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python - 为什么函数以不同的格式和顺序返回结果

问题描述

此代码运行良好且可预期。制作拟合、预测和计数损失函数：

    features = set(df_train_cat.columns)
    features.remove('SalePrice')
    features.remove('Id')

    X_train_cat = df_train_cat[features].values
    y_train_cat = df_train_cat.SalePrice.values

    X_train, X_val, y_train, y_val = train_test_split( X_train_cat, y_train_cat, test_size=0.33,                         random_state=17)


    regression = linear_model.Ridge(alpha=.5, random_state = 17)
    regression.fit(X_train,y_train)
    predictions = regression.predict(X_val)

    rmse = (mean_squared_error(y_val, predictions))**(1/2)

    mae = mean_absolute_error(y_val, predictions)

    print('RMSE on run = {}, MAE = {}'.format(rmse, mae))
    -------------------------------------------------------
    in returns: 
    RMSE on run = 27696.648368899852, MAE = 17553.925337213957

但是当我试图将它包装在函数中时，结果是不同的：

def regress_without_uncorrelated(delete_feture_list, data):
    features = set(data.columns)

    delete_feture_list = delete_feture_list + ['SalePrice', 'Id']
    if len(delete_feture_list) > 2:
        features.remove(set(delete_feture_list))


    X = data[features].values
    y = data.SalePrice.values

    X_train, X_val, y_train, y_val = train_test_split( X, y, test_size=0.33, random_state=17)

    regression = linear_model.Ridge(alpha=.5, random_state = 17)
    regression.fit(X_train,y_train)
    predictions = regression.predict(X_val)


    rmse = (mean_squared_error(y_val, predictions))**(1/2)
    mae = mean_absolute_error(y_val, predictions)
    print('rmse = {}, mae = {}'.format(rmse,  mae))
    return rmse, mae
    -------------------------------------
    result is:         
    rmse = 2.760396036595275e-08, mae = 1.7484699770097167e-08

问题：

  1. 为什么结果会以 e 返回？如何让它像第一个街区一样？
  2. 为什么在具有固定 random_state 的相同数据处损失顺序会发生变化？

标签： pythonpandasscikit-learn

解决方案

问题出在 set.remove() 方法中，该方法不适用于可迭代。

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