hyperparameter_hunter.optimization package¶
Submodules¶
hyperparameter_hunter.optimization.protocol_core module¶
This module defines the base Optimization Protocol classes. The classes defined herein are not
intended for direct use, but are rather parent classes to those defined in
hyperparameter_hunter.optimization.backends.skopt.protocols
Module contents¶

class
hyperparameter_hunter.optimization.
BayesianOptPro
(target_metric=None, iterations=1, verbose=1, read_experiments=True, reporter_parameters=None, warn_on_re_ask=False, base_estimator='GP', n_initial_points=10, acquisition_function='gp_hedge', acquisition_optimizer='auto', random_state=32, acquisition_function_kwargs=None, acquisition_optimizer_kwargs=None, n_random_starts='DEPRECATED', callbacks=None, base_estimator_kwargs=None)¶ Bases:
hyperparameter_hunter.optimization.protocol_core.SKOptPro
Base class for SKOptbased Optimization Protocols
There are two important methods for all
SKOptPro
descendants that should be invoked after initialization: Parameters
 target_metric: Tuple, default=(“oof”, <:attr:`environment.Environment.metrics`[0]>)
Rarely necessary to explicitly provide this, as the default is usually sufficient. Path denoting the metric to be used to compare Experiment performance. The first value should be one of [“oof”, “holdout”, “in_fold”]. The second value should be the name of a metric being recorded according to
environment.Environment.metrics_params
. See the documentation formetrics.get_formatted_target_metric()
for more info. Any values returned by, or given as the target_metric input to,get_formatted_target_metric()
are acceptable values forBaseOptPro.target_metric
 iterations: Int, default=1
Number of Experiments to conduct during optimization upon invoking
BaseOptPro.go()
 verbose: {0, 1, 2}, default=1
Verbosity mode for console logging. 0: Silent. 1: Show only logs from the Optimization Protocol. 2: In addition to logs shown when verbose=1, also show the logs from individual Experiments
 read_experiments: Boolean, default=True
If True, all Experiment records that fit in the current
space
and guidelines, and matchalgorithm_name
, will be read in and used to fit any optimizers reporter_parameters: Dict, or None, default=None
Additional parameters passed to
reporting.OptimizationReporter.__init__()
. Note: Unless provided explicitly, the key “do_maximize” will be added by default to reporter_params, with a value inferred from the direction oftarget_metric
in G.Env.metrics. In nearly all cases, the “do_maximize” key should be ignored, as there are very few reasons to explicitly include it warn_on_re_ask: Boolean, default=False
If True, and the internal optimizer recommends a point that has already been evaluated on invocation of ask, a warning is logged before recommending a random point. Either way, a random point is used instead of alreadyevaluated recommendations. However, logging the fact that this has taken place can be useful to indicate that the optimizer may be stalling, especially if it repeatedly recommends the same point. In these cases, if the suggested point is not optimal, it can be helpful to switch a different OptPro (especially DummyOptPro), which will suggest points using different criteria
 Other Parameters
 base_estimator: {SKLearn Regressor, “GP”, “RF”, “ET”, “GBRT”, “DUMMY”}, default=”GP”
If not string, should inherit from sklearn.base.RegressorMixin. In addition, the predict method should have an optional return_std argument, which returns std(Y  x), along with E[Y  x].
If base_estimator is a string in {“GP”, “RF”, “ET”, “GBRT”, “DUMMY”}, a surrogate model corresponding to the relevant X_minimize function is created
 n_initial_points: Int, default=10
Number of complete evaluation points necessary before allowing Experiments to be approximated with base_estimator. Any valid Experiment records found will count as initialization points. If enough Experiment records are not found, additional points will be randomly sampled
 acquisition_function:{“LCB”, “EI”, “PI”, “gp_hedge”}, default=”gp_hedge”
Function to minimize over the posterior distribution. Can be any of the following:
“LCB”: Lower confidence bound
“EI”: Negative expected improvement
“PI”: Negative probability of improvement
“gp_hedge”: Probabilistically choose one of the above three acquisition functions at every iteration
The gains g_i are initialized to zero
At every iteration,
Each acquisition function is optimised independently to propose a candidate point X_i
Out of all these candidate points, the next point X_best is chosen by softmax(eta g_i)
After fitting the surrogate model with (X_best, y_best), the gains are updated such that g_i = mu(X_i)
 acquisition_optimizer: {“sampling”, “lbfgs”, “auto”}, default=”auto”
Method to minimize the acquisition function. The fit model is updated with the optimal value obtained by optimizing acq_func with acq_optimizer
“sampling”: acq_func is optimized by computing acq_func at n_initial_points randomly sampled points.
“lbfgs”: acq_func is optimized by
Randomly sampling n_restarts_optimizer (from acq_optimizer_kwargs) points
“lbfgs” is run for 20 iterations with these initial points to find local minima
The optimal of these local minima is used to update the prior
“auto”: acq_optimizer is configured on the basis of the base_estimator and the search space. If the space is Categorical or if the provided estimator is based on treemodels, then this is set to “sampling”
 random_state: Int, `RandomState` instance, or None, default=None
Set to something other than None for reproducible results
 acquisition_function_kwargs: Dict, or None, default=dict(xi=0.01, kappa=1.96)
Additional arguments passed to the acquisition function
 acquisition_optimizer_kwargs: Dict, or None, default=dict(n_points=10000, n_restarts_optimizer=5, n_jobs=1)
Additional arguments passed to the acquisition optimizer
 n_random_starts: …
Deprecated since version 3.0.0: Use n_initial_points, instead. Will be removed in 3.2.0
 callbacks: Callable, list of callables, or None, default=[]
If callable, then callbacks(self.optimizer_result) is called after each update to
optimizer
. If list, then each callable is called base_estimator_kwargs: Dict, or None, default={}
Additional arguments passed to base_estimator when it is initialized
Notes
To provide initial input points for evaluation, individual Experiments can be executed prior to instantiating an Optimization Protocol. The results of these Experiments will automatically be detected and cherished by the optimizer.
SKOptPro
and its children inoptimization
rely heavily on the utilities provided by the ScikitOptimize library, so thank you to the creators and contributors for their excellent work. Attributes
search_space_size
The number of different hyperparameter permutations possible given the current
 source_script
Methods
forge_experiment
(self, model_initializer[, …])Define hyperparameter search scaffold for building Experiments during optimization
go
(self[, force_ready])Execute hyperparameter optimization, building an Experiment for each iteration

source_script
= None¶

class
hyperparameter_hunter.optimization.
GradientBoostedRegressionTreeOptPro
(target_metric=None, iterations=1, verbose=1, read_experiments=True, reporter_parameters=None, warn_on_re_ask=False, base_estimator='GBRT', n_initial_points=10, acquisition_function='EI', acquisition_optimizer='sampling', random_state=32, acquisition_function_kwargs=None, acquisition_optimizer_kwargs=None, n_random_starts='DEPRECATED', callbacks=None, base_estimator_kwargs=None)¶ Bases:
hyperparameter_hunter.optimization.protocol_core.SKOptPro
Base class for SKOptbased Optimization Protocols
There are two important methods for all
SKOptPro
descendants that should be invoked after initialization: Parameters
 target_metric: Tuple, default=(“oof”, <:attr:`environment.Environment.metrics`[0]>)
Rarely necessary to explicitly provide this, as the default is usually sufficient. Path denoting the metric to be used to compare Experiment performance. The first value should be one of [“oof”, “holdout”, “in_fold”]. The second value should be the name of a metric being recorded according to
environment.Environment.metrics_params
. See the documentation formetrics.get_formatted_target_metric()
for more info. Any values returned by, or given as the target_metric input to,get_formatted_target_metric()
are acceptable values forBaseOptPro.target_metric
 iterations: Int, default=1
Number of Experiments to conduct during optimization upon invoking
BaseOptPro.go()
 verbose: {0, 1, 2}, default=1
Verbosity mode for console logging. 0: Silent. 1: Show only logs from the Optimization Protocol. 2: In addition to logs shown when verbose=1, also show the logs from individual Experiments
 read_experiments: Boolean, default=True
If True, all Experiment records that fit in the current
space
and guidelines, and matchalgorithm_name
, will be read in and used to fit any optimizers reporter_parameters: Dict, or None, default=None
Additional parameters passed to
reporting.OptimizationReporter.__init__()
. Note: Unless provided explicitly, the key “do_maximize” will be added by default to reporter_params, with a value inferred from the direction oftarget_metric
in G.Env.metrics. In nearly all cases, the “do_maximize” key should be ignored, as there are very few reasons to explicitly include it warn_on_re_ask: Boolean, default=False
If True, and the internal optimizer recommends a point that has already been evaluated on invocation of ask, a warning is logged before recommending a random point. Either way, a random point is used instead of alreadyevaluated recommendations. However, logging the fact that this has taken place can be useful to indicate that the optimizer may be stalling, especially if it repeatedly recommends the same point. In these cases, if the suggested point is not optimal, it can be helpful to switch a different OptPro (especially DummyOptPro), which will suggest points using different criteria
 Other Parameters
 base_estimator: {SKLearn Regressor, “GP”, “RF”, “ET”, “GBRT”, “DUMMY”}, default=”GP”
If not string, should inherit from sklearn.base.RegressorMixin. In addition, the predict method should have an optional return_std argument, which returns std(Y  x), along with E[Y  x].
If base_estimator is a string in {“GP”, “RF”, “ET”, “GBRT”, “DUMMY”}, a surrogate model corresponding to the relevant X_minimize function is created
 n_initial_points: Int, default=10
Number of complete evaluation points necessary before allowing Experiments to be approximated with base_estimator. Any valid Experiment records found will count as initialization points. If enough Experiment records are not found, additional points will be randomly sampled
 acquisition_function:{“LCB”, “EI”, “PI”, “gp_hedge”}, default=”gp_hedge”
Function to minimize over the posterior distribution. Can be any of the following:
“LCB”: Lower confidence bound
“EI”: Negative expected improvement
“PI”: Negative probability of improvement
“gp_hedge”: Probabilistically choose one of the above three acquisition functions at every iteration
The gains g_i are initialized to zero
At every iteration,
Each acquisition function is optimised independently to propose a candidate point X_i
Out of all these candidate points, the next point X_best is chosen by softmax(eta g_i)
After fitting the surrogate model with (X_best, y_best), the gains are updated such that g_i = mu(X_i)
 acquisition_optimizer: {“sampling”, “lbfgs”, “auto”}, default=”auto”
Method to minimize the acquisition function. The fit model is updated with the optimal value obtained by optimizing acq_func with acq_optimizer
“sampling”: acq_func is optimized by computing acq_func at n_initial_points randomly sampled points.
“lbfgs”: acq_func is optimized by
Randomly sampling n_restarts_optimizer (from acq_optimizer_kwargs) points
“lbfgs” is run for 20 iterations with these initial points to find local minima
The optimal of these local minima is used to update the prior
“auto”: acq_optimizer is configured on the basis of the base_estimator and the search space. If the space is Categorical or if the provided estimator is based on treemodels, then this is set to “sampling”
 random_state: Int, `RandomState` instance, or None, default=None
Set to something other than None for reproducible results
 acquisition_function_kwargs: Dict, or None, default=dict(xi=0.01, kappa=1.96)
Additional arguments passed to the acquisition function
 acquisition_optimizer_kwargs: Dict, or None, default=dict(n_points=10000, n_restarts_optimizer=5, n_jobs=1)
Additional arguments passed to the acquisition optimizer
 n_random_starts: …
Deprecated since version 3.0.0: Use n_initial_points, instead. Will be removed in 3.2.0
 callbacks: Callable, list of callables, or None, default=[]
If callable, then callbacks(self.optimizer_result) is called after each update to
optimizer
. If list, then each callable is called base_estimator_kwargs: Dict, or None, default={}
Additional arguments passed to base_estimator when it is initialized
Notes
To provide initial input points for evaluation, individual Experiments can be executed prior to instantiating an Optimization Protocol. The results of these Experiments will automatically be detected and cherished by the optimizer.
SKOptPro
and its children inoptimization
rely heavily on the utilities provided by the ScikitOptimize library, so thank you to the creators and contributors for their excellent work. Attributes
search_space_size
The number of different hyperparameter permutations possible given the current
 source_script
Methods
forge_experiment
(self, model_initializer[, …])Define hyperparameter search scaffold for building Experiments during optimization
go
(self[, force_ready])Execute hyperparameter optimization, building an Experiment for each iteration

source_script
= None¶

hyperparameter_hunter.optimization.
GBRT
¶ alias of
hyperparameter_hunter.optimization.backends.skopt.protocols.GradientBoostedRegressionTreeOptPro

class
hyperparameter_hunter.optimization.
RandomForestOptPro
(target_metric=None, iterations=1, verbose=1, read_experiments=True, reporter_parameters=None, warn_on_re_ask=False, base_estimator='RF', n_initial_points=10, acquisition_function='EI', acquisition_optimizer='sampling', random_state=32, acquisition_function_kwargs=None, acquisition_optimizer_kwargs=None, n_random_starts='DEPRECATED', callbacks=None, base_estimator_kwargs=None)¶ Bases:
hyperparameter_hunter.optimization.protocol_core.SKOptPro
Base class for SKOptbased Optimization Protocols
There are two important methods for all
SKOptPro
descendants that should be invoked after initialization: Parameters
 target_metric: Tuple, default=(“oof”, <:attr:`environment.Environment.metrics`[0]>)
Rarely necessary to explicitly provide this, as the default is usually sufficient. Path denoting the metric to be used to compare Experiment performance. The first value should be one of [“oof”, “holdout”, “in_fold”]. The second value should be the name of a metric being recorded according to
environment.Environment.metrics_params
. See the documentation formetrics.get_formatted_target_metric()
for more info. Any values returned by, or given as the target_metric input to,get_formatted_target_metric()
are acceptable values forBaseOptPro.target_metric
 iterations: Int, default=1
Number of Experiments to conduct during optimization upon invoking
BaseOptPro.go()
 verbose: {0, 1, 2}, default=1
Verbosity mode for console logging. 0: Silent. 1: Show only logs from the Optimization Protocol. 2: In addition to logs shown when verbose=1, also show the logs from individual Experiments
 read_experiments: Boolean, default=True
If True, all Experiment records that fit in the current
space
and guidelines, and matchalgorithm_name
, will be read in and used to fit any optimizers reporter_parameters: Dict, or None, default=None
Additional parameters passed to
reporting.OptimizationReporter.__init__()
. Note: Unless provided explicitly, the key “do_maximize” will be added by default to reporter_params, with a value inferred from the direction oftarget_metric
in G.Env.metrics. In nearly all cases, the “do_maximize” key should be ignored, as there are very few reasons to explicitly include it warn_on_re_ask: Boolean, default=False
If True, and the internal optimizer recommends a point that has already been evaluated on invocation of ask, a warning is logged before recommending a random point. Either way, a random point is used instead of alreadyevaluated recommendations. However, logging the fact that this has taken place can be useful to indicate that the optimizer may be stalling, especially if it repeatedly recommends the same point. In these cases, if the suggested point is not optimal, it can be helpful to switch a different OptPro (especially DummyOptPro), which will suggest points using different criteria
 Other Parameters
 base_estimator: {SKLearn Regressor, “GP”, “RF”, “ET”, “GBRT”, “DUMMY”}, default=”GP”
If not string, should inherit from sklearn.base.RegressorMixin. In addition, the predict method should have an optional return_std argument, which returns std(Y  x), along with E[Y  x].
If base_estimator is a string in {“GP”, “RF”, “ET”, “GBRT”, “DUMMY”}, a surrogate model corresponding to the relevant X_minimize function is created
 n_initial_points: Int, default=10
Number of complete evaluation points necessary before allowing Experiments to be approximated with base_estimator. Any valid Experiment records found will count as initialization points. If enough Experiment records are not found, additional points will be randomly sampled
 acquisition_function:{“LCB”, “EI”, “PI”, “gp_hedge”}, default=”gp_hedge”
Function to minimize over the posterior distribution. Can be any of the following:
“LCB”: Lower confidence bound
“EI”: Negative expected improvement
“PI”: Negative probability of improvement
“gp_hedge”: Probabilistically choose one of the above three acquisition functions at every iteration
The gains g_i are initialized to zero
At every iteration,
Each acquisition function is optimised independently to propose a candidate point X_i
Out of all these candidate points, the next point X_best is chosen by softmax(eta g_i)
After fitting the surrogate model with (X_best, y_best), the gains are updated such that g_i = mu(X_i)
 acquisition_optimizer: {“sampling”, “lbfgs”, “auto”}, default=”auto”
Method to minimize the acquisition function. The fit model is updated with the optimal value obtained by optimizing acq_func with acq_optimizer
“sampling”: acq_func is optimized by computing acq_func at n_initial_points randomly sampled points.
“lbfgs”: acq_func is optimized by
Randomly sampling n_restarts_optimizer (from acq_optimizer_kwargs) points
“lbfgs” is run for 20 iterations with these initial points to find local minima
The optimal of these local minima is used to update the prior
“auto”: acq_optimizer is configured on the basis of the base_estimator and the search space. If the space is Categorical or if the provided estimator is based on treemodels, then this is set to “sampling”
 random_state: Int, `RandomState` instance, or None, default=None
Set to something other than None for reproducible results
 acquisition_function_kwargs: Dict, or None, default=dict(xi=0.01, kappa=1.96)
Additional arguments passed to the acquisition function
 acquisition_optimizer_kwargs: Dict, or None, default=dict(n_points=10000, n_restarts_optimizer=5, n_jobs=1)
Additional arguments passed to the acquisition optimizer
 n_random_starts: …
Deprecated since version 3.0.0: Use n_initial_points, instead. Will be removed in 3.2.0
 callbacks: Callable, list of callables, or None, default=[]
If callable, then callbacks(self.optimizer_result) is called after each update to
optimizer
. If list, then each callable is called base_estimator_kwargs: Dict, or None, default={}
Additional arguments passed to base_estimator when it is initialized
Notes
To provide initial input points for evaluation, individual Experiments can be executed prior to instantiating an Optimization Protocol. The results of these Experiments will automatically be detected and cherished by the optimizer.
SKOptPro
and its children inoptimization
rely heavily on the utilities provided by the ScikitOptimize library, so thank you to the creators and contributors for their excellent work. Attributes
search_space_size
The number of different hyperparameter permutations possible given the current
 source_script
Methods
forge_experiment
(self, model_initializer[, …])Define hyperparameter search scaffold for building Experiments during optimization
go
(self[, force_ready])Execute hyperparameter optimization, building an Experiment for each iteration

source_script
= None¶

hyperparameter_hunter.optimization.
RF
¶ alias of
hyperparameter_hunter.optimization.backends.skopt.protocols.RandomForestOptPro

class
hyperparameter_hunter.optimization.
ExtraTreesOptPro
(target_metric=None, iterations=1, verbose=1, read_experiments=True, reporter_parameters=None, warn_on_re_ask=False, base_estimator='ET', n_initial_points=10, acquisition_function='EI', acquisition_optimizer='sampling', random_state=32, acquisition_function_kwargs=None, acquisition_optimizer_kwargs=None, n_random_starts='DEPRECATED', callbacks=None, base_estimator_kwargs=None)¶ Bases:
hyperparameter_hunter.optimization.protocol_core.SKOptPro
Base class for SKOptbased Optimization Protocols
There are two important methods for all
SKOptPro
descendants that should be invoked after initialization: Parameters
 target_metric: Tuple, default=(“oof”, <:attr:`environment.Environment.metrics`[0]>)
Rarely necessary to explicitly provide this, as the default is usually sufficient. Path denoting the metric to be used to compare Experiment performance. The first value should be one of [“oof”, “holdout”, “in_fold”]. The second value should be the name of a metric being recorded according to
environment.Environment.metrics_params
. See the documentation formetrics.get_formatted_target_metric()
for more info. Any values returned by, or given as the target_metric input to,get_formatted_target_metric()
are acceptable values forBaseOptPro.target_metric
 iterations: Int, default=1
Number of Experiments to conduct during optimization upon invoking
BaseOptPro.go()
 verbose: {0, 1, 2}, default=1
Verbosity mode for console logging. 0: Silent. 1: Show only logs from the Optimization Protocol. 2: In addition to logs shown when verbose=1, also show the logs from individual Experiments
 read_experiments: Boolean, default=True
If True, all Experiment records that fit in the current
space
and guidelines, and matchalgorithm_name
, will be read in and used to fit any optimizers reporter_parameters: Dict, or None, default=None
Additional parameters passed to
reporting.OptimizationReporter.__init__()
. Note: Unless provided explicitly, the key “do_maximize” will be added by default to reporter_params, with a value inferred from the direction oftarget_metric
in G.Env.metrics. In nearly all cases, the “do_maximize” key should be ignored, as there are very few reasons to explicitly include it warn_on_re_ask: Boolean, default=False
If True, and the internal optimizer recommends a point that has already been evaluated on invocation of ask, a warning is logged before recommending a random point. Either way, a random point is used instead of alreadyevaluated recommendations. However, logging the fact that this has taken place can be useful to indicate that the optimizer may be stalling, especially if it repeatedly recommends the same point. In these cases, if the suggested point is not optimal, it can be helpful to switch a different OptPro (especially DummyOptPro), which will suggest points using different criteria
 Other Parameters
 base_estimator: {SKLearn Regressor, “GP”, “RF”, “ET”, “GBRT”, “DUMMY”}, default=”GP”
If not string, should inherit from sklearn.base.RegressorMixin. In addition, the predict method should have an optional return_std argument, which returns std(Y  x), along with E[Y  x].
If base_estimator is a string in {“GP”, “RF”, “ET”, “GBRT”, “DUMMY”}, a surrogate model corresponding to the relevant X_minimize function is created
 n_initial_points: Int, default=10
Number of complete evaluation points necessary before allowing Experiments to be approximated with base_estimator. Any valid Experiment records found will count as initialization points. If enough Experiment records are not found, additional points will be randomly sampled
 acquisition_function:{“LCB”, “EI”, “PI”, “gp_hedge”}, default=”gp_hedge”
Function to minimize over the posterior distribution. Can be any of the following:
“LCB”: Lower confidence bound
“EI”: Negative expected improvement
“PI”: Negative probability of improvement
“gp_hedge”: Probabilistically choose one of the above three acquisition functions at every iteration
The gains g_i are initialized to zero
At every iteration,
Each acquisition function is optimised independently to propose a candidate point X_i
Out of all these candidate points, the next point X_best is chosen by softmax(eta g_i)
After fitting the surrogate model with (X_best, y_best), the gains are updated such that g_i = mu(X_i)
 acquisition_optimizer: {“sampling”, “lbfgs”, “auto”}, default=”auto”
Method to minimize the acquisition function. The fit model is updated with the optimal value obtained by optimizing acq_func with acq_optimizer
“sampling”: acq_func is optimized by computing acq_func at n_initial_points randomly sampled points.
“lbfgs”: acq_func is optimized by
Randomly sampling n_restarts_optimizer (from acq_optimizer_kwargs) points
“lbfgs” is run for 20 iterations with these initial points to find local minima
The optimal of these local minima is used to update the prior
“auto”: acq_optimizer is configured on the basis of the base_estimator and the search space. If the space is Categorical or if the provided estimator is based on treemodels, then this is set to “sampling”
 random_state: Int, `RandomState` instance, or None, default=None
Set to something other than None for reproducible results
 acquisition_function_kwargs: Dict, or None, default=dict(xi=0.01, kappa=1.96)
Additional arguments passed to the acquisition function
 acquisition_optimizer_kwargs: Dict, or None, default=dict(n_points=10000, n_restarts_optimizer=5, n_jobs=1)
Additional arguments passed to the acquisition optimizer
 n_random_starts: …
Deprecated since version 3.0.0: Use n_initial_points, instead. Will be removed in 3.2.0
 callbacks: Callable, list of callables, or None, default=[]
If callable, then callbacks(self.optimizer_result) is called after each update to
optimizer
. If list, then each callable is called base_estimator_kwargs: Dict, or None, default={}
Additional arguments passed to base_estimator when it is initialized
Notes
To provide initial input points for evaluation, individual Experiments can be executed prior to instantiating an Optimization Protocol. The results of these Experiments will automatically be detected and cherished by the optimizer.
SKOptPro
and its children inoptimization
rely heavily on the utilities provided by the ScikitOptimize library, so thank you to the creators and contributors for their excellent work. Attributes
search_space_size
The number of different hyperparameter permutations possible given the current
 source_script
Methods
forge_experiment
(self, model_initializer[, …])Define hyperparameter search scaffold for building Experiments during optimization
go
(self[, force_ready])Execute hyperparameter optimization, building an Experiment for each iteration

source_script
= None¶

hyperparameter_hunter.optimization.
ET
¶ alias of
hyperparameter_hunter.optimization.backends.skopt.protocols.ExtraTreesOptPro

class
hyperparameter_hunter.optimization.
DummyOptPro
(target_metric=None, iterations=1, verbose=1, read_experiments=True, reporter_parameters=None, warn_on_re_ask=False, base_estimator='DUMMY', n_initial_points=10, acquisition_function='EI', acquisition_optimizer='sampling', random_state=32, acquisition_function_kwargs=None, acquisition_optimizer_kwargs=None, n_random_starts='DEPRECATED', callbacks=None, base_estimator_kwargs=None)¶ Bases:
hyperparameter_hunter.optimization.protocol_core.SKOptPro
Base class for SKOptbased Optimization Protocols
There are two important methods for all
SKOptPro
descendants that should be invoked after initialization: Parameters
 target_metric: Tuple, default=(“oof”, <:attr:`environment.Environment.metrics`[0]>)
Rarely necessary to explicitly provide this, as the default is usually sufficient. Path denoting the metric to be used to compare Experiment performance. The first value should be one of [“oof”, “holdout”, “in_fold”]. The second value should be the name of a metric being recorded according to
environment.Environment.metrics_params
. See the documentation formetrics.get_formatted_target_metric()
for more info. Any values returned by, or given as the target_metric input to,get_formatted_target_metric()
are acceptable values forBaseOptPro.target_metric
 iterations: Int, default=1
Number of Experiments to conduct during optimization upon invoking
BaseOptPro.go()
 verbose: {0, 1, 2}, default=1
Verbosity mode for console logging. 0: Silent. 1: Show only logs from the Optimization Protocol. 2: In addition to logs shown when verbose=1, also show the logs from individual Experiments
 read_experiments: Boolean, default=True
If True, all Experiment records that fit in the current
space
and guidelines, and matchalgorithm_name
, will be read in and used to fit any optimizers reporter_parameters: Dict, or None, default=None
Additional parameters passed to
reporting.OptimizationReporter.__init__()
. Note: Unless provided explicitly, the key “do_maximize” will be added by default to reporter_params, with a value inferred from the direction oftarget_metric
in G.Env.metrics. In nearly all cases, the “do_maximize” key should be ignored, as there are very few reasons to explicitly include it warn_on_re_ask: Boolean, default=False
If True, and the internal optimizer recommends a point that has already been evaluated on invocation of ask, a warning is logged before recommending a random point. Either way, a random point is used instead of alreadyevaluated recommendations. However, logging the fact that this has taken place can be useful to indicate that the optimizer may be stalling, especially if it repeatedly recommends the same point. In these cases, if the suggested point is not optimal, it can be helpful to switch a different OptPro (especially DummyOptPro), which will suggest points using different criteria
 Other Parameters
 base_estimator: {SKLearn Regressor, “GP”, “RF”, “ET”, “GBRT”, “DUMMY”}, default=”GP”
If not string, should inherit from sklearn.base.RegressorMixin. In addition, the predict method should have an optional return_std argument, which returns std(Y  x), along with E[Y  x].
If base_estimator is a string in {“GP”, “RF”, “ET”, “GBRT”, “DUMMY”}, a surrogate model corresponding to the relevant X_minimize function is created
 n_initial_points: Int, default=10
Number of complete evaluation points necessary before allowing Experiments to be approximated with base_estimator. Any valid Experiment records found will count as initialization points. If enough Experiment records are not found, additional points will be randomly sampled
 acquisition_function:{“LCB”, “EI”, “PI”, “gp_hedge”}, default=”gp_hedge”
Function to minimize over the posterior distribution. Can be any of the following:
“LCB”: Lower confidence bound
“EI”: Negative expected improvement
“PI”: Negative probability of improvement
“gp_hedge”: Probabilistically choose one of the above three acquisition functions at every iteration
The gains g_i are initialized to zero
At every iteration,
Each acquisition function is optimised independently to propose a candidate point X_i
Out of all these candidate points, the next point X_best is chosen by softmax(eta g_i)
After fitting the surrogate model with (X_best, y_best), the gains are updated such that g_i = mu(X_i)
 acquisition_optimizer: {“sampling”, “lbfgs”, “auto”}, default=”auto”
Method to minimize the acquisition function. The fit model is updated with the optimal value obtained by optimizing acq_func with acq_optimizer
“sampling”: acq_func is optimized by computing acq_func at n_initial_points randomly sampled points.
“lbfgs”: acq_func is optimized by
Randomly sampling n_restarts_optimizer (from acq_optimizer_kwargs) points
“lbfgs” is run for 20 iterations with these initial points to find local minima
The optimal of these local minima is used to update the prior
“auto”: acq_optimizer is configured on the basis of the base_estimator and the search space. If the space is Categorical or if the provided estimator is based on treemodels, then this is set to “sampling”
 random_state: Int, `RandomState` instance, or None, default=None
Set to something other than None for reproducible results
 acquisition_function_kwargs: Dict, or None, default=dict(xi=0.01, kappa=1.96)
Additional arguments passed to the acquisition function
 acquisition_optimizer_kwargs: Dict, or None, default=dict(n_points=10000, n_restarts_optimizer=5, n_jobs=1)
Additional arguments passed to the acquisition optimizer
 n_random_starts: …
Deprecated since version 3.0.0: Use n_initial_points, instead. Will be removed in 3.2.0
 callbacks: Callable, list of callables, or None, default=[]
If callable, then callbacks(self.optimizer_result) is called after each update to
optimizer
. If list, then each callable is called base_estimator_kwargs: Dict, or None, default={}
Additional arguments passed to base_estimator when it is initialized
Notes
To provide initial input points for evaluation, individual Experiments can be executed prior to instantiating an Optimization Protocol. The results of these Experiments will automatically be detected and cherished by the optimizer.
SKOptPro
and its children inoptimization
rely heavily on the utilities provided by the ScikitOptimize library, so thank you to the creators and contributors for their excellent work. Attributes
search_space_size
The number of different hyperparameter permutations possible given the current
 source_script
Methods
forge_experiment
(self, model_initializer[, …])Define hyperparameter search scaffold for building Experiments during optimization
go
(self[, force_ready])Execute hyperparameter optimization, building an Experiment for each iteration

source_script
= None¶

class
hyperparameter_hunter.optimization.
BayesianOptimization
(**kwargs)¶ Bases:
hyperparameter_hunter.optimization.backends.skopt.protocols.BayesianOptPro
Deprecated since version 3.0.0a2: Will be removed in 3.2.0. Renamed to BayesianOptPro
 Attributes
search_space_size
The number of different hyperparameter permutations possible given the current
 source_script
Methods
forge_experiment
(self, model_initializer[, …])Define hyperparameter search scaffold for building Experiments during optimization
get_ready
(self)Prepare for optimization by finalizing hyperparameter space and identifying similar Experiments.
go
(self[, force_ready])Execute hyperparameter optimization, building an Experiment for each iteration
set_dimensions
(self)Locate given hyperparameters that are space choice declarations and add them to
dimensions
set_experiment_guidelines
(self, \*args, …)Deprecated since version 3.0.0a2.

source_script
= None¶

class
hyperparameter_hunter.optimization.
GradientBoostedRegressionTreeOptimization
(**kwargs)¶ Bases:
hyperparameter_hunter.optimization.backends.skopt.protocols.GradientBoostedRegressionTreeOptPro
Deprecated since version 3.0.0a2: Will be removed in 3.2.0. Renamed to GradientBoostedRegressionTreeOptPro
 Attributes
search_space_size
The number of different hyperparameter permutations possible given the current
 source_script
Methods
forge_experiment
(self, model_initializer[, …])Define hyperparameter search scaffold for building Experiments during optimization
get_ready
(self)Prepare for optimization by finalizing hyperparameter space and identifying similar Experiments.
go
(self[, force_ready])Execute hyperparameter optimization, building an Experiment for each iteration
set_dimensions
(self)Locate given hyperparameters that are space choice declarations and add them to
dimensions
set_experiment_guidelines
(self, \*args, …)Deprecated since version 3.0.0a2.

source_script
= None¶

class
hyperparameter_hunter.optimization.
RandomForestOptimization
(**kwargs)¶ Bases:
hyperparameter_hunter.optimization.backends.skopt.protocols.RandomForestOptPro
Deprecated since version 3.0.0a2: Will be removed in 3.2.0. Renamed to RandomForestOptPro
 Attributes
search_space_size
The number of different hyperparameter permutations possible given the current
 source_script
Methods
forge_experiment
(self, model_initializer[, …])Define hyperparameter search scaffold for building Experiments during optimization
get_ready
(self)Prepare for optimization by finalizing hyperparameter space and identifying similar Experiments.
go
(self[, force_ready])Execute hyperparameter optimization, building an Experiment for each iteration
set_dimensions
(self)Locate given hyperparameters that are space choice declarations and add them to
dimensions
set_experiment_guidelines
(self, \*args, …)Deprecated since version 3.0.0a2.

source_script
= None¶

class
hyperparameter_hunter.optimization.
ExtraTreesOptimization
(**kwargs)¶ Bases:
hyperparameter_hunter.optimization.backends.skopt.protocols.ExtraTreesOptPro
Deprecated since version 3.0.0a2: Will be removed in 3.2.0. Renamed to ExtraTreesOptPro
 Attributes
search_space_size
The number of different hyperparameter permutations possible given the current
 source_script
Methods
forge_experiment
(self, model_initializer[, …])Define hyperparameter search scaffold for building Experiments during optimization
get_ready
(self)Prepare for optimization by finalizing hyperparameter space and identifying similar Experiments.
go
(self[, force_ready])Execute hyperparameter optimization, building an Experiment for each iteration
set_dimensions
(self)Locate given hyperparameters that are space choice declarations and add them to
dimensions
set_experiment_guidelines
(self, \*args, …)Deprecated since version 3.0.0a2.

source_script
= None¶

class
hyperparameter_hunter.optimization.
DummySearch
(**kwargs)¶ Bases:
hyperparameter_hunter.optimization.backends.skopt.protocols.DummyOptPro
Deprecated since version 3.0.0a2: Will be removed in 3.2.0. Renamed to DummyOptPro
 Attributes
search_space_size
The number of different hyperparameter permutations possible given the current
 source_script
Methods
forge_experiment
(self, model_initializer[, …])Define hyperparameter search scaffold for building Experiments during optimization
get_ready
(self)Prepare for optimization by finalizing hyperparameter space and identifying similar Experiments.
go
(self[, force_ready])Execute hyperparameter optimization, building an Experiment for each iteration
set_dimensions
(self)Locate given hyperparameters that are space choice declarations and add them to
dimensions
set_experiment_guidelines
(self, \*args, …)Deprecated since version 3.0.0a2.

source_script
= None¶