Random Search in hyperparam

pyFTS/common/fts.py

@@ -410,10 +410,6 @@ class FTS(object):
         if save:
             Util.persist_obj(self, file_path)
 
-        if 'statistics' in kw:
-            kwargs['statistics'] = kw['statistics']
-            print(kwargs['statistics'])
-
 
     def clone_parameters(self, model):
         """

pyFTS/hyperparam/mvfts.py

@@ -29,7 +29,7 @@ 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
+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
 from pyFTS.models.seasonal.common import DateTime
@@ -315,6 +315,7 @@ def crossover_variable_params(best, worst, var):
     param = {'partitioner': partitioner, 'npart': npart, 'alpha': alpha, 'mf': mf}
     return param
 
+
 def mutation(individual, **kwargs):
     """
     Mutation operator
@@ -356,6 +357,51 @@ def mutation(individual, **kwargs):
 
     return individual
 
+def mutation_random_search(individual, **kwargs):
+    """
+    Mutation operator
+
+    :param individual: an individual genotype
+    :param pmut: individual probability o
+    :return:
+    """
+
+    vars = kwargs.get('variables', None)
+    tvar = kwargs.get('target_variable', None)
+    l = len(vars)
+
+    il = len(individual['explanatory_variables'])
+    #
+    if il > 1:
+        for l in range(il):
+            il = len(individual['explanatory_variables'])
+            rnd = random.uniform(0, 1)
+            if rnd > .5:
+                rnd = random.randint(0, il-1)
+                val = individual['explanatory_variables'][rnd]
+                individual['explanatory_variables'].remove(val)
+                individual['explanatory_params'].pop(rnd)
+    else:
+        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], vars[ct])
+
+    rnd = random.uniform(0, 1)
+    if rnd > .5:
+        mutate_variable_params(individual['target_params'], tvar)
+
+    individual['f1'] = None
+    individual['f2'] = None
+
+    return individual
+
 
 def mutate_variable_params(param, var):
     if var['type']=='common':
@@ -458,3 +504,31 @@ def log_result(datasetname, fts_method, result):
         file.write(json.dumps(result))
 
         print(result)
+
+
+def random_search(datasetname, dataset, **kwargs):
+    experiments = kwargs.get('experiments', 30)
+
+    distributed = kwargs.get('distributed', False)
+
+    fts_method = kwargs.get('fts_method', hofts.WeightedHighOrderFTS)
+    shortname = str(fts_method.__module__).split('.')[-1]
+
+    kwargs['mutation_operator'] = mutation_random_search
+    kwargs['evaluation_operator'] = evaluate
+    kwargs['random_individual'] = random_genotype
+
+    ret = []
+    for i in np.arange(experiments):
+        print("Experiment {}".format(i))
+
+        start = time.time()
+        ret, statistics = RS.execute (dataset, **kwargs)
+        end = time.time()
+        ret['time'] = end - start
+        experiment = {'individual': ret, 'statistics': statistics}
+
+        ret = process_experiment(shortname, experiment, datasetname)
+
+
+    return ret

pyFTS/hyperparam/random_search.py

@@ -0,0 +1,84 @@
+"""
+Simple Random Search Hyperparameter Optimization
+"""
+
+from pyFTS.hyperparam import Evolutionary
+import time
+
+__measures = ['f1', 'f2', 'rmse', 'size']
+
+
+def execute( dataset, **kwargs):
+    """
+    Batch execution of Random Search Hyperparameter Optimization
+
+    :param datasetname:
+    :param dataset: The time series to optimize the FTS
+    :keyword ngen: An integer value with the maximum number of generations, default value: 30
+    :keyword mgen: An integer value with the maximum number of generations without improvement to stop, default value 7
+    :keyword fts_method: The FTS method to optimize
+    :keyword parameters: dict with model specific arguments for fts_method
+    :keyword random_individual: create an random genotype
+    :keyword evalutation_operator: a function that receives a dataset and an individual and return its fitness
+    :keyword mutation_operator: a function that receives one individual and return a changed individual
+    :keyword window_size: An integer value with the the length of scrolling window for train/test on dataset
+    :keyword train_rate: A float value between 0 and 1 with the train/test split ([0,1])
+    :keyword increment_rate: A float value between 0 and 1 with the the increment of the scrolling window,
+             relative to the window_size ([0,1])
+    :keyword collect_statistics: A boolean value indicating to collect statistics for each generation
+    :keyword distributed: A value indicating it the execution will be local and sequential (distributed=False),
+             or parallel and distributed (distributed='dispy' or distributed='spark')
+    :keyword cluster: If distributed='dispy' the list of cluster nodes, else if distributed='spark' it is the master node
+    :return: the best genotype
+    """
+
+    ngen = kwargs.get('ngen',30)
+    mgen = kwargs.get('mgen', 7)
+
+    kwargs['pmut'] = 1.0
+
+    random_individual = kwargs.get('random_individual', Evolutionary.random_genotype)
+    evaluation_operator = kwargs.get('evaluation_operator', Evolutionary.evaluate)
+    mutation_operator = kwargs.get('mutation_operator', Evolutionary.mutation)
+
+    no_improvement_count = 0
+
+    individual = random_individual(**kwargs)
+
+    stat = {}
+
+    stat[0] = {}
+
+    ret = evaluation_operator(dataset, individual, **kwargs)
+    for key in __measures:
+        individual[key] = ret[key]
+        stat[0][key] = ret[key]
+
+    print(individual)
+
+    for i in range(1,ngen+1):
+        print("GENERATION {} {}".format(i, time.time()))
+
+        new = mutation_operator(individual, **kwargs)
+        ret = evaluation_operator(dataset, new, **kwargs)
+        new_stat = {}
+        for key in __measures:
+            new[key] = ret[key]
+            new_stat[key] = ret[key]
+
+        print(new)
+
+        if new['f1'] <= individual['f1'] and new['f2'] <= individual['f2']:
+            individual = new
+            no_improvement_count = 0
+            stat[i] = new_stat
+            print(individual)
+        else:
+            stat[i] = stat[i-1]
+            no_improvement_count += 1
+            print("WITHOUT IMPROVEMENT {}".format(no_improvement_count))
+
+        if no_improvement_count == mgen:
+            break
+
+    return individual, stat

pyFTS/partitioners/partitioner.py

@@ -162,12 +162,10 @@ class Partitioner(object):
         nearest = self.search(data, type='index')
         mv = np.zeros(self.partitions)
 
-        try:
+        for ix in nearest:
-            for ix in nearest:
+            tmp = self[ix].membership(data)
-                tmp = self[ix].membership(data)
+            mv[ix] = tmp if tmp >= alpha_cut else 0.
-                mv[ix] = tmp if tmp >= alpha_cut else 0.
+
-        except:
-            print(ix)
 
         ix = np.ravel(np.argwhere(mv > 0.))
 

pyFTS/tests/hyperparam.py

@@ -56,6 +56,20 @@ explanatory_variables =[
 
 target_variable = {'name': 'Load', 'data_label': 'load', 'type': 'common'}
 nodes=['192.168.28.38']
+
+deho_mv.random_search(datsetname, dataset,
+              ngen=200, mgen=200,
+              window_size=2000, train_rate=.9, increment_rate=1,
+              experiments=1,
+              fts_method=wmvfts.WeightedMVFTS,
+              variables=explanatory_variables,
+              target_variable=target_variable,
+              #distributed='dispy', nodes=nodes,
+              parameters=dict(num_batches=5)
+              #parameters=dict(distributed='dispy', nodes=nodes, num_batches=5)
+              )
+
+'''
 deho_mv.execute(datsetname, dataset,
               ngen=20, npop=15,psel=0.6, pcross=.5, pmut=.3,
               window_size=2000, train_rate=.9, increment_rate=1,
@@ -67,7 +81,7 @@ deho_mv.execute(datsetname, dataset,
               parameters=dict(num_batches=5)
               #parameters=dict(distributed='dispy', nodes=nodes, num_batches=5)
               )
-
+'''
 '''
 ret = Evolutionary.execute(datsetname, dataset,
                            ngen=30, npop=20,psel=0.6, pcross=.5, pmut=.3,