Parallel benchmarks - sliding window por point forecasts
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Petrônio Cândido de Lima e Silva
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@ -2,6 +2,178 @@ from copy import deepcopy
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from joblib import Parallel, delayed
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import multiprocessing
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import numpy as np
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import pandas as pd
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import time
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import matplotlib as plt
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import matplotlib.colors as pltcolors
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import matplotlib.cm as cmx
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import matplotlib.pyplot as plt
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from mpl_toolkits.mplot3d import Axes3D
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# from sklearn.cross_validation import KFold
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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
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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
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from pyFTS.benchmarks import benchmarks
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def get_first_order_models():
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return [chen.ConventionalFTS, yu.WeightedFTS, ismailefendi.ImprovedWeightedFTS,
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sadaei.ExponentialyWeightedFTS]
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def get_high_order_models():
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return [hofts.HighOrderFTS, pwfts.ProbabilisticWeightedFTS]
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def run_first_order(method, partitioner, train_data, test_data, transformation = None, indexer=None ):
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mfts = method("")
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pttr = str(partitioner.__module__).split('.')[-1]
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_key = mfts.shortname + " " + 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)
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_end = time.time()
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times = _end - _start
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_start = time.time()
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_rmse, _smape, _u = benchmarks.get_point_statistics(test_data, mfts, indexer)
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_end = time.time()
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times += _end - _start
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except Exception as e:
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print(e)
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_rmse = np.nan
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_smape= np.nan
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_u = np.nan
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times = np.nan
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ret = {'key':_key, 'obj': mfts, 'rmse': _rmse, 'smape': _smape, 'u': _u, 'time': times }
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print(ret)
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return ret
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def run_high_order(method, order, partitioner, train_data, test_data, transformation=None, indexer=None):
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mfts = method("")
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if order >= mfts.minOrder:
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pttr = str(partitioner.__module__).split('.')[-1]
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_key = mfts.shortname + " n = " + str(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=order)
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_end = time.time()
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times = _end - _start
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_start = time.time()
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_rmse, _smape, _u = benchmarks.get_point_statistics(test_data, mfts, indexer)
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_end = time.time()
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times += _end - _start
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except Exception as e:
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print(e)
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_rmse = np.nan
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_smape = np.nan
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_u = np.nan
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times = np.nan
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ret = {'key': _key, 'obj': mfts, 'rmse': _rmse, 'smape': _smape, 'u': _u, 'time': times}
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print(ret)
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return ret
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return {'key': None, 'obj': mfts, 'rmse': np.nan, 'smape': np.nan, 'u': np.nan, 'time': np.nan}
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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):
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num_cores = multiprocessing.cpu_count()
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objs = {}
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rmse = {}
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smape = {}
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u = {}
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times = {}
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for ct, train,test in Util.sliding_window(data, windowsize, train):
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mocks = {}
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for partition in partitions:
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for partitioner in partitioners:
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pttr = str(partitioner.__module__).split('.')[-1]
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data_train_fs = partitioner(train, partition, transformation=transformation)
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results = Parallel(n_jobs=num_cores)(delayed(run_first_order)(m, deepcopy(data_train_fs), deepcopy(train), deepcopy(test), transformation)
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for m in get_first_order_models())
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for tmp in results:
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if tmp['key'] not in objs:
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objs[tmp['key']] = tmp['obj']
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rmse[tmp['key']] = []
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smape[tmp['key']] = []
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u[tmp['key']] = []
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times[tmp['key']] = []
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rmse[tmp['key']].append(tmp['rmse'])
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smape[tmp['key']].append(tmp['smape'])
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u[tmp['key']].append(tmp['u'])
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times[tmp['key']].append(tmp['time'])
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for count, model in enumerate(get_high_order_models(), start=0):
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results = Parallel(n_jobs=num_cores)(
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delayed(run_high_order)(model, order, deepcopy(data_train_fs), deepcopy(train), deepcopy(test),
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transformation)
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for order in np.arange(1, max_order + 1))
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for tmp in results:
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if tmp['key'] not in objs:
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objs[tmp['key']] = tmp['obj']
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rmse[tmp['key']] = []
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smape[tmp['key']] = []
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u[tmp['key']] = []
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times[tmp['key']] = []
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rmse[tmp['key']].append(tmp['rmse'])
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smape[tmp['key']].append(tmp['smape'])
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u[tmp['key']].append(tmp['u'])
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times[tmp['key']].append(tmp['time'])
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ret = []
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for k in sorted(objs.keys()):
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try:
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mod = []
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tmp = objs[k]
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mod.append(tmp.shortname)
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mod.append(tmp.order )
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mod.append(tmp.partitioner.name)
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mod.append(tmp.partitioner.partitions)
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mod.append(np.round(np.nanmean(rmse[k]),2))
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mod.append(np.round(np.nanstd(rmse[k]), 2))
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mod.append(np.round(np.nanmean(smape[k]), 2))
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mod.append(np.round(np.nanstd(smape[k]), 2))
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mod.append(np.round(np.nanmean(u[k]), 2))
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mod.append(np.round(np.nanstd(u[k]), 2))
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mod.append(np.round(np.nanmean(times[k]), 4))
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mod.append(np.round(np.nanstd(times[k]), 4))
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mod.append(len(tmp))
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ret.append(mod)
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except Exception as ex:
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print("Erro ao salvar ",k)
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print("Exceção ", ex)
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columns = ["Model","Order","Scheme","Partitions","RMSEAVG","RMSESTD","SMAPEAVG","SMAPESTD","UAVG","USTD","TIMEAVG","TIMESTD","SIZE"]
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dat = pd.DataFrame(ret,columns=columns)
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if save: dat.to_csv(Util.uniquefilename(file),sep=";")
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return dat
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@ -28,9 +28,14 @@ os.chdir("/home/petronio/dados/Dropbox/Doutorado/Codigos/")
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taiexpd = pd.read_csv("DataSets/TAIEX.csv", sep=",")
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taiex = np.array(taiexpd["avg"][:5000])
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from pyFTS.partitioners import parallel_util
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from pyFTS.benchmarks import parallel_benchmarks as bchmk
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parallel_util.explore_partitioners(taiex,20)
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bchmk.point_sliding_window(taiex,2000,train=0.8, #transformation=diff, #models=[pwfts.ProbabilisticWeightedFTS], # #
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partitioners=[Grid.GridPartitioner], #Entropy.EntropyPartitioner], # FCM.FCMPartitioner, ],
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partitions= np.arange(10,200,step=5), #
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dump=True, save=True, file="experiments/nasdaq_point_paralllel.csv")
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#parallel_util.explore_partitioners(taiex,20)
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#nasdaqpd = pd.read_csv("DataSets/NASDAQ_IXIC.csv", sep=",")
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#nasdaq = np.array(nasdaqpd["avg"][:5000])
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