def run_interval(mfts, partitioner, train_data, test_data, window_key=None, **kwargs):
"""
Run the interval forecasting benchmarks
:param mfts: FTS model
:param partitioner: Universe of Discourse partitioner
:param train_data: data used to train the model
:param test_data: ata used to test the model
:param window_key: id of the sliding window
:param transformation: data transformation
:param indexer: seasonal indexer
:return: a dictionary with the benchmark results
"""
import time
from pyFTS.models import hofts,ifts,pwfts
from pyFTS.partitioners import Grid, Entropy, FCM
from pyFTS.benchmarks import Measures, arima, quantreg
tmp = [hofts.HighOrderFTS, ifts.IntervalFTS, pwfts.ProbabilisticWeightedFTS]
tmp2 = [Grid.GridPartitioner, Entropy.EntropyPartitioner, FCM.FCMPartitioner]
tmp4 = [arima.ARIMA, quantreg.QuantileRegression]
tmp3 = [Measures.get_interval_statistics]
steps_ahead = kwargs.get('steps_ahead', 1)
method = kwargs.get('method', None)
if mfts.benchmark_only:
_key = mfts.shortname + str(mfts.order if mfts.order is not None else "") + str(mfts.alpha)
else:
pttr = str(partitioner.__module__).split('.')[-1]
_key = mfts.shortname + " n = " + str(mfts.order) + " " + pttr + " q = " + str(partitioner.partitions)
mfts.partitioner = partitioner
mfts.append_transformation(partitioner.transformation)
_key += str(steps_ahead)
_key += str(method) if method is not None else ""
_start = time.time()
mfts.fit(train_data, **kwargs)
_end = time.time()
times = _end - _start
_start = time.time()
#_sharp, _res, _cov, _q05, _q25, _q75, _q95, _w05, _w25
metrics = Measures.get_interval_statistics(test_data, mfts, **kwargs)
_end = time.time()
times += _end - _start
ret = {'key': _key, 'obj': mfts, 'sharpness': metrics[0], 'resolution': metrics[1], 'coverage': metrics[2],
'time': times,'Q05': metrics[3], 'Q25': metrics[4], 'Q75': metrics[5], 'Q95': metrics[6],
'winkler05': metrics[7], 'winkler25': metrics[8],
'window': window_key,'steps': steps_ahead, 'method': method}
return ret
def print_interval_statistics(original, models):
ret = "Model & Order & Sharpness & Resolution & Coverage & .05 & .25 & .75 & .95 \\\\ \n"
for fts in models:
_sharp, _res, _cov, _q5, _q25, _q75, _q95 = Measures.get_interval_statistics(
original, fts)
ret += fts.shortname + " & "
ret += str(fts.order) + " & "
ret += str(_sharp) + " & "
ret += str(_res) + " & "
ret += str(_cov) + " &"
ret += str(_q5) + " &"
ret += str(_q25) + " &"
ret += str(_q75) + " &"
ret += str(_q95) + "\\\\ \n"
print(ret)
def print_interval_statistics(original, models):
"""
Run interval benchmarks on given models and data and print the results
:param data: test data
:param models: a list of FTS models to benchmark
:return:
"""
ret = "Model & Order & Sharpness & Resolution & Coverage & .05 & .25 & .75 & .95 \\\\ \n"
for fts in models:
_sharp, _res, _cov, _q5, _q25, _q75, _q95 = Measures.get_interval_statistics(original, fts)
ret += fts.shortname + " & "
ret += str(fts.order) + " & "
ret += str(_sharp) + " & "
ret += str(_res) + " & "
ret += str(_cov) + " &"
ret += str(_q5) + " &"
ret += str(_q25) + " &"
ret += str(_q75) + " &"
ret += str(_q95) + "\\\\ \n"
print(ret)