85 lines
3.1 KiB
Python
85 lines
3.1 KiB
Python
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"""
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Simple Random Search Hyperparameter Optimization
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"""
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from pyFTS.hyperparam import Evolutionary
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import time
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__measures = ['f1', 'f2', 'rmse', 'size']
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def execute( dataset, **kwargs):
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"""
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Batch execution of Random Search Hyperparameter Optimization
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:param datasetname:
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:param dataset: The time series to optimize the FTS
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:keyword ngen: An integer value with the maximum number of generations, default value: 30
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:keyword mgen: An integer value with the maximum number of generations without improvement to stop, default value 7
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:keyword fts_method: The FTS method to optimize
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:keyword parameters: dict with model specific arguments for fts_method
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:keyword random_individual: create an random genotype
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:keyword evalutation_operator: a function that receives a dataset and an individual and return its fitness
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:keyword mutation_operator: a function that receives one individual and return a changed individual
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:keyword window_size: An integer value with the the length of scrolling window for train/test on dataset
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:keyword train_rate: A float value between 0 and 1 with the train/test split ([0,1])
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:keyword increment_rate: A float value between 0 and 1 with the the increment of the scrolling window,
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relative to the window_size ([0,1])
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:keyword collect_statistics: A boolean value indicating to collect statistics for each generation
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:keyword distributed: A value indicating it the execution will be local and sequential (distributed=False),
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or parallel and distributed (distributed='dispy' or distributed='spark')
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:keyword cluster: If distributed='dispy' the list of cluster nodes, else if distributed='spark' it is the master node
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:return: the best genotype
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"""
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ngen = kwargs.get('ngen',30)
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mgen = kwargs.get('mgen', 7)
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kwargs['pmut'] = 1.0
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random_individual = kwargs.get('random_individual', Evolutionary.random_genotype)
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evaluation_operator = kwargs.get('evaluation_operator', Evolutionary.evaluate)
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mutation_operator = kwargs.get('mutation_operator', Evolutionary.mutation)
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no_improvement_count = 0
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individual = random_individual(**kwargs)
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stat = {}
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stat[0] = {}
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ret = evaluation_operator(dataset, individual, **kwargs)
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for key in __measures:
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individual[key] = ret[key]
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stat[0][key] = ret[key]
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print(individual)
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for i in range(1,ngen+1):
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print("GENERATION {} {}".format(i, time.time()))
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new = mutation_operator(individual, **kwargs)
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ret = evaluation_operator(dataset, new, **kwargs)
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new_stat = {}
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for key in __measures:
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new[key] = ret[key]
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new_stat[key] = ret[key]
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print(new)
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if new['f1'] <= individual['f1'] and new['f2'] <= individual['f2']:
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individual = new
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no_improvement_count = 0
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stat[i] = new_stat
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print(individual)
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else:
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stat[i] = stat[i-1]
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no_improvement_count += 1
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print("WITHOUT IMPROVEMENT {}".format(no_improvement_count))
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if no_improvement_count == mgen:
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break
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return individual, stat
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