+
+from pyFTS.common import Util, Membership
+from pyFTS.models import hofts
+from pyFTS.partitioners import Grid, Entropy
+from pyFTS.benchmarks import Measures
+from pyFTS.hyperparam import Util as hUtil
+import numpy as np
+import dispy
+from itertools import product
+
+
+[docs]def dict_individual(mf, partitioner, partitions, order, lags, alpha_cut):
+
return {
+
'mf': mf,
+
'partitioner': partitioner,
+
'npart': partitions,
+
'alpha': alpha_cut,
+
'order': order,
+
'lags': lags
+
}
+
+
+[docs]def metodo_cluster(individual, train, test):
+
from pyFTS.common import Util, Membership
+
from pyFTS.models import hofts
+
from pyFTS.partitioners import Grid, Entropy
+
from pyFTS.benchmarks import Measures
+
+
if individual['mf'] == 1:
+
mf = Membership.trimf
+
elif individual['mf'] == 2:
+
mf = Membership.trapmf
+
elif individual['mf'] == 3 and individual['partitioner'] != 2:
+
mf = Membership.gaussmf
+
else:
+
mf = Membership.trimf
+
+
if individual['partitioner'] == 1:
+
partitioner = Grid.GridPartitioner(data=train, npart=individual['npart'], func=mf)
+
elif individual['partitioner'] == 2:
+
npart = individual['npart'] if individual['npart'] > 10 else 10
+
partitioner = Entropy.EntropyPartitioner(data=train, npart=npart, func=mf)
+
+
+
model = hofts.HighOrderFTS(partitioner=partitioner,
+
lags=individual['lags'],
+
alpha_cut=individual['alpha'],
+
order=individual['order'])
+
+
model.fit(train)
+
+
rmse, mape, u = Measures.get_point_statistics(test, model)
+
+
return individual, rmse
+
+
+[docs]def execute(hyperparams, datasetname, train, test, **kwargs):
+
+
nodes = kwargs.get('nodes',['127.0.0.1'])
+
+
individuals = []
+
+
if 'lags' in hyperparams:
+
lags = hyperparams.pop('lags')
+
else:
+
lags = [k for k in np.arange(50)]
+
+
keys_sorted = [k for k in sorted(hyperparams.keys())]
+
+
index = {}
+
for k in np.arange(len(keys_sorted)):
+
index[keys_sorted[k]] = k
+
+
hp_values = [
+
[v for v in hyperparams[hp]]
+
for hp in keys_sorted
+
]
+
+
for instance in product(*hp_values):
+
partitions = instance[index['partitions']]
+
partitioner = instance[index['partitioner']]
+
mf = instance[index['mf']]
+
alpha_cut = instance[index['alpha']]
+
order = instance[index['order']]
+
for lag1 in lags: # o é o lag1
+
_lags = [lag1]
+
if order > 1:
+
for lag2 in lags: # o é o lag1
+
_lags2 = [lag1, lag1+lag2]
+
if order > 2:
+
for lag3 in lags: # o é o lag1
+
_lags3 = [lag1, lag1 + lag2, lag1 + lag2+lag3 ]
+
individuals.append(dict_individual(mf, partitioner, partitions, order, _lags3, alpha_cut))
+
else:
+
individuals.append(
+
dict_individual(mf, partitioner, partitions, order, _lags2, alpha_cut))
+
else:
+
individuals.append(dict_individual(mf, partitioner, partitions, order, _lags, alpha_cut))
+
+
+
cluster, http_server = Util.start_dispy_cluster(metodo_cluster, nodes=nodes)
+
+
jobs = []
+
+
for ind in individuals:
+
job = cluster.submit(ind, train, test)
+
jobs.append(job)
+
+
+
conn = hUtil.open_hyperparam_db('hyperparam.db')
+
+
for job in jobs:
+
result, rmse = job()
+
if job.status == dispy.DispyJob.Finished and result is not None:
+
print(result)
+
+
record = (datasetname, 'GridSearch', 'HOFTS', None, result['mf'],
+
result['order'], result['partitioner'], result['npart'],
+
result['alpha'], str(result['lags']), 'rmse', rmse)
+
+
hUtil.insert_hyperparam(record, conn)
+
+
else:
+
print(job.exception)
+
print(job.stdout)
+
+
Util.stop_dispy_cluster(cluster, http_server)
+
+ 
+ 
+