def extractAlerts(self, predictions_monitoring):
detection_threshold = self.alerts_conf.detection_threshold
alerts = matrix_tools.extractRowsWithThresholds(
predictions_monitoring.predictions, detection_threshold, None,
'predicted_proba')
alerts = matrix_tools.sortDataFrame(alerts, 'predicted_proba', False,
False)
return alerts
def generateAnnotationQueries(self):
unsure_df = matrix_tools.extractRowsWithThresholds(self.predictions,
self.proba_min,
self.proba_max,
'predicted_proba',
deepcopy=True)
unsure_df['predicted_proba'] = abs(unsure_df['predicted_proba'] - 0.5)
matrix_tools.sortDataFrame(unsure_df, 'predicted_proba', True, True)
if self.num_annotations is not None and len(
unsure_df) > self.num_annotations:
unsure_df = unsure_df.head(n=self.num_annotations)
for instance_id, row in unsure_df.iterrows():
query = self.generateAnnotationQuery(instance_id,
row['predicted_proba'], None,
None)
self.annotation_queries.append(query)
def runModels(self, already_queried=None):
df = matrix_tools.extractRowsWithThresholds(self.predictions,
self.proba_min,
self.proba_max,
'predicted_proba')
if already_queried is not None:
self.predicted_ids = list(
set(df.index).difference(set(already_queried)))
else:
self.predicted_ids = list(df.index)
datasets = self.iteration.datasets
self.annotated_instances = datasets.getAnnotatedInstances(
label=self.label)
self.families_analysis = self.familiesAnalysis()
if self.families_analysis:
self.annotations_type = 'families'
start_time = time.time()
self.buildCategories()
self.analysis_time = time.time() - start_time
self.categories.setLikelihood()
else:
self.annotations_type = 'individual'
self.categories = None
self.analysis_time = 0