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), compose.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.