Kaggle competition: Tweet Sentiment Extraction
<SOS> metric: Jaccard Kaggle data source: tweet-sentiment-extraction<categorify>
print('TF version',tf.__version__ )
for f in('Exterior1st', 'Exterior2nd', 'SaleType','Utilities'):
features[f] = features[f].fillna(features[f].mode() [0])
 features['Functional'] = features['Functional'].fillna('Typ')
 features['Electrical'] = features['Electrical'].fillna("SBrkr")
 features['KitchenQual'] = features['KitchenQual'].fillna("TA")
 features['MSZoning'] = features.groupby('MSSubClass')['MSZoning'].transform(lambda x: x.fillna(x.mode() [0]))
objects = []
for i in features.columns:
if features[i].dtype == object:
objects.append(i)
 features.update(features[objects].fillna('None'))<filter>
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 
)
 sentiment_id = {'positive': 1313, 'negative': 2430, 'neutral': 7974}
train = pd.read_csv('.. /input/tweet-sentiment-extraction/train.csv' ).fillna('')
 train.head()
features[features['GarageYrBlt'] == 2207][['YearBuilt','YearRemodAdd','GarageYrBlt']]<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)+5] = [0] + enc.ids + [2,2] + [s_tok] + [2]
attention_mask[k,:len(enc.ids)+5] = 1
if len(toks)>0:
start_tokens[k,toks[0]+1] = 1
end_tokens[k,toks[-1]+1] = 1
features.GarageYrBlt.iloc[2592] = 2007<data_type_conversions>
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)+5] = [0] + enc.ids + [2,2] + [s_tok] + [2]
attention_mask_t[k,:len(enc.ids)+5] = 1
for f in ['GarageYrBlt', 'GarageArea', 'GarageCars']:
features[f] = features[f].fillna(0 )<data_type_conversions>
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)
 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(1,1 )(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(1,1 )(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='categorical_crossentropy', optimizer=optimizer)
 return model
for f in ['BsmtFinSF1', 'BsmtFinSF2', 'BsmtUnfSF','TotalBsmtSF', 'BsmtFullBath', 'BsmtHalfBath']:
features[f] = features[f].fillna(0 )<feature_engineering>
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))
features['MasVnrArea'] = features['MasVnrArea'].fillna(0 )<groupby>
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=777)
 for fold,(idxT,idxV)in enumerate(skf.split(input_ids,train.sentiment.values)) :
print('
print('
print('
K.clear_session()
model = build_model()
sv = tf.keras.callbacks.ModelCheckpoint(
 '%s-roberta-%i.h5'%(VER,fold), monitor='val_loss', verbose=1, save_best_only=True,
save_weights_only=True, mode='auto', save_freq='epoch')
 model.fit([input_ids[idxT,], attention_mask[idxT,], token_type_ids[idxT,]], [start_tokens[idxT,], end_tokens[idxT,]],
epochs=3, batch_size=32, verbose=DISPLAY, callbacks=[sv],
validation_data=([input_ids[idxV,],attention_mask[idxV,],token_type_ids[idxV,]],
[start_tokens[idxV,], end_tokens[idxV,]]))
print('Loading model...')
 model.load_weights('%s-roberta-%i.h5'%(VER,fold))
print('Predicting OOF...')
 oof_start[idxV,],oof_end[idxV,] = model.predict([input_ids[idxV,],attention_mask[idxV,],token_type_ids[idxV,]],verbose=DISPLAY)
 print('Predicting Test...')
 preds = 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-1:b])
 all.append(jaccard(st,train.loc[k,'selected_text']))
jac.append(np.mean(all))
print('>>>> FOLD %i Jaccard ='%(fold+1),np.mean(all))
print()
features['LotFrontage'] = features.groupby('Neighborhood')['LotFrontage'].apply(lambda x: x.fillna(x.median()))<feature_engineering>
print('>>>> OVERALL 5Fold CV Jaccard =',np.mean(jac))
numeric_dtypes = ['int16', 'int32', 'int64', 'float16', 'float32', 'float64']
numerics2 = []
for i in features.columns:
if features[i].dtype in numeric_dtypes:
numerics2.append(i)
 skew_features = features[numerics2].apply(lambda x: stats.skew(x)).sort_values(ascending=False)
 high_skew = skew_features[skew_features > 0.5]
skew_index = high_skew.index
for i in skew_index:
features[i] = boxcox1p(features[i], boxcox_normmax(features[i] + 1))<feature_engineering>
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-1:b])
 all.append(st )
features['HasPool'] = features['PoolArea'].apply(lambda x: 1 if x > 0 else 0)
 features['Has2ndfloor'] = features['2ndFlrSF'].apply(lambda x: 1 if x > 0 else 0)
 features['HasGarage'] = features['GarageArea'].apply(lambda x: 1 if x > 0 else 0)
 features['HasBsmt'] = features['TotalBsmtSF'].apply(lambda x: 1 if x > 0 else 0)
 features['HasFireplace'] = features['Fireplaces'].apply(lambda x: 1 if x > 0 else 0)
 features['YrBltAndRemod']=features['YearBuilt']+features['YearRemodAdd']
features['TotalSF']=features['TotalBsmtSF'] + features['1stFlrSF'] + features['2ndFlrSF']
features['Total_SQR_Footage'] =(features['BsmtFinSF1'] + features['BsmtFinSF2'] +
features['1stFlrSF'] + features['2ndFlrSF'])
 features['Total_Bathrooms'] =(features['FullBath'] +(0.5 * features['HalfBath'])+
features['BsmtFullBath'] +(0.5 * features['BsmtHalfBath']))
features['Total_Porch_SF'] =(features['OpenPorchSF'] + features['3SsnPorch'] +
features['EnclosedPorch'] + features['ScreenPorch'] +
features['WoodDeckSF'])
 features = features.drop(['Utilities', 'Street', 'PoolQC',], axis=1 )<categorify>
test['selected_text'] = all
test[['textID','selected_text']].to_csv('submission.csv',index=False)
 pd.set_option('max_colwidth', 60)
 test.sample(25 )