problem_definition.md

title	Problem definition

Problem definition

ProblemDefinition defines the feature selection and split configuration used on top of a PLAID dataset.

In the current API, a problem definition stores:

• input_features (list[str], required and non-empty)
• output_features (list[str], required and non-empty)
• train_split and test_split (required)

The problem identifier is not stored in the model. On disk, it is the YAML filename stem; in memory, it is the dictionary key used for the definition.

Basic usage

from plaid import ProblemDefinition

pb = ProblemDefinition(
    input_features=[
        "Base/Zone/GridCoordinates/CoordinateX",
        "Base/Zone/GridCoordinates/CoordinateY",
    ],
    output_features=[
        "Base/Zone/VertexFields/pressure",
    ],
    train_split={"train": [0, 1, 2]},
    test_split={"test": [3, 4]},
)

Feature lists are normalized by the model: entries are converted to strings, sorted, checked for duplicates, and rejected if empty.

Problem definitions can also be validated from a plain mapping, for instance after reading YAML:

pb = ProblemDefinition.model_validate(
    {
        "input_features": ["Base/Zone/GridCoordinates/CoordinateX"],
        "output_features": ["Base/Zone/VertexFields/pressure"],
        "train_split": {"train": [0, 1, 2]},
        "test_split": {"test": [3, 4]},
    }
)

Loading from disk

Load a definition from a dataset path:

pb = ProblemDefinition.from_path(
    "/path/to/plaid_dataset/problem_definitions/regression_1.yaml"
)

At storage level, problem definitions are loaded as a dictionary keyed by YAML filename stem:

from plaid.storage import load_problem_definitions_from_disk

pb_defs = load_problem_definitions_from_disk("/path/to/plaid_dataset")
pb = pb_defs["regression_1"]

Saving

Save to YAML:

pb.save_to_file("problem_definitions/regression_1.yaml")

This writes no name: key; regression_1 is inferred from the filename by the storage loader.

Notes

• Input/output features are plain strings corresponding to CGNS paths.
• Splits are represented by train_split and test_split dictionaries and are accessed directly as model attributes.
• Split values can be explicit index sequences or the string "all".
• add_input_features(...) and add_output_features(...) accept either a single string or a sequence of strings after initialization.