#!/usr/bin/python # -*- coding: utf8 -*- """Benchmarks to FTS methods""" import numpy as np import pandas as pd import time import datetime import matplotlib as plt import matplotlib.colors as pltcolors import matplotlib.cm as cmx import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D # from sklearn.cross_validation import KFold from pyFTS.partitioners import partitioner, Grid, Huarng, Entropy, FCM from pyFTS.benchmarks import Measures, naive, arima, ResidualAnalysis, ProbabilityDistribution, Util, quantreg from pyFTS.common import Membership, FuzzySet, FLR, Transformations, Util from pyFTS import fts, song, chen, yu, ismailefendi, sadaei, hofts, hwang, pwfts, ifts, cheng, ensemble, hwang from copy import deepcopy colors = ['grey', 'rosybrown', 'maroon', 'red','orange', 'yellow', 'olive', 'green', 'cyan', 'blue', 'darkblue', 'purple', 'darkviolet'] ncol = len(colors) styles = ['-','--','-.',':','.'] nsty = len(styles) def get_benchmark_point_methods(): """Return all non FTS methods for point forecasting""" return [naive.Naive, arima.ARIMA, quantreg.QuantileRegression] def get_point_methods(): """Return all FTS methods for point forecasting""" return [song.ConventionalFTS, chen.ConventionalFTS, yu.WeightedFTS, ismailefendi.ImprovedWeightedFTS, cheng.TrendWeightedFTS, sadaei.ExponentialyWeightedFTS, hofts.HighOrderFTS, hwang.HighOrderFTS, pwfts.ProbabilisticWeightedFTS] def get_benchmark_interval_methods(): """Return all non FTS methods for interval forecasting""" return [quantreg.QuantileRegression] def get_interval_methods(): """Return all FTS methods for interval forecasting""" return [ifts.IntervalFTS, pwfts.ProbabilisticWeightedFTS] def get_probabilistic_methods(): """Return all FTS methods for probabilistic forecasting""" return [quantreg.QuantileRegression, ensemble.EnsembleFTS, pwfts.ProbabilisticWeightedFTS] def external_point_sliding_window(models, parameters, data, windowsize,train=0.8, dump=False, save=False, file=None, sintetic=True): """ Sliding window benchmarks for non FTS point forecasters :param models: non FTS point forecasters :param parameters: parameters for each model :param data: data set :param windowsize: size of sliding window :param train: percentual of sliding window data used to train the models :param dump: :param save: save results :param file: file path to save the results :param sintetic: if true only the average and standard deviation of the results :return: DataFrame with the results """ objs = {} lcolors = {} rmse = {} smape = {} u = {} times = {} experiments = 0 for ct, train, test in Util.sliding_window(data, windowsize, train): experiments += 1 for count, method in enumerate(models, start=0): model = method("") _start = time.time() model.train(train, None, parameters=parameters[count]) _end = time.time() _key = model.shortname if dump: print(ct, _key) if _key not in objs: objs[_key] = model lcolors[_key] = colors[count % ncol] rmse[_key] = [] smape[_key] = [] u[_key] = [] times[_key] = [] _tdiff = _end - _start try: _start = time.time() _rmse, _smape, _u = Measures.get_point_statistics(test, model, None) _end = time.time() rmse[_key].append(_rmse) smape[_key].append(_smape) u[_key].append(_u) _tdiff += _end - _start times[_key].append(_tdiff) if dump: print(_rmse, _smape, _u, _tdiff) except: rmse[_key].append(np.nan) smape[_key].append(np.nan) u[_key].append(np.nan) times[_key].append(np.nan) return Util.save_dataframe_point(experiments, file, objs, rmse, save, sintetic, smape, times, u) def point_sliding_window(data, windowsize, train=0.8,models=None,partitioners=[Grid.GridPartitioner], partitions=[10], max_order=3,transformation=None,indexer=None,dump=False, save=False, file=None, sintetic=True): """ Sliding window benchmarks for FTS point forecasters :param data: :param windowsize: size of sliding window :param train: percentual of sliding window data used to train the models :param models: FTS point forecasters :param partitioners: Universe of Discourse partitioner :param partitions: the max number of partitions on the Universe of Discourse :param max_order: the max order of the models (for high order models) :param transformation: data transformation :param indexer: seasonal indexer :param dump: :param save: save results :param file: file path to save the results :param sintetic: if true only the average and standard deviation of the results :return: DataFrame with the results """ _process_start = time.time() print("Process Start: {0: %H:%M:%S}".format(datetime.datetime.now())) if models is None: models = get_point_methods() objs = {} lcolors = {} rmse = {} smape = {} u = {} times = {} experiments = 0 for ct, train,test in Util.sliding_window(data, windowsize, train): experiments += 1 for partition in partitions: for partitioner in partitioners: pttr = str(partitioner.__module__).split('.')[-1] data_train_fs = partitioner(train, partition, transformation=transformation) for count, model in enumerate(models, start=0): mfts = model("") _key = mfts.shortname + " " + pttr + " q = " + str(partition) mfts.partitioner = data_train_fs if not mfts.is_high_order: if dump: print(ct,_key) if _key not in objs: objs[_key] = mfts lcolors[_key] = colors[count % ncol] rmse[_key] = [] smape[_key] = [] u[_key] = [] times[_key] = [] if transformation is not None: mfts.appendTransformation(transformation) _start = time.time() mfts.train(train, data_train_fs.sets) _end = time.time() times[_key].append(_end - _start) _start = time.time() _rmse, _smape, _u = Measures.get_point_statistics(test, mfts, indexer) _end = time.time() rmse[_key].append(_rmse) smape[_key].append(_smape) u[_key].append(_u) times[_key].append(_end - _start) if dump: print(_rmse, _smape, _u) else: for order in np.arange(1, max_order + 1): if order >= mfts.min_order: mfts = model("") _key = mfts.shortname + " n = " + str(order) + " " + pttr + " q = " + str(partition) mfts.partitioner = data_train_fs if dump: print(ct,_key) if _key not in objs: objs[_key] = mfts lcolors[_key] = colors[count % ncol] rmse[_key] = [] smape[_key] = [] u[_key] = [] times[_key] = [] if transformation is not None: mfts.appendTransformation(transformation) try: _start = time.time() mfts.train(train, data_train_fs.sets, order=order) _end = time.time() times[_key].append(_end - _start) _start = time.time() _rmse, _smape, _u = Measures.get_point_statistics(test, mfts, indexer) _end = time.time() rmse[_key].append(_rmse) smape[_key].append(_smape) u[_key].append(_u) times[_key].append(_end - _start) if dump: print(_rmse, _smape, _u) except Exception as e: print(e) rmse[_key].append(np.nan) smape[_key].append(np.nan) u[_key].append(np.nan) times[_key].append(np.nan) _process_end = time.time() print("Process End: {0: %H:%M:%S}".format(datetime.datetime.now())) print("Process Duration: {0}".format(_process_end - _process_start)) return Util.save_dataframe_point(experiments, file, objs, rmse, save, sintetic, smape, times, u) def all_point_forecasters(data_train, data_test, partitions, max_order=3, statistics=True, residuals=True, series=True, save=False, file=None, tam=[20, 5], models=None, transformation=None, distributions=False): """ Fixed data benchmark for FTS point forecasters :param data_train: data used to train the models :param data_test: data used to test the models :param partitions: the max number of partitions on the Universe of Discourse :param max_order: the max order of the models (for high order models) :param statistics: print statistics :param residuals: print and plot residuals :param series: plot time series :param save: save results :param file: file path to save the results :param tam: figure dimensions to plot the graphs :param models: list of models to benchmark :param transformation: data transformation :param distributions: plot distributions :return: """ if models is None: models = get_point_methods() objs = [] data_train_fs = Grid.GridPartitioner(data_train, partitions, transformation=transformation) count = 1 lcolors = [] for count, model in enumerate(models, start=0): #print(model) mfts = model("") if not mfts.is_high_order: if transformation is not None: mfts.appendTransformation(transformation) mfts.train(data_train, data_train_fs.sets) objs.append(mfts) lcolors.append( colors[count % ncol] ) else: for order in np.arange(1,max_order+1): if order >= mfts.min_order: mfts = model(" n = " + str(order)) if transformation is not None: mfts.appendTransformation(transformation) mfts.train(data_train, data_train_fs.sets, order=order) objs.append(mfts) lcolors.append(colors[(count + order) % ncol]) if statistics: print_point_statistics(data_test, objs) if residuals: print(ResidualAnalysis.compareResiduals(data_test, objs)) ResidualAnalysis.plot_residuals(data_test, objs, save=save, file=file, tam=tam) if series: plot_compared_series(data_test, objs, lcolors, typeonlegend=False, save=save, file=file, tam=tam, intervals=False) if distributions: lcolors.insert(0,'black') pmfs = [] pmfs.append( ProbabilityDistribution.ProbabilityDistribution("Original", 100, [min(data_test), max(data_test)], data=data_test) ) for m in objs: forecasts = m.forecast(data_test) pmfs.append( ProbabilityDistribution.ProbabilityDistribution(m.shortname, 100, [min(data_test), max(data_test)], data=forecasts)) print(getProbabilityDistributionStatistics(pmfs,data_test)) plot_probability_distributions(pmfs, lcolors, tam=tam) def print_point_statistics(data, models, externalmodels = None, externalforecasts = None, indexers=None): ret = "Model & Order & RMSE & SMAPE & Theil's U \\\\ \n" for count,model in enumerate(models,start=0): _rmse, _smape, _u = Measures.get_point_statistics(data, model, indexers) ret += model.shortname + " & " ret += str(model.order) + " & " ret += str(_rmse) + " & " ret += str(_smape)+ " & " ret += str(_u) #ret += str(round(Measures.TheilsInequality(np.array(data[fts.order:]), np.array(forecasts[:-1])), 4)) ret += " \\\\ \n" if externalmodels is not None: l = len(externalmodels) for k in np.arange(0,l): ret += externalmodels[k] + " & " ret += " 1 & " ret += str(round(Measures.rmse(data, externalforecasts[k][:-1]), 2)) + " & " ret += str(round(Measures.smape(data, externalforecasts[k][:-1]), 2))+ " & " ret += str(round(Measures.UStatistic(data, externalforecasts[k][:-1]), 2)) ret += " \\\\ \n" print(ret) def getProbabilityDistributionStatistics(pmfs, data): ret = "Model & Entropy & Empirical Likelihood & Pseudo Likelihood \\\\ \n" for k in pmfs: ret += k.name + " & " ret += str(k.entropy()) + " & " ret += str(k.empiricalloglikelihood())+ " & " ret += str(k.pseudologlikelihood(data)) ret += " \\\\ \n" return ret def save_dataframe_interval(coverage, experiments, file, objs, resolution, save, sharpness, sintetic, times): ret = [] if sintetic: for k in sorted(objs.keys()): mod = [] mfts = objs[k] mod.append(mfts.shortname) mod.append(mfts.order) mod.append(mfts.partitioner.name) mod.append(mfts.partitioner.partitions) mod.append(round(np.nanmean(sharpness[k]), 2)) mod.append(round(np.nanstd(sharpness[k]), 2)) mod.append(round(np.nanmean(resolution[k]), 2)) mod.append(round(np.nanstd(resolution[k]), 2)) mod.append(round(np.nanmean(coverage[k]), 2)) mod.append(round(np.nanstd(coverage[k]), 2)) mod.append(round(np.nanmean(times[k]), 2)) mod.append(round(np.nanstd(times[k]), 2)) mod.append(len(mfts)) ret.append(mod) columns = ["Model", "Order", "Scheme", "Partitions", "SHARPAVG", "SHARPSTD", "RESAVG", "RESSTD", "COVAVG", "COVSTD", "TIMEAVG", "TIMESTD", "SIZE"] else: for k in sorted(objs.keys()): try: mfts = objs[k] tmp = [mfts.shortname, mfts.order, mfts.partitioner.name, mfts.partitioner.partitions, len(mfts), 'Sharpness'] tmp.extend(sharpness[k]) ret.append(deepcopy(tmp)) tmp = [mfts.shortname, mfts.order, mfts.partitioner.name, mfts.partitioner.partitions, len(mfts), 'Resolution'] tmp.extend(resolution[k]) ret.append(deepcopy(tmp)) tmp = [mfts.shortname, mfts.order, mfts.partitioner.name, mfts.partitioner.partitions, len(mfts), 'Coverage'] tmp.extend(coverage[k]) ret.append(deepcopy(tmp)) tmp = [mfts.shortname, mfts.order, mfts.partitioner.name, mfts.partitioner.partitions, len(mfts), 'TIME'] tmp.extend(times[k]) ret.append(deepcopy(tmp)) except Exception as ex: print("Erro ao salvar ", k) print("Exceção ", ex) columns = [str(k) for k in np.arange(0, experiments)] columns.insert(0, "Model") columns.insert(1, "Order") columns.insert(2, "Scheme") columns.insert(3, "Partitions") columns.insert(4, "Size") columns.insert(5, "Measure") dat = pd.DataFrame(ret, columns=columns) if save: dat.to_csv(Util.uniquefilename(file), sep=";") return dat def interval_sliding_window(data, windowsize, train=0.8,models=None,partitioners=[Grid.GridPartitioner], partitions=[10], max_order=3,transformation=None,indexer=None,dump=False, save=False, file=None, sintetic=True): if models is None: models = get_interval_methods() objs = {} lcolors = {} sharpness = {} resolution = {} coverage = {} times = {} experiments = 0 for ct, training,test in Util.sliding_window(data, windowsize, train): experiments += 1 for partition in partitions: for partitioner in partitioners: pttr = str(partitioner.__module__).split('.')[-1] data_train_fs = partitioner(training, partition, transformation=transformation) for count, model in enumerate(models, start=0): mfts = model("") _key = mfts.shortname + " " + pttr+ " q = " +str(partition) mfts.partitioner = data_train_fs if not mfts.is_high_order: if dump: print(ct,_key) if _key not in objs: objs[_key] = mfts lcolors[_key] = colors[count % ncol] sharpness[_key] = [] resolution[_key] = [] coverage[_key] = [] times[_key] = [] if transformation is not None: mfts.appendTransformation(transformation) _start = time.time() mfts.train(training, data_train_fs.sets) _end = time.time() _tdiff = _end - _start _start = time.time() _sharp, _res, _cov = Measures.get_interval_statistics(test, mfts) _end = time.time() _tdiff += _end - _start sharpness[_key].append(_sharp) resolution[_key].append(_res) coverage[_key].append(_cov) times[_key].append(_tdiff) else: for order in np.arange(1, max_order + 1): if order >= mfts.min_order: mfts = model("") _key = mfts.shortname + " n = " + str(order) + " " + pttr + " q = " + str(partition) mfts.partitioner = data_train_fs if dump: print(ct,_key) if _key not in objs: objs[_key] = mfts lcolors[_key] = colors[count % ncol] sharpness[_key] = [] resolution[_key] = [] coverage[_key] = [] times[_key] = [] if transformation is not None: mfts.appendTransformation(transformation) _start = time.time() mfts.train(training, data_train_fs.sets, order=order) _end = time.time() _tdiff = _end - _start _start = time.time() _sharp, _res, _cov = Measures.get_interval_statistics(test, mfts) _end = time.time() _tdiff += _end - _start sharpness[_key].append(_sharp) resolution[_key].append(_res) coverage[_key].append(_cov) times[_key].append(_tdiff) return save_dataframe_interval(coverage, experiments, file, objs, resolution, save, sharpness, sintetic, times) def all_interval_forecasters(data_train, data_test, partitions, max_order=3,save=False, file=None, tam=[20, 5], models=None, transformation=None): if models is None: models = get_interval_methods() objs = [] data_train_fs = Grid.GridPartitioner(data_train,partitions, transformation=transformation).sets lcolors = [] for count, model in Util.enumerate2(models, start=0, step=2): mfts = model("") if not mfts.is_high_order: if transformation is not None: mfts.appendTransformation(transformation) mfts.train(data_train, data_train_fs) objs.append(mfts) lcolors.append( colors[count % ncol] ) else: for order in np.arange(1,max_order+1): if order >= mfts.min_order: mfts = model(" n = " + str(order)) if transformation is not None: mfts.appendTransformation(transformation) mfts.train(data_train, data_train_fs, order=order) objs.append(mfts) lcolors.append(colors[count % ncol]) print_interval_statistics(data_test, objs) plot_compared_series(data_test, objs, lcolors, typeonlegend=False, save=save, file=file, tam=tam, intervals=True) def print_interval_statistics(original, models): ret = "Model & Order & Sharpness & Resolution & Coverage \\\\ \n" for fts in models: _sharp, _res, _cov = Measures.get_interval_statistics(original, fts) ret += fts.shortname + " & " ret += str(fts.order) + " & " ret += str(_sharp) + " & " ret += str(_res) + " & " ret += str(_cov) + " \\\\ \n" print(ret) def plot_distribution(dist): for k in dist.index: alpha = np.array([dist[x][k] for x in dist]) * 100 x = [k for x in np.arange(0, len(alpha))] y = dist.columns plt.scatter(x, y, c=alpha, marker='s', linewidths=0, cmap='Oranges', norm=pltcolors.Normalize(vmin=0, vmax=1), vmin=0, vmax=1, edgecolors=None) def plot_compared_series(original, models, colors, typeonlegend=False, save=False, file=None, tam=[20, 5], points=True, intervals=True, linewidth=1.5): fig = plt.figure(figsize=tam) ax = fig.add_subplot(111) mi = [] ma = [] legends = [] ax.plot(original, color='black', label="Original", linewidth=linewidth*1.5) for count, fts in enumerate(models, start=0): if fts.has_point_forecasting and points: forecasted = fts.forecast(original) mi.append(min(forecasted) * 0.95) ma.append(max(forecasted) * 1.05) for k in np.arange(0, fts.order): forecasted.insert(0, None) lbl = fts.shortname if typeonlegend: lbl += " (Point)" ax.plot(forecasted, color=colors[count], label=lbl, ls="-",linewidth=linewidth) if fts.has_interval_forecasting and intervals: forecasted = fts.forecastInterval(original) lower = [kk[0] for kk in forecasted] upper = [kk[1] for kk in forecasted] mi.append(min(lower) * 0.95) ma.append(max(upper) * 1.05) for k in np.arange(0, fts.order): lower.insert(0, None) upper.insert(0, None) lbl = fts.shortname if typeonlegend: lbl += " (Interval)" ax.plot(lower, color=colors[count], label=lbl, ls="--",linewidth=linewidth) ax.plot(upper, color=colors[count], ls="--",linewidth=linewidth) handles0, labels0 = ax.get_legend_handles_labels() lgd = ax.legend(handles0, labels0, loc=2, bbox_to_anchor=(1, 1)) legends.append(lgd) # ax.set_title(fts.name) ax.set_ylim([min(mi), max(ma)]) ax.set_ylabel('F(T)') ax.set_xlabel('T') ax.set_xlim([0, len(original)]) Util.showAndSaveImage(fig, file, save, lgd=legends) def plot_probability_distributions(pmfs, lcolors, tam=[15, 7]): fig = plt.figure(figsize=tam) ax = fig.add_subplot(111) for k,m in enumerate(pmfs,start=0): m.plot(ax, color=lcolors[k]) handles0, labels0 = ax.get_legend_handles_labels() ax.legend(handles0, labels0) def save_dataframe_ahead(experiments, file, objs, crps_interval, crps_distr, times1, times2, save, sintetic): """ Save benchmark results for m-step ahead probabilistic forecasters :param experiments: :param file: :param objs: :param crps_interval: :param crps_distr: :param times1: :param times2: :param save: :param sintetic: :return: """ ret = [] if sintetic: for k in sorted(objs.keys()): try: ret = [] for k in sorted(objs.keys()): try: mod = [] mfts = objs[k] mod.append(mfts.shortname) mod.append(mfts.order) mod.append(mfts.partitioner.name) mod.append(mfts.partitioner.partitions) mod.append(np.round(np.nanmean(crps_interval[k]), 2)) mod.append(np.round(np.nanstd(crps_interval[k]), 2)) mod.append(np.round(np.nanmean(crps_distr[k]), 2)) mod.append(np.round(np.nanstd(crps_distr[k]), 2)) mod.append(len(mfts)) mod.append(np.round(np.nanmean(times1[k]), 4)) mod.append(np.round(np.nanmean(times2[k]), 4)) ret.append(mod) except Exception as e: print('Erro: %s' % e) except Exception as ex: print("Erro ao salvar ", k) print("Exceção ", ex) columns = ["Model", "Order", "Scheme", "Partitions", "CRPS1AVG", "CRPS1STD", "CRPS2AVG", "CRPS2STD", "SIZE", "TIME1AVG", "TIME2AVG"] else: for k in sorted(objs.keys()): try: mfts = objs[k] tmp = [mfts.shortname, mfts.order, mfts.partitioner.name, mfts.partitioner.partitions, len(mfts), 'CRPS_Interval'] tmp.extend(crps_interval[k]) ret.append(deepcopy(tmp)) tmp = [mfts.shortname, mfts.order, mfts.partitioner.name, mfts.partitioner.partitions, len(mfts), 'CRPS_Distribution'] tmp.extend(crps_distr[k]) ret.append(deepcopy(tmp)) tmp = [mfts.shortname, mfts.order, mfts.partitioner.name, mfts.partitioner.partitions, len(mfts), 'TIME_Interval'] tmp.extend(times1[k]) ret.append(deepcopy(tmp)) tmp = [mfts.shortname, mfts.order, mfts.partitioner.name, mfts.partitioner.partitions, len(mfts), 'TIME_Distribution'] tmp.extend(times2[k]) ret.append(deepcopy(tmp)) except Exception as ex: print("Erro ao salvar ", k) print("Exceção ", ex) columns = [str(k) for k in np.arange(0, experiments)] columns.insert(0, "Model") columns.insert(1, "Order") columns.insert(2, "Scheme") columns.insert(3, "Partitions") columns.insert(4, "Size") columns.insert(5, "Measure") dat = pd.DataFrame(ret, columns=columns) if save: dat.to_csv(Util.uniquefilename(file), sep=";") return dat def ahead_sliding_window(data, windowsize, train, steps, models=None, resolution = None, partitioners=[Grid.GridPartitioner], partitions=[10], max_order=3,transformation=None,indexer=None,dump=False, save=False, file=None, sintetic=False): if models is None: models = [pwfts.ProbabilisticWeightedFTS] objs = {} lcolors = {} crps_interval = {} crps_distr = {} times1 = {} times2 = {} experiments = 0 for ct, train,test in Util.sliding_window(data, windowsize, train): experiments += 1 for partition in partitions: for partitioner in partitioners: pttr = str(partitioner.__module__).split('.')[-1] data_train_fs = partitioner(train, partition, transformation=transformation) for count, model in enumerate(models, start=0): mfts = model("") _key = mfts.shortname + " " + pttr+ " q = " +str(partition) mfts.partitioner = data_train_fs if not mfts.is_high_order: if dump: print(ct,_key) if _key not in objs: objs[_key] = mfts lcolors[_key] = colors[count % ncol] crps_interval[_key] = [] crps_distr[_key] = [] times1[_key] = [] times2[_key] = [] if transformation is not None: mfts.appendTransformation(transformation) _start = time.time() mfts.train(train, data_train_fs.sets) _end = time.time() _tdiff = _end - _start _crps1, _crps2, _t1, _t2 = Measures.get_distribution_statistics(test,mfts,steps=steps,resolution=resolution) crps_interval[_key].append(_crps1) crps_distr[_key].append(_crps2) times1[_key] = _tdiff + _t1 times2[_key] = _tdiff + _t2 if dump: print(_crps1, _crps2, _tdiff, _t1, _t2) else: for order in np.arange(1, max_order + 1): if order >= mfts.min_order: mfts = model("") _key = mfts.shortname + " n = " + str(order) + " " + pttr + " q = " + str(partition) mfts.partitioner = data_train_fs if dump: print(ct,_key) if _key not in objs: objs[_key] = mfts lcolors[_key] = colors[count % ncol] crps_interval[_key] = [] crps_distr[_key] = [] times1[_key] = [] times2[_key] = [] if transformation is not None: mfts.appendTransformation(transformation) _start = time.time() mfts.train(train, data_train_fs.sets, order=order) _end = time.time() _tdiff = _end - _start _crps1, _crps2, _t1, _t2 = Measures.get_distribution_statistics(test, mfts, steps=steps, resolution=resolution) crps_interval[_key].append(_crps1) crps_distr[_key].append(_crps2) times1[_key] = _tdiff + _t1 times2[_key] = _tdiff + _t2 if dump: print(_crps1, _crps2, _tdiff, _t1, _t2) return save_dataframe_ahead(experiments, file, objs, crps_interval, crps_distr, times1, times2, save, sintetic) def all_ahead_forecasters(data_train, data_test, partitions, start, steps, resolution = None, max_order=3,save=False, file=None, tam=[20, 5], models=None, transformation=None, option=2): if models is None: models = [pwfts.ProbabilisticWeightedFTS] if resolution is None: resolution = (max(data_train) - min(data_train)) / 100 objs = [] data_train_fs = Grid.GridPartitioner(data_train, partitions, transformation=transformation).sets lcolors = [] for count, model in Util.enumerate2(models, start=0, step=2): mfts = model("") if not mfts.is_high_order: if transformation is not None: mfts.appendTransformation(transformation) mfts.train(data_train, data_train_fs) objs.append(mfts) lcolors.append( colors[count % ncol] ) else: for order in np.arange(1,max_order+1): if order >= mfts.min_order: mfts = model(" n = " + str(order)) if transformation is not None: mfts.appendTransformation(transformation) mfts.train(data_train, data_train_fs, order=order) objs.append(mfts) lcolors.append(colors[count % ncol]) distributions = [False for k in objs] distributions[0] = True print_distribution_statistics(data_test[start:], objs, steps, resolution) plot_compared_intervals_ahead(data_test, objs, lcolors, distributions=distributions, time_from=start, time_to=steps, interpol=False, save=save, file=file, tam=tam, resolution=resolution, option=option) def print_distribution_statistics(original, models, steps, resolution): ret = "Model & Order & Interval & Distribution \\\\ \n" for fts in models: _crps1, _crps2, _t1, _t2 = Measures.get_distribution_statistics(original, fts, steps, resolution) ret += fts.shortname + " & " ret += str(fts.order) + " & " ret += str(_crps1) + " & " ret += str(_crps2) + " \\\\ \n" print(ret) def plot_compared_intervals_ahead(original, models, colors, distributions, time_from, time_to, interpol=False, save=False, file=None, tam=[20, 5], resolution=None, cmap='Blues',option=2): fig = plt.figure(figsize=tam) ax = fig.add_subplot(111) cm = plt.get_cmap(cmap) cNorm = pltcolors.Normalize(vmin=0, vmax=1) scalarMap = cmx.ScalarMappable(norm=cNorm, cmap=cm) if resolution is None: resolution = (max(original) - min(original)) / 100 mi = [] ma = [] for count, fts in enumerate(models, start=0): if fts.has_probability_forecasting and distributions[count]: density = fts.forecastAheadDistribution(original[time_from - fts.order:time_from], time_to, resolution=resolution, method=option) Y = [] X = [] C = [] S = [] y = density.columns t = len(y) ss = time_to ** 2 for k in density.index: #alpha = [scalarMap.to_rgba(density[col][k]) for col in density.columns] col = [density[col][k]*5 for col in density.columns] x = [time_from + k for x in np.arange(0, t)] s = [ss for x in np.arange(0, t)] ic = resolution/10 for cc in np.arange(0, resolution, ic): Y.append(y + cc) X.append(x) C.append(col) S.append(s) Y = np.hstack(Y) X = np.hstack(X) C = np.hstack(C) S = np.hstack(S) s = ax.scatter(X, Y, c=C, marker='s',s=S, linewidths=0, edgecolors=None, cmap=cmap) s.set_clim([0, 1]) cb = fig.colorbar(s) cb.set_label('Density') if fts.has_interval_forecasting: forecasts = fts.forecastAheadInterval(original[time_from - fts.order:time_from], time_to) lower = [kk[0] for kk in forecasts] upper = [kk[1] for kk in forecasts] mi.append(min(lower)) ma.append(max(upper)) for k in np.arange(0, time_from - fts.order): lower.insert(0, None) upper.insert(0, None) ax.plot(lower, color=colors[count], label=fts.shortname) ax.plot(upper, color=colors[count]) else: forecasts = fts.forecast(original) mi.append(min(forecasts)) ma.append(max(forecasts)) for k in np.arange(0, time_from): forecasts.insert(0, None) ax.plot(forecasts, color=colors[count], label=fts.shortname) ax.plot(original, color='black', label="Original") handles0, labels0 = ax.get_legend_handles_labels() ax.legend(handles0, labels0, loc=2) # ax.set_title(fts.name) _mi = min(mi) if _mi < 0: _mi *= 1.1 else: _mi *= 0.9 _ma = max(ma) if _ma < 0: _ma *= 0.9 else: _ma *= 1.1 ax.set_ylim([_mi, _ma]) ax.set_ylabel('F(T)') ax.set_xlabel('T') ax.set_xlim([0, len(original)]) #plt.colorbar() Util.showAndSaveImage(fig, file, save) def plotCompared(original, forecasts, labels, title): fig = plt.figure(figsize=[13, 6]) ax = fig.add_subplot(111) ax.plot(original, color='k', label="Original") for c in range(0, len(forecasts)): ax.plot(forecasts[c], label=labels[c]) handles0, labels0 = ax.get_legend_handles_labels() ax.legend(handles0, labels0) ax.set_title(title) ax.set_ylabel('F(T)') ax.set_xlabel('T') ax.set_xlim([0, len(original)]) ax.set_ylim([min(original), max(original)]) def SelecaoSimples_MenorRMSE(original, parameters, modelo): ret = [] errors = [] forecasted_best = [] print("Série Original") fig = plt.figure(figsize=[20, 12]) fig.suptitle("Comparação de modelos ") ax0 = fig.add_axes([0, 0.5, 0.65, 0.45]) # left, bottom, width, height ax0.set_xlim([0, len(original)]) ax0.set_ylim([min(original), max(original)]) ax0.set_title('Série Temporal') ax0.set_ylabel('F(T)') ax0.set_xlabel('T') ax0.plot(original, label="Original") min_rmse = 100000.0 best = None for p in parameters: sets = Grid.GridPartitioner(original, p).sets fts = modelo(str(p) + " particoes") fts.train(original, sets) # print(original) forecasted = fts.forecast(original) forecasted.insert(0, original[0]) # print(forecasted) ax0.plot(forecasted, label=fts.name) error = Measures.rmse(np.array(forecasted), np.array(original)) print(p, error) errors.append(error) if error < min_rmse: min_rmse = error best = fts forecasted_best = forecasted handles0, labels0 = ax0.get_legend_handles_labels() ax0.legend(handles0, labels0) ax1 = fig.add_axes([0.7, 0.5, 0.3, 0.45]) # left, bottom, width, height ax1.set_title('Comparação dos Erros Quadráticos Médios') ax1.set_ylabel('RMSE') ax1.set_xlabel('Quantidade de Partições') ax1.set_xlim([min(parameters), max(parameters)]) ax1.plot(parameters, errors) ret.append(best) ret.append(forecasted_best) # Modelo diferencial print("\nSérie Diferencial") difffts = Transformations.differential(original) errors = [] forecastedd_best = [] ax2 = fig.add_axes([0, 0, 0.65, 0.45]) # left, bottom, width, height ax2.set_xlim([0, len(difffts)]) ax2.set_ylim([min(difffts), max(difffts)]) ax2.set_title('Série Temporal') ax2.set_ylabel('F(T)') ax2.set_xlabel('T') ax2.plot(difffts, label="Original") min_rmse = 100000.0 bestd = None for p in parameters: sets = Grid.GridPartitionerTrimf(difffts, p) fts = modelo(str(p) + " particoes") fts.train(difffts, sets) forecasted = fts.forecast(difffts) forecasted.insert(0, difffts[0]) ax2.plot(forecasted, label=fts.name) error = Measures.rmse(np.array(forecasted), np.array(difffts)) print(p, error) errors.append(error) if error < min_rmse: min_rmse = error bestd = fts forecastedd_best = forecasted handles0, labels0 = ax2.get_legend_handles_labels() ax2.legend(handles0, labels0) ax3 = fig.add_axes([0.7, 0, 0.3, 0.45]) # left, bottom, width, height ax3.set_title('Comparação dos Erros Quadráticos Médios') ax3.set_ylabel('RMSE') ax3.set_xlabel('Quantidade de Partições') ax3.set_xlim([min(parameters), max(parameters)]) ax3.plot(parameters, errors) ret.append(bestd) ret.append(forecastedd_best) return ret def compareModelsPlot(original, models_fo, models_ho): fig = plt.figure(figsize=[13, 6]) fig.suptitle("Comparação de modelos ") ax0 = fig.add_axes([0, 0, 1, 1]) # left, bottom, width, height rows = [] for model in models_fo: fts = model["model"] ax0.plot(model["forecasted"], label=model["name"]) for model in models_ho: fts = model["model"] ax0.plot(model["forecasted"], label=model["name"]) handles0, labels0 = ax0.get_legend_handles_labels() ax0.legend(handles0, labels0) def compareModelsTable(original, models_fo, models_ho): fig = plt.figure(figsize=[12, 4]) fig.suptitle("Comparação de modelos ") columns = ['Modelo', 'Ordem', 'Partições', 'RMSE', 'MAPE (%)'] rows = [] for model in models_fo: fts = model["model"] error_r = Measures.rmse(model["forecasted"], original) error_m = round(Measures.mape(model["forecasted"], original) * 100, 2) rows.append([model["name"], fts.order, len(fts.sets), error_r, error_m]) for model in models_ho: fts = model["model"] error_r = Measures.rmse(model["forecasted"][fts.order:], original[fts.order:]) error_m = round(Measures.mape(model["forecasted"][fts.order:], original[fts.order:]) * 100, 2) rows.append([model["name"], fts.order, len(fts.sets), error_r, error_m]) ax1 = fig.add_axes([0, 0, 1, 1]) # left, bottom, width, height ax1.set_xticks([]) ax1.set_yticks([]) ax1.table(cellText=rows, colLabels=columns, cellLoc='center', bbox=[0, 0, 1, 1]) sup = "\\begin{tabular}{" header = "" body = "" footer = "" for c in columns: sup = sup + "|c" if len(header) > 0: header = header + " & " header = header + "\\textbf{" + c + "} " sup = sup + "|} \\hline\n" header = header + "\\\\ \\hline \n" for r in rows: lin = "" for c in r: if len(lin) > 0: lin = lin + " & " lin = lin + str(c) body = body + lin + "\\\\ \\hline \n" return sup + header + body + "\\end{tabular}" def simpleSearch_RMSE(train, test, model, partitions, orders, save=False, file=None, tam=[10, 15], plotforecasts=False, elev=30, azim=144, intervals=False,parameters=None, partitioner=Grid.GridPartitioner,transformation=None,indexer=None): _3d = len(orders) > 1 ret = [] errors = np.array([[0 for k in range(len(partitions))] for kk in range(len(orders))]) forecasted_best = [] fig = plt.figure(figsize=tam) # fig.suptitle("Comparação de modelos ") if plotforecasts: ax0 = fig.add_axes([0, 0.4, 0.9, 0.5]) # left, bottom, width, height ax0.set_xlim([0, len(train)]) ax0.set_ylim([min(train) * 0.9, max(train) * 1.1]) ax0.set_title('Forecasts') ax0.set_ylabel('F(T)') ax0.set_xlabel('T') min_rmse = 1000000.0 best = None for pc, p in enumerate(partitions, start=0): sets = partitioner(train, p, transformation=transformation).sets for oc, o in enumerate(orders, start=0): fts = model("q = " + str(p) + " n = " + str(o)) fts.appendTransformation(transformation) fts.train(train, sets, o, parameters=parameters) if not intervals: forecasted = fts.forecast(test) if not fts.has_seasonality: error = Measures.rmse(np.array(test[o:]), np.array(forecasted[:-1])) else: error = Measures.rmse(np.array(test[o:]), np.array(forecasted)) for kk in range(o): forecasted.insert(0, None) if plotforecasts: ax0.plot(forecasted, label=fts.name) else: forecasted = fts.forecastInterval(test) error = 1.0 - Measures.rmse_interval(np.array(test[o:]), np.array(forecasted[:-1])) errors[oc, pc] = error if error < min_rmse: min_rmse = error best = fts forecasted_best = forecasted # print(min_rmse) if plotforecasts: # handles0, labels0 = ax0.get_legend_handles_labels() # ax0.legend(handles0, labels0) ax0.plot(test, label="Original", linewidth=3.0, color="black") if _3d: ax1 = Axes3D(fig, rect=[0, 1, 0.9, 0.9], elev=elev, azim=azim) if not plotforecasts: ax1 = Axes3D(fig, rect=[0, 1, 0.9, 0.9], elev=elev, azim=azim) # ax1 = fig.add_axes([0.6, 0.5, 0.45, 0.45], projection='3d') if _3d: ax1.set_title('Error Surface') ax1.set_ylabel('Model order') ax1.set_xlabel('Number of partitions') ax1.set_zlabel('RMSE') X, Y = np.meshgrid(partitions, orders) surf = ax1.plot_surface(X, Y, errors, rstride=1, cstride=1, antialiased=True) else: ax1 = fig.add_axes([0, 1, 0.9, 0.9]) ax1.set_title('Error Curve') ax1.set_ylabel('Number of partitions') ax1.set_xlabel('RMSE') ax0.plot(errors,partitions) ret.append(best) ret.append(forecasted_best) ret.append(min_rmse) # plt.tight_layout() Util.showAndSaveImage(fig, file, save) return ret def sliding_window_simple_search(data, windowsize, model, partitions, orders, save=False, file=None, tam=[10, 15], plotforecasts=False, elev=30, azim=144, intervals=False, parameters=None): _3d = len(orders) > 1 ret = [] errors = np.array([[0 for k in range(len(partitions))] for kk in range(len(orders))]) forecasted_best = [] fig = plt.figure(figsize=tam) # fig.suptitle("Comparação de modelos ") if plotforecasts: ax0 = fig.add_axes([0, 0.4, 0.9, 0.5]) # left, bottom, width, height ax0.set_xlim([0, len(data)]) ax0.set_ylim([min(data) * 0.9, max(data) * 1.1]) ax0.set_title('Forecasts') ax0.set_ylabel('F(T)') ax0.set_xlabel('T') min_rmse = 1000000.0 best = None for pc, p in enumerate(partitions, start=0): sets = Grid.GridPartitioner(data, p).sets for oc, o in enumerate(orders, start=0): _error = [] for ct, train, test in Util.sliding_window(data, windowsize, 0.8): fts = model("q = " + str(p) + " n = " + str(o)) fts.train(data, sets, o, parameters=parameters) if not intervals: forecasted = fts.forecast(test) if not fts.has_seasonality: _error.append( Measures.rmse(np.array(test[o:]), np.array(forecasted[:-1])) ) else: _error.append( Measures.rmse(np.array(test[o:]), np.array(forecasted)) ) for kk in range(o): forecasted.insert(0, None) if plotforecasts: ax0.plot(forecasted, label=fts.name) else: forecasted = fts.forecastInterval(test) _error.append( 1.0 - Measures.rmse_interval(np.array(test[o:]), np.array(forecasted[:-1])) ) error = np.nanmean(_error) errors[oc, pc] = error if error < min_rmse: min_rmse = error best = fts forecasted_best = forecasted # print(min_rmse) if plotforecasts: # handles0, labels0 = ax0.get_legend_handles_labels() # ax0.legend(handles0, labels0) ax0.plot(test, label="Original", linewidth=3.0, color="black") if _3d: ax1 = Axes3D(fig, rect=[0, 1, 0.9, 0.9], elev=elev, azim=azim) if not plotforecasts: ax1 = Axes3D(fig, rect=[0, 1, 0.9, 0.9], elev=elev, azim=azim) # ax1 = fig.add_axes([0.6, 0.5, 0.45, 0.45], projection='3d') if _3d: ax1.set_title('Error Surface') ax1.set_ylabel('Model order') ax1.set_xlabel('Number of partitions') ax1.set_zlabel('RMSE') X, Y = np.meshgrid(partitions, orders) surf = ax1.plot_surface(X, Y, errors, rstride=1, cstride=1, antialiased=True) else: ax1 = fig.add_axes([0, 1, 0.9, 0.9]) ax1.set_title('Error Curve') ax1.set_ylabel('Number of partitions') ax1.set_xlabel('RMSE') ax0.plot(errors,partitions) ret.append(best) ret.append(forecasted_best) # plt.tight_layout() Util.showAndSaveImage(fig, file, save) return ret def pftsExploreOrderAndPartitions(data,save=False, file=None): fig, axes = plt.subplots(nrows=4, ncols=1, figsize=[6, 8]) data_fs1 = Grid.GridPartitioner(data, 10).sets mi = [] ma = [] axes[0].set_title('Point Forecasts by Order') axes[2].set_title('Interval Forecasts by Order') for order in np.arange(1, 6): fts = pwfts.ProbabilisticWeightedFTS("") fts.shortname = "n = " + str(order) fts.train(data, data_fs1, order=order) point_forecasts = fts.forecast(data) interval_forecasts = fts.forecastInterval(data) lower = [kk[0] for kk in interval_forecasts] upper = [kk[1] for kk in interval_forecasts] mi.append(min(lower) * 0.95) ma.append(max(upper) * 1.05) for k in np.arange(0, order): point_forecasts.insert(0, None) lower.insert(0, None) upper.insert(0, None) axes[0].plot(point_forecasts, label=fts.shortname) axes[2].plot(lower, label=fts.shortname) axes[2].plot(upper) axes[1].set_title('Point Forecasts by Number of Partitions') axes[3].set_title('Interval Forecasts by Number of Partitions') for partitions in np.arange(5, 11): data_fs = Grid.GridPartitioner(data, partitions).sets fts = pwfts.ProbabilisticWeightedFTS("") fts.shortname = "q = " + str(partitions) fts.train(data, data_fs, 1) point_forecasts = fts.forecast(data) interval_forecasts = fts.forecastInterval(data) lower = [kk[0] for kk in interval_forecasts] upper = [kk[1] for kk in interval_forecasts] mi.append(min(lower) * 0.95) ma.append(max(upper) * 1.05) point_forecasts.insert(0, None) lower.insert(0, None) upper.insert(0, None) axes[1].plot(point_forecasts, label=fts.shortname) axes[3].plot(lower, label=fts.shortname) axes[3].plot(upper) for ax in axes: ax.set_ylabel('F(T)') ax.set_xlabel('T') ax.plot(data, label="Original", color="black", linewidth=1.5) handles, labels = ax.get_legend_handles_labels() ax.legend(handles, labels, loc=2, bbox_to_anchor=(1, 1)) ax.set_ylim([min(mi), max(ma)]) ax.set_xlim([0, len(data)]) plt.tight_layout() Util.showAndSaveImage(fig, file, save)