def test_trace_grad(self):
self.history_options = HistoryOptions(
trace_record=True,
trace_record_grad=True,
)
self.check_history()
def test_trace_schi2(self):
self.history_options = HistoryOptions(
trace_record=True,
trace_record_chi2=False,
trace_record_schi2=True,
)
self.check_history()
def test_trace_grad_integrated(self):
self.obj = rosen_for_sensi(max_sensi_order=2, integrated=True)['obj']
self.history_options = HistoryOptions(
trace_record=True,
trace_record_grad=True,
trace_record_hess=False,
)
self.check_history()
def test_trace_all(self):
self.history_options = HistoryOptions(
trace_record=True,
trace_record_grad=True,
trace_record_hess=True,
trace_record_res=True,
trace_record_sres=True,
trace_record_chi2=True,
trace_record_schi2=True,
)
self.fix_pars = False
self.check_history()
def test_trace_all_aggregated(self):
self.history_options = HistoryOptions(
trace_record=True,
trace_record_grad=True,
trace_record_hess=True,
trace_record_res=True,
trace_record_sres=True,
trace_record_chi2=True,
trace_record_schi2=True,
)
self.obj = pypesto.objective.AggregatedObjective([self.obj, self.obj])
self.fix_pars = False
self.check_history()
def test_trace_all(self):
self.obj = rosen_for_sensi(max_sensi_order=2, integrated=True)['obj']
self.history_options = HistoryOptions(
trace_record=True,
trace_record_grad=True,
trace_record_hess=True,
trace_record_res=True,
trace_record_sres=True,
trace_record_chi2=True,
trace_record_schi2=True,
)
self.fix_pars = False
self.check_history()
def test_trace_all_aggregated(self):
self.obj = rosen_for_sensi(max_sensi_order=2, integrated=True)['obj']
self.history_options = HistoryOptions(
trace_record=True,
trace_record_grad=True,
trace_record_hess=True,
trace_record_res=True,
trace_record_sres=True,
trace_record_chi2=True,
trace_record_schi2=True,
)
self.obj = pypesto.objective.AggregatedObjective([self.obj, self.obj])
self.fix_pars = False
self.check_history()
def train(ae: MechanisticAutoEncoder,
optimizer: str = 'fides',
ftol: float = 1e-3,
maxiter: int = 1e4,
n_starts: int = 1,
seed: int = 0) -> Result:
"""
Trains the provided autoencoder by solving the optimization problem
generated by :py:func:`create_pypesto_problem`
:param ae:
Autoencoder that will be trained
:param optimizer:
Optimizer string that specifies the optimizer that will be used
:param ftol:
function tolerance that is used to assess optimizer convergence
:param maxiter:
maximum number of optimization iterations
:param n_starts:
number of local starts that will be performed
:param seed:
random seed that will be used to generate the randomly sampled
initial startpoints
:returns:
Pypesto optimization results.
"""
pypesto_problem = create_pypesto_problem(ae)
if optimizer == 'ipopt':
opt = IpoptOptimizer(options={
'maxiter': maxiter,
'tol': ftol,
'disp': 5,
})
elif optimizer.startswith('NLOpt_'):
opt = NLoptOptimizer(method=getattr(nlopt,
optimizer.replace('NLOpt_', '')),
options={
'maxtime': 3600,
'ftol_abs': ftol,
})
elif optimizer == 'fides':
opt = FidesOptimizer(hessian_update=fides.BFGS(),
options={
'maxtime': 3600,
fides.Options.FATOL: ftol,
fides.Options.MAXTIME: 3600,
fides.Options.MAXITER: maxiter,
fides.Options.SUBSPACE_DIM:
fides.SubSpaceDim.FULL
},
verbose=logging.INFO)
os.makedirs(trace_path, exist_ok=True)
history_options = HistoryOptions(trace_record=True,
trace_record_hess=False,
trace_record_res=False,
trace_record_sres=False,
trace_record_schi2=False,
storage_file=os.path.join(
trace_path,
TRACE_FILE_TEMPLATE.format(
pathway=ae.pathway_name,
data=ae.data_name,
optimizer=optimizer,
n_hidden=ae.n_hidden,
job=seed)),
trace_save_iter=10)
np.random.seed(seed)
optimize_options = OptimizeOptions(
startpoint_resample=False,
allow_failed_starts=True,
)
decoder_par_pretraining = os.path.join(
'pretraining', f'{ae.pathway_name}__{ae.data_name}__{ae.n_hidden}'
f'__decoder_inflate.csv')
has_decoder_par_pretraing = os.path.exists(decoder_par_pretraining)
if has_decoder_par_pretraing:
decoder_pars = pd.read_csv(decoder_par_pretraining)[ae.x_names]
lb = np.asarray([
parameter_boundaries_scales[name.split('_')[-1]][0]
for name in pypesto_problem.x_names
])
ub = np.asarray([
parameter_boundaries_scales[name.split('_')[-1]][1]
for name in pypesto_problem.x_names
])
def startpoint(**kwargs):
if has_decoder_par_pretraing and seed < len(decoder_pars):
xs = decoder_pars.iloc[seed, :]
else:
xs = np.random.random((kwargs['n_starts'],
ae.n_encoder_pars + ae.n_kin_params)) \
* (ub - lb) + lb
return xs
return minimize(pypesto_problem,
opt,
n_starts=n_starts,
options=optimize_options,
history_options=history_options,
startpoint_method=startpoint)
