fairness_instru.py
def sklearn_to_dataflow_graph(pipeline, log_list, parent_vertices=[]):
    """
    Function translating sklearn operations to DAGs.

    args:
        pipeline_func: raw pipeline codes. String.
        log_list: log_list storing all operation identifiers. List.
        parent_vertices: parent nodes. default to be None. No operations before pandas. List.

    return:
        graph: list of nodes in the eligible format from graphviz.
        log_list: log_list storing all operation identifiers.
    """
    graph = pipeline_to_dataflow_graph_full(pipeline)
    graph_dict = pipeline_to_dataflow_graph(pipeline)
    for node in graph_dict:
        log_list.append(line_cleansing(f"{node.name}__{str(node.operation).split('(')[0]}"))
    for node in graph:
        if node.parent_vertices==[]:
            node.parent_vertices = parent_vertices
    return graph, log_list

    Function translating sklearn operations to DAGs.

    args:
        pipeline_func: raw pipeline codes. String.
        log_list: log_list storing all operation identifiers. List.
        parent_vertices: parent nodes. default to be None. No operations before pandas. List.

    return:
        graph: list of nodes in the eligible format from graphviz.
        log_list: log_list storing all operation identifiers.
    
Function translating sklearn operations to DAGs.
args:
pipeline_func: raw pipeline codes. String.

list of nodes in the eligible format from graphviz.
Function translating sklearn operations to DAGs . args : pipeline_func : raw pipeline codes . String . list of nodes in the eligible format from graphviz .
def sklearn_to_dataflow_graph(pipeline, log_list, parent_vertices=[]):
    graph = pipeline_to_dataflow_graph_full(pipeline)
    graph_dict = pipeline_to_dataflow_graph(pipeline)
    for node in graph_dict:
        log_list.append(line_cleansing(f"{node.name}__{str(node.operation).split('(')[0]}"))
    for node in graph:
        if node.parent_vertices==[]:
            node.parent_vertices = parent_vertices
    return graph, log_list
def sklearn_to_dataflow_graph ( pipeline , log_list , parent_vertices = [ ] ) : graph = pipeline_to_dataflow_graph_full ( pipeline ) graph_dict = pipeline_to_dataflow_graph ( pipeline ) for node in graph_dict : log_list . append ( line_cleansing ( f"{node.name}__{str(node.operation).split('(')[0]}" ) ) for node in graph : if node . parent_vertices == [ ] : node . parent_vertices = parent_vertices return graph , log_list
Function translating sklearn operations to DAGs.
Function translating sklearn operations to DAGs .
"""
    Function translating sklearn operations to DAGs.

    args:
        pipeline_func: raw pipeline codes. String.
        log_list: log_list storing all operation identifiers. List.
        parent_vertices: parent nodes. default to be None. No operations before pandas. List.

    return:
        graph: list of nodes in the eligible format from graphviz.
        log_list: log_list storing all operation identifiers.
    """
pipeline
log_list
parent_vertices