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import os
import tensorflow as tf
import torch
import pandas as pd
import numpy as np
from glob import glob
from tqdm import tqdm
from itertools import islice
from megnet.data.molecule import MolecularGraph
from megnet.data.molecule import MolecularGraph
from megnet.data.molecule import SimpleMolGraph
from megnet.data.crystal import CrystalGraph
from megnet.callbacks import ReduceLRUponNan, ManualStop
from megnet.data.graph import GraphBatchDistanceConvert, GaussianDistance
from megnet.models import MEGNetModel
import dataset
from dataset import make_datasets
def set_seed(seed):
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
np.random.seed(seed)
random.seed(seed)
class trainer():
def __init__(self, base_dir,
atom_features=['element', 'formal_charge', 'hybridization', 'donor', 'acceptor'], bond_features=['spatial_distance', 'same_ring', 'bond_type'], global_features=['fedility'],
nblocks=3, global_embedding_dim=16, npass=2,
num_xtb_data=None, num_g09_data=None, load=False, random_state=1000, g09_data_cut=None, with_rdkit=False, rdkit_rank=10, to_class=False, class_num=5, cut_mode='qcut', onehot=False):
self.base_dir = base_dir
self.atom_features = atom_features
self.bond_features = bond_features
self.global_features = global_features
self.nblocks = nblocks
self.global_embedding_dim = global_embedding_dim
self.npass = npass
self.num_xtb_data = num_xtb_data
self.num_g09_data = num_g09_data
self.load = load
self.random_state = random_state
self.g09_data_cut = g09_data_cut
self.with_rdkit = with_rdkit
self.rdkit_rank = rdkit_rank
self.to_class = to_class
self.class_num = class_num
self.cut_mode = cut_mode
self.onehot = onehot
def make_train_val_set(self):
known_elements = ['H', 'C', 'N', 'O', 'F', 'Cl', 'Br', 'S', 'P', 'I', 'Si', 'Li', 'B', 'Ge']
molecule_graph = MolecularGraph(atom_features=self.atom_features, bond_features=self.bond_features, known_elements=known_elements)
dataset_maker = make_datasets(self.base_dir)
''' make graph datas '''
train_graphs, train_targets, val_graphs, val_targets = dataset_maker.make_graph_datas(self.num_xtb_data, self.num_g09_data, self.atom_features, self.bond_features,
load=self.load, random_state=self.random_state, g09_data_cut=self.g09_data_cut,
with_rdkit=self.with_rdkit, rdkit_rank=self.rdkit_rank,
to_class=self.to_class, class_num=self.class_num, cut_mode=self.cut_mode, onehot=self.onehot)
return train_graphs, train_targets, val_graphs, val_targets
def train(self, epochs=200, lr=1e-3, batch_size=128, dropout=None,
wandb_on=True, wandb_project=None, wandb_name=None, wandb_memo=None, wandb_group=None):
''' 기타 변수 정의 '''
#tf.compat.v1.disable_eager_execution()
GPU_INDEX = '0'
tf.random.set_seed(self.random_state)
#os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
known_elements = ['H', 'C', 'N', 'O', 'F', 'Cl', 'Br', 'S', 'P', 'I', 'Si', 'Li', 'B', 'Ge']
molecule_graph = MolecularGraph(atom_features=self.atom_features, bond_features=self.bond_features, known_elements=known_elements)
train_graphs, train_targets, val_graphs, val_targets = self.make_train_val_set()
train_num = len(train_targets)
val_num = len(val_targets)
''' wandb settings '''
if wandb_on:
import wandb
from wandb.keras import WandbCallback
wandb.init(project=wandb_project, reinit=True, group=wandb_group, notes=wandb_memo)
if wandb_project is None:
wandb.init(project="testing", reinit=True, group=wandb_group, notes=wandb_memo)
wandb.run.name = wandb_name
wandb.run.save()
parameters = wandb.config
parameters.epochs = epochs
parameters.nblocks = self.nblocks
parameters.lr = lr
parameters.batch_size = batch_size
parameters.dropout = dropout
parameters.atom_features = self.atom_features
parameters.bond_features = self.bond_features
parameters.global_features = self.global_features
parameters.global_embedding_dim = self.global_embedding_dim
parameters.npass = self.npass
parameters.train_num = train_num
parameters.val_num = val_num
parameters.with_rdkit = self.with_rdkit
if self.with_rdkit:
parameters.rdkit_rank = self.rdkit_rank
if self.to_class:
parameters.to_class = self.to_class
parameters.class_num = self.class_num
parameters.cut_mode = self.cut_mode
format_ = val_graphs[0]
num_atom_fea = torch.tensor(format_['atom']).shape[1]
num_bond_fea = torch.tensor(format_['bond']).shape[1]
num_state_fea = torch.tensor(format_['state']).shape[1]
if wandb_on:
parameters.num_atom_fea = num_atom_fea
parameters.num_bond_fea = num_bond_fea
parameters.num_state_fea = num_state_fea
nfeat_node = num_atom_fea
nfeat_edge = num_bond_fea
nfeat_global = num_state_fea
''' model defination '''
if wandb_on:
callbacks = [ReduceLRUponNan(patience=200), ManualStop(), WandbCallback(monitor="val_mae", log_evaluation=True)]
else:
callbacks = [ReduceLRUponNan(patience=200), ManualStop()]
model = MEGNetModel(nfeat_edge=nfeat_edge, nfeat_global=nfeat_global, nfeat_node=nfeat_node,
global_embedding_dim=self.global_embedding_dim, nblocks=self.nblocks, dropout=dropout,
npass=self.npass, graph_converter=molecule_graph, learning_rate=lr, batch_size=batch_size, metrics=['mae'])
''' training '''
EPOCHS = epochs
model.train_from_graphs(train_graphs, train_targets, val_graphs, val_targets,
epochs=EPOCHS, verbose=2, initial_epoch=0, callbacks=callbacks)
wandb.finish()
def eval(selected_model=None, xtb_state=0, g09_state=1, batch_size=128, dropout=None):
''' 기타 변수 정의 '''
#tf.compat.v1.disable_eager_execution()
GPU_INDEX = '0'
tf.random.set_seed(self.random_state)
#os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
train_graphs, train_targets, val_graphs, val_targets = self.make_train_val_set()
train_data = model.graph_converter.get_flat_data(train_graphs, train_targets)
val_data = model.graph_converter.get_flat_data(val_graphs, val_targets)
train_gen = GraphBatchDistanceConvert(*train_data, distance_converter=model.graph_converter.bond_converter, batch_size=batch_size)
val_gen = GraphBatchDistanceConvert(*val_data, distance_converter=model.graph_converter.bond_converter, batch_size=batch_size)
train_num = len(train_targets)
val_num = len(val_targets)
format_ = val_graphs[0]
num_atom_fea = torch.tensor(format_['atom']).shape[1]
num_bond_fea = torch.tensor(format_['bond']).shape[1]
num_state_fea = torch.tensor(format_['state']).shape[1]
nfeat_node = num_atom_fea
nfeat_edge = num_bond_fea
nfeat_global = num_state_fea
''' model defination '''
model = MEGNetModel(nfeat_edge=nfeat_edge, nfeat_global=nfeat_global, nfeat_node=nfeat_node,
global_embedding_dim=self.global_embedding_dim, nblocks=self.nblocks, dropout=dropout,
npass=self.npass, graph_converter=molecule_graph, learning_rate=lr, batch_size=batch_size, metrics=['mae'])
model.load_weights(selected_model)
''' evaluate '''
train_preds = []
train_trues = []
val_preds = []
val_trues = []
for i in range(len(train_gen)):
d = train_gen[i]
train_preds.extend(model.predict(d[0]).ravel().tolist())
train_trues.extend(d[1].ravel().tolist())
for i in range(len(val_gen)):
d = val_gen[i]
val_preds.extend(model.predict(d[0]).ravel().tolist())
val_trues.extend(d[1].ravel().tolist())
return train_preds, train_trues, val_preds, val_trues