Decode

The decode service turns raw dataset records into the tensors a run consumes. It is the bridge between the record format and the model’s input and output spaces.

Usage

Configured under services.decode. The decoders for attributes and records are themselves selectable plugins.

services:
  decode:
    features: []
    targets:  [ bundle ]
    auxiliary: []
    keymap:
      truth: targets
      edge_attr: edge_weight
    attrs:
      name: standard
      kwargs: {}
    records:
      name: standard
      kwargs: {}

How it works

Two decoders divide the work. The attribute decoder reads dataset-level metadata, such as column names, per-column statistics, and the set of observed label values. The record decoder reads an individual record and extracts relevant tensors (such as features, targets, etc).

Configuration

Option	Description	Type	Default
`features`	Column names used as input features.	list[str]	`[]`
`targets`	Column names used as training targets.	list[str]	`[]`
`auxiliary`	Extra columns to decode but exclude from the loss (useful for plotting and analysis).	list[str]	`[]`
`keymap`	Overrides mapping logical roles to the keys used in the stored data.	mapping	identity
`attrs`	Attribute-decoder plugin selection (`name` / `kwargs`).	mapping	required
`records`	Record-decoder plugin selection (`name` / `kwargs`).	mapping	required

The keymap accepts the keys truth (the source of both targets and auxiliary), features, edge_index, edge_attr, and simweights; each defaults to its own name.

Sub-slots