seqgra.evaluator.gradientbased.feedbackevaluator module

Feedback Evaluator

class FeedbackEvaluator(learner: seqgra.learner.learner.Learner, output_dir: str, importance_threshold: Optional[float] = None, input_size=None, class_num=1000, lr=0.1, lambd=0.01, max_iters=30, silent: bool = False)

Bases: seqgra.evaluator.gradientbased.abstractgradientevaluator.AbstractGradientEvaluator

Feedback evaluator for PyTorch models

evaluate_model(set_name: str = 'test', subset_idx: Optional[List[int]] = None, subset_n: Optional[int] = None, subset_labels: Optional[List[str]] = None, subset_n_per_label: bool = True, subset_shuffle: bool = True, subset_threshold: Optional[float] = None, suppress_plots: bool = False) → Any

explain(x, y)

get_layer(key_list)

select_examples(set_name: str = 'test', labels: Optional[Set[str]] = None, threshold: Optional[float] = None) → seqgra.schema.AnnotatedExampleSet

Returns all correctly classified examples that exceed the threshold.

for the specified labels and set that exceed the threshold.

Parameters

TODO –

Returns

TODO

select_n_examples(set_name: str = 'test', n: Optional[int] = None, labels: Optional[Set[str]] = None, n_per_label: bool = True, shuffle: bool = True, threshold: Optional[float] = None) → seqgra.schema.AnnotatedExampleSet

first_forward(self, x)

mask_forward(self, x)

replace_first(m)

replace_mask(m)