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20cdc88 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 | {
"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"]
}
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