104 lines
2.8 KiB
Python
104 lines
2.8 KiB
Python
#!/usr/bin/python
|
|
# -*- coding: utf8 -*-
|
|
|
|
import os
|
|
import numpy as np
|
|
import pandas as pd
|
|
import matplotlib as plt
|
|
import matplotlib.pyplot as plt
|
|
from mpl_toolkits.mplot3d import Axes3D
|
|
|
|
import datetime
|
|
|
|
import pandas as pd
|
|
from pyFTS.partitioners import Grid, CMeans, FCM, Entropy
|
|
from pyFTS.common import FLR,FuzzySet,Membership,Transformations,Util
|
|
from pyFTS import fts,sfts
|
|
from pyFTS.models import msfts
|
|
from pyFTS.benchmarks import benchmarks as bchmk
|
|
from pyFTS.benchmarks import Measures
|
|
|
|
os.chdir("/home/petronio/dados/Dropbox/Doutorado/Disciplinas/AdvancedFuzzyTimeSeriesModels/")
|
|
|
|
sonda = pd.read_csv("DataSets/SONDA_BSB_MOD.csv", sep=";")
|
|
|
|
sonda['data'] = pd.to_datetime(sonda['data'])
|
|
|
|
sonda = sonda[:][527041:]
|
|
|
|
sonda.index = np.arange(0,len(sonda.index))
|
|
|
|
#data = []
|
|
|
|
#for i in sonda.index:
|
|
|
|
#inst = []
|
|
|
|
#year = int( sonda["year"][i] )
|
|
#day_of_year = int( sonda["day"][i] )
|
|
#minute = int (sonda["min"][i] )
|
|
|
|
#glo_avg = sonda["glo_avg"][i]
|
|
|
|
#inst.append( datetime.datetime(year, 1, 1) + datetime.timedelta(day_of_year - 1, minutes=minute) )
|
|
|
|
#inst.append( glo_avg )
|
|
|
|
#data.append(inst)
|
|
|
|
#nov = pd.DataFrame(data,columns=["data","glo_avg"])
|
|
|
|
#nov.to_csv("DataSets/SONDA_BSB_MOD.csv", sep=";")
|
|
|
|
sonda_treino = sonda[:1051200]
|
|
sonda_teste = sonda[1051201:]
|
|
|
|
|
|
#res = bchmk.simpleSearch_RMSE(sonda_treino, sonda_teste,
|
|
# sfts.SeasonalFTS,np.arange(3,30),[1],parameters=1440,
|
|
# tam=[15,8], plotforecasts=False,elev=45, azim=40,
|
|
# save=False,file="pictures/sonda_sfts_error_surface", intervals=False)
|
|
|
|
from pyFTS.models.seasonal import SeasonalIndexer
|
|
from pyFTS.models import msfts
|
|
from pyFTS.common import FLR
|
|
|
|
ix = SeasonalIndexer.DateTimeSeasonalIndexer('data',[SeasonalIndexer.DateTime.month,
|
|
SeasonalIndexer.DateTime.hour, SeasonalIndexer.DateTime.minute],
|
|
[None, None,15],'glo_avg')
|
|
|
|
tmp = ix.get_data(sonda_treino)
|
|
for max_part in [10, 20, 30, 40, 50]:
|
|
|
|
fs1 = Grid.GridPartitionerTrimf(tmp,max_part)
|
|
|
|
Util.persist_obj(fs1,"models/sonda_fs_grid_" + str(max_part) + ".pkl")
|
|
|
|
fs2 = FCM.FCMPartitionerTrimf(tmp, max_part)
|
|
|
|
Util.persist_obj(fs2, "models/sonda_fs_fcm_" + str(max_part) + ".pkl")
|
|
|
|
fs3 = Entropy.EntropyPartitionerTrimf(tmp, max_part)
|
|
|
|
Util.persist_obj(fs3, "models/sonda_fs_entropy_" + str(max_part) + ".pkl")
|
|
|
|
|
|
#fs = Util.load_obj("models/sonda_fs_grid_50.pkl")
|
|
|
|
#for f in fs:
|
|
# print(f)
|
|
|
|
#mfts = msfts.MultiSeasonalFTS("",ix)
|
|
|
|
#mfts.train(sonda_treino,fs)
|
|
|
|
#print(str(mfts))
|
|
|
|
#plt.plot(mfts.forecast(sonda_teste))
|
|
|
|
#[10, 508]
|
|
|
|
#flrs = FLR.generateIndexedFLRs(fs, ix, sonda_treino[110000:111450])
|
|
|
|
#for i in mfts.forecast(sonda_teste):
|
|
# print(i) |