Figure 2.38 - UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored about introduction_to_ml_with_python HOT 6 OPEN

Am also working thru the scripts in chapter 2 using Jupyter Lab in Windows 11. Tried numerous various but couldn't solve the cmap problem so I rewrote it using scatter3D. This works:

import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits import mplot3d

X_new = np.hstack([X, X[:, 1:] ** 2])

ax = plt.figure().add_subplot(projection='3d')
ax.view_init(elev=-152, azim=-26)

mask = y == 0

ax.scatter3D(X_new[mask, 0], X_new[mask, 1], X_new[mask, 2], c='b', s=60, edgecolor='k')
ax.scatter3D(X_new[~mask, 0], X_new[~mask, 1], X_new[~mask, 2], c='r', marker='^', s=60, edgecolor='k')
ax.set_xlabel("feature0")
ax.set_ylabel("feature1")
ax.set_zlabel("feature1 ** 2")
plt.show()

Script [77] has the same problem. If you run these sequentially in Jupyter do not need the first three lines. This works:

linear_svm_3d = LinearSVC().fit(X_new, y)
coef, intercept = linear_svm_3d.coef_.ravel(), linear_svm_3d.intercept_

ax = plt.figure().add_subplot(projection='3d')
ax.view_init(elev=-152, azim=-26)
xx = np.linspace(X_new[:, 0].min() - 2, X_new[:, 0].max() + 2, 50)
yy = np.linspace(X_new[:, 1].min() - 2, X_new[:, 1].max() + 2, 50)

XX, YY = np.meshgrid(xx, yy)
ZZ = (coef[0] * XX + coef[1] * YY + intercept) / -coef[2]
ax.plot_surface(XX, YY, ZZ, rstride=8, cstride=8, alpha=0.3)
ax.scatter3D(X_new[mask, 0], X_new[mask, 1], X_new[mask, 2], c='b', s=60, edgecolor='k')
ax.scatter3D(X_new[~mask, 0], X_new[~mask, 1], X_new[~mask, 2], c='r', marker='^', s=60, edgecolor='k')

ax.set_xlabel("feature0")
ax.set_ylabel("feature1")
ax.set_zlabel("feature1 ** 2")

from introduction_to_ml_with_python.

Legend thankyou

from introduction_to_ml_with_python.

thx for the solution

from introduction_to_ml_with_python.

from introduction_to_ml_with_python.

from introduction_to_ml_with_python.

you, my friend, hero!
thanks for the solution.

from introduction_to_ml_with_python.