我正在尝试在 Iris 数据集中的目标列('Species')上使用一个热编码器。
但我收到以下错误:
ValueError: Expected 2D array, got 1D array instead:
Reshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample.
Id SepalLengthCm SepalWidthCm PetalLengthCm PetalWidthCm Species
0 1 5.1 3.5 1.4 0.2 Iris-setosa
1 2 4.9 3.0 1.4 0.2 Iris-setosa
2 3 4.7 3.2 1.3 0.2 Iris-setosa
3 4 4.6 3.1 1.5 0.2 Iris-setosa
4 5 5.0 3.6 1.4 0.2 Iris-setosa
我确实在谷歌上搜索了这个问题,我发现大多数 scikit 学习估计器需要一个二维数组而不是一维数组。
同时,我也发现我们可以尝试通过dataframe及其索引来对单列进行编码,但是没有用
onehotencoder = OneHotEncoder(categorical_features=[df.columns.tolist().index('pattern_id')
X = dataset.iloc[:,1:5].values
y = dataset.iloc[:, 5].values
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
labelencoder= LabelEncoder()
y = labelencoder.fit_transform(y)
onehotencoder = OneHotEncoder(categorical_features=[0])
y = onehotencoder.fit_transform(y)
我正在尝试对单个分类列进行编码并拆分为多列(编码通常的工作方式)
最佳答案
ValueError: Expected 2D array, got 1D array instead: Reshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample.
说您需要将数组转换为向量。
你可以这样做:
from sklearn import datasets
from sklearn.decomposition import PCA
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
import pandas as pd
import numpy as np
# load iris dataset
>>> iris = datasets.load_iris()
>>> iris = pd.DataFrame(data= np.c_[iris['data'], iris['target']], columns= iris['feature_names'] + ['target'])
>>> y = iris.target.values
>>> onehotencoder = OneHotEncoder(categories='auto')
>>> y = onehotencoder.fit_transform(y.reshape(-1,1))
# y - will be sparse matrix of type '<class 'numpy.float64'>
# if you want it to be a array you need to
>>> print(y.toarray())
[[1. 0. 0.]
[1. 0. 0.]
. . . .
[0. 0. 1.]
[0. 0. 1.]]
您也可以使用
get_dummies
函数 ( docs )>>> pd.get_dummies(iris.target).head()
0.0 1.0 2.0
0 1 0 0
1 1 0 0
2 1 0 0
3 1 0 0
4 1 0 0
希望有帮助!
关于python-3.x - 单列热编码,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/56355312/