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:
- keepColumnTypesLike, optional
Which feature types (‘X_types’) to keep. If not specified, ‘keep’ defaults to ‘continuous’.
- row_select_col_typestr or list of str or set of str or ColumnTypes
The column types needed to select rows (default is None) Not really useful for this one, but here for compatibility.
- 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) 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:
- Xpd.DataFrame
The data to filter the columns on.
- Returns:
- DataLike
The filtered data.
- get_feature_names_out(input_features=None)#
Get names of features to be returned.
- Parameters:
- input_featuresNone
Parameter to ensure scikit-learn compatibility. It is not used by the method.
- Returns:
- list
Names of features to be kept in the output pd.DataFrame.
- 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_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
MetadataRequestencapsulating routing information.
- get_needed_types()#
Get the column types needed by the estimator.
- Returns:
- ColumnTypes
The column types needed by the estimator.
- get_params(deep=True)#
Get parameters for this estimator.
- Parameters:
- deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators.
- Returns:
- paramsdict
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”}, default=None
Configure output of transform and fit_transform.
“default”: Default output format of a transformer
“pandas”: DataFrame output
None: Transform configuration is unchanged
- Returns:
- selfestimator instance
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:
- **paramsdict
Estimator parameters.
- Returns:
- selfestimator instance
Estimator instance.