LightGBM model returning the same score with different parameters

I'm trying to train a LightGBM model on the Kaggle Iowa housing dataset and I wrote a small script to randomly try different parameters within a given range. I'm not sure what's wrong with my code, but the script returns the same score with different parameters, which shouldn't be happening. I tried the same script with Catboost and it works as expected, so I'm guessing the issue is with LGBM.

The code:

import numpy as np
import pandas as pd
from lightgbm import LGBMRegressor
from sklearn.metrics import mean_absolute_error
from sklearn.model_selection import train_test_split
from random import choice, randrange, uniform 

complete_train = pd.read_csv(
    "train.csv",
    encoding = "UTF-8", 
    index_col = "Id")

complete_test = pd.read_csv(
    "test.csv",
    encoding = "UTF-8",
    index_col = "Id")

def encode_impute(*datasets):
    for dataset in datasets:
        for column in dataset.columns:
            dataset[
                column].fillna(
                -999,
                inplace = True)
            if dataset[
                column].dtype ==  "object":
                dataset[
                    column] = dataset[
                    column].astype("category", copy = False)
encode_impute(
    complete_train, 
    complete_test)

X = complete_train.drop(
    columns = "SalePrice")

y = complete_train[
    "SalePrice"]

X_train, X_valid, y_train, y_valid = train_test_split(X, y)

def objective():

    while True:

        params = {
            "boosting_type": choice(["gbdt", "goss", "dart", "rf"]),
            "num_leaves": randrange(10000),
            "learning_rate": uniform(0.01, 1),
            "subsample_for_bin": randrange(100000000),
            "min_data_in_leaf": randrange(100000000),
            "reg_alpha": uniform(0, 1),
            "reg_lambda": uniform(0, 1),
            "feature_fraction": uniform(0, 1),
            "bagging_fraction": uniform(0, 1),
            "bagging_freq": randrange(1, 100)}

        params["bagging_fraction"] = 1.0 if params[
                        "boosting_type"] == "goss" else params[
                        "bagging_fraction"]

        model = LGBMRegressor().set_params(**params)

        model.fit(X_train, y_train)

        predictions = model.predict(X_valid)

        error_rate = mean_absolute_error(
          y_valid, predictions)

        print(f"Score = {error_rate} with parameters: {params}","\n" *5)

objective()

Example of the output I'm getting:

Score = 55967.70375930444 with parameters: {'boosting_type': 'gbdt', 'num_leaves': 6455, 'learning_rate': 0.2479700848039991, 'subsample_for_bin': 83737077, 'min_data_in_leaf': 51951103, 'reg_alpha': 0.1856001984332697, 'reg_lambda': 0.7849262049058852, 'feature_fraction': 0.10550627738309537, 'bagging_fraction': 0.2613298736131875, 'bagging_freq': 96}

Score = 55967.70375930444 with parameters: {'boosting_type': 'dart', 'num_leaves': 9678, 'learning_rate': 0.28670432435369037, 'subsample_for_bin': 24246091, 'min_data_in_leaf': 559094, 'reg_alpha': 0.07261459695501371, 'reg_lambda': 0.8834743560240725, 'feature_fraction': 0.5361519020265366, 'bagging_fraction': 0.9120030047714073, 'bagging_freq': 10}

Score = 55967.70375930444 with parameters: {'boosting_type': 'goss', 'num_leaves': 4898, 'learning_rate': 0.09237499846487345, 'subsample_for_bin': 32620066, 'min_data_in_leaf': 71317820, 'reg_alpha': 0.9818297737748625, 'reg_lambda': 0.11638265354331834, 'feature_fraction': 0.4230342728468828, 'bagging_fraction': 1.0, 'bagging_freq': 64}