def __init__(self,
filename,
disable_morphing=False,
include_nuisance_parameters=True):
# Save setup
self.include_nuisance_parameters = include_nuisance_parameters
self.madminer_filename = filename
# Load data
logger.info("Loading data from %s", filename)
(
self.parameters,
self.benchmarks,
self.benchmark_is_nuisance,
self.morphing_components,
self.morphing_matrix,
self.observables,
self.n_samples,
_,
self.reference_benchmark,
self.nuisance_parameters,
self.n_events_generated_per_benchmark,
self.n_events_backgrounds,
) = load_madminer_settings(
filename, include_nuisance_benchmarks=include_nuisance_parameters)
self.n_parameters = len(self.parameters)
self.n_benchmarks = len(self.benchmarks)
self.n_benchmarks_phys = np.sum(
np.logical_not(self.benchmark_is_nuisance))
self.n_observables = 0 if self.observables is None else len(
self.observables)
self.n_nuisance_parameters = 0
if self.nuisance_parameters is not None and include_nuisance_parameters:
self.n_nuisance_parameters = len(self.nuisance_parameters)
else:
self.nuisance_parameters = None
# Morphing
self.morpher = None
if self.morphing_matrix is not None and self.morphing_components is not None and not disable_morphing:
self.morpher = PhysicsMorpher(self.parameters)
self.morpher.set_components(self.morphing_components)
self.morpher.set_basis(self.benchmarks,
morphing_matrix=self.morphing_matrix)
# Nuisance morphing
self.nuisance_morpher = None
if self.nuisance_parameters is not None:
self.nuisance_morpher = NuisanceMorpher(
self.nuisance_parameters, list(self.benchmarks.keys()),
self.reference_benchmark)
else:
self.include_nuisance_parameters = False
# Check event numbers
self._check_n_events()
self._report_setup()
def __init__(self,
filename,
disable_morphing=False,
include_nuisance_parameters=True):
# Save setup
self.include_nuisance_parameters = include_nuisance_parameters
self.madminer_filename = filename
logger.info("Loading data from %s", filename)
# Load data
(
self.parameters,
self.benchmarks,
self.benchmark_is_nuisance,
self.morphing_components,
self.morphing_matrix,
self.observables,
self.n_samples,
_,
self.reference_benchmark,
self.nuisance_parameters,
) = load_madminer_settings(
filename, include_nuisance_benchmarks=include_nuisance_parameters)
self.n_parameters = len(self.parameters)
self.n_benchmarks = len(self.benchmarks)
self.n_benchmarks_phys = np.sum(
np.logical_not(self.benchmark_is_nuisance))
self.n_observables = len(self.observables)
self.n_nuisance_parameters = 0
if self.nuisance_parameters is not None and include_nuisance_parameters:
self.n_nuisance_parameters = len(self.nuisance_parameters)
else:
self.nuisance_parameters = None
logger.info("Found %s parameters", self.n_parameters)
for key, values in six.iteritems(self.parameters):
logger.debug(
" %s (LHA: %s %s, maximal power in squared ME: %s, range: %s)",
key,
values[0],
values[1],
values[2],
values[3],
)
if self.nuisance_parameters is not None:
logger.info("Found %s nuisance parameters",
self.n_nuisance_parameters)
for key, values in six.iteritems(self.nuisance_parameters):
logger.debug(" %s (%s)", key, values)
else:
logger.info("Did not find nuisance parameters")
logger.info("Found %s benchmarks, of which %s physical",
self.n_benchmarks, self.n_benchmarks_phys)
for (key, values), is_nuisance in zip(six.iteritems(self.benchmarks),
self.benchmark_is_nuisance):
if is_nuisance:
logger.debug(" %s: systematics", key)
else:
logger.debug(" %s: %s", key, format_benchmark(values))
logger.info("Found %s observables", len(self.observables))
for i, obs in enumerate(self.observables):
logger.debug(" %2.2s %s", i, obs)
logger.info("Found %s events", self.n_samples)
# Morphing
self.morpher = None
if self.morphing_matrix is not None and self.morphing_components is not None and not disable_morphing:
self.morpher = PhysicsMorpher(self.parameters)
self.morpher.set_components(self.morphing_components)
self.morpher.set_basis(self.benchmarks,
morphing_matrix=self.morphing_matrix)
logger.info("Found morphing setup with %s components",
len(self.morphing_components))
else:
logger.info("Did not find morphing setup.")
# Nuisance morphing
self.nuisance_morpher = None
if self.nuisance_parameters is not None:
self.nuisance_morpher = NuisanceMorpher(
self.nuisance_parameters, list(self.benchmarks.keys()),
self.reference_benchmark)
logger.info("Found nuisance morphing setup")
def set_morphing(self,
max_overall_power=4,
n_bases=1,
include_existing_benchmarks=True,
n_trials=100,
n_test_thetas=100):
"""
Sets up the morphing environment.
Sets benchmarks, i.e. parameter points that will be evaluated by MadGraph, for a morphing algorithm, and
calculates all information required for morphing. Morphing is a technique that allows MadMax to infer the full
probability distribution `p(x_i | theta)` for each simulated event `x_i` and any `theta`, not just the
benchmarks.
The morphing basis is optimized with respect to the expected mean squared morphing weights over the parameter
region of interest. If keep_existing_benchmarks=True, benchmarks defined previously will be incorporated in the
morphing basis and only the remaining basis points will be optimized.
Note that any subsequent call to `set_benchmarks` or `add_benchmark` will overwrite the morphing setup. The
correct order is therefore to manually define benchmarks first, using `set_benchmarks` or `add_benchmark`, and
then to create the morphing setup and complete the basis by calling
`set_benchmarks_from_morphing(keep_existing_benchmarks=True)`.
Parameters
----------
max_overall_power : int or tuple of int, optional
The maximal sum of powers of all parameters contributing to the squared matrix element. If a tuple is given,
gives the maximal sum of powers for each of several operator configurations (see `add_parameter`).
Typically, if parameters can affect the couplings at n vertices, this number is 2n. Default value: 4.
n_bases : int, optional
The number of morphing bases generated. If n_bases > 1, multiple bases are combined, and the
weights for each basis are reduced by a factor 1 / n_bases. Currently only the default choice of 1 is
fully implemented. Do not use any other value for now. Default value: 1.
include_existing_benchmarks : bool, optional
If True, the previously defined benchmarks are included in the morphing basis. In that case, the number of
free parameters in the optimization routine is reduced. If False, the existing benchmarks will still be
simulated, but are not part of the morphing routine. Default value: True.
n_trials : int, optional
Number of random basis configurations tested in the optimization procedure. A larger number will increase
the run time of the optimization, but lead to better results. Default value: 100.
n_test_thetas : int, optional
Number of random parameter points used to evaluate the expected mean squared morphing weights. A larger
number will increase the run time of the optimization, but lead to better results. Default value: 100.
Returns
-------
None
"""
logger.info("Optimizing basis for morphing")
if isinstance(max_overall_power, int):
max_overall_power = (max_overall_power, )
morpher = PhysicsMorpher(parameters_from_madminer=self.parameters)
morpher.find_components(max_overall_power)
if include_existing_benchmarks:
n_predefined_benchmarks = len(self.benchmarks)
basis = morpher.optimize_basis(
n_bases=n_bases,
fixed_benchmarks_from_madminer=self.benchmarks,
n_trials=n_trials,
n_test_thetas=n_test_thetas,
)
else:
n_predefined_benchmarks = 0
basis = morpher.optimize_basis(n_bases=n_bases,
fixed_benchmarks_from_madminer=None,
n_trials=n_trials,
n_test_thetas=n_test_thetas)
basis.update(self.benchmarks)
self.set_benchmarks(basis, verbose=False)
self.morpher = morpher
self.export_morphing = True
logger.info(
"Set up morphing with %s parameters, %s morphing components, %s predefined basis points, and %s "
"new basis points",
morpher.n_parameters,
morpher.n_components,
n_predefined_benchmarks,
morpher.n_components - n_predefined_benchmarks,
)
def load(self, filename, disable_morphing=False):
"""
Loads MadMiner setup from a file. All parameters, benchmarks, and morphing settings are overwritten. See `save`
for more details.
Parameters
----------
filename : str
Path to the MadMiner file.
disable_morphing : bool, optional
If True, the morphing setup is not loaded from the file. Default value: False.
Returns
-------
None
"""
# Load data
(
self.parameters,
self.benchmarks,
_,
morphing_components,
morphing_matrix,
_,
_,
self.systematics,
_,
_,
_,
_,
) = load_madminer_settings(filename, include_nuisance_benchmarks=False)
logger.info("Found %s parameters:", len(self.parameters))
for key, values in six.iteritems(self.parameters):
logger.info(
" %s (LHA: %s %s, maximal power in squared ME: %s, range: %s)",
key,
values[0],
values[1],
values[2],
values[3],
)
logger.info("Found %s benchmarks:", len(self.benchmarks))
for key, values in six.iteritems(self.benchmarks):
logger.info(" %s: %s", key, format_benchmark(values))
if self.default_benchmark is None:
self.default_benchmark = key
# Morphing
self.morpher = None
self.export_morphing = False
if morphing_matrix is not None and morphing_components is not None and not disable_morphing:
self.morpher = PhysicsMorpher(self.parameters)
self.morpher.set_components(morphing_components)
self.morpher.set_basis(self.benchmarks,
morphing_matrix=morphing_matrix)
self.export_morphing = True
logger.info("Found morphing setup with %s components",
len(morphing_components))
else:
logger.info("Did not find morphing setup.")
# Systematics setup
if len(self.systematics) == 0:
logger.info("Did not find systematics setup.")
else:
logger.info(
"Found systematics setup with %s nuisance parameter groups",
len(self.systematics))
for key, value in six.iteritems(self.systematics):
logger.debug(" %s: %s", key,
" / ".join(str(x) for x in value))
def load(self, filename, disable_morphing=False):
"""
Loads MadMiner setup from a file. All parameters, benchmarks, and morphing settings are overwritten.
See `save` for more details.
Parameters
----------
filename : str
Path to the MadMiner file.
disable_morphing : bool, optional
If True, the morphing setup is not loaded from the file. Default value: False.
Returns
-------
None
"""
# Load data
(
self.parameters,
self.benchmarks,
_,
morphing_components,
morphing_matrix,
_,
_,
self.systematics,
_,
_,
_,
_,
self.finite_difference_benchmarks,
self.finite_difference_epsilon,
) = load_madminer_settings(filename, include_nuisance_benchmarks=False)
logger.info("Found %s parameters:", len(self.parameters))
for param in self.parameters.values():
logger.info(" %s", param)
logger.info("Found %s benchmarks:", len(self.benchmarks))
for benchmark in self.benchmarks.values():
logger.info(" %s", benchmark)
if self.default_benchmark is None:
self.default_benchmark = benchmark.name
# Morphing
self.morpher = None
self.export_morphing = False
if morphing_matrix is not None and morphing_components is not None and not disable_morphing:
self.morpher = PhysicsMorpher(self.parameters)
self.morpher.set_components(morphing_components)
self.morpher.set_basis(self.benchmarks,
morphing_matrix=morphing_matrix)
self.export_morphing = True
logger.info("Found morphing setup with %s components",
len(morphing_components))
else:
logger.info("Did not find morphing setup.")
# Systematics setup
if len(self.systematics) == 0:
logger.info("Did not find systematics setup.")
else:
logger.info("Found systematics setup with %s groups",
len(self.systematics))
for name, systematic in self.systematics.items():
logger.debug(" %s: %s", name, systematic)