Metadata-Version: 2.1
Name: DedupliPy
Version: 0.5
Summary: End-to-end deduplication solution
Home-page: https://github.com/fritshermans/deduplipy
Author: Frits Hermans
License: UNKNOWN
Description: [![Version](https://img.shields.io/pypi/v/deduplipy)](https://pypi.org/project/deduplipy/)
        ![](https://img.shields.io/github/license/fritshermans/deduplipy)
        [![Downloads](https://pepy.tech/badge/deduplipy)](https://pepy.tech/project/deduplipy)
        
        # DedupliPy
        
        <a href="https://deduplipy.readthedocs.io/en/latest/"><img src="https://deduplipy.readthedocs.io/en/latest/_images/logo.png" width="15%" height="15%" align="left" /></a>
        
        Deduplication is the task to combine different representations of the same real world entity. This package implements
        deduplication using active learning. Active learning allows for rapid training without having to provide a large,
        manually labelled dataset.
        
        DedupliPy is an end-to-end solution with advantages over existing solutions:
        
        - active learning; no large manually labelled dataset required
        - during active learning, the user gets notified when the model converged and training may be finished
        - works out of the box, advanced users can choose settings as desired (custom blocking rules, custom metrics,
          interaction features)
        
        Developed by [Frits Hermans](https://www.linkedin.com/in/frits-hermans-data-scientist/)
        
        ## Documentation
        
        Documentation can be found [here](https://deduplipy.readthedocs.io/en/latest/)
        
        ## Installation
        
        ### Normal installation
        
        Install directly from Pypi:
        
        ```
        pip install deduplipy
        ```
        
        ### Install to contribute
        
        Clone this Github repo and install in editable mode:
        
        ```
        python -m pip install -e ".[dev]"
        python setup.py develop
        ```
        
        ## Usage
        
        Apply deduplication your Pandas dataframe `df` as follows:
        
        ```python
        myDedupliPy = Deduplicator(col_names=['name', 'address'])
        myDedupliPy.fit(df)
        ```
        
        This will start the interactive learning session in which you provide input on whether a pair is a match (y) or not (n).
        During active learning you will get the message that training may be finished once algorithm training has converged.
        Predictions on (new) data are obtained as follows:
        
        ```python
        result = myDedupliPy.predict(df)
        ```
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6.9
Description-Content-Type: text/markdown
Provides-Extra: base
Provides-Extra: dev
Provides-Extra: docs
