✨ 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,
bigtree.dag.construct
list_to_dag
Construct DAG from list of tuples containing parent-child names. Note that node names must be unique.
Examples:
>>> 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:
Name | Type | Description | Default |
---|---|---|---|
relations |
List[Tuple[str, str]]
|
list containing tuple of parent-child names |
required |
node_type |
Type[DAGNode]
|
node type of DAG to be created, defaults to |
DAGNode
|
Returns:
Type | Description |
---|---|
DAGNode
|
(DAGNode) |
dict_to_dag
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.
Examples:
>>> 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:
Name | Type | Description | Default |
---|---|---|---|
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 |
required |
parent_key |
str
|
key of dictionary to retrieve list of parents name, defaults to 'parent' |
'parents'
|
node_type |
Type[DAGNode]
|
node type of DAG to be created, defaults to |
DAGNode
|
Returns:
Type | Description |
---|---|
DAGNode
|
(DAGNode) |
dataframe_to_dag
Construct DAG from pandas DataFrame. Note that node names must be unique.
child_col
andparent_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 areattribute_cols
.
Only attributes in attribute_cols
with non-null values will be added to the tree.
Examples:
>>> 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:
Name | Type | Description | Default |
---|---|---|---|
data |
DataFrame
|
data containing path and node attribute information |
required |
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 |
[]
|
node_type |
Type[DAGNode]
|
node type of DAG to be created, defaults to |
DAGNode
|
Returns:
Type | Description |
---|---|
DAGNode
|
(DAGNode) |