- Issue #3 - Code documentation with PEP 257 compliance
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Petrônio Cândido de Lima e Silva
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cbfbf47f54
@ -4,6 +4,7 @@
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pyFTS module for common benchmark metrics
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"""
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import time
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import numpy as np
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import pandas as pd
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from pyFTS.common import FuzzySet,SortedCollection
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@ -240,3 +241,29 @@ def get_interval_statistics(original, model):
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ret.append(round(resolution(forecasts), 2))
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ret.append(round(coverage(original[model.order:], forecasts[:-1]), 2))
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return ret
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def get_distribution_statistics(original, model, steps, resolution):
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ret = list()
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try:
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_s1 = time.time()
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densities1 = model.forecastAheadDistribution(original, steps, parameters=3)
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_e1 = time.time()
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ret.append(round(crps(original, densities1), 3))
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ret.append(round(_e1 - _s1, 3))
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except Exception as e:
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print('Erro: ', e)
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ret.append(np.nan)
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ret.append(np.nan)
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try:
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_s2 = time.time()
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densities2 = model.forecastAheadDistribution(original, steps, parameters=2)
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_e2 = time.time()
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ret.append( round(crps(original, densities2), 3))
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ret.append(round(_e2 - _s2, 3))
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except:
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ret.append(np.nan)
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ret.append(np.nan)
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return ret
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@ -17,7 +17,7 @@ from mpl_toolkits.mplot3d import Axes3D
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from pyFTS.partitioners import partitioner, Grid, Huarng, Entropy, FCM
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from pyFTS.benchmarks import Measures, naive, arima, ResidualAnalysis, ProbabilityDistribution, Util, quantreg
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from pyFTS.common import Membership, FuzzySet, FLR, Transformations, Util
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from pyFTS import fts, chen, yu, ismailefendi, sadaei, hofts, hwang, pwfts, ifts, cheng
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from pyFTS import fts, song, chen, yu, ismailefendi, sadaei, hofts, hwang, pwfts, ifts, cheng, ensemble
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from copy import deepcopy
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colors = ['grey', 'rosybrown', 'maroon', 'red','orange', 'yellow', 'olive', 'green',
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@ -29,24 +29,34 @@ styles = ['-','--','-.',':','.']
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nsty = len(styles)
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def get_benchmark_point_methods():
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"""Return all non FTS methods for point forecast"""
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"""Return all non FTS methods for point forecasting"""
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return [naive.Naive, arima.ARIMA, quantreg.QuantileRegression]
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def get_point_methods():
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"""Return all FTS methods for point forecast"""
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return [chen.ConventionalFTS, yu.WeightedFTS, ismailefendi.ImprovedWeightedFTS, cheng.TrendWeightedFTS,
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sadaei.ExponentialyWeightedFTS, hofts.HighOrderFTS, pwfts.ProbabilisticWeightedFTS]
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"""Return all FTS methods for point forecasting"""
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return [song.ConventionalFTS, chen.ConventionalFTS, yu.WeightedFTS, ismailefendi.ImprovedWeightedFTS,
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cheng.TrendWeightedFTS, sadaei.ExponentialyWeightedFTS, hofts.HighOrderFTS,
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pwfts.ProbabilisticWeightedFTS]
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def get_benchmark_interval_methods():
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"""Return all non FTS methods for interval forecast"""
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"""Return all non FTS methods for interval forecasting"""
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return [quantreg.QuantileRegression]
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def get_interval_methods():
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"""Return all FTS methods for interval forecast"""
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"""Return all FTS methods for interval forecasting"""
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return [ifts.IntervalFTS, pwfts.ProbabilisticWeightedFTS]
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def get_probabilistic_methods():
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"""Return all FTS methods for probabilistic forecasting"""
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return [quantreg.QuantileRegression, ensemble.EnsembleFTS, pwfts.ProbabilisticWeightedFTS]
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def external_point_sliding_window(models, parameters, data, windowsize,train=0.8, dump=False,
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save=False, file=None, sintetic=True):
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"""
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@ -628,6 +638,19 @@ def plot_probability_distributions(pmfs, lcolors, tam=[15, 7]):
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def save_dataframe_ahead(experiments, file, objs, crps_interval, crps_distr, times1, times2, save, sintetic):
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"""
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Save benchmark results for m-step ahead probabilistic forecasters
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:param experiments:
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:param file:
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:param objs:
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:param crps_interval:
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:param crps_distr:
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:param times1:
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:param times2:
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:param save:
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:param sintetic:
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:return:
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"""
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ret = []
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if sintetic:
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@ -738,7 +761,7 @@ def ahead_sliding_window(data, windowsize, train, steps, models=None, resolution
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_tdiff = _end - _start
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_crps1, _crps2, _t1, _t2 = get_distribution_statistics(test,mfts,steps=steps,resolution=resolution)
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_crps1, _crps2, _t1, _t2 = Measures.get_distribution_statistics(test,mfts,steps=steps,resolution=resolution)
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crps_interval[_key].append(_crps1)
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crps_distr[_key].append(_crps2)
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@ -773,7 +796,7 @@ def ahead_sliding_window(data, windowsize, train, steps, models=None, resolution
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_tdiff = _end - _start
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_crps1, _crps2, _t1, _t2 = get_distribution_statistics(test, mfts, steps=steps,
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_crps1, _crps2, _t1, _t2 = Measures.get_distribution_statistics(test, mfts, steps=steps,
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resolution=resolution)
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crps_interval[_key].append(_crps1)
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@ -826,36 +849,13 @@ def all_ahead_forecasters(data_train, data_test, partitions, start, steps, resol
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interpol=False, save=save, file=file, tam=tam, resolution=resolution, option=option)
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def get_distribution_statistics(original, model, steps, resolution):
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ret = list()
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try:
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_s1 = time.time()
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densities1 = model.forecastAheadDistribution(original, steps, parameters=3)
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_e1 = time.time()
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ret.append(round(Measures.crps(original, densities1), 3))
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ret.append(round(_e1 - _s1, 3))
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except Exception as e:
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print('Erro: ', e)
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ret.append(np.nan)
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ret.append(np.nan)
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try:
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_s2 = time.time()
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densities2 = model.forecastAheadDistribution(original, steps, parameters=2)
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_e2 = time.time()
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ret.append( round(Measures.crps(original, densities2), 3))
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ret.append(round(_e2 - _s2, 3))
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except:
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ret.append(np.nan)
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ret.append(np.nan)
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return ret
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def print_distribution_statistics(original, models, steps, resolution):
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ret = "Model & Order & Interval & Distribution \\\\ \n"
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for fts in models:
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_crps1, _crps2, _t1, _t2 = get_distribution_statistics(original, fts, steps, resolution)
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_crps1, _crps2, _t1, _t2 = Measures.get_distribution_statistics(original, fts, steps, resolution)
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ret += fts.shortname + " & "
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ret += str(fts.order) + " & "
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ret += str(_crps1) + " & "
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@ -172,7 +172,7 @@ def point_sliding_window(data, windowsize, train=0.8, models=None, partitioners=
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return bUtil.save_dataframe_point(experiments, file, objs, rmse, save, sintetic, smape, times, u)
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def run_interval(mfts, partitioner, train_data, test_data, transformation=None, indexer=None):
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def run_interval(mfts, partitioner, train_data, test_data, window_key=None, transformation=None, indexer=None):
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"""
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Interval forecast benchmark function to be executed on cluster nodes
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:param mfts: FTS model
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@ -211,7 +211,8 @@ def run_interval(mfts, partitioner, train_data, test_data, transformation=None,
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_end = time.time()
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times += _end - _start
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ret = {'key': _key, 'obj': mfts, 'sharpness': _sharp, 'resolution': _res, 'coverage': _cov, 'time': times}
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ret = {'key': _key, 'obj': mfts, 'sharpness': _sharp, 'resolution': _res, 'coverage': _cov, 'time': times,
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'window': window_key}
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return ret
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@ -321,3 +322,163 @@ def interval_sliding_window(data, windowsize, train=0.8, models=None, partitione
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return benchmarks.save_dataframe_interval(coverage, experiments, file, objs, resolution, save, sharpness, sintetic,
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times)
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def run_ahead(mfts, partitioner, train_data, test_data, steps, resolution, window_key=None, transformation=None, indexer=None):
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"""
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Probabilistic m-step ahead forecast benchmark function to be executed on cluster nodes
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:param mfts: FTS model
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:param partitioner: Universe of Discourse partitioner
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:param train_data: data used to train the model
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:param test_data: ata used to test the model
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:param steps:
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:param resolution:
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:param window_key: id of the sliding window
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:param transformation: data transformation
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:param indexer: seasonal indexer
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:return: a dictionary with the benchmark results
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"""
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import time
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from pyFTS import hofts, ifts, pwfts
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from pyFTS.partitioners import Grid, Entropy, FCM
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from pyFTS.benchmarks import Measures, arima, quantreg
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tmp = [hofts.HighOrderFTS, ifts.IntervalFTS, pwfts.ProbabilisticWeightedFTS, arima.ARIMA, quantreg.QuantileRegression]
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tmp2 = [Grid.GridPartitioner, Entropy.EntropyPartitioner, FCM.FCMPartitioner]
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tmp3 = [Measures.get_distribution_statistics]
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pttr = str(partitioner.__module__).split('.')[-1]
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_key = mfts.shortname + " n = " + str(mfts.order) + " " + pttr + " q = " + str(partitioner.partitions)
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mfts.partitioner = partitioner
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if transformation is not None:
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mfts.appendTransformation(transformation)
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try:
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_start = time.time()
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mfts.train(train_data, partitioner.sets, order=mfts.order)
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_end = time.time()
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times = _end - _start
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_crps1, _crps2, _t1, _t2 = Measures.get_distribution_statistics(test_data, mfts, steps=steps,
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resolution=resolution)
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_t1 += times
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_t2 += times
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except Exception as e:
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print(e)
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_crps1 = np.nan
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_crps2 = np.nan
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_t1 = np.nan
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_t2 = np.nan
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ret = {'key': _key, 'obj': mfts, 'CRPS_Interval': _crps1, 'CRPS_Distribution': _crps2, 'TIME_Interval': _t1,
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'TIME_Distribution': _t2, 'window': window_key}
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return ret
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def ahead_sliding_window(data, windowsize, train, steps,resolution, models=None, partitioners=[Grid.GridPartitioner],
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partitions=[10], max_order=3, transformation=None, indexer=None, dump=False,
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save=False, file=None, sintetic=False,nodes=None, depends=None):
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"""
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Distributed sliding window benchmarks for FTS probabilistic forecasters
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:param data:
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:param windowsize: size of sliding window
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:param train: percentual of sliding window data used to train the models
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:param steps:
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:param resolution:
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:param models: FTS point forecasters
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:param partitioners: Universe of Discourse partitioner
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:param partitions: the max number of partitions on the Universe of Discourse
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:param max_order: the max order of the models (for high order models)
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:param transformation: data transformation
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:param indexer: seasonal indexer
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:param dump:
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:param save: save results
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:param file: file path to save the results
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:param sintetic: if true only the average and standard deviation of the results
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:param nodes: list of cluster nodes to distribute tasks
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:param depends: list of module dependencies
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:return: DataFrame with the results
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"""
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cluster = dispy.JobCluster(run_point, nodes=nodes) # , depends=dependencies)
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http_server = dispy.httpd.DispyHTTPServer(cluster)
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_process_start = time.time()
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print("Process Start: {0: %H:%M:%S}".format(datetime.datetime.now()))
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pool = []
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jobs = []
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objs = {}
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crps_interval = {}
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crps_distr = {}
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times1 = {}
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times2 = {}
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if models is None:
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models = benchmarks.get_probabilistic_methods()
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for model in models:
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mfts = model("")
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if mfts.is_high_order:
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for order in np.arange(1, max_order + 1):
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if order >= mfts.min_order:
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mfts = model("")
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mfts.order = order
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pool.append(mfts)
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else:
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pool.append(mfts)
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experiments = 0
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for ct, train, test in Util.sliding_window(data, windowsize, train):
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experiments += 1
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if dump: print('\nWindow: {0}\n'.format(ct))
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for partition in partitions:
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for partitioner in partitioners:
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data_train_fs = partitioner(train, partition, transformation=transformation)
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for id, m in enumerate(pool,start=0):
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job = cluster.submit(m, data_train_fs, train, test, ct, transformation)
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job.id = id # associate an ID to identify jobs (if needed later)
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jobs.append(job)
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for job in jobs:
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tmp = job()
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if job.status == dispy.DispyJob.Finished and tmp is not None:
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if tmp['key'] not in objs:
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objs[tmp['key']] = tmp['obj']
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crps_interval[tmp['key']] = []
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crps_distr[tmp['key']] = []
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times1[tmp['key']] = []
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times2[tmp['key']] = []
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crps_interval[tmp['key']].append(tmp['CRPS_Interval'])
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crps_distr[tmp['key']].append(tmp['CRPS_Distribution'])
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times1[tmp['key']].append(tmp['TIME_Interval'])
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times2[tmp['key']].append(tmp['TIME_Distribution'])
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else:
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print(job.exception)
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print(job.stdout)
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_process_end = time.time()
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print("Process End: {0: %H:%M:%S}".format(datetime.datetime.now()))
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print("Process Duration: {0}".format(_process_end - _process_start))
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cluster.wait() # wait for all jobs to finish
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cluster.print_status()
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http_server.shutdown() # this waits until browser gets all updates
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cluster.close()
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return benchmarks.save_dataframe_ahead(experiments, file, objs, crps_interval, crps_distr, times1, times2, save, sintetic)
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@ -18,6 +18,17 @@ from pyFTS.benchmarks import benchmarks
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def run_point(mfts, partitioner, train_data, test_data, transformation=None, indexer=None):
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"""
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Point forecast benchmark function to be executed on threads
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:param mfts: FTS model
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:param partitioner: Universe of Discourse partitioner
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:param train_data: data used to train the model
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:param test_data: ata used to test the model
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:param window_key: id of the sliding window
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:param transformation: data transformation
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:param indexer: seasonal indexer
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:return: a dictionary with the benchmark results
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"""
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pttr = str(partitioner.__module__).split('.')[-1]
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_key = mfts.shortname + " n = " + str(mfts.order) + " " + pttr + " q = " + str(partitioner.partitions)
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mfts.partitioner = partitioner
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@ -51,6 +62,23 @@ def run_point(mfts, partitioner, train_data, test_data, transformation=None, ind
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def point_sliding_window(data, windowsize, train=0.8, models=None, partitioners=[Grid.GridPartitioner],
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partitions=[10], max_order=3, transformation=None, indexer=None, dump=False,
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save=False, file=None, sintetic=False):
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"""
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Parallel sliding window benchmarks for FTS point forecasters
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:param data:
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:param windowsize: size of sliding window
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:param train: percentual of sliding window data used to train the models
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:param models: FTS point forecasters
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:param partitioners: Universe of Discourse partitioner
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:param partitions: the max number of partitions on the Universe of Discourse
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:param max_order: the max order of the models (for high order models)
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:param transformation: data transformation
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:param indexer: seasonal indexer
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:param dump:
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:param save: save results
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:param file: file path to save the results
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:param sintetic: if true only the average and standard deviation of the results
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:return: DataFrame with the results
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"""
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_process_start = time.time()
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print("Process Start: {0: %H:%M:%S}".format(datetime.datetime.now()))
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@ -116,6 +144,17 @@ def point_sliding_window(data, windowsize, train=0.8, models=None, partitioners=
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def run_interval(mfts, partitioner, train_data, test_data, transformation=None, indexer=None):
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"""
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Interval forecast benchmark function to be executed on threads
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:param mfts: FTS model
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:param partitioner: Universe of Discourse partitioner
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:param train_data: data used to train the model
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:param test_data: ata used to test the model
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:param window_key: id of the sliding window
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:param transformation: data transformation
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:param indexer: seasonal indexer
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:return: a dictionary with the benchmark results
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"""
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pttr = str(partitioner.__module__).split('.')[-1]
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_key = mfts.shortname + " n = " + str(mfts.order) + " " + pttr + " q = " + str(partitioner.partitions)
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mfts.partitioner = partitioner
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@ -149,6 +188,23 @@ def run_interval(mfts, partitioner, train_data, test_data, transformation=None,
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def interval_sliding_window(data, windowsize, train=0.8, models=None, partitioners=[Grid.GridPartitioner],
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partitions=[10], max_order=3, transformation=None, indexer=None, dump=False,
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save=False, file=None, sintetic=False):
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"""
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Parallel sliding window benchmarks for FTS interval forecasters
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:param data:
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:param windowsize: size of sliding window
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:param train: percentual of sliding window data used to train the models
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:param models: FTS point forecasters
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:param partitioners: Universe of Discourse partitioner
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: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()))
|
||||
@ -215,6 +271,18 @@ def interval_sliding_window(data, windowsize, train=0.8, models=None, partitione
|
||||
|
||||
|
||||
def run_ahead(mfts, partitioner, train_data, test_data, steps, resolution, transformation=None, indexer=None):
|
||||
"""
|
||||
Probabilistic m-step ahead forecast benchmark function to be executed on threads
|
||||
:param mfts: FTS model
|
||||
:param partitioner: Universe of Discourse partitioner
|
||||
:param train_data: data used to train the model
|
||||
:param test_data: ata used to test the model
|
||||
:param steps:
|
||||
:param resolution:
|
||||
:param transformation: data transformation
|
||||
:param indexer: seasonal indexer
|
||||
:return: a dictionary with the benchmark results
|
||||
"""
|
||||
pttr = str(partitioner.__module__).split('.')[-1]
|
||||
_key = mfts.shortname + " n = " + str(mfts.order) + " " + pttr + " q = " + str(partitioner.partitions)
|
||||
mfts.partitioner = partitioner
|
||||
@ -248,6 +316,25 @@ def run_ahead(mfts, partitioner, train_data, test_data, steps, resolution, trans
|
||||
def ahead_sliding_window(data, windowsize, train, steps,resolution, models=None, partitioners=[Grid.GridPartitioner],
|
||||
partitions=[10], max_order=3, transformation=None, indexer=None, dump=False,
|
||||
save=False, file=None, sintetic=False):
|
||||
"""
|
||||
Parallel sliding window benchmarks for FTS probabilistic forecasters
|
||||
:param data:
|
||||
:param windowsize: size of sliding window
|
||||
:param train: percentual of sliding window data used to train the models
|
||||
:param steps:
|
||||
:param resolution:
|
||||
: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()))
|
||||
|
||||
4
song.py
4
song.py
@ -5,8 +5,8 @@ from pyFTS import fts
|
||||
class ConventionalFTS(fts.FTS):
|
||||
"""Conventional Fuzzy Time Series"""
|
||||
def __init__(self, name, **kwargs):
|
||||
super(ConventionalFTS, self).__init__(1, "CFTS " + name)
|
||||
self.name = "Conventional FTS"
|
||||
super(ConventionalFTS, self).__init__(1, "FTS " + name)
|
||||
self.name = "Traditional FTS"
|
||||
self.detail = "Song & Chissom"
|
||||
self.R = None
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user