Particle swarm optimization in regression problem - dataframe

Question

I am trying to optimize my Random forest regression model using a particle swarm optimizer to minimize the prediction error. But getting this error:UFuncTypeError: ufunc 'add' did not contain a loop with signature matching types (dtype('<U33'), dtype('<U33')) -> None
Can anyone please help me with this. I really appreciate any help you can provide.
My dataset contains 24 independent variables (X) and one dependent variable (y).
CODE:
import numpy as np
import pandas as pd
import re
import os
import random
import seaborn as sns
import matplotlib.pyplot as plt
from matplotlib import animation, rc
%matplotlib inline
from matplotlib.animation import FuncAnimation
from sklearn.model_selection import cross_val_predict, train_test_split
from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score, mean_absolute_percentage_error
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.20, random_state=1)
from sklearn.ensemble import RandomForestRegressor
RFR = RandomForestRegressor()
RFR.fit(X_train, y_train)
######################### Particle swarm optimization ##################################
#### error rate
#### The objective is to find minimum error
def fitness_function(xtest, ytest):
# Prediction
ypred = RFR.predict(X_test)
error = mean_squared_error(y_test, ypred)
#r2 = r2_score(ytest, ypred)
return f'The error is: {error}'
from matplotlib import animation
import random
####### velocity #######################################
def update_velocity(particle, velocity, pbest, gbest, w_min=0.5, max=1.0, c=0.1):
# Initialise new velocity array
num_particle = len(particle)
new_velocity = np.array([0.2 for i in range(num_particle)])
# Randomly generate r1, r2 and inertia weight from normal distribution
r1 = random.uniform(0,max)
r2 = random.uniform(0,max)
w = random.uniform(w_min,max)
c1 = c
c2 = c
# Calculate new velocity
for i in range(num_particle):
new_velocity[i] = w*velocity[i] + c1*r1*(pbest[i]-particle[i])+c2*r2*(gbest[i]-particle[i])
return new_velocity
############## update position ##############
def update_position(particle, velocity):
# Move particles by adding velocity
new_particle = particle + velocity
return new_particle
######################################### PSO Main function ###################################
def pso_2d(population, dimension, position_min, position_max, generation, fitness_criterion):
# Initialization
# Population
particles = [[np.random.uniform(position_min[0:24], position_max[0 :24]) for j in range(population)] for i in range(dimension)] # generating random particle position
# Particle's best position
pbest_position = particles # personal best position
# Fitness
pbest_fitness = [fitness_function(p[0:24],p[1]) for p in particles] # personal best fitness
# Index of the best particle
# np.reshape(np.ravel(p[0]), (2, 31))
gbest_index = np.argmin(pbest_fitness)
# Global best particle position
gbest_position = pbest_position[gbest_index]
# Velocity (starting from 0 speed)
velocity = [[0.0 for j in range(dimension)] for i in range(population)]
# Loop for the number of generation
for t in range(generation):
# Stop if the average fitness value reached a predefined success criterion
if np.average(pbest_fitness) <= fitness_criterion:
break
else:
for n in range(population):
# Update the velocity of each particle
velocity[n] = update_velocity(particles[n], velocity[n], pbest_position[n], gbest_position)
# Move the particles to new position
particles[n] = update_position(particles[n], velocity[n])
# Calculate the fitness value
pbest_fitness = [fitness_function(p[0:24],p[1]) for p in particles]
# Find the index of the best particle
gbest_index = np.argmin(pbest_fitness)
# Update the position of the best particle
gbest_position = pbest_position[gbest_index]
# Print the results
print('Global Best Position: ', gbest_position)
print('Best Fitness Value: ', min(pbest_fitness))
print('Average Particle Best Fitness Value: ', np.average(pbest_fitness))
print('Number of Generation: ', t)
position_min = [-0.44306155, -0.52971118, -0.10311188, -0.60053201, -0.78198029,
-0.37737778, -0.14371436, -0.01623235, -0.88660182, -0.06182274,
-0.30084403, -0.98080838, -0.11787062, -0.84172055, -0.709991 ,
-0.9841236 , -0.32976052, -0.26586302, -0.87641669, -0.23728611,
-0.08874495, -0.03091284, -0.29987714, -0.96795309]
position_max = [0.44306155, 0.52971118, 0.10311188, 0.60053201, 0.78198029,
0.37737778, 0.14371436, 0.01623235, 0.88660182, 0.06182274,
0.30084403, 0.98080838, 0.11787062, 0.84172055, 0.709991 ,
0.9841236 , 0.32976052, 0.26586302, 0.87641669, 0.23728611,
0.08874495, 0.03091284, 0.29987714, 0.96795309]
pso_2d(100, 24, position_min, position_max, 400, 10e-4)
Output: UFuncTypeError: ufunc 'add' did not contain a loop with signature matching types (dtype('<U33'), dtype('<U33')) -> None