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data:
root: data
format_version: cme_structured_var_seasonal_v1
samples: 1
time_steps: 2100
nodes: 50
seasons: [DJF, MAM, JJA, SON]
model_count: 4
grid_shape: [73, 144]
years_per_segment: 70
time_step_days: 3
model:
max_lag: 10
alpha: 0.02
ridge: 1.0e-6
exclude_self_links: true
paper_model:
nodes: 50
time_step_days: 3
max_lag: 10
maximum_delay_days: 30
alpha: 1.0e-4
method: PCMCI with partial correlation
engineering_approximation: target-history conditional regression with partial correlation
evaluation:
lag_tolerance: 2
reference_f1_for_projection: 0.64
paths:
dataset: data/cme_fake.npz
checkpoint: result/checkpoints/causalmodelevaluation.pt
training_metrics: result/training/metrics.json
inference: result/output/inference.npz
evaluation_dir: result/evaluation
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