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deep_aai_kmer_embedding_pred_demo.py
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deep_aai_kmer_embedding_pred_demo.py
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#!/usr/bin/env
# coding:utf-8
"""
Created on 2020/11/9 下午4:18
base Info
"""
__author__ = 'xx'
__version__ = '1.0'
from dataset.abs_dataset_cls import AbsDataset
from metrics.evaluate import evaluate_classification
from models.deep_aai_kmer_embedding_cls import DeepAAIKmerEmbeddingCls
import numpy as np
import torch.nn as nn
import random
import torch
import math
import os.path as osp
from utils.index_map import get_map_index_for_sub_arr
import pandas as pd
from dataset.amino_seq_to_ft import amino_seq_to_kmer, amino_seq_to_num
from dataset.dataset_tools import get_padding_ft_dict
_, _, amino_map_idx = get_padding_ft_dict()
current_path = osp.dirname(osp.realpath(__file__))
demo_data_file = osp.join(current_path, 'dataset_demo.csv')
class DeepAAIPred(object):
def __init__(self):
self.param_dict = {
'seed': 2,
'kmer_min_df': 0.1,
'label_type': 'label_10',
'batch_size': 32,
'h_dim': 512,
'dropout_num': 0.4,
'lr': 5e-5,
'adj_loss_coef': 5e-4,
'param_l2_coef': 5e-4,
'amino_embedding_dim': 7,
'kernel_cfg': [7, 9, 11],
'channel_cfg': [256, 256, 256],
'dilation_cfg': [1, 1, 1],
'add_res': True,
'add_bn': False,
'max_antibody_len': 344,
'max_virus_len': 912,
}
self.setup_seed(self.param_dict['seed'])
self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
self.ft_dict = {}
self.load_data()
self.build_model()
def load_data(self):
# load test data --> kmer_ft, amino_num
test_data_df = pd.read_csv(demo_data_file)
self.label_mat = test_data_df['label_10'].to_numpy()
virus_seq_list = test_data_df['virus_seq'].to_list()
virus_kmer_ft = amino_seq_to_kmer(virus_seq_list)
self.ft_dict['test_virus_kmer'] = torch.FloatTensor(virus_kmer_ft).to(self.device)
virus_amino_num_mat = amino_seq_to_num(virus_seq_list, protein_type='virus')
self.ft_dict['test_virus_amino_num'] = torch.LongTensor(virus_amino_num_mat).to(self.device)
antibody_seq_list = test_data_df['heavy_sequence'].str.cat(test_data_df['light_sequence']).to_list()
antibody_kmer_ft = amino_seq_to_kmer(antibody_seq_list)
self.ft_dict['test_antibody_kmer'] = torch.FloatTensor(antibody_kmer_ft).to(self.device)
antibody_amino_num_mat = amino_seq_to_num(antibody_seq_list, protein_type='antibody')
self.ft_dict['test_antibody_amino_num'] = torch.LongTensor(antibody_amino_num_mat).to(self.device)
# load bg graph
antibody_graph_node_kmer_ft = torch.load(
osp.join(current_path, 'dataset', 'processed_mat', 'antibody_graph_node_kmer_ft.pkl')
)
self.ft_dict['antibody_graph_node_kmer_ft'] = torch.FloatTensor(antibody_graph_node_kmer_ft).to(self.device)
virus_graph_node_kmer_ft = torch.load(
osp.join(current_path, 'dataset', 'processed_mat', 'virus_graph_node_kmer_ft.pkl')
)
self.ft_dict['virus_graph_node_kmer_ft'] = torch.FloatTensor(virus_graph_node_kmer_ft).to(self.device)
self.param_dict['kmer_dim'] = self.ft_dict['test_antibody_kmer'].shape[-1]
self.param_dict['amino_type_num'] = max(amino_map_idx.values()) + 1
self.param_dict['max_antibody_len'] = self.ft_dict['test_antibody_amino_num'].shape[1]
self.param_dict['max_virus_len'] = self.ft_dict['test_antibody_amino_num'].shape[1]
def build_model(self):
self.model = DeepAAIKmerEmbeddingCls(**self.param_dict).to(self.device)
def setup_seed(self, seed):
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
np.random.seed(seed)
random.seed(seed)
torch.backends.cudnn.deterministic = True
def prediction(self):
self.model.eval()
all_pred = []
bg_virus_graph_node_num = self.ft_dict['virus_graph_node_kmer_ft'].size()[0]
bg_antibody_graph_node_num = self.ft_dict['antibody_graph_node_kmer_ft'].size()[0]
test_pair_num = self.ft_dict['test_antibody_kmer'].size()[0]
range_idx = np.arange(0, test_pair_num)
for i in range(math.ceil(test_pair_num/self.param_dict['batch_size'])):
right_bound = min((i + 1)*self.param_dict['batch_size'], test_pair_num + 1)
batch_idx = range_idx[i * self.param_dict['batch_size']: right_bound]
batch_antibody_amino_ft = self.ft_dict['test_antibody_amino_num'][batch_idx]
batch_virus_amino_ft = self.ft_dict['test_virus_amino_num'][batch_idx]
batch_test_antibody_kmer_ft = self.ft_dict['test_antibody_kmer'][batch_idx]
batch_test_virus_kmer_ft = self.ft_dict['test_virus_kmer'][batch_idx]
all_antibody_kmer_ft = torch.cat(
[self.ft_dict['antibody_graph_node_kmer_ft'], batch_test_antibody_kmer_ft], dim=0)
all_virus_kmer_ft = torch.cat(
[self.ft_dict['virus_graph_node_kmer_ft'], batch_test_virus_kmer_ft], dim=0)
batch_antibody_node_idx_in_graph = np.arange(0, batch_idx.shape[0]) + bg_antibody_graph_node_num
batch_virus_node_idx_in_graph = np.arange(0, batch_idx.shape[0]) + bg_virus_graph_node_num
model_input_ft_dict = {
'antibody_graph_node_kmer_ft': all_antibody_kmer_ft,
'virus_graph_node_kmer_ft': all_virus_kmer_ft,
'antibody_amino_ft': batch_antibody_amino_ft,
'virus_amino_ft': batch_virus_amino_ft,
'antibody_idx': batch_antibody_node_idx_in_graph,
'virus_idx': batch_virus_node_idx_in_graph
}
pred, _, _ = self.model(**model_input_ft_dict)
pred = pred.view(-1)
pred = pred.detach().to('cpu').numpy()
all_pred = np.hstack([all_pred, pred])
return all_pred
def evaluate_model(self):
# load param
model_file_path = osp.join(current_path, 'save_model_param_pred', 'deep_aai_k+e',
'deep_aai_kmer_embedding_cls_seed=2_param.pkl')
self.model.load_state_dict(torch.load(model_file_path))
print('load_param ', model_file_path)
test_pred = self.prediction()
test_acc, test_p, test_r, test_f1, test_auc, test_mcc = \
evaluate_classification(predict_proba=test_pred, label=self.label_mat)
log_str = \
'Evaluate Result: ACC \tP \tR \tF1 \tAUC \tMCC \n' \
'Seen Test: {:.5f}\t{:.5f}\t{:.5f}\t{:.5f}\t{:.5f}\t{:.5f} '.format(
test_acc, test_p, test_r, test_f1, test_auc, test_mcc)
print(log_str)
if __name__ == '__main__':
demo = DeepAAIPred()
demo.evaluate_model()