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GridSearch improvements for stability and scalability

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This commit is contained in:
Petrônio Cândido 2018-11-14 12:21:59 -02:00
parent edceece6e2
commit 4742498ec5
2 changed files with 21 additions and 18 deletions
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29
pyFTS/hyperparam/GridSearch.py
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@ -20,7 +20,7 @@ def dict_individual(mf, partitioner, partitions, order, lags, alpha_cut):
} }
def metodo_cluster(individual, train, test): def cluster_method(individual, train, test):
from pyFTS.common import Util, Membership from pyFTS.common import Util, Membership
from pyFTS.models import hofts from pyFTS.models import hofts
from pyFTS.partitioners import Grid, Entropy from pyFTS.partitioners import Grid, Entropy
@ -53,23 +53,21 @@ def metodo_cluster(individual, train, test):
size = len(model) size = len(model)
return individual, rmse, size return individual, rmse, size, mape, u
def process_jobs(jobs, datasetname, conn): def process_jobs(jobs, datasetname, conn):
for job in jobs: for job in jobs:
result, rmse, size = job() result, rmse, size, mape, u = job()
if job.status == dispy.DispyJob.Finished and result is not None: if job.status == dispy.DispyJob.Finished and result is not None:
print(result) 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'], record = (datasetname, 'GridSearch', 'WHOFTS', None, result['mf'],
result['order'], result['partitioner'], result['npart'], result['order'], result['partitioner'], result['npart'],
result['alpha'], str(result['lags']), 'rmse', rmse) result['alpha'], str(result['lags']), metric, metrics[metric])
hUtil.insert_hyperparam(record, conn)
record = (datasetname, 'GridSearch', 'WHOFTS', None, result['mf'],
result['order'], result['partitioner'], result['npart'],
result['alpha'], str(result['lags']), 'size', size)
hUtil.insert_hyperparam(record, conn) hUtil.insert_hyperparam(record, conn)
@ -95,15 +93,19 @@ def execute(hyperparams, datasetname, train, test, **kwargs):
for k in np.arange(len(keys_sorted)): for k in np.arange(len(keys_sorted)):
index[keys_sorted[k]] = k index[keys_sorted[k]] = k
print("Evaluation order: \n {}".format(index))
hp_values = [ hp_values = [
[v for v in hyperparams[hp]] [v for v in hyperparams[hp]]
for hp in keys_sorted for hp in keys_sorted
] ]
cluster, http_server = Util.start_dispy_cluster(metodo_cluster, nodes=nodes) 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') conn = hUtil.open_hyperparam_db('hyperparam.db')
for ct, instance in enumerate(product(*hp_values)): for instance in product(*hp_values):
partitions = instance[index['partitions']] partitions = instance[index['partitions']]
partitioner = instance[index['partitioner']] partitioner = instance[index['partitioner']]
mf = instance[index['mf']] mf = instance[index['mf']]
@ -132,6 +134,7 @@ def execute(hyperparams, datasetname, train, test, **kwargs):
jobs = [] jobs = []
for ind in individuals: for ind in individuals:
print("Testing individual {}".format(ind))
job = cluster.submit(ind, train, test) job = cluster.submit(ind, train, test)
jobs.append(job) jobs.append(job)

4
pyFTS/tests/hyperparam.py
View File

@ -17,10 +17,10 @@ hyperparams = {
'partitioner': [1,2], 'partitioner': [1,2],
'mf': [1, 2, 3, 4], 'mf': [1, 2, 3, 4],
'lags': np.arange(1,35,2), 'lags': np.arange(1,35,2),
'alpha': np.arange(0,.5, .05) 'alpha': np.arange(.0, .5, .05)
} }
nodes = ['192.168.0.110','192.168.0.106', '192.168.0.107'] nodes = ['192.168.0.106', '192.168.0.110'] #, '192.168.0.107']
ds, train, test = get_train_test() ds, train, test = get_train_test()