roberta_regressor_head_from_scratch

roberta_regressor_head_from_scratch/predict.py

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+import os
+import torch
+import random
+import copy
+from fairseq.models.roberta import RobertaModel, RobertaHubInterface
+from fairseq import hub_utils
+from fairseq.data.data_utils import collate_tokens
+from tqdm import tqdm
+import numpy as np
+from sklearn.preprocessing import MinMaxScaler
+
+
+EVAL_OFTEN = True
+EVAL_EVERY = 3000
+BATCH_SIZE = 5
+model_type = 'base' # base or large
+
+
+
+roberta = torch.hub.load('pytorch/fairseq', f'roberta.{model_type}')
+roberta.cuda()
+device='cuda'
+
+
+# LOAD DATA
+train_in = [l.rstrip('\n') for l in open('../train/in.tsv',newline='\n').readlines()] # shuffled
+dev_in = [l.rstrip('\n') for l in open('../dev-0/in.tsv',newline='\n').readlines()] # shuffled
+
+train_year = [float(l.rstrip('\n')) for l in open('../train/expected.tsv',newline='\n').readlines()]
+dev_year = [float(l.rstrip('\n')) for l in open('../dev-0/expected.tsv',newline='\n').readlines()]
+
+dev_in_not_shuffled = copy.deepcopy(dev_in) # not shuffled
+test_in = [l.rstrip('\n') for l in open('../test-A/in.tsv',newline='\n').readlines()] # not shuffled
+
+# SHUFFLE DATA
+c = list(zip(train_in,train_year))
+random.shuffle(c)
+train_in, train_year = zip(*c) 
+c = list(zip(dev_in,dev_year))
+random.shuffle(c)
+dev_in, dev_year = zip(*c) 
+
+# SCALE DATA
+scaler = MinMaxScaler()
+train_year_scaled = scaler.fit_transform(np.array(train_year).reshape(-1,1))
+dev_year_scaled = scaler.transform(np.array(dev_year).reshape(-1,1))
+
+
+class RegressorHead(torch.nn.Module):
+    def __init__(self):
+        super(RegressorHead, self).__init__()
+        in_dim = 768 if model_type == 'base' else 1024
+        self.linear = torch.nn.Linear(in_dim, 1)
+        self.m = torch.nn.LeakyReLU(0.1)
+    def forward(self, x):
+        x = self.linear(x)
+        x = self.m(x)
+        x = - self.m(-x + 1 ) +1
+        return x 
+
+def get_features_and_year(dataset_in,dataset_y):
+    for i in tqdm(range(0,len(dataset_in), BATCH_SIZE)):
+        batch_of_text = dataset_in[i:i+BATCH_SIZE]
+        
+        batch = collate_tokens([roberta.encode(p)[:512] for p in batch_of_text], pad_idx=1)
+        features = roberta.extract_features(batch).mean(1)
+        years = torch.FloatTensor(dataset_y[i:i+BATCH_SIZE]).to(device)
+
+        yield features, years
+
+def eval_dev(short=False):
+    criterion_eval = torch.nn.MSELoss(reduction='sum')
+    roberta.eval()
+    regressor_head.eval()
+
+    loss = 0.0
+    loss_clipped = 0.0
+    loss_scaled = 0.0
+
+    if short:
+        dataset_in = dev_in[:1000]
+        dataset_years = dev_year_scaled[:1000]
+    else:
+        dataset_in = dev_in
+        dataset_years = dev_year_scaled
+
+    predictions_sum = 0
+    for batch, year in tqdm(get_features_and_year(dataset_in, dataset_years)):
+
+        predictions_sum += year.shape[0]
+        x = regressor_head(batch.to(device))
+        x_clipped = torch.clamp(x,0.0,1.0)
+
+        original_x = torch.FloatTensor(scaler.inverse_transform(x.detach().cpu().numpy().reshape(1,-1)))
+        original_x_clipped = torch.FloatTensor(scaler.inverse_transform(x_clipped.detach().cpu().numpy().reshape(1,-1)))
+        original_year =  torch.FloatTensor(scaler.inverse_transform(year.detach().cpu().numpy().reshape(1,-1)))
+
+        loss_scaled += criterion_eval(x, year).item()
+        loss += criterion_eval(original_x, original_year).item()
+        loss_clipped += criterion_eval(original_x_clipped, original_year).item()
+
+    print('valid loss scaled: ' + str(np.sqrt(loss_scaled/predictions_sum)))
+    print('valid loss: ' + str(np.sqrt(loss/predictions_sum)))
+    print('valid loss clipped: ' + str(np.sqrt(loss_clipped/predictions_sum)))
+
+
+
+def train_one_epoch():
+    roberta.train()
+    regressor_head.train()
+    loss_value=0.0
+    iteration = 0
+    for batch, year in get_features_and_year(train_in,train_year_scaled):
+        iteration +=1
+        roberta.zero_grad()
+        regressor_head.zero_grad()
+
+        predictions = regressor_head(batch.to(device))
+
+        loss = criterion(predictions, year)
+        loss_value += loss.item()
+        loss.backward()
+        optimizer.step()
+
+        roberta.zero_grad()
+        regressor_head.zero_grad()
+
+
+        if EVAL_OFTEN and (iteration > 1) and (iteration % EVAL_EVERY == 1):
+            print('train loss: ' + str(np.sqrt(loss_value / (EVAL_EVERY*BATCH_SIZE))))
+            eval_dev(True)
+            roberta.train()
+            regressor_head.train()
+            loss_value = 0.0
+
+
+def predict(dataset='dev'):
+    if dataset=='dev':
+        f_out_path = '../dev-0/out.tsv'
+        dataset_in_not_shuffled = dev_in_not_shuffled
+    elif dataset=='test':
+        f_out_path = '../test-A/out.tsv'
+        dataset_in_not_shuffled = test_in
+    roberta.eval()
+    regressor_head.eval()
+    f_out = open(f_out_path,'w')
+    for batch, year in tqdm(get_features_and_year(dataset_in_not_shuffled, dev_year_scaled)):
+        x = regressor_head(batch)
+        x_clipped = torch.clamp(x,0.0,1.0)
+        original_x_clipped =  scaler.inverse_transform(x_clipped.detach().cpu().numpy().reshape(1,-1))
+        for y in original_x_clipped[0]:
+            f_out.write(str(y) + '\n')
+    f_out.close()
+
+
+regressor_head = RegressorHead().to(device)
+
+optimizer = torch.optim.Adam(list(roberta.parameters()) + list(regressor_head.parameters()), lr=1e-6)
+criterion = torch.nn.MSELoss(reduction='sum').to(device)
+
+
+#for i in range(12):
+#    print('epoch ' + str(i))
+#    train_one_epoch()
+#
+#    print(f'epoch {i} done, EVALUATION ON FULL DEV:')
+#    eval_dev()
+#    print('evaluation done')
+#    predict('dev')
+#    predict('test')
+#
+#    torch.save(roberta.state_dict(),'checkpoints/roberta_to_regressor' + str(i) + '.pt')
+#    torch.save(regressor_head.state_dict(),'checkpoints/regressor_head' + str(i) + '.pt')
+
+
+roberta.load_state_dict(torch.load('checkpoints/roberta_to_regressor2.pt'))
+regressor_head.load_state_dict(torch.load('checkpoints/regressor_head2.pt'))
+predict('dev')
+predict('test')

roberta_regressor_head_from_scratch/train.py

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+import os
+import torch
+import random
+import copy
+from fairseq.models.roberta import RobertaModel, RobertaHubInterface
+from fairseq import hub_utils
+from fairseq.data.data_utils import collate_tokens
+from tqdm import tqdm
+import numpy as np
+from sklearn.preprocessing import MinMaxScaler
+
+
+EVAL_OFTEN = False
+EVAL_EVERY = 3000
+BATCH_SIZE = 5
+model_type = 'base' # base or large
+
+
+
+roberta = torch.hub.load('pytorch/fairseq', f'roberta.{model_type}', pretrained=False)
+roberta.cuda()
+device='cuda'
+
+
+# LOAD DATA
+train_in = [l.rstrip('\n') for l in open('../train/in.tsv',newline='\n').readlines()] # shuffled
+dev_in = [l.rstrip('\n') for l in open('../dev-0/in.tsv',newline='\n').readlines()] # shuffled
+
+train_year = [float(l.rstrip('\n')) for l in open('../train/expected.tsv',newline='\n').readlines()]
+dev_year = [float(l.rstrip('\n')) for l in open('../dev-0/expected.tsv',newline='\n').readlines()]
+
+dev_in_not_shuffled = copy.deepcopy(dev_in) # not shuffled
+test_in = [l.rstrip('\n') for l in open('../test-A/in.tsv',newline='\n').readlines()] # not shuffled
+
+# SHUFFLE DATA
+c = list(zip(train_in,train_year))
+random.shuffle(c)
+train_in, train_year = zip(*c) 
+c = list(zip(dev_in,dev_year))
+random.shuffle(c)
+dev_in, dev_year = zip(*c) 
+
+# SCALE DATA
+scaler = MinMaxScaler()
+train_year_scaled = scaler.fit_transform(np.array(train_year).reshape(-1,1))
+dev_year_scaled = scaler.transform(np.array(dev_year).reshape(-1,1))
+
+
+class RegressorHead(torch.nn.Module):
+    def __init__(self):
+        super(RegressorHead, self).__init__()
+        in_dim = 768 if model_type == 'base' else 1024
+        self.linear = torch.nn.Linear(in_dim, 1)
+        self.m = torch.nn.LeakyReLU(0.1)
+    def forward(self, x):
+        x = self.linear(x)
+        x = self.m(x)
+        x = - self.m(-x + 1 ) +1
+        return x 
+
+def get_features_and_year(dataset_in,dataset_y):
+    for i in tqdm(range(0,len(dataset_in), BATCH_SIZE)):
+        batch_of_text = dataset_in[i:i+BATCH_SIZE]
+        
+        batch = collate_tokens([roberta.encode(p)[:512] for p in batch_of_text], pad_idx=1)
+        features = roberta.extract_features(batch).mean(1)
+        years = torch.FloatTensor(dataset_y[i:i+BATCH_SIZE]).to(device)
+
+        yield features, years
+
+def eval_dev(short=False):
+    criterion_eval = torch.nn.MSELoss(reduction='sum')
+    roberta.eval()
+    regressor_head.eval()
+
+    loss = 0.0
+    loss_clipped = 0.0
+    loss_scaled = 0.0
+
+    if short:
+        dataset_in = dev_in[:1000]
+        dataset_years = dev_year_scaled[:1000]
+    else:
+        dataset_in = dev_in
+        dataset_years = dev_year_scaled
+
+    predictions_sum = 0
+    for batch, year in tqdm(get_features_and_year(dataset_in, dataset_years)):
+
+        predictions_sum += year.shape[0]
+        x = regressor_head(batch.to(device))
+        x_clipped = torch.clamp(x,0.0,1.0)
+
+        original_x = torch.FloatTensor(scaler.inverse_transform(x.detach().cpu().numpy().reshape(1,-1)))
+        original_x_clipped = torch.FloatTensor(scaler.inverse_transform(x_clipped.detach().cpu().numpy().reshape(1,-1)))
+        original_year =  torch.FloatTensor(scaler.inverse_transform(year.detach().cpu().numpy().reshape(1,-1)))
+
+        loss_scaled += criterion_eval(x, year).item()
+        loss += criterion_eval(original_x, original_year).item()
+        loss_clipped += criterion_eval(original_x_clipped, original_year).item()
+
+    print('valid loss scaled: ' + str(np.sqrt(loss_scaled/predictions_sum)))
+    print('valid loss: ' + str(np.sqrt(loss/predictions_sum)))
+    print('valid loss clipped: ' + str(np.sqrt(loss_clipped/predictions_sum)))
+
+
+
+def train_one_epoch():
+    roberta.train()
+    regressor_head.train()
+    loss_value=0.0
+    iteration = 0
+    for batch, year in get_features_and_year(train_in,train_year_scaled):
+        iteration +=1
+        roberta.zero_grad()
+        regressor_head.zero_grad()
+
+        predictions = regressor_head(batch.to(device))
+
+        loss = criterion(predictions, year)
+        loss_value += loss.item()
+        loss.backward()
+        optimizer.step()
+
+        roberta.zero_grad()
+        regressor_head.zero_grad()
+
+
+        if EVAL_OFTEN and (iteration > 1) and (iteration % EVAL_EVERY == 1):
+            print('train loss: ' + str(np.sqrt(loss_value / (EVAL_EVERY*BATCH_SIZE))))
+            eval_dev(True)
+            roberta.train()
+            regressor_head.train()
+            loss_value = 0.0
+
+
+def predict(dataset='dev'):
+    if dataset=='dev':
+        f_out_path = '../dev-0/out.tsv'
+        dataset_in_not_shuffled = dev_in_not_shuffled
+    elif dataset=='test':
+        f_out_path = '../test-A/out.tsv'
+        dataset_in_not_shuffled = test_in
+    roberta.eval()
+    regressor_head.eval()
+    f_out = open(f_out_path,'w')
+    for batch, year in tqdm(get_features_and_year(dataset_in_not_shuffled, dev_year_scaled)):
+        x = regressor_head(batch)
+        x_clipped = torch.clamp(x,0.0,1.0)
+        original_x_clipped =  scaler.inverse_transform(x_clipped.detach().cpu().numpy().reshape(1,-1))
+        for y in original_x_clipped[0]:
+            f_out.write(str(y) + '\n')
+    f_out.close()
+
+
+regressor_head = RegressorHead().to(device)
+
+optimizer = torch.optim.Adam(list(roberta.parameters()) + list(regressor_head.parameters()), lr=1e-6)
+criterion = torch.nn.MSELoss(reduction='sum').to(device)
+
+
+roberta.load_state_dict(torch.load('checkpoints/roberta_to_regressor0.pt'))
+regressor_head.load_state_dict(torch.load('checkpoints/regressor_head0.pt'))
+for i in range(1,3):
+    print('epoch ' + str(i))
+    train_one_epoch()
+
+    print(f'epoch {i} done, EVALUATION ON FULL DEV:')
+    eval_dev()
+    print('evaluation done')
+    predict('dev')
+    predict('test')
+
+    torch.save(roberta.state_dict(),'checkpoints/roberta_to_regressor' + str(i) + '.pt')
+    torch.save(regressor_head.state_dict(),'checkpoints/regressor_head' + str(i) + '.pt')
+
+
+roberta.load_state_dict(torch.load('checkpoints/roberta_to_regressor2.pt'))
+regressor_head.load_state_dict(torch.load('checkpoints/regressor_head2.pt'))
+predict('dev')
+predict('test')