def process_pairwise_edges(dataset_label, matrixfile, pairwised_file, pvlambda, config, results_path, do_pubcrawl, contacts, keep_unmapped, featureInterestingFile):
"""
Include edges where nodes are in original set, direction does not matter so do not populate edge if A->B if B->A are in hash
Expected tab delimited columns are nodeA nodeB pvalue correlation numNonNA
"""
edges_hash = {}
max_pv = -1000.0
max_pv_corr = -1000.0
mydb = db_util.getDBSchema(config) #config.get("mysql_jdbc_configs", "db")
myuser = db_util.getDBUser(config) #config.get("mysql_jdbc_configs", "username")
mypw = db_util.getDBPassword(config) #config.get("mysql_jdbc_configs", "password")
myhost = db_util.getDBHost(config)
myport = db_util.getDBPort(config)
mysolr = db_util.getSolrPath(config)
edges_file = open(pairwised_file)
fIntHash = parse_features_rfex.get_feature_interest_hash(featureInterestingFile)
edge_table = mydb + ".mv_" + dataset_label + "_feature_networks"
efshout = open(results_path + 'load_edges_' + dataset_label + '.sh','w')
solrshout = open(results_path + 'load_solr_' + dataset_label + '.sh','w')
edges_out_re = open(results_path + 'edges_out_' + dataset_label + '_pw_re.tsv','w')
edges_out_pc = open(results_path + 'edges_out_' + dataset_label + '_pw_pc.tsv','w')
edges_meta_json = open(results_path + 'edges_out_' + dataset_label + '_meta.json','w')
unmappedPath = results_path + 'edges_out_' + dataset_label + '_pw_unmapped.tsv'
unmappedout = open(unmappedPath,'w')
features_file = open(results_path + dataset_label + '_features_out.tsv','r')
features_hash = {}
for fl in features_file.readlines():
ftk = fl.strip().split("\t")
features_hash[ftk[1]] = ftk
features_file.close()
validEdgeId = 1
invalidEdges = 0
dupeEdges = 0
totalEdges = 0
cnan = 0
pcc = 0
unMapped = 0
for line in edges_file:
totalEdges += 1
line = line.strip()
tokens = line.split('\t')
if (len(tokens) < 11):
if (validEdgeId == 1):
print "Skipping header/line 1 for insufficient token reasons"
continue
print "ERROR: requires 11 tokens, found:" + str(len(tokens)) + " Skipping line\n" + line
continue
nodeA = tokens[0]
nodeB = tokens[1]
try:
f1genescore = fIntHash[nodeA]
except KeyError:
f1genescore = 0
try:
f2genescore = fIntHash[nodeB]
except KeyError:
f2genescore = 0
if (db_util.isUnmappedAssociation(nodeA, nodeB) and keep_unmapped == 0):
unmappedout.write(nodeA + "\t" + nodeB + "\n")
unMapped += 1
continue
#nodeA = nodeA.replace('|', '_')
#nodeB = nodeB.replace('|', '_')
try:
features_hash[nodeA]
except KeyError:
print "key error in resolving featureId for " + nodeA + " skipping edge."
continue
try:
features_hash[nodeB]
except KeyError:
print "key error in resolving featureId for " + nodeB + " skipping edge."
continue
if (features_hash[nodeA] and features_hash[nodeB]):
if (not edges_hash.get(nodeA + "_" + nodeB) and not edges_hash.get(nodeA + "_" + nodeB)):
feature1id = ""#str(features_hash[nodeA])
feature2id = ""#str(features_hash[nodeB])
#This will need to be improve once all pairs has annotations
try:
feature1id = str(features_hash[nodeA][0])
except KeyError:
print "ERROR: key error in resolving featureId for " + nodeA
try:
feature2id = str(features_hash[nodeB][0])
except:
print "ERROR: key error in resolving featureId for " + nodeB
edges_hash[nodeA + "_" + nodeB] = validEdgeId
validEdgeId += 1
dataA = process_feature_alias(nodeA)
label1_desc = ""
dataB = process_feature_alias(nodeB)
label2_desc = ""
if (len(dataA) == 7):
dataA.append("")
nodeA = nodeA + ":"
if (len(dataB) == 7):
dataB.append("")
nodeB = nodeB + ":"
correlation_str = tokens[2]
try:
correlation = float(correlation_str)
except ValueError:
#Align correlation value to NaN
cnan += 1
correlation = float('nan')
correlation_str = ''
numna = tokens[3]
pv_str = tokens[4]
bonf = tokens[5]
pv_bonf_str = tokens[6]
numnaf1 = tokens[7]
pvf1_str = tokens[8]
numnaf2 = tokens[9]
pvf2_str = tokens[10]
try:
pv = str(pvlambda(float(pv_str)))
pv_bonf = str(pvlambda(float(pv_bonf_str)))
pvf1 = str(pvlambda(float(pvf1_str)))
pvf2 = str(pvlambda(float(pvf2_str)))
except ValueError:
#error in pairwise script, ignore these associations for now
continue;
if (float(pv) > max_pv):
max_pv = float(pv)
if (float(pv_bonf) > max_pv_corr):
max_pv_corr = float(pv_bonf)
rho = str(db_util.sign(correlation)*abs(float(pv)))
link_distance = 500000000
if ( len(tokens) >= 12 ):
link_distance = int(tokens[11])
else:
if (len(dataA) >=5 and len(dataB)>=5 and db_util.is_numeric(dataA[4]) >= 1 and db_util.is_numeric(dataB[4]) >= 1 and dataA[3] == dataB[3]):
link_distance = abs(int(dataB[4]) - int(dataA[4]))
edges_out_re.write(feature1id + "\t" + feature2id + "\t" + nodeA + "\t" + "\t".join(dataA) + "\t" + nodeB + "\t" + "\t".join(dataB) + "\t" + correlation_str + "\t" + numna + "\t" + pv + "\t" + bonf + "\t" + pv_bonf + "\t" + numnaf1 + "\t" + pvf1 + "\t" + numnaf2 + "\t" + pvf2 + "\t" + rho + "\t" + str(link_distance) + "\t" + str(f1genescore) + "\t" + str(f2genescore) + "\n")
if (do_pubcrawl == "yes"):
#call andrea code
getPairwiseInfo.processLine(line, edges_out_pc)
pcc += 1
else:
print "duplicated edge:" + nodeA + "_" + nodeB
dupeEdges += 1
else:
print "invalid edge nodeA and nodeB not in features:" + nodeA + "_" + nodeB
invalidEdges += 1
print "Report: Valid Edges %i Duped %i cNAN %i \nunMapped %i Saved to %s \nTotal %i max_pvalue %f max_pvalue_corr %f" %(validEdgeId-1, dupeEdges, cnan, unMapped,unmappedPath, totalEdges, max_pv, max_pv_corr)
edges_meta_json.write('{"max_logpv":%f}' %(max_pv))
edges_file.close()
edges_out_re.close()
edges_out_pc.close()
edges_meta_json.close()
unmappedout.close()
efshout.write("#!/bin/bash\n")
efshout.write("mysql -h %s --port %s --user=%s --password=%s --database=%s<<EOFMYSQL\n" %(myhost, myport, myuser, mypw, mydb))
efshout.write("load data local infile '" + edges_out_re.name + "' replace INTO TABLE " + edge_table + " fields terminated by '\\t' LINES TERMINATED BY '\\n';\n")
efshout.write("\ncommit;")
efshout.write("\nEOFMYSQL")
efshout.close()
print "Begin pairwise db bulk upload " + time.ctime()
os.system("sh " + efshout.name)
#create sharded association files for solr import
solrshout.write("#!/bin/bash\n");
solrshout.write("python createPWShardedDataset.py " + edges_out_re.name + " " + dataset_label + "\n")
solrshout.write("curl '" + mysolr + "/core0/update/?commit=true' -H 'Content-type:text/xml' --data-binary '<delete><query>dataset:\"" + dataset_label + "\"</query></delete>'\n")
solrshout.write("curl '" + mysolr + "/core1/update/?commit=true' -H 'Content-type:text/xml' --data-binary '<delete><query>dataset:\"" + dataset_label + "\"</query></delete>'\n")
solrshout.write("curl '" + mysolr + "/core2/update/?commit=true' -H 'Content-type:text/xml' --data-binary '<delete><query>dataset:\"" + dataset_label + "\"</query></delete>'\n")
solrshout.write("curl '" + mysolr + "/core3/update/?commit=true' -H 'Content-type:text/xml' --data-binary '<delete><query>dataset:\"" + dataset_label + "\"</query></delete>'\n")
solrshout.write("curl '" + mysolr + "/core4/update/?commit=true' -H 'Content-type:text/xml' --data-binary '<delete><query>dataset:\"" + dataset_label + "\"</query></delete>'\n")
solrshout.write("curl '" + mysolr + "/core5/update/?commit=true' -H 'Content-type:text/xml' --data-binary '<delete><query>dataset:\"" + dataset_label + "\"</query></delete>'\n")
solrshout.write("curl '" + mysolr + "/core6/update/?commit=true' -H 'Content-type:text/xml' --data-binary '<delete><query>dataset:\"" + dataset_label + "\"</query></delete>'\n")
solrshout.write("curl '" + mysolr + "/core7/update/?commit=true' -H 'Content-type:text/xml' --data-binary '<delete><query>dataset:\"" + dataset_label + "\"</query></delete>'\n")
solrshout.write("curl '" + mysolr + "/core0/update/csv?commit=true&separator=%09&overwrite=false&escape=\ ' --data-binary @" + edges_out_re.name + "_core0_final.tsv -H 'Content-type:text/plain;charset=utf-8' &\n")
solrshout.write("curl '" + mysolr + "/core1/update/csv?commit=true&separator=%09&overwrite=false&escape=\ ' --data-binary @" + edges_out_re.name + "_core1_final.tsv -H 'Content-type:text/plain;charset=utf-8' &\n")
solrshout.write("curl '" + mysolr + "/core2/update/csv?commit=true&separator=%09&overwrite=false&escape=\ ' --data-binary @" + edges_out_re.name + "_core2_final.tsv -H 'Content-type:text/plain;charset=utf-8' &\n")
solrshout.write("curl '" + mysolr + "/core3/update/csv?commit=true&separator=%09&overwrite=false&escape=\ ' --data-binary @" + edges_out_re.name + "_core3_final.tsv -H 'Content-type:text/plain;charset=utf-8' &\n")
solrshout.write("curl '" + mysolr + "/core4/update/csv?commit=true&separator=%09&overwrite=false&escape=\ ' --data-binary @" + edges_out_re.name + "_core4_final.tsv -H 'Content-type:text/plain;charset=utf-8' &\n")
solrshout.write("curl '" + mysolr + "/core5/update/csv?commit=true&separator=%09&overwrite=false&escape=\ ' --data-binary @" + edges_out_re.name + "_core5_final.tsv -H 'Content-type:text/plain;charset=utf-8' &\n")
solrshout.write("curl '" + mysolr + "/core6/update/csv?commit=true&separator=%09&overwrite=false&escape=\ ' --data-binary @" + edges_out_re.name + "_core6_final.tsv -H 'Content-type:text/plain;charset=utf-8' &\n")
solrshout.write("curl '" + mysolr + "/core7/update/csv?commit=true&separator=%09&overwrite=false&escape=\ ' --data-binary @" + edges_out_re.name + "_core7_final.tsv -H 'Content-type:text/plain;charset=utf-8' &\n")
solrshout.close()
print "Begin pairwise solr upload " + time.ctime()
os.system("sh " + solrshout.name)
if (do_pubcrawl == "yes"):
print "senting Pubcrawl notification to " + contacts
smtp.main("*****@*****.**", contacts, "Notification - New Pairwise Associations for PubCrawl", "New pairwise associations ready for PubCrawl load\n" + edges_out_pc.name + "\n\n" + str(pcc) + " Total Edges\n\n" + edges_out_re.name + " loaded into RegulomeExplorer, dataset label is " + dataset_label + " \n\n")
def Email(adjuntos):
smtp.main(adjuntos)
gui.msgbox('Mensaje Enviado')
def main(mydict):
print("开始训练时间：")
start_time = time.strftime('%Y-%m-%d %H:%M:%S',
time.localtime(time.time()))
print(start_time)
# py脚本额外参数
args = get_args()
# main函数传入参数
my_data_dir = mydict["data_dir"]
my_tensorboard = mydict["tensorboard"]
my_checkpoint = mydict["checkpoint"]
my_ifSE = mydict["ifSE"]
my_l1loss = mydict["l1loss"]
if my_l1loss:
l1loss = 0.1 # 0.1
# l1loss = my_l1value
else:
l1loss = 0.0
if args.opts:
cfg.merge_from_list(args.opts)
cfg.freeze()
start_epoch = 0
# checkpoint_dir = Path(args.checkpoint)
checkpoint_dir = Path(my_checkpoint)
checkpoint_dir.mkdir(parents=True, exist_ok=True)
# create model_dir
print("=> creating model_dir '{}'".format("se_resnext50_32x4d"))
# model = get_model(model_name="se_resnext50_32x4d")
model = my_model(my_ifSE)
if cfg.TRAIN.OPT == "sgd":
optimizer = torch.optim.SGD(model.parameters(),
lr=cfg.TRAIN.LR,
momentum=cfg.TRAIN.MOMENTUM,
weight_decay=cfg.TRAIN.WEIGHT_DECAY)
else:
optimizer = torch.optim.Adam(model.parameters(), lr=cfg.TRAIN.LR)
device = "cuda" if torch.cuda.is_available() else "cpu"
model = model.to(device)
# optionally resume from a checkpoint
resume_path = args.resume
if resume_path:
print(Path(resume_path).is_file())
if Path(resume_path).is_file():
print("=> loading checkpoint '{}'".format(resume_path))
checkpoint = torch.load(resume_path, map_location="cpu")
start_epoch = checkpoint['epoch']
model.load_state_dict(checkpoint['state_dict'])
print("=> loaded checkpoint '{}' (epoch {})".format(
resume_path, checkpoint['epoch']))
optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
else:
print("=> no checkpoint found at '{}'".format(resume_path))
if args.multi_gpu:
model = nn.DataParallel(model)
if device == "cuda":
cudnn.benchmark = True
# 损失计算准则
criterion = nn.CrossEntropyLoss().to(device)
train_dataset = FaceDataset_ceface(my_data_dir,
"train",
img_size=cfg.MODEL.IMG_SIZE,
augment=True,
age_stddev=cfg.TRAIN.AGE_STDDEV)
train_loader = DataLoader(train_dataset,
batch_size=cfg.BATCH_SIZE,
shuffle=True,
num_workers=cfg.TRAIN.WORKERS,
drop_last=True)
val_dataset = FaceDataset_ceface(my_data_dir,
"valid",
img_size=cfg.MODEL.IMG_SIZE,
augment=False)
val_loader = DataLoader(val_dataset,
batch_size=cfg.BATCH_SIZE,
shuffle=False,
num_workers=cfg.TRAIN.WORKERS,
drop_last=False)
scheduler = StepLR(optimizer,
step_size=cfg.TRAIN.LR_DECAY_STEP,
gamma=cfg.TRAIN.LR_DECAY_RATE,
last_epoch=start_epoch - 1)
best_val_mae = 10000.0
train_writer = None
val_mae_list = []
train_loss_list = []
val_loss_list = []
if my_tensorboard is not None:
opts_prefix = "_".join(args.opts)
train_writer = SummaryWriter(log_dir=my_tensorboard + "/" +
opts_prefix + "_train")
val_writer = SummaryWriter(log_dir=my_tensorboard + "/" + opts_prefix +
"_val")
for epoch in range(start_epoch, 80): # cfg.TRAIN.EPOCHS):
# train
train_loss, train_acc = train(train_loader, model, criterion,
optimizer, epoch, device, l1loss)
train_loss_list.append(train_loss)
# validate
val_loss, val_acc, val_mae = validate(val_loader, model, criterion,
epoch, device, l1loss)
val_mae_list.append(val_mae)
val_loss_list.append(val_loss)
if my_tensorboard is not None:
train_writer.add_scalar("loss", train_loss, epoch)
train_writer.add_scalar("acc", train_acc, epoch)
val_writer.add_scalar("loss", val_loss, epoch)
val_writer.add_scalar("acc", val_acc, epoch)
val_writer.add_scalar("mae", val_mae, epoch)
if val_mae < best_val_mae or val_mae > 0:
print(
f"=> [epoch {epoch:03d}] best val mae was improved from {best_val_mae:.3f} to {val_mae:.3f}"
)
best_val_mae = val_mae
# checkpoint
if val_mae < 4.0:
model_state_dict = model.module.state_dict(
) if args.multi_gpu else model.state_dict()
torch.save(
{
'epoch': epoch + 1,
'arch': cfg.MODEL.ARCH,
'state_dict': model_state_dict,
'optimizer_state_dict': optimizer.state_dict()
},
str(
checkpoint_dir.joinpath(
"epoch{:03d}_{:.5f}_{:.4f}.pth".format(
epoch, val_loss, val_mae))))
else:
print(
f"=> [epoch {epoch:03d}] best val mae was not improved from {best_val_mae:.3f} ({val_mae:.3f})"
)
# adjust learning rate
scheduler.step()
print("=> training finished")
print(f"additional opts: {args.opts}")
print(f"best val mae: {best_val_mae:.3f}")
print("结束训练时间：")
end_time = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(time.time()))
print(end_time)
print("训练耗时: " + smtp.date_gap(start_time, end_time))
# 发邮件
smtp.main(
dict_={
"共训练epochs: ": cfg.TRAIN.EPOCHS,
"训练耗时: ": smtp.date_gap(start_time, end_time),
"最低val_mae: ": best_val_mae,
"平均val_mae: ": np.array(val_mae_list).mean(),
"vale_mae_list: ": val_mae_list,
"train_loss_list: ": train_loss_list,
"val_loss_list: ": val_loss_list,
"MODEL.IMG_SIZE: ": cfg.MODEL.IMG_SIZE,
"BATCH_SIZE: ": cfg.BATCH_SIZE,
"LOSS.l1: ": l1loss,
"TRAIN.LR: ": cfg.TRAIN.LR,
"TRAIN.LR_DECAY_STEP: ": cfg.TRAIN.LR_DECAY_STEP,
"TRAIN.LR_DECAY_RATE:": cfg.TRAIN.LR_DECAY_RATE,
"TRAIN.OPT: ": cfg.TRAIN.OPT,
"MODEL.ARCH:": cfg.MODEL.ARCH
})
return best_val_mae
"l1loss": False
})
time.sleep(180)
main({
"data_dir": data_dir,
"tensorboard": tf_log,
"checkpoint": ckpt,
"ifSE": True,
"l1loss": False
})
time.sleep(180)
main({
"data_dir": data_dir,
"tensorboard": tf_log,
"checkpoint": ckpt,
"ifSE": True,
"l1loss": True
})
time.sleep(180)
main({
"data_dir": data_dir,
"tensorboard": tf_log,
"checkpoint": ckpt,
"ifSE": True,
"l1loss": True
})
###########################################################################################################
end_time = smtp.print_time("全部训练结束!!!")
print(smtp.date_gap(start_time, end_time))
smtp.main(dict_={"ceface全部训练耗时: ": smtp.date_gap(start_time, end_time)})
def process_pairwise_edges(dataset_label, matrixfile, pairwised_file, pvlambda,
config, results_path, do_pubcrawl, contacts,
keep_unmapped, featureInterestingFile):
"""
Include edges where nodes are in original set, direction does not matter so do not populate edge if A->B if B->A are in hash
Expected tab delimited columns are nodeA nodeB pvalue correlation numNonNA
"""
edges_hash = {}
max_pv = -1000.0
max_pv_corr = -1000.0
mydb = db_util.getDBSchema(config) #config.get("mysql_jdbc_configs", "db")
myuser = db_util.getDBUser(
config) #config.get("mysql_jdbc_configs", "username")
mypw = db_util.getDBPassword(
config) #config.get("mysql_jdbc_configs", "password")
myhost = db_util.getDBHost(config)
myport = db_util.getDBPort(config)
mysolr = db_util.getSolrPath(config)
edges_file = open(pairwised_file)
fIntHash = parse_features_rfex.get_feature_interest_hash(
featureInterestingFile)
edge_table = mydb + ".mv_" + dataset_label + "_feature_networks"
efshout = open(results_path + 'load_edges_' + dataset_label + '.sh', 'w')
solrshout = open(results_path + 'load_solr_' + dataset_label + '.sh', 'w')
edges_out_re = open(
results_path + 'edges_out_' + dataset_label + '_pw_re.tsv', 'w')
edges_out_pc = open(
results_path + 'edges_out_' + dataset_label + '_pw_pc.tsv', 'w')
edges_meta_json = open(
results_path + 'edges_out_' + dataset_label + '_meta.json', 'w')
unmappedPath = results_path + 'edges_out_' + dataset_label + '_pw_unmapped.tsv'
unmappedout = open(unmappedPath, 'w')
features_file = open(results_path + dataset_label + '_features_out.tsv',
'r')
features_hash = {}
for fl in features_file.readlines():
ftk = fl.strip().split("\t")
features_hash[ftk[1]] = ftk
features_file.close()
validEdgeId = 1
invalidEdges = 0
dupeEdges = 0
totalEdges = 0
cnan = 0
pcc = 0
unMapped = 0
for line in edges_file:
totalEdges += 1
line = line.strip()
tokens = line.split('\t')
if (len(tokens) < 11):
if (validEdgeId == 1):
print "Skipping header/line 1 for insufficient token reasons"
continue
print "ERROR: requires 11 tokens, found:" + str(
len(tokens)) + " Skipping line\n" + line
continue
nodeA = tokens[0]
nodeB = tokens[1]
try:
f1genescore = fIntHash[nodeA]
except KeyError:
f1genescore = 0
try:
f2genescore = fIntHash[nodeB]
except KeyError:
f2genescore = 0
if (db_util.isUnmappedAssociation(nodeA, nodeB)
and keep_unmapped == 0):
unmappedout.write(nodeA + "\t" + nodeB + "\n")
unMapped += 1
continue
#nodeA = nodeA.replace('|', '_')
#nodeB = nodeB.replace('|', '_')
try:
features_hash[nodeA]
except KeyError:
print "key error in resolving featureId for " + nodeA + " skipping edge."
continue
try:
features_hash[nodeB]
except KeyError:
print "key error in resolving featureId for " + nodeB + " skipping edge."
continue
if (features_hash[nodeA] and features_hash[nodeB]):
if (not edges_hash.get(nodeA + "_" + nodeB)
and not edges_hash.get(nodeA + "_" + nodeB)):
feature1id = "" #str(features_hash[nodeA])
feature2id = "" #str(features_hash[nodeB])
#This will need to be improve once all pairs has annotations
try:
feature1id = str(features_hash[nodeA][0])
except KeyError:
print "ERROR: key error in resolving featureId for " + nodeA
try:
feature2id = str(features_hash[nodeB][0])
except:
print "ERROR: key error in resolving featureId for " + nodeB
edges_hash[nodeA + "_" + nodeB] = validEdgeId
validEdgeId += 1
dataA = process_feature_alias(nodeA)
label1_desc = ""
dataB = process_feature_alias(nodeB)
label2_desc = ""
if (len(dataA) == 7):
dataA.append("")
nodeA = nodeA + ":"
if (len(dataB) == 7):
dataB.append("")
nodeB = nodeB + ":"
correlation_str = tokens[2]
try:
correlation = float(correlation_str)
except ValueError:
#Align correlation value to NaN
cnan += 1
correlation = float('nan')
correlation_str = ''
numna = tokens[3]
pv_str = tokens[4]
bonf = tokens[5]
pv_bonf_str = tokens[6]
numnaf1 = tokens[7]
pvf1_str = tokens[8]
numnaf2 = tokens[9]
pvf2_str = tokens[10]
try:
pv = str(pvlambda(float(pv_str)))
pv_bonf = str(pvlambda(float(pv_bonf_str)))
pvf1 = str(pvlambda(float(pvf1_str)))
pvf2 = str(pvlambda(float(pvf2_str)))
except ValueError:
#error in pairwise script, ignore these associations for now
continue
if (float(pv) > max_pv):
max_pv = float(pv)
if (float(pv_bonf) > max_pv_corr):
max_pv_corr = float(pv_bonf)
rho = str(db_util.sign(correlation) * abs(float(pv)))
link_distance = 500000000
if (len(tokens) >= 12):
link_distance = int(tokens[11])
else:
if (len(dataA) >= 5 and len(dataB) >= 5
and db_util.is_numeric(dataA[4]) >= 1
and db_util.is_numeric(dataB[4]) >= 1
and dataA[3] == dataB[3]):
link_distance = abs(int(dataB[4]) - int(dataA[4]))
edges_out_re.write(feature1id + "\t" + feature2id + "\t" +
nodeA + "\t" + "\t".join(dataA) + "\t" +
nodeB + "\t" + "\t".join(dataB) + "\t" +
correlation_str + "\t" + numna + "\t" + pv +
"\t" + bonf + "\t" + pv_bonf + "\t" +
numnaf1 + "\t" + pvf1 + "\t" + numnaf2 +
"\t" + pvf2 + "\t" + rho + "\t" +
str(link_distance) + "\t" +
str(f1genescore) + "\t" + str(f2genescore) +
"\n")
if (do_pubcrawl == "yes"):
#call andrea code
getPairwiseInfo.processLine(line, edges_out_pc)
pcc += 1
else:
print "duplicated edge:" + nodeA + "_" + nodeB
dupeEdges += 1
else:
print "invalid edge nodeA and nodeB not in features:" + nodeA + "_" + nodeB
invalidEdges += 1
print "Report: Valid Edges %i Duped %i cNAN %i \nunMapped %i Saved to %s \nTotal %i max_pvalue %f max_pvalue_corr %f" % (
validEdgeId - 1, dupeEdges, cnan, unMapped, unmappedPath, totalEdges,
max_pv, max_pv_corr)
edges_meta_json.write('{"max_logpv":%f}' % (max_pv))
edges_file.close()
edges_out_re.close()
edges_out_pc.close()
edges_meta_json.close()
unmappedout.close()
efshout.write("#!/bin/bash\n")
efshout.write(
"mysql -h %s --port %s --user=%s --password=%s --database=%s<<EOFMYSQL\n"
% (myhost, myport, myuser, mypw, mydb))
efshout.write("load data local infile '" + edges_out_re.name +
"' replace INTO TABLE " + edge_table +
" fields terminated by '\\t' LINES TERMINATED BY '\\n';\n")
efshout.write("\ncommit;")
efshout.write("\nEOFMYSQL")
efshout.close()
print "Begin pairwise db bulk upload " + time.ctime()
os.system("sh " + efshout.name)
#create sharded association files for solr import
solrshout.write("#!/bin/bash\n")
solrshout.write("python createPWShardedDataset.py " + edges_out_re.name +
" " + dataset_label + "\n")
solrshout.write(
"curl '" + mysolr +
"/core0/update/?commit=true' -H 'Content-type:text/xml' --data-binary '<delete><query>dataset:\""
+ dataset_label + "\"</query></delete>'\n")
solrshout.write(
"curl '" + mysolr +
"/core1/update/?commit=true' -H 'Content-type:text/xml' --data-binary '<delete><query>dataset:\""
+ dataset_label + "\"</query></delete>'\n")
solrshout.write(
"curl '" + mysolr +
"/core2/update/?commit=true' -H 'Content-type:text/xml' --data-binary '<delete><query>dataset:\""
+ dataset_label + "\"</query></delete>'\n")
solrshout.write(
"curl '" + mysolr +
"/core3/update/?commit=true' -H 'Content-type:text/xml' --data-binary '<delete><query>dataset:\""
+ dataset_label + "\"</query></delete>'\n")
solrshout.write(
"curl '" + mysolr +
"/core4/update/?commit=true' -H 'Content-type:text/xml' --data-binary '<delete><query>dataset:\""
+ dataset_label + "\"</query></delete>'\n")
solrshout.write(
"curl '" + mysolr +
"/core5/update/?commit=true' -H 'Content-type:text/xml' --data-binary '<delete><query>dataset:\""
+ dataset_label + "\"</query></delete>'\n")
solrshout.write(
"curl '" + mysolr +
"/core6/update/?commit=true' -H 'Content-type:text/xml' --data-binary '<delete><query>dataset:\""
+ dataset_label + "\"</query></delete>'\n")
solrshout.write(
"curl '" + mysolr +
"/core7/update/?commit=true' -H 'Content-type:text/xml' --data-binary '<delete><query>dataset:\""
+ dataset_label + "\"</query></delete>'\n")
solrshout.write(
"curl '" + mysolr +
"/core0/update/csv?commit=true&separator=%09&overwrite=false&escape=\ ' --data-binary @"
+ edges_out_re.name +
"_core0_final.tsv -H 'Content-type:text/plain;charset=utf-8' &\n")
solrshout.write(
"curl '" + mysolr +
"/core1/update/csv?commit=true&separator=%09&overwrite=false&escape=\ ' --data-binary @"
+ edges_out_re.name +
"_core1_final.tsv -H 'Content-type:text/plain;charset=utf-8' &\n")
solrshout.write(
"curl '" + mysolr +
"/core2/update/csv?commit=true&separator=%09&overwrite=false&escape=\ ' --data-binary @"
+ edges_out_re.name +
"_core2_final.tsv -H 'Content-type:text/plain;charset=utf-8' &\n")
solrshout.write(
"curl '" + mysolr +
"/core3/update/csv?commit=true&separator=%09&overwrite=false&escape=\ ' --data-binary @"
+ edges_out_re.name +
"_core3_final.tsv -H 'Content-type:text/plain;charset=utf-8' &\n")
solrshout.write(
"curl '" + mysolr +
"/core4/update/csv?commit=true&separator=%09&overwrite=false&escape=\ ' --data-binary @"
+ edges_out_re.name +
"_core4_final.tsv -H 'Content-type:text/plain;charset=utf-8' &\n")
solrshout.write(
"curl '" + mysolr +
"/core5/update/csv?commit=true&separator=%09&overwrite=false&escape=\ ' --data-binary @"
+ edges_out_re.name +
"_core5_final.tsv -H 'Content-type:text/plain;charset=utf-8' &\n")
solrshout.write(
"curl '" + mysolr +
"/core6/update/csv?commit=true&separator=%09&overwrite=false&escape=\ ' --data-binary @"
+ edges_out_re.name +
"_core6_final.tsv -H 'Content-type:text/plain;charset=utf-8' &\n")
solrshout.write(
"curl '" + mysolr +
"/core7/update/csv?commit=true&separator=%09&overwrite=false&escape=\ ' --data-binary @"
+ edges_out_re.name +
"_core7_final.tsv -H 'Content-type:text/plain;charset=utf-8' &\n")
solrshout.close()
print "Begin pairwise solr upload " + time.ctime()
os.system("sh " + solrshout.name)
if (do_pubcrawl == "yes"):
print "senting Pubcrawl notification to " + contacts
smtp.main(
"*****@*****.**", contacts,
"Notification - New Pairwise Associations for PubCrawl",
"New pairwise associations ready for PubCrawl load\n" +
edges_out_pc.name + "\n\n" + str(pcc) + " Total Edges\n\n" +
edges_out_re.name +
" loaded into RegulomeExplorer, dataset label is " +
dataset_label + " \n\n")
if __name__ == '__main__':
# fgnet train 82 group
start_time = smtp.print_time("全部开始训练!!!")
fgnet_root = cfg.dataset.fgnet_leave1out
best_val_mae_arr = []
for i in range(1, 83):
tmp = str(i) if i > 9 else "0" + str(i)
data_dir = Path(fgnet_root).joinpath(tmp)
best_val_mae_arr.append(main(str(data_dir)))
print(
f"fgnet all train finished and best_val_mae_arr is:{best_val_mae_arr}")
end_time = smtp.print_time("全部训练结束!!!")
print(smtp.date_gap(start_time, end_time))
smtp.main(
dict_={
"fgnet全部训练耗时: ": smtp.date_gap(start_time, end_time),
"best_val_mae_arr": best_val_mae_arr
})
time.sleep(600) # sleep 10 min
# fgnet_align train 82 group
start_time = smtp.print_time("全部开始训练!!!")
fgnet_align_root = cfg.dataset.fgnet_align_leave1out
best_val_mae_arr = []
for i in range(1, 83):
tmp = str(i) if i > 9 else "0" + str(i)
data_dir = Path(fgnet_align_root).joinpath(tmp)
best_val_mae_arr.append(main(str(data_dir)))
print(
f"fgnet_align all train finished and best_val_mae_arr is:{best_val_mae_arr}"
def process_associations_rfex(dataset_label, matrixfile, associationsfile, config, annotations, collapse_direction, reverse_direction, results_path, pv_lambda, do_pubcrawl, contacts, keep_unmapped, featureInterestingFile):
mydb = db_util.getDBSchema(config)
myuser = db_util.getDBUser(config)
mypw = db_util.getDBPassword(config)
myhost = db_util.getDBHost(config)
myport = db_util.getDBPort(config)
mysolr = db_util.getSolrPath(config)
if (not os.path.isfile(associationsfile)):
print associationsfile + " does not exist; unrecoverable ERROR"
sys.exit(-1)
associations_table = mydb + ".mv_" + dataset_label + "_feature_networks"
print "Begin processing associations %s Applying processing_pubcrawl %s" %(time.ctime(), do_pubcrawl)
fIntHash = parse_features_rfex.get_feature_interest_hash(featureInterestingFile)
edges_out_re = open(results_path + 'edges_out_' + dataset_label + '_rface_re.tsv','w')
associations_in = open(associationsfile,'r')
annotation_hash, ftype = parse_features_rfex.process_feature_annotations(annotations)
fshout = open(results_path + 'load_sql_associations_' + dataset_label + '.sh','w')
solrshout = open(results_path + 'load_solr_assocations_' + dataset_label + '.sh','w')
unmappedPath = results_path + 'edges_out_' + dataset_label + '_rface_unmapped.tsv'
unmappedout = open(unmappedPath,'w')
features_file = open(results_path + dataset_label + '_features_out.tsv','r')
features_hash = {}
for fl in features_file.readlines():
ftk = fl.strip().split("\t")
features_hash[ftk[1]] = ftk
features_file.close()
aliasid_file = open(results_path + dataset_label + '_features_alias_id.tsv','r')
aliasid_hash = {}
for fl in aliasid_file.readlines():
ftk = fl.strip().split("\t")
aliasid_hash[ftk[0]] = ftk
aliasid_file.close()
tsvout = open(results_path + 'edges_out_' + dataset_label + '_rface_re.tsv','w')
pubcrawl_tsvout = open(results_path + 'edges_out_' + dataset_label + '_rface_pc.tsv','w')
lc = 0
edgeCount = 0
pcc = 0
unMapped = 0
pvalueCutCount = 0
impCut = 0
lines = associations_in.readlines()
associations_in.close()
associations_dic = {}
for line in lines:
lc = lc + 1
columns = line.strip().split('\t')
if (len(columns) < 5):
print "Missing required tokens in associations lineIndex %i lineValue %s" %(lc, line)
continue
f1alias = columns[0]
#afm_ids will be used for directionality collapsing, if needed
f1afm_id = columns[0]
f2afm_id = columns[1]
if (len(f1alias.split(":")) < 3):
annotated_feature = annotation_hash.get(f1alias)
if (annotated_feature == None):
print "ERROR: Target feature %s is not in afm/annotation %i" %(f1alias, len(annotation_hash))
continue
f1alias = annotated_feature.replace("\t", ":")
f2alias = columns[1]
if (len(f2alias.split(":")) < 3):
annotated_feature = annotation_hash.get(f2alias)
if (annotated_feature == None):
print "ERROR: Predictor feature %s is not in afm/annotation" %(f2alias)
continue
f2alias = annotated_feature.replace("\t", ":")
try:
f1genescore = fIntHash[f1alias]
except KeyError:
f1genescore = 0
try:
f2genescore = fIntHash[f2alias]
except KeyError:
f2genescore = 0
f1data = f1alias.split(':')
f2data = f2alias.split(':')
if len(f1data) > 4:
f1data[3] = f1data[3][3:]
if len(f2data) > 4:
f2data[3] = f2data[3][3:]
if (len(f1data) <= 7 and (f1data[1] == 'CLIN' or f1data[1] == 'SAMP')):
f1alias = ":".join(f1data[0:3]) + ":::::"
f1data = f1alias.split(':')
elif (len(f1data) == 7):
f1data.append("")
if (len(f2data) <= 7 and (f2data[1] == 'CLIN' or f2data[1] == 'SAMP')):
f2alias = ":".join(f2data[0:3]) + ":::::"
f2data = f2alias.split(':')
elif (len(f2data) == 7):
f2data.append("")
f1aliasOmic = f1alias
f2aliasOmic = f2alias
#for annotations
try:
f1id = features_hash[f1alias][0]
except KeyError:
try:
f1id = aliasid_hash[f1alias][1]
f1aliasOmic = aliasid_hash[f1alias][2]
f1data = f1aliasOmic.split(':')
f1data[3] = f1data[3][3:]
except KeyError:
print "Skipping Key error with alias1 " + f1alias
continue
try:
f2id = features_hash[f2alias][0]#f2alias.split(":")[-1]
except KeyError:
try:
f2id = aliasid_hash[f2alias][1]
f2aliasOmic = aliasid_hash[f2alias][2]
f2data = f2aliasOmic.split(':')
f2data[3] = f2data[3][3:]
except KeyError:
print "Skipping Key error with alias2 " + f2alias
continue
pvalue = float(columns[2])
pvalue = str(pv_lambda(pvalue))
importance = columns[3]
correlation = columns[4]
patientct = columns[5]
if (db_util.isUnmappedAssociation(f1alias, f2alias) and keep_unmapped == 0):
unmappedout.write(f1alias + "\t" + f2alias + "\n")
unMapped += 1
continue
rhoscore = ""
link_distance = -1
if (len(f1data) >=5 and len(f2data)>=5 and db_util.is_numeric(f1data[4]) >= 1 and db_util.is_numeric(f2data[4]) >= 1 and f1data[3] == f2data[3]):
link_distance = abs(int(f2data[4]) - int(f1data[4]))
if (collapse_direction == 0):
associations_dic[f1afm_id + "_" + f2afm_id] = f1aliasOmic + "\t" + f2aliasOmic + "\t" + pvalue + "\t" + importance + "\t" + correlation + "\t" + patientct + "\t" + f1id + "\t" + "\t".join(f1data) + "\t" + f2id + "\t" + "\t".join(f2data) + "\t" + str(f1genescore) + "\t" + str(f2genescore) + "\t" + rhoscore + "\t" + str(link_distance) + "\n"
else:
#check whether (f1 -> f2 or f2 -> f1) exists, if yes, take the more important
#if not, store pair
if ((associations_dic.get(f1afm_id + "_" + f2afm_id) == None) and (associations_dic.get(f2afm_id + "_" + f1afm_id) == None)):
associations_dic[f1afm_id + "_" + f2afm_id] = f1aliasOmic + "\t" + f2aliasOmic + "\t" + pvalue + "\t" + importance + "\t" + correlation + "\t" + patientct + "\t" + f1id + "\t" + "\t".join(f1data) + "\t" + f2id + "\t" + "\t".join(f2data) + "\t" + str(f1genescore) + "\t" + str(f2genescore) + "\t" + rhoscore + "\t" + str(link_distance) + "\n"
else:
existingLink = associations_dic.get(f1afm_id + "_" + f2afm_id)
ekey = f1afm_id + "_" + f2afm_id
if (existingLink == None):
existingLink = associations_dic.get(f2afm_id + "_" + f1afm_id)
ekey = f2afm_id + "_" + f1afm_id
prevImportance = existingLink.split("\t")[3]
if (float(importance) > float(prevImportance)):
associations_dic[ekey] = f1aliasOmic + "\t" + f2aliasOmic + "\t" + pvalue + "\t" + importance + "\t" + correlation + "\t" + patientct + "\t" + f1id + "\t" + "\t".join(f1data) + "\t" + f2id + "\t" + "\t".join(f2data) + "\t" + str(f1genescore) + "\t" + str(f2genescore) + "\t" + rhoscore + "\t" + str(link_distance) + "\n"
if (reverse_direction == 1):
associations_dic[f2afm_id + "_" + f1afm_id] = f2aliasOmic + "\t" + f1aliasOmic + "\t" + pvalue + "\t" + importance + "\t" + correlation + "\t" + patientct + "\t" + f2id + "\t" + "\t".join(f2data) + "\t" + f1id + "\t" + "\t".join(f1data) + "\t" + str(f2genescore) + "\t" + str(f1genescore) + "\t" + rhoscore + "\t" + str(link_distance) + "\n"
edgeCount = edgeCount + 1
edgeCount = edgeCount + 1
if (do_pubcrawl == "yes"):
getRFACEInfo.processLine(line, pubcrawl_tsvout)
pcc += 1
for ei in associations_dic:
tsvout.write(associations_dic[ei])
fshout.write("#!/bin/bash\n")
fshout.write("mysql -h %s --port %s --user=%s --password=%s --database=%s<<EOFMYSQL\n" %(myhost, myport, myuser, mypw, mydb))
fshout.write("load data local infile '" + tsvout.name + "' replace INTO TABLE " + associations_table + " fields terminated by '\\t' LINES TERMINATED BY '\\n';")
fshout.write("\nEOFMYSQL\n")
tsvout.close()
unmappedout.close()
pubcrawl_tsvout.close()
fshout.close()
print "\nReport: ValidEdges %i ImportanceCutoff %i edges filtered %i \nunMapped Edges %i Saved to %s" %(len(associations_dic), impCut, pvalueCutCount, unMapped, unmappedPath)
print "Begin RF-ACE db bulk upload %s os.system sh %s" %(time.ctime(), fshout.name)
os.system("sh " + fshout.name)
solrshout.write("#!/bin/bash\n")
solrshout.write("python createRFShardedDataset.py " + edges_out_re.name + " " + dataset_label + "\n")
solrshout.write("curl '" + mysolr + "/core0/update/?commit=true' -H 'Content-type:text/xml' --data-binary '<delete><query>dataset:\"" + dataset_label + "\"</query></delete>'\n")
solrshout.write("curl '" + mysolr + "/core1/update/?commit=true' -H 'Content-type:text/xml' --data-binary '<delete><query>dataset:\"" + dataset_label + "\"</query></delete>'\n")
solrshout.write("curl '" + mysolr + "/core2/update/?commit=true' -H 'Content-type:text/xml' --data-binary '<delete><query>dataset:\"" + dataset_label + "\"</query></delete>'\n")
solrshout.write("curl '" + mysolr + "/core3/update/?commit=true' -H 'Content-type:text/xml' --data-binary '<delete><query>dataset:\"" + dataset_label + "\"</query></delete>'\n")
solrshout.write("curl '" + mysolr + "/core4/update/?commit=true' -H 'Content-type:text/xml' --data-binary '<delete><query>dataset:\"" + dataset_label + "\"</query></delete>'\n")
solrshout.write("curl '" + mysolr + "/core5/update/?commit=true' -H 'Content-type:text/xml' --data-binary '<delete><query>dataset:\"" + dataset_label + "\"</query></delete>'\n")
solrshout.write("curl '" + mysolr + "/core6/update/?commit=true' -H 'Content-type:text/xml' --data-binary '<delete><query>dataset:\"" + dataset_label + "\"</query></delete>'\n")
solrshout.write("curl '" + mysolr + "/core7/update/?commit=true' -H 'Content-type:text/xml' --data-binary '<delete><query>dataset:\"" + dataset_label + "\"</query></delete>'\n")
solrshout.write("curl '" + mysolr + "/core0/update/csv?commit=true&separator=%09&overwrite=false&escape=\ ' --data-binary @" + edges_out_re.name + "_core0_final.tsv -H 'Content-type:text/plain;charset=utf-8' &\n")
solrshout.write("curl '" + mysolr + "/core1/update/csv?commit=true&separator=%09&overwrite=false&escape=\ ' --data-binary @" + edges_out_re.name + "_core1_final.tsv -H 'Content-type:text/plain;charset=utf-8' &\n")
solrshout.write("curl '" + mysolr + "/core2/update/csv?commit=true&separator=%09&overwrite=false&escape=\ ' --data-binary @" + edges_out_re.name + "_core2_final.tsv -H 'Content-type:text/plain;charset=utf-8' &\n")
solrshout.write("curl '" + mysolr + "/core3/update/csv?commit=true&separator=%09&overwrite=false&escape=\ ' --data-binary @" + edges_out_re.name + "_core3_final.tsv -H 'Content-type:text/plain;charset=utf-8' &\n")
solrshout.write("curl '" + mysolr + "/core4/update/csv?commit=true&separator=%09&overwrite=false&escape=\ ' --data-binary @" + edges_out_re.name + "_core4_final.tsv -H 'Content-type:text/plain;charset=utf-8' &\n")
solrshout.write("curl '" + mysolr + "/core5/update/csv?commit=true&separator=%09&overwrite=false&escape=\ ' --data-binary @" + edges_out_re.name + "_core5_final.tsv -H 'Content-type:text/plain;charset=utf-8' &\n")
solrshout.write("curl '" + mysolr + "/core6/update/csv?commit=true&separator=%09&overwrite=false&escape=\ ' --data-binary @" + edges_out_re.name + "_core6_final.tsv -H 'Content-type:text/plain;charset=utf-8' &\n")
solrshout.write("curl '" + mysolr + "/core7/update/csv?commit=true&separator=%09&overwrite=false&escape=\ ' --data-binary @" + edges_out_re.name + "_core7_final.tsv -H 'Content-type:text/plain;charset=utf-8' &\n")
solrshout.close()
print "Begin rface solr upload " + time.ctime()
os.system("sh " + solrshout.name)
if (do_pubcrawl == 'yes'):
smtp.main("*****@*****.**", contacts, "Notification - New RFAce " + dataset_label + " Associations for PubCrawl", "New RFAce associations ready for PubCrawl load\n" + pubcrawl_tsvout.name + "\n" + str(pcc) + " Total Edges\n" + tsvout.name + " loaded into RegulomeExplorer, dataset label is " + dataset_label + "\n\n")
print "Done processing associations %s" %(time.ctime())
associations_dic = None
