Using a transformer (estimator) to transform the target labels in sklearn.pipeline

Question

I understand that one can chain several estimators that implement the transform method to transform X (the feature set) in sklearn.pipeline. However I have a use case where I would like also transform the target labels (like transform the labels to [1...K] instead of [0, K-1] and I would love to do that as a component in my pipeline. Is it possible to that at all using the sklearn.pipeline.?

Answer 1

There is now a nicer way to do this built into scikit-learn; using a compose.TransformedTargetRegressor.

When constructing these objects you give them a regressor and a transformer. When you .fit() them they transform the targets before regressing, and when you .predict() them they transform their predicted targets back to the original space.

It's important to note that you can pass them a pipeline object, so they should interface nicely with your existing setup. For example, take the following setup where I train a ridge regression to predict 1 target given 2 features:

# Imports
import numpy as np
from sklearn import compose, linear_model, metrics, pipeline, preprocessing

# Generate some training and test features and targets
X_train = np.random.rand(200).reshape(100,2)
y_train = 1.2*X_train[:, 0]+3.4*X_train[:, 1]+5.6
X_test = np.random.rand(20).reshape(10,2)
y_test = 1.2*X_test[:, 0]+3.4*X_test[:, 1]+5.6

# Define my model and scalers
ridge = linear_model.Ridge(alpha=1e-2)
scaler = preprocessing.StandardScaler()
minmax = preprocessing.MinMaxScaler(feature_range=(-1,1))

# Construct a pipeline using these methods
pipe = pipeline.make_pipeline(scaler, ridge)

# Construct a TransformedTargetRegressor using this pipeline
# ** So far the set-up has been standard **
regr = compose.TransformedTargetRegressor(regressor=pipe, transformer=minmax)

# Fit and train the regr like you would a pipeline
regr.fit(X_train, y_train)
y_pred = regr.predict(X_test)
print("MAE: {}".format(metrics.mean_absolute_error(y_test, y_pred)))

This still isn't quite as smooth as I'd like it to be, for example you can access the regressor that contained by a TransformedTargetRegressor using .regressor_ but the coefficients stored there are untransformed. This means there are some extra hoops to jump through if you want to work your way back to the equation that generated the data.