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Bugfix in benchmarks.mv_run methods

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This commit is contained in:
Petrônio Cândido 2020-01-27 21:30:38 -03:00
parent 715772bbcc
commit dbfa1ac86e
2 changed files with 58 additions and 6 deletions
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12
pyFTS/benchmarks/benchmarks.py
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@ -770,7 +770,9 @@ def mv_run_point2(mfts, train_data, test_data, window_key=None, **kwargs):
_end = time.time()
times += _end - _start
eval = Measures.get_point_ahead_statistics(test_data[mfts.order:mfts.order + steps_ahead], forecasts)
tmp_test = test_data[mfts.target_variable.data_label].values[mfts.order:mfts.order + steps_ahead]
eval = Measures.get_point_ahead_statistics(tmp_test, forecasts)
for key in eval.keys():
eval[key]["time"] = times
@ -898,7 +900,9 @@ def mv_run_interval2(mfts,train_data, test_data, window_key=None, **kwargs):
_end = time.time()
times += _end - _start
eval = Measures.get_interval_ahead_statistics(test_data[mfts.order:mfts.order+steps_ahead], intervals)
tmp_test = test_data[mfts.target_variable.data_label].values[mfts.order:mfts.order + steps_ahead]
eval = Measures.get_interval_ahead_statistics(tmp_test, intervals)
for key in eval.keys():
eval[key]["time"] = times
@ -1017,7 +1021,9 @@ def mv_run_probabilistic2(mfts, train_data, test_data, window_key=None, **kwargs
_end = time.time()
times += _end - _start
eval = Measures.get_distribution_ahead_statistics(test_data[mfts.order:mfts.order+steps_ahead], distributions)
tmp_test = test_data[mfts.target_variable.data_label].values[mfts.order:mfts.order + steps_ahead]
eval = Measures.get_distribution_ahead_statistics(tmp_test, distributions)
for key in eval.keys():
eval[key]["time"] = times

52
pyFTS/tests/transformations.py
View File

@ -69,9 +69,9 @@ detrend = trend.apply(data)
plt.plot(trend.inverse(detrend, data))
'''
dataset = pd.read_csv('https://query.data.world/s/nxst4hzhjrqld4bxhbpn6twmjbwqk7')
dataset['data'] = pd.to_datetime([str(y)+'-'+str(m) for y,m in zip(dataset['Ano'].values, dataset['Mes'].values)],
format='%Y-%m')
#dataset = pd.read_csv('https://query.data.world/s/nxst4hzhjrqld4bxhbpn6twmjbwqk7')
#dataset['data'] = pd.to_datetime([str(y)+'-'+str(m) for y,m in zip(dataset['Ano'].values, dataset['Mes'].values)],
# format='%Y-%m')
roi = Transformations.ROI()
'''
@ -93,6 +93,8 @@ model.fit(train)
fig, ax = plt.subplots(nrows=1, ncols=1, figsize=[10,5])
ax.plot(test)
'''
'''
train = dataset.iloc[:30]
test = dataset.iloc[30:]
@ -129,3 +131,47 @@ ax.plot(forecast)
plt.show()
print(dataset)
'''
eto = pd.read_csv('https://raw.githubusercontent.com/PatriciaLucas/Evapotranspiracao/master/ETo_setelagoas.csv', sep=',')
eto['Data'] = pd.to_datetime(eto["Data"], format='%Y-%m-%d')
from pyFTS.models.multivariate import common, variable, mvfts, wmvfts, granular
from pyFTS.models import hofts, pwfts
from pyFTS.partitioners import Grid, Entropy
from pyFTS.common import Membership
from pyFTS.models.seasonal.common import DateTime
from pyFTS.models.seasonal import partitioner as seasonal
from pyFTS.benchmarks import Measures
from pyFTS.benchmarks import arima, quantreg, knn, benchmarks as bchmk
variables = {
"Month": dict(data_label="Data", partitioner=seasonal.TimeGridPartitioner, npart=6),
"Eto": dict(data_label="Eto", alias='eto',
partitioner=Grid.GridPartitioner, npart=50)
}
methods = [mvfts.MVFTS, wmvfts.WeightedMVFTS, granular.GranularWMVFTS]
time_generator = lambda x : pd.to_datetime(x) + pd.to_timedelta(1, unit='d')
parameters = [
{},{},
dict(fts_method=pwfts.ProbabilisticWeightedFTS, fuzzyfy_mode='both',
order=1, knn=3)
]
bchmk.multivariate_sliding_window_benchmarks2(eto, 2000, train=0.8, inc=0.2,
methods=methods,
methods_parameters=parameters,
variables=variables,
target_variable='Eto',
type='point',
steps_ahead=[7],
file="hyperparam.db", dataset='Eto',
tag="experiments",
generators= {'Data': time_generator}
)