Metadata-Version: 2.1
Name: feature_stuff
Version: 0.0.dev3
Summary: Feature extraction, processing and interpretation algorithms and functions for machine learning and data science.
Home-page: https://github.com/hiflyin/Advanced-Feature-Stuff-Lib
Author: Mihaela Mares
Author-email: mihaela.andreea.mares@gmail.com
License: UNKNOWN
Description: 
        -----------------
        
        # feature_stuff: a python algorithms distribution for advanced feature extraction, processing and interpretation in machine learning, data science, AI.
        
        <table>
        
        <tr>
          <td>Latest Release</td>
          <td>
            <a href="https://pypi.org/project/feature-stuff/">
            </a>
          </td>
        </tr>
        
        <tr>
          <td>Package Status</td>
          <td>
        		<a href="https://pypi.org/project/feature-stuff/"></a>
            </td>
        </tr>
        
        <tr>
          <td>License</td>
          <td>
            <a href="https://github.com/hiflyin/Feature-Stuff/blob/master/LICENSE">
            </a>
        </td>
        </tr>
        
        <tr>
          <td>Build Status</td>
          <td>
            <a href="https://travis-ci.org/hiflyin/Feature-Stuff/">
            </a>
          </td>
        </tr>
        </table>
        
        
        
        ## What is it
        
        **feature_stuff** is a Python package providing fast and flexible algorithms and functions
        for extracting, processing and interpreting features. It includes functions like feature interaction extraction
        from from boosted decision tree based models, generic target encoding and memory efficient enrichment of features
        dataframe with group values.
        
        
        ## How to get it
        
        Binary installers for the latest released version are available at the [Python
        package index](https://pypi.org/project/feature-stuff) .
        
        ```sh
        # or PyPI
        pip install feature_stuff
        ```
        
        The source code is currently hosted on GitHub at:
        https://github.com/hiflyin/Feature-Stuff
        
        
        ## Installation from sources
        
        In the `Feature-Stuff` directory (same one where you found this file after
        cloning the git repo), execute:
        
        ```sh
        python setup.py install
        ```
        
        or for installing in [development mode](https://pip.pypa.io/en/latest/reference/pip_install.html#editable-installs):
        
        ```sh
        python setup.py develop
        ```
        
        Alternatively, you can use `pip` if you want all the dependencies pulled
        in automatically (the `-e` option is for installing it in [development
        mode](https://pip.pypa.io/en/latest/reference/pip_install.html#editable-installs)):
        
        ```sh
        pip install -e .
        ``
        
        ## How to use it
        
        Example on extracting interactions form tree based models and adding
        them as new features to your dataset.
        
        ```sh
        import feature_stuff as fs
        import pandas as pd
        import xgboost as xgb
        
        data = pd.DataFrame({"x0":[0,1,0,1], "x1":range(4), "x2":[1,0,1,0]})
        print data
           x0  x1  x2
        0   0   0   1
        1   1   1   0
        2   0   2   1
        3   1   3   0
        
        target = data.x0 * data.x1 + data.x2*data.x1
        print target.tolist()
        [0, 1, 2, 3]
        
        model = xgb.train({'max_depth': 4, "seed": 123}, xgb.DMatrix(data, label=target), num_boost_round=2)
        fs.addInteractions(data, model)
        
        # at least one of the interactions in target must have been discovered by xgboost
        print data
           x0  x1  x2  inter_0
        0   0   0   1        0
        1   1   1   0        1
        2   0   2   1        0
        3   1   3   0        3
        
        # if we want to inspect the interactions extracted
        from feature_stuff import model_features_insights_extractions as insights
        print insights.get_xgboost_interactions(model)
        [['x0', 'x1']]
        
        ``
        
        ## Contributing to feature-stuff
        
        All contributions, bug reports, bug fixes, documentation improvements, enhancements and ideas are welcome.
        
        
Keywords: machine_learning data_science AI ML feature_extraction
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Description-Content-Type: text/markdown
Provides-Extra: dev
Provides-Extra: test
