back to the matplotlib-gallery at https://github.com/rasbt/matplotlib-gallery
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%load_ext watermark
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%watermark -u -v -d -p matplotlib,numpy
[More info](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/ipython_magic/watermark.ipynb) about the `%watermark` extension
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%matplotlib inline
Often, we see code that explicitely instantiates a new figure object:
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import matplotlib.pyplot as plt
fig = plt.figure()
plt.plot([0, 1], [0, 1])
plt.show()
If we are not planning to manipulate the figure object or add subplots to the figure, this may be redundant. Why?
As nicely explained on SO, the plot function retrieves the current figure automatically via gcf ("get current figure") nested inside a gca ("get current axes") call. Thus, it really doesn't matter if we create a figure prior to plot unless we are planning to modify it in some way.
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import matplotlib.pyplot as plt
plt.plot([0, 1], [0, 1])
plt.show()
Although a plot appears perfectly fine inline in a IPython notebook, sometimes we may notice that certain figure elements went missing if we save it to an image file via savefig. The tight_layout() function often helps to produce a "tighter" appearence of the plot, it is not sufficient if to fit our graph nicely into the figure boundaries if we plot outside it's margins. Consider the following example:
Typically, this issue can be easily resolved by setting the bbox_inches attribute to 'tight' inside savefig. You can find the complete code example for producing the plot below:
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import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline
def gini(p):
return (p)*(1 - (p)) + (1-p)*(1 - (1-p))
def entropy(p):
return - p*np.log2(p) - (1 - p)*np.log2((1 - p))
def error(p):
return 1 - np.max([p, 1 - p])
x = np.arange(0.0, 1.0, 0.01)
ent = [entropy(p) if p != 0 else None for p in x]
sc_ent = [e*0.5 if e else None for e in ent]
err = [error(i) for i in x]
fig = plt.figure()
ax = plt.subplot(111)
for i, lab, ls, c, in zip([ent, sc_ent, gini(x), err],
['Entropy', 'Entropy (scaled)',
'Gini Impurity', 'Misclassification Error'],
['-', '-', '--', '-.'],
['black', 'lightgray', 'red', 'green', 'cyan']):
line = ax.plot(x, i, label=lab, linestyle=ls, lw=2, color=c)
ax.legend(loc='upper center', bbox_to_anchor=(0.5, 1.15),
ncol=3, fancybox=True, shadow=False)
ax.axhline(y=0.5, linewidth=1, color='k', linestyle='--')
ax.axhline(y=1.0, linewidth=1, color='k', linestyle='--')
plt.ylim([0, 1.1])
plt.xlabel('p(i=1)')
plt.ylabel('Impurity Index')
plt.tight_layout()
# uncomment the following line to save the image to disk
# plt.savefig('./impurity.png', dpi=300, bbox_inches='tight')
plt.show()
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