sklearn ColumnTransformer 与 MultilabelBinarizer [英] sklearn ColumnTransformer with MultilabelBinarizer
问题描述
我想知道是否可以在 ColumnTransformer 中使用 MultilabelBinarizer.
I wonder if it is possible to use a MultilabelBinarizer within a ColumnTransformer.
我有一个玩具熊猫数据框,例如:
I have a toy pandas dataframe like:
df = pd.DataFrame({"id":[1,2,3],
"text": ["some text", "some other text", "yet another text"],
"label": [["white", "cat"], ["black", "cat"], ["brown", "dog"]]})
preprocess = ColumnTransformer(
[
('vectorizer', CountVectorizer(), 'text'),
('binarizer', MultiLabelBinarizer(), ['label']),
],
remainder='drop')
然而,这段代码抛出了一个异常:
this code, however, throws an exception:
~/lib/python3.7/site-packages/sklearn/pipeline.py in _fit_transform_one(transformer, X, y, weight, message_clsname, message, **fit_params)
714 with _print_elapsed_time(message_clsname, message):
715 if hasattr(transformer, 'fit_transform'):
--> 716 res = transformer.fit_transform(X, y, **fit_params)
717 else:
718 res = transformer.fit(X, y, **fit_params).transform(X)
TypeError: fit_transform() takes 2 positional arguments but 3 were given
使用 OneHotEncoder,ColumnTransformer 可以正常工作.
With OneHotEncoder the ColumnTransformer does work.
推荐答案
对于输入 X
,MultiLabelBinarizer
适合一次处理一列(作为每一行应该是一系列类别),而 OneHotEncoder
可以处理多列.要使 ColumnTransformer
兼容 MultiHotEncoder
,您需要遍历 X
的所有列并使用 MultiLabelBinarizer 拟合/转换每一列代码>.以下应该适用于
pandas.DataFrame
输入.
For input X
, MultiLabelBinarizer
is suited to deal with one column at a time (as each row is supposed to be a sequence of categories), while OneHotEncoder
can deal with multiple columns. To make a ColumnTransformer
compatible MultiHotEncoder
, you will need to iterate through all columns of X
and fit/transform each column with a MultiLabelBinarizer
. The following should work with pandas.DataFrame
input.
from sklearn.base import BaseEstimator, TransformerMixin
class MultiHotEncoder(BaseEstimator, TransformerMixin):
"""Wraps `MultiLabelBinarizer` in a form that can work with `ColumnTransformer`. Note
that input X has to be a `pandas.DataFrame`.
"""
def __init__(self):
self.mlbs = list()
self.n_columns = 0
self.categories_ = self.classes_ = list()
def fit(self, X:pd.DataFrame, y=None):
for i in range(X.shape[1]): # X can be of multiple columns
mlb = MultiLabelBinarizer()
mlb.fit(X.iloc[:,i])
self.mlbs.append(mlb)
self.classes_.append(mlb.classes_)
self.n_columns += 1
return self
def transform(self, X:pd.DataFrame):
if self.n_columns == 0:
raise ValueError('Please fit the transformer first.')
if self.n_columns != X.shape[1]:
raise ValueError(f'The fit transformer deals with {self.n_columns} columns '
f'while the input has {X.shape[1]}.'
)
result = list()
for i in range(self.n_columns):
result.append(self.mlbs[i].transform(X.iloc[:,i]))
result = np.concatenate(result, axis=1)
return result
# test
temp = pd.DataFrame({
"id":[1,2,3],
"text": ["some text", "some other text", "yet another text"],
"label": [["white", "cat"], ["black", "cat"], ["brown", "dog"]],
"label2": [["w", "c"], ["b", "c"], ["b", "d"]]
})
col_transformer = ColumnTransformer([
('one-hot', OneHotEncoder(), ['id','text']),
('multi-hot', MultiHotEncoder(), ['label', 'label2'])
])
col_transformer.fit_transform(temp)
你应该得到:
array([[1., 0., 0., 0., 1., 0., 0., 0., 1., 0., 1., 0., 1., 0., 1.],
[0., 1., 0., 1., 0., 0., 1., 0., 1., 0., 0., 1., 1., 0., 0.],
[0., 0., 1., 0., 0., 1., 0., 1., 0., 1., 0., 1., 0., 1., 0.]])
注意前 3 列和后 3 列是单热编码的,而后面的 5 和最后 4 列是多热编码的.并且可以像往常一样找到类别信息:
Note how the first 3 and second 3 columns are one-hot coded while the following 5 and last 4 are multi-hot coded. And the categories info can be found as you normally do:
col_transformer.named_transformers_['one-hot'].categories_
>>> [array([1, 2, 3], dtype=object),
array(['some other text', 'some text', 'yet another text'], dtype=object)]
col_transformer.named_transformers_['multi-hot'].categories_
>>> [array(['black', 'brown', 'cat', 'dog', 'white'], dtype=object),
array(['b', 'c', 'd', 'w'], dtype=object)]
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