# pyFTS.distributed.spark — pyFTS 1.4 documentation

# Source code for pyFTS.distributed.spark

    import numpy as np
    import pandas as pd

    from pyFTS.data import Enrollments, TAIEX
    from pyFTS.partitioners import Grid, Simple
    from pyFTS.models.multivariate import partitioner as mv_partitioner
    from pyFTS.models import hofts

    from pyspark import SparkConf
    from pyspark import SparkContext

    import os
    # make sure pyspark tells workers to use python3 not 2 if both are installed
    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=[]):
        """

        :param part:
        :return:
        """
        if type=='common':
            fs_tmp = Simple.SimplePartitioner()

        for fset in shared_partitioner.value.keys():
            fz = shared_partitioner.value[fset]
            if type=='common':
                fs_tmp.append_complex(fz)
            elif type == 'multivariate':
                fs_tmp.append(fz)

        return fs_tmp

    def get_clustered_partitioner(explanatory_variables, target_variable, **parameters):
        from pyFTS.models.multivariate.common import MultivariateFuzzySet
        fs_tmp = mv_partitioner.MultivariatePartitioner(explanatory_variables=explanatory_variables,
                                               target_variable=target_variable)
        for tmp in parameters['partitioner_names'].value:
            fs = MultivariateFuzzySet(target_variable=target_variable)
            for var, fset in parameters['partitioner_{}'.format(tmp)].value:
                fs.append_set(var, fset)
            fs_tmp.append(fs)

        fs_tmp.build_index()

        return fs_tmp

    def get_variables(**parameters):
        explanatory_variables = []
        target_variable = None
        for name in parameters['variables'].value:
            from pyFTS.models.multivariate import common, variable
            var = variable.Variable(name,
                                    type=parameters['{}_type'.format(name)].value,
                                    data_label=parameters['{}_label'.format(name)].value,
                                    alpha_cut=parameters['{}_alpha'.format(name)].value,
                                    #data_type=parameters['{}_data_type'.format(name)].value,
                                    #mask=parameters['{}_mask'.format(name)].value,
                                    )
            var.partitioner = get_partitioner(parameters['{}_partitioner'.format(name)])
            var.partitioner.type = parameters['{}_partitioner_type'.format(name)].value

            explanatory_variables.append(var)

            if var.name == parameters['target'].value:
                target_variable = var

        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):
        """

        :param data:
        :return:
        """

        model = create_univariate_model(**parameters)

        ndata = [k for k in data]

        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 create_multivariate_model(**parameters):
        explanatory_variables, target_variable = get_variables(**parameters)
        #vars = [(v.name, v.name) for v in explanatory_variables]

        #return [('vars', vars), ('target',[target_variable.name])]

        if parameters['type'].value == 'clustered':
            fs = get_clustered_partitioner(explanatory_variables, target_variable, **parameters)
            model = parameters['method'].value(explanatory_variables=explanatory_variables,
                                               target_variable=target_variable,
                                               partitioner=fs,
                                               order=parameters['order'].value,
                                               alpha_cut=parameters['alpha_cut'].value,
                                               lags=parameters['lags'].value)
        else:

            if parameters['order'].value > 1:
                model = parameters['method'].value(explanatory_variables=explanatory_variables,
                                                   target_variable=target_variable,
                                                   order=parameters['order'].value,
                                                   alpha_cut=parameters['alpha_cut'].value,
                                                   lags=parameters['lags'].value)
            else:
                model = parameters['method'].value(explanatory_variables=explanatory_variables,
                                                   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)

        model.train(ndata)

        if parameters['type'].value == 'clustered':
            counts = [(fset, count) for fset,count in model.partitioner.count.items()]
            flrgs = [(k, v) for k,v in model.flrgs.items()]

            return [('counts', counts), ('flrgs', flrgs)]
        else:
            return [(k, v) for k,v in model.flrgs.items()]

    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):
        """

        :param model:
        :param data:
        :param url:
        :param app:
        :return:
        """

        num_batches = kwargs.get("num_batches", 4)

        conf = create_spark_conf(**kwargs)

        with SparkContext(conf=conf) as context:

            nodes = context.defaultParallelism

            parameters = share_parameters(model, context, data)

            if not model.is_multivariate:
                func = lambda x: slave_train_univariate(x, **parameters)

                flrgs = context.parallelize(data).repartition(nodes*num_batches).mapPartitions(func)

                for k in flrgs.collect():
                    model.append_rule(k[1])

            else:

                data = data.to_dict(orient='records')

                func = lambda x: slave_train_multivariate(x, **parameters)

                flrgs = context.parallelize(data).mapPartitions(func)

                for k in flrgs.collect():
                    if parameters['type'].value == 'clustered':
                        if k[0] == 'counts':
                            for fset, count in k[1]:
                                model.partitioner.count[fset] = count
                        elif k[0] == 'flrgs':
                            model.append_rule(k[1])
                    else:
                        model.append_rule(k[1])

        return model

    def distributed_predict(data, model, **kwargs):
        """

        :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