Bugfixes in FTS, HOFTS and CVFTS
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Petrônio Cândido
parent
c6f9af8e00
commit
f5652f7029
@ -332,11 +332,11 @@ def get_point_statistics(data, model, **kwargs):
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if not isinstance(forecasts, (list, np.ndarray)):
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forecasts = [forecasts]
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nforecasts = np.array(forecasts[:-1])
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forecasts = np.array(forecasts[:-1])
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ret.append(np.round(rmse(ndata[model.max_lag:], nforecasts), 2))
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ret.append(np.round(mape(ndata[model.max_lag:], nforecasts), 2))
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ret.append(np.round(UStatistic(ndata[model.max_lag:], nforecasts), 2))
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ret.append(np.round(rmse(ndata[model.max_lag:], forecasts), 2))
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ret.append(np.round(mape(ndata[model.max_lag:], forecasts), 2))
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ret.append(np.round(UStatistic(ndata[model.max_lag:], forecasts), 2))
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else:
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steps_ahead_sampler = kwargs.get('steps_ahead_sampler', 1)
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nforecasts = []
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@ -7,6 +7,7 @@
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import datetime
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import time
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from copy import deepcopy
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import traceback
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import matplotlib as plt
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import matplotlib.cm as cmx
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@ -245,8 +246,8 @@ def sliding_window_benchmarks(data, windowsize, train=0.8, **kwargs):
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job = experiment_method(deepcopy(model), None, train, test, **kwargs)
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synthesis_method(dataset, tag, job, conn)
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except Exception as ex:
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print("Error evaluating model " + model.shortname)
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print(ex)
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print('EXCEPTION! ', model.shortname, model.order)
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traceback.print_exc()
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else:
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job = cluster.submit(deepcopy(model), None, train, test, **kwargs)
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jobs.append(job)
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@ -279,11 +280,14 @@ def sliding_window_benchmarks(data, windowsize, train=0.8, **kwargs):
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if progress:
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progressbar.update(1)
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try:
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print(model.shortname, model.order, partitioner.name,
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partitioner.partitions, str(partitioner.transformation))
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job = experiment_method(deepcopy(model), deepcopy(partitioner), train, test, **kwargs)
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synthesis_method(dataset, tag, job, conn)
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except Exception as ex:
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print("Error evaluating model " + model.shortname + " " + str(partitioner))
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print(ex)
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print('EXCEPTION! ',model.shortname, model.order, partitioner.name,
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partitioner.partitions, str(partitioner.transformation))
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traceback.print_exc()
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else:
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job = cluster.submit(deepcopy(model), deepcopy(partitioner), train, test, **kwargs)
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job.id = id # associate an ID to identify jobs (if needed later)
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@ -144,7 +144,7 @@ class FTS(object):
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if not self.is_multivariate:
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kwargs['type'] = type
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ret = self.apply_inverse_transformations(ret, params=[data[self.order - 1:]], **kwargs)
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ret = self.apply_inverse_transformations(ret, params=[data[self.max_lag - 1:]], **kwargs)
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return ret
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@ -38,6 +38,7 @@ class ConditionalVarianceFTS(hofts.HighOrderFTS):
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self.shortname = "CVFTS "
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self.detail = ""
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self.flrgs = {}
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self.is_high_order = False
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if self.partitioner is not None:
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self.append_transformation(self.partitioner.transformation)
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@ -132,3 +132,19 @@ class SimpleNonStationaryPartitioner(partitioner.Partitioner):
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def build(self, data):
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return {}
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def simplenonstationary_gridpartitioner_builder(data, npart, transformation):
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from pyFTS.partitioners import Grid
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from pyFTS.models.nonstationary import perturbation, partitioners
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tmp_fs = Grid.GridPartitioner(data=data, npart=npart, transformation=transformation)
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fs = partitioners.SimpleNonStationaryPartitioner(data, tmp_fs,
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location=perturbation.polynomial,
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location_params=[1, 0],
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location_roots=0,
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width=perturbation.polynomial,
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width_params=[1, 0],
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width_roots=0)
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return fs
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@ -1,7 +1,7 @@
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import os
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import numpy as np
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from pyFTS.common import Membership, Transformations
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from pyFTS.models.nonstationary import common, perturbation, partitioners, util
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from pyFTS.models.nonstationary import common, perturbation, partitioners as nspart, util
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from pyFTS.models.nonstationary import nsfts, cvfts
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from pyFTS.partitioners import Grid, Entropy
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import matplotlib.pyplot as plt
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@ -33,36 +33,47 @@ tdiff = Transformations.Differential(1)
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boxcox = Transformations.BoxCox(0)
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transformations = {'None': None, 'Differential(1)': tdiff, 'BoxCox(0)': boxcox }
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transformations = {
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'None': None,
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'Differential(1)': tdiff,
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'BoxCox(0)': boxcox
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}
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from pyFTS.partitioners import Grid, Util as pUtil
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from pyFTS.benchmarks import benchmarks as bchmk
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from pyFTS.models import chen
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from pyFTS.models import chen, hofts, pwfts, hwang
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partitions = {'CMIV': {'BoxCox(0)': 17, 'Differential(1)': 7, 'None': 13},
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'IMCV': {'BoxCox(0)': 22, 'Differential(1)': 9, 'None': 25},
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'IMIV': {'BoxCox(0)': 27, 'Differential(1)': 11, 'None': 6},
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'NASDAQ': {'BoxCox(0)': 39, 'Differential(1)': 10, 'None': 34},
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'SP500': {'BoxCox(0)': 38, 'Differential(1)': 15, 'None': 39},
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'TAIEX': {'BoxCox(0)': 36, 'Differential(1)': 18, 'None': 38}}
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partitions = {'CMIV': {'BoxCox(0)': 36, 'Differential(1)': 11, 'None': 8},
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'IMCV': {'BoxCox(0)': 36, 'Differential(1)': 20, 'None': 16},
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'IMIV': {'BoxCox(0)': 39, 'Differential(1)': 12, 'None': 6},
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'IMIV0': {'BoxCox(0)': 39, 'Differential(1)': 12, 'None': 3},
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'NASDAQ': {'BoxCox(0)': 39, 'Differential(1)': 13, 'None': 36},
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'SP500': {'BoxCox(0)': 33, 'Differential(1)': 7, 'None': 33},
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'TAIEX': {'BoxCox(0)': 39, 'Differential(1)': 31, 'None': 33}}
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tag = 'benchmarks'
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'''
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for ds in datasets.keys():
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dataset = datasets[ds]
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def nsfts_partitioner_builder(data, npart, transformation):
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from pyFTS.partitioners import Grid
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from pyFTS.models.nonstationary import perturbation, partitioners
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for tf in transformations.keys():
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transformation = transformations[tf]
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tmp_fs = Grid.GridPartitioner(data=data, npart=npart, transformation=transformation)
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fs = partitioners.SimpleNonStationaryPartitioner(data, tmp_fs,
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location=perturbation.polynomial,
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location_params=[1, 0],
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location_roots=0,
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width=perturbation.polynomial,
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width_params=[1, 0],
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width_roots=0)
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return fs
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partitioning = partitions[ds][tf]
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bchmk.sliding_window_benchmarks(dataset, 2000, train=0.2, inc=0.2,
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methods=[
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hwang.HighOrderFTS,
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hofts.HighOrderFTS,
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pwfts.ProbabilisticWeightedFTS],
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#orders = [3],
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benchmark_models=False,
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transformations=[transformation],
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partitions=[partitioning],
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progress=False, type='point',
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file="nsfts_benchmarks.db", dataset=ds, tag=tag)
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'''
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for ds in datasets.keys():
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dataset = datasets[ds]
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@ -75,7 +86,7 @@ for ds in datasets.keys():
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bchmk.sliding_window_benchmarks(dataset, 2000, train=0.2, inc=0.2,
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benchmark_models=False,
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methods=[cvfts.ConditionalVarianceFTS],
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partitioners_methods=[nsfts_partitioner_builder],
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partitioners_methods=[nspart.simplenonstationary_gridpartitioner_builder],
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transformations=[transformation],
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partitions=[partitioning],
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progress=False, type='point',
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