- Optimizations for interval distributed benchmarks
This commit is contained in:
Petrônio Cândido de Lima e Silva
parent
3b21889d6c
commit
2c0e685e87
@ -164,6 +164,36 @@ def coverage(targets, forecasts):
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return np.mean(preds)
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def pinball(tau, target, forecast):
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"""
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Pinball loss function. Measure the distance of forecast to the tau-quantile of the target
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:param tau: quantile value in the range (0,1)
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:param target:
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:param forecast:
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:return: distance of forecast to the tau-quantile of the target
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"""
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if target >= forecast:
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return (target - forecast) * tau
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else:
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return (forecast - target) * (1 - tau)
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def pinball_mean(tau, targets, forecasts):
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"""
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Mean pinball loss value of the forecast for a given tau-quantile of the targets
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:param tau: quantile value in the range (0,1)
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:param targets: list of target values
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:param forecasts: list of prediction intervals
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:return:
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"""
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preds = []
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if tau <= 0.5:
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preds = [pinball(tau, targets[i], forecasts[i][0]) for i in np.arange(0, len(forecasts))]
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else:
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preds = [pinball(tau, targets[i], forecasts[i][1]) for i in np.arange(0, len(forecasts))]
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return np.nanmean(preds)
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def pmf_to_cdf(density):
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ret = []
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for row in density.index:
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@ -248,6 +278,10 @@ def get_interval_statistics(original, model):
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ret.append(round(sharpness(forecasts), 2))
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ret.append(round(resolution(forecasts), 2))
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ret.append(round(coverage(original[model.order:], forecasts[:-1]), 2))
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ret.append(round(pinball_mean(0.05, original[model.order:], forecasts[:-1]), 2))
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ret.append(round(pinball_mean(0.25, original[model.order:], forecasts[:-1]), 2))
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ret.append(round(pinball_mean(0.75, original[model.order:], forecasts[:-1]), 2))
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ret.append(round(pinball_mean(0.95, original[model.order:], forecasts[:-1]), 2))
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return ret
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@ -240,8 +240,7 @@ def plot_dataframe_point(file_synthetic, file_analytic, experiments, tam):
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plt.show()
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def save_dataframe_interval(coverage, experiments, file, objs, resolution, save, sharpness, synthetic, times):
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def save_dataframe_interval(coverage, experiments, file, objs, resolution, save, sharpness, synthetic, times, q05, q25, q75, q95):
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ret = []
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if synthetic:
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for k in sorted(objs.keys()):
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@ -265,6 +264,14 @@ def save_dataframe_interval(coverage, experiments, file, objs, resolution, save,
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mod.append(round(np.nanstd(coverage[k]), 2))
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mod.append(round(np.nanmean(times[k]), 2))
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mod.append(round(np.nanstd(times[k]), 2))
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mod.append(round(np.nanmean(q05[k]), 2))
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mod.append(round(np.nanstd(q05[k]), 2))
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mod.append(round(np.nanmean(q25[k]), 2))
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mod.append(round(np.nanstd(q25[k]), 2))
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mod.append(round(np.nanmean(q75[k]), 2))
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mod.append(round(np.nanstd(q75[k]), 2))
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mod.append(round(np.nanmean(q95[k]), 2))
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mod.append(round(np.nanstd(q95[k]), 2))
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mod.append(l)
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ret.append(mod)
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@ -296,6 +303,18 @@ def save_dataframe_interval(coverage, experiments, file, objs, resolution, save,
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tmp = [n, o, s, p, l, 'TIME']
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tmp.extend(times[k])
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ret.append(deepcopy(tmp))
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tmp = [n, o, s, p, l, 'Q05']
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tmp.extend(q05[k])
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ret.append(deepcopy(tmp))
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tmp = [n, o, s, p, l, 'Q25']
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tmp.extend(q25[k])
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ret.append(deepcopy(tmp))
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tmp = [n, o, s, p, l, 'Q75']
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tmp.extend(q75[k])
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ret.append(deepcopy(tmp))
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tmp = [n, o, s, p, l, 'Q95']
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tmp.extend(q95[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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@ -317,7 +336,7 @@ def interval_dataframe_analytic_columns(experiments):
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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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"COVSTD", "TIMEAVG", "TIMESTD", "Q05AVG", "Q05STD", "Q25AVG", "Q25STD", "Q75AVG", "Q75STD", "Q95AVG", "Q95STD", "SIZE"]
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return columns
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@ -3,6 +3,7 @@
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import numpy as np
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from statsmodels.tsa.arima_model import ARIMA as stats_arima
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import scipy.stats as st
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from pyFTS import fts
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@ -15,6 +16,8 @@ class ARIMA(fts.FTS):
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self.name = "ARIMA"
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self.detail = "Auto Regressive Integrated Moving Average"
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self.is_high_order = True
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self.has_point_forecasting = True
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self.has_interval_forecasting = True
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self.model = None
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self.model_fit = None
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self.trained_data = None
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@ -23,6 +26,7 @@ class ARIMA(fts.FTS):
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self.q = 0
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self.benchmark_only = True
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self.min_order = 1
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self.alpha = (1 - kwargs.get("alpha", 0.90))/2
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def train(self, data, sets, order, parameters=None):
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self.p = order[0]
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@ -35,6 +39,7 @@ class ARIMA(fts.FTS):
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try:
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self.model = stats_arima(data, order=(self.p, self.d, self.q))
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self.model_fit = self.model.fit(disp=0)
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print(np.sqrt(self.model_fit.sigma2))
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except Exception as ex:
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print(ex)
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self.model_fit = None
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@ -72,3 +77,64 @@ class ARIMA(fts.FTS):
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ret = self.doInverseTransformations(ret, params=[data[self.order - 1:]])
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return ret
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def forecastInterval(self, data, **kwargs):
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if self.model_fit is None:
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return np.nan
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sigma = np.sqrt(self.model_fit.sigma2)
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ndata = np.array(self.doTransformations(data))
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l = len(ndata)
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ret = []
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for k in np.arange(self.order, l+1):
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tmp = []
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sample = [ndata[i] for i in np.arange(k - self.order, k)]
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mean = self.forecast(sample)[0]
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tmp.append(mean + st.norm.ppf(self.alpha) * sigma)
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tmp.append(mean + st.norm.ppf(1 - self.alpha) * sigma)
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ret.append(tmp)
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ret = self.doInverseTransformations(ret, params=[data[self.order - 1:]])
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return ret
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def forecastAheadInterval(self, data, steps, **kwargs):
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if self.model_fit is None:
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return np.nan
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smoothing = kwargs.get("smoothing",0.2)
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alpha = (1 - kwargs.get("alpha", 0.95))/2
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sigma = np.sqrt(self.model_fit.sigma2)
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ndata = np.array(self.doTransformations(data))
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l = len(ndata)
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means = self.forecastAhead(data,steps,kwargs)
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ret = []
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for k in np.arange(0, steps):
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tmp = []
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hsigma = (1 + k*smoothing)*sigma
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tmp.append(means[k] + st.norm.ppf(alpha) * hsigma)
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tmp.append(means[k] + st.norm.ppf(1 - alpha) * hsigma)
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ret.append(tmp)
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ret = self.doInverseTransformations(ret, params=[data[self.order - 1:]])
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return ret
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@ -73,7 +73,7 @@ def run_point(mfts, partitioner, train_data, test_data, window_key=None, transfo
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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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def point_sliding_window(data, windowsize, train=0.8, inc=0.1, 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=False,nodes=None, depends=None):
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@ -82,6 +82,7 @@ def point_sliding_window(data, windowsize, train=0.8, models=None, partitioners=
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:param data:
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:param windowsize: size of sliding window
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:param train: percentual of sliding window data used to train the models
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:param inc: percentual of window is used do increment
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:param models: FTS point forecasters
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:param partitioners: Universe of Discourse partitioner
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:param partitions: the max number of partitions on the Universe of Discourse
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@ -146,7 +147,7 @@ def point_sliding_window(data, windowsize, train=0.8, models=None, partitioners=
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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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for ct, train, test in Util.sliding_window(data, windowsize, train, inc):
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experiments += 1
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benchmarks_only = {}
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@ -223,13 +224,18 @@ def run_interval(mfts, partitioner, train_data, test_data, window_key=None, tran
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tmp2 = [Grid.GridPartitioner, Entropy.EntropyPartitioner, FCM.FCMPartitioner]
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tmp4 = [arima.ARIMA, quantreg.QuantileRegression]
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tmp3 = [Measures.get_interval_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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if mfts.benchmark_only:
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_key = mfts.shortname + str(mfts.order if mfts.order is not None else "") + str(mfts.alpha)
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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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@ -237,24 +243,26 @@ def run_interval(mfts, partitioner, train_data, test_data, window_key=None, tran
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times = _end - _start
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_start = time.time()
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_sharp, _res, _cov = Measures.get_interval_statistics(test_data, mfts)
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_sharp, _res, _cov, _q05, _q25, _q75, _q95 = Measures.get_interval_statistics(test_data, mfts)
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_end = time.time()
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times += _end - _start
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ret = {'key': _key, 'obj': mfts, 'sharpness': _sharp, 'resolution': _res, 'coverage': _cov, 'time': times,
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'window': window_key}
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'Q05': _q05, 'Q25': _q25, 'Q75': _q75, 'Q95': _q95, 'window': window_key}
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return ret
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def interval_sliding_window(data, windowsize, train=0.8, models=None, partitioners=[Grid.GridPartitioner],
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partitions=[10], max_order=3, transformation=None, indexer=None, dump=False,
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save=False, file=None, sintetic=False,nodes=None, depends=None):
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def interval_sliding_window(data, windowsize, train=0.8, inc=0.1, 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=False,nodes=None, depends=None):
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"""
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Distributed sliding window benchmarks for FTS interval forecasters
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:param data:
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:param windowsize: size of sliding window
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:param train: percentual of sliding window data used to train the models
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:param inc:
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:param models: FTS point forecasters
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:param partitioners: Universe of Discourse partitioner
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:param partitions: the max number of partitions on the Universe of Discourse
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@ -262,6 +270,8 @@ def interval_sliding_window(data, windowsize, train=0.8, models=None, partitione
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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:
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:param benchmark_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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@ -270,6 +280,15 @@ def interval_sliding_window(data, windowsize, train=0.8, models=None, partitione
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:return: DataFrame with the results
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"""
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alphas = [0.5, 0.25]
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if benchmark_models is None and models is None:
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benchmark_models = [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, 0, 0), (1, 0, 1), (2, 0, 1), (2, 0, 2), 1, 2]
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cluster = dispy.JobCluster(run_point, nodes=nodes) #, depends=dependencies)
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http_server = dispy.httpd.DispyHTTPServer(cluster)
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@ -284,6 +303,10 @@ def interval_sliding_window(data, windowsize, train=0.8, models=None, partitione
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sharpness = {}
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resolution = {}
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coverage = {}
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q05 = {}
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q25 = {}
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q75 = {}
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q95 = {}
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times = {}
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if models is None:
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@ -299,12 +322,23 @@ def interval_sliding_window(data, windowsize, train=0.8, models=None, partitione
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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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if benchmark_models is not None:
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for count, model in enumerate(benchmark_models, start=0):
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for a in alphas:
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par = benchmark_models_parameters[count]
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mfts = model(str(par if par is not None else ""), alpha=a)
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mfts.order = par
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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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for ct, train, test in Util.sliding_window(data, windowsize, train, inc=inc):
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experiments += 1
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benchmarks_only = {}
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if dump: print('\nWindow: {0}\n'.format(ct))
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for partition in partitions:
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@ -314,6 +348,10 @@ def interval_sliding_window(data, windowsize, train=0.8, models=None, partitione
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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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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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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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@ -327,11 +365,19 @@ def interval_sliding_window(data, windowsize, train=0.8, models=None, partitione
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resolution[tmp['key']] = []
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coverage[tmp['key']] = []
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times[tmp['key']] = []
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q05[tmp['key']] = []
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q25[tmp['key']] = []
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q75[tmp['key']] = []
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q95[tmp['key']] = []
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sharpness[tmp['key']].append(tmp['sharpness'])
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resolution[tmp['key']].append(tmp['resolution'])
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coverage[tmp['key']].append(tmp['coverage'])
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times[tmp['key']].append(tmp['time'])
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q05[tmp['key']].append(tmp['Q05'])
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q25[tmp['key']].append(tmp['Q25'])
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q75[tmp['key']].append(tmp['Q75'])
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q95[tmp['key']].append(tmp['Q95'])
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print(tmp['key'])
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else:
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print(job.exception)
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@ -350,8 +396,8 @@ def interval_sliding_window(data, windowsize, train=0.8, models=None, partitione
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http_server.shutdown() # this waits until browser gets all updates
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cluster.close()
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return benchmarks.save_dataframe_interval(coverage, experiments, file, objs, resolution, save, sharpness, sintetic,
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times)
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return bUtil.save_dataframe_interval(coverage, experiments, file, objs, resolution, save, sharpness, sintetic,
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times, q05, q25, q75, q95)
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def run_ahead(mfts, partitioner, train_data, test_data, steps, resolution, window_key=None, transformation=None, indexer=None):
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@ -19,7 +19,7 @@ class QuantileRegression(fts.FTS):
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self.has_probability_forecasting = True
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self.benchmark_only = True
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self.minOrder = 1
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self.alpha = 0.5
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self.alpha = kwargs.get("alpha", 0.05)
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self.upper_qt = None
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self.mean_qt = None
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self.lower_qt = None
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@ -27,8 +27,6 @@ class QuantileRegression(fts.FTS):
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def train(self, data, sets, order=1, parameters=None):
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self.order = order
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self.alpha = parameters
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tmp = np.array(self.doTransformations(data))
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lagdata, ndata = lagmat(tmp, maxlag=order, trim="both", original='sep')
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@ -72,7 +72,7 @@ class EnsembleFTS(fts.FTS):
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ret = []
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for k in np.arange(0, l):
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for k in np.arange(0, l+1):
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tmp = []
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for model in self.models:
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if k >= model.minOrder - 1:
|
||||
|
||||
8
fts.py
8
fts.py
@ -88,7 +88,13 @@ class FTS(object):
|
||||
:param kwargs:
|
||||
:return:
|
||||
"""
|
||||
pass
|
||||
ret = []
|
||||
for k in np.arange(0,steps):
|
||||
tmp = self.forecast(data[-self.order:],kwargs)
|
||||
ret.append(tmp)
|
||||
data.append(tmp)
|
||||
|
||||
return ret
|
||||
|
||||
def forecastAheadInterval(self, data, steps, **kwargs):
|
||||
"""
|
||||
|
||||
@ -22,40 +22,36 @@ os.chdir("/home/petronio/dados/Dropbox/Doutorado/Codigos/")
|
||||
#enrollments = pd.read_csv("DataSets/Enrollments.csv", sep=";")
|
||||
#enrollments = np.array(enrollments["Enrollments"])
|
||||
|
||||
#from pyFTS import song
|
||||
|
||||
#enrollments_fs = Grid.GridPartitioner(enrollments, 10).sets
|
||||
|
||||
#model = song.ConventionalFTS('')
|
||||
#model.train(enrollments,enrollments_fs)
|
||||
#teste = model.forecast(enrollments)
|
||||
|
||||
#print(teste)
|
||||
|
||||
|
||||
#print(FCM.FCMPartitionerTrimf.__module__)
|
||||
"""
|
||||
DATASETS
|
||||
"""
|
||||
|
||||
#gauss = random.normal(0,1.0,5000)
|
||||
#gauss_teste = random.normal(0,1.0,400)
|
||||
|
||||
taiexpd = pd.read_csv("DataSets/TAIEX.csv", sep=",")
|
||||
taiex = np.array(taiexpd["avg"][:2000])
|
||||
#taiexpd = pd.read_csv("DataSets/TAIEX.csv", sep=",")
|
||||
#taiex = np.array(taiexpd["avg"][:5000])
|
||||
|
||||
#nasdaqpd = pd.read_csv("DataSets/NASDAQ_IXIC.csv", sep=",")
|
||||
#nasdaq = np.array(nasdaqpd["avg"][0:5000])
|
||||
|
||||
#from statsmodels.tsa.arima_model import ARIMA as stats_arima
|
||||
from statsmodels.tsa.tsatools import lagmat
|
||||
#sp500pd = pd.read_csv("DataSets/S&P500.csv", sep=",")
|
||||
#sp500 = np.array(sp500pd["Avg"][11000:])
|
||||
#del(sp500pd)
|
||||
|
||||
#tmp = np.arange(10)
|
||||
sondapd = pd.read_csv("DataSets/SONDA_BSB_HOURLY_AVG.csv", sep=";")
|
||||
sondapd = sondapd.dropna(axis=0, how='any')
|
||||
sonda = np.array(sondapd["ws_10m"])
|
||||
del(sondapd)
|
||||
|
||||
#lag, a = lagmat(tmp, maxlag=2, trim="both", original='sep')
|
||||
#bestpd = pd.read_csv("DataSets/BEST_TAVG.csv", sep=";")
|
||||
#best = np.array(bestpd["Anomaly"])
|
||||
|
||||
#print(lag)
|
||||
#print(a)
|
||||
|
||||
from pyFTS.benchmarks import benchmarks as bchmk
|
||||
#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
|
||||
@ -66,9 +62,9 @@ from pyFTS.benchmarks import arima, quantreg
|
||||
|
||||
#tmp = arima.ARIMA("")
|
||||
#tmp.train(taiex[:1600], None, order=(2,0,2))
|
||||
#teste = tmp.forecast(taiex[1600:1605])
|
||||
#teste = tmp.forecastInterval(taiex[1600:1605])
|
||||
|
||||
#tmp = quantreg.QuantileRegression("")
|
||||
#tmp = quan#treg.QuantileRegression("")
|
||||
#tmp.train(taiex[:1600], None, order=2)
|
||||
#teste = tmp.forecast(taiex[1600:1605])
|
||||
|
||||
@ -80,21 +76,20 @@ from pyFTS.benchmarks import arima, quantreg
|
||||
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], # #
|
||||
bchmk.point_sliding_window(sonda, 9000, train=0.8, inc=0.4,#models=[yu.WeightedFTS], # #
|
||||
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])
|
||||
partitions= np.arange(10,200,step=10), #transformation=diff,
|
||||
dump=True, save=True, file="experiments/sondaws_point_analytic.csv",
|
||||
nodes=['192.168.0.103', '192.168.0.106', '192.168.0.108', '192.168.0.109']) #, depends=[hofts, ifts])
|
||||
|
||||
"""
|
||||
diff = Transformations.Differential(1)
|
||||
|
||||
bchmk.point_sliding_window(taiex,2000,train=0.8, #models=[yu.WeightedFTS], # #
|
||||
bchmk.point_sliding_window(sonda, 9000, train=0.8, inc=0.4, #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_diff.csv",
|
||||
nodes=['192.168.0.102', '192.168.0.109', '192.168.0.106']) #, depends=[hofts, ifts])
|
||||
"""
|
||||
partitions= np.arange(3,20,step=2), #transformation=diff,
|
||||
dump=True, save=True, file="experiments/sondaws_point_analytic_diff.csv",
|
||||
nodes=['192.168.0.103', '192.168.0.106', '192.168.0.108', '192.168.0.109']) #, 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