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Bugfixes in DEHO

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This commit is contained in:
Petrônio Cândido 2019-08-05 17:51:15 -03:00
parent 5b7e4edcd7
commit 9efe7ba453
3 changed files with 70 additions and 44 deletions
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72
pyFTS/hyperparam/mvfts.py
View File

@ -76,6 +76,19 @@ def random_genotype(**kwargs):
for v in explanatory_variables:
var = vars[v]
param = random_param(var)
explanatory_params.append(param)
target_params = random_param(tvar)
return genotype(
explanatory_variables,
explanatory_params,
target_params
)
def random_param(var):
if var['type'] == 'common':
npart = random.randint(7, 50)
else:
@ -83,23 +96,10 @@ def random_genotype(**kwargs):
param = {
'mf': random.randint(1, 4),
'npart': npart,
'partitioner': 1, #random.randint(1, 2),
'partitioner': 1, # random.randint(1, 2),
'alpha': random.uniform(0, .5)
}
explanatory_params.append(param)
target_params = {
'mf': random.randint(1, 4),
'npart': random.randint(7, 50),
'partitioner': 1, #random.randint(1, 2),
'alpha': random.uniform(0, .5)
}
return genotype(
explanatory_variables,
explanatory_params,
target_params
)
return param
def phenotype(individual, train, fts_method, parameters={}, **kwargs):
@ -246,6 +246,10 @@ def crossover(population, **kwargs):
"""
import random
vars = kwargs.get('variables', None)
tvar = kwargs.get('target_variable', None)
n = len(population) - 1
r1,r2 = 0,0
@ -280,7 +284,7 @@ def crossover(population, **kwargs):
if ix in best['explanatory_variables'] and ix in worst['explanatory_variables']:
bix = best['explanatory_variables'].index(ix)
wix = worst['explanatory_variables'].index(ix)
param = crossover_variable_params(best['explanatory_params'][bix], worst['explanatory_params'][wix])
param = crossover_variable_params(best['explanatory_params'][bix], worst['explanatory_params'][wix], vars[ix])
elif ix in best['explanatory_variables']:
bix = best['explanatory_variables'].index(ix)
param = best['explanatory_params'][bix]
@ -291,15 +295,18 @@ def crossover(population, **kwargs):
explanatory_variables.append(ix)
explanatory_params.append(param)
tparams = crossover_variable_params(best['target_params'], worst['target_params'])
tparams = crossover_variable_params(best['target_params'], worst['target_params'], tvar)
descendent = genotype(explanatory_variables, explanatory_params, tparams, None, None)
descendent = genotype(explanatory_variables, explanatory_params, tparams)
return descendent
def crossover_variable_params(best, worst):
def crossover_variable_params(best, worst, var):
if var['type'] == 'common':
npart = int(round(.7 * best['npart'] + .3 * worst['npart']))
else:
npart = best['npart']
alpha = float(.7 * best['alpha'] + .3 * worst['alpha'])
rnd = random.uniform(0, 1)
mf = best['mf'] if rnd < .7 else worst['mf']
@ -317,14 +324,32 @@ def mutation(individual, **kwargs):
:return:
"""
vars = kwargs.get('variables', None)
tvar = kwargs.get('target_variable', None)
l = len(vars)
il = len(individual['explanatory_variables'])
rnd = random.uniform(0, 1)
if rnd > .9 and il > 1:
rnd = random.randint(0, il-1)
val = individual['explanatory_variables'][rnd]
individual['explanatory_variables'].remove(val)
individual['explanatory_params'].pop(rnd)
elif rnd < .1 and il < l:
rnd = random.randint(0, l-1)
while rnd in individual['explanatory_variables']:
rnd = random.randint(0, l-1)
individual['explanatory_variables'].append(rnd)
individual['explanatory_params'].append(random_param(vars[rnd]))
for ct in np.arange(len(individual['explanatory_variables'])):
rnd = random.uniform(0, 1)
if rnd > .5:
mutate_variable_params(individual['explanatory_params'][ct])
mutate_variable_params(individual['explanatory_params'][ct], vars[ct])
rnd = random.uniform(0, 1)
if rnd > .5:
mutate_variable_params(individual['target_params'])
mutate_variable_params(individual['target_params'], tvar)
individual['f1'] = None
individual['f2'] = None
@ -332,7 +357,8 @@ def mutation(individual, **kwargs):
return individual
def mutate_variable_params(param):
def mutate_variable_params(param, var):
if var['type']=='common':
param['npart'] = min(50, max(3, int(param['npart'] + np.random.normal(0, 4))))
param['alpha'] = min(.5, max(0, param['alpha'] + np.random.normal(0, .5)))
param['mf'] = random.randint(1, 4)
@ -428,7 +454,7 @@ def persist_statistics(datasetname, statistics):
def log_result(datasetname, fts_method, result):
import json
with open('result_{}{}.json'.format(fts_method,datasetname), 'w') as file:
with open('result_{}{}.json'.format(fts_method,datasetname), 'a+') as file:
file.write(json.dumps(result))
print(result)

6
pyFTS/partitioners/partitioner.py
View File

@ -156,9 +156,12 @@ class Partitioner(object):
nearest = self.search(data, type='index')
mv = np.zeros(self.partitions)
try:
for ix in nearest:
tmp = self[ix].membership(data)
mv[ix] = tmp if tmp >= alpha_cut else 0.
except:
print(ix)
ix = np.ravel(np.argwhere(mv > 0.))
@ -316,7 +319,6 @@ class Partitioner(object):
it represents the fuzzy set name.
:return: the fuzzy set
"""
try:
if isinstance(item, (int, np.int, np.int8, np.int16, np.int32, np.int64)):
if item < 0 or item >= self.partitions:
raise ValueError("The fuzzy set index must be between 0 and {}.".format(self.partitions))
@ -327,8 +329,6 @@ class Partitioner(object):
return self.sets[item]
else:
raise ValueError("The parameter 'item' must be an integer or a string and the value informed was {} of type {}!".format(item, type(item)))
except Exception as ex:
logging.exception("Error")
def __iter__(self):

4
pyFTS/tests/hyperparam.py
View File

@ -50,14 +50,14 @@ explanatory_variables =[
{'name': 'Temperature', 'data_label': 'temperature', 'type': 'common'},
{'name': 'Daily', 'data_label': 'time', 'type': 'seasonal', 'seasonality': DateTime.minute_of_day, 'npart': 24 },
{'name': 'Weekly', 'data_label': 'time', 'type': 'seasonal', 'seasonality': DateTime.day_of_week, 'npart': 7 },
#{'name': 'Monthly', 'data_label': 'time', 'type': 'seasonal', 'seasonality': DateTime.day_of_month, 'npart': 4 },
{'name': 'Monthly', 'data_label': 'time', 'type': 'seasonal', 'seasonality': DateTime.day_of_month, 'npart': 4 },
{'name': 'Yearly', 'data_label': 'time', 'type': 'seasonal', 'seasonality': DateTime.day_of_year, 'npart': 12 }
]
target_variable = {'name': 'Load', 'data_label': 'load', 'type': 'common'}
nodes=['192.168.28.38']
deho_mv.execute(datsetname, dataset,
ngen=10, npop=10,psel=0.6, pcross=.5, pmut=.3,
ngen=20, npop=15,psel=0.6, pcross=.5, pmut=.3,
window_size=2000, train_rate=.9, increment_rate=1,
experiments=1,
fts_method=wmvfts.WeightedMVFTS,