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[MRG] SVR plot updates size and color #12207

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[MRG] SVR plot updates size and color #12207

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944be8c
Modify SVR example for better plot
rishikksh20 Feb 15, 2017
73ba352
Modify plot_svm_regression for removing flake8 error
rishikksh20 Feb 16, 2017
4eda965
change the marker sizea
Sep 29, 2018
4b2eb9a
Updates colors of lines and markers to make them consistent
vasudhathinks Sep 29, 2018
aefb778
Merge branch 'master' into svr_plot
ml4713 Sep 29, 2018
50b7d19
Merge pull request #1 from vasudhathinks/svr_plot
ml4713 Sep 29, 2018
cef9bea
change marker size
ml4713 Sep 29, 2018
3505e4c
remove hold
ml4713 Sep 29, 2018
dfe2f88
coef0 to 1
ml4713 Sep 29, 2018
f93b737
add other training data + fix fig size
ml4713 Nov 23, 2018
a975949
remove marker setting
ml4713 Nov 27, 2018
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51 changes: 40 additions & 11 deletions examples/svm/plot_svm_regression.py
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Original file line number Diff line number Diff line change
Expand Up @@ -23,22 +23,51 @@

# #############################################################################
# Fit regression model
svr_rbf = SVR(kernel='rbf', C=1e3, gamma=0.1)
svr_lin = SVR(kernel='linear', C=1e3)
svr_poly = SVR(kernel='poly', C=1e3, degree=2)
svr_rbf = SVR(kernel='rbf', C=100, gamma=0.1, epsilon=.1)
svr_lin = SVR(kernel='linear', C=100)
svr_poly = SVR(kernel='poly', C=100, degree=3, epsilon=.1, coef0=1)
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)

# #############################################################################
# Look at the 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()
fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(15, 10), sharey=True)

ax1.plot(X, y_rbf, color='m', lw=lw, label='RBF model')
ax1.scatter(X[svr_rbf.support_], y[svr_rbf.support_], facecolor="none",
edgecolor="m", label='rbf support vectors', s=50)
ax1.scatter(X[np.setdiff1d(np.arange(len(X)), svr_rbf.support_)],
y[np.setdiff1d(np.arange(len(X)), svr_rbf.support_)],
facecolor="none",
edgecolor="k", label='other training data', s=50)
ax1.legend(loc='upper center', bbox_to_anchor=(0.5, 1.1),
ncol=1, fancybox=True, shadow=True)


ax2.plot(X, y_lin, color='c', lw=lw, label='Linear model')
ax2.scatter(X[svr_lin.support_], y[svr_lin.support_], facecolor="none",
edgecolor="c", label='linear support vectors', s=50)
ax2.scatter(X[np.setdiff1d(np.arange(len(X)), svr_lin.support_)],
y[np.setdiff1d(np.arange(len(X)), svr_lin.support_)],
facecolor="none",
edgecolor="k", label='other training data', s=50)
ax2.legend(loc='upper center', bbox_to_anchor=(0.5, 1.1),
ncol=1, fancybox=True, shadow=True)


ax3.plot(X, y_poly, color='g', lw=lw, label='Polynomial model')
ax3.scatter(X[svr_poly.support_], y[svr_poly.support_], facecolor="none",
edgecolor="g", label='poly support vectors', s=50)
ax3.scatter(X[np.setdiff1d(np.arange(len(X)), svr_poly.support_)],
y[np.setdiff1d(np.arange(len(X)), svr_poly.support_)],
facecolor="none",
edgecolor="k", label='other training data', s=50)
ax3.legend(loc='upper center', bbox_to_anchor=(0.5, 1.1),
ncol=1, fancybox=True, shadow=True)

fig.text(0.5, 0.04, 'data', ha='center', va='center')
fig.text(0.06, 0.5, 'target', ha='center', va='center', rotation='vertical')
fig.suptitle("Support Vector Regression", fontsize=14)
plt.show()