das.evaluate#
- das.evaluate.cli_evaluate(model_save_name: str, verbose: int = 1)[source]#
Evaluate model.
- Parameters
model_save_name (str) – Stem of the path for the model (and parameters). File to load will be MODEL_SAVE_NAME + _model.h5.
verbose (int) – Display progress bar during prediction. Defaults to 1.
- das.evaluate.evaluate(model_save_name: str, custom_objects: Optional[Dict[str, Callable]] = None, full_output: bool = True, verbose: int = 1)[source]#
_summary_
- Parameters
model_save_name (str) – Stem of the path for the model (and parameters). File to load will be MODEL_SAVE_NAME + _model.h5.
custom_objects (Dict[str, Callable], optional) – Unused.
full_output (bool, optional) – If True, function will return
verbose (int, optional) – Display progress bar during prediction. Defaults to 1.
- Returns
_description_
- Return type
_type_
- das.evaluate.evaluate_probabilities(x, y, model: Optional[keras.src.engine.training.Model] = None, params: Optional[Dict] = None, model_savename: Optional[str] = None, verbose: int = 1)[source]#
[summary]
evaluate_probabilities(x, y, model=keras_model, params=params_dict) evaluate_probabilities(x, y, model_savename=save_string) -> will load model and params
- Parameters
x ([type]) – [description]
y ([type]) – [description]
model (Union[models.keras.models.Model], optional) – [description]. Defaults to None.
params (Union[Dict], optional) – [description]. Defaults to None.
model_savename (Union[str], optional) – [description]. Defaults to None.
verbose (int, optional) – [description]. Defaults to 1.
- Returns
[description]
- Return type
[type]
- das.evaluate.evaluate_segment_timing(segment_labels_true, segment_labels_pred, samplerate: float, event_tol: float)[source]#
[summary]
- Parameters
segment_labels_true ([type]) – [description]
segment_labels_pred ([type]) – [description]
samplerate ([type]) – Hz [description]
event_tol ([type]) – seconds [description]
- Returns
[description]
- Return type
[type]
- das.evaluate.evaluate_segments(labels_test, labels_pred, class_names, confmat_as_pandas: bool = False, report_as_dict: bool = False, labels=None)[source]#
- Parameters
labels_test (List) – [nb_samples,]
labels_pred (List) – [nb_samples,]
class_names ([type]) – [description]
confmat_as_pandas (bool, optional) – [description]. Defaults to False.
report_as_dict (bool, optional) – [description]. Defaults to False.
labels ([type], optional) – [description]. Defaults to None.
- Returns
conf_mat report