Additional Configuration

Some additional model configuration must be specified, and some fields are optional. This will be explained below and it is relevant for both Python and R models.

Specifying prediction length constraints

Include min_prediction_length and max_prediction_length in your model configuration to define how many time periods your model can predict ahead. When users need predictions beyond your max_prediction_length, CHAP automatically uses ExtendedPredictor to make iterative predictions (see supporting functionality).

Model Configuration Options

You can define configurable parameters in your MLproject file using user_options. This allows users to customize model behavior when running your model, without modifying the model code itself.

Schema structure

Each option in user_options has the following fields:

title: Display name for the parameter
type: One of string, integer, number, boolean, or array
description: What the parameter does
default: Optional default value. If omitted, the parameter is required

Example MLproject with user_options

name: my_model

docker_env:
  image: python:3.11

entry_points:
  train:
    parameters:
      train_data: str
      model: str
    command: "python train.py {train_data} {model}"
  predict:
    parameters:
      historic_data: str
      future_data: str
      model: str
      out_file: str
    command: "python predict.py {model} {historic_data} {future_data} {out_file}"

user_options:
  n_lag_periods:
    title: n_lag_periods
    type: integer
    default: 3
    description: "Number of lag periods to include in the model"
  learning_rate:
    title: learning_rate
    type: number
    description: "Learning rate for training (required)"

Providing configuration values

Configuration values can be provided via the --model-configuration-yaml CLI flag when running eval or other commands:

chap eval my_model data.csv results.nc --model-configuration-yaml config.yaml

The configuration YAML file should contain the parameter values:

n_lag_periods: 5
learning_rate: 0.01

Validation rules

Options without a default value are required and must be provided
Only options defined in user_options are allowed in the configuration file
Values must match the specified type (e.g., integers for integer type)

Examples in the codebase

See the following examples that use user_options: