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)#
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 MetadataRequest encapsulating 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”}, 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.