distributed.spark optimizations and bugfixes
This commit is contained in:
Petrônio Cândido
parent
6373afe136
commit
e91d44afd1
@ -8,7 +8,6 @@ import dill
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import numpy as np
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def plot_rules(model, size=[5, 5], axis=None, rules_by_axis=None, columns=1):
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if axis is None and rules_by_axis is None:
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rows = 1
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@ -126,8 +125,8 @@ def show_and_save_image(fig, file, flag, lgd=None):
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:param flag: if True the image will be saved
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:param lgd: legend
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"""
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plt.show()
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if flag:
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plt.show()
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if lgd is not None:
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fig.savefig(file, additional_artists=lgd,bbox_inches='tight') #bbox_extra_artists=(lgd,), )
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else:
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@ -163,9 +163,7 @@ class FTS(object):
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elif distributed == 'spark':
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from pyFTS.distributed import spark
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nodes = kwargs.get("nodes", 'spark://192.168.0.110:7077')
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app = kwargs.get("app", 'pyFTS')
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ret = spark.distributed_predict(data=ndata, model=self, url=nodes, app=app)
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ret = spark.distributed_predict(data=ndata, model=self, **kwargs)
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if not self.is_multivariate:
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@ -16,7 +16,21 @@ SPARK_ADDR = 'spark://192.168.0.110:7077'
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os.environ['PYSPARK_PYTHON'] = '/usr/bin/python3'
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os.environ['PYSPARK_DRIVER_PYTHON'] = '/usr/bin/python3'
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def create_spark_conf(**kwargs):
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spark_executor_memory = kwargs.get("spark_executor_memory", "2g")
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spark_driver_memory = kwargs.get("spark_driver_memory", "2g")
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url = kwargs.get("url", SPARK_ADDR)
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app = kwargs.get("app", 'pyFTS')
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conf = SparkConf()
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conf.setMaster(url)
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conf.setAppName(app)
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conf.set("spark.executor.memory", spark_executor_memory)
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conf.set("spark.driver.memory", spark_driver_memory)
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conf.set("spark.memory.offHeap.enabled",True)
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conf.set("spark.memory.offHeap.size","16g")
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return conf
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def get_partitioner(shared_partitioner, type='common', variables=[]):
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"""
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@ -74,6 +88,16 @@ def get_variables(**parameters):
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return (explanatory_variables, target_variable)
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def create_univariate_model(**parameters):
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if parameters['order'].value > 1:
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model = parameters['method'].value(partitioner=get_partitioner(parameters['partitioner']),
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order=parameters['order'].value, alpha_cut=parameters['alpha_cut'].value,
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lags=parameters['lags'].value)
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else:
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model = parameters['method'].value(partitioner=get_partitioner(parameters['partitioner']),
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alpha_cut=parameters['alpha_cut'].value)
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return model
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def slave_train_univariate(data, **parameters):
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"""
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@ -82,27 +106,32 @@ def slave_train_univariate(data, **parameters):
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:return:
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"""
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if parameters['type'].value == 'common':
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model = create_univariate_model(**parameters)
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if parameters['order'].value > 1:
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model = parameters['method'].value(partitioner=get_partitioner(parameters['partitioner']),
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order=parameters['order'].value, alpha_cut=parameters['alpha_cut'].value,
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lags=parameters['lags'].value)
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else:
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model = parameters['method'].value(partitioner=get_partitioner(parameters['partitioner']),
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alpha_cut=parameters['alpha_cut'].value)
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ndata = [k for k in data]
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else:
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pass
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ndata = [k for k in data]
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model.train(ndata)
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return [(k, model.flrgs[k]) for k in model.flrgs.keys()]
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def slave_train_multivariate(data, **parameters):
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def slave_forecast_univariate(data, **parameters):
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"""
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:param data:
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:return:
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"""
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model = create_univariate_model(**parameters)
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ndata = [k for k in data]
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forecasts = model.predict(ndata)
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return [(k, k) for k in forecasts]
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def create_multivariate_model(**parameters):
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explanatory_variables, target_variable = get_variables(**parameters)
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#vars = [(v.name, v.name) for v in explanatory_variables]
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@ -129,8 +158,13 @@ def slave_train_multivariate(data, **parameters):
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target_variable=target_variable,
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alpha_cut=parameters['alpha_cut'].value)
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return model
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def slave_train_multivariate(data, **parameters):
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model = create_multivariate_model(**parameters)
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rows = [k for k in data]
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ndata = pd.DataFrame.from_records(rows, columns=parameters['columns'].value)
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@ -145,7 +179,68 @@ def slave_train_multivariate(data, **parameters):
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return [(k, v) for k,v in model.flrgs.items()]
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def distributed_train(model, data, url=SPARK_ADDR, app='pyFTS'):
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def slave_forecast_multivariate(data, **parameters):
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model = create_multivariate_model(**parameters)
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rows = [k for k in data]
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ndata = pd.DataFrame.from_records(rows, columns=parameters['columns'].value)
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forecasts = model.predict(ndata)
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return [(k, k) for k in forecasts]
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def share_parameters(model, context, data):
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parameters = {}
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if not model.is_multivariate:
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parameters['type'] = context.broadcast('common')
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parameters['partitioner'] = context.broadcast(model.partitioner.sets)
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parameters['alpha_cut'] = context.broadcast(model.alpha_cut)
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parameters['order'] = context.broadcast(model.order)
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parameters['method'] = context.broadcast(type(model))
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parameters['lags'] = context.broadcast(model.lags)
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parameters['max_lag'] = context.broadcast(model.max_lag)
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else:
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if model.is_clustered:
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parameters['type'] = context.broadcast('clustered')
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names = []
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for name, fset in model.partitioner.sets.items():
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names.append(name)
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parameters['partitioner_{}'.format(name)] = context.broadcast([(k,v) for k,v in fset.sets.items()])
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parameters['partitioner_names'] = context.broadcast(names)
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else:
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parameters['type'] = context.broadcast('multivariate')
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names = []
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for var in model.explanatory_variables:
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#if var.data_type is None:
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# raise Exception("It is mandatory to inform the data_type parameter for each variable when the training is distributed! ")
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names.append(var.name)
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parameters['{}_type'.format(var.name)] = context.broadcast(var.type)
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#parameters['{}_data_type'.format(var.name)] = context.broadcast(var.data_type)
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#parameters['{}_mask'.format(var.name)] = context.broadcast(var.mask)
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parameters['{}_label'.format(var.name)] = context.broadcast(var.data_label)
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parameters['{}_alpha'.format(var.name)] = context.broadcast(var.alpha_cut)
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parameters['{}_partitioner'.format(var.name)] = context.broadcast(var.partitioner.sets)
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parameters['{}_partitioner_type'.format(var.name)] = context.broadcast(var.partitioner.type)
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parameters['variables'] = context.broadcast(names)
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parameters['target'] = context.broadcast(model.target_variable.name)
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parameters['columns'] = context.broadcast(data.columns.values)
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parameters['alpha_cut'] = context.broadcast(model.alpha_cut)
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parameters['order'] = context.broadcast(model.order)
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parameters['method'] = context.broadcast(type(model))
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parameters['lags'] = context.broadcast(model.lags)
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parameters['max_lag'] = context.broadcast(model.max_lag)
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return parameters
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def distributed_train(model, data, **kwargs):
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"""
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@ -156,84 +251,33 @@ def distributed_train(model, data, url=SPARK_ADDR, app='pyFTS'):
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:return:
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"""
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conf = SparkConf()
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conf.setMaster(url)
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conf.setAppName(app)
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conf.set("spark.executor.memory", "2g")
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conf.set("spark.driver.memory", "2g")
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conf.set("spark.memory.offHeap.enabled",True)
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conf.set("spark.memory.offHeap.size","16g")
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parameters = {}
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num_batches = kwargs.get("num_batches", 4)
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conf = create_spark_conf(**kwargs)
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with SparkContext(conf=conf) as context:
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nodes = context.defaultParallelism
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if not model.is_multivariate:
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parameters['type'] = context.broadcast('common')
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parameters['partitioner'] = context.broadcast(model.partitioner.sets)
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parameters['alpha_cut'] = context.broadcast(model.alpha_cut)
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parameters['order'] = context.broadcast(model.order)
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parameters['method'] = context.broadcast(type(model))
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parameters['lags'] = context.broadcast(model.lags)
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parameters['max_lag'] = context.broadcast(model.max_lag)
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parameters = share_parameters(model, context, data)
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if not model.is_multivariate:
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func = lambda x: slave_train_univariate(x, **parameters)
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flrgs = context.parallelize(data).repartition(nodes*4).mapPartitions(func)
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flrgs = context.parallelize(data).repartition(nodes*num_batches).mapPartitions(func)
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for k in flrgs.collect():
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model.append_rule(k[1])
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return model
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else:
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if model.is_clustered:
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parameters['type'] = context.broadcast('clustered')
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names = []
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for name, fset in model.partitioner.sets.items():
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names.append(name)
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parameters['partitioner_{}'.format(name)] = context.broadcast([(k,v) for k,v in fset.sets.items()])
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parameters['partitioner_names'] = context.broadcast(names)
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else:
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parameters['type'] = context.broadcast('multivariate')
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names = []
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for var in model.explanatory_variables:
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#if var.data_type is None:
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# raise Exception("It is mandatory to inform the data_type parameter for each variable when the training is distributed! ")
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names.append(var.name)
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parameters['{}_type'.format(var.name)] = context.broadcast(var.type)
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#parameters['{}_data_type'.format(var.name)] = context.broadcast(var.data_type)
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#parameters['{}_mask'.format(var.name)] = context.broadcast(var.mask)
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parameters['{}_label'.format(var.name)] = context.broadcast(var.data_label)
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parameters['{}_alpha'.format(var.name)] = context.broadcast(var.alpha_cut)
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parameters['{}_partitioner'.format(var.name)] = context.broadcast(var.partitioner.sets)
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parameters['{}_partitioner_type'.format(var.name)] = context.broadcast(var.partitioner.type)
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parameters['variables'] = context.broadcast(names)
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parameters['target'] = context.broadcast(model.target_variable.name)
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parameters['columns'] = context.broadcast(data.columns.values)
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data = data.to_dict(orient='records')
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parameters['alpha_cut'] = context.broadcast(model.alpha_cut)
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parameters['order'] = context.broadcast(model.order)
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parameters['method'] = context.broadcast(type(model))
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parameters['lags'] = context.broadcast(model.lags)
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parameters['max_lag'] = context.broadcast(model.max_lag)
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func = lambda x: slave_train_multivariate(x, **parameters)
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flrgs = context.parallelize(data).mapPartitions(func)
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for k in flrgs.collect():
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print(k)
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#for g in k:
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# print(g)
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#return
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if parameters['type'].value == 'clustered':
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if k[0] == 'counts':
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for fset, count in k[1]:
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@ -243,8 +287,46 @@ def distributed_train(model, data, url=SPARK_ADDR, app='pyFTS'):
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else:
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model.append_rule(k[1])
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return model
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return model
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def distributed_predict(data, model, url=SPARK_ADDR, app='pyFTS'):
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return None
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def distributed_predict(data, model, **kwargs):
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"""
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:param model:
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:param data:
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:param url:
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:param app:
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:return:
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"""
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num_batches = kwargs.get("num_batches", 4)
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conf = create_spark_conf(**kwargs)
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ret = []
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with SparkContext(conf=conf) as context:
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nodes = context.defaultParallelism
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parameters = share_parameters(model, context)
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if not model.is_multivariate:
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func = lambda x: slave_forecast_univariate(x, **parameters)
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forecasts = context.parallelize(data).repartition(nodes * num_batches).mapPartitions(func)
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else:
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data = data.to_dict(orient='records')
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func = lambda x: slave_forecast_multivariate(x, **parameters)
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forecasts = context.parallelize(data).repartition(nodes * num_batches).mapPartitions(func)
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for k in forecasts.collect():
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ret.extend(k)
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return ret
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@ -96,7 +96,7 @@ t1 = time()
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Evolutionary.execute('SONDA', dataset,
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ngen=20, mgen=5, npop=15, pcruz=.5, pmut=.3,
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window_size=35000, train_rate=.6, increment_rate=1,
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collect_statistics=True, experiments=1)
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collect_statistics=True, experiments=5)
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#distributed='dispy', nodes=['192.168.0.110','192.168.0.106','192.168.0.107'])
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t2 = time()
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@ -1,20 +1,98 @@
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import numpy as np
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import pandas as pd
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import matplotlib.pylab as plt
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from pyFTS.data import TAIEX, Malaysia
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from pyFTS.common import Transformations
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import time
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from pyFTS.benchmarks import Measures
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from pyFTS.partitioners import Grid, Util as pUtil
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from pyFTS.common import Transformations, Util
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from pyFTS.models import pwfts
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from pyFTS.models.multivariate import common, variable, mvfts, wmvfts
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from pyFTS.data import Enrollments, TAIEX, SONDA
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from pyFTS.partitioners import Grid, Simple
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from pyFTS.common import Util
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from pyspark import SparkConf
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from pyspark import SparkContext
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import os
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# make sure pyspark tells workers to use python3 not 2 if both are installed
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os.environ['PYSPARK_PYTHON'] = '/usr/bin/python3'
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os.environ['PYSPARK_DRIVER_PYTHON'] = '/usr/bin/python3'
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#'''
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data = SONDA.get_dataframe()
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data = data[['datahora','glo_avg']]
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data = data[~(np.isnan(data['glo_avg']) | np.equal(data['glo_avg'], 0.0))]
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train = data.iloc[:1500000]
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test = data.iloc[1500000:]
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from pyFTS.models.multivariate import common, variable, wmvfts
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from pyFTS.models.seasonal import partitioner as seasonal
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from pyFTS.models.seasonal.common import DateTime
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from pyFTS.partitioners import Grid
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bc = Transformations.BoxCox(0)
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tdiff = Transformations.Differential(1)
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import matplotlib.pyplot as plt
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from pyFTS.models.multivariate import common, variable, mvfts, cmvfts
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'''
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#fig, ax = plt.subplots(nrows=3, ncols=1, figsize=[15,5])
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sp = {'seasonality': DateTime.day_of_year , 'names': ['Jan','Feb','Mar','Apr','May','Jun','Jul', 'Aug','Sep','Oct','Nov','Dec']}
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vmonth = variable.Variable("Month", data_label="datahora", partitioner=seasonal.TimeGridPartitioner, npart=12, alpha_cut=.25,
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data=train, partitioner_specific=sp)
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#vmonth.partitioner.plot(ax[0])
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sp = {'seasonality': DateTime.minute_of_day, 'names': [str(k) for k in range(0,24)]}
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vhour = variable.Variable("Hour", data_label="datahora", partitioner=seasonal.TimeGridPartitioner, npart=24, alpha_cut=.2,
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data=train, partitioner_specific=sp)
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#vhour.partitioner.plot(ax[1])
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vavg = variable.Variable("Radiation", data_label="glo_avg", alias='R',
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partitioner=Grid.GridPartitioner, npart=35, alpha_cut=.3,
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data=train)
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#vavg.partitioner.plot(ax[2])
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#plt.tight_layout()
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#Util.show_and_save_image(fig, 'variables', True)
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model = wmvfts.WeightedMVFTS(explanatory_variables=[vmonth,vhour,vavg], target_variable=vavg)
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_s1 = time.time()
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model.fit(train)
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#model.fit(data, distributed='spark', url='spark://192.168.0.106:7077', num_batches=4)
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_s2 = time.time()
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print(_s2-_s1)
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Util.persist_obj(model, 'sonda_wmvfts')
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'''
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model = Util.load_obj('sonda_wmvfts')
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'''
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from pyFTS.benchmarks import Measures
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_s1 = time.time()
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print(Measures.get_point_statistics(test, model))
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_s2 = time.time()
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print(_s2-_s1)
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'''
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print(len(model))
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#
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#model.fit(data, distributed='dispy', nodes=['192.168.0.110'])
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'''
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from pyFTS.models.multivariate import common, variable, mvfts, wmvfts, cmvfts, grid
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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 = [
|
||||
{},{},
|
||||
{'order':2, 'knn': 1},
|
||||
{'order':2, 'knn': 2},
|
||||
{'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)
|
||||
|
||||
fs = grid.GridCluster(explanatory_variables=[vhour, vvalue], target_variable=vvalue)
|
||||
#model = wmvfts.WeightedMVFTS(explanatory_variables=[vhour, vvalue], target_variable=vvalue)
|
||||
model = cmvfts.ClusteredMVFTS(explanatory_variables=[vhour, vvalue], target_variable=vvalue,
|
||||
partitioner=fs)
|
||||
|
||||
model.fit(train_mv, distributed='spark', url='spark://192.168.0.106:7077')
|
||||
#'''
|
||||
#print(model)
|
||||
|
||||
'''
|
||||
from pyFTS.data import henon
|
||||
df = henon.get_dataframe(iterations=1000)
|
||||
def fun(x):
|
||||
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)
|
||||
vy = variable.Variable("y", data_label="y", partitioner=Grid.GridPartitioner, npart=15, data=df)
|
||||
def get_fs():
|
||||
fs_tmp = Simple.SimplePartitioner()
|
||||
|
||||
model = cmvfts.ClusteredMVFTS(pre_fuzzyfy=False, knn=3, order=2, fts_method=pwfts.ProbabilisticWeightedFTS)
|
||||
model.append_variable(vx)
|
||||
model.append_variable(vy)
|
||||
model.target_variable = vx
|
||||
for fset in part.value.keys():
|
||||
fz = part.value[fset]
|
||||
fs_tmp.append(fset, fz.mf, fz.parameters)
|
||||
|
||||
model.fit(df.iloc[:800])
|
||||
return fs_tmp
|
||||
|
||||
test = df.iloc[800:]
|
||||
def fuzzyfy(x):
|
||||
|
||||
forecasts = model.predict(test, type='multivariate')
|
||||
fs_tmp = get_fs()
|
||||
|
||||
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)
|
||||
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)
|
||||
|
||||
print(forecasts)
|
||||
'''
|
||||
|
||||
|
||||
|
||||
|
||||
@ -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):
|
||||
|
||||
Loading…
Reference in New Issue
Block a user