# Source code for pyFTS.models.ensemble.ensemble

    """
    EnsembleFTS wraps several FTS methods to ensemble their forecasts, providing point,
    interval and probabilistic forecasting.

    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
    import pandas as pd
    from pyFTS.common import SortedCollection, fts, tree
    from pyFTS.models import chen, cheng, hofts, hwang, ismailefendi, sadaei, song, yu
    from pyFTS.probabilistic import ProbabilityDistribution
    from pyFTS.partitioners import Grid
    import scipy.stats as st
    from itertools import product

    def sampler(data, quantiles, bounds=False):
        ret = []
        for qt in quantiles:
            ret.append(np.nanpercentile(data, q=qt * 100))
        if bounds:
            ret.insert(0, min(data))
            ret.append(max(data))
        return ret

    class EnsembleFTS(fts.FTS):
        """
        Ensemble FTS
        """
        def __init__(self, **kwargs):
            super(EnsembleFTS, self).__init__(**kwargs)
            self.shortname = "EnsembleFTS"
            self.name = "Ensemble FTS"
            self.flrgs = {}
            self.is_wrapper = True
            self.has_point_forecasting = True
            self.has_interval_forecasting = True
            self.has_probability_forecasting = True
            self.is_high_order = True
            self.models = []
            """A list of FTS models, the ensemble components"""
            self.parameters = []
            """A list with the parameters for each component model"""
            self.alpha = kwargs.get("alpha", 0.05)
            """The quantiles """
            self.point_method = kwargs.get('point_method', 'mean')
            """The method used to mix the several model's forecasts into a unique point forecast. Options: mean, median, quantile, exponential"""
            self.interval_method = kwargs.get('interval_method', 'quantile')
            """The method used to mix the several model's forecasts into a interval forecast. Options: quantile, extremum, normal"""

        def append_model(self, model):
            """
            Append a new trained model to the ensemble

            :param model: FTS model

            """
            self.models.append(model)
            if model.order > self.order:
                self.order = model.order

            if model.is_multivariate:
                self.is_multivariate = True

            if model.has_seasonality:
                self.has_seasonality = True

            if model.original_min < self.original_min:
                self.original_min = model.original_min
            elif model.original_max > self.original_max:
                self.original_max = model.original_max

        def get_UoD(self):
            return [self.original_min, self.original_max]

        def train(self, data, **kwargs):
            pass

        def get_models_forecasts(self,data):
            tmp = []
            for model in self.models:
                if model.is_multivariate or model.has_seasonality:
                    forecast = model.forecast(data)
                else:

                    if isinstance(data, pd.DataFrame) and self.indexer is not None:
                        data = self.indexer.get_data(data)

                    sample = data[-model.order:]
                    forecast = model.predict(sample)
                    if isinstance(forecast, (list,np.ndarray)) and len(forecast) > 0:
                        forecast = forecast[-1]
                    elif isinstance(forecast, (list,np.ndarray)) and len(forecast) == 0:
                        forecast = np.nan
                if isinstance(forecast, list):
                    tmp.extend(forecast)
                else:
                    tmp.append(forecast)
            return tmp

        def get_point(self,forecasts, **kwargs):
            if self.point_method == 'mean':
                ret = np.nanmean(forecasts)
            elif self.point_method == 'median':
                ret = np.nanpercentile(forecasts, 50)
            elif self.point_method == 'quantile':
                alpha = kwargs.get("alpha",0.05)
                ret = np.nanpercentile(forecasts, alpha*100)
            elif self.point_method == 'exponential':
                l = len(self.models)
                if l == 1:
                    return forecasts[0]
                w = np.array([np.exp(-(l - k)) for k in range(l)])
                w = w / np.nansum(w)
                ret = np.nansum([w[k] * forecasts[k] for k in range(l)])

            return ret

        def get_interval(self, forecasts):
            ret = []
            if self.interval_method == 'extremum':
                ret.append([min(forecasts), max(forecasts)])
            elif self.interval_method == 'quantile':
                qt_lo = np.nanpercentile(forecasts, q=self.alpha * 100)
                qt_up = np.nanpercentile(forecasts, q=(1-self.alpha) * 100)
                ret.append([qt_lo, qt_up])
            elif self.interval_method == 'normal':
                mu = np.nanmean(forecasts)
                sigma = np.sqrt(np.nanvar(forecasts))
                ret.append(mu + st.norm.ppf(self.alpha) * sigma)
                ret.append(mu + st.norm.ppf(1 - self.alpha) * sigma)

            return ret

        def get_distribution_interquantile(self,forecasts, alpha):
            size = len(forecasts)
            qt_lower = int(np.ceil(size * alpha)) - 1
            qt_upper = int(np.ceil(size * (1- alpha))) - 1

            ret = sorted(forecasts)[qt_lower : qt_upper]

            return ret

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

            if "method" in kwargs:
                self.point_method = kwargs.get('method','mean')

            l = len(data)
            ret = []

            for k in np.arange(self.order, l+1):
                sample = data[k - self.max_lag : k]
                tmp = self.get_models_forecasts(sample)
                point = self.get_point(tmp)
                ret.append(point)

            return ret

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

            if "method" in kwargs:
                self.interval_method = kwargs.get('method','quantile')

            self.alpha = kwargs.get('alpha', self.alpha)

            l = len(data)

            ret = []

            for k in np.arange(self.order, l+1):
                sample = data[k - self.order : k]
                tmp = self.get_models_forecasts(sample)
                interval = self.get_interval(tmp)
                if len(interval) == 1:
                    interval = interval[-1]
                ret.append(interval)

            return ret

        def forecast_ahead_interval(self, data, steps, **kwargs):

            if 'method' in kwargs:
                self.interval_method = kwargs.get('method','quantile')

            self.alpha = kwargs.get('alpha', self.alpha)

            ret = []

            start = kwargs.get('start_at', self.order)

            sample = [[k] for k in data[start: start+self.order]]

            for k in np.arange(self.order, steps + self.order):
                forecasts = []

                lags = []
                for i in np.arange(0, self.order):
                    lags.append(sample[i - self.order])

                # Trace the possible paths
                for path in product(*lags):
                    forecasts.extend(self.get_models_forecasts(path))

                sample.append(sampler(forecasts, np.arange(.1, 1, 0.1), bounds=True))

                interval = self.get_interval(forecasts)

                if len(interval) == 1:
                    interval = interval[0]

                ret.append(interval)

            return ret[-steps:]

        def forecast_distribution(self, data, **kwargs):
            ret = []

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

            uod = self.get_UoD()

            for k in np.arange(self.order, len(data)):

                sample = data[k-self.order : k]

                forecasts = self.get_models_forecasts(sample)

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

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

                ret.append(dist)

            return ret

        def forecast_ahead_distribution(self, data, steps, **kwargs):
            if 'method' in kwargs:
                self.point_method = kwargs.get('method','mean')

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

            ret = []

            start = kwargs.get('start_at', self.order)

            uod = self.get_UoD()

            sample = [[k] for k in data[start: start+self.order]]

            for k in np.arange(self.order, steps+self.order):
                forecasts = []

                lags = []
                for i in np.arange(0, self.order):
                    lags.append(sample[i - self.order])

                # Trace the possible paths
                for path in product(*lags):
                    forecasts.extend(self.get_models_forecasts(path))

                sample.append(sampler(forecasts, np.arange(.1, 1, 0.1), bounds=True))

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

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

                ret.append(dist)

            return ret[-steps:]

    class SimpleEnsembleFTS(EnsembleFTS):
        '''
        An homogeneous FTS method ensemble with variations on partitionings and orders.
        '''
        def __init__(self, **kwargs):
            super(SimpleEnsembleFTS, self).__init__(**kwargs)
            self.method = kwargs.get('fts_method', hofts.WeightedHighOrderFTS)
            """FTS method class that will be used on internal models"""
            self.partitioner_method = kwargs.get('partitioner_method', Grid.GridPartitioner)
            """UoD partitioner class that will be used on internal methods"""
            self.partitions = kwargs.get('partitions', np.arange(15,35,10))
            """Possible variations of number of partitions on internal models"""
            self.orders = kwargs.get('orders', [1,2,3])
            """Possible variations of order on internal models"""
            self.uod_clip = False

            self.shortname = kwargs.get('name', 'EnsembleFTS-' + str(self.method.__module__).split('.')[-1])

        def train(self, data, **kwargs):
            for k in self.partitions:
                fs = self.partitioner_method(data=data, npart=k)

                for order in self.orders:
                    tmp = self.method(partitioner=fs, order=order)

                    tmp.fit(data)

                    self.append_model(tmp)

    class AllMethodEnsembleFTS(EnsembleFTS):
        """
        Creates an EnsembleFTS with all point forecast methods, sharing the same partitioner
        """
        def __init__(self, **kwargs):
            super(AllMethodEnsembleFTS, self).__init__(**kwargs)
            self.min_order = 3
            self.shortname ="Ensemble FTS"

        def set_transformations(self, model):
            for t in self.transformations:
                model.append_transformation(t)

        def train(self, data, **kwargs):
            fo_methods = [song.ConventionalFTS, chen.ConventionalFTS, yu.WeightedFTS, cheng.TrendWeightedFTS,
                          sadaei.ExponentialyWeightedFTS, ismailefendi.ImprovedWeightedFTS]

            ho_methods = [hofts.HighOrderFTS, hwang.HighOrderFTS]

            for method in fo_methods:
                model = method(partitioner=self.partitioner)
                self.set_transformations(model)
                model.fit(data, **kwargs)
                self.append_model(model)

            for method in ho_methods:
                for o in np.arange(1, self.order+1):
                    model = method(partitioner=self.partitioner)
                    if model.min_order >= o:
                        model.order = o
                        self.set_transformations(model)
                        model.fit(data, **kwargs)
                        self.append_model(model)