Kaggle competition: Tweet Sentiment Extraction
<SOS> metric: Jaccard Kaggle data source: tweet-sentiment-extraction<categorify>
tqdm.pandas()
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')
 train.head()
data_all["LotFrontage"] = data_all.groupby("Neighborhood")["LotFrontage"].transform(
 lambda x: x.fillna(x.median())) 
<count_values>
train.isnull().sum()
data_all['MSZoning'].value_counts(normalize = True)
<feature_engineering>
train.isnull().sum()
data_all['MSZoning'] = data_all['MSZoning'].fillna('RL')
 data_all['MSZoning'].isnull().any()<count_values>
train = train.dropna(axis = 0)
 train.isnull().sum()
data_all['Utilities'].value_counts(normalize = True )<data_type_conversions>
def process(text, selected_text, ib_space):
added_extra_space = False
splitted = text.split(selected_text)
 if splitted[0][-1] == ' ':
added_extra_space = True
splitted = text.split(" " + selected_text)
 sub = len(splitted[0])- len(" ".join(splitted[0].split()))
if sub == 1 and text[0] == ' ':
splitted = text.split(selected_text)
 add_space = True if splitted[0] and splitted[0][-1] == ' ' else False
add_space = False
if splitted[0] and splitted[0][-1] == ' ':
add_space = True
if add_space == False:
start = text.find(selected_text)
 else:
start = text.find(selected_text)- 1
elif sub == 2:
start = text.find(selected_text)
 else:
start = text.find(selected_text)
 splitted = text.split(selected_text)
 add_space = False
if splitted[0] and splitted[0][-1] == ' ':
add_space = True
text_pr = " ".join(splitted[0].split())
 if add_space:
text_pr += " "
text_pr = text_pr + selected_text
if len(splitted)> 1:
text_pr = text_pr + splitted[1]
if sub > 1 :
if text[0] == ' ':
end = start + len(selected_text)- 1 + ib_space
else:
end = start + len(selected_text)
 else:
end = start + len(selected_text)
 new_st = text_pr[start : end]
return new_st
def process_selected_text(text, selected_text):
splitted = text.split(selected_text)
 add_space = True if splitted[0] and splitted[0][-1] == ' ' else False
sub = len(splitted[0])- len(" ".join(splitted[0].split()))
in_between_space = len(selected_text)- len(" ".join(selected_text.split()))
new_selected_text = selected_text
if sub > 0 and text.strip() != selected_text.strip() and add_space == False and text.find(selected_text)!= 0:
if in_between_space == 0:
new_selected_text = process(text, selected_text, in_between_space)
 return new_selected_text
data_all['Utilities'] = data_all['Utilities'].fillna('AllPub' )<count_values>
train['new_selected_text'] = train.selected_text
train = train[train.textID != '12f21c8f19']
train['new_selected_text'] = train.progress_apply(lambda x: process_selected_text(x.text, x.selected_text), axis=1 )
data_all['Functional'].value_counts(normalize = True )<data_type_conversions>
train.to_csv('new_train.csv',index = False )
data_all['Functional'] = data_all['Functional'].fillna('Typ' )<categorify>
train = pd.read_csv('/kaggle/working/new_train.csv')
 train.head(5 )
data_all['Electrical'] = data_all['Electrical'].fillna(data_all['Electrical'].mode() [0])
 data_all['KitchenQual'] = data_all['KitchenQual'].fillna(data_all['KitchenQual'].mode() [0])
 data_all['Exterior1st'] = data_all['Exterior1st'].fillna(data_all['Exterior1st'].mode() [0])
 data_all['Exterior2nd'] = data_all['Exterior2nd'].fillna(data_all['Exterior2nd'].mode() [0])
 data_all['SaleType'] = data_all['SaleType'].fillna(data_all['SaleType'].mode() [0] )<sort_values>
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,'new_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
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
null_values_2 = data_all.columns[data_all.isnull().any() ]
null_features_2 = data_all[null_values_2].isnull().sum().sort_values(ascending = False)
 missing_data_2 = pd.DataFrame({'No of Nulls' :null_features_2})
 missing_data_2
print('|\t\t NO MORE MISSING VALUES REMAINING.
 \t\t\t...IMPUTING COMPLETED...' )<count_values>
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.Conv1D(64, 2,padding='same' )(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.Conv1D(64, 2, padding='same' )(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
data_all.dtypes.value_counts()<count_values>
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))
train_new.dtypes.value_counts()<count_values>
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()
test_new.dtypes.value_counts()<categorify>
print('>>>> OVERALL 5Fold CV Jaccard =',np.mean(jac))
<count_values><EOS> 
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)
 test['selected_text'] = all
test[['textID','selected_text']].to_csv('submission.csv',index=False)
 pd.set_option('max_colwidth', 60)
 test.sample(25 )