Kaggle competition: Tweet Sentiment Extraction
print(pd.isnull(train ).sum() )<drop_column>
print('Number of submissions above: ' + str(len(lbs)) )
train = train.drop(['Cabin'], axis = 1)
 test = test.drop(['Cabin'], axis = 1)
 print('Feature Dropped!!' )<drop_column>
print('TF version',tf.__version__ )
train = train.drop(['Ticket'], axis = 1)
 test = test.drop(['Ticket'], axis = 1)
 print('Feature Dropped!!' )<data_type_conversions>
MAX_LEN = 96
PATH = '.. /input/tf-roberta/'
tokenizer = tokenizers.ByteLevelBPETokenizer(
 vocab_file=PATH+'vocab-roberta-base.json',
merges_file=PATH+'merges-roberta-base.txt',
lowercase=True,
add_prefix_space=True 
)
 EPOCHS = 3
BATCH_SIZE = 32
PAD_ID = 1
SEED = 88888
LABEL_SMOOTHING = 0.1
tf.random.set_seed(SEED)
 np.random.seed(SEED)
 sentiment_id = {'positive': 1313, 'negative': 2430, 'neutral': 7974}
train = pd.read_csv('.. /input/tweet-sentiment-extraction/train.csv' ).fillna('')
 train.head()
train = train.fillna({"Embarked": "S"})
 train.head()<feature_engineering>
ct = train.shape[0]
input_ids = np.ones(( ct,MAX_LEN),dtype='int32')
 attention_mask = np.zeros(( ct,MAX_LEN),dtype='int32')
 token_type_ids = np.zeros(( ct,MAX_LEN),dtype='int32')
 start_tokens = np.zeros(( ct,MAX_LEN),dtype='int32')
 end_tokens = np.zeros(( ct,MAX_LEN),dtype='int32')
 for k in range(train.shape[0]):
text1 = " "+" ".join(train.loc[k,'text'].split())
 text2 = " ".join(train.loc[k,'selected_text'].split())
 idx = text1.find(text2)
 chars = np.zeros(( len(text1)))
 chars[idx:idx+len(text2)]=1
if text1[idx-1]==' ': chars[idx-1] = 1
enc = tokenizer.encode(text1)
 offsets = []; idx=0
for t in enc.ids:
w = tokenizer.decode([t])
 offsets.append(( idx,idx+len(w)))
 idx += len(w)
 toks = []
for i,(a,b)in enumerate(offsets):
sm = np.sum(chars[a:b])
 if sm>0: toks.append(i)
 s_tok = sentiment_id[train.loc[k,'sentiment']]
input_ids[k,:len(enc.ids)+3] = [0, s_tok] + enc.ids + [2]
attention_mask[k,:len(enc.ids)+3] = 1
if len(toks)>0:
start_tokens[k,toks[0]+2] = 1
end_tokens[k,toks[-1]+2] = 1
for dataset in combine:
dataset['Title'] = dataset['Title'].replace(['Lady', 'Capt', 'Col',
'Don', 'Dr', 'Major', 'Rev', 'Jonkheer', 'Dona'], 'Rare')
 dataset['Title'] = dataset['Title'].replace(['Countess', 'Lady', 'Sir'], 'Royal')
 dataset['Title'] = dataset['Title'].replace('Mlle', 'Miss')
 dataset['Title'] = dataset['Title'].replace('Ms', 'Miss')
 dataset['Title'] = dataset['Title'].replace('Mme', 'Mrs')
 train[['Title', 'Survived']].groupby(['Title'], as_index=False ).mean()<categorify>
test = pd.read_csv('.. /input/tweet-sentiment-extraction/test.csv' ).fillna('')
 ct = test.shape[0]
input_ids_t = np.ones(( ct,MAX_LEN),dtype='int32')
 attention_mask_t = np.zeros(( ct,MAX_LEN),dtype='int32')
 token_type_ids_t = np.zeros(( ct,MAX_LEN),dtype='int32')
 for k in range(test.shape[0]):
text1 = " "+" ".join(test.loc[k,'text'].split())
 enc = tokenizer.encode(text1)
 s_tok = sentiment_id[test.loc[k,'sentiment']]
input_ids_t[k,:len(enc.ids)+3] = [0, s_tok] + enc.ids + [2]
attention_mask_t[k,:len(enc.ids)+3] = 1
title_mapping = {"Mr": 1, "Miss": 2, "Mrs": 3, "Master": 4, "Royal": 5, "Rare": 6}
for dataset in combine:
dataset['Title'] = dataset['Title'].map(title_mapping)
 dataset['Title'] = dataset['Title'].fillna(0)
 train.head()<categorify>
def save_weights(model, dst_fn):
weights = model.get_weights()
with open(dst_fn, 'wb')as f:
pickle.dump(weights, f)
 def load_weights(model, weight_fn):
with open(weight_fn, 'rb')as f:
weights = pickle.load(f)
 model.set_weights(weights)
 return model
def loss_fn(y_true, y_pred):
ll = tf.shape(y_pred)[1]
y_true = y_true[:, :ll]
loss = tf.keras.losses.categorical_crossentropy(y_true, y_pred,
from_logits=False, label_smoothing=LABEL_SMOOTHING)
 loss = tf.reduce_mean(loss)
 return loss
def build_model() :
ids = tf.keras.layers.Input(( MAX_LEN,), dtype=tf.int32)
 att = tf.keras.layers.Input(( MAX_LEN,), dtype=tf.int32)
 tok = tf.keras.layers.Input(( MAX_LEN,), dtype=tf.int32)
 padding = tf.cast(tf.equal(ids, PAD_ID), tf.int32)
 lens = MAX_LEN - tf.reduce_sum(padding, -1)
 max_len = tf.reduce_max(lens)
 ids_ = ids[:, :max_len]
att_ = att[:, :max_len]
tok_ = tok[:, :max_len]
config = RobertaConfig.from_pretrained(PATH+'config-roberta-base.json')
 bert_model = TFRobertaModel.from_pretrained(PATH+'pretrained-roberta-base.h5',config=config)
 x = bert_model(ids_,attention_mask=att_,token_type_ids=tok_)
 x1 = tf.keras.layers.Dropout(0.1 )(x[0])
 x1 = tf.keras.layers.Conv1D(768, 2,padding='same' )(x1)
 x1 = tf.keras.layers.LeakyReLU()(x1)
 x1 = tf.keras.layers.Dense(1 )(x1)
 x1 = tf.keras.layers.Flatten()(x1)
 x1 = tf.keras.layers.Activation('softmax' )(x1)
 x2 = tf.keras.layers.Dropout(0.1 )(x[0])
 x2 = tf.keras.layers.Conv1D(768, 2,padding='same' )(x2)
 x2 = tf.keras.layers.LeakyReLU()(x2)
 x2 = tf.keras.layers.Dense(1 )(x2)
 x2 = tf.keras.layers.Flatten()(x2)
 x2 = tf.keras.layers.Activation('softmax' )(x2)
 model = tf.keras.models.Model(inputs=[ids, att, tok], outputs=[x1,x2])
 optimizer = tf.keras.optimizers.Adam(learning_rate=3e-5)
 model.compile(loss=loss_fn, optimizer=optimizer)
 x1_padded = tf.pad(x1, [[0, 0], [0, MAX_LEN - max_len]], constant_values=0.)
x2_padded = tf.pad(x2, [[0, 0], [0, MAX_LEN - max_len]], constant_values=0.)
padded_model = tf.keras.models.Model(inputs=[ids, att, tok], outputs=[x1_padded,x2_padded])
 return model, padded_model
mr_age = train[train["Title"] == 1]["AgeGroup"].mode()
miss_age = train[train["Title"] == 2]["AgeGroup"].mode()
mrs_age = train[train["Title"] == 3]["AgeGroup"].mode()
master_age = train[train["Title"] == 4]["AgeGroup"].mode()
royal_age = train[train["Title"] == 5]["AgeGroup"].mode()
rare_age = train[train["Title"] == 6]["AgeGroup"].mode()
age_title_mapping = {1: "Young Adult", 2: "Student", 3: "Adult", 4: "Baby", 5: "Adult", 6: "Adult"}
for x in range(len(train["AgeGroup"])) :
if train["AgeGroup"][x] == "Unknown":
train["AgeGroup"][x] = age_title_mapping[train["Title"][x]]
for x in range(len(test["AgeGroup"])) :
if test["AgeGroup"][x] == "Unknown":
test["AgeGroup"][x] = age_title_mapping[test["Title"][x]]<categorify>
def jaccard(str1, str2):
a = set(str1.lower().split())
 b = set(str2.lower().split())
 if(len(a)==0)&(len(b)==0): return 0.5
c = a.intersection(b)
 return float(len(c)) /(len(a)+ len(b)- len(c))
age_mapping = {'Baby': 1, 'Child': 2, 'Teenager': 3, 'Student': 4, 'Young Adult': 5, 'Adult': 6, 'Senior': 7}
train['AgeGroup'] = train['AgeGroup'].map(age_mapping)
 test['AgeGroup'] = test['AgeGroup'].map(age_mapping)
 train.head()
train = train.drop(['Age'], axis = 1)
 test = test.drop(['Age'], axis = 1)
 print('Dropped!!' )<drop_column>
jac = []; VER='v0'; DISPLAY=1
oof_start = np.zeros(( input_ids.shape[0],MAX_LEN))
oof_end = np.zeros(( input_ids.shape[0],MAX_LEN))
preds_start = np.zeros(( input_ids_t.shape[0],MAX_LEN))
preds_end = np.zeros(( input_ids_t.shape[0],MAX_LEN))
skf = StratifiedKFold(n_splits=5,shuffle=True,random_state=SEED)
 for fold,(idxT,idxV)in enumerate(skf.split(input_ids,train.sentiment.values)) :
print('
print('
print('
K.clear_session()
model, padded_model = build_model()
inpT = [input_ids[idxT,], attention_mask[idxT,], token_type_ids[idxT,]]
targetT = [start_tokens[idxT,], end_tokens[idxT,]]
inpV = [input_ids[idxV,],attention_mask[idxV,],token_type_ids[idxV,]]
targetV = [start_tokens[idxV,], end_tokens[idxV,]]
shuffleV = np.int32(sorted(range(len(inpV[0])) , key=lambda k:(inpV[0][k] == PAD_ID ).sum() , reverse=True))
inpV = [arr[shuffleV] for arr in inpV]
targetV = [arr[shuffleV] for arr in targetV]
weight_fn = '%s-roberta-%i.h5'%(VER,fold)
 for epoch in range(1, EPOCHS + 1):
shuffleT = np.int32(sorted(range(len(inpT[0])) , key=lambda k:(inpT[0][k] == PAD_ID ).sum() + np.random.randint(-3, 3), reverse=True))
num_batches = math.ceil(len(shuffleT)/ BATCH_SIZE)
 batch_inds = np.random.permutation(num_batches)
 shuffleT_ = []
for batch_ind in batch_inds:
shuffleT_.append(shuffleT[batch_ind * BATCH_SIZE:(batch_ind + 1)* BATCH_SIZE])
 shuffleT = np.concatenate(shuffleT_)
 inpT = [arr[shuffleT] for arr in inpT]
targetT = [arr[shuffleT] for arr in targetT]
model.fit(inpT, targetT,
epochs=epoch, initial_epoch=epoch - 1, batch_size=BATCH_SIZE, verbose=DISPLAY, callbacks=[],
validation_data=(inpV, targetV), shuffle=False)
 save_weights(model, weight_fn)
 print('Loading model...')
 load_weights(model, weight_fn)
 print('Predicting OOF...')
 oof_start[idxV,],oof_end[idxV,] = padded_model.predict([input_ids[idxV,],attention_mask[idxV,],token_type_ids[idxV,]],verbose=DISPLAY)
 print('Predicting Test...')
 preds = padded_model.predict([input_ids_t,attention_mask_t,token_type_ids_t],verbose=DISPLAY)
 preds_start += preds[0]/skf.n_splits
preds_end += preds[1]/skf.n_splits
all = []
for k in idxV:
a = np.argmax(oof_start[k,])
 b = np.argmax(oof_end[k,])
 if a>b:
st = train.loc[k,'text']
else:
text1 = " "+" ".join(train.loc[k,'text'].split())
 enc = tokenizer.encode(text1)
 st = tokenizer.decode(enc.ids[a-2:b-1])
 all.append(jaccard(st,train.loc[k,'selected_text']))
jac.append(np.mean(all))
print('>>>> FOLD %i Jaccard ='%(fold+1),np.mean(all))
print()
train = train.drop(['Name'], axis = 1)
 test = test.drop(['Name'], axis = 1)
 print('Dropped!!' )<categorify>
print('>>>> OVERALL 5Fold CV Jaccard =',np.mean(jac))
sex_mapping = {"male": 0, "female": 1}
train['Sex'] = train['Sex'].map(sex_mapping)
 test['Sex'] = test['Sex'].map(sex_mapping)
 train.head()<categorify>
all = []
for k in range(input_ids_t.shape[0]):
a = np.argmax(preds_start[k,])
 b = np.argmax(preds_end[k,])
 if a>b:
st = test.loc[k,'text']
else:
text1 = " "+" ".join(test.loc[k,'text'].split())
 enc = tokenizer.encode(text1)
 st = tokenizer.decode(enc.ids[a-2:b-1])
 all.append(st )
<split><EOS> 
test['selected_text'] = all
test[['textID','selected_text']].to_csv('submission.csv',index=False)
 pd.set_option('max_colwidth', 60)
 test.sample(25 )