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September 5, 2018 12:09
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Separation plot
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# separation plot | |
# Author: Cameron Davidson-Pilon,2013 | |
# see http://mdwardlab.com/sites/default/files/GreenhillWardSacks.pdf | |
import matplotlib.pyplot as plt | |
import numpy as np | |
def separation_plot( p, y, **kwargs ): | |
""" | |
This function creates a separation plot for logistic and probit classification. | |
See http://mdwardlab.com/sites/default/files/GreenhillWardSacks.pdf | |
p: The proportions/probabilities, can be a nxM matrix which represents M models. | |
y: the 0-1 response variables. | |
""" | |
assert p.shape[0] == y.shape[0], "p.shape[0] != y.shape[0]" | |
n = p.shape[0] | |
try: | |
M = p.shape[1] | |
except: | |
p = p.reshape( n, 1 ) | |
M = p.shape[1] | |
colors_bmh = np.array( ["#eeeeee", "#348ABD"] ) | |
fig = plt.figure( ) | |
for i in range(M): | |
ax = fig.add_subplot(M, 1, i+1) | |
ix = np.argsort( p[:,i] ) | |
#plot the different bars | |
bars = ax.bar( np.arange(n), np.ones(n), width=1., | |
color = colors_bmh[ y[ix].astype(int) ], | |
edgecolor = 'none') | |
ax.plot( np.arange(n+1), np.append(p[ix,i], p[ix,i][-1]), "k", | |
linewidth = 1.,drawstyle="steps-post" ) | |
#create expected value bar. | |
ax.vlines( [(1-p[ix,i]).sum()], [0], [1] ) | |
plt.xlim( 0, n) | |
plt.tight_layout() | |
return | |
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