- Distributed sliding window benchmarks with dispy
This commit is contained in:
Petrônio Cândido de Lima e Silva
parent
fa676f9f1d
commit
97246f1510
@ -145,3 +145,35 @@ def crps(targets, densities):
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_crps += sum([ (Ff[col][k]-Fa[col][k])**2 for col in densities.columns])
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return _crps / float(l * n)
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def get_point_statistics(data, model, indexer=None):
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if indexer is not None:
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ndata = np.array(indexer.get_data(data[model.order:]))
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else:
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ndata = np.array(data[model.order:])
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if model.isMultivariate or indexer is None:
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forecasts = model.forecast(data)
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elif not model.isMultivariate and indexer is not None:
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forecasts = model.forecast(indexer.get_data(data))
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if model.hasSeasonality:
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nforecasts = np.array(forecasts)
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else:
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nforecasts = np.array(forecasts[:-1])
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ret = list()
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try:
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ret.append(np.round(rmse(ndata, nforecasts), 2))
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except:
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ret.append(np.nan)
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try:
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ret.append(np.round(smape(ndata, nforecasts), 2))
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except:
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ret.append(np.nan)
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try:
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ret.append(np.round(UStatistic(ndata, nforecasts), 2))
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except:
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ret.append(np.nan)
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return ret
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62
benchmarks/Util.py
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62
benchmarks/Util.py
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@ -0,0 +1,62 @@
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import numpy as np
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import pandas as pd
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from copy import deepcopy
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from pyFTS.common import Util
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def save_dataframe_point(experiments, file, objs, rmse, save, sintetic, smape, times, u):
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ret = []
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if sintetic:
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for k in sorted(objs.keys()):
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try:
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mod = []
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mfts = objs[k]
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mod.append(mfts.shortname)
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mod.append(mfts.order)
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mod.append(mfts.partitioner.name)
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mod.append(mfts.partitioner.partitions)
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mod.append(len(mfts))
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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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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", "Size", "RMSEAVG", "RMSESTD", "SMAPEAVG", "SMAPESTD", "UAVG", "USTD", "TIMEAVG"]
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else:
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for k in sorted(objs.keys()):
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try:
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mfts = objs[k]
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tmp = [mfts.shortname, mfts.order, mfts.partitioner.name, mfts.partitioner.partitions, len(mfts), 'RMSE']
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tmp.extend(rmse[k])
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ret.append(deepcopy(tmp))
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tmp = [mfts.shortname, mfts.order, mfts.partitioner.name, mfts.partitioner.partitions, len(mfts), 'SMAPE']
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tmp.extend(smape[k])
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ret.append(deepcopy(tmp))
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tmp = [mfts.shortname, mfts.order, mfts.partitioner.name, mfts.partitioner.partitions, len(mfts), 'U']
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tmp.extend(u[k])
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ret.append(deepcopy(tmp))
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tmp = [mfts.shortname, mfts.order, mfts.partitioner.name, mfts.partitioner.partitions, len(mfts), 'TIME']
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tmp.extend(times[k])
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ret.append(deepcopy(tmp))
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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 = [str(k) for k in np.arange(0, experiments)]
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columns.insert(0, "Model")
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columns.insert(1, "Order")
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columns.insert(2, "Scheme")
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columns.insert(3, "Partitions")
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columns.insert(4, "Size")
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columns.insert(5, "Measure")
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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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@ -12,7 +12,7 @@ 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.benchmarks import Measures, naive, arima, ResidualAnalysis, ProbabilityDistribution, Util
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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 copy import deepcopy
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@ -37,64 +37,6 @@ def get_interval_methods():
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return [ifts.IntervalFTS, pwfts.ProbabilisticWeightedFTS]
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def save_dataframe_point(experiments, file, objs, rmse, save, sintetic, smape, times, u):
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ret = []
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if sintetic:
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for k in sorted(objs.keys()):
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try:
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mod = []
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mfts = objs[k]
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mod.append(mfts.shortname)
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mod.append(mfts.order)
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mod.append(mfts.partitioner.name)
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mod.append(mfts.partitioner.partitions)
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mod.append(len(mfts))
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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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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", "Size", "RMSEAVG", "RMSESTD", "SMAPEAVG", "SMAPESTD", "UAVG", "USTD", "TIMEAVG"]
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else:
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for k in sorted(objs.keys()):
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try:
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mfts = objs[k]
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tmp = [mfts.shortname, mfts.order, mfts.partitioner.name, mfts.partitioner.partitions, len(mfts), 'RMSE']
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tmp.extend(rmse[k])
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ret.append(deepcopy(tmp))
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tmp = [mfts.shortname, mfts.order, mfts.partitioner.name, mfts.partitioner.partitions, len(mfts), 'SMAPE']
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tmp.extend(smape[k])
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ret.append(deepcopy(tmp))
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tmp = [mfts.shortname, mfts.order, mfts.partitioner.name, mfts.partitioner.partitions, len(mfts), 'U']
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tmp.extend(u[k])
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ret.append(deepcopy(tmp))
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tmp = [mfts.shortname, mfts.order, mfts.partitioner.name, mfts.partitioner.partitions, len(mfts), 'TIME']
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tmp.extend(times[k])
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ret.append(deepcopy(tmp))
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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 = [str(k) for k in np.arange(0, experiments)]
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columns.insert(0, "Model")
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columns.insert(1, "Order")
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columns.insert(2, "Scheme")
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columns.insert(3, "Partitions")
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columns.insert(4, "Size")
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columns.insert(5, "Measure")
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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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def external_point_sliding_window(models, parameters, data, windowsize,train=0.8, dump=False, save=False, file=None, sintetic=True):
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objs = {}
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lcolors = {}
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@ -129,7 +71,7 @@ def external_point_sliding_window(models, parameters, data, windowsize,train=0.8
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try:
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_start = time.time()
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_rmse, _smape, _u = get_point_statistics(test, model, None)
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_rmse, _smape, _u = Measures.get_point_statistics(test, model, None)
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_end = time.time()
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rmse[_key].append(_rmse)
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smape[_key].append(_smape)
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@ -143,7 +85,7 @@ def external_point_sliding_window(models, parameters, data, windowsize,train=0.8
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u[_key].append(np.nan)
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times[_key].append(np.nan)
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return save_dataframe_point(experiments, file, objs, rmse, save, sintetic, smape, times, u)
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return Util.save_dataframe_point(experiments, file, objs, rmse, save, sintetic, smape, times, u)
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def point_sliding_window(data, windowsize, train=0.8,models=None,partitioners=[Grid.GridPartitioner],
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@ -240,7 +182,7 @@ def point_sliding_window(data, windowsize, train=0.8,models=None,partitioners=[G
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times[_key].append(_end - _start)
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_start = time.time()
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_rmse, _smape, _u = get_point_statistics(test, mfts, indexer)
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_rmse, _smape, _u = Measures.get_point_statistics(test, mfts, indexer)
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_end = time.time()
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rmse[_key].append(_rmse)
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smape[_key].append(_smape)
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@ -262,7 +204,7 @@ def point_sliding_window(data, windowsize, train=0.8,models=None,partitioners=[G
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print("Process Duration: {0}".format(_process_end - _process_start))
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return save_dataframe_point(experiments, file, objs, rmse, save, sintetic, smape, times, u)
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return Util.save_dataframe_point(experiments, file, objs, rmse, save, sintetic, smape, times, u)
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def all_point_forecasters(data_train, data_test, partitions, max_order=3, statistics=True, residuals=True,
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@ -326,38 +268,6 @@ def all_point_forecasters(data_train, data_test, partitions, max_order=3, statis
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plot_probability_distributions(pmfs, lcolors, tam=tam)
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def get_point_statistics(data, model, indexer=None):
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if indexer is not None:
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ndata = np.array(indexer.get_data(data[model.order:]))
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else:
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ndata = np.array(data[model.order:])
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if model.isMultivariate or indexer is None:
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forecasts = model.forecast(data)
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elif not model.isMultivariate and indexer is not None:
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forecasts = model.forecast(indexer.get_data(data))
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if model.hasSeasonality:
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nforecasts = np.array(forecasts)
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else:
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nforecasts = np.array(forecasts[:-1])
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ret = list()
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try:
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ret.append(np.round(Measures.rmse(ndata, nforecasts), 2))
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except:
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ret.append(np.nan)
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try:
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ret.append(np.round(Measures.smape(ndata, nforecasts), 2))
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except:
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ret.append(np.nan)
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try:
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ret.append(np.round(Measures.UStatistic(ndata, nforecasts), 2))
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except:
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ret.append(np.nan)
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return ret
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def print_point_statistics(data, models, externalmodels = None, externalforecasts = None, indexers=None):
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ret = "Model & Order & RMSE & SMAPE & Theil's U \\\\ \n"
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for count,model in enumerate(models,start=0):
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179
benchmarks/distributed_benchmarks.py
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179
benchmarks/distributed_benchmarks.py
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import random
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import dispy
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import dispy.httpd
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from copy import deepcopy
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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 datetime
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import pyFTS
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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, parallel_benchmarks, Util as bUtil
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def run_point(mfts, partitioner, train_data, test_data, window_key=None, transformation=None, indexer=None):
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import time
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from pyFTS import yu,chen,hofts,ifts,pwfts,ismailefendi,sadaei
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from pyFTS.partitioners import Grid, Entropy, FCM
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from pyFTS.benchmarks import Measures
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tmp = [yu.WeightedFTS, chen.ConventionalFTS, hofts.HighOrderFTS, ifts.IntervalFTS,
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pwfts.ProbabilisticWeightedFTS, ismailefendi.ImprovedWeightedFTS, sadaei.ExponentialyWeightedFTS]
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tmp2 = [Grid.GridPartitioner, Entropy.EntropyPartitioner, FCM.FCMPartitioner]
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tmp3 = [Measures.get_point_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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_start = time.time()
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_rmse, _smape, _u = Measures.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, 'window': window_key}
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# print(ret)
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return ret
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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,nodes=None, depends=None):
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# dependencies = [fts, Membership, benchmarks]
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# if depends is not None: dependencies.extend(depends)
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# if models is not None:
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# dependencies.extend(models)
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# else:
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# dependencies.extend(benchmarks.get_point_methods())
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# dependencies.extend(partitioners)
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# if transformation is not None: dependencies.extend(transformation)
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# if indexer is not None: dependencies.extend(indexer)
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cluster = dispy.JobCluster(run_point, nodes=nodes) #, depends=dependencies)
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# import dispy's httpd module, create http server for this cluster
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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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rmse = {}
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smape = {}
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u = {}
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times = {}
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if models is None:
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models = benchmarks.get_point_methods()
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for model in models:
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mfts = model("")
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if mfts.isHighOrder:
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for order in np.arange(1, max_order + 1):
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if order >= mfts.minOrder:
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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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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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print(tmp['key'], tmp['window'])
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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 bUtil.save_dataframe_point(experiments, file, objs, rmse, save, sintetic, smape, times, u)
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def compute(data):
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import socket
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return (socket.gethostname(), data)
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def teste(data,nodes):
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cluster = dispy.JobCluster(compute, nodes=nodes)
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jobs = []
|
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for ct, train, test in Util.sliding_window(data, 2000, 0.8):
|
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job = cluster.submit(ct)
|
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jobs.append(job)
|
||||
|
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for job in jobs:
|
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x = job()
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print(x)
|
||||
|
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cluster.wait()
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cluster.close()
|
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|
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|
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@ -6,12 +6,6 @@ import numpy as np
|
||||
import pandas as pd
|
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import time
|
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import datetime
|
||||
import matplotlib as plt
|
||||
import matplotlib.colors as pltcolors
|
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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
|
||||
from pyFTS.common import Membership, FuzzySet, FLR, Transformations, Util
|
||||
|
||||
2
fts.py
2
fts.py
@ -2,8 +2,6 @@ import numpy as np
|
||||
import pandas as pd
|
||||
from pyFTS import tree
|
||||
from pyFTS.common import FuzzySet, SortedCollection
|
||||
from pyFTS.benchmarks import Measures
|
||||
|
||||
|
||||
class FTS(object):
|
||||
def __init__(self, order, name):
|
||||
|
||||
@ -44,3 +44,6 @@ class Partitioner(object):
|
||||
tmpx = [kk for kk in np.arange(s.lower, s.upper)]
|
||||
tmpy = [s.membership(kk) for kk in np.arange(s.lower, s.upper)]
|
||||
ax.plot(tmpx, tmpy)
|
||||
|
||||
def __str__(self):
|
||||
return self.name
|
||||
@ -28,13 +28,20 @@ os.chdir("/home/petronio/dados/Dropbox/Doutorado/Codigos/")
|
||||
taiexpd = pd.read_csv("DataSets/TAIEX.csv", sep=",")
|
||||
taiex = np.array(taiexpd["avg"][:5000])
|
||||
|
||||
from pyFTS.benchmarks import parallel_benchmarks as bchmk
|
||||
from pyFTS.benchmarks import distributed_benchmarks as bchmk
|
||||
#from pyFTS.benchmarks import parallel_benchmarks as bchmk
|
||||
#from pyFTS.benchmarks import benchmarks as bchmk
|
||||
from pyFTS import yu
|
||||
|
||||
bchmk.point_sliding_window(taiex,2000,train=0.8, #transformation=diff, #models=[pwfts.ProbabilisticWeightedFTS], # #
|
||||
#bchmk.teste(taiex,['192.168.0.109', '192.168.0.101'])
|
||||
|
||||
bchmk.point_sliding_window(taiex,2000,train=0.8, #models=[yu.WeightedFTS], # #
|
||||
partitioners=[Grid.GridPartitioner], #Entropy.EntropyPartitioner], # FCM.FCMPartitioner, ],
|
||||
partitions= np.arange(10,200,step=5), #
|
||||
dump=False, save=True, file="experiments/nasdaq_point_parallel.csv")
|
||||
partitions= np.arange(10,200,step=5), #transformation=diff,
|
||||
dump=False, save=True, file="experiments/nasdaq_point_distributed.csv",
|
||||
nodes=['192.168.0.109', '192.168.0.101']) #, depends=[hofts, ifts])
|
||||
|
||||
#bchmk.testa(taiex,[10,20],partitioners=[Grid.GridPartitioner], nodes=['192.168.0.109', '192.168.0.101'])
|
||||
|
||||
#parallel_util.explore_partitioners(taiex,20)
|
||||
|
||||
@ -51,7 +58,7 @@ bchmk.point_sliding_window(taiex,2000,train=0.8, #transformation=diff, #models=[
|
||||
|
||||
#, ,
|
||||
|
||||
diff = Transformations.Differential(1)
|
||||
#diff = Transformations.Differential(1)
|
||||
|
||||
|
||||
#bchmk.external_point_sliding_window([naive.Naive, arima.ARIMA, arima.ARIMA, arima.ARIMA, arima.ARIMA, arima.ARIMA, arima.ARIMA],
|
||||
|
||||
Loading…
Reference in New Issue
Block a user