63 lines
1.7 KiB
Python
63 lines
1.7 KiB
Python
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#!/usr/bin/python
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# -*- coding: utf8 -*-
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import os
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import numpy as np
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import pandas as pd
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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, 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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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_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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sonda_treino = sonda[:1051200]
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sonda_teste = sonda[1051201:]
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#res = bchmk.simpleSearch_RMSE(sonda_treino, sonda_teste,
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# sfts.SeasonalFTS,np.arange(3,30),[1],parameters=1440,
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# tam=[15,8], plotforecasts=False,elev=45, azim=40,
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# save=False,file="pictures/sonda_sfts_error_surface", intervals=False)
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from pyFTS.common import Util
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from pyFTS.models import cmsfts
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partitions = ['grid', 'entropy']
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indexers = ['m15', 'Mh', 'Mhm15']
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for max_part in [40, 50]:
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for part in partitions:
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fs = Util.load_obj("models/sonda_fs_" + part + "_" + str(max_part) + ".pkl")
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for ind in indexers:
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ix = Util.load_obj("models/sonda_ix_" + ind + ".pkl")
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model = cmsfts.ContextualMultiSeasonalFTS(part + " " + ind, ix)
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model.train(sonda_treino, fs)
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Util.persist_obj(model, "models/sonda_cmsfts_" + part + "_" + str(max_part) + "_" + ind + ".pkl")
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