diff --git a/lc_classification_step/lc_classification/core/parsers/scribe_parser.py b/lc_classification_step/lc_classification/core/parsers/scribe_parser.py index 1a3ac125d..d992d5a0c 100644 --- a/lc_classification_step/lc_classification/core/parsers/scribe_parser.py +++ b/lc_classification_step/lc_classification/core/parsers/scribe_parser.py @@ -219,3 +219,107 @@ def get_scribe_commands( ) return KafkaOutput(commands_list) + + +class TopBottomScribeParser(KafkaParser): + def __init__(self, *, classifier_name: str): + super().__init__(self) + self.classifier_name = classifier_name + + def parse(self, to_parse: OutputDTO, **kwargs) -> KafkaOutput[List[dict]]: + """Parse data output from the Random Forest to scribe commands. + Parameters + ---------- + to_parse : OutputDTO + Output from the model. Has two attributes: + probabilities and hierarchical. + + OutputDTO.probabilities is a dataframe + OutputDTO.hierarchical is a Dict[str,pd.DataFrame] + with two keys: "top" and "children" where children is a Dict[str, pd.DataFrame] + + Examples + -------- + to_parse.hierarchical + + { + 'top': Periodic Stochastic Transient + oid + vbKsodtqMI 0.434 0.21 0.356, + 'children': { + 'Transient': SLSN SNII SNIa SNIbc + oid + vbKsodtqMI 0.082 0.168 0.444 0.306, + 'Stochastic': AGN Blazar CV/Nova QSO YSO + oid + vbKsodtqMI 0.032 0.056 0.746 0.01 0.156, + 'Periodic': CEP DSCT E LPV Periodic-Other RRL + oid + vbKsodtqMI 0.218 0.082 0.158 0.028 0.12 0.394 + } + } + + to_parse.probabilities + SLSN SNII SNIa SNIbc ... E LPV Periodic-Other RRL + oid ... + vbKsodtqMI 0.029192 0.059808 0.158064 0.108936 ... 0.068572 0.012152 0.05208 0.170996, + } + """ + if len(to_parse.probabilities) == 0: + return KafkaOutput([]) + probabilities = to_parse.probabilities + top = to_parse.hierarchical["top"] + hierarchical = [ + to_parse.hierarchical["children"][ch] + for ch in to_parse.hierarchical["children"].keys() + ] + hierarchical = pd.concat(hierarchical) + probabilities["classifier_name"] = self._get_classifier_name() + top["classifier_name"] = self._get_classifier_name("top") + hierarchical["classifier_name"] = self._get_classifier_name("bottom") + + results = [top, hierarchical, probabilities] + + results = pd.concat(results) + if not results.index.name == "oid": + try: + results.set_index("oid", inplace=True) + except KeyError as e: + if not is_all_strings(results.index.values): + raise e + + commands = [] + + def get_scribe_messages(classifications_by_classifier: pd.DataFrame): + class_names = classifications_by_classifier.columns + for idx, row in classifications_by_classifier.iterrows(): + command = { + "collection": "object", + "type": "update_probabilities", + "criteria": { + "_id": idx, + }, + "data": { + "classifier_name": row["classifier_name"], + "classifier_version": kwargs["classifier_version"], + }, + "options": {"upsert": True, "set_on_insert": False}, + } + for class_name in class_names: + command["data"].update({class_name: row[class_name]}) + commands.append(command) + return classifications_by_classifier + + for oid in results.index.unique(): + results.loc[[oid], :].groupby( + "classifier_name", group_keys=False + ).apply(get_scribe_messages) + + return KafkaOutput(commands) + + def _get_classifier_name(self, suffix=None): + return ( + self.classifier_name + if suffix is None + else f"{self.classifier_name}_{suffix}" + ) diff --git a/lc_classification_step/poetry.lock b/lc_classification_step/poetry.lock index 63aa7e3de..34f23697e 100644 --- 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