Created
March 22, 2013 05:24
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from benchy.api import Benchmark, BenchmarkSuite, BenchmarkRunner | |
import matplotlib.pyplot as plt | |
common_setup = """ | |
import numpy | |
X = numpy.random.uniform(1,5,(1000,)) | |
""" | |
statement = "cosine_distances(X, X)" | |
setup_bk1 = common_setup + """ | |
import scipy.spatial.distance as ssd | |
X = X.reshape(-1,1) | |
def cosine_distances(X, Y): | |
return 1. - ssd.cdist(X, Y, 'cosine') | |
""" | |
benchmark1 = Benchmark(statement, setup_bk1, name="scipy.spatial 0.8.0") | |
setup_bk2 = common_setup + """ | |
from sklearn.metrics.pairwise import cosine_similarity as cosine_distances | |
""" | |
benchmark2 = Benchmark(statement, setup_bk2, name="sklearn 0.13.1") | |
setup_bk3 = common_setup + """ | |
from nltk import cluster | |
def cosine_distances(X, Y): | |
return 1. - cluster.util.cosine_distance(X, Y) | |
""" | |
benchmark3 = Benchmark(statement, setup_bk3, name="nltk.cluster") | |
setup_bk4 = common_setup + """ | |
import numpy, math | |
def cosine_distances(X, Y): | |
return 1. - numpy.dot(X, Y) / (math.sqrt(numpy.dot(X, X)) * | |
math.sqrt(numpy.dot(Y, Y))) | |
""" | |
benchmark4 = Benchmark(statement, setup_bk4, name="numpy") |
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