skrub.DataOp.skb.get_vars#

DataOp.skb.get_vars(all_named_ops=False)[source]#

Get all the variables used in the DataOp.

Parameters:

all_named_opsbool, default = False: If False, return only actual variables (DataOps created with var(), X() or y()). If False, return all nodes that have a name (ie for which a value can be passed in the environment).

Returns:

dict: Keys are names, and values the corresponding DataOp.

Examples

>>> import skrub

>>> a = skrub.var("a")
>>> b = skrub.var("b")
>>> c = (a + b).skb.set_name("c")
>>> d = c + c
>>> d
<BinOp: add>

Our DataOp, d, contains 2 variables: “a” and “b”:

>>> d.skb.get_vars()
{'a': <Var 'a'>, 'b': <Var 'b'>}

Those are the keys for which we need to provide values in the environment when evaluating d:

>>> d.skb.eval({"a": 10, "b": 3}) # (10 + 3) + (10 + 3) = 26
26

In addition, we set a name on the internal node c. It is not a variable, and normally it is computed as (a + b). But as it has a name, we can override its output by passing a value for “c” in the environment. When we do, the computation of c never happens (nor of a or b, here, because they are only used to compute c) – it is bypassed and the provided value is used instead.

>>> d.skb.eval({"c": 7}) # 7 + 7 = 14
14

If we want get_vars to also list nodes like our example c which have a name and can be passed in the environment, we pass all_named_ops=True:

>>> d.skb.get_vars(all_named_ops=True)
{'a': <Var 'a'>, 'b': <Var 'b'>, 'c': <c | BinOp: add>}

Note get_vars can be particularly useful when we have a learner (e.g. loaded from a pickle file) and we want to check what inputs we should pass to its methods such as fit and transform:

>>> learner = d.skb.make_learner()
>>> list(learner.data_op.skb.get_vars().keys())
['a', 'b']

The output above tells us what keys the dict we pass to learner.fit() should contain:

>>> learner.fit({'a': 2, 'b': 3})
SkrubLearner(data_op=<BinOp: add>)

skrub.DataOp.skb.get_vars#

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