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Merge pull request #4 from Fung-Lab/dev
feature: initial framework for training and evaluating models
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‎.gitignore‎

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# Byte-compiled / optimized / DLL files
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__pycache__/
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*.py[cod]
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*$py.class
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# C extensions
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*.so
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# Distribution / packaging
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.Python
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build/
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develop-eggs/
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dist/
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downloads/
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eggs/
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.eggs/
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lib/
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lib64/
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parts/
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sdist/
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var/
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wheels/
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pip-wheel-metadata/
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share/python-wheels/
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*.egg-info/
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.installed.cfg
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*.egg
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MANIFEST
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# PyInstaller
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# Usually these files are written by a python script from a template
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# before PyInstaller builds the exe, so as to inject date/other infos into it.
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# Translations
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# IPython
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profile_default/
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ipython_config.py
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# pyenv
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.python-version
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# pipenv
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# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
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# However, in case of collaboration, if having platform-specific dependencies or dependencies
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# having no cross-platform support, pipenv may install dependencies that don't work, or not
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# Celery stuff
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# SageMath parsed files
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# Environments
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.idea/**/sqlDataSources.xml
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.idea/**/uiDesigner.xml
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.idea/**/gradle.xml
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# Visual Studio Code
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.vscode/*
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!.vscode/settings.json
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# mac
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.DS_Store
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# data
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./data/*
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./config/*
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server/
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main.py
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test*.py
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test*.ipynb

‎README.md‎

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pip install -e .

‎configs/config.yml‎

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trainer: property
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task:
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# run_mode: train
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name: "my_train_job"
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reprocess: "False"
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parallel: "True"
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seed: 0
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#seed=0 means random initalization
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write_output: "True"
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parallel: "True"
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#Training print out frequency (print per n number of epochs)
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verbosity: 5
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#Ratios for train/val/test split out of a total of 1
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train_ratio: 0.8
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val_ratio: 0.05
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test_ratio: 0.15
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model:
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name: CGCNN
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load_model: "False"
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save_model: "True"
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model_path: "my_model.pth"
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edge_steps: 50
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self_loop: True
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#model attributes
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dim1: 100
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dim2: 150
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pre_fc_count: 1
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gc_count: 4
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post_fc_count: 3
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pool: "global_mean_pool"
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pool_order: "early"
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batch_norm: "True"
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batch_track_stats: "True"
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act: "relu"
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dropout_rate: 0.0
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optim:
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max_epochs: 250
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lr: 0.002
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#Loss functions (from pytorch) examples: l1_loss, mse_loss, binary_cross_entropy
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loss_fn: "l1_loss"
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batch_size: 100
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optimizer:
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optimizer_type: "AdamW"
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optimizer_args: {}
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scheduler:
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scheduler_type: "ReduceLROnPlateau"
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scheduler_args: {"mode":"min", "factor":0.8, "patience":10, "min_lr":0.00001, "threshold":0.0002}
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dataset:
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processed: False # if False, need to preprocessor data and generate .pt file
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# Whether to use "inmemory" or "large" format for pytorch-geometric dataset. Reccomend inmemory unless the dataset is too large
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# dataset_type: "inmemory"
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#Path to data files
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src: "/global/cfs/projectdirs/m3641/Shared/Materials_datasets/MP_data_npj/raw/"
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#Path to target file within data_path
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target_path: "/global/cfs/projectdirs/m3641/Shared/Materials_datasets/MP_data_npj/targets.csv"
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#Path to save processed data.pt file
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pt_path: "/global/homes/s/shuyijia/datasets/MP_data_npj/"
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#Format of data files (limit to those supported by ASE)
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data_format: "json"
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#Method of obtaining atom idctionary: available:(onehot)
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node_representation: "onehot"
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#Print out processing info
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verbose: "True"
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#Loading dataset params
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#Index of target column in targets.csv
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target_index: 0
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#graph specific settings
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cutoff_radius : 8.0
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n_neighbors : 12
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edge_steps : 50
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‎data/bulk_data_500/processed/data.pt‎

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‎data/bulk_data_500/raw/0.json‎

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‎data/bulk_data_500/raw/1.json‎

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‎data/bulk_data_500/raw/10.json‎

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‎data/bulk_data_500/raw/100.json‎

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‎data/bulk_data_500/raw/101.json‎

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‎data/bulk_data_500/raw/102.json‎

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