skrub.Expr.skb.train_test_split#

Expr.skb.train_test_split(environment=None, *, keep_subsampling=False, splitter=<function train_test_split>, **splitter_kwargs)[source]#

Split an environment into a training an testing environments.

Parameters:

environmentdict, optional: The environment (dict mapping variable names to values) containing the full data. If None (the default), the data is retrieved from the expression.
keep_subsamplingbool, default=False: If True, and if subsampling has been configured (see Expr.skb.subsample()), use a subsample of the data. By default subsampling is not applied and all the data is used.
splitterfunction, optional: The function used to split X and y once they have been computed. By default, sklearn.train_test_split is used.
splitter_kwargs: Additional named arguments to pass to the splitter.

Returns:

dict

The return value is slightly different than scikit-learn’s. Rather than a tuple, it returns a dictionary with the following keys:

train: a dictionary containing the training environment
test: a dictionary containing the test environment
X_train: the value of the variable marked with skb.mark_as_x() in the train environment
X_test: the value of the variable marked with skb.mark_as_x() in the test environment
y_train: the value of the variable marked with skb.mark_as_y() in the train environment, if there is one (may not be the case for unsupervised learning).
y_test: the value of the variable marked with skb.mark_as_y() in the test environment, if there is one (may not be the case for unsupervised learning).

Examples

>>> import skrub
>>> from sklearn.dummy import DummyClassifier
>>> from sklearn.metrics import accuracy_score

>>> orders = skrub.var("orders", skrub.toy_orders().orders)
>>> X = orders.skb.drop("delayed").skb.mark_as_X()
>>> y = orders["delayed"].skb.mark_as_y()
>>> delayed = X.skb.apply(skrub.TableVectorizer()).skb.apply(
...     DummyClassifier(), y=y
... )

>>> split = delayed.skb.train_test_split(random_state=0)
>>> split.keys()
dict_keys(['train', 'test', 'X_train', 'X_test', 'y_train', 'y_test'])
>>> pipeline = delayed.skb.get_pipeline()
>>> pipeline.fit(split["train"])
SkrubPipeline(expr=<Apply DummyClassifier>)
>>> pipeline.score(split["test"])
0.0
>>> predictions = pipeline.predict(split["test"])
>>> accuracy_score(split["y_test"], predictions)
0.0

skrub.Expr.skb.train_test_split#

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