8000 [MRG + 1] Improve the output of example plot_iris.py after matplotlib2.0 by qinhanmin2014 · Pull Request #9541 · scikit-learn/scikit-learn · GitHub
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[MRG + 1] Improve the output of example plot_iris.py after matplotlib2.0 #9541

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Merged
merged 2 commits into from
Aug 15, 2017
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[MRG + 1] Improve the output of example plot_iris.py after matplotlib2.0 #9541

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2b6d767
improve plot_iris.py
qinhanmin2014 Aug 13, 2017
c974d04
update
qinhanmin2014 Aug 13, 2017
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13 changes: 6 additions & 7 deletions examples/tree/plot_iris.py
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Original file line number Diff line number Diff line change
Expand Up @@ -22,7 +22,7 @@

# Parameters
n_classes = 3
plot_colors = "bry"
plot_colors = "ryb"
plot_step = 0.02

# Load data
Expand All @@ -44,23 +44,22 @@
y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
xx, yy = np.meshgrid(np.arange(x_min, x_max, plot_step),
np.arange(y_min, y_max, plot_step))
plt.tight_layout(h_pad=0.5, w_pad=0.5, pad=2.5)

Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shape)
cs = plt.contourf(xx, yy, Z, cmap=plt.cm.Paired)
cs = plt.contourf(xx, yy, Z, cmap=plt.cm.RdYlBu)

plt.xlabel(iris.feature_names[pair[0]])
plt.ylabel(iris.feature_names[pair[1]])
plt.axis("tight")

# Plot the training points
for i, color in zip(range(n_classes), plot_colors):
idx = np.where(y == i)
plt.scatter(X[idx, 0], X[idx, 1], c=color, label=iris.target_names[i],
cmap=plt.cm.Paired)

plt.axis("tight")
cmap=plt.cm.RdYlBu, edgecolor='black', s=15)

plt.suptitle("Decision surface of a decision tree using paired features")
plt.legend()
plt.legend(loc='lower right', borderpad=0, handletextpad=0)
plt.axis("tight")
plt.show()
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