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Petrônio Cândido 2018-05-08 23:59:04 -03:00
parent ffd97bacfc
commit 7d5dae21e8
2 changed files with 34 additions and 15 deletions
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1
pyFTS/benchmarks/Measures.py
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@ -165,6 +165,7 @@ def sharpness(forecasts):
return np.mean(tmp) return np.mean(tmp)
def resolution(forecasts): def resolution(forecasts):
"""Resolution - Standard deviation of the intervals""" """Resolution - Standard deviation of the intervals"""
shp = sharpness(forecasts) shp = sharpness(forecasts)

48
pyFTS/tests/general.py
View File

@ -24,7 +24,7 @@ partitioner = Grid.GridPartitioner(data=dataset[:800], npart=10) #, transformati
from pyFTS.benchmarks import benchmarks as bchmk, Util as bUtil, Measures, knn, quantreg, arima, naive from pyFTS.benchmarks import benchmarks as bchmk, Util as bUtil, Measures, knn, quantreg, arima, naive
from pyFTS.models import pwfts, song, ifts from pyFTS.models import pwfts, song, ifts, hofts
from pyFTS.models.ensemble import ensemble from pyFTS.models.ensemble import ensemble
''' '''
@ -39,33 +39,33 @@ print(Measures.get_distribution_statistics(dataset[800:1000], model))
#for tmp2 in tmp: #for tmp2 in tmp:
# print(tmp2) # print(tmp2)
#''' #'''
#''' '''
bchmk.sliding_window_benchmarks(dataset, 1000, train=0.8, inc=0.2, bchmk.sliding_window_benchmarks(dataset, 1000, train=0.8, inc=0.2,
methods=[pwfts.ProbabilisticWeightedFTS], methods=[hofts.HighOrderFTS], #[pwfts.ProbabilisticWeightedFTS],
benchmark_models=False, benchmark_models=False,
transformations=[None], transformations=[None],
orders=[1, 2, 3], orders=[1, 2, 3],
partitions=np.arange(10, 90, 5), partitions=np.arange(30, 80, 5),
progress=False, type="point", progress=False, type="point",
#steps_ahead=[1,2,4,6,8,10], #steps_ahead=[1,2,4,6,8,10],
distributed=True, nodes=['192.168.0.110', '192.168.0.107', '192.168.0.106'], distributed=True, nodes=['192.168.0.110', '192.168.0.107', '192.168.0.106'],
file="benchmarks.db", dataset="SP500", tag="partitioning") file="benchmarks.db", dataset="NASDAQ", tag="comparisons")
bchmk.sliding_window_benchmarks(dataset, 1000, train=0.8, inc=0.2, bchmk.sliding_window_benchmarks(dataset, 1000, train=0.8, inc=0.2,
methods=[pwfts.ProbabilisticWeightedFTS], methods=[hofts.HighOrderFTS], # [pwfts.ProbabilisticWeightedFTS],
benchmark_models=False, benchmark_models=False,
transformations=[tdiff], transformations=[tdiff],
orders=[1, 2, 3], orders=[1, 2, 3],
partitions=np.arange(3, 30, 2), partitions=np.arange(3, 25, 2),
progress=False, type="point", progress=False, type="point",
#steps_ahead=[1,2,4,6,8,10], #steps_ahead=[1,2,4,6,8,10],
distributed=True, nodes=['192.168.0.110', '192.168.0.107', '192.168.0.106'], distributed=True, nodes=['192.168.0.110', '192.168.0.107', '192.168.0.106'],
file="benchmarks.db", dataset="SP500", tag="partitioning") file="benchmarks.db", dataset="NASDAQ", tag="comparisons")
#''' '''
''' '''
from pyFTS.partitioners import Grid, Util as pUtil from pyFTS.partitioners import Grid, Util as pUtil
partitioner = Grid.GridPartitioner(data=dataset[:800], npart=10, transformation=tdiff) partitioner = Grid.GridPartitioner(data=dataset[:800], npart=10, transformation=tdiff)
@ -78,16 +78,34 @@ print(Measures.get_distribution_statistics(dataset[800:1000], model, steps_ahead
#for tmp2 in tmp: #for tmp2 in tmp:
# print(tmp2) # print(tmp2)
''' '''
''' #'''
types = ['point','interval','distribution'] types = ['interval']#['point','interval','distribution']
benchmark_methods=[[arima.ARIMA for k in range(8)] + [quantreg.QuantileRegression for k in range(4)]]
'''
benchmark_methods=[ benchmark_methods=[
[arima.ARIMA for k in range(4)] + [naive.Naive], [arima.ARIMA for k in range(4)] + [naive.Naive],
[arima.ARIMA for k in range(8)] + [quantreg.QuantileRegression for k in range(4)], [arima.ARIMA for k in range(8)] + [quantreg.QuantileRegression for k in range(4)],
[arima.ARIMA for k in range(4)] + [quantreg.QuantileRegression for k in range(2)] [arima.ARIMA for k in range(4)] + [quantreg.QuantileRegression for k in range(2)]
+ [knn.KNearestNeighbors for k in range(3)] + [knn.KNearestNeighbors for k in range(3)]
] ]'''
benchmark_methods_parameters= [ benchmark_methods_parameters= [
[
{'order': (1, 0, 0), 'alpha': .05},
{'order': (1, 0, 0), 'alpha': .25},
{'order': (1, 0, 1), 'alpha': .05},
{'order': (1, 0, 1), 'alpha': .25},
{'order': (2, 0, 1), 'alpha': .05},
{'order': (2, 0, 1), 'alpha': .25},
{'order': (2, 0, 2), 'alpha': .05},
{'order': (2, 0, 2), 'alpha': .25},
{'order': 1, 'alpha': .05},
{'order': 1, 'alpha': .25},
{'order': 2, 'alpha': .05},
{'order': 2, 'alpha': .25}
]
]
'''benchmark_methods_parameters= [
[ [
{'order': (1, 0, 0)}, {'order': (1, 0, 0)},
{'order': (1, 0, 1)}, {'order': (1, 0, 1)},
@ -116,8 +134,8 @@ benchmark_methods_parameters= [
{'order': 2, 'dist': True}, {'order': 2, 'dist': True},
{'order': 1}, {'order': 2}, {'order': 3}, {'order': 1}, {'order': 2}, {'order': 3},
] ]
] ]'''
dataset_name = "NASDAQ" dataset_name = "SP500"
tag = "comparisons" tag = "comparisons"
from pyFTS.benchmarks import arima, naive, quantreg from pyFTS.benchmarks import arima, naive, quantreg
@ -147,7 +165,7 @@ for ct, type in enumerate(types):
file="benchmarks.db", dataset=dataset_name, tag=tag) file="benchmarks.db", dataset=dataset_name, tag=tag)
''' #'''
''' '''
dat = pd.read_csv('pwfts_taiex_partitioning.csv', sep=';') dat = pd.read_csv('pwfts_taiex_partitioning.csv', sep=';')
print(bUtil.analytic_tabular_dataframe(dat)) print(bUtil.analytic_tabular_dataframe(dat))