src/datacenter_verification_modeling/README.md

datacenter verification modeling

this package trains and evaluates the first public baseline model for the synthetic v0 datacenter training-run verification dataset.

the training unit is one row from:

data/synthetic_v0/features/window_features_all.csv

the model does not read raw telemetry samples, events, or snapshots directly

outputs

the default model run is written to:

data/model_runs/synthetic_v0_baseline/

it contains the fitted calibrated model, preprocessing pipeline, split manifest, feature metadata, predictions, metrics, calibration diagnostics, feature importance, and an evidence audit sample

leakage controls

rows are split by episode_id, not randomly. This is required because the same latent episode appears in multiple adjacent windows. The default split is scenario-stratified at the episode level with seed 20260510:

train: 60%
validation/calibration: 20%
test: 20%

the supervised model excludes identifiers, direct labels, site id, episode id, raw manifest hashes, and synthetic-only audit columns such as latent_workload_class and synthetic_evidence_profile

model

the supervised baseline is:

SimpleImputer + OneHotEncoder preprocessing
HistGradientBoostingClassifier
CalibratedClassifierCV on the validation split

the package also includes rule_baseline.py, a deterministic evidence-rule baseline that encodes broad study logic:

• capacity below threshold with strong coverage is negative evidence
• capacity alone does not prove training
• missing data is not zero activity
• integrity anomalies are warnings, not positive proof
• labels 3 and 4 require coherent multi-layer evidence

commands

train, evaluate, and generate all default artifacts:

python src/datacenter_verification_modeling/train_model.py \
  --features data/synthetic_v0/features/window_features_all.csv \
  --output data/model_runs/synthetic_v0_baseline \
  --seed 20260510

Evaluate an existing run:

python src/datacenter_verification_modeling/evaluate_model.py \
  --model-run data/model_runs/synthetic_v0_baseline \
  --features data/synthetic_v0/features/window_features_all.csv

Generate predictions from an existing run:

python src/datacenter_verification_modeling/predict.py \
  --model-run data/model_runs/synthetic_v0_baseline \
  --features data/synthetic_v0/features/window_features_all.csv \
  --output data/model_runs/synthetic_v0_baseline/predictions_all.csv

Prepare split and feature metadata without training:

python src/datacenter_verification_modeling/prepare_features.py \
  --features data/synthetic_v0/features/window_features_all.csv \
  --output data/model_runs/synthetic_v0_baseline \
  --seed 20260510

governance outputs

prediction files include:

• p_label_0 through p_label_4
• raw_p_label_0 through raw_p_label_4
• p_large_training
• severity_score
• capacity_possible
• negative_certification_confidence
• integrity_warning
• critical_missing_layers
• top_evidence

the p_label_* columns are post-processed with the capacity gate; the raw_p_label_* columns preserve the calibrated model probabilities before that gate

limitations

This is a synthetic v0 prototype ! Metrics are useful for testing the pipeline, not for deployment claims. Real datacenter use would require real telemetry, controlled drills, operational calibration, and independent review