Metadata-Version: 1.1
Name: kmodes
Version: 0.11.1
Summary: Python implementations of the k-modes and k-prototypes clustering algorithms for clustering categorical data.
Home-page: https://github.com/nicodv/kmodes
Author: Nelis J de Vos
Author-email: njdevos@gmail.com
License: MIT
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        kmodes
        ======
        
        Description
        -----------
        
        Python implementations of the k-modes and k-prototypes clustering
        algorithms. Relies on numpy for a lot of the heavy lifting.
        
        k-modes is used for clustering categorical variables. It defines clusters
        based on the number of matching categories between data points. (This is
        in contrast to the more well-known k-means algorithm, which clusters
        numerical data based on Euclidean distance.) The k-prototypes algorithm
        combines k-modes and k-means and is able to cluster mixed numerical /
        categorical data.
        
        Implemented are:
        
        - k-modes [HUANG97]_ [HUANG98]_
        - k-modes with initialization based on density [CAO09]_
        - k-prototypes [HUANG97]_
        
        The code is modeled after the clustering algorithms in :code:`scikit-learn`
        and has the same familiar interface.
        
        I would love to have more people play around with this and give me
        feedback on my implementation. If you come across any issues in running or
        installing kmodes,
        `please submit a bug report <https://github.com/nicodv/kmodes/issues>`_.
        
        Enjoy!
        
        Installation
        ------------
        
        kmodes can be installed using pip:
        
        .. code:: bash
        
            pip install kmodes
        
        To upgrade to the latest version (recommended), run it like this:
        
        .. code:: bash
        
            pip install --upgrade kmodes
        
        Alternatively, you can build the latest development version from source:
        
        .. code:: bash
        
            git clone https://github.com/nicodv/kmodes.git
            cd kmodes
            python setup.py install
        
        Usage
        -----
        .. code:: python
        
            import numpy as np
            from kmodes.kmodes import KModes
        
            # random categorical data
            data = np.random.choice(20, (100, 10))
        
            km = KModes(n_clusters=4, init='Huang', n_init=5, verbose=1)
        
            clusters = km.fit_predict(data)
        
            # Print the cluster centroids
            print(km.cluster_centroids_)
        
        The examples directory showcases simple use cases of both k-modes
        ('soybean.py') and k-prototypes ('stocks.py').
        
        Parallel execution
        ------------------
        
        The k-modes and k-prototypes implementations both offer support for
        multiprocessing via the 
        `joblib library <https://pythonhosted.org/joblib/generated/joblib.Parallel.html>`_,
        similar to e.g. scikit-learn's implementation of k-means, using the
        :code:`n_jobs` parameter. It generally does not make sense to set more jobs
        than there are processor cores available on your system.
        
        This potentially speeds up any execution with more than one initialization try,
        :code:`n_init > 1`, which may be helpful to reduce the execution time for
        larger problems. Note that it depends on your problem whether multiprocessing
        actually helps, so be sure to try that out first. You can check out the
        examples for some benchmarks.
        
        FAQ
        ---
        
        **Q: I'm seeing errors such as "TypeError: '<' not supported between instances of 'str' and 'float'"
        when using the kprototypes algorithm.**
        
        A: One or more of your numerical feature columns have string values in them. Make sure that all 
        columns have consistent data types.
        
        ----
        
        **Q: How does k-protypes know which of my features are numerical and which are categorical?**
        
        A: You tell it which column indices are categorical using the :code:`categorical` argument. All others are assumed numerical. E.g., :code:`clusters = KPrototypes().fit_predict(X, categorical=[1, 2])`
        
        ----
        
        **Q: I'm getting the following error, what gives? "ModuleNotFoundError: No module named 'kmodes.kmodes'; 'kmodes' is not a package".**
        
        A: Make sure your working file is not called 'kmodes.py', because it might overrule the :code:`kmodes` package.
        
        ----
        
        **Q: I'm getting the following error: "ValueError: Clustering algorithm could not initialize. Consider assigning the initial clusters manually."**
        
        A: This is a feature, not a bug. :code:`kmodes` is telling you that it can't make sense of the data you are presenting it. At least, not with the parameters you are setting the algorithm with. It is up to you, the data scientist, to figure out why. Some hints to possible solutions:
        
        - Run with fewer clusters as the data might not support a large number of clusters
        - Explore and visualize your data, checking for weird distributions, outliers, etc.
        - Clean and normalize the data
        - Increase the ratio of rows to columns
        
        ----
        
        **Q: I'm getting the following error: "ValueError: Input contains NaN, infinity, or a value too large for dtype('float64')."**
        
        A: Following scikit-learn, the k-modes algorithm does not accept :code:`np.NaN` 
        values in the :code:`X` matrix. Users are suggested to fill in the missing 
        data in a way that makes sense for the problem at hand.
        
        ----
        
        **Q: How would like your library to be cited?**
        
        A: Something along these lines would do nicely:
        
        .. code-block::
        
          @Misc{devos2015,
            author = {Nelis J. de Vos},
            title = {kmodes categorical clustering library},
            howpublished = {\url{https://github.com/nicodv/kmodes}},
            year = {2015--2021}
          }
        
        
        References
        ----------
        
        .. [HUANG97] Huang, Z.: Clustering large data sets with mixed numeric and
           categorical values, Proceedings of the First Pacific Asia Knowledge
           Discovery and Data Mining Conference, Singapore, pp. 21-34, 1997.
        
        .. [HUANG98] Huang, Z.: Extensions to the k-modes algorithm for clustering
           large data sets with categorical values, Data Mining and Knowledge
           Discovery 2(3), pp. 283-304, 1998.
        
        .. [CAO09] Cao, F., Liang, J, Bai, L.: A new initialization method for
           categorical data clustering, Expert Systems with Applications 36(7),
           pp. 10223-10228., 2009.
        
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Topic :: Scientific/Engineering
