#!/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 pandas as pd from pyFTS.partitioners import Grid, Entropy, FCM, Huarng from pyFTS.common import FLR,FuzzySet,Membership,Transformations from pyFTS import fts,hofts,ifts,pwfts,tree, chen #from pyFTS.benchmarks import benchmarks as bchmk from pyFTS.benchmarks import naive, arima from pyFTS.benchmarks import Measures from numpy import random from pyFTS.models.seasonal import SeasonalIndexer os.chdir("/home/petronio/dados/Dropbox/Doutorado/Codigos/") diff = Transformations.Differential(1) ix = SeasonalIndexer.LinearSeasonalIndexer([12, 24], [720, 1],[False, False]) """ DATASETS """ #enrollments = pd.read_csv("DataSets/Enrollments.csv", sep=";") #enrollments = np.array(enrollments["Enrollments"]) #passengers = pd.read_csv("DataSets/AirPassengers.csv", sep=",") #passengers = np.array(passengers["Passengers"]) #sunspots = pd.read_csv("DataSets/sunspots.csv", sep=",") #sunspots = np.array(sunspots["SUNACTIVITY"]) #gauss = random.normal(0,1.0,5000) #gauss_teste = random.normal(0,1.0,400) #taiexpd = pd.read_csv("DataSets/TAIEX.csv", sep=",") #taiex = np.array(taiexpd["avg"][:5000]) #nasdaqpd = pd.read_csv("DataSets/NASDAQ_IXIC.csv", sep=",") #nasdaq = np.array(nasdaqpd["avg"][0:5000]) #sp500pd = pd.read_csv("DataSets/S&P500.csv", sep=",") #sp500 = np.array(sp500pd["Avg"][11000:]) #del(sp500pd) sondapd = pd.read_csv("DataSets/SONDA_BSB_HOURLY_AVG.csv", sep=";") sondapd = sondapd.dropna(axis=0, how='any') sonda = np.array(sondapd["glo_avg"]) del(sondapd) #bestpd = pd.read_csv("DataSets/BEST_TAVG.csv", sep=";") #best = np.array(bestpd["Anomaly"]) #del(bestpd) #print(lag) #print(a) #from pyFTS.benchmarks import benchmarks as bchmk from pyFTS.benchmarks import distributed_benchmarks as bchmk #from pyFTS.benchmarks import parallel_benchmarks as bchmk from pyFTS.benchmarks import Util from pyFTS.benchmarks import arima, quantreg, Measures #Util.cast_dataframe_to_synthetic_point("experiments/taiex_point_analitic.csv","experiments/taiex_point_sintetic.csv",11) #Util.plot_dataframe_point("experiments/taiex_point_sintetic.csv","experiments/taiex_point_analitic.csv",11) """ arima100 = arima.ARIMA("", alpha=0.25) #tmp.appendTransformation(diff) arima100.train(passengers, None, order=(1,0,0)) arima101 = arima.ARIMA("", alpha=0.25) #tmp.appendTransformation(diff) arima101.train(passengers, None, order=(1,0,1)) arima200 = arima.ARIMA("", alpha=0.25) #tmp.appendTransformation(diff) arima200.train(passengers, None, order=(2,0,0)) arima201 = arima.ARIMA("", alpha=0.25) #tmp.appendTransformation(diff) arima201.train(passengers, None, order=(2,0,1)) #tmp = quantreg.QuantileRegression("", alpha=0.25, dist=True) #tmp.appendTransformation(diff) #tmp.train(sunspots[:150], None, order=1) #teste = tmp.forecastAheadInterval(sunspots[150:155], 5) #teste = tmp.forecastAheadDistribution(nasdaq[1600:1604], steps=5, resolution=50) bchmk.plot_compared_series(enrollments,[tmp], ['blue','red'], points=False, intervals=True) #print(sunspots[150:155]) #print(teste) #kk = Measures.get_interval_statistics(nasdaq[1600:1605], tmp) #print(kk) #""" """ bchmk.point_sliding_window(sonda, 9000, train=0.8, inc=0.4,#models=[yu.WeightedFTS], # # partitioners=[Grid.GridPartitioner], #Entropy.EntropyPartitioner], # FCM.FCMPartitioner, ], partitions= np.arange(10,200,step=10), #transformation=diff, dump=True, save=True, file="experiments/sondaws_point_analytic.csv", nodes=['192.168.0.103', '192.168.0.106', '192.168.0.108', '192.168.0.109']) #, depends=[hofts, ifts]) bchmk.point_sliding_window(sonda, 9000, train=0.8, inc=0.4, #models=[yu.WeightedFTS], # # partitioners=[Grid.GridPartitioner], #Entropy.EntropyPartitioner], # FCM.FCMPartitioner, ], partitions= np.arange(3,20,step=2), #transformation=diff, dump=True, save=True, file="experiments/sondaws_point_analytic_diff.csv", nodes=['192.168.0.103', '192.168.0.106', '192.168.0.108', '192.168.0.109']) #, depends=[hofts, ifts]) """ """ bchmk.interval_sliding_window(best, 5000, train=0.8, inc=0.8,#models=[yu.WeightedFTS], # # partitioners=[Grid.GridPartitioner], #Entropy.EntropyPartitioner], # FCM.FCMPartitioner, ], partitions= np.arange(10,200,step=10), dump=True, save=True, file="experiments/best" "_interval_analytic.csv", nodes=['192.168.0.103', '192.168.0.106', '192.168.0.108', '192.168.0.109']) #, depends=[hofts, ifts]) bchmk.interval_sliding_window(sp500, 2000, train=0.8, inc=0.2, #models=[yu.WeightedFTS], # # partitioners=[Grid.GridPartitioner], #Entropy.EntropyPartitioner], # FCM.FCMPartitioner, ], partitions= np.arange(3,20,step=2), transformation=diff, dump=True, save=True, file="experiments/sp500_analytic_diff.csv", nodes=['192.168.0.103', '192.168.0.106', '192.168.0.108', '192.168.0.109']) #, depends=[hofts, ifts]) """ #""" bchmk.ahead_sliding_window(sonda, 10000, steps=10, resolution=10, train=0.2, inc=0.5, partitioners=[Grid.GridPartitioner], partitions= np.arange(10,200,step=10), indexer=ix, dump=True, save=True, file="experiments/sondasolar_ahead_analytic.csv", nodes=['192.168.0.106', '192.168.0.108', '192.168.0.109']) #, depends=[hofts, ifts]) bchmk.ahead_sliding_window(sonda, 10000, steps=10, resolution=10, train=0.2, inc=0.5, partitioners=[Grid.GridPartitioner], partitions= np.arange(3,20,step=2), transformation=diff, indexer=ix, dump=True, save=True, file="experiments/sondasolar_ahead_analytic_diff.csv", nodes=['192.168.0.106', '192.168.0.108', '192.168.0.109']) #, depends=[hofts, ifts]) """ from pyFTS.partitioners import Grid from pyFTS import sfts #print(ix.get_season_of_data(best[:2000])) #print(ix.get_season_by_index(45)) #ix = SeasonalIndexer.LinearSeasonalIndexer([720,24],[False,True,False]) #print(ix.get_season_of_data(sonda[6500:9000])[-20:]) diff = Transformations.Differential(1) fs = Grid.GridPartitioner(sonda[:9000], 10, transformation=diff) tmp = sfts.SeasonalFTS("") tmp.indexer = ix tmp.appendTransformation(diff) #tmp = pwfts.ProbabilisticWeightedFTS("") #tmp.appendTransformation(diff) tmp.train(sonda[:9000], fs.sets, order=1) x = tmp.forecast(sonda[:1610]) #print(taiex[1600:1610]) print(x) #"""