Improvements on hyperparam.Evolutive

This commit is contained in:
Petrônio Cândido 2019-03-20 13:25:54 -03:00
parent 6fd161468b
commit 99b0203389

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@ -376,7 +376,7 @@ def GeneticAlgorithm(dataset, **kwargs):
if distributed == 'dispy': if distributed == 'dispy':
cluster = kwargs.pop('cluster', None) cluster = kwargs.pop('cluster', None)
collect_statistics = kwargs.get('collect_statistics', False) collect_statistics = kwargs.get('collect_statistics', True)
no_improvement_count = 0 no_improvement_count = 0
@ -454,7 +454,7 @@ def GeneticAlgorithm(dataset, **kwargs):
if job.status == dispy.DispyJob.Finished and result is not None: if job.status == dispy.DispyJob.Finished and result is not None:
for key in __measures: for key in __measures:
new_population[job.id][key] = result[key] new_population[job.id][key] = result[key]
if collect_statistics: stats[key].append(ret[key]) if collect_statistics: stats[key].append(result[key])
else: else:
print(job.exception) print(job.exception)
print(job.stdout) print(job.stdout)
@ -535,7 +535,7 @@ def execute(datasetname, dataset, **kwargs):
kwargs['cluster'] = cluster kwargs['cluster'] = cluster
ret = [] ret = []
for i in range(experiments): for i in np.arange(experiments):
print("Experiment {}".format(i)) print("Experiment {}".format(i))
start = time.time() start = time.time()