Configuration¶

This page summarizes the main configuration objects exposed through the current API.

The canonical configuration definitions live in:

foreblocks/aux/config.py for ModelConfig and TrainingConfig

`ModelConfig`¶

ModelConfig is a lightweight dataclass for architecture-level settings.

Core fields:

Field	Purpose
`model_type`	selects the backbone family such as LSTM-style vs transformer-style workflows
`input_size`	feature dimension presented to the model
`output_size`	output feature dimension
`hidden_size`	latent width for recurrent or sequence blocks
`seq_len`	source window length
`target_len`	forecast horizon
`strategy`	forecasting strategy such as `seq2seq`
`teacher_forcing_ratio`	decoder teacher forcing during training

Extended architecture fields include dim_feedforward, dropout, num_encoder_layers, num_decoder_layers, multi_encoder_decoder, latent_size, and nheads.

It is useful when you want a typed container for model-level settings, even though many examples instantiate modules directly.

`TrainingConfig`¶

TrainingConfig controls the training loop.

Core training¶

Field	Purpose
`num_epochs`	maximum epoch count
`learning_rate`	optimizer learning rate
`weight_decay`	optimizer weight decay
`batch_size`	default batch size used in examples and helpers
`patience`	early-stopping patience
`min_delta`	minimum improvement threshold for early stopping
`use_amp`	enables automatic mixed precision
`gradient_clip_val`	gradient clipping threshold
`gradient_accumulation_steps`	number of mini-batches to accumulate before stepping
`l1_regularization`	optional L1 penalty
`kl_weight`	optional KL weight for compatible models

Scheduler and logging¶

Field	Purpose
`scheduler_type`	scheduler selector
`lr_step_size`	step scheduler interval
`lr_gamma`	step scheduler decay
`min_lr`	lower learning-rate bound
`verbose`	trainer logging verbosity
`log_interval`	batch logging cadence
`save_best_model`	whether to retain best checkpoint state
`save_model_path`	optional filesystem path for saved weights
`experiment_name`	MLTracker experiment name when tracking is enabled

Mixture-of-experts logging¶

Field	Purpose
`moe_logging`	enables MoE report collection
`moe_log_latency`	records latency metrics in MoE logs
`moe_condition_name`	condition label for segmented MoE analysis
`moe_condition_cardinality`	category count for condition-aware reports

NAS settings¶

Field	Purpose
`train_nas`	enables alternating optimization for architecture parameters
`nas_alpha_lr`	learning rate for architecture parameters
`nas_alpha_weight_decay`	weight decay for architecture parameters
`nas_warmup_epochs`	epochs before alpha updates begin
`nas_alternate_steps`	alpha-step frequency
`nas_use_val_for_alpha`	uses validation loss for alpha updates
`nas_discretize_at_end`	discretizes architecture choices after training
`nas_discretize_threshold`	threshold used during discretization
`nas_log_alphas`	logs alpha values during training

Conformal prediction settings¶

Shared fields:

Field	Purpose
`conformal_enabled`	master switch
`conformal_method`	method name such as `split`, `rolling`, `agaci`, `enbpi`, `cptc`, or `afocp`
`conformal_quantile`	target coverage level
`conformal_knn_k`	neighborhood size for local conformal methods
`conformal_local_window`	calibration window for local methods
`conformal_aci_gamma`	adaptation rate for ACI-style methods
`conformal_rolling_alpha`	rolling update rate
`conformal_agaci_gammas`	gamma grid for AgACI
`conformal_enbpi_B`	bootstrap count for EnbPI
`conformal_enbpi_window`	rolling window for EnbPI
`conformal_tsp_lambda`	regularization parameter for TSP
`conformal_tsp_window`	calibration window for TSP
`conformal_cptc_window`	state-aware rolling window
`conformal_cptc_tau`	state filter sharpness
`conformal_cptc_hard_state_filter`	switches CPTC filtering mode
`conformal_afocp_feature_dim`	feature dimension for AFOCP
`conformal_afocp_attn_hidden`	attention hidden size for AFOCP
`conformal_afocp_window`	context window for AFOCP
`conformal_afocp_tau`	temperature-like scaling for AFOCP
`conformal_afocp_internal_feat_hidden`	hidden size of the internal feature network
`conformal_afocp_internal_feat_depth`	depth of the internal feature network
`conformal_afocp_internal_feat_dropout`	dropout inside the internal feature network
`conformal_afocp_online_lr`	online update learning rate
`conformal_afocp_online_steps`	number of online update steps

Helper methods¶

TrainingConfig also provides:

update(**kwargs) for safe field updates
get_conformal_params() to build the trainer-facing conformal configuration payload

Starter recipes¶

Minimal local smoke test:

config = TrainingConfig(
    num_epochs=5,
    batch_size=16,
    patience=3,
    use_amp=False,
)

Tracking-oriented run:

config = TrainingConfig(
    experiment_name="baseline_direct",
    save_best_model=True,
)

`TimeSeriesHandler` settings¶

Important preprocessor controls include:

window_size
horizon
normalize
differencing
detrend
apply_filter
apply_ewt
remove_outliers
apply_imputation
generate_time_features
self_tune

Use the dedicated guide for more detail:

Preprocessor Guide

Practical guidance¶

For first runs, keep configuration small and explicit.
Disable optional features until the core training loop is working.
Enable conformal and NAS only after validating your baseline training path.
Use auto_track=False on Trainer when you do not want MLTracker involved in local smoke tests.