Improvements on Seasonal and Multivariate models
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Petrônio Cândido
parent
e010df344a
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66e41f42ee
@ -177,8 +177,11 @@ def persist_obj(obj, file):
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:param obj: object on memory
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:param file: file name to store the object
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"""
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with open(file, 'wb') as _file:
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dill.dump(obj, _file)
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try:
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with open(file, 'wb') as _file:
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dill.dump(obj, _file)
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except Exception as ex:
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print("File {} could not be saved due exception {}".format(file, ex))
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def load_obj(file):
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@ -42,9 +42,12 @@ class MultivariateFuzzySet(Composite.FuzzySet):
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return np.nanmin(mv)
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def fuzzyfy_instance(data_point, var):
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def fuzzyfy_instance(data_point, var, tuples=True):
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fsets = FuzzySet.fuzzyfy(data_point, var.partitioner, mode='sets', method='fuzzy', alpha_cut=var.alpha_cut)
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return [(var.name, fs) for fs in fsets]
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if tuples:
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return [(var.name, fs) for fs in fsets]
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else:
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return fsets
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def fuzzyfy_instance_clustered(data_point, cluster, **kwargs):
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@ -1,11 +1,24 @@
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from pyFTS.common import fts, FuzzySet, FLR, Membership, tree
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from pyFTS.common import fts, FuzzySet, FLR, Membership
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from pyFTS.partitioners import Grid
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from pyFTS.models.multivariate import FLR as MVFLR, common, flrg as mvflrg
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from itertools import product
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import numpy as np
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import pandas as pd
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def product_dict(**kwargs):
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'''
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Code by Seth Johnson
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:param kwargs:
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:return:
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'''
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keys = kwargs.keys()
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vals = kwargs.values()
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for instance in product(*vals):
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yield dict(zip(keys, instance))
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class MVFTS(fts.FTS):
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"""
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Multivariate extension of Chen's ConventionalFTS method
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@ -52,22 +65,18 @@ class MVFTS(fts.FTS):
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lags = {}
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for vc, var in enumerate(self.explanatory_variables):
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data_point = data[var.name]
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lags[vc] = common.fuzzyfy_instance(data_point, var)
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root = tree.FLRGTreeNode(None)
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tree.build_tree_without_order(root, lags, 0)
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for p in root.paths():
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path = list(reversed(list(filter(None.__ne__, p))))
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lags[var.name] = common.fuzzyfy_instance(data_point, var, tuples=False)
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for path in product_dict(**lags):
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flr = MVFLR.FLR()
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for v, s in path:
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flr.set_lhs(v, s)
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for var, fset in path.items():
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flr.set_lhs(var, fset)
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if len(flr.LHS.keys()) == len(self.explanatory_variables):
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flrs.append(flr)
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else:
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print(flr)
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return flrs
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@ -110,6 +119,7 @@ class MVFTS(fts.FTS):
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def forecast(self, data, **kwargs):
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ret = []
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ndata = self.apply_transformations(data)
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c = 0
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for index, row in ndata.iterrows():
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data_point = self.format_data(row)
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flrs = self.generate_lhs_flrs(data_point)
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@ -118,8 +128,17 @@ class MVFTS(fts.FTS):
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for flr in flrs:
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flrg = mvflrg.FLRG(lhs=flr.LHS)
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if flrg.get_key() not in self.flrgs:
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mvs.append(0.)
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mps.append(0.)
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#Naïve approach is applied when no rules were found
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if self.target_variable.name in flrg.LHS:
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fs = flrg.LHS[self.target_variable.name]
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fset = self.target_variable.partitioner.sets[fs]
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mp = fset.centroid
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mv = fset.membership(data_point[self.target_variable.name])
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mvs.append(mv)
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mps.append(mp)
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else:
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mvs.append(0.)
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mps.append(0.)
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else:
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mvs.append(self.flrgs[flrg.get_key()].get_membership(data_point, self.explanatory_variables))
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mps.append(self.flrgs[flrg.get_key()].get_midpoint(self.target_variable.partitioner.sets))
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@ -6,9 +6,15 @@ from pyFTS.partitioners import partitioner, Grid
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from datetime import date as dt, datetime as dtm
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class DateTime(Enum):
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"""
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Data and Time granularity for time granularity and seasonality identification
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"""
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year = 1
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half = 2 # six months
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third = 3 # four months
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quarter = 4 # three months
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sixth = 6 # two months
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month = 12
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day_of_month = 30
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day_of_year = 364
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@ -37,6 +43,8 @@ def strip_datepart(date, date_part, mask=''):
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tmp = date.year
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elif date_part == DateTime.month:
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tmp = date.month
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elif date_part in (DateTime.half, DateTime.third, DateTime.quarter, DateTime.sixth):
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tmp = (date.month // date_part.value) + 1
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elif date_part == DateTime.day_of_year:
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tmp = date.timetuple().tm_yday
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elif date_part == DateTime.day_of_month:
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@ -55,10 +55,13 @@ class TimeGridPartitioner(partitioner.Partitioner):
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dlen = (self.max - self.min)
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partlen = dlen / self.partitions
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elif self.season == DateTime.day_of_week:
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self.min = 1
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self.max = 8
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partlen = 1
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pl2 = 1
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self.min, self.max, partlen, pl2 = 0, 7, 1, 1
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elif self.season == DateTime.hour:
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self.min, self.max, partlen, pl2 = 0, 24, 1, 1
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elif self.season == DateTime.month:
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self.min, self.max, partlen, pl2 = 1, 13, 1, 1
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elif self.season in (DateTime.half, DateTime.third, DateTime.quarter, DateTime.sixth):
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self.min, self.max, partlen, pl2 = 1, self.season.value+1, 1, 1
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else:
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partlen = self.season.value / self.partitions
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pl2 = partlen / 2
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@ -92,14 +92,14 @@ from pyFTS.models.seasonal.common import DateTime
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dataset = pd.read_csv('/home/petronio/Downloads/gefcom12.csv')
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dataset = dataset.dropna()
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train_mv = dataset.iloc[:25000]
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test_mv = dataset.iloc[25000:]
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train_mv = dataset.iloc[:15000]
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test_mv = dataset.iloc[15000:]
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from pyFTS.models.multivariate import common, variable, mvfts
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from pyFTS.models.seasonal import partitioner as seasonal
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from pyFTS.models.seasonal.common import DateTime
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#'''
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sp = {'seasonality': DateTime.minute_of_day, 'names': [str(k) for k in range(0,24)]}
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vhour = variable.Variable("Hour", data_label="date", partitioner=seasonal.TimeGridPartitioner, npart=24,
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@ -110,56 +110,48 @@ sp = {'seasonality': DateTime.day_of_week, 'names': ['mon','tue','wed','tur','fr
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vday = variable.Variable("DayOfWeek", data_label="date", partitioner=seasonal.TimeGridPartitioner, npart=7,
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data=train_mv, partitioner_specific=sp)
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sp = {'seasonality': DateTime.day_of_year, 'names': ['jan','feb','mar','apr','may','jun','jul','aug','sep','oct','nov','dec']}
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#sp = {'seasonality': DateTime.day_of_month, 'names': ['jan','feb','mar','apr','may','jun','jul','aug','sep','oct','nov','dec']}
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vmonth = variable.Variable("Month", data_label="date", partitioner=seasonal.TimeGridPartitioner, npart=12,
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sp = {'seasonality': DateTime.quarter}
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vmonth = variable.Variable("Month", data_label="date", partitioner=seasonal.TimeGridPartitioner, npart=4,
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data=train_mv, partitioner_specific=sp)
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vload = variable.Variable("Load", data_label="load", alias='load',
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partitioner=Grid.GridPartitioner, npart=35,
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partitioner=Grid.GridPartitioner, npart=20,
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data=train_mv)
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vtemp = variable.Variable("Temperature", data_label="temperature", alias='temperature',
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partitioner=Grid.GridPartitioner, npart=35,
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partitioner=Grid.GridPartitioner, npart=20,
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data=train_mv)
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from pyFTS.models.multivariate import mvfts, wmvfts, cmvfts, grid
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from itertools import combinations
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models = []
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vars = [vhour, vday, vload]
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variables = [vhour, vday, vmonth, vtemp]
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#fs = grid.GridCluster(explanatory_variables=vars, target_variable=vload)
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parameters = [
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{}, {},
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{'order': 2, 'knn': 1},
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{'order': 2, 'knn': 2},
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{'order': 2, 'knn': 3},
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]
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#model = mvfts.MVFTS(explanatory_variables=vars, target_variable=vload)
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model = wmvfts.WeightedMVFTS(explanatory_variables=vars, target_variable=vload)
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#model = cmvfts.ClusteredMVFTS(explanatory_variables=vars, target_variable=vload,order=2, knn=3, partitioner=fs)
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model.fit(train_mv)
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print(model.shortname)
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Util.persist_obj(model, model.shortname)
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#'''
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#model = Util.load_obj('MVFTS')
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for ct, method in enumerate([mvfts.MVFTS, wmvfts.WeightedMVFTS,
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cmvfts.ClusteredMVFTS, cmvfts.ClusteredMVFTS, cmvfts.ClusteredMVFTS]):
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for nc in np.arange(1, 5):
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for comb in combinations(variables, nc):
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_vars = []
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_vars.extend(comb)
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_vars.append(vload)
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with open("rules.txt","w") as file:
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file.write(str(model))
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if not method == cmvfts.ClusteredMVFTS:
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model = method(explanatory_variables=_vars, target_variable=vload, **parameters[ct])
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else:
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fs = grid.GridCluster(explanatory_variables=_vars, target_variable=vload)
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model = method(explanatory_variables=_vars, target_variable=vload, partitioner=fs, **parameters[ct])
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forecasts = model.predict(test_mv.iloc[:100])
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forecasts.insert(0,None)
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for _v in comb:
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model.shortname += _v.name
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fig, ax = plt.subplots(nrows=1, ncols=1, figsize=[15,3])
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ax.plot(test_mv['load'].values[:100],label='Original')
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ax.plot(forecasts, label='predicted')
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handles, labels = ax.get_legend_handles_labels()
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lgd = ax.legend(handles, labels, loc=2, bbox_to_anchor=(1, 1))
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model.fit(train_mv)
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models.append(model.shortname)
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#Util.persist_obj(model, model.shortname)
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forecasts = model.predict(test_mv.iloc[:100])
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Util.show_and_save_image(fig, model.shortname, True)
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