Removing direct dispy import from hyperparam modules

pyFTS/hyperparam/Evolutionary.py

@@ -8,7 +8,6 @@ import math
 import time
 from functools import reduce
 from operator import itemgetter
-import dispy
 
 import random
 from pyFTS.common import Util
@@ -17,7 +16,7 @@ from pyFTS.partitioners import Grid, Entropy  # , Huarng
 from pyFTS.common import Membership
 from pyFTS.models import hofts, ifts, pwfts
 from pyFTS.hyperparam import Util as hUtil
-from pyFTS.distributed import dispy as dUtil
+
 
 __measures = ['f1', 'f2', 'rmse', 'size']
 
@@ -415,6 +414,8 @@ def GeneticAlgorithm(dataset, **kwargs):
             for key in __measures:
                 individual[key] = ret[key]
     elif distributed=='dispy':
+        from pyFTS.distributed import dispy as dUtil
+        import dispy
         jobs = []
         for ct, individual in enumerate(population):
             job = cluster.submit(dataset, individual, **kwargs)
@@ -602,6 +603,7 @@ def execute(datasetname, dataset, **kwargs):
     shortname = str(fts_method.__module__).split('.')[-1]
 
     if distributed == 'dispy':
+        from pyFTS.distributed import dispy as dUtil
         nodes = kwargs.get('nodes', ['127.0.0.1'])
         cluster, http_server = dUtil.start_dispy_cluster(evaluate, nodes=nodes)
         kwargs['cluster'] = cluster

pyFTS/hyperparam/GridSearch.py

@@ -4,8 +4,7 @@ from pyFTS.models import hofts
 from pyFTS.partitioners import Grid, Entropy
 from pyFTS.benchmarks import Measures
 from pyFTS.hyperparam import Util as hUtil
-from pyFTS.distributed import dispy as dUtil
-import dispy
+
 import numpy as np
 from itertools import product
 
@@ -73,6 +72,8 @@ def cluster_method(individual, dataset, **kwargs):
 
 
 def process_jobs(jobs, datasetname, conn):
+    from pyFTS.distributed import dispy as dUtil
+    import dispy
     for ct, job in enumerate(jobs):
         print("Processing job {}".format(ct))
         result = job()
@@ -98,6 +99,8 @@ def process_jobs(jobs, datasetname, conn):
     
 
 def execute(hyperparams, datasetname, dataset, **kwargs):
+    from pyFTS.distributed import dispy as dUtil
+    import dispy
 
     nodes = kwargs.get('nodes',['127.0.0.1'])
 

pyFTS/hyperparam/mvfts.py

@@ -28,7 +28,7 @@ from pyFTS.partitioners import Grid, Entropy  # , Huarng
 from pyFTS.common import Membership
 from pyFTS.models import hofts, ifts, pwfts
 from pyFTS.hyperparam import Util as hUtil
-from pyFTS.distributed import dispy as dUtil
+
 from pyFTS.hyperparam import Evolutionary, random_search as RS
 from pyFTS.models.multivariate import mvfts, wmvfts, variable
 from pyFTS.models.seasonal import partitioner as seasonal
@@ -458,6 +458,8 @@ def execute(datasetname, dataset, **kwargs):
     kwargs['random_individual'] = random_genotype
 
     if distributed == 'dispy':
+        from pyFTS.distributed import dispy as dUtil
+        import dispy
         nodes = kwargs.get('nodes', ['127.0.0.1'])
         cluster, http_server = dUtil.start_dispy_cluster(evaluate, nodes=nodes)
         kwargs['cluster'] = cluster