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dSalmon-0.1
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مشاهده بیشترتوضیحات
dSalmon is a framework for analyzing data streams
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | dSalmon-0.1 |
| نام | dSalmon |
| نسخه کتابخانه | 0.1 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Alexander Hartl |
| ایمیل نویسنده | alexander.hartl@tuwien.ac.at |
| آدرس صفحه اصلی | https://github.com/CN-TU/dSalmon |
| آدرس اینترنتی | https://pypi.org/project/dSalmon/ |
| مجوز | LGPL-3.0 |
dSalmon
=======
.. image:: https://img.shields.io/github/license/CN-TU/dSalmon.svg
:target: https://github.com/CN-TU/dSalmon/blob/master/LICENSE
:alt: License
.. image:: https://readthedocs.org/projects/dsalmon/badge/?version=latest
:target: https://dsalmon.readthedocs.io/en/latest/?badge=latest
:alt: Documentation Status
dSalmon (**D**\ ata **S**\ tream **A**\ nalysis A\ **l**\ gorith\ **m**\ s f\ **o**\ r the Impatie\ **n**\ t) is a framework for analyzing data streams. Implementation of the core algorithms is done in C++, focusing on superior processing speed and allowing even vast amounts of data to be processed. Python bindings are provided to allow seamless integration in data science development.
Installation
------------
dSalmon can be installed using ``pip`` by running
.. code-block:: sh
pip3 install git+https://github.com/CN-TU/dSalmon
Outlier Detectors
-----------------
dSalmon provides several algorithms for detecting outliers in data streams. Usage is easiest using the Python interface, which provides an interface similar to the algorithms from scikit-learn. The following example performs k-nearest neighbor outlier detection with a window size of 100 samples.
.. code-block:: python
from dSalmon import outlier
import pandas
X = pandas.read_csv('my_dataset.csv')
detector = outlier.SWKNN(window=100,k=5)
outlier_scores = detector.fit_predict(X)
print ('Outlier scores: ', outlier_scores)
Individual rows of the passed data are processed sequentially. Hence, while being substantially faster, the above code provides similar results as the following example.
.. code-block:: python
from dSalmon import outlier
import pandas
X = pandas.read_csv('my_dataset.csv')
detector = outlier.SWKNN(window=100,k=5)
outlier_scores = [ detector.fit_predict(X.iloc[i,:]) for i in range(len(X)) ]
print ('Outlier scores: ', outlier_scores)
M-Tree Usage
------------
dSalmon uses an M-Tree for several of its algorithms. An M-Tree is a spatial indexing data structure for metric spaces, allowing fast nearest-neighbor and range queries. The benefit of an M-Tree compared to, e.g., a KD-Tree or Ball-Tree is that insertion, updating and removal of points is fast after having built the tree.
For the development of custom algorithms, an M-Tree interface is provided for Python.
A point within a tree can be accessed either via ``tree[k]`` using the point's key ``k``, or via ``tree.ix[i]`` using the point's index ``i``. Keys can be arbitrary integers and are returned by ``insert()``, ``knn()`` and
``neighbors()``. Indices are integers in the range ``0...len(tree)``, sorted according to the points' keys in ascending order.
KNN queries can be performed using the ``knn()`` function and range queries can be performed using the ``neighbors()`` function.
The following example shows how to modify points within a tree and how to find nearest neighbors.
.. code-block:: python
from dSalmon.trees import MTree
import numpy as np
tree = MTree()
# insert a point [1,2,3,4] with key 5
tree[5] = [1,2,3,4]
# insert some random test data
X = np.random.rand(1000,4)
inserted_keys = tree.insert(X)
# delete every second point
del tree.ix[::2]
# Set the coordinates of the point with the lowest key
tree.ix[0] = [0,0,0,0]
# find the 3 nearest neighbors to [0.5, 0.5, 0.5, 0.5]
neighbor_keys, neighbor_distances, _ = tree.knn([.5,.5,.5,.5], k=3)
print ('Neighbor keys:', neighbor_keys)
print ('Neighbor distances:', neighbor_distances)
# find all neighbors to [0.5, 0.5, 0.5, 0.5] within a radius of 0.2
neighbor_keys, neighbor_distances, _ = tree.neighbors([.5,.5,.5,.5], radius=0.2)
print ('Neighbor keys:', neighbor_keys)
print ('Neighbor distances:', neighbor_distances)
Extending dSalmon
-----------------
dSalmon uses `SWIG <http://www.swig.org/>`_ for generating wrapper code for the C++ core algorithms and instantiates single and double precision floating point variants of each algorithm.
Architecture
^^^^^^^^^^^^
The ``cpp`` folder contains the code for the C++ core algorithms, which might be used directly by C++ projects.
When using dSalmon from Python, the C++ algorithms are wrapped by the interfaces in the SWIG folder. These wrapper functions are translated to a Python interface and have the main purpose of providing an interface which can easily be parsed by SWIG.
Finally, the ``python`` folder contains the Python interface invoking the Python interface provided by SWIG.
Rebuilding
^^^^^^^^^^
When adding new algorithms or modifying the interface, the SWIG wrappers have to be rebuilt. To this end, SWIG has to be installed and a ``pip`` package can be created and installed using
.. code-block:: sh
make && pip3 install dSalmon.tar.xz
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.5 | Python |
نحوه نصب
نصب پکیج whl dSalmon-0.1:
pip install dSalmon-0.1.whl
نصب پکیج tar.gz dSalmon-0.1:
pip install dSalmon-0.1.tar.gz