pythonplotscikit-learnsvmsvc

How to plot SVM decision boundary in sklearn Python?


Using SVM with sklearn library, I would like to plot the data with each labels representing its color. I don't want to color the points but filling area with colors.

I have now :

d_pred, d_train_std, d_test_std, l_train, l_test

d_pred are the labels predicted. I would plot d_pred with d_train_std with shape : (70000,2) where X-axis are the first column and Y-Axis the second column.

Thank you.


Solution

  • You cannot visualize the decision surface for a lot of features. This is because the dimensions will be too many and there is no way to visualize an N-dimensional surface.

    However, you can use 2 features and plot nice decision surfaces as follows.

    I have also written an article about this here: https://towardsdatascience.com/support-vector-machines-svm-clearly-explained-a-python-tutorial-for-classification-problems-29c539f3ad8?source=friends_link&sk=80f72ab272550d76a0cc3730d7c8af35

    Case 1: 2D plot for 2 features and using the iris dataset

    from sklearn.svm import SVC
    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.
    y = iris.target
    
    def make_meshgrid(x, y, h=.02):
        x_min, x_max = x.min() - 1, x.max() + 1
        y_min, y_max = y.min() - 1, y.max() + 1
        xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
        return xx, yy
    
    def plot_contours(ax, clf, xx, yy, **params):
        Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
        Z = Z.reshape(xx.shape)
        out = ax.contourf(xx, yy, Z, **params)
        return out
    
    model = svm.SVC(kernel='linear')
    clf = model.fit(X, y)
    
    fig, ax = plt.subplots()
    # title for the plots
    title = ('Decision surface of linear SVC ')
    # Set-up grid for plotting.
    X0, X1 = X[:, 0], X[:, 1]
    xx, yy = make_meshgrid(X0, X1)
    
    plot_contours(ax, clf, xx, yy, cmap=plt.cm.coolwarm, alpha=0.8)
    ax.scatter(X0, X1, c=y, cmap=plt.cm.coolwarm, s=20, edgecolors='k')
    ax.set_ylabel('y label here')
    ax.set_xlabel('x label here')
    ax.set_xticks(())
    ax.set_yticks(())
    ax.set_title(title)
    ax.legend()
    plt.show()
    

    enter image description here

    Case 2: 3D plot for 3 features and using the iris dataset

    from sklearn.svm import SVC
    import numpy as np
    import matplotlib.pyplot as plt
    from sklearn import svm, datasets
    from mpl_toolkits.mplot3d import Axes3D
    
    iris = datasets.load_iris()
    X = iris.data[:, :3]  # we only take the first three features.
    Y = iris.target
    
    #make it binary classification problem
    X = X[np.logical_or(Y==0,Y==1)]
    Y = Y[np.logical_or(Y==0,Y==1)]
    
    model = svm.SVC(kernel='linear')
    clf = model.fit(X, Y)
    
    # The equation of the separating plane is given by all x so that np.dot(svc.coef_[0], x) + b = 0.
    # Solve for w3 (z)
    z = lambda x,y: (-clf.intercept_[0]-clf.coef_[0][0]*x -clf.coef_[0][1]*y) / clf.coef_[0][2]
    
    tmp = np.linspace(-5,5,30)
    x,y = np.meshgrid(tmp,tmp)
    
    fig = plt.figure()
    ax  = fig.add_subplot(111, projection='3d')
    ax.plot3D(X[Y==0,0], X[Y==0,1], X[Y==0,2],'ob')
    ax.plot3D(X[Y==1,0], X[Y==1,1], X[Y==1,2],'sr')
    ax.plot_surface(x, y, z(x,y))
    ax.view_init(30, 60)
    plt.show()
    

    enter image description here