1. get_odata.py

The Parliament has a service to retrieve the data named ODATA.
Here I used this service and extracted the relevant data to a csv file.

The votes.csv consists of the data as is -- the votes for each law by each member. 7057 laws X 143 members.
The values of the vote results are 0 ,1, 2, 3, 4. Where 1 - for the law, 2 - opposed to the law, 3 - abstained, 0 - didn’t attend. 4 - I don’t know.

2. create_csv.py

outputs: votes.csv and law2vector.pickle.
Creates a csv readable for humans -- votes.csv.
7057 law ids (rows) x 143 kmmbrs ids (cols)
1 - for the law, 2 - opposed to the law, 0 - didn't attend.
In addition, we dump the same data structure to a pickle -- 'law2vector.pickle'


3.to_game.py

Here, we convert the original data to be suitable for our problem -- cooperative game. We create sets of players (members) and calculate value for each set.
outputs: A, b -- inputs for LP


4. helpers/lp.py and lp_cvx.py
both outputs x -- a payoff division.
lp_cvx - more efficient

This is an implementation of the optimization problem represented at: Theorem 3.1 (2) in 2016 paper -- https://arxiv.org/pdf/1505.00039.pdf
(see also the image in helpers/opt_problem.png)

Linear Programming is intended to solve the following problem form:

Minimize:     c^T * x

Subject to:   A_ub * x <= b_ub

We multiply A and b by -1 to suite the input for the LP (to flip the equality).

c = [1]*len(A[0])
from scipy.optimize import linprog
res = linprog(c, A_ub=A, b_ub=b, bounds=(0, None), options={"disp": True},method='interior-point')