def main():
# Specify number of poitns to be used in the training set
# Validation data can be provided optionally
ndata= 50
nval = 500
lb = [-15,-15]
ub = [15,15]
x = np.random.uniform(lb,ub,(ndata,2))
xval = np.random.uniform(lb,ub,(nval,2))
z = [0]*ndata
zval = [0]*nval
# specify simulator as examples.sixcamel
sim = examples.ackley
for i in range(ndata):
z[i]=sim(x[i][0],x[i][1])
for i in range(nval):
zval[i] = sim(xval[i][0],xval[i][1])
# Use alamopy's python function wrapper to avoid using ALAMO's I/O format
almsim = alamopy.wrapwriter(sim)
# Call alamo through the alamopy wrapper
res = alamopy.doalamo(x,z,xval=xval,zval=zval,almname='ackley',monomialpower=(1,2,3,4,5,6),expfcns=1,multi2power=(1,2),expandoutput=True)
# Calculate confidence intervals
conf_inv = alamopy.almconfidence(res)
print('Model: {}'.format(res['model']))
print('Confidence Intervals : {}'.format(conf_inv['conf_inv']))
def _main():
# Specify number of poitns to be used in the training set
# Validation data can be provided optionally
ndata = 10
x = np.random.uniform([-2, -1], [2, 1], (ndata, 2))
z = [0] * ndata
# specify simulator as examples.sixcamel
sim = examples.sixcamel
for i in range(ndata):
z[i] = sim(x[i][0], x[i][1])
# Use alamopy's python function wrapper to avoid using ALAMO's I/O format
almsim = alamopy.wrapwriter(sim)
# Call alamo through the alamopy wrapper
res = alamopy.doalamo(
x,
z,
almname="cam6",
monomialpower=(1, 2, 3, 4, 5, 6),
multi2power=(1, 2),
simulator=almsim,
expandoutput=True,
maxiter=20,
cvfun=True,
)
# print res
print("Model: {}".format(res["model"]))
def main():
# Specify number of poitns to be used in the training set
# Validation data can be provided optionally
ndata = 20
nval = 100
x = np.random.uniform([-5, 0], [10, 15], (ndata, 2))
xval = np.random.uniform([-5, 0], [10, 15], (nval, 2))
z = [0] * ndata
zval = [0] * nval
# specify simulator as examples.sixcamel
sim = examples.branin
for i in range(ndata):
z[i] = sim(x[i][0], x[i][1])
for i in range(nval):
zval[i] = sim(xval[i][0], xval[i][1])
# Use alamopy's python function wrapper to avoid using ALAMO's I/O format
# Call alamo through the alamopy wrapper
res = alamopy.doalamo(x,
z,
xval=xval,
zval=zval,
almname='branin',
xmin=(-5, 0),
xmax=(10, 15),
monomialpower=(1, 2, 3, 4),
expfcns=1,
multi2power=(1, 2),
expandoutput=True)
conf_inv = alamopy.almconfidence(res)
print('Model: {}'.format(res['model']))
print('Confidence Intervals : {}'.format(conf_inv['conf_inv']))
def test_single_input_CV():
if not alamopy.has_alamo():
return False
# Specify number of points to be used in the training set
# Validation data can be provided optionally
ndata = 10
x = np.random.uniform([-2, -1], [2, 1], (ndata, 2))
z = np.zeros((ndata, 1))
# specify simulator as examples.sixcamel
sim = sixcamel
for i in range(ndata):
z[i, 0] = sim(x[i][0], x[i][1])
# z[i,1] = sim(x[i][0], x[i][1])
# # Use alamopy's python function wrapper to avoid using ALAMO's I/O format
almsim = alamopy.wrapwriter(sim)
# NON GENERIC
alamo_settings = {
'almname': "cam6",
'monomialpower': (1, 2, 3, 4, 5, 6),
'multi2power': (1, 2),
'simulator': almsim,
'maxiter': 20,
'cvfun': True
}
res = alamopy.doalamo(x, z, lmo=3)
def _run_alamo(self):
kwargs = self._kwargs.copy()
if self._xv is not None and self._zv is not None:
kwargs['xval'], kwargs['zval'] = self._xv, self._zv
# run alamo to generate model
try:
results = alamopy.doalamo(self._x, self._z, **kwargs)
except alamopy.AlamoError as err:
raise errors.AlamoError(str(err))
# return the generated model
return results
def allcard(xdata, zdata, xval, zval, **kwargs):
# enumerate all model cardinalities via ccmiqp and
# use validation/cross-validaiton to determine
from idaes.surrogate import alamopy
# PYLINT-TODO-FIX alamopy.writethis.writethis doesn't seem to exist
# pylint: disable=import-error
from alamopy.writethis import writethis
# pylint: enable=import-error
import numpy as np
import math
# import sympy
import random
from random import shuffle
# from scipy.optimize import curve_fit #2.7
# from scipy.optimize import minimize #2.7
from scipy.linalg import lstsq # 2.7
import os
# from alamopy import almlsq as almfun
# from alamopy import almlsqjac as almjac
import time
# from sympy.parsing.sympy_parser import parse_expr
import sys
random.seed(100)
# kwargs=opdict
trans = list([
'linfcns', 'expfcns', 'logfcns', 'sinfcns', 'cosfcns', 'monomialpower',
'multi2power', 'multi3power', 'ratiopower'
])
et = list(['multi2power', 'multi3power', 'ratiopower'])
# datacc = {}
ins = list(['cvfold'])
for opt in ins:
if (opt in kwargs.keys()):
cvfold = kwargs['cvfold']
ntrans = 0
ndata = np.shape(xdata)[0]
ninputs = np.shape(xdata)[1]
for t in trans:
if t in kwargs.keys():
if (type(kwargs[t]) == list()):
nt = len(kwargs[t])
else:
nt = 1
if t not in et:
ntrans = ntrans + ninputs * nt
else:
ntrans = ntrans + math.factorial(ninputs) * nt
else:
kwargs[t] = 0
# mseold = 0.0
rmseold = 0.0
startt = time.time()
oldres = {}
oldp = ()
ntrans = min(ntrans, 1000)
# split training and validationd ata before looping through cc
tlist = list([])
vlist = list([])
# ndlist = range(ndata)
if (cvfold == 'loo'):
for i in range(ndata):
vlist.append(np.asarray([i]))
temp = [x for x in range(ndata) if x != i]
tlist.append(np.asarray(temp))
else:
temp = range(ndata)
shuffle(temp)
# tlist = np.asarray(temp)
if (cvfold == 'valset'):
tlist = np.asarray(temp)[0:int(0.7 * ndata)]
vlist = np.asarray(temp)[int(0.7 * ndata):-1]
else:
vlist = np.array_split(np.asarray(temp), int(cvfold))
tlist = [1] * int(cvfold)
for v in range(len(vlist)):
tlist[v] = range(ndata)
for this in vlist[v]:
tlist[v].remove(this)
for ccon in list(range(1, ntrans + 1)):
# reload(alamopy)
# try:
# del cvalsim, almsim
# except Exception:
# pass
# res = alamopy.doalamo(xdata,zdata,kwargs.values())
if (cvfold != 'valset'):
res = alamopy.doalamo(xdata,
zdata,
xval=xval,
zval=zval,
linfcns=kwargs['linfcns'],
expfcns=kwargs['expfcns'],
logfcns=kwargs['logfcns'],
sinfcns=kwargs['sinfcns'],
cosfcns=kwargs['cosfcns'],
monomialpower=kwargs['monomialpower'],
multi2power=kwargs['multi2power'],
multi3power=kwargs['multi3power'],
ratiopower=kwargs['ratiopower'],
sigma=kwargs['sigma'],
xlabels=kwargs['xlabels'],
zlabels=kwargs['zlabels'],
modeler=6,
convpen=0,
maxterms=ccon,
almname=kwargs['almname'],
expandoutput=kwargs['expandoutput'],
xmax=kwargs['xmax'],
xmin=kwargs['xmin'],
savescratch=kwargs['savescratch'])
else:
res = alamopy.doalamo(xdata[tlist, :],
zdata[tlist],
xval=xdata[vlist, :],
zval=zdata[vlist],
linfcns=kwargs['linfcns'],
expfcns=kwargs['expfcns'],
logfcns=kwargs['logfcns'],
sinfcns=kwargs['sinfcns'],
cosfcns=kwargs['cosfcns'],
monomialpower=kwargs['monomialpower'],
multi2power=kwargs['multi2power'],
multi3power=kwargs['multi3power'],
ratiopower=kwargs['ratiopower'],
sigma=kwargs['sigma'],
xlabels=kwargs['xlabels'],
zlabels=kwargs['zlabels'],
modeler=6,
convpen=0,
maxterms=ccon,
almname=kwargs['almname'],
expandoutput=kwargs['expandoutput'],
xmax=kwargs['xmax'],
xmin=kwargs['xmin'],
savescratch=kwargs['savescratch'])
for fn in ['cvalsim.py', 'cvalsim.pyc', 'almsim.py', 'almsim.pyc']:
try:
os.remove(fn)
except Exception:
pass
os.system('cp ' + kwargs['almname'].split('.')[0] + 'cv.py cvalsim.py')
os.system('cp ' + kwargs['almname'].split('.')[0] + 'alm.py almsim.py')
if (ccon == 1):
if (ndata < 10): # not enough data to do cross validation
writethis('Not enough data to facilitate cross validation')
endt = time.time()
res['totaltime'] = endt - startt
return res
# import cvalsim
import almsim
xalm = alamopy.almfeatmat(xdata, ccon)
# Lets do the cross validatione error
mseval = 0.0
rmse = {}
if (cvfold == 'valset'):
resid = np.sum((zdata[vlist] - almsim.f(xdata[vlist, :]))**2)
mseval = resid / float(len(vlist))
params = 'ALM params used for valset'
rmse = {}
rmse['val'] = res['rmseval']
rmse['train'] = res['rmse']
else:
mseval = 0.0
# track = 0
for tl, vl in zip(tlist, vlist):
# initb = [ 0.0 for x in range(ccon)]
xd, xmax, xmin = alamopy.mapminmax(xalm)
zd, zmax, zmin = alamopy.mapminmax(zdata)
fitres = lstsq(xd[tl], zd[tl])
params = fitres[0]
resid = zd[vl] - np.matmul(xd[vl, :], params[:])
resid = alamopy.remapminmax(resid, zmax, zmin)
resid = sum(resid**2)
if (cvfold == 'loo'):
mseval = mseval + resid
else:
mseval = mseval + resid / float(len(vl))
mseval = mseval / float(len(vlist))
rmse['val'] = np.sqrt(mseval)
rmse['train'] = 'not'
if (ccon > 1):
if (float(rmse['val']) >= float(rmseold)):
sys.stdout.write(' Problem name : ' +
kwargs['almname'] + '\n')
sys.stdout.write(' rMSEval : ' + str(rmse['val']) +
' MSEold : ' + str(rmseold) + '\n')
if (cvfold == 'valset'):
sys.stdout.write(' Ntrain : ' + str(len(tlist)) +
' Nval : ' + str(len(vlist)) + '\n')
else:
sys.stdout.write(' Ntrain : ' + str(len(tlist[0])) +
' Nval : ' + str(len(vlist[0])) + '\n')
sys.stdout.write(' optimal model size is : ' +
str(ccon - 1) + '\n')
sys.stdout.write(' optimal coefficients are : ' +
str(oldp) + '\n')
os.remove(kwargs['almname'].split('.')[0] + 'alm.py')
os.system('cp ' + 'oldalmsim.sv ' +
kwargs['almname'].split('.')[0] + 'alm.py')
endt = time.time()
oldres['totaltime'] = endt - startt
return oldres
elif (ccon == ntrans):
endt = time.time()
sys.stdout.write('optimal model size is :' + str(ccon) + '\n')
res['totaltime'] = endt - startt
return res
else:
# mseold = mseval
oldres = res
else:
# mseold = float(mseval)
rmseold = float(rmse['val'])
oldres = res
oldp = params
# keep track of alm model of old iteratoin
try:
os.remove('oldalmsim.sv')
except Exception:
pass
os.system('mv almsim.py oldalmsim.sv')
