skrub.DataOp.skb.mark_as_X#

DataOp.skb.mark_as_X(*, cv=None, split_kwargs=None)[source]#

Mark this DataOp as being the X table.

Returns a copy; the original DataOp is left unchanged.

This is used for train/test splits and cross-validation. To create a split,

The nodes that are marked as X and y are materialized: all the operations that come before are executed, to compute the value of X and y.
Then the resulting tables are divided into train and test parts according to the splitting strategy.
For each split, the rest of the DataOp is fitted on the train set and tested on the test set.

In addition, mark_as_X can be passed a splitter (cv) and named arguments for the splitter. Those will be evaluated at the same time as X and y and used to create the train/test splits.

Parameters:

cvint, cross-validation iterator or iterable, default=None

Cross-validation splitting strategy. It can be:

None: 5-fold (stratified) cross-validation
integer: specify the number of folds
sklearn CV splitter
iterable yielding (train, test) splits as arrays of indices.

split_kwargsdict or None, default=None

Named arguments for the split() function (such as groups for a GroupKFold). Note that split_kwargs (and cv) can themselves be DataOps.

Returns:

A new DataOp, which has been marked as being the X table (which
must be split for cross-validation).

See also

DataOp.skb.mark_as_y(): The equivalent of this function for the targets: mark a node as being the y table.
skrub.X(): skrub.X(value) can be used as a shorthand for skrub.var('X', value).skb.mark_as_X().
DataOp.skb.train_test_split(): Prepare training and testing sets for a DataOp.
DataOp.skb.cross_validate(): Perform cross-validation on a DataOp.
DataOp.skb.make_randomized_search(): Perform hyperparameter tuning driven by cross-validation scores.
DataOp.skb.make_grid_search(): Perform hyperparameter tuning driven by cross-validation scores.

Notes

During cross-validation, all the previous steps are first executed, until X and y (and the splitter and its additional arguments, if any) have been materialized. Then, X and y are split into training and testing sets. The following steps in the DataOp are fitted on the train data, and applied to test data, within each split.

This means that any step that comes before mark_as_X() or mark_as_y(), meaning that it is needed to compute X and y, sees the full dataset and cannot benefit from hyperparameter tuning. So we should be careful to start our learner by building X and y, and to use mark_as_X() and mark_as_y() as soon as possible.

skrub.X(value) can be used as a shorthand for skrub.var('X', value).skb.mark_as_X().

Examples

>>> import skrub
>>> orders = skrub.var('orders', skrub.datasets.toy_orders(split='all').orders)
>>> features = orders.drop(columns='delayed', errors='ignore')
>>> features.skb.is_X
False
>>> X = features.skb.mark_as_X()
>>> X.skb.is_X
True

Note the original is left unchanged

>>> features.skb.is_X
False

>>> y = orders['delayed'].skb.mark_as_y()
>>> y.skb.is_y
True

Now if we run cross-validation:

>>> from sklearn.dummy import DummyClassifier
>>> pred = X.skb.apply(DummyClassifier(), y=y)
>>> pred.skb.cross_validate(cv=2)['test_score']
0    0.666667
1    0.666667
Name: test_score, dtype: float64

First (outside of the cross-validation loop) X and y are computed. Then, they are split into training and test sets. Then the rest of the learner (in this case the last step, the DummyClassifier) is evaluated on those splits.

We can pass additional data to the cross-validation splitter by using the cv and split_kwargs parameters:

>>> df = skrub.datasets.toy_products()
>>> df
   description  price            seller     category
0        mouse     10   supermarket.com  electronics
1       hammer     15  bestproducts.com        tools
2     keyboard     20   supermarket.com  electronics
3      usb key      9  bestproducts.com  electronics
4      charger     13  bestproducts.com  electronics
5  screwdriver     12   supermarket.com        tools

Suppose we want to assess generalization to new sellers. While splitting for cross-validation we must group products by seller. We do it with sklearn.model_selection.LeaveOneGroupOut.

>>> from sklearn.dummy import DummyClassifier
>>> from sklearn.model_selection import LeaveOneGroupOut

>>> data = skrub.var("df", df)
>>> groups = data["seller"]
>>> X = data[["description", "price"]].skb.mark_as_X(
...     cv=LeaveOneGroupOut(), split_kwargs={"groups": groups}
... )
>>> y = data["category"].skb.mark_as_y()
>>> pred = X.skb.apply(DummyClassifier(), y=y)
>>> split = pred.skb.train_test_split()

The train set only contains data from the “supermarket.com” seller.

>>> split["X_train"]
   description  price
0        mouse     10
2     keyboard     20
5  screwdriver     12

The test set only contains data from the “bestproducts.com” seller.

>>> split["X_test"]
  description  price
1      hammer     15
3     usb key      9
4     charger     13

skrub.DataOp.skb.mark_as_X#

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