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.FilterColumns¶

class julearn.transformers.FilterColumns(keep=None, row_select_col_type=None, row_select_vals=None)¶

Filter columns of a DataFrame.

Parameters:

keep (list[str] | set[str] | str | ColumnTypes | None, default: None) – Which feature types (‘X_types’) to keep. If not specified, ‘keep’ defaults to ‘continuous’.
row_select_col_type (list[str] | set[str] | str | ColumnTypes | None, default: None) – The column types needed to select rows (default is None) Not really useful for this one, but here for compatibility.
row_select_vals (str | int | list | bool | None, default: None) – The value(s) which should be selected in the row_select_col_type to select the rows used for training (default is None) Not really useful for this one, but here for compatibility.

__init__(keep=None, row_select_col_type=None, row_select_vals=None)¶

transform(X)¶

Transform the data.

Parameters:: X (DataFrame) – The data to filter the columns on.
Returns:: The filtered data.

get_feature_names_out(input_features=None)¶

Get names of features to be returned.

Parameters:: input_features (list[str] | None, default: None) – Parameter to ensure scikit-learn compatibility. It is not used by the method.
Returns:: Names of features to be kept in the output pd.DataFrame.

filter_columns(X)¶

Get the apply_to columns of a pandas DataFrame.

Parameters:: X (DataFrame) – The DataFrame to filter.
Returns:: 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:

X (DataFrame) – The data to fit the model on.
y (Series | None, default: None) – The target data (default is None).
**fit_params (Any) – Additional parameters to pass to the model’s fit method.

Returns:

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:

X (array-like of shape (n_samples, n_features)) – Input samples.
y (array-like of shape (n_samples,) or (n_samples, n_outputs), default=None) – Target values (None for unsupervised transformations).
**fit_params (dict) – Additional fit parameters. Pass only if the estimator accepts additional params in its fit method.

Returns:

Transformed array.

get_apply_to()¶

Get the column types the estimator applies to.

Returns:: 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:: A MetadataRequest encapsulating routing information.

get_needed_types()¶

Get the column types needed by the estimator.

Returns:: The column types needed by the estimator.

get_params(deep=True)¶

Get parameters for this estimator.

Parameters:: deep (bool, default=True) – If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns:: Parameter names mapped to their values.

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, default: None) – Configure output of transform and fit_transform.
Returns:: Estimator instance.

set_params(**params)¶

Set the parameters of this estimator.

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

Parameters:: **params (dict) – Estimator parameters.
Returns:: Estimator instance.