From cf88343a1c817fa537da961f5426a9a6d4094eab Mon Sep 17 00:00:00 2001 From: Katsuhiko Ishiguro Date: Tue, 21 Jul 2020 12:05:17 +0900 Subject: [PATCH] remove internal scripts --- .../molnet/molnet_config.py.moleculenet | 370 ------------------ .../evaluate_models_molnet_hiv_cnle.sh | 43 -- .../evaluate_models_molnet_qm9_comparison.sh | 73 ---- .../summary_evaluate_models_molnet_qm9.sh | 41 -- 4 files changed, 527 deletions(-) delete mode 100644 chainer_chemistry/datasets/molnet/molnet_config.py.moleculenet delete mode 100644 examples/molnet/scripts/evaluate_models_molnet_hiv_cnle.sh delete mode 100644 examples/molnet/scripts/evaluate_models_molnet_qm9_comparison.sh delete mode 100644 examples/molnet/scripts/summary_evaluate_models_molnet_qm9.sh diff --git a/chainer_chemistry/datasets/molnet/molnet_config.py.moleculenet b/chainer_chemistry/datasets/molnet/molnet_config.py.moleculenet deleted file mode 100644 index ddbf7d65..00000000 --- a/chainer_chemistry/datasets/molnet/molnet_config.py.moleculenet +++ /dev/null @@ -1,370 +0,0 @@ -import chainer.functions as F -import chainer_chemistry - -from chainer_chemistry.datasets.molnet.chembl_tasks import chembl_tasks -from chainer_chemistry.datasets.molnet.toxcast_tasks import toxcast_tasks -from chainer_chemistry.functions import mean_absolute_error -from chainer_chemistry.functions import mean_squared_error -from chainer_chemistry.training.extensions.prc_auc_evaluator import PRCAUCEvaluator # NOQA -from chainer_chemistry.training.extensions.roc_auc_evaluator import ROCAUCEvaluator # NOQA - -molnet_base = 'http://deepchem.io.s3-website-us-west-1.amazonaws.com/datasets/' -featurized_base = 'http://deepchem.io.s3-website-us-west-1.amazonaws.com/' \ - + 'featurized_datasets/' - - -def mae(x, t): - return mean_absolute_error(x, t, ignore_nan=True) - - -def mse(x, t): - return mean_squared_error(x, t, ignore_nan=True) - - -def rmse(x, t): - return F.sqrt(mse(x, t)) - - -def r2_score(x, t): - return chainer_chemistry.functions.r2_score(x, t, ignore_nan=True) - - -molnet_default_config = { - "bace_Class": { - "dataset_type": 'one_file_csv', - "loss": F.sigmoid_cross_entropy, - "metrics": {'binary_accuracy': F.binary_accuracy, - 'roc_auc': ROCAUCEvaluator}, - "smiles_columns": 'mol', - "split": 'random', - "task_type": 'classification', - "tasks": ["Class"], - "url": molnet_base + 'bace.csv', - }, - "bace_pIC50": { - "dataset_type": 'one_file_csv', - "loss": mse, - "metrics": {'MAE': mae}, - "smiles_columns": 'mol', - "split": 'random', - "task_type": 'regression', - "tasks": ["pIC50"], - "url": molnet_base + 'bace.csv', - }, - "bbbp": { - "dataset_type": 'one_file_csv', - "loss": F.sigmoid_cross_entropy, - "metrics": {'binary_accuracy': F.binary_accuracy, - 'roc_auc': ROCAUCEvaluator}, - "smiles_columns": 'smiles', - "split": 'dc_scaffold', - "task_type": 'classification', - "tasks": ["p_np"], - "url": molnet_base + 'BBBP.csv', - }, - # TODO(mottodora): There are many separating ways for chembl dataset - # TODO(mottodora): only use 5thresh dataset(sparse dataset is not used.) - # TODO(mottodora): support mix dataset type in example - "chembl": { - "dataset_type": 'one_file_csv', - "loss": mse, - "smiles_columns": 'smiles', - "split": 'random', - "task_type": 'mix', - "tasks": chembl_tasks, - "url": molnet_base + 'chembl_5thresh.csv.gz', - }, - "clearance": { - "dataset_type": 'one_file_csv', - "loss": mse, - "metrics": {'RMSE': rmse}, - "smiles_columns": 'smile', - "split": 'random', - "task_type": 'regression', - "tasks": ["target"], - "url": molnet_base + 'clearance.csv', - }, - "clintox": { - "dataset_type": 'one_file_csv', - "loss": F.sigmoid_cross_entropy, - "metrics": {'binary_accuracy': F.binary_accuracy, - 'roc_auc': ROCAUCEvaluator}, - "smiles_columns": 'smiles', - "split": 'random', - "task_type": 'classification', - "tasks": ["FDA_APPROVED", "CT_TOX"], - "url": molnet_base + 'clintox.csv.gz', - }, - "delaney": { - "dataset_type": 'one_file_csv', - "loss": mse, - "metrics": {'RMSE': rmse}, - "smiles_columns": 'smiles', - "split": 'random', - "task_type": 'regression', - "tasks": ['measured log solubility in mols per litre'], - "url": molnet_base + 'delaney-processed.csv', - }, - "HIV": { - "dataset_type": 'one_file_csv', - "loss": F.sigmoid_cross_entropy, - "metrics": {'binary_accuracy': F.binary_accuracy, - 'roc_auc': ROCAUCEvaluator}, - "smiles_columns": 'smiles', - "split": 'dc_scaffold', - "task_type": 'classification', - "tasks": ["HIV_active"], - "url": molnet_base + 'HIV.csv', - }, - "hopv": { - "dataset_type": 'one_file_csv', - "loss": mse, - "metrics": {'RMSE': rmse}, - "smiles_columns": 'hopv.csv', - "split": 'random', - "task_type": 'regression', - "tasks": ['HOMO', 'LUMO', 'electrochemical_gap', 'optical_gap', - 'PCE', 'V_OC', 'J_SC', 'fill_factor'], - "url": molnet_base + 'hopv.tar.gz', - }, - "kaggle": { - "dataset_type": 'separate_csv', - "loss": mse, - "metrics": {'RMSE': rmse}, - "smiles_columns": 'smiles', - "split": 'random', - "task_type": 'regression', - "tasks": ['3A4', 'CB1', 'DPP4', 'HIVINT', 'HIV_PROT', 'LOGD', 'METAB', - 'NK1', 'OX1', 'OX2', 'PGP', 'PPB', 'RAT_F', 'TDI', 'THROMBIN' - ], - "test_url": molnet_base + 'KAGGLE_test2_' - 'disguised_combined_full.csv.gz', - "train_url": molnet_base + 'KAGGLE_training_' - 'disguised_combined_full.csv.gz', - - "valid_url": molnet_base + 'KAGGLE_test1_' - 'disguised_combined_full.csv.gz', - }, - - "lipo": { - "dataset_type": 'one_file_csv', - "loss": mse, - "metrics": {'RMSE': rmse}, - "smiles_columns": 'smiles', - "split": 'random', - "task_type": 'regression', - "tasks": ['exp'], - "url": molnet_base + 'Lipophilicity.csv', - }, - "muv": { - "dataset_type": 'one_file_csv', - "loss": F.sigmoid_cross_entropy, - "metrics": {'binary_accuracy': F.binary_accuracy, - 'prc_auc': PRCAUCEvaluator}, - "smiles_columns": 'smiles', - "split": 'random', - "task_type": 'classification', - "tasks": ['MUV-692', 'MUV-689', 'MUV-846', 'MUV-859', 'MUV-644', - 'MUV-548', 'MUV-852', 'MUV-600', 'MUV-810', 'MUV-712', - 'MUV-737', 'MUV-858', 'MUV-713', 'MUV-733', 'MUV-652', - 'MUV-466', 'MUV-832'], - "url": molnet_base + 'muv.csv.gz', - }, - "nci": { - "dataset_type": 'one_file_csv', - "loss": mse, - "metrics": {'RMSE': rmse}, - "smiles_columns": 'smiles', - "split": 'random', - "task_type": 'regression', - "tasks": ['CCRF-CEM', 'HL-60(TB)', 'K-562', 'MOLT-4', 'RPMI-8226', - 'SR', 'A549/ATCC', 'EKVX', 'HOP-62', 'HOP-92', 'NCI-H226', - 'NCI-H23', 'NCI-H322M', 'NCI-H460', 'NCI-H522', 'COLO 205', - 'HCC-2998', 'HCT-116', 'HCT-15', 'HT29', 'KM12', 'SW-620', - 'SF-268', 'SF-295', 'SF-539', 'SNB-19', 'SNB-75', 'U251', - 'LOX IMVI', 'MALME-3M', 'M14', 'MDA-MB-435', 'SK-MEL-2', - 'SK-MEL-28', 'SK-MEL-5', 'UACC-257', 'UACC-62', 'IGR-OV1', - 'OVCAR-3', 'OVCAR-4', 'OVCAR-5', 'OVCAR-8', 'NCI/ADR-RES', - 'SK-OV-3', '786-0', 'A498', 'ACHN', 'CAKI-1', 'RXF 393', - 'SN12C', 'TK-10', 'UO-31', 'PC-3', 'DU-145', 'MCF7', - 'MDA-MB-231/ATCC', 'MDA-MB-468', 'HS 578T', 'BT-549', 'T-47D' - ], - "url": molnet_base + 'nci_unique.csv', - }, - "pcba": { - "dataset_type": 'one_file_csv', - "loss": F.sigmoid_cross_entropy, - "metrics": {'binary_accuracy': F.binary_accuracy, - 'prc_auc': PRCAUCEvaluator}, - "smiles_columns": 'smiles', - "split": 'random', - "task_type": 'classification', - "tasks": - ['PCBA-1030', 'PCBA-1379', 'PCBA-1452', 'PCBA-1454', 'PCBA-1457', - 'PCBA-1458', 'PCBA-1460', 'PCBA-1461', 'PCBA-1468', 'PCBA-1469', - 'PCBA-1471', 'PCBA-1479', 'PCBA-1631', 'PCBA-1634', 'PCBA-1688', - 'PCBA-1721', 'PCBA-2100', 'PCBA-2101', 'PCBA-2147', 'PCBA-2242', - 'PCBA-2326', 'PCBA-2451', 'PCBA-2517', 'PCBA-2528', 'PCBA-2546', - 'PCBA-2549', 'PCBA-2551', 'PCBA-2662', 'PCBA-2675', 'PCBA-2676', - 'PCBA-411', 'PCBA-463254', 'PCBA-485281', 'PCBA-485290', - 'PCBA-485294', 'PCBA-485297', 'PCBA-485313', 'PCBA-485314', - 'PCBA-485341', 'PCBA-485349', 'PCBA-485353', 'PCBA-485360', - 'PCBA-485364', 'PCBA-485367', 'PCBA-492947', 'PCBA-493208', - 'PCBA-504327', 'PCBA-504332', 'PCBA-504333', 'PCBA-504339', - 'PCBA-504444', 'PCBA-504466', 'PCBA-504467', 'PCBA-504706', - 'PCBA-504842', 'PCBA-504845', 'PCBA-504847', 'PCBA-504891', - 'PCBA-540276', 'PCBA-540317', 'PCBA-588342', 'PCBA-588453', - 'PCBA-588456', 'PCBA-588579', 'PCBA-588590', 'PCBA-588591', - 'PCBA-588795', 'PCBA-588855', 'PCBA-602179', 'PCBA-602233', - 'PCBA-602310', 'PCBA-602313', 'PCBA-602332', 'PCBA-624170', - 'PCBA-624171', 'PCBA-624173', 'PCBA-624202', 'PCBA-624246', - 'PCBA-624287', 'PCBA-624288', 'PCBA-624291', 'PCBA-624296', - 'PCBA-624297', 'PCBA-624417', 'PCBA-651635', 'PCBA-651644', - 'PCBA-651768', 'PCBA-651965', 'PCBA-652025', 'PCBA-652104', - 'PCBA-652105', 'PCBA-652106', 'PCBA-686970', 'PCBA-686978', - 'PCBA-686979', 'PCBA-720504', 'PCBA-720532', 'PCBA-720542', - 'PCBA-720551', 'PCBA-720553', 'PCBA-720579', 'PCBA-720580', - 'PCBA-720707', 'PCBA-720708', 'PCBA-720709', 'PCBA-720711', - 'PCBA-743255', 'PCBA-743266', 'PCBA-875', 'PCBA-881', 'PCBA-883', - 'PCBA-884', 'PCBA-885', 'PCBA-887', 'PCBA-891', 'PCBA-899', - 'PCBA-902', 'PCBA-903', 'PCBA-904', 'PCBA-912', 'PCBA-914', - 'PCBA-915', 'PCBA-924', 'PCBA-925', 'PCBA-926', 'PCBA-927', - 'PCBA-938', 'PCBA-995'], - "url": molnet_base + 'pcba.csv.gz', - }, - "pdbbind_smiles": { - "subset": ["core", "full", "refined"], - "dataset_type": 'one_file_csv', - "url": {'core': molnet_base + 'core_smiles_labels.csv', - 'full': molnet_base + 'full_smiles_labels.csv', - 'refined': molnet_base + 'refined_smiles_labels.csv'}, - "smiles_columns": 'smiles', - "metrics": {'R2': r2_score}, - "split": 'time', - "task_type": 'regression', - "tasks": ["-logKd/Ki"], - }, - "pdbbind_grid": { - "pdbbind_subset": ["core", "full", "refined"], - "dataset_type": 'joblib', - "url": {'core': featurized_base + 'core_grid.tar.gz', - 'full': featurized_base + 'full_grid.tar.gz', - 'refined': featurized_base + 'refined_grid.tar.gz'}, - "smiles_columns": '', - "metrics": {'R2': r2_score}, - "split": 'time', - "task_type": 'regression', - "tasks": ["-logKd/Ki"], - }, - "ppb": { - "dataset_type": 'one_file_csv', - "loss": mse, - "metrics": {'RMSE': rmse}, - "smiles_columns": 'smiles', - "split": 'random', - "task_type": 'regression', - "tasks": ["exp"], - "url": molnet_base + 'PPB.csv', - }, - # TODO(motoki): there are multiple data types in qm7 dataset. - "qm7": { - "dataset_type": 'one_file_csv', - "loss": mse, - "metrics": {'MAE': mae}, - "smiles_columns": 'smiles', - "split": 'stratified', - "task_type": 'regression', - "tasks": ["u0_atom"], - "url": molnet_base + 'qm7.csv', - }, - # TODO(motoki): there are sdf data types in qm8 dataset. - "qm8": { - "dataset_type": 'one_file_csv', - "loss": mse, - "metrics": {'MAE': mae}, - "smiles_columns": 'smiles', - "split": 'random', - "task_type": 'regression', - "tasks": ["E1-CC2", "E2-CC2", "f1-CC2", "f2-CC2", "E1-PBE0", "E2-PBE0", - "f1-PBE0", "f2-PBE0", "E1-PBE0", "E2-PBE0", "f1-PBE0", - "f2-PBE0", "E1-CAM", "E2-CAM", "f1-CAM", "f2-CAM"], - "url": molnet_base + 'qm8.csv', - }, - # TODO(motoki): there are sdf data types in qm9 dataset. - "qm9": { - "dataset_type": 'one_file_csv', - "loss": mse, - "metrics": {'MAE': mae}, - "smiles_columns": 'smiles', - "split": 'random', - "task_type": 'regression', - "tasks": ["mu", "alpha", "homo", "lumo", "gap", "r2", "zpve", "cv", - "u0", "u298", "h298", "g298"], - "url": molnet_base + 'qm9.csv', - }, - "SAMPL": { - "dataset_type": 'one_file_csv', - "loss": mse, - "metrics": {'RMSE': rmse}, - "smiles_columns": 'smiles', - "split": 'random', - "task_type": 'regression', - "tasks": ["expt"], - "url": molnet_base + 'SAMPL.csv', - }, - "sider": { - "dataset_type": 'one_file_csv', - "loss": F.sigmoid_cross_entropy, - "metrics": {'binary_accuracy': F.binary_accuracy, - 'roc_auc': ROCAUCEvaluator}, - "smiles_columns": 'smiles', - "split": 'random', - "task_type": 'classification', - "tasks": ['Hepatobiliary disorders', - 'Metabolism and nutrition disorders', 'Product issues', - 'Eye disorders', 'Investigations', - 'Musculoskeletal and connective tissue disorders', - 'Gastrointestinal disorders', 'Social circumstances', - 'Immune system disorders', - 'Reproductive system and breast disorders', - 'Neoplasms benign, malignant and unspecified ' - '(incl cysts and polyps)', - 'General disorders and administration site conditions', - 'Endocrine disorders', 'Surgical and medical procedures', - 'Vascular disorders', 'Blood and lymphatic system disorders', - 'Skin and subcutaneous tissue disorders', - 'Congenital, familial and genetic disorders', - 'Infections and infestations', - 'Respiratory, thoracic and mediastinal disorders', - 'Psychiatric disorders', 'Renal and urinary disorders', - 'Pregnancy, puerperium and perinatal conditions', - 'Ear and labyrinth disorders', 'Cardiac disorders', - 'Nervous system disorders', - 'Injury, poisoning and procedural complications'], - "url": molnet_base + 'sider.csv.gz', - }, - "tox21": { - "dataset_type": 'one_file_csv', - "loss": F.sigmoid_cross_entropy, - "metrics": {'binary_accuracy': F.binary_accuracy, - 'roc_auc': ROCAUCEvaluator}, - "smiles_columns": 'smiles', - "split": 'random', - "task_type": 'classification', - "tasks": ['NR-AR', 'NR-AR-LBD', 'NR-AhR', 'NR-Aromatase', 'NR-ER', - 'NR-ER-LBD', 'NR-PPAR-gamma', 'SR-ARE', 'SR-ATAD5', 'SR-HSE', - 'SR-MMP', 'SR-p53'], - "url": molnet_base + 'tox21.csv.gz', - }, - "toxcast": { - "dataset_type": 'one_file_csv', - "loss": F.sigmoid_cross_entropy, - "metrics": {'binary_accuracy': F.binary_accuracy, - 'roc_auc': ROCAUCEvaluator}, - "smiles_columns": 'smiles', - "split": 'random', - "task_type": 'classification', - "tasks": toxcast_tasks, - "url": molnet_base + 'toxcast_data.csv.gz', - }, -} diff --git a/examples/molnet/scripts/evaluate_models_molnet_hiv_cnle.sh b/examples/molnet/scripts/evaluate_models_molnet_hiv_cnle.sh deleted file mode 100644 index 3c257388..00000000 --- a/examples/molnet/scripts/evaluate_models_molnet_hiv_cnle.sh +++ /dev/null @@ -1,43 +0,0 @@ -#!/bin/bash -eu - -run=$1 - -device=0 -dataset_list=(bace_Class bace_pIC50 bbbp clearance clintox delaney HIV hopv lipo muv nci pcba ppb qm7 qm8 qm9 SAMPL sider tox21 toxcast) -methods=(rsgcn_cnle relgat_cnle ggnn_cnle) -epoch=500 -prefix=eval_test -runs=10 -batchsizes=(128 32 128) -nlayers=(2 3 2) -unit_nums=(45 20 45) -adam_alphas=(0.0011 0.000065 0.0017) - -dataset=HIV - -prefix=${prefix}_CNLE - - -for m in `seq 0 2` -do - batchsize=${batchsizes[${m}]} - method=${methods[${m}]} - unit_num=${unit_nums[${m}]} - nlayer=${nlayers[${m}]} - adam_alpha=${adam_alphas[${m}]} - echo "" - echo "nlayer=$nlayer " - echo "method=$method " - echo "unit_num=$unit_num " - echo "adam_alpha=$adam_alpha " - echo "run=$run " - - result_dir=${prefix}_layer${nlayer}_${dataset}_${method}_${run} - - PYTHONPATH=/home/ishiguro/workspace/chainer-chemistry-v6 python examples/molnet/train_molnet.py --dataset ${dataset} --method ${method} --conv-layers ${nlayer} --device ${device} --epoch ${epoch} --batchsize ${batchsize} --unit-num ${unit_num} --out ${result_dir} --adam-alpha ${adam_alpha} --apply-cnle - - PYTHONPATH=/home/ishiguro/workspace/chainer-chemistry-v6 python examples/molnet/predict_molnet.py --device ${device} --dataset ${dataset} --method ${method} --in-dir ${result_dir} - - #cp -r ${result_dir} /mnt/vol12/ishiguro/temp_hiv/${result_dir} - -done diff --git a/examples/molnet/scripts/evaluate_models_molnet_qm9_comparison.sh b/examples/molnet/scripts/evaluate_models_molnet_qm9_comparison.sh deleted file mode 100644 index fc80a253..00000000 --- a/examples/molnet/scripts/evaluate_models_molnet_qm9_comparison.sh +++ /dev/null @@ -1,73 +0,0 @@ -#!/bin/bash -eu - -run=$1 -nle=$2 - -device=0 -dataset_list=(bace_Class bace_pIC50 bbbp clearance clintox delaney HIV hopv lipo muv nci pcba ppb qm7 qm8 qm9 SAMPL sider tox21 toxcast) -#methods=(rsgcn) -methods=(rsgcn relgcn relgat ggnn) -epoch=500 -prefix=eval_test -#prefix=eval -runs=10 -batchsize=128 -nlayers=8 -#unit_num=100 -unit_nums=(100 40 40 72) - -dataset=qm9 - -if [ $nle = "1" ] ; then - prefix=${prefix}_NLE -fi - -for nlayer in `seq 2 $nlayers` -do - for m in `seq 0 0` - #for method in ${methods[@]} - do - method=${methods[${m}]} - unit_num=${unit_nums[${m}]} - echo "" - echo "nlayer=$nlayer " - echo "method=$method " - echo "unit_num=$unit_num " - echo "run=$run " - echo "nle=$nle " - - result_dir=${prefix}_layer${nlayer}_${dataset}_${method}_${run} - - if [ $nle = "0" ] ; then - echo "NLE disabled" - - #echo "PYTHONPATH=/home/ishiguro/workspace/chainer-chemistry-v6 python train_molnet.py --dataset ${dataset} --method ${method} --conv-layers ${nlayer} --device ${device} --epoch ${epoch} --batchsize ${batchsize} --unit-num ${unit_num} --out ${result_dir}" - #PYTHONPATH=/home/ishiguro/Project/nn_theory/191003_NeighborLabelExpansion/chainer-chemistry-v6 python examples/molnet/train_molnet.py --dataset ${dataset} --method ${method} --conv-layers ${nlayer} --device ${device} --epoch ${epoch} --batchsize ${batchsize} --unit-num ${unit_num} --out ${result_dir} - PYTHONPATH=/home/ishiguro/workspace/chainer-chemistry-v6 python examples/molnet/train_molnet.py --dataset ${dataset} --method ${method} --conv-layers ${nlayer} --device ${device} --epoch ${epoch} --batchsize ${batchsize} --unit-num ${unit_num} --out ${result_dir} --scale none - - elif [ $nle = "1" ] ; then - echo "NLE enabled" - - #echo "PYTHONPATH=/home/ishiguro/workspace/chainer-chemistry-v6 python train_molnet.py --dataset ${dataset} --method ${method} --conv-layers ${nlayer} --device ${device} --epoch ${epoch} --batchsize ${batchsize} --unit-num ${unit_num} --out ${result_dir} --apply_nle" - - #PYTHONPATH=/home/ishiguro/Project/nn_theory/191003_NeighborLabelExpansion/chainer-chemistry-v6 python examples/molnet/train_molnet.py --dataset ${dataset} --method ${method} --conv-layers ${nlayer} --device ${device} --epoch ${epoch} --batchsize ${batchsize} --unit-num ${unit_num} --out ${result_dir} --apply-nle --num-data 500 - PYTHONPATH=/home/ishiguro/workspace/chainer-chemistry-v6 python examples/molnet/train_molnet.py --dataset ${dataset} --method ${method} --conv-layers ${nlayer} --device ${device} --epoch ${epoch} --batchsize ${batchsize} --unit-num ${unit_num} --out ${result_dir} --scale none --apply-nle - else - echo "fishy nle" - fi - - - #echo "PYTHONPATH=/home/ishiguro/workspace/chainer-chemistry-v6 python predict_molnet.py --device ${device} --dataset ${dataset} --method ${method} --in-dir ${result_dir}" - #PYTHONPATH=/home/ishiguro/Project/nn_theory/191003_NeighborLabelExpansion/chainer-chemistry-v6 python examples/molnet/predict_molnet.py --device ${device} --dataset ${dataset} --method ${method} --in-dir ${result_dir} --num-data 100 - PYTHONPATH=/home/ishiguro/workspace/chainer-chemistry-v6 python examples/molnet/predict_molnet.py --device ${device} --dataset ${dataset} --method ${method} --in-dir ${result_dir} - - cp -r ${result_dir} /mnt/vol12/ishiguro/temp/${result_dir} - done # end method-for - - #result_dir=${prefix}_${dataset}_${method}_${nlayer} - #PYTHONPATH=/home/ishiguro/workspace/chainer-chemistry-v6 python example/molent/summary_eval_molnet.py --prefix /mnt/vol12/ishiguro/temp/${result_dir} --methods ${methods[@]} --dataset ${dataset} --runs ${runs} --out_prefix /mnt/vol12/ishiguro/temp_eval/${result_dir}/${prefix}_{$ - -done # end layer-for - - - diff --git a/examples/molnet/scripts/summary_evaluate_models_molnet_qm9.sh b/examples/molnet/scripts/summary_evaluate_models_molnet_qm9.sh deleted file mode 100644 index 79c79d39..00000000 --- a/examples/molnet/scripts/summary_evaluate_models_molnet_qm9.sh +++ /dev/null @@ -1,41 +0,0 @@ -#!/bin/bash -eu - -dataset_list=(bace_Class bace_pIC50 bbbp clearance clintox delaney HIV hopv lipo muv nci pcba ppb qm7 qm8 qm9 SAMPL sider tox21 toxcast) -methods=(rsgcn) -#methods=(rsgcn relgat ggnn) -#epoch=500 -in_dir=/mnt/vol12/ishiguro/Project/nn-theory/191003_NeighborLabelExpansion/191014_feasibilityExp/ -temp_dir=/mnt/vo12/ishiguro/temp_temp -out_dir=/mnt/vol12/ishiguro/temp/ -prefix=eval_test -runs=10 - -#batchsize=128 -nlayers=8 -#unit_num=100 - -datasets=(qm9 tox21 HIV lipo) - - -for dataset in ${datasets[@]} -do - - for nlayer in `seq 2 $nlayers` - do - - # run the summary script - indir_prefix=${in_dir}${prefix}_layer${nlayer} - out_prefix=${out_dir}${prefix}_layer${nlayer}_${dataset}/result - - echo "" - echo "indir_prefix=$indir_prefix " - echo "out_prefix=$out_prefix " - - PYTHONPATH=/home/ishiguro/workspace/chainer-chemistry-v6 python examples/molnet/summary_eval_molnet.py --indir_prefix ${indir_prefix} --methods ${methods[@]} --dataset ${dataset} --runs ${runs} --out_prefix ${out_prefix} - - - done # end layer-for -done # end method-for - - -