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.DropColumns#
- class julearn.transformers.DropColumns(apply_to, row_select_col_type=None, row_select_vals=None)#
Drop columns of a DataFrame.
- Parameters:
- apply_toColumnTypesLike
The feature types (‘X_types’) to drop.
- 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__(apply_to, row_select_col_type=None, row_select_vals=None)#
- transform(X)#
Drop the columns.
- Parameters:
- Xpd.DataFrame
Data to drop columns.
- Returns:
- pd.DataFrame
Data with dropped columns.
- get_support(indices=False)#
Get the support mask.
- Parameters:
- indicesbool
If true, return indices.
- Returns:
- support_masknumpy.array
The support mask
- 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
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.