Bugfix in models.multivariate.grid
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pyFTS
@ -43,7 +43,7 @@ class MultivariateFuzzySet(Composite.FuzzySet):
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def fuzzyfy_instance(data_point, var, tuples=True):
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def fuzzyfy_instance(data_point, var, tuples=True):
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fsets = var.partitioner(data_point, mode='sets', method='fuzzy', alpha_cut=var.alpha_cut)
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fsets = var.partitioner.fuzzyfy(data_point, mode='sets', method='fuzzy', alpha_cut=var.alpha_cut)
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if tuples:
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if tuples:
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return [(var.name, fs) for fs in fsets]
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return [(var.name, fs) for fs in fsets]
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else:
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else:
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@ -14,17 +14,73 @@ from pyFTS.benchmarks import benchmarks as bchmk, Measures
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from pyFTS.models import chen, yu, cheng, ismailefendi, hofts, pwfts, tsaur, song, sadaei
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from pyFTS.models import chen, yu, cheng, ismailefendi, hofts, pwfts, tsaur, song, sadaei
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from pyFTS.common import Transformations, Membership
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from pyFTS.common import Transformations, Membership
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from pyFTS.data import TAIEX
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dataset = pd.read_csv('https://query.data.world/s/2bgegjggydd3venttp3zlosh3wpjqj', sep=';')
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data = TAIEX.get_data()
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dataset['data'] = pd.to_datetime(dataset["data"], format='%Y-%m-%d %H:%M:%S')
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fs = Grid.GridPartitioner(data=data, npart=23)
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train_mv = dataset.iloc[:24505]
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test_mv = dataset.iloc[24505:]
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from itertools import product
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levels = ['VL', 'L', 'M', 'H', 'VH']
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sublevels = [str(k) for k in np.arange(0, 7)]
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names = []
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for combination in product(*[levels, sublevels]):
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names.append(combination[0] + combination[1])
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print(names)
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from pyFTS.models.multivariate import common, variable, mvfts
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from pyFTS.models.seasonal import partitioner as seasonal
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from pyFTS.models.seasonal.common import DateTime
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sp = {'seasonality': DateTime.day_of_year , 'names': ['Jan','Feb','Mar','Apr','May',
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'Jun','Jul', 'Aug','Sep','Oct',
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'Nov','Dec']}
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vmonth = variable.Variable("Month", data_label="data", partitioner=seasonal.TimeGridPartitioner, npart=12,
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data=train_mv, partitioner_specific=sp)
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sp = {'seasonality': DateTime.minute_of_day, 'names': [str(k)+'hs' for k in range(0,24)]}
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vhour = variable.Variable("Hour", data_label="data", partitioner=seasonal.TimeGridPartitioner, npart=24,
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data=train_mv, partitioner_specific=sp)
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vavg = variable.Variable("Radiation", data_label="glo_avg", alias='rad',
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partitioner=Grid.GridPartitioner, npart=35, partitioner_specific={'names': names},
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data=train_mv)
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from pyFTS.models.multivariate import mvfts, wmvfts, cmvfts, grid
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parameters = [
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{}, {},
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{'order': 2, 'knn': 1},
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{'order': 2, 'knn': 2},
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{'order': 2, 'knn': 3},
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]
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for ct, method in enumerate([mvfts.MVFTS, wmvfts.WeightedMVFTS,
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cmvfts.ClusteredMVFTS, cmvfts.ClusteredMVFTS, cmvfts.ClusteredMVFTS]):
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if method != cmvfts.ClusteredMVFTS:
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model = method(explanatory_variables=[vmonth, vhour, vavg], target_variable=vavg, **parameters[ct])
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else:
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fs = grid.GridCluster(explanatory_variables=[vmonth, vhour, vavg], target_variable=vavg)
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model = method(explanatory_variables=[vmonth, vhour, vavg], target_variable=vavg, partitioner=fs,
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**parameters[ct])
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model.shortname += str(ct)
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model.fit(train_mv)
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forecasts = model.predict(test_mv.iloc[:100])
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print(model.shortname, forecasts)
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test = [2000, 5000, 5500, 12000]
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for method in [yu.WeightedFTS, tsaur.MarkovWeightedFTS, song.ConventionalFTS, sadaei.ExponentialyWeightedFTS, ismailefendi.ImprovedWeightedFTS,
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chen.ConventionalFTS, cheng.TrendWeightedFTS, hofts.HighOrderFTS, pwfts.ProbabilisticWeightedFTS]:
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model = method(partitioner=fs)
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model.fit(data)
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print(model.forecast(test))
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