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Merge pull request #268 from sadmanomee/deepergatgnn
DeeperGATGNN results added
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,372 @@ | ||
| Job: | ||
| run_mode: "Training" | ||
| #{Training, Predict, Repeat, CV, Hyperparameter, Ensemble, Analysis} | ||
| Training: | ||
| job_name: "my_train_job" | ||
| reprocess: "False" | ||
| model: CGCNN_demo | ||
| load_model: "False" | ||
| save_model: "True" | ||
| model_path: "my_model.pth" | ||
| write_output: "True" | ||
| parallel: "True" | ||
| #seed=0 means random initalization | ||
| seed: 0 | ||
| Predict: | ||
| job_name: "my_predict_job" | ||
| reprocess: "False" | ||
| model_path: "my_model.pth" | ||
| write_output: "True" | ||
| seed: 0 | ||
| Repeat: | ||
| job_name: "my_repeat_job" | ||
| reprocess: "False" | ||
| model: CGCNN_demo | ||
| model_path: "my_model.pth" | ||
| write_output: "False" | ||
| parallel: "True" | ||
| seed: 0 | ||
| ###specific options | ||
| #number of repeat trials | ||
| repeat_trials: 5 | ||
| CV: | ||
| job_name: "my_CV_job" | ||
| reprocess: "False" | ||
| model: CGCNN_demo | ||
| write_output: "True" | ||
| parallel: "True" | ||
| seed: 18012019 | ||
| ###specific options | ||
| #number of folds for n-fold CV | ||
| cv_folds: 5 | ||
| Hyperparameter: | ||
| job_name: "my_hyperparameter_job" | ||
| reprocess: "False" | ||
| model: CGCNN_demo | ||
| seed: 0 | ||
| ###specific options | ||
| hyper_trials: 10 | ||
| #number of concurrent trials (can be greater than number of GPUs) | ||
| hyper_concurrency: 8 | ||
| #frequency of checkpointing and update (default: 1) | ||
| hyper_iter: 1 | ||
| #resume a previous hyperparameter optimization run | ||
| hyper_resume: "True" | ||
| #Verbosity of ray tune output; available: (1, 2, 3) | ||
| hyper_verbosity: 1 | ||
| #Delete processed datasets | ||
| hyper_delete_processed: "True" | ||
| Ensemble: | ||
| job_name: "my_ensemble_job" | ||
| reprocess: "False" | ||
| save_model: "False" | ||
| model_path: "my_model.pth" | ||
| write_output: "Partial" | ||
| parallel: "True" | ||
| seed: 0 | ||
| ###specific options | ||
| #List of models to use: (Example: "CGCNN_demo,MPNN_demo,SchNet_demo,MEGNet_demo" or "CGCNN_demo,CGCNN_demo,CGCNN_demo,CGCNN_demo") | ||
| ensemble_list: "CGCNN_demo,CGCNN_demo,CGCNN_demo,CGCNN_demo,CGCNN_demo" | ||
| Analysis: | ||
| job_name: "my_job" | ||
| reprocess: "False" | ||
| model: CGCNN_demo | ||
| model_path: "my_model.pth" | ||
| write_output: "True" | ||
| seed: 0 | ||
|
|
||
| Processing: | ||
| #Whether to use "inmemory" or "large" format for pytorch-geometric dataset. Reccomend inmemory unless the dataset is too large | ||
| dataset_type: "inmemory" | ||
| #Path to data files | ||
| data_path: "/data" | ||
| #Path to target file within data_path | ||
| target_path: "targets.csv" | ||
| #Method of obtaining atom idctionary: available:(provided, default, blank, generated) | ||
| dictionary_source: "default" | ||
| #Path to atom dictionary file within data_path | ||
| dictionary_path: "atom_dict.json" | ||
| #Format of data files (limit to those supported by ASE) | ||
| data_format: "json" | ||
| #Print out processing info | ||
| verbose: "True" | ||
| #graph specific settings | ||
| graph_max_radius : 8.0 | ||
| graph_max_neighbors : 12 | ||
| voronoi: "False" | ||
| edge_features: "True" | ||
| graph_edge_length : 50 | ||
| #SM specific settings | ||
| SM_descriptor: "False" | ||
| #SOAP specific settings | ||
| SOAP_descriptor: "False" | ||
| SOAP_rcut : 8.0 | ||
| SOAP_nmax : 6 | ||
| SOAP_lmax : 4 | ||
| SOAP_sigma : 0.3 | ||
|
|
||
| Training: | ||
| #Index of target column in targets.csv | ||
| target_index: 0 | ||
| #Loss functions (from pytorch) examples: l1_loss, mse_loss, binary_cross_entropy | ||
| loss: "l1_loss" | ||
| #Ratios for train/val/test split out of a total of 1 | ||
| train_ratio: 0.8 | ||
| val_ratio: 0.05 | ||
| test_ratio: 0.15 | ||
| #Training print out frequency (print per n number of epochs) | ||
| verbosity: 5 | ||
|
|
||
| Models: | ||
| CGCNN_demo: | ||
| model: CGCNN | ||
| dim1: 100 | ||
| dim2: 150 | ||
| pre_fc_count: 1 | ||
| gc_count: 4 | ||
| post_fc_count: 3 | ||
| pool: "global_mean_pool" | ||
| pool_order: "early" | ||
| batch_norm: "True" | ||
| batch_track_stats: "True" | ||
| act: "relu" | ||
| dropout_rate: 0.0 | ||
| epochs: 250 | ||
| lr: 0.002 | ||
| batch_size: 100 | ||
| optimizer: "AdamW" | ||
| optimizer_args: {} | ||
| scheduler: "ReduceLROnPlateau" | ||
| scheduler_args: {"mode":"min", "factor":0.8, "patience":10, "min_lr":0.00001, "threshold":0.0002} | ||
| SUPER_CGCNN_demo: | ||
| model: SUPER_CGCNN | ||
| dim1: 100 | ||
| dim2: 150 | ||
| pre_fc_count: 1 | ||
| gc_count: 10 | ||
| post_fc_count: 3 | ||
| pool: "global_mean_pool" | ||
| pool_order: "early" | ||
| batch_norm: "True" | ||
| batch_track_stats: "True" | ||
| act: "relu" | ||
| dropout_rate: 0.0 | ||
| epochs: 250 | ||
| lr: 0.002 | ||
| batch_size: 100 | ||
| optimizer: "AdamW" | ||
| optimizer_args: {} | ||
| scheduler: "ReduceLROnPlateau" | ||
| scheduler_args: {"mode":"min", "factor":0.8, "patience":10, "min_lr":0.00001, "threshold":0.0002} | ||
| GATGNN_demo: | ||
| model: GATGNN | ||
| dim1: 64 | ||
| dim2: 150 | ||
| pre_fc_count: 1 | ||
| gc_count: 5 | ||
| post_fc_count: 0 | ||
| pool: "global_add_pool" | ||
| pool_order: "early" | ||
| batch_norm: "True" | ||
| batch_track_stats: "True" | ||
| act: "softplus" | ||
| dropout_rate: 0.0 | ||
| epochs: 250 | ||
| lr: 0.005 | ||
| batch_size: 100 | ||
| optimizer: "AdamW" | ||
| optimizer_args: {} | ||
| scheduler: "ReduceLROnPlateau" | ||
| scheduler_args: {"mode":"min", "factor":0.8, "patience":10, "min_lr":0.00001, "threshold":0.0002} | ||
| DEEP_GATGNN_demo: | ||
| model: DEEP_GATGNN | ||
| dim1: 64 | ||
| dim2: 150 | ||
| pre_fc_count: 1 | ||
| gc_count: 20 | ||
| post_fc_count: 0 | ||
| pool: "global_add_pool" | ||
| pool_order: "early" | ||
| batch_norm: "True" | ||
| batch_track_stats: "True" | ||
| act: "softplus" | ||
| dropout_rate: 0.0 | ||
| epochs: 500 | ||
| lr: 0.005 | ||
| batch_size: 100 | ||
| optimizer: "AdamW" | ||
| optimizer_args: {} | ||
| scheduler: "ReduceLROnPlateau" | ||
| scheduler_args: {"mode":"min", "factor":0.8, "patience":10, "min_lr":0.00001, "threshold":0.0002} | ||
| MPNN_demo: | ||
| model: MPNN | ||
| dim1: 100 | ||
| dim2: 100 | ||
| dim3: 100 | ||
| pre_fc_count: 1 | ||
| gc_count: 4 | ||
| post_fc_count: 3 | ||
| pool: "global_mean_pool" | ||
| pool_order: "early" | ||
| batch_norm: "True" | ||
| batch_track_stats: "True" | ||
| act: "relu" | ||
| dropout_rate: 0.0 | ||
| epochs: 250 | ||
| lr: 0.001 | ||
| batch_size: 100 | ||
| optimizer: "AdamW" | ||
| optimizer_args: {} | ||
| scheduler: "ReduceLROnPlateau" | ||
| scheduler_args: {"mode":"min", "factor":0.8, "patience":10, "min_lr":0.00001, "threshold":0.0002} | ||
| SUPER_MPNN_demo: | ||
| model: SUPER_MPNN | ||
| dim1: 100 | ||
| dim2: 100 | ||
| dim3: 100 | ||
| pre_fc_count: 1 | ||
| gc_count: 10 | ||
| post_fc_count: 3 | ||
| pool: "global_mean_pool" | ||
| pool_order: "early" | ||
| batch_norm: "True" | ||
| batch_track_stats: "True" | ||
| act: "relu" | ||
| dropout_rate: 0.0 | ||
| epochs: 250 | ||
| lr: 0.001 | ||
| batch_size: 100 | ||
| optimizer: "AdamW" | ||
| optimizer_args: {} | ||
| scheduler: "ReduceLROnPlateau" | ||
| scheduler_args: {"mode":"min", "factor":0.8, "patience":10, "min_lr":0.00001, "threshold":0.0002} | ||
| SchNet_demo: | ||
| model: SchNet | ||
| dim1: 100 | ||
| dim2: 100 | ||
| dim3: 150 | ||
| cutoff: 8 | ||
| pre_fc_count: 1 | ||
| gc_count: 4 | ||
| post_fc_count: 3 | ||
| pool: "global_max_pool" | ||
| pool_order: "early" | ||
| batch_norm: "True" | ||
| batch_track_stats: "True" | ||
| act: "relu" | ||
| dropout_rate: 0.0 | ||
| epochs: 250 | ||
| lr: 0.0005 | ||
| batch_size: 100 | ||
| optimizer: "AdamW" | ||
| optimizer_args: {} | ||
| scheduler: "ReduceLROnPlateau" | ||
| scheduler_args: {"mode":"min", "factor":0.8, "patience":10, "min_lr":0.00001, "threshold":0.0002} | ||
| SUPER_SchNet_demo: | ||
| model: SUPER_SchNet | ||
| dim1: 100 | ||
| dim2: 100 | ||
| dim3: 150 | ||
| cutoff: 8 | ||
| pre_fc_count: 1 | ||
| gc_count: 10 | ||
| post_fc_count: 3 | ||
| pool: "global_max_pool" | ||
| pool_order: "early" | ||
| batch_norm: "True" | ||
| batch_track_stats: "True" | ||
| act: "relu" | ||
| dropout_rate: 0.0 | ||
| epochs: 250 | ||
| lr: 0.0005 | ||
| batch_size: 100 | ||
| optimizer: "AdamW" | ||
| optimizer_args: {} | ||
| scheduler: "ReduceLROnPlateau" | ||
| scheduler_args: {"mode":"min", "factor":0.8, "patience":10, "min_lr":0.00001, "threshold":0.0002} | ||
| MEGNet_demo: | ||
| model: MEGNet | ||
| dim1: 100 | ||
| dim2: 100 | ||
| dim3: 100 | ||
| pre_fc_count: 1 | ||
| gc_count: 4 | ||
| gc_fc_count: 1 | ||
| post_fc_count: 3 | ||
| pool: "global_mean_pool" | ||
| pool_order: "early" | ||
| batch_norm: "True" | ||
| batch_track_stats: "True" | ||
| act: "relu" | ||
| dropout_rate: 0.0 | ||
| epochs: 250 | ||
| lr: 0.0005 | ||
| batch_size: 100 | ||
| optimizer: "AdamW" | ||
| optimizer_args: {} | ||
| scheduler: "ReduceLROnPlateau" | ||
| scheduler_args: {"mode":"min", "factor":0.8, "patience":10, "min_lr":0.00001, "threshold":0.0002} | ||
| SUPER_MEGNet_demo: | ||
| model: SUPER_MEGNet | ||
| dim1: 100 | ||
| dim2: 100 | ||
| dim3: 100 | ||
| pre_fc_count: 1 | ||
| gc_count: 10 | ||
| gc_fc_count: 1 | ||
| post_fc_count: 3 | ||
| pool: "global_mean_pool" | ||
| pool_order: "early" | ||
| batch_norm: "True" | ||
| batch_track_stats: "True" | ||
| act: "relu" | ||
| dropout_rate: 0.0 | ||
| epochs: 250 | ||
| lr: 0.0005 | ||
| batch_size: 100 | ||
| optimizer: "AdamW" | ||
| optimizer_args: {} | ||
| scheduler: "ReduceLROnPlateau" | ||
| scheduler_args: {"mode":"min", "factor":0.8, "patience":10, "min_lr":0.00001, "threshold":0.0002} | ||
| GCN_demo: | ||
| model: GCN | ||
| dim1: 100 | ||
| dim2: 150 | ||
| pre_fc_count: 1 | ||
| gc_count: 4 | ||
| post_fc_count: 3 | ||
| pool: "global_mean_pool" | ||
| pool_order: "early" | ||
| batch_norm: "True" | ||
| batch_track_stats: "True" | ||
| act: "relu" | ||
| dropout_rate: 0.0 | ||
| epochs: 250 | ||
| lr: 0.002 | ||
| batch_size: 100 | ||
| optimizer: "AdamW" | ||
| optimizer_args: {} | ||
| scheduler: "ReduceLROnPlateau" | ||
| scheduler_args: {"mode":"min", "factor":0.8, "patience":10, "min_lr":0.00001, "threshold":0.0002} | ||
| SM_demo: | ||
| model: SM | ||
| dim1: 100 | ||
| fc_count: 2 | ||
| epochs: 200 | ||
| lr: 0.002 | ||
| batch_size: 100 | ||
| optimizer: "AdamW" | ||
| optimizer_args: {} | ||
| scheduler: "ReduceLROnPlateau" | ||
| scheduler_args: {"mode":"min", "factor":0.8, "patience":10, "min_lr":0.00001, "threshold":0.0002} | ||
| SOAP_demo: | ||
| model: SOAP | ||
| dim1: 100 | ||
| fc_count: 2 | ||
| epochs: 200 | ||
| lr: 0.002 | ||
| batch_size: 100 | ||
| optimizer: "AdamW" | ||
| optimizer_args: {} | ||
| scheduler: "ReduceLROnPlateau" | ||
| scheduler_args: {"mode":"min", "factor":0.8, "patience":10, "min_lr":0.00001, "threshold":0.0002} |
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