如何更快地优化 Python 代码中的均方误差

Question

(I'm new to stack overflow, but I will try to write my problem the best way I can) For my thesis, I need to do the optimization for a mean squares error problem as fast as possible. For this problem, I used to use the scipy.optimize.minimize method (with and without jacobian). However; the optimization was still too slow for what we wanted to do. (This program is running on mac with python 3.9)

So first, this is the function to minimize (I already tried to simplify the formula, but it didn't change the speed of the program

       def _residuals_mse(self, coef, unshimmed_vec, coil_mat, factor):
        """ Objective function to minimize the mean squared error (MSE)

        Args:
            coef (numpy.ndarray): 1D array of channel coefficients
            unshimmed_vec (numpy.ndarray): 1D flattened array (point) 
            coil_mat (numpy.ndarray): 2D flattened array (point, channel) of masked coils
                                      (axis 0 must align with unshimmed_vec)
            factor (float): Devise the result by 'factor'. This allows to scale the output for the minimize function to avoid positive directional linesearch

        Returns:
            scalar: Residual for least squares optimization 
        """

        # MSE regularized to minimize currents
        return np.mean((unshimmed_vec + np.sum(coil_mat * coef, axis=1, keepdims=False)) ** 2) / factor + \ (self.reg_factor * np.mean(np.abs(coef) / self.reg_factor_channel))

This is the jacobian of the function ( There is maybe a way to make it faster but I didn't succeed to do it)

     def _residuals_mse_jacobian(self, coef, unshimmed_vec, coil_mat, factor):
        """ Jacobian of the function that we want to minimize, note that normally b is calculates somewhere else 
        Args:
            coef (numpy.ndarray): 1D array of channel coefficients
            unshimmed_vec (numpy.ndarray): 1D flattened array (point) of the masked unshimmed map
            coil_mat (numpy.ndarray): 2D flattened array (point, channel) of masked coils
                                      (axis 0 must align with unshimmed_vec)
            factor (float): integer

        Returns:
            jacobian (numpy.ndarray) : 1D array of the gradient of the mse function to minimize
        """
        b = (2 / (unshimmed_vec.size * factor))
        jacobian = np.array([
            self.b * np.sum((unshimmed_vec + np.matmul(coil_mat, coef)) *            coil_mat[:, j]) + \
            np.sign(coef[j]) * (self.reg_factor / (9 * self.reg_factor_channel[j]))
            for j in range(coef.size)
        ])

        return jacobian

And so this is the "main" program

import numpy as np 
import scipy.optimize as opt
from numpy.random import default_rng
rand = default_rng(seed=0)
reg_factor_channel = rand.integers(1, 10, size=9) 
coef = np.zeros(9)
unshimmed_vec = np.random.randint(100, size=(150))
coil_mat = np.random.randint(100, size=(150,9))
factor = 2 
self.reg_factor = 5 
currents_sp = opt.minimize(self.residuals_mse, coef,
                               args=(unshimmed_vec, coil_mat, factor),
                               method='SLSQP',
                               jac = self._residuals_mse_jacobian,
                               options={'maxiter': 1000})

On my computer, the optimization takes around 40 ms for a dataset of this size.

The matrices in the example are usually obtained after some modifications and can be way way bigger, but here to make it clear and easy to test, I choose some arbitrary ones. In addition, this optimization is done many times (Sometimes up to 50 times), so, we are already doing multiprocessing (To do different optimization at the same time). However on mac, mp is slow to start because of the spawning method (because fork is not stable on python 3.9). For this reason, I am trying to make the optimization as fast as possible to maybe remove multiprocessing.

Do any of you know how to make this code faster in python? Also, this code will be available in open source for users, so I can only free solver (unlike MOSEK)

Edit: I tried to run the code by using the CVXPY model, with this code after the one just above:

        m = currents_0.size
        n = unshimmed_vec.size
        coef = cp.Variable(m)
        unshimmed_vec2 = cp.Parameter((n))
        coil_mat2 = cp.Parameter((n,m))
        unshimmed_vec2.value = unshimmed_vec
        coil_mat2.value = coil_mat

        x1 = unshimmed_vec2 + cp.matmul(coil_mat2,coef)
        x2 = cp.sum_squares(x1) / (factor*n)
        x3 = self.reg_factor / self.reg_factor_channel@ cp.abs(coef) / m
        obj = cp.Minimize(x2 + x3)
        prob = cp.Problem(obj)

        prob.solve(solver=SCS)

However, this is slowing even more my code, and it gives me a different value than with scipy.optimize.minimize, so does anyone see a problem in this code?

Answer 1

I would suggest trying the library NLOpt. It also has SLSQP as nonlinear solver (among many others), and I found it to be faster in many instances than SciPy optimize.

However , you're talking 50 ms per run, you won't get down to 5 ms.

If you're looking to squeeze as much performance as possible, I would probably go to the metal and re-implement the objective function and Jacobian in Fortran (or C) and then use f2py (or Cython) to bridge them to Python. Looks a bit of an overkill to me though.

Answer 2

I will make some sweeping assumptions:

• that we can ignore _criteria_func and instead optimize _residuals_mse ;
• that none of this needs to be in a class;
• that, unlike in your example, reg_factor_channel will never have zeros; and
• that your bounds and constraints can all be ignored (though you have not made this clear).

Recognize that your inner expressions can be simplified:

• np.sum(coil_mat * coef) , since it uses a broadcast, is really just a matrix multiplication
• mean(**2) on a vector is really just a self-dot-product divided by the length
• Some of your scalar factors and vector coefficients can be combined outside of the function

This leaves us with the following, starting without the Jacobian:

import numpy as np
from numpy.random import default_rng
from scipy import optimize as opt
from timeit import timeit

rand = default_rng(seed=0)
reg_factor = 5
reg_factor_channel = rand.integers(1, 10, size=9)
reg_vector = reg_factor / len(reg_factor_channel) / reg_factor_channel


def residuals_mse(
    coef: np.ndarray,
    unshimmed_vec: np.ndarray,
    coil_mat: np.ndarray,
    factor: float,
) -> float:
    inner = unshimmed_vec + coil_mat@coef
    return inner.dot(inner)/len(inner)/factor + np.abs(coef).dot(reg_vector)


def old_residuals_mse(coef, unshimmed_vec, coil_mat, factor):
    return np.mean(
        (unshimmed_vec + np.sum(coil_mat * coef, axis=1, keepdims=False)) ** 2) / factor + (
            reg_factor * np.mean(np.abs(coef) / reg_factor_channel))


def main() -> None:
    unshimmed_vec = rand.integers(100, size=150)
    coil_mat = rand.integers(100, size=(150, 9))
    factor = 2
    args = unshimmed_vec, coil_mat, factor

    currents_sp = None
    def run():
        nonlocal currents_sp
        currents_sp = opt.minimize(
            fun=residuals_mse,
            x0=np.zeros_like(reg_factor_channel),
            args=args,
            method='SLSQP',
        )

    t = timeit(run, number=1)
    print(currents_sp)
    print(t, 'seconds')

    r_old = old_residuals_mse(currents_sp.x, *args)
    assert np.isclose(r_old, currents_sp.fun)


if __name__ == '__main__':
    main()

with output

 message: Optimization terminated successfully
 success: True
  status: 0
     fun: 435.166150155064
       x: [-1.546e-01 -8.305e-02 -1.637e-01 -1.106e-01 -1.033e-01
           -8.792e-02 -9.908e-02 -8.666e-02 -1.217e-01]
     nit: 7
     jac: [-1.179e-01 -1.621e-01 -1.112e-01 -1.765e-01 -1.678e-01
           -1.570e-01 -1.456e-01 -1.722e-01 -1.299e-01]
    nfev: 94
    njev: 7
0.012324300012551248 seconds

The Jacobian does indeed help, but has been written in a way that is not properly vectorised. Once vectorised it looks like:

import numpy as np
from numpy.random import default_rng
from scipy import optimize as opt
from timeit import timeit

rand = default_rng(seed=0)
reg_factor = 5
reg_factor_channel = rand.integers(1, 10, size=9)
reg_vector = reg_factor / len(reg_factor_channel) / reg_factor_channel


def residuals_mse(
    coef: np.ndarray,
    unshimmed_vec: np.ndarray,
    coil_mat: np.ndarray,
    factor: float,
) -> float:
    inner = unshimmed_vec + coil_mat@coef
    return inner.dot(inner)/len(inner)/factor + np.abs(coef).dot(reg_vector)


def old_residuals_mse(coef, unshimmed_vec, coil_mat, factor):
    return np.mean(
        (unshimmed_vec + np.sum(coil_mat * coef, axis=1, keepdims=False)) ** 2) / factor + (
            reg_factor * np.mean(np.abs(coef) / reg_factor_channel))


def residuals_mse_jacobian(
    coef: np.ndarray,
    unshimmed_vec: np.ndarray,
    coil_mat: np.ndarray,
    factor: float,
) -> np.ndarray:
    b = 2 / unshimmed_vec.size / factor
    return b * (
        unshimmed_vec + coil_mat@coef
    )@coil_mat + np.sign(coef) * reg_factor/coef.size/reg_factor_channel


def old_residuals_mse_jacobian(coef, unshimmed_vec, coil_mat, factor):
    b = (2 / (unshimmed_vec.size * factor))
    jacobian = np.array([
        b * np.sum((unshimmed_vec + np.matmul(coil_mat, coef)) *            coil_mat[:, j]) +
        np.sign(coef[j]) * (reg_factor / (9 * reg_factor_channel[j]))
        for j in range(coef.size)
    ])

    return jacobian


def main() -> None:
    unshimmed_vec = rand.integers(100, size=150)
    coil_mat = rand.integers(100, size=(150, 9))
    factor = 2
    args = unshimmed_vec, coil_mat, factor

    currents_sp = None
    def run():
        nonlocal currents_sp
        currents_sp = opt.minimize(
            fun=residuals_mse,
            x0=np.zeros_like(reg_factor_channel),
            args=args,
            method='SLSQP',
            jac=residuals_mse_jacobian,
        )

    t = timeit(run, number=1)
    print(currents_sp)
    print(t, 'seconds')

    r_old = old_residuals_mse(currents_sp.x, *args)
    assert np.isclose(r_old, currents_sp.fun)

    j_new = residuals_mse_jacobian(currents_sp.x, *args)
    j_old = old_residuals_mse_jacobian(currents_sp.x, *args)
    assert np.allclose(j_old, j_new)


if __name__ == '__main__':
    main()

 message: Optimization terminated successfully
 success: True
  status: 0
     fun: 435.1661470650057
       x: [-1.546e-01 -8.305e-02 -1.637e-01 -1.106e-01 -1.033e-01
           -8.792e-02 -9.908e-02 -8.666e-02 -1.217e-01]
     nit: 7
     jac: [-4.396e-02 -8.791e-02 -3.385e-02 -9.817e-02 -9.516e-02
           -8.223e-02 -7.154e-02 -9.907e-02 -5.939e-02]
    nfev: 31
    njev: 7
0.005370599974412471 seconds