我正在尝试使用 OneclassSVM 检测数据集的异常值，我的代码如下

from sklearn.svm import OneclassSVM
import pandas as pd

input_file = "training.csv"
training_data = pd.read_csv(input_file)
   
X = training_data[['header1','header2','header3']].values
model = OneclassSVM(gamma='auto').fit(X)

但我收到以下错误：

> Traceback (most recent call last):   File
> "C:/Users/user1/PycharmProjects/sv/main.py",line 13,in <module>
>     model = svm.OneclassSVM(gamma='auto').fit(X)   File "C:\Users\user1\PycharmProjects\sv\venv\lib\site-packages\sklearn\svm\_classes.py",> line 1376,in fit
>     super().fit(X,np.ones(_num_samples(X)),File "C:\Users\user1\PycharmProjects\sv\venv\lib\site-packages\sklearn\svm\_base.py",> line 169,in fit
>     X,y = self._validate_data(X,y,dtype=np.float64,File "C:\Users\user1\PycharmProjects\sv\venv\lib\site-packages\sklearn\base.py",> line 433,in _validate_data
>     X,y = check_X_y(X,**check_params)   File "C:\Users\user1\PycharmProjects\sv\venv\lib\site-packages\sklearn\utils\validation.py",> line 63,in inner_f
>     return f(*args,**kwargs)   File "C:\Users\user1\PycharmProjects\sv\venv\lib\site-packages\sklearn\utils\validation.py",> line 814,in check_X_y
>     X = check_array(X,accept_sparse=accept_sparse,File "C:\Users\user1\PycharmProjects\sv\venv\lib\site-packages\sklearn\utils\validation.py",> line 616,in check_array
>     array = np.asarray(array,order=order,dtype=dtype)   File "C:\Users\user1\PycharmProjects\sv\venv\lib\site-packages\numpy\core\_asarray.py",> line 102,in asarray
>     return array(a,dtype,copy=False,order=order) ValueError: could not convert string to float: 'SDS'
> 
> Process finished with exit code 1

有人可以帮忙解决这个问题吗？