def test_empty_prior():
"""Check that priors are zero when none are defined."""
# define negative log posterior
posterior_fun = pypesto.Objective(fun=negative_log_posterior)
# define pypesto problem without prior object
test_problem = pypesto.Problem(objective=posterior_fun,
lb=-10,
ub=10,
x_names=['x'])
sampler = sample.AdaptiveMetropolisSampler()
result = sample.sample(
test_problem,
n_samples=50,
sampler=sampler,
x0=np.array([0.0]),
filename=None,
)
# get log prior values of first chain
logprior_trace = -result.sample_result.trace_neglogprior[0, :]
# check that all entries are zero
assert (logprior_trace == 0.0).all()
def get_objective(
self,
fun: bool = True,
res: bool = True,
max_sensi_order: int = 2,
fim_for_hess: bool = False,
):
"""Full pyPESTO objective function.
Parameters
----------
fun: Whether the objective can calculate function values.
res: Whether the objective can calculate residuals.
max_sensi_order: Maximum sensitivity order the function can calculate.
fim_for_hess: Whether to use the FIM instead of the Hessian.
"""
if fim_for_hess:
fhess = self.get_ffim()
else:
fhess = self.get_fs2nllh()
return pypesto.Objective(
fun=self.get_fnllh() if fun else None,
grad=self.get_fsnllh() if fun and max_sensi_order >= 1 else None,
hess=fhess if fun and max_sensi_order >= 2 else None,
res=self.get_fres() if res else None,
sres=self.get_fsres() if res and max_sensi_order >= 1 else None,
)
def test_x_names():
"""Test that `x_names` are handled properly."""
kwargs = {
'objective': pypesto.Objective(),
'lb': [-5] * 3,
'ub': [4] * 3,
'x_fixed_indices': [1],
'x_fixed_vals': [42.0],
}
# non-unique values
with pytest.raises(ValueError):
pypesto.Problem(x_names=['x1', 'x2', 'x2'], **kwargs)
# too few or too many arguments
with pytest.raises(AssertionError):
pypesto.Problem(x_names=['x1', 'x2'], **kwargs)
with pytest.raises(AssertionError):
pypesto.Problem(x_names=['x1', 'x2', 'x3', 'x4'], **kwargs)
# all fine
problem = pypesto.Problem(x_names=['a', 'b', 'c'], **kwargs)
assert problem.x_names == ['a', 'b', 'c']
# defaults
problem = pypesto.Problem(**kwargs)
assert problem.x_names == ['x0', 'x1', 'x2']
def test_finite_difference_checks():
"""
Test the finite difference gradient check methods by expected relative
error.
"""
x = sp.Symbol('x')
# Setup single-parameter objective function
fun_expr = x**10
grad_expr = fun_expr.diff()
theta = 0.1
fun = sp.lambdify(x, fun_expr)
grad = sp.lambdify(x, grad_expr)
objective = pypesto.Objective(fun=fun, grad=grad)
def rel_err(eps_):
"""Expected relative error."""
central_difference = (fun(theta + eps_) - fun(theta - eps_)) / (2 *
eps_)
return abs(
(grad(theta) - central_difference) / (central_difference + eps_))
# Test the single step size `check_grad` method.
eps = 1e-5
result_single_eps = objective.check_grad(np.array([theta]), eps=eps)
assert result_single_eps['rel_err'].squeeze() == rel_err(eps)
# Test the multiple step size `check_grad_multi_eps` method.
multi_eps = {1e-1, 1e-3, 1e-5, 1e-7, 1e-9}
result_multi_eps = \
objective.check_grad_multi_eps([theta], multi_eps=multi_eps)
assert result_multi_eps['rel_err'].squeeze() == \
min(rel_err(_eps) for _eps in multi_eps)
def gaussian_problem():
def nllh(x):
return -gaussian_llh(x)
objective = pypesto.Objective(fun=nllh)
problem = pypesto.Problem(objective=objective, lb=[-10], ub=[10])
return problem
def test_prior():
"""Check that priors are defined for sampling."""
# define negative log posterior
posterior_fun = pypesto.Objective(fun=negative_log_posterior)
# define negative log prior
prior_fun = pypesto.Objective(fun=negative_log_prior)
# define pypesto prior object
prior_object = pypesto.NegLogPriors(objectives=[prior_fun])
# define pypesto problem using prior object
test_problem = pypesto.Problem(
objective=posterior_fun,
x_priors_defs=prior_object,
lb=-10,
ub=10,
x_names=['x'],
)
sampler = sample.AdaptiveMetropolisSampler()
result = sample.sample(
test_problem,
n_samples=1e4,
sampler=sampler,
x0=np.array([0.0]),
filename=None,
)
# get log prior values of first chain
logprior_trace = -result.sample_result.trace_neglogprior[0, :]
# check that not all entries are zero
assert (logprior_trace != 0.0).any()
# get samples of first chain
samples = result.sample_result.trace_x[0, :, 0]
# generate ground-truth samples
rvs = norm.rvs(size=5000, loc=-1.0, scale=np.sqrt(0.7))
# check sample distribution agreement with the ground-truth
statistic, pval = ks_2samp(rvs, samples)
print(statistic, pval)
assert statistic < 0.1
def test_mpipoolengine():
"""
Test the MPIPoolEngine by calling an example script with mpiexec.
"""
try:
# get the path to this file:
path = os.path.dirname(__file__)
# run the example file.
subprocess.check_call( # noqa: S603,S607
[
'mpiexec',
'-np',
'2',
'python',
'-m',
'mpi4py.futures',
f'{path}/../../doc/example/example_MPIPool.py',
]
)
# read results
result1 = read_result('temp_result.h5', problem=True, optimize=True)
# set optimizer
optimizer = optimize.FidesOptimizer(verbose=0)
# initialize problem with x_guesses and objective
objective = pypesto.Objective(
fun=sp.optimize.rosen,
grad=sp.optimize.rosen_der,
hess=sp.optimize.rosen_hess,
)
x_guesses = np.array(
[result1.optimize_result.list[i]['x0'] for i in range(2)]
)
problem = pypesto.Problem(
objective=objective,
ub=result1.problem.ub,
lb=result1.problem.lb,
x_guesses=x_guesses,
)
result2 = optimize.minimize(
problem=problem,
optimizer=optimizer,
n_starts=2,
engine=pypesto.engine.MultiProcessEngine(),
filename=None,
)
for ix in range(2):
assert_almost_equal(
result1.optimize_result.list[ix]['x'],
result2.optimize_result.list[ix]['x'],
err_msg='The final parameter values '
'do not agree for the engines.',
)
finally:
if os.path.exists('temp_result.h5'):
# delete data
os.remove('temp_result.h5')
def gaussian_mixture_problem():
"""Problem based on a mixture of gaussians."""
def nllh(x):
return - gaussian_mixture_llh(x)
objective = pypesto.Objective(fun=nllh)
problem = pypesto.Problem(objective=objective, lb=[-10], ub=[10],
x_names=['x'])
return problem
def gaussian_mixture_separated_modes_problem():
"""Problem based on a mixture of gaussians with far/separated modes."""
def nllh(x):
return - gaussian_mixture_separated_modes_llh(x)
objective = pypesto.Objective(fun=nllh)
problem = pypesto.Problem(objective=objective, lb=[-100], ub=[200],
x_names=['x'])
return problem
def obj_for_sensi(fun, grad, hess, max_sensi_order, integrated, x):
"""
Create a pypesto.Objective able to compute up to the speficied
max_sensi_order. Returns a dict containing the objective obj as well
as max_sensi_order and fval, grad, hess for the passed x.
Parameters
----------
fun, grad, hess: callable
Functions computing the fval, grad, hess.
max_sensi_order: int
Maximum sensitivity order the pypesto.Objective should be capable of.
integrated: bool
True if fun, grad, hess should be integrated into one function, or
passed to pypesto.Objective separately (both is possible)
x: np.array
Value at which to evaluate the function to obtain true values.
Returns
-------
ret: dict
With fields obj, max_sensi_order, x, fval, grad, hess.
"""
if integrated:
if max_sensi_order == 2:
def arg_fun(x):
return (fun(x), grad(x), hess(x))
arg_grad = arg_hess = True
elif max_sensi_order == 1:
def arg_fun(x):
return (fun(x), grad(x))
arg_grad = True
arg_hess = False
else:
def arg_fun(x):
return fun(x)
arg_grad = arg_hess = False
else: # integrated
if max_sensi_order >= 2:
arg_hess = hess
else:
arg_hess = None
if max_sensi_order >= 1:
arg_grad = grad
else:
arg_grad = None
arg_fun = fun
obj = pypesto.Objective(fun=arg_fun, grad=arg_grad, hess=arg_hess)
return {'obj': obj,
'max_sensi_order': max_sensi_order,
'x': x,
'fval': fun(x),
'grad': grad(x),
'hess': hess(x)}
def rosenbrock_problem():
"""Problem based on rosenbrock objective."""
objective = pypesto.Objective(fun=so.rosen)
dim_full = 2
lb = -5 * np.ones((dim_full, 1))
ub = 5 * np.ones((dim_full, 1))
problem = pypesto.Problem(objective=objective, lb=lb, ub=ub)
return problem
def create_problem():
# define a pypesto objective
objective = pypesto.Objective(fun=so.rosen,
grad=so.rosen_der,
hess=so.rosen_hess)
# define a pypesto problem
(lb, ub) = create_bounds()
problem = pypesto.Problem(objective=objective, lb=lb, ub=ub)
return problem
def problem():
"""A very basic problem."""
lb = [-5] * 10
ub = [6] * 10
objective = pypesto.Objective()
problem = pypesto.Problem(objective=objective,
lb=lb,
ub=ub,
x_fixed_indices=[0, 1, 5],
x_fixed_vals=[42, 43, 44])
return problem
def create_problem(n_parameters: int = 2, x_names: Sequence[str] = None):
# define a pypesto objective
objective = pypesto.Objective(
fun=so.rosen, grad=so.rosen_der, hess=so.rosen_hess, x_names=x_names
)
# define a pypesto problem
(lb, ub) = create_bounds(n_parameters)
problem = pypesto.Problem(objective=objective, lb=lb, ub=ub)
return problem
def _multistart_on_kernel(fun, lb, ub):
objective = pypesto.Objective(fun=fun)
problem = pypesto.Problem(objective=objective, lb=lb, ub=ub)
optimizer = pypesto.ScipyOptimizer(options={
'maxiter': 10000,
'maxfun': 10000,
'disp': False
})
result = pypesto.minimize(problem=problem,
optimizer=optimizer,
n_starts=100)
return result
def test_ensemble_from_optimization():
"""
Test reading an ensemble from optimization result.
"""
objective = pypesto.Objective(fun=so.rosen,
grad=so.rosen_der,
hess=so.rosen_hess)
dim_full = 10
lb = -5 * np.ones((dim_full, 1))
ub = 5 * np.ones((dim_full, 1))
n_starts = 5
problem = pypesto.Problem(objective=objective, lb=lb, ub=ub)
optimizer = optimize.ScipyOptimizer(options={'maxiter': 10})
history_options = pypesto.HistoryOptions(trace_record=True)
result = optimize.minimize(
problem=problem,
optimizer=optimizer,
n_starts=n_starts,
history_options=history_options,
)
# change fvals of each start
for i_start, optimizer_result in enumerate(result.optimize_result.list):
optimizer_result['fval'] = i_start + 1
for i_iter in range(len(optimizer_result['history']._trace['fval'])):
optimizer_result['history']._trace['fval'][i_iter] = (
len(optimizer_result['history']._trace['fval']) + i_start -
i_iter)
# test_endpoints
ensemble_ep = Ensemble.from_optimization_endpoints(result=result,
cutoff=4,
max_size=10)
ensemble_hist = Ensemble.from_optimization_history(result=result,
cutoff=4,
max_size=10,
max_per_start=5)
# compare vector_tags with the expected values:
ep_tags = [(int(result.optimize_result.list[i]['id']), -1)
for i in [0, 1, 2, 3]]
hist_tags = [(
int(result.optimize_result.list[i]['id']),
len(result.optimize_result.list[i]['history']._trace['fval']) - 1 - j,
) for i in range(4) for j in reversed(range(4 - i))]
assert hist_tags == ensemble_hist.vector_tags
assert ep_tags == ensemble_ep.vector_tags
def lsq_residual_objective(d: float):
"""
Returns an objective for the function
f(x) = (x-d)^2
"""
def f(x):
return np.sum((x[0] - d) ** 2)
def grad(x):
return 2 * (x - d)
return pypesto.Objective(fun=f, grad=grad)
def create_problem():
# define a pypesto objective (with tracing options)
objective_options = pypesto.ObjectiveOptions(trace_record=True,
trace_save_iter=1)
objective = pypesto.Objective(fun=sp.optimize.rosen,
grad=sp.optimize.rosen_der,
hess=sp.optimize.rosen_hess,
options=objective_options)
# define a pypesto problem
(lb, ub) = create_bounds()
problem = pypesto.Problem(objective=objective, lb=lb, ub=ub)
return problem
def rosenbrock_problem():
"""Problem based on rosenbrock objective.
Features
--------
* 3-dim
* has fixed parameters
"""
objective = pypesto.Objective(fun=so.rosen)
dim_full = 2
lb = -5 * np.ones((dim_full, 1))
ub = 5 * np.ones((dim_full, 1))
problem = pypesto.Problem(
objective=objective, lb=lb, ub=ub,
x_fixed_indices=[1], x_fixed_vals=[2])
return problem
def test_dim_vs_dim_full():
"""Test passing arrays in the full or reduced dimension."""
objective = pypesto.Objective()
# define problem with bounds including fixed parameters
pypesto.Problem(objective=objective,
lb=[-1] * 4,
ub=[1] * 4,
dim_full=4,
x_fixed_indices=[0, 3],
x_fixed_vals=[42, 43])
# define problem with incomplete bounds
pypesto.Problem(objective=objective,
lb=[-1] * 2,
ub=[1] * 2,
dim_full=4,
x_fixed_indices=[0, 3],
x_fixed_vals=[42, 43])
def test_optimize():
# logging
pypesto.logging.log_to_console(logging.WARN)
filename = ".test_logging.tmp"
pypesto.logging.log_to_file(logging.DEBUG, filename)
logger = logging.getLogger('pypesto')
if os.path.exists(filename):
os.remove(filename)
fh = logging.FileHandler(filename)
fh.setLevel(logging.DEBUG)
logger.addHandler(fh)
logger.info("start test")
# problem definition
def fun(_):
raise Exception("This function cannot be called.")
objective = pypesto.Objective(fun=fun)
problem = pypesto.Problem(objective, -1, 1)
optimizer = pypesto.optimize.ScipyOptimizer()
options = {'allow_failed_starts': True}
# optimization
pypesto.optimize.minimize(problem,
optimizer,
5,
options=options,
filename=None)
# assert logging worked
assert os.path.exists(filename)
f = open(filename, 'rb')
content = str(f.read())
f.close()
# tidy up
os.remove(filename)
# check if error message got inserted
assert "fail" in content
def setup_rosen_problem(n_starts: int = 2):
"""Set up the rosenbrock problem and return
a pypesto.Problem"""
objective = pypesto.Objective(
fun=sp.optimize.rosen,
grad=sp.optimize.rosen_der,
hess=sp.optimize.rosen_hess,
)
dim_full = 10
lb = -5 * np.ones((dim_full, 1))
ub = 5 * np.ones((dim_full, 1))
# fixing startpoints
startpoints = pypesto.startpoint.latin_hypercube(
n_starts=n_starts, lb=lb, ub=ub
)
problem = pypesto.Problem(
objective=objective, lb=lb, ub=ub, x_guesses=startpoints
)
return problem
# CREATE LOGICLE OBJECTIVE FUNCTION __________________________________________________________________________________
obj_lin = importer.create_objective(solver=solver)
# logicle parameter
T = 1
end_lin = 1e-5
logicle_obj = LogicleScale.LogicleObject(T=T, end_lin=end_lin)
f = lambda x: obj_lin.get_fval(
logicleInverseTransform(par=x, logicle_object=logicle_obj))
g = lambda x: obj_lin.get_grad(logicleInverseTransform(x, logicle_obj)
) * logicleInverseGradient(x, logicle_obj)
obj = pypesto.Objective(fun=f, grad=g) #, hess=h)
print('optimal x = ', petab_problem.x_nominal)
print('optimal lh value', obj(petab_problem.x_nominal))
# check gradient at optimum and at random point
check_grad_1 = obj.check_grad(petab_problem.x_nominal)
print(check_grad_1[np.array(['grad', 'fd_c', 'abs_err', 'rel_err'])])
x_random = np.random.normal(0.5, 0.005, 22)
check_grad_2 = obj.check_grad(x_random)
print(check_grad_2[np.array(['grad', 'fd_c', 'abs_err', 'rel_err'])])
# OPTIMIZATION WITHOUT PRIOR ___________________________________________________________________________________________
optimizer = pypesto.ScipyOptimizer(method='L-BFGS-B')
# play with FSA tolerances
solver.setRelativeToleranceFSA(rtol=1e-10)
solver.setAbsoluteToleranceFSA(atol=1e-10)
# CREATE LOG(1+x) OBJECTIVE FUNCTION ___________________________________________________________________________________
obj_lin = importer.create_objective(solver=solver)
# define offset
eps = 1e-5
f = lambda x: obj_lin.get_fval(10**np.array(x) - eps)
g = lambda x: obj_lin.get_grad(10**np.array(x) - eps) * 10**np.array(
x) * np.log(10)
obj = pypesto.Objective(fun=f, grad=g)
print('optimal x = ', petab_problem.x_nominal)
print('optimal lh value', obj(petab_problem.x_nominal))
# check gradient at optimum and at random point
check_grad_1 = obj.check_grad(petab_problem.x_nominal)
print(check_grad_1[np.array(['grad', 'fd_c', 'abs_err', 'rel_err'])])
x_random = np.random.normal(0.5, 0.005, 12)
check_grad_2 = obj.check_grad(x_random)
print(check_grad_2[np.array(['grad', 'fd_c', 'abs_err', 'rel_err'])])
# OPTIMIZATION WITHOUT PRIOR ___________________________________________________________________________________________
optimizer = pypesto.ScipyOptimizer(method='L-BFGS-B')
def test_hdf5_history_mp():
"""Test whether hdf5-History works with a MultiProcessEngine."""
objective1 = pypesto.Objective(fun=so.rosen,
grad=so.rosen_der,
hess=so.rosen_hess)
objective2 = pypesto.Objective(fun=so.rosen,
grad=so.rosen_der,
hess=so.rosen_hess)
dim_full = 10
lb = -5 * np.ones((dim_full, 1))
ub = 5 * np.ones((dim_full, 1))
n_starts = 5
startpoints = pypesto.startpoint.latin_hypercube(n_starts=n_starts,
lb=lb,
ub=ub)
problem1 = pypesto.Problem(objective=objective1,
lb=lb,
ub=ub,
x_guesses=startpoints)
problem2 = pypesto.Problem(objective=objective2,
lb=lb,
ub=ub,
x_guesses=startpoints)
optimizer1 = pypesto.optimize.ScipyOptimizer(options={'maxiter': 10})
optimizer2 = pypesto.optimize.ScipyOptimizer(options={'maxiter': 10})
with tempfile.TemporaryDirectory(dir=".") as tmpdirname:
_, fn = tempfile.mkstemp(".hdf5", dir=f"{tmpdirname}")
history_options_mp = pypesto.HistoryOptions(trace_record=True,
storage_file=fn)
history_options_mem = pypesto.HistoryOptions(trace_record=True)
# optimize with Memory History
result_hdf5_mem = pypesto.optimize.minimize(
problem=problem1,
optimizer=optimizer1,
n_starts=n_starts,
history_options=history_options_mem,
engine=MultiProcessEngine(),
filename=None,
)
# optimizing with history saved in hdf5 and MultiProcessEngine
result_memory_mp = pypesto.optimize.minimize(
problem=problem2,
optimizer=optimizer2,
n_starts=n_starts,
history_options=history_options_mp,
engine=MultiProcessEngine(),
filename=None,
)
history_entries = [X, FVAL, GRAD, HESS, RES, SRES, CHI2, SCHI2]
assert len(result_hdf5_mem.optimize_result.list) == len(
result_memory_mp.optimize_result.list)
for mp_res in result_memory_mp.optimize_result.list:
for mem_res in result_hdf5_mem.optimize_result.list:
if mp_res['id'] == mem_res['id']:
for entry in history_entries:
hdf5_entry_trace = getattr(mp_res['history'],
f'get_{entry}_trace')()
mem_entry_trace = getattr(mem_res['history'],
f'get_{entry}_trace')()
for iteration in range(len(hdf5_entry_trace)):
# comparing nan and None difficult
if (hdf5_entry_trace[iteration] is None
or np.isnan(
hdf5_entry_trace[iteration]).all()):
continue
np.testing.assert_array_equal(
mem_entry_trace[iteration],
hdf5_entry_trace[iteration],
)
def test_storage_all():
"""Test `read_result` and `write_result`.
It currently does not test read/write of the problem as this
is know to not work completely. Also excludes testing the history
key of an optimization result.
"""
objective = pypesto.Objective(fun=so.rosen,
grad=so.rosen_der,
hess=so.rosen_hess)
dim_full = 10
lb = -5 * np.ones((dim_full, 1))
ub = 5 * np.ones((dim_full, 1))
n_starts = 5
problem = pypesto.Problem(objective=objective, lb=lb, ub=ub)
optimizer = optimize.ScipyOptimizer()
# Optimization
result = optimize.minimize(
problem=problem,
optimizer=optimizer,
n_starts=n_starts,
filename=None,
)
# Profiling
result = profile.parameter_profile(
problem=problem,
result=result,
profile_index=[0],
optimizer=optimizer,
filename=None,
)
# Sampling
sampler = sample.AdaptiveMetropolisSampler()
result = sample.sample(
problem=problem,
sampler=sampler,
n_samples=100,
result=result,
filename=None,
)
# Read and write
filename = 'test_file.hdf5'
try:
write_result(result=result, filename=filename)
result_read = read_result(filename=filename)
# test optimize
for i, opt_res in enumerate(result.optimize_result.list):
for key in opt_res:
if key == 'history':
continue
if isinstance(opt_res[key], np.ndarray):
np.testing.assert_array_equal(
opt_res[key], result_read.optimize_result.list[i][key])
else:
assert (opt_res[key] == result_read.optimize_result.list[i]
[key])
# test profile
for key in result.profile_result.list[0][0].keys():
if (result.profile_result.list[0][0].keys is None
or key == 'time_path'):
continue
elif isinstance(result.profile_result.list[0][0][key], np.ndarray):
np.testing.assert_array_equal(
result.profile_result.list[0][0][key],
result_read.profile_result.list[0][0][key],
)
elif isinstance(result.profile_result.list[0][0][key], int):
assert (result.profile_result.list[0][0][key] ==
result_read.profile_result.list[0][0][key])
# test sample
for key in result.sample_result.keys():
if result.sample_result[key] is None or key == 'time':
continue
elif isinstance(result.sample_result[key], np.ndarray):
np.testing.assert_array_equal(
result.sample_result[key],
result_read.sample_result[key],
)
elif isinstance(result.sample_result[key], (float, int)):
np.testing.assert_almost_equal(
result.sample_result[key],
result_read.sample_result[key],
)
finally:
if os.path.exists(filename):
os.remove(filename)
def test_storage_sampling():
"""
This test tests the saving and loading of samples
into HDF5 through pypesto.store.SamplingResultHDF5Writer
and pypesto.store.SamplingResultHDF5Reader. Tests all entries
aside from time and message.
"""
objective = pypesto.Objective(fun=so.rosen,
grad=so.rosen_der,
hess=so.rosen_hess)
dim_full = 10
lb = -5 * np.ones((dim_full, 1))
ub = 5 * np.ones((dim_full, 1))
n_starts = 5
startpoints = pypesto.startpoint.latin_hypercube(n_starts=n_starts,
lb=lb,
ub=ub)
problem = pypesto.Problem(objective=objective,
lb=lb,
ub=ub,
x_guesses=startpoints)
optimizer = optimize.ScipyOptimizer()
result_optimization = optimize.minimize(
problem=problem,
optimizer=optimizer,
n_starts=n_starts,
filename=None,
)
x_0 = result_optimization.optimize_result.list[0]['x']
sampler = sample.AdaptiveParallelTemperingSampler(
internal_sampler=sample.AdaptiveMetropolisSampler(), n_chains=1)
sample_original = sample.sample(
problem=problem,
sampler=sampler,
n_samples=100,
x0=[x_0],
filename=None,
)
fn = 'test_file.hdf5'
try:
pypesto_sample_writer = SamplingResultHDF5Writer(fn)
pypesto_sample_writer.write(sample_original)
pypesto_sample_reader = SamplingResultHDF5Reader(fn)
sample_read = pypesto_sample_reader.read()
for key in sample_original.sample_result.keys():
if sample_original.sample_result[key] is None or key == 'time':
continue
elif isinstance(sample_original.sample_result[key], np.ndarray):
np.testing.assert_array_equal(
sample_original.sample_result[key],
sample_read.sample_result[key],
)
elif isinstance(sample_original.sample_result[key], (float, int)):
np.testing.assert_almost_equal(
sample_original.sample_result[key],
sample_read.sample_result[key],
)
finally:
if os.path.exists(fn):
os.remove(fn)
def test_storage_profiling():
"""
This test tests the saving and loading of profiles
into HDF5 through pypesto.store.ProfileResultHDF5Writer
and pypesto.store.ProfileResultHDF5Reader. Tests all entries
aside from times and message.
"""
objective = pypesto.Objective(fun=so.rosen,
grad=so.rosen_der,
hess=so.rosen_hess)
dim_full = 10
lb = -5 * np.ones((dim_full, 1))
ub = 5 * np.ones((dim_full, 1))
n_starts = 5
startpoints = pypesto.startpoint.latin_hypercube(n_starts=n_starts,
lb=lb,
ub=ub)
problem = pypesto.Problem(objective=objective,
lb=lb,
ub=ub,
x_guesses=startpoints)
optimizer = optimize.ScipyOptimizer()
result_optimization = optimize.minimize(
problem=problem,
optimizer=optimizer,
n_starts=n_starts,
filename=None,
)
profile_original = profile.parameter_profile(
problem=problem,
result=result_optimization,
profile_index=[0],
optimizer=optimizer,
filename=None,
)
fn = 'test_file.hdf5'
try:
pypesto_profile_writer = ProfileResultHDF5Writer(fn)
pypesto_profile_writer.write(profile_original)
pypesto_profile_reader = ProfileResultHDF5Reader(fn)
profile_read = pypesto_profile_reader.read()
for key in profile_original.profile_result.list[0][0].keys():
if (profile_original.profile_result.list[0][0].keys is None
or key == 'time_path'):
continue
elif isinstance(profile_original.profile_result.list[0][0][key],
np.ndarray):
np.testing.assert_array_equal(
profile_original.profile_result.list[0][0][key],
profile_read.profile_result.list[0][0][key],
)
elif isinstance(profile_original.profile_result.list[0][0][key],
int):
assert (profile_original.profile_result.list[0][0][key] ==
profile_read.profile_result.list[0][0][key])
finally:
if os.path.exists(fn):
os.remove(fn)
def test_storage_trace(hdf5_file):
objective1 = pypesto.Objective(fun=so.rosen,
grad=so.rosen_der,
hess=so.rosen_hess)
objective2 = pypesto.Objective(fun=so.rosen,
grad=so.rosen_der,
hess=so.rosen_hess)
dim_full = 10
lb = -5 * np.ones((dim_full, 1))
ub = 5 * np.ones((dim_full, 1))
n_starts = 5
startpoints = pypesto.startpoint.latin_hypercube(n_starts=n_starts,
lb=lb,
ub=ub)
problem1 = pypesto.Problem(objective=objective1,
lb=lb,
ub=ub,
x_guesses=startpoints)
problem2 = pypesto.Problem(objective=objective2,
lb=lb,
ub=ub,
x_guesses=startpoints)
optimizer1 = optimize.ScipyOptimizer(options={'maxiter': 10})
optimizer2 = optimize.ScipyOptimizer(options={'maxiter': 10})
history_options_hdf5 = pypesto.HistoryOptions(trace_record=True,
storage_file=hdf5_file)
# optimize with history saved to hdf5
result_hdf5 = optimize.minimize(
problem=problem1,
optimizer=optimizer1,
n_starts=n_starts,
history_options=history_options_hdf5,
)
# optimizing with history saved in memory
history_options_memory = pypesto.HistoryOptions(trace_record=True)
result_memory = optimize.minimize(
problem=problem2,
optimizer=optimizer2,
n_starts=n_starts,
history_options=history_options_memory,
filename=None,
)
history_entries = [X, FVAL, GRAD, HESS, RES, SRES, CHI2, SCHI2]
assert len(result_hdf5.optimize_result.list) == len(
result_memory.optimize_result.list)
for mem_res in result_memory.optimize_result.list:
for hdf_res in result_hdf5.optimize_result.list:
if mem_res['id'] == hdf_res['id']:
for entry in history_entries:
hdf5_entry_trace = getattr(hdf_res['history'],
f'get_{entry}_trace')()
for iteration in range(len(hdf5_entry_trace)):
# comparing nan and None difficult
if (hdf5_entry_trace[iteration] is None or np.isnan(
hdf5_entry_trace[iteration]).all()):
continue
np.testing.assert_array_equal(
getattr(mem_res['history'],
f'get_{entry}_trace')()[iteration],
hdf5_entry_trace[iteration],
)
# enable sensitivities
solver.setSensitivityOrder(amici.SensitivityOrder_first) # First-order ...
solver.setSensitivityMethod(amici.SensitivityMethod_forward) # ... forward sensitivities
model.requireSensitivitiesForAllParameters() # ... w.r.t. all parameters
# CREATE OBJECTIVE FUNCTION_____________________________________________________________________________________________
obj_lin = importer.create_objective(solver=solver)
# logicle parameter
T = 1
end_lin = 1e-5
logicle_obj = LogicleScale.LogicleObject(T=T, end_lin=end_lin)
f = lambda x: obj_lin.get_fval(logicleInverseTransform(par=x, logicle_object=logicle_obj))
g = lambda x: obj_lin.get_grad(logicleInverseTransform(x, logicle_obj)) * logicleInverseGradient(x, logicle_obj)
obj_logicle = pypesto.Objective(fun=f, grad=g) #, hess=h)
# MODEL SELECTION ______________________________________________________________________________________________________
def import_stuff(file_name):
import_opt = import_modelSelection_results(file_name)
lambdas = import_opt['lambdas'][~np.isnan(import_opt['lambdas'])]
sigma = import_opt['sigma'][0]
par_names_temp = import_opt['par_names']
par_names_index = [i for i, y in enumerate(par_names_temp) if y is not np.nan]
par_names = par_names_temp[par_names_index]
conv_points = import_opt['conv_points'][:len(lambdas)]
n_starts = import_opt['n_starts'][0]
options = import_opt['options'][0]