Step

Step()

A single step in a machine learning pipeline that wraps a scikit-learn estimator.

This class represents an individual processing step that can either transform data (transformers like StandardScaler, SelectKBest) or make predictions (classifiers like KNeighborsClassifier, LinearSVC). Steps can be combined into Pipeline objects to create complex ML workflows.

Parameters

Name	Type	Description	Default
typ	type	The scikit-learn estimator class (must inherit from BaseEstimator).	required
name	str	A unique name for this step. If None, generates a name from the class name and ID.	required
params_tuple	tuple	Tuple of (parameter_name, parameter_value) pairs for the estimator. Parameters are automatically sorted for consistency.	required

Attributes

Name	Type	Description
typ	type	The scikit-learn estimator class.
name	str	The unique name for this step in the pipeline.
params_tuple	tuple	Sorted tuple of parameter key-value pairs.

Examples

Create a scaler step:

>>> from xorq.ml import Step
>>> from sklearn.preprocessing import StandardScaler
>>> scaler_step = Step(typ=StandardScaler, name="scaler")
>>> scaler_step.instance

StandardScaler()

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Create a classifier step with parameters:

>>> from sklearn.neighbors import KNeighborsClassifier
>>> knn_step = Step(
...     typ=KNeighborsClassifier,
...     name="knn",
...     params_tuple=(("n_neighbors", 5), ("weights", "uniform"))
... )
>>> knn_step.instance

KNeighborsClassifier()

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Notes

• The Step class is frozen (immutable) using attrs.
• All estimators must inherit from sklearn.base.BaseEstimator.
• Parameter tuples are automatically sorted for hash consistency.
• Steps can be fitted to data using the fit() method which returns a FittedStep.

Methods

Name	Description
fit	Fit this step to the given expression data.
from_fit_predict	Create a Step from custom fit and predict functions.
from_instance_name	Create a Step from an existing scikit-learn estimator instance.
from_name_instance	Create a Step from a name and estimator instance.
set_params	Create a new Step with updated parameters.

fit

fit(expr, features=None, target=None, storage=None, dest_col=None)

Fit this step to the given expression data.

Parameters

Name	Type	Description	Default
expr	Expr	The xorq expression containing the training data.	required
features	tuple of str	Column names to use as features. If None, infers from expr.columns.	None
target	str	Target column name. Required for prediction steps.	None
storage	Storage	Storage backend for caching fitted models.	None
dest_col	str	Destination column name for transformed output.	None

Returns

Name	Type	Description
	FittedStep	A fitted step that can transform or predict on new data.

from_fit_predict

from_fit_predict(fit, predict, return_type, klass_name=None, name=None)

Create a Step from custom fit and predict functions.

Parameters

Name	Type	Description	Default
fit	callable	Function to fit the model.	required
predict	callable	Function to make predictions.	required
return_type	DataType	The return type for predictions.	required
klass_name	str	Name for the generated estimator class.	None
name	str	Name for the step.	None

Returns

Name	Type	Description
	Step	A new Step with a dynamically created estimator type.

from_instance_name

from_instance_name(instance, name=None)

Create a Step from an existing scikit-learn estimator instance.

Parameters

Name	Type	Description	Default
instance	object	A scikit-learn estimator instance.	required
name	str	Name for the step. If None, generates from instance class name.	None

Returns

Name	Type	Description
	Step	A new Step wrapping the estimator instance.

from_name_instance

from_name_instance(name, instance)

Create a Step from a name and estimator instance.

Parameters

Name	Type	Description	Default
name	str	Name for the step.	required
instance	object	A scikit-learn estimator instance.	required

Returns

Name	Type	Description
	Step	A new Step wrapping the estimator instance.

set_params

set_params(**kwargs)

Create a new Step with updated parameters.

Parameters

Name	Type	Description	Default
**kwargs		Parameter names and values to update.	{}

Returns

Name	Type	Description
	Step	A new Step instance with updated parameters.

Examples

>>> knn_step = Step(typ=KNeighborsClassifier, name="knn")
>>> updated_step = knn_step.set_params(n_neighbors=10, weights="distance")