semvec/pypi-packages huggingface dataset:
uniqed 0.0.2
Simple implementation of the Temporal Outlier Factor
License: Simplified BSD

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The uniqed package
==================

Simple python implementation of the Temporal Outlier Factor [1]_ (TOF) anomaly detection method.


Installation instructions
-------------------------

Install it directly from PyPI:

.. code-block:: bash

    pip install uniqed


Simple example
--------------
This is a simple example:

.. code-block:: python

    from uniqed.data.gen_logmap import generate_logmapdata
    from uniqed.runners.tof_run import detect_outlier
    import matplotlib.pyplot as plt
    
    # Generate some data
    data_df = generate_logmapdata(rseed=359)
    
    # Detect outliers
    res_df = detect_outlier(data_df[['value']], cutoff_n=80)
    
    
    # plot the results
    fig, axs = plt.subplots(2, 1, sharex=True)
    fig.suptitle('TOF anomaly detection demo')
    
    axs[0].plot(res_df['value'], color='tab:blue', label='time series')
    axs[0].plot(res_df['value'].loc[data_df.query("is_anomaly==1").index.values],
             color='tab:green', label='anomaly')
    axs[0].plot(res_df.query("TOF==1")['value'], lw=0, marker='o',
             color='tab:orange', label='TOF detections')
    axs[0].set_ylabel('values')
    axs[0].legend(loc='upper left', framealpha=1)
    
    
    axs[1].plot(res_df['TOF_score'], color='k', label='TOF score')
    axs[1].plot(res_df['TOF_score'].loc[data_df.query("is_anomaly==1").index.values],
             color='tab:green', label='anomaly')
    axs[1].plot(res_df.query("TOF==1")['TOF_score'], lw=0, marker='o',
             color='tab:orange', label='TOF')
    axs[1].set_ylabel('TOF score')
    axs[1].set_xlabel('t')
    axs[1].legend(['TOF score', 'anomaly', 'TOF detections'],
                  loc='upper left',
                  framealpha=1)
    
    axs[1].set_xlim(0, 2000)
    axs[0].grid(True)
    axs[1].grid(True)
    
    fig.tight_layout(rect=[0, 0, 1, 1], pad=1, h_pad=0, w_pad=0)
    fig.savefig("example_run.png")
    plt.show()


.. image:: https://raw.githubusercontent.com/phrenico/uniqed/master/examples/example_run.png


References
----------

.. [1] Benkő, Z., Bábel, T., & Somogyvári, Z. (2020). How to find a unicorn: a novel model-free, unsupervised anomaly detection method for time series. http://arxiv.org/abs/2004.11468