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Merge pull request #268 from sadmanomee/deepergatgnn
DeeperGATGNN results added
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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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