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PERF: Add accurate performance measurement
Add accurate performance measurement
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‎ci/performance.py‎

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from collections import OrderedDict
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import timeit
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import numpy as np
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import pandas as pd
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from randomstate.prng import (mt19937, sfmt, dsfmt, xoroshiro128plus,
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xorshift1024, pcg64)
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REPS = 3
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SIZE = 100000
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SETUP = """
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import numpy
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from numpy import array, random
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from randomstate.prng import (mt19937, sfmt, dsfmt, xoroshiro128plus,
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xorshift1024, pcg64)
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import randomstate
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import randomstate.prng
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rs = {prng}.RandomState(123456)
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f = rs.__getattribute__('{fn}')
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args = {args}
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"""
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prngs = (np.random, mt19937, sfmt, dsfmt, xoroshiro128plus, xorshift1024, pcg64)
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functions = {'randint': {'low': 2 ** 31, 'dtype': 'uint32'},
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'random_sample': {},
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'random_raw': {'output': False},
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'standard_exponential': {},
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'standard_gamma': {'shape': 2.4},
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'standard_normal': {'method': 'zig'},
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'multinomial': {'n': 20, 'pvals': [1.0 / 6.0] * np.ones(6)},
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'negative_binomial': {'n': 5, 'p': 0.16},
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'poisson': {'lam': 3.0},
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'complex_normal': {'gamma': 2 + 0j, 'relation': 1 + 0.5j, 'method': 'zig'},
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'laplace': {'loc': 1, 'scale': 3},
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'binomial': {'n': 35, 'p': 0.25}}
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def timer(prng: str, fn: str, args: dict):
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if prng == 'random':
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# Differences with NumPy
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if fn in ('random_raw', 'complex_normal'):
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return np.nan
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if fn == 'standard_normal':
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args = {k: v for k, v in args.items() if k != 'method'}
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elif prng == 'mt19937' and fn == 'random_raw': # To make comparable
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args['size'] = 2 * args['size']
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setup = SETUP.format(prng=prng, fn=fn, args=args)
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return min(timeit.Timer('f(**args)', setup=setup).repeat(10, REPS)) / SIZE / REPS
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results = OrderedDict()
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for prng in prngs:
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name = prng.__name__.split('.')[-1]
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speeds = OrderedDict()
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for fn, args in functions.items():
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args['size'] = SIZE
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speeds[fn] = np.round(timer(name, fn, args) * 10 ** 9, 2)
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results[name] = pd.Series(speeds)
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print(name)
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print(results[name])
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results = pd.DataFrame(results)
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results = results.loc[results.mean(1).sort_values().index]
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index = {'randint': 'Random Integers',
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'random_sample': 'Uniforms',
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'random_raw': 'Raw',
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'standard_exponential': 'Exponential',
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'standard_gamma': 'Gamma',
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'standard_normal': 'Normal',
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'multinomial': 'Multinomial',
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'negative_binomial': 'Neg. Binomial',
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'poisson': 'Poisson',
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'complex_normal': 'Complex Normal',
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'laplace': 'Laplace',
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'binomial': 'Binomial'}
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cols = {'sfmt': 'SFMT', 'dsfmt': 'dSFMT',
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'xoroshiro128plus': 'xoroshirt128+',
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'xorshift1024': 'xorshift1024', 'pcg64': 'PCG64',
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'mt19937': 'MT19937', 'random': 'NumPy MT19937'}
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results.columns = [cols[c] for c in results]
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results.index = [index[i] for i in results.index]
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print(results)

‎doc/source/change-log.rst‎

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Since Version 1.13
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------------------
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* Add SIMD-oriented Fast Mersenne Twister (`SFMT <http://www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/SFMT/>`) generator.
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* Add complex normal (:meth:`~randomstate.prng.mt19937.complex_normal`)
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Version 1.13

‎doc/source/index.rst‎

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MT19937 <mt19937>
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dSFMT <dsfmt>
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SFMT <sfmt>
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XorShift128+ <xorshift128>
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XoroShiro128+ <xoroshiro128plus>
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XorShift1024* <xorshift1024>

‎doc/source/performance.rst‎

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Performance
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-----------
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‎doc/source/sfmt.rst‎

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SFMT Randomstate
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****************
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.. currentmodule:: randomstate.prng.sfmt
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Random generator
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================
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.. autoclass::
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RandomState
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.. autosummary::
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:toctree: generated/
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seed
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get_state
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set_state
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Simple random data
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==================
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.. autosummary::
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:toctree: generated/
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rand
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randn
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randint
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random_integers
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random_sample
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random
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ranf
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sample
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choice
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bytes
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random_uintegers
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random_raw
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Permutations
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============
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.. autosummary::
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:toctree: generated/
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shuffle
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permutation
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Distributions
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=============
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.. autosummary::
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:toctree: generated/
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beta
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binomial
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chisquare
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complex_normal
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dirichlet
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exponential
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f
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gamma
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geometric
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gumbel
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hypergeometric
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laplace
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logistic
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lognormal
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logseries
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multinomial
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multivariate_normal
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negative_binomial
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noncentral_chisquare
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noncentral_f
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normal
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pareto
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poisson
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power
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rayleigh
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standard_cauchy
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standard_exponential
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standard_gamma
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standard_normal
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standard_t
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triangular
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uniform
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vonmises
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wald
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weibull
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zipf

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