Metadata-Version: 1.1
Name: imperio
Version: 0.1.0
Summary: Imperio is a python sci-kit learn inspired package for feature engineering.
Home-page: https://github.com/user/reponame
Author: SigmoidAI - Păpăluță Vasile
Author-email: vpapaluta06@gmail.com
License: MIT
Download-URL: https://github.com/ScienceKot/kydavra/archive/v1.0.tar.gz
Description: 
        Imperio is a python sci-kit learn inspired package for feature engineering. It contains a some feature transformers to make your data more easy to learn from for Machine Learning Algorithms.
        
        This version of kydavra has the next methods of feature selection:
        
        1) Box-Cox (BoxCoxTransformer).
        
        2) Clusterize (ClusterizeTransformer).
        
        3) Combinator (CombinatorTransformer).
        
        4) Expanding Mean Transformer (ExpandingMeanTransformer).
        
        5) Frequency Imputation Transformer (FrequencyImputationTransformer).
        
        6) log Transformer (LogTransformer).
        
        7) Smoothing (SmoothingTransformer).
        
        8) Spatial-Sign Transformer (SpatialSignTransformer).
        
        9) Target Imputation Transformer (TargetImputationTransformer).
        
        10) Whitening (WhiteningTransformer).
        
        11) Yeo-Johnson Transformer (YeoJohnsonTransformer).
        
        12) ZCA (ZCATransformer).
        
        All these methods work like normal sklearn transformers. They have fit, transform and fit_transform functions implemented.
        
        Additionally every imperio transformer has an apply function which allows to apply an transformation on a pandas Data Frame.
        
        How to use imperio
        
        To use a transformer from imperio you should just import the transformer from imperio in the following framework:
        
        ```from imperio import <class name>```
        
        class names are written above in parantheses.
        
        Next create a object of this algorithm (I will use Box-Cox as an example).
        
        ```method = BoxCoxTransformer()```
        
        Firstly you should fit the transformer, passing to it a feature matrix (X) and the target array (y). y argument is really used only by Target-Imputation
        
        ```mathod.fit(X, y)```
        
        After you fit the model, you can use it for transforming new data, using the transform function. To transform function you should pass only the feature matrix (X).
        
        ```X_transformed = method.transform(X)```
        
        Also you can fit and transform the data at the same time using the fit_transform function.
        
        ```X_transformed = method.fit_transform(X)```
        
        Also you can apply a transformation directly on a pandas DataFrame, choosing the columns that you want to change.
        
        ```new_df = method.apply(df, 'target', ['col1', 'col2'])```
        
        Some advices.
        
        1) Use ```FrequencyImputationTransformer``` and ```TargetImputationTransformer``` for categorical features.
        
        2) Use ```BoxCoxTransformer``` and ```YeoJohnsonTransformer``` for numerical features to normalize a feature distribution.
        
        3) Use ```SpatialSignTransformer``` on normalized data to bring outlayers to normal features..
        
        With love from Sigmoid.
        
        We are open for feedback. Please send your impression to papaluta.vasile@isa.utm.md
        
        
Keywords: ml,machine learning,feature engineering,python,data science
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Framework :: Jupyter
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Topic :: Software Development :: Build Tools
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
