A user wants to compute the entropy of 0-1 vector many times. Is there a faster way to do this?
He used the following code
def entropy(labels):
""" Computes entropy of 0-1 vector. """
n_labels = len(labels)
if n_labels <= 1:
return 0
counts = np.bincount(labels)
probs = counts[np.nonzero(counts)] / n_labels
n_classes = len(probs)
if n_classes <= 1:
return 0
return - np.sum(probs * np.log(probs)) / np.log(n_classes)
We can use four different approaches and see which library performs faster.Those approaches are
scipy/numpy, numpy/math, pandas/numpy, Numpy
import numpy as np
from scipy.stats import entropy
from math import log, e
import pandas as pd
import timeit
def entropy1(labels, base=None):
value,counts = np.unique(labels, return_counts=True)
return entropy(counts, base=base)
def entropy2(labels, base=None):
""" Computes entropy of label distribution. """
n_labels = len(labels)
if n_labels <= 1:
return 0
value,counts = np.unique(labels, return_counts=True)
probs = counts / n_labels
n_classes = np.count_nonzero(probs)
if n_classes <= 1:
return 0
ent = 0.
# Compute entropy
base = e if base is None else base
for i in probs:
ent -= i * log(i, base)
return ent
def entropy3(labels, base=None):
vc = pd.Series(labels).value_counts(normalize=True, sort=False)
base = e if base is None else base
return -(vc * np.log(vc)/np.log(base)).sum()
def entropy4(labels, base=None):
value,counts = np.unique(labels, return_counts=True)
norm_counts = counts / counts.sum()
base = e if base is None else base
return -(norm_counts * np.log(norm_counts)/np.log(base)).sum()
Now lets input the timeit operations
repeat_number = 1000000
a = timeit.repeat(stmt='''entropy1(labels)''',
setup='''labels=[1,3,5,2,3,5,3,2,1,3,4,5];from __main__ import entropy1''',
repeat=3, number=repeat_number)
b = timeit.repeat(stmt='''entropy2(labels)''',
setup='''labels=[1,3,5,2,3,5,3,2,1,3,4,5];from __main__ import entropy2''',
repeat=3, number=repeat_number)
c = timeit.repeat(stmt='''entropy3(labels)''',
setup='''labels=[1,3,5,2,3,5,3,2,1,3,4,5];from __main__ import entropy3''',
repeat=3, number=repeat_number)
d = timeit.repeat(stmt='''entropy4(labels)''',
setup='''labels=[1,3,5,2,3,5,3,2,1,3,4,5];from __main__ import entropy4''',
repeat=3, number=repeat_number)
Here is the timeit results
# for loop to print out results of timeit
for approach,timeit_results in zip(['scipy/numpy', 'numpy/math', 'pandas/numpy', 'numpy'], [a,b,c,d]):
print('Method: {}, Avg.: {:.6f}'.format(approach, np.array(timeit_results).mean()))
Method: scipy/numpy, Avg.: 63.315312
Method: numpy/math, Avg.: 49.256894
Method: pandas/numpy, Avg.: 884.644023
Method: numpy, Avg.: 60.026938