SciKit-Learn¶
SklearnEstimator¶
-
class
SklearnEstimator
(module: sklearn.base.BaseEstimator)[source]¶ Bases:
delira.models.abstract_network.AbstractNetwork
Wrapper Class to wrap all
sklearn
estimators and provide delira compatibility-
_init_kwargs
= {}¶
-
static
closure
(model, data_dict: dict, optimizers: dict, losses={}, metrics={}, fold=0, **kwargs)[source]¶ default closure method to do a single training step; Could be overwritten for more advanced models
- Parameters
model (
SkLearnEstimator
) – trainable modeldata_dict (dict) – dictionary containing the data
optimizers (dict) – dictionary of optimizers to optimize model’s parameters; ignored here, just passed for compatibility reasons
losses (dict) – dict holding the losses to calculate errors; ignored here, just passed for compatibility reasons
metrics (dict) – dict holding the metrics to calculate
fold (int) – Current Fold in Crossvalidation (default: 0)
**kwargs – additional keyword arguments
- Returns
dict – Metric values (with same keys as input dict metrics)
dict – Loss values (with same keys as input dict losses; will always be empty here)
dict – dictionary containing all predictions
-
property
init_kwargs
¶ Returns all arguments registered as init kwargs
- Returns
init kwargs
- Return type
-
property
iterative_training
¶ Property indicating, whether a the current module can be trained iteratively (batchwise)
- Returns
True: if current module can be trained iteratively False: else
- Return type
-
static
prepare_batch
(batch: dict, input_device, output_device)[source]¶ Helper Function to prepare Network Inputs and Labels (convert them to correct type and shape and push them to correct devices)
- Parameters
batch (dict) – dictionary containing all the data
input_device (Any) – device for module inputs (will be ignored here; just given for compatibility)
output_device (Any) – device for module outputs (will be ignored here; just given for compatibility)
- Returns
dictionary containing data in correct type and shape and on correct device
- Return type
-