Support Vector Regression (SVR) plots no graph in Ubuntu 18.04 LTS

Question

I am using Python 2.7.15rc1 in Ubuntu 18.04 LTS. I was trying to plot graph of Support Vector Regression but I dint get any output.

import matplotlib
matplotlib.use("Agg")
import numpy as np
from sklearn.svm import SVR
import matplotlib.pyplot as plt

#Generate Sample data
x = np.sort(5 * np.random.rand(40, 1), axis = 0)
y = np.sin(x).ravel()

#Add noise to targets
y[::5] += 3 * (0.5 - np.random.rand(8))

#create classifier regression model
svr_rbf = SVR(kernel="rbf", C=1000, gamma=0.1)
svr_lin = SVR(kernel="linear", C=1000, gamma=0.1)
svr_poly = SVR(kernel="poly", C=1000, gamma=0.1)

#Fit regression model
y_rbf = svr_rbf.fit(x,y).predict(x)
y_lin = svr_lin.fit(x,y).predict(x)
y_poly = svr_poly.fit(x,y).predict(x)

#Plotting of results
lw = 2
plt.scatter(x, y, color="darkorange", label="data")
plt.plot(x, y_rbf, color="navy", lw=lw, label="RBF Model")
plt.plot(x, y_lin, color="c", lw=lw, label="Linear Model")
plt.plot(x, y_poly, color="cornflowerblue", lw=lw, label="Polynomial Model")
plt.xlabel("data")
plt.ylabel("target")
plt.title("Support Vector Regression")
plt.legend()
plt.show()

python svm.py outputs nothing. Did I missed something to import? or we can not plot graph of this? I am new to machine learning

Answer 1

Matplotlib can use one of several "backends" for producing graphs. Those backends do different things. In your case you specified Agg backend that is used to write PNG files:

matplotlib.use("Agg")

So the solution is to remove that line to use the default backend for your system or choose a backend that produces graphs on screen. You might these first:

matplotlib.use("GTK3Agg")
matplotlib.use("WXAgg")
matplotlib.use("TkAgg")
matplotlib.use("Qt5Agg")

See https://matplotlib.org/faq/usage_faq.html#what-is-a-backend for the complete list of backends.

Answer 2

You just need to add %matplotlib inline at the top of your code if you are running on Jupyter Ipython notebook. You can read more about it here and here.

Otherwise, I copied your code and removed matplotlib.use("Agg") , it works for me on Ubuntu 18.04, matplotlib version 2.2.2. Can you specify which version you are using ?

Also here is the code,

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

#Generate Sample data
x = np.sort(5 * np.random.rand(40, 1), axis = 0)
y = np.sin(x).ravel()

#Add noise to targets
y[::5] += 3 * (0.5 - np.random.rand(8))

#create classifier regression model
svr_rbf = SVR(kernel="rbf", C=1000, gamma=0.1)
svr_lin = SVR(kernel="linear", C=1000, gamma=0.1)
svr_poly = SVR(kernel="poly", C=1000, gamma=0.1)

#Fit regression model
y_rbf = svr_rbf.fit(x,y).predict(x)
y_lin = svr_lin.fit(x,y).predict(x)
y_poly = svr_poly.fit(x,y).predict(x)

#Plotting of results
lw = 2
plt.scatter(x, y, color="darkorange", label="data")
plt.plot(x, y_rbf, color="navy", lw=lw, label="RBF Model")
plt.plot(x, y_lin, color="c", lw=lw, label="Linear Model")
plt.plot(x, y_poly, color="cornflowerblue", lw=lw, label="Polynomial Model")
plt.xlabel("data")
plt.ylabel("target")
plt.title("Support Vector Regression")
plt.legend()
plt.show()