Metadata-Version: 2.1
Name: pandas-ml-utils
Version: 0.2.5
Summary: Augment pandas DataFrame with methods for machine learning
Home-page: https://github.com/KIC/pandas-ml-quant/pandas-ml-utils
Author: KIC
Author-email: 
License: MIT
Description: # Pandas ML Utils
        
        Pandas Machine Learning Utilities is part of a bigger set of libraries for a convenient experience. Usually exploring
        statistical models start with a pandas `DataFrame`. 
        
        But soon enough you will find yourself converting your data frames to numpy, splitting arrays, applying min
        max scalers, lagging and concatenating columns etc. As a result your notebook looks messy and became and 
        unreadable beast. Yet the mess becomes only worse once you start to deploy your research into a productive
        application. The untested hard coded data pipelines need be be maintained at two places. 
        
        The aim of this library is to conveniently operate with data frames without and abstract away the ugly unreproducible 
        data pipelines. The only thing you need is the original unprocessed data frame where you started.
        The data pipeline becomes a part of your model and gets saved that way. Going into production is as easy as 
        this:
        
        ```python
        
        import pandas as pd
        import pandas_ml_utils  # monkey patch the `DataFrame`
        from pandas_ml_utils import Model
        # alternatively as a one liner `from pandas_ml_utils import pd, Model` 
        
        model = Model.load('your_saved.model')
        df = pd.read_csv('your_raw_data.csv')
        df_prediction = df.model.predict(model)
        
        # do something with your prediction
        df_prediction.plot()
        ``` 
        
        
        is intended to help you through your journey of statistical or machine learning models, 
        while you never need to leave the world of pandas.
        
        ## Installation
        The basic implementation supports [scikit learn][e1] classifiers and regressors.
        ```shell script
        
        pip install pandas-ml-utils
        ```
        
        Additional machine learning libraries are available as an add on:
        ```shell script
        
        pip install pandas-ml-utils-torch  # pytorch implementation
        pip install pandas-ml-utils-keras  # keras + tensorflow 1.x implementation
        ```
        
        Note that the keras/tensorflow version is currently stalled as I focus on pytorch recently. This might change
        with PyMC4 and tensorflow probability
         
        ## Example
        You will find some demo projects in the [examples][ghl1] directory. But It might also be worth it to check
        the unit tests and the [integration tests][ghl2]. Here is how classification challenge
        might look like:
          
        ![Classification Example][ghi1]
        
        
        [e1]: https://scikit-learn.org/stable/
        [ghl1]: ./examples/
        [ghl2]: ../pandas-ml-1ntegration-test
        [ghi1]: ../.readme/images/classification.png
        
        
Keywords: pandas,ml,util,quant
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Topic :: Software Development :: Build Tools
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Description-Content-Type: text/markdown
Provides-Extra: dev
