def setUp(self):
self.tempdirpath = tempfile.mkdtemp()
# assuming we are in the test subdir
import pyomo.contrib.parmest.examples.semibatch as sbroot
p = str(sbroot.__path__)
l = p.find("'")
r = p.find("'", l + 1)
sbrootpath = p[l + 1:r]
for file in glob.glob(sbrootpath + os.sep + 'exp*.out'):
shutil.copy(file, self.tempdirpath)
self.save_cwd = os.getcwd()
os.chdir(self.tempdirpath)
num_experiments = 10
exp_list = range(1, num_experiments +
1) # callback uses one-based file names
thetalist = ['k1', 'k2', 'E1', 'E2']
np.random.seed(1134)
from pyomo.contrib.parmest.examples.semibatch.semibatch import pysp_instance_creation_callback
# non-string callback specification
self.pest = parmest.ParmEstimator(None,
pysp_instance_creation_callback,
"SecondStageCost", exp_list,
thetalist)
def setUp(self):
self.thetalist = ['asymptote', 'rate_constant']
self.num_samples = 6
self.sample_list = range(self.num_samples)
self.fsfilename = \
"pyomo.contrib.parmest.examples.rooney_biegler.rooney_biegler"
self.callback_funcname = "instance_creation_callback"
experiment_data = pd.DataFrame(data=[[1, 8.3], [2, 10.3], [3, 19.0],
[4, 16.0], [5, 15.6], [6, 19.8]],
columns=['hour', 'y'])
self.pest = parmest.ParmEstimator(self.fsfilename,
self.callback_funcname,
"SecondStageCost",
self.sample_list,
self.thetalist,
cb_data=experiment_data)
# prepare for the parmest object construction
experiment_data = pd.DataFrame(data=[[1, 8.3], [2, 10.3], [3, 19.0], [4, 16.0],
[5, 15.6], [6, 19.8]],
columns=['hour', 'y'])
num_samples = 6
sample_list = list(range(num_samples)) # callback uses zero-based indexes
thetalist = ['asymptote', 'rate_constant']
fsfilename = "rooney_biegler"
np.random.seed(1134)
# Generate parmest object
pest = parmest.ParmEstimator(fsfilename,
"instance_creation_callback",
"SecondStageCost",
sample_list,
thetalist,
cb_data=experiment_data)
mpii = mpiu.MPIInterface() # works with, or without, mpi
### Parameter estimation with entire data set
objval, thetavals = pest.theta_est()
if mpii.rank in [0, None]:
print("objective value=", str(objval))
print("theta-star=", str(thetavals))
### Parameter estimation with bootstrap
alpha = 0.8
num_bootstraps = 10
# parmest example using mea
import numpy as np
import pyomo.contrib.parmest.parmest as parmest
import pyomo.contrib.parmest.mpi_utils as mpiu
import pyomo.contrib.parmest.graphics as grph
if __name__ == "__main__":
thetalist = ['pp.Keq_a[1]', 'pp.Keq_a[2]']
S = 4
fsfilename = "examples.contrib.projects.mea_simple.parameter_estimate.mea_estimate_pysp"
pest = parmest.ParmEstimator(fsfilename, "pysp_instance_creation_callback",
"SecondStageCost", range(1, S + 1), thetalist)
mpii = mpiu.MPIInterface() # works with, or without, mpi
objval, thetavals = pest.theta_est()
if mpii.rank in [0, None]:
print("objective value=", str(objval))
for t in thetavals:
print(t, "=", thetavals[t])
print("====")
### Parameter estimation with bootstrap
np.random.seed(1134) # make it reproducible
num_bootstraps = 10
bootstrap_theta = pest.bootstrap(num_bootstraps)
if mpii.rank in [0, None]:
if __name__ == '__main__':
# Very simple, with just theta estimation and bootstrap
# Not done in parallel.
import pyomo.contrib.parmest.parmest as parmest
import pyomo.contrib.parmest.graphics as grph
# prepare for the parmest object construction
num_samples = 6
sample_list = list(range(num_samples))
thetalist = ['asymptote', 'rate_constant']
np.random.seed(1134)
# Generate parmest object using the callback function itself,
# rather than a module and a function name
pest = parmest.ParmEstimator(None, pysp_instance_creation_callback,
"SecondStageCost", sample_list, thetalist)
### Parameter estimation with entire data set
objval, thetavals = pest.theta_est()
print("objective value=", str(objval))
print("theta-star=", str(thetavals))
### Parameter estimation with bootstrap
num_bootstraps = 10
bootstrap_theta = pest.bootstrap(num_bootstraps)
print("Bootstrap:")
print(bootstrap_theta)
grph.pairwise_bootstrap_plot(bootstrap_theta,
thetavals,
0.8,