"""
Hyperparameter (neural network options) management
"""
from __future__ import (
print_function,
division,
absolute_import,
)
import itertools
[docs]class HyperparameterDefaults(object):
"""
Class for managing hyperparameters. Thin wrapper around a dict.
Instances of this class are a specification of the hyperparameters
*supported* by a model and their defaults. The particular
hyperparameter settings to be used, for example, to train a model
are kept in plain dicts.
"""
def __init__(self, **defaults):
self.defaults = dict(defaults)
[docs] def extend(self, other):
"""
Return a new HyperparameterDefaults instance containing the
hyperparameters from the current instance combined with
those from other.
It is an error if self and other have any hyperparameters in
common.
"""
overlap = [key for key in other.defaults if key in self.defaults]
if overlap:
raise ValueError(
"Duplicate hyperparameter(s): %s" % " ".join(overlap))
new = dict(self.defaults)
new.update(other.defaults)
return HyperparameterDefaults(**new)
[docs] def with_defaults(self, obj):
"""
Given a dict of hyperparameter settings, return a dict containing
those settings augmented by the defaults for any keys missing from
the dict.
"""
self.check_valid_keys(obj)
obj = dict(obj)
for (key, value) in self.defaults.items():
if key not in obj:
obj[key] = value
return obj
[docs] def subselect(self, obj):
"""
Filter a dict of hyperparameter settings to only those keys defined
in this HyperparameterDefaults .
"""
return dict(
(key, value) for (key, value)
in obj.items()
if key in self.defaults)
[docs] def check_valid_keys(self, obj):
"""
Given a dict of hyperparameter settings, throw an exception if any
keys are not defined in this HyperparameterDefaults instance.
"""
invalid_keys = [
x for x in obj if x not in self.defaults
]
if invalid_keys:
raise ValueError(
"No such model parameters: %s. Valid parameters are: %s"
% (" ".join(invalid_keys), " ".join(self.defaults)))
[docs] def models_grid(self, **kwargs):
'''
Make a grid of models by taking the cartesian product of all specified
model parameter lists.
Parameters
-----------
The valid kwarg parameters are the entries of this
HyperparameterDefaults instance. Each parameter must be a list
giving the values to search across.
Returns
-----------
list of dict giving the parameters for each model. The length of the
list is the product of the lengths of the input lists.
'''
# Check parameters
self.check_valid_keys(kwargs)
for (key, value) in kwargs.items():
if not isinstance(value, list):
raise ValueError(
"All parameters must be lists, but %s is %s"
% (key, str(type(value))))
# Make models, using defaults.
parameters = dict(
(key, [value]) for (key, value) in self.defaults.items())
parameters.update(kwargs)
parameter_names = list(parameters)
parameter_values = [parameters[name] for name in parameter_names]
models = [
dict(zip(parameter_names, model_values))
for model_values in itertools.product(*parameter_values)
]
return models