def __init__(self, exp, instance_ids=None): if instance_ids is None: dataset_id = exp.exp_conf.dataset_conf.dataset_id instance_ids = Ids(get_dataset_ids(exp.session, dataset_id)) values = FeaturesFromExp.get_matrix(exp.exp_conf.features_conf.files) Features.__init__(self, values, exp.exp_conf.features_conf.info, instance_ids)
def __init__(self, exp, instance_ids=None): if instance_ids is None: dataset_id = exp.exp_conf.dataset_conf.dataset_id instance_ids = Ids(get_dataset_ids(exp.session, dataset_id)) self._set_exp_conf(exp) ids, names, descriptions = self._set_paths_masks_names() values = self._get_matrix() Features.__init__(self, values, ids, names, descriptions, instance_ids)
def deepcopy(predictions): return Predictions(deepcopy(predictions.values), Ids.deepcopy(predictions.ids), predictions.info.multiclass, all_probas=deepcopy(predictions.all_probas), probas=deepcopy(predictions.probas), all_scores=deepcopy(predictions.all_scores), scores=deepcopy(predictions.scores), ground_truth=deepcopy(predictions.ground_truth))
def __init__(self, experiment, streaming=False, stream_batch=None): self._set_exp_conf(experiment) ids, timestamps, gt_labels, gt_families = self._get_instances_from_db() ids = Ids(ids, timestamps=timestamps) ground_truth = Annotations(gt_labels, gt_families, ids) features = FeaturesFromExp(experiment, ids, streaming=streaming, stream_batch=stream_batch) annotations = self._get_annotations(ids, ground_truth) CoreInstances.__init__(self, ids, features, annotations, ground_truth)
def add_fold(self, predictions): if self.predictions is None: ids = Ids(deepcopy(predictions.ids.ids), deepcopy(predictions.ids.idents), deepcopy(predictions.ids.timestamps)) self.predictions = Predictions(deepcopy(predictions.values), ids, predictions.info.multiclass, deepcopy(predictions.all_probas), deepcopy(predictions.probas), deepcopy(predictions.scores), deepcopy(predictions.ground_truth)) else: self.predictions.union(predictions)
def __init__(self, exp, instance_ids=None, streaming=False, stream_batch=None): if instance_ids is None: dataset_id = exp.exp_conf.dataset_conf.dataset_id instance_ids = Ids(get_dataset_ids(exp.session, dataset_id)) features_conf = exp.exp_conf.features_conf num_instances = instance_ids.num_instances() if streaming: values = FeaturesFromExp.get_matrix_iterator( features_conf.files, num_instances) else: values = FeaturesFromExp.get_matrix(features_conf.files, num_instances, sparse=features_conf.sparse) Features.__init__(self, values, exp.exp_conf.features_conf.info, instance_ids, streaming=streaming, stream_batch=stream_batch, sparse=features_conf.sparse)
def get_ids(self): dataset_id = self.dataset_conf.dataset_id ids = get_dataset_ids(self.session, dataset_id) timestamps = get_dataset_timestamps(self.session, dataset_id) return Ids(ids, timestamps=timestamps)