Metadata-Version: 2.1
Name: vectograph
Version: 0.0.2
Summary: A set of python modules for applying knowledge graph embedding on tabular data
Home-page: UNKNOWN
Author: Caglar Demir
Author-email: caglardemir8@gmail.com
License: UNKNOWN
Description: # Vectograph
        
        Vectograph is an open-source software library for applying knowledge graph embedding approaches on tabular data. 
        To this end, Vectograph enables users to converts tabular data into RDF knowledge graph and apply KGE approaches.
        
        - [Framework](#Framework)
                
        - [Installation](#installation)
        
        # Installation
        ### Installation from source
        ```
        1) git clone https://github.com/dice-group/Vectograph.git
        2) conda create -n temp python=3.6.2 # Or be sure that your have Python => 3.6.
        3) conda activate temp
        4) python ontolearn/setup.py install
        # After you receive this Finished processing dependencies for OntoPy==0.0.1
        5) python -c "import vectograph"
        ```
        ### Installation via pip
        
        ```python
        pip install vectograph
        ```
        
        ## Usage
        
        
        ```python
        import pandas as pd
        from sklearn.pipeline import Pipeline
        from vectograph.transformers import ApplyKGE, KGCreator
        
        path_of_folder = '/.../data_files/'
        tabular_csv_data_name = 'example'  
        df = pd.read_csv(path_of_folder + tabular_csv_data_name + '.csv', index_col=0, low_memory=False)
        ####################################
        #### Data Preprocessing ####
        ####################################
        kg_path = path_of_folder + tabular_csv_data_name
        pipe = Pipeline([('createkg', KGCreator(path=kg_path)),
                         ('embeddings', ApplyKGE(params={'kge': 'Conve',  # Distmult,Complex,Tucker,Hyper, Conve
                                                         'embedding_dim': 10,
                                                         'batch_size': 256,
                                                         'num_epochs': 10}))])
        
        model = pipe.fit_transform(X=df.select_dtypes(include='category'))
        print(model)
        
        ```
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Requires-Python: >=3.6
Description-Content-Type: text/markdown
