1340 lines
52 KiB
Python
1340 lines
52 KiB
Python
#!/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, chen, yu, ismailefendi, sadaei, hofts, hwang, pwfts, ifts, cheng
|
|
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 forecast"""
|
|
return [naive.Naive, arima.ARIMA, quantreg.QuantileRegression]
|
|
|
|
def get_point_methods():
|
|
"""Return all FTS methods for point forecast"""
|
|
return [chen.ConventionalFTS, yu.WeightedFTS, ismailefendi.ImprovedWeightedFTS, cheng.TrendWeightedFTS,
|
|
sadaei.ExponentialyWeightedFTS, hofts.HighOrderFTS, pwfts.ProbabilisticWeightedFTS]
|
|
|
|
def get_benchmark_interval_methods():
|
|
"""Return all non FTS methods for interval forecast"""
|
|
return [quantreg.QuantileRegression]
|
|
|
|
def get_interval_methods():
|
|
"""Return all FTS methods for interval forecast"""
|
|
return [ifts.IntervalFTS, 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):
|
|
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 = 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 = 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 get_distribution_statistics(original, model, steps, resolution):
|
|
ret = list()
|
|
try:
|
|
_s1 = time.time()
|
|
densities1 = model.forecastAheadDistribution(original, steps, parameters=3)
|
|
_e1 = time.time()
|
|
ret.append(round(Measures.crps(original, densities1), 3))
|
|
ret.append(round(_e1 - _s1, 3))
|
|
except Exception as e:
|
|
print('Erro: ', e)
|
|
ret.append(np.nan)
|
|
ret.append(np.nan)
|
|
|
|
try:
|
|
_s2 = time.time()
|
|
densities2 = model.forecastAheadDistribution(original, steps, parameters=2)
|
|
_e2 = time.time()
|
|
ret.append( round(Measures.crps(original, densities2), 3))
|
|
ret.append(round(_e2 - _s2, 3))
|
|
except:
|
|
ret.append(np.nan)
|
|
ret.append(np.nan)
|
|
|
|
return ret
|
|
|
|
|
|
def print_distribution_statistics(original, models, steps, resolution):
|
|
ret = "Model & Order & Interval & Distribution \\\\ \n"
|
|
for fts in models:
|
|
_crps1, _crps2, _t1, _t2 = 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)
|
|
|