Bugfixes in DEHO
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@ -76,6 +76,19 @@ def random_genotype(**kwargs):
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for v in explanatory_variables:
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var = vars[v]
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param = random_param(var)
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explanatory_params.append(param)
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target_params = random_param(tvar)
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return genotype(
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explanatory_variables,
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explanatory_params,
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target_params
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)
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def random_param(var):
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if var['type'] == 'common':
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npart = random.randint(7, 50)
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else:
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@ -86,20 +99,7 @@ def random_genotype(**kwargs):
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'partitioner': 1, # random.randint(1, 2),
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'alpha': random.uniform(0, .5)
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}
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explanatory_params.append(param)
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target_params = {
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'mf': random.randint(1, 4),
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'npart': random.randint(7, 50),
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'partitioner': 1, #random.randint(1, 2),
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'alpha': random.uniform(0, .5)
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}
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return genotype(
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explanatory_variables,
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explanatory_params,
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target_params
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)
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return param
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def phenotype(individual, train, fts_method, parameters={}, **kwargs):
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@ -246,6 +246,10 @@ def crossover(population, **kwargs):
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"""
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import random
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vars = kwargs.get('variables', None)
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tvar = kwargs.get('target_variable', None)
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n = len(population) - 1
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r1,r2 = 0,0
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@ -280,7 +284,7 @@ def crossover(population, **kwargs):
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if ix in best['explanatory_variables'] and ix in worst['explanatory_variables']:
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bix = best['explanatory_variables'].index(ix)
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wix = worst['explanatory_variables'].index(ix)
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param = crossover_variable_params(best['explanatory_params'][bix], worst['explanatory_params'][wix])
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param = crossover_variable_params(best['explanatory_params'][bix], worst['explanatory_params'][wix], vars[ix])
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elif ix in best['explanatory_variables']:
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bix = best['explanatory_variables'].index(ix)
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param = best['explanatory_params'][bix]
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@ -291,15 +295,18 @@ def crossover(population, **kwargs):
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explanatory_variables.append(ix)
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explanatory_params.append(param)
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tparams = crossover_variable_params(best['target_params'], worst['target_params'])
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tparams = crossover_variable_params(best['target_params'], worst['target_params'], tvar)
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descendent = genotype(explanatory_variables, explanatory_params, tparams, None, None)
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descendent = genotype(explanatory_variables, explanatory_params, tparams)
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return descendent
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def crossover_variable_params(best, worst):
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def crossover_variable_params(best, worst, var):
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if var['type'] == 'common':
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npart = int(round(.7 * best['npart'] + .3 * worst['npart']))
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else:
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npart = best['npart']
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alpha = float(.7 * best['alpha'] + .3 * worst['alpha'])
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rnd = random.uniform(0, 1)
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mf = best['mf'] if rnd < .7 else worst['mf']
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@ -317,14 +324,32 @@ def mutation(individual, **kwargs):
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:return:
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"""
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vars = kwargs.get('variables', None)
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tvar = kwargs.get('target_variable', None)
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l = len(vars)
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il = len(individual['explanatory_variables'])
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rnd = random.uniform(0, 1)
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if rnd > .9 and il > 1:
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rnd = random.randint(0, il-1)
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val = individual['explanatory_variables'][rnd]
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individual['explanatory_variables'].remove(val)
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individual['explanatory_params'].pop(rnd)
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elif rnd < .1 and il < l:
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rnd = random.randint(0, l-1)
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while rnd in individual['explanatory_variables']:
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rnd = random.randint(0, l-1)
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individual['explanatory_variables'].append(rnd)
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individual['explanatory_params'].append(random_param(vars[rnd]))
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for ct in np.arange(len(individual['explanatory_variables'])):
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rnd = random.uniform(0, 1)
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if rnd > .5:
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mutate_variable_params(individual['explanatory_params'][ct])
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mutate_variable_params(individual['explanatory_params'][ct], vars[ct])
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rnd = random.uniform(0, 1)
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if rnd > .5:
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mutate_variable_params(individual['target_params'])
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mutate_variable_params(individual['target_params'], tvar)
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individual['f1'] = None
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individual['f2'] = None
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@ -332,7 +357,8 @@ def mutation(individual, **kwargs):
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return individual
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def mutate_variable_params(param):
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def mutate_variable_params(param, var):
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if var['type']=='common':
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param['npart'] = min(50, max(3, int(param['npart'] + np.random.normal(0, 4))))
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param['alpha'] = min(.5, max(0, param['alpha'] + np.random.normal(0, .5)))
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param['mf'] = random.randint(1, 4)
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@ -428,7 +454,7 @@ def persist_statistics(datasetname, statistics):
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def log_result(datasetname, fts_method, result):
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import json
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with open('result_{}{}.json'.format(fts_method,datasetname), 'w') as file:
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with open('result_{}{}.json'.format(fts_method,datasetname), 'a+') as file:
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file.write(json.dumps(result))
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print(result)
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@ -156,9 +156,12 @@ class Partitioner(object):
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nearest = self.search(data, type='index')
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mv = np.zeros(self.partitions)
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try:
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for ix in nearest:
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tmp = self[ix].membership(data)
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mv[ix] = tmp if tmp >= alpha_cut else 0.
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except:
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print(ix)
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ix = np.ravel(np.argwhere(mv > 0.))
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@ -316,7 +319,6 @@ class Partitioner(object):
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it represents the fuzzy set name.
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:return: the fuzzy set
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"""
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try:
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if isinstance(item, (int, np.int, np.int8, np.int16, np.int32, np.int64)):
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if item < 0 or item >= self.partitions:
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raise ValueError("The fuzzy set index must be between 0 and {}.".format(self.partitions))
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@ -327,8 +329,6 @@ class Partitioner(object):
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return self.sets[item]
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else:
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raise ValueError("The parameter 'item' must be an integer or a string and the value informed was {} of type {}!".format(item, type(item)))
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except Exception as ex:
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logging.exception("Error")
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def __iter__(self):
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@ -50,14 +50,14 @@ explanatory_variables =[
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{'name': 'Temperature', 'data_label': 'temperature', 'type': 'common'},
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{'name': 'Daily', 'data_label': 'time', 'type': 'seasonal', 'seasonality': DateTime.minute_of_day, 'npart': 24 },
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{'name': 'Weekly', 'data_label': 'time', 'type': 'seasonal', 'seasonality': DateTime.day_of_week, 'npart': 7 },
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#{'name': 'Monthly', 'data_label': 'time', 'type': 'seasonal', 'seasonality': DateTime.day_of_month, 'npart': 4 },
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{'name': 'Monthly', 'data_label': 'time', 'type': 'seasonal', 'seasonality': DateTime.day_of_month, 'npart': 4 },
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{'name': 'Yearly', 'data_label': 'time', 'type': 'seasonal', 'seasonality': DateTime.day_of_year, 'npart': 12 }
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]
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target_variable = {'name': 'Load', 'data_label': 'load', 'type': 'common'}
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nodes=['192.168.28.38']
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deho_mv.execute(datsetname, dataset,
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ngen=10, npop=10,psel=0.6, pcross=.5, pmut=.3,
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ngen=20, npop=15,psel=0.6, pcross=.5, pmut=.3,
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window_size=2000, train_rate=.9, increment_rate=1,
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experiments=1,
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fts_method=wmvfts.WeightedMVFTS,
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