pandas 如何在 sklearn 中使用 OneHotEncoder 的输出？
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How to use the output from OneHotEncoder in sklearn?
提问by Bert Carremans
I have a Pandas Dataframe with 2 categorical variables, and ID variable and a target variable (for classification). I managed to convert the categorical values with OneHotEncoder. This results in a sparse matrix.
我有一个 Pandas 数据框，其中包含 2 个分类变量、ID 变量和一个目标变量（用于分类）。我设法将分类值转换为OneHotEncoder. 这导致稀疏矩阵。
ohe = OneHotEncoder()
# First I remapped the string values in the categorical variables to integers as OneHotEncoder needs integers as input
... remapping code ...
ohe.fit(df[['col_a', 'col_b']])
ohe.transform(df[['col_a', 'col_b']])
But I have no clue how I can use this sparse matrix in a DecisionTreeClassifier? Especially when I want to add some other non-categorical variables in my dataframe later on. Thanks!
但是我不知道如何在 DecisionTreeClassifier 中使用这个稀疏矩阵？特别是当我稍后想在我的数据框中添加一些其他非分类变量时。谢谢！
EDITIn reply to the comment of miraculixx: I also tried the DataFrameMapper in sklearn-pandas
编辑回复 miraculixx 的评论：我也在 sklearn-pandas 中尝试了 DataFrameMapper
mapper = DataFrameMapper([
('id_col', None),
('target_col', None),
(['col_a'], OneHotEncoder()),
(['col_b'], OneHotEncoder())
])
t = mapper.fit_transform(df)
But then I get this error:
但是后来我收到了这个错误：
TypeError: no supported conversion for types : (dtype('O'), dtype('int64'), dtype('float64'), dtype('float64')).
类型错误：不支持类型转换：(dtype('O')、dtype('int64')、dtype('float64')、dtype('float64'))。
回答by Guiem Bosch
I see you are already using Pandas, so why not using its get_dummiesfunction?
我看到你已经在使用 Pandas，那么为什么不使用它的get_dummies功能呢？
import pandas as pd
df = pd.DataFrame([['rick','young'],['phil','old'],['john','teenager']],columns=['name','age-group'])
result
结果
name age-group
0 rick young
1 phil old
2 john teenager
now you encode with get_dummies
现在你用 get_dummies 编码
pd.get_dummies(df)
result
结果
name_john name_phil name_rick age-group_old age-group_teenager \
0 0 0 1 0 0
1 0 1 0 1 0
2 1 0 0 0 1
age-group_young
0 1
1 0
2 0
And you can actually use the new Pandas DataFrame in your Sklearn's DecisionTreeClassifier.
您实际上可以在 Sklearn 的 DecisionTreeClassifier 中使用新的 Pandas DataFrame。
回答by Merlin
Look at this example from scikit-learn: http://scikit-learn.org/stable/auto_examples/ensemble/plot_feature_transformation.html#example-ensemble-plot-feature-transformation-py
从 scikit-learn 看这个例子：http://scikit-learn.org/stable/auto_examples/ensemble/plot_feature_transformation.html#example-ensemble-plot-feature-transformation-py
Problem is that you are not using the sparse matrices to xx.fit(). You are using the original data.
问题是您没有将稀疏矩阵用于xx.fit(). 您正在使用原始数据。
