pyFTS/ensemble/ensemble.py

#!/usr/bin/python
# -*- coding: utf8 -*-

import numpy as np
import pandas as pd
import math
from operator import itemgetter
from pyFTS.common import FLR, FuzzySet, SortedCollection
from pyFTS import fts, chen, cheng, hofts, hwang, ismailefendi, sadaei, song, yu, sfts
from pyFTS.benchmarks import arima, quantreg
from pyFTS.common import Transformations
import scipy.stats as st
from pyFTS import tree
from pyFTS.models import msfts
from pyFTS.probabilistic import ProbabilityDistribution, kde

def sampler(data, quantiles):
    ret = []
    for qt in quantiles:
        ret.append(np.nanpercentile(data, q=qt * 100))
    return ret


class EnsembleFTS(fts.FTS):
    def __init__(self, name, **kwargs):
        super(EnsembleFTS, self).__init__(1, "Ensemble FTS")
        self.shortname = "Ensemble FTS " + name
        self.name = "Ensemble FTS"
        self.flrgs = {}
        self.has_point_forecasting = True
        self.has_interval_forecasting = True
        self.has_probability_forecasting = True
        self.is_high_order = True
        self.models = []
        self.parameters = []
        self.alpha = kwargs.get("alpha", 0.05)
        self.order = 1
        self.point_method = kwargs.get('point_method', 'mean')
        self.interval_method = kwargs.get('interval_method', 'quantile')

    def appendModel(self, model):
        self.models.append(model)
        if model.order > self.order:
            self.order = model.order

    def train(self, data, sets, order=1,parameters=None):
        self.original_max = max(data)
        self.original_min = min(data)

    def get_models_forecasts(self,data):
        tmp = []
        for model in self.models:
            if self.is_multivariate or self.has_seasonality:
                forecast = model.forecast(data)
            else:
                sample = data[-model.order:]
                forecast = model.forecast(sample)
                if isinstance(forecast, (list,np.ndarray)) and len(forecast) > 0:
                    forecast = int(forecast[-1])
                elif isinstance(forecast, (list,np.ndarray)) and len(forecast) == 0:
                    forecast = np.nan
            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.percentile(forecasts, alpha*100)

        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 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.order : k]
            tmp = self.get_models_forecasts(sample)
            point = self.get_point(tmp)
            ret.append(point)

        return ret

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

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

        if 'alpha' in kwargs:
            self.alpha = kwargs.get('alpha',0.05)

        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 forecastAheadInterval(self, data, steps, **kwargs):

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

        if 'alpha' in kwargs:
            self.alpha = kwargs.get('alpha', self.alpha)

        ret = []

        samples = [[k] for k in data[-self.order:]]

        for k in np.arange(self.order, steps + self.order):
            forecasts = []
            lags = {}
            for i in np.arange(0, self.order): lags[i] = samples[k - self.order + i]

            # Build the tree with all possible paths

            root = tree.FLRGTreeNode(None)

            tree.buildTreeWithoutOrder(root, lags, 0)

            for p in root.paths():
                path = list(reversed(list(filter(None.__ne__, p))))

                forecasts.extend(self.get_models_forecasts(path))

            samples.append(sampler(forecasts, np.arange(0.1, 1, 0.2)))
            interval = self.get_interval(forecasts)

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

            ret.append(interval)

        return ret

    def empty_grid(self, resolution):
        return self.get_empty_grid(-(self.original_max*2), self.original_max*2, resolution)

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

        percentile_size = (self.original_max - self.original_min) / 100

        resolution = kwargs.get('resolution', percentile_size)

        grid = self.empty_grid(resolution)

        index = SortedCollection.SortedCollection(iterable=grid.keys())

        ret = []

        samples = [[k] for k in data[-self.order:]]

        for k in np.arange(self.order, steps + self.order):
            forecasts = []
            lags = {}
            for i in np.arange(0, self.order): lags[i] = samples[k - self.order + i]

            # Build the tree with all possible paths

            root = tree.FLRGTreeNode(None)

            tree.buildTreeWithoutOrder(root, lags, 0)

            for p in root.paths():
                path = list(reversed(list(filter(None.__ne__, p))))

                forecasts.extend(self.get_models_forecasts(path))

            samples.append(sampler(forecasts, np.arange(0.1, 1, 0.1)))

            grid = self.gridCountPoint(grid, resolution, index, forecasts)

            tmp = np.array([grid[i] for i in sorted(grid)])

            ret.append(tmp / sum(tmp))

        grid = self.empty_grid(resolution)
        df = pd.DataFrame(ret, columns=sorted(grid))
        return df


class AllMethodEnsembleFTS(EnsembleFTS):
    def __init__(self, name, **kwargs):
        super(AllMethodEnsembleFTS, self).__init__(name="Ensemble FTS", **kwargs)
        self.min_order = 3

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

    def train(self, data, sets, order=1, parameters=None):
        self.original_max = max(data)
        self.original_min = min(data)

        fo_methods = [song.ConventionalFTS, chen.ConventionalFTS, yu.WeightedFTS, cheng.TrendWeightedFTS,
                      sadaei.ExponentialyWeightedFTS,  ismailefendi.ImprovedWeightedFTS, sfts.SeasonalFTS]

        ho_methods = [hofts.HighOrderFTS, hwang.HighOrderFTS]

        for method in fo_methods:
            model = method("")
            self.set_transformations(model)
            model.train(data, sets)
            self.appendModel(model)

        for method in ho_methods:
            for o in np.arange(1, order+1):
                model = method("")
                if model.min_order >= o:
                    self.set_transformations(model)
                    model.train(data, sets, order=o)
                    self.appendModel(model)