Rating class

Class for all rating related things.

class pyanp.rating.Rating

Represents rating a full group of alternatives for a group of users. The data is essentially a dataframe and a WordEval object to evaluate that to scores.

add_alt(alt_name, ignore_existing=True)

Adds an alternative/s, by name

Parameters:

• alt_name – A str name, or a list of names to add.
• ignore_existing – If True and we try to add an existing alternative we simply skip by, otherwise we throw an error.

Returns:

Nothing

add_user(uname)

Adds one or more uses to this system.

Parameters:uname – The str name of the user to add, or a list of str names of users to add. Returns:Nothing

alt_names()

Returns:A list of str alternative names in this system. Ordered as the data in the ratings votes are ordered (columns).

is_alt(alt: str) → bool

Tells if the item is an alternative

Parameters:alt – The name of the alternative to check for. Returns:True/False

is_user(uname: str)

Parameters:uname – The name of the user to check for Returns:True/False if the given user exists in the system.

nalts() → int

Returns:The number of alternatives in this system.

nusers() → int

The number of users in this system.

Returns:The number of users

priority(username=None, ptype: pyanp.prioritizer.PriorityType = None)

Calculates the alternative priority for the specified user/users and the given normalizer type.

Parameters:

• username – The name (this of names) of the user (users) to get the overall priority of. If None, then we return the total group average.
• ptype – How should we normalize?

Returns:

A pandas.Series whose index is self.alt_names() and whose values are the priorities.

set_word_eval(param)

Sets the WordEval object

Parameters:param – This could either be a WordEval object, or a something that WordEval(param) would work with Returns:None

user_names()

Returns:A list of str names of users in this system. Ordered as the data in the ratings votes are ordered (the rows).

vote_column(alt_name, votes, createUnknownUsers=True)

Specifies all votes (across all users) for a specific alternative.

Parameters:

• alt_name – The name of the alternative to set the data for
• votes – Should either be a list with self.nusers() items, or a pandas.Series or dict with usernames as index.
• createUnknownUsers – If True and unknown users appear in the index of votes, we will create those users before trying to do the assignment.

Returns:

Nothing

vote_values(username=None, alt_name=None)

Gets the numeric vote values for the given user/alternative (or whole column or dataframe).

Parameters:

• username – If None, we get the values for all users. If a list get the values for each user in the list, or it could just be a single username.
• alt_name – Either None, meaing get it for all alternatives, or a single alternative name (to get one column).

Returns:

If username=None and alt_name=None, returns a pandas.DataFrame of the numeric values. Otherwise returns a pandas.Series of values as the result.

class pyanp.rating.WordEval(vals)

Information for a Word Evaluator, i.e. a function that inputs a word and outputs a numeric value.

eval(word)

Evaluates a word, or a pandas.Series of words.

Parameters:word – The string word to evaluate to a number, or a pandas.Series of data. Returns:The float value if we can evaluate, or None if a single value is passed in. If the word was actually a pandas.Series, we return a pandas.Series with the same index.

get_key(word)

Find the key word for this word. A WordEval has a list of words that represent different levels/numerical values. Those words are called keys. In addition, each key has a list of synonyms. For instance the keyword “high” might have a synonym “hi” or “h”. In that case get_key(“hi”) would return “high”.

Parameters:word – The word to look up a synonym for. Returns:The key if this word is a key or a synonym. If it is not a synonym or key, we return None.

keys_match_score(word_list)

This function tells us how well this WordEval interprets a list of words. It is used for searhcing through the “standard list” of words to find the best match for a data set.

Parameters:word_list – The list-like of words to see how we can match. Returns:A score <= 1. A positive number means no missing words, i.e. every word in word_list has a value in this WordEval object. The larger number means our word_list uses more of the names in this WordEval object.

class pyanp.rating.WordEvalType

What kind of WordEval will we use.

pyanp.rating.best_std_word_evaluator(list_of_words, return_name=True)

Finds the WordEval in STD_WORD_EVALUATOR that best matches the list of words

Parameters:

• list_of_words – The list of words to look for best matches of
• return_name – Should we return the best WordEval or its name in the STD_WORD_EVALUATOR.

Returns:

The name of the best match, or the best match WordEval

pyanp.rating.clean_word(word: str) → str

Cleans a word before subjecting it to ratings lookup

Parameters:word – The word to clean. Returns:The sanitized word