2-Class Support Vector Machine using Custom Kernel

Question

Is there a sample python code for 2-class SVM classification using the custom kernel or sigmoid kernel?

The code below uses 3-class classification. How to modify this to a 2-class SVM?

http://scikit-learn.org/stable/auto_examples/svm/plot_custom_kernel.html

import numpy as np
import matplotlib.pyplot as plt
from sklearn import svm, datasets

iris = datasets.load_iris()
X = iris.data[:, :2]  # we only take the first two features. We could
                      # avoid this ugly slicing by using a two-dim dataset
Y = iris.target

def my_kernel(X, Y):
    """
    We create a custom kernel:

                 (2  0)
    k(X, Y) = X  (    ) Y.T
                 (0  1)
    """
    M = np.array([[2, 0], [0, 1.0]])
    return np.dot(np.dot(X, M), Y.T)


h = .02  # step size in the mesh

# we create an instance of SVM and fit out data.
clf = svm.SVC(kernel=my_kernel)
clf.fit(X, Y)

# Plot the decision boundary. For that, we will assign a color to each
# point in the mesh [x_min, x_max]x[y_min, y_max].
x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])

# Put the result into a color plot
Z = Z.reshape(xx.shape)
plt.pcolormesh(xx, yy, Z, cmap=plt.cm.Paired)

# Plot also the training points
plt.scatter(X[:, 0], X[:, 1], c=Y, cmap=plt.cm.Paired, edgecolors='k')
plt.title('3-Class classification using Support Vector Machine with custom'
          ' kernel')
plt.axis('tight')
plt.show()

Just use the data for first two classes. The iris dataset has first 50 samples for class1, next 50 for class2 and last 50 for class3. You can use only the first 100 samples. Do X = X[:100] and Y = Y[:100] — Vivek Kumar

Finn Finn · Accepted Answer · 2018-01-30T18:26:52

You have 3 classes, since your data is categorized into 3 classes. You can manipulate the target vector to only contain two classes.

Y = iris.target
for index, value in enumerate(Y):
  Y[index] = value % 2

But this is a bit of an ugly hack, since it effectively merges every class > 1 into class 1.

2-Class Support Vector Machine using Custom Kernel

1 Answers