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Fix merge issues for better SVR plot #12877

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Merged
merged 4 commits into from
Jan 9, 2019
Merged

Fix merge issues for better SVR plot #12877

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fa509fb
remove marker setting
mferrari3 Dec 27, 2018
78018d9
retry ml4713 PR with updated master
mferrari3 Dec 27, 2018
dd1669c
added empty line at end of the file
mferrari3 Dec 27, 2018
83104ea
created loop for demo plots and added default gamma 'auto' for SVR
mferrari3 Jan 9, 2019
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37 changes: 26 additions & 11 deletions examples/svm/plot_svm_regression.py
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Expand Up @@ -23,22 +23,37 @@

# #############################################################################
# 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, gamma='auto')
svr_poly = SVR(kernel='poly', C=100, gamma='auto', 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()

svrs = [svr_rbf, svr_lin, svr_poly]
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I would perhaps rather an idiom with models = [{'est': svr_rbf, 'label': 'RBF', 'color': 'm', 'ax': axes[0]}], but this is fine by me.

kernel_label = ['RBF', 'Linear', 'Polynomial']
model_color = ['m', 'c', 'g']

fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(15, 10), sharey=True)
for ix, svr in enumerate(svrs):
axes[ix].plot(X, svr.fit(X, y).predict(X), color=model_color[ix], lw=lw,
label='{} model'.format(kernel_label[ix]))
axes[ix].scatter(X[svr.support_], y[svr.support_], facecolor="none",
edgecolor=model_color[ix], s=50,
label='{} support vectors'.format(kernel_label[ix]))
axes[ix].scatter(X[np.setdiff1d(np.arange(len(X)), svr.support_)],
y[np.setdiff1d(np.arange(len(X)), svr.support_)],
facecolor="none", edgecolor="k", s=50,
label='other training data')
axes[ix].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()