def top_features(self, patient): if isinstance(patient, dict): import json patient = json.dumps(patient) named_features = ssf.extract_named_features(self._gateway.jvm, patient, self._features_spec) return [k for k, _ in named_features.items() if k in self._top_features]
def score(self, patient): if isinstance(patient, dict): import json patient = json.dumps(patient) # feature construction named_features = ssf.extract_named_features(self._gateway.jvm, patient, self._features_spec) feature_vector = ssf.to_sparse_feature_vector(self._feature_mapping, named_features) return self._pipeline.calibrated_score(feature_vector)