bugfix: default to multiclass accuracy (#146)

Author: manujosephv

src/pytorch_tabular/models/base_model.py

@@ -104,7 +104,7 @@ def __init__(
                 for i, metric_params in enumerate(config.metrics_params):
                     if "task" not in metric_params:
                         # For classification task, output_dim == number of classses
-                        config.metrics_params[i]["task"] = "binary" if inferred_config.output_dim == 2 else "multiclass"
+                        config.metrics_params[i]["task"] = "multiclass"
                     if "num_classes" not in metric_params:
                         config.metrics_params[i]["num_classes"] = inferred_config.output_dim
 

src/pytorch_tabular/tabular_model.py

@@ -838,15 +838,15 @@ def create_finetune_model(
                 metrics = [torchmetrics.functional.accuracy]
                 metrics_params = [
                     {
-                        "task": "binary" if inferred_config.output_dim == 2 else "multiclass",
+                        "task": "multiclass",
                         "num_classes": inferred_config.output_dim,
                     }
                 ]
             else:
                 for i, mp in enumerate(metrics_params):
                     if "task" not in mp:
                         # For classification task, output_dim == number of classses
-                        metrics_params[i]["task"] = "binary" if inferred_config.output_dim == 2 else "multiclass"
+                        metrics_params[i]["task"] = "multiclass"
                     if "num_classes" not in mp:
                         metrics_params[i]["num_classes"] = inferred_config.output_dim
         # Forming partial callables using metrics and metric params