Support new multi-class objectives in lgbm_classification_learner (#232)

src/fklearn/training/classification.py

@@ -620,10 +620,14 @@ def lgbm_classification_learner(df: pd.DataFrame,
 
     import lightgbm as lgbm
 
+    LGBM_MULTICLASS_OBJECTIVES = {'multiclass', 'softmax', 'multiclassova', 'multiclass_ova', 'ova', 'ovr'}
+
     params = extra_params if extra_params else {}
     params = assoc(params, "eta", learning_rate)
     params = params if "objective" in params else assoc(params, "objective", 'binary')
 
+    is_multiclass_classification = params["objective"] in LGBM_MULTICLASS_OBJECTIVES
+
     weights = df[weight_column].values if weight_column else None
 
     features = features if not encode_extra_cols else expand_features_encoded(df, features)
@@ -637,7 +641,7 @@ def lgbm_classification_learner(df: pd.DataFrame,
                      callbacks=callbacks)
 
     def p(new_df: pd.DataFrame, apply_shap: bool = False) -> pd.DataFrame:
-        if params["objective"] == "multiclass":
+        if is_multiclass_classification:
             col_dict = {prediction_column + "_" + str(key): value
                         for (key, value) in enumerate(bst.predict(new_df[features].values).T)}
         else:
@@ -649,7 +653,7 @@ def p(new_df: pd.DataFrame, apply_shap: bool = False) -> pd.DataFrame:
             shap_values = explainer.shap_values(new_df[features])
             shap_expected_value = explainer.expected_value
 
-            if params["objective"] == "multiclass":
+            if is_multiclass_classification:
                 shap_values_multiclass = {f"shap_values_{class_index}": list(value)
                                           for (class_index, value) in enumerate(shap_values)}
                 shap_expected_value_multiclass = {