Improvement on FCM GA including both average and standard deviation on the learning optimization objective
This commit is contained in:
Petrônio Cândido
parent
f28fcf0a66
commit
048bb64927
@ -6,6 +6,7 @@ import time
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import matplotlib.pyplot as plt
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import dill
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import numpy as np
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import pandas as pd
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import matplotlib.cm as cmx
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import matplotlib.colors as pltcolors
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from pyFTS.probabilistic import ProbabilityDistribution
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@ -340,7 +341,10 @@ def sliding_window(data, windowsize, train=0.8, inc=0.1, **kwargs):
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:param inc: percentual of data used for slide the window
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:return: window count, training set, test set
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"""
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l = len(data)
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multivariate = True if isinstance(data, pd.DataFrame) else False
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l = len(data) if not multivariate else len(data.index)
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ttrain = int(round(windowsize * train, 0))
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ic = int(round(windowsize * inc, 0))
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@ -357,6 +361,9 @@ def sliding_window(data, windowsize, train=0.8, inc=0.1, **kwargs):
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_end = l
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else:
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_end = count + windowsize
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if multivariate:
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yield (count, data.iloc[count: count + ttrain], data.iloc[count + ttrain: _end])
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else:
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yield (count, data[count : count + ttrain], data[count + ttrain : _end] )
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@ -125,9 +125,10 @@ def evaluate(dataset, individual, **kwargs):
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errors.append(rmse)
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_rmse = np.nanmean(errors)
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_std = np.nanstd(errors)
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#print("EVALUATION {}".format(individual))
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return {'rmse': _rmse}
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return {'rmse': .6 * _rmse + .4 * _std}
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@ -6,193 +6,144 @@ from pyFTS.data import Enrollments, TAIEX, SONDA
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from pyFTS.partitioners import Grid, Simple, Entropy
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from pyFTS.common import Util
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from pyspark import SparkConf
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from pyspark import SparkContext
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import os
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# make sure pyspark tells workers to use python3 not 2 if both are installed
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os.environ['PYSPARK_PYTHON'] = '/usr/bin/python3'
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os.environ['PYSPARK_DRIVER_PYTHON'] = '/usr/bin/python3'
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#'''
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from pyFTS.models.multivariate import common, variable, wmvfts
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from pyFTS.models.seasonal import partitioner as seasonal
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from pyFTS.models.seasonal.common import DateTime
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from pyFTS.partitioners import Grid
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import matplotlib.pyplot as plt
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'''
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#fig, ax = plt.subplots(nrows=3, ncols=1, figsize=[15,5])
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sp = {'seasonality': DateTime.day_of_year , 'names': ['Jan','Feb','Mar','Apr','May','Jun','Jul', 'Aug','Sep','Oct','Nov','Dec']}
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vmonth = variable.Variable("Month", data_label="datahora", partitioner=seasonal.TimeGridPartitioner, npart=12, alpha_cut=.25,
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data=train, partitioner_specific=sp)
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#vmonth.partitioner.plot(ax[0])
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sp = {'seasonality': DateTime.minute_of_day, 'names': [str(k) for k in range(0,24)]}
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vhour = variable.Variable("Hour", data_label="datahora", partitioner=seasonal.TimeGridPartitioner, npart=24, alpha_cut=.2,
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data=train, partitioner_specific=sp)
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#vhour.partitioner.plot(ax[1])
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vavg = variable.Variable("Radiation", data_label="glo_avg", alias='R',
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partitioner=Grid.GridPartitioner, npart=35, alpha_cut=.3,
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data=train)
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#vavg.partitioner.plot(ax[2])
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#plt.tight_layout()
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#Util.show_and_save_image(fig, 'variables', True)
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model = wmvfts.WeightedMVFTS(explanatory_variables=[vmonth,vhour,vavg], target_variable=vavg)
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_s1 = time.time()
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model.fit(train)
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#model.fit(data, distributed='spark', url='spark://192.168.0.106:7077', num_batches=4)
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_s2 = time.time()
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print(_s2-_s1)
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Util.persist_obj(model, 'sonda_wmvfts')
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'''
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#model = Util.load_obj('sonda_wmvfts')
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'''
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from pyFTS.models.multivariate import mvfts, wmvfts, cmvfts, grid, granular
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from pyFTS.benchmarks import Measures
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from pyFTS.common import Util as cUtil
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_s1 = time.time()
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print(Measures.get_point_statistics(test, model))
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_s2 = time.time()
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print(_s2-_s1)
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'''
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#print(len(model))
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#
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#model.fit(data, distributed='dispy', nodes=['192.168.0.110'])
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#'''
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'''
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from pyFTS.models.multivariate import common, variable, mvfts, wmvfts, cmvfts, grid
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from pyFTS.models.seasonal import partitioner as seasonal
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from pyFTS.models.seasonal.common import DateTime
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dataset = pd.read_csv('/home/petronio/Downloads/gefcom12.csv')
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dataset = dataset.dropna()
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train_mv = dataset.iloc[:15000]
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test_mv = dataset.iloc[15000:]
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from pyFTS.models.multivariate import common, variable, mvfts
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from pyFTS.models.seasonal import partitioner as seasonal
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from pyFTS.models.seasonal.common import DateTime
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from pyFTS.partitioners import Grid
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from pyFTS.common import Membership
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sp = {'seasonality': DateTime.minute_of_day, 'names': [str(k) for k in range(0,24)]}
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import os
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'''
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from pyFTS.data import lorentz
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df = lorentz.get_dataframe(iterations=5000)
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train = df.iloc[:4000]
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#test = df.iloc[4000:]
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npart=120
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import sys
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vx = variable.Variable("x", data_label="x", alias='x', partitioner=Grid.GridPartitioner,
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partitioner_specific={'mf': Membership.gaussmf}, npart=npart, data=train)
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vy = variable.Variable("y", data_label="y", alias='y', partitioner=Grid.GridPartitioner,
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partitioner_specific={'mf': Membership.gaussmf}, npart=int(npart*1.5), data=train)
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vz = variable.Variable("z", data_label="z", alias='z', partitioner=Grid.GridPartitioner,
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partitioner_specific={'mf': Membership.gaussmf}, npart=int(npart*1.2), data=train)
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rows = []
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for ct, train, test in cUtil.sliding_window(df, windowsize=4100, train=.97, inc=.05):
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print('Window {}'.format(ct))
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for order in [1, 2, 3]:
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for knn in [1, 2, 3]:
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model = granular.GranularWMVFTS(explanatory_variables=[vx, vy, vz], target_variable=vx, order=order,
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knn=knn)
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model.fit(train)
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forecasts1 = model.predict(test, type='multivariate')
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forecasts2 = model.predict(test, type='multivariate', steps_ahead=100)
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for var in ['x', 'y', 'z']:
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row = [order, knn, var, len(model)]
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for horizon in [1, 25, 50, 75, 100]:
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if horizon == 1:
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row.append( Measures.mape(test[var].values[model.order:model.order+10],
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forecasts1[var].values[:10]))
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else:
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row.append( Measures.mape(test[var].values[:horizon],
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forecasts2[var].values[:horizon]))
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print(row)
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rows.append(row)
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columns = ['Order', 'knn', 'var', 'Rules']
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for horizon in [1, 25, 50, 75, 100]:
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columns.append('h{}'.format(horizon))
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final = pd.DataFrame(rows, columns=columns)
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final.to_csv('gmvfts_lorentz1.csv',sep=';',index=False)
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'''
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import pandas as pd
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df = pd.read_csv('https://query.data.world/s/ftb7bzgobr6bsg6bsuxuqowja6ew4r')
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#df.dropna()
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mload = np.nanmean(df["load"].values)
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df['load'] = np.where(pd.isna(df["load"]), mload, df["load"])
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mtemp = np.nanmean(df["temperature"].values)
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df['temperature'] = np.where(pd.isna(df["temperature"]), mtemp, df["temperature"])
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df['date'] = pd.to_datetime(df['date'], format='%Y-%m-%d %H:%M:%S')
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df['hour'] = np.float64(df['date'].apply(lambda x: x.strftime('%H')))
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df['weekday'] = np.float64(df['date'].apply(lambda x: x.strftime('%w')))
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df['month'] = np.float64(df['date'].apply(lambda x: x.strftime('%m')))
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train_mv = df.iloc[:31000]
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test_mv = df.iloc[:31000:]
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sp = {'seasonality': DateTime.minute_of_day, 'names': [str(k)+'hs' for k in range(0,24)]}
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vhour = variable.Variable("Hour", data_label="date", partitioner=seasonal.TimeGridPartitioner, npart=24,
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data=train_mv, partitioner_specific=sp)
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sp = {'seasonality': DateTime.day_of_week, 'names': ['mon','tue','wed','tur','fri','sat','sun']}
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vday = variable.Variable("DayOfWeek", data_label="date", partitioner=seasonal.TimeGridPartitioner, npart=7,
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data=train_mv, partitioner_specific=sp)
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#sp = {'seasonality': DateTime.day_of_month, 'names': ['jan','feb','mar','apr','may','jun','jul','aug','sep','oct','nov','dec']}
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sp = {'seasonality': DateTime.quarter}
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vmonth = variable.Variable("Month", data_label="date", partitioner=seasonal.TimeGridPartitioner, npart=4,
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data=train_mv, partitioner_specific=sp)
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data=train_mv, partitioner_specific=sp, alpha_cut=.3)
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vtemp = variable.Variable("Temperature", data_label="temperature", alias='temp',
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partitioner=Grid.GridPartitioner, npart=15, func=Membership.gaussmf,
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data=train_mv, alpha_cut=.3)
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vload = variable.Variable("Load", data_label="load", alias='load',
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partitioner=Grid.GridPartitioner, npart=20,
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data=train_mv)
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vtemp = variable.Variable("Temperature", data_label="temperature", alias='temperature',
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partitioner=Grid.GridPartitioner, npart=20,
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data=train_mv)
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from pyFTS.models.multivariate import mvfts, wmvfts, cmvfts, grid
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from pyFTS.models.multivariate import mvfts, wmvfts, cmvfts, grid
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mtemp = wmvfts.WeightedMVFTS(explanatory_variables=[vhour, vmonth, vtemp], target_variable=vtemp)
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mtemp.fit(train_mv)
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Util.persist_obj(mtemp, 'mtemp')
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from pyFTS.models import hofts
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#mtemp = hofts.WeightedHighOrderFTS(order=2, partitioner=vtemp.partitioner)
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#mtemp.fit(train_mv['temperature'].values)
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from pyFTS.models.multivariate import mvfts, wmvfts, cmvfts, grid
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mload = wmvfts.WeightedMVFTS(explanatory_variables=[vtemp, vload], target_variable=vload)
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mload.fit(train_mv)
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Util.persist_obj(mload, 'mload')
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partitioner=Grid.GridPartitioner, npart=20, func=Membership.gaussmf,
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data=train_mv, alpha_cut=.3)
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rows = []
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time_generator = lambda x : pd.to_datetime(x) + pd.to_timedelta(1, unit='h')
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for ct, train, test in cUtil.sliding_window(df, windowsize=32000, train=.98, inc=.05):
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print('Window {}'.format(ct))
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for order in [1, 2, 3]:
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for knn in [1, 2, 3]:
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model = granular.GranularWMVFTS(explanatory_variables=[vhour, vtemp, vload], target_variable=vload,
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order=order, knn=knn)
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forecasts = mload.predict(test_mv.iloc[:1], steps_ahead=48, generators={'date': time_generator,
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'temperature': mtemp})
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model.fit(train)
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'''
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forecasts1 = model.predict(test, type='multivariate')
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forecasts2 = model.predict(test, type='multivariate', generators={'date': time_generator},
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steps_ahead=100)
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for var in ['temperature','load']:
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row = [order, knn, var, len(model)]
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for horizon in [1, 25, 50, 75, 100]:
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if horizon == 1:
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row.append(Measures.mape(test[var].values[model.order:model.order + 10],
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forecasts1[var].values[:10]))
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else:
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row.append(Measures.mape(test[var].values[:horizon],
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forecasts2[var].values[:horizon]))
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data = pd.read_csv('https://query.data.world/s/6xfb5useuotbbgpsnm5b2l3wzhvw2i', sep=';')
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print(row)
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rows.append(row)
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train = data.iloc[:9000]
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test = data.iloc[9000:9200]
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columns = ['Order', 'knn', 'var', 'Rules']
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for horizon in [1, 25, 50, 75, 100]:
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columns.append('h{}'.format(horizon))
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final = pd.DataFrame(rows, columns=columns)
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from pyFTS.models.multivariate import common, variable, mvfts
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from pyFTS.models.seasonal import partitioner as seasonal
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from pyFTS.models.seasonal.common import DateTime
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from pyFTS.partitioners import Grid
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sp = {'seasonality': DateTime.day_of_year , 'names': ['Jan','Fev','Mar','Abr','Mai','Jun','Jul', 'Ago','Set','Out','Nov','Dez']}
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vmonth = variable.Variable("Month", data_label="data", partitioner=seasonal.TimeGridPartitioner, npart=12,
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data=train, partitioner_specific=sp, alpha_cut=.5)
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sp = {'seasonality': DateTime.minute_of_day, 'names': [str(k) for k in range(0,24)]}
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vhour = variable.Variable("Hour", data_label="data", partitioner=seasonal.TimeGridPartitioner, npart=24,
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data=train, partitioner_specific=sp, alpha_cut=.5)
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#print(vhour.partitioner)
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#print(vmonth.partitioner.fuzzyfy(180))
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vavg = variable.Variable("Radiation", data_label="glo_avg", alias='rad',
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partitioner=Grid.GridPartitioner, npart=25, alpha_cut=.3,
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data=train)
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from pyFTS.models.multivariate import mvfts, wmvfts, cmvfts, grid, granular
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model = granular.GranularWMVFTS(explanatory_variables=[vmonth, vhour, vavg], target_variable=vavg,
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order=2, knn=7)
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model.fit(train)
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print(model)
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#model.predict(test)
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final.to_csv('gmvfts_gefcom12.csv', sep=';', index=False)
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