✨ Construct

Construct Directed Acyclic Graph (DAG) from list, dictionary, and pandas DataFrame.

DAG Construct Methods

Construct DAG from	Using parent-child relation	Add node attributes
List	list_to_dag	No
Dictionary	dict_to_dag	Yes
DataFrame	dataframe_to_dag	Yes

These functions are not standalone functions. Under the hood, they have the following dependency,

Functions:

dataframe_to_dag(data[, child_col, ...])	Construct DAG from pandas DataFrame.
dict_to_dag(relation_attrs[, parent_key, ...])	Construct DAG from nested dictionary, key: child name, value: dictionary of parent names, attribute name, and attribute value.
list_to_dag(relations[, node_type])	Construct DAG from list of tuples containing parent-child names.

bigtree.dag.construct.dataframe_to_dag(data: ~pandas.core.frame.DataFrame, child_col: str = '', parent_col: str = '', attribute_cols: ~typing.List[str] = [], node_type: ~typing.Type[~bigtree.node.dagnode.DAGNode] = <class 'bigtree.node.dagnode.DAGNode'>) → DAGNode

Construct DAG from pandas DataFrame. Note that node names must be unique.

child_col and parent_col specify columns for child name and parent name to construct DAG. attribute_cols specify columns for node attribute for child name If columns are not specified, child_col takes first column, parent_col takes second column, and all other columns are attribute_cols.

>>> import pandas as pd
>>> from bigtree import dataframe_to_dag, dag_iterator
>>> relation_data = pd.DataFrame([
...     ["a", None, 1],
...     ["b", None, 1],
...     ["c", "a", 2],
...     ["c", "b", 2],
...     ["d", "a", 2],
...     ["d", "c", 2],
...     ["e", "d", 3],
... ],
...     columns=["child", "parent", "step"]
... )
>>> dag = dataframe_to_dag(relation_data)
>>> [(parent.node_name, child.node_name) for parent, child in dag_iterator(dag)]
[('a', 'd'), ('c', 'd'), ('d', 'e'), ('a', 'c'), ('b', 'c')]

Parameters:

• data (pd.DataFrame) – data containing path and node attribute information

• child_col (str) – column of data containing child name information, defaults to ‘’ if not set, it will take the first column of data

• parent_col (str) – column of data containing parent name information, defaults to ‘’ if not set, it will take the second column of data

• attribute_cols (List[str]) – columns of data containing child node attribute information, if not set, it will take all columns of data except child_col and parent_col

• node_type (Type[DAGNode]) – node type of DAG to be created, defaults to DAGNode

Returns:

(DAGNode)

bigtree.dag.construct.dict_to_dag(relation_attrs: ~typing.Dict[str, ~typing.Any], parent_key: str = 'parents', node_type: ~typing.Type[~bigtree.node.dagnode.DAGNode] = <class 'bigtree.node.dagnode.DAGNode'>) → DAGNode

Construct DAG from nested dictionary, key: child name, value: dictionary of parent names, attribute name, and attribute value. Note that node names must be unique.

>>> from bigtree import dict_to_dag, dag_iterator
>>> relation_dict = {
...     "a": {"step": 1},
...     "b": {"step": 1},
...     "c": {"parents": ["a", "b"], "step": 2},
...     "d": {"parents": ["a", "c"], "step": 2},
...     "e": {"parents": ["d"], "step": 3},
... }
>>> dag = dict_to_dag(relation_dict, parent_key="parents")
>>> [(parent.node_name, child.node_name) for parent, child in dag_iterator(dag)]
[('a', 'd'), ('c', 'd'), ('d', 'e'), ('a', 'c'), ('b', 'c')]

Parameters:

• relation_attrs (Dict[str, Any]) – dictionary containing node, node parents, and node attribute information, key: child name, value: dictionary of parent names, node attribute, and attribute value

• parent_key (str) – key of dictionary to retrieve list of parents name, defaults to ‘parent’

• node_type (Type[DAGNode]) – node type of DAG to be created, defaults to DAGNode

Returns:

(DAGNode)

bigtree.dag.construct.list_to_dag(relations: ~typing.List[~typing.Tuple[str, str]], node_type: ~typing.Type[~bigtree.node.dagnode.DAGNode] = <class 'bigtree.node.dagnode.DAGNode'>) → DAGNode

Construct DAG from list of tuples containing parent-child names. Note that node names must be unique.

>>> from bigtree import list_to_dag, dag_iterator
>>> relations_list = [("a", "c"), ("a", "d"), ("b", "c"), ("c", "d"), ("d", "e")]
>>> dag = list_to_dag(relations_list)
>>> [(parent.node_name, child.node_name) for parent, child in dag_iterator(dag)]
[('a', 'd'), ('c', 'd'), ('d', 'e'), ('a', 'c'), ('b', 'c')]

Parameters:

• relations (List[Tuple[str, str]]) – list containing tuple of parent-child names

• node_type (Type[DAGNode]) – node type of DAG to be created, defaults to DAGNode

Returns:

(DAGNode)