使用 StandardScaler 仅标准化数值特征

Question

我有以下数据集：

df=pd.read_csv('https://raw.githubusercontent.com/michalis0/DataMining_and_MachineLearning/master/data/HR_comma_sep.csv')

我先用标签编码器le_salary编码salary，然后用序数编码器oe_salary编码。然后我用 OneHotEncoder ohe_department 编码 department。我把它全部连接起来，现在有一个 concat_df。现在我想做一个逻辑回归，但要标准化，这就是我遇到问题的地方。这是我的价值观和 train/test 分裂：

X=concat_df[[ 'satisfaction_level', 'last_evaluation', 'number_project', 'average_monthly_hours', 'time_spent_company', 'work_accident', 'promotion_last_5years', ('IT',), ('RandD',), ('accounting',), ('hr',), ('management',), ('marketing',), ('product_mng',), ('sales',), ('support',), ('technical',), 'oe_salary', 'eval_spent']].values
y=concat_df["left"].values

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=72)

然后我尝试使用以下代码仅标准化数值：

from sklearn.compose import ColumnTransformer
scaler = StandardScaler()
#select cols to standardize
Cols = ['satisfaction_level', 'last_evaluation', 'number_project', 'average_monthly_hours', 'time_spent_company', 'eval_spent']
#set up preprocessor
preprocessor = ColumnTransformer([('standard', scaler, Cols)], remainder = 'passthrough')
#fit preprocessor
X_train_std = preprocessor.fit_transform(X_train)
X_test_std = preprocessor.transform(X_test)

但是我得到了以下错误，我没有理解，因为我之前已经标准化了，没有任何问题。

AttributeError                            Traceback (most recent call last)
/usr/local/lib/python3.7/dist-packages/sklearn/utils/__init__.py in _get_column_indices(X, key)
    408         try:
--> 409             all_columns = X.columns
    410         except AttributeError:

AttributeError: 'numpy.ndarray' object has no attribute 'columns'

During handling of the above exception, another exception occurred:

ValueError                                Traceback (most recent call last)
3 frames
/usr/local/lib/python3.7/dist-packages/sklearn/utils/__init__.py in _get_column_indices(X, key)
    410         except AttributeError:
    411             raise ValueError(
--> 412                 "Specifying the columns using strings is only "
    413                 "supported for pandas DataFrames"
    414             )

ValueError: Specifying the columns using strings is only supported for pandas DataFrames

为什么会出现此错误，这是什么意思？

Answer 1

像这样将 .values 删除到 DataFrame 中：

X=concat_df[[ 'satisfaction_level', 'last_evaluation', 'number_project', 'average_monthly_hours', 'time_spent_company', 'work_accident', 'promotion_last_5years', ('IT',), ('RandD',), ('accounting',), ('hr',), ('management',), ('marketing',), ('product_mng',), ('sales',), ('support',), ('technical',), 'oe_salary', 'eval_spent']]
y=concat_df["left"]

我们应该能够保留 DataFrame 格式并使用列名调用它们。

此外，要删除那些关于列名的警告，我们可以通过在开始时执行以下操作来修改它们：

concat_df.columns = ['satisfaction_level',
    'last_evaluation',
    'number_project',
    'average_monthly_hours',
    'time_spent_company',
    'work_accident',
    'promotion_last_5years',
    'IT',
    'RandD',
    'accounting',
    'hr',
    'management',
    'marketing',
    'product_mng',
    'sales',
    'support',
    'technical',
    'oe_salary',
    'eval_spent',
    'left']

然后我们可以调用新的列名：

X=concat_df[['satisfaction_level',
    'last_evaluation',
    'number_project',
    'average_monthly_hours',
    'time_spent_company',
    'work_accident',
    'promotion_last_5years',
    'IT',
    'RandD',
    'accounting',
    'hr',
    'management',
    'marketing',
    'product_mng',
    'sales',
    'support',
    'technical',
    'oe_salary',
    'eval_spent']]]
y=concat_df["left"]

使用 StandardScaler 仅标准化数值特征

Standardize only numerical features with StandardScaler

python

data-mining

pandas

scikit-learn

standardization