src.eval.deeplearn.dl_model_evaluator

Classes

class src.eval.deeplearn.dl_model_evaluator.DLModelEvaluator(**kwargs)

Class to evaluate deep learning models.

Variables:

dlmodel (Model) – The deep learning model to be evaluated
pwise_output_path (str) – Where to export the point-wise output.
pwise_activations_path (str) – Where to export the point-wise activations.
accept_pipeline_state_predictions (bool) – Whether to accept predictions from a pipeline’s state (True) or not (False).

static extract_eval_args(spec)

Extract the arguments to initialize/instantiate a DLModelEValuator from a key-word specification.

Parameters:: spec – The key-word specification containing the arguments.
Returns:: The arguments to initialize/instantiate a DLModelEvaluator.

__init__(**kwargs)

Initialize/instantiate a DLModelEvaluator.

eval(X, y=None, **kwargs)

Evaluate the DL model.

Potential evaluations are the point-wise outputs of the model and the point-wise activations of a hidden layer.

Parameters:

Returns:

The evaluation of the deep learning model.

Return type:

DLModelEvaluation

__call__(x, **kwargs)

Evaluate with extra logic that is convenient for pipeline-based execution.

See evaluator.Evaluator.eval().

lazy_prepare(state)

Prepare the DLModelEvaluator, so it can take the dlmodel from the pipeline’s state that was not available when instantiating the evaluator.

Parameters:: state (SimplePipelineState) – The pipeline’s state.
Returns:: Nothing, but the internal state of the DLModelEvaluator is updated.

eval_args_from_state(state)

Obtain the arguments to call the DLModelEvaluator from the current pipeline’s state.