Release - v0.0.3

.travis.yml

@@ -2,8 +2,10 @@ dist: xenial
 language: python
 python:
   - "3.7"
+git:
+  depth: 1
 install:
   - pip install pipenv
   - pipenv install --dev
 script:
-  - pipenv run mamba ./tests/*.py
+  - pipenv run mamba ./tests/*/*.py

README.rst

@@ -1,6 +1,153 @@
+.. image:: https://img.shields.io/pypi/v/estream.svg
+    :target: https://pypi.python.org/pypi/estream
+    :alt: PyPI Version
+.. image:: https://img.shields.io/pypi/l/estream.svg
+    :target: https://github.com/mickeycj/estream/blob/master/LICENSE
+    :alt: License
+.. image:: https://travis-ci.org/mickeycj/estream.svg
+    :target: https://travis-ci.org/mickeycj/estream
+    :alt: Travis CI Build Status
+
+====================================
 An E-Stream implementation in Python
-=====================================
+====================================
+
+E-Stream is an evolution-based technique for stream clustering which supports
+five behaviors:
+
+1. Appearance
+2. Disappearance
+3. Self-evolution
+4. Merge
+5. Split
+
+These behaviors are achieved by representing each cluster as a *Fading Cluster
+Structure with Histogram (FCH)*, utilizing a histogram for each feature of the
+data.
+
+The details for the underlying concepts can be found `here <https://www.researchgate.net/publication/221571035_E-Stream_Evolution-Based_Technique_for_Stream_Clustering>`_:
+
+Udommanetanakit, K, Rakthanmanon, T, Waiyamai, K, *E-Stream: Evolution-Based
+Technique for Stream Clustering*, Advanced Data Mining and Applications: Third
+International Conference, 2007
+
+-------------------
+How to use E-Stream
+-------------------
+
+The ``estream`` package aims to be substibutable with ``sklearn`` classes so it
+can be used interchangably with other transformers with similar API.
+
+.. code-block:: python
+
+    from estream import EStream
+    from sklearn.datasets.samples_generator import make_blobs
+
+    estream = EStream()
+    data, _ = make_blobs()
+
+    estream.fit(data)
+
+E-Stream contains a number of parameters that can be set; the major ones are as
+follows:
+
+- ``max_clusters``: This limits the number of clusters the clustering can have
+  before the existing clusters have to be merged. The default is set to
+  *10*.
+- ``stream_speed/decay_rate``: These determine the fading factor of the
+  clusters. In this implementation, the fading function is constant derived
+  from the default values of *10* and *0.1*, respectively.
+- ``remove_threshold``: This sets the lower bound for each cluster's weight
+  before they are considered to be removed. The default is set to *0.1*.
+- ``merge_threshold``: This determines whether two close clusters can be merged
+  togther. The default is set to *1.25*.
+- ``radius_threshold``: This determines the minimum range from an existing
+  cluster that a new data must be in order to be merged into one. The default
+  is set to *3.0*.
+- ``active_threshold``: This sets the minimum weight of each cluster before
+  they are considered active. The default is set to *5.0*.
+
+An example of setting these parameters:
+
+.. code-block:: python
+
+    from estream import EStream
+    from sklearn.datasets.samples_generator import make_blobs
+
+    estream = EStream(max_clusters=5,
+                      merge_threshold=0.5,
+                      radius_threshold=1.5,
+                      active_threshold=3.0)
+    data, _ = make_blobs()
+
+    estream.fit(data)
+
+------------
+Installation
+------------
+
+Currently, the package is only available through either ``PyPI``:
+
+.. code-block:: bash
+
+    pip install estream
+
+or a manual install:
+
+.. code-block:: bash
+
+    wget https://github.com/mickeycj/estream/archive/master.zip
+    unzip master.zip
+    rm master.zip
+    cd estream-master
+    python setup.py install
+
+--------------
+Help & Support
+--------------
+
+Currently, there is no dedicated documentation available, so any questions or
+issues can be asked via my `email <chanonjenakom@gmail.com>`_.
+
+--------
+Citation
+--------
+
+If you make use of this software for your work, please cite the paper from the
+Advanced Data Mining and Applications: Third International Conference:
+
+.. code-block:: bibtex
+
+    @inproceedings{inproceedings,
+        author = {Udommanetanakit, Komkrit, and Rakthanmanon, Thanawin and Waiyamai, Kitsana},
+        year = {2007},
+        month = {08},
+        pages = {605-615},
+        title = {E-Stream: Evolution-Based Technique for Stream Clustering},
+        volume = {4632},
+        doi = {10.1007/978-3-540-73871}
+    }
+
+Moreover, this implementation is based on a MOA implementaion of E-Stream (and
+other related algorithms) by `David Ratier <https://gitub.com/ratierd>`_. The
+original source code can be found in this `repository <https://gitub.com/ratierd/MOA>`_.
+
+-------
+License
+-------
+
+The ``estream`` package is under the GNU General Public License.
+
+------------
+Contributing
+------------
+
+Contributions are always welcome! Everything ranging from code to notebooks and
+examples/documentation will be very valuable to the growth of this project. To
+contribute, please `fork this project <https://github.com/mickeycj/estream/issues#fork-destination-box>`_
+, make your changes and submit a pull request. I will do my best to fix any
+issues and merge your code into the main branch.
 
 :Author: Chanon Jenakom
-:Version: $Revision: 0.0.1 $
+:Version: 0.0.3
 :Dedicated: To DAKDL, Kasetsart University

estream/__init__.py

@@ -0,0 +1,3 @@
+from estream.estream import EStream
+from estream.fading_cluster import FadingCluster
+from estream.histogram import Histogram