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utils.py
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utils.py
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#!/usr/bin/env python
# -*- encoding: utf-8 -*-
'''
@author: Jiahua Rao
@license: BSD-3-Clause, For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause
@contact: jiahua.rao@gmail.com
@time: 05/2023
'''
import os
import copy
import random
from rdkit import Chem
from gensim.models import Word2Vec, word2vec
import torch
import numpy as np
import pandas as pd
from tqdm import tqdm
from itertools import product
from torch_geometric.data import Batch
from sklearn.metrics import precision_recall_curve, auc, roc_curve
from sklearn.metrics import roc_auc_score, accuracy_score, precision_score, recall_score, f1_score, average_precision_score
from mol2vec.features import (DfVec, MolSentence, mol2alt_sentence,
mol2sentence, sentences2vec)
from descriptastorus.descriptors import rdNormalizedDescriptors
def filter_invalid_smiles(smiles_list):
valid_smiles_list = []
for smiles in smiles_list:
mol = Chem.MolFromSmiles(smiles)
if mol is not None and mol.GetNumHeavyAtoms() > 0:
valid_smiles_list.append(smiles)
return valid_smiles_list
def replace_numpy_with_torchtensor(obj):
# assume obj comprises either list or dictionary
# replace all the numpy instance with torch tensor.
if isinstance(obj, dict):
for key in obj.keys():
if isinstance(obj[key], np.ndarray):
obj[key] = torch.from_numpy(obj[key])
else:
replace_numpy_with_torchtensor(obj[key])
elif isinstance(obj, list):
for i in range(len(obj)):
if isinstance(obj[i], np.ndarray):
obj[i] = torch.from_numpy(obj[i])
else:
replace_numpy_with_torchtensor(obj[i])
# if the original input obj is numpy array
elif isinstance(obj, np.ndarray):
obj = torch.from_numpy(obj)
return obj
def write_log(logFile, text, isPrint=True):
if isPrint:
print(text)
if logFile != print:
logFile.write(text)
logFile.write('\n')
def compute_loss(logits, labels, loss_func, is_gold=None, pl_weight=0.5, is_augmented=False):
"""
Combine two types of losses: (1-alpha)*MLE (CE loss on gold) + alpha*Pl_loss (CE loss on pseudo labels)
"""
import torch as th
if is_augmented:# and ((n_pseudo := sum(~is_gold)) > 0):
deal_nan = lambda x: 0 if th.isnan(x) else x
mle_loss = deal_nan(loss_func(logits[is_gold], labels[is_gold]))
pl_loss = deal_nan(loss_func(logits[~is_gold], labels[~is_gold]))
loss = pl_weight * pl_loss + (1 - pl_weight) * mle_loss
else:
loss = loss_func(logits, labels)
return loss
def adjust_lr(optimizer, decay_ratio, lr):
lr_ = lr * (1 - decay_ratio)
lr_min = lr * 0.0001
if lr_ < lr_min:
lr_ = lr_min
for param_group in optimizer.param_groups:
param_group['lr'] = lr_
return lr_
def collate(data_list):
r"""Collates a python list of data objects to the internal storage
format of :class:`torch_geometric.data.InMemoryDataset`."""
keys = data_list[0].keys
data = data_list[0].__class__()
for key in keys:
data[key] = []
slices = {key: [0] for key in keys}
for item, key in product(data_list, keys):
data[key].append(item[key])
if torch.is_tensor(item[key]):
s = slices[key][-1] + item[key].size(
item.__cat_dim__(key, item[key]))
else:
s = slices[key][-1] + 1
slices[key].append(s)
if hasattr(data_list[0], '__num_nodes__'):
data.__num_nodes__ = []
for item in data_list:
data.__num_nodes__.append(item.num_nodes)
for key in keys:
item = data_list[0][key]
if torch.is_tensor(item) and len(data_list) > 1:
data[key] = torch.cat(data[key],
dim=data.__cat_dim__(key, item))
elif torch.is_tensor(item): # Don't duplicate attributes...
data[key] = data[key][0]
elif isinstance(item, int) or isinstance(item, float):
data[key] = torch.tensor(data[key])
slices[key] = torch.tensor(slices[key], dtype=torch.long)
batch = Batch.from_data_list(data)
return batch
class Metrictor_PPI:
def __init__(self, pre_y, truth_y, true_prob, is_binary=False, logging=False):
self.logging = logging
self.is_binary = is_binary
self.TP = 0
self.FP = 0
self.TN = 0
self.FN = 0
self.pre = np.array(pre_y).squeeze()
self.tru = np.array(truth_y).squeeze()
self.true_prob = np.array(true_prob).squeeze()
if is_binary:
length = pre_y.shape[0]
for i in range(length):
if pre_y[i] == truth_y[i]:
if truth_y[i] == 1:
self.TP += 1
else:
self.TN += 1
elif truth_y[i] == 1:
self.FN += 1
elif pre_y[i] == 1:
self.FP += 1
self.num = length
else:
N, C = pre_y.shape
for i in range(N):
for j in range(C):
if pre_y[i][j] == truth_y[i][j]:
if truth_y[i][j] == 1:
self.TP += 1
else:
self.TN += 1
elif truth_y[i][j] == 1:
self.FN += 1
elif truth_y[i][j] == 0:
self.FP += 1
self.num = N * C
def show_result(self, is_print=True):
self.Accuracy = (self.TP + self.TN) / (self.num + 1e-10)
self.Precision = self.TP / (self.TP + self.FP + 1e-10)
self.Recall = self.TP / (self.TP + self.FN + 1e-10)
self.F1 = 2 * self.Precision * self.Recall / (self.Precision + self.Recall + 1e-10)
# fpr, tpr, thresholds = metrics.roc_curve(self.tru, self.pre, pos_label=1)
# self.Auc = metrics.auc(fpr, tpr)
if self.is_binary:
fpr, tpr, thresholds = roc_curve(self.tru, self.pre)
self.Auc = auc(fpr, tpr)
aupr_entry_1 = self.tru
aupr_entry_2 = self.true_prob
precision, recall, _ = precision_recall_curve(aupr_entry_1, aupr_entry_2)
aupr = auc(recall,precision)
self.Aupr = aupr
else:
aupr_entry_1 = self.tru
aupr_entry_2 = self.true_prob
aupr = np.zeros(7)
for i in range(7):
precision, recall, _ = precision_recall_curve(aupr_entry_1[:,i], aupr_entry_2[:,i])
aupr[i] = auc(recall,precision)
self.Aupr = np.mean(aupr)
roc_auc = np.zeros(7)
for i in range(7):
fpr, tpr, _ = roc_curve(aupr_entry_1[:,i], aupr_entry_2[:,i])
roc_auc[i] = auc(fpr, tpr)
self.Auc = np.mean(roc_auc)
# write_log(self.logging, "Accuracy: {}".format(self.Accuracy), isPrint=False)
# write_log(self.logging, "Precision: {}".format(self.Precision), isPrint=False)
# write_log(self.logging, "Recall: {}".format(self.Recall), isPrint=False)
# write_log(self.logging, "F1-Score: {}".format(self.F1), isPrint=False)
# write_log(self.logging, "AUC: {}".format(self.Auc), isPrint=False)
# write_log(self.logging, "Aupr: {}".format(self.Aupr), isPrint=False)
print("Accuracy: {}".format(self.Accuracy))
print("Precision: {}".format(self.Precision))
print("Recall: {}".format(self.Recall))
print("F1-Score: {}".format(self.F1))
print("AUC: {}".format(self.Auc))
print("Aupr: {}".format(self.Aupr))
def Metrictor_DPI(correct_labels, scores, thres=0.5):
auc = roc_auc_score(correct_labels, scores)
aupr = average_precision_score(correct_labels, scores)
predict_labels = [1. if i >= thres else 0. for i in scores]
acc = accuracy_score(correct_labels, predict_labels)
precision = precision_score(correct_labels, predict_labels)
recall = recall_score(correct_labels, predict_labels)
f1 = f1_score(correct_labels, predict_labels)
return {"auc": auc, "acc": acc, "aupr": aupr,
"precision": precision, "recall": recall,
"f1": f1}
def Metrictor_DDI():
return
def Metrictor_PPBS(preds, labels):
precisions, recalls, _ = precision_recall_curve(labels, preds)
AUPR = auc(recalls, precisions)
if not(np.any(labels)) or np.all(labels):
return 0, AUPR
AUC = roc_auc_score(labels, preds)
return AUC, AUPR
def get_bfs_sub_graph(ppi_list, node_num, node_to_edge_index, sub_graph_size):
candiate_node = []
selected_edge_index = []
selected_node = []
random_node = random.randint(0, node_num - 1)
while len(node_to_edge_index[random_node]) > 5:
random_node = random.randint(0, node_num - 1)
candiate_node.append(random_node)
while len(selected_edge_index) < sub_graph_size:
cur_node = candiate_node.pop(0)
selected_node.append(cur_node)
for edge_index in node_to_edge_index[cur_node]:
if edge_index not in selected_edge_index:
selected_edge_index.append(edge_index)
end_node = -1
if ppi_list[edge_index][0] == cur_node:
end_node = ppi_list[edge_index][1]
else:
end_node = ppi_list[edge_index][0]
if end_node not in selected_node and end_node not in candiate_node:
candiate_node.append(end_node)
else:
continue
# print(len(selected_edge_index), len(candiate_node))
node_list = candiate_node + selected_node
print(len(node_list), len(selected_edge_index))
return selected_edge_index
def get_dfs_sub_graph(ppi_list, node_num, node_to_edge_index, sub_graph_size):
stack = []
selected_edge_index = []
selected_node = []
random_node = random.randint(0, node_num - 1)
while len(node_to_edge_index[random_node]) > 5:
random_node = random.randint(0, node_num - 1)
stack.append(random_node)
while len(selected_edge_index) < sub_graph_size:
# print(len(selected_edge_index), len(stack), len(selected_node))
cur_node = stack[-1]
if cur_node in selected_node:
flag = True
for edge_index in node_to_edge_index[cur_node]:
if flag:
end_node = -1
if ppi_list[edge_index][0] == cur_node:
end_node = ppi_list[edge_index][1]
else:
end_node = ppi_list[edge_index][0]
if end_node in selected_node:
continue
else:
stack.append(end_node)
flag = False
else:
break
if flag:
stack.pop()
continue
else:
selected_node.append(cur_node)
for edge_index in node_to_edge_index[cur_node]:
if edge_index not in selected_edge_index:
selected_edge_index.append(edge_index)
return selected_edge_index
def ppi_split_dataset(split_mode='random', test_size=0.2, seed=2, consistent=False):
prefix = "./data/high_ppi/"
# seed = random.randint(0, 10000)
random.seed(seed)
print(f"!!!!!!!! New split. seed:{seed}")
fp = open(prefix+f"split/{split_mode}/seed.txt", "w")
fp.write(str(seed))
fp.close()
protein_path = os.path.join(prefix, "raw", "protein.SHS27k.sequences.dictionary.pro3.tsv")
ppi_path = os.path.join(prefix, 'raw', "protein.actions.SHS27k.STRING.pro2.txt")
proteins = pd.read_csv(protein_path, sep='\t', header=None, names=['Protein', 'Sequence'])
protein_idx = 0
protein_nodes = {}
for i in tqdm(range(len(proteins))):
node = proteins.loc[i, "Protein"]
if node not in protein_nodes.keys():
protein_nodes[node] = protein_idx
protein_idx += 1
protein_node_df = proteins[['Protein']]
protein_node_df["node_idx"] = proteins.index.tolist()
protein_node_df.to_csv(os.path.join(prefix, "mapping", "nodeidx2proteinid.csv"), index=False)
raw_ppi_df = pd.read_csv(ppi_path, sep='\t', header=0)[['item_id_a', 'item_id_b', 'mode', 'is_directional', 'a_is_acting', 'score']]
class_map = {'reaction': 0, 'binding': 1, 'ptmod': 2, 'activation': 3, 'inhibition': 4, 'catalysis': 5,
'expression': 6}
ppi_idx = 0
ppi_label_list = []
ppi_dict = {}
ppi_list = []
for i in tqdm(range(len(raw_ppi_df))):
from_node = raw_ppi_df.loc[i, "item_id_a"]
to_node = raw_ppi_df.loc[i, "item_id_b"]
relation_mode = raw_ppi_df.loc[i, "mode"]
if from_node < to_node:
temp_data = from_node + "__" + to_node
else:
temp_data = to_node + "__" + from_node
if temp_data not in ppi_dict.keys():
ppi_dict[temp_data] = ppi_idx
temp_label = [0, 0, 0, 0, 0, 0, 0]
temp_label[class_map[relation_mode]] = 1
ppi_label_list.append(temp_label)
ppi_idx += 1
else:
index = ppi_dict[temp_data]
temp_label = ppi_label_list[index]
temp_label[class_map[relation_mode]] = 1
ppi_label_list[index] = temp_label
i = 0
for ppi in tqdm(ppi_dict.keys()):
name = ppi_dict[ppi]
assert name == i
i += 1
temp = ppi.strip().split('__')
ppi_list.append(temp)
ppi_num = len(ppi_list)
origin_ppi_list = copy.deepcopy(ppi_list)
assert len(ppi_list) == len(ppi_label_list)
for i in tqdm(range(ppi_num)):
seq1_name = ppi_list[i][0]
seq2_name = ppi_list[i][1]
# print(len(protein_name))
ppi_list[i][0] = protein_nodes[seq1_name]
ppi_list[i][1] = protein_nodes[seq2_name]
ppi_num = len(ppi_list)
if split_mode == 'random':
import json
splits = json.load(open("/data/user/raojh/worksapce/MUSE/data/high_ppi/split/random/train_val_split_1.json", "r"))
# random_list = [i for i in range(ppi_num)]
# random.shuffle(random_list)
train_index = splits['train_index']
valid_index = splits['valid_index']
# train_index = random_list[: int(ppi_num * (1 - test_size))]
# valid_index = random_list[int(ppi_num * (1 - test_size)):]
elif split_mode == 'bfs' or split_mode == 'dfs':
node_to_edge_index = {}
edge_num = ppi_num # int(edge_num // 2)
print("edge_num: ", edge_num)
print("ppi_list: ", len(ppi_list))
for i in range(edge_num):
edge = ppi_list[i]
if edge[0] not in node_to_edge_index.keys():
node_to_edge_index[edge[0]] = []
node_to_edge_index[edge[0]].append(i)
if edge[1] not in node_to_edge_index.keys():
node_to_edge_index[edge[1]] = []
node_to_edge_index[edge[1]].append(i)
node_num = len(node_to_edge_index)
sub_graph_size = int(edge_num * test_size)
if split_mode == 'bfs':
selected_edge_index = get_bfs_sub_graph(ppi_list, node_num, node_to_edge_index, sub_graph_size)
elif split_mode == 'dfs':
selected_edge_index = get_dfs_sub_graph(ppi_list, node_num, node_to_edge_index, sub_graph_size)
all_edge_index = [i for i in range(edge_num)]
unselected_edge_index = list(set(all_edge_index).difference(set(selected_edge_index)))
train_index = unselected_edge_index
valid_index = selected_edge_index
elif split_mode == 'default':
test_split_df = pd.read_csv(os.path.join(prefix, "raw", "high_ppi_split.tsv"), sep='\t', header=0, index_col=0)
test_split_df['row'] = test_split_df.Protein_1.map(protein_nodes)
test_split_df['col'] = test_split_df.Protein_2.map(protein_nodes)
test_edges = test_split_df[['row', 'col']].values.tolist()
test_edges_tuple = [tuple(i) for i in test_edges]
edges_tuple = [tuple(i) for i in ppi_list]
unselected_edges = list(set(edges_tuple).difference(set(test_edges_tuple)))
train_edge = unselected_edges
train_index = [edges_tuple.index(e) for e in train_edge]
valid_index = [edges_tuple.index(e) for e in test_edges_tuple]
os.makedirs(f'{prefix}/split/{split_mode}', exist_ok=True)
pd.DataFrame(sorted(train_index)).to_csv(f'{prefix}/split/{split_mode}/train.csv.gz', compression='gzip', header=False, index=False)
pd.DataFrame(sorted(valid_index)).to_csv(f'{prefix}/split/{split_mode}/test.csv.gz', compression='gzip', header=False, index=False)
train_edge = np.array(ppi_list).T[:, train_index].T
valid_edge = np.array(ppi_list).T[:, valid_index].T
train_label = np.array(ppi_label_list)[train_index]
valid_label = np.array(ppi_label_list)[valid_index]
torch.save(
{"edge": train_edge, "label": train_label},
os.path.join(prefix, "split", split_mode, "train.pt")
)
torch.save(
{"edge": valid_edge, "label": valid_label},
os.path.join(prefix, "split", split_mode, "test.pt")
)
pd.DataFrame(train_edge).to_csv(os.path.join(prefix, "raw/edge.csv.gz"), compression='gzip', header=False, index=False)
pd.DataFrame(train_label).to_csv(os.path.join(prefix, "raw/edge-label.csv.gz"), compression='gzip', header=False, index=False)
node_num = len(protein_nodes)
edge_num = len(ppi_list)
pd.DataFrame([node_num]).to_csv(os.path.join(prefix, "raw", "num-node-list.csv.gz"), compression='gzip', header=False, index=False)
pd.DataFrame([edge_num]).to_csv(os.path.join(prefix, "raw", "num-edge-list.csv.gz"), compression='gzip', header=False, index=False)
return {'train': torch.LongTensor(train_index), 'test': torch.LongTensor(valid_index)}
# NUM_ATOM_FEAT = 34
NUM_ATOM_FEAT = 144
# NUM_PROTEIN_FEAT = 100
NUM_PROTEIN_FEAT = 1956
NUM_PLAIN_FEAT = 200
PT = Chem.GetPeriodicTable()
RNG = rdNormalizedDescriptors.RDKit2DNormalized()
ELEMENT_LIST = [
'C', 'N', 'O', 'S', 'F', 'Si', 'P', 'Cl', 'Br', 'Mg', 'Na', 'Ca', 'Fe', 'As', 'Al', 'I', 'B', 'V', 'K', 'Tl', 'Yb',
'Sb', 'Sn', 'Ag', 'Pd', 'Co', 'Se', 'Ti', 'Zn', 'H', 'Li', 'Ge', 'Cu', 'Au', 'Ni', 'Cd', 'In', 'Mn', 'Zr', 'Cr',
'Pt', 'Hg', 'Pb', 'W', 'Ru', 'Nb', 'Re', 'Te', 'Rh', 'Tc', 'Ba', 'Bi', 'Hf', 'Mo', 'U', 'Sm', 'Os', 'Ir', 'Ce',
'Gd', 'Ga', 'Cs', 'unknown'
]
ATOM_CLASS_TABLE = {}
NOBLE_GAS_ATOMIC_NUM = {2, 10, 18, 36, 54, 86}
OTHER_NON_METAL_ATOMIC_NUM = {1, 6, 7, 8, 9, 15, 16, 17, 34, 35, 53}
METALLOID_ATOMIC_NUM = {5, 14, 32, 33, 51, 52, 85}
POST_TRANSITION_METAL_ATOMIC_NUM = {13, 31, 49, 50, 81, 82, 83, 84, 114}
TRANSITION_METAL_ATOMIC_NUM = set(range(21, 30 + 1)) | set(range(39, 48 + 1)) | set(range(72, 80 + 1)) | set(
range(104, 108 + 1)) | {112}
ALKALI_METAL_ATOMIC_NUM = {3, 11, 19, 37, 55, 87}
ALKALI_EARCH_METAL_ATOMIC_NUM = {4, 12, 20, 38, 56, 88}
LANTHANOID_ATOMIC_NUM = set(range(57, 71 + 1))
ACTINOID_ATOMIC_NUM = set(range(89, 103 + 1))
ATOM_CLASSES = [
NOBLE_GAS_ATOMIC_NUM, OTHER_NON_METAL_ATOMIC_NUM, METALLOID_ATOMIC_NUM, POST_TRANSITION_METAL_ATOMIC_NUM,
TRANSITION_METAL_ATOMIC_NUM, ALKALI_EARCH_METAL_ATOMIC_NUM, ALKALI_METAL_ATOMIC_NUM, LANTHANOID_ATOMIC_NUM,
ACTINOID_ATOMIC_NUM
]
for class_index, atom_class in enumerate(ATOM_CLASSES):
for num in atom_class:
ATOM_CLASS_TABLE[num] = class_index + 1
ALLEN_NEGATIVITY_TABLE = {
1: 2.3,
2: 4.16,
3: 0.912,
4: 1.576,
5: 2.051,
6: 2.544,
7: 3.066,
8: 3.61,
9: 4.193,
10: 4.787,
11: 0.869,
12: 1.293,
13: 1.613,
14: 1.916,
15: 2.253,
16: 2.589,
17: 2.869,
18: 3.242,
19: 0.734,
20: 1.034,
21: 1.19,
22: 1.38,
23: 1.53,
24: 1.65,
25: 1.75,
26: 1.8,
27: 1.84,
28: 1.88,
29: 1.85,
30: 1.59,
31: 1.756,
32: 1.994,
33: 2.211,
34: 2.424,
35: 2.685,
36: 2.966,
37: 0.706,
38: 0.963,
39: 1.12,
40: 1.32,
41: 1.41,
42: 1.47,
43: 1.51,
44: 1.54,
45: 1.56,
46: 1.58,
47: 1.87,
48: 1.52,
49: 1.656,
50: 1.824,
51: 1.984,
52: 2.158,
53: 2.359,
54: 2.582,
55: 0.659,
56: 0.881,
71: 1.09,
72: 1.16,
73: 1.34,
74: 1.47,
75: 1.6,
76: 1.65,
77: 1.68,
78: 1.72,
79: 1.92,
80: 1.76,
81: 1.789,
82: 1.854,
83: 2.01,
84: 2.19,
85: 2.39,
86: 2.6,
87: 0.67,
88: 0.89
}
ELECTRON_AFFINITY_TABLE = (
(1, 0.75),
(1, 0.75),
(2, -0.52),
(3, 0.62),
(4, -0.52),
(5, 0.28),
(6, 1.26),
(6, 1.26),
(7, 0.00),
(7, 0.01),
(7, 0.01),
(8, 1.46),
(8, 1.46),
(8, 1.46),
(8, -7.71),
(9, 3.40),
(10, -1.20),
(11, 0.55),
(12, -0.41),
(13, 0.43),
(14, 1.39),
(15, 0.75),
(15, -4.85),
(15, -9.18),
(16, 2.08),
(16, 2.08),
(16, -4.72),
(17, 3.61),
(18, -1.00),
(19, 0.50),
(20, 0.02),
(21, 0.19),
(22, 0.08),
(23, 0.53),
(24, 0.68),
(25, -0.52),
(26, 0.15),
(27, 0.66),
(28, 1.16),
(29, 1.24),
(30, -0.62),
(31, 0.43),
(32, 1.23),
(33, 0.80),
(34, 2.02),
(35, 3.36),
(36, -0.62),
(37, 0.49),
(38, 0.05),
(39, 0.31),
(40, 0.43),
(41, 0.92),
(42, 0.75),
(43, 0.55),
(44, 1.05),
(45, 1.14),
(46, 0.56),
(47, 1.30),
(48, -0.72),
(49, 0.30),
(50, 1.11),
(51, 1.05),
(52, 1.97),
(53, 3.06),
(54, -0.83),
(55, 0.47),
(56, 0.14),
(57, 0.47),
(58, 0.65),
(59, 0.96),
(60, 1.92),
(61, 0.13),
(62, 0.16),
(63, 0.86),
(64, 0.14),
(65, 1.17),
(66, 0.35),
(67, 0.34),
(68, 0.31),
(69, 1.03),
(70, -0.02),
(71, 0.35),
(72, 0.02),
(73, 0.32),
(74, 0.82),
(75, 0.06),
(76, 1.10),
(77, 1.56),
(78, 2.13),
(79, 2.31),
(80, -0.52),
(81, 0.38),
(82, 0.36),
(83, 0.94),
(84, 1.90),
(85, 2.30),
(86, -0.72),
(87, 0.49),
(88, 0.10),
(89, 0.35),
(90, 1.17),
(91, 0.55),
(92, 0.53),
(93, 0.48),
(94, -0.50),
(95, 0.10),
(96, 0.28),
(97, -1.72),
(98, -1.01),
(99, -0.30),
(100, 0.35),
(101, 0.98),
(102, -2.33),
(103, -0.31),
(111, 1.57),
(113, 0.69),
(115, 0.37),
(116, 0.78),
(117, 1.72),
(118, 0.06),
(119, 0.66),
(120, 0.02),
(121, 0.57),
)
ELECTRON_AFFINITY_TABLE = {k: v for (k, v) in ELECTRON_AFFINITY_TABLE}
def one_of_k_encoding(x, allowable_set):
# if x not in allowable_set:
# raise Exception("input {0} not in allowable set{1}:".format(
# x, allowable_set))
return [x == s for s in allowable_set]
def one_of_k_encoding_unk(x, allowable_set):
"""Maps inputs not in the allowable set to the last element."""
if x not in allowable_set:
x = allowable_set[-1]
return [x == s for s in allowable_set]
def atom_features(atom, explicit_H=False, use_chirality=True):
"""Generate atom features including atom symbol(10),degree(7),formal charge,
radical electrons,hybridization(6),aromatic(1),Chirality(3)
"""
symbol = ['C', 'N', 'O', 'F', 'P', 'S', 'Cl', 'Br', 'I', 'other'] # 10-dim
degree = [0, 1, 2, 3, 4, 5, 6] # 7-dim
hybridizationType = [
Chem.rdchem.HybridizationType.SP, Chem.rdchem.HybridizationType.SP2, Chem.rdchem.HybridizationType.SP3,
Chem.rdchem.HybridizationType.SP3D, Chem.rdchem.HybridizationType.SP3D2, 'other'
] # 6-dim
results = (one_of_k_encoding_unk(atom.GetSymbol(), symbol) + one_of_k_encoding(atom.GetDegree(), degree) +
[atom.GetFormalCharge(), atom.GetNumRadicalElectrons()] +
one_of_k_encoding_unk(atom.GetHybridization(), hybridizationType) + [atom.GetIsAromatic()]
) # 10+7+2+6+1=26
# In case of explicit hydrogen(QM8, QM9), avoid calling `GetTotalNumHs`
if not explicit_H:
results = results + one_of_k_encoding_unk(atom.GetTotalNumHs(), [0, 1, 2, 3, 4]) # 26+5=31
if use_chirality:
try:
results = results + one_of_k_encoding_unk(atom.GetProp('_CIPCode'),
['R', 'S']) + [atom.HasProp('_ChiralityPossible')]
except Exception:
results = results + [False, False] + [atom.HasProp('_ChiralityPossible')] # 31+3 =34
return results
def atomic_features(atomic_num):
# Symbol
# symbol = PT.GetElementSymbol(atomic_num)
# symbol_k = one_of_k_encoding_unk(symbol, ELEMENT_LIST)
# Period
outer_electrons = PT.GetNOuterElecs(atomic_num)
outer_electrons_k = one_of_k_encoding(outer_electrons, list(range(0, 8 + 1)))
# Default Valence
default_electrons = PT.GetDefaultValence(atomic_num) # -1 for transition metals
default_electrons_k = one_of_k_encoding(default_electrons, list(range(-1, 8 + 1)))
# Orbitals / Group / ~Row
orbitals = next(j + 1 for j, val in enumerate([2, 10, 18, 36, 54, 86, 120]) if val >= atomic_num)
orbitals_k = one_of_k_encoding(orbitals, list(range(0, 7 + 1)))
# IUPAC Series
atom_series = ATOM_CLASS_TABLE[atomic_num]
atom_series_k = one_of_k_encoding(atom_series, list(range(0, 9 + 1)))
# Centered Electrons
centered_oec = abs(outer_electrons - 4)
# Electronegativity & Electron Affinity
try:
allen_electronegativity = ALLEN_NEGATIVITY_TABLE[atomic_num]
except KeyError:
allen_electronegativity = 0
try:
electron_affinity = ELECTRON_AFFINITY_TABLE[atomic_num]
except KeyError:
electron_affinity = 0
# Mass & Radius (van der waals / covalent / bohr 0)
floats = [
centered_oec, allen_electronegativity, electron_affinity,
PT.GetAtomicWeight(atomic_num),
PT.GetRb0(atomic_num),
PT.GetRvdw(atomic_num),
PT.GetRcovalent(atomic_num), outer_electrons, default_electrons, orbitals
]
# print(symbol_k + outer_electrons_k + default_electrons_k + orbitals_k + atom_series_k + floats)
# Compose feature array
# feature_array = np.array(symbol_k + outer_electrons_k + default_electrons_k + orbitals_k + atom_series_k + floats,
# dtype=np.float32)
feature_array = np.array(outer_electrons_k + default_electrons_k + orbitals_k + atom_series_k + floats,
dtype=np.float32)
# Cache in dict for future use
return feature_array
def adjacent_matrix(mol):
adjacency = Chem.GetAdjacencyMatrix(mol)
return np.array(adjacency) + np.eye(adjacency.shape[0])
def get_mol_features(smiles, atom_dim):
try:
mol = Chem.MolFromSmiles(smiles)
except Exception:
raise RuntimeError("SMILES cannot been parsed!")
# mol = Chem.AddHs(mol)
# atom_feat = np.zeros((mol.GetNumAtoms(), NUM_ATOM_FEAT))
atom_feat = np.zeros((mol.GetNumAtoms(), atom_dim))
map_dict = dict()
for atom in mol.GetAtoms():
# atomic_features(atom.GetAtomicNum())
# atom_feat[atom.GetIdx(), :] = np.append(atom_features(atom), atomic_features(atom.GetAtomicNum()))
atom_feat[atom.GetIdx(), :] = atom_features(atom)
map_dict[atom.GetIdx()] = atom.GetSmarts()
adj_matrix = adjacent_matrix(mol)
return atom_feat, adj_matrix, map_dict
def get_mol2vec_features(model, smiles):
mol = Chem.MolFromSmiles(smiles)
sen = (MolSentence(mol2alt_sentence(mol, 0)))
mol_vec = (sentences2vec(sen, model, unseen='UNK'))
return mol_vec
def molecule_collate_fn(batch):
batch_size = len(batch)
if not isinstance(batch[0], dict):
new_batch = []
for i in range(batch_size):
new_batch.append(
{"compound_node_features": batch[i].compound_node_features,
"compound_adj_matrix": batch[i].compound_adj_matrix,
"compound_word_embedding": batch[i].compound_word_embedding}
)
batch = new_batch
compound_node_nums = [item['compound_node_features'].shape[0] for item in batch]
max_compound_len = max(compound_node_nums)
compound_node_features = torch.zeros((batch_size, max_compound_len, batch[0]['compound_node_features'].shape[1]))
compound_adj_matrix = torch.zeros((batch_size, max_compound_len, max_compound_len))
compound_word_embedding = torch.zeros(
(batch_size, max_compound_len, batch[0]['compound_word_embedding'].shape[1]))
for i, item in enumerate(batch):
v = item['compound_node_features']
compound_node_features[i, :v.shape[0], :] = torch.FloatTensor(v)
v = item['compound_adj_matrix']
compound_adj_matrix[i, :v.shape[0], :v.shape[0]] = torch.FloatTensor(v)
v = item['compound_word_embedding']
compound_word_embedding[i, :v.shape[0], :] = torch.FloatTensor(v)
compound_node_nums = torch.LongTensor(compound_node_nums)
return {
'compound_node_feat': compound_node_features,
'compound_adj': compound_adj_matrix,
'compound_node_num': compound_node_nums,
'compound_word_embedding': compound_word_embedding,
}
# def protein_collate_fn(batch):
# batch_size = len(batch)
# protein_node_nums = [item.protein_node_feat.shape[0] for item in batch]
# max_protein_len = max(protein_node_nums)
# protein_node_features = torch.zeros((batch_size, max_protein_len, batch[0].protein_node_feat.shape[1]))
# protein_contact_map = torch.zeros((batch_size, max_protein_len, max_protein_len))
# protein_seq_embedding = torch.zeros((batch_size, max_protein_len, batch[0].protein_embedding.shape[1]))
# for i, item in enumerate(batch):
# v = item.protein_node_feat
# protein_node_features[i, :v.shape[0], :] = torch.FloatTensor(v)
# v = item.protein_map
# protein_contact_map[i, :v.shape[0], :v.shape[0]] = torch.FloatTensor(v)
# v = item.protein_embedding
# # if args.pretrained == 1 and args.objective == 'classification':
# v[:, 358] = (v[:, 358] - 7.8) / 6.5
# protein_seq_embedding[i, :v.shape[0], :] = torch.FloatTensor(v)[:max_protein_len, :]
# protein_node_nums = torch.LongTensor(protein_node_nums)
# return {
# 'protein_node_feat': protein_node_features,
# 'protein_map': protein_contact_map,
# 'protein_embedding': protein_seq_embedding,
# 'protein_node_num': protein_node_nums,
# }
def protein_collate_fn(batch):
protein_node_nums = [item.x.shape[0] for item in batch]
return {
"protein_node_num": torch.LongTensor(protein_node_nums),
"protein_data": Batch.from_data_list(batch),
}
def check_gpus():
'''
GPU available check
http://pytorch-cn.readthedocs.io/zh/latest/package_references/torch-cuda/
'''
if not torch.cuda.is_available():
print('This script could only be used to manage NVIDIA GPUs,but no GPU found in your device')
return False
elif not 'NVIDIA System Management' in os.popen('nvidia-smi -h').read():
print("'nvidia-smi' tool not found.")
return False
return True
def check_gpus():
'''
GPU available check
http://pytorch-cn.readthedocs.io/zh/latest/package_references/torch-cuda/
'''
if not torch.cuda.is_available():
print('This script could only be used to manage NVIDIA GPUs,but no GPU found in your device')
return False
elif not 'NVIDIA System Management' in os.popen('nvidia-smi -h').read():
print("'nvidia-smi' tool not found.")
return False
return True
if check_gpus():
def parse(line, qargs):
"""
line:
a line of text
qargs:
query arguments
return:
a dict of gpu infos
Pasing a line of csv format text returned by nvidia-smi
解析一行nvidia-smi返回的csv格式文本
"""
numberic_args = ['memory.free', 'memory.total', 'power.draw', 'power.limit'] # 可计数的参数
power_manage_enable = lambda v: (not 'Not Support' in v) # lambda表达式，显卡是否滋瓷power management（笔记本可能不滋瓷）
to_numberic = lambda v: float(v.upper().strip().replace('MIB', '').replace('W', '')) # 带单位字符串去掉单位
process = lambda k, v: (
(int(to_numberic(v)) if power_manage_enable(v) else 1) if k in numberic_args else v.strip())
return {k: process(k, v) for k, v in zip(qargs, line.strip().split(','))}
def query_gpu(qargs=[]):
"""
qargs:
query arguments
return:
a list of dict
Querying GPUs infos
查询GPU信息
"""