Expressions

Generalizing the scikit-learn pipeline. See skrub expression for further details.

var	Create a skrub variable.
X	Create a skrub variable and mark it as being X.
y	Create a skrub variable and mark it as being y.
as_expr	Create an expression Expr that evaluates to the given value.
deferred	Wrap function calls in an expression Expr.

The expression object.

Expr	Representation of a computation that can be used to build ML pipelines.

Inline hyperparameters selection within your expressions.

choose_bool	A choice between True and False.
choose_float	A choice of floating-point numbers from a numeric range.
choose_int	A choice of integers from a numeric range.
choose_from	A choice among several possible outcomes.
optional	A choice between value and None.

Evaluate your expressions.

cross_validate	Cross-validate a pipeline built from an expression.
eval_mode	Return the mode in which the expression is currently being evaluated.

The skb accessor exposes all expressions methods and attributes.

Expr.skb.apply	Apply a scikit-learn estimator to a dataframe or numpy array.
Expr.skb.apply_func	Apply the given function.
Expr.skb.clone	Get an independent clone of the expression.
Expr.skb.concat	Concatenate dataframes vertically or horizontally.
Expr.skb.cross_validate	Cross-validate the expression.
Expr.skb.describe_defaults	Describe the hyper-parameters used by the default pipeline.
Expr.skb.describe_param_grid	Describe the hyper-parameters extracted from choices in the expression.
Expr.skb.describe_steps	Get a text representation of the computation graph.
Expr.skb.draw_graph	Get an SVG string representing the computation graph.
Expr.skb.drop	Drop some columns.
Expr.skb.eval	Evaluate the expression.
Expr.skb.freeze_after_fit	Freeze the result during pipeline fitting.
Expr.skb.full_report	Generate a full report of the expression's evaluation.
Expr.skb.get_data	Collect the values of the variables contained in the expression.
Expr.skb.get_pipeline	Get a skrub pipeline for this expression.
Expr.skb.get_grid_search	Find the best parameters with grid search.
Expr.skb.get_randomized_search	Find the best parameters with randomized search.
Expr.skb.if_else	Create a conditional expression.
Expr.skb.iter_pipelines_grid	Get pipelines with different parameter combinations.
Expr.skb.iter_pipelines_randomized	Get pipelines with different parameter combinations.
Expr.skb.mark_as_X	Mark this expression as being the X table.
Expr.skb.mark_as_y	Mark this expression as being the y table.
Expr.skb.match	Select based on the value of an expression.
Expr.skb.preview	Get the value computed for previews (shown when printing the expression).
Expr.skb.select	Select a subset of columns.
Expr.skb.set_description	Give a description to this expression.
Expr.skb.set_name	Give a name to this expression.
Expr.skb.subsample	Configure subsampling of a dataframe or numpy array.
Expr.skb.train_test_split	Split an environment into a training an testing environments.

Accessor attributes.

Expr.skb.description	A user-defined description or comment about the expression.
Expr.skb.is_X	Whether this expression has been marked with .skb.mark_as_X().
Expr.skb.is_y	Whether this expression has been marked with .skb.mark_as_y().
Expr.skb.name	A user-chosen name for the expression.
Expr.skb.applied_estimator	Retrieve the estimator applied in the previous step, as an expression.

Objects generated by the expressions.

SkrubPipeline	Pipeline that evaluates a skrub expression.
ParamSearch	Pipeline that evaluates a skrub expression with hyperparameter tuning.