Source code for pyFTS.models.ensemble.multiseasonal

"""
Silva, P. C. L et al. Probabilistic Forecasting with Seasonal Ensemble Fuzzy Time-Series
XIII Brazilian Congress on Computational Intelligence, 2017. Rio de Janeiro, Brazil.
"""

import numpy as np
from pyFTS.common import Util as cUtil
from pyFTS.models.ensemble import ensemble
from pyFTS.models.seasonal import cmsfts
from pyFTS.probabilistic import ProbabilityDistribution
from copy import deepcopy
from joblib import Parallel, delayed
import multiprocessing


def train_individual_model(partitioner, train_data, indexer):
    pttr = str(partitioner.__module__).split('.')[-1]
    diff = "_diff" if partitioner.transformation is not None else ""
    _key = "msfts_" + pttr + str(partitioner.partitions) + diff + "_" + indexer.name

    print(_key)

    model = cmsfts.ContextualMultiSeasonalFTS(_key, indexer=indexer)
    model.append_transformation(partitioner.transformation)
    model.train(train_data, partitioner.sets, order=1)

    cUtil.persist_obj(model, "models/"+_key+".pkl")

    return model


class SeasonalEnsembleFTS(ensemble.EnsembleFTS):
    def __init__(self, name, **kwargs):
        super(SeasonalEnsembleFTS, self).__init__(name="Seasonal Ensemble FTS", **kwargs)
        self.min_order = 1
        self.indexers = []
        self.partitioners = []
        self.is_multivariate = True
        self.has_seasonality = True
        self.has_probability_forecasting = True

    def update_uod(self, data):
        self.original_max = max(self.indexer.get_data(data))
        self.original_min = min(self.indexer.get_data(data))

    def train(self, data, **kwargs):
        self.original_max = max(self.indexer.get_data(data))
        self.original_min = min(self.indexer.get_data(data))

        num_cores = multiprocessing.cpu_count()

        pool = {}
        count = 0
        for ix in self.indexers:
            for pt in self.partitioners:
                pool[count] = {'ix': ix, 'pt': pt}
                count += 1

        results = Parallel(n_jobs=num_cores)(
            delayed(train_individual_model)(deepcopy(pool[m]['pt']), data, deepcopy(pool[m]['ix']))
            for m in pool.keys())

        for tmp in results:
            self.append_model(tmp)

        cUtil.persist_obj(self, "models/"+self.name+".pkl")

    def forecast_distribution(self, data, **kwargs):

        ret = []

        smooth = kwargs.get("smooth", "KDE")
        alpha = kwargs.get("alpha", None)

        uod = self.get_UoD()

        for k in data.index:

            tmp = self.get_models_forecasts(data.ix[k])

            if alpha is None:
                tmp = np.ravel(tmp).tolist()
            else:
                tmp = self.get_distribution_interquantile( np.ravel(tmp).tolist(), alpha)

            name = str(self.indexer.get_index(data.ix[k]))

            dist = ProbabilityDistribution.ProbabilityDistribution(smooth, uod=uod, data=tmp,
                                                                   name=name, **kwargs)

            ret.append(dist)

        return ret