152 lines
4.7 KiB
Python
152 lines
4.7 KiB
Python
#!/usr/bin/python
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# -*- coding: utf8 -*-
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import numpy as np
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import pandas as pd
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import math
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from operator import itemgetter
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from pyFTS.common import FLR, FuzzySet, SortedCollection
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from pyFTS import fts
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class EnsembleFTS(fts.FTS):
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def __init__(self, name, **kwargs):
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super(EnsembleFTS, self).__init__("Ensemble FTS")
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self.shortname = "Ensemble FTS " + name
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self.name = "Ensemble FTS"
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self.flrgs = {}
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self.has_point_forecasting = True
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self.has_interval_forecasting = True
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self.has_probability_forecasting = True
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self.is_high_order = True
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self.models = []
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self.parameters = []
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def build(self, data, models, partitioners, partitions, max_order=3, transformation=None, indexer=None):
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self.models = []
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for count, model in enumerate(models, start=0):
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mfts = model("")
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if mfts.benchmark_only:
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if transformation is not None:
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mfts.appendTransformation(transformation)
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mfts.train(data,None, order=1, parameters=None)
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self.models.append(mfts)
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else:
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for partition in partitions:
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for partitioner in partitioners:
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data_train_fs = partitioner(data, partition, transformation=transformation)
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mfts = model("")
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mfts.partitioner = data_train_fs
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if not mfts.is_high_order:
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if transformation is not None:
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mfts.appendTransformation(transformation)
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mfts.train(data, data_train_fs.sets)
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self.models.append(mfts)
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else:
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for order in np.arange(1, max_order + 1):
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if order >= mfts.min_order:
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mfts = model("")
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mfts.partitioner = data_train_fs
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if transformation is not None:
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mfts.appendTransformation(transformation)
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mfts.train(data, data_train_fs.sets, order=order)
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self.models.append(mfts)
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def train(self, data, sets, order=1,parameters=None):
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pass
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def forecast(self, data, **kwargs):
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method = kwargs.get('method','mean')
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ndata = np.array(self.doTransformations(data))
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l = len(ndata)
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ret = []
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for k in np.arange(0, l):
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tmp = []
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for model in self.models:
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if k >= model.minOrder - 1:
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sample = ndata[k - model.order : k]
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tmp.append( model.forecast(sample) )
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if method == 'mean':
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ret.append( np.nanmean(tmp))
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elif method == 'median':
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ret.append(np.percentile(tmp,50))
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ret = self.doInverseTransformations(ret, params=[data[self.order - 1:]])
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return ret
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def forecastInterval(self, data, **kwargs):
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method = kwargs.get('method', 'extremum')
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ndata = np.array(self.doTransformations(data))
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l = len(ndata)
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ret = []
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for k in np.arange(0, l):
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tmp = []
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for model in self.models:
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if k >= model.minOrder - 1:
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sample = ndata[k - model.order : k]
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tmp.append( model.forecast(sample) )
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if method == 'extremum':
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ret.append( [ min(tmp), max(tmp) ] )
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elif method == 'quantile':
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q = kwargs.get('q', [.05, .95])
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ret.append(np.percentile(tmp,q=q*100))
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return ret
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def forecastAhead(self, data, steps, **kwargs):
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pass
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def forecastAheadInterval(self, data, steps, **kwargs):
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pass
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def getGridClean(self, resolution):
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grid = {}
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if len(self.transformations) == 0:
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_min = self.sets[0].lower
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_max = self.sets[-1].upper
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else:
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_min = self.original_min
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_max = self.original_max
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for sbin in np.arange(_min,_max, resolution):
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grid[sbin] = 0
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return grid
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def gridCount(self, grid, resolution, index, interval):
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#print(interval)
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for k in index.inside(interval[0],interval[1]):
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#print(k)
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grid[k] += 1
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return grid
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def gridCountPoint(self, grid, resolution, index, point):
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k = index.find_ge(point)
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# print(k)
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grid[k] += 1
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return grid
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def forecastAheadDistribution(self, data, steps, **kwargs):
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pass
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