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)