- DateTimeSeasonalIndexer
- persist_obj, load_obj, persist_env, load_env
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Petrônio Cândido de Lima e Silva
parent
bb42a6be07
commit
55d3deadfc
@ -40,9 +40,9 @@ def generateIndexedFLRs(sets, indexer, data):
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flrs = []
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index = indexer.get_season_of_data(data)
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ndata = indexer.get_data(data)
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for k in np.arange(0,len(data)-1):
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lhs = FuzzySet.getMaxMembershipFuzzySet(ndata[k],sets)
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rhs = FuzzySet.getMaxMembershipFuzzySet(ndata[k+1], sets)
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for k in np.arange(1,len(data)):
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lhs = FuzzySet.getMaxMembershipFuzzySet(ndata[k-1],sets)
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rhs = FuzzySet.getMaxMembershipFuzzySet(ndata[k], sets)
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season = index[k]
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flr = IndexedFLR(season,lhs,rhs)
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flrs.append(flr)
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@ -1,5 +1,6 @@
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import time
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import matplotlib.pyplot as plt
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import dill
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current_milli_time = lambda: int(round(time.time() * 1000))
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@ -27,3 +28,19 @@ def enumerate2(xs, start=0, step=1):
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for x in xs:
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yield (start, x)
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start += step
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def persist_obj(obj, file):
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with open(file, 'wb') as _file:
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dill.dump(obj, _file)
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def load_obj(file):
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with open(file, 'rb') as _file:
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obj = dill.load(_file)
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return obj
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def persist_env(file):
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dill.dump_session(file)
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def load_env(file):
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dill.load_session(file)
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@ -2,6 +2,7 @@ import numpy as np
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from pyFTS.common import FuzzySet,FLR
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from pyFTS import fts, sfts
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class MultiSeasonalFTS(sfts.SeasonalFTS):
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def __init__(self, name, indexer):
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super(MultiSeasonalFTS, self).__init__("MSFTS")
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@ -18,44 +19,50 @@ class MultiSeasonalFTS(sfts.SeasonalFTS):
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def generateFLRG(self, flrs):
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flrgs = {}
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for index, season in enumerate(self.indexer.get_season_of_data(flrs),start=0):
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for flr in flrs:
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print(index)
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print(season)
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if str(flr.index) not in self.flrgs:
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flrgs[str(flr.index)] = sfts.SeasonalFLRG(flr.index)
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if str(season) not in self.flrgs:
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flrgs[str(season)] = sfts.SeasonalFLRG(season)
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flrgs[str(season)].append(flrs[index].RHS)
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flrgs[str(flr.index)].append(flr.RHS)
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return (flrgs)
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def train(self, data, sets, order=1, parameters=None):
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self.sets = sets
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self.seasonality = parameters
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ndata = self.indexer.set_data(data,self.doTransformations(self.indexer.get_data(data)))
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tmpdata = FuzzySet.fuzzySeries(ndata, sets)
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flrs = FLR.generateRecurrentFLRs(tmpdata)
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#ndata = self.indexer.set_data(data,self.doTransformations(self.indexer.get_data(data)))
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flrs = FLR.generateIndexedFLRs(self.sets, self.indexer, data)
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self.flrgs = self.generateFLRG(flrs)
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def forecast(self, data):
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ndata = np.array(self.doTransformations(self.indexer.get_data(data)))
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l = len(ndata)
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ret = []
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for k in np.arange(1, l):
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index = self.indexer.get_season_of_data(data)
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ndata = self.indexer.get_data(data)
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season = self.indexer.get_season_index(k)
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for k in np.arange(1, len(data)):
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flrg = self.flrgs[str(season)]
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flrg = self.flrgs[str(index[k])]
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mp = self.getMidpoints(flrg)
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ret.append(sum(mp) / len(mp))
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ret = self.doInverseTransformations(ret, params=[data[self.order - 1:]])
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ret = self.doInverseTransformations(ret, params=[ndata[self.order - 1:]])
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return ret
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def forecastAhead(self, data, steps):
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ret = []
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for i in steps:
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flrg = self.flrgs[str(i)]
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mp = self.getMidpoints(flrg)
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ret.append(sum(mp) / len(mp))
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ret = self.doInverseTransformations(ret, params=data)
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return ret
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@ -1,4 +1,5 @@
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import numpy as np
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from enum import Enum
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class SeasonalIndexer(object):
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def __init__(self,num_seasons):
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@ -68,6 +69,7 @@ class DataFrameSeasonalIndexer(SeasonalIndexer):
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self.data_fields = data_fields
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def get_season_of_data(self,data):
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#data = data.copy()
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ret = []
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for ix in data.index:
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season = []
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@ -75,7 +77,8 @@ class DataFrameSeasonalIndexer(SeasonalIndexer):
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if self.seasons[c] is None:
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season.append(data[f][ix])
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else:
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season.append(data[f][ix] // self.seasons[c])
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a = data[f][ix]
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season.append(a // self.seasons[c])
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ret.append(season)
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return ret
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@ -98,5 +101,73 @@ class DataFrameSeasonalIndexer(SeasonalIndexer):
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return data[self.data_fields].tolist()
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def set_data(self, data, value):
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data[self.data_fields] = value
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data.loc[:,self.data_fields] = value
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return data
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class DateTime(Enum):
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year = 1
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month = 2
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day_of_month = 3
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day_of_year = 4
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day_of_week = 5
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hour = 6
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minute = 7
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second = 8
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class DateTimeSeasonalIndexer(SeasonalIndexer):
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def __init__(self,date_field, index_fields, index_seasons, data_fields):
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super(DateTimeSeasonalIndexer, self).__init__(len(index_seasons))
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self.fields = index_fields
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self.seasons = index_seasons
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self.data_fields = data_fields
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self.date_field = date_field
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def strip_datepart(self, date, date_part, resolution):
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if date_part == DateTime.year:
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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 == 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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tmp = date.day
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elif date_part == DateTime.day_of_week:
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tmp = date.weekday()
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elif date_part == DateTime.hour:
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tmp = date.hour
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elif date_part == DateTime.minute:
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tmp = date.minute
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elif date_part == DateTime.second:
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tmp = date.second
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if resolution is None:
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return tmp
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else:
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return tmp // resolution
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def get_season_of_data(self, data):
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# data = data.copy()
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ret = []
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for ix in data.index:
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date = data[self.date_field][ix]
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season = []
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for c, f in enumerate(self.fields, start=0):
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season.append( self.strip_datepart(date, f, self.seasons[c]) )
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ret.append(season)
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return ret
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def get_season_by_index(self, index):
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raise Exception("Operation not available!")
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def get_data_by_season(self, data, indexes):
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raise Exception("Operation not available!")
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def get_index_by_season(self, indexes):
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raise Exception("Operation not available!")
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def get_data(self, data):
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return data[self.data_fields].tolist()
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def set_data(self, data, value):
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raise Exception("Operation not available!")
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@ -8,9 +8,11 @@ import matplotlib as plt
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import matplotlib.pyplot as plt
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from mpl_toolkits.mplot3d import Axes3D
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import datetime
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import pandas as pd
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from pyFTS.partitioners import Grid
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from pyFTS.common import FLR,FuzzySet,Membership,Transformations
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from pyFTS.partitioners import Grid, CMeans, FCM, Entropy
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from pyFTS.common import FLR,FuzzySet,Membership,Transformations,Util
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from pyFTS import fts,sfts
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from pyFTS.models import msfts
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from pyFTS.benchmarks import benchmarks as bchmk
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@ -18,12 +20,36 @@ from pyFTS.benchmarks import Measures
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os.chdir("/home/petronio/dados/Dropbox/Doutorado/Disciplinas/AdvancedFuzzyTimeSeriesModels/")
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sonda = pd.read_csv("DataSets/SONDA_BSB_CLEAN.csv", sep=";")
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sonda = pd.read_csv("DataSets/SONDA_BSB_MOD.csv", sep=";")
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sonda['data'] = pd.to_datetime(sonda['data'])
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sonda = sonda[:][527041:]
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sonda.index = np.arange(0,len(sonda.index))
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#data = []
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#for i in sonda.index:
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#inst = []
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#year = int( sonda["year"][i] )
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#day_of_year = int( sonda["day"][i] )
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#minute = int (sonda["min"][i] )
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#glo_avg = sonda["glo_avg"][i]
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#inst.append( datetime.datetime(year, 1, 1) + datetime.timedelta(day_of_year - 1, minutes=minute) )
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#inst.append( glo_avg )
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#data.append(inst)
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#nov = pd.DataFrame(data,columns=["data","glo_avg"])
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#nov.to_csv("DataSets/SONDA_BSB_MOD.csv", sep=";")
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sonda_treino = sonda[:1051200]
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sonda_teste = sonda[1051201:]
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@ -37,19 +63,42 @@ from pyFTS.models.seasonal import SeasonalIndexer
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from pyFTS.models import msfts
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from pyFTS.common import FLR
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ix = SeasonalIndexer.DataFrameSeasonalIndexer(['day','min'],[30, 60],'glo_avg')
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ix = SeasonalIndexer.DateTimeSeasonalIndexer('data',[SeasonalIndexer.DateTime.month,
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SeasonalIndexer.DateTime.hour, SeasonalIndexer.DateTime.minute],
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[None, None,15],'glo_avg')
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fs = Grid.GridPartitionerTrimf(ix.get_data(sonda_treino),20)
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tmp = ix.get_data(sonda_treino)
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for max_part in [10, 20, 30, 40, 50]:
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fs1 = Grid.GridPartitionerTrimf(tmp,max_part)
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Util.persist_obj(fs1,"models/sonda_fs_grid_" + str(max_part) + ".pkl")
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fs2 = FCM.FCMPartitionerTrimf(tmp, max_part)
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Util.persist_obj(fs2, "models/sonda_fs_fcm_" + str(max_part) + ".pkl")
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fs3 = Entropy.EntropyPartitionerTrimf(tmp, max_part)
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Util.persist_obj(fs3, "models/sonda_fs_entropy_" + str(max_part) + ".pkl")
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#fs = Util.load_obj("models/sonda_fs_grid_50.pkl")
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#for f in fs:
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# print(f)
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#mfts = msfts.MultiSeasonalFTS("",ix)
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#mfts.train(sonda_teste,fs)
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#mfts.train(sonda_treino,fs)
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#print(str(mfts))
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#plt.plot(mfts.forecast(sonda_teste))
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#[10, 508]
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flrs = FLR.generateIndexedFLRs(fs, ix, sonda_treino[110000:111450])
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#flrs = FLR.generateIndexedFLRs(fs, ix, sonda_treino[110000:111450])
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for i in flrs: #ix.get_data(sonda_treino[111430:111450]):
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print(i)
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#for i in mfts.forecast(sonda_teste):
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# print(i)
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