skrub.DataOp.skb.describe_steps#

DataOp.skb.describe_steps()[source]#

Get a text representation of the computation graph.

Usually the graphical representation provided by DataOp.skb.draw_graph() or DataOp.skb.full_report() is more useful. This is a fallback for inspecting the computation graph when only text output is available.

Returns:

str: A string representing the different computation steps, one on each line.

See also

sklearn.model_selection.cross_validate(): Evaluate metric(s) by cross-validation and also record fit/score times.
skrub.DataOp.skb.make_learner(): Get a skrub learner for this DataOp.

Examples

>>> import skrub
>>> a = skrub.var('a')
>>> b = skrub.var('b')
>>> c = a + b
>>> d = c * c
>>> print(d.skb.describe_steps())
Var 'a'
Var 'b'
BinOp: add
( Var 'a' )*
( Var 'b' )*
( BinOp: add )*
BinOp: mul
* Cached, not recomputed

The above should be read from top to bottom as instructions for a simple stack machine: load the variable ‘a’, load the variable ‘b’, compute the addition leaving the result of (a + b) on the stack, then repeat this operation (but the second time no computation actually runs because the result of evaluating c has been cached in-memory), and finally evaluate the multiplication.

skrub.DataOp.skb.describe_steps#

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