def test_lambda_callback_deprecations(add_kwarg, drop_kwarg):
"""This function tests the deprecations of the following `lambda_callback` kwargs in 3.0.0:
* `on_experiment_start` -> `on_exp_start`
* `on_experiment_end` -> `on_exp_end`
* `on_repetition_start` -> `on_rep_start`
* `on_repetition_end` -> `on_rep_end`"""
bad_lambda_callback_kwargs = deepcopy(good_lambda_callback_kwargs)
del bad_lambda_callback_kwargs[drop_kwarg]
bad_lambda_callback_kwargs[add_kwarg] = printer_helper
with pytest.deprecated_call():
lambda_callback(**bad_lambda_callback_kwargs)
def printer_callback():
"""This is a simple callback example that will print out :attr:`CrossValidationExperiment.last_evaluation_results` at all
available time intervals, along with the repetition, fold, and run number. Of course, printing evaluations at the beginning
of each of the intervals, as is shown below, is pretty much useless. However, this shows that if you want to, you can do it
anyways and create your own replacement for the default logger... Or make anything else you might want"""
return lambda_callback(
# The contents of `required_attributes` are the Experiment attributes that we want sent to our callables at each interval
required_attributes=[
'_rep', '_fold', '_run', 'last_evaluation_results'
],
# Notice that below, each callable expects the four attributes we listed above in `required_attributes`
on_experiment_start=lambda _rep, _fold, _run, evals: print(
F'{_rep}.{_fold}.{_run} {evals}'),
on_experiment_end=lambda _rep, _fold, _run, evals: print(
F'{_rep}.{_fold}.{_run} {evals}'),
on_repetition_start=lambda _rep, _fold, _run, evals: print(
F'{_rep}.{_fold}.{_run} {evals}'),
on_repetition_end=lambda _rep, _fold, _run, evals: print(
F'{_rep}.{_fold}.{_run} {evals}'),
on_fold_start=lambda _rep, _fold, _run, evals: print(
F'{_rep}.{_fold}.{_run} {evals}'),
on_fold_end=lambda _rep, _fold, _run, evals: print(
F'{_rep}.{_fold}.{_run} {evals}'),
on_run_start=lambda _rep, _fold, _run, evals: print(
F'{_rep}.{_fold}.{_run} {evals}'),
on_run_end=lambda _rep, _fold, _run, evals: print(
F'{_rep}.{_fold}.{_run} {evals}'),
)
def aggregator_epochs_elapsed(on_run=True,
on_fold=True,
on_repetition=True,
on_experiment=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_repetition: Boolean, default=True
If False, skip making epochs-elapsed averages for individual Experiment repetitions
on_experiment: Boolean, default=True
If False, skip making epochs-elapsed average for the Experiment
Returns
-------
LambdaCallback
An uninitialized :class:`LambdaCallback` to aggregate the number of epochs elapsed during
training, produced by :func:`hyperparameter_hunter.callbacks.bases.lambda_callback`"""
def _on_run_end(model):
"""Return the number of epochs elapsed after fitting model"""
if model.epochs_elapsed is not None:
return model.epochs_elapsed
def _on_fold_end(stat_aggregates, _run):
"""Average the number of epochs elapsed across all runs in the fold"""
run_results = stat_aggregates["_epochs_elapsed"]["runs"]
if len(run_results) > 0:
return np.average(run_results[-_run:])
def _on_repetition_end(stat_aggregates, _run, _fold):
"""Average the number of epochs elapsed across all runs in the repetition"""
run_results = stat_aggregates["_epochs_elapsed"]["runs"]
if len(run_results) > 0:
num_runs = _run * _fold
return np.average(run_results[-num_runs:])
def _on_experiment_end(stat_aggregates, _run, _fold, _rep):
"""Average the number of epochs elapsed across all runs in the Experiment"""
run_results = stat_aggregates["_epochs_elapsed"]["runs"]
if len(run_results) > 0:
num_runs = _run * _fold * _rep
return np.average(run_results[-num_runs:])
return lambda_callback(
on_run_end=_on_run_end if on_run else None,
on_fold_end=_on_fold_end if on_fold else None,
on_repetition_end=_on_repetition_end if on_repetition else None,
on_experiment_end=_on_experiment_end if on_experiment else None,
agg_name="epochs_elapsed",
)
def printer_callback():
"""This is a simple callback example that will print out :attr:`CVExperiment.last_evaluation_results` at all
available time intervals, along with the repetition, fold, and run number. Of course, printing evaluations at the beginning
of each of the intervals, as is shown below, is pretty much useless. However, this shows that if you want to, you can do it
anyways and create your own replacement for the default logger... Or make anything else you might want"""
def printer_helper(_rep, _fold, _run, last_evaluation_results):
print(f"{_rep}.{_fold}.{_run} {last_evaluation_results}")
return lambda_callback(
on_exp_start=printer_helper,
on_exp_end=printer_helper,
on_rep_start=printer_helper,
on_rep_end=printer_helper,
on_fold_start=printer_helper,
on_fold_end=printer_helper,
on_run_start=printer_helper,
on_run_end=printer_helper,
)
def dataset_recorder(save_transformed=False):
"""Build a `LambdaCallback` that records the current state of all datasets `on_fold_start` and
`on_fold_end` in order to validate modifications made by
:class:`feature_engineering.FeatureEngineer`/:class:`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"""
def _on_fold(
fold_train_input,
fold_train_target,
fold_validation_input,
fold_validation_target,
fold_holdout_input,
fold_holdout_target,
fold_test_input,
kwargs,
):
d = dict(
fold_train_input=fold_train_input,
fold_train_target=fold_train_target,
fold_validation_input=fold_validation_input,
fold_validation_target=fold_validation_target,
fold_holdout_input=fold_holdout_input,
fold_holdout_target=fold_holdout_target,
fold_test_input=fold_test_input,
)
if save_transformed:
for k in ["transformed_fold_holdout_target", "transformed_run_validation_target"]:
if k in kwargs:
d[k] = kwargs[k]
return {k: v if not isinstance(v, pd.DataFrame) else v.copy() for k, v in d.items()}
return lambda_callback(
on_fold_start=_on_fold, on_fold_end=_on_fold, agg_name="datasets", method_agg_keys=True
)
def dataset_recorder():
"""Build a `LambdaCallback` that records the current state of all datasets `on_fold_start` in
order to validate modifications made by
:class:`feature_engineering.FeatureEngineer`/:class:`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"""
def _on_fold(data_train, data_oof, data_holdout, data_test):
d = dict(
data_train=deepcopy(data_train),
data_oof=deepcopy(data_oof),
data_holdout=deepcopy(data_holdout),
data_test=deepcopy(data_test),
)
return d
return lambda_callback(on_fold_start=_on_fold,
agg_name="datasets",
method_agg_keys=True)
def confusion_matrix_oof(on_run=True,
on_fold=True,
on_repetition=True,
on_experiment=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_repetition: Boolean, default=True
If False, skip making confusion matrices for individual Experiment repetitions
on_experiment: Boolean, default=True
If False, skip making final confusion matrix for the Experiment
Returns
-------
LambdaCallback
An uninitialized :class:`LambdaCallback` to generate confusion matrices, produced by
:func:`hyperparameter_hunter.callbacks.bases.lambda_callback`
Notes
-----
Unlike :func:`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_callback`
If the size of this `lambda_callback` implementation is daunting, minimize the helper functions'
docstrings. It's surprisingly simple"""
# noinspection PyUnusedLocal
def _on_run_end(fold_validation_target, run_validation_predictions,
**kwargs):
"""Callback to execute upon ending an Experiment's run. Note that parameters are
named after Experiment attributes
Parameters
----------
fold_validation_target: Array-like
:attr:`hyperparameter_hunter.experiments.BaseCVExperiment.fold_validation_target`
run_validation_predictions: Array-like
:attr:`hyperparameter_hunter.experiments.BaseCVExperiment.run_validation_predictions`
**kwargs: Dict
If this is given as a parameter to one of `lambda_callback`\'s functions, the entirety
of the Experiment's attributes will be given as a dict. This can be used for debugging
Returns
-------
Array-like"""
return _confusion_matrix(fold_validation_target,
run_validation_predictions)
def _on_fold_end(fold_validation_target, repetition_oof_predictions,
validation_index):
"""Callback to execute upon ending an Experiment's fold. Note that parameters are
named after Experiment attributes
Parameters
----------
fold_validation_target: Array-like
:attr:`hyperparameter_hunter.experiments.BaseCVExperiment.fold_validation_target`
repetition_oof_predictions: Array-like
:attr:`hyperparameter_hunter.experiments.BaseCVExperiment.repetition_oof_predictions`
validation_index: Array-like
:attr:`hyperparameter_hunter.experiments.BaseCVExperiment.validation_index`
Returns
-------
Array-like"""
return _confusion_matrix(
fold_validation_target,
repetition_oof_predictions.iloc[validation_index])
def _on_repetition_end(train_target_data, repetition_oof_predictions):
"""Callback to execute upon ending an Experiment's repetition. Note that parameters are
named after Experiment attributes
Parameters
----------
train_target_data: Array-like
:attr:`hyperparameter_hunter.experiments.BaseExperiment.train_target_data`
repetition_oof_predictions: Array-like
:attr:`hyperparameter_hunter.experiments.BaseCVExperiment.repetition_oof_predictions`
Returns
-------
Array-like"""
return _confusion_matrix(train_target_data, repetition_oof_predictions)
def _on_experiment_end(train_target_data, final_oof_predictions):
"""Callback to execute upon ending an Experiment. Note that parameters are
named after Experiment attributes
Parameters
----------
train_target_data: Array-like
:attr:`hyperparameter_hunter.experiments.BaseExperiment.train_target_data`
final_oof_predictions: Array-like
:attr:`hyperparameter_hunter.experiments.BaseCVExperiment.final_oof_predictions`
Returns
-------
Array-like"""
return _confusion_matrix(train_target_data, final_oof_predictions)
return lambda_callback(
on_run_end=_on_run_end if on_run else None,
on_fold_end=_on_fold_end if on_fold else None,
on_repetition_end=_on_repetition_end if on_repetition else None,
on_experiment_end=_on_experiment_end if on_experiment else None,
agg_name="confusion_matrix_oof",
)
def confusion_matrix_holdout(on_run=True,
on_fold=True,
on_repetition=True,
on_experiment=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_repetition: Boolean, default=True
If False, skip making confusion matrices for individual Experiment repetitions
on_experiment: Boolean, default=True
If False, skip making final confusion matrix for the Experiment
Returns
-------
LambdaCallback
An uninitialized :class:`LambdaCallback` to generate confusion matrices, produced by
:func:`hyperparameter_hunter.callbacks.bases.lambda_callback`
Notes
-----
Unlike :func:`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 in :attr:`hyperparameter_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 :func:`_on_experiment_start`.
Note also that each of the "on..." functions must append their values to an explicitly named
container in :attr:`hyperparameter_hunter.experiments.BaseExperiment.stat_aggregates` when using
this method as opposed to :func:`hyperparameter_hunter.callbacks.recipes.confusion_matrix_oof`\'s
If the size of this `lambda_callback` implementation is daunting, minimize the helper functions'
docstrings. It's surprisingly simple"""
def _on_experiment_start(stat_aggregates):
"""Callback to instantiate container for Experiment values to be aggregated
Parameters
----------
stat_aggregates: Dict
:attr:`hyperparameter_hunter.experiments.BaseExperiment.stat_aggregates`"""
stat_aggregates["confusion_matrix_holdout"] = dict(runs=[],
folds=[],
reps=[],
final=None)
def _on_run_end(stat_aggregates, holdout_target_data,
run_holdout_predictions):
"""Callback to execute upon ending an Experiment's run. Note that parameters are
named after Experiment attributes
Parameters
----------
stat_aggregates: Dict
:attr:`hyperparameter_hunter.experiments.BaseExperiment.stat_aggregates`
holdout_target_data: Array-like
:attr:`hyperparameter_hunter.experiments.BaseExperiment.holdout_target_data`
run_holdout_predictions: Array-like
:attr:`hyperparameter_hunter.experiments.BaseCVExperiment.run_holdout_predictions`"""
stat_aggregates["confusion_matrix_holdout"]["runs"].append(
_confusion_matrix(holdout_target_data, run_holdout_predictions))
def _on_fold_end(stat_aggregates, holdout_target_data,
fold_holdout_predictions):
"""Callback to execute upon ending an Experiment's fold. Note that parameters are
named after Experiment attributes
Parameters
----------
stat_aggregates: Dict
:attr:`hyperparameter_hunter.experiments.BaseExperiment.stat_aggregates`
holdout_target_data: Array-like
:attr:`hyperparameter_hunter.experiments.BaseExperiment.holdout_target_data`
fold_holdout_predictions: Array-like
:attr:`hyperparameter_hunter.experiments.BaseCVExperiment.fold_holdout_predictions`"""
stat_aggregates["confusion_matrix_holdout"]["folds"].append(
_confusion_matrix(holdout_target_data, fold_holdout_predictions))
def _on_repetition_end(stat_aggregates, holdout_target_data,
repetition_holdout_predictions):
"""Callback to execute upon ending an Experiment's repetition. Note that parameters are
named after Experiment attributes
Parameters
----------
stat_aggregates: Dict
:attr:`hyperparameter_hunter.experiments.BaseExperiment.stat_aggregates`
holdout_target_data: Array-like
:attr:`hyperparameter_hunter.experiments.BaseExperiment.holdout_target_data`
repetition_holdout_predictions: Array-like
:attr:`hyperparameter_hunter.experiments.BaseCVExperiment.repetition_holdout_predictions`"""
stat_aggregates["confusion_matrix_holdout"]["reps"].append(
_confusion_matrix(holdout_target_data,
repetition_holdout_predictions))
def _on_experiment_end(stat_aggregates, holdout_target_data,
final_holdout_predictions):
"""Callback to execute upon ending an Experiment. Note that parameters are
named after Experiment attributes
Parameters
----------
stat_aggregates: Dict
:attr:`hyperparameter_hunter.experiments.BaseExperiment.stat_aggregates`
holdout_target_data: Array-like
:attr:`hyperparameter_hunter.experiments.BaseExperiment.holdout_target_data`
final_holdout_predictions: Array-like
:attr:`hyperparameter_hunter.experiments.BaseCVExperiment.final_holdout_predictions`"""
stat_aggregates["confusion_matrix_holdout"][
"final"] = _confusion_matrix(holdout_target_data,
final_holdout_predictions)
return lambda_callback(
on_experiment_start=_on_experiment_start,
on_run_end=_on_run_end if on_run else None,
on_fold_end=_on_fold_end if on_fold else None,
on_repetition_end=_on_repetition_end if on_repetition else None,
on_experiment_end=_on_experiment_end if on_experiment else None,
agg_name="confusion_matrix_holdout",
)
def 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 :class:`LambdaCallback` to generate confusion matrices, produced by
:func:`hyperparameter_hunter.callbacks.bases.lambda_callback`
Notes
-----
Unlike :func:`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_callback`
If the size of this `lambda_callback` implementation is daunting, minimize the helper functions'
docstrings. It's surprisingly simple"""
# noinspection PyUnusedLocal
def _on_run_end(data_oof, **kwargs):
"""Callback to execute upon ending an Experiment's run. Note that parameters are
named after Experiment attributes
Parameters
----------
data_oof: BaseDataset
:attr:`hyperparameter_hunter.experiments.BaseCVExperiment.data_oof`
**kwargs: Dict
If this is given as a parameter to one of `lambda_callback`\'s functions, the entirety
of the Experiment's attributes will be given as a dict. This can be used for debugging
Returns
-------
Array-like"""
return _confusion_matrix(data_oof.target.fold, data_oof.prediction.run)
def _on_fold_end(data_oof, validation_index):
"""Callback to execute upon ending an Experiment's fold. Note that parameters are
named after Experiment attributes
Parameters
----------
data_oof: BaseDataset
:attr:`hyperparameter_hunter.experiments.BaseCVExperiment.data_oof`
validation_index: Array-like
:attr:`hyperparameter_hunter.experiments.BaseCVExperiment.validation_index`
Returns
-------
Array-like"""
return _confusion_matrix(
data_oof.target.fold,
data_oof.prediction.rep.iloc[validation_index])
def _on_rep_end(data_train, data_oof):
"""Callback to execute upon ending an Experiment's repetition. Note that parameters are
named after Experiment attributes
Parameters
----------
data_train: BaseDataset
:attr:`hyperparameter_hunter.experiments.BaseCVExperiment.data_train`
data_oof: BaseDataset
:attr:`hyperparameter_hunter.experiments.BaseCVExperiment.data_oof`
Returns
-------
Array-like"""
# return _confusion_matrix(data_oof.target.final, data_oof.prediction.rep) # TODO: Should use this when collecting transformed targets
return _confusion_matrix(data_train.target.d,
data_oof.prediction.rep) # FLAG: TEMPORARY
def _on_exp_end(data_train, data_oof):
"""Callback to execute upon ending an Experiment. Note that parameters are
named after Experiment attributes
Parameters
----------
data_train: BaseDataset
:attr:`hyperparameter_hunter.experiments.BaseCVExperiment.data_train`
data_oof: BaseDataset
:attr:`hyperparameter_hunter.experiments.BaseCVExperiment.data_oof`
Returns
-------
Array-like"""
# return _confusion_matrix(data_oof.target.final, data_oof.prediction.final) # TODO: Should use this when collecting transformed targets
return _confusion_matrix(data_train.target.d,
data_oof.prediction.final) # FLAG: TEMPORARY
return lambda_callback(
on_run_end=_on_run_end if on_run else None,
on_fold_end=_on_fold_end if on_fold else None,
on_rep_end=_on_rep_end if on_rep else None,
on_exp_end=_on_exp_end if on_exp else None,
agg_name="confusion_matrix_oof",
)
def lambda_check_train_targets(
on_exp_start: Optional[List[TrainTargetChunk]] = None,
on_rep_start: Optional[List[TrainTargetChunk]] = None,
on_fold_start: Optional[List[TrainTargetChunk]] = None,
on_run_start: Optional[List[TrainTargetChunk]] = None,
on_run_end: Optional[List[TrainTargetChunk]] = None,
on_fold_end: Optional[List[TrainTargetChunk]] = None,
on_rep_end: Optional[List[TrainTargetChunk]] = None,
on_exp_end: Optional[List[TrainTargetChunk]] = None,
):
"""LambdaCallback to check the values of an experiment's `data_train.target` attribute
The list of :class:`~hyperparameter_hunter.data.data_chunks.target_chunks.TrainTargetChunk`
instances given to each parameter represents the expected value of
:attr:`hyperparameter_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
:attr:`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
:attr:`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"""
on_exp_start = on_exp_start if on_exp_start is not None else []
on_rep_start = on_rep_start if on_rep_start is not None else []
on_fold_start = on_fold_start if on_fold_start is not None else []
on_run_start = on_run_start if on_run_start is not None else []
on_run_end = on_run_end if on_run_end is not None else []
on_fold_end = on_fold_end if on_fold_end is not None else []
on_rep_end = on_rep_end if on_rep_end is not None else []
on_exp_end = on_exp_end if on_exp_end is not None else []
#################### Division Start Points ####################
def _on_exp_start(data_train):
assert data_train.target == on_exp_start[0]
def _on_rep_start(data_train, _rep):
assert data_train.target == on_rep_start[_rep]
def _on_fold_start(data_train, _rep, _fold):
assert data_train.target == on_fold_start[((_rep + 1) * (_fold + 1) -
1)]
def _on_run_start(data_train, _rep, _fold, _run):
assert data_train.target == on_run_start[((_rep + 1) * (_fold + 1) *
(_run + 1) - 1)]
#################### Division End Points ####################
def _on_run_end(data_train, _rep, _fold, _run):
assert data_train.target == on_run_end[((_rep + 1) * (_fold + 1) *
(_run + 1) - 1)]
def _on_fold_end(data_train, _rep, _fold):
assert data_train.target == on_fold_end[((_rep + 1) * (_fold + 1) - 1)]
def _on_rep_end(data_train, _rep):
assert data_train.target == on_rep_end[_rep]
def _on_exp_end(data_train):
assert data_train.target == on_exp_end[0]
return lambda_callback(
on_exp_start=_on_exp_start if on_exp_start else None,
on_rep_start=_on_rep_start if on_rep_start else None,
on_fold_start=_on_fold_start if on_fold_start else None,
on_run_start=_on_run_start if on_run_start else None,
on_run_end=_on_run_end if on_run_end else None,
on_fold_end=_on_fold_end if on_fold_end else None,
on_rep_end=_on_rep_end if on_rep_end else None,
on_exp_end=_on_exp_end if on_exp_end else None,
)
