带有 MultilabelBinarizer 的 sklearn ColumnTransformer

Posted 2023-03-12

【中文标题】带有 MultilabelBinarizer 的 sklearn ColumnTransformer

【英文标题】：sklearn ColumnTransformer with MultilabelBinarizer

【发布时间】：2020-04-02 21:33:04

【问题描述】：

我想知道是否可以在 ColumnTransformer 中使用 MultilabelBinarizer。

我有一个玩具熊猫数据框，例如：

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')

但是，这段代码会引发异常：

~/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 确实可以工作。

【参考方案1】：

我在测试中并没有特别勤奋地了解确切为什么下面的工作，但我能够构建一个自定义的<Transformer>，它基本上“包装”了MultiLabelBinarizer，但也是兼容<ColumnTransformer>：

class MultiLabelBinarizerFixedTransformer(BaseEstimator, TransformerMixin):
    """       
    Wraps `MultiLabelBinarizer` in a form that can work with `ColumnTransformer`
    """
    def __init__(
            self 
        ):
        self.feature_name = ["mlb"]
        self.mlb = MultiLabelBinarizer(sparse_output=False)

    def fit(self, X, y=None):
        self.mlb.fit(X)
        return self

    def transform(self, X):
        return self.mlb.transform(X)

    def get_feature_names(self, input_features=None):
        cats = self.mlb.classes_
        if input_features is None:
            input_features = ['x%d' % i for i in range(len(cats))]
            print(input_features)
        elif len(input_features) != len(self.categories_):
            raise ValueError(
                "input_features should have length equal to number of "
                "features (), got ".format(len(self.categories_),
                                               len(input_features)))

        feature_names = [f"input_features[i]_cats[i]" for i in range(len(cats))]
        return np.array(feature_names, dtype=object)

我的预感是MultiLabelBinarizer 对transform() 使用的set of inputs 与<ColumnTransformer> 预期的不同。

【参考方案2】：

对于输入X，MultiLabelBinarizer 适合一次处理一列（因为每一行都应该是一个类别序列），而OneHotEncoder 可以处理多列。要使ColumnTransformer 与MultiHotEncoder 兼容，您需要遍历X 的所有列，并使用MultiLabelBinarizer 拟合/转换每一列。以下应与pandas.DataFrame 输入一起使用。

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 列是如何单热编码的，而后 5 列和后 4 列是多热编码的。并且可以像往常一样找到类别信息：

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)]