freated folder with example configs

example_configs/logp_gcnn_config.py

@@ -0,0 +1,95 @@
+from openchem.models.Graph2Label import Graph2Label
+from openchem.modules.encoders.gcn_encoder import GraphCNNEncoder
+from openchem.modules.mlp.openchem_mlp import OpenChemMLP
+from openchem.data.graph_data_layer import GraphDataset
+
+from openchem.utils.graph import Attribute
+from openchem.utils.utils import identity
+
+import torch.nn as nn
+from torch.optim import RMSprop, SGD, Adam
+from torch.optim.lr_scheduler import ExponentialLR, StepLR
+import torch.nn.functional as F
+from sklearn.metrics import r2_score, mean_squared_error
+
+import pandas as pd
+
+import copy
+import pickle
+
+
+def get_atomic_attributes(atom):
+    attr_dict = {}
+
+    atomic_num = atom.GetAtomicNum()
+    atomic_mapping = {5: 0, 7: 1, 6: 2, 8: 3, 9: 4, 15: 5, 16: 6, 17: 7, 35: 8,
+                      53: 9}
+    if atomic_num in atomic_mapping.keys():
+        attr_dict['atom_element'] = atomic_mapping[atomic_num]
+    else:
+        attr_dict['atom_element'] = 10
+    attr_dict['valence'] = atom.GetTotalValence()
+    attr_dict['charge'] = atom.GetFormalCharge()
+    attr_dict['hybridization'] = atom.GetHybridization().real
+    attr_dict['aromatic'] = int(atom.GetIsAromatic())
+    return attr_dict
+
+
+node_attributes = {}
+node_attributes['valence'] = Attribute('node', 'valence', one_hot=True, values=[1, 2, 3, 4, 5, 6])
+node_attributes['charge'] = Attribute('node', 'charge', one_hot=True, values=[-1, 0, 1, 2, 3, 4])
+node_attributes['hybridization'] = Attribute('node', 'hybridization',
+                                             one_hot=True, values=[0, 1, 2, 3, 4, 5, 6, 7])
+node_attributes['aromatic'] = Attribute('node', 'aromatic', one_hot=True,
+                                        values=[0, 1])
+node_attributes['atom_element'] = Attribute('node', 'atom_element',
+                                            one_hot=True,
+                                            values=list(range(11)))
+
+train_dataset = GraphDataset(get_atomic_attributes, node_attributes,
+                             './benchmark_datasets/Lipophilicity_dataset/Lipophilicity_train.csv',
+                             delimiter=',', cols_to_read=[0, 1])
+test_dataset = GraphDataset(get_atomic_attributes, node_attributes,
+                             './benchmark_datasets/Lipophilicity_dataset/Lipophilicity_test.csv',
+                             delimiter=',', cols_to_read=[0, 1])
+
+model = Graph2Label
+
+model_params = {
+    'task': 'regression',
+    'data_layer': GraphDataset,
+    'use_clip_grad': False,
+    'batch_size': 256,
+    'num_epochs': 101,
+    'logdir': '/home/user/Work/OpenChem/logs/logp_gcnn_logs',
+    'print_every': 10,
+    'save_every': 5,
+    'train_data_layer': train_dataset,
+    'val_data_layer': test_dataset,
+    'eval_metrics': r2_score,
+    'criterion': nn.MSELoss(),
+    'optimizer': Adam,
+    'optimizer_params': {
+        'lr': 0.0005,
+    },
+    'lr_scheduler': StepLR,
+    'lr_scheduler_params': {
+        'step_size': 15,
+        'gamma': 0.8
+    },
+    'encoder': GraphCNNEncoder,
+    'encoder_params': {
+        'input_size': train_dataset.num_features,
+        'encoder_dim': 128,
+        'n_layers': 5,
+        'hidden_size': [128, 128, 128, 128, 128],
+    },
+    'mlp': OpenChemMLP,
+    'mlp_params': {
+        'input_size': 128,
+        'n_layers': 2,
+        'hidden_size': [128, 1],
+        'activation': [F.relu, identity]
+    }
+}
+

example_configs/tox21_rnn_config.py

@@ -0,0 +1,111 @@
+from openchem.models.Smiles2Label import Smiles2Label
+from openchem.modules.embeddings.basic_embedding import Embedding
+from openchem.modules.encoders.rnn_encoder import RNNEncoder
+from openchem.modules.mlp.openchem_mlp import OpenChemMLP
+from openchem.data.smiles_data_layer import SmilesDataset
+from openchem.criterion.multitask_loss import MultitaskLoss
+
+import torch
+import torch.nn as nn
+
+import numpy as np
+
+from torch.optim import RMSprop, Adam
+from torch.optim.lr_scheduler import ExponentialLR, StepLR
+import torch.nn.functional as F
+from sklearn.metrics import roc_auc_score, mean_squared_error
+
+from openchem.data.utils import read_smiles_property_file
+data = read_smiles_property_file('./benchmark_datasets/tox21/tox21.csv',
+                                 cols_to_read=[13] + list(range(0,12)))
+smiles = data[0]
+labels = np.array(data[1:])
+
+labels[np.where(labels=='')] = '999'
+labels = labels.T
+
+from openchem.data.utils import get_tokens
+tokens, _, _ = get_tokens(smiles)
+tokens = tokens + ' '
+
+from sklearn.model_selection import train_test_split
+X_train, X_test, y_train, y_test = train_test_split(smiles, labels, test_size=0.2,
+                                                    random_state=42)
+
+from openchem.data.utils import save_smiles_property_file
+save_smiles_property_file('./benchmark_datasets/tox21/train.smi', X_train, y_train)
+save_smiles_property_file('./benchmark_datasets/tox21/test.smi', X_test, y_test)
+
+from openchem.data.smiles_data_layer import SmilesDataset
+train_dataset = SmilesDataset('./benchmark_datasets/tox21/train.smi',
+                              delimiter=',', cols_to_read=list(range(13)),
+                              tokens=tokens, augment=True)
+test_dataset = SmilesDataset('./benchmark_datasets/tox21/test.smi',
+                            delimiter=',', cols_to_read=list(range(13)),
+                            tokens=tokens)
+
+def multitask_auc(ground_truth, predicted):
+    from sklearn.metrics import roc_auc_score
+    import numpy as np
+    import torch
+    ground_truth = np.array(ground_truth)
+    predicted = np.array(predicted)
+    n_tasks = ground_truth.shape[1]
+    auc = []
+    for i in range(n_tasks):
+        ind = np.where(ground_truth[:, i] != 999)[0]
+        auc.append(roc_auc_score(ground_truth[ind, i], predicted[ind, i]))
+    #if torch.distributed.get_rank() == 0:
+    #    print(auc)
+    return np.mean(auc)
+
+model = Smiles2Label
+
+model_params = {
+    'use_cuda': True,
+    'task': 'multitask',
+    'random_seed': 5,
+    'use_clip_grad': True,
+    'max_grad_norm': 10.0,
+    'batch_size': 256,
+    'num_epochs': 31,
+    'logdir': '/home/mpopova/Work/OpenChem/logs/rnn_log',
+    'print_every': 5,
+    'save_every': 5,
+    'train_data_layer': train_dataset,
+    'val_data_layer': test_dataset,
+    'eval_metrics': multitask_auc,
+    'criterion': MultitaskLoss(ignore_index=999, n_tasks=12).cuda(),
+    'optimizer': RMSprop,
+    'optimizer_params': {
+        'lr': 0.001,
+        },
+    'lr_scheduler': StepLR,
+    'lr_scheduler_params': {
+        'step_size': 10,
+        'gamma': 0.8
+    },
+    'embedding': Embedding,
+    'embedding_params': {
+        'num_embeddings': train_dataset.num_tokens,
+        'embedding_dim': 128,
+        'padding_idx': train_dataset.tokens.index(' ')
+    },
+    'encoder': RNNEncoder,
+    'encoder_params': {
+        'input_size': 128,
+        'layer': "LSTM",
+        'encoder_dim': 128,
+        'n_layers': 4,
+        'dropout': 0.8,
+        'is_bidirectional': False
+    },
+    'mlp': OpenChemMLP,
+    'mlp_params': {
+        'input_size': 128,
+        'n_layers': 2,
+        'hidden_size': [128, 12],
+        'activation': [F.relu, torch.sigmoid],
+        'dropout': 0.0
+    }
+}