kernel_id int64 24.2k 23.3M | prompt stringlengths 8 1.85M | completetion stringlengths 1 182k | comp_name stringlengths 5 57 |
|---|---|---|---|
10,038,839 | count_nusvc.fit(count_train_sub ,y)
count_nusvc_sub = count_nusvc.predict(count_sub)
<create_dataframe> | PROCESS_TWEETS = False
if PROCESS_TWEETS:
total['text'] = total['text'].apply(lambda x: x.lower())
total['text'] = total['text'].apply(lambda x: re.sub(r'https?://\S+|www\.\S+', '', x, flags = re.MULTILINE))
total['text'] = total['text'].apply(remove_punctuation)
total['text'] = total['text'].apply(remove_stopwords)
... | Natural Language Processing with Disaster Tweets |
10,038,839 | sub=pd.DataFrame({'id':sample_sub['id'].values.tolist() ,'target':count_nusvc_sub} )<save_to_csv> | contractions = {
"ain't": "am not / are not / is not / has not / have not",
"aren't": "are not / am not",
"can't": "cannot",
"can't've": "cannot have",
"'cause": "because",
"could've": "could have",
"couldn't": "could not",
"couldn't've": "could not have",
"didn't": "did not",
"doesn't": "does not",
"don't": "do not",
... | Natural Language Processing with Disaster Tweets |
10,038,839 | sub.to_csv('submission.csv',index=False )<import_modules> | total['text'] = total['text'].apply(expand_contractions ) | Natural Language Processing with Disaster Tweets |
10,038,839 | from sklearn.linear_model import LogisticRegression<load_from_csv> | def clean(tweet):
tweet = re.sub(r"tnwx", "Tennessee Weather", tweet)
tweet = re.sub(r"azwx", "Arizona Weather", tweet)
tweet = re.sub(r"alwx", "Alabama Weather", tweet)
tweet = re.sub(r"wordpressdotcom", "wordpress", tweet)
tweet = re.sub(r"gawx", "Georgia Weather", tweet)
tweet = re.sub(r"scwx", "South Carolina ... | Natural Language Processing with Disaster Tweets |
10,038,839 | x_train = pd.read_csv("/kaggle/input/titanic/train.csv")
y_train = x_train['Survived']
x_train = x_train.drop(columns=['Survived'])
x_test = pd.read_csv("/kaggle/input/titanic/test.csv" )<feature_engineering> | tweets = [tweet for tweet in total['text']]
train = total[:len(train)]
test = total[len(train):] | Natural Language Processing with Disaster Tweets |
10,038,839 | def preprocessing(df):
df["Fare"] =(df["Fare"] - df["Fare"].min())/(df["Fare"].max() - df["Fare"].min())
df["Fare"] = df["Fare"].fillna(-999)
df["Sex"] = df["Sex"].factorize() [0]
df["Embarked"] = df["Embarked"].factorize() [0]
for i in range(len(df["Name"])) : df["Name"][i] = df["Name"][i].split(',')[0]
df["Name"] =... | def generate_ngrams(text, n_gram=1):
token = [token for token in text.lower().split(' ')if token != '' if token not in wordcloud.STOPWORDS]
ngrams = zip(*[token[i:] for i in range(n_gram)])
return [' '.join(ngram)for ngram in ngrams]
disaster_unigrams = defaultdict(int)
for word in total[train['target'] == 1]['text']... | Natural Language Processing with Disaster Tweets |
10,038,839 | x_train_processed = preprocessing(x_train)
x_test_processed = preprocessing(x_test )<drop_column> | to_exclude = '*+-/() %
[\\]{|}^_`~\t'
to_tokenize = '!"
tokenizer = Tokenizer(filters = to_exclude)
text = 'Why are you so f%
text = re.sub(r'(['+to_tokenize+'])', r' \1 ', text)
tokenizer.fit_on_texts([text])
print(tokenizer.word_index ) | Natural Language Processing with Disaster Tweets |
10,038,839 | x_train_processed = x_train.drop(columns=["Ticket"])
x_test_processed = x_test.drop(columns=["Ticket"] )<predict_on_test> | Natural Language Processing with Disaster Tweets | |
10,038,839 | model = LogisticRegression(random_state=0, max_iter=2500 ).fit(x_train_processed, y_train)
pred = model.predict(x_test_processed)
pred<compute_test_metric> | tokenizer = Tokenizer()
tokenizer.fit_on_texts(tweets)
sequences = tokenizer.texts_to_sequences(tweets)
word_index = tokenizer.word_index
print('Found %s unique tokens.' % len(word_index))
data = pad_sequences(sequences)
labels = train['target']
print('Shape of data tensor:', data.shape)
print('Shape of label tenso... | Natural Language Processing with Disaster Tweets |
10,038,839 | model.score(x_train_processed, y_train )<prepare_output> | embeddings_index = {}
with open('.. /input/glove-global-vectors-for-word-representation/glove.6B.200d.txt','r')as f:
for line in tqdm(f):
values = line.split()
word = values[0]
coefs = np.asarray(values[1:], dtype='float32')
embeddings_index[word] = coefs
f.close()
print('Found %s word vectors in the GloVe library' % ... | Natural Language Processing with Disaster Tweets |
10,038,839 | df = pd.DataFrame(pred, columns=["Survived"])
df.head()<feature_engineering> | EMBEDDING_DIM = 200 | Natural Language Processing with Disaster Tweets |
10,038,839 | df["PassengerId"] = x_test["PassengerId"].values
df<save_to_csv> | embedding_matrix = np.zeros(( len(word_index)+ 1, EMBEDDING_DIM))
for word, i in tqdm(word_index.items()):
embedding_vector = embeddings_index.get(word)
if embedding_vector is not None:
embedding_matrix[i] = embedding_vector
print("Our embedded matrix is of dimension", embedding_matrix.shape ) | Natural Language Processing with Disaster Tweets |
10,038,839 | df.to_csv('predicts.csv',index=False )<load_from_csv> | embedding = Embedding(len(word_index)+ 1, EMBEDDING_DIM, weights = [embedding_matrix],
input_length = MAX_SEQUENCE_LENGTH, trainable = False)
| Natural Language Processing with Disaster Tweets |
10,038,839 | data_train = pd.read_csv("/kaggle/input/titanic/train.csv")
data_test = pd.read_csv("/kaggle/input/titanic/test.csv")
y = data_train.Survived<groupby> | def scale(df, scaler):
return scaler.fit_transform(df.iloc[:, 2:])
meta_train = scale(train, StandardScaler())
meta_test = scale(test, StandardScaler() ) | Natural Language Processing with Disaster Tweets |
10,038,839 | data_train.groupby('Sex' ).Survived.mean()<groupby> | def create_lstm(spatial_dropout, dropout, recurrent_dropout, learning_rate, bidirectional = False):
activation = LeakyReLU(alpha = 0.01)
nlp_input = Input(shape =(MAX_SEQUENCE_LENGTH,), name = 'nlp_input')
meta_input_train = Input(shape =(7,), name = 'meta_train')
emb = embedding(nlp_input)
emb = SpatialDropout1D(d... | Natural Language Processing with Disaster Tweets |
10,038,839 | data_train.groupby('SibSp' ).Survived.agg(['mean','count'] )<groupby> | lstm = create_lstm(spatial_dropout =.2, dropout =.2, recurrent_dropout =.2,
learning_rate = 3e-4, bidirectional = True)
lstm.summary() | Natural Language Processing with Disaster Tweets |
10,038,839 | data_train.groupby('Parch' ).Survived.agg(['mean','count'] )<drop_column> | history1 = lstm.fit([nlp_train, meta_train], labels, validation_split =.2,
epochs = 5, batch_size = 21, verbose = 1 ) | Natural Language Processing with Disaster Tweets |
10,038,839 | X_train = data_train.drop(['Name','Ticket','PassengerId'],axis=1)
X_test = data_test.drop(['Name','Ticket','PassengerId'],axis=1 )<drop_column> | callback = EarlyStopping(monitor = 'val_loss', patience = 4)
| Natural Language Processing with Disaster Tweets |
10,038,839 | X_train = X_train.drop(['Cabin'],axis=1)
X_test = X_test.drop(['Cabin'],axis=1 )<filter> | def create_lstm_2(spatial_dropout, dropout, recurrent_dropout, learning_rate, bidirectional = False):
activation = LeakyReLU(alpha = 0.01)
nlp_input = Input(shape =(MAX_SEQUENCE_LENGTH,), name = 'nlp_input')
meta_input_train = Input(shape =(7,), name = 'meta_train')
emb = embedding(nlp_input)
emb = SpatialDropout1D... | Natural Language Processing with Disaster Tweets |
10,038,839 | X_train[X_train.Embarked.isnull() ]<groupby> | lstm_2 = create_lstm_2(spatial_dropout =.4, dropout =.4, recurrent_dropout =.4,
learning_rate = 3e-4, bidirectional = True)
lstm_2.summary() | Natural Language Processing with Disaster Tweets |
10,038,839 | X_train.groupby('Embarked' ).Embarked.count()<data_type_conversions> | history2 = lstm_2.fit([nlp_train, meta_train], labels, validation_split =.2,
epochs = 30, batch_size = 21, verbose = 1 ) | Natural Language Processing with Disaster Tweets |
10,038,839 | X_train.Embarked=X_train.Embarked.fillna('S' )<filter> | submission_lstm = pd.DataFrame()
submission_lstm['id'] = test_id
submission_lstm['prob'] = lstm_2.predict([nlp_test, meta_test])
submission_lstm['target'] = submission_lstm['prob'].apply(lambda x: 0 if x <.5 else 1)
submission_lstm.head(10 ) | Natural Language Processing with Disaster Tweets |
10,038,839 | X_train[X_train.Embarked.isnull() ]<categorify> | def create_dual_lstm(spatial_dropout, dropout, recurrent_dropout, learning_rate, bidirectional = False):
activation = LeakyReLU(alpha = 0.01)
nlp_input = Input(shape =(MAX_SEQUENCE_LENGTH,), name = 'nlp_input')
meta_input_train = Input(shape =(7,), name = 'meta_train')
emb = embedding(nlp_input)
emb = SpatialDropou... | Natural Language Processing with Disaster Tweets |
10,038,839 | def impute(cols):
Age = cols[0]
Pclass = cols[1]
if(pd.isnull(Age)) :
if Pclass==1:
return 38
elif Pclass==2:
return 30
else:
return 25
return Age<feature_engineering> | history3 = dual_lstm.fit([nlp_train, meta_train], labels, validation_split =.2,
epochs = 25, batch_size = 21, verbose = 1 ) | Natural Language Processing with Disaster Tweets |
10,038,839 | X_train.Age = X_train[['Age','Pclass']].apply(impute,axis=1)
X_test.Age = X_test[['Age','Pclass']].apply(impute,axis=1 )<filter> | submission_lstm2 = pd.DataFrame()
submission_lstm2['id'] = test_id
submission_lstm2['prob'] = dual_lstm.predict([nlp_test, meta_test])
submission_lstm2['target'] = submission_lstm2['prob'].apply(lambda x: 0 if x <.5 else 1)
submission_lstm2.head(10 ) | Natural Language Processing with Disaster Tweets |
10,038,839 | X_test[X_test.Fare.isnull() ]<feature_engineering> | BATCH_SIZE = 32
EPOCHS = 2
USE_META = True
ADD_DENSE = False
DENSE_DIM = 64
ADD_DROPOUT = False
DROPOUT =.2 | Natural Language Processing with Disaster Tweets |
10,038,839 | X_test['Fare']=X_test['Fare'].fillna(13 )<drop_column> | !pip install --quiet transformers
| Natural Language Processing with Disaster Tweets |
10,038,839 | X_train = X_train.drop(['Survived'],axis=1 )<groupby> | TOKENIZER = AutoTokenizer.from_pretrained("bert-large-uncased")
enc = TOKENIZER.encode("Encode me!")
dec = TOKENIZER.decode(enc)
print("Encode: " + str(enc))
print("Decode: " + str(dec)) | Natural Language Processing with Disaster Tweets |
10,038,839 | X_test.groupby('Sex' ).Sex.count()<import_modules> | def bert_encode(data,maximum_len):
input_ids = []
attention_masks = []
for i in range(len(data.text)) :
encoded = TOKENIZER.encode_plus(data.text[i],
add_special_tokens=True,
max_length=maximum_len,
pad_to_max_length=True,
return_attention_mask=True)
input_ids.append(encoded['input_ids'])
attention_masks.append(encod... | Natural Language Processing with Disaster Tweets |
10,038,839 | from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import OneHotEncoder , LabelEncoder<categorify> | def build_model(model_layer, learning_rate, use_meta = USE_META, add_dense = ADD_DENSE,
dense_dim = DENSE_DIM, add_dropout = ADD_DROPOUT, dropout = DROPOUT):
input_ids = tf.keras.Input(shape=(60,),dtype='int32')
attention_masks = tf.keras.Input(shape=(60,),dtype='int32')
meta_input = tf.keras.Input(shape =(meta_train... | Natural Language Processing with Disaster Tweets |
10,038,839 | le =LabelEncoder()
X_train['Sex']=le.fit_transform(X_train['Sex'])
X_test['Sex'] = le.fit_transform(X_test['Sex'])
X_train.head()<categorify> | train = pd.read_csv('.. /input/nlp-getting-started/train.csv')
test = pd.read_csv('.. /input/nlp-getting-started/test.csv' ) | Natural Language Processing with Disaster Tweets |
10,038,839 | onh = OneHotEncoder(handle_unknown='ignore', sparse=False)
X_train_trans = pd.DataFrame(onh.fit_transform(X_train[['Embarked']]))
X_test_trans = pd.DataFrame(onh.fit_transform(X_test[['Embarked']]))
X_train_trans.index = X_train.index
X_test_trans.index = X_test.index
X_train_conc = X_train.drop(['Embarked'],axis=1)
... | bert_large = TFAutoModel.from_pretrained('bert-large-uncased')
TOKENIZER = AutoTokenizer.from_pretrained("bert-large-uncased")
train_input_ids,train_attention_masks = bert_encode(train,60)
test_input_ids,test_attention_masks = bert_encode(test,60)
print('Train length:', len(train_input_ids))
print('Test length:', l... | Natural Language Processing with Disaster Tweets |
10,038,839 | sc= StandardScaler()
X_train_final = sc.fit_transform(X_train_final)
X_test_final = sc.transform(X_test_final )<train_model> | history_bert = BERT_large.fit([train_input_ids,train_attention_masks, meta_train], train.target,
validation_split =.2, epochs = EPOCHS, callbacks = [checkpoint], batch_size = BATCH_SIZE ) | Natural Language Processing with Disaster Tweets |
10,038,839 | clf = SVC(kernel='rbf', degree = 5)
clf.fit(X_train_final,y )<save_to_csv> | BERT_large.load_weights('large_model.h5')
preds_bert = BERT_large.predict([test_input_ids,test_attention_masks,meta_test] ) | Natural Language Processing with Disaster Tweets |
10,038,839 | pred = clf.predict(X_test_final)
output = pd.DataFrame({'PassengerId':data_test.PassengerId,'Survived':pred})
output.to_csv('submission.csv',index=False )<set_options> | submission_bert = pd.DataFrame()
submission_bert['id'] = test_id
submission_bert['prob'] = preds_bert
submission_bert['target'] = np.round(submission_bert['prob'] ).astype(int)
submission_bert.head(10 ) | Natural Language Processing with Disaster Tweets |
10,038,839 | <load_from_csv><EOS> | submission_bert = submission_bert[['id', 'target']]
submission_bert.to_csv('submission_bert.csv', index = False)
print('Blended submission has been saved to disk' ) | Natural Language Processing with Disaster Tweets |
9,998,024 | <SOS> metric: meanfscore Kaggle data source: natural-language-processing-with-disaster-tweets<feature_engineering> | !pip install bert-for-tf2 | Natural Language Processing with Disaster Tweets |
9,998,024 | df = pd.concat([traindf, testdf], axis=0, sort=False)
df['Title'] = df.Name.str.split(',' ).str[1].str.split('.' ).str[0].str.strip()
df['Title'] = df.Name.str.split(',' ).str[1].str.split('.' ).str[0].str.strip()
df['IsWomanOrBoy'] =(( df.Title == 'Master')|(df.Sex == 'female'))
df['LastName'] = df.Name.str.split(','... | import numpy as np
import pandas as pd
import re
import tensorflow as tf
from tensorflow_core.python.keras.layers import Dense, Input
from tensorflow.keras.optimizers import Adam
from tensorflow_core.python.keras.models import Model
from tensorflow_core.python.keras.callbacks import ModelCheckpoint
import tensorflow_hu... | Natural Language Processing with Disaster Tweets |
9,998,024 | numerics = ['int8', 'int16', 'int32', 'int64', 'float16', 'float32', 'float64']
categorical_columns = []
features = train.columns.values.tolist()
for col in features:
if train[col].dtype in numerics: continue
categorical_columns.append(col)
for col in categorical_columns:
if col in train.columns:
le = LabelEncoder()
l... | def clean_text(text):
new_text = []
for each in text.split() :
if each.isalpha() :
new_text.append(each)
cleaned_text = ' '.join(new_text)
cleaned_text = re.sub(r'https?:\/\/t.co\/[A-Za-z0-9]+','',cleaned_text)
return cleaned_text | Natural Language Processing with Disaster Tweets |
9,998,024 | Xtrain, Xval, Ztrain, Zval = train_test_split(train, target, test_size=0.2, random_state=0)
train_set = lgbm.Dataset(Xtrain, Ztrain, silent=False)
valid_set = lgbm.Dataset(Xval, Zval, silent=False )<init_hyperparams> | def bert_encode(texts, tokenizer, max_len =512):
all_tokens = []
all_masks = []
all_segments = []
for text in texts:
text = tokenizer.tokenize(text)
text = text[:max_len-2]
input_sequence = ['[CLS]'] + text +['[SEP]']
pad_len = max_len - len(input_sequence)
tokens = tokenizer.convert_tokens_to_ids(input_sequence)
to... | Natural Language Processing with Disaster Tweets |
9,998,024 | params = {
'boosting_type':'gbdt',
'objective': 'binary',
'num_leaves': 31,
'learning_rate': 0.05,
'max_depth': -1,
'subsample': 0.8,
'bagging_fraction' : 1,
'max_bin' : 50 ,
'bagging_freq': 20,
'colsample_bytree': 0.6,
'metric': 'binary',
'min_split_gain': 0.5,
'min_child_weight': 1,
'min_child_samples': 2,
'scale_pos... | test_text = list(test_data['text'])
test_input = bert_encode(test_text, tokenizer, max_len=100)
min_loss_index = all_loss.index(min(all_loss))
results = all_models[min_loss_index].predict(test_input)
submission_data = pd.read_csv('/kaggle/input/nlp-getting-started/sample_submission.csv')
submission_data['target'] =... | Natural Language Processing with Disaster Tweets |
9,516,555 | feature_score = pd.DataFrame(train.columns, columns = ['feature'])
feature_score['LGB'] = modelL.feature_importance()<predict_on_test> | train = pd.read_csv('.. /input/nlp-getting-started/train.csv')
test = pd.read_csv('.. /input/nlp-getting-started/test.csv')
sample = pd.read_csv('.. /input/nlp-getting-started/sample_submission.csv' ) | Natural Language Processing with Disaster Tweets |
9,516,555 | y_preds_lgb = modelL.predict(test, num_iteration=modelL.best_iteration )<prepare_x_and_y> | sns.countplot(train.text.duplicated() ) | Natural Language Processing with Disaster Tweets |
9,516,555 | data_tr = xgb.DMatrix(Xtrain, label=Ztrain)
data_cv = xgb.DMatrix(Xval , label=Zval)
data_train = xgb.DMatrix(train)
data_test = xgb.DMatrix(test)
evallist = [(data_tr, 'train'),(data_cv, 'valid')]<train_model> | duplicate_index = train[train.text.duplicated() ].index
train.drop(index = duplicate_index, inplace = True)
train.reset_index(drop = True, inplace = True ) | Natural Language Processing with Disaster Tweets |
9,516,555 | parms = {'max_depth':5,
'objective':'reg:logistic',
'eval_metric':'error',
'learning_rate':0.01,
'subsample':0.8,
'colsample_bylevel':0.9,
'min_child_weight': 2,
'seed': 0}
modelx = xgb.train(parms, data_tr, num_boost_round=2000, evals = evallist,
early_stopping_rounds=300, maximize=False,
verbose_eval=100)
print('sco... | shortforms = {"ain't": "am not",
"aren't": "are not",
"can't": "cannot",
"can't've": "cannot have",
"'cause": "because",
"could've": "could have",
"couldn't": "could not",
"couldn't've": "could not have",
"didn't": "did not",
"doesn't": "does not",
"don't": "do not",
"hadn't": "had not",
"hadn't've": "had not have",
"h... | Natural Language Processing with Disaster Tweets |
9,516,555 | feature_score['XGB'] = feature_score['feature'].map(modelx.get_score(importance_type='weight'))<predict_on_test> | def cleaner(text):
text = str(text ).lower()
text = re.sub(r'<*?>',' ',text)
text = re.sub(r'https?://\S+|www\.\S+',' ',text)
text = ' '.join([shortforms[word]
if word in shortforms.keys() else word
for word in text.split() ])
text = str(text ).lower()
text = re.sub(r'^\s','',text)
text = re.sub(r'\s+',' ',text)
r... | Natural Language Processing with Disaster Tweets |
9,516,555 | y_preds_xgb = modelx.predict(data_test )<normalization> | %%time
train['cleaner_text'] = train.text.progress_apply(lambda x: cleaner(x))
test['cleaner_text'] = test.text.progress_apply(lambda x: cleaner(x)) | Natural Language Processing with Disaster Tweets |
9,516,555 | Scaler_train = preprocessing.MinMaxScaler().fit(train)
train = pd.DataFrame(Scaler_train.transform(train), columns=train.columns, index=train.index)
test = pd.DataFrame(Scaler_train.transform(test), columns=test.columns, index=test.index )<train_on_grid> | case = 'roberta-base'
tokenizer = RobertaTokenizer.from_pretrained(case)
config = AutoConfig.from_pretrained(case, output_attentions = True, output_hidden_states = True)
model = TFAutoModel.from_pretrained(case, config = config)
bert = TFRobertaMainLayer(config ) | Natural Language Processing with Disaster Tweets |
9,516,555 | linreg = LinearRegression()
linreg.fit(train, target )<load_pretrained> | %%time
def convert2token(all_text):
token_id, attention_id = [], []
for i, sent in tqdm.tqdm(enumerate(all_text)) :
token_dict = tokenizer.encode_plus(sent, max_length=60, pad_to_max_length=True, return_attention_mask=True,
return_tensors='tf', add_special_tokens= True)
token_id.append(token_dict['input_ids'])
attent... | Natural Language Processing with Disaster Tweets |
9,516,555 | eli5.show_weights(linreg )<merge> | def building_model(need_emb):
inp_1 = tf.keras.layers.Input(shape =(60,), name = 'token_id', dtype = 'int32')
inp_2 = tf.keras.layers.Input(shape =(60,), name = 'mask_id', dtype = 'int32')
x1 = tf.keras.layers.Reshape(( 60,))(inp_1)
x2 = tf.keras.layers.Reshape(( 60,))(inp_2)
if need_emb:
emb = model(x1, attention_... | Natural Language Processing with Disaster Tweets |
9,516,555 | coeff_linreg["LinRegress"] = coeff_linreg["LinRegress"].abs()
feature_score = pd.merge(feature_score, coeff_linreg, on='feature')
feature_score = feature_score.fillna(0)
feature_score = feature_score.set_index('feature')
feature_score<predict_on_test> | Emb_Model.compile(metrics=['accuracy'], optimizer=tf.keras.optimizers.Adam(learning_rate = 4e-5), loss='binary_crossentropy')
Emb_Model.fit([np.reshape(train_token_id,(7503,60)) , np.reshape(train_attention_id,(7503,60)) ], train.target, epochs=10,
batch_size=64, validation_split=0.20, shuffle = True ) | Natural Language Processing with Disaster Tweets |
9,516,555 | y_preds_linreg = linreg.predict(test )<prepare_output> | %%time
Emb_Model_Answer = Emb_Model.predict([np.reshape(test_token_id,(3263,60)) , np.reshape(test_attention_id,(3263,60)) ] ) | Natural Language Processing with Disaster Tweets |
9,516,555 | feature_score = pd.DataFrame(
preprocessing.MinMaxScaler().fit_transform(feature_score),
columns=feature_score.columns,
index=feature_score.index
)
feature_score['Mean'] = feature_score.mean(axis=1 )<feature_engineering> | Tune_Bert.compile(metrics=['accuracy'], optimizer=tf.keras.optimizers.Adam(learning_rate=1e-5), loss='binary_crossentropy')
Tune_Bert.fit([np.reshape(train_token_id,(7503,60)) , np.reshape(train_attention_id,(7503,60)) ], train.target, epochs=10,
batch_size=64, validation_split=0.20, shuffle = True, callbacks = [callb... | Natural Language Processing with Disaster Tweets |
9,516,555 | w_lgb = 0.4
w_xgb = 0.5
w_linreg = 1 - w_lgb - w_xgb
w_linreg
feature_score['Merging'] = w_lgb*feature_score['LGB'] + w_xgb*feature_score['XGB'] + w_linreg*feature_score['LinRegress']
feature_score.sort_values('Merging', ascending=False )<feature_engineering> | Tune_Bert.load_weights('best.hdf5')
Tune_answer = Tune_Bert.predict([np.reshape(test_token_id,(3263,60)) , np.reshape(test_attention_id,(3263,60)) ] ) | Natural Language Processing with Disaster Tweets |
9,516,555 | def features_selection_by_weights(df, threshold):
features_list = df.feature.tolist()
features_best = []
for i in range(len(df)) :
feature_name = features_list[i]
feature_is_best = False
for col in feature_score_columns:
if df.loc[i, col] > threshold:
feature_is_best = True
if feature_is_best:
features_best.append(feat... | answer_Emb = pd.DataFrame({'id': sample.id, 'target': np.where(Emb_Model_Answer>0.5,1,0 ).reshape(Emb_Model_Answer.shape[0])})
answer_tune = pd.DataFrame({'id': sample.id, 'target': np.where(Tune_answer>0.5,1,0 ).reshape(Tune_answer.shape[0])} ) | Natural Language Processing with Disaster Tweets |
9,516,555 | <prepare_output><EOS> | answer_Emb.to_csv('submission_emb.csv', index = False)
answer_tune.to_csv('submission_tune.csv', index = False ) | Natural Language Processing with Disaster Tweets |
9,641,083 | <SOS> metric: meanfscore Kaggle data source: natural-language-processing-with-disaster-tweets<save_to_csv> | !wget --quiet https://raw.githubusercontent.com/tensorflow/models/master/official/nlp/bert/tokenization.py | Natural Language Processing with Disaster Tweets |
9,641,083 | submission.to_csv('submission.csv', index=False )<save_to_csv> | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import os
from wordcloud import WordCloud
from nltk.corpus import stopwords
from tqdm.notebook import tqdm
import tensorflow as tf
from tensorflow.keras.layers import Dense, Input
from tensorflow.keras.optimizers import Adam
fr... | Natural Language Processing with Disaster Tweets |
9,641,083 | submission.to_csv('submission.csv', index=False )<import_modules> | pd.set_option('display.max_rows', 500)
pd.set_option('display.max_columns', 500)
pd.set_option('display.width', 1000)
plt.style.use('fivethirtyeight' ) | Natural Language Processing with Disaster Tweets |
9,641,083 | import numpy as np
import pandas as pd<load_from_csv> | train_data = pd.read_csv("/kaggle/input/nlp-getting-started/train.csv")
test_data = pd.read_csv("/kaggle/input/nlp-getting-started/test.csv" ) | Natural Language Processing with Disaster Tweets |
9,641,083 | train_data = pd.read_csv("/kaggle/input/titanic/train.csv")
test_data = pd.read_csv("/kaggle/input/titanic/test.csv" )<count_missing_values> | print("Shape of the training dataset: {}.".format(train_data.shape))
print("Shape of the testing dataset: {}".format(test_data.shape))
for col in train_data.columns:
nan_vals = train_data[col].isna().sum()
pcent =(train_data[col].isna().sum() / train_data[col].count())* 100
print("Total NaN values in column '{}' are: {... | Natural Language Processing with Disaster Tweets |
9,641,083 | train_data.isnull().sum()<filter> | def bert_encode(texts, tokenizer, max_len=512):
all_tokens, all_masks, all_segments = [], [], []
for text in tqdm(texts):
text = tokenizer.tokenize(text)
text = text[:max_len-2]
input_sequence = ["[CLS]"] + text + ["[SEP]"]
pad_len = max_len - len(input_sequence)
tokens = tokenizer.convert_tokens_to_ids(input_sequenc... | Natural Language Processing with Disaster Tweets |
9,641,083 | train_data[train_data['Age'].isnull() ]<count_missing_values> | %%time
url = "https://tfhub.dev/tensorflow/bert_en_uncased_L-24_H-1024_A-16/1"
bert_layer = hub.KerasLayer(url, trainable=True ) | Natural Language Processing with Disaster Tweets |
9,641,083 | test_data.isnull().sum()<filter> | vocab_fl = bert_layer.resolved_object.vocab_file.asset_path.numpy()
lower_case = bert_layer.resolved_object.do_lower_case.numpy()
tokenizer = tokenization.FullTokenizer(vocab_fl, lower_case ) | Natural Language Processing with Disaster Tweets |
9,641,083 | train_data[train_data['Embarked'].isnull() ]<filter> | %%time
train_input = bert_encode(train_data['text'].values, tokenizer, max_len=160)
test_input = bert_encode(test_data['text'].values, tokenizer, max_len=160)
train_labels = train_data['target'].values | Natural Language Processing with Disaster Tweets |
9,641,083 | train_data[train_data['Ticket'] == '113572']<drop_column> | def build_model(transformer, max_len=512):
input_word_ids = Input(shape=(max_len,), dtype=tf.int32, name='input_word_ids')
input_mask = Input(shape=(max_len,), dtype=tf.int32, name='input_mask')
segment_ids = Input(shape=(max_len,), dtype=tf.int32, name='segment_ids')
_, seq_op = transformer([input_word_ids, input_m... | Natural Language Processing with Disaster Tweets |
9,641,083 |
<categorify> | model = build_model(bert_layer, max_len=160)
model.summary() | Natural Language Processing with Disaster Tweets |
9,641,083 | train_data['Age'] = train_data['Age'].fillna(train_data.groupby(['Pclass','Sex','Survived'])['Age'].transform('median'))
test_data['Age'] = test_data['Age'].fillna(test_data.groupby(['Pclass','Sex'])['Age'].transform('median'))<feature_engineering> | checkpoint = ModelCheckpoint('model.h5', monitor='val_loss', save_best_only=True)
train_history = model.fit(
train_input, train_labels,
validation_split=0.1,
epochs=3,
callbacks=[checkpoint],
batch_size=16
) | Natural Language Processing with Disaster Tweets |
9,641,083 | train_data['IsChild'] = np.where(train_data['Age'] <= 10, 'Yes', 'No')
test_data['IsChild'] = np.where(test_data['Age'] <= 10, 'Yes', 'No' )<drop_column> | preds = model.predict(test_input ) | Natural Language Processing with Disaster Tweets |
9,641,083 | train_data = train_data[train_data['Ticket'] != '113572']<save_to_csv> | sub_fl = pd.read_csv("/kaggle/input/nlp-getting-started/sample_submission.csv")
sub_fl['target'] = preds.round().astype(int)
sub_fl.to_csv("submission.csv", index=False ) | Natural Language Processing with Disaster Tweets |
7,409,740 | y = train_data["Survived"]
features = ["Pclass", "Sex", "SibSp", "Parch", "Age"]
X = pd.get_dummies(train_data[features])
X_test = pd.get_dummies(test_data[features])
model = RandomForestClassifier(n_estimators=100, max_depth=5, random_state=1)
model.fit(X, y)
predictions = model.predict(X_test)
output = pd.DataFr... | !pip install bert-for-tf2
!pip install sentencepiece | Natural Language Processing with Disaster Tweets |
7,409,740 | %matplotlib inline<load_from_csv> | try:
%tensorflow_version 2.x
except Exception:
pass
| Natural Language Processing with Disaster Tweets |
7,409,740 | train= pd.read_csv('/kaggle/input/titanic/train.csv' )<categorify> | FullTokenizer = bert.bert_tokenization.FullTokenizer
bert_layer = hub.KerasLayer("https://tfhub.dev/tensorflow/bert_en_uncased_L-24_H-1024_A-16/1", trainable=False)
vocab_file = bert_layer.resolved_object.vocab_file.asset_path.numpy()
do_lower_case = bert_layer.resolved_object.do_lower_case.numpy()
tokenizer = FullTok... | Natural Language Processing with Disaster Tweets |
7,409,740 | def impute_age(cols):
Age = cols[0]
Pclass = cols[1]
if pd.isnull(Age):
if Pclass==1:
return 37
elif Pclass == 2:
return 29
else :
return 24
else:
return Age<feature_engineering> | train_cols = ["id", "keyword", "location", "text", "target"]
train = pd.read_csv(
"/kaggle/input/nlp-getting-started/train.csv",
header=None,
names=train_cols,
skiprows=1,
engine="python",
encoding="latin1"
)
test_cols = ["id", "keyword", "location", "text"]
test = pd.read_csv(
"/kaggle/input/nlp-getting-started/te... | Natural Language Processing with Disaster Tweets |
7,409,740 | train['Age']=train[['Age','Pclass']].apply(impute_age,axis = 1 )<drop_column> | def get_ids(tokens):
return tokenizer.convert_tokens_to_ids(tokens)
def get_mask(tokens):
return np.char.not_equal(tokens, "[PAD]" ).astype(int)
def get_segments(tokens):
seg_ids = []
current_seg_id = 0
for tok in tokens:
seg_ids.append(current_seg_id)
if tok == "[SEP]":
current_seg_id = 1-current_seg_id
return seg_... | Natural Language Processing with Disaster Tweets |
7,409,740 | train.drop('Cabin',inplace = True,axis =1 )<categorify> | data_with_len = [[sent, train_labels[i], len(sent)]
for i, sent in enumerate(train_inputs)]
random.shuffle(data_with_len)
data_with_len.sort(key=lambda x: x[2])
train_all = [
(
[
get_ids(sent_lab[0]),
get_mask(sent_lab[0]),
get_segments(sent_lab[0])
],
sent_lab[1]
)
for sent_lab in data_with_len]
| Natural Language Processing with Disaster Tweets |
7,409,740 | sex = pd.get_dummies(train['Sex'],drop_first=True)
embark = pd.get_dummies(train['Embarked'],drop_first=True )<concatenate> | all_dataset = tf.data.Dataset.from_generator(lambda: train_all, output_types=(tf.int32, tf.int32)) | Natural Language Processing with Disaster Tweets |
7,409,740 | train = pd.concat([train,sex,embark],axis=1 )<drop_column> | BATCH_SIZE = 32
all_batched = all_dataset.padded_batch(BATCH_SIZE,
padded_shapes=(( 3, None),()),
padding_values=(0, 0))
NB_BATCHES = math.ceil(len(train_all)/ BATCH_SIZE)
NB_BATCHES_TEST = NB_BATCHES // 10
all_batched.shuffle(NB_BATCHES)
test_dataset = all_batched.take(NB_BATCHES_TEST)
train_dataset = all_batched.s... | Natural Language Processing with Disaster Tweets |
7,409,740 | train.drop(['Sex','Embarked','PassengerId','Name','Ticket'],axis=1,inplace=True )<normalization> | class DCNNBERTEmbedding(tf.keras.Model):
def __init__(self,
nb_filters=50,
FFN_units=512,
nb_classes=2,
dropout_rate=0.1,
name="dcnn"):
super(DCNNBERTEmbedding, self ).__init__(name=name)
self.bert_layer = hub.KerasLayer(
"https://tfhub.dev/tensorflow/bert_en_uncased_L-24_H-1024_A-16/1",
trainable=True)
self.bigram ... | Natural Language Processing with Disaster Tweets |
7,409,740 | st = StandardScaler()<categorify> | NB_FILTERS = 128
FFN_UNITS = 256
NB_CLASSES = 2
DROPOUT_RATE = 0.2
BATCH_SIZE = 32
NB_EPOCHS = 3 | Natural Language Processing with Disaster Tweets |
7,409,740 | feature_scale = ['Age','Fare']
train[feature_scale] = st.fit_transform(train[feature_scale] )<prepare_x_and_y> | Dcnn = DCNNBERTEmbedding(nb_filters=NB_FILTERS,
FFN_units=FFN_UNITS,
nb_classes=NB_CLASSES,
dropout_rate=DROPOUT_RATE)
if NB_CLASSES == 2:
Dcnn.compile(loss="binary_crossentropy",
optimizer=tf.optimizers.Adam(learning_rate=2e-5),
metrics=["accuracy"])
else:
Dcnn.compile(loss="sparse_categorical_crossentropy",
optimiz... | Natural Language Processing with Disaster Tweets |
7,409,740 | x = train.drop(['Survived'],axis=1)
y = train['Survived']<import_modules> | results = Dcnn.evaluate(test_dataset)
print(results ) | Natural Language Processing with Disaster Tweets |
7,409,740 | from sklearn.model_selection import GridSearchCV
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.svm import SVC
from sklearn.neighbors import KNeighborsClassifier<train_model> | cols = ["id", "keyword", "location", "text"]
test = pd.read_csv(
"/kaggle/input/nlp-getting-started/test.csv",
header=None,
names=cols,
skiprows=1,
engine="python",
encoding="latin1"
)
test.keyword = test.apply(fix_keyword, axis=1)
test['new_text'] = test.apply(new_text, axis=1)
test_clean = [clean_tweet(tweet)for... | Natural Language Processing with Disaster Tweets |
7,409,740 | tree = DecisionTreeClassifier()
tree.fit(x,y)
tree.score(x,y )<load_from_csv> | preds = []
for sentence in test_inputs:
input = [[
get_ids(sentence),
get_mask(sentence),
get_segments(sentence)
]]
preds.append(int(np.round(Dcnn.predict(input)[0][0])))
if len(preds)% 100 == 0:
print('Predictions made:', len(preds))
| Natural Language Processing with Disaster Tweets |
7,409,740 | <create_dataframe><EOS> | test['target'] = preds
submission = test[['id', 'target']]
submission.to_csv('submission.csv', index=False)
submission.target.hist() | Natural Language Processing with Disaster Tweets |
10,644,856 | <SOS> metric: meanfscore Kaggle data source: natural-language-processing-with-disaster-tweets<feature_engineering> | !wget --quiet https://raw.githubusercontent.com/tensorflow/models/master/official/nlp/bert/tokenization.py | Natural Language Processing with Disaster Tweets |
10,644,856 | test['Age']=test[['Age','Pclass']].apply(impute_age,axis = 1 )<drop_column> | sns.set(style="darkgrid")
warnings.filterwarnings('ignore' ) | Natural Language Processing with Disaster Tweets |
10,644,856 | test.drop('Cabin',inplace = True,axis =1 )<categorify> | train = pd.read_csv('.. /input/nlp-getting-started/train.csv')
print('Training data shape: ', train.shape)
train.head() | Natural Language Processing with Disaster Tweets |
10,644,856 | sex = pd.get_dummies(test['Sex'],drop_first=True)
embark = pd.get_dummies(test['Embarked'],drop_first=True )<concatenate> | test = pd.read_csv('.. /input/nlp-getting-started/test.csv')
print('Testing data shape: ', test.shape)
test.head() | Natural Language Processing with Disaster Tweets |
10,644,856 | test = pd.concat([test,sex,embark],axis=1 )<drop_column> | train.isnull().sum() | Natural Language Processing with Disaster Tweets |
10,644,856 | test.drop(['Sex','Embarked','PassengerId','Name','Ticket'],axis=1,inplace=True )<correct_missing_values> | test.isnull().sum() | Natural Language Processing with Disaster Tweets |
10,644,856 | test['Fare'].fillna(test['Fare'].mean() ,inplace=True )<categorify> | train['target'].value_counts() | Natural Language Processing with Disaster Tweets |
10,644,856 | feature_scale = ['Age','Fare']
test[feature_scale] = st.fit_transform(test[feature_scale] )<import_modules> | train1 = train.copy()
test1 = test.copy() | Natural Language Processing with Disaster Tweets |
10,644,856 | from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
from sklearn.neighbors import KNeighborsClassifier<choose_model_class> | def clean_text(text):
text = text.lower()
text = re.sub('\[.*?\]', '', text)
text = re.sub('https?://\S+|www\.\S+', '', text)
text = re.sub('<.*?>+', '', text)
text = re.sub('[%s]' % re.escape(string.punctuation), '', text)
text = re.sub('
', '', text)
text = re.sub('\w*\d\w*', '', text)
text = re.sub('[‘’“”…]'... | Natural Language Processing with Disaster Tweets |
10,644,856 | level1 = LogisticRegression()
model = StackingClassifier(estimators=level0,final_estimator=level1,cv=5 )<choose_model_class> | def remove_emoji(text):
emoji_pattern = re.compile("["
u"\U0001F600-\U0001F64F"
u"\U0001F300-\U0001F5FF"
u"\U0001F680-\U0001F6FF"
u"\U0001F1E0-\U0001F1FF"
u"\U00002702-\U000027B0"
u"\U000024C2-\U0001F251"
"]+", flags=re.UNICODE)
return emoji_pattern.sub(r'', text)
train1['text'] = train1['text'].apply(lambda x: remov... | Natural Language Processing with Disaster Tweets |
10,644,856 | level1 = LogisticRegression()
model = StackingClassifier(estimators=level0,final_estimator=level1,cv=5 )<train_model> | def text_preprocessing(text):
tokenizer_reg = nltk.tokenize.RegexpTokenizer(r'\w+')
nopunc = clean_text(text)
tokenized_text = tokenizer_reg.tokenize(nopunc)
remove_stopwords = [w for w in tokenized_text if w not in stopwords.words('english')]
combined_text = ' '.join(remove_stopwords)
return combined_text
train1... | Natural Language Processing with Disaster Tweets |
10,644,856 | model.fit(x,y )<predict_on_test> | count_vectorizer = CountVectorizer(ngram_range =(1,1), min_df = 1)
train_vectors = count_vectorizer.fit_transform(train1['text'])
test_vectors = count_vectorizer.transform(test1["text"])
train_vectors.shape | Natural Language Processing with Disaster Tweets |
10,644,856 | y_predicted = model.predict(test )<create_dataframe> | tfidf = TfidfVectorizer(ngram_range=(1, 2), min_df = 2, max_df = 0.5)
train_tfidf = tfidf.fit_transform(train1['text'])
test_tfidf = tfidf.transform(test1["text"])
train_tfidf.shape | Natural Language Processing with Disaster Tweets |
10,644,856 | submission = pd.DataFrame({
"PassengerId":test2['PassengerId'],
"Survived":y_predicted
} )<save_to_csv> | logreg_bow = LogisticRegression(C=1.0)
scores = model_selection.cross_val_score(logreg_bow, train_vectors, train["target"], cv=5, scoring="f1")
scores.mean() | Natural Language Processing with Disaster Tweets |
10,644,856 | submission.to_csv('first_kaggale_titanic_submission.csv',index=False )<load_from_csv> | logreg_tfidf = LogisticRegression(C=1.0)
scores = model_selection.cross_val_score(logreg_tfidf, train_tfidf, train["target"], cv=5, scoring="f1")
scores.mean() | Natural Language Processing with Disaster Tweets |
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