def __init__(self,
wrapped,
hashers: List[BaseHasher] = None,
cache_folder_when_no_handle=None):
MetaStep.__init__(self, wrapped, hashers=hashers)
ForceHandleOnlyMixin.__init__(self, cache_folder_when_no_handle)
_DidProcessInputOutputHandlerMixin.__init__(self)
def __init__(self,
hyperparameter_optimizer: BaseHyperparameterOptimizer,
higher_score_is_better=True,
cache_folder_when_no_handle=None):
MetaStep.__init__(self, None)
ForceHandleOnlyMixin.__init__(self, cache_folder_when_no_handle)
self.higher_score_is_better = higher_score_is_better
self.hyperparameter_optimizer = hyperparameter_optimizer
def __init__(self,
wrapped,
from_data_inputs=False,
cache_folder_when_no_handle=None):
BaseStep.__init__(self)
MetaStepMixin.__init__(self, wrapped)
ForceHandleOnlyMixin.__init__(self, cache_folder_when_no_handle)
self.from_data_inputs = from_data_inputs
def __init__(self, handle_fit_callback, handle_transform_callback,
handle_fit_transform_callback):
BaseStep.__init__(self)
ForceHandleOnlyMixin.__init__(self)
self.handle_fit_callback = handle_fit_callback
self.handle_fit_transform_callback = handle_fit_transform_callback
self.handle_transform_callback = handle_transform_callback
def __init__(self, wrapped, epochs, fit_only=False, repeat_in_test_mode=False, cache_folder_when_no_handle=None):
BaseStep.__init__(self)
MetaStepMixin.__init__(self, wrapped)
ForceHandleOnlyMixin.__init__(self, cache_folder=cache_folder_when_no_handle)
self.repeat_in_test_mode = repeat_in_test_mode
self.fit_only = fit_only
self.epochs = epochs
def __init__(self,
wrapped: BaseStep,
is_train_only=True,
cache_folder_when_no_handle=None):
MetaStep.__init__(self, wrapped=wrapped)
ForceHandleOnlyMixin.__init__(self,
cache_folder=cache_folder_when_no_handle)
self.is_train_only = is_train_only
def __init__(self, wrapped=None, scoring_function=r2_score, joiner=NumpyConcatenateOuterBatch(),
cache_folder_when_no_handle=None,
split_data_container_during_fit=True, predict_after_fit=True):
BaseValidation.__init__(self, wrapped=wrapped, scoring_function=scoring_function)
ForceHandleOnlyMixin.__init__(self, cache_folder=cache_folder_when_no_handle)
EvaluableStepMixin.__init__(self)
self.split_data_container_during_fit = split_data_container_during_fit
self.predict_after_fit = predict_after_fit
self.joiner = joiner
def __init__(self,
scoring_function=r2_score,
joiner=NumpyConcatenateOuterBatch(),
cache_folder_when_no_handle=None):
BaseValidation.__init__(self, scoring_function)
ForceHandleOnlyMixin.__init__(self,
cache_folder=cache_folder_when_no_handle)
EvaluableStepMixin.__init__(self)
self.joiner = joiner
def __init__(
self,
pipeline: BaseStep,
validation_splitter: 'BaseValidationSplitter',
refit_trial: bool,
scoring_callback: ScoringCallback,
hyperparams_optimizer: BaseHyperparameterSelectionStrategy = None,
hyperparams_repository: HyperparamsRepository = None,
n_trials: int = 10,
epochs: int = 1,
callbacks: List[BaseCallback] = None,
refit_scoring_function: Callable = None,
print_func: Callable = None,
cache_folder_when_no_handle=None):
BaseStep.__init__(self)
ForceHandleOnlyMixin.__init__(self,
cache_folder=cache_folder_when_no_handle)
self.validation_split_function: BaseValidationSplitter = validation_splitter
if print_func is None:
print_func = print
if hyperparams_optimizer is None:
hyperparams_optimizer = RandomSearchHyperparameterSelectionStrategy(
)
self.hyperparameter_optimizer: BaseHyperparameterSelectionStrategy = hyperparams_optimizer
if hyperparams_repository is None:
hyperparams_repository = HyperparamsJSONRepository(
hyperparams_optimizer, cache_folder_when_no_handle)
else:
hyperparams_repository.set_strategy(hyperparams_optimizer)
self.hyperparams_repository: HyperparamsJSONRepository = hyperparams_repository
self.pipeline: BaseStep = pipeline
self.print_func: Callable = print_func
self.n_trial: int = n_trials
self.hyperparams_repository: HyperparamsRepository = hyperparams_repository
self.refit_scoring_function: Callable = refit_scoring_function
if callbacks is None:
callbacks = []
callbacks: List[BaseCallback] = [scoring_callback] + callbacks
self.refit_trial: bool = refit_trial
self.trainer = Trainer(callbacks=callbacks,
epochs=epochs,
print_func=self.print_func)
def __init__(self,
wrapped: BaseTransformer,
copy_op=copy.deepcopy,
cache_folder_when_no_handle=None):
MetaStep.__init__(self, wrapped=wrapped)
ForceHandleOnlyMixin.__init__(self, cache_folder_when_no_handle)
self.savers.append(TruncableJoblibStepSaver())
self.set_step(wrapped)
self.steps_as_tuple: List[NamedTupleList] = []
self.copy_op = copy_op
def __init__(self,
wrapped: BaseStep,
copy_op=copy.deepcopy,
cache_folder_when_no_handle=None):
BaseStep.__init__(self)
MetaStepMixin.__init__(self, wrapped)
ForceHandleOnlyMixin.__init__(self, cache_folder_when_no_handle)
self.set_step(wrapped)
self.steps: List[BaseStep] = []
self.copy_op = copy_op
def __init__(self,
wrapped: BaseStep,
enabled: bool = True,
nullified_return_value=None,
cache_folder_when_no_handle=None):
BaseStep.__init__(self,
hyperparams=HyperparameterSamples(
{OPTIONAL_ENABLED_HYPERPARAM: enabled}))
MetaStepMixin.__init__(self, wrapped)
ForceHandleOnlyMixin.__init__(self, cache_folder_when_no_handle)
if nullified_return_value is None:
nullified_return_value = []
self.nullified_return_value = nullified_return_value
def __init__(self,
steps_as_tuple: NamedTupleList,
joiner: NonFittableMixin = NumpyConcatenateInnerFeatures(),
n_jobs: int = None,
backend: str = "threading",
cache_folder_when_no_handle: str = None):
"""
Create a feature union.
:param steps_as_tuple: the NamedTupleList of steps to process in parallel and to join.
:param joiner: What will be used to join the features. For example, ``NumpyConcatenateInnerFeatures()``.
:param n_jobs: The number of jobs for the parallelized ``joblib.Parallel`` loop in fit and in transform.
:param backend: The type of parallelization to do with ``joblib.Parallel``. Possible values: "loky", "multiprocessing", "threading", "dask" if you use dask, and more.
"""
steps_as_tuple.append(('joiner', joiner))
TruncableSteps.__init__(self, steps_as_tuple)
self.n_jobs = n_jobs
self.backend = backend
ForceHandleOnlyMixin.__init__(self,
cache_folder=cache_folder_when_no_handle)
def __init__(self, wrapped: BaseTransformer, enabled: bool = True, nullified_return_value=None,
cache_folder_when_no_handle=None, use_hyperparameter_space=True, nullify_hyperparams=True):
hyperparameter_space = HyperparameterSpace({
OPTIONAL_ENABLED_HYPERPARAM: Boolean()
}) if use_hyperparameter_space else {}
MetaStep.__init__(
self,
hyperparams=HyperparameterSamples({
OPTIONAL_ENABLED_HYPERPARAM: enabled
}),
hyperparams_space=hyperparameter_space,
wrapped=wrapped
)
ForceHandleOnlyMixin.__init__(self, cache_folder_when_no_handle)
if nullified_return_value is None:
nullified_return_value = []
self.nullified_return_value = nullified_return_value
self.nullify_hyperparams = nullify_hyperparams
def __init__(self,
wrapped: BaseStep,
auto_ml_algorithm: AutoMLAlgorithm,
hyperparams_repository: HyperparamsRepository = None,
n_iters: int = 100,
refit=True,
cache_folder_when_no_handle=None):
if not isinstance(wrapped, EvaluableStepMixin):
raise ValueError(
'AutoML algorithm needs evaluable steps that implement the function get_score. Please use a validation technique, or implement EvaluableStepMixin.'
)
MetaStep.__init__(self, auto_ml_algorithm.set_step(wrapped))
ForceHandleOnlyMixin.__init__(self, cache_folder_when_no_handle)
if hyperparams_repository is None:
hyperparams_repository = InMemoryHyperparamsRepository()
self.hyperparams_repository = hyperparams_repository
self.n_iters = n_iters
self.refit = refit
def __init__(self,
wrapped: BaseStep = None,
test_size: float = 0.2,
scoring_function=r2_score,
run_validation_split_in_test_mode=True,
metrics_already_enabled=True,
cache_folder_when_no_handle=None):
"""
:param wrapped: wrapped step
:param test_size: ratio for test size between 0 and 1
:param scoring_function: scoring function with two arguments (y_true, y_pred)
"""
BaseStep.__init__(self)
MetaStepMixin.__init__(self, wrapped)
ForceHandleOnlyMixin.__init__(self, cache_folder_when_no_handle)
EvaluableStepMixin.__init__(self)
self.run_validation_split_in_test_mode = run_validation_split_in_test_mode
self.test_size = test_size
self.scoring_function = scoring_function
self.metrics_enabled = metrics_already_enabled
def __init__(self, wrapped, cache_folder_when_no_handle=None):
MetaStep.__init__(self, wrapped)
ForceHandleOnlyMixin.__init__(self, cache_folder_when_no_handle)
def __init__(self, sub_data_container_names=None):
BaseTransformer.__init__(self)
ForceHandleOnlyMixin.__init__(self)
self.data_sources = sub_data_container_names
