214 lines
7.3 KiB
Python
214 lines
7.3 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 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])
|
|
#del(taiexpd)
|
|
|
|
#nasdaqpd = pd.read_csv("DataSets/NASDAQ_IXIC.csv", sep=",")
|
|
#nasdaq = np.array(nasdaqpd["avg"][0:5000])
|
|
#del(nasdaqpd)
|
|
|
|
#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(taiex, 2000, train=0.8, inc=0.1, #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/taiex_interval_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=0.2,
|
|
partitioners=[Grid.GridPartitioner],
|
|
partitions= np.arange(10,200,step=10), indexer=ix,
|
|
dump=True, save=True, file="experiments/sondawind_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=0.2,
|
|
partitioners=[Grid.GridPartitioner],
|
|
partitions= np.arange(3,20,step=2), transformation=diff, indexer=ix,
|
|
dump=True, save=True, file="experiments/sondawind_ahead_analytic_diff.csv",
|
|
nodes=['192.168.0.106', '192.168.0.108', '192.168.0.109']) #, depends=[hofts, ifts])
|
|
|
|
"""
|
|
|
|
from pyFTS import pwfts
|
|
from pyFTS.common import Transformations
|
|
from pyFTS.partitioners import Grid
|
|
|
|
diff = Transformations.Differential(1)
|
|
fs = Grid.GridPartitioner(best, 190) #, transformation=diff)
|
|
|
|
|
|
model = pwfts.ProbabilisticWeightedFTS("FTS 1")
|
|
#model.appendTransformation(diff)
|
|
model.train(best[0:1600],fs.sets, order=3)
|
|
|
|
bchmk.plot_compared_intervals_ahead(best[1600:1700],[model], ['blue','red'],
|
|
distributions=[True], save=True, file="pictures/best_ahead_forecasts",
|
|
time_from=40, time_to=60, resolution=100)
|
|
|
|
"""
|
|
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)
|
|
#""" |