hyperparameter_hunter.optimization package

Subpackages

• hyperparameter_hunter.optimization.backends package
  • Subpackages
    • hyperparameter_hunter.optimization.backends.skopt package
      • Submodules
      • hyperparameter_hunter.optimization.backends.skopt.engine module
        • Related
        • Notes
      • hyperparameter_hunter.optimization.backends.skopt.protocols module
        • Related
      • Module contents
  • Module contents

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

Related

hyperparameter_hunter.optimization.backends.skopt.protocols

Defines the optimization classes that are intended for direct use. All classes defined in hyperparameter_hunter.optimization.backends.skopt.protocols should be descendants of BaseOptPro

hyperparameter_hunter.result_reader

Used to locate result files for Experiments that are similar to the current optimization constraints, and produce data to learn from in the case of SKOptPro

hyperparameter_hunter.space

Defines the child classes of hyperparameter_hunter.space.Dimension, which are used to define the hyperparameters to optimize

hyperparameter_hunter.utils.optimization_utils:

Provides utility functions for locating saved Experiments that fit within the constraints currently being optimized

class hyperparameter_hunter.optimization.protocol_core.OptProMeta

Bases: type

Metaclass to accurately set source_script for its descendants even if the original call was the product of scripts calling other scripts that eventually instantiated an optimization protocol

Methods

__call__(cls, \*args, \*\*kwargs)	Set the instance’s source_script to the absolute path of the file that instantiated the OptimizationProtocol
mro()	return a type’s method resolution order

class hyperparameter_hunter.optimization.protocol_core.MergedOptProMeta

Bases: hyperparameter_hunter.optimization.protocol_core.OptProMeta, abc.ABCMeta

Metaclass to combine OptProMeta, and ABCMeta

Methods

__call__(cls, \*args, \*\*kwargs)	Set the instance’s source_script to the absolute path of the file that instantiated the OptimizationProtocol
mro()	return a type’s method resolution order
register(cls, subclass)	Register a virtual subclass of an ABC.

class hyperparameter_hunter.optimization.protocol_core.BaseOptPro(target_metric=None, iterations=1, verbose=1, read_experiments=True, reporter_parameters=None, warn_on_re_ask=False)

Bases: object

Base class for intermediate base optimization protocol classes

There are two important methods for all BaseOptPro descendants that should be invoked after initialization:

1. forge_experiment()

2. go()

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 for metrics.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 for BaseOptPro.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 match algorithm_name, will be read in and used to fit any optimizers

reporter_parameters: Dict, or None, default={}

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 of target_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 already-evaluated 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

Notes

By default, ‘script_backup’ for Experiments is blacklisted when executed within BaseOptPro since it would just repeatedly create copies of the same, unchanged file. So don’t expect any script_backup files for Experiments executed by OptPros

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

forge_experiment(self, model_initializer, model_init_params=None, model_extra_params=None, feature_engineer=None, feature_selector=None, notes=None, do_raise_repeated=True)

Define hyperparameter search scaffold for building Experiments during optimization

OptPros use this method to guide Experiment construction behind the scenes, which is why it looks just like hyperparameter_hunter.experiments.BaseExperiment.__init__(). forge_experiment offers one major upgrade to standard Experiment initialization: it accepts hyperparameters not only as concrete values, but also as space choices – using Real, Integer, and Categorical. This functionality applies to the model_init_params, model_extra_params and feature_engineer kwargs. Any Dimensions provided to forge_experiment are detected by the OptPro and used to define the hyperparameter search space to be optimized

Parameters

model_initializer: Class, or functools.partial, or class instance

Algorithm class used to initialize a model, such as XGBoost’s XGBRegressor, or SKLearn’s KNeighborsClassifier; although, there are hundreds of possibilities across many different ML libraries. model_initializer is expected to define at least fit and predict methods. model_initializer will be initialized with model_init_params, and its extra methods (fit, predict, etc.) will be invoked with parameters in model_extra_params

model_init_params: Dict, or object (optional)

Dictionary of arguments given to create an instance of model_initializer. Any kwargs that are considered valid by the __init__ method of model_initializer are valid in model_init_params.

In addition to providing concrete values, hyperparameters can be expressed as choices (dimensions to optimize) by using instances of Real, Integer, or Categorical. Furthermore, hyperparameter choices and concrete values can be used together in model_init_params.

Using XGBoost’s XGBClassifier to illustrate, the model_init_params kwarg of CVExperiment is limited to using concrete values, such as dict(max_depth=10, learning_rate=0.1, booster="gbtree"). This is still valid for forge_experiment(). However, forge_experiment() also allows model_init_params to consist entirely of space choices, such as dict(max_depth=Integer(2, 20), learning_rate=Real(0.001, 0.5), booster=Categorical(["gbtree", "dart"])), or as any combination of concrete values and choices, for instance, dict(max_depth=10, learning_rate=Real(0.001, 0.5), booster="gbtree").

One of the key features that makes HyperparameterHunter so magical is that ALL hyperparameters in the signature of model_initializer (and their default values) are discovered – whether or not they are explicitly given in model_init_params. Not only does this make Experiment result descriptions incredibly thorough, it also makes optimization smoother, more effective, and far less work for the user. For example, take LightGBM’s LGBMRegressor, with model_init_params`=`dict(learning_rate=0.2). HyperparameterHunter recognizes that this differs from the default of 0.1. It also recognizes that LGBMRegressor is actually initialized with more than a dozen other hyperparameters we didn’t bother mentioning, and it records their values, too. So if we want to optimize num_leaves tomorrow, the OptPro doesn’t start from scratch. It knows that we ran an Experiment that didn’t explicitly mention num_leaves, but its default value was 31, and it uses this information to fuel optimization – all without us having to manually keep track of tons of janky collections of hyperparameters. In fact, we really don’t need to go out of our way at all. HyperparameterHunter just acts as our faithful lab assistant, keeping track of all the stuff we’d rather not worry about

model_extra_params: Dict (optional)

Dictionary of extra parameters for models’ non-initialization methods (like fit, predict, predict_proba, etc.), and for neural networks. To specify parameters for an extra method, place them in a dict named for the extra method to which the parameters should be given. For example, to call fit with early_stopping_rounds`=5, use `model_extra_params`=`dict(fit=dict(early_stopping_rounds=5)).

Declaring hyperparameter space choices works identically to model_init_params, meaning that in addition to concrete values, extra parameters can be given as instances of Real, Integer, or Categorical. To optimize over a space in which early_stopping_rounds is between 3 and 9, use model_extra_params`=`dict(fit=dict(early_stopping_rounds=Real(3, 9))).

For models whose fit methods have a kwarg like eval_set (such as XGBoost’s), one can use the DatasetSentinel attributes of the current active Environment, documented under its “Attributes” section and under train_input. An example using several DatasetSentinels can be found in HyperparameterHunter’s [XGBoost Classification Example](https://github.com/HunterMcGushion/hyperparameter_hunter/blob/master/examples/xgboost_examples/classification.py)

feature_engineer: `FeatureEngineer`, or list (optional)

Feature engineering/transformation/pre-processing steps to apply to datasets defined in Environment. If list, will be used to initialize FeatureEngineer, and can contain any of the following values:

1. EngineerStep instance

2. Function input to :class:~hyperparameter_hunter.feature_engineering.EngineerStep`

3. Categorical, with categories comprising a selection of the previous two values (optimization only)

For important information on properly formatting EngineerStep functions, please see the documentation of EngineerStep.

To search a space optionally including an EngineerStep, use the optional kwarg of Categorical. This functionality is illustrated in FeatureEngineer. If using a FeatureEngineer instance to optimize feature_engineer, this instance cannot be used with CVExperiment because Experiments can’t handle space choices

feature_selector: List of str, callable, or list of booleans (optional)

Column names to include as input data for all provided DataFrames. If None, feature_selector is set to all columns in train_dataset, less target_column, and id_column. feature_selector is provided as the second argument for calls to pandas.DataFrame.loc when constructing datasets

notes: String (optional)

Additional information about the Experiment that will be saved with the Experiment’s description result file. This serves no purpose other than to facilitate saving Experiment details in a more readable format

do_raise_repeated: Boolean, default=False

If True and this Experiment locates a previous Experiment’s results with matching Environment and Hyperparameter Keys, a RepeatedExperimentError will be raised. Else, a warning will be logged

See also

hyperparameter_hunter.experiments.BaseExperiment

One-off experimentation counterpart to an OptPro’s forge_experiment(). Internally, OptPros feed the processed arguments from forge_experiment to initialize Experiments. This hand-off to Experiments takes place in _execute_experiment()

Notes

The auto_start kwarg is not available here because _execute_experiment() sets it to False in order to check for duplicated keys before running the whole Experiment. This and target_metric being moved to __init__() are the most notable differences between calling forge_experiment() and instantiating CVExperiment

A more accurate name for this method might be something like “build_experiment_forge”, since forge_experiment itself does not actually execute any Experiments. However, forge_experiment sounds cooler and much less clunky

set_experiment_guidelines(self, *args, **kwargs)

Deprecated since version 3.0.0a2: Will be removed in 3.2.0. Renamed to forge_experiment

set_dimensions(self)

Locate given hyperparameters that are space choice declarations and add them to dimensions

get_ready(self)

Prepare for optimization by finalizing hyperparameter space and identifying similar Experiments. This method is automatically invoked when go() is called if necessary

go(self, force_ready=True)

Execute hyperparameter optimization, building an Experiment for each iteration

This method may only be invoked after invoking forge_experiment(), which defines experiment guidelines and search dimensions. go performs a few important tasks: 1) Formally setting the hyperparameter space; 2) Locating similar experiments to be used as learning material (for OptPros that suggest incumbent search points by estimating utilities using surrogate models); and 3) Actually setting off the optimization process, via _optimization_loop()

Parameters

force_ready: Boolean, default=False

If True, get_ready() will be invoked even if it has already been called. This will re-initialize the hyperparameter space and similar_experiments. Standard behavior is for go() to invoke get_ready(), so force_ready is ignored unless get_ready() has been manually invoked

abstract property search_space_size

The number of different hyperparameter permutations possible given the current hyperparameter search space

class hyperparameter_hunter.optimization.protocol_core.SKOptPro(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.BaseOptPro

Base class for SKOpt-based Optimization Protocols

There are two important methods for all SKOptPro descendants that should be invoked after initialization:

1. forge_experiment()

2. go()

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 for metrics.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 for BaseOptPro.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 match algorithm_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 of target_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 already-evaluated 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 tree-models, 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 in optimization rely heavily on the utilities provided by the Scikit-Optimize 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

property search_space_size

The number of different hyperparameter permutations possible given the current hyperparameter search dimensions

Returns

_search_space_size: Int, or numpy.inf

Infinity returned if any of the following constraints are met: 1) the hyperparameter dimensions include any real-valued boundaries, 2) the boundaries include values that are neither categorical nor integer, or 3) search space size is otherwise incalculable

source_script = None

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

Bayesian optimization with Gaussian Processes

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.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

Sequential optimization with gradient boosted regression trees

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

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

Sequential optimization with random forest regressor decision trees

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

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

Sequential optimization with extra trees regressor decision trees

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

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

Random search by uniform sampling

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.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