hyperparameter_hunter.callbacks package¶
Subpackages¶
Submodules¶
hyperparameter_hunter.callbacks.aggregators module¶
-
class
hyperparameter_hunter.callbacks.aggregators.
AggregatorTimes
¶ Bases:
hyperparameter_hunter.callbacks.bases.BaseAggregatorCallback
Methods
on_exp_end
(self)Perform tasks when an Experiment ends
on_exp_start
(self)Perform tasks when an Experiment is started
on_fold_end
(self)Perform tasks on fold end in an Experiment’s cross-validation scheme
on_fold_start
(self)Perform tasks on fold start in an Experiment’s cross-validation scheme
on_rep_end
(self)Perform tasks on repetition end in an Experiment’s repeated cross-validation scheme
on_rep_start
(self)Perform tasks on repetition start in an Experiment’s repeated cross-validation scheme
on_run_end
(self)Perform tasks on run end in an Experiment’s multiple-run-averaging phase
on_run_start
(self)Perform tasks on run start in an Experiment’s multiple-run-averaging phase
-
on_exp_start
(self)¶ Perform tasks when an Experiment is started
-
on_rep_start
(self)¶ Perform tasks on repetition start in an Experiment’s repeated cross-validation scheme
-
on_fold_start
(self)¶ Perform tasks on fold start in an Experiment’s cross-validation scheme
-
on_run_start
(self)¶ Perform tasks on run start in an Experiment’s multiple-run-averaging phase
-
on_run_end
(self)¶ Perform tasks on run end in an Experiment’s multiple-run-averaging phase
-
on_fold_end
(self)¶ Perform tasks on fold end in an Experiment’s cross-validation scheme
-
on_rep_end
(self)¶ Perform tasks on repetition end in an Experiment’s repeated cross-validation scheme
-
on_exp_end
(self)¶ Perform tasks when an Experiment ends
-
-
class
hyperparameter_hunter.callbacks.aggregators.
AggregatorEvaluations
¶ Bases:
hyperparameter_hunter.callbacks.bases.BaseAggregatorCallback
Methods
on_exp_end
(self)Perform tasks when an Experiment ends
on_exp_start
(self)Perform tasks when an Experiment is started
on_fold_end
(self)Perform tasks on fold end in an Experiment’s cross-validation scheme
on_fold_start
(self)Perform tasks on fold start in an Experiment’s cross-validation scheme
on_rep_end
(self)Perform tasks on repetition end in an Experiment’s repeated cross-validation scheme
on_rep_start
(self)Perform tasks on repetition start in an Experiment’s repeated cross-validation scheme
on_run_end
(self)Perform tasks on run end in an Experiment’s multiple-run-averaging phase
on_run_start
(self)Perform tasks on run start in an Experiment’s multiple-run-averaging phase
-
on_run_end
(self)¶ Perform tasks on run end in an Experiment’s multiple-run-averaging phase
-
on_fold_end
(self)¶ Perform tasks on fold end in an Experiment’s cross-validation scheme
-
on_rep_end
(self)¶ Perform tasks on repetition end in an Experiment’s repeated cross-validation scheme
-
on_exp_end
(self)¶ Perform tasks when an Experiment ends
-
-
class
hyperparameter_hunter.callbacks.aggregators.
AggregatorOOF
¶ Bases:
hyperparameter_hunter.callbacks.bases.BaseAggregatorCallback
Methods
on_exp_end
(self)Perform tasks when an Experiment ends
on_exp_start
(self)Perform tasks when an Experiment is started
on_fold_end
(self)Perform tasks on fold end in an Experiment’s cross-validation scheme
on_fold_start
(self)Perform tasks on fold start in an Experiment’s cross-validation scheme
on_rep_end
(self)Perform tasks on repetition end in an Experiment’s repeated cross-validation scheme
on_rep_start
(self)Perform tasks on repetition start in an Experiment’s repeated cross-validation scheme
on_run_end
(self)Perform tasks on run end in an Experiment’s multiple-run-averaging phase
on_run_start
(self)Perform tasks on run start in an Experiment’s multiple-run-averaging phase
-
class
hyperparameter_hunter.callbacks.aggregators.
AggregatorHoldout
¶ Bases:
hyperparameter_hunter.callbacks.bases.BaseAggregatorCallback
Methods
on_exp_end
(self)Perform tasks when an Experiment ends
on_exp_start
(self)Perform tasks when an Experiment is started
on_fold_end
(self)Perform tasks on fold end in an Experiment’s cross-validation scheme
on_fold_start
(self)Perform tasks on fold start in an Experiment’s cross-validation scheme
on_rep_end
(self)Perform tasks on repetition end in an Experiment’s repeated cross-validation scheme
on_rep_start
(self)Perform tasks on repetition start in an Experiment’s repeated cross-validation scheme
on_run_end
(self)Perform tasks on run end in an Experiment’s multiple-run-averaging phase
on_run_start
(self)Perform tasks on run start in an Experiment’s multiple-run-averaging phase
-
class
hyperparameter_hunter.callbacks.aggregators.
AggregatorTest
¶ Bases:
hyperparameter_hunter.callbacks.bases.BaseAggregatorCallback
Methods
on_exp_end
(self)Perform tasks when an Experiment ends
on_exp_start
(self)Perform tasks when an Experiment is started
on_fold_end
(self)Perform tasks on fold end in an Experiment’s cross-validation scheme
on_fold_start
(self)Perform tasks on fold start in an Experiment’s cross-validation scheme
on_rep_end
(self)Perform tasks on repetition end in an Experiment’s repeated cross-validation scheme
on_rep_start
(self)Perform tasks on repetition start in an Experiment’s repeated cross-validation scheme
on_run_end
(self)Perform tasks on run end in an Experiment’s multiple-run-averaging phase
on_run_start
(self)Perform tasks on run start in an Experiment’s multiple-run-averaging phase
-
class
hyperparameter_hunter.callbacks.aggregators.
AggregatorLosses
¶ Bases:
hyperparameter_hunter.callbacks.bases.BaseAggregatorCallback
Methods
on_exp_end
(self)Perform tasks when an Experiment ends
on_exp_start
(self)Perform tasks when an Experiment is started
on_fold_end
(self)Perform tasks on fold end in an Experiment’s cross-validation scheme
on_fold_start
(self)Perform tasks on fold start in an Experiment’s cross-validation scheme
on_rep_end
(self)Perform tasks on repetition end in an Experiment’s repeated cross-validation scheme
on_rep_start
(self)Perform tasks on repetition start in an Experiment’s repeated cross-validation scheme
on_run_end
(self)Perform tasks on run end in an Experiment’s multiple-run-averaging phase
on_run_start
(self)Perform tasks on run start in an Experiment’s multiple-run-averaging phase
hyperparameter_hunter.callbacks.bases module¶
This module defines the base callback classes, from which all other callback classes in
hyperparameter_hunter.callbacks
are descendants. Importantly, the specific base callback
classes contained herein are all descendants of
hyperparameter_hunter.callbacks.bases.BaseCallback
, ensuring all callbacks descend from the
same base class. This module also defines
hyperparameter_hunter.callbacks.bases.lambda_callback()
, which can be used to define custom
callbacks to be executed during Experiments when passed to
hyperparameter_hunter.environment.Environment.__init__()
via the experiment_callbacks
argument
hyperparameter_hunter.callbacks.evaluators module¶
This module defines Evaluator callbacks to score predictions generated during the different time
divisions of the BaseExperiment
by invoking
hyperparameter_hunter.metrics.ScoringMixIn.evaluate()
Related¶
hyperparameter_hunter.metrics
Defines
ScoringMixIn
, which is inherited byBaseExperiment
, and provides the evaluate method that is called by the classes inevaluators
Notes¶
Regarding evaluation when G.Env.save_transformed_metrics is False, target data will be either
fold (for on_run_end/on_fold_end) or d (for on_rep_end/on_exp_end). Prediction data used
for evaluation in this case does not follow this abnormal pattern. Target data is limited to either
the fold or d data_chunks when G.Env.save_transformed_metrics is False because targets for
run and rep are identical to the targets for fold and d, respectively. This is still the
case even if performing inverse target transformation via
EngineerStep
. Because the target values do not
change between these two pairs of divisions, their values may be unset, so the targets for the
division immediately above are used instead. As noted in
hyperparameter_hunter.data.data_chunks.target_chunks
, both itself and
hyperparameter_hunter.callback.wranglers.target_wranglers
are concerned only with transformed
targets–not with original targets (or inverted targets). That is because original targets and
inverted targets should be identical. Original targets are updated only in
hyperparameter_hunter.experiments.BaseExperiment.on_exp_start()
(through
hyperparameter_hunter.data.data_core.BaseDataset
initialization) and in
hyperparameter_hunter.experiments.BaseCVExperiment.on_fold_start()
-
class
hyperparameter_hunter.callbacks.evaluators.
EvaluatorOOF
¶ Bases:
hyperparameter_hunter.callbacks.bases.BaseEvaluatorCallback
Methods
on_exp_end
(self)Evaluate final (run/repetition-averaged) out-of-fold predictions
on_exp_start
(self)Perform tasks when an Experiment is started
on_fold_end
(self)Evaluate (run-averaged) out-of-fold predictions for the fold
on_fold_start
(self)Perform tasks on fold start in an Experiment’s cross-validation scheme
on_rep_end
(self)Evaluate (run-averaged) out-of-fold predictions for the repetition
on_rep_start
(self)Perform tasks on repetition start in an Experiment’s repeated cross-validation scheme
on_run_end
(self)Evaluate out-of-fold predictions for the run
on_run_start
(self)Perform tasks on run start in an Experiment’s multiple-run-averaging phase
-
on_run_end
(self)¶ Evaluate out-of-fold predictions for the run
-
on_fold_end
(self)¶ Evaluate (run-averaged) out-of-fold predictions for the fold
-
on_rep_end
(self)¶ Evaluate (run-averaged) out-of-fold predictions for the repetition
-
on_exp_end
(self)¶ Evaluate final (run/repetition-averaged) out-of-fold predictions
-
-
class
hyperparameter_hunter.callbacks.evaluators.
EvaluatorHoldout
¶ Bases:
hyperparameter_hunter.callbacks.bases.BaseEvaluatorCallback
Methods
on_exp_end
(self)Evaluate final (run/repetition-averaged) holdout predictions
on_exp_start
(self)Perform tasks when an Experiment is started
on_fold_end
(self)Evaluate (run-averaged) holdout predictions for the fold
on_fold_start
(self)Perform tasks on fold start in an Experiment’s cross-validation scheme
on_rep_end
(self)Evaluate (run-averaged) holdout predictions for the repetition
on_rep_start
(self)Perform tasks on repetition start in an Experiment’s repeated cross-validation scheme
on_run_end
(self)Evaluate holdout predictions for the run
on_run_start
(self)Perform tasks on run start in an Experiment’s multiple-run-averaging phase
-
on_run_end
(self)¶ Evaluate holdout predictions for the run
-
on_fold_end
(self)¶ Evaluate (run-averaged) holdout predictions for the fold
-
on_rep_end
(self)¶ Evaluate (run-averaged) holdout predictions for the repetition
-
on_exp_end
(self)¶ Evaluate final (run/repetition-averaged) holdout predictions
-
hyperparameter_hunter.callbacks.loggers module¶
-
class
hyperparameter_hunter.callbacks.loggers.
LoggerFitStatus
¶ Bases:
hyperparameter_hunter.callbacks.bases.BaseLoggerCallback
Methods
on_exp_end
(self)Perform tasks when an Experiment ends
on_exp_start
(self)Perform tasks when an Experiment is started
on_fold_end
(self)Perform tasks on fold end in an Experiment’s cross-validation scheme
on_fold_start
(self)Perform tasks on fold start in an Experiment’s cross-validation scheme
on_rep_end
(self)Perform tasks on repetition end in an Experiment’s repeated cross-validation scheme
on_rep_start
(self)Perform tasks on repetition start in an Experiment’s repeated cross-validation scheme
on_run_end
(self)Perform tasks on run end in an Experiment’s multiple-run-averaging phase
on_run_start
(self)Perform tasks on run start in an Experiment’s multiple-run-averaging phase
-
float_format
= '{:.5f}'¶
-
log_separator
= ' | '¶
-
on_exp_start
(self)¶ Perform tasks when an Experiment is started
-
on_rep_start
(self)¶ Perform tasks on repetition start in an Experiment’s repeated cross-validation scheme
-
on_fold_start
(self)¶ Perform tasks on fold start in an Experiment’s cross-validation scheme
-
on_run_start
(self)¶ Perform tasks on run start in an Experiment’s multiple-run-averaging phase
-
on_run_end
(self)¶ Perform tasks on run end in an Experiment’s multiple-run-averaging phase
-
on_fold_end
(self)¶ Perform tasks on fold end in an Experiment’s cross-validation scheme
-
on_rep_end
(self)¶ Perform tasks on repetition end in an Experiment’s repeated cross-validation scheme
-
on_exp_end
(self)¶ Perform tasks when an Experiment ends
-
-
class
hyperparameter_hunter.callbacks.loggers.
LoggerOOF
¶ Bases:
hyperparameter_hunter.callbacks.bases.BaseLoggerCallback
Methods
on_exp_end
(self)Perform tasks when an Experiment ends
on_exp_start
(self)Perform tasks when an Experiment is started
on_fold_end
(self)Perform tasks on fold end in an Experiment’s cross-validation scheme
on_fold_start
(self)Perform tasks on fold start in an Experiment’s cross-validation scheme
on_rep_end
(self)Perform tasks on repetition end in an Experiment’s repeated cross-validation scheme
on_rep_start
(self)Perform tasks on repetition start in an Experiment’s repeated cross-validation scheme
on_run_end
(self)Perform tasks on run end in an Experiment’s multiple-run-averaging phase
on_run_start
(self)Perform tasks on run start in an Experiment’s multiple-run-averaging phase
-
class
hyperparameter_hunter.callbacks.loggers.
LoggerHoldout
¶ Bases:
hyperparameter_hunter.callbacks.bases.BaseLoggerCallback
Methods
on_exp_end
(self)Perform tasks when an Experiment ends
on_exp_start
(self)Perform tasks when an Experiment is started
on_fold_end
(self)Perform tasks on fold end in an Experiment’s cross-validation scheme
on_fold_start
(self)Perform tasks on fold start in an Experiment’s cross-validation scheme
on_rep_end
(self)Perform tasks on repetition end in an Experiment’s repeated cross-validation scheme
on_rep_start
(self)Perform tasks on repetition start in an Experiment’s repeated cross-validation scheme
on_run_end
(self)Perform tasks on run end in an Experiment’s multiple-run-averaging phase
on_run_start
(self)Perform tasks on run start in an Experiment’s multiple-run-averaging phase
-
class
hyperparameter_hunter.callbacks.loggers.
LoggerTest
¶ Bases:
hyperparameter_hunter.callbacks.bases.BaseLoggerCallback
Methods
on_exp_end
(self)Perform tasks when an Experiment ends
on_exp_start
(self)Perform tasks when an Experiment is started
on_fold_end
(self)Perform tasks on fold end in an Experiment’s cross-validation scheme
on_fold_start
(self)Perform tasks on fold start in an Experiment’s cross-validation scheme
on_rep_end
(self)Perform tasks on repetition end in an Experiment’s repeated cross-validation scheme
on_rep_start
(self)Perform tasks on repetition start in an Experiment’s repeated cross-validation scheme
on_run_end
(self)Perform tasks on run end in an Experiment’s multiple-run-averaging phase
on_run_start
(self)Perform tasks on run start in an Experiment’s multiple-run-averaging phase
-
class
hyperparameter_hunter.callbacks.loggers.
LoggerEvaluation
¶ Bases:
hyperparameter_hunter.callbacks.bases.BaseLoggerCallback
Methods
on_exp_end
(self)Perform tasks when an Experiment ends
on_exp_start
(self)Perform tasks when an Experiment is started
on_fold_end
(self)Perform tasks on fold end in an Experiment’s cross-validation scheme
on_fold_start
(self)Perform tasks on fold start in an Experiment’s cross-validation scheme
on_rep_end
(self)Perform tasks on repetition end in an Experiment’s repeated cross-validation scheme
on_rep_start
(self)Perform tasks on repetition start in an Experiment’s repeated cross-validation scheme
on_run_end
(self)Perform tasks on run end in an Experiment’s multiple-run-averaging phase
on_run_start
(self)Perform tasks on run start in an Experiment’s multiple-run-averaging phase
hyperparameter_hunter.callbacks.recipes module¶
This module contains extra callbacks that can add commonly-used functionality to Experiments.
This module also serves as an example for how users can properly construct their own custom
callbacks using hyperparameter_hunter.callbacks.bases.lambda_callback()
Related¶
hyperparameter_hunter.callbacks.bases
This module defines
hyperparameter_hunter.callbacks.bases.lambda_callback()
, which is how all extra callbacks created inhyperparameter_hunter.callbacks.recipes
are createdhyperparameter_hunter.environment
This module provides the means to use custom callbacks made by
hyperparameter_hunter.callbacks.bases.lambda_callback()
through the experiment_callbacks argument ofhyperparameter_hunter.environment.Environment.__init__()
Notes¶
For the purposes of aggregating additional Experiment information, this module describes two methods
outlined in hyperparameter_hunter.callbacks.recipes.confusion_matrix_oof()
, and
hyperparameter_hunter.callbacks.recipes.confusion_matrix_holdout()
. The first automatically
handles aggregating new values; whereas, the second provides an example for manually aggregating new
values, which offers greater customization at the cost of slightly more overhead
-
hyperparameter_hunter.callbacks.recipes.
confusion_matrix_oof
(on_run=True, on_fold=True, on_rep=True, on_exp=True)¶ Callback function to produce confusion matrices for out-of-fold predictions at each stage of the Experiment
- Parameters
- on_run: Boolean, default=True
If False, skip making confusion matrices for individual Experiment runs
- on_fold: Boolean, default=True
If False, skip making confusion matrices for individual Experiment folds
- on_rep: Boolean, default=True
If False, skip making confusion matrices for individual Experiment repetitions
- on_exp: Boolean, default=True
If False, skip making final confusion matrix for the Experiment
- Returns
- LambdaCallback
An uninitialized
LambdaCallback
to generate confusion matrices, produced byhyperparameter_hunter.callbacks.bases.lambda_callback()
Notes
Unlike
hyperparameter_hunter.callbacks.recipes.confusion_matrix_holdout()
, this callback function allows lambda_callback to automatically aggregate the stats returned by each of the “on…” functions given to lambda_callbackIf the size of this lambda_callback implementation is daunting, minimize the helper functions’ docstrings. It’s surprisingly simple
-
hyperparameter_hunter.callbacks.recipes.
confusion_matrix_holdout
(on_run=True, on_fold=True, on_rep=True, on_exp=True)¶ Callback function to produce confusion matrices for holdout predictions at each stage of the Experiment
- Parameters
- on_run: Boolean, default=True
If False, skip making confusion matrices for individual Experiment runs
- on_fold: Boolean, default=True
If False, skip making confusion matrices for individual Experiment folds
- on_rep: Boolean, default=True
If False, skip making confusion matrices for individual Experiment repetitions
- on_exp: Boolean, default=True
If False, skip making final confusion matrix for the Experiment
- Returns
- LambdaCallback
An uninitialized
LambdaCallback
to generate confusion matrices, produced byhyperparameter_hunter.callbacks.bases.lambda_callback()
Notes
Unlike
hyperparameter_hunter.callbacks.recipes.confusion_matrix_oof()
, this callback bypasses lambda_callback’s ability to automatically aggregate stats returned by the “on…” functions. It does this simply by not returning values in the “on…” functions, and manually aggregating the stats inhyperparameter_hunter.experiments.BaseExperiment.stat_aggregates
. This offers greater control over how your values are collected, but also requires additional overhead, namely, instantiating a dict to collect the values via_on_exp_start()
. Note also that each of the “on…” functions must append their values to an explicitly named container inhyperparameter_hunter.experiments.BaseExperiment.stat_aggregates
when using this method as opposed tohyperparameter_hunter.callbacks.recipes.confusion_matrix_oof()
’sIf the size of this lambda_callback implementation is daunting, minimize the helper functions’ docstrings. It’s surprisingly simple
-
hyperparameter_hunter.callbacks.recipes.
dataset_recorder
()¶ Build a LambdaCallback that records the current state of all datasets on_fold_start in order to validate modifications made by
feature_engineering.FeatureEngineer
/feature_engineering.EngineerStep
- Returns
- LambdaCallback
Aggregator-like LambdaCallback whose values are aggregated under the name “_datasets” and whose keys are named after the corresponding callback methods
-
hyperparameter_hunter.callbacks.recipes.
lambda_check_train_targets
(on_exp_start:Union[List[hyperparameter_hunter.data.data_chunks.target_chunks.TrainTargetChunk], NoneType]=None, on_rep_start:Union[List[hyperparameter_hunter.data.data_chunks.target_chunks.TrainTargetChunk], NoneType]=None, on_fold_start:Union[List[hyperparameter_hunter.data.data_chunks.target_chunks.TrainTargetChunk], NoneType]=None, on_run_start:Union[List[hyperparameter_hunter.data.data_chunks.target_chunks.TrainTargetChunk], NoneType]=None, on_run_end:Union[List[hyperparameter_hunter.data.data_chunks.target_chunks.TrainTargetChunk], NoneType]=None, on_fold_end:Union[List[hyperparameter_hunter.data.data_chunks.target_chunks.TrainTargetChunk], NoneType]=None, on_rep_end:Union[List[hyperparameter_hunter.data.data_chunks.target_chunks.TrainTargetChunk], NoneType]=None, on_exp_end:Union[List[hyperparameter_hunter.data.data_chunks.target_chunks.TrainTargetChunk], NoneType]=None)¶ LambdaCallback to check the values of an experiment’s data_train.target attribute
The list of
TrainTargetChunk
instances given to each parameter represents the expected value ofhyperparameter_hunter.experiments.CVExperiment.data_train.target
for each call of that particular callback method. In other words, the number of items in each parameter’s list should correspond to the number of times that callback method is expected to be invoked.This means that on_exp_start and on_exp_end should both contain only a single TrainTargetChunk (because they are only ever invoked once by an experiment), and their values should be the expected states of data_train.target on experiment start and end, respectively.
- Parameters
- on_exp_start: List[TrainTargetChunk], or None, default=None
Expected value of train targets when on_exp_start is invoked. Should contain only a single TrainTargetChunk instance
- on_rep_start: List[TrainTargetChunk], or None, default=None
Expected value of train targets on each invocation of on_rep_start. Should contain as many TrainTargetChunk instances as repetitions will be conducted during the experiment. Should contain only a single value if the number or repetitions is one, or if
hyperparameter_hunter.environment.Environment.cv_type
is not a repeated CV scheme- on_fold_start: List[TrainTargetChunk], or None, default=None
Expected value of train targets on each invocation of on_fold_start. The values to provide are not as straight-forward, as they depend on the number of repetitions as well. If only a single repetition will be conducted, then on_fold_start should simply contain as many TrainTargetChunk instances as folds will be conducted. However, if multiple repetitions will be conducted, then the length of on_fold_start should be (<# of reps> * <# of folds>). For example, if performing RepeatedKFold cross validation with 2 repetitions, and 3 folds/splits, then on_fold_start should contain 6 values
- on_run_start: List[TrainTargetChunk], or None, default=None
Expected value of train targets on each invocation of on_run_start. Similarly to on_fold_start, the length/values of on_run_start depends on the number of repetitions, as well as the number of folds that will be conducted. The length of on_run_start should be (<# of reps> * <# of folds> * <# of runs>). If performing standard, non-repeated KFold-like cross validation, with 3 folds, and only a single run, then on_run_start should contain 3 values. Just as in the on_fold_start description example, if performing RepeatedKFold CV with 2 repetitions, and 3 folds, and 1 run, then on_run_start should contain 6 values. On the extreme end, if performing RepeatedKFold CV with 2 repetitions, and 3 folds, and 4 runs, then on_run_start should contain 24 values
- on_run_end: List[TrainTargetChunk], or None, default=None
See `on_run_start` description
- on_fold_end: List[TrainTargetChunk], or None, default=None
See `on_fold_start` description
- on_rep_end: List[TrainTargetChunk], or None, default=None
See `on_rep_start` description
- on_exp_end: List[TrainTargetChunk], or None, default=None
See `on_exp_start` description
Notes
As is always the case, on_run_start and on_run_end will still be invoked even if
hyperparameter_hunter.environment.Environment.runs
is 1. In this case, they will be invoked as many times as on_fold_start and on_fold_end are invoked; however, this does not mean that the values of data_train.target are identical between fold and run divisions
-
hyperparameter_hunter.callbacks.recipes.
aggregator_epochs_elapsed
(on_run=True, on_fold=True, on_rep=True, on_exp=True)¶ Callback function to aggregate and average the number of epochs elapsed during model training at each stage of the Experiment
- Parameters
- on_run: Boolean, default=True
If False, skip recording epochs elapsed for individual Experiment runs
- on_fold: Boolean, default=True
If False, skip making epochs-elapsed averages for individual Experiment folds
- on_rep: Boolean, default=True
If False, skip making epochs-elapsed averages for individual Experiment repetitions
- on_exp: Boolean, default=True
If False, skip making epochs-elapsed average for the Experiment
- Returns
- LambdaCallback
An uninitialized
LambdaCallback
to aggregate the number of epochs elapsed during training, produced byhyperparameter_hunter.callbacks.bases.lambda_callback()