logai.algorithms.categorical_encoding_algo package

Submodules

logai.algorithms.categorical_encoding_algo.label_encoding module

class logai.algorithms.categorical_encoding_algo.label_encoding.LabelEncoding

Bases: CategoricalEncodingAlgo

This is a wrapper class for LabelEncoder from scikit-learn library. For more details see https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.LabelEncoder.html.

fit_transform(log_attributes: DataFrame)

Fits and transforms log_attributes into label encoding categories.

Parameters:

log_attributes – A list of log attributes in text format.

Returns:

The label encoding categories.

logai.algorithms.categorical_encoding_algo.one_hot_encoding module

class logai.algorithms.categorical_encoding_algo.one_hot_encoding.OneHotEncoding(params: OneHotEncodingParams)

Bases: CategoricalEncodingAlgo

This is a wrapper class for OneHotEncoder from scikit-learn library https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.OneHotEncoder.html.

fit_transform(log_attributes: DataFrame) → DataFrame

Fits and transforms log attributes into one-hot encoding categories.

Parameters:

log_attributes – A list of log attributes in text form.

Returns:

A pandas dataframe of categories in on-hot encoding.

class logai.algorithms.categorical_encoding_algo.one_hot_encoding.OneHotEncodingParams

Bases: Config

Configuration for One-Hot Encoding. For more details on the parameters see https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.OneHotEncoder.html.

Parameters:

  • categories – Categories (unique values) per feature.

  • drop – Specifies a methodology to use to drop one of the categories per feature.

  • dtype – Desired dtype of output.

  • handle_unknown – Specifies the way unknown categories are handled during transform.

categories: str = 'auto'
drop: object = None
dtype

alias of float64

handle_unknown: str = 'error'

logai.algorithms.categorical_encoding_algo.ordinal_encoding module

class logai.algorithms.categorical_encoding_algo.ordinal_encoding.OrdinalEncoding(params: OrdinalEncodingParams)

Bases: CategoricalEncodingAlgo

Implementation of ordinal encoder. This is a wrapper class for the OrdinalEncoder from scikit learn https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.OrdinalEncoder.html.

fit_transform(log_attributes: DataFrame) → DataFrame

Fits and transforms log attributes into ordinal encoding categories.

Parameters:

log_attributes – A list of log attributes in text format.

Returns:

A pandas dataframe of ordinal encoding categories.

class logai.algorithms.categorical_encoding_algo.ordinal_encoding.OrdinalEncodingParams(categories: str = 'auto', dtype: ~numpy.float64 = <class 'numpy.float64'>, handle_unknown: str = 'error', unknown_value: object | None = None)

Bases: Config

Configuration of OrdinalEncoding. For more details on the parameters see https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.OrdinalEncoder.html.

Parameters:

  • categories – Categories (unique values) per feature.

  • dtype – Desired dtype of output.

  • handle_unknown – Specifies the way unknown categories are handled during transform.

  • unknown_value – When the parameter handle_unknown is set to ‘use_encoded_value’, this parameter is required and will set the encoded value of unknown categories.

categories: str
dtype: float64
handle_unknown: str
unknown_value: object

Module contents