HOFTS bugfix
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Petrônio Cândido
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0017d471b5
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@ -187,6 +187,8 @@ class HighOrderFTS(fts.FTS):
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fuzzyfied = kwargs.get('fuzzyfied', False)
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fuzzyfied = kwargs.get('fuzzyfied', False)
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mode = kwargs.get('mode', 'mean')
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ret = []
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ret = []
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l = len(ndata) if not explain else self.max_lag + 1
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l = len(ndata) if not explain else self.max_lag + 1
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@ -234,7 +236,11 @@ class HighOrderFTS(fts.FTS):
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print("\t {} \t Midpoint: {}\n".format(str(flrg), mp))
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print("\t {} \t Midpoint: {}\n".format(str(flrg), mp))
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print("\t {} \t Membership: {}\n".format(str(flrg), mv))
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print("\t {} \t Membership: {}\n".format(str(flrg), mv))
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final = np.dot(midpoints, memberships) if not fuzzyfied else np.nanmean(midpoints)
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if mode == "mean" or fuzzyfied:
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final = np.nanmean(midpoints)
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else:
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final = np.dot(midpoints, memberships)
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ret.append(final)
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ret.append(final)
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if explain:
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if explain:
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@ -25,235 +25,13 @@ from pyFTS.data import TAIEX, SP500, NASDAQ, Malaysia, Enrollments
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from pyFTS.partitioners import Grid
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from pyFTS.partitioners import Grid
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from pyFTS.models import pwfts, tsaur
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from pyFTS.models import pwfts, tsaur
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dataset = pd.read_csv('/home/petronio/Downloads/Klang-daily Max.csv', sep=',')
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x = [k for k in np.arange(-2*np.pi, 2*np.pi, 0.5)]
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y = [np.sin(k) for k in x]
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dataset['date'] = pd.to_datetime(dataset["Day/Month/Year"], format='%m/%d/%Y')
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part = Grid.GridPartitioner(data=y, npart=35)
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dataset['value'] = dataset['Daily-Max API']
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model = hofts.HighOrderFTS(order=2, partitioner=part)
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model.fit(y)
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forecasts = model.predict(y)
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print([round(k,2) for k in y[2:]])
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train_uv = dataset['value'].values[:732]
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print([round(k,2) for k in forecasts[:-1]])
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test_uv = dataset['value'].values[732:]
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from itertools import product
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levels = ['VeryLow', 'Low', 'Medium', 'High', 'VeryHigh']
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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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#partitioner = Grid.GridPartitioner(data=train_uv, npart=35, names=names)
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partitioner = Entropy.EntropyPartitioner(data=train_uv,npart=35, names=names)
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print(partitioner)
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model = pwfts.ProbabilisticWeightedFTS(partitioner=partitioner) #, order=2, lags=[3,4])
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#model = tsaur.MarkovWeightedFTS(partitioner=partitioner)
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model.fit(train_uv)
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from pyFTS.benchmarks import benchmarks as bchmk
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print(model)
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print(model.forecast(test_uv))
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#distributions = model.predict(y[800:820])
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#print(distributions)
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'''
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#dataset = SP500.get_data()[11500:16000]
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#dataset = NASDAQ.get_data()
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#print(len(dataset))
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bchmk.sliding_window_benchmarks(dataset, 1000, train=0.8, inc=0.2,
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methods=[chen.ConventionalFTS], #[pwfts.ProbabilisticWeightedFTS],
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benchmark_models=False,
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transformations=[None],
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#orders=[1, 2, 3],
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partitions=np.arange(10, 100, 2),
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progress=False, type="point",
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#steps_ahead=[1,2,4,6,8,10],
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distributed=False, nodes=['192.168.0.110', '192.168.0.107', '192.168.0.106'],
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file="benchmarks.db", dataset="TAIEX", tag="comparisons")
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bchmk.sliding_window_benchmarks(dataset, 1000, train=0.8, inc=0.2,
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methods=[chen.ConventionalFTS], # [pwfts.ProbabilisticWeightedFTS],
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benchmark_models=False,
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transformations=[tdiff],
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#orders=[1, 2, 3],
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partitions=np.arange(3, 30, 1),
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progress=False, type="point",
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#steps_ahead=[1,2,4,6,8,10],
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distributed=False, nodes=['192.168.0.110', '192.168.0.107', '192.168.0.106'],
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file="benchmarks.db", dataset="NASDAQ", tag="comparisons")
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'''
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'''
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from pyFTS.partitioners import Grid, Util as pUtil
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partitioner = Grid.GridPartitioner(data=dataset[:800], npart=10, transformation=tdiff)
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model = pwfts.ProbabilisticWeightedFTS('',partitioner=partitioner)
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model.append_transformation(tdiff)
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model.fit(dataset[:800])
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print(Measures.get_distribution_statistics(dataset[800:1000], model, steps_ahead=7))
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#tmp = model.predict(dataset[800:1000], type='distribution', steps_ahead=7)
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#for tmp2 in tmp:
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# print(tmp2)
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'''
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'''
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types = ['point','interval','distribution']
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benchmark_methods=[[arima.ARIMA for k in range(8)] + [quantreg.QuantileRegression for k in range(4)]]
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benchmark_methods=[
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[arima.ARIMA for k in range(4)] + [naive.Naive],
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[arima.ARIMA for k in range(8)] + [quantreg.QuantileRegression for k in range(4)],
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[arima.ARIMA for k in range(4)] + [quantreg.QuantileRegression for k in range(2)]
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+ [knn.KNearestNeighbors for k in range(3)]
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]
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benchmark_methods_parameters= [
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[
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{'order': (1, 0, 0), 'alpha': .05},
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{'order': (1, 0, 0), 'alpha': .25},
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{'order': (1, 0, 1), 'alpha': .05},
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{'order': (1, 0, 1), 'alpha': .25},
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{'order': (2, 0, 1), 'alpha': .05},
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{'order': (2, 0, 1), 'alpha': .25},
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{'order': (2, 0, 2), 'alpha': .05},
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{'order': (2, 0, 2), 'alpha': .25},
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{'order': 1, 'alpha': .05},
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{'order': 1, 'alpha': .25},
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{'order': 2, 'alpha': .05},
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{'order': 2, 'alpha': .25}
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]
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]
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benchmark_methods_parameters= [
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[
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{'order': (1, 0, 0)},
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{'order': (1, 0, 1)},
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{'order': (2, 0, 1)},
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{'order': (2, 0, 2)},
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{},
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],[
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{'order': (1, 0, 0), 'alpha': .05},
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{'order': (1, 0, 0), 'alpha': .25},
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{'order': (1, 0, 1), 'alpha': .05},
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{'order': (1, 0, 1), 'alpha': .25},
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{'order': (2, 0, 1), 'alpha': .05},
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{'order': (2, 0, 1), 'alpha': .25},
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{'order': (2, 0, 2), 'alpha': .05},
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{'order': (2, 0, 2), 'alpha': .25},
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{'order': 1, 'alpha': .05},
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{'order': 1, 'alpha': .25},
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{'order': 2, 'alpha': .05},
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{'order': 2, 'alpha': .25}
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],[
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{'order': (1, 0, 0)},
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{'order': (1, 0, 1)},
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{'order': (2, 0, 1)},
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{'order': (2, 0, 2)},
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{'order': 1, 'dist': True},
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{'order': 2, 'dist': True},
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{'order': 1}, {'order': 2}, {'order': 3},
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]
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]
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dataset_name = "SP500"
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tag = "ahead2"
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from pyFTS.benchmarks import arima, naive, quantreg
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for ct, type in enumerate(types):
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bchmk.sliding_window_benchmarks(dataset, 1000, train=0.8, inc=0.2,
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methods=[pwfts.ProbabilisticWeightedFTS],
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benchmark_models=False,
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#benchmark_methods=benchmark_methods[ct],
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#benchmark_methods_parameters=benchmark_methods_parameters[ct],
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transformations=[tdiff],
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orders=[1], #, 2, 3],
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partitions=[5], #np.arange(3, 35, 2),
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progress=False, type=type,
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steps_ahead=[2, 4, 6, 8, 10],
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distributed=True, nodes=['192.168.0.110', '192.168.0.107', '192.168.0.106'],
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file="benchmarks.db", dataset=dataset_name, tag=tag)
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bchmk.sliding_window_benchmarks(dataset, 1000, train=0.8, inc=0.2,
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methods=[pwfts.ProbabilisticWeightedFTS],
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benchmark_models=False,
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#benchmark_methods=benchmark_methods[ct],
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#benchmark_methods_parameters=benchmark_methods_parameters[ct],
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transformations=[None],
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orders=[1], #,2,3],
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partitions=[30], #np.arange(15, 85, 5),
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progress=False, type=type,
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steps_ahead=[2, 4, 6, 8, 10],
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distributed=True, nodes=['192.168.0.110', '192.168.0.107','192.168.0.106'],
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file="benchmarks.db", dataset=dataset_name, tag=tag)
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'''
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'''
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dat = pd.read_csv('pwfts_taiex_partitioning.csv', sep=';')
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print(bUtil.analytic_tabular_dataframe(dat))
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#print(dat["Size"].values[0])
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'''
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'''
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train_split = 2000
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test_length = 200
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from pyFTS.partitioners import Grid, Util as pUtil
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partitioner = Grid.GridPartitioner(data=dataset[:train_split], npart=30)
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#partitioner = Grid.GridPartitioner(data=dataset[:train_split], npart=10, transformation=tdiff)
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from pyFTS.common import fts,tree
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from pyFTS.models import hofts, pwfts
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pfts1_taiex = pwfts.ProbabilisticWeightedFTS("1", partitioner=partitioner)
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#pfts1_taiex.append_transformation(tdiff)
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pfts1_taiex.fit(dataset[:train_split], save_model=True, file_path='pwfts')
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pfts1_taiex.shortname = "1st Order"
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print(pfts1_taiex)
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tmp = pfts1_taiex.predict(dataset[train_split:train_split+200], type='point',
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method='heuristic')
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print(tmp)
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tmp = pfts1_taiex.predict(dataset[train_split:train_split+200], type='point',
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method='expected_value')
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print(tmp)
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'''
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'''
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tmp = pfts1_taiex.predict(dataset[train_split:train_split+200], type='diPedro Pazzini
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stribution', steps_ahead=20)
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f, ax = plt.subplots(3, 4, figsize=[20,15])
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tmp[0].plot(ax[0][0], title='t=1')
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tmp[2].plot(ax[0][1], title='t=20')
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tmp[4].plot(ax[0][2], title='t=40')
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tmp[6].plot(ax[0][3], title='t=60')
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tmp[8].plot(ax[1][0], title='t=80')
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tmp[10].plot(ax[1][1], title='t=100')
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tmp[12].plot(ax[1][2], title='t=120')
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tmp[14].plot(ax[1][3], title='t=140')
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tmp[16].plot(ax[2][0], title='t=160')
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tmp[18].plot(ax[2][1], title='t=180')
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tmp[20].plot(ax[2][2], title='t=200')
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f, ax = plt.subplots(1, 1, figsize=[20,15])
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bchmk.plot_distribution(ax, 'blue', tmp, f, 0, reference_data=dataset[train_split:train_split+200])
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'''
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