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Enh/logging #9

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3b3a61c
Provide generation and individual id to objective in snes
jakobj May 16, 2018
c37083c
Add logging to snes
jakobj Jul 2, 2018
5fa10c5
Add more logging output
jakobj Jul 2, 2018
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42 changes: 34 additions & 8 deletions es/separable_natural_es.py
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Original file line number Diff line number Diff line change
@@ -1,19 +1,32 @@
import logging
import multiprocessing as mp
import numpy as np
from . import lib

logger = logging.getLogger(__name__)


def optimize(func, mu, sigma,
learning_rate_mu=None, learning_rate_sigma=None, population_size=None,
max_iter=2000,
fitness_shaping=True, mirrored_sampling=True, record_history=False,
rng=None,
parallel_threads=None):
parallel_threads=None,
verbosity=logging.WARNING):
"""
Evolution strategies using the natural gradient of multinormal search distributions in natural coordinates.
Does not consider covariances between parameters.
See Wierstra et al. (2014). Natural evolution strategies. Journal of Machine Learning Research, 15(1), 949-980.
"""

logger_ch = logging.StreamHandler()
logger_fh = logging.FileHandler('snes.log', 'w')
logger_ch.setFormatter(logging.Formatter('%(asctime)s %(levelname)s:%(name)s %(message)s'))
logger_fh.setFormatter(logging.Formatter('%(asctime)s %(levelname)s:%(name)s %(message)s'))
logger.setLevel(verbosity)
logger.addHandler(logger_ch)
logger.addHandler(logger_fh)

if not isinstance(mu, np.ndarray):
raise TypeError('mu needs to be of type np.ndarray')
if not isinstance(sigma, np.ndarray):
Expand All @@ -37,6 +50,11 @@ def optimize(func, mu, sigma,
history_pop = []
history_fitness = []

n_total_individuals = population_size * (1 + int(mirrored_sampling))
logger.info('starting evolution with {} individuals per generation on {} threads'.format(n_total_individuals, parallel_threads))
if (parallel_threads is None and n_total_individuals > 1) or (parallel_threads is not None and n_total_individuals > parallel_threads):
logger.warning('more individuals than parallel threads. expect long runtime')
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@mschmidt87 mschmidt87 Jul 31, 2018

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"expect long runtime" sounds a bit casual. What about

"more individuals ({}) than parallel threads ({}). consider increasing the number of parallel threads.".format(n_total_individuals, parallel_threads)

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@mschmidt87 mschmidt87 Jul 31, 2018

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Is it a convention to use only lowercase in logging messages?


while True:
s = rng.normal(0, 1, size=(population_size, *np.shape(mu)))
z = mu + sigma * s
Expand All @@ -45,13 +63,13 @@ def optimize(func, mu, sigma,
z = np.vstack([z, mu - sigma * s])
s = np.vstack([s, -s])

generations_list = [generation] * len(z)
individual_list = range(len(z))
if parallel_threads is None:
fitness = np.fromiter((func(zi) for zi in z), np.float)
fitness = np.fromiter((func(zi, gi, ii) for zi, gi, ii in zip(z, generations_list, individual_list)), np.float)
else:
pool = mp.Pool(processes=parallel_threads)
fitness = np.fromiter(pool.map(func, z), np.float)
pool.close()
pool.join()
with mp.Pool(processes=parallel_threads) as pool:
fitness = np.fromiter(pool.starmap(func, zip(z, generations_list, individual_list)), np.float)

ni = np.logical_not(np.isnan(fitness))
z = z[ni]
Expand All @@ -69,6 +87,11 @@ def optimize(func, mu, sigma,
mu += learning_rate_mu * sigma * np.dot(utility, s)
sigma *= np.exp(learning_rate_sigma / 2. * np.dot(utility, s ** 2 - 1))

logger.info('generation {}, average fitness {}'.format(generation, np.mean(fitness)))
logger.debug('fitness {}'.format(fitness))
logger.debug('mu {}'.format(mu))
logger.debug('sigma {}'.format(sigma))

if record_history:
history_mu.append(mu.copy())
history_sigma.append(sigma.copy())
Expand All @@ -78,7 +101,11 @@ def optimize(func, mu, sigma,
generation += 1

# exit if max iterations reached
if generation > max_iter or np.all(sigma < 1e-10):
if generation > max_iter:
logger.info('maximum number of iterations reached - exiting')
break
elif np.all(sigma < 1e-10):
logger.info('convergence of sigma detected - exiting')
break

return {'mu': mu,
Expand All @@ -87,4 +114,3 @@ def optimize(func, mu, sigma,
'history_sigma': history_sigma,
'history_fitness': history_fitness,
'history_pop': history_pop}