jesterTOV.inference.flows.config.FlowTrainingConfig

class FlowTrainingConfig(**data)

Bases: BaseModel

Configuration for training normalizing flows on posterior samples.

Variables:

• posterior_file (str) – Path to .npz file with posterior samples

• output_dir (str) – Directory to save model weights, kwargs, and plots

• parameter_names (list[str]) – List of parameter names to extract from posterior file. Examples: GW parameters [“mass_1_source”, “mass_2_source”, “lambda_1”, “lambda_2”], NICER [“mass”, “radius”]

• num_epochs (int) – Number of training epochs (default: 600)

• learning_rate (float) – Learning rate for training (default: 1e-3)

• max_patience (int) – Early stopping patience (default: 50)

• nn_depth (int) – Depth of neural network blocks (default: 5)

• nn_block_dim (int) – Dimension of neural network blocks (default: 8)

• flow_layers (int) – Number of flow layers (default: 1)

• invert (bool) – Whether to invert the flow (default: True)

• cond_dim (int | None) – Conditional dimension for conditional flows (default: None)

• max_samples (int) – Maximum number of samples to use for training (default: 50,000)

• seed (int) – Random seed for reproducibility (default: 0)

• plot_corner (bool) – Generate corner plot comparison (default: True)

• plot_losses (bool) – Plot training and validation losses (default: True)

• flow_type (Literal["block_neural_autoregressive_flow", "masked_autoregressive_flow", "coupling_flow"]) – Type of normalizing flow to use (default: masked_autoregressive_flow)

• nn_width (int) – Width of neural network hidden layers (default: 50)

• standardize (bool) – Whether to standardize input data (default: True, changed from False)

• standardization_method (Literal["zscore", "minmax"]) – Method for standardizing input data (default: zscore). - “zscore”: Standardize to mean=0, std=1 (recommended for most cases) - “minmax”: Standardize to [0, 1] range (legacy, for backward compatibility) Only used if standardize=True.

• transformer (Literal["affine", "rational_quadratic_spline"]) – Transformer type for masked_autoregressive_flow and coupling_flow (default: rational_quadratic_spline, changed from affine)

• transformer_knots (int) – Number of knots for RationalQuadraticSpline transformer (default: 10, changed from 8)

• transformer_interval (float) – Interval for RationalQuadraticSpline transformer (default: 5.0, changed from 4.0)

• val_prop (float) – Proportion of data to use for validation (default: 0.2)

• batch_size (int) – Batch size for training (default: 128)

__init__(**data)

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)
from_yaml(filepath)	Load configuration from a YAML file.
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_parameter_names(v)	Validate that parameter_names is a non-empty list.
validate_positive_float(v)	Validate that float value is positive.
validate_positive_int(v)	Validate that integer value is positive.
validate_val_prop_range(v)	Validate that validation proportion is in (0, 1).

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.
posterior_file
output_dir
parameter_names
num_epochs
learning_rate
max_patience
nn_depth
nn_block_dim
flow_layers
invert
cond_dim
max_samples
seed
plot_corner
plot_losses
flow_type
nn_width
standardize
standardization_method
transformer
transformer_knots
transformer_interval
val_prop
batch_size

batch_size: int

cond_dim: int | None

flow_layers: int

flow_type: Literal['block_neural_autoregressive_flow', 'masked_autoregressive_flow', 'coupling_flow']

classmethod from_yaml(filepath)

Load configuration from a YAML file.

Parameters:

filepath (str | Path) – Path to YAML configuration file

Return type:

FlowTrainingConfig

Returns:

FlowTrainingConfig instance with loaded configuration

Example

>>> config = FlowTrainingConfig.from_yaml("config.yaml")

invert: bool

learning_rate: float

max_patience: int

max_samples: int

nn_block_dim: int

nn_depth: int

nn_width: int

num_epochs: int

output_dir: str

parameter_names: list[str]

plot_corner: bool

plot_losses: bool

posterior_file: str

seed: int

standardization_method: Literal['zscore', 'minmax']

standardize: bool

transformer: Literal['affine', 'rational_quadratic_spline']

transformer_interval: float

transformer_knots: int

val_prop: float

classmethod validate_parameter_names(v)

Validate that parameter_names is a non-empty list.

Return type:

list[str]

classmethod validate_positive_float(v)

Validate that float value is positive.

Return type:

float

classmethod validate_positive_int(v)

Validate that integer value is positive.

Return type:

int

classmethod validate_val_prop_range(v)

Validate that validation proportion is in (0, 1).

Return type:

float