Kaggle competition: Titanic - Machine Learning from Disaster
<SOS> metric: categorizationaccuracy Kaggle data source: titanic-machine-learning-from-disaster<compute_train_metric>
%matplotlib inline
def OOF_preds(test_df, target, embs_vocab, epochs = 4, alias='prediction', cv=skf,
loss_fn = torch.nn.BCEWithLogitsLoss(reduction='mean', pos_weight=(torch.Tensor([2.7])).to(device)) ,
bs = 512, embedding_dim = 300, bidirectional=True, n_hidden = 64):
print('Embedding vocab size: ', embs_vocab.size() [0])
 test_df[alias] = 0.
 for train, _ in splits_cv(train_ds, cv, target):
train = data.BucketIterator(train, batch_size=bs, device=device,
sort_key=lambda x: len(x.question_text),
sort_within_batch=True,
shuffle=True, repeat=False)
 model = RecNN(embs_vocab, n_hidden, dropout=0., bidirectional=bidirectional ).to(device)
 opt = optim.Adam(filter(lambda p: p.requires_grad, model.parameters()), 1e-3,
betas=(0.75, 0.999), eps=1e-08, weight_decay=0, amsgrad=True)
 print('
')
 for epoch in range(epochs):
y_true_train = np.empty(0)
 y_pred_train = np.empty(0)
 total_loss_train = 0
model.train()
for(_, x), y in train:
y = y.type(dtype=torch.cuda.FloatTensor)
 opt.zero_grad()
pred = model(x)
 loss = loss_fn(pred, y)
 loss.backward()
opt.step()
y_true_train = np.concatenate([y_true_train, y.cpu().data.numpy() ], axis = 0)
 y_pred_train = np.concatenate([y_pred_train, pred.cpu().squeeze().data.numpy() ], axis = 0)
 total_loss_train += loss.item()
tacc = f1_score(y_true_train, y_pred_train>0)
 tloss = total_loss_train/len(train)
 print(f'Epoch {epoch+1}: Train loss: {tloss:.4f}, F1: {tacc:.4f}')
 preds = torch.empty(0)
 qids = []
for(y, x), _ in test_loader:
pred = model(x)
 qids.append(y)
 preds = torch.cat([preds, pred.detach().cpu() ])
 preds = torch.sigmoid(preds ).numpy()
qids = [item for sublist in qids for item in sublist]
test_df.at[qids, alias] = test_df.loc[qids][alias].values + preds/n_folds
gc.enable() ;
del train
gc.collect() ;
gc.enable() ;
del embs_vocab, model
gc.collect() ;
return test_df<load_pretrained>
from sklearn.preprocessing import OneHotEncoder, LabelEncoder, label_binarize, StandardScaler
def preload_gnews() :
vector_google = KeyedVectors.load_word2vec_format(os.path.join(emb_path, embs_file['gnews']), binary=True)
 stoi = {s:idx for idx, s in enumerate(vector_google.index2word)}
itos = {idx:s for idx, s in enumerate(vector_google.index2word)}
cache='cache/'
path_cache = os.path.join(cache, 'GoogleNews-vectors-negative300.bin')
 file_suffix = '.pt'
path_pt = path_cache + file_suffix
torch.save(( itos, stoi, torch.from_numpy(vector_google.vectors), vector_google.vectors.shape[1]), path_pt)
 embs_file = {}
embs_file['wiki'] = 'wiki-news-300d-1M/wiki-news-300d-1M.vec'
embs_file['gnews'] = 'GoogleNews-vectors-negative300/GoogleNews-vectors-negative300.bin'
embs_file['glove'] = 'glove.840B.300d/glove.840B.300d.txt'
embs_file['gram'] = 'paragram_300_sl999/paragram_300_sl999.txt'
embs_vocab = {}
!mkdir cache
preload_gnews()
vec = vocab.Vectors(os.path.join(emb_path, embs_file['gnews']), cache='cache/')
 txt_field.build_vocab(train_ds, test_ds, max_size=350000, vectors=vec)
 embs_vocab['gnews'] = train_ds.fields['question_text'].vocab.vectors
!rm -r cache
vec = vocab.Vectors(os.path.join(emb_path, embs_file['wiki']), cache='cache/')
 txt_field.build_vocab(train_ds, test_ds, max_size=350000, vectors=vec)
 embs_vocab['wiki'] = train_ds.fields['question_text'].vocab.vectors
vec = vocab.Vectors(os.path.join(emb_path, embs_file['glove']), cache='cache/')
 txt_field.build_vocab(train_ds, test_ds, max_size=350000, vectors=vec)
 embs_vocab['glove'] = train_ds.fields['question_text'].vocab.vectors
print('Embedding loaded, vocab size: ', embs_vocab['glove'].size() [0])
 !rm -r cache
gc.enable()
del vec
gc.collect() ;<feature_engineering>
from sklearn.metrics import mean_absolute_error as MAE
from sklearn.model_selection import cross_val_score
from sklearn.preprocessing import OneHotEncoder
def fill_unknown(vector):
data = torch.zeros_like(vector)
 data.copy_(vector)
 idx = torch.nonzero(data.sum(dim=1)== 0)
 data[idx] = embs_vocab['glove'][idx]
idx = torch.nonzero(data.sum(dim=1)== 0)
 data[idx] = embs_vocab['wiki'][idx]
idx = torch.nonzero(data.sum(dim=1)== 0)
 data[idx] = embs_vocab['gnews'][idx]
return data<compute_train_metric>
from sklearn.ensemble import RandomForestClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.linear_model import LogisticRegression
from sklearn import svm
from sklearn.neighbors import KNeighborsClassifier
from sklearn.ensemble import GradientBoostingClassifier
%%time
subm = OOF_preds(subm, target, epochs = 5, alias='wiki',
embs_vocab=fill_unknown(embs_vocab['wiki']),
cv = StratifiedKFold(n_splits = n_folds, shuffle = True, random_state = seed),
embedding_dim = 300, bidirectional=True, n_hidden = 64 )<compute_train_metric>
from catboost import CatBoostClassifier, Pool, cv
%%time
subm = OOF_preds(subm, target, epochs = 5, alias='glove',
embs_vocab=fill_unknown(embs_vocab['glove']),
cv = StratifiedKFold(n_splits = n_folds, shuffle = True, random_state = seed+15),
bs = 512, embedding_dim = 300, bidirectional=True, n_hidden = 64 )<compute_train_metric>
from sklearn.ensemble import VotingClassifier
%%time
subm = OOF_preds(subm, target, epochs = 5, alias='gnews',
embs_vocab=fill_unknown(embs_vocab['gnews']),
cv = StratifiedKFold(n_splits = n_folds, shuffle = True, random_state = seed+25),
bs = 512, embedding_dim = 300, bidirectional=True, n_hidden = 64 )<feature_engineering>
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import RandomizedSearchCV
submission = np.mean(subm.values, axis = 1 )<save_to_csv>
train_data_path = ".. /input/titanic/train.csv"
test_data_path = ".. /input/titanic/test.csv"
subm['prediction'] = submission > 0.55
subm.prediction = subm.prediction.astype('int')
 subm.to_csv('submission.csv', columns=['prediction'] )<import_modules>
train_data = pd.read_csv(train_data_path)
 test_data = pd.read_csv(test_data_path )
tqdm.pandas(desc='Progress')
<define_variables>
num_var = ['Age', 'SibSp', 'Parch', 'Fare']
cat_var = ['Survived', 'Pclass', 'Sex', 'Ticket', 'Cabin', 'Embarked']
embed_size = 300
max_features = 120000
maxlen = 70
batch_size = 512
n_epochs = 5
n_splits = 5
SEED = 10
debug =0<choose_model_class>
df_num = train_data[num_var]
df_cat = train_data[cat_var]
loss_fn = torch.nn.BCEWithLogitsLoss(reduction='sum' )<set_options>
pd.pivot_table(train_data, index='Survived', values = num_var )
def seed_everything(seed=10):
random.seed(seed)
 os.environ['PYTHONHASHSEED'] = str(seed)
 np.random.seed(seed)
 torch.manual_seed(seed)
 torch.cuda.manual_seed(seed)
 torch.backends.cudnn.deterministic = True
seed_everything()<features_selection>
[i for i in cat_var if i not in ['Survived', 'Ticket']]
def load_glove(word_index):
EMBEDDING_FILE = '.. /input/embeddings/glove.840B.300d/glove.840B.300d.txt'
def get_coefs(word,*arr): return word, np.asarray(arr, dtype='float32')[:300]
embeddings_index = dict(get_coefs(*o.split(" ")) for o in open(EMBEDDING_FILE))
all_embs = np.stack(embeddings_index.values())
 emb_mean,emb_std = -0.005838499,0.48782197
embed_size = all_embs.shape[1]
nb_words = min(max_features, len(word_index))
embedding_matrix = np.random.normal(emb_mean, emb_std,(nb_words, embed_size))
for word, i in word_index.items() :
if i >= max_features: continue
embedding_vector = embeddings_index.get(word)
 if embedding_vector is not None:
embedding_matrix[i] = embedding_vector
else:
embedding_vector = embeddings_index.get(word.capitalize())
 if embedding_vector is not None:
embedding_matrix[i] = embedding_vector
return embedding_matrix
def load_fasttext(word_index):
EMBEDDING_FILE = '.. /input/embeddings/wiki-news-300d-1M/wiki-news-300d-1M.vec'
def get_coefs(word,*arr): return word, np.asarray(arr, dtype='float32')
 embeddings_index = dict(get_coefs(*o.split(" ")) for o in open(EMBEDDING_FILE)if len(o)>100)
 all_embs = np.stack(embeddings_index.values())
 emb_mean,emb_std = all_embs.mean() , all_embs.std()
embed_size = all_embs.shape[1]
nb_words = min(max_features, len(word_index))
embedding_matrix = np.random.normal(emb_mean, emb_std,(nb_words, embed_size))
for word, i in word_index.items() :
if i >= max_features: continue
embedding_vector = embeddings_index.get(word)
 if embedding_vector is not None: embedding_matrix[i] = embedding_vector
return embedding_matrix
def load_para(word_index):
EMBEDDING_FILE = '.. /input/embeddings/paragram_300_sl999/paragram_300_sl999.txt'
def get_coefs(word,*arr): return word, np.asarray(arr, dtype='float32')
 embeddings_index = dict(get_coefs(*o.split(" ")) for o in open(EMBEDDING_FILE, encoding="utf8", errors='ignore')if len(o)>100)
 all_embs = np.stack(embeddings_index.values())
 emb_mean,emb_std = -0.0053247833,0.49346462
embed_size = all_embs.shape[1]
nb_words = min(max_features, len(word_index))
embedding_matrix = np.random.normal(emb_mean, emb_std,(nb_words, embed_size))
for word, i in word_index.items() :
if i >= max_features: continue
embedding_vector = embeddings_index.get(word)
 if embedding_vector is not None: embedding_matrix[i] = embedding_vector
return embedding_matrix<feature_engineering>
train_data['cabin_count'] = train_data.Cabin.apply(lambda x: 0 if pd.isna(x)else len(x.split()))
train_data['cabin_count'].value_counts()
def build_vocab(texts):
sentences = texts.apply(lambda x: x.split() ).values
vocab = {}
for sentence in sentences:
for word in sentence:
try:
vocab[word] += 1
except KeyError:
vocab[word] = 1
return vocab
def known_contractions(embed):
known = []
for contract in contraction_mapping:
if contract in embed:
known.append(contract)
 return known
def clean_contractions(text, mapping):
specials = ["’", "‘", "´", "`"]
for s in specials:
text = text.replace(s, "'")
 text = ' '.join([mapping[t] if t in mapping else t for t in text.split(" ")])
 return text
def correct_spelling(x, dic):
for word in dic.keys() :
x = x.replace(word, dic[word])
 return x
def unknown_punct(embed, punct):
unknown = ''
for p in punct:
if p not in embed:
unknown += p
unknown += ' '
return unknown
def clean_special_chars(text, punct, mapping):
for p in mapping:
text = text.replace(p, mapping[p])
 for p in punct:
text = text.replace(p, f' {p} ')
 specials = {'\u200b': ' ', '…': '...', '\ufeff': '', 'करना': '', 'है': ''}
for s in specials:
text = text.replace(s, specials[s])
 return text
def add_lower(embedding, vocab):
count = 0
for word in vocab:
if word in embedding and word.lower() not in embedding:
embedding[word.lower() ] = embedding[word]
count += 1
print(f"Added {count} words to embedding")
<define_variables>
pd.pivot_table(train_data, index='Survived', columns='cabin_count', values='Ticket', aggfunc='count' )
puncts = [',', '.', '"', ':', ')', '(', '-', '!', '?', '|', ';', "'", '$', '&', '/', '[', ']', '>', '%', '=', '
'·', '_', '{', '}', '©', '^', '®', '`', '<', '→', '°', '€', '™', '›', '♥', '←', '×', '§', '″', '′', 'Â', '█', '½', 'à', '…',
'“', '★', '”', '–', '●', 'â', '►', '−', '¢', '²', '¬', '░', '¶', '↑', '±', '¿', '▾', '═', '¦', '║', '―', '¥', '▓', '—', '‹', '─',
'▒', '：', '¼', '⊕', '▼', '▪', '†', '■', '’', '▀', '¨', '▄', '♫', '☆', 'é', '¯', '♦', '¤', '▲', 'è', '¸', '¾', 'Ã', '⋅', '‘', '∞',
'∙', '）', '↓', '、', '│', '（', '»', '，', '♪', '╩', '╚', '³', '・', '╦', '╣', '╔', '╗', '▬', '❤', 'ï', 'Ø', '¹', '≤', '‡', '√', ]
def clean_text(x):
x = str(x)
 for punct in puncts:
if punct in x:
x = x.replace(punct, f' {punct} ')
 return x
def clean_numbers(x):
if bool(re.search(r'\d', x)) :
x = re.sub('[0-9]{5,}', '
x = re.sub('[0-9]{4}', '
x = re.sub('[0-9]{3}', '
x = re.sub('[0-9]{2}', '
return x
mispell_dict = {"ain't": "is not", "aren't": "are not","can't": "cannot", "'cause": "because", "could've": "could have", "couldn't": "could not", "didn't": "did not", "doesn't": "does not", "don't": "do not", "hadn't": "had not", "hasn't": "has not", "haven't": "have not", "he'd": "he would","he'll": "he will", "he's": "he is", "how'd": "how did", "how'd'y": "how do you", "how'll": "how will", "how's": "how is", "I'd": "I would", "I'd've": "I would have", "I'll": "I will", "I'll've": "I will have","I'm": "I am", "I've": "I have", "i'd": "i would", "i'd've": "i would have", "i'll": "i will", "i'll've": "i will have","i'm": "i am", "i've": "i have", "isn't": "is not", "it'd": "it would", "it'd've": "it would have", "it'll": "it will", "it'll've": "it will have","it's": "it is", "let's": "let us", "ma'am": "madam", "mayn't": "may not", "might've": "might have","mightn't": "might not","mightn't've": "might not have", "must've": "must have", "mustn't": "must not", "mustn't've": "must not have", "needn't": "need not", "needn't've": "need not have","o'clock": "of the clock", "oughtn't": "ought not", "oughtn't've": "ought not have", "shan't": "shall not", "sha'n't": "shall not", "shan't've": "shall not have", "she'd": "she would", "she'd've": "she would have", "she'll": "she will", "she'll've": "she will have", "she's": "she is", "should've": "should have", "shouldn't": "should not", "shouldn't've": "should not have", "so've": "so have","so's": "so as", "this's": "this is","that'd": "that would", "that'd've": "that would have", "that's": "that is", "there'd": "there would", "there'd've": "there would have", "there's": "there is", "here's": "here is","they'd": "they would", "they'd've": "they would have", "they'll": "they will", "they'll've": "they will have", "they're": "they are", "they've": "they have", "to've": "to have", "wasn't": "was not", "we'd": "we would", "we'd've": "we would have", "we'll": "we will", "we'll've": "we will have", "we're": "we are", "we've": "we have", "weren't": "were not", "what'll": "what will", "what'll've": "what will have", "what're": "what are", "what's": "what is", "what've": "what have", "when's": "when is", "when've": "when have", "where'd": "where did", "where's": "where is", "where've": "where have", "who'll": "who will", "who'll've": "who will have", "who's": "who is", "who've": "who have", "why's": "why is", "why've": "why have", "will've": "will have", "won't": "will not", "won't've": "will not have", "would've": "would have", "wouldn't": "would not", "wouldn't've": "would not have", "y'all": "you all", "y'all'd": "you all would","y'all'd've": "you all would have","y'all're": "you all are","y'all've": "you all have","you'd": "you would", "you'd've": "you would have", "you'll": "you will", "you'll've": "you will have", "you're": "you are", "you've": "you have", 'colour': 'color', 'centre': 'center', 'favourite': 'favorite', 'travelling': 'traveling', 'counselling': 'counseling', 'theatre': 'theater', 'cancelled': 'canceled', 'labour': 'labor', 'organisation': 'organization', 'wwii': 'world war 2', 'citicise': 'criticize', 'youtu ': 'youtube ', 'Qoura': 'Quora', 'sallary': 'salary', 'Whta': 'What', 'narcisist': 'narcissist', 'howdo': 'how do', 'whatare': 'what are', 'howcan': 'how can', 'howmuch': 'how much', 'howmany': 'how many', 'whydo': 'why do', 'doI': 'do I', 'theBest': 'the best', 'howdoes': 'how does', 'mastrubation': 'masturbation', 'mastrubate': 'masturbate', "mastrubating": 'masturbating', 'pennis': 'penis', 'Etherium': 'Ethereum', 'narcissit': 'narcissist', 'bigdata': 'big data', '2k17': '2017', '2k18': '2018', 'qouta': 'quota', 'exboyfriend': 'ex boyfriend', 'airhostess': 'air hostess', "whst": 'what', 'watsapp': 'whatsapp', 'demonitisation': 'demonetization', 'demonitization': 'demonetization', 'demonetisation': 'demonetization'}
def _get_mispell(mispell_dict):
mispell_re = re.compile('(%s)' % '|'.join(mispell_dict.keys()))
return mispell_dict, mispell_re
mispellings, mispellings_re = _get_mispell(mispell_dict)
 def replace_typical_misspell(text):
def replace(match):
return mispellings[match.group(0)]
return mispellings_re.sub(replace, text)
<concatenate>
train_data['cabin_adv'] = train_data.Cabin.apply(lambda x: str(x)[0])
 train_data['cabin_adv'].value_counts()
def parallelize_apply(df,func,colname,num_process,newcolnames):
pool =Pool(processes=num_process)
 arraydata = pool.map(func,tqdm(df[colname].values))
pool.close()
newdf = pd.DataFrame(arraydata,columns = newcolnames)
 df = pd.concat([df,newdf],axis=1)
 return df
def parallelize_dataframe(df, func):
df_split = np.array_split(df, 4)
 pool = Pool(4)
 df = pd.concat(pool.map(func, df_split))
pool.close()
pool.join()
return df
def count_regexp_occ(regexp="", text=None):

 return len(re.findall(regexp, text))
def add_features(df):
df['question_text'] = df['question_text'].progress_apply(lambda x:str(x))
df["lower_question_text"] = df["question_text"].apply(lambda x: x.lower())
 df['total_length'] = df['question_text'].progress_apply(len)
 df['capitals'] = df['question_text'].progress_apply(lambda comment: sum(1 for c in comment if c.isupper()))
df['caps_vs_length'] = df.progress_apply(lambda row: float(row['capitals'])/float(row['total_length']),
axis=1)
 df['num_words'] = df.question_text.str.count('\S+')
 df['num_unique_words'] = df['question_text'].progress_apply(lambda comment: len(set(w for w in comment.split())))
 df['words_vs_unique'] = df['num_unique_words'] / df['num_words']
return df
def load_and_prec() :
if debug:
train_df = pd.read_csv(".. /input/train.csv")[:80000]
test_df = pd.read_csv(".. /input/test.csv")[:20000]
else:
train_df = pd.read_csv(".. /input/train.csv")
 test_df = pd.read_csv(".. /input/test.csv")
 print("Train shape : ",train_df.shape)
 print("Test shape : ",test_df.shape)

train = parallelize_dataframe(train_df, add_features)
 test = parallelize_dataframe(test_df, add_features)
 train_df["question_text"] = train_df["question_text"].apply(lambda x: x.lower())
 test_df["question_text"] = test_df["question_text"].apply(lambda x: x.lower())
 train_df["question_text"] = train_df["question_text"].progress_apply(lambda x: clean_text(x))
test_df["question_text"] = test_df["question_text"].apply(lambda x: clean_text(x))
train_df["question_text"] = train_df["question_text"].progress_apply(lambda x: clean_numbers(x))
test_df["question_text"] = test_df["question_text"].apply(lambda x: clean_numbers(x))
train_df["question_text"] = train_df["question_text"].progress_apply(lambda x: replace_typical_misspell(x))
test_df["question_text"] = test_df["question_text"].apply(lambda x: replace_typical_misspell(x))
train_X = train_df["question_text"].fillna("_
test_X = test_df["question_text"].fillna("_
features = train[['num_unique_words','words_vs_unique']].fillna(0)
 test_features = test[['num_unique_words','words_vs_unique']].fillna(0)
 ss = StandardScaler()
pc = PCA(n_components=5)
 ss.fit(np.vstack(( features, test_features)))
 features = ss.transform(features)
 test_features = ss.transform(test_features)
 tokenizer = Tokenizer(num_words=max_features)
 tokenizer.fit_on_texts(list(train_X))
train_X = tokenizer.texts_to_sequences(train_X)
 test_X = tokenizer.texts_to_sequences(test_X)
 train_X = pad_sequences(train_X, maxlen=maxlen)
 test_X = pad_sequences(test_X, maxlen=maxlen)
 train_y = train_df['target'].values
np.random.seed(SEED)
 trn_idx = np.random.permutation(len(train_X))
train_X = train_X[trn_idx]
train_y = train_y[trn_idx]
features = features[trn_idx]
return train_X, test_X, train_y, features, test_features, tokenizer.word_index 
<categorify>
pd.pivot_table(train_data, index='Survived', columns='cabin_adv', values='Ticket', aggfunc='count' )
start = time.time()
x_train, x_test, y_train, features, test_features, word_index = load_and_prec()
print(time.time() -start )<normalization>
train_data['numeric_ticket'] = train_data.Ticket.apply(lambda x: 1 if x.isnumeric() else 0)
 train_data['numeric_ticket']
seed_everything()
if debug:
paragram_embeddings = np.random.randn(120000,300)
 glove_embeddings = np.random.randn(120000,300)
 embedding_matrix = np.mean([glove_embeddings, paragram_embeddings], axis=0)
 else:
glove_embeddings = load_glove(word_index)
 paragram_embeddings = load_para(word_index)
 embedding_matrix = np.mean([glove_embeddings, paragram_embeddings], axis=0 )<choose_model_class>
pd.pivot_table(train_data, index='Survived', columns='numeric_ticket', values='Ticket', aggfunc='count' )
class CyclicLR(object):
def __init__(self, optimizer, base_lr=1e-3, max_lr=6e-3,
step_size=2000, mode='triangular', gamma=1.,
scale_fn=None, scale_mode='cycle', last_batch_iteration=-1):
if not isinstance(optimizer, Optimizer):
raise TypeError('{} is not an Optimizer'.format(
 type(optimizer ).__name__))
self.optimizer = optimizer
if isinstance(base_lr, list)or isinstance(base_lr, tuple):
if len(base_lr)!= len(optimizer.param_groups):
raise ValueError("expected {} base_lr, got {}".format(
 len(optimizer.param_groups), len(base_lr)))
 self.base_lrs = list(base_lr)
 else:
self.base_lrs = [base_lr] * len(optimizer.param_groups)
 if isinstance(max_lr, list)or isinstance(max_lr, tuple):
if len(max_lr)!= len(optimizer.param_groups):
raise ValueError("expected {} max_lr, got {}".format(
 len(optimizer.param_groups), len(max_lr)))
 self.max_lrs = list(max_lr)
 else:
self.max_lrs = [max_lr] * len(optimizer.param_groups)
 self.step_size = step_size
if mode not in ['triangular', 'triangular2', 'exp_range'] \
and scale_fn is None:
raise ValueError('mode is invalid and scale_fn is None')
 self.mode = mode
self.gamma = gamma
if scale_fn is None:
if self.mode == 'triangular':
self.scale_fn = self._triangular_scale_fn
self.scale_mode = 'cycle'
elif self.mode == 'triangular2':
self.scale_fn = self._triangular2_scale_fn
self.scale_mode = 'cycle'
elif self.mode == 'exp_range':
self.scale_fn = self._exp_range_scale_fn
self.scale_mode = 'iterations'
else:
self.scale_fn = scale_fn
self.scale_mode = scale_mode
self.batch_step(last_batch_iteration + 1)
 self.last_batch_iteration = last_batch_iteration
def batch_step(self, batch_iteration=None):
if batch_iteration is None:
batch_iteration = self.last_batch_iteration + 1
self.last_batch_iteration = batch_iteration
for param_group, lr in zip(self.optimizer.param_groups, self.get_lr()):
param_group['lr'] = lr
def _triangular_scale_fn(self, x):
return 1.
 def _triangular2_scale_fn(self, x):
return 1 /(2.**(x - 1))
def _exp_range_scale_fn(self, x):
return self.gamma**(x)
 def get_lr(self):
step_size = float(self.step_size)
 cycle = np.floor(1 + self.last_batch_iteration /(2 * step_size))
x = np.abs(self.last_batch_iteration / step_size - 2 * cycle + 1)
 lrs = []
param_lrs = zip(self.optimizer.param_groups, self.base_lrs, self.max_lrs)
 for param_group, base_lr, max_lr in param_lrs:
base_height =(max_lr - base_lr)* np.maximum(0,(1 - x))
if self.scale_mode == 'cycle':
lr = base_lr + base_height * self.scale_fn(cycle)
 else:
lr = base_lr + base_height * self.scale_fn(self.last_batch_iteration)
 lrs.append(lr)
 return lrs 
<categorify>
print(142/88, 407/254 )
class MyDataset(Dataset):
def __init__(self,dataset):
self.dataset = dataset
def __getitem__(self,index):
data,target = self.dataset[index]
return data,target,index
def __len__(self):
return len(self.dataset )<compute_train_metric>
train_data['name_title'] = train_data.Name.apply(lambda x: x.split(',', 1)[1].split('.', 1)[0])
 train_data['name_title']
def pytorch_model_run_cv(x_train,y_train,features,x_test, model_obj, feats = False,clip = True):
seed_everything()
avg_losses_f = []
avg_val_losses_f = []
train_preds = np.zeros(( len(x_train)))
 test_preds = np.zeros(( len(x_test)))
 splits = list(StratifiedKFold(n_splits=n_splits, shuffle=True, random_state=SEED ).split(x_train, y_train))
for i,(train_idx, valid_idx)in enumerate(splits):
seed_everything(i*1000+i)
 x_train = np.array(x_train)
 y_train = np.array(y_train)
 if feats:
features = np.array(features)
 x_train_fold = torch.tensor(x_train[train_idx.astype(int)], dtype=torch.long ).cuda()
y_train_fold = torch.tensor(y_train[train_idx.astype(int), np.newaxis], dtype=torch.float32 ).cuda()
if feats:
kfold_X_features = features[train_idx.astype(int)]
kfold_X_valid_features = features[valid_idx.astype(int)]
x_val_fold = torch.tensor(x_train[valid_idx.astype(int)], dtype=torch.long ).cuda()
y_val_fold = torch.tensor(y_train[valid_idx.astype(int), np.newaxis], dtype=torch.float32 ).cuda()
model = copy.deepcopy(model_obj)
 model.cuda()
loss_fn = torch.nn.BCEWithLogitsLoss(reduction='sum')
 step_size = 300
base_lr, max_lr = 0.001, 0.003
optimizer = torch.optim.Adam(filter(lambda p: p.requires_grad, model.parameters()),
lr=max_lr)
 scheduler = CyclicLR(optimizer, base_lr=base_lr, max_lr=max_lr,
step_size=step_size, mode='exp_range',
gamma=0.99994)
 train = MyDataset(torch.utils.data.TensorDataset(x_train_fold, y_train_fold))
valid = MyDataset(torch.utils.data.TensorDataset(x_val_fold, y_val_fold))
train_loader = torch.utils.data.DataLoader(train, batch_size=batch_size, shuffle=True)
 valid_loader = torch.utils.data.DataLoader(valid, batch_size=batch_size, shuffle=False)
 print(f'Fold {i + 1}')
 for epoch in range(n_epochs):
start_time = time.time()
model.train()
avg_loss = 0.
 for i,(x_batch, y_batch, index)in enumerate(train_loader):
if feats:
f = kfold_X_features[index]
y_pred = model([x_batch,f])
 else:
y_pred = model(x_batch)
 if scheduler:
scheduler.batch_step()
loss = loss_fn(y_pred, y_batch)
 optimizer.zero_grad()
loss.backward()
if clip:
nn.utils.clip_grad_norm_(model.parameters() ,1)
 optimizer.step()
avg_loss += loss.item() / len(train_loader)
 model.eval()
valid_preds_fold = np.zeros(( x_val_fold.size(0)))
 test_preds_fold = np.zeros(( len(x_test)))
 avg_val_loss = 0.
 for i,(x_batch, y_batch,index)in enumerate(valid_loader):
if feats:
f = kfold_X_valid_features[index]
y_pred = model([x_batch,f] ).detach()
else:
y_pred = model(x_batch ).detach()
avg_val_loss += loss_fn(y_pred, y_batch ).item() / len(valid_loader)
 valid_preds_fold[index] = sigmoid(y_pred.cpu().numpy())[:, 0]
elapsed_time = time.time() - start_time
print('Epoch {}/{} \t loss={:.4f} \t val_loss={:.4f} \t time={:.2f}s'.format(
 epoch + 1, n_epochs, avg_loss, avg_val_loss, elapsed_time))
avg_losses_f.append(avg_loss)
 avg_val_losses_f.append(avg_val_loss)
 for i,(x_batch,)in enumerate(test_loader):
if feats:
f = test_features[i * batch_size:(i+1)* batch_size]
y_pred = model([x_batch,f] ).detach()
else:
y_pred = model(x_batch ).detach()
test_preds_fold[i * batch_size:(i+1)* batch_size] = sigmoid(y_pred.cpu().numpy())[:, 0]
train_preds[valid_idx] = valid_preds_fold
test_preds += test_preds_fold / len(splits)
 print('All \t loss={:.4f} \t val_loss={:.4f} \t '.format(np.average(avg_losses_f),np.average(avg_val_losses_f)))
 return train_preds, test_preds 
<choose_model_class>
train_data['name_title'].value_counts()
class Alex_NeuralNet_Meta(nn.Module):
def __init__(self,hidden_size,lin_size, embedding_matrix=embedding_matrix):
super(Alex_NeuralNet_Meta, self ).__init__()
self.hidden_size = hidden_size
drp = 0.1
self.embedding = nn.Embedding(max_features, embed_size)
 self.embedding.weight = nn.Parameter(torch.tensor(embedding_matrix, dtype=torch.float32))
self.embedding.weight.requires_grad = False
self.embedding_dropout = nn.Dropout2d(0.1)
 self.lstm = nn.LSTM(embed_size, hidden_size, bidirectional=True, batch_first=True)
 for name, param in self.lstm.named_parameters() :
if 'bias' in name:
nn.init.constant_(param, 0.0)
 elif 'weight_ih' in name:
nn.init.kaiming_normal_(param)
 elif 'weight_hh' in name:
nn.init.orthogonal_(param)
 self.gru = nn.GRU(hidden_size*2, hidden_size, bidirectional=True, batch_first=True)
 for name, param in self.gru.named_parameters() :
if 'bias' in name:
nn.init.constant_(param, 0.0)
 elif 'weight_ih' in name:
nn.init.kaiming_normal_(param)
 elif 'weight_hh' in name:
nn.init.orthogonal_(param)
 self.linear = nn.Linear(hidden_size*6 + features.shape[1], lin_size)
 self.relu = nn.ReLU()
self.dropout = nn.Dropout(drp)
 self.out = nn.Linear(lin_size, 1)
 def forward(self, x):

 h_embedding = self.embedding(x[0])
 embeddings = h_embedding.unsqueeze(2)
 embeddings = embeddings.permute(0, 3, 2, 1)
 embeddings = self.embedding_dropout(embeddings)
 embeddings = embeddings.permute(0, 3, 2, 1)
 h_embedding = embeddings.squeeze(2)
 h_lstm, _ = self.lstm(h_embedding)
 h_gru, hh_gru = self.gru(h_lstm)
 hh_gru = hh_gru.view(-1, 2*self.hidden_size)
 avg_pool = torch.mean(h_gru, 1)
 max_pool, _ = torch.max(h_gru, 1)
 f = torch.tensor(x[1], dtype=torch.float ).cuda()
conc = torch.cat(( hh_gru, avg_pool, max_pool,f), 1)
 conc = self.relu(self.linear(conc))
conc = self.dropout(conc)
 out = self.out(conc)
 return out<normalization>
def feature_engineer_df(df):

 print("adding column 'cabin_count'...")
 df['cabin_count'] = df.Cabin.apply(lambda x: 0 if pd.isna(x)else len(x.split()))
print("adding column 'cabin_adv'...")
 df['cabin_adv'] = df.Cabin.apply(lambda x: str(x)[0])
 print("adding column 'numeric_ticket'...")
 df['numeric_ticket'] = df.Ticket.apply(lambda x: 1 if x.isnumeric() else 0)
 print("adding column 'name_title'...")
 df['name_title'] = df.Name.apply(lambda x: x.split(',', 1)[1].split('.', 1)[0])
 print('imputing null values...')
 print("adding column 'age missing'...")
 df['age_missing'] = df.Age.apply(lambda x: 1 if pd.isnull(x)else 0)
 df.Age = df.Age.fillna(df.Age.mean())
 print("adding column 'fare_missing'...")
 df['fare_missing'] = df.Fare.apply(lambda x: 1 if pd.isnull(x)else 0)
 df.Fare = df.Fare.fillna(df.Fare.median())
 print("dropping rows with missing 'Embarked'...")
 print(f"{np.count_nonzero(df['Embarked'].isnull().values)} rows with missing 'Embarked' will be dropped")
 df.Embarked = df.dropna(subset=['Embarked'])
 print("normalizing 'sibsp'...")
 df['norm_sibsp'] = np.log(df.SibSp+1)
 print("normalizing 'fare'...")
 df['norm_fare'] = np.log(df.Fare+1 )
def sigmoid(x):
return 1 /(1 + np.exp(-x))
seed_everything()
x_test_cuda = torch.tensor(x_test, dtype=torch.long ).cuda()
test = torch.utils.data.TensorDataset(x_test_cuda)
 test_loader = torch.utils.data.DataLoader(test, batch_size=batch_size, shuffle=False )<compute_train_metric>
def solve_mismatch(df_train, df_test):
df_train['train_data'] = 1
df_test['train_data'] = 0
df_test['Survived'] = 0
combined_df = pd.concat([df_train, df_test])
 combined_df.Pclass = combined_df.Pclass.astype(str)
 combined_dummy = pd.get_dummies(combined_df[['PassengerId', 'Survived', 'Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'norm_fare', 'Embarked', 'cabin_adv',
'cabin_count', 'numeric_ticket', 'name_title', 'age_missing', 'fare_missing', 'train_data']])
 new_train = combined_dummy[combined_dummy['train_data']==1]
new_test = combined_dummy[combined_dummy['train_data']==0]
new_train.drop(['train_data'], axis=1, inplace=True)
 new_test.drop(['train_data', 'Survived'], axis=1, inplace=True)
 return(new_train, new_test )
train_preds , test_preds = pytorch_model_run_cv(x_train,y_train,features,x_test,Alex_NeuralNet_Meta(70,16, embedding_matrix=embedding_matrix), feats = True )<compute_test_metric>
def ft_splitted(df, drop=['Survived', 'PassengerId']):
X = df.drop(drop, axis=1)
 y = df[drop[0]]
return([X, y] )
def bestThresshold(y_train,train_preds):
tmp = [0,0,0]
delta = 0
for tmp[0] in tqdm(np.arange(0.1, 0.501, 0.01)) :
tmp[1] = f1_score(y_train, np.array(train_preds)>tmp[0])
 if tmp[1] > tmp[2]:
delta = tmp[0]
tmp[2] = tmp[1]
print('best threshold is {:.4f} with F1 score: {:.4f}'.format(delta, tmp[2]))
return delta , tmp[2]
delta, _ = bestThresshold(y_train,train_preds )<save_to_csv>
feature_engineer_df(train_data )
if debug:
df_test = pd.read_csv(".. /input/test.csv")[:20000]
else:
df_test = pd.read_csv(".. /input/test.csv")
 submission = df_test[['qid']].copy()
submission['prediction'] =(test_preds > delta ).astype(int)
 submission.to_csv('submission.csv', index=False )<import_modules>
train_data['age_missing'].value_counts()
from numpy import array
from numpy import asarray
from numpy import zeros
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pad_sequences
from keras.models import Sequential
from keras.layers import LSTM
from keras.layers import Dense
from keras.layers import Flatten
from keras.layers import Dropout
from keras.layers import Bidirectional
from keras.layers import CuDNNGRU
from keras.layers.embeddings import Embedding<load_from_csv>
feature_engineer_df(test_data )
df_train = pd.read_csv(".. /input/train.csv" )<load_from_csv>
test_data['age_missing'].value_counts()
df_test = pd.read_csv(".. /input/test.csv" )<feature_engineering>
train_data, test_data = solve_mismatch(train_data, test_data )
tokenizer = Tokenizer()
tokenizer.fit_on_texts(df_train['question_text'] )<define_variables>
X_train, y_train = ft_splitted(train_data )
vocab_size = len(tokenizer.word_index)+ 1<string_transform>
def scale_data(X):
scale = StandardScaler()
X_scaled = X.copy()
X_scaled[['Age', 'SibSp', 'Parch', 'norm_fare']] = scale.fit_transform(X_scaled[['Age', 'SibSp', 'Parch', 'norm_fare']])
 return(X_scaled )
ts_train=tokenizer.texts_to_sequences(df_train['question_text'] )<string_transform>
X_train_scaled = scale_data(X_train)
 X_train_scaled.head()
ts_test=tokenizer.texts_to_sequences(df_test['question_text'] )<concatenate>
X_test_scaled = scale_data(test_data.drop(['PassengerId'], axis=1))
X_test_scaled.head()
X_train_vectorized=pad_sequences(ts_train,maxlen=135,padding='post' )<concatenate>
def get_model_accuracy(model, cv=5):
cv_score = cross_val_score(model, X_train_scaled, y_train, cv=cv)
 print(cv_score)
 print(f"{model.__class__.__name__}({format(cv_score.mean() *100, '.2f')}%)")
 return(cv_score.mean() )
X_test_vectorized=pad_sequences(ts_test,maxlen=135,padding='post' )<prepare_x_and_y>
GNB = GaussianNB()
get_model_accuracy(GNB )
y_train = df_train['target']<feature_engineering>
LR = LogisticRegression()
get_model_accuracy(LR )
embeddings_index = {}
f = open('.. /input/embeddings/glove.840B.300d/glove.840B.300d.txt', encoding='utf8')
 for line in f:
values = line.split()
word = ''.join(values[:-300])
 coefs = np.asarray(values[-300:], dtype='float32')
 embeddings_index[word] = coefs
f.close()
print('Loaded %s word vectors.' % len(embeddings_index))<define_variables>
SVC = svm.SVC(probability=True)
 get_model_accuracy(SVC )
embedding_matrix = zeros(( vocab_size, 300))
for word, i in tokenizer.word_index.items() :
embedding_vector = embeddings_index.get(word)
 if embedding_vector is not None:
embedding_matrix[i] = embedding_vector<choose_model_class>
KNN = KNeighborsClassifier()
get_model_accuracy(KNN )
model = Sequential()
e = Embedding(vocab_size, 300, weights=[embedding_matrix], input_length=135, trainable=False)
 model.add(e)
 model.add(Bidirectional(LSTM(128, return_sequences=True)))
 model.add(Flatten())
 model.add(Dense(128, activation='sigmoid'))
model.add(Dropout(0.2))
model.add(Dense(1, activation='sigmoid'))
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
 print(model.summary() )<train_model>
RFC = RandomForestClassifier(random_state=42)
 get_model_accuracy(RFC )
model.fit(X_train_vectorized, y_train, epochs=3, batch_size=1024, verbose=0 )<predict_on_test>
GB = GradientBoostingClassifier(random_state=42)
 get_model_accuracy(GB )
predictions = model.predict(X_test_vectorized )<data_type_conversions>
voting_clf_all = VotingClassifier(
 estimators=[('GNB', GNB),('LR', LR),('SVC', SVC),('KNN', KNN),('RFC', RFC),('GB', GB)],
voting='soft')
 get_model_accuracy(voting_clf_all )
preds_class =(predictions > 0.33 ).astype(np.int )<define_variables>
voting_clf_best_3 = VotingClassifier(
 estimators=[('SVC', SVC),('RFC', RFC),('KNN', KNN)],
voting='soft')
 get_model_accuracy(voting_clf_best_3 )
qid = df_test['qid']<concatenate>
def clf_performance(classifier):
print(classifier.__class__.__name__)
 print(f"Best Score: {classifier.best_score_}")
 print(f"Best Parameters: {classifier.best_params_}" )
submission_df = pd.concat([qid, prediction], axis=1 )<save_to_csv>
param_grid = {
'random_state': [42],
'max_iter': [100, 500, 2000],
'penalty': ['l1', 'l2'],
'C': np.logspace(-4, 4, 20),
'solver': ['liblinear', 'lbfgs']
}
clf_LR = GridSearchCV(LR, param_grid=param_grid, cv=5, verbose=True, n_jobs=-1)
 best_clf_LR = clf_LR.fit(X_train_scaled, y_train)
 clf_performance(best_clf_LR )
submission_df.to_csv("submission.csv", columns = submission_df.columns, index=False )<import_modules>
print_valid_params("'C': 11.288378916846883, 'max_iter': 100, 'penalty': 'l1', 'random_state': 42, 'solver': 'liblinear'" )
tqdm.pandas(desc='Progress')
<define_variables>
tuned_LR = LogisticRegression(C= 11.288378916846883, max_iter= 100, penalty= 'l1', random_state= 42, solver= 'liblinear')
 get_model_accuracy(tuned_LR )
embed_size = 300
max_features = 120000
maxlen = 70
batch_size = 512
n_epochs = 5
n_splits = 5
SEED = 1029<set_options>
param_grid = {
'random_state': [42],
'C': [.1,.3, 1, 3],
'kernel': ['rbf'],
'gamma': [.03,.1,.3, 1]
}
clf_SVC = GridSearchCV(SVC, param_grid=param_grid, cv=5, verbose=True, n_jobs=-1)
 best_clf_SVC = clf_SVC.fit(X_train_scaled, y_train)
 clf_performance(best_clf_SVC )
def seed_everything(seed=1029):
random.seed(seed)
 os.environ['PYTHONHASHSEED'] = str(seed)
 np.random.seed(seed)
 torch.manual_seed(seed)
 torch.cuda.manual_seed(seed)
 torch.backends.cudnn.deterministic = True
seed_everything()<features_selection>
print_valid_params("'C': 3, 'gamma': 0.03, 'kernel': 'rbf', 'random_state': 42" )
def load_glove(word_index):
EMBEDDING_FILE = '.. /input/embeddings/glove.840B.300d/glove.840B.300d.txt'
def get_coefs(word,*arr): return word, np.asarray(arr, dtype='float32')[:300]
embeddings_index = dict(get_coefs(*o.split(" ")) for o in open(EMBEDDING_FILE))
all_embs = np.stack(embeddings_index.values())
 emb_mean,emb_std = -0.005838499,0.48782197
embed_size = all_embs.shape[1]
nb_words = min(max_features, len(word_index))
embedding_matrix = np.random.normal(emb_mean, emb_std,(nb_words, embed_size))
for word, i in word_index.items() :
if i >= max_features: continue
embedding_vector = embeddings_index.get(word)
 if embedding_vector is not None: embedding_matrix[i] = embedding_vector
return embedding_matrix
def load_fasttext(word_index):
EMBEDDING_FILE = '.. /input/embeddings/wiki-news-300d-1M/wiki-news-300d-1M.vec'
def get_coefs(word,*arr): return word, np.asarray(arr, dtype='float32')
 embeddings_index = dict(get_coefs(*o.split(" ")) for o in open(EMBEDDING_FILE)if len(o)>100)
 all_embs = np.stack(embeddings_index.values())
 emb_mean,emb_std = all_embs.mean() , all_embs.std()
embed_size = all_embs.shape[1]
nb_words = min(max_features, len(word_index))
embedding_matrix = np.random.normal(emb_mean, emb_std,(nb_words, embed_size))
for word, i in word_index.items() :
if i >= max_features: continue
embedding_vector = embeddings_index.get(word)
 if embedding_vector is not None: embedding_matrix[i] = embedding_vector
return embedding_matrix
def load_para(word_index):
EMBEDDING_FILE = '.. /input/embeddings/paragram_300_sl999/paragram_300_sl999.txt'
def get_coefs(word,*arr): return word, np.asarray(arr, dtype='float32')
 embeddings_index = dict(get_coefs(*o.split(" ")) for o in open(EMBEDDING_FILE, encoding="utf8", errors='ignore')if len(o)>100)
 all_embs = np.stack(embeddings_index.values())
 emb_mean,emb_std = -0.0053247833,0.49346462
embed_size = all_embs.shape[1]
nb_words = min(max_features, len(word_index))
embedding_matrix = np.random.normal(emb_mean, emb_std,(nb_words, embed_size))
for word, i in word_index.items() :
if i >= max_features: continue
embedding_vector = embeddings_index.get(word)
 if embedding_vector is not None: embedding_matrix[i] = embedding_vector
return embedding_matrix<load_from_csv>
tuned_SVC = svm.SVC(C= 3, gamma= 0.03, kernel= 'rbf', random_state= 42, probability=True)
 get_model_accuracy(tuned_SVC )
df_train = pd.read_csv(".. /input/train.csv")
 df_test = pd.read_csv(".. /input/test.csv")
 df = pd.concat([df_train ,df_test],sort=True )<feature_engineering>
param_grid = {
'n_neighbors': [3, 5, 7, 9],
'weights': ['uniform', 'distance'],
'algorithm': ['auto', 'ball_tree', 'kd_tree'],
'p': [1, 2]
}
clf_KNN = GridSearchCV(KNN, param_grid=param_grid, cv=5, verbose=True, n_jobs=-1)
 best_clf_KNN = clf_KNN.fit(X_train_scaled, y_train)
 clf_performance(best_clf_KNN )
def build_vocab(texts):
sentences = texts.apply(lambda x: x.split() ).values
vocab = {}
for sentence in sentences:
for word in sentence:
try:
vocab[word] += 1
except KeyError:
vocab[word] = 1
return vocab
vocab = build_vocab(df['question_text'] )<define_variables>
print_valid_params("'algorithm': 'auto', 'n_neighbors': 7, 'p': 2, 'weights': 'uniform'" )
sin = len(df_train[df_train["target"]==0])
 insin = len(df_train[df_train["target"]==1])
 persin =(sin/(sin+insin)) *100
perinsin =(insin/(sin+insin)) *100
print("
print("<feature_engineering>
tuned_KNN = KNeighborsClassifier(algorithm= 'auto', n_neighbors= 7, p= 2, weights= 'uniform')
 get_model_accuracy(tuned_KNN )
def build_vocab(texts):
sentences = texts.apply(lambda x: x.split() ).values
vocab = {}
for sentence in sentences:
for word in sentence:
try:
vocab[word] += 1
except KeyError:
vocab[word] = 1
return vocab
def known_contractions(embed):
known = []
for contract in contraction_mapping:
if contract in embed:
known.append(contract)
 return known
def clean_contractions(text, mapping):
specials = ["’", "‘", "´", "`"]
for s in specials:
text = text.replace(s, "'")
 text = ' '.join([mapping[t] if t in mapping else t for t in text.split(" ")])
 return text
def correct_spelling(x, dic):
for word in dic.keys() :
x = x.replace(word, dic[word])
 return x
def unknown_punct(embed, punct):
unknown = ''
for p in punct:
if p not in embed:
unknown += p
unknown += ' '
return unknown
def clean_numbers(x):
x = re.sub('[0-9]{5,}', '
x = re.sub('[0-9]{4}', '
x = re.sub('[0-9]{3}', '
x = re.sub('[0-9]{2}', '
return x
def clean_special_chars(text, punct, mapping):
for p in mapping:
text = text.replace(p, mapping[p])
 for p in punct:
text = text.replace(p, f' {p} ')
 specials = {'\u200b': ' ', '…': '...', '\ufeff': '', 'करना': '', 'है': ''}
for s in specials:
text = text.replace(s, specials[s])
 return text
def add_lower(embedding, vocab):
count = 0
for word in vocab:
if word in embedding and word.lower() not in embedding:
embedding[word.lower() ] = embedding[word]
count += 1
print(f"Added {count} words to embedding" )<define_variables>
param_grid = {
'random_state': [42],
'n_estimators': [10, 30, 100, 300, 1000],
'bootstrap': [True, False],
'max_depth': [1, 3, 10, 30, 100, None],
'max_features': ['auto', 'sqrt'],
'min_samples_leaf': [1, 3, 10, 30],
'min_samples_split': [2, 4, 7, 10]
}
clf_RFC = RandomizedSearchCV(RFC, param_distributions=param_grid, n_iter=100, cv=5, verbose=True, n_jobs=-1)
 best_clf_RFC = clf_RFC.fit(X_train_scaled, y_train)
 clf_performance(best_clf_RFC )
puncts = [',', '.', '"', ':', ')', '(', '-', '!', '?', '|', ';', "'", '$', '&', '/', '[', ']', '>', '%', '=', '
'·', '_', '{', '}', '©', '^', '®', '`', '<', '→', '°', '€', '™', '›', '♥', '←', '×', '§', '″', '′', 'Â', '█', '½', 'à', '…',
'“', '★', '”', '–', '●', 'â', '►', '−', '¢', '²', '¬', '░', '¶', '↑', '±', '¿', '▾', '═', '¦', '║', '―', '¥', '▓', '—', '‹', '─',
'▒', '：', '¼', '⊕', '▼', '▪', '†', '■', '’', '▀', '¨', '▄', '♫', '☆', 'é', '¯', '♦', '¤', '▲', 'è', '¸', '¾', 'Ã', '⋅', '‘', '∞',
'∙', '）', '↓', '、', '│', '（', '»', '，', '♪', '╩', '╚', '³', '・', '╦', '╣', '╔', '╗', '▬', '❤', 'ï', 'Ø', '¹', '≤', '‡', '√', ]
def clean_text(x):
x = str(x)
 for punct in puncts:
x = x.replace(punct, f' {punct} ')
 return x
def clean_numbers(x):
x = re.sub('[0-9]{5,}', '
x = re.sub('[0-9]{4}', '
x = re.sub('[0-9]{3}', '
x = re.sub('[0-9]{2}', '
return x
mispell_dict = {"ain't": "is not", "aren't": "are not","can't": "cannot", "'cause": "because", "could've": "could have", "couldn't": "could not", "didn't": "did not", "doesn't": "does not", "don't": "do not", "hadn't": "had not", "hasn't": "has not", "haven't": "have not", "he'd": "he would","he'll": "he will", "he's": "he is", "how'd": "how did", "how'd'y": "how do you", "how'll": "how will", "how's": "how is", "I'd": "I would", "I'd've": "I would have", "I'll": "I will", "I'll've": "I will have","I'm": "I am", "I've": "I have", "i'd": "i would", "i'd've": "i would have", "i'll": "i will", "i'll've": "i will have","i'm": "i am", "i've": "i have", "isn't": "is not", "it'd": "it would", "it'd've": "it would have", "it'll": "it will", "it'll've": "it will have","it's": "it is", "let's": "let us", "ma'am": "madam", "mayn't": "may not", "might've": "might have","mightn't": "might not","mightn't've": "might not have", "must've": "must have", "mustn't": "must not", "mustn't've": "must not have", "needn't": "need not", "needn't've": "need not have","o'clock": "of the clock", "oughtn't": "ought not", "oughtn't've": "ought not have", "shan't": "shall not", "sha'n't": "shall not", "shan't've": "shall not have", "she'd": "she would", "she'd've": "she would have", "she'll": "she will", "she'll've": "she will have", "she's": "she is", "should've": "should have", "shouldn't": "should not", "shouldn't've": "should not have", "so've": "so have","so's": "so as", "this's": "this is","that'd": "that would", "that'd've": "that would have", "that's": "that is", "there'd": "there would", "there'd've": "there would have", "there's": "there is", "here's": "here is","they'd": "they would", "they'd've": "they would have", "they'll": "they will", "they'll've": "they will have", "they're": "they are", "they've": "they have", "to've": "to have", "wasn't": "was not", "we'd": "we would", "we'd've": "we would have", "we'll": "we will", "we'll've": "we will have", "we're": "we are", "we've": "we have", "weren't": "were not", "what'll": "what will", "what'll've": "what will have", "what're": "what are", "what's": "what is", "what've": "what have", "when's": "when is", "when've": "when have", "where'd": "where did", "where's": "where is", "where've": "where have", "who'll": "who will", "who'll've": "who will have", "who's": "who is", "who've": "who have", "why's": "why is", "why've": "why have", "will've": "will have", "won't": "will not", "won't've": "will not have", "would've": "would have", "wouldn't": "would not", "wouldn't've": "would not have", "y'all": "you all", "y'all'd": "you all would","y'all'd've": "you all would have","y'all're": "you all are","y'all've": "you all have","you'd": "you would", "you'd've": "you would have", "you'll": "you will", "you'll've": "you will have", "you're": "you are", "you've": "you have", 'colour': 'color', 'centre': 'center', 'favourite': 'favorite', 'travelling': 'traveling', 'counselling': 'counseling', 'theatre': 'theater', 'cancelled': 'canceled', 'labour': 'labor', 'organisation': 'organization', 'wwii': 'world war 2', 'citicise': 'criticize', 'youtu ': 'youtube ', 'Qoura': 'Quora', 'sallary': 'salary', 'Whta': 'What', 'narcisist': 'narcissist', 'howdo': 'how do', 'whatare': 'what are', 'howcan': 'how can', 'howmuch': 'how much', 'howmany': 'how many', 'whydo': 'why do', 'doI': 'do I', 'theBest': 'the best', 'howdoes': 'how does', 'mastrubation': 'masturbation', 'mastrubate': 'masturbate', "mastrubating": 'masturbating', 'pennis': 'penis', 'Etherium': 'Ethereum', 'narcissit': 'narcissist', 'bigdata': 'big data', '2k17': '2017', '2k18': '2018', 'qouta': 'quota', 'exboyfriend': 'ex boyfriend', 'airhostess': 'air hostess', "whst": 'what', 'watsapp': 'whatsapp', 'demonitisation': 'demonetization', 'demonitization': 'demonetization', 'demonetisation': 'demonetization'}
def _get_mispell(mispell_dict):
mispell_re = re.compile('(%s)' % '|'.join(mispell_dict.keys()))
return mispell_dict, mispell_re
mispellings, mispellings_re = _get_mispell(mispell_dict)
 def replace_typical_misspell(text):
def replace(match):
return mispellings[match.group(0)]
return mispellings_re.sub(replace, text )<feature_engineering>
print_valid_params("'random_state': 42, 'n_estimators': 30, 'min_samples_split': 7, 'min_samples_leaf': 1, 'max_features': 'sqrt', 'max_depth': None, 'bootstrap': False" )
def add_features(df):
df['question_text'] = df['question_text'].progress_apply(lambda x:str(x))
df['total_length'] = df['question_text'].progress_apply(len)
 df['capitals'] = df['question_text'].progress_apply(lambda comment: sum(1 for c in comment if c.isupper()))
df['caps_vs_length'] = df.progress_apply(lambda row: float(row['capitals'])/float(row['total_length']),
axis=1)
 df['num_words'] = df.question_text.str.count('\S+')
 df['num_unique_words'] = df['question_text'].progress_apply(lambda comment: len(set(w for w in comment.split())))
 df['words_vs_unique'] = df['num_unique_words'] / df['num_words']
return df
def load_and_prec() :
train_df = pd.read_csv(".. /input/train.csv")
 test_df = pd.read_csv(".. /input/test.csv")
 print("Train shape : ",train_df.shape)
 print("Test shape : ",test_df.shape)
 train_df["question_text"] = train_df["question_text"].progress_apply(lambda x: clean_text(x))
test_df["question_text"] = test_df["question_text"].apply(lambda x: clean_text(x))
train_df["question_text"] = train_df["question_text"].progress_apply(lambda x: clean_numbers(x))
test_df["question_text"] = test_df["question_text"].apply(lambda x: clean_numbers(x))
train_df["question_text"] = train_df["question_text"].progress_apply(lambda x: replace_typical_misspell(x))
test_df["question_text"] = test_df["question_text"].apply(lambda x: replace_typical_misspell(x))
train = add_features(train_df)
 test = add_features(test_df)
 train_df["question_text"] = train_df["question_text"].apply(lambda x: x.lower())
 test_df["question_text"] = test_df["question_text"].apply(lambda x: x.lower())
 train_X = train_df["question_text"].fillna("_
test_X = test_df["question_text"].fillna("_
features = train[['caps_vs_length', 'words_vs_unique']].fillna(0)
 test_features = test[['caps_vs_length', 'words_vs_unique']].fillna(0)
 ss = StandardScaler()
ss.fit(np.vstack(( features, test_features)))
 features = ss.transform(features)
 test_features = ss.transform(test_features)
 tokenizer = Tokenizer(num_words=max_features)
 tokenizer.fit_on_texts(list(train_X))
train_X = tokenizer.texts_to_sequences(train_X)
 test_X = tokenizer.texts_to_sequences(test_X)
 train_X = pad_sequences(train_X, maxlen=maxlen)
 test_X = pad_sequences(test_X, maxlen=maxlen)
 train_y = train_df['target'].values
np.random.seed(SEED)
 trn_idx = np.random.permutation(len(train_X))
train_X = train_X[trn_idx]
train_y = train_y[trn_idx]
features = features[trn_idx]
return train_X, test_X, train_y, features, test_features, tokenizer.word_index 
<train_model>
tuned_RFC = RandomForestClassifier(random_state= 42, n_estimators= 30, min_samples_split= 7, min_samples_leaf= 1, max_features= 'sqrt', max_depth= None, bootstrap= False)
 get_model_accuracy(tuned_RFC )
x_train, x_test, y_train, features, test_features, word_index = load_and_prec() 
<save_model>
param_grid = {
'random_state': [42],
'n_estimators': [100, 300],
'learning_rate': [.1,.3, 1],
'max_depth': [1, 2, 3, 10],
'min_samples_split': [.1,.3, 1, 3, 10],
'min_samples_leaf': [.1,.3, 1, 3]
}
clf_GB = GridSearchCV(GB, param_grid=param_grid, cv=5, verbose=True, n_jobs=-1)
 best_clf_GB = clf_GB.fit(X_train_scaled, y_train)
 clf_performance(best_clf_GB )
np.save("x_train",x_train)
 np.save("x_test",x_test)
 np.save("y_train",y_train)
 np.save("features",features)
 np.save("test_features",test_features)
 np.save("word_index.npy",word_index )<load_pretrained>
print_valid_params("'learning_rate': 0.1, 'max_depth': 3, 'min_samples_leaf': 3, 'min_samples_split': 0.1, 'n_estimators': 300, 'random_state': 42" )
x_train = np.load("x_train.npy")
 x_test = np.load("x_test.npy")
 y_train = np.load("y_train.npy")
 features = np.load("features.npy")
 test_features = np.load("test_features.npy")
 word_index = np.load("word_index.npy" ).item()<normalization>
tuned_GB = GradientBoostingClassifier(learning_rate= 0.1, max_depth= 3, min_samples_leaf= 3, min_samples_split= 0.1, n_estimators= 300, random_state= 42)
 get_model_accuracy(tuned_GB )
seed_everything()
glove_embeddings = load_glove(word_index)
 paragram_embeddings = load_para(word_index)
 fasttext_embeddings = load_fasttext(word_index)
 embedding_matrix = np.mean([glove_embeddings, paragram_embeddings, fasttext_embeddings], axis=0)
 del glove_embeddings, paragram_embeddings, fasttext_embeddings
gc.collect()
np.shape(embedding_matrix )<split>
params = {
'weights': [[1, 1, 1], [1, 1, 2], [1, 2, 1], [2, 1, 1], [1, 2, 2], [2, 1, 2], [2, 2, 1]]
}
vote_weight = GridSearchCV(voting_clf_best_3, param_grid=params, cv=5, verbose=True, n_jobs=-1)
 best_clf_weight = vote_weight.fit(X_train_scaled, y_train)
 clf_performance(best_clf_weight )
splits = list(StratifiedKFold(n_splits=n_splits, shuffle=True, random_state=SEED ).split(x_train, y_train))
splits[:3]<choose_model_class>
print_valid_params("'weights': [1, 1, 2]" )
class CyclicLR(object):
def __init__(self, optimizer, base_lr=1e-3, max_lr=6e-3,
step_size=2000, mode='triangular', gamma=1.,
scale_fn=None, scale_mode='cycle', last_batch_iteration=-1):
if not isinstance(optimizer, Optimizer):
raise TypeError('{} is not an Optimizer'.format(
 type(optimizer ).__name__))
self.optimizer = optimizer
if isinstance(base_lr, list)or isinstance(base_lr, tuple):
if len(base_lr)!= len(optimizer.param_groups):
raise ValueError("expected {} base_lr, got {}".format(
 len(optimizer.param_groups), len(base_lr)))
 self.base_lrs = list(base_lr)
 else:
self.base_lrs = [base_lr] * len(optimizer.param_groups)
 if isinstance(max_lr, list)or isinstance(max_lr, tuple):
if len(max_lr)!= len(optimizer.param_groups):
raise ValueError("expected {} max_lr, got {}".format(
 len(optimizer.param_groups), len(max_lr)))
 self.max_lrs = list(max_lr)
 else:
self.max_lrs = [max_lr] * len(optimizer.param_groups)
 self.step_size = step_size
if mode not in ['triangular', 'triangular2', 'exp_range'] \
and scale_fn is None:
raise ValueError('mode is invalid and scale_fn is None')
 self.mode = mode
self.gamma = gamma
if scale_fn is None:
if self.mode == 'triangular':
self.scale_fn = self._triangular_scale_fn
self.scale_mode = 'cycle'
elif self.mode == 'triangular2':
self.scale_fn = self._triangular2_scale_fn
self.scale_mode = 'cycle'
elif self.mode == 'exp_range':
self.scale_fn = self._exp_range_scale_fn
self.scale_mode = 'iterations'
else:
self.scale_fn = scale_fn
self.scale_mode = scale_mode
self.batch_step(last_batch_iteration + 1)
 self.last_batch_iteration = last_batch_iteration
def batch_step(self, batch_iteration=None):
if batch_iteration is None:
batch_iteration = self.last_batch_iteration + 1
self.last_batch_iteration = batch_iteration
for param_group, lr in zip(self.optimizer.param_groups, self.get_lr()):
param_group['lr'] = lr
def _triangular_scale_fn(self, x):
return 1.
 def _triangular2_scale_fn(self, x):
return 1 /(2.**(x - 1))
def _exp_range_scale_fn(self, x):
return self.gamma**(x)
 def get_lr(self):
step_size = float(self.step_size)
 cycle = np.floor(1 + self.last_batch_iteration /(2 * step_size))
x = np.abs(self.last_batch_iteration / step_size - 2 * cycle + 1)
 lrs = []
param_lrs = zip(self.optimizer.param_groups, self.base_lrs, self.max_lrs)
 for param_group, base_lr, max_lr in param_lrs:
base_height =(max_lr - base_lr)* np.maximum(0,(1 - x))
if self.scale_mode == 'cycle':
lr = base_lr + base_height * self.scale_fn(cycle)
 else:
lr = base_lr + base_height * self.scale_fn(self.last_batch_iteration)
 lrs.append(lr)
 return lrs<choose_model_class>
weighted_voting_clf_best_3 = VotingClassifier(
 estimators=[('SVC', SVC),('RFC', RFC),('KNN', KNN)],
voting='soft',
weights= [1, 1, 2] 
)
 get_model_accuracy(weighted_voting_clf_best_3 )
embedding_dim = 300
embedding_path = '.. /save/embedding_matrix.npy'
use_pretrained_embedding = True
hidden_size = 60
gru_len = hidden_size
Routings = 4
Num_capsule = 5
Dim_capsule = 5
dropout_p = 0.25
rate_drop_dense = 0.28
LR = 0.001
T_epsilon = 1e-7
num_classes = 30
class Embed_Layer(nn.Module):
def __init__(self, embedding_matrix=None, vocab_size=None, embedding_dim=300):
super(Embed_Layer, self ).__init__()
self.encoder = nn.Embedding(vocab_size + 1, embedding_dim)
 if use_pretrained_embedding:
self.encoder.weight.data.copy_(t.from_numpy(embedding_matrix))
def forward(self, x, dropout_p=0.25):
return nn.Dropout(p=dropout_p )(self.encoder(x))
class GRU_Layer(nn.Module):
def __init__(self):
super(GRU_Layer, self ).__init__()
self.gru = nn.GRU(input_size=300,
hidden_size=gru_len,
bidirectional=True)

def init_weights(self):
ih =(param.data for name, param in self.named_parameters() if 'weight_ih' in name)
 hh =(param.data for name, param in self.named_parameters() if 'weight_hh' in name)
 b =(param.data for name, param in self.named_parameters() if 'bias' in name)
 for k in ih:
nn.init.xavier_uniform_(k)
 for k in hh:
nn.init.orthogonal_(k)
 for k in b:
nn.init.constant_(k, 0)
 def forward(self, x):
return self.gru(x)
 class Caps_Layer(nn.Module):
def __init__(self, input_dim_capsule=gru_len * 2, num_capsule=Num_capsule, dim_capsule=Dim_capsule, \
routings=Routings, kernel_size=(9, 1), share_weights=True,
activation='default', **kwargs):
super(Caps_Layer, self ).__init__(**kwargs)
 self.num_capsule = num_capsule
self.dim_capsule = dim_capsule
self.routings = routings
self.kernel_size = kernel_size
self.share_weights = share_weights
if activation == 'default':
self.activation = self.squash
else:
self.activation = nn.ReLU(inplace=True)
 if self.share_weights:
self.W = nn.Parameter(
 nn.init.xavier_normal_(t.empty(1, input_dim_capsule, self.num_capsule * self.dim_capsule)))
 else:
self.W = nn.Parameter(
 t.randn(BATCH_SIZE, input_dim_capsule, self.num_capsule * self.dim_capsule))
def forward(self, x):
if self.share_weights:
u_hat_vecs = t.matmul(x, self.W)
 else:
print('add later')
 batch_size = x.size(0)
 input_num_capsule = x.size(1)
 u_hat_vecs = u_hat_vecs.view(( batch_size, input_num_capsule,
self.num_capsule, self.dim_capsule))
u_hat_vecs = u_hat_vecs.permute(0, 2, 1, 3)
 b = t.zeros_like(u_hat_vecs[:, :, :, 0])
 for i in range(self.routings):
b = b.permute(0, 2, 1)
 c = F.softmax(b, dim=2)
 c = c.permute(0, 2, 1)
 b = b.permute(0, 2, 1)
 outputs = self.activation(t.einsum('bij,bijk->bik',(c, u_hat_vecs)))
 if i < self.routings - 1:
b = t.einsum('bik,bijk->bij',(outputs, u_hat_vecs))
return outputs
def squash(self, x, axis=-1):
s_squared_norm =(x ** 2 ).sum(axis, keepdim=True)
 scale = t.sqrt(s_squared_norm + T_epsilon)
 return x / scale
class Capsule_Main(nn.Module):
def __init__(self, embedding_matrix=None, vocab_size=None):
super(Capsule_Main, self ).__init__()
self.embed_layer = Embed_Layer(embedding_matrix, vocab_size)
 self.gru_layer = GRU_Layer()
self.gru_layer.init_weights()
self.caps_layer = Caps_Layer()
self.dense_layer = Dense_Layer()
def forward(self, content):
content1 = self.embed_layer(content)
 content2, _ = self.gru_layer(
 content1)
 content3 = self.caps_layer(content2)
 output = self.dense_layer(content3)
 return output 
<normalization>
def submission_to_csv(y_preds, filename='submission.csv'):
submission = {'PassengerId': test_data.PassengerId, 'survived': y_preds}
submission_df = pd.DataFrame(data=submission)
 submission_csv = submission_df.to_csv(filename, index=False)
 return(submission_csv )
class Attention(nn.Module):
def __init__(self, feature_dim, step_dim, bias=True, **kwargs):
super(Attention, self ).__init__(**kwargs)
 self.supports_masking = True
self.bias = bias
self.feature_dim = feature_dim
self.step_dim = step_dim
self.features_dim = 0
weight = torch.zeros(feature_dim, 1)
 nn.init.xavier_uniform_(weight)
 self.weight = nn.Parameter(weight)
 if bias:
self.b = nn.Parameter(torch.zeros(step_dim))
def forward(self, x, mask=None):
feature_dim = self.feature_dim
step_dim = self.step_dim
eij = torch.mm(
 x.contiguous().view(-1, feature_dim),
self.weight
).view(-1, step_dim)
 if self.bias:
eij = eij + self.b
eij = torch.tanh(eij)
 a = torch.exp(eij)
 if mask is not None:
a = a * mask
a = a / torch.sum(a, 1, keepdim=True)+ 1e-10
weighted_input = x * torch.unsqueeze(a, -1)
 return torch.sum(weighted_input, 1)
 class NeuralNet(nn.Module):
def __init__(self):
super(NeuralNet, self ).__init__()
fc_layer = 16
fc_layer1 = 16
self.embedding = nn.Embedding(max_features, embed_size)
 self.embedding.weight = nn.Parameter(torch.tensor(embedding_matrix, dtype=torch.float32))
self.embedding.weight.requires_grad = False
self.embedding_dropout = nn.Dropout2d(0.1)
 self.lstm = nn.LSTM(embed_size, hidden_size, bidirectional=True, batch_first=True)
 self.gru = nn.GRU(hidden_size * 2, hidden_size, bidirectional=True, batch_first=True)
 self.lstm2 = nn.LSTM(hidden_size * 2, hidden_size, bidirectional=True, batch_first=True)
 self.lstm_attention = Attention(hidden_size * 2, maxlen)
 self.gru_attention = Attention(hidden_size * 2, maxlen)
 self.bn = nn.BatchNorm1d(16, momentum=0.5)
 self.linear = nn.Linear(hidden_size*8+3, fc_layer1)
 self.relu = nn.ReLU()
self.dropout = nn.Dropout(0.1)
 self.fc = nn.Linear(fc_layer**2,fc_layer)
 self.out = nn.Linear(fc_layer, 1)
 self.lincaps = nn.Linear(Num_capsule * Dim_capsule, 1)
 self.caps_layer = Caps_Layer()
def forward(self, x):
h_embedding = self.embedding(x[0])
 h_embedding = torch.squeeze(
 self.embedding_dropout(torch.unsqueeze(h_embedding, 0)))
 h_lstm, _ = self.lstm(h_embedding)
 h_gru, _ = self.gru(h_lstm)
 content3 = self.caps_layer(h_gru)
 content3 = self.dropout(content3)
 batch_size = content3.size(0)
 content3 = content3.view(batch_size, -1)
 content3 = self.relu(self.lincaps(content3))
h_lstm_atten = self.lstm_attention(h_lstm)
 h_gru_atten = self.gru_attention(h_gru)
 avg_pool = torch.mean(h_gru, 1)
 max_pool, _ = torch.max(h_gru, 1)
 f = torch.tensor(x[1], dtype=torch.float ).cuda()
conc = torch.cat(( h_lstm_atten, h_gru_atten,content3, avg_pool, max_pool,f), 1)
 conc = self.relu(self.linear(conc))
conc = self.bn(conc)
 conc = self.dropout(conc)
 out = self.out(conc)
 return out<categorify>
tuned_RFC.fit(X_train_scaled, y_train)
 tuned_RFC_preds = tuned_RFC.predict(X_test_scaled)
 submission_to_csv(tuned_RFC_preds, 'RFC_submission.csv' )
class MyDataset(Dataset):
def __init__(self,dataset):
self.dataset = dataset
def __getitem__(self, index):
data, target = self.dataset[index]
return data, target, index
def __len__(self):
return len(self.dataset )<compute_train_metric>
tuned_GB.fit(X_train_scaled, y_train)
 tuned_GB_preds = tuned_GB.predict(X_test_scaled)
 submission_to_csv(tuned_GB_preds, 'GB_clf_submission.csv' )
class FocalLoss(nn.Module):
def __init__(self, alpha=1, gamma=2, logits=True, reduction='elementwise_mean'):
super(FocalLoss, self ).__init__()
self.alpha = alpha
self.gamma = gamma
self.logits = logits
self.reduction = reduction
def forward(self, inputs, targets):
if self.logits:
BCE_loss = F.binary_cross_entropy_with_logits(inputs, targets, reduction='none')
 else:
BCE_loss = F.binary_cross_entropy(inputs, targets, reduction='none')
 pt = torch.exp(-BCE_loss)
 F_loss = self.alpha *(1-pt)**self.gamma * BCE_loss
if self.reduction is None:
return F_loss
else:
return torch.mean(F_loss )<define_variables>
weighted_voting_clf_best_3.fit(X_train_scaled, y_train)
 weighted_voting_clf_best_3_preds = weighted_voting_clf_best_3.predict(X_test_scaled)
 submission_to_csv(weighted_voting_clf_best_3_preds, 'soft_voting_clf_submission.csv' )