Machine-Learning-Collection/ML/algorithms/svm/svm.py

"""
Implementation of SVM using cvxopt package. Implementation uses 
soft margin and I've defined linear, polynomial and gaussian kernels.

To understand the theory (which is a bit challenging) I recommend reading the following:
http://cs229.stanford.edu/notes/cs229-notes3.pdf
https://www.youtube.com/playlist?list=PLoROMvodv4rMiGQp3WXShtMGgzqpfVfbU (Lectures 6,7 by Andrew Ng)

To understand how to reformulate the optimization problem we obtain
to get the input to cvxopt QP solver this blogpost can be useful:
https://xavierbourretsicotte.github.io/SVM_implementation.html

Programmed by Aladdin Persson <aladdin.persson at hotmail dot com>
*    2020-04-26 Initial coding

"""

import numpy as np
import cvxopt
from utils import create_dataset, plot_contour


def linear(x, z):
    return np.dot(x, z.T)


def polynomial(x, z, p=5):
    return (1 + np.dot(x, z.T)) ** p


def gaussian(x, z, sigma=0.1):
    return np.exp(-np.linalg.norm(x - z, axis=1) ** 2 / (2 * (sigma ** 2)))


class SVM:
    def __init__(self, kernel=gaussian, C=1):
        self.kernel = kernel
        self.C = C

    def fit(self, X, y):
        self.y = y
        self.X = X
        m, n = X.shape

        # Calculate Kernel
        self.K = np.zeros((m, m))
        for i in range(m):
            self.K[i, :] = self.kernel(X[i, np.newaxis], self.X)

        # Solve with cvxopt final QP needs to be reformulated
        # to match the input form for cvxopt.solvers.qp
        P = cvxopt.matrix(np.outer(y, y) * self.K)
        q = cvxopt.matrix(-np.ones((m, 1)))
        G = cvxopt.matrix(np.vstack((np.eye(m) * -1, np.eye(m))))
        h = cvxopt.matrix(np.hstack((np.zeros(m), np.ones(m) * self.C)))
        A = cvxopt.matrix(y, (1, m), "d")
        b = cvxopt.matrix(np.zeros(1))
        cvxopt.solvers.options["show_progress"] = False
        sol = cvxopt.solvers.qp(P, q, G, h, A, b)
        self.alphas = np.array(sol["x"])

    def predict(self, X):
        y_predict = np.zeros((X.shape[0]))
        sv = self.get_parameters(self.alphas)

        for i in range(X.shape[0]):
            y_predict[i] = np.sum(
                self.alphas[sv]
                * self.y[sv, np.newaxis]
                * self.kernel(X[i], self.X[sv])[:, np.newaxis]
            )

        return np.sign(y_predict + self.b)

    def get_parameters(self, alphas):
        threshold = 1e-5

        sv = ((alphas > threshold) * (alphas < self.C)).flatten()
        self.w = np.dot(X[sv].T, alphas[sv] * self.y[sv, np.newaxis])
        self.b = np.mean(
            self.y[sv, np.newaxis]
            - self.alphas[sv] * self.y[sv, np.newaxis] * self.K[sv, sv][:, np.newaxis]
        )
        return sv


if __name__ == "__main__":
    np.random.seed(1)
    X, y = create_dataset(N=50)

    svm = SVM(kernel=gaussian)
    svm.fit(X, y)
    y_pred = svm.predict(X)
    plot_contour(X, y, svm)

    print(f"Accuracy: {sum(y==y_pred)/y.shape[0]}")