gpu_ss_generic.py

#CUDA_LAUNCH_BLOCKING=0 python3 gpu_ss_generic.py /tmp l1 0 l3 l4 0 l6 qg 1 ../Semi_Supervised_LFs/Data/TREC 6 nn 0 32 0.0003 0.01 none f1 adjust

import torch
import sys
import numpy as np
#Own models
from logistic_regression import *
from deep_net import *
#from deep_lstm import *
###
import warnings
warnings.filterwarnings("ignore")
from gpu_cage import *
from sklearn.feature_extraction.text import TfidfVectorizer
from losses import *
import pickle
from torch.utils.data import TensorDataset, DataLoader
# import wandb
# wandb.init(project='generic', entity='spear-plus')
# conf = wandb.config
# CUDA for PyTorch
use_cuda = torch.cuda.is_available()
device = torch.device("cuda" if use_cuda else "cpu")
torch.backends.cudnn.benchmark = True

torch.set_default_dtype(torch.float64)
torch.set_printoptions(threshold=20)



objs = []
dset_directory = sys.argv[10]
n_classes = int(sys.argv[11])
feat_model = sys.argv[12]
qg_available = int(sys.argv[13])
batch_size = int(sys.argv[14])
lr_fnetwork = float(sys.argv[15])
lr_gm = float(sys.argv[16])
name_dset = dset_directory.split("/")[-1].lower()
dataset = dset_directory.split("/")[-2].lower()
print('dset is ', dataset, name_dset)
mode = sys.argv[17] #''
metric = sys.argv[18]
adjust = sys.argv[19]
# conf.learning_rate = lr_fnetwork #wandb
# wrunname = name_dset + "_" + mode +"_generic"#wandb
# wandb.run.name = wrunname #wandb


if feat_model == 'lstm':
    print('dataset is ', dataset)
    loader_file = "reef.data." + dataset+"_loader"

    import importlib

    load = importlib.import_module(loader_file)

    dl = load.DataLoader()
    train_primitive_matrix, val_primitive_matrix, test_primitive_matrix, train_ground,\
        val_ground, test_ground, _,_,_, train_text, val_text, test_text = dl.load_data(dataset=dataset, split_val = 0.1)


from sklearn.metrics import precision_score as prec_score
from sklearn.metrics import recall_score as recall_score
if metric=='accuracy':
    from sklearn.metrics import accuracy_score as score
    print('inside accuracy')
elif metric =='binary':
	from sklearn.metrics import f1_score as score
	metric_avg = 'binary'
else:
    from sklearn.metrics import f1_score as score
    metric_avg = 'macro'


if mode =='normal':
    fname = dset_directory + "/" + mode + "_d_processed.p"
else:
    fname = dset_directory + "/d_processed.p"
with open(fname, 'rb') as f:
    while 1:
        try:
            o = pickle.load(f)
        except EOFError:
            break
        objs.append(o)

x_supervised = torch.tensor(objs[0]).double()
y_supervised = torch.tensor(objs[3]).long()
l_supervised = torch.tensor(objs[2]).long()
s_supervised = torch.tensor(objs[2]).double()
print('supervised shape', objs[2].shape)

objs = []
if mode =='normal':
    fname = dset_directory + "/" + mode + "_U_processed.p"
else:
    fname = dset_directory + "/U_processed.p"

with open(fname, 'rb') as f:
    while 1:
        try:
            o = pickle.load(f)
        except EOFError:
            break
        objs.append(o)

excl= []
idx=0
for x in objs[1]:
    if(all(x==int(n_classes))):
        excl.append(idx)
    idx+=1
print('no of excludings are ', len(excl))

x_unsupervised = torch.tensor(np.delete(objs[0],excl, axis=0)).double()
y_unsupervised = torch.tensor(np.delete(objs[3],excl, axis=0)).long()
l_unsupervised = torch.tensor(np.delete(objs[2],excl, axis=0)).long()
s_unsupervised = torch.tensor(np.delete(objs[2],excl, axis=0)).double()
print('UNsupervised shape', objs[2].shape)
print('Length of U is', len(x_unsupervised))

objs = []
if mode =='normal':
    fname = dset_directory + "/" + mode + "_validation_processed.p"
else:
    fname = dset_directory + "/validation_processed.p"

with open(fname, 'rb') as f:
    while 1:
        try:
            o = pickle.load(f)
        except EOFError:
            break
        objs.append(o)

x_valid = torch.tensor(objs[0]).double()
y_valid = torch.tensor(objs[3]).long()
l_valid = torch.tensor(objs[2]).long()
s_valid = torch.tensor(objs[2]).double()
print('Valid shape', x_valid.shape)
adjust = 'adjust'
if adjust =='adjust':
    tmp = torch.cat((x_supervised, x_valid))
    leng = int(len(tmp)/2)

    x_supervised, x_valid = tmp[0:leng], tmp[leng:]

    tmp = torch.cat((y_supervised, y_valid))
    y_supervised, y_valid = tmp[0:leng], tmp[leng:]

    tmp = torch.cat((s_supervised, s_valid))
    s_supervised, s_valid = tmp[0:leng], tmp[leng:]

    tmp = torch.cat((l_supervised, l_valid))
    l_supervised, l_valid = tmp[0:leng], tmp[leng:]

print('after adjusting Valid shape', x_valid.shape)
print('after adjusting supervised shape', x_supervised.shape)
# exit()
objs1 = []
if mode =='normal':
    fname = dset_directory + "/" + mode + "_test_processed.p"
else:
    fname = dset_directory + "/test_processed.p"


with open(fname, 'rb') as f:
    while 1:
        try:
            o = pickle.load(f)
        except EOFError:
            break
        objs1.append(o)
x_test = torch.tensor(objs1[0]).double()
y_test = objs1[3]
l_test = torch.tensor(objs1[2]).long()
s_test = torch.tensor(objs1[2]).double()
print('Test shape', x_test.shape)

n_features = x_supervised.shape[1]

# Labeling Function Classes
# k = torch.from_numpy(np.array([0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0])).long()
#lf_classes_file = sys.argv[11]

if mode =='normal':
    fname = dset_directory + '/' + mode + '_k.npy'
else:
    fname = dset_directory + '/k.npy'
k = torch.from_numpy(np.load(fname)).to(device=device).long()
n_lfs = len(k)
print('LFs are ',k)
print('no of lfs are ', n_lfs)

# a = torch.ones(n_lfs).double() * 0.9
# print('before ',a)
if qg_available:
    a = torch.from_numpy(np.load(dset_directory+'/prec.npy')).to(device=device).double()
else:
    # a = torch.ones(n_lfs).double() * 0.9

    prec_lfs=[]
    for i in range(n_lfs):
       correct = 0
       for j in range(len(y_valid)):
           if y_valid[j] == l_valid[j][i]:
               correct+=1
       prec_lfs.append(correct/len(y_valid))
    a = torch.tensor(prec_lfs, device=device).double()

# n_lfs = int(len(k))
# print('number of lfs ', n_lfs)
# a = torch.ones(n_lfs).double() * 0.9
continuous_mask = torch.zeros(n_lfs, device=device).double()


for i in range(s_supervised.shape[0]):
    for j in range(s_supervised.shape[1]):
        if s_supervised[i, j].item() > 0.999:
            s_supervised[i, j] = 0.999
        if s_supervised[i, j].item() < 0.001:
            s_supervised[i, j] = 0.001

for i in range(s_unsupervised.shape[0]):
    for j in range(s_unsupervised.shape[1]):
        if s_unsupervised[i, j].item() > 0.999:
            s_unsupervised[i, j] = 0.999
        if s_unsupervised[i, j].item() < 0.001:
            s_unsupervised[i, j] = 0.001

for i in range(s_valid.shape[0]):
    for j in range(s_valid.shape[1]):
        if s_valid[i, j].item() > 0.999:
            s_valid[i, j] = 0.999
        if s_valid[i, j].item() < 0.001:
            s_valid[i, j] = 0.001

for i in range(s_test.shape[0]):
    for j in range(s_test.shape[1]):
        if s_test[i, j].item() > 0.999:
            s_test[i, j] = 0.999
        if s_test[i, j].item() < 0.001:
            s_test[i, j] = 0.001



l = torch.cat([l_supervised, l_unsupervised])
s = torch.cat([s_supervised, s_unsupervised])
x_train = torch.cat([x_supervised, x_unsupervised])
y_train = torch.cat([y_supervised, y_unsupervised])
supervised_mask = torch.cat([torch.ones(l_supervised.shape[0]), torch.zeros(l_unsupervised.shape[0])])


## Quality Guides ##



## End Quality Quides##
# a =  torch.tensor(np.load(dset_directory + '/precision_values.npy'))
# print('after ',a)

#Setting |validation|=|supevised|
# x_valid = x_valid[0:len(x_supervised)]
# y_valid = y_valid[0:len(x_supervised)]
# s_valid = s_valid[0:len(x_supervised)]
# l_valid = l_valid[0:len(x_supervised)]

print('l_valid',l_valid.shape)
# print(l_valid[0])

num_runs = int(sys.argv[9])

final_score_gm, final_score_lr, final_score_gm_val, final_score_lr_val = [],[],[],[]

final_score_lr_prec, final_score_lr_recall, final_score_gm_prec, final_score_gm_recall = [],[],[],[]
for lo in range(0,num_runs):
    pi = torch.ones((n_classes, n_lfs), device=device).double()
    pi.requires_grad = True

    theta = torch.ones((n_classes, n_lfs), device=device).double() * 1
    theta.requires_grad = True

    pi_y = torch.ones(n_classes, device=device).double()
    pi_y.requires_grad = True

    if feat_model == 'lr':
        lr_model = LogisticReg(n_features, n_classes).to(device=device)
        # print(lr_model)
        supervised_criterion = torch.nn.CrossEntropyLoss()
    elif feat_model =='nn':
        n_hidden = 512
        lr_model = DeepNet(n_features, n_hidden, n_classes).to(device=device)
        supervised_criterion = torch.nn.CrossEntropyLoss()
    elif feat_model == 'lstm':
        mkt = MakeTokens()
        _,_,_, vocab_size, embedding_vector_length, max_sentence_length =\
         mkt.make(train_text, val_text, test_text)
        print('vocab size is ', vocab_size)
        lr_model = DeepLSTM(vocab_size=vocab_size, embedding_vector_length=32, max_sentence_length=500).to(device=device)
        supervised_criterion = torch.nn.BCELoss()
    else:
        print('Please provide feature based model : lr or nn')
        exit()


    # optimizer = torch.optim.Adam([{"params": lr_model.parameters()}, {"params": [pi, pi_y, theta]}], lr=0.001)
    optimizer_lr = torch.optim.Adam(lr_model.parameters(), lr=lr_fnetwork)
    optimizer_gm = torch.optim.Adam([theta, pi, pi_y], lr=lr_gm, weight_decay=0)
    # optimizer = torch.optim.Adam([theta, pi, pi_y], lr=0.01, weight_decay=0)
    


    # print('X_train', X_train.shape)

    dataset = TensorDataset(x_train, y_train, l, s, supervised_mask)

    loader = DataLoader(dataset, batch_size=batch_size, shuffle=True)#,pin_memory=True)
    save_folder = sys.argv[1]
    print('num runs are ', sys.argv[1], num_runs)
    best_score_lr,best_score_gm,best_epoch_lr,best_epoch_gm,best_score_lr_val, best_score_gm_val = 0,0,0,0,0,0
    best_score_lr_prec,best_score_lr_recall ,best_score_gm_prec,best_score_gm_recall= 0,0,0,0

    stop_pahle, stop_pahle_gm = [], []
    # wandb.watch(lr_model)
    for epoch in range(100):
        lr_model.train()

        for batch_ndx, sample in enumerate(loader):
            
            optimizer_lr.zero_grad()
            optimizer_gm.zero_grad()
            for i in range(len(sample)):
                sample[i] = sample[i].to(device=device)
            unsup = []
            sup = []
            supervised_indices = sample[4].nonzero().view(-1)
            # unsupervised_indices = indices  ## Uncomment for entropy
            unsupervised_indices = (1-sample[4]).nonzero().squeeze().view(-1)
            # print('sample[2][unsupervised_indices].shape', sample[2][unsupervised_indices].shape)
            # print('sample[3][unsupervised_indices].shape', sample[3][unsupervised_indices].shape)
            # if feat_model != 'lstm':
            #     sample[0] = sample[0].double()
            #     sample[1] = sample[1].long()
            # print(sample[0][supervised_indices].shape, sample[1][supervised_indices].shape)

            if(sys.argv[2] =='l1'):
                if len(supervised_indices) > 0:
                    if feat_model == 'lstm':
                        loss_1 = supervised_criterion(lr_model(sample[0][supervised_indices].long()), sample[1][supervised_indices].double())
                    else:
                        loss_1 = supervised_criterion(lr_model(sample[0][supervised_indices]), sample[1][supervised_indices])
                else:
                    loss_1 = 0
            else:
                loss_1=0
            # print('loss 1', loss_1.item())
            if(sys.argv[3] =='l2'):
                if feat_model == 'lstm':
                    unsupervised_lr_probability = torch.nn.Softmax()(lr_model(sample[0][unsupervised_indices].long()))
                else:
                    unsupervised_lr_probability = torch.nn.Softmax()(lr_model(sample[0][unsupervised_indices]))
                loss_2 = entropy(unsupervised_lr_probability)
            else:
                loss_2=0
            if(sys.argv[4] =='l3'):
                # print(theta)
                y_pred_unsupervised = np.argmax(probability(theta, pi_y, pi, sample[2][unsupervised_indices],\
                 sample[3][unsupervised_indices], k, n_classes,continuous_mask , device=device).cpu().detach().numpy(), 1)
                y_pred_unsupervised =torch.tensor(y_pred_unsupervised, device=device)
                if feat_model =='lstm':
                    loss_3 = supervised_criterion(lr_model(sample[0][unsupervised_indices].long()), torch.tensor(y_pred_unsupervised))
                else:
                    loss_3 = supervised_criterion(lr_model(sample[0][unsupervised_indices]), torch.tensor(y_pred_unsupervised))
            else:
                loss_3 = 0

            if (sys.argv[5] == 'l4' and len(supervised_indices) > 0):
                loss_4 = log_likelihood_loss_supervised(theta, pi_y, pi, sample[1][supervised_indices], sample[2][supervised_indices], sample[3][supervised_indices], k, n_classes, continuous_mask, device=device)
            else:
                loss_4 = 0

            if(sys.argv[6] =='l5'):
                loss_5 = log_likelihood_loss(theta, pi_y, pi, sample[2][unsupervised_indices], sample[3][unsupervised_indices], k, n_classes, continuous_mask, device=device)
            else:
                loss_5 =0

            if(sys.argv[7] =='l6'):
                if(len(supervised_indices) >0):
                    supervised_indices = supervised_indices.tolist()
                    probs_graphical = probability(theta, pi_y, pi, torch.cat([sample[2][unsupervised_indices], sample[2][supervised_indices]]),\
                    torch.cat([sample[3][unsupervised_indices],sample[3][supervised_indices]]), k, n_classes, continuous_mask, device=device)
                else:
                    probs_graphical = probability(theta, pi_y, pi,sample[2][unsupervised_indices],sample[3][unsupervised_indices],\
                         k, n_classes, continuous_mask, device=device)
                probs_graphical = (probs_graphical.t() / probs_graphical.sum(1)).t()
                if feat_model == 'lstm':
                    probs_lr = lr_model(sample[0].long())
                else:
                    probs_lr = torch.nn.Softmax()(lr_model(sample[0]))
                loss_6 = kl_divergence(probs_lr, probs_graphical)
                # loss_6 = kl_divergence(probs_graphical, probs_lr) #original version

            else:
                loss_6= 0
            # loss_6 = - torch.log(1 - probs_graphical * (1 - probs_lr)).sum(1).mean()
            if(sys.argv[8] =='qg'):
                prec_loss = precision_loss(theta, k, n_classes, a, device=device)
            else:
                prec_loss =0

            loss = loss_1 + loss_2 + loss_3 + loss_4 + loss_6+loss_5 + prec_loss
#            print('loss is',loss_1, loss_2, loss_3, loss_4, loss_5, loss_6, prec_loss)
            # print('loss ', loss)
            if loss != 0:
                loss.backward()
                optimizer_gm.step()
                optimizer_lr.step()
        # wname = "Run_"+str(lo)+" Train Loss" #wandb
        # wandb.log({wname:loss, 'custom_step':epoch}) #wandb
        if epoch %5==0:
            y_pred = np.argmax(probability(theta, pi_y, pi, l_test.to(device=device), s_test.to(device=device), k, n_classes, continuous_mask, device=device).cpu().detach().numpy(), 1)
            if metric=='accuracy':
                gm_acc = score(y_test, y_pred)
                lr_prec = prec_score(y_test, y_pred, average=None) 
                lr_recall = recall_score(y_test, y_pred, average=None)
                gm_prec, gm_recall = 0,0
            else:
                gm_acc = score(y_test, y_pred, average=metric_avg)
                gm_prec = prec_score(y_test, y_pred, average=metric_avg)
                gm_recall = recall_score(y_test, y_pred, average=metric_avg)
        #Valid
        y_pred = np.argmax(probability(theta, pi_y, pi, l_valid.to(device=device), s_valid.to(device=device), k, n_classes, continuous_mask, device=device).cpu().detach().numpy(), 1)
        
        if metric=='accuracy':
            gm_valid_acc = score(y_valid, y_pred)
            gm_prec, gm_recall = 0,0
        else:
#            print(y_pred,y_valid)
            gm_valid_acc = score(y_valid, y_pred, average=metric_avg)

        #LR Test
        if epoch %5==0:
            if feat_model =='lstm':
                probs = lr_model(x_test.long().to(device=device))
                y_pred = probs.cpu().detach().numpy()
                y_pred = np.round(y_pred)
            else:
                # x_test = x_test.double()
                probs = torch.nn.Softmax()(lr_model(x_test.to(device=device)))
                y_pred = np.argmax(probs.cpu().detach().numpy(), 1)
            # if name_dset =='youtube' or name_dset=='census' or name_dset =='sms':
            if metric=='accuracy':
            	# print('inside accuracy LR test')
            	lr_acc =score(y_test, y_pred)
            	lr_prec = prec_score(y_test, y_pred, average=None)
            	lr_recall = recall_score(y_test, y_pred, average=None)
            	gm_prec, gm_recall = 0,0

            else:
                lr_acc =score(y_test, y_pred, average=metric_avg)
                lr_prec = prec_score(y_test, y_pred, average=metric_avg)
                lr_recall = recall_score(y_test, y_pred, average=metric_avg)
            #LR Valid

        if feat_model =='lstm':
            probs = lr_model(x_valid.long().to(device=device))
            y_pred = probs.cpu().detach().numpy()
            y_pred = np.round(y_pred)
        else:
            x_valid = x_valid.double()
            probs = torch.nn.Softmax()(lr_model(x_valid.to(device=device)))
            y_pred = np.argmax(probs.cpu().detach().numpy(), 1)
            
        
        if metric=='accuracy':
            lr_valid_acc = score(y_valid, y_pred)
            gm_prec, gm_recall = 0,0
        else:
            lr_valid_acc = score(y_valid, y_pred, average=metric_avg)
        if epoch %1 ==0:
            print("Epoch: {}\t Test GM accuracy_score: {}".format(epoch, gm_acc ))
            print("Epoch: {}\tGM accuracy_score(Valid): {}".format(epoch, gm_valid_acc))
            print("Epoch: {}\tTest LR accuracy_score: {}".format(epoch, lr_acc))    
            print("Epoch: {}\tLR accuracy_score(Valid): {}".format(epoch, lr_valid_acc))
        wname = "Run_"+str(lo)+" LR valid score"
        wnamegm = 'Run_' + str(lo) + ' GM valid score'
        # wandb.log({wname:lr_valid_acc, 
            # wnamegm:gm_valid_acc,'custom_step':epoch})

        # if epoch > 5 and gm_valid_acc >= best_score_gm_val and gm_valid_acc >= best_score_lr_val:
        #     # print("Inside Best hu Epoch: {}\t Test GM accuracy_score: {}".format(epoch, gm_acc ))
        #     # print("Inside Best hu Epoch: {}\tGM accuracy_score(Valid): {}".format(epoch, gm_valid_acc))
        #     if gm_valid_acc == best_score_gm_val or gm_valid_acc == best_score_lr_val:
        #         if best_score_gm < gm_acc or best_score_lr < lr_acc:
        #             best_epoch_lr = epoch
        #             best_score_lr_val = lr_valid_acc
        #             best_score_lr = lr_acc

        #             best_epoch_gm = epoch
        #             best_score_gm_val = gm_valid_acc
        #             best_score_gm = gm_acc

        #             best_score_lr_prec = lr_prec
        #             best_score_lr_recall  = lr_recall
        #             best_score_gm_prec = gm_prec
        #             best_score_gm_recall  = gm_recall
        #     else:
        #         best_epoch_lr = epoch
        #         best_score_lr_val = lr_valid_acc
        #         best_score_lr = lr_acc

        #         best_epoch_gm = epoch
        #         best_score_gm_val = gm_valid_acc
        #         best_score_gm = gm_acc

        #         best_score_lr_prec = lr_prec
        #         best_score_lr_recall  = lr_recall
        #         best_score_gm_prec = gm_prec
        #         best_score_gm_recall  = gm_recall
        #         stop_pahle = []
        #         stop_pahle_gm = []
        #     checkpoint = {'theta': theta,'pi': pi}
        #     # torch.save(checkpoint, save_folder+"/gm_"+str(epoch)    +".pt")
        #     checkpoint = {'params': lr_model.state_dict()}
        #     # torch.save(checkpoint, save_folder+"/lr_"+ str(epoch)+".pt")
            

        if epoch > 5 and lr_valid_acc >= best_score_lr_val:# and lr_valid_acc >= best_score_lr_val:
            # print("Inside Best hu Epoch: {}\tTest LR accuracy_score: {}".format(epoch, lr_acc ))
            # print("Inside Best hu Epoch: {}\tLR accuracy_score(Valid): {}".format(epoch, lr_valid_acc))
            if lr_valid_acc == best_score_lr_val:
                if best_score_lr < lr_acc:
                    
                    best_epoch_lr = epoch
                    best_score_lr_val = lr_valid_acc
                    best_score_lr = lr_acc

                    best_epoch_gm = epoch
                    best_score_gm_val = gm_valid_acc
                    best_score_gm = gm_acc

                    best_score_lr_prec = lr_prec
                    best_score_lr_recall  = lr_recall
                    best_score_gm_prec = gm_prec
                    best_score_gm_recall  = gm_recall
            else:
                best_epoch_lr = epoch
                best_score_lr_val = lr_valid_acc
                best_score_lr = lr_acc
                best_epoch_gm = epoch
                best_score_gm_val = gm_valid_acc
                best_score_gm = gm_acc
                best_score_lr_prec = lr_prec
                best_score_lr_recall  = lr_recall
                best_score_gm_prec = gm_prec
                best_score_gm_recall  = gm_recall
                stop_pahle = []
                stop_pahle_gm = []
            print('Best score lr val is ', best_score_lr_val , ' at epoch ', epoch)
            checkpoint = {'theta': theta,'pi': pi}
            # torch.save(checkpoint, save_folder+"/gm_"+str(epoch)    +".pt")
            checkpoint = {'params': lr_model.state_dict()}
            # torch.save(checkpoint, save_folder+"/lr_"+ str(epoch)+".pt")
            


        # if len(stop_pahle) > 20 and len(stop_pahle_gm) > 20 and (all(best_score_lr_val >= k for k in stop_pahle) and all(best_score_gm_val >= k for k in stop_pahle_gm)):
        # if len(stop_pahle) > 10 and len(stop_pahle_gm) > 10 and (all(best_score_lr_val >= k for k in stop_pahle)):
        # if len(stop_pahle) > 10  and (all(best_score_lr_val >= k for k in stop_pahle)):
        if len(stop_pahle_gm) > 10 and all(best_score_gm_val >= k for k in stop_pahle_gm):
        
            print('Early Stopping at', best_epoch_gm, best_score_gm, best_score_lr)
            print('Validation score Early Stopping at', best_epoch_gm, best_score_lr_val, best_score_gm_val)
            break
        else:
            # print('inside else stop pahle epoch', epoch)
            stop_pahle.append(lr_valid_acc)
            stop_pahle_gm.append(gm_valid_acc)

    # print("Run \t",lo, "Epoch Gm, Epoch LR, GM, LR \t", best_epoch_gm, best_epoch_lr,best_score_gm, best_score_lr)
    # print("Run \t",lo, "GM Val, LR Val \t", best_score_gm_val, best_score_lr_val)
    print('Best Epoch LR', best_epoch_lr)
    print('Best Precision LR', best_score_lr_prec)
    print('Best Recall LR', best_score_lr_recall)
    print('Best Epoch GM', best_epoch_gm)
    print('Best Precision GM ', best_score_gm_prec)
    print('Best Recall GM ', best_score_gm_recall)
    print("Run \t",lo, "Epoch, GM, LR \t", best_score_gm, best_score_lr)
    print("Run \t",lo, "GM Val, LR Val \t", best_score_gm_val, best_score_lr_val)
    final_score_gm.append(best_score_gm)
    final_score_lr.append(best_score_lr)
#    final_score_lr_prec.append(best_score_lr_prec)
#    final_score_lr_recall.append(best_score_lr_recall)

#    final_score_gm_prec.append(best_score_gm_prec)
#    final_score_gm_recall.append(best_score_gm_recall)

    final_score_gm_val.append(best_score_gm_val)
    final_score_lr_val.append(best_score_lr_val)


print("===================================================")
print("TEST Averaged scores are for LR", np.mean(final_score_lr))
print("TEST Precision average scores are for LR", np.mean(best_score_lr_prec))
print("TEST Recall average scores are for LR", np.mean(best_score_lr_recall))
print("===================================================")
print("TEST Averaged scores are for GM",  np.mean(final_score_gm))
print("TEST Precision average scores are for GM", np.mean(final_score_gm_prec))
print("TEST Recall average scores are for GM", np.mean(final_score_gm_recall))
print("===================================================")
print("VALIDATION Averaged scores are for GM,LR", np.mean(final_score_gm_val), np.mean(final_score_lr_val))
print("TEST STD  are for GM,LR", np.std(final_score_gm), np.std(final_score_lr))
print("VALIDATION STD  are for GM,LR", np.std(final_score_gm_val), np.std(final_score_lr_val))

# wandb.log({'test_lr':np.mean(final_score_lr),'test_gm':np.mean(final_score_gm)})#wandb
# wandb.log({'test_mean_GM ':np.mean(final_score_lr), 'test_mean_GM': np.mean(final_score_gm)}) #wandb
# wandb.log({'test_STD_LR ':np.std(final_score_lr), 'test_STD_GM': np.std(final_score_gm)}) #wandb