Changes

'''To general users:''' your input file (usually a single ".txt" file contains many examples each as a row) will be split into a training set (80% by default) and a testing set (20% by default). The labels you want to predict will be the folder names. The content (usually a block of text) of the examples will go into separate ".txt" files. To run the script, you basically need to specify the following: 

The script will generate a pickle file with an ".pkl" extension and the name will be the same as your input. Please change the name properly to indicate the target label information as have been discussed above. And make sure place this pickle file is under the same directory with your classification code, i.e. "classification_MMM_LLL.py" '''To advanced users:''' one important step in data preprocessing is to convert words (strings) to integers. That is we need to build a dictionary mapping words to their corresponding indices. Our dictionary is ordered by the words' frequency. Higher the frequency smaller the index, i.e. you should expect to see "the, a, ..." these words in the smallest 10 indices : 2, 3, 4, .... Please also notice that 0 and 1 these two indices are not assigned to any words intentionally. The advantage of doing this is that you can specify easily ignore those common and meaningless words by simply say I want to consider words with the index > 20 for example.