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)
