def print_distribution_statistics(original, models, steps, resolution):
ret = "Model & Order & Interval & Distribution \\\\ \n"
for fts in models:
_crps1, _crps2, _t1, _t2 = Measures.get_distribution_statistics(
original, fts, steps, resolution)
ret += fts.shortname + " & "
ret += str(fts.order) + " & "
ret += str(_crps1) + " & "
ret += str(_crps2) + " \\\\ \n"
print(ret)
def print_distribution_statistics(original, models, steps, resolution):
"""
Run probabilistic 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 & Interval & Distribution \\\\ \n"
for fts in models:
_crps1, _crps2, _t1, _t2 = Measures.get_distribution_statistics(original, fts, steps, resolution)
ret += fts.shortname + " & "
ret += str(fts.order) + " & "
ret += str(_crps1) + " & "
ret += str(_crps2) + " \\\\ \n"
print(ret)
def run_probabilistic(mfts,
partitioner,
train_data,
test_data,
window_key=None,
**kwargs):
"""
Probabilistic forecast benchmark function to be executed on cluster nodes
: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 steps:
:param resolution:
: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
import numpy as np
from pyFTS.models import hofts, ifts, pwfts
from pyFTS.models.ensemble import ensemble
from pyFTS.partitioners import Grid, Entropy, FCM
from pyFTS.benchmarks import Measures, arima, quantreg, knn
from pyFTS.models.seasonal import SeasonalIndexer
tmp = [
hofts.HighOrderFTS, ifts.IntervalFTS, pwfts.ProbabilisticWeightedFTS,
arima.ARIMA, ensemble.AllMethodEnsembleFTS, knn.KNearestNeighbors
]
tmp2 = [
Grid.GridPartitioner, Entropy.EntropyPartitioner, FCM.FCMPartitioner
]
tmp3 = [
Measures.get_distribution_statistics, SeasonalIndexer.SeasonalIndexer,
SeasonalIndexer.LinearSeasonalIndexer
]
indexer = kwargs.get('indexer', None)
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 ""
if mfts.has_seasonality:
mfts.indexer = indexer
_start = time.time()
mfts.fit(train_data, **kwargs)
_end = time.time()
times = _end - _start
_crps1, _t1, _brier = Measures.get_distribution_statistics(
test_data, mfts, **kwargs)
_t1 += times
ret = {
'key': _key,
'obj': mfts,
'CRPS': _crps1,
'time': _t1,
'brier': _brier,
'window': window_key,
'steps': steps_ahead,
'method': method
}
return ret