cursus.core.base.hyperparameters_base

class ModelHyperparameters(*, full_field_list, cat_field_list, tab_field_list, id_name, label_name, multiclass_categories, categorical_features_to_encode=<factory>, model_class='base_model', device=-1, header=0, lr=3e-05, batch_size=2, eval_batch_size_multiplier=2.0, max_epochs=3, metric_choices=['f1_score', 'auroc'], optimizer='SGD', class_weights=None, **extra_data)

Bases: BaseModel

Base model hyperparameters for training tasks.

Fields are organized into three tiers: 1. Tier 1: Essential User Inputs - fields that users must explicitly provide 2. Tier 2: System Inputs with Defaults - fields with reasonable defaults that can be overridden 3. Tier 3: Derived Fields - fields calculated from other fields (private attributes with properties)

full_field_list: List[str]

cat_field_list: List[str]

tab_field_list: List[str]

id_name: str

label_name: str

multiclass_categories: List[str | int]

categorical_features_to_encode: List[str]

model_class: str

device: int

header: int

lr: float

batch_size: int

eval_batch_size_multiplier: float

max_epochs: int

metric_choices: List[str]

optimizer: str

class_weights: List[float] | None

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True, 'extra': 'allow', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

property input_tab_dim: int

Get input tabular dimension derived from tab_field_list.

property num_classes: int

Get number of classes derived from multiclass_categories.

property is_binary: bool

Determine if this is a binary classification task based on num_classes.

validate_dimensions()

Validate model dimensions and configurations

categorize_fields()

Categorize all fields into three tiers: 1. Tier 1: Essential User Inputs - fields with no defaults (required) 2. Tier 2: System Inputs - fields with defaults (optional) 3. Tier 3: Derived Fields - properties that access private attributes

Returns:

Dict with keys ‘essential’, ‘system’, and ‘derived’ mapping to lists of field names

Return type:

Dict[str, List[str]]

print_hyperparam()

Print complete hyperparameter information organized by tiers. This method automatically categorizes fields by examining their characteristics.

get_public_init_fields()

Get a dictionary of public fields suitable for initializing a child hyperparameter. Only includes fields that should be passed to child class constructors. Both essential user inputs and system inputs with defaults or user-overridden values are included to ensure all user customizations are properly propagated.

Returns:

Dictionary of field names to values for child initialization

Return type:

Dict[str, Any]

classmethod from_base_hyperparam(base_hyperparam, **kwargs)

Create a new hyperparameter instance from a base hyperparameter. This is a virtual method that all derived classes can use to inherit from a parent config.

Parameters:

• base_hyperparam (ModelHyperparameters) – Parent ModelHyperparameters instance

• **kwargs (Any) – Additional arguments specific to the derived class

Returns:

A new instance of the derived class initialized with parent fields and additional kwargs

Return type:

ModelHyperparameters

get_config()

Get the complete configuration dictionary.

serialize_config()

Serialize configuration for SageMaker.

model_post_init(context, /)

This function is meant to behave like a BaseModel method to initialize private attributes.

It takes context as an argument since that’s what pydantic-core passes when calling it.

Parameters:

• self (BaseModel) – The BaseModel instance.

• context (Any) – The context.