OneScience-Group/ML-MODIS

1{2  "model_name": "ML-MODIS",3  "model_type": "ml_modis",4  "architectures": ["BootstrapRandomForestRegressor"],5  "framework": "PyTorch",6  "domain": "earth-science",7  "task": "counterfactual-cloud-property-regression",8  "implementation": {9    "entry_point": "model/ml_modis.py",10    "scope": "independent paper-method engineering reproduction",11    "train_script": "scripts/train.py",12    "inference_script": "scripts/inference.py",13    "evaluation_script": "scripts/result.py",14    "synthetic_data_script": "scripts/fake_data.py"15  },16  "architecture": {17    "family": "bootstrap random regression forest",18    "input_features": 114,19    "output_targets": 4,20    "independent_models": 8,21    "months": [9, 10],22    "targets": ["Nd", "reff", "LWP", "CF"]23  },24  "data": {25    "format": "NPZ",26    "protocol": "ml_modis_npz_v1",27    "input_shape": ["N", 114],28    "target_shape": ["N", 4],29    "alignment_key": ["year", "month", "platform", "latitude", "longitude"]30  },31  "configuration_sources": [32    "conf/config.yaml",33    "model/ml_modis.py",34    "scripts/fake_data.py",35    "scripts/train.py",36    "scripts/inference.py",37    "scripts/result.py"38  ]39}40