distributed.spark optimizations and bugfixes

pyFTS/common/Util.py

@@ -8,7 +8,6 @@ import dill
 import numpy as np
 
 
-
 def plot_rules(model, size=[5, 5], axis=None, rules_by_axis=None, columns=1):
     if axis is None and rules_by_axis is None:
         rows = 1
@@ -126,8 +125,8 @@ def show_and_save_image(fig, file, flag, lgd=None):
     :param flag: if True the image will be saved
     :param lgd: legend
     """
+    plt.show()
     if flag:
-        plt.show()
         if lgd is not None:
             fig.savefig(file, additional_artists=lgd,bbox_inches='tight')  #bbox_extra_artists=(lgd,), )
         else:

pyFTS/common/fts.py

@@ -163,9 +163,7 @@ class FTS(object):
             elif distributed == 'spark':
                 from pyFTS.distributed import spark
 
-                nodes = kwargs.get("nodes", 'spark://192.168.0.110:7077')
+                ret = spark.distributed_predict(data=ndata, model=self, **kwargs)
-                app  = kwargs.get("app", 'pyFTS')
-                ret = spark.distributed_predict(data=ndata, model=self, url=nodes, app=app)
 
 
         if not self.is_multivariate:

pyFTS/distributed/spark.py

@@ -16,7 +16,21 @@ SPARK_ADDR = 'spark://192.168.0.110:7077'
 os.environ['PYSPARK_PYTHON'] = '/usr/bin/python3'
 os.environ['PYSPARK_DRIVER_PYTHON'] = '/usr/bin/python3'
 
+def create_spark_conf(**kwargs):
+    spark_executor_memory = kwargs.get("spark_executor_memory", "2g")
+    spark_driver_memory = kwargs.get("spark_driver_memory", "2g")
+    url = kwargs.get("url", SPARK_ADDR)
+    app  = kwargs.get("app", 'pyFTS')
 
+    conf = SparkConf()
+    conf.setMaster(url)
+    conf.setAppName(app)
+    conf.set("spark.executor.memory", spark_executor_memory)
+    conf.set("spark.driver.memory", spark_driver_memory)
+    conf.set("spark.memory.offHeap.enabled",True)
+    conf.set("spark.memory.offHeap.size","16g")
+    
+    return conf
 
 def get_partitioner(shared_partitioner, type='common', variables=[]):
     """
@@ -74,6 +88,16 @@ def get_variables(**parameters):
 
     return (explanatory_variables, target_variable)
 
+def create_univariate_model(**parameters):
+    if parameters['order'].value > 1:
+        model = parameters['method'].value(partitioner=get_partitioner(parameters['partitioner']),
+                                           order=parameters['order'].value, alpha_cut=parameters['alpha_cut'].value,
+                                           lags=parameters['lags'].value)
+    else:
+        model = parameters['method'].value(partitioner=get_partitioner(parameters['partitioner']),
+                                           alpha_cut=parameters['alpha_cut'].value)
+    
+    return model
 
 def slave_train_univariate(data, **parameters):
     """
@@ -82,27 +106,32 @@ def slave_train_univariate(data, **parameters):
     :return:
     """
 
-    if parameters['type'].value == 'common':
+    model = create_univariate_model(**parameters)
 
-        if parameters['order'].value > 1:
+    ndata = [k for k in data]
-            model = parameters['method'].value(partitioner=get_partitioner(parameters['partitioner']),
-                                               order=parameters['order'].value, alpha_cut=parameters['alpha_cut'].value,
-                                               lags=parameters['lags'].value)
-        else:
-            model = parameters['method'].value(partitioner=get_partitioner(parameters['partitioner']),
-                                               alpha_cut=parameters['alpha_cut'].value)
-
-        ndata = [k for k in data]
-
-    else:
-        pass
 
     model.train(ndata)
 
     return [(k, model.flrgs[k]) for k in model.flrgs.keys()]
+    
+
+def slave_forecast_univariate(data, **parameters):
+    """
+
+    :param data:
+    :return:
+    """
+
+    model = create_univariate_model(**parameters)
+
+    ndata = [k for k in data]
+
+    forecasts = model.predict(ndata)
+
+    return [(k, k) for k in forecasts]
 
 
-def slave_train_multivariate(data, **parameters):
+def create_multivariate_model(**parameters):
     explanatory_variables, target_variable = get_variables(**parameters)
     #vars = [(v.name, v.name) for v in explanatory_variables]
 
@@ -129,8 +158,13 @@ def slave_train_multivariate(data, **parameters):
                                                target_variable=target_variable,
                                                alpha_cut=parameters['alpha_cut'].value)
 
+    return model
 
 
+def slave_train_multivariate(data, **parameters):
+    
+    model = create_multivariate_model(**parameters)
+
     rows = [k for k in data]
     ndata = pd.DataFrame.from_records(rows, columns=parameters['columns'].value)
 
@@ -145,7 +179,68 @@ def slave_train_multivariate(data, **parameters):
         return [(k, v) for k,v in model.flrgs.items()]
 
 
-def distributed_train(model, data, url=SPARK_ADDR, app='pyFTS'):
+def slave_forecast_multivariate(data, **parameters):
+    
+    model = create_multivariate_model(**parameters)
+
+    rows = [k for k in data]
+    ndata = pd.DataFrame.from_records(rows, columns=parameters['columns'].value)
+
+    forecasts = model.predict(ndata)
+
+    return [(k, k) for k in forecasts]
+
+
+def share_parameters(model, context, data):
+    parameters = {}
+    if not model.is_multivariate:
+        parameters['type'] = context.broadcast('common')
+        parameters['partitioner'] = context.broadcast(model.partitioner.sets)
+        parameters['alpha_cut'] = context.broadcast(model.alpha_cut)
+        parameters['order'] = context.broadcast(model.order)
+        parameters['method'] = context.broadcast(type(model))
+        parameters['lags'] = context.broadcast(model.lags)
+        parameters['max_lag'] = context.broadcast(model.max_lag)
+    else:
+        if model.is_clustered:
+            parameters['type'] = context.broadcast('clustered')
+            names = []
+            for name, fset in model.partitioner.sets.items():
+                names.append(name)
+                parameters['partitioner_{}'.format(name)] = context.broadcast([(k,v) for k,v in fset.sets.items()])
+
+            parameters['partitioner_names'] = context.broadcast(names)
+
+        else:
+            parameters['type'] = context.broadcast('multivariate')
+        names = []
+        for var in model.explanatory_variables:
+            #if var.data_type is None:
+            #    raise Exception("It is mandatory to inform the data_type parameter for each variable when the training is distributed! ")
+            names.append(var.name)
+            parameters['{}_type'.format(var.name)] = context.broadcast(var.type)
+            #parameters['{}_data_type'.format(var.name)] = context.broadcast(var.data_type)
+            #parameters['{}_mask'.format(var.name)] = context.broadcast(var.mask)
+            parameters['{}_label'.format(var.name)] = context.broadcast(var.data_label)
+            parameters['{}_alpha'.format(var.name)] = context.broadcast(var.alpha_cut)
+            parameters['{}_partitioner'.format(var.name)] = context.broadcast(var.partitioner.sets)
+            parameters['{}_partitioner_type'.format(var.name)] = context.broadcast(var.partitioner.type)
+
+        parameters['variables'] = context.broadcast(names)
+        parameters['target'] = context.broadcast(model.target_variable.name)
+
+        parameters['columns'] = context.broadcast(data.columns.values)
+
+        parameters['alpha_cut'] = context.broadcast(model.alpha_cut)
+        parameters['order'] = context.broadcast(model.order)
+        parameters['method'] = context.broadcast(type(model))
+        parameters['lags'] = context.broadcast(model.lags)
+        parameters['max_lag'] = context.broadcast(model.max_lag)
+        
+    return parameters
+    
+
+def distributed_train(model, data, **kwargs):
     """
 
 
@@ -155,85 +250,34 @@ def distributed_train(model, data, url=SPARK_ADDR, app='pyFTS'):
     :param app:
     :return:
     """
-
+    
-    conf = SparkConf()
+    num_batches = kwargs.get("num_batches", 4)
-    conf.setMaster(url)
+    
-    conf.setAppName(app)
+    conf = create_spark_conf(**kwargs)
-    conf.set("spark.executor.memory", "2g")
-    conf.set("spark.driver.memory", "2g")
-    conf.set("spark.memory.offHeap.enabled",True)
-    conf.set("spark.memory.offHeap.size","16g")
-    parameters = {}
 
     with SparkContext(conf=conf) as context:
 
         nodes = context.defaultParallelism
+        
+        parameters = share_parameters(model, context, data)
 
         if not model.is_multivariate:
-            parameters['type'] = context.broadcast('common')
-            parameters['partitioner'] = context.broadcast(model.partitioner.sets)
-            parameters['alpha_cut'] = context.broadcast(model.alpha_cut)
-            parameters['order'] = context.broadcast(model.order)
-            parameters['method'] = context.broadcast(type(model))
-            parameters['lags'] = context.broadcast(model.lags)
-            parameters['max_lag'] = context.broadcast(model.max_lag)
-
             func = lambda x: slave_train_univariate(x, **parameters)
 
-            flrgs = context.parallelize(data).repartition(nodes*4).mapPartitions(func)
+            flrgs = context.parallelize(data).repartition(nodes*num_batches).mapPartitions(func)
 
             for k in flrgs.collect():
                 model.append_rule(k[1])
 
-            return model
         else:
-            if model.is_clustered:
+            
-                parameters['type'] = context.broadcast('clustered')
-                names = []
-                for name, fset in model.partitioner.sets.items():
-                    names.append(name)
-                    parameters['partitioner_{}'.format(name)] = context.broadcast([(k,v) for k,v in fset.sets.items()])
-
-                parameters['partitioner_names'] = context.broadcast(names)
-
-            else:
-                parameters['type'] = context.broadcast('multivariate')
-            names = []
-            for var in model.explanatory_variables:
-                #if var.data_type is None:
-                #    raise Exception("It is mandatory to inform the data_type parameter for each variable when the training is distributed! ")
-                names.append(var.name)
-                parameters['{}_type'.format(var.name)] = context.broadcast(var.type)
-                #parameters['{}_data_type'.format(var.name)] = context.broadcast(var.data_type)
-                #parameters['{}_mask'.format(var.name)] = context.broadcast(var.mask)
-                parameters['{}_label'.format(var.name)] = context.broadcast(var.data_label)
-                parameters['{}_alpha'.format(var.name)] = context.broadcast(var.alpha_cut)
-                parameters['{}_partitioner'.format(var.name)] = context.broadcast(var.partitioner.sets)
-                parameters['{}_partitioner_type'.format(var.name)] = context.broadcast(var.partitioner.type)
-
-            parameters['variables'] = context.broadcast(names)
-            parameters['target'] = context.broadcast(model.target_variable.name)
-
-            parameters['columns'] = context.broadcast(data.columns.values)
-
             data = data.to_dict(orient='records')
 
-            parameters['alpha_cut'] = context.broadcast(model.alpha_cut)
-            parameters['order'] = context.broadcast(model.order)
-            parameters['method'] = context.broadcast(type(model))
-            parameters['lags'] = context.broadcast(model.lags)
-            parameters['max_lag'] = context.broadcast(model.max_lag)
-
             func = lambda x: slave_train_multivariate(x, **parameters)
 
             flrgs = context.parallelize(data).mapPartitions(func)
 
             for k in flrgs.collect():
-                print(k)
-                #for g in k:
-                #    print(g)
-
-                #return
                 if parameters['type'].value == 'clustered':
                     if k[0] == 'counts':
                         for fset, count in k[1]:
@@ -243,8 +287,46 @@ def distributed_train(model, data, url=SPARK_ADDR, app='pyFTS'):
                 else:
                     model.append_rule(k[1])
 
-            return model
+    return model
 
 
-def distributed_predict(data, model, url=SPARK_ADDR, app='pyFTS'):
+def distributed_predict(data, model, **kwargs):
-    return None
+    """
+
+
+    :param model:
+    :param data:
+    :param url:
+    :param app:
+    :return:
+    """
+    
+    num_batches = kwargs.get("num_batches", 4)
+    
+    conf = create_spark_conf(**kwargs)
+
+    ret = []
+
+    with SparkContext(conf=conf) as context:
+
+        nodes = context.defaultParallelism
+        
+        parameters = share_parameters(model, context)
+
+        if not model.is_multivariate:
+            func = lambda x: slave_forecast_univariate(x, **parameters)
+
+            forecasts = context.parallelize(data).repartition(nodes * num_batches).mapPartitions(func)
+
+        else:
+
+            data = data.to_dict(orient='records')
+
+            func = lambda x: slave_forecast_multivariate(x, **parameters)
+
+            forecasts = context.parallelize(data).repartition(nodes * num_batches).mapPartitions(func)
+
+    for k in forecasts.collect():
+        ret.extend(k)
+
+    return ret

pyFTS/tests/hyperparam.py

@@ -96,7 +96,7 @@ t1 = time()
 Evolutionary.execute('SONDA', dataset,
                      ngen=20, mgen=5, npop=15, pcruz=.5, pmut=.3,
                      window_size=35000, train_rate=.6, increment_rate=1,
-                     collect_statistics=True, experiments=1)
+                     collect_statistics=True, experiments=5)
                      #distributed='dispy', nodes=['192.168.0.110','192.168.0.106','192.168.0.107'])
 
 t2 = time()

pyFTS/tests/multivariate.py

@@ -1,20 +1,98 @@
+import numpy as np
 import pandas as pd
-import matplotlib.pylab as plt
+import time
-from pyFTS.data import TAIEX, Malaysia
-from pyFTS.common import Transformations
 
-from pyFTS.benchmarks import Measures
+from pyFTS.data import Enrollments, TAIEX, SONDA
-from pyFTS.partitioners import Grid, Util as pUtil
+from pyFTS.partitioners import Grid, Simple
-from pyFTS.common import Transformations, Util
+from pyFTS.common import Util
-from pyFTS.models import pwfts
+
-from pyFTS.models.multivariate import common, variable, mvfts, wmvfts
+from pyspark import SparkConf
+from pyspark import SparkContext
+
+import os
+# make sure pyspark tells workers to use python3 not 2 if both are installed
+os.environ['PYSPARK_PYTHON'] = '/usr/bin/python3'
+os.environ['PYSPARK_DRIVER_PYTHON'] = '/usr/bin/python3'
+#'''
+data = SONDA.get_dataframe()
+
+data = data[['datahora','glo_avg']]
+
+data = data[~(np.isnan(data['glo_avg']) | np.equal(data['glo_avg'], 0.0))]
+
+train = data.iloc[:1500000]
+test = data.iloc[1500000:]
+
+from pyFTS.models.multivariate import common, variable, wmvfts
 from pyFTS.models.seasonal import partitioner as seasonal
 from pyFTS.models.seasonal.common import DateTime
+from pyFTS.partitioners import Grid
 
-bc = Transformations.BoxCox(0)
+import matplotlib.pyplot as plt
-tdiff = Transformations.Differential(1)
 
-from pyFTS.models.multivariate import common, variable, mvfts, cmvfts
+'''
+#fig, ax = plt.subplots(nrows=3, ncols=1, figsize=[15,5])
+
+
+sp = {'seasonality': DateTime.day_of_year , 'names': ['Jan','Feb','Mar','Apr','May','Jun','Jul', 'Aug','Sep','Oct','Nov','Dec']}
+
+vmonth = variable.Variable("Month", data_label="datahora", partitioner=seasonal.TimeGridPartitioner, npart=12, alpha_cut=.25,
+                           data=train, partitioner_specific=sp)
+
+#vmonth.partitioner.plot(ax[0])
+
+sp = {'seasonality': DateTime.minute_of_day, 'names': [str(k) for k in range(0,24)]}
+
+vhour = variable.Variable("Hour", data_label="datahora", partitioner=seasonal.TimeGridPartitioner, npart=24, alpha_cut=.2,
+                          data=train, partitioner_specific=sp)
+
+#vhour.partitioner.plot(ax[1])
+
+
+vavg = variable.Variable("Radiation", data_label="glo_avg", alias='R',
+                         partitioner=Grid.GridPartitioner, npart=35, alpha_cut=.3,
+                         data=train)
+
+#vavg.partitioner.plot(ax[2])
+
+#plt.tight_layout()
+
+#Util.show_and_save_image(fig, 'variables', True)
+
+model = wmvfts.WeightedMVFTS(explanatory_variables=[vmonth,vhour,vavg], target_variable=vavg)
+
+
+_s1 = time.time()
+model.fit(train)
+#model.fit(data, distributed='spark', url='spark://192.168.0.106:7077', num_batches=4)
+_s2 = time.time()
+
+print(_s2-_s1)
+
+Util.persist_obj(model, 'sonda_wmvfts')
+'''
+
+model = Util.load_obj('sonda_wmvfts')
+
+'''
+from pyFTS.benchmarks import Measures
+
+_s1 = time.time()
+print(Measures.get_point_statistics(test, model))
+_s2 = time.time()
+
+print(_s2-_s1)
+'''
+
+print(len(model))
+
+
+#
+
+#model.fit(data, distributed='dispy', nodes=['192.168.0.110'])
+'''
+
+from pyFTS.models.multivariate import common, variable, mvfts, wmvfts, cmvfts, grid
 from pyFTS.models.seasonal import partitioner as seasonal
 from pyFTS.models.seasonal.common import DateTime
 
@@ -22,70 +100,83 @@ dataset = pd.read_csv('/home/petronio/Downloads/kalang.csv', sep=',')
 
 dataset['date'] = pd.to_datetime(dataset["date"], format='%Y-%m-%d %H:%M:%S')
 
-train_uv = dataset['value'].values[:24505]
-test_uv = dataset['value'].values[24505:]
-
 train_mv = dataset.iloc[:24505]
 test_mv = dataset.iloc[24505:]
 
 sp = {'seasonality': DateTime.minute_of_day, 'names': [str(k)+'hs' for k in range(0,24)]}
 
 vhour = variable.Variable("Hour", data_label="date", partitioner=seasonal.TimeGridPartitioner, npart=24,
-                          data=train_mv, partitioner_specific=sp)
+                          data=train_mv, partitioner_specific=sp, data_type=pd.datetime, mask='%Y-%m-%d %H:%M:%S')
 
 vvalue = variable.Variable("Pollution", data_label="value", alias='value',
-                         partitioner=Grid.GridPartitioner, npart=35,
+                         partitioner=Grid.GridPartitioner, npart=35, data_type=np.float64,
                          data=train_mv)
 
-parameters = [
+fs = grid.GridCluster(explanatory_variables=[vhour, vvalue], target_variable=vvalue)
-    {},{},
+#model = wmvfts.WeightedMVFTS(explanatory_variables=[vhour, vvalue], target_variable=vvalue)
-    {'order':2, 'knn': 1},
+model = cmvfts.ClusteredMVFTS(explanatory_variables=[vhour, vvalue], target_variable=vvalue,
-    {'order':2, 'knn': 2},
+                              partitioner=fs)
-    {'order':2, 'knn': 3},
-]
-
-#for ct, method in enumerate([, wmvfts.WeightedMVFTS,
-#                             cmvfts.ClusteredMVFTS,cmvfts.ClusteredMVFTS,cmvfts.ClusteredMVFTS]):
-model = mvfts.MVFTS()
-
-model.append_variable(vhour)
-model.append_variable(vvalue)
-model.target_variable = vvalue
-model.fit(train_mv)
-
-print(model)
-
 
+model.fit(train_mv, distributed='spark', url='spark://192.168.0.106:7077')
+#'''
+#print(model)
 
 '''
-from pyFTS.data import henon
+def fun(x):
-df = henon.get_dataframe(iterations=1000)
+    return (x, x % 2)
 
-from pyFTS.models.multivariate import variable, cmvfts
 
-vx = variable.Variable("x", data_label="x", partitioner=Grid.GridPartitioner, npart=15, data=df)
+def get_fs():
-vy = variable.Variable("y", data_label="y", partitioner=Grid.GridPartitioner, npart=15, data=df)
+    fs_tmp = Simple.SimplePartitioner()
 
-model = cmvfts.ClusteredMVFTS(pre_fuzzyfy=False, knn=3, order=2, fts_method=pwfts.ProbabilisticWeightedFTS)
+    for fset in part.value.keys():
-model.append_variable(vx)
+        fz = part.value[fset]
-model.append_variable(vy)
+        fs_tmp.append(fset, fz.mf, fz.parameters)
-model.target_variable = vx
 
-model.fit(df.iloc[:800])
+    return fs_tmp
 
-test = df.iloc[800:]
+def fuzzyfy(x):
+
+    fs_tmp = get_fs()
+
+    ret = []
+
+    for k in x:
+        ret.append(fs_tmp.fuzzyfy(k, mode='both'))
+
+    return ret
+
+
+def train(fuzzyfied):
+    model = hofts.WeightedHighOrderFTS(partitioner=get_fs(), order=order.value)
+
+    ndata = [k for k in fuzzyfied]
+
+    model.train(ndata)
+
+    return [(k, model.flrgs[k]) for k in model.flrgs]
+
+
+with SparkContext(conf=conf) as sc:
+
+    part = sc.broadcast(fs.sets)
+
+    order = sc.broadcast(2)
+
+    #ret = sc.parallelize(np.arange(0,100)).map(fun)
+
+    #fuzzyfied = sc.parallelize(data).mapPartitions(fuzzyfy)
+
+    flrgs = sc.parallelize(data).mapPartitions(train)
+
+    model = hofts.WeightedHighOrderFTS(partitioner=fs, order=order.value)
+
+    for k in flrgs.collect():
+        model.append_rule(k[1])
+
+    print(model)
+
+'''
 
-forecasts = model.predict(test, type='multivariate')
 
-fig, ax = plt.subplots(nrows=2, ncols=2, figsize=[15,7])
-ax[0][0].plot(test['x'].values)
-ax[0][0].plot(forecasts['x'].values)
-ax[0][1].scatter(test['x'].values,test['y'].values)
-ax[0][1].scatter(forecasts['x'].values,forecasts['y'].values)
-ax[1][0].scatter(test['y'].values,test['x'].values)
-ax[1][0].scatter(forecasts['y'].values,forecasts['x'].values)
-ax[1][1].plot(test['y'].values)
-ax[1][1].plot(forecasts['y'].values)
 
-print(forecasts)
-'''

pyFTS/tests/spark.py

@@ -4,7 +4,7 @@ import time
 
 from pyFTS.data import Enrollments, TAIEX, SONDA
 from pyFTS.partitioners import Grid, Simple
-from pyFTS.models import hofts
+from pyFTS.common import Util
 
 from pyspark import SparkConf
 from pyspark import SparkContext
@@ -14,18 +14,66 @@ import os
 os.environ['PYSPARK_PYTHON'] = '/usr/bin/python3'
 os.environ['PYSPARK_DRIVER_PYTHON'] = '/usr/bin/python3'
 #'''
-data = SONDA.get_data('glo_avg')
+data = SONDA.get_dataframe()
 
-fs = Grid.GridPartitioner(data=data, npart=50)
+data = data[['datahora','glo_avg']]
+
+data = data[~(np.isnan(data['glo_avg']) | np.equal(data['glo_avg'], 0.0))]
+
+train = data.iloc[:1500000]
+test = data.iloc[1500000:]
+
+from pyFTS.models.multivariate import common, variable, wmvfts
+from pyFTS.models.seasonal import partitioner as seasonal
+from pyFTS.models.seasonal.common import DateTime
+from pyFTS.partitioners import Grid
+
+import matplotlib.pyplot as plt
+
+#fig, ax = plt.subplots(nrows=3, ncols=1, figsize=[15,5])
+
+
+sp = {'seasonality': DateTime.day_of_year , 'names': ['Jan','Feb','Mar','Apr','May','Jun','Jul', 'Aug','Sep','Oct','Nov','Dec']}
+
+vmonth = variable.Variable("Month", data_label="datahora", partitioner=seasonal.TimeGridPartitioner, npart=12, alpha_cut=.25,
+                           data=train, partitioner_specific=sp)
+
+#vmonth.partitioner.plot(ax[0])
+
+sp = {'seasonality': DateTime.minute_of_day, 'names': [str(k) for k in range(0,24)]}
+
+vhour = variable.Variable("Hour", data_label="datahora", partitioner=seasonal.TimeGridPartitioner, npart=24, alpha_cut=.2,
+                          data=train, partitioner_specific=sp)
+
+#vhour.partitioner.plot(ax[1])
+
+
+vavg = variable.Variable("Radiation", data_label="glo_avg", alias='R',
+                         partitioner=Grid.GridPartitioner, npart=35, alpha_cut=.3,
+                         data=train)
+
+#vavg.partitioner.plot(ax[2])
+
+#plt.tight_layout()
+
+#Util.show_and_save_image(fig, 'variables', True)
+
+model = wmvfts.WeightedMVFTS(explanatory_variables=[vmonth,vhour,vavg], target_variable=vavg)
 
-model = hofts.WeightedHighOrderFTS(partitioner=fs, order=2)
 
 _s1 = time.time()
-model.fit(data, distributed='spark', url='spark://192.168.0.106:7077')
+#model.fit(train)
+model.fit(data, distributed='spark', url='spark://192.168.0.106:7077', num_batches=5)
 _s2 = time.time()
 
 print(_s2-_s1)
 
+Util.persist_obj(model, 'sonda_wmvfts')
+
+from pyFTS.benchmarks import Measures
+
+#print(Measures.get_point_statistics(test, model))
+
 #model.fit(data, distributed='dispy', nodes=['192.168.0.110'])
 '''
 
@@ -56,7 +104,7 @@ model = cmvfts.ClusteredMVFTS(explanatory_variables=[vhour, vvalue], target_vari
 
 model.fit(train_mv, distributed='spark', url='spark://192.168.0.106:7077')
 #'''
-print(model)
+#print(model)
 
 '''
 def fun(x):