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