Even if thecategory_encoders.one_hot.OneHotEncoderdoesn't encode any features, we would expect it to convert a pd.DataFrame into a numpy.ndarray if we set the parameter : return_df=False Actual Behavior When thecategory_encoders.one_hot.OneHotEncoderdeals with a dataframe with only numerical...
OneHotEncoder(categorical_features='all', dtype=<class'numpy.float64'>, handle_unknown='error', n_values='auto', sparse=True) >>> enc.n_values_ array([2,3,4]) >>> enc.feature_indices_ array([0,2,5,9]) >>> enc.transform([[0,1,1]]) <1x9 sparse matrix oftype'<class 'n...
X = onehotencoder1.fit_transform(X).toarray() (我希望你的数据集不再有分类值。我建议你先对所有内容进行标签编码,然后再进行 onehotencode。 我得到了同样的错误,在错误消息之后有如下建议: "Reshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape...
1, 0, 1]data = array(data)print(data)# one hot encodeencoded = to_categorical(data)print(en...
data = np.array(['red', 'blue', 'green', 'blue', 'red']) one_hot_encoded = one_hot_encode(data) print(one_hot_encoded) 在上面的代码中,我们首先创建了一个包含颜色数据的NumPy数组,然后使用one_hot_encode函数将颜色数据转换为one-hot编码。输出结果如下: ...
classsklearn.preprocessing.OneHotEncoder(*,categories='auto',drop=None,sparse=True,dtype=<class 'numpy.float64'>,handle_unknown='error') Encode categorical features as a one-hot numeric array. The input to this transformer should be an array-like of integers or strings, denoting the values ta...
from numpy import argmax from sklearn.preprocessing import LabelEncoder from sklearn.preprocessing import OneHotEncoder# define exampledata = ['cold','cold','warm','cold','hot','hot','warm','cold','warm','hot'] values = array(data)print(values)# integer encodelabel_encoder = LabelEncoder...
#基于scikit-learn from numpy import array from numpy import argmax from sklearn.preprocessing import LabelEncoder from sklearn.preprocessing import OneHotEncoder # define example data = ['cold', 'cold', 'warm', 'cold', 'hot', 'hot', 'warm', 'cold', 'warm', 'hot'] values = array(da...
# need to be global or remembered to use it later def one_hot_encode(x):"""One hot encode a list of sample labels. Return a one-hot encoded vector for each label.: x: List of sample Labels : return: Numpy array of one-hot encoded labels """return label_binarizer.transform(x)
X = np.array(ct.fit_transform(X)) 如您所见,X没有改变,也没有进行编码。我已经测试过多次了。因此,似乎OneHotEncoder最多只能处理5个不同的类别值。有没有可以更改的参数或其他方法来编码具有5个以上值的分类变量? PS-我知道在编码后删除虚拟变量;) ...