Note

This page is a reference documentation. It only explains the class signature, and not how to use it. Please refer to the What you really need to know section for the big picture.

julearn.transformers.JuColumnTransformer

class julearn.transformers.JuColumnTransformer(name, transformer, apply_to, needed_types=None, row_select_col_type=None, row_select_vals=None, **params)

Column transformer that can be used in a julearn pipeline.

This column transformer is a wrapper around the sklearn column transformer, so it can be used directly with julearn pipelines.

Parameters:
namestr

Name of the transformer.

transformerEstimatorLike

The transformer to apply to the columns.

apply_toColumnTypesLike

To which column types the transformer needs to be applied to.

needed_typesColumnTypesLike, optional

Which feature types are needed for the transformer to work.

row_select_col_typestr or list of str or set of str or ColumnTypes

The column types needed to select rows (default is None).

row_select_valsstr, int, bool or list of str, int, bool

The value(s) which should be selected in the row_select_col_type to select the rows used for training (default is None).

**paramsdict

Extra keyword arguments for the transformer.

__init__(name, transformer, apply_to, needed_types=None, row_select_col_type=None, row_select_vals=None, **params)
transform(X)

Apply the transformer.

Parameters:
Xpd.DataFrame

Data to be transformed.

Returns:
pd.DataFrame

Transformed data.

get_feature_names_out(input_features=None)

Get names of features to be returned.

Parameters:
input_featuresarray-like of str or None, default=None

Input features to use.

  • If None, then feature_names_in_ is used as input feature names if it’s defined. If feature_names_in_ is undefined, then the following input feature names are generated: ["x0", "x1", ..., "x(n_features_in_ - 1)"].

  • If array-like, then input_features must match feature_names_in_ if it’s defined.

Returns:
list of str

Names of features to be kept in the output pd.DataFrame.

get_params(deep=True)

Get parameters for this estimator.

Parameters:
deepbool, default=True

Not used. Kept for compatibility with scikit-learn.

Returns:
dict

Parameter names mapped to their values.

set_params(**kwargs)

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as sklearn.pipeline.Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:
**kwargsdict

Estimator parameters.

Returns:
JuColumnTransformer

JuColumnTransformer instance with params set.

filter_columns(X)

Get the apply_to columns of a pandas DataFrame.

Parameters:
Xpd.DataFrame

The DataFrame to filter.

Returns:
pd.DataFrame

The DataFrame with only the apply_to columns.

fit(X, y=None, **fit_params)

Fit the model.

This method will fit the model using only the columns selected by apply_to.

Parameters:
Xpd.DataFrame

The data to fit the model on.

yDataLike, optional

The target data (default is None).

**fit_paramsAny

Additional parameters to pass to the model’s fit method.

Returns:
JuTransformer

The fitted model.

fit_transform(X, y=None, **fit_params)

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

Parameters:
Xarray-like of shape (n_samples, n_features)

Input samples.

yarray-like of shape (n_samples,) or (n_samples, n_outputs), default=None

Target values (None for unsupervised transformations).

**fit_paramsdict

Additional fit parameters.

Returns:
X_newndarray array of shape (n_samples, n_features_new)

Transformed array.

get_apply_to()

Get the column types the estimator applies to.

Returns:
ColumnTypes

The column types the estimator applies to.

get_metadata_routing()

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:
routingMetadataRequest

A MetadataRequest encapsulating routing information.

get_needed_types()

Get the column types needed by the estimator.

Returns:
ColumnTypes

The column types needed by the estimator.

set_output(*, transform=None)

Set output container.

See Introducing the set_output API for an example on how to use the API.

Parameters:
transform{“default”, “pandas”, “polars”}, default=None

Configure output of transform and fit_transform.

  • “default”: Default output format of a transformer

  • “pandas”: DataFrame output

  • “polars”: Polars output

  • None: Transform configuration is unchanged

Added in version 1.4: “polars” option was added.

Returns:
selfestimator instance

Estimator instance.