Metadata-Version: 2.1
Name: pyrea
Version: 1.0.2
Summary: Multi-view clustering with deep ensemble structures.
Home-page: https://github.com/mdbloice/Pyrea
Author: Marcus D. Bloice, Bastian Pfeifer
Author-email: marcus.bloice@medunigraz.at
License: MIT
Description: # Pyrea
        
        <p align="center">
        <img src="https://raw.githubusercontent.com/mdbloice/AugmentorFiles/master/Pyrea/Pyrea-logos_transparent.png" width="400">
        </p>
        
        Multi-view clustering with flexible ensemble structures.
        
        _The name Pyrea is derived from the Greek word Parea, meaning a group of friends who gather to share experiences, values, and ideas._
        
        ![PyPI](https://img.shields.io/pypi/v/Pyrea) ![PyPI - License](https://img.shields.io/pypi/l/Pyrea) ![PyPI - Python Version](https://img.shields.io/pypi/pyversions/Pyrea)
        
        ## Installation
        
        Install Pyrea using `pip`:
        
        ```bash
        pip install pyrea
        ```
        
        This will install the latest version of Pyrea from PyPI.
        
        ## Usage
        
        ### API
        
        In Pyrea, your data are organised in to views. A view consists of the data in the
        form of a 2D matrix, and an associated clustering algorithm (a _clusterer_).
        
        To create a view you must have some data, and a clusterer:
        
        ```python
        import pyrea
        
        # Create your data, which must be a 2-dimensional array/matrix.
        d = [[1,2,3],
             [4,5,6],
             [7,8,9]]
        
        # Create a clusterer
        c = pyrea.clusterer("ward")
        
        v = pyrea.view(d, c)
        ```
        
        You now have a view `v`, containing the data `d` using the clustering algorithm
        `c`. Note that many views can share the same clusterer, or each view may have a
        unique clusterer.
        
        As this is a library for multi-view ensemble learning, you will normally have
        multiple views.
        
        A fusion algorithm is therefore used to fused the clusters created from multiple
        views. Therefore, our next step is to create a *fuser* object:
        
        ```python
        f = pyrea.fuser('parea')
        ```
        
        With you fusion algorithm `f`, you can create an *ensemble*. The ensemble is
        created with your views, the fusion algorithm, and a clustering algorithm:
        
        ```pythom
        e = pyrea.ensemble([v1, v2, v3], f, c)
        ```
        
        To perform this operation you execute the ensemble:
        
        ```python
        e.execute()
        ```
        
        which returns your clustered fusion matrix.
        
        A full example is shown below:
        
        ```python
        import pyrea
        import numpy as np
        
        # Create two datasets with random values of 1000 samples and 100 features per sample.
        d1 = np.random.rand(1000,100)
        d2 = np.random.rand(1000,100)
        
        # Define the clustering algorithm(s) you want to use. In this case we used the same
        # algorithm for both views
        c = pyrea.clusterer('ward')
        
        # Create the views using the data and clusterer
        v1 = pyrea.view(d1, c)
        v2 = pyrea.view(d1, c)
        
        # Create a fusion object
        f = pyrea.fuser('parea')
        
        # Create your ensemble
        e = pyrea.ensemble([v1, v2], f, c)
        
        # Execute the ensemble
        e.execute()
        ```
        
        ## Ensemble Structures
        Complex structures can be built using Pyrea.
        
        For example, examine the two structures below:
        
        ![Ensemble Structures](https://raw.githubusercontent.com/mdbloice/AugmentorFiles/master/Pyrea/parea.png)
        
        We will demonstrate how to create deep and flexible ensemble structures using the examples  a) and b) from the image above.
        
        ### Example A
        This ensemble consists of two sets of three views, which are clustered, fused, and then once again combined in a second layer.
        
        **Work in progress**
        
        We create two ensembles, which represent the first layer of the structure A in the image above:
        
        ```python
        import pyrea
        import numpy as np
        
        # Define our clustering algorithms(s) and fusion algorimth(s) first that we
        # will use multiple times.
        # Clusterers:
        hc1 = pyrea.clusterer('ward')
        hc2 = pyrea.clusterer('complete')
        # Fusers:
        parea_fuser = pyrea.fuser('parea')
        
        # Data (random data for now)
        d1 = np.random.rand(100,10)
        d2 = np.random.rand(100,10)
        d3 = np.random.rand(100,10)
        
        # Views for ensemble 1
        view1_e1 = pyrea.view(d1, hc1)
        view2_e1 = pyrea.view(d2, hc1)
        view3_e1 = pyrea.view(d3, hc1)
        
        # Ensemble 1
        e1 = pyrea.ensemble([view1_e1, view2_e1, view3_e1], f, hc1)
        
        # Views for ensemble 2
        view1_e2 = pyrea.view(d1, hc2)
        view2_e2 = pyrea.view(d2, hc2)
        view3_e2 = pyrea.view(d3, hc2)
        
        e2 = pyrea.ensemble([view1_e2, view2_e2, view3_e2], f, hc1)
        
        e3 = pyrea.ensemble([e1, e2], f, [c1, c2])
        
        e3.execute()
        ```
        
        As for structure b) above, this can implemented as follows:
        
        ```python
        import pyrea
        import numpy as np
        
        # Clustering algorithms
        c1 = pyrea.clusterer('ward')
        c2 = pyrea.clusterer('complete')
        c3 = pyrea.clusterer('single')
        
        # Fusion algorithm
        fuser = pyrea.fuser('disagreement')
        
        # Data
        d1 = np.random.rand(100,10)
        d2 = np.random.rand(100,10)
        d3 = np.random.rand(100,10)
        
        v1 = pyrea.view(d1, c1)
        v2 = pyrea.view(d2, c2)
        v3 = pyrea.view(d3, c3)
        
        e = pyrea.ensemble([v1, v2, v3], f, [c1, c2, c3])
        
        e.execute()
        ```
        
        Notice how the ensemble is passed 3 clustering algorithms, and these are combined for a final clustering.
        
        ## Deep Ensembles
        Pyrea can be used to create deep ensembles.
        
        ## Genetic Algorithm
        Pyrea can select the best clustering algorithms and fusion algorithms based on a genetic algorithm optimisation technique.
        
        **Work in progress...**
        
        ## Clustering Methods
        
        See [`scipy`](https://docs.scipy.org/doc/scipy/reference/cluster.html) and [`sklearn`](https://scikit-learn.org/stable/modules/clustering.html) for details.
        
        ## Extensible
        
        Pyrea has been designed to be extensible. It allows you to use Pyrea's data fusion techniques with custom clustering algorithms that can be loaded in to Pyrea at run-time.
        
        By providing a `View` with a `ClusterMethod` object, it makes providing custom clustering algorithms uncomplicated. See [`Extending Pyrea`](https://pyrea.readthedocs.org/pyrea/extending.html#custom-clustering-algorithms) for details.
        
Keywords: multi-view,clustering,ensemble clustering
Platform: UNKNOWN
Classifier: Development Status :: 1 - Planning
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
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: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Topic :: Software Development
Classifier: Topic :: Scientific/Engineering
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX :: Linux
Classifier: Operating System :: MacOS
Requires-Python: >=3.6
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