Version 0.6.X#

Version 0.6.1#

Deployed: Friday, September 18, 2020

Contributors: Soledad Galli

Minor Changes:

Updated docs: updated and expanded Contributing guidelines, added Governance, updated references to Feature-engine online.
Updated Readme: updated and expanded readme.

Deployed: Friday, August 14, 2020

Contributors:

Major Changes:

New Transformer: the MathematicalCombinator allows you combine multiple features into new variables by performing mathematical operations like sum, product, mean, standard deviation, or finding the minimum and maximum values (by Michał Gromiec).
New Transformer: the DropFeatures allows you remove specified variables from a dataset (by Karthik Kothareddy).
New Transformer: the DecisionTreeCategoricalEncoder encodes categorical variables with a decision tree (by Surya Krishnamurthy).
Bug fix: the SklearnTransformerWrapper can now automatically select numerical or numerical and categorical variables depending on the Scikit-learn transformer the user implements (by Michał Gromiec).
Bug fix: the SklearnTransformerWrapper can now wrap Scikit-learn’s OneHotEncoder and concatenate the binary features back to the original dataframe (by Michał Gromiec).
Added functionality: the ArbitraryNumberImputer can now take a dictionary of variable, arbitrary number pairs, to impute different variables with different numbers (by Michał Gromiec).
Added functionality: the CategoricalVariableImputer can now replace missing data in categorical variables by a string defined by the user (by Gleb Levitskiy).
Added functionality: the RareLabelEnoder now allows the user to determine the maximum number of categories that the variable should have when grouping infrequent values (by Surya Krishnamurthy).

Minor Changes:

Improved docs: fixed typos and tidy Readme.md (by Richard Cornelius Suwandi)
Improved engineering practices: added Manifest.in to include md and licenses in tar ball in pypi (by Chris Samiullah)
Improved engineering practices: updated circleci yaml and created release branch for orchestrated release of new versions with significant changes (by Soledad Galli and Chris Samiullah)
Improved engineering practices: added test for doc build in circleci yaml (by Soledad Galli and Chris Samiullah)
Transformer fix: removed parameter return_object from the RareLabelEncoder as it was not working as intended(by Karthik Kothareddy and Soledad Galli)

Major Changes:

Bug fix: fixed error in weight of evidence formula in the WoERatioCategoricalEncoder. The old formula, that is np.log( p(1) / p(0) ) is preserved, and can be obtained by setting the encoding_method to ‘log_ratio’. If encoding_method is set to ‘woe’, now the correct formula will operate.
- Added functionality: most categorical encoders have the option inverse_transform, to obtain the original value of the variable from the transformed dataset.
Added functionality: the 'Winsorizer`, OutlierTrimmer and ArbitraryOutlierCapper have now the option to ignore missing values, and obtain the parameters from the original variable distribution, or raise an error if the dataframe contains na, by setting the parameter missing_values to raise or ignore.
New Transformer: the UserInputDiscretiser allows users to discretise numerical variables into arbitrarily defined buckets.

Major Changes:

New Transformer: the 'SklearnTransformerWrapper` allows you to use most Scikit-learn transformers just on a subset of features. Works with the SimpleImputer, the OrdinalEncoder and most scalers.

Minor Changes:

Added functionality: the 'EqualFrequencyDiscretiser` and EqualWidthDiscretiser now have the ability to return interval boundaries as well as integers, to identify the bins. To return boundareis set the parameter return_boundaries=True.
Improved docs: added contibuting section, where you can find information on how to participate in the development of Feature-engine’s code base, and more.

Major Changes:

Deprecated: the FrequentCategoryImputer was integrated into the class CategoricalVariableImputer. To perform frequent category imputation now use: CategoricalVariableImputer(imputation_method='frequent')
Renamed: the AddNaNBinaryImputer is now called AddMissingIndicator.
New: the OutlierTrimmer was introduced into the package and allows you to remove outliers from the dataset

Minor Changes:

Improved: the EndTailImputer now has the additional option to place outliers at a factor of the maximum value.
Improved: the FrequentCategoryImputer has now the functionality to return numerical variables cast as object, in case you want to operate with them as if they were categorical. Set return_object=True.
Improved: the RareLabelEncoder now allows the user to define the name for the label that will replace rare categories.
Improved: All feature engine transformers (except missing data imputers) check that the data sets do not contain missing values.
Improved: the LogTransformer will raise an error if a variable has zero or negative values.
Improved: the ReciprocalTransformer now works with variables of type integer.
Improved: the ReciprocalTransformer will raise an error if the variable contains the value zero.
Improved: the BoxCoxTransformer will raise an error if the variable contains negative values.
Improved: the OutlierCapper now finds and removes outliers based of percentiles.
Improved: Feature-engine is now compatible with latest releases of Pandas and Scikit-learn.

Major Changes:

New: the RandomSampleImputer now has the option to set one seed for batch imputation or set a seed observation per observations based on 1 or more additional numerical variables for that observation, which can be combined with multiplication or addition.
New: the YeoJohnsonTransfomer has been included to perform Yeo-Johnson transformation of numerical variables.
Renamed: the ExponentialTransformer is now called PowerTransformer.
Improved: the DecisionTreeDiscretiser now allows to provide a grid of parameters to tune the decision trees which is done with a GridSearchCV under the hood.
New: Extended documentation for all Feature-engine’s transformers.
New: Quickstart guide to jump on straight onto how to use Feature-engine.
New: Changelog to track what is new in Feature-engine.
Updated: new Jupyter notebooks with examples on how to use Feature-engine’s transformers.

Minor Changes:

Unified: dictionary attributes in transformers, which contain the transformation mappings, now end with _, for example binner_dict_.