from sklearn import metrics print('Mean Absolute Error:', metrics.mean_absolute_error(Y_validation,y_pred)) print('Mean Squared Error:', metrics.mean_squared_error(Y_validation,y_pred)) print('Root Mean Squared Error:',np.sqrt(metrics.mean_squared_error(Y_validation,y_pred))) print('R2 =',metrics.r2_score(Y_validation,y_pred))请优化

当处理大型数据集时，计算一些评估指标可能会导致内存不足错误。为了优化这段代码，你可以采取以下措施：

1. 分批计算：将验证集分成小批次进行评估，而不是一次性计算所有指标。这样可以减少内存的使用量。示例代码如下：

batch_size = 1000
num_batches = len(Y_validation) // batch_size

mae = 0
mse = 0
rmse = 0
r2 = 0

for i in range(num_batches):
    start_idx = i * batch_size
    end_idx = (i + 1) * batch_size
    
    batch_Y_validation = Y_validation[start_idx:end_idx]
    batch_y_pred = y_pred[start_idx:end_idx]
    
    mae += metrics.mean_absolute_error(batch_Y_validation, batch_y_pred)
    mse += metrics.mean_squared_error(batch_Y_validation, batch_y_pred)
    r2 += metrics.r2_score(batch_Y_validation, batch_y_pred)

mae /= num_batches
mse /= num_batches
rmse = np.sqrt(mse)
r2 /= num_batches

print('Mean Absolute Error:', mae)
print('Mean Squared Error:', mse)
print('Root Mean Squared Error:', rmse)
print('R2 =', r2)

2. 释放内存：在每次计算指标后，手动释放不再需要的变量占用的内存。示例代码如下：

mae = metrics.mean_absolute_error(Y_validation, y_pred)
print('Mean Absolute Error:', mae)
del mae

mse = metrics.mean_squared_error(Y_validation, y_pred)
print('Mean Squared Error:', mse)
del mse

rmse = np.sqrt(metrics.mean_squared_error(Y_validation, y_pred))
print('Root Mean Squared Error:', rmse)
del rmse

r2 = metrics.r2_score(Y_validation, y_pred)
print('R2 =', r2)
del r2

通过这些优化措施，你应该能够减少内存的使用，从而避免出现内存不足错误。