example of multiprocessing usage

multiprocessing_pool

import numpy as np
import pandas as pd
from scipy.optimize import minimize
import sys
from multiprocessing import Pool
# local modules and functions imported here
sys.path.append('../experiment/simulation')
from utils import softmax, autocorrelation

class MaxLike(object):
    def __init__(self, df):
        self.number_of_actions = 2
        self.dataframe = df

    def neg_log_likelihood(self, alphabeta):
        df = self.dataframe

        alpha = alphabeta[0]
        beta = alphabeta[1]

        actions, rewards = df.action.values, df['reward'].values
        prob_log = 0
        Q = np.zeros(self.number_of_actions)
        for action, reward in zip(actions, rewards):
            Q[action] += alpha * (reward - Q[action])
            prob_log += np.log(softmax(Q, beta)[action])

        return -2 * prob_log # -2 because reasons

    def ml_estimation(self, start_parameters):
        # print("running estimation with parameters: {}".format(start_parameters))
        rawr = minimize(self.neg_log_likelihood, start_parameters,
                        method='L-BFGS-B', bounds=((0.01,2), (0.01,5)),
                        options={'disp': False})
        return rawr

    def ml_estimation_multicore(self, start_parameters):
        """ start_parameters is a list of the form [[0.1, 0.2]..[0.9, 0.9]]"""

        print("booting up extra core engines to run estimation...")
        with Pool() as pooler:
            results = pooler.map(self.ml_estimation, start_parameters)

        best_bic = 10000
        best_index = None
        for index, result in enumerate(results):
            lnlike = -result['fun']
            bic = 2 * np.log(80) - lnlike
            if bic < best_bic:
                best_bic = bic
                best_index = index

        return results[best_index]