141 lines
4.9 KiB
Python
141 lines
4.9 KiB
Python
#!/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 pandas as pd
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from pyFTS.partitioners import Grid, Entropy, FCM, Huarng
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from pyFTS.common import FLR,FuzzySet,Membership,Transformations
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from pyFTS import fts,hofts,ifts,pwfts,tree, chen
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#from pyFTS.benchmarks import benchmarks as bchmk
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from pyFTS.benchmarks import naive, arima
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from pyFTS.benchmarks import Measures
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from numpy import random
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os.chdir("/home/petronio/dados/Dropbox/Doutorado/Codigos/")
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#enrollments = pd.read_csv("DataSets/Enrollments.csv", sep=";")
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#enrollments = np.array(enrollments["Enrollments"])
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#from pyFTS import song
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#enrollments_fs = Grid.GridPartitioner(enrollments, 10).sets
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#model = song.ConventionalFTS('')
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#model.train(enrollments,enrollments_fs)
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#teste = model.forecast(enrollments)
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#print(teste)
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#print(FCM.FCMPartitionerTrimf.__module__)
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#gauss = random.normal(0,1.0,5000)
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#gauss_teste = random.normal(0,1.0,400)
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taiexpd = pd.read_csv("DataSets/TAIEX.csv", sep=",")
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taiex = np.array(taiexpd["avg"][:5000])
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#nasdaqpd = pd.read_csv("DataSets/NASDAQ_IXIC.csv", sep=",")
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#nasdaq = np.array(nasdaqpd["avg"][0:5000])
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#from statsmodels.tsa.arima_model import ARIMA as stats_arima
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from statsmodels.tsa.tsatools import lagmat
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#tmp = np.arange(10)
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#lag, a = lagmat(tmp, maxlag=2, trim="both", original='sep')
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#print(lag)
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#print(a)
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from pyFTS.benchmarks import distributed_benchmarks as bchmk
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#from pyFTS.benchmarks import parallel_benchmarks as bchmk
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from pyFTS.benchmarks import Util
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#from pyFTS.benchmarks import arima
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#Util.cast_dataframe_to_sintetic_point("experiments/taiex_point_analitic.csv","experiments/taiex_point_sintetic.csv",11)
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#Util.plot_dataframe_point("experiments/taiex_point_sintetic.csv","experiments/taiex_point_analitic.csv",11)
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#tmp = arima.ARIMA("")
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#tmp.train(taiex[:1600],None,parameters=(2,0,1))
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#teste = tmp.forecast(taiex[1598:1601])
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#print(teste)
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#bchmk.teste(taiex,['192.168.0.109', '192.168.0.101'])
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bchmk.point_sliding_window(taiex,2000,train=0.8, #models=[yu.WeightedFTS], # #
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partitioners=[Grid.GridPartitioner], #Entropy.EntropyPartitioner], # FCM.FCMPartitioner, ],
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partitions= np.arange(10,200,step=10), #transformation=diff,
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dump=True, save=True, file="experiments/taiex_point_analytic.csv",
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nodes=['192.168.0.102', '192.168.0.109']) #, depends=[hofts, ifts])
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#bchmk.testa(taiex,[10,20],partitioners=[Grid.GridPartitioner], nodes=['192.168.0.109', '192.168.0.101'])
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#parallel_util.explore_partitioners(taiex,20)
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#nasdaqpd = pd.read_csv("DataSets/NASDAQ_IXIC.csv", sep=",")
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#nasdaq = np.array(nasdaqpd["avg"][:5000])
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#taiex = pd.read_csv("DataSets/TAIEX.csv", sep=",")
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#taiex_treino = np.array(taiex["avg"][2500:3900])
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#taiex_teste = np.array(taiex["avg"][3901:4500])
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#print(len(taiex))
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#from pyFTS.common import Util
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#, ,
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#diff = Transformations.Differential(1)
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#bchmk.external_point_sliding_window([naive.Naive, arima.ARIMA, arima.ARIMA, arima.ARIMA, arima.ARIMA, arima.ARIMA, arima.ARIMA],
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# [None, (1,0,0),(1,1,0),(2,0,0), (2,1,0), (1,1,1), (1,0,1)],
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# gauss,2000,train=0.8, dump=True, save=True, file="experiments/arima_gauss.csv")
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#bchmk.interval_sliding_window(gauss,2000,train=0.8, #transformation=diff, #models=[pwfts.ProbabilisticWeightedFTS], # #
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# partitioners=[Grid.GridPartitioner], #Entropy.EntropyPartitioner], # FCM.FCMPartitioner, ],
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# partitions= np.arange(10,200,step=5), #
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# dump=True, save=False, file="experiments/nasdaq_interval.csv")
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#3bchmk.ahead_sliding_window(taiex,2000,train=0.8, steps=20, resolution=250, #transformation=diff, #models=[pwfts.ProbabilisticWeightedFTS], # #
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# partitioners=[Grid.GridPartitioner], #Entropy.EntropyPartitioner], # FCM.FCMPartitioner, ],
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# partitions= np.arange(10,200,step=10), #
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# dump=True, save=True, file="experiments/taiex_ahead.csv")
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#bchmk.allPointForecasters(taiex_treino, taiex_treino, 95, #transformation=diff,
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# models=[ naive.Naive, pfts.ProbabilisticFTS, pwfts.ProbabilisticWeightedFTS],
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# statistics=True, residuals=False, series=False)
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#data_train_fs = Grid.GridPartitioner(nasdaq[:1600], 95).sets
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#fts1 = pwfts.ProbabilisticWeightedFTS("")
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#fts1.appendTransformation(diff)
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#fts1.train(nasdaq[:1600], data_train_fs, order=1)
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#_crps1, _crps2, _t1, _t2 = bchmk.get_distribution_statistics(nasdaq[1600:2000], fts1, steps=20, resolution=200)
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#print(_crps1, _crps2, _t1, _t2)
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#print(fts1.forecast([5000, 5000]))
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#fts2 = pwfts.ProbabilisticWeightedFTS("")
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#fts2.appendTransformation(diff)
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#fts2.train(taiex_treino, data_train_fs, order=1)
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#print(fts2.forecast([5000, 5000]))
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#tmp = Grid.GridPartitioner(taiex_treino,7,transformation=diff)
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#for s in tmp.sets: print(s) |