var#

skrub.var(name, value=NULL)[source]#

Create a skrub variable.

Variables represent inputs to a machine-learning pipeline. They can be combined with other variables, constants, operators, function calls etc. to build up complex expressions, which implicitly define the pipeline.

See the example gallery for more information about skrub pipelines.

Parameters:
namestr

The name for this input. It corresponds to a key in the dictionary that is passed to the pipeline’s fit() method (see Examples below). Names must be unique within a pipeline and must not start with "_skrub_"

valueobject, optional

Optionally, an initial value can be given to the variable. When it is available, it is used to provide a preview of the pipeline’s results, to detect errors in the pipeline early, and to provide better help and tab-completion in interactive Python shells.

Returns:
A skrub variable

See also

skrub.X

Create a skrub variable and mark it as being X.

skrub.y

Create a skrub variable and mark it as being y.

Examples

Variables without a value:

>>> import skrub
>>> a = skrub.var('a')
>>> a
<Var 'a'>
>>> b = skrub.var('b')
>>> c = a + b
>>> c
<BinOp: add>
>>> print(c.skb.describe_steps())
VAR 'a'
VAR 'b'
BINOP: add

The names of variables correspond to keys in the inputs:

>>> c.skb.eval({'a': 10, 'b': 6})
16

And also to keys to the inputs to the pipeline:

>>> pipeline = c.skb.get_pipeline()
>>> pipeline.fit_transform({'a': 5, 'b': 4})
9

When providing a value, we see what the pipeline produces for the values we provided:

>>> a = skrub.var('a', 2)
>>> b = skrub.var('b', 3)
>>> b
<Var 'b'>
Result:
―――――――
3
>>> c = a + b
>>> c
<BinOp: add>
Result:
―――――――
5

The values are also used as defaults for eval():

>>> c.skb.eval()
5

But we can still override them. And inputs must be provided explicitly when using the pipeline returned by .skb.get_pipeline().

>>> c.skb.eval({'a': 10, 'b': 6})
16

Much more information about skrub variables is provided in the examples gallery.

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