230 lines
5.9 KiB
Python
230 lines
5.9 KiB
Python
import numpy as np
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import pandas as pd
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from pyFTS import tree
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from pyFTS.common import FuzzySet, SortedCollection
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class FTS(object):
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"""
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Fuzzy Time Series
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"""
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def __init__(self, order, name, **kwargs):
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"""
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Create a Fuzzy Time Series model
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:param order: model order
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:param name: model name
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:param kwargs: model specific parameters
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"""
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self.sets = {}
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self.flrgs = {}
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self.order = order
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self.shortname = name
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self.name = name
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self.detail = name
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self.is_high_order = False
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self.min_order = 1
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self.has_seasonality = False
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self.has_point_forecasting = True
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self.has_interval_forecasting = False
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self.has_probability_forecasting = False
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self.is_multivariate = False
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self.dump = False
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self.transformations = []
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self.transformations_param = []
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self.original_max = 0
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self.original_min = 0
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self.partitioner = None
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self.auto_update = False
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self.benchmark_only = False
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def fuzzy(self, data):
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"""
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Fuzzify a data point
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:param data: data point
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:return: maximum membership fuzzy set
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"""
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best = {"fuzzyset": "", "membership": 0.0}
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for f in self.sets:
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fset = self.sets[f]
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if best["membership"] <= fset.membership(data):
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best["fuzzyset"] = fset.name
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best["membership"] = fset.membership(data)
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return best
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def forecast(self, data, **kwargs):
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"""
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Point forecast one step ahead
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:param data: time series with minimal length to the order of the model
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:param kwargs:
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:return:
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"""
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pass
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def forecastInterval(self, data, **kwargs):
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"""
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Interval forecast one step ahead
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:param data:
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:param kwargs:
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:return:
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"""
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pass
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def forecastDistribution(self, data, **kwargs):
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"""
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Probabilistic forecast one step ahead
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:param data:
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:param kwargs:
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:return:
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"""
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pass
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def forecastAhead(self, data, steps, **kwargs):
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"""
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Point forecast n steps ahead
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:param data:
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:param steps:
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:param kwargs:
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:return:
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"""
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ndata = [k for k in self.doTransformations(data[- self.order:])]
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ret = []
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for k in np.arange(0,steps):
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tmp = self.forecast(ndata[-self.order:], **kwargs)
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if isinstance(tmp,(list, np.ndarray)):
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tmp = tmp[0]
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ret.append(tmp)
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ndata.append(tmp)
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ret = self.doInverseTransformations(ret, params=[ndata[self.order - 1:]])
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return ret
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def forecastAheadInterval(self, data, steps, **kwargs):
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"""
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Interval forecast n steps ahead
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:param data:
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:param steps:
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:param kwargs:
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:return:
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"""
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pass
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def forecastAheadDistribution(self, data, steps, **kwargs):
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"""
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Probabilistic forecast n steps ahead
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:param data:
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:param steps:
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:param kwargs:
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:return:
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"""
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pass
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def train(self, data, sets, order=1, parameters=None):
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"""
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:param data:
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:param sets:
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:param order:
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:param parameters:
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:return:
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"""
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pass
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def getMidpoints(self, flrg):
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ret = np.array([s.centroid for s in flrg.RHS])
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return ret
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def appendTransformation(self, transformation):
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if transformation is not None:
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self.transformations.append(transformation)
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def doTransformations(self,data,params=None,updateUoD=False, **kwargs):
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ndata = data
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if updateUoD:
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if min(data) < 0:
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self.original_min = min(data) * 1.1
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else:
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self.original_min = min(data) * 0.9
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if max(data) > 0:
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self.original_max = max(data) * 1.1
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else:
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self.original_max = max(data) * 0.9
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if len(self.transformations) > 0:
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if params is None:
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params = [ None for k in self.transformations]
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for c, t in enumerate(self.transformations, start=0):
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ndata = t.apply(ndata,params[c])
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return ndata
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def doInverseTransformations(self, data, params=None, **kwargs):
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ndata = data
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if len(self.transformations) > 0:
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if params is None:
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params = [None for k in self.transformations]
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for c, t in enumerate(reversed(self.transformations), start=0):
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ndata = t.inverse(ndata, params[c], **kwargs)
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return ndata
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def __str__(self):
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tmp = self.name + ":\n"
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for r in sorted(self.flrgs):
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tmp = tmp + str(self.flrgs[r]) + "\n"
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return tmp
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def __len__(self):
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return len(self.flrgs)
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def len_total(self):
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return sum([len(k) for k in self.flrgs])
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def get_empty_grid(self, _min, _max, resolution):
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grid = {}
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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 getGridClean(self, resolution):
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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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return self.get_empty_grid(_min, _max, resolution)
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def gridCount(self, grid, resolution, index, interval):
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#print(point_to_interval)
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for k in index.inside(interval[0],interval[1]):
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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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if not isinstance(point, (list, np.ndarray)):
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point = [point]
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for p in point:
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k = index.find_ge(p)
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grid[k] += 1
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return grid
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