Scenario fields¶
Generated from tapestry.model.scenario.Scenario (CODES, MEANING) by tapestry.evaluation.plots.write_scenario_key, rewritten by every report; do not edit by hand. A scenario string names only the fields that differ from these defaults, as key=value tokens joined by ,; booleans are written 0/1. "Design" = the architecture; execution is described in Workflow.
| Field | Type | Default | Allowed values | Meaning |
|---|---|---|---|---|
task |
str | 'forecast' |
forecast, nowcast, pipeline |
forecast, nowcast, or an independently fitted nowcast-to-forecast pipeline. |
nowcast_weeks |
int | 2 |
any int (checked in Scenario.__post_init__) |
Completed weeks reconstructed, ending at the context Saturday. |
nowcast.<field> |
stage override | inherit | Model/training fields | Pipeline stage overrides written as nowcast. |
forecast.<field> |
stage override | inherit | Model/training fields | Pipeline stage overrides written as forecast. |
nowcast_members |
int | 16 |
any int (checked in Scenario.__post_init__) |
Cached out-of-fold history draws per training episode. |
lookback |
int | 12 |
any int (checked in Scenario.__post_init__) |
Context weeks per episode (the history the network sees). |
count_transform |
str | 'fourth_root' |
fourth_root, log1p, rate, raw, sqrt |
Admissions in model space: raw counts, rate per 100,000, or its sqrt / fourth root / log1p (model/network.py transform_counts). |
ed_transform |
str | 'linear' |
fourth_root, linear, logit |
ED proportions in model space (model/network.py); scores stay in native units. |
geography |
bool | 1 |
0, 1 |
Adds log population and a native-US flag per location as features. |
coordinates |
bool | 0 |
0, 1 |
Census state internal-point latitude/longitude and non-US indicator. |
dynamics |
bool | 1 |
0, 1 |
Recent-dynamics feature block (30 slope/acceleration/age/validity features); see architecture.md. |
loss_weights |
str | 'objective' |
balanced_admissions, flu_only, influenza_first, objective |
Training-loss weight per channel (model/objective.py LOSS_WEIGHTS); objective = the score's target weights. |
encoder |
str | 'mlp' |
conv, mlp, multiscale_conv |
Temporal context encoder; see architecture.md. |
spatial |
str | 'none' |
attention, distance, gated_pool, gravity, joint_location_target, national_broadcast, neighbors, none, pathogen_spatial, pooled, target_spatial |
Cross-location information exchange (none, shared attention, pathogen/target/joint scopes); see architecture.md. |
heads |
str | 'shared' |
shared, state_us |
State and US output heads shared or separate (state_us). |
decoder |
str | 'legacy' |
legacy, residual2 |
Horizon decoder: existing modulated residual (legacy) or residual2; see architecture.md. |
noise |
str | 'global' |
global, local |
Global latent noise, or global plus a per-location latent (local). |
us_error |
str | 'none' |
none, shared_factor |
Extra common noise factor (shared_factor); see architecture.md. |
latent |
int | 16 |
any int (checked in Scenario.__post_init__) |
Global latent (noise) dimension. |
width |
int | 64 |
any int (checked in Scenario.__post_init__) |
Hidden width of the network. |
epochs |
int | 50 |
any int (checked in Scenario.__post_init__) |
Epoch cap (the fixed number of epochs when patience = 0). |
patience |
int | 0 |
any int (checked in Scenario.__post_init__) |
Early-stopping patience in epochs; 0 = fixed epochs, no validation weeks; > 0 selects the epoch on the validation weeks, then refits on the full training seasons (design §4). |
batch_size |
int | 8 |
any int (checked in Scenario.__post_init__) |
Episodes per optimizer step. |
members |
int | 128 |
any int (checked in Scenario.__post_init__) |
Sampled members per training episode (fair CRPS loss). |
lr |
float | 0.001 |
any float (checked in Scenario.__post_init__) |
Adam learning rate. |
head_sharing |
str | 'shared' |
pathogen, shared, target |
Output sharing: shared, three pathogen heads, or six target heads; see architecture.md. |
annual_calendar |
bool | 1 |
0, 1 |
Annual sine/cosine and Christmas-timing features. |
location_embedding |
int | 0 |
any int (checked in Scenario.__post_init__) |
Dimension of a learned location-ID embedding (0 = none). |
fit_partition |
str | 'all' |
all, pathogen, target |
One model for all six targets, or separately fitted models per pathogen / target group (each sees all six inputs); see architecture.md. |
validation_members |
int | 256 |
any int (checked in Scenario.__post_init__) |
Members drawn for the early-stopping validation loss. |
weight_decay |
float | 0.0 |
any float (checked in Scenario.__post_init__) |
Adam weight decay. |
supplied_final |
bool | 0 |
0, 1 |
The network receives a known-final flag channel per cell; see architecture.md. |
mask_rate |
float | 0.0 |
any float (checked in Scenario.__post_init__) |
Probability an episode receives an artificial missingness pattern in training (not a fraction of cells); see architecture.md. |
mask_recent |
float | 0.5 |
any float (checked in Scenario.__post_init__) |
Share of masked episodes whose pattern hides recent reports (with mask_gap, mask_outage sums to 1). |
mask_gap |
float | 0.3 |
any float (checked in Scenario.__post_init__) |
Share of masked episodes whose pattern is a local gap in one location history. |
mask_outage |
float | 0.2 |
any float (checked in Scenario.__post_init__) |
Share of masked episodes whose pattern is a whole-channel outage. |
covariate_encoder |
str | 'raw' |
raw, shared, smooth, summary |
Raw standardized history, signed-log trailing-three-week smoothing, six summaries, or a shared 4-dimensional encoder plus coverage/age. |
signal_features |
str | 'none' |
multiscale, none, smooth_multiscale |
Optional causal 3/6/12-week level, slope and curvature features for targets and covariates, with optional three-week smoothing. |
covariate_set |
str | '' |
+-joined subset of inpatient, outpatient, ww_wval_like, ww_pct_rank, kinsa, ilinet, clinical_lab, flusurv |
+-joined covariate source groups fed to the context encoder; '' = none; see workflow.md and experiments/b-2-t0/index.md#protocol. |
input_mode |
str | 'finalized' |
finalized, finalized_available, scheduled_final, vintaged |
scheduled_final supplies T-0 final targets and source-specific T-0/T-1 covariates; finalized truth, finalized_available (final truth masked by Wednesday reporting availability), or Wednesday-vintage context (design §3). |
training_inputs |
str | 'same' |
finalized, same |
same as forecasting, or complete finalized target and covariate histories during fitting only. |
input_normalization |
str | 'none' |
b0, none |
none, or B0 per-location transformed target scales fitted on training contexts only. |
validation_calendar |
str | 'season' |
b0, season |
season-relative blocks, or B0 blocks counted from the first stored week of each season. |
evaluation_seasons |
str | 'all' |
all, recent_two |
all three held-out seasons, or recent_two (2025-26 and 2024-25). |
asof_weeks |
int | 2 |
any int (checked in Scenario.__post_init__) |
Standalone forecast only (nowcast/pipeline use all-as-of history): most recent context weeks whose targets are as visible at the issuance; older weeks take final truth (design §3). |
validation_weeks |
int | 3 |
any int (checked in Scenario.__post_init__) |
patience > 0 only: consecutive early-stopping weeks hidden per block (design §4). |
validation_spacing |
int | 16 |
any int (checked in Scenario.__post_init__) |
patience > 0 only: one validation block every this many weeks of a training season. |
validation_offset |
int | 4 |
any int (checked in Scenario.__post_init__) |
patience > 0 only: week of each training season where the first block starts (0-based, counted from the season's first epiweek, CDC week 31). |