How to make a function pickable so that it can be minimized through the parallel version of scipy.optimize.differential_evolution

Question

I need to minimize a function using the scipy implementation of differential evolution. I'd like to exploit parallelism to speed up the computation and I tried setting workers=-1.

I get an error and searching I found that the problem is that the function that I'm trying to minimize is not pickable. I need help to understand how to make it pickable.

The function to minimize works in the following way:

A class object has an attribute vector, the observed data.
One method of the class takes some parameters and compute an estimate of the vector.
The function to minimize compute the mean square error between the vector and the computed estimate.

The pseudocode of the function could be something like that:

def function_to_minimize(self, parameters):
    true_vector = self.true_vector
    estimated_vector = self.estimate_vector(parameters)
    return mse(true_vector, estimated_vector)

score 0 · Answer 1 · answered Jan 05 '21 at 04:00

Something like this should work:

class Objective(object):
    def __init__(self, data):
        self.measured_data = data

    def __call__(self, parameters):
        # need to return a scalar value
        estimated_vector = self.estimate_vector(parameters)
        return np.sum(np.power(self.measured_data - estimated_vector, 2))

    def estimate_vector(parameters):
        # calculate what you expect to happen with the parameters
        pass

you should pass Objective(data) to differential_evolution as the function to minimize. On macOS and Windows that use spawn as the default way of creating new processes this function should be defined in a file that is importable.

How to make a function pickable so that it can be minimized through the parallel version of scipy.optimize.differential_evolution

1 Answers1