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.base.JuTransformer#
- class julearn.base.JuTransformer(apply_to, needed_types=None, row_select_col_type=None, row_select_vals=None)#
Base class for julearn transformers.
- Parameters:
- apply_tostr or list of str or set of str or ColumnTypes
The column types to apply the estimator to.
- needed_typesstr or list of str or set of str or ColumnTypes
The column types needed by the estimator. If None, there are no needed types (default is None)
- 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)
- __init__(apply_to, needed_types=None, row_select_col_type=None, row_select_vals=None)#
- 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.
- get_needed_types()#
Get the column types needed by the estimator.
- Returns:
- ColumnTypes
The column types needed by the estimator.
- 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_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_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”, “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
New in version 1.4: “polars” option was added.
- 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.