cellmil.interfaces.MILTrainerConfig

class cellmil.interfaces.MILTrainerConfig(*, root: Path, folder: Path, excel_path: Path, label: str, model: MILType, gpu: int = 0, extractor: cellmil.interfaces.FeatureExtractorConfig.ExtractorType | list[cellmil.interfaces.FeatureExtractorConfig.ExtractorType], segmentation_model: ModelType, graph_creator: GraphCreatorType, ckpt_path: Path, normalization: bool = False, correlation_filter: float = 0.0, cell_type: bool = False, n_bins: int = 4)

Bases: BaseModel

Configuration for MIL prediction using CLAM or standard MIL models

__init__(**data: Any) → None

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Methods

__init__(**data)	Create a new model by parsing and validating input data from keyword arguments.
construct([_fields_set])
copy(*[, include, exclude, update, deep])	Returns a copy of the model.
dict(*[, include, exclude, by_alias, ...])
from_orm(obj)
json(*[, include, exclude, by_alias, ...])
model_construct([_fields_set])	Creates a new instance of the Model class with validated data.
model_copy(*[, update, deep])	!!! abstract "Usage Documentation"
model_dump(*[, mode, include, exclude, ...])	!!! abstract "Usage Documentation"
model_dump_json(*[, indent, ensure_ascii, ...])	!!! abstract "Usage Documentation"
model_json_schema([by_alias, ref_template, ...])	Generates a JSON schema for a model class.
model_parametrized_name(params)	Compute the class name for parametrizations of generic classes.
model_post_init(context, /)	Override this method to perform additional initialization after __init__ and model_construct.
model_rebuild(*[, force, raise_errors, ...])	Try to rebuild the pydantic-core schema for the model.
model_validate(obj, *[, strict, extra, ...])	Validate a pydantic model instance.
model_validate_json(json_data, *[, strict, ...])	!!! abstract "Usage Documentation"
model_validate_strings(obj, *[, strict, ...])	Validate the given object with string data against the Pydantic model.
parse_file(path, *[, content_type, ...])
parse_obj(obj)
parse_raw(b, *[, content_type, encoding, ...])
schema([by_alias, ref_template])
schema_json(*[, by_alias, ref_template])
update_forward_refs(**localns)
validate(value)
validate_model(v)

Attributes

model_computed_fields
model_config	Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
model_extra	Get extra fields set during validation.
model_fields
model_fields_set	Returns the set of fields that have been explicitly set on this model instance.
root
folder
excel_path
label
model
gpu
extractor
segmentation_model
graph_creator
ckpt_path
normalization
correlation_filter
cell_type
n_bins

root: Path

folder: Path

excel_path: Path

label: str

model: MILType

gpu: int

extractor: cellmil.interfaces.FeatureExtractorConfig.ExtractorType | list[cellmil.interfaces.FeatureExtractorConfig.ExtractorType]

segmentation_model: ModelType

graph_creator: GraphCreatorType

ckpt_path: Path

normalization: bool

correlation_filter: float

cell_type: bool

n_bins: int

classmethod validate_model(v: str) → str

class Config

Bases: object

arbitrary_types_allowed = True

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True}

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