CondorOrdinalEncoder

CondorOrdinalEncoder(nclasses=0, dtype=, kwargs)

Base class for all estimators in scikit-learn.

Notes

All estimators should specify all the parameters that can be set at the class level in their __init__ as explicit keyword arguments (no *args or **kwargs).

Methods

fit(X, y=None)

Fit the CondorOrdinalEncoder to X.

Parameters

  • X : array-like of shape (n_samples, n_features)

    The data to determine the categories of each feature.

  • y : None

    Ignored. This parameter exists only for compatibility with :class:~sklearn.pipeline.Pipeline.

Returns

self

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.

Returns

  • X_new : ndarray array of shape (n_samples, n_features_new)

    Transformed array.

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

  • params : dict

    Parameter names mapped to their values.

set_params(params)

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects
(such as :class:`~sklearn.pipeline.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

  • self : estimator instance

    Estimator instance.

transform(X)

Transform X to ordinal arrays.

Parameters

  • X : array-like of shape (n_samples, 1)

    The labels data to encode.

Returns

  • X_out : ndarray of shape (n_samples, n_classes-1)

    Transformed input.

Properties