def sample(config, model=None):
""" Create sampler and sample per options in configuration file.
If there is a configuration option 'load_model_from_pickle' in
section 'sampling' the function tries to load that model, ignoring
the data argument (the option value shold be the path to a pickle
file containing a single LogisticRuleModel object.) If the model
argument is None, and that configuration option is not present, an
exception results.
"""
if config.has_option('sampling','load_model_from_pickle'):
with open(config.get('sampling','load_model_from_pickle')) as f:
model = pickle.load(f)
if model is None:
raise ValueError('Model must be passed as argument if not specified in config file.')
l = [hamming(model.data.y,t) for t in model.rule_population.flat_truth]
arbitrary_rl = rules.RuleList(
[[model.rule_population.flat_rules[np.argmin(l)]]]
)
sampler = logit_rules.LogisticRuleSampler(model,
arbitrary_rl)
if config.has_option('sampling','sampling_time'):
sampling_time = config.getfloat('sampling','sampling_time')
logger.info('Starting sampling: will continue for %.1f seconds' %
sampling_time)
sampler.sample_for(sampling_time)
elif config.has_option('sampling','total_samples'):
total_samples = config.getint('sampling','total_samples')
logger.info('Starting to draw %d samples' % total_samples)
sampler.sample(total_samples)
else:
raise ValueError('Either number of samples or sampling time must be specified.')
if config.has_option('sampling', 'pickle_sampler'):
prefix = config.get('description','tag')
if config.getboolean('sampling','pickle_sampler'):
filename = prefix + '_sampler_object.pickle'
with open(filename, 'w') as f:
pickle.dump(sampler,f)
logger.info('Sampler written to %s' % filename)
return sampler
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