def method_param_selector(callback, uniquekey):
import itertools
from constants import methods
s = [
methods.preprocessing, methods.segmentation, methods.activity_fetcher,
methods.feature_extraction, methods.classifier
]
permut = list(itertools.product(*s))
allpool = []
for item in permut:
func = Data('Functions')
func.uniquekey = uniquekey
func.preprocessor = createFunction(item[0])
func.segmentor = createFunction(item[1])
func.activityFetcher = createFunction(item[2])
func.featureExtractor = createFunction(item[3])
func.classifier = createFunction(item[4])
func.combiner = createFunction(methods.combiner[0])
func.classifier_metric = createFunction(methods.classifier_metric[0])
func.event_metric = createFunction(methods.classifier_metric[0])
func.shortrunname = ''
for k in func.__dict__:
obj = func.__dict__[k]
if isinstance(obj, MyTask):
obj.func = func
func.shortrunname += obj.shortname() + '_'
optl = OptLearn(func, callback)
allpool.append(optl)
# break
success, fail = run(allpool, True)
bestJobscore = success[0].result['optq']['q']
bestJob = success[0]
for job in success:
if (bestJobscore > job.result['optq']['q']):
bestJobscore = job.result['optq']['q']
bestJob = job
return bestJob
{'method': lambda: metric.classical.Accuracy()},
#{'method': lambda: Accuracy()},
]
methods.event_metric = [
# {'method': lambda: metric.Accuracy.Accuracy()},
#{'method': lambda: Accuracy()},
]
methods.activity_fetcher = [
{'method': lambda: activity_fetcher.MaxActivityFetcher.MaxActivityFetcher()},
# {'method': lambda: activity_fetcher.CookActivityFetcher.CookActivityFetcher()}
]
methods.combiner = [
{'method':lambda: combiner.SimpleCombiner.EmptyCombiner2()},
# {'method':lambda: combiner.SimpleCombiner.SimpleCombiner()},
# {'method':lambda: combiner.SimpleCombiner.EmptyCombiner()},
]
methods.evaluation = [
{'method': lambda: evaluation.KFoldEval.KFoldEval(5)},
{'method': lambda: evaluation.KFoldEval.PKFoldEval(5)},
{'method': lambda: evaluation.SimpleEval.SimpleEval()},
]
methods.feature_extraction = [
{'method': lambda:feature_extraction.KHistory.KHistory(), 'params':[{'k':2},{'method':feature_extraction.Simple.Simple()}],'findopt':False},
{'method': lambda:feature_extraction.KHistory.KHistory(), 'params':[{'k':1},{'method':feature_extraction.Cook.Cook1()}],'findopt':False},
{'method': lambda:feature_extraction.KHistory.KHistory(), 'params':[{'k':1},{'method':feature_extraction.Simple.Simple()}],'findopt':False},
{'method': lambda:feature_extraction.Simple.Simple(), 'params':[], 'findopt':False},
{'method': lambda:feature_extraction.DeepLearningFeatureExtraction.DeepLearningFeatureExtraction(), 'params':[