在Scikit Learn中将管道与自定义转换器一起使用

问题描述：

在最后一次线性回归估计器拟合之前，我尝试使用"y"列中的值对"X"列进行转换(这是一个玩具示例，仅用于显示使用 y 进行转换).但是为什么 df ['y'] 不传递给 MyTransformer ?

I tried to transform the column 'X' using values in column 'y' (this is a toy example, just to show using y for transformation) before fitted by the last linear regression estimator. But why df['y'] is not passed to MyTransformer?

from sklearn.base import TransformerMixin
class MyTransformer(TransformerMixin):
    def __init__(self):
        pass
    def fit(self, X, y=None):
        return self
    def transform(self, X, y=None):
        print(y)
        return X + np.sum(y)

df = pd.DataFrame(np.array([[2, 3], [1, 5], [1, 1], [5, 6], [1, 2]]), columns=['X', 'y'])
pip =  Pipeline([('my_transformer', MyTransformer()), 
             ('sqrt', FunctionTransformer(np.sqrt, validate=False)),
             ('lr', LinearRegression())])
pip.fit(df[['X']], df['y'])

运行此脚本将在返回X + np.sum(y)的行上引发错误，看起来y为 None .

Running this script will raise an error at line return X + np.sum(y), looks like y is None.

答

如前所述，fit_transform方法不会传递y进行变换.我之前所做的是实现自己的fit_transform.不是您的代码，但是这是我最近写的一个示例:

As stated previously, the fit_transform method doesn't pass y off to transform. What I've done previously is implement my own fit_transform. Not your code, but here's an example I wrote recently:

class MultiColumnLabelEncoder:
    def __init__(self, *args, **kwargs):
        self.encoder = StandardLabelEncoder(*args, **kwargs)
    def fit(self, X, y=None):
        return self
    def transform(self,X):
        data = X.copy()
        for i in range(data.shape[1]):
            data[:, i] = LabelEncoder().fit_transform(data[:, i])
        return data
    def fit_transform(self, X, y=None):
        return self.fit(X, y).transform(X)

还有其他方法.您可以将y作为类参数，并在transform方法中对其进行访问.

There are other ways. You could have y as a class param and access it in the transform method.

我应该注意，您可以将y传递给您的transform版本.所以:

I should note that you can pass y off to your version of transform. So:

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

在Scikit Learn中将管道与自定义转换器一起使用

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