OneHotEncoder 的分类特征问题 [英] Issue with OneHotEncoder for categorical features

问题描述

我想对数据集中的 10 个特征中的 3 个分类特征进行编码.我使用 sklearn.preprocessing<中的 preprocessing/a> 这样做:

I want to encode 3 categorical features out of 10 features in my datasets. I use preprocessing from sklearn.preprocessing to do so as the following:

from sklearn import preprocessing
cat_features = ['color', 'director_name', 'actor_2_name']
enc = preprocessing.OneHotEncoder(categorical_features=cat_features)
enc.fit(dataset.values)

但是，我无法继续，因为我收到此错误:

However, I couldn't proceed as I am getting this error:

    array = np.array(array, dtype=dtype, order=order, copy=copy)
ValueError: could not convert string to float: PG

我很惊讶为什么它会抱怨字符串，因为它应该转换它！！我在这里遗漏了什么吗?

I am surprised why it is complaining about the string as it is supposed to convert it!! Am I missing something here?

推荐答案

如果您阅读 OneHotEncoder 的文档，您将看到 fit 的输入是输入数组类型为 int".所以你需要为你的一个热编码数据做两个步骤

If you read the docs for OneHotEncoder you'll see the input for fit is "Input array of type int". So you need to do two steps for your one hot encoded data

from sklearn import preprocessing
cat_features = ['color', 'director_name', 'actor_2_name']
enc = preprocessing.LabelEncoder()
enc.fit(cat_features)
new_cat_features = enc.transform(cat_features)
print new_cat_features # [1 2 0]
new_cat_features = new_cat_features.reshape(-1, 1) # Needs to be the correct shape
ohe = preprocessing.OneHotEncoder(sparse=False) #Easier to read
print ohe.fit_transform(new_cat_features)

输出:

[[ 0.  1.  0.]
 [ 0.  0.  1.]
 [ 1.  0.  0.]]

编辑

从 0.20 开始，这变得更容易了，不仅因为 OneHotEncoder 现在可以很好地处理字符串，还因为我们可以使用 ColumnTransformer，看下面的例子

As of 0.20 this became a bit easier, not only because OneHotEncoder now handles strings nicely, but also because we can transform multiple columns easily using ColumnTransformer, see below for an example

from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
import numpy as np

X = np.array([['apple', 'red', 1, 'round', 0],
              ['orange', 'orange', 2, 'round', 0.1],
              ['bannana', 'yellow', 2, 'long', 0],
              ['apple', 'green', 1, 'round', 0.2]])
ct = ColumnTransformer(
    [('oh_enc', OneHotEncoder(sparse=False), [0, 1, 3]),],  # the column numbers I want to apply this to
    remainder='passthrough'  # This leaves the rest of my columns in place
)
print(ct2.fit_transform(X)) # Notice the output is a string

输出:

[['1.0' '0.0' '0.0' '0.0' '0.0' '1.0' '0.0' '0.0' '1.0' '1' '0']
 ['0.0' '0.0' '1.0' '0.0' '1.0' '0.0' '0.0' '0.0' '1.0' '2' '0.1']
 ['0.0' '1.0' '0.0' '0.0' '0.0' '0.0' '1.0' '1.0' '0.0' '2' '0']
 ['1.0' '0.0' '0.0' '1.0' '0.0' '0.0' '0.0' '0.0' '1.0' '1' '0.2']]

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