def _update_current_hyperparameters(self):
"""Update :attr:`current_init_params`, and :attr:`current_extra_params` according to the upcoming set of hyperparameters
to be searched"""
current_hyperparameters = self._get_current_hyperparameters()
init_params = {
_k[1:]: _v
for _k, _v in current_hyperparameters.items()
if _k[0] == 'model_init_params'
}
extra_params = {
_k[1:]: _v
for _k, _v in current_hyperparameters.items()
if _k[0] == 'model_extra_params'
}
self.current_init_params = deep_restricted_update(
self.model_init_params,
init_params,
iter_attrs=self.init_iter_attrs)
self.current_extra_params = deep_restricted_update(
self.model_extra_params,
extra_params,
iter_attrs=self.extra_iter_attrs)
if (self.module_name == 'keras') and ('callbacks'
in self.current_extra_params):
self.current_extra_params['callbacks'] = reinitialize_callbacks(
self.current_extra_params['callbacks'])
def _update_current_hyperparameters(self):
"""Update :attr:`current_init_params`, and :attr:`current_extra_params` according to the
upcoming set of hyperparameters to be searched"""
current_hyperparameters = self._get_current_hyperparameters().items()
init_params = {
_k[1:]: _v for _k, _v in current_hyperparameters if _k[0] == "model_init_params"
}
extra_params = {
_k[1:]: _v for _k, _v in current_hyperparameters if _k[0] == "model_extra_params"
}
# TODO: Replace above two with `general_utils.subdict` call that modifies key to a slice
# FLAG: At this point, `dummy_layers` shows "kernel_initializer" as `orthogonal` instance with "__hh" attrs
# FLAG: HOWEVER, the `orthogonal` instance does have `gain` set to the correct dummy value, ...
# FLAG: ... so it might be ok, as long as experiment matching can still work with that
self.current_init_params = deep_restricted_update(
self.model_init_params, init_params, iter_attrs=self.init_iter_attrs
)
self.current_extra_params = deep_restricted_update(
self.model_extra_params, extra_params, iter_attrs=self.extra_iter_attrs
)
if (self.module_name == "keras") and ("callbacks" in self.current_extra_params):
self.current_extra_params["callbacks"] = reinitialize_callbacks(
self.current_extra_params["callbacks"]
)
def keras_prep_workflow(model_initializer, build_fn, extra_params,
source_script):
"""Conduct preparation steps necessary before hyperparameter optimization on a `Keras` model.
Such steps include parsing and modifying `build_fn` to be of the form used by
:class:`hyperparameter_hunter.optimization_core.BaseOptimizationProtocol`, compiling a dummy
model to identify universal locations of given hyperparameter choices, and creating a simplified
characterization of the models to be built during optimization in order to enable collection of
similar Experiments
Parameters
----------
model_initializer: :class:`keras.wrappers.scikit_learn.<KerasClassifier; KerasRegressor>`
A descendant of :class:`keras.wrappers.scikit_learn.BaseWrapper` used to build a Keras model
build_fn: Callable
The `build_fn` value provided to :meth:`keras.wrappers.scikit_learn.BaseWrapper.__init__`.
Expected to return a compiled Keras model. May contain hyperparameter space choices
extra_params: Dict
The parameters expected to be passed to the extra methods of the compiled Keras model. Such
methods include (but are not limited to) `fit`, `predict`, and `predict_proba`. Some of the
common parameters given here include `epochs`, `batch_size`, and `callbacks`
source_script: Str
Absolute path to a Python file. Expected to end with one of the following extensions:
'.py', '.ipynb'
Returns
-------
reusable_build_fn: Callable
Modified `build_fn` in which hyperparameter space choices are replaced by dict lookups, and
the signature is given a standard name, and additional input parameters necessary for reuse
reusable_wrapper_params: Dict
The parameters expected to be passed to the extra methods of the compiled Keras model. Such
methods include (but are not limited to) `fit`, `predict`, and `predict_proba`. Some of the
common parameters given here include `epochs`, `batch_size`, and `callbacks`
dummy_layers: List
The layers of a compiled dummy Keras model constructed according to the given
hyperparameters, in which each layer is a dict containing at least the following: the name
of the layer class, allowed and used args, and default and used kwargs
dummy_compile_params: Dict
The parameters used on the `compile` call for the dummy model. If a parameter is accepted
by the `compile` method, but is not explicitly given, its default value is included in
`dummy_compile_params`"""
#################### Prepare Model-Builder String ####################
temp_builder_name = "__temp_model_builder"
reusable_build_fn, expected_params = rewrite_model_builder(
stringify_model_builder(build_fn))
temp_model_file_str = build_temp_model_file(reusable_build_fn,
source_script)
#################### Save and Import Temporary Model Builder ####################
write_python_source(
temp_model_file_str,
"{}/{}.py".format(os.path.split(__file__)[0], temp_builder_name))
if temp_builder_name in sys.modules:
del sys.modules[temp_builder_name]
try:
from .__temp_model_builder import build_fn as temp_build_fn
except:
raise
#################### Translate Hyperparameter Names to Universal Paths ####################
wrapper_params = dict(
params={_k: eval(_v)
for _k, _v in expected_params.items()},
**extra_params)
wrapper_params, dummified_params = check_dummy_params(wrapper_params)
if ("optimizer_params" in dummified_params) and ("optimizer"
in dummified_params):
raise ValueError(
"Unable to optimize both `optimizer` and `optimizer_params`. Please try optimizing them separately"
)
compiled_dummy = initialize_dummy_model(model_initializer, temp_build_fn,
wrapper_params)
dummy_layers, dummy_compile_params = parameterize_compiled_keras_model(
compiled_dummy)
merged_compile_params = merge_compile_params(dummy_compile_params,
dummified_params)
# FLAG: Will need to deal with capitalization conflicts when comparing similar experiments: `optimizer`='Adam' vs 'adam'
consolidated_layers = consolidate_layers(dummy_layers,
class_name_key=False,
separate_args=False)
wrapper_params = deep_restricted_update(wrapper_params, dummified_params)
return (temp_build_fn, wrapper_params, consolidated_layers,
merged_compile_params)
def keras_prep_workflow(model_initializer, build_fn, extra_params, source_script):
"""Conduct preparation steps necessary before hyperparameter optimization on a `Keras` model.
Such steps include parsing and modifying `build_fn` to be of the form used by
:class:`hyperparameter_hunter.optimization.protocol_core.BaseOptPro`, compiling a dummy model to
identify universal locations of given hyperparameter choices, and creating a simplified
characterization of the models to be built during optimization in order to enable similar
Experiment collection
Parameters
----------
model_initializer: :class:`keras.wrappers.scikit_learn.<KerasClassifier; KerasRegressor>`
A descendant of :class:`keras.wrappers.scikit_learn.BaseWrapper` used to build a Keras model
build_fn: Callable
The `build_fn` value provided to :meth:`keras.wrappers.scikit_learn.BaseWrapper.__init__`.
Expected to return a compiled Keras model. May contain hyperparameter space choices
extra_params: Dict
The parameters expected to be passed to the extra methods of the compiled Keras model. Such
methods include (but are not limited to) `fit`, `predict`, and `predict_proba`. Some of the
common parameters given here include `epochs`, `batch_size`, and `callbacks`
source_script: Str
Absolute path to a Python file. Should end with one of following extensions: ".py", ".ipynb"
Returns
-------
reusable_build_fn: Callable
Modified `build_fn` in which hyperparameter space choices are replaced by dict lookups, and
the signature is given a standard name, and additional input parameters necessary for reuse
reusable_wrapper_params: Dict
The parameters expected to be passed to the extra methods of the compiled Keras model. Such
methods include (but are not limited to) `fit`, `predict`, and `predict_proba`. Some of the
common parameters given here include `epochs`, `batch_size`, and `callbacks`
dummy_layers: List
The layers of a compiled dummy Keras model constructed according to the given
hyperparameters, in which each layer is a dict containing at least the following: the name
of the layer class, allowed and used args, and default and used kwargs
dummy_compile_params: Dict
The parameters used on the `compile` call for the dummy model. If a parameter is accepted
by the `compile` method, but is not explicitly given, its default value is included in
`dummy_compile_params`"""
#################### Set Temporary Model-Builder Module Location ####################
temp_module_name = f"__temp_model_builder_{datetime.now().strftime('%Y-%m-%d_%H-%M-%S-%f')}"
temp_module_dot_path = f"{TEMP_MODULES_DOT_PATH}.{temp_module_name}"
temp_module_filepath = f"{TEMP_MODULES_DIR_PATH}/{temp_module_name}.py"
#################### Prepare Model-Builder String ####################
reusable_build_fn, expected_params = rewrite_model_builder(stringify_model_builder(build_fn))
temp_module_str = build_temp_model_file(reusable_build_fn, source_script)
#################### Save and Import Temporary Model Builder ####################
write_python(temp_module_str, temp_module_filepath)
if temp_module_name in sys.modules:
del sys.modules[temp_module_name]
temp_module_spec = spec_from_file_location(temp_module_dot_path, temp_module_filepath)
temp_module = module_from_spec(temp_module_spec)
temp_module_spec.loader.exec_module(temp_module)
temp_build_fn = temp_module.build_fn
#################### Translate Hyperparameter Names to Universal Paths ####################
wrapper_params = dict(params={k: eval(v) for k, v in expected_params.items()}, **extra_params)
# TODO: Intercept space choices that use callables (like `Categorical([glorot_normal(), orthogonal()])`)
# TODO: Can't deal with them yet, due to imports unavailable in this context. Raise exception
wrapper_params, dummified_params = check_dummy_params(wrapper_params)
if ("optimizer_params" in dummified_params) and ("optimizer" in dummified_params):
raise ValueError("Can't optimize `optimizer` with `optimizer_params`. Try them separately")
compiled_dummy = initialize_dummy_model(model_initializer, temp_build_fn, wrapper_params)
dummy_layers, dummy_compile_params = parameterize_compiled_keras_model(compiled_dummy)
merged_compile_params = merge_compile_params(dummy_compile_params, dummified_params)
# FLAG: Will need to deal with capitalization conflicts when comparing similar experiments: `optimizer`="Adam" vs "adam"
consolidated_layers = consolidate_layers(dummy_layers, class_name_key=False, split_args=False)
wrapper_params = deep_restricted_update(wrapper_params, dummified_params)
return (temp_build_fn, wrapper_params, consolidated_layers, merged_compile_params)