deferred#

skrub.deferred(func)[source]#

Wrap function calls in an expression Expr.

When this decorator is applied, the resulting function returns expressions. The returned expression wraps the call to the original function, and the call is actually executed when the expression is evaluated.

This allows including a call to any function as a step in a pipeline, rather than executing it immediately.

See the examples gallery for an in-depth explanation of skrub expressions and deferred.

Parameters:
funcfunction

The function to wrap

Returns:
A new function

When called, rather than applying the original function immediately, it returns an expression. Evaluating the expression applies the original function.

See also

as_expr

Create an expression that evaluates to the given value.

Expr

Representation of a computation that can be used to build ML estimators.

Examples

>>> def tokenize(text):
...     words = text.split()
...     return [w for w in words if w not in ['the', 'of']]
>>> tokenize('the first day of the week')
['first', 'day', 'week']

>>> import skrub
>>> text = skrub.var('text')

Calling tokenize on a skrub expression raises an exception: tokenize tries to iterate immediately over the tokens to remove stop words, but the text will only be known when we run the pipeline.

>>> tokens = tokenize(text)
Traceback (most recent call last):
    ...
TypeError: This object is an expression that will be evaluated later, when your pipeline runs. So it is not possible to eagerly iterate over it now.

We can defer the call to tokenize until we are evaluating the expression:

>>> tokens = skrub.deferred(tokenize)(text)
>>> tokens
<Call 'tokenize'>
>>> tokens.skb.eval({'text': 'the first month of the year'})
['first', 'month', 'year']

Like any decorator deferred can be called explicitly as shown above or used with the @ syntax:

>>> @skrub.deferred
... def log(x):
...     print('INFO x =', x)
...     return x
>>> x = skrub.var('x')
>>> e = log(x)
>>> e.skb.eval({'x': 3})
INFO x = 3
3
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