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salary
先用标签编码器编码le_salary
,然后用序数编码器编码oe_salary
。然后我department
用 OneHotEncoder编码ohe_department
。我将所有内容合并,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。这是我的价值观和训练/测试拆分:
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,我收到以下错误,我并不理解,因为我之前已经对其进行了标准化,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
为什么我会收到此错误,这是什么意思?