| { |
| "model_name": "CausalModelEvaluation", |
| "model_type": "causalmodelevaluation", |
| "architectures": ["LaggedPartialCorrelationCME", "PrecipitationConstraintGP"], |
| "framework": "PyTorch/NumPy/scikit-learn", |
| "domain": "climate-science", |
| "task": "causal-network-model-evaluation-and-constrained-precipitation-projection", |
| "implementation": { |
| "entry_point": "model/causalmodelevaluation.py", |
| "scope": "linear PCMCI-ParCorr engineering approximation at the paper node and lag dimensions", |
| "train_script": "scripts/train.py", |
| "inference_script": "scripts/inference.py", |
| "evaluation_script": "scripts/result.py", |
| "synthetic_data_script": "scripts/fake_data.py" |
| }, |
| "architecture": { |
| "family": "lagged partial correlation conditional regression and Gaussian process", |
| "nodes": 50, |
| "maximum_lag_steps": 10, |
| "time_step_days": 3, |
| "paper_significance_threshold": 0.0001, |
| "engineering_fake_data_threshold": 0.02, |
| "network_layout": ["source", "target", "lag"], |
| "network_shape": [50, 50, 10] |
| }, |
| "data": { |
| "protocol": "cme_structured_var_seasonal_v1", |
| "node_series_layout": ["member", "three_day_step", "node"], |
| "node_series_shape": ["B", 2100, 50], |
| "years_per_segment": 70, |
| "seasons": ["DJF", "MAM", "JJA", "SON"], |
| "precipitation_grid_shape": [73, 144], |
| "precipitation_grid_degrees": 2.5, |
| "network_output_shape": [50, 50, 10], |
| "model_count": 4 |
| }, |
| "configuration_sources": ["conf/config.yaml", "model/causalmodelevaluation.py", "scripts/fake_data.py", "scripts/train.py", "scripts/inference.py", "scripts/result.py"] |
| } |
|
|
