def _filter_by_guidelines_multi(self, location):
"""Helper to filter by guidelines when one of the guideline hyperparameters is directly
affected by a hyperparameter that is given as a space choice
Parameters
----------
location: Tuple
Location of the hyperparameter space choice that affects the acceptable guideline values
of a particular hyperparameter. In other words, this is the path of a hyperparameter,
which, if changed, would change the expected default value of another hyperparameter
Notes
-----
This is used for Keras Experiments when the `optimizer` value in a model's `compile_params`
is given as a hyperparameter space choice. Each possible value of `optimizer` prescribes
different default values for the `optimizer_params` argument, so special measures need to be
taken to ensure the correct Experiments are declared to fit within the constraints"""
_model_params = deepcopy(self.model_params)
if location == ("model_init_params", "compile_params", "optimizer"):
from keras.optimizers import get as k_opt_get
update_location = ("model_init_params", "compile_params",
"optimizer_params")
allowed_values = get_path(_model_params, location).bounds
#################### Handle First Value (Dummy) ####################
self._filter_by_guidelines()
allowed_values = allowed_values[1:]
#################### Handle Remaining Values ####################
for allowed_val in allowed_values:
updated_value = k_opt_get(allowed_val).get_config()
def _visit(path, key, value):
"""If `path` + `key` == `update_location`, return default for this choice. Else,
default_visit"""
if path + (key, ) == update_location:
return (key, updated_value)
return (key, value)
self._filter_by_guidelines(
model_params=remap(_model_params, visit=_visit))
self.similar_experiments = sorted(self.similar_experiments,
key=lambda _: _[1],
reverse=True)
else:
raise ValueError(
"Received unhandled location: {}".format(location))
def does_match_init_params_guidelines_multi(self, exp_id, params, score,
location) -> bool:
"""Check candidate compatibility with `model_init_params` template guidelines when a
guideline hyperparameter is directly affected by another hyperparameter that is given as
a space choice
Parameters
----------
exp_id: String
Candidate Experiment ID
params: Dict
Candidate "model_init_params" to compare to the template in :attr:`model_params`
score: Number
Value of the candidate Experiment's target metric
location: Tuple
Location of the hyperparameter space choice that affects the acceptable guideline values
of a particular hyperparameter. In other words, this is the path of a hyperparameter,
which, if changed, would change the expected default value of another hyperparameter
Returns
-------
Boolean
True if candidate `params` match `model_init_params` guidelines. Else, False
Notes
-----
This is used for Keras Experiments when the `optimizer` value in a model's `compile_params`
is given as a hyperparameter space choice. Each possible value of `optimizer` prescribes
different default values for the `optimizer_params` argument, so special measures need to be
taken to ensure the correct Experiments are declared to fit within the constraints"""
_model_params = deepcopy(self.model_params["model_init_params"])
if location == ("compile_params", "optimizer"):
from keras.optimizers import get as k_opt_get
update_location = ("compile_params", "optimizer_params")
# `update_location` = Path to hyperparameter whose default value depends on `location`
allowed_values = get_path(_model_params, location).bounds
# `allowed_values` = Good `("model_init_params", "compile_params", "optimizer")` values
#################### Handle First Value (Dummy) ####################
is_match = self.does_match_init_params_guidelines(
exp_id, params, score)
# The first value gets handled separately from the rest because the value at
# `update_location` is set according to `allowed_values[0]`. For the remaining
# `allowed_values`, we need to manually set `update_location` for each
# If the first value was a match, the below `while` loop will never be entered because
# `is_match` is already True
#################### Handle Remaining Values ####################
allowed_val_index = 1
while is_match is not True and allowed_val_index < len(
allowed_values):
allowed_val = allowed_values[allowed_val_index]
# Determine current default value for the dependent hyperparameter
updated_val = k_opt_get(allowed_val).get_config()
# Set value at `update_location` to `updated_val`, then check if params match
def _visit(path, key, value):
"""If `path` + `key` == `update_location`, return default for this choice. Else,
default_visit"""
if path + (key, ) == update_location:
return (key, updated_val)
return (key, value)
is_match = self.does_match_init_params_guidelines(
exp_id,
params,
score,
template_params=remap(_model_params, visit=_visit))
# If `is_match` is True, the loop stops and :attr:`match_status`'s value at `exp_id`
# for `does_match_init_params_guidelines` remains truthy
allowed_val_index += 1
return is_match
else:
raise ValueError(
"Received unhandled location: {}".format(location))