def train_model(self, params, X, y):
early_stop = EarlyStopping({"metric": {"name": "logloss"}})
time_constraint = TimeConstraint({"train_seconds_time_limit": self._time_limit})
il = IterativeLearner(params, callbacks=[early_stop, time_constraint])
il_key = il.get_params_key()
if il_key in self._models_params_keys:
return None
self._models_params_keys += [il_key]
if self.should_train_next(il.get_name()):
il.train({"train": {"X": X, "y": y}})
return il
return None
def train_model(self, params, X, y):
metric_logger = MetricLogger({"metric_names": ["logloss", "auc"]})
early_stop = EarlyStopping({"metric": {"name": self._optimize_metric}})
time_constraint = TimeConstraint({"train_seconds_time_limit": self._time_limit})
il = IterativeLearner(
params, callbacks=[early_stop, time_constraint, metric_logger]
)
il_key = il.get_params_key()
if il_key in self._models_params_keys:
self._progress_bar.update(1)
return None
self._models_params_keys += [il_key]
if self.should_train_next(il.get_name()):
il.train({"train": {"X": X, "y": y}})
self._progress_bar.update(1)
return il
self._progress_bar.update(1)
return None