Base Classes

To work properly with all the sklearn methods and components, each transformer needs to have a specific set of characteristics. This class ensures just that. Nearly every other class in TubesML inherits from this class.

class tubesml.base.BaseTransformer

This is the base class for all the transformers.

Attributes:

columns: an empty list that gets reset by the fit method, populated by the transform method,

returned by the get_feature_names_out method

fit(X, y=None)

Method to train the transformer.

It also reset the columns attribute

Parameters:

  • X – {array-like} of shape (n_samples, n_features) The training input samples.

  • y – array-like of shape (n_samples,) or (n_samples, n_outputs), optional The target values (class labels) as integers or strings.

get_feature_names_out()

Returns the columns attribute, useful to well behave with other sklearn methods

set_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → BaseTransformer

Request metadata passed to the score method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to score if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to score.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters

sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for sample_weight parameter in score.

Returns

selfobject

The updated object.

transform(X, y=None)

Method to transform the input data.

It populates the columns attribute with the columns of the output data

Parameters:

  • X – {array-like} of shape (n_samples, n_features) The input samples.

  • y – array-like of shape (n_samples,) or (n_samples, n_outputs), optional The target values (class labels) as integers or strings.

tubesml.base.fit_wrapper(func)

Wrapper for the fit method. It stores the column order and resets the columns attribute

tubesml.base.transform_wrapper(func)

Wrapper for the transform method. It makes sure the columns are in the same order as when the fit method was called and it populates the columns attribute