请用python写一段代码,将数据输入到mlp回归模型中,并用optuna调参,score为R平方,并打印每次调参使用的超参数和对应的R平方,在三个随机种子上测试最佳模型,得到score为R平方和spearman相关系数,并保存代码运行过程的输出
时间: 2024-06-09 13:06:23 浏览: 16
以下是代码示例:
```python
import numpy as np
import pandas as pd
from sklearn.neural_network import MLPRegressor
from sklearn.metrics import r2_score, spearmanr
import optuna
import random
import time
# 读取数据
data = pd.read_csv('data.csv')
X = data.drop('target', axis=1).values
y = data['target'].values
# MLP回归模型
def MLPRegressor_model(trial):
hidden_layer_sizes = []
for i in range(trial.suggest_int('n_layers', 1, 4)):
hidden_layer_sizes.append(trial.suggest_int(f'n_units_l{i}', 1, 100))
activation = trial.suggest_categorical('activation', ['identity', 'logistic', 'tanh', 'relu'])
solver = trial.suggest_categorical('solver', ['lbfgs', 'sgd', 'adam'])
alpha = trial.suggest_loguniform('alpha', 1e-7, 1e-1)
learning_rate = trial.suggest_categorical('learning_rate', ['constant', 'invscaling', 'adaptive'])
learning_rate_init = trial.suggest_loguniform('learning_rate_init', 1e-5, 1e-1)
max_iter = trial.suggest_int('max_iter', 100, 1000)
tol = trial.suggest_loguniform('tol', 1e-7, 1e-1)
mlp = MLPRegressor(hidden_layer_sizes=hidden_layer_sizes, activation=activation, solver=solver, alpha=alpha,
learning_rate=learning_rate, learning_rate_init=learning_rate_init, max_iter=max_iter,
tol=tol, random_state=0)
return mlp
# 定义优化目标函数
def objective(trial):
mlp = MLPRegressor_model(trial)
r2_scores = []
for i in range(3):
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=i)
mlp.fit(X_train, y_train)
y_pred = mlp.predict(X_test)
r2_scores.append(r2_score(y_test, y_pred))
r2_score_mean = np.mean(r2_scores)
return r2_score_mean
if __name__ == '__main__':
study = optuna.create_study(direction='maximize')
start_time = time.time()
study.optimize(objective, n_trials=50)
end_time = time.time()
print(f'Optimization finished in {end_time - start_time:.2f} seconds.')
print(f'Best trial: {study.best_trial.number}')
print(f'Best R2 score: {study.best_value:.4f}')
print(f'Best hyperparameters: {study.best_params}')
# 保存输出
with open('output.txt', 'w') as f:
f.write(f'Optimization finished in {end_time - start_time:.2f} seconds.\n')
f.write(f'Best trial: {study.best_trial.number}\n')
f.write(f'Best R2 score: {study.best_value:.4f}\n')
f.write(f'Best hyperparameters: {study.best_params}\n')
for i, trial in enumerate(study.trials):
f.write(f'Trial {i}: R2 score={trial.value:.4f}, params={trial.params}\n')
# 测试最佳模型
best_params = study.best_params
r2_scores = []
spearman_correlations = []
for i in range(3):
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=i)
mlp = MLPRegressor_model(best_params)
mlp.fit(X_train, y_train)
y_pred = mlp.predict(X_test)
r2_scores.append(r2_score(y_test, y_pred))
spearman_correlations.append(spearmanr(y_test, y_pred)[0])
print(f'Testing finished: R2 score={np.mean(r2_scores):.4f}, Spearman correlation={np.mean(spearman_correlations):.4f}')
```
其中,`data.csv`是数据文件,包含特征和目标变量;`MLPRegressor_model`函数定义了MLP回归模型,使用Optuna搜索超参数;`objective`函数定义了优化目标,即最大化R平方;`study`对象使用`create_study`方法创建,`optimize`方法用于搜索最佳超参数;最后,将输出保存到文件`output.txt`中,并使用`train_test_split`函数将数据随机划分为训练集和测试集,使用三个随机种子分别测试最佳模型,计算R平方和Spearman相关系数。
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