How can I create custom transformers in an sklearn pipeline?

How can I create custom transformers in an sklearn pipeline?

You can create custom transformers by subclassing BaseEstimator and TransformerMixin from sklearn.base. Would you like an example?

Yes, please provide an example of a custom transformer.

Here's a simple example:

from sklearn.base import BaseEstimator, TransformerMixin

class CustomScaler(BaseEstimator, TransformerMixin):
    def fit(self, X, y=None):
        self.mean_ = X.mean(axis=0)
        self.scale_ = X.std(axis=0)
        return self

    def transform(self, X):
        return (X - self.mean_) / self.scale_

This transformer scales the data to have a mean of 0 and a standard deviation of 1.

How do I integrate this custom transformer into a pipeline?

You can integrate it using Pipeline from sklearn.pipeline. Here's how:

from sklearn.pipeline import Pipeline

pipeline = Pipeline([
    ('scaler', CustomScaler()),
    ('model', SomeModel())
])

This will apply your custom scaling before fitting the model.