- Bugfixes in naive, arima and quantreg and optimizations on benchmarks
This commit is contained in:
Petrônio Cândido de Lima e Silva
parent
5f7c0f0bfa
commit
3b21889d6c
@ -202,6 +202,7 @@ def crps(targets, densities):
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def get_point_statistics(data, model, indexer=None):
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"""Condensate all measures for point forecasters"""
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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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@ -212,22 +213,29 @@ def get_point_statistics(data, model, indexer=None):
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elif not model.is_multivariate and indexer is not None:
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forecasts = model.forecast(indexer.get_data(data))
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try:
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if model.has_seasonality:
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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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except Exception as ex:
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print(ex)
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return [np.nan,np.nan,np.nan]
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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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except Exception as ex:
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print(ex)
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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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except Exception as ex:
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print(ex)
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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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except Exception as ex:
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print(ex)
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ret.append(np.nan)
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return ret
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@ -48,7 +48,7 @@ def find_best(dataframe, criteria, ascending):
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return ret
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def point_dataframe_sintetic_columns():
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def point_dataframe_synthetic_columns():
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return ["Model", "Order", "Scheme", "Partitions", "Size", "RMSEAVG", "RMSESTD", "SMAPEAVG", "SMAPESTD", "UAVG",
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"USTD", "TIMEAVG", "TIMESTD"]
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@ -64,7 +64,7 @@ def point_dataframe_analytic_columns(experiments):
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return columns
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def save_dataframe_point(experiments, file, objs, rmse, save, sintetic, smape, times, u):
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def save_dataframe_point(experiments, file, objs, rmse, save, synthetic, smape, times, u):
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"""
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Create a dataframe to store the benchmark results
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:param experiments: dictionary with the execution results
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@ -72,7 +72,7 @@ def save_dataframe_point(experiments, file, objs, rmse, save, sintetic, smape, t
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:param objs:
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:param rmse:
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:param save:
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:param sintetic:
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:param synthetic:
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:param smape:
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:param times:
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:param u:
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@ -80,7 +80,7 @@ def save_dataframe_point(experiments, file, objs, rmse, save, sintetic, smape, t
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"""
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ret = []
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if sintetic:
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if synthetic:
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for k in sorted(objs.keys()):
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try:
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@ -109,7 +109,7 @@ def save_dataframe_point(experiments, file, objs, rmse, save, sintetic, smape, t
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print("Erro ao salvar ", k)
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print("Exceção ", ex)
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columns = point_dataframe_sintetic_columns()
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columns = point_dataframe_synthetic_columns()
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else:
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for k in sorted(objs.keys()):
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try:
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@ -152,7 +152,7 @@ def save_dataframe_point(experiments, file, objs, rmse, save, sintetic, smape, t
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print(ret)
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def cast_dataframe_to_sintetic_point(infile, outfile, experiments):
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def cast_dataframe_to_synthetic_point(infile, outfile, experiments):
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columns = point_dataframe_analytic_columns(experiments)
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dat = pd.read_csv(infile, sep=";", usecols=columns)
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models = dat.Model.unique()
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@ -190,7 +190,7 @@ def cast_dataframe_to_sintetic_point(infile, outfile, experiments):
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mod.append(np.round(np.nanstd(times), 4))
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ret.append(mod)
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dat = pd.DataFrame(ret, columns=point_dataframe_sintetic_columns())
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dat = pd.DataFrame(ret, columns=point_dataframe_synthetic_columns())
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dat.to_csv(Util.uniquefilename(outfile), sep=";", index=False)
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@ -208,7 +208,7 @@ def plot_dataframe_point(file_synthetic, file_analytic, experiments, tam):
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axes[2].set_title('U Statistic')
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axes[3].set_title('Execution Time')
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dat_syn = pd.read_csv(file_synthetic, sep=";", usecols=point_dataframe_sintetic_columns())
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dat_syn = pd.read_csv(file_synthetic, sep=";", usecols=point_dataframe_synthetic_columns())
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bests = find_best(dat_syn, ['UAVG','RMSEAVG','USTD','RMSESTD'], [1,1,1,1])
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@ -241,9 +241,9 @@ def plot_dataframe_point(file_synthetic, file_analytic, experiments, tam):
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def save_dataframe_interval(coverage, experiments, file, objs, resolution, save, sharpness, sintetic, times):
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def save_dataframe_interval(coverage, experiments, file, objs, resolution, save, sharpness, synthetic, times):
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ret = []
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if sintetic:
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if synthetic:
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for k in sorted(objs.keys()):
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mod = []
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mfts = objs[k]
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@ -268,7 +268,7 @@ def save_dataframe_interval(coverage, experiments, file, objs, resolution, save,
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mod.append(l)
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ret.append(mod)
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columns = interval_dataframe_sintetic_columns()
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columns = interval_dataframe_synthetic_columns()
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else:
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for k in sorted(objs.keys()):
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try:
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@ -315,13 +315,13 @@ def interval_dataframe_analytic_columns(experiments):
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return columns
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def interval_dataframe_sintetic_columns():
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def interval_dataframe_synthetic_columns():
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columns = ["Model", "Order", "Scheme", "Partitions", "SHARPAVG", "SHARPSTD", "RESAVG", "RESSTD", "COVAVG",
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"COVSTD", "TIMEAVG", "TIMESTD", "SIZE"]
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return columns
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def save_dataframe_ahead(experiments, file, objs, crps_interval, crps_distr, times1, times2, save, sintetic):
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def save_dataframe_ahead(experiments, file, objs, crps_interval, crps_distr, times1, times2, save, synthetic):
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"""
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Save benchmark results for m-step ahead probabilistic forecasters
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:param experiments:
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@ -332,12 +332,12 @@ def save_dataframe_ahead(experiments, file, objs, crps_interval, crps_distr, tim
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:param times1:
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:param times2:
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:param save:
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:param sintetic:
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:param synthetic:
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:return:
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"""
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ret = []
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if sintetic:
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if synthetic:
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for k in sorted(objs.keys()):
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try:
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@ -370,7 +370,7 @@ def save_dataframe_ahead(experiments, file, objs, crps_interval, crps_distr, tim
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print("Erro ao salvar ", k)
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print("Exceção ", ex)
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columns = ahead_dataframe_sintetic_columns()
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columns = ahead_dataframe_synthetic_columns()
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else:
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for k in sorted(objs.keys()):
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try:
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@ -417,7 +417,7 @@ def ahead_dataframe_analytic_columns(experiments):
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return columns
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def ahead_dataframe_sintetic_columns():
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def ahead_dataframe_synthetic_columns():
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columns = ["Model", "Order", "Scheme", "Partitions", "CRPS1AVG", "CRPS1STD", "CRPS2AVG", "CRPS2STD",
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"SIZE", "TIME1AVG", "TIME2AVG"]
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return columns
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@ -18,7 +18,8 @@ from mpl_toolkits.mplot3d import Axes3D
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from probabilistic import ProbabilityDistribution
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from pyFTS import song, chen, yu, ismailefendi, sadaei, hofts, pwfts, ifts, cheng, ensemble, hwang
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from pyFTS.benchmarks import Measures, naive, arima, ResidualAnalysis, Util, quantreg
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from pyFTS.benchmarks import Measures, naive, arima, ResidualAnalysis, quantreg
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from pyFTS.benchmarks import Util as bUtil
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from pyFTS.common import Transformations, Util
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# from sklearn.cross_validation import KFold
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from pyFTS.partitioners import Grid
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@ -60,10 +61,47 @@ def get_probabilistic_methods():
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return [quantreg.QuantileRegression, ensemble.EnsembleFTS, pwfts.ProbabilisticWeightedFTS]
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def run_point(mfts, partitioner, train_data, test_data, window_key=None, transformation=None, indexer=None):
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"""
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Point forecast benchmark function to be executed on sliding window
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:param mfts: FTS model
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:param partitioner: Universe of Discourse partitioner
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:param train_data: data used to train the model
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:param test_data: ata used to test the model
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:param window_key: id of the sliding window
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:param transformation: data transformation
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:param indexer: seasonal indexer
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:return: a dictionary with the benchmark results
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"""
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if mfts.benchmark_only:
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_key = mfts.shortname + str(mfts.order if mfts.order is not None else "")
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else:
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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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_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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ret = {'key': _key, 'obj': mfts, 'rmse': _rmse, 'smape': _smape, 'u': _u, 'time': times, 'window': window_key}
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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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benchmark_models=None, benchmark_models_parameters = None,
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save=False, file=None, sintetic=True):
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save=False, file=None, sintetic=False):
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"""
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Sliding window benchmarks for FTS point forecasters
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:param data:
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@ -76,116 +114,89 @@ def point_sliding_window(data, windowsize, train=0.8,models=None,partitioners=[G
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:param transformation: data transformation
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:param indexer: seasonal indexer
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:param dump:
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:param benchmark_models: Non FTS models to benchmark
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:param benchmark_models_parameters: Non FTS models parameters
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:param save: save results
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:param file: file path to save the results
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:param sintetic: if true only the average and standard deviation of the results
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:return: DataFrame with the results
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"""
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if benchmark_models is None: # and models is None:
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benchmark_models = [naive.Naive, arima.ARIMA, arima.ARIMA, arima.ARIMA, arima.ARIMA,
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quantreg.QuantileRegression, quantreg.QuantileRegression]
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if benchmark_models_parameters is None:
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benchmark_models_parameters = [1, (1, 0, 0), (1, 0, 1), (2, 0, 1), (2, 0, 2), 1, 2]
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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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if models is None:
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models = get_point_methods()
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pool = []
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jobs = []
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objs = {}
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lcolors = {}
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rmse = {}
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smape = {}
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u = {}
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times = {}
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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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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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for count, model in enumerate(models, start=0):
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if models is None:
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models = get_point_methods()
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for model in models:
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mfts = model("")
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_key = mfts.shortname + " " + pttr + " q = " + str(partition)
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mfts.partitioner = data_train_fs
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if not mfts.is_high_order:
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if dump: print(ct,_key)
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if _key not in objs:
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objs[_key] = mfts
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lcolors[_key] = colors[count % ncol]
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rmse[_key] = []
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smape[_key] = []
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u[_key] = []
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times[_key] = []
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if transformation is not None:
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mfts.appendTransformation(transformation)
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_start = time.time()
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mfts.train(train, data_train_fs.sets)
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_end = time.time()
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times[_key].append(_end - _start)
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_start = time.time()
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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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u[_key].append(_u)
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times[_key].append(_end - _start)
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if dump: print(_rmse, _smape, _u)
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else:
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if mfts.is_high_order:
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for order in np.arange(1, max_order + 1):
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if order >= mfts.min_order:
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mfts = model("")
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mfts.order = order
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pool.append(mfts)
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else:
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mfts.order = 1
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pool.append(mfts)
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_key = mfts.shortname + " n = " + str(order) + " " + pttr + " q = " + str(partition)
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if benchmark_models is not None:
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for count, model in enumerate(benchmark_models, start=0):
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par = benchmark_models_parameters[count]
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mfts = model(str(par if par is not None else ""))
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mfts.order = par
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pool.append(mfts)
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mfts.partitioner = data_train_fs
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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(ct,_key)
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benchmarks_only = {}
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if _key not in objs:
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objs[_key] = mfts
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lcolors[_key] = colors[count % ncol]
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rmse[_key] = []
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smape[_key] = []
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u[_key] = []
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times[_key] = []
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if dump: print('\nWindow: {0}\n'.format(ct))
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if transformation is not None:
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mfts.appendTransformation(transformation)
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for partition in partitions:
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try:
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_start = time.time()
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mfts.train(train, data_train_fs.sets, order=order)
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_end = time.time()
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times[_key].append(_end - _start)
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for partitioner in partitioners:
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_start = time.time()
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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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u[_key].append(_u)
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times[_key].append(_end - _start)
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data_train_fs = partitioner(train, partition, transformation=transformation)
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if dump: print(_rmse, _smape, _u)
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for _id, m in enumerate(pool,start=0):
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if m.benchmark_only and m.shortname in benchmarks_only:
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continue
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else:
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benchmarks_only[m.shortname] = m
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except Exception as e:
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print(e)
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rmse[_key].append(np.nan)
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smape[_key].append(np.nan)
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u[_key].append(np.nan)
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times[_key].append(np.nan)
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tmp = run_point(deepcopy(m), data_train_fs, train, test, ct, transformation)
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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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_process_end = time.time()
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@ -193,7 +204,8 @@ 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 Util.save_dataframe_point(experiments, file, objs, rmse, save, sintetic, smape, times, u)
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return bUtil.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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@ -311,7 +323,7 @@ def getProbabilityDistributionStatistics(pmfs, data):
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def interval_sliding_window(data, windowsize, train=0.8, models=None, partitioners=[Grid.GridPartitioner],
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partitions=[10], max_order=3, transformation=None, indexer=None, dump=False,
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save=False, file=None, sintetic=True):
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save=False, file=None, synthetic=True):
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if models is None:
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models = get_interval_methods()
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@ -400,7 +412,7 @@ def interval_sliding_window(data, windowsize, train=0.8,models=None,partitioners
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coverage[_key].append(_cov)
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times[_key].append(_tdiff)
|
||||
|
||||
return Util.save_dataframe_interval(coverage, experiments, file, objs, resolution, save, sharpness, sintetic, times)
|
||||
return bUtil.save_dataframe_interval(coverage, experiments, file, objs, resolution, save, sharpness, synthetic, times)
|
||||
|
||||
|
||||
def all_interval_forecasters(data_train, data_test, partitions, max_order=3,save=False, file=None, tam=[20, 5],
|
||||
@ -523,7 +535,7 @@ def plot_probability_distributions(pmfs, lcolors, tam=[15, 7]):
|
||||
|
||||
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):
|
||||
save=False, file=None, synthetic=False):
|
||||
if models is None:
|
||||
models = [pwfts.ProbabilisticWeightedFTS]
|
||||
|
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@ -614,7 +626,7 @@ def ahead_sliding_window(data, windowsize, train, steps, models=None, resolution
|
||||
|
||||
if dump: print(_crps1, _crps2, _tdiff, _t1, _t2)
|
||||
|
||||
return Util.save_dataframe_ahead(experiments, file, objs, crps_interval, crps_distr, times1, times2, save, sintetic)
|
||||
return bUtil.save_dataframe_ahead(experiments, file, objs, crps_interval, crps_distr, times1, times2, save, synthetic)
|
||||
|
||||
|
||||
def all_ahead_forecasters(data_train, data_test, partitions, start, steps, resolution = None, max_order=3,save=False, file=None, tam=[20, 5],
|
||||
|
||||
@ -99,12 +99,12 @@ def point_sliding_window(data, windowsize, train=0.8, models=None, partitioners=
|
||||
:return: DataFrame with the results
|
||||
"""
|
||||
|
||||
if benchmark_models is None:
|
||||
if benchmark_models is None and models is None:
|
||||
benchmark_models = [naive.Naive, arima.ARIMA, arima.ARIMA, arima.ARIMA, arima.ARIMA,
|
||||
quantreg.QuantileRegression, quantreg.QuantileRegression]
|
||||
|
||||
if benchmark_models_parameters is None:
|
||||
benchmark_models_parameters = [None, (1, 0, 0), (1, 0, 1), (2, 0, 1), (2, 0, 2), 1, 2]
|
||||
benchmark_models_parameters = [1, (1, 0, 0), (1, 0, 1), (2, 0, 1), (2, 0, 2), 1, 2]
|
||||
|
||||
cluster = dispy.JobCluster(run_point, nodes=nodes) #, depends=dependencies)
|
||||
|
||||
@ -135,10 +135,10 @@ def point_sliding_window(data, windowsize, train=0.8, models=None, partitioners=
|
||||
mfts.order = order
|
||||
pool.append(mfts)
|
||||
else:
|
||||
pool.append(mfts)
|
||||
mfts.order = 1
|
||||
pool.append(mfts)
|
||||
|
||||
if benchmark_models is not None:
|
||||
for count, model in enumerate(benchmark_models, start=0):
|
||||
par = benchmark_models_parameters[count]
|
||||
mfts = model(str(par if par is not None else ""))
|
||||
|
||||
@ -14,5 +14,5 @@ class Naive(fts.FTS):
|
||||
self.is_high_order = False
|
||||
|
||||
def forecast(self, data, **kwargs):
|
||||
return [k for k in data]
|
||||
return data
|
||||
|
||||
|
||||
@ -55,7 +55,7 @@ class QuantileRegression(fts.FTS):
|
||||
|
||||
ret = []
|
||||
|
||||
for k in np.arange(self.order, l):
|
||||
for k in np.arange(self.order, l+1): #+1 to forecast one step ahead given all available lags
|
||||
sample = ndata[k - self.order : k]
|
||||
|
||||
ret.append(self.linearmodel(sample, self.mean_qt))
|
||||
@ -72,7 +72,7 @@ class QuantileRegression(fts.FTS):
|
||||
|
||||
ret = []
|
||||
|
||||
for k in np.arange(self.order - 1, l):
|
||||
for k in np.arange(self.order , l):
|
||||
sample = ndata[k - self.order: k]
|
||||
up = self.linearmodel(sample, self.upper_qt)
|
||||
down = self.linearmodel(sample, self.down_qt)
|
||||
|
||||
@ -39,7 +39,7 @@ os.chdir("/home/petronio/dados/Dropbox/Doutorado/Codigos/")
|
||||
#gauss_teste = random.normal(0,1.0,400)
|
||||
|
||||
taiexpd = pd.read_csv("DataSets/TAIEX.csv", sep=",")
|
||||
taiex = np.array(taiexpd["avg"][:5000])
|
||||
taiex = np.array(taiexpd["avg"][:2000])
|
||||
|
||||
#nasdaqpd = pd.read_csv("DataSets/NASDAQ_IXIC.csv", sep=",")
|
||||
#nasdaq = np.array(nasdaqpd["avg"][0:5000])
|
||||
@ -54,34 +54,39 @@ from statsmodels.tsa.tsatools import lagmat
|
||||
#print(lag)
|
||||
#print(a)
|
||||
|
||||
from pyFTS.benchmarks import distributed_benchmarks as bchmk
|
||||
from pyFTS.benchmarks import benchmarks as bchmk
|
||||
#from pyFTS.benchmarks import distributed_benchmarks as bchmk
|
||||
#from pyFTS.benchmarks import parallel_benchmarks as bchmk
|
||||
from pyFTS.benchmarks import Util
|
||||
from pyFTS.benchmarks import arima, quantreg
|
||||
|
||||
#Util.cast_dataframe_to_sintetic_point("experiments/taiex_point_analitic.csv","experiments/taiex_point_sintetic.csv",11)
|
||||
#Util.cast_dataframe_to_synthetic_point("experiments/taiex_point_analitic.csv","experiments/taiex_point_sintetic.csv",11)
|
||||
|
||||
#Util.plot_dataframe_point("experiments/taiex_point_sintetic.csv","experiments/taiex_point_analitic.csv",11)
|
||||
|
||||
#tmp = arima.ARIMA("")
|
||||
#tmp.train(taiex[:1600], None, order=(1,0,1))
|
||||
#teste = tmp.forecast(taiex[1600:1610])
|
||||
#tmp.train(taiex[:1600], None, order=(2,0,2))
|
||||
#teste = tmp.forecast(taiex[1600:1605])
|
||||
|
||||
#tmp = quantreg.QuantileRegression("")
|
||||
#tmp.train(taiex[:1600], None, order=1)
|
||||
#teste = tmp.forecast(taiex[1600:1610])
|
||||
#tmp.train(taiex[:1600], None, order=2)
|
||||
#teste = tmp.forecast(taiex[1600:1605])
|
||||
|
||||
#print(taiex[1600:1610])
|
||||
#print(taiex[1600:1605])
|
||||
#print(teste)
|
||||
|
||||
#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=10), #transformation=diff,
|
||||
dump=True, save=True, file="experiments/taiex_point_analytic.csv",
|
||||
nodes=['192.168.0.102', '192.168.0.109', '192.168.0.106']) #, depends=[hofts, ifts])
|
||||
from pyFTS import song, chen, yu, cheng
|
||||
|
||||
|
||||
bchmk.point_sliding_window(taiex,1000,train=0.8, models=[], #song.ConventionalFTS, chen.ConventionalFTS], #[yu.WeightedFTS, cheng.TrendWeightedFTS], # #
|
||||
partitioners=[Grid.GridPartitioner], #Entropy.EntropyPartitioner], # FCM.FCMPartitioner, ],
|
||||
partitions= [10], #np.arange(10,200,step=10), #transformation=diff,
|
||||
dump=True, save=True, file="experiments/XXXtaiex_point_analytic.csv") #,
|
||||
# nodes=['192.168.0.102', '192.168.0.109', '192.168.0.106']) #, depends=[hofts, ifts])
|
||||
|
||||
"""
|
||||
diff = Transformations.Differential(1)
|
||||
|
||||
bchmk.point_sliding_window(taiex,2000,train=0.8, #models=[yu.WeightedFTS], # #
|
||||
@ -89,7 +94,7 @@ bchmk.point_sliding_window(taiex,2000,train=0.8, #models=[yu.WeightedFTS], # #
|
||||
partitions= np.arange(10,200,step=10), transformation=diff,
|
||||
dump=True, save=True, file="experiments/taiex_point_analytic_diff.csv",
|
||||
nodes=['192.168.0.102', '192.168.0.109', '192.168.0.106']) #, 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)
|
||||
|
||||
Loading…
Reference in New Issue
Block a user