dmlc--dgl
Mufei Li
828a5e5bc6
* First commit * Update * Update splitters * Update * Update * Update * Update * Update * Update * Migrate ACNN * Fix * Fix * Update * Update * Update * Update * Update * Update * Finish classification * Update * Fix * Update * Update * Update * Fix * Fix * Fix * Update * Update * Update * trigger CI * Fix CI * Update * Update * Update * Add default values * Rename * Update deprecation message
201 行
6.4 KiB
Python
201 行
6.4 KiB
Python
import numpy as np
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import torch
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ACNN_PDBBind_core_pocket_random = {
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'dataset': 'PDBBind',
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'subset': 'core',
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'load_binding_pocket': True,
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'random_seed': 123,
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'frac_train': 0.8,
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'frac_val': 0.,
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'frac_test': 0.2,
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'batch_size': 24,
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'shuffle': False,
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'hidden_sizes': [32, 32, 16],
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'weight_init_stddevs': [1. / float(np.sqrt(32)), 1. / float(np.sqrt(32)),
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1. / float(np.sqrt(16)), 0.01],
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'dropouts': [0., 0., 0.],
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'atomic_numbers_considered': torch.tensor([
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1., 6., 7., 8., 9., 11., 12., 15., 16., 17., 20., 25., 30., 35., 53.]),
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'radial': [[12.0], [0.0, 4.0, 8.0], [4.0]],
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'lr': 0.001,
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'num_epochs': 120,
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'metrics': ['r2', 'mae'],
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'split': 'random'
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}
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ACNN_PDBBind_core_pocket_scaffold = {
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'dataset': 'PDBBind',
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'subset': 'core',
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'load_binding_pocket': True,
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'random_seed': 123,
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'frac_train': 0.8,
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'frac_val': 0.,
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'frac_test': 0.2,
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'batch_size': 24,
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'shuffle': False,
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'hidden_sizes': [32, 32, 16],
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'weight_init_stddevs': [1. / float(np.sqrt(32)), 1. / float(np.sqrt(32)),
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1. / float(np.sqrt(16)), 0.01],
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'dropouts': [0., 0., 0.],
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'atomic_numbers_considered': torch.tensor([
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1., 6., 7., 8., 9., 11., 12., 15., 16., 17., 20., 25., 30., 35., 53.]),
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'radial': [[12.0], [0.0, 4.0, 8.0], [4.0]],
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'lr': 0.001,
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'num_epochs': 170,
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'metrics': ['r2', 'mae'],
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'split': 'scaffold'
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}
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ACNN_PDBBind_core_pocket_stratified = {
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'dataset': 'PDBBind',
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'subset': 'core',
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'load_binding_pocket': True,
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'random_seed': 123,
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'frac_train': 0.8,
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'frac_val': 0.,
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'frac_test': 0.2,
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'batch_size': 24,
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'shuffle': False,
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'hidden_sizes': [32, 32, 16],
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'weight_init_stddevs': [1. / float(np.sqrt(32)), 1. / float(np.sqrt(32)),
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1. / float(np.sqrt(16)), 0.01],
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'dropouts': [0., 0., 0.],
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'atomic_numbers_considered': torch.tensor([
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1., 6., 7., 8., 9., 11., 12., 15., 16., 17., 20., 25., 30., 35., 53.]),
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'radial': [[12.0], [0.0, 4.0, 8.0], [4.0]],
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'lr': 0.001,
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'num_epochs': 110,
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'metrics': ['r2', 'mae'],
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'split': 'stratified'
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}
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ACNN_PDBBind_core_pocket_temporal = {
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'dataset': 'PDBBind',
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'subset': 'core',
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'load_binding_pocket': True,
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'random_seed': 123,
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'frac_train': 0.8,
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'frac_val': 0.,
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'frac_test': 0.2,
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'batch_size': 24,
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'shuffle': False,
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'hidden_sizes': [32, 32, 16],
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'weight_init_stddevs': [1. / float(np.sqrt(32)), 1. / float(np.sqrt(32)),
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1. / float(np.sqrt(16)), 0.01],
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'dropouts': [0., 0., 0.],
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'atomic_numbers_considered': torch.tensor([
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1., 6., 7., 8., 9., 11., 12., 15., 16., 17., 20., 25., 30., 35., 53.]),
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'radial': [[12.0], [0.0, 4.0, 8.0], [4.0]],
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'lr': 0.001,
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'num_epochs': 80,
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'metrics': ['r2', 'mae'],
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'split': 'temporal'
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}
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ACNN_PDBBind_refined_pocket_random = {
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'dataset': 'PDBBind',
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'subset': 'refined',
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'load_binding_pocket': True,
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'random_seed': 123,
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'frac_train': 0.8,
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'frac_val': 0.,
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'frac_test': 0.2,
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'batch_size': 24,
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'shuffle': False,
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'hidden_sizes': [128, 128, 64],
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'weight_init_stddevs': [0.125, 0.125, 0.177, 0.01],
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'dropouts': [0.4, 0.4, 0.],
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'atomic_numbers_considered': torch.tensor([
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1., 6., 7., 8., 9., 11., 12., 15., 16., 17., 19., 20., 25., 26., 27., 28.,
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29., 30., 34., 35., 38., 48., 53., 55., 80.]),
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'radial': [[12.0], [0.0, 2.0, 4.0, 6.0, 8.0], [4.0]],
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'lr': 0.001,
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'num_epochs': 200,
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'metrics': ['r2', 'mae'],
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'split': 'random'
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}
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ACNN_PDBBind_refined_pocket_scaffold = {
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'dataset': 'PDBBind',
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'subset': 'refined',
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'load_binding_pocket': True,
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'random_seed': 123,
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'frac_train': 0.8,
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'frac_val': 0.,
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'frac_test': 0.2,
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'batch_size': 24,
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'shuffle': False,
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'hidden_sizes': [128, 128, 64],
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'weight_init_stddevs': [0.125, 0.125, 0.177, 0.01],
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'dropouts': [0.4, 0.4, 0.],
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'atomic_numbers_considered': torch.tensor([
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1., 6., 7., 8., 9., 11., 12., 15., 16., 17., 19., 20., 25., 26., 27., 28.,
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29., 30., 34., 35., 38., 48., 53., 55., 80.]),
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'radial': [[12.0], [0.0, 2.0, 4.0, 6.0, 8.0], [4.0]],
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'lr': 0.001,
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'num_epochs': 350,
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'metrics': ['r2', 'mae'],
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'split': 'scaffold'
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}
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ACNN_PDBBind_refined_pocket_stratified = {
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'dataset': 'PDBBind',
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'subset': 'refined',
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'load_binding_pocket': True,
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'random_seed': 123,
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'frac_train': 0.8,
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'frac_val': 0.,
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'frac_test': 0.2,
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'batch_size': 24,
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'shuffle': False,
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'hidden_sizes': [128, 128, 64],
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'weight_init_stddevs': [0.125, 0.125, 0.177, 0.01],
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'dropouts': [0.4, 0.4, 0.],
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'atomic_numbers_considered': torch.tensor([
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1., 6., 7., 8., 9., 11., 12., 15., 16., 17., 19., 20., 25., 26., 27., 28.,
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29., 30., 34., 35., 38., 48., 53., 55., 80.]),
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'radial': [[12.0], [0.0, 2.0, 4.0, 6.0, 8.0], [4.0]],
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'lr': 0.001,
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'num_epochs': 400,
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'metrics': ['r2', 'mae'],
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'split': 'stratified'
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}
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ACNN_PDBBind_refined_pocket_temporal = {
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'dataset': 'PDBBind',
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'subset': 'refined',
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'load_binding_pocket': True,
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'random_seed': 123,
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'frac_train': 0.8,
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'frac_val': 0.,
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'frac_test': 0.2,
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'batch_size': 24,
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'shuffle': False,
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'hidden_sizes': [128, 128, 64],
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'weight_init_stddevs': [0.125, 0.125, 0.177, 0.01],
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'dropouts': [0.4, 0.4, 0.],
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'atomic_numbers_considered': torch.tensor([
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1., 6., 7., 8., 9., 11., 12., 15., 16., 17., 19., 20., 25., 26., 27., 28.,
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29., 30., 34., 35., 38., 48., 53., 55., 80.]),
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'radial': [[12.0], [0.0, 2.0, 4.0, 6.0, 8.0], [4.0]],
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'lr': 0.001,
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'num_epochs': 350,
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'metrics': ['r2', 'mae'],
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'split': 'temporal'
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}
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experiment_configures = {
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'ACNN_PDBBind_core_pocket_random': ACNN_PDBBind_core_pocket_random,
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'ACNN_PDBBind_core_pocket_scaffold': ACNN_PDBBind_core_pocket_scaffold,
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'ACNN_PDBBind_core_pocket_stratified': ACNN_PDBBind_core_pocket_stratified,
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'ACNN_PDBBind_core_pocket_temporal': ACNN_PDBBind_core_pocket_temporal,
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'ACNN_PDBBind_refined_pocket_random': ACNN_PDBBind_refined_pocket_random,
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'ACNN_PDBBind_refined_pocket_scaffold': ACNN_PDBBind_refined_pocket_scaffold,
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'ACNN_PDBBind_refined_pocket_stratified': ACNN_PDBBind_refined_pocket_stratified,
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'ACNN_PDBBind_refined_pocket_temporal': ACNN_PDBBind_refined_pocket_temporal
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}
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def get_exp_configure(exp_name):
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return experiment_configures[exp_name]
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