pyFTS/tests/general.py

#!/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

os.chdir("/home/petronio/dados/Dropbox/Doutorado/Codigos/")

diff = Transformations.Differential(1)

"""
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(best, 4000, steps=10, resolution=100, train=0.8, inc=0.5,
                     partitioners=[Grid.GridPartitioner],
                     partitions= np.arange(10,200,step=10),
                     dump=True, save=True, file="experiments/best_ahead_analytic.csv",
                     nodes=['192.168.0.106', '192.168.0.108', '192.168.0.109']) #, depends=[hofts, ifts])


bchmk.ahead_sliding_window(best, 4000, steps=10, resolution=100, train=0.8, inc=0.5,
                     partitioners=[Grid.GridPartitioner],
                     partitions= np.arange(3,20,step=2), transformation=diff,
                     dump=True, save=True, file="experiments/best_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.models.seasonal import SeasonalIndexer
from pyFTS import sfts

ix = SeasonalIndexer.LinearSeasonalIndexer([10])

#print(ix.get_season_of_data(best[:2000]))

#print(ix.get_season_by_index(45))

diff = Transformations.Differential(1)

fs = Grid.GridPartitioner(best[:2000], 10, transformation=diff)

tmp = sfts.SeasonalFTS("")
tmp.indexer = ix
tmp.appendTransformation(diff)

#tmp = pwfts.ProbabilisticWeightedFTS("")

#tmp.appendTransformation(diff)

tmp.train(best[:1600], fs.sets, order=1)

x = tmp.forecast(best[1600:1610])

#print(taiex[1600:1610])
print(x)
#"""