Kaggle competition: Tweet Sentiment Extraction
<SOS> metric: Jaccard Kaggle data source: tweet-sentiment-extraction<sort_values>
import os
import torch
import pandas as pd
import torch.nn as nn
import numpy as np
import torch.nn.functional as F
from torch.optim import lr_scheduler
from sklearn import model_selection
from sklearn.model_selection import KFold
from sklearn import metrics
from transformers import RobertaModel, RobertaConfig
import tokenizers
from transformers import AdamW
from transformers import get_linear_schedule_with_warmup
from tqdm.autonotebook import tqdm
import utils
null_values = test_encoded.columns[test_encoded.isnull().any() ]
null_features = test_encoded[null_values].isnull().sum().sort_values(ascending = False)
 missing = pd.DataFrame({'No of Nulls' :null_features})
 missing<categorify>
class config:
MAX_LEN = 128
TRAIN_BATCH_SIZE = 64
VALID_BATCH_SIZE = 16
EPOCHS = 3
MODEL_PATH = ".. /input/roberta-base/pytorch_model.bin"
TRAINING_FILE = ".. /input/tweet-sentiment-extraction/train.csv"
TOKENIZER = tokenizers.ByteLevelBPETokenizer(
 vocab_file='.. /input/roberta-base/vocab.json',
merges_file='.. /input/roberta-base/merges.txt',
lowercase=True,
add_prefix_space=True)
 def process_data(tweet, selected_text, sentiment, tokenizer, max_len):
tweet = " " + " ".join(str(tweet ).split())
 selected_text = " " + " ".join(str(selected_text ).split())
 len_st = len(selected_text)- 1
idx0 = None
idx1 = None
for ind in(i for i, e in enumerate(tweet)if e == selected_text[1]):
if " " + tweet[ind: ind+len_st] == selected_text:
idx0 = ind
idx1 = ind + len_st - 1
break
char_targets = [0] * len(tweet)
 if idx0 != None and idx1 != None:
for ct in range(idx0, idx1 + 1):
char_targets[ct] = 1
tok_tweet = tokenizer.encode(tweet)
 input_ids_orig = tok_tweet.ids
tweet_offsets = tok_tweet.offsets
target_idx = []
for j,(offset1, offset2)in enumerate(tweet_offsets):
if sum(char_targets[offset1: offset2])> 0:
target_idx.append(j)
 targets_start = target_idx[0]
targets_end = target_idx[-1]
sentiment_id = {
'positive': 1313,
'negative': 2430,
'neutral': 7974
}
input_ids = [0] + [sentiment_id[sentiment]] + [2] + [2] + input_ids_orig + [2]
token_type_ids = [0, 0, 0, 0] + [0] *(len(input_ids_orig)+ 1)
 mask = [1] * len(token_type_ids)
 tweet_offsets = [(0, 0)] * 4 + tweet_offsets + [(0, 0)]
targets_start += 4
targets_end += 4
padding_length = max_len - len(input_ids)
 if padding_length > 0:
input_ids = input_ids +([1] * padding_length)
 mask = mask +([0] * padding_length)
 token_type_ids = token_type_ids +([0] * padding_length)
 tweet_offsets = tweet_offsets +([(0, 0)] * padding_length)
 return {
'ids': input_ids,
'mask': mask,
'token_type_ids': token_type_ids,
'targets_start': targets_start,
'targets_end': targets_end,
'orig_tweet': tweet,
'orig_selected': selected_text,
'sentiment': sentiment,
'offsets': tweet_offsets
}
class TweetDataset:
def __init__(self, tweet, sentiment, selected_text):
self.tweet = tweet
self.sentiment = sentiment
self.selected_text = selected_text
self.tokenizer = config.TOKENIZER
self.max_len = config.MAX_LEN
def __len__(self):
return len(self.tweet)
 def __getitem__(self, item):
data = process_data(
 self.tweet[item],
self.selected_text[item],
self.sentiment[item],
self.tokenizer,
self.max_len 
)
 return {
'ids': torch.tensor(data["ids"], dtype=torch.long),
'mask': torch.tensor(data["mask"], dtype=torch.long),
'token_type_ids': torch.tensor(data["token_type_ids"], dtype=torch.long),
'targets_start': torch.tensor(data["targets_start"], dtype=torch.long),
'targets_end': torch.tensor(data["targets_end"], dtype=torch.long),
'orig_tweet': data["orig_tweet"],
'orig_selected': data["orig_selected"],
'sentiment': data["sentiment"],
'offsets': torch.tensor(data["offsets"], dtype=torch.long)
 }
class TweetModel(nn.Module):
def __init__(self):
super(TweetModel, self ).__init__()
config = RobertaConfig.from_pretrained(
 '.. /input/roberta-base/config.json', output_hidden_states=True)
 self.roberta = RobertaModel.from_pretrained(
 '.. /input/roberta-base/pytorch_model.bin', config=config)
 self.drop_out = nn.Dropout(0.2)
 self.l0 = nn.Linear(768 * 2, 2)
 torch.nn.init.normal_(self.l0.weight, std=0.02)
 def forward(self, ids, mask, token_type_ids):
_, _, out = self.roberta(
 ids,
attention_mask=mask,
token_type_ids=token_type_ids 
)
 out = torch.cat(( out[-1], out[-2]), dim=-1)
 out = self.drop_out(out)
 logits = self.l0(out)
 start_logits, end_logits = logits.split(1, dim=-1)
 start_logits = start_logits.squeeze(-1)
 end_logits = end_logits.squeeze(-1)
 return start_logits, end_logits
def loss_fn(start_logits, end_logits, start_positions, end_positions):
loss_fct = nn.CrossEntropyLoss()
start_loss = loss_fct(start_logits, start_positions)
 end_loss = loss_fct(end_logits, end_positions)
 total_loss =(start_loss + end_loss)
 return total_loss
def train_fn(data_loader, model, optimizer, device, scheduler=None):
model.train()
losses = utils.AverageMeter()
jaccards = utils.AverageMeter()
tk0 = tqdm(data_loader, total=len(data_loader))
for bi, d in enumerate(tk0):
ids = d["ids"]
token_type_ids = d["token_type_ids"]
mask = d["mask"]
targets_start = d["targets_start"]
targets_end = d["targets_end"]
sentiment = d["sentiment"]
orig_selected = d["orig_selected"]
orig_tweet = d["orig_tweet"]
targets_start = d["targets_start"]
targets_end = d["targets_end"]
offsets = d["offsets"]
ids = ids.to(device, dtype=torch.long)
 token_type_ids = token_type_ids.to(device, dtype=torch.long)
 mask = mask.to(device, dtype=torch.long)
 targets_start = targets_start.to(device, dtype=torch.long)
 targets_end = targets_end.to(device, dtype=torch.long)
 model.zero_grad()
outputs_start, outputs_end = model(
 ids=ids,
mask=mask,
token_type_ids=token_type_ids, 
)
 loss = loss_fn(outputs_start, outputs_end, targets_start, targets_end)
 loss.backward()
optimizer.step()
scheduler.step()
outputs_start = torch.softmax(outputs_start, dim=1 ).cpu().detach().numpy()
outputs_end = torch.softmax(outputs_end, dim=1 ).cpu().detach().numpy()
jaccard_scores = []
for px, tweet in enumerate(orig_tweet):
selected_tweet = orig_selected[px]
tweet_sentiment = sentiment[px]
jaccard_score, _ = calculate_jaccard_score(
 original_tweet=tweet,
target_string=selected_tweet,
sentiment_val=tweet_sentiment,
idx_start=np.argmax(outputs_start[px, :]),
idx_end=np.argmax(outputs_end[px, :]),
offsets=offsets[px] 
)
 jaccard_scores.append(jaccard_score)
 jaccards.update(np.mean(jaccard_scores), ids.size(0))
losses.update(loss.item() , ids.size(0))
tk0.set_postfix(loss=losses.avg, jaccard=jaccards.avg)
 def calculate_jaccard_score(
 original_tweet,
target_string,
sentiment_val,
idx_start,
idx_end,
offsets,
verbose=False):
if idx_end < idx_start:
idx_end = idx_start
filtered_output = ""
for ix in range(idx_start, idx_end + 1):
filtered_output += original_tweet[offsets[ix][0]: offsets[ix][1]]
if(ix+1)< len(offsets)and offsets[ix][1] < offsets[ix+1][0]:
filtered_output += " "
if sentiment_val == "neutral" or len(original_tweet.split())< 2:
filtered_output = original_tweet
jac = utils.jaccard(target_string.strip() , filtered_output.strip())
 return jac, filtered_output
def eval_fn(data_loader, model, device):
model.eval()
losses = utils.AverageMeter()
jaccards = utils.AverageMeter()
with torch.no_grad() :
tk0 = tqdm(data_loader, total=len(data_loader))
for bi, d in enumerate(tk0):
ids = d["ids"]
token_type_ids = d["token_type_ids"]
mask = d["mask"]
sentiment = d["sentiment"]
orig_selected = d["orig_selected"]
orig_tweet = d["orig_tweet"]
targets_start = d["targets_start"]
targets_end = d["targets_end"]
offsets = d["offsets"].numpy()
ids = ids.to(device, dtype=torch.long)
 token_type_ids = token_type_ids.to(device, dtype=torch.long)
 mask = mask.to(device, dtype=torch.long)
 targets_start = targets_start.to(device, dtype=torch.long)
 targets_end = targets_end.to(device, dtype=torch.long)
 outputs_start, outputs_end = model(
 ids=ids,
mask=mask,
token_type_ids=token_type_ids 
)
 loss = loss_fn(outputs_start, outputs_end, targets_start, targets_end)
 outputs_start = torch.softmax(outputs_start, dim=1 ).cpu().detach().numpy()
outputs_end = torch.softmax(outputs_end, dim=1 ).cpu().detach().numpy()
jaccard_scores = []
for px, tweet in enumerate(orig_tweet):
selected_tweet = orig_selected[px]
tweet_sentiment = sentiment[px]
jaccard_score, _ = calculate_jaccard_score(
 original_tweet=tweet,
target_string=selected_tweet,
sentiment_val=tweet_sentiment,
idx_start=np.argmax(outputs_start[px, :]),
idx_end=np.argmax(outputs_end[px, :]),
offsets=offsets[px] 
)
 jaccard_scores.append(jaccard_score)
 jaccards.update(np.mean(jaccard_scores), ids.size(0))
losses.update(loss.item() , ids.size(0))
tk0.set_postfix(loss=losses.avg, jaccard=jaccards.avg)
 print(f"Jaccard = {jaccards.avg}")
 return jaccards.avg
test_encoded = test_encoded.fillna(0 )<predict_on_test>
def run(fold):
dfx = pd.read_csv(config.TRAINING_FILE)
 kf = KFold(n_splits = 10, shuffle = True, random_state = 42)
 kf_r = next(kf.split(dfx), None)
 df_train = dfx.iloc[kf_r[0]].reset_index(drop=True)
 df_valid = dfx.iloc[kf_r[1]].reset_index(drop=True)
 train_dataset = TweetDataset(
 tweet=df_train.text.values,
sentiment=df_train.sentiment.values,
selected_text=df_train.selected_text.values 
)
 train_data_loader = torch.utils.data.DataLoader(
 train_dataset,
batch_size=config.TRAIN_BATCH_SIZE,
num_workers=4 
)
 valid_dataset = TweetDataset(
 tweet=df_valid.text.values,
sentiment=df_valid.sentiment.values,
selected_text=df_valid.selected_text.values 
)
 valid_data_loader = torch.utils.data.DataLoader(
 valid_dataset,
batch_size=config.VALID_BATCH_SIZE,
num_workers=2 
)
 device = torch.device("cuda")
 model = TweetModel()
model.to(device)
 num_train_steps = int(len(df_train)/ config.TRAIN_BATCH_SIZE * config.EPOCHS)
 param_optimizer = list(model.named_parameters())
 no_decay = ["bias", "LayerNorm.bias", "LayerNorm.weight"]
optimizer_parameters = [
{'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)], 'weight_decay': 0.001},
{'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay)], 'weight_decay': 0.0},
]
optimizer = AdamW(optimizer_parameters, lr=3e-5)
 scheduler = get_linear_schedule_with_warmup(
 optimizer,
num_warmup_steps=0,
num_training_steps=num_train_steps 
)
 es = utils.EarlyStopping(patience=2, mode="max")
 for epoch in range(3):
train_fn(train_data_loader, model, optimizer, device, scheduler=scheduler)
 jaccard = eval_fn(valid_data_loader, model, device)
 print(f"Jaccard Score = {jaccard}")
 es(jaccard, model, model_path=f"modelo_{fold}.bin")
 if es.early_stop:
print("Early stopping")
 break
prediction = lassoCV.predict(test_encoded)
 final_prediction = np.exp(prediction )<save_to_csv>
for fold in range(3):
print(f"Fold={fold}")
 run(fold )
House_submission = pd.DataFrame({'Id': id_test, 'SalePrice': final_prediction})
 print(House_submission.shape)
 House_submission.to_csv('House_prediction.csv', index = False)
<load_from_csv>
device = torch.device("cuda")
 modelo1 = TweetModel()
modelo1.to(device)
 modelo1.load_state_dict(torch.load("modelo_0.bin"))
modelo1.eval()
modelo2 = TweetModel()
modelo2.to(device)
 modelo2.load_state_dict(torch.load("modelo_1.bin"))
modelo2.eval()
modelo3 = TweetModel()
modelo3.to(device)
 modelo3.load_state_dict(torch.load("modelo_2.bin"))
modelo3.eval()
%matplotlib inline
plt.style.use('fivethirtyeight')
 warnings.filterwarnings('ignore')
 train = pd.read_csv('.. /input/train.csv')
 test = pd.read_csv('.. /input/test.csv')
 merged = train.append(test)
<groupby>
final_output = []
df_test = pd.read_csv(".. /input/tweet-sentiment-extraction/test.csv")
 df_test.loc[:, "selected_text"] = df_test.text.values
merged['PTitle'] = merged['PTitle'].replace(['Lady', 'the Countess','Mlle', 'Ms', 'Mme', 'Dona','Capt', 'Col','Don', 'Dr', 'Major', 'Rev', 'Sir', 'Jonkheer'], 'Rare')
 merged[['Sex','PTitle','Pclass','Survived']].groupby(['Sex','PTitle','Pclass'] ).mean()<feature_engineering>
test_dataset = TweetDataset(
 tweet=df_test.text.values,
sentiment=df_test.sentiment.values,
selected_text=df_test.selected_text.values 
)
 data_loader = torch.utils.data.DataLoader(
 test_dataset,
shuffle=False,
batch_size=config.VALID_BATCH_SIZE,
num_workers=1 
)
 with torch.no_grad() :
tk0 = tqdm(data_loader, total=len(data_loader))
for bi, d in enumerate(tk0):
ids = d["ids"]
token_type_ids = d["token_type_ids"]
mask = d["mask"]
sentiment = d["sentiment"]
orig_selected = d["orig_selected"]
orig_tweet = d["orig_tweet"]
targets_start = d["targets_start"]
targets_end = d["targets_end"]
offsets = d["offsets"].numpy()
ids = ids.to(device, dtype=torch.long)
 token_type_ids = token_type_ids.to(device, dtype=torch.long)
 mask = mask.to(device, dtype=torch.long)
 targets_start = targets_start.to(device, dtype=torch.long)
 targets_end = targets_end.to(device, dtype=torch.long)
 outputs_start1, outputs_end1 = modelo1(
 ids=ids,
mask=mask,
token_type_ids=token_type_ids 
)
 outputs_start2, outputs_end2 = modelo2(
 ids=ids,
mask=mask,
token_type_ids=token_type_ids 
)
 outputs_start3, outputs_end3 = modelo3(
 ids=ids,
mask=mask,
token_type_ids=token_type_ids 
)
 outputs_start =(outputs_start1 + outputs_start2 + outputs_start3)/ 3
outputs_end =(outputs_end1 + outputs_end2 + outputs_end3)/ 3
outputs_start = torch.softmax(outputs_start, dim=1 ).cpu().detach().numpy()
outputs_end = torch.softmax(outputs_end, dim=1 ).cpu().detach().numpy()
jaccard_scores = []
for px, tweet in enumerate(orig_tweet):
selected_tweet = orig_selected[px]
tweet_sentiment = sentiment[px]
_, output_sentence = calculate_jaccard_score(
 original_tweet=tweet,
target_string=selected_tweet,
sentiment_val=tweet_sentiment,
idx_start=np.argmax(outputs_start[px, :]),
idx_end=np.argmax(outputs_end[px, :]),
offsets=offsets[px] 
)
 final_output.append(output_sentence)
<groupby><EOS> 
submission = pd.read_csv(".. /input/tweet-sentiment-extraction/sample_submission.csv")
 submission.loc[:, 'selected_text'] = final_output
submission.to_csv("submission.csv", index=False )