Scikit Linear SVM plot decision boundary for input array of size n

I am currently performing multi class SVM with linear kernel using python's scikit library. The sample training data and testing data are as given below:

Model data:

x = [[20,32,45,33,32,44,0],[23,32,45,12,32,66,11],[16,32,45,12,32,44,23],[120,2,55,62,82,14,81],[30,222,115,12,42,64,91],[220,12,55,222,82,14,181],[30,222,315,12,222,64,111]]
y = [0,0,0,1,1,2,2]

I want to plot the decision boundary and visualize the datasets. Can someone please help to plot this type of data.

The data given above is just mock data so feel free to change the values. It would be helpful if at least if you could suggest the steps that are to followed. Thanks in advance

1 answer

  • answered 2018-07-12 09:01 seralouk

    You have to choose only 2 features to do this. The reason is that you cannot plot a 7D plot. After selecting the 2 features use only these for the visualization of the decision surface.

    Now, the next question that you would ask if How can I choose these 2 features?. Well, there are a lot of ways. You could do a univariate F-value (feature ranking) test and see what features/variables are the most important. Then you could use these for the plot. Also, we could reduce the dimensionality from 7 to 2 using PCA for example.


    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()
    # Select 2 features / variable for the 2D plot that we are going to create.
    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


    EDIT: Apply PCA to reduce dimensionality.

    from sklearn.svm import SVC
    import numpy as np
    import matplotlib.pyplot as plt
    from sklearn import svm, datasets
    from sklearn.decomposition import PCA
    
    iris = datasets.load_iris()
    
    X = iris.data  
    y = iris.target
    
    pca = PCA(n_components=2)
    Xreduced = pca.fit_transform(X)
    
    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(Xreduced, y)
    
    fig, ax = plt.subplots()
    # title for the plots
    title = ('Decision surface of linear SVC ')
    # Set-up grid for plotting.
    X0, X1 = Xreduced[:, 0], Xreduced[:, 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('PC2')
    ax.set_xlabel('PC1')
    ax.set_xticks(())
    ax.set_yticks(())
    ax.set_title('Decison surface using the PCA transformed/projected features')
    ax.legend()
    plt.show()
    

    enter image description here