ExtractCategories

ExtractCategories(categories=None, target=None)

This transformer filters the selected dataset categories.

Attributes

categories: list of categories that you want to keep.

Examples

For usage examples, please see https://jaisenbe58r.github.io/MLearner/user_guide/preprocessing/ReplaceTransformer/

Methods

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fit(X, y=None, fit_params)

Gets the columns to make filters the selected dataset categories.

Parameters

• X : {Dataframe}, shape = [n_samples, n_features]

  Dataframe, where n_samples is the number of samples and n_features is the number of features.

Returns

self

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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 : numpy array of shape [n_samples, n_features]

  Training set.

• y : numpy array of shape [n_samples]

  Target values.

Returns

• X_new : numpy array of shape [n_samples, n_features_new]

  Transformed array.

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get_params(deep=True)

Get parameters for this estimator.

Parameters

• deep : boolean, optional

  If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns

• params : mapping of string to any

  Parameter names mapped to their values.

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set_params(params)

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form <component>__<parameter> so that it's possible to update each component of a nested object.

Returns

self

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transform(X)

Gets the columns to make filters the selected dataset categories.

Parameters

• X : {Dataframe}, shape = [n_samples, n_features]

  Dataframe of samples, where n_samples is the number of samples and n_features is the number of features.

Returns

• X_transform : {Dataframe}, shape = [n_samples, n_features]

  A copy of the input Dataframe with the columns replaced.