rewrite for reproduction purpose only

libmultilabel/linear/linear.py

@@ -186,9 +186,6 @@ def train_thresholding(
     Returns:
         A model which can be used in predict_values.
     """
-    if not is_multilabel:
-        raise ValueError("thresholding method doesn't support binary/multiclass datasets.")
-
     x, options, bias = _prepare_options(x, options)
 
     y = y.tocsc()
@@ -413,9 +410,6 @@ def train_cost_sensitive(
     Returns:
         A model which can be used in predict_values.
     """
-    if not is_multilabel:
-        raise ValueError("cost_sensitive method doesn't support binary/multiclass datasets.")
-
     # Follows the MATLAB implementation at https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/multilabel/
     x, options, bias = _prepare_options(x, options)
 
@@ -520,9 +514,6 @@ def train_cost_sensitive_micro(
     Returns:
         A model which can be used in predict_values.
     """
-    if not is_multilabel:
-        raise ValueError("cost_sensitive_micro method doesn't support binary/multiclass datasets.")
-
     # Follows the MATLAB implementation at https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/multilabel/
     x, options, bias = _prepare_options(x, options)
 

linear_trainer.py

@@ -40,19 +40,7 @@ def linear_test(config, model, datasets, label_mapping):
 
 def linear_train(datasets, config):
     # detect task type
-    is_multilabel = config.get("is_multilabel", "auto")
-    if is_multilabel == "auto":
-        is_multilabel = not is_multiclass_dataset(datasets["train"], "y")
-    elif not isinstance(is_multilabel, bool):
-        raise ValueError(
-            f'"is_multilabel" is expected to be either "auto", "True", or "False". But got "{is_multilabel}" instead.'
-        )
-
-    task_type = "multilabel" if is_multilabel else "binary/multiclass"
-    logging.info(
-        f'is_multilabel is set to "{config.get("is_multilabel", "auto")}". '
-        f"Model will be trained in {task_type} mode."
-    )
+    is_multilabel = not is_multiclass_dataset(datasets["train"], "y")
 
     # train
     if config.linear_technique == "tree":