I have a function of multiple arguments. I want to optimize it with respect to a single variable while holding others constant. For that I want to use minimize_scalar from spicy.optimize. I read the documentation, but I am still confused how to tell minimize_scalar that I want to minimize with respect to variable:w1. Below is a minimal working code.
import numpy as np from scipy.optimize import minimize_scalar def error(w0,w1,x,y_actual): y_pred = w0+w1*x mse = ((y_actual-y_pred)**2).mean() return mse w0=50 x = np.array([1,2,3]) y = np.array([52,54,56]) minimize_scalar(error,args=(w0,x,y),bounds=(-5,5)) 3 Answers
You can use a lambda function
minimize_scalar(lambda w1: error(w0,w1,x,y),bounds=(-5,5)) You can also use a partial function.
from functools import partial error_partial = partial(error, w0=w0, x=x, y_actual=y) minimize_scalar(error_partial, bounds=(-5, 5)) In case you are wondering about the performance ... it is the same as with lambdas.
import time from functools import partial import numpy as np from scipy.optimize import minimize_scalar def error(w1, w0, x, y_actual): y_pred = w0 + w1 * x mse = ((y_actual - y_pred) ** 2).mean() return mse w0 = 50 x = np.arange(int(1e5)) y = np.arange(int(1e5)) + 52 error_partial = partial(error, w0=w0, x=x, y_actual=y) p_time = [] for _ in range(100): p_time_ = time.time() p = minimize_scalar(error_partial, bounds=(-5, 5)) p_time_ = time.time() - p_time_ p_time.append(p_time_ / p.nfev) l_time = [] for _ in range(100): l_time_ = time.time() l = minimize_scalar(lambda w1: error(w1, w0, x, y), bounds=(-5, 5)) l_time_ = time.time() - l_time_ l_time.append(l_time_ / l.nfev) print(f'Same performance? {np.median(p_time) == np.median(l_time)}') # Same performance? True The marked correct answer is actually minimizing with respect to W0. It should be:
minimize_scalar(lambda w1: error(w1,w0,x,y),bounds=(-5,5)) 
