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import sys
import time
import numpy as np
import pandas as pd
import logging
sys.path.append(os.path.dirname(os.path.dirname(__file__)))
import utils
import torch
from scipy.stats import pearsonr
from torch import optim
from sklearn.metrics import r2_score, f1_score, roc_auc_score
from models.ScheduleOptimizer import ScheduledOptim , find_lr
from models.loss import Dynamic_Task_Priority as DTP
def train(dataloader,model,optimizer,popen,epoch,lr=None, verbose=True):
logger = logging.getLogger("VAE")
loader_len = len(dataloader) # number of iteration
device = 'cuda' if torch.cuda.is_available() else 'cpu'
model = model.to(device)
model.train()
verbose_list=[]
verbose_df = pd.DataFrame()
for idx,data in enumerate(dataloader):
X,Y = put_data_to_cuda(data,popen,require_grad=True)
optimizer.zero_grad()
out = model(X)
loss_dict = model.compute_loss(out,X,Y,popen)
loss = loss_dict['Total']
acc_dict = model.compute_acc(out,X,Y,popen)
loss_dict.update(acc_dict) # adding the acc ditc into loss dict
loss.backward()
optimizer.step()
# ====== update lr =======
if type(optimizer) == ScheduledOptim:
lr = optimizer.update_learning_rate() # see model.optim.py
loss_dict["lr"]=lr
elif popen.optimizer == 'Adam':
lr = optimizer.param_groups[0]['lr']
if popen.loss_schema != 'constant':
popen.chimerla_weight = popen.loss_schedualer._update(loss_dict)
for t in popen.tasks:
loss_dict[popen.loss_schema+"_wt_"+t] = popen.chimerla_weight[t]
with torch.no_grad():
loss_dict= utils.clean_value_dict(loss_dict)
verbose_list.append(loss_dict)
# ======== verbose ========
# record result 5 times for a epoch
if verbose:
if idx % int(loader_len/5) == 0:
# plot that in loss dict
loss_dict_keys = list(loss_dict.keys())
train_verbose = "{:5d} / {:5d} ({:s}%):"
verbose_args = [idx,loader_len,str(int(idx/loader_len*100)).zfill(3)]
for key in loss_dict_keys:
train_verbose += "\t %s:{:.7f}"%key
verbose_args.append(loss_dict[key])
# plot the cumulative mean total loss
short_batch_df = pd.json_normalize(verbose_list) # this will be cumulative
mean_total = short_batch_df.loc[:,'Total'].mean()
train_verbose += "\t %s:{:.7f}"%"Mean_Total"
verbose_args.append(mean_total)
train_verbose = train_verbose.format(*verbose_args)
logger.info(train_verbose)
if popen.cuda_id != torch.device('cpu'):
with torch.cuda.device(popen.cuda_id):
torch.cuda.empty_cache()
def validate(dataloader,model,popen,epoch):
logger = logging.getLogger("VAE")
device = 'cuda' if torch.cuda.is_available() else 'cpu'
model = model.to(device)
model.teacher_forcing = False # turn off teacher_forcing
# ====== set up empty =====
verbose_list=[]
Y_ls = []
pred_ls = []
metric_dict = {}
# ======== evaluate =======
model.eval()
with torch.no_grad():
for idx,data in enumerate(dataloader):
X,Y = put_data_to_cuda(data,popen,require_grad=False)
Y_ls.append(Y.cpu().numpy())
out = model(X)
pred_ls.append(out.cpu().numpy())
loss_dict = model.compute_loss(out,X,Y,popen)
loss = loss_dict['Total']
acc_dict = model.compute_acc(out,X,Y,popen)
loss_dict.update(acc_dict)
loss_dict = utils.clean_value_dict(loss_dict) # convert possible torch to single item
verbose_list.append(loss_dict)
if popen.cuda_id != torch.device('cpu'):
with torch.cuda.device(popen.cuda_id):
torch.cuda.empty_cache()
# # average among batch
# ======== verbose ========
Y_ay = np.concatenate(Y_ls,axis=0).flatten()
pred_ay = np.concatenate(pred_ls,axis=0).flatten()
if popen.model_type == 'RL_clf':
metric_dict["F1"] = f1_score(Y_ay, pred_ay>0.5, average='binary')
metric_dict["AUROC"] = roc_auc_score(Y_ay, pred_ay)
else:
metric_dict[f"r2"] = r2_score(Y_ay, pred_ay)
metric_dict[f"pr"] = pearsonr(Y_ay, pred_ay)[0]
verbose_df = pd.json_normalize(verbose_list)
val_verbose = ""
verbose_args = []
verbose_dict = {key:verbose_df[key].mean() for key in verbose_df.columns}
verbose_dict.update(metric_dict)
for key,values in verbose_dict.items():
val_verbose += "\t %s:{:.7f}"%key
verbose_args.append(values)
val_verbose = val_verbose.format(*verbose_args)
logger.info(val_verbose)
# what avg acc return : mean of RL_Acc , Recons_Acc, Motif_Acc
acc_col = list(acc_dict.keys())
Avg_acc = np.mean(verbose_df.loc[:,acc_col].mean(axis=0))
# return these to save current performance
return (verbose_df['Total'].mean(),Avg_acc) if 'RL_loss' not in verbose_df.keys() else (verbose_df['RL_loss'].mean(),verbose_df['RL_Acc'].mean())
def iter_train(loader_dict, model, optimizer, popen, epoch, verbose=True):
logger = logging.getLogger("VAE")
# loader_len = len(dataloader) # number of iteration
all_len = [len(loader[0]) for loader in loader_dict.values()]
max_len = np.max(all_len)
n_task = len(popen.cycle_set)
total_len = max_len*n_task
all_train = {task : iter(loader[0]) for task,loader in loader_dict.items()}
def try_next(all_train, task):
try:
data = next(all_train[task])
return data
except StopIteration:
all_train[task] = iter(loader_dict[task][0])
data = next(all_train[task])
return data
device = 'cuda' if torch.cuda.is_available() else 'cpu'
model = model.to(device)
model.train()
verbose_list=[]
for idx in range(total_len):
task = popen.cycle_set[idx%n_task]
model.task = task
data = try_next(all_train, task)
X,Y = put_data_to_cuda(data,popen,require_grad=True)
optimizer.zero_grad()
out = model(X)
loss_dict = model.compute_loss(out,X,Y,popen)
loss = loss_dict['Total']
acc_dict = model.compute_acc(out,X,Y,popen)
loss_dict.update(acc_dict) # adding the acc ditc into loss dict
loss.backward()
optimizer.step()
# ====== update lr =======
if type(optimizer) == ScheduledOptim:
lr = optimizer.update_learning_rate() # see model.optim.py
loss_dict["lr"]=lr
elif popen.optimizer == 'Adam':
lr = optimizer.param_groups[0]['lr']
if popen.loss_schema != 'constant':
popen.chimerla_weight = popen.loss_schedualer._update(loss_dict)
for t in popen.tasks:
loss_dict[popen.loss_schema+"_wt_"+t] = popen.chimerla_weight[t]
with torch.no_grad():
loss_dict= utils.clean_value_dict(loss_dict)
verbose_list.append(loss_dict)
# ======== verbose ========
# record result 5 times for a epoch
if verbose:
if idx % int(total_len/5) == 0:
# plot that in loss dict
loss_dict_keys = list(loss_dict.keys())
train_verbose = "{:5d} / {:5d} ({:s}%):"
verbose_args = [idx,total_len,str(int(idx/total_len*100)).zfill(3)]
for key in loss_dict_keys:
train_verbose += "\t %s:{:.7f}"%key
verbose_args.append(loss_dict[key])
# plot the cumulative mean total loss
short_batch_df = pd.json_normalize(verbose_list) # this will be cumulative
mean_total = short_batch_df.loc[:,'Total'].mean()
train_verbose += "\t %s:{:.7f}"%"Mean_Total"
verbose_args.append(mean_total)
train_verbose = train_verbose.format(*verbose_args)
logger.info(train_verbose)
if popen.cuda_id != torch.device('cpu'):
with torch.cuda.device(popen.cuda_id):
torch.cuda.empty_cache()
def cycle_validate(loader_dict, model, optimizer, popen, epoch , which_set=1, return_=False):
logger = logging.getLogger("VAE")
device = 'cuda' if torch.cuda.is_available() else 'cpu'
model = model.to(device)
# ====== set up empty =====
# model.loss_dict_keys = ['RL_loss', 'Recons_loss', 'Motif_loss', 'Total', 'RL_Acc', 'Recons_Acc', 'Motif_Acc']
verbose_list=[]
r2_dict = {}
Y_n_pred = {}
# ======== evaluate =======
for subset, dataloader in loader_dict.items():
# fix
model.task = subset
# # logger.info(" =======================| fix |======================= ")
# model = utils.fix_parameter(model, popen.modual_to_fix[0])
# train(dataloader[0], model, optimizer, popen, epoch, verbose=True)
Y_ls = []
pred_ls = []
with torch.no_grad():
model.eval()
for idx,data in enumerate(dataloader[which_set]):
X,Y = put_data_to_cuda(data,popen,require_grad=False)
out = model(X)
Y_ls.append(Y.detach().cpu().numpy())
pred_ls.append(out.detach().cpu().numpy())
loss_dict = model.compute_loss(out,X,Y,popen)
loss_dict['%s_loss'%subset] = loss_dict['Total']
acc_dict = model.compute_acc(out,X,Y,popen)
loss_dict.update(acc_dict)
loss_dict = utils.clean_value_dict(loss_dict) # convert possible torch to single item
verbose_list.append(loss_dict)
if popen.cuda_id != torch.device('cpu'):
with torch.cuda.device(popen.cuda_id):
torch.cuda.empty_cache()
Y_ay = np.concatenate(Y_ls,axis=0).flatten()
pred_ay = np.concatenate(pred_ls,axis=0).flatten()
if return_:
Y_n_pred[subset] = (Y_ay, pred_ay)
r2_dict[f"{subset}_r2"] = r2_score(Y_ay, pred_ay)
r2_dict[f"{subset}_pr"] = pearsonr(Y_ay, pred_ay)[0]
# # average among batch
# ======== verbose ========
verbose_df = pd.json_normalize(verbose_list)
val_verbose = ""
verbose_args = []
verbose_dict = {key:verbose_df[key].mean() for key in verbose_df.columns}
verbose_dict.update(r2_dict)
for key,values in verbose_dict.items():
val_verbose += "\t %s:{:.7f}"%key
verbose_args.append(values)
val_verbose = val_verbose.format(*verbose_args)
logger.info(val_verbose)
# what avg acc return : mean of RL_Acc , Recons_Acc, Motif_Acc
acc_col = list(acc_dict.keys())
Avg_acc = np.mean(verbose_df.loc[:,acc_col].mean(axis=0))
if return_:
return verbose_dict, Y_n_pred
else:
return verbose_dict
def put_data_to_cuda(data,popen,require_grad=True):
X,y = data
device = popen.cuda_id
# for X is a list [seq, uAUG ....]
if popen.other_input_columns is not None:
X = [x_i.float().to(device) for x_i in X]
if require_grad:
for x_i in X:
x_i.required_grad = True
# X is not a list : seq
else:
X = X.float().to(device)
if require_grad:
X.required_grad = True # check !!!
Y = y.float().to(device)
# Y = Y if X.shape == Y.shape else None # for mask data
return X,Y |