renaming k_fold parameter to num_folds everywhere but in full_kfold_c…

…ross_validate

ludwig/api.py

@@ -1002,7 +1002,7 @@ def test(
 
 
 def kfold_cross_validate(
-        k_fold,
+        num_folds,
         model_definition=None,
         model_definition_file=None,
         data_csv=None,
@@ -1015,7 +1015,7 @@ def kfold_cross_validate(
 
     # Inputs
     
-    :param k_fold: (int) number of folds to create for the cross-validation
+    :param num_folds: (int) number of folds to create for the cross-validation
     :param model_definition: (dict, default: None) a dictionary containing
              information needed to build a model. Refer to the [User Guide]
             (http://ludwig.ai/user_guide/#model-definition) for details.
@@ -1037,7 +1037,7 @@ def kfold_cross_validate(
 
     (kfold_cv_stats,
      kfold_split_indices) = experiment_kfold_cross_validate(
-        k_fold,
+        num_folds,
         model_definition=model_definition,
         model_definition_file=model_definition_file,
         data_csv=data_csv,

ludwig/experiment.py

@@ -396,7 +396,7 @@ def full_experiment(
 
 
 def kfold_cross_validate(
-        k_fold,
+        num_folds,
         model_definition=None,
         model_definition_file=None,
         data_csv=None,
@@ -405,7 +405,7 @@ def kfold_cross_validate(
         **kwargs
 ):
     # check for k_fold
-    if k_fold is None:
+    if num_folds is None:
         raise ValueError(
             'k_fold parameter must be specified'
         )
@@ -422,7 +422,7 @@ def kfold_cross_validate(
             'model_definition_file can be provided'
         )
 
-    logger.info('starting {:d}-fold cross validation'.format(k_fold))
+    logger.info('starting {:d}-fold cross validation'.format(num_folds))
 
     # extract out model definition for use
     if model_definition_file is not None:
@@ -444,7 +444,7 @@ def kfold_cross_validate(
     kfold_split_indices = {}
 
     for train_indices, test_indices, fold_num in \
-            generate_kfold_splits(data_df, k_fold, random_seed):
+            generate_kfold_splits(data_df, num_folds, random_seed):
         with tempfile.TemporaryDirectory(dir=data_dir) as temp_dir_name:
             curr_train_df = data_df.iloc[train_indices]
             curr_test_df = data_df.iloc[test_indices]
@@ -519,7 +519,7 @@ def kfold_cross_validate(
 
     kfold_cv_stats['overall'] = overall_kfold_stats
 
-    logger.info('completed {:d}-fold cross validation'.format(k_fold))
+    logger.info('completed {:d}-fold cross validation'.format(num_folds))
 
     return kfold_cv_stats, kfold_split_indices