def from_dict(hyps_mask, verbose=False, init_spec=[]):
"""convert dictionary mask to HM instance
This function is not tested yet
"""
Parameters.check_instantiation(
hyps_mask["hyps"], hyps_mask["cutoffs"], hyps_mask["kernels"], hyps_mask
)
pm = ParameterHelper(verbose=verbose)
nspecie = hyps_mask["nspecie"]
if nspecie > 1:
max_species = np.max(hyps_mask["specie_mask"])
specie_mask = hyps_mask["specie_mask"]
for i in range(max_species + 1):
elelist = np.where(specie_mask == i)[0]
if len(elelist) > 0:
for ele in elelist:
if ele != 0:
elename = Z_to_element(ele)
if len(init_spec) > 0:
if elename in init_spec:
pm.define_group("specie", i, [elename])
else:
pm.define_group("specie", i, [elename])
else:
pm.define_group("specie", i, ["*"])
for kernel in hyps_mask["kernels"] + ParameterHelper.cutoff_types_keys:
n = hyps_mask.get("n" + kernel, 0)
if n >= 0:
if kernel not in ParameterHelper.cutoff_types:
chyps, copt = Parameters.get_component_hyps(
hyps_mask, kernel, constraint=True, noise=False
)
sig = chyps[0]
ls = chyps[1]
csig = copt[0]
cls = copt[1]
cutoff = hyps_mask["cutoffs"][kernel]
pm.set_parameters("cutoff_" + kernel, cutoff)
cutoff_list = hyps_mask.get(
f"{kernel}_cutoff_list", np.ones(len(sig)) * cutoff
)
elif kernel in ParameterHelper.cutoff_types and n > 1:
cutoff_list = hyps_mask[
ParameterHelper.cutoff_types[kernel] + "_cutoff_list"
]
if n > 1:
all_specie = np.arange(nspecie)
all_comb = combinations_with_replacement(
all_specie, ParameterHelper.ndim[kernel]
)
for comb in all_comb:
mask_id = 0
for ele in comb:
mask_id += ele
mask_id *= nspecie
mask_id = mask_id // nspecie
ttype = hyps_mask[f"{kernel}_mask"][mask_id]
pm.define_group(f"{kernel}", f"{kernel}{ttype}", comb)
if (kernel not in ParameterHelper.cutoff_types) and (
kernel not in ParameterHelper.cutoff_types_values
):
pm.set_parameters(
f"{kernel}{ttype}",
[sig[ttype], ls[ttype], cutoff_list[ttype]],
opt=[csig[ttype], cls[ttype]],
)
elif kernel in ParameterHelper.cutoff_types_values:
pm.set_parameters(
f"{kernel}{ttype}",
[sig[ttype], ls[ttype]],
opt=[csig[ttype], cls[ttype]],
)
else:
pm.set_parameters(f"{kernel}{ttype}", cutoff_list[ttype])
else:
pm.define_group(
kernel, kernel, ["*"] * ParameterHelper.ndim[kernel]
)
if kernel not in ParameterHelper.cutoff_types_keys:
pm.set_parameters(
kernel, parameters=np.hstack([sig, ls, cutoff]), opt=copt
)
else:
pm.set_parameters(kernel, parameters=cutoff)
hyps = Parameters.get_hyps(hyps_mask)
pm.set_parameters("noise", hyps[-1])
if "cutoffs" in hyps_mask:
cutoffs = hyps_mask["cutoffs"]
for k in cutoffs:
pm.set_parameters(f"cutoff_{k}", cutoffs[k])
return pm
def from_mask_to_args(hyps, cutoffs, hyps_mask=None):
"""Return the tuple of arguments needed for kernel function.
The order of the tuple has to be exactly the same as the one taken by
the kernel function.
:param hyps: list of hyperparmeter values
:type hyps: nd.array
:param hyps_mask: all the remaining parameters needed
:type hyps_mask: dictionary
:param cutoffs: cutoffs used
:return: args
"""
# no special setting
multihyps = True
if hyps_mask is None:
multihyps = False
elif hyps_mask["nspecie"] == 1:
multihyps = False
if not multihyps:
cutoffs_array = [0, 0, 0]
cutoffs_array[0] = cutoffs.get("twobody", 0)
cutoffs_array[1] = cutoffs.get("threebody", 0)
cutoffs_array[2] = cutoffs.get("manybody", 0)
return (hyps, cutoffs_array)
# setting for mc_sephyps
nspecie = hyps_mask["nspecie"]
n2b = hyps_mask.get("ntwobody", 0)
n3b = hyps_mask.get("nthreebody", 0)
nmanybody = hyps_mask.get("nmanybody", 0)
ncut3b = hyps_mask.get("ncut3b", 0)
twobody_mask = hyps_mask.get("twobody_mask", None)
threebody_mask = hyps_mask.get("threebody_mask", None)
manybody_mask = hyps_mask.get("manybody_mask", None)
cut3b_mask = hyps_mask.get("cut3b_mask", None)
# TO DO , should instead use the non-sephyps kernel
if n2b == 1:
twobody_mask = np.zeros(nspecie ** 2, dtype=int)
if n3b == 1:
threebody_mask = np.zeros(nspecie ** 3, dtype=int)
if nmanybody == 1:
manybody_mask = np.zeros(nspecie ** 2, dtype=int)
cutoff_2b = cutoffs.get("twobody", 0)
cutoff_3b = cutoffs.get("threebody", 0)
cutoff_mb = cutoffs.get("manybody", 0)
if "bond_cutoff_list" in hyps_mask:
cutoff_2b = hyps_mask["bond_cutoff_list"]
else:
cutoff_2b = np.ones(nspecie ** 2, dtype=float) * cutoff_2b
if "threebody_cutoff_list" in hyps_mask:
cutoff_3b = hyps_mask["threebody_cutoff_list"]
if "manybody_cutoff_list" in hyps_mask:
cutoff_mb = hyps_mask["manybody_cutoff_list"]
(sig2, ls2) = Parameters.get_component_hyps(hyps_mask, "twobody", hyps=hyps)
(sig3, ls3) = Parameters.get_component_hyps(hyps_mask, "threebody", hyps=hyps)
(sigm, lsm) = Parameters.get_component_hyps(hyps_mask, "manybody", hyps=hyps)
return (
cutoff_2b,
cutoff_3b,
cutoff_mb,
nspecie,
np.array(hyps_mask["species_mask"]),
n2b,
twobody_mask,
n3b,
threebody_mask,
ncut3b,
cut3b_mask,
nmanybody,
manybody_mask,
sig2,
ls2,
sig3,
ls3,
sigm,
lsm,
)
