added info about data_prefix

README.md

@@ -154,7 +154,7 @@ pred_file | Name of the output prediction file.
 train_file | Name of the training file. 
 test_file | Name of the test file. 
 output_name | Prefix of the models to be printed per iteration. This is to allow the Meta features of each iteration to be printed. Defaults to nothing.
-data_prefix | (**NEW**) prefix to be used when the user supplies own pairs of [X_train,X_cv] datasets for each fold as well as an X file for the whole training data. This is particularly useful for when likelihood features are needed or generally features than must be computed within cv.  Each train/valid pair is identified by prefix_train[fold_index_starting_from_zero].txt/prefix_cv[fold_index_starting_from_zero].txt and prefix_train.txt for the final set. For example if prefix=mystack and folds=2 then stacknet is expecting 2 pairs of train/cv files. e.g [[mystack_train0.txt,mystack_cv0.txt],[mystack_train1.txt,mystack_cv1.txt]]. It also expects a [mystack_train.txt]  for the final train set. These files can be either dense or sparse ( when 'sparse=True') and need to have the target variable in the beginning. 
+data_prefix | (**NEW**) prefix to be used when the user supplies own pairs of [X_train,X_cv] datasets for each fold as well as an X file for the whole training data. This is particularly useful for when likelihood features are needed or generally features than must be computed within cv.  Each train/valid pair is identified by prefix_train[fold_index_starting_from_zero].txt/prefix_cv[fold_index_starting_from_zero].txt and prefix_train.txt for the final set. For example if prefix=mystack and folds=2 then stacknet is expecting 2 pairs of train/cv files. e.g [[mystack_train0.txt,mystack_cv0.txt],[mystack_train1.txt,mystack_cv1.txt]]. It also expects a [mystack_train.txt]  for the final train set. These files can be either dense or sparse ( when 'sparse=True') and need to have the target variable in the beginning. If you use **output_name** to extract the predictions, these will be stacked vertically in the same order as the cv files. 
 indices_name |  A prefix. When given any value it prints a .csv file for each fold with the corresponding train(0) and valiation(1) indices stacked vertically .The format is “row_index,[0 if train else 1 for validation]”. First it prints the train indices and then the validation indices in exactly the same order as they appear when modelling inside StackNet.
 test_target | True if the test file has a target variable in the beginning (left) else false (only predictors in the file).
 params | Parameter file where each line is a model. empty lines correspond to the creation of new levels