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Here, we discuss common patterns for working with graphs in the Kumo SDK. Graphs are created from Table objects, and represent the relational (primary key / foreign key) connections between their constituent tables. These specified relationships are used by Kumo to learn across multiple tables, in a way that derives optimal cross-table representations for your specified task and avoids manual feature engineering. The metadata required for graph creation is simple:
  • Each constituent Table, with a corresponding name to be used within the Graph.
  • The relationships between the Tables, specifying the source table (table with the foreign key), name of the foreign key, and destination table (table with the primary key corresponding to the foreign key).

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How do I create a graph?

Creating a graph requires creating all of the Table objects that participate in the graph; see faq_tables for answers to frequently asked questions for table creation. Once you have created all of your tables, you can create a Graph explicitly by passing the relevant arguments to the constructor: 
table_1 = kumoai.Table(...)
table_2 = kumoai.Table(...)  # assume this table has a primary key.
table_3 = kumoai.Table(...)

graph = kumoai.Graph(
    # A dictionary mapping the names of the tables to the table objects:
    tables = {
        'table_1_name': table_1,
        'table_2_name': table_2,
        'table_3_name': table_3,
    },

    # A list of edges, either specified as kumoai.Edge objects or as
    # dictionaries, that describe the relationships between the tables.
    # Note that edges are always bidirectional:
    edges = [
        kumoai.Edge('table_1_name', 'table_1_fkey', 'table_2_name'),
        dict(src_table='table_3_name', fkey='table_3_fkey', dst_table='table_2_name'),
    ]
)
In the above graph, we have included three tables, and two primary/foreign key relationships between them: one from a foreign key in table 1 to the primary key in table 2, and another from a foreign key in table 3 to the primary key in table 2.

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How do I edit a graph?

Multiple methods exist to support adding/removing tables and edges in a graph. Concretely:
  • add_table() adds a table to a Graph
  • remove_table() removes a table from a Graph
  • link() adds an edge between two tables in a Graph
  • unlink() removes an edge from a Graph

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What does it mean to snapshot a graph?

The snapshot() method allows you to ingest all of the tables in a graph, so that multiple calls to train a model will use the same version of data even while the data in the source connector changes. Snapshotting a graph is also required to view that graph’s edge health statistics, which contain information about the number of matches between primary and foreign keys across all edges in the graph.