Plot Hyperplane Linear Svm Python

I am trying to plot the hyperplane for the model I trained with LinearSVC and sklearn. Note that I am working with natural languages; before fitting the model I extracted features

Solution 1:

What about leaving the support_ out, which is not defined for a LinearSVC?

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

np.random.seed(0)
X = np.r_[np.random.randn(20, 2) - [2, 2], np.random.randn(20, 2) + [2, 2]]
Y = [0] * 20 + [1] * 20

fig, ax = plt.subplots()
clf2 = svm.LinearSVC(C=1).fit(X, Y)

# get the separating hyperplane
w = clf2.coef_[0]
a = -w[0] / w[1]
xx = np.linspace(-5, 5)
yy = a * xx - (clf2.intercept_[0]) / w[1]

# create a mesh to plot in
x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
xx2, yy2 = np.meshgrid(np.arange(x_min, x_max, .2),
                     np.arange(y_min, y_max, .2))
Z = clf2.predict(np.c_[xx2.ravel(), yy2.ravel()])

Z = Z.reshape(xx2.shape)
ax.contourf(xx2, yy2, Z, cmap=plt.cm.coolwarm, alpha=0.3)
ax.scatter(X[:, 0], X[:, 1], c=Y, cmap=plt.cm.coolwarm, s=25)
ax.plot(xx,yy)

ax.axis([x_min, x_max,y_min, y_max])
plt.show()