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 cluster_method(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.WeightedHighOrderFTS(partitioner=partitioner,
lags=individual['lags'],
alpha_cut=individual['alpha'],
order=individual['order'])
model.fit(train)
rmse, mape, u = Measures.get_point_statistics(test, model)
size = len(model)
return individual, rmse, size, mape, u
[docs]def process_jobs(jobs, datasetname, conn):
for job in jobs:
result, rmse, size, mape, u = job()
if job.status == dispy.DispyJob.Finished and result is not None:
print("Processing result of {}".format(result))
metrics = {'rmse': rmse, 'size': size, 'mape': mape, 'u': u }
for metric in metrics.keys():
record = (datasetname, 'GridSearch', 'WHOFTS', None, result['mf'],
result['order'], result['partitioner'], result['npart'],
result['alpha'], str(result['lags']), metric, metrics[metric])
print(record)
hUtil.insert_hyperparam(record, conn)
else:
print(job.exception)
print(job.stdout)
[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
print("Evaluation order: \n {}".format(index))
hp_values = [
[v for v in hyperparams[hp]]
for hp in keys_sorted
]
print("Evaluation values: \n {}".format(hp_values))
cluster, http_server = Util.start_dispy_cluster(cluster_method, nodes=nodes)
conn = hUtil.open_hyperparam_db('hyperparam.db')
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']]
count = 0
for lag1 in lags: # o é o lag1
_lags = [lag1]
count += 1
if order > 1:
for lag2 in lags: # o é o lag1
_lags2 = [lag1, lag1+lag2]
count += 1
if order > 2:
for lag3 in lags: # o é o lag1
count += 1
_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))
if count > 50:
jobs = []
for ind in individuals:
print("Testing individual {}".format(ind))
job = cluster.submit(ind, train, test)
jobs.append(job)
process_jobs(jobs, datasetname, conn)
count = 0
individuals = []
Util.stop_dispy_cluster(cluster, http_server)
