- Several bugfixes in benchmarks methods and optimizations
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Petrônio Cândido de Lima e Silva
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ff67356d64
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692503704e
@ -10,7 +10,7 @@ class ARIMA(fts.FTS):
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"""
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Façade for statsmodels.tsa.arima_model
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"""
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def __init__(self, order, **kwargs):
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def __init__(self, name, **kwargs):
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super(ARIMA, self).__init__(1, "ARIMA")
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self.name = "ARIMA"
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self.detail = "Auto Regressive Integrated Moving Average"
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@ -24,34 +24,41 @@ class ARIMA(fts.FTS):
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self.benchmark_only = True
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self.min_order = 1
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def train(self, data, sets, order=1, parameters=None):
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if parameters is not None:
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self.p = parameters[0]
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self.d = parameters[1]
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self.q = parameters[2]
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def train(self, data, sets, order=(2,1,1), parameters=None):
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self.p = order[0]
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self.d = order[1]
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self.q = order[2]
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self.order = max([self.p, self.d, self.q])
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self.shortname = "ARIMA(" + str(self.p) + "," + str(self.d) + "," + str(self.q) + ")"
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old_fit = self.model_fit
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self.model = stats_arima(data, order=(self.p, self.d, self.q))
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try:
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self.model_fit = self.model.fit(disp=0)
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except:
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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=1)
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except:
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self.model_fit = old_fit
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self.trained_data = data #.tolist()
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def ar(self, data):
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return data.dot(self.model_fit.arparams)
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def ma(self, data):
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return data.dot(self.model_fit.maparams)
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def forecast(self, data, **kwargs):
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if self.model_fit is None:
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return np.nan
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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 t in data:
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output = self.model_fit.forecast()
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ret.append( output[0] )
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self.trained_data = np.append(self.trained_data, t) #.append(t)
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self.train(self.trained_data,None,order=self.order, parameters=(self.p, self.d, self.q))
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ar = np.array([self.ar(ndata[k - self.p: k]) for k in np.arange(self.p, l)])
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residuals = np.array([ar[k - self.p] - ndata[k] for k in np.arange(self.p, l)])
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ma = np.array([self.ma(residuals[k - self.q: k]) for k in np.arange(self.q, len(ar) + 1)])
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ret = ar + ma
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ret = self.doInverseTransformations(ret, params=[data[self.order - 1:]])
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return ret
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@ -60,73 +60,9 @@ def get_probabilistic_methods():
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return [quantreg.QuantileRegression, ensemble.EnsembleFTS, pwfts.ProbabilisticWeightedFTS]
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def external_point_sliding_window(models, parameters, data, windowsize,train=0.8, dump=False,
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save=False, file=None, sintetic=True):
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"""
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Sliding window benchmarks for non FTS point forecasters
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:param models: non FTS point forecasters
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:param parameters: parameters for each model
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:param data: data set
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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 dump:
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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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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 count, method in enumerate(models, start=0):
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model = method("")
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_start = time.time()
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model.train(train, None, parameters=parameters[count])
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_end = time.time()
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_key = model.shortname
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if dump: print(ct, _key)
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if _key not in objs:
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objs[_key] = model
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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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_tdiff = _end - _start
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try:
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_start = time.time()
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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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u[_key].append(_u)
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_tdiff += _end - _start
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times[_key].append(_tdiff)
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if dump: print(_rmse, _smape, _u, _tdiff)
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except:
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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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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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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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"""
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Sliding window benchmarks for FTS point forecasters
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@ -153,7 +89,6 @@ def point_sliding_window(data, windowsize, train=0.8,models=None,partitioners=[G
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if models is None:
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models = get_point_methods()
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objs = {}
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lcolors = {}
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rmse = {}
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@ -13,7 +13,7 @@ import dispy
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import dispy.httpd
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import numpy as np
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from pyFTS.benchmarks import benchmarks, Util as bUtil
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from pyFTS.benchmarks import benchmarks, Util as bUtil, naive, quantreg, arima
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from pyFTS.common import Util
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from pyFTS.partitioners import Grid
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@ -31,17 +31,23 @@ def run_point(mfts, partitioner, train_data, test_data, window_key=None, transfo
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:return: a dictionary with the benchmark results
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"""
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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 import yu,chen,hofts,ifts,pwfts,ismailefendi,sadaei, song, cheng, hwang
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from pyFTS.partitioners import Grid, Entropy, FCM
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from pyFTS.benchmarks import Measures
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from pyFTS.benchmarks import Measures, naive, arima, quantreg
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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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tmp = [song.ConventionalFTS, chen.ConventionalFTS, yu.WeightedFTS, ismailefendi.ImprovedWeightedFTS,
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cheng.TrendWeightedFTS, sadaei.ExponentialyWeightedFTS, hofts.HighOrderFTS, hwang.HighOrderFTS,
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pwfts.ProbabilisticWeightedFTS]
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tmp2 = [Grid.GridPartitioner, Entropy.EntropyPartitioner, FCM.FCMPartitioner]
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tmp4 = [naive.Naive, arima.ARIMA, quantreg.QuantileRegression]
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tmp3 = [Measures.get_point_statistics]
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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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@ -65,6 +71,7 @@ def run_point(mfts, partitioner, train_data, test_data, window_key=None, transfo
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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=False,nodes=None, depends=None):
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"""
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Distributed sliding window benchmarks for FTS point forecasters
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@ -78,6 +85,8 @@ def point_sliding_window(data, windowsize, train=0.8, models=None, partitioners=
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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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@ -86,6 +95,13 @@ def point_sliding_window(data, windowsize, train=0.8, models=None, partitioners=
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:return: DataFrame with the results
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"""
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if benchmark_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 = [None, (1, 0, 1), (1, 1, 1), (2, 1, 1), (2, 1, 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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@ -116,11 +132,20 @@ def point_sliding_window(data, windowsize, train=0.8, models=None, partitioners=
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pool.append(mfts)
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else:
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pool.append(mfts)
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mfts.order = 1
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pool.append(mfts)
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for count, model in enumerate(benchmark_models, start=0):
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mfts = model("")
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mfts.order = benchmark_models_parameters[count]
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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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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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@ -129,9 +154,13 @@ def point_sliding_window(data, windowsize, train=0.8, models=None, 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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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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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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@ -6,7 +6,7 @@ from pyFTS import fts
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class Naive(fts.FTS):
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"""Naïve Forecasting method"""
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def __init__(self, order, name, **kwargs):
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def __init__(self, name, **kwargs):
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super(Naive, self).__init__(1, "Naive " + name)
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self.name = "Naïve Model"
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self.detail = "Naïve Model"
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@ -9,7 +9,7 @@ from pyFTS import fts
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class QuantileRegression(fts.FTS):
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"""Façade for statsmodels.regression.quantile_regression"""
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def __init__(self, order, **kwargs):
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def __init__(self, name, **kwargs):
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super(QuantileRegression, self).__init__(1, "QR")
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self.name = "QR"
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self.detail = "Quantile Regression"
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@ -27,18 +27,20 @@ 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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if parameters is not None:
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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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uqt = QuantReg(ndata, lagdata).fit(1 - self.alpha)
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mqt = QuantReg(ndata, lagdata).fit(0.5)
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if self.alpha is not None:
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uqt = QuantReg(ndata, lagdata).fit(1 - self.alpha)
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lqt = QuantReg(ndata, lagdata).fit(self.alpha)
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self.mean_qt = [k for k in mqt.params]
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if self.alpha is not None:
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self.upper_qt = [uqt.params[k] for k in uqt.params.keys()]
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self.mean_qt = [mqt.params[k] for k in mqt.params.keys()]
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self.lower_qt = [lqt.params[k] for k in lqt.params.keys()]
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def linearmodel(self,data,params):
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@ -38,9 +38,8 @@ os.chdir("/home/petronio/dados/Dropbox/Doutorado/Codigos/")
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#gauss = random.normal(0,1.0,5000)
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#gauss_teste = random.normal(0,1.0,400)
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#taiexpd = pd.read_csv("DataSets/TAIEX.csv", sep=",")
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#taiex = np.array(taiexpd["avg"][:5000])
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taiexpd = pd.read_csv("DataSets/TAIEX.csv", sep=",")
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taiex = np.array(taiexpd["avg"][:5000])
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#nasdaqpd = pd.read_csv("DataSets/NASDAQ_IXIC.csv", sep=",")
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#nasdaq = np.array(nasdaqpd["avg"][0:5000])
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@ -62,7 +61,7 @@ from pyFTS.benchmarks import Util
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#Util.cast_dataframe_to_sintetic_point("experiments/taiex_point_analitic.csv","experiments/taiex_point_sintetic.csv",11)
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Util.plot_dataframe_point("experiments/taiex_point_sintetic.csv","experiments/taiex_point_analitic.csv",11)
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#Util.plot_dataframe_point("experiments/taiex_point_sintetic.csv","experiments/taiex_point_analitic.csv",11)
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#tmp = arima.ARIMA("")
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#tmp.train(taiex[:1600],None,parameters=(2,0,1))
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@ -72,11 +71,11 @@ Util.plot_dataframe_point("experiments/taiex_point_sintetic.csv","experiments/ta
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#bchmk.teste(taiex,['192.168.0.109', '192.168.0.101'])
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#bchmk.point_sliding_window(gauss,2000,train=0.8, #models=[yu.WeightedFTS], # #
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# partitioners=[Grid.GridPartitioner], #Entropy.EntropyPartitioner], # FCM.FCMPartitioner, ],
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# partitions= np.arange(3,10,step=1), #transformation=diff,
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# dump=True, save=True, file="experiments/gauss_point_distributed.csv",
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# nodes=['192.168.0.102', '192.168.0.109']) #, depends=[hofts, ifts])
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bchmk.point_sliding_window(taiex,2000,train=0.8, #models=[yu.WeightedFTS], # #
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partitioners=[Grid.GridPartitioner], #Entropy.EntropyPartitioner], # FCM.FCMPartitioner, ],
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partitions= np.arange(10,200,step=10), #transformation=diff,
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dump=True, save=True, file="experiments/taiex_point_analytic.csv",
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nodes=['192.168.0.102', '192.168.0.109']) #, depends=[hofts, ifts])
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#bchmk.testa(taiex,[10,20],partitioners=[Grid.GridPartitioner], nodes=['192.168.0.109', '192.168.0.101'])
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