Removes occurrences of DataFrame.values (ndarray)

Uses the DataFrame everywhere it's possible.

Author: fberanizo

src/fklearn/training/classification.py

@@ -571,21 +571,21 @@ def lgbm_classification_learner(df: pd.DataFrame,
     params = assoc(params, "eta", learning_rate)
     params = params if "objective" in params else assoc(params, "objective", 'binary')
 
-    weights = df[weight_column].values if weight_column else None
+    weights = df[weight_column] if weight_column else None
 
     features = features if not encode_extra_cols else expand_features_encoded(df, features)
 
     dtrain = lgbm.Dataset(df[features], label=df[target], feature_name=list(map(str, features)), weight=weights,
                           silent=True, categorical_feature=categorical_features)
 
-    bst = lgbm.train(params, dtrain, num_estimators)
+    bst = lgbm.train(params, dtrain, num_estimators, categorical_feature=categorical_features)
 
     def p(new_df: pd.DataFrame, apply_shap: bool = False) -> pd.DataFrame:
         if params["objective"] == "multiclass":
             col_dict = {prediction_column + "_" + str(key): value
-                        for (key, value) in enumerate(bst.predict(new_df[features].values).T)}
+                        for (key, value) in enumerate(bst.predict(new_df[features]).T)}
         else:
-            col_dict = {prediction_column: bst.predict(new_df[features].values)}
+            col_dict = {prediction_column: bst.predict(new_df[features])}
 
         if apply_shap:
             import shap