#!/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 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: 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)