warm_up_mode

A context manager for training pipelines during a warm-up phase.

You don't have to worry about anything when you call predict_one and learn_one with a pipeline during in a training loop. The methods at each step of the pipeline will be called in the correct order.

However, during a warm-up phase, you might just be calling learn_one because you don't need the out-of-sample predictions. In this case the unsupervised estimators in the pipeline won't be updated, because they are usually updated when predict_one is called.

This context manager allows you to override that behavior and make it so that unsupervised estimators are updated when learn_one is called.

Examples

Let's first see what methods are called if we just call learn_one.

>>> import io
>>> import logging
>>> from river import anomaly
>>> from river import compose
>>> from river import datasets
>>> from river import preprocessing
>>> from river import utils

>>> model = compose.Pipeline(
...     preprocessing.MinMaxScaler(),
...     anomaly.HalfSpaceTrees()
... )

>>> class_condition = lambda x: x.__class__.__name__ in ('MinMaxScaler', 'HalfSpaceTrees')

>>> logger = logging.getLogger()
>>> logger.setLevel(logging.DEBUG)

>>> logs = io.StringIO()
>>> sh = logging.StreamHandler(logs)
>>> sh.setLevel(logging.DEBUG)
>>> logger.addHandler(sh)

>>> with utils.log_method_calls(class_condition):
...     for x, y in datasets.CreditCard().take(1):
...         model = model.learn_one(x)

>>> print(logs.getvalue())
MinMaxScaler.transform_one
HalfSpaceTrees.learn_one

Now let's use the context manager and see what methods get called.

>>> logs = io.StringIO()
>>> sh = logging.StreamHandler(logs)
>>> sh.setLevel(logging.DEBUG)
>>> logger.addHandler(sh)

>>> with utils.log_method_calls(class_condition), utils.warm_up_mode():
...     for x, y in datasets.CreditCard().take(1):
...         model = model.learn_one(x)

>>> print(logs.getvalue())
MinMaxScaler.learn_one
MinMaxScaler.transform_one
HalfSpaceTrees.learn_one

We can see that the scaler got updated before transforming the data.