Spam filter - step 1

We are going to create a function, which can read the information from files !truth.txt or !prediction.txt into the dictionary data structure.

• ++Working with a dictionary * See [Pilgrim2009], chapter 2.7, or [Wentworth2012], chapter 20). * How to create an empty dictionary. * How to add a key-value pair. * How to read a value of a key. * How to browse the dictionary by items using method items():

  eng_to_cz = {'cat': 'kocka', 'dog': 'pes', 'house': 'dum' }
  for eng, cz in eng_to_cz.items():
      print(eng, ',', cz)

  ++++Working with (text) files *(viz [Pilgrim2009], chapter 11, or [Wentworth2012], chapter 13). * How to open and close a text file. * How to use the with command. * Reading a file line by line. * Reading the whole file contents as a single string. The usage of section <code>if __name__ == "__main__":</code> * See [Pilgrim2009], chapter 1.10). ++++Method ''split()'' of string values * See the Python docs for str.split()) ++

Task:

• In a module called utils.py, create a function read_classification_from_file() that will read the mail classes from a text file.

Why do we need it:

• We will need this function if we want to create a learning filter, and during the evaluation of the filter quality.

Function read_classification_from_file() (in module utils.py) has to conform to the following specifications:

The function loads a text file contaning a pair of strings per line, separated by single space, like this:

email01 OK
email02 OK
email03 SPAM
email1234 OK
...

{'email1234': 'OK', 'email03': 'SPAM', 'email02': 'OK', 'email01': 'OK'}

If the file is empty, it returns an empty dictionary.