The Data Science Lab

How to Do Kernel Logistic Regression Using C#

Dr. James McCaffrey of Microsoft Research uses code samples, a full C# program and screenshots to detail the ins and outs of kernal logistic regression, a machine learning technique that extends regular logistic regression -- used for binary classification -- to deal with data that is not linearly separable.

Regular logistic regression is a machine learning technique that can be used for binary classification. An example is predicting whether a person is male or female based on predictor variables such as age, height, weight, and so on. One weakness of regular logistic regression is that the technique doesn't work well with data that is not linearly separable. Kernel logistic regression is a technique that extends regular logistic regression to deal with data that is not linearly separable.

A good way to understand what kernel logistic regression is and to see where this article is headed is to examine the screenshot of a demo program in Figure 1 and a graph of the associated data in Figure 2. The goal of the demo is to predict whether a data item is class 0 or class 1 based on two predictor variables. The demo uses just two predictors so that the data can be visualized in a graph, but kernel logistic regression can handle any number of predictors. You can imagine that the demo corresponds to a problem where you want to predict if a person is male (class 0, red) or female (class 1, blue) based on age (predictor x0) and weight (predictor x1).

Figure 1: Kernel Logistic Regression in Action
[Click on image for larger view.] Figure 1: Kernel Logistic Regression in Action
Figure 2: The Training/Reference Data
[Click on image for larger view.] Figure 2: The Training/Reference Data

Linearly separable data is data where it is possible to split the two classes by drawing a straight line (or by a hyperplane when there are three or more predictor variables). The demo data is far from being linearly separable and regular logistic regression would give you a model with only about 57 percent accuracy -- not much better than randomly guessing the class. Notice that there are 21 data items.

The demo program begins by loading the training data into memory. Kernel logistic regression requires you to specify a kernel function and parameters for the kernel function. The demo uses a radial basis function (RBF) kernel function. The RBF function has a single parameter, sigma, which is set to 0.2 by the demo. The choice of which kernel function to use and the values of the function's parameters are meta hyperparameters that must be determined using trial and error.

Behind the scenes, the demo program uses stochastic gradient descent to train the kernel logistic regression model. Training is an iterative process. The demo sets the number of training iterations to 1,000 and the learning rate, which controls how much the model's parameters change on each update, to 0.001. The number of training iterations and the learning rate are hyperparameters.

After training completed, the demo displayed a few of the values of the model's 21 alpha values and the one bias value. A kernel logistic regression model will have one alpha value for each training item, and one bias value.

The resulting model has 100 percent accuracy -- predicting the class of all 21 training items correctly. The demo concludes by making a prediction for a new, previously unseen data item with predictor values (0.15, 0.45). The model output is p = 0.4713 and because that value is less than 0.5 the predicted class is class 0.

This article assumes you have intermediate or better skill with C# but doesn’t assume you know anything about kernel logistic regression binary classification. The code for demo program is a bit too long to present in its entirety in this article but the complete code is available in the associated file download.

Understanding the Radial Basis Function
The most common kernel function used by kernel logistic regression, and the one used in the demo program, is the radial basis function (RBF). The RBF definition, expressed in math terms, is shown as equation (1) in Figure 3. The function accepts two vectors, v1 and v2, and a sigma value. The double-bar notation is the norm/length of a vector, which is the square root of the sum of the squared components of the vector. For example, if v = (1.0, 4.0, 2.0) then || v || = sqrt( 1.0^2 + 4.0^2 + 2.0^2 ) = sqrt(1.0 + 16.0 + 4.0) = sqrt(21.0) = 4.58.

Figure 3: Equations for Kernel Logistic Regression
[Click on image for larger view.] Figure 3: Equations for Kernel Logistic Regression

Notice that equation (1) squares the norm, which undoes the square root operation. Because of this, || v || is sometimes called the squared length of vector v.

Suppose v1 = (4, 7, 3) and v2 = (1, 6, 5) and sigma = 1.5. The vector difference is v1 -- v2 = (4-1, 7-6, 3-5) = (3, 1, -2). The squared length of the difference is 3^2 + 1^2 + (-2)^2 = 9 +1 + 4 = 14.0. The denominator is 2 * sigma^2 = 2 * (1.5)^2 = 2 * 2.25 = 4.5. The final value of the kernel function is exp(-14.0 / 4.5) = exp(-3.1111) = 0.0446.

The demo program implements the RBF function like so:

static double Kernel(double[] v1, double[] v2,
  double sigma)
{
  double num = 0.0;
  for (int i = 0; i < v1.Length; ++i) 
    num += (v1[i] - v2[i]) * (v1[i] - v2[i]);
  double denom = 2.0 * sigma * sigma;
  double z = num / denom;
  return Math.Exp(-z);
}

The function could be tested using these statements:

double[] v1 = new double[] { 4, 7, 3 };
double[] v2 = new double[] { 1, 6, 5 };
double x = Kernel(v1, v2, 1.5);
Console.WriteLine(x);  // 0.0446

One way to think about the RBF function is that it's a metric that describes the similarity of two vectors. If the two vectors have the same values, RBF returns 1.0 (maximum similarity). For example, if v1 = (2, 6, 4) and v2 = (2, 6, 4) then RBF is 1.0. The more different the two vectors are, the smaller the value of RBF is, approaching but never quite reaching 0.0 (minimum similarity).

Understanding Kernel Logistic Regression
The input-output mechanism for kernel logistic regression, expressed mathematically, is shown as equations (2) and (3) in Figure 3. The mechanism is a bit tricky and is perhaps best explained by a concrete example. Suppose you have just four training items and each training item has just two predictor variables:

[0]  0.2  0.6
[1]  0.3  0.4
[2]  0.1  0.9
[3]  0.5  0.7

A trained kernel logistic regression model will have four alpha values and one bias value. Suppose the alpha values are (1.0, -1.2, 1.3, 1.4) and the bias (sometimes called the beta value) is -2.0 and you set sigma to 0.25. Now suppose you want to make a prediction for an input of (0.8, 0.2). The calculations are:

z = 1.0 * K( (0.2, 0.6), (0.8, 0.2), 0.25 ) +
   -1.2 * K( (0.3, 0.4), (0.8, 0.2), 0.25 ) +
    1.3 * K( (0.1, 0.9), (0.8, 0.2), 0.25 ) +
    1.4 * K( (0.5, 0.7), (0.8, 0.2), 0.25 ) + (-2.0)

  = 1.0 * 0.0156 +
   -1.2 * 0.0983 +
    1.3 * 0.0004 +
    1.4 * 0.0659 + (-2.0)

  = -2.0096

p = logsig(-2.0096) = 1.0 / (1.0 + exp(+2.0096))
  = 0.1182

In words, to compute the output for an input vector, you compute the similarity metric of the input vector with each of the training items, weighting each similarity term by an associated alpha value, then add the bias value, and then apply the logistic sigmoid function. The resulting p-value will be between 0.0 and 1.0 and if the p-value is less than 0.5 the prediction is class 0, otherwise the prediction is class 1.

OK, but where do the alpha values and the beta/bias value come from? These values must be determined by training the model using data with known correct input and output values. The alphas and bias are initially set to 0.0 and then iteratively adjusted by a small amount so that the computed output p-value is closer to the target 0 or 1 correct value.

Notice that after training a kernel logistic regression binary classification model, you still need the training data to make a prediction. This is different from techniques like regular logistic regression or neural network models where the training data is not needed after the model has been created. For this reason, in a kernel logistic regression context, the training data is sometimes called reference data.

Training a Kernel Logistic Regression Model
There are several techniques that can be used to find good values for kernel logistic regression alphas and the bias. The demo program uses a simplified form of stochastic gradient descent (SGD). The update delta value for the alpha associated with training item [j] and associated input vector [i] is shown as equation (4) in Figure 3. In the equation, y(i) is the target output value (0 or 1) for input [i] and p(i) is the computed p-value (between 0.0 and 1.0).

Suppose, for a given set of alphas and bias, the computed output p(i) is 0.7 and the target value is y(i) = 1. The computed output gives a correct prediction but you'd like p(i) to be somewhat greater so that it's closer to the target value. The difference y(i) -- p(i) is 1 -- 0.7 = +0.3 so you could add this delta to the current value of alpha and p(i) would increase. However, this update strategy is too aggressive so you throttle the delta using two terms. The learning rate, a value typically something like 0.01 or 0.05, will reduce the delta value. And the value of the kernel function will adjust the delta value so that training items that are close to the input vector will get more weight than training items that are dissimilar to the input vector.

To update the single bias value, you can use equation (4) in Figure 3 and use a dummy value of 1.0 in place of the value of the kernel function. The form of SGD used in the demo program is simple and relatively crude compared to other forms of SGD. In particular, the update equation uses the Calculus derivative of the error term but does not use the derivative of the logistic sigmoid function, which is p(i) * (1 - p(i)). However, the simple approach usually works well in practice.

The Demo Program
To create the demo program, I launched Visual Studio 2019. I used the Community (free) edition but any relatively recent version of Visual Studio will work fine. From the main Visual Studio start window I selected the "Create a new project" option. Next, I selected C# from the Language dropdown control and Console from the Project Type dropdown, and then picked the "Console App (.NET Core)" item.

The code presented in this article will run as a .NET Core console application or as a .NET Framework application. Many of the newer Microsoft technologies, such as the ML.NET code library, specifically target .NET Core so it makes sense to develop most new C# machine learning code in that environment.

I entered "LogisticKernel" as the Project Name, specified C:\VSM on my local machine as the Location (you can use any convenient directory), and checked the "Place solution and project in the same directory" box.

After the template code loaded into Visual Studio, at the top of the editor window I removed all using statements to unneeded namespaces, leaving just the reference to the top-level System namespace. The demo needs no other assemblies and uses no external code libraries.

In the Solution Explorer window, I renamed file Program.cs to the more descriptive LogisticKernelProgram.cs and then in the editor window I renamed class Program to class LogisticKernelProgram to match the file name. The structure of the demo program, with a few minor edits to save space, is shown in Listing 1.

Listing 1. Kernel Logistic Regression Demo Program Structure

using System;
namespace LogisticKernel
{
  class LogisticKernelProgram
  {
    static void Main(string[] args)
    {
      Console.WriteLine("Begin kernel LR demo ");
      Console.WriteLine("Loading training data");
      Console.WriteLine("train [0] =  (0.2, 0.3) 0");
      Console.WriteLine("train [1] =  (0.1, 0.5) 0");
      Console.WriteLine(" . . . ");
      Console.WriteLine("train [20] = (0.5, 0.6) 1");

      double[][] trainX = new double[21][];
      trainX[0] = new double[] { 0.2, 0.3 };
      trainX[1] = new double[] { 0.1, 0.5 };
      . . .
      trainX[19] = new double[] { 0.5, 0.5 };
      trainX[20] = new double[] { 0.5, 0.6 };

      int[] trainY = new int[21] {
        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
        1, 1, 1, 1, 1, 1, 1, 1, 1 };

      int maxIter = 1000;
      double lr = 0.001;
      double sigma = 0.2;

      Console.WriteLine("sigma = " + sigma);
      Console.WriteLine("lr = " + lr +
        " and maxIter = " + maxIter);
      Console.WriteLine("Starting training");
      double[] alphas = Train(trainX, trainY,
        lr, maxIter, sigma);
      Console.WriteLine("Training complete");

      ShowSomeAlphas(alphas, 3, 2);

      double accTrain = Accuracy(trainX, trainY,
        trainX, alphas, sigma);
      Console.WriteLine("accuracy = " +
        accTrain.ToString("F4") + "\n");

      Console.WriteLine("Predicting (0.15, 0.45)");
      double[] unknown = new double[] { 0.15, 0.45 };
      double p = ComputeOutput(unknown, alphas,
        sigma, trainX);
      if (p < 0.5)
        Console.WriteLine("Computed p = " +
          p.ToString("F4") +
          " Predicted class = 0");
      else
        Console.WriteLine("Computed p = " +
          p.ToString("F4") +
          " Predicted class = 1");

      Console.WriteLine("End kernel LR demo ");
      Console.ReadLine();

    } // Main

    static double[] Train(double[][] trainX,
      int[] trainY, double lr, int maxIter,
      double sigma, int seed = 0) { . . }

    static double Kernel(double[] v1, double[] v2,
      double sigma) { . . }
    
    static double ComputeOutput(double[] x,
      double[] alphas, double sigma,
      double[][] trainX) { . . }
    
    static double LogSig(double x) { . . }
    
    static double Error(double[][] dataX,
      int[] dataY, double[][] trainX,
      double[] alphas, double sigma) { . . }
    
    static double Accuracy(double[][] dataX,
      int[] dataY, double[][] trainX,
      double[] alphas, double sigma) { . . }
    
    static void Shuffle(int[] indices,
      Random rnd) { . . }
    
    static void ShowSomeAlphas(double[] alphas,
      int first, int last) { . . }
    
  } // Program class
} // ns

All of the program logic is contained in the Main() method. The demo uses a static method approach rather than an OOP approach for simplicity. All normal error checking has been removed to keep the main ideas as clear as possible.

The demo begins by setting up the training/reference data:

double[][] trainX = new double[21][];
trainX[0] = new double[] { 0.2, 0.3 };
trainX[1] = new double[] { 0.1, 0.5 };
. . .
int[] trainY = new int[21] {
  0, 0, 0, . . . 1, 1, 1 };
. . .

The predictor values are hard-coded and stored into an array-of-arrays style matrix. The class labels are stored in a single integer array. In a non-demo scenario you'd likely want to store your training data as a text file:

0.2, 0.3, 0
0.1, 0.5, 0
. . .
0.5, 0.6, 1

And then you'd read the training data into memory using helper functions along the lines of:

double[][] trainX = MatLoad("..\\data.txt",
  new int[] { 0, 1 }, ",");
int[] trainY = VecLoad("..\\data.txt", 2, ",");

In most scenarios you'd want to set aside some of your source data as a test dataset. After training you'd compute the prediction accuracy of the model on the held-out test dataset. This accuracy metric would be a rough estimate of the accuracy you could expect on new, previously unseen data.

After setting up the training data, the demo program trains the model using these statements:

int maxIter = 1000;
double lr = 0.001;
double sigma = 0.2;
double[] alphas = Train(trainX, trainY, lr,
  maxIter, sigma);

The maxIter variable holds the number of training iterations to perform, the lr variable holds the learning rate, and sigma is a parameter that is passed to the radial basis function kernel. These three values are hyperparameters that must be determined using trial and error.

The Train() function returns an array named alphas that holds the model alpha-weights and the bias, where the bias is stored as the last value in the array.

After training, the demo program displays the values of a few of the alphas and the beta using a helper function named ShowSomeAlphas() and then computes the model's accuracy on the training data using a helper function Accuracy():

ShowSomeAlphas(alphas, 3, 2);  // first 3, last 2
double accTrain = Accuracy(trainX, trainY, trainX,
  alphas, sigma);
Console.WriteLine(accTrain.ToString("F4"));

The Accuracy() function computes the number of correct and incorrect predictions on the training data. Notice that Accuracy() accepts the training predictor values twice. A call to Accuracy() on test data would be Accuracy(testX, testY, trainX, alphas, sigma). The second instance of trainX is the reference data needed to compute output.

The demo concludes by using the trained model to make a prediction:

double[] unknown = new double[] { 0.15, 0.45 };
double p = ComputeOutput(unknown, alphas,
  sigma, trainX);
if (p < 0.5)
  Console.WriteLine("Computed p = " +
    p.ToString("F4") +
    " Predicted class = 0");
else
  Console.WriteLine("Computed p = " +
    p.ToString("F4") +
    " Predicted class = 1");

The choice of whether a p-value of exactly 0.5 is associated with class 0 or class 1 is arbitrary to some extent. The threshold boundary value 0.5 can be adjusted if you want to give more influence to one of the two classes. For example, by setting the threshold to 0.8 more predictions would be class 0.

In some scenarios, after you get a good kernel logistic regression model, you might want to save the trained alphas and bias and the reference data to a text file so you wouldn't have to retrain your model every time you want to use it to make a prediction.

Computing Output
The demo program implements a function ComputeOutput() like so:

static double ComputeOutput(double[] x,
  double[] alphas, double sigma,
  double[][] trainX)
{
  int n = trainX.Length;
  double sum = 0.0;
  for (int i = 0; i < n; ++i)
    sum += (alphas[i] * Kernel(x, trainX[i], sigma));
  sum += alphas[n];  // add the bias
  return LogSig(sum);  // result is [0.0, 1.0]
}

Function ComputeOutput() mirrors the equations (2) and (3) in Figure 3 in a very straightforward way, omitting all error-checking and emphasizing clarity over performance. The program-defined function LogSig() is implemented like so:

static double LogSig(double x)
{
  if (x < -10.0)
    return 0.0;
  else if (x > 10.0)
    return 1.0;
  else
    return 1.0 / (1.0 + Math.Exp(-x));
}

The short-circuiting for input values (less than -10 or greater than 10) is a common technique in machine learning code. The idea is that the logistic sigmoid function approaches 0 very quickly for negative values of x and approaches 1 very quickly for positive values of x. The threshold values of -10 and +10 are arbitrary to a large extent. Other common values are -20 and +20, -6 and +6, and -40 and +40.

Training a Kernel Logistic Regression Model
The key statements in function Train() are:

Shuffle(indices, rnd);
foreach (int i in indices) { // each train item
  double p = ComputeOutput(trainX[i], alphas,
    sigma, trainX);
  int y = trainY[i];  // target 0 or 1 output
  for (int j = 0; j < alphas.Length - 1; ++j)  // alphas
    alphas[j] += lr * (y - p) * kernels[i][j];
  alphas[alphas.Length - 1] += lr * (y - p) * 1;  // bias
}

The training items are processed in a different, random order on each pass. This helps prevent a harmonic oscillation where the updates to an alpha are immediately undone by the next training item. The Shuffle() function is a program-defined helper that scrambles the indices of the training items using the Fisher-Yates mini-algorithm.

Wrapping Up
Kernel logistic regression (KLR) is a relatively rare machine learning technique. Because KLR requires the entire training/reference data to make a prediction, the technique does not scale well to problems with huge datasets. However, KLR is often effective for binary classification problems with a relatively small dataset (under 1,000 training items) where the data is not linearly separable. An alternative to KLR is a neural network binary classifier with a single hidden layer.

KLR binary classification shares characteristics with k-nearest neighbors classification. In k-nearest neighbors, a specified number of reference items (k) is used to make a prediction. In KLR, all reference items are used to make a prediction. In k-nearest neighbors, you compare the item-to-classify with reference items using Euclidean distance. In KLR, you compare the item-to-classify with reference items using a kernel function, usually RBF, that measures similarity.

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