19000,
 MaximumBinCountPerFeature = 16,
 LearningRate = 0.001
 });

 // Fit the model using both of training and validation sets. GAM can use a technique called 
 // pruning to tune the model to the validation set after training to improve generalization.
 var model = trainer.Fit(trainSet, validSet);

 // Extract the model parameters.
 var gam = model.Model.SubModel;

 // Now we can inspect the parameters of the Generalized Additive Model to understand the fit
 // and potentially learn about our dataset.
 // First, we will look at the bias; the bias represents the average prediction for the training data.
 Console.WriteLine($"Average prediction: {gam.Bias:0.00}");

 // Now look at the shape functions that the model has learned. Similar to a linear model, we have
 // one response per feature, and they are independent. Unlike a linear model, this response is a 
 // generic function instead of a line. Because we have included a bias term, each feature response 
 // represents the deviation from the average prediction as a function of the feature value.
 for (int i = 0; i < gam.NumberOfShapeFunctions; i++)
 {
 // Break a line.
 Console.WriteLine();

 // Get the bin upper bounds for the feature.
 var binUpperBounds = gam.GetBinUpperBounds(i);

 // Get the bin effects; these are the function values for each bin.
 var binEffects = gam.GetBinEffects(i);

 // Now, write the function to the console. The function is a set of bins, and the corresponding
 // function values. You can think of GAMs as building a bar-chart or lookup table for each feature.
 Console.WriteLine($"Feature{i}");
 for (int j = 0; j < binUpperBounds.Count; j++)
 Console.WriteLine($"x < {binUpperBounds[j]:0.00} => {binEffects[j]:0.000}");
 }

 // Expected output:
 // Average prediction: 0.82
 //
 // Feature0
 // x < -0.44 => 0.286
 // x < -0.38 => 0.225
 // x < -0.32 => 0.048
 // x < -0.26 => -0.110
 // x < -0.20 => -0.116
 // x < 0.18 => -0.143
 // x < 0.25 => -0.115
 // x < 0.31 => -0.005
 // x < 0.37 => 0.097
 // x < 0.44 => 0.263
 // x < ∞ => 0.284
 //
 // Feature1
 // x < 0.00 => -0.350
 // x < 0.24 => 0.875
 // x < 0.31 => -0.138
 // x < ∞ => -0.188

 // Let's consider this output. To score a given example, we look up the first bin where the inequality
 // is satisfied for the feature value. We can look at the whole function to get a sense for how the
 // model responds to the variable on a global level.
 // The model can be seen to reconstruct the parabolic and step-wise function, shifted with respect to the average
 // expected output over the training set. Very few bins are used to model the second feature because the GAM model
 // discards unchanged bins to create smaller models.
 // One last thing to notice is that these feature functions can be noisy. While we know that Feature1 should be
 // symmetric, this is not captured in the model. This is due to noise in the data. Common practice is to use 
 // resampling methods to estimate a confidence interval at each bin. This will help to determine if the effect is 
 // real or just sampling noise. See for example:
 // Tan, Caruana, Hooker, and Lou. "Distill-and-Compare: Auditing Black-Box Models Using Transparent Model 
 // Distillation." <a href='https://arxiv.org/abs/1710.06169'>arXiv:1710.06169</a>."
 }

 private class Data
 {
 public bool Label { get; set; }

 [VectorType(2)]
 public float[] Features { get; set; }
 }

 /// <summary>
 /// Creates a dataset, an IEnumerable of Data objects, for a GAM sample. Feature1 is a parabola centered around 0,
 /// while Feature2 is a simple piecewise function.
 /// </summary>
 /// <param name="numExamples">The number of examples to generate.</param>
 /// <param name="seed">The seed for the random number generator used to produce data.</param>
 /// <returns></returns>
 private static IEnumerable<Data> GenerateData(int numExamples = 25000, int seed = 1)
 {
 var rng = new Random(seed);
 float centeredFloat() => (float)(rng.NextDouble() - 0.5);
 for (int i = 0; i < numExamples; i++)
 {
 // Generate random, uncoupled features.
 var data = new Data
 {
 Features = new float[2] { centeredFloat(), centeredFloat() }
 };
 // Compute the label from the shape functions and add noise.
 data.Label = Sigmoid(Parabola(data.Features[0]) + SimplePiecewise(data.Features[1]) + centeredFloat()) > 0.5;

 yield return data;
 }
 }

 private static float Parabola(float x) => x * x;

 private static float SimplePiecewise(float x)
 {
 if (x < 0)
 return 0;
 else if (x < 0.25)
 return 1;
 else
 return 0;
 }

 private static double Sigmoid(double x) => 1.0 / (1.0 + Math.Exp(-1 * x));
 }
}
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
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} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
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Extend Gam Samples
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fce8ef4

Adding Regression GAM samples.

Apr 10, 2019
7d335d0

Adding GAM Binary Classification samples.

Apr 10, 2019
73d8270

Update docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClass…


wschin Apr 11, 2019
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Update docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClass…


wschin Apr 11, 2019
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Update docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClass…


wschin Apr 11, 2019
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Update docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClass…


wschin Apr 11, 2019
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Addressing PR comments; rewriting comments.

Apr 11, 2019
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Addressing PR comments.

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docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/Gam.cs

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Original file line numberDiff line numberDiff line change
@@ -0,0 +1,154 @@
using System;
using System.Collections.Generic;
using Microsoft.ML;
using Microsoft.ML.Data;

namespace Samples.Dynamic.Trainers.BinaryClassification
{
 public static class Gam
 {
 // This example requires installation of additional NuGet package
 // <a href="https://www.nuget.org/packages/Microsoft.ML.FastTree/">Microsoft.ML.FastTree</a>.
 public static void Example()
 {
 // Create a new context for ML.NET operations. It can be used for exception tracking and logging, 
 // as a catalog of available operations and as the source of randomness.
 var mlContext = new MLContext();

 // Create the dataset.
 var samples = GenerateData();

 // Convert the dataset to an IDataView.
 var data = mlContext.Data.LoadFromEnumerable(samples);

 // Create training and validation sets.
 var dataSets = mlContext.Data.TrainTestSplit(data);
 var trainSet = dataSets.TrainSet;
 var validSet = dataSets.TestSet;

 // Create a GAM trainer.
 // Use a small number of bins for this example. The setting below means for each feature, 
 // we divide its range into 16 discrete regions for the training process. Note that these
 // regions are not evenly spaced, and that the final model may contain fewer bins, as
 // neighboring bins with identical values will be combined. In general, we recommend using
 // at least the default number of bins, as a small number of bins limits the capacity of
 // the model.
 var trainer = mlContext.BinaryClassification.Trainers.Gam(maximumBinCountPerFeature: 16);

 // Fit the model using both of training and validation sets. GAM can use a technique called 
 // pruning to tune the model to the validation set after training to improve generalization.
 var model = trainer.Fit(trainSet, validSet);

 // Extract the model parameters.
 var gam = model.Model.SubModel;

 // Now we can inspect the parameters of the Generalized Additive Model to understand the fit
 // and potentially learn about our dataset.
 // First, we will look at the bias; the bias represents the average prediction for the training data.
 Console.WriteLine($"Average prediction: {gam.Bias:0.00}");

 // Now look at the shape functions that the model has learned. Similar to a linear model, we have
 // one response per feature, and they are independent. Unlike a linear model, this response is a 
 // generic function instead of a line. Because we have included a bias term, each feature response 
 // represents the deviation from the average prediction as a function of the feature value.
 for (int i = 0; i < gam.NumberOfShapeFunctions; i++)
 {
 // Break a line.
 Console.WriteLine();

 // Get the bin upper bounds for the feature.
 var binUpperBounds = gam.GetBinUpperBounds(i);

 // Get the bin effects; these are the function values for each bin.
 var binEffects = gam.GetBinEffects(i);

 // Now, write the function to the console. The function is a set of bins, and the corresponding
 // function values. You can think of GAMs as building a bar-chart or lookup table for each feature.
 Console.WriteLine($"Feature{i}");
 for (int j = 0; j < binUpperBounds.Count; j++)
 Console.WriteLine($"x < {binUpperBounds[j]:0.00} => {binEffects[j]:0.000}");
 }

 // Expected output:
 // Average prediction: 0.82
 //
 // Feature0
 // x < -0.44 => 0.286
 // x < -0.38 => 0.225
 // x < -0.32 => 0.048
 // x < -0.26 => -0.110
 // x < -0.20 => -0.116
 // x < 0.18 => -0.143
 // x < 0.25 => -0.115
 // x < 0.31 => -0.005
 // x < 0.37 => 0.097
 // x < 0.44 => 0.263
 // x < ∞ => 0.284
 //
 // Feature1
 // x < 0.00 => -0.350
 // x < 0.24 => 0.875
 // x < 0.31 => -0.138
 // x < ∞ => -0.188

 // Let's consider this output. To score a given example, we look up the first bin where the inequality
 // is satisfied for the feature value. We can look at the whole function to get a sense for how the
 // model responds to the variable on a global level.
 // The model can be seen to reconstruct the parabolic and step-wise function, shifted with respect to the average
 // expected output over the training set. Very few bins are used to model the second feature because the GAM model
 // discards unchanged bins to create smaller models.
 // One last thing to notice is that these feature functions can be noisy. While we know that Feature1 should be
 // symmetric, this is not captured in the model. This is due to noise in the data. Common practice is to use 
 // resampling methods to estimate a confidence interval at each bin. This will help to determine if the effect is 
 // real or just sampling noise. See for example:
 // Tan, Caruana, Hooker, and Lou. "Distill-and-Compare: Auditing Black-Box Models Using Transparent Model 
 // Distillation." <a href='https://arxiv.org/abs/1710.06169'>arXiv:1710.06169</a>."
 }

 private class Data
 {
 public bool Label { get; set; }

 [VectorType(2)]
 public float[] Features { get; set; }
 }

 /// <summary>
 /// Creates a dataset, an IEnumerable of Data objects, for a GAM sample. Feature1 is a parabola centered around 0,
 /// while Feature2 is a simple piecewise function.
 /// </summary>
 /// <param name="numExamples">The number of examples to generate.</param>
 /// <param name="seed">The seed for the random number generator used to produce data.</param>
 /// <returns></returns>
 private static IEnumerable<Data> GenerateData(int numExamples = 25000, int seed = 1)
 {
 var rng = new Random(seed);
 float centeredFloat() => (float)(rng.NextDouble() - 0.5);
 for (int i = 0; i < numExamples; i++)
 {
 // Generate random, uncoupled features.
 var data = new Data {
 Features = new float[2] { centeredFloat(), centeredFloat() }
 };
 // Compute the label from the shape functions and add noise.
 data.Label = Sigmoid(Parabola(data.Features[0]) + SimplePiecewise(data.Features[1]) + centeredFloat()) > 0.5;

 yield return data;
 }
 }

 private static float Parabola(float x) => x * x;

 private static float SimplePiecewise(float x)
 {
 if (x < 0)
 return 0;
 else if (x < 0.25)
 return 1;
 else
 return 0;
@shmoradimsshmoradimsApr 12, 2019•

edited by rogancarr

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0 [](start = 23, length = 1)
is this the same as x < 0 value? #Resolved
@rogancarrrogancarrApr 12, 2019

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That is correct.
In reply to: 274927257 [](ancestors = 274927257)
 }

 private static double Sigmoid(double x) => 1.0 / (1.0 + Math.Exp(-1 * x));
 }
}

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docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/GamWithOptions.cs

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using System;
@wschinwschinApr 11, 2019•

edited by rogancarr

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Similar comments for Gam without Options apply here (and other sample files) #Resolved
using System.Collections.Generic;
using Microsoft.ML;
using Microsoft.ML.Data;
using Microsoft.ML.Trainers.FastTree;

namespace Samples.Dynamic.Trainers.BinaryClassification
{
 public static class GamWithOptions
 {
 // This example requires installation of additional NuGet package
 // <a href="https://www.nuget.org/packages/Microsoft.ML.FastTree/">Microsoft.ML.FastTree</a>.
 public static void Example()
 {
 // Create a new context for ML.NET operations. It can be used for exception tracking and logging, 
 // as a catalog of available operations and as the source of randomness.
 var mlContext = new MLContext();

 // Create the dataset.
 var samples = GenerateData();

 // Convert the dataset to an IDataView.
 var data = mlContext.Data.LoadFromEnumerable(samples);

 // Create training and validation datasets.
 var dataSets = mlContext.Data.TrainTestSplit(data);
 var trainSet = dataSets.TrainSet;
 var validSet = dataSets.TestSet;

 // Create a GAM trainer.
 // Use a small number of bins for this example. The setting below means for each feature, 
 // we divide its range into 16 discrete regions for the training process. Note that these
 // regions are not evenly spaced, and that the final model may contain fewer bins, as
 // neighboring bins with identical values will be combined. In general, we recommend using
 // at least the default number of bins, as a small number of bins limits the capacity of
 // the model.
 // Also, set the learning rate to half the default to slow down the gradient descent, and
 // double the number of iterations to compensate.
 var trainer = mlContext.BinaryClassification.Trainers.Gam(
 new GamBinaryTrainer.Options {
 NumberOfIterations = 19000,
 MaximumBinCountPerFeature = 16,
 LearningRate = 0.001
 });

 // Fit the model using both of training and validation sets. GAM can use a technique called 
 // pruning to tune the model to the validation set after training to improve generalization.
 var model = trainer.Fit(trainSet, validSet);

 // Extract the model parameters.
 var gam = model.Model.SubModel;

 // Now we can inspect the parameters of the Generalized Additive Model to understand the fit
 // and potentially learn about our dataset.
 // First, we will look at the bias; the bias represents the average prediction for the training data.
 Console.WriteLine($"Average prediction: {gam.Bias:0.00}");

 // Now look at the shape functions that the model has learned. Similar to a linear model, we have
 // one response per feature, and they are independent. Unlike a linear model, this response is a 
 // generic function instead of a line. Because we have included a bias term, each feature response 
 // represents the deviation from the average prediction as a function of the feature value.
 for (int i = 0; i < gam.NumberOfShapeFunctions; i++)
 {
 // Break a line.
 Console.WriteLine();

 // Get the bin upper bounds for the feature.
 var binUpperBounds = gam.GetBinUpperBounds(i);

 // Get the bin effects; these are the function values for each bin.
 var binEffects = gam.GetBinEffects(i);

 // Now, write the function to the console. The function is a set of bins, and the corresponding
 // function values. You can think of GAMs as building a bar-chart or lookup table for each feature.
 Console.WriteLine($"Feature{i}");
 for (int j = 0; j < binUpperBounds.Count; j++)
 Console.WriteLine($"x < {binUpperBounds[j]:0.00} => {binEffects[j]:0.000}");
 }

 // Expected output:
 // Average prediction: 0.82
 //
 // Feature0
 // x < -0.44 => 0.286
 // x < -0.38 => 0.225
 // x < -0.32 => 0.048
 // x < -0.26 => -0.110
 // x < -0.20 => -0.116
 // x < 0.18 => -0.143
 // x < 0.25 => -0.115
 // x < 0.31 => -0.005
 // x < 0.37 => 0.097
 // x < 0.44 => 0.263
 // x < ∞ => 0.284
 //
 // Feature1
 // x < 0.00 => -0.350
 // x < 0.24 => 0.875
 // x < 0.31 => -0.138
 // x < ∞ => -0.188

 // Let's consider this output. To score a given example, we look up the first bin where the inequality
 // is satisfied for the feature value. We can look at the whole function to get a sense for how the
 // model responds to the variable on a global level.
 // The model can be seen to reconstruct the parabolic and step-wise function, shifted with respect to the average
 // expected output over the training set. Very few bins are used to model the second feature because the GAM model
 // discards unchanged bins to create smaller models.
 // One last thing to notice is that these feature functions can be noisy. While we know that Feature1 should be
 // symmetric, this is not captured in the model. This is due to noise in the data. Common practice is to use 
 // resampling methods to estimate a confidence interval at each bin. This will help to determine if the effect is 
 // real or just sampling noise. See for example:
 // Tan, Caruana, Hooker, and Lou. "Distill-and-Compare: Auditing Black-Box Models Using Transparent Model 
 // Distillation." <a href='https://arxiv.org/abs/1710.06169'>arXiv:1710.06169</a>."
 }

 private class Data
 {
 public bool Label { get; set; }

 [VectorType(2)]
 public float[] Features { get; set; }
 }

 /// <summary>
 /// Creates a dataset, an IEnumerable of Data objects, for a GAM sample. Feature1 is a parabola centered around 0,
 /// while Feature2 is a simple piecewise function.
 /// </summary>
 /// <param name="numExamples">The number of examples to generate.</param>
 /// <param name="seed">The seed for the random number generator used to produce data.</param>
 /// <returns></returns>
 private static IEnumerable<Data> GenerateData(int numExamples = 25000, int seed = 1)
 {
 var rng = new Random(seed);
 float centeredFloat() => (float)(rng.NextDouble() - 0.5);
 for (int i = 0; i < numExamples; i++)
 {
 // Generate random, uncoupled features.
 var data = new Data
 {
 Features = new float[2] { centeredFloat(), centeredFloat() }
 };
 // Compute the label from the shape functions and add noise.
 data.Label = Sigmoid(Parabola(data.Features[0]) + SimplePiecewise(data.Features[1]) + centeredFloat()) > 0.5;

 yield return data;
 }
 }

 private static float Parabola(float x) => x * x;

 private static float SimplePiecewise(float x)
 {
 if (x < 0)
 return 0;
 else if (x < 0.25)
 return 1;
 else
 return 0;
 }

 private static double Sigmoid(double x) => 1.0 / (1.0 + Math.Exp(-1 * x));
 }
}
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