Metadata-Version: 2.1
Name: build2vec
Version: 0.0.6
Summary: Python package for building data embeddings
Home-page: UNKNOWN
Author: Mahmoud Abdelrahman
Author-email: <arch.mahmoud.ouf111@gmail.com>
License: UNKNOWN
Description: 
        # build2Vec
        
        Graph Neural Networks based building representation in the vector space
        
        ## Installation
        
        ```
        $ pip install build2vec
        ```
        
        ## Examples
        
        ```Python
        import networkx as nx
        from build2vec import Build2Vec
        emb_dimensions = 10
        # Create a graph using networkx -- you can generate the graph from dataframe of edges
        
        graph = nx.from_pandas_edgelist(df_links_graph)
        
        build2vec = Build2Vec(graph, dimensions=emb_dimensions, walk_length=50, num_walks=50, workers=1)
        
        model = build2vec.fit(window=50, min_count=1, batch_words=10)
        ```
        
        ## Todos:
        
        1. Add automatic grid generation method.
        2. Add automatic graph construction method.
        3. Add visualization moddule.
        4. Add ML clustering, classification, and prediction moduels.
        5. Define other builing-related random walks methods.
        
        ## Citation:
        
        ```bib
        @inproceedings{10.5555/3465085.3465155,
        author = {Abdelrahman, Mahmoud M. and Chong, Adrian and Miller, Clayton},
        title = {Build2Vec: Building Representation in Vector Space},
        year = {2020},
        abstract = {In this paper, we represent a methodology of a graph embeddings algorithm that is
        used to transform labeled property graphs obtained from a Building Information Model
        (BIM). Industrial Foundation Classes (IFC) is a standard schema for BIM, which is
        utilized to convert the building data into a graph representation. We used node2Vec
        with biased random walks to extract semantic similarities between different building
        components and represent them in a multi-dimensional vector space. A case study implementation
        is conducted on a net-zero-energy building located at the National University of Singapore
        (SDE4). This approach shows promising machine learning applications in capturing the
        semantic relations and similarities of different building objects, more specifically,
        spatial and spatio-temporal data.},
        booktitle = {Proceedings of the 11th Annual Symposium on Simulation for Architecture and Urban Design},
        articleno = {70},
        numpages = {4},
        keywords = {graph embeddings, STAR, node2vec, feature learning, representation learning},
        location = {Virtual Event, Austria},
        series = {SimAUD '20},
        }
        ```
        
Keywords: graph,network,building,spatial,spatiotemporal,bim,gis
Platform: UNKNOWN
Classifier: Development Status :: 1 - Planning
Classifier: Intended Audience :: Developers
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: Microsoft :: Windows
Description-Content-Type: text/markdown
