This is a TU-Berlin Master's Thesis. It is created and implemented for research and educational purposes only, not for commercial use.

To begin with, install the requirements:

python -m pip install -r requirements.txt

Switch to virtual environment and install Regraph by executing following command inside the third_library/Regraph folder:

python setup.py install

To use CLI:

1. start virtual environment
2. cd into the project folder data-science-pipelines
3. run commands

Commands:

initialize database:    python data_science_pipelines init_db 
create pipeline:        python data_science_pipelines create_pipeline path_to_script language --hpath hook_path --wtfile True --opath output_file_path 
extract rule:           python data_science_pipelines extract_rule path_to_g1 path_to_g2 rule_type 
confirm rule:           python data_science_pipelines path_to_pattern path_to_result rule_name rule_description language rule_type rule_priority
list rules by language: python data_science_pipelines list_rules language 
visualize rule:         python data_science_pipelines visualize_rule rule_name 
delete a rule:          python data_science_pipelines delete_rule rule_name
add a rule:             python data_science_pipelines add_rule path_to_rule
add new module to kb:   python data_science_pipelines add_module module_name module_version date(yyyy-mm-dd) language
add function to kb:     python data_science_pipelines add_function module_name function_title function_description function_language ds_task --dlink link_to_documentation
add data science dask:  python data_science_pipelines add_ds_task module_name function_title language ds_task 
add description:        python data_science_pipelines add_description module_name function_title language description 

To populate our knowledge base, we used publicly available information from Pandas (BSD-3-Clause license) and Scikit-learn (BSD-3-Clause license) documentation. We also used some of the data provided by the Data Science Ontology project (CC-BY-4.0 license).