Kaggle competition: Quora Insincere Questions Classification
<SOS> metric: FScore_1 Kaggle data source: quora-insincere-questions-classification<categorify>
tqdm.pandas(desc='Progress')
train_ = pd.get_dummies(train_,columns=cat_feats,drop_first=False )<categorify>
concat =0
if concat:
embed_size = 604
else:
embed_size = 304
max_features = 120000
maxlen = 70
batch_size = 512
n_epochs = 5
n_splits = 5
SEED = 1029
debug = 0
test['Age'] = test['Age'].mask(test['Age'].eq(0)).fillna(test['PassengerId'].map(df.set_index('PassengerId')['Age']))
test['Fare'] = test['Fare'].mask(test['Fare'].eq(0)).fillna(test['PassengerId'].map(df.set_index('PassengerId')['Fare']))
test=pd.merge(test,df[['PassengerId','CabinOccupancy','block','Titles']],how='left',on='PassengerId')
 test.info()<categorify>
loss_fn = torch.nn.BCEWithLogitsLoss(reduction='sum' )
test_ = pd.get_dummies(test_,columns=cat_feats,drop_first=False )<prepare_x_and_y>
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()
X_train = train_.drop('Survived',axis=1)
 y_train = train_['Survived'] 
<prepare_x_and_y>
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
X_test=test_<drop_column>
def load_glove_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))
return embeddings_index
def load_fasttext_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)
 return embeddings_index
def load_para_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)
 return embeddings_index
def create_glove(word_index,embeddings_index):
emb_mean,emb_std = -0.005838499,0.48782197
all_embs = np.stack(embeddings_index.values())
 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+4))
count_found = nb_words
for word, i in tqdm(word_index.items()):
if i >= max_features: continue
embedding_vector = embeddings_index.get(word)
 extra_embed =np.array([0,0,0,0])
 if word.istitle() :
extra_embed[0]=1
elif word.isupper() :
extra_embed[1]=1
word_sent = TextBlob(word ).sentiment
extra_embed[2:] = [word_sent.polarity,word_sent.subjectivity]
if embedding_vector is not None:
embedding_matrix[i] = np.append(embedding_vector,extra_embed)
 else:
if word.istitle() :
embedding_vector = embeddings_index.get(word.lower())
 if embedding_vector is not None:
embedding_matrix[i] = np.append(embedding_vector,extra_embed)
 else:
embedding_matrix[i,300:] = extra_embed
count_found-=1
elif word.islower() :
embedding_vector = embeddings_index.get(word.capitalize())
 if embedding_vector is not None:
embedding_matrix[i] = np.append(embedding_vector,extra_embed)
 else:
embedding_matrix[i,300:] = extra_embed
count_found-=1
else:
embedding_matrix[i,300:] = extra_embed
count_found-=1
print("Got embedding for ",count_found," words.")
 return embedding_matrix
def create_para(word_index,embeddings_index):
emb_mean,emb_std = -0.0053247833,0.49346462
all_embs = np.stack(embeddings_index.values())
 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+4))
count_found = nb_words
for word, i in word_index.items() :
if i >= max_features: continue
embedding_vector = embeddings_index.get(word.lower())
 extra_embed =np.array([0,0,0,0])
 if word.istitle() :
extra_embed[0]=1
elif word.isupper() :
extra_embed[1]=1
word_sent = TextBlob(word ).sentiment
extra_embed[2:] = [word_sent.polarity,word_sent.subjectivity]
if embedding_vector is not None:
embedding_matrix[i] = np.append(embedding_vector,extra_embed)
 else:
embedding_matrix[i,300:] = extra_embed
count_found -=1
print("Got embedding for ",count_found," words.")
 return embedding_matrix
X_train=X_train.drop(columns=['Titles_ the Countess','Titles_ Sir','Titles_ Mme',
'Titles_ Mlle','Titles_ Major','Titles_ Lady','Titles_ Jonkheer',
'Titles_ Don','Titles_ Capt'])
 X_test=X_test.drop(columns=['Titles_ Dona'] )<train_model>
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")
dtree = DecisionTreeClassifier()
dtree.fit(X_train,y_train )<predict_on_test>
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)
 def mid_padder(sequence,maxlen):
for i, data in enumerate(sequence):
if len(data)>=maxlen:
sequence[i] = data[:maxlen]
elif len(data)<maxlen:
mid_point = int(len(data)/2)
 num_zeros = maxlen - len(data)
 n_seq = data[:mid_point] + [0]*num_zeros + data[mid_point:]
sequence[i] = n_seq
return np.array(sequence,dtype='int32')
 def fraction_padder(sequence,maxlen):
for i, data in enumerate(sequence):
if len(data)>=maxlen:
mid_value = int(maxlen*2/3)
 sequence[i] = data[:mid_value]+data[-(maxlen-mid_value):]
elif len(data)<maxlen:
mid_point = int(len(data)/2)
 num_zeros = maxlen - len(data)
 n_seq = data[:mid_point] + [0]*num_zeros + data[mid_point:]
sequence[i] = n_seq
return np.array(sequence,dtype='int32')
predictions_dtree = dtree.predict(X_test )<prepare_x_and_y>
token_dict_manageable = {'GPE': ['india', 'us', 'china', 'usa', 'canada', 'america', 'uk', 'pakistan', 'russia', 'germany', 'australia', 'the united states', 'israel', 'delhi', 'japan', 'mumbai', 'instagram', 'u.s.', 'hyderabad', 'north korea', 'singapore', 'california', 'france', 'iran', 'london', 'dubai', 'turkey', 'england', 'quora', 'indonesia', 'mexico', 'philippines', 'malaysia', 'italy', 'syria', 'android', 'britain', 'spain', 'hollywood', 'nigeria', 'vietnam', 'new york', 'texas', 'nepal', 'saudi arabia', 'sweden', 'bangladesh', 'bollywood', 'new zealand', 'brazil', 'florida', 'egypt', 'chennai', 'greece', 'south korea', 'ireland', 'dc', 'korea', 'kerala', 'south africa', 'thailand', 'gujarat', 'jio', 'hong kong', 'poland', 'twitter', 'tamil', 'iraq', 'taiwan', 'afghanistan', 'paris', 'san francisco', 'ai', 'switzerland', 'uae', 'gate', 'chicago', 'toronto', 'u.s', 'ukraine', 'rome', 'netherlands', 'palestine', 'bitsat', 'norway', 'los angeles', 'united states', 'jerusalem', 'du', 'noida', 'bihar', 'karnataka', 'jaipur', 'scotland', 'portugal', 'west bengal', 'sri lanka', 'new york city', 'ussr', 'argentina', 'georgia', 'denmark', 'ahmedabad', 'gmat', 'the soviet union', 'las vegas', 'kenya', 'ib', 'qatar', 'hawaii', 'mangalore', 'austria', 'boston', 'sydney', 'sanskrit', 'finland', 'washington', 'melbourne', 'venezuela', 'barcelona', 'newton', 'seattle', 'galaxy', 'h1b', 'new delhi', 'belgium', 'hadoop', 'myanmar', 'naruto', 'berlin', 'macbook', 'neet', 'sc', 'cambridge', 'ayurveda', 'ohio', 'vr', 'bhutan', 'puerto rico', 'miami', 'tibet', 'u.k.', 'cuba', 'morocco', 'ontario', 'houston', 'tokyo', 'arizona', 'melania', 'atlanta', 'chandigarh', 'vancouver', 'romania', 'illuminati', 'colorado', 'rajasthan', 'michigan', 'new jersey', 'iceland', 'montreal', 'bengal', 'telangana', 'indore', 'the united kingdom', 'ghana', 'the united states of america', 'amsterdam', 'manhattan', 'dallas', 'alaska', 'urdu', 'patna', 'rs', 'hungary', 'la', 'tn', 'comedk', 'oregon', 'viteee', 'mahabharata', 'arjuna', 'ny', 'kashmiri', 'illinois', 'md', 'photoshop', 'trump', 'bhubaneswar', 'serbia', 'tennessee', 'beijing', 'colombia', 'bluetooth', 'great britain', 'physics', 'wikipedia', 'ethereum', 'jammu', 'cet', 'gurgaon', 'lebanon', 'aiims', 'odisha', 'kuwait', 'bulgaria', 'cisco', 'quebec', 'bali', 'shanghai', 'whom', 'haryana', 'massachusetts', 'phoenix', 'chile', 'bangkok', 'canon', 'moscow', 'cm', 'wwi', 'iphones', 'jakarta', 'alabama', 'catalonia', 'san diego', 'redmi', 'm.sc', 'pennsylvania', 'maryland', 'ca', 'istanbul', '°', 'toefl', 'jordan', 'ethiopia', 'modi', 'virginia', 'aiims delhi', 'manchester', 'mongolia', 'nc', 'the roman empire', 'django', 'indiana', 'va', 'honduras', 'mysore', 'alibaba', 'guwahati', 'zimbabwe', 'ubuntu', 'cs', 'philadelphia', 'youtuber', 'oman', 'colonies', 'bangalore', 'cambodia', 'yemen', 'aadhaar', 'libya', 'rings', 'algeria', 'punjab', 'kuala lumpur', 'mecca', 'minnesota', 'north carolina', 'wales', 'manaphy', 'oklahoma', 'saturn', 'kanpur', 'gaza', 'karachi', 'ww2', 'capgemini', 'portland', 'gb', 'seoul', 'narendra modi', 'northern ireland', 'uber', 'karna', "north korea 's", 'detroit', 'kota', 'croatia', 'ug', 'dublin', 'orlando', 'iim', 'jamaica', 'macedonia', 'columbus', 'albania', 'mobile', 'ranchi', 'walmart', 'peru', 'wisconsin', 'denver', 'vedas', 'north', 'jon', 'the british empire', 'titanic', 'austin', 'azerbaijan', 'bharat', 'munich', 'ola', 'hermione', 'xbox', 'utah', 'costa rica', 'connecticut', 'empire', 'uttarakhand', 'armenia', 'louisiana', 'jee', 'panama', 'sri lankan', 'kosovo', 'missouri', 'santa', 'athens', 'madrid', 'prague', 'nevada', 'uc', 'kashmiris', 'raipur', 'ashoka', 'visakhapatnam', 'upsc', 'madurai', "qur'an", 'san jose', 'united kingdom', 'venice', 'seo', 'vienna', 'indo', 'estonia', 'kindle', 'greenland', 'bhopal', 'somalia', 'romanian', 'greeks', 'delaware', 'kazakhstan', 'vadodara', 'new orleans', 'nyc', 'itunes', 'south carolina', 'north india', 'iiit', 'faridabad', 'san antonio', 'brooklyn', 'kochi', 'berkeley', 'yugoslavia', 'kathmandu', 'prc', 'hiroshima', 'firefox', 'soviet union', 'the ottoman empire', 'tyrion', 'edinburgh', 'mississippi', 'haiti', 'srinagar', 'gk', 'bombay', 'lithuania', 'ww1', 'washington dc', 'welsh', 'legends', 'kannada', 'xi', 'b.a.', 'trivandrum', 'emirates', 'uganda', 'manipur', 'ktm', 'manila', 'maine', 'persia', 'sf', 'cognizant', 'thane', 'kentucky', 'laos', 'liverpool', 'the west bank', 'u.s.a.', 'kcet', 'abu dhabi', 'u.s.a', 'sudan', 'cleveland', 'alberta', 'stockholm', 'uzbekistan', 'arkansas', 'tanzania', 'states', 'hindi', 'congo', 'kansas', 'pittsburgh', 'ghaziabad', 'tunisia', 'ronaldo', 'turkic', 'monaco', 'xrp', 'tolkien', 'bahrain', 'kotlin', 'vizag', 'obama', 'charlotte', 'oxford', 'kurdistan', 'logan', 'scala', 'guam', 'brunei', 'draupadi', 'baltimore', 'charlottesville', 'new mexico', 'british columbia', 'telugu', 'b12', 'f1', 'ba', 'mckinsey', 'luxembourg', 'angular', 'angularjs', 'ecuador', 'modiji', 'ladakh', 'manali', 'karma', 'balochistan', 'idaho', 'lagos', 'zurich', 'holland', 'titan', 'eu', 'calgary', 'imei', 'mbbs', 'brussels', 'bolivia', 'sacramento', 'amrita', 'burma', 'slovenia', 'gay', 'jodhpur', 'gujarati', 'iowa', 'surat', 'milan', 'psychopaths', 'victoria', 'shenzhen', 'ggsipu', 'slavs', 'nagasaki', 'outlook', 'nagaland', 'caucasian', 'budapest', 'cochin', 'voldemort', 'south india', 'nashville', 'quota', 'belarus', 'maldives', 'vlsi', 'allahabad', 'tampa', 'columbia', 'avenger', 'namibia', 'madagascar', 'aristotle', 'hindustan', 'jharkhand', 'barack obama', 'bosnia', 'gtx', 'guatemala', 'millennials', 'rishikesh', 'uruguay', 'nmat', 'perth', 'montana', 'tehran', 'cgl', 'cape town', 'north africa', 'latvia', 'white', 'vatican', 'madara', 'islamabad', 'roman empire', 'facebook', 'coursera', 'zambia', 'mexico city', 'hulu', 'kolkata', 'intp', 'malta', 'hero', 'frankfurt', 'n.korea', 'formula', 'kumar', 'palo alto', 'phuket', 'assassin', 'us', 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merchant of venice', 'skulduggery pleasant', 'the nobel prize in physics', 'revelation', 'the tesla semi', 'the way of the world', 'the maze runner', 'the chronicles of narnia', 'star wars episode', 'the kane chronicles', 'fortune 500', 'i++', 'the things they carried', 'the young and the restless', 'the next generation', 'wonder woman', 'the cosby show', 'a wrinkle in time', 'saturday night live', 'ghost hunters', 'batman beyond', 'seinfeld', 'the originals', 'the bell jar', 'the catcher in the rye', 'venus', 'danes', 'economic and philosophic manuscripts of 1844', "star trek 's", 'trump', 'the wizard of oz', "do n't die , my love", 'love', 'sword art online', 'sirius', 'genius', 'the lovely bones', 'the waltons', 'star trek : the next generation', 'the lion king', 't20', 'finance', 'wuthering heights', 'avatar', 'a brief history of time', 'the stepford wives', 'dragons', 'the coldest winter ever', 'jurisprudence', 'the picture of dorian gray', 'toys r us', 'family ties', 'spider - man : homecoming', 'one love', 'chicken', 'the deerslayer', 'star trek beyond', 'grammy', 'punch', 'into the wild', 'insta', 'perfect', 'network', 'civil peace', 'already taken 2', 'adventure', 'sister , sister', 'yu - gi - oh', 'enduring love', 'loser', 'a clockwork orange', 'game', 'sunny leone', 'the chamber of secrets', 'the metamorphosis', 'natural law and natural rights', 'dexter', 'rurouni kenshin', 'wizards of waverly place', 'the twilight zone', 'end of the spear', 'starship troopers', 'primer', 'the secret garden', '3000', 'the game : penetrating the secret society of pickup artists', 'return of the jedi', 'star wars legends', "' the prince '", "5'3", 'teen wolf', 'redwall', 'sargon of akkad', 'the gruffalo', 'the contender', 'the fog horn', 'wolf quest', 'blood on the river', 'the law of life', 'where the wild things are', 'angel eyes', 'ecstasy of saint teresa', 'the red pill', 'i am legend', "no man 's sky", 'road to nowhere', 'tesla', 'the island', 'the little prince', 'before i go to sleep', 'house of night', "' the dark knight '", 'the untouchables', 'harry potter and the half - blood prince', 'the blood of olympus', 'le havre', 'dear white people', 'the brothers karamazov', 'the voice', 'the red badge of courage', 'depression', 'hansel and gretel', 'tuck everlasting', 'miracle at st.anna', 'dolphin tale', 'a christmas carol', 'heart of the ocean', 'ca', 'the brook', 'the light in the forest', 'harry potter and the deathly hallows – part 1', 'the solitary reaper', 'lazytown', 'life', 'the neverending story', 'star trek : discovery', 'hacker', 'the nobel prizes', 'the hobbit', 'eat , pray , love', 'a faraway home', 'heat', 'the phantom of the opera', 'gods', 'the gathering', 'winnie - the - pooh', 'gentlemen of the road', 'cse', 'the mandela effect', 'deadpool', 'hetalia : axis powers', 'html and css', 'hatchet', 'fallen angels', 'the dust bowl', '" the catcher in the rye "', 'jacket', 'catcher in the rye', "cat 's cradle", 'dance moms', 'the matrix', 'a walk to remember', 'the city of ember', 'the jungle', 'alone', 'the master in management', 'out of the flames', 'the canterbury tales', 'cengage', 'the green mile', 'what is ancient philosophy', 'the real housewives of atlanta', 'the mill and the cross', "look up daughter : ca n't you see i love you", 'days of our lives "', 'mobile suit gundam wing', 'i can only imagine', 'requiem for a dream', 'crime and the american dream', 'hope for the flowers', 'style', 'durgapur', 'black', 'the alchemist', 'spring boot', "the night king 's", 'the invisible man', 'the last jedi', 'the flintstones', 'the tell - tale heart', 'gnu', 'amazing grace', 'the view', 'the melancholy of haruhi suzumiya', 'the rocking - horse winner', 'the notebook', 'a modest', 'the 10th kingdom', 'a series of unfortunate events', 'art of war', 'pulitzer prize', 'gone with the wind', "a master 's", 'apocalypse now', 'fahrenheit 451', 'star wars : the force awakens', 'human', 'miracle on 34th street', 'harry potter and the prisoner of azkaban', "a master 's in computer science", 'breitbart', 'the book thief', 'the devil and tom walker', 'what is the what', 'the fifth element', 'the methods of rationality', '" the dark tower "', 'massacre at myall creek', 'the interpretation of dreams', 'thriller', 'me too', 'cheers', 'weeds', 'sugar land', 'gold medal', 'prodigal son', 'ww 2', 'in light of india', 'the rain came', 'the subtle knife', 'the long dark night', 'lion', 'star trek the next generation', 'quran', 'the god delusion', 'woodbury , ny', 'poverty is where the money is', "theodore ' the beaver ' cleaver", 'leave it to beaver', 'the black panther', 'the adventures of precious the dog', 'star wars : the old republic', 'the furnished room', '" the golden child "', 'the silver lotus', 'les liaisons dangereuses', 'the lottery', 'gaffer', 'ghost whisperer', 'sympathy for mr.vengeance', 'the charmer', 'the tenant of wildfell hall', 'the bell curve', 'xenomorph queen', 'star trek : enterprise', 'bates motel', 'arjun reddy', "the guv'nor", '" the book thief "', 'a raisin in the sun', 'the big bang', 'the end of love', 'soul surfer', "5'9.5", 'when the scorpion stings : the history of the 3rd cavalry regiment , south vietnam', 'sicario', 'the good fairies of new york', 'the bluest eye', 'break a leg : a memoir', 'by night in chile', 'more than you know', 'thirst', 'digging', 'a corner of the universe', 'the story of my experiments with truth', 'the mortal immortal', '“ family guy ”', 'the adventures of pinocchio', 'seven deadly sins', 'b17', 'why earls fall in love', 'the original six', 'brandi love', 'beowulf', 'zorba the greek', 'israel’', 'glowstone', 'an inspector calls', 'the curious case of benjamin button', '" falling up "', 'the prince', 'the power trip', 'i know you got soul', 'the egypt game', 'tales from the crypt', 'intelligent', 'the gift of the magi', 'howard the duck', 'la duchesse de langeais', 'nobody knows but me', 'the soul of a new machine', 'first light', 'a prayer for owen meany', 'canterwood crest', 'person of interest', 'freedom or death', 'the twilight saga', 'stig of the dump', 'forrest gump', 'home', 'where the red fern grows', 'battle royale', 'srm main campus', 'absalom and achitophel', 'the land of silver rain', 'in search of deep throat', 'el chavo del ocho', 'this is america', 'i want to be a police', 'the garden of forking paths', 'the temple of master hotei', 'afternoon of a faun', 'the gun', 'bosniaks', 'ambition', 'the wellness solution', 'raspberry pi', 'out of the ordinary', "peter 's lullaby", 'people you may know', 'the sleeper awakes', 'the influence of sea power upon history', 'a long way down', 'the true story of christopher emmanuel balestrero', 'the jerry springer show', 'pokémon : diamond and pearl', 'the pearl', '" love waves "', 'phora', 'the mindy project', 'cryptonomicon', 'the return of the king', 'lovelorn', 'where good ideas come from', 'blood brothers', 'the wars of the roses', 'somewhere over the rainbow', 'the night listener', 'libra moon', 'his needs , her needs : building an affair - proof marriage', 'the secret of the great red spot : the jupiter chronicles - book 1', 'the late show', 'the king of queens', 'the post', 'the great', 'poughkeepsie', 'pride and prejudice', '1408', 'the wreck of the hesperus', 'clear light of day', '2/2/2', 'raising the bar', 'star trek conventions', 'anne of green gables', 'october sky', 'dancing with the stars', 'the power of now', 'the rock : exodus', 'a mystery of heroism', 'spring', 'touched by an angel', 'angels and demons', 'buffy the vampire slayer', 'the lincoln hunters', 'the scooby - doo show', "black 's law dictionary", 'black and white', 'gun shy', 'a wonderful life', 'cover up', 'hubble', 'the word of god for the people of god', 'the ten commandments', 'the diary of anne frank', 'on the sidewalk bleeding', 'soul to soul : a black russian american family 1865 - 1992', 'the jee mains', 'the second sin', 'lie to me', 'poison', 'world wars', 'runner', 'something borrowed', 'the edge of evolution', 'uptown funk', 'the holy land', 'the winds of winter', 'the sign of the beaver', 'ai', 'a game of thrones', 'the portrait of a lady', 'two to the fifth', 'sweat', 'where lies the land', 'vande matram', 'the land of blood and honey : the rise of modern israel', 'mathematical universe hypothesis', 'a new earth : awakening to your life ’s purpose', 'down through the years', 'conservative', 'bones', 'switch', "gulliver 's travels", 'the alamo', 'gates of hades', 'close your eyes', 'the c programming language', 'the secrets of the immortal nicholas flamel', 'the fall', 'you are old', 'a new china : an intermediate reader of modern chinese', 'cat and mouse', 'bright', 'a little cloud', 'barn burning', 'the usual suspects', 'the neverending story', 'the " hitler myth "', 'barking up the wrong tree', 'the dawn patrol', 'the rum diary', 'listography journal : your life in lists', 'the law of the sea', 'the avengers', 'traction', 'the baby in the bottle', 'yu - gi - oh gx', 'messages to the world', 'the wheel of time', 'doctor who', 'the mars project', 'i love you', 'star trek tos', "' the room '", 'the 13th floor', 'touch blue', 'where grace abounds : an amish widow romance', 'the american crisis', 'dear john', 'the golden twine', 'remember me', 'the sea of monsters', "olive 's ocean", 'dl', 'faces in the crowd', 'the gods of the copybook headings', 'age of empires', '2lakhs', 'city of bones', 'gentle rogue', 'where treetops glisten : three stories of heartwarming courage', 'get away with murder', 'surrender', 'pavitra rishta', 'ghost hunt', 'kafka on the shore', 'trouble river', 'killing pablo', 'demon hunters : trinity', 'the star child', 'bayonet charge', 'book', 'tapas on the ramblas', 'god in me', 'maggie', 'the federalist papers', 'the battle of the labyrinth', 'the southern vampire mysteries', "where 's wally", 'beyonce', 'personal narrative of a pilgrimage to al - madinah and meccah', 'we have met the enemy : self - control', 'the terminator', 'sushi pack', 'the missing gospels', 'the god of small things', 'big words for little people', 'where mercy flows', 'what dreams may come', 'star wars rebels', 'black cat', 'point blanc', 'rich dad , poor dad', 'true blue', 'the black pearl', 'facebook', 'the valley of the shadow of death : a tale of tragedy and redemption', 'i see you', 'jagua nana', 'noah', 'refugee blues', 'swades', 'cs', 'the crown', 'home to harlem', "let 's stay together", "' to sleep with the angels ''", 'phasma(star wars): journey to star wars : the last jedi', 'boot camp', 'imagine me and you : a novel "', 'smallville', 'marvel ’s black panther', '6s', '" water for elephants "', 'boost your bust', 'the book of five rings', 'the moon is a harsh mistress', 'looking for alaska', 'the man from earth', 'the messenger', 'the last laugh', 'i love you , beth cooper', 'thorn in my pride : a novel of the l.a.riots of 1992', '" the vampire diaries "', 'every last one', 'amber addison', 'fullmetal alchemist', 'the trip', 'the edge of seventeen', 'remember this titan : the bill yoast story', 'allah hu akbar', 'wounded', 'the purge', 'spring breakers', 'the day of the triffids', 'a master', 'bahadur shah', 'star trek voyager', 'pi', 'zodiac', 'the lucifer effect', 'm.d.', 'chain saw confidential', 'the townshend acts', "le morte d'arthur", 'all lives matter', 'how do i love thee', 'the thin man', 'avatar : the last airbender', 'star trek : voyager', 'lipstick jungle', 'the greatest generation', 'the summer day', 'the bungalow mystery', 'glee', 'billions', 'dceu', 'the rockford files', 'secret angels', 'maiden rose', 'profit from your losses', 'boardwalk empire', 'bigg boss', 'dharma & greg', 'the adventures of tom sawyer', "the international jew '", 'times now', 'the loved one', 'muppet treasure island : sailing for adventure', 'grudge match', 'the lightning thief', 'the fault in our stars', 'timothy of the cay', "the millionaire next door : the surprising secrets of america 's wealthy", 'conflict and compromise', 'daytime emmy award', 'the flowers of may', 'android to android', 'an afrocentric manifesto', 'ultra moon', 'star wars vii', 'safe', 'star wars : episode ix', 'the l word', 'a light in the attic', 'a week in the woods', 'ergo proxy', 'the call of the wild', 'dear mr.henshaw', 'the game', 'chasing redbird', 'pink ribbons , inc : breast cancer', 'deus ex machina', 'the gifted', 'californication', 'heavily suppressed)', 'war and peace', 'picasso', "spring 's sprung", 'roll of thunder , hear my cry', 'rockstar', '1 and', 'the soul stone', 'islamophobia', 'zatch bell', 'the diary of a young girl', 'down these mean streets', 'tangerine', 'the man who invented christmas', 'gotti', 'the name of the rose', 'the fun they had', 'the candymakers', 'the bean trees', 'for the love of money : a memoir', 'cat person', 'alice 19th', 'jedi master', 'barnes', 'the seventh tower', 'witch hunt', 'tuxedo mask', 'cut the deck', 'life is good : the book', 'all about the benjamins', 'after ever after', 'sanctuary', 'the way things ought to be', 'the world is flat', 'alice in the country of hearts', 'mistborn', 'wish you were here', 'snow crash', 'shooter', 'gnss', 'sweet home alabama', 'sjw', 'the power of habit', 'independence', 'gypsies', 'the red king', 'fish in the trap', 'star wars and marvel', 'black people', 'the unfoldment : the organic path to clarity , power , and transformation', 'the good earth', 'the smell of war', 'inspector george gently', 'the amnesiac', 'the mathematics of life', 'panama papers', 'picnic', 'look what you made me do', 'the grey lady', 'all quiet on the western front', 'recovery of your inner child : the highly acclaimed method for liberating your inner self', 'big nate : flips out', 'death by chocolate', 'the odyssey of kp2', 'dynasty', 'the freedom writers diary', 'the rescuers down under', 'the adventures of huckleberry finn', 'calcutta', 'the forsaken', 'computer science', 'harry potter and the cursed child', 'inkdeath', 'lock and key', 'the seussification of romeo and juliet', 'mickey mouse clubhouse', 'infinity bee pollen', 'gorgias : encomium of helen', 'the sleeping gypsy', 'sentinel event', 'twilight', 'internal revenue code', 'the art of exceptional living', 'city of glass', 'the little match girl', 'the age of innocence', 'runescape', 'x - men', 'donmeh', 'zimmerman', 'midnight for charlie bone', 'the best birth : your guide', 'do you believe in miracles', 'star wars : rebels', 'death to america', 'the official preppy handbook', 'the great dictator', 'the wolf of wall street', 'where the lost things go', 'the phantom tollbooth', 'the great khali', 'james and the giant peach', 'the sea of trolls', 'ender', 'rightmove', 'the birds', 'the black knight', "irresistible : why we ca n't stop checking , scrolling , clicking and watching", 'tales of the frog princess', 'notification', 'a master of science', "a master 's or", 'the burning bridge', 'the sisters grimm', 'furio giunta', 'the nightmare before christmas', 'saturday night', 'playing the game : a novel', 'the open window', 'mischief night', 'star', "when nothing else matters : michael jordan 's last comeback", 'dark side of the moon', 'naruto', 'young goodman brown', 'bit of rough', 'the art of noises', 'origin', 'going too far', 'jeopardy', 'king of new york', 'the fly', 'watchmen', 'sixteen candles', 'consumed by you', 'mcdougal littell biology', 'the closing of the american mind', 'only cows allowed', 'i dream of jeannie', 'in living color', "a midsummer night 's dream", "my master 's", 'kaun banega crorepati', 'the white giraffe', '" love across the salt desert "', 'star trek v : the final frontier', 'their eyes were watching god', 'a few good men', 'the addams family', 'dragon ball', "the doll 's house", 'nobel laureates', 'saw', 'the book of fred', 'mummy', 'batman & robin', 'flowers for algernon', 'the book of eli', 'fight or flight', 'sons of anarchy', 'star trek enterprise', 'angels & demons', "raven 's lucky numbers dream book : the only lottery book you 'll ever need", 'the pacific', "cook 's country", 'the deathly hallows', 'revelations', 'the children of the forest', 'deep is the night : dark fire', 'bengaluru in karnataka', 'the giver quartet', 'princess hours', 'little house on the prairie', 'commissary kitchen', 'the egg', 'goi', 'the metamorphosis of prime intellect', 'the language of dance', 'jon snow', 'sunrise over fallujah', 'football genius', 'i light a candle', 'thought architectures', 'the wild swans at coole', 'taboo', 'the hunchback of notre - dame', 'boa constrictor', 'the black cassock', 'the princess bride', 'ga - rei', 'final cut pro x', 'torah - observant', 'the wealth of nations', 'the first day', 'i just want to be loved by you and you and you', 'the deer hunter', 'shipping business', 'boogie nights', 'the hollow kingdom trilogy', 'steel magnolias', 'whose line is it anyway', 'sing down the moon', 'the incredibles 2', 'the turn of the screw', 'my little pony : friendship', 'huswifery', 'scrum master', 'build', 'stark', 'hellboy', 'bits hd', 'wide sargasso sea', 'the sand reckoner', 'shut the door', 'foreigner', 'the royal tenenbaums', 'graph theory', 'once upon a time', 'a book of opposites', 'the doll people', 'world of warcraft', 'tragedy in tin can holler', 'the vantage point', 'lethal', 'a long way gone', 'comedk', 'unbornness : human pre - existence and the journey toward birth', 'the truman show', 'political science', 'fear factor', 'the moon', 'super mario run', 'the vampire huntress legend series', 'the rainbow bridge : a visit to pet paradise', 'goosebumps', 'war', '" true blood "', 'sense and sensibility', 'wild at heart', 'marvel cinematic universe', '" the outsiders "', 'the lord of the rings', 'nougat bits', 'e.t.', 'hemlock grove', 'the ted knight show', 'liberalism', 'rocket man', 'the last airbender', 'the fall of troy', 'the sunil gavaskar omnibus', 'they made me do', 'the poisonwood bible', 'the dark knight rises', 'bored to death', 'think out of the box', 'goonda', 'the western tradition : from the ancient world to louis xiv', 'flint', 'confessions of a dangerous mind', 'sex and the city', 'a child called it', 'notes on a scandal', 'midnight sun', 'attack on titan', 'mev', 'the tale of despereaux', 'my life in ruins', 'ai no kusabi', 'othello', 'last shot', 'pluralsight', "grey 's anatomy", 'the missing', 'casablanca', 'when the wind blows', 'the nanny', 'toddlers & tiaras', "night 's kiss", 'dark shadows', 'stargate atlantis', 'napster', 'the wind in the willows', 'the manchurian candidate', 'pulitzer', 'the old testament', 'quora user', 'on the take "', 'the scholar gipsy', 'b.c.', 'mein kampf', 'danny phantom', 'sufi vision and initiation : meetings with remarkable', 'green light', 'when autumn falls', 'the white queen', "' love '", 'the tyger', 'hazing meri sugarman', "blu 's hanging", 'this american life', 'hoops', 'sick', 'labyrinth of memories : a child in the spanish civil war', 'the hills have eyes : the beginning', 'toy story', 'the story of tracy beaker', 'the last of the really great whangdoodles', 'the open boat', 'a bitter brew : faith , power , and poison in a small new england town', 'maple skate', 'elements of art', 'dunkirk', 'demons', 'rules of the road', 'the young turks', 'manchild in the promised land', 'malice', 'the red pyramid', 'the restless gun', 'roll the bones : the history of gambling "', 'the fountainhead', 'american pie', "rennie 's way", 'usa', 'the last supper', 'op', 'bloomability', 'from the dark tower', 'memoirs of a geisha', 'the inner world of jimi hendrix', 'mark', 'neighbours', 'emily windsnap', 'gambit', 'nagpur', 'the killer angels : a novel of the civil war', 'p90', 's.t.o.r.m.- the black sphere', 'm.sc mathematics', 'the circuit : stories from the life of a migrant child', "ca n't take my eyes off of you", 'under a war - torn sky', 'a tesla semi', 'the paul and young ron show', 'don quixote', 'artemis fowl', "lucifer 's hammer", 'impossible', 'the belly of paris', "i 'll see you again", 'amulet', 'the cask of amontillado', 'bronx masquerade', 'forest gump', "constructor theory '", 'nothing succeeds like success', 'beetle', 'fort of chittorgarh', 'aladdin', 'beyond the hills : the journey of waite phillips', 'six cherry blossoms : and other stories', 'dwarf fortress', 'the count of monte cristo', 'mea culpa', 'the " maximum ride : school 's out forever "', 'individual vs society', 'star trek :', 'friendly fire', 'an evolutionary view of women at work', 'installed applications', 'trump and the attack of the bowling ball', 'the house of the spirits " by isabel allende', 'the love of my life', 'the oedipus plays', 'what i know for sure', 'proofs', 'the king in yellow', 'oliver nocturne', 'pawn stars', 'parallel lives', 'the manhood of the master', 'soy un socialista , soy un comunista , soy chicano', 'the doomsday book', 'daily stormer or', 'kool - aid', 'la haine', 'joe " brown', 'quoran q&a', 'the great khali "', "the parson 's tale", 'promoting the common good : bringing economics and theology together again', 'weinstein , moore and franken', 'currency manipulator', 'grand sponsors', 'heavy rain', 'dirty money', '" confidence man "', 'how we roll', 'euro', 'the c++ programming language', 'plan', 'graylog', 'american dragon : jake long', 'a " gun ban "', 'satellite c655-s5512 laptop be improved ?', '“ chopper of republic of anzakistan ”', 'ethnic', 'madame butterfly', 'the white tiger', '" master class "', 'edible bones', "their holy book as quran rather than '", 'engineering , sweden', 'the american dream', 'secret society', '" the game : penetrating the secret society of pickup artists "', '“ now more than ever', 'k.j.h.p.h"at the top of king xi punjab', "harry and the wrinkles '", 'the force awakens', 'food science and quality', 'smile guide', "' yeti obhijaan '", 'the world god only knows', 'bakrichods', 'turkish delight', 'to do list "', "a master 's degree from", 'war for planet of the apes', 'differenz', 'schellingschen systems', 'kiss me', 'two whatever', 'everyday use', 'master of the game']}
y_test=gender_submission['Survived'].values<compute_test_metric>
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 sentiment(comment):
testimonial = TextBlob(comment)
 a = testimonial.sentiment
return [a.polarity,a.subjectivity]
def fword(x,word):
if(x.split(" ")[0].lower() ==word):
return 1
else: return 0
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']
df['num_exclamation_marks'] = df['question_text'].progress_apply(lambda comment: comment.count('!'))
df['num_question_marks'] = df['question_text'].progress_apply(lambda comment: comment.count('?'))
df['num_punctuation'] = df['question_text'].progress_apply(lambda comment: sum(comment.count(w)for w in '.,;:'))
df['num_symbols'] = df['question_text'].progress_apply(lambda comment: sum(comment.count(w)for w in '*&$%'))
df['num_smilies'] = df['question_text'].progress_apply(lambda comment: sum(comment.count(w)for w in(':-)', ':)', ';-)', ';)')))
 df['num_sad'] = df['question_text'].progress_apply(lambda comment: sum(comment.count(w)for w in(':-<', ':() ', ';-() ', ';(')))
 df["mean_word_len"] = df["question_text"].progress_apply(lambda x: np.mean([len(w)for w in str(x ).split() ]))
df["nb_fk"] = df["question_text"].progress_apply(lambda x: count_regexp_occ(r"[Ff]\S{2}[Kk]", x))
df["nb_sk"] = df["question_text"].progress_apply(lambda x: count_regexp_occ(r"[Ss]\S{2}[Kk]", x))
df["nb_dk"] = df["question_text"].progress_apply(lambda x: count_regexp_occ(r"[dD]ick", x))
df["nb_you"] = df["question_text"].progress_apply(lambda x: count_regexp_occ(r"\W[Yy]ou\W", x))
df["nb_mother"] = df["question_text"].progress_apply(lambda x: count_regexp_occ(r"\W[Mm]other\W", x))
df["nb_ng"] = df["question_text"].progress_apply(lambda x: count_regexp_occ(r"\W[Nn]igger\W", x))
df['sum_expletive'] = df["nb_fk"] + df["nb_sk"] + df["nb_dk"] + df["nb_ng"]
df['people_person'] = df["lower_question_text"].progress_apply(lambda comment: sum(comment.count(w)for w in('people', 'person')))
 df['trump'] = df["lower_question_text"].progress_apply(lambda comment: sum(comment.count(w)for w in('trump','donald','president')))
 df['women'] = df["lower_question_text"].progress_apply(lambda comment: sum(comment.count(w)for w in('woman','women', 'girl', 'girls','womens')))
 df['men'] = df["lower_question_text"].progress_apply(lambda comment: sum(comment.count(w)for w in('man','men','mens')))
 df['race'] = df["lower_question_text"].progress_apply(lambda comment: sum(comment.count(w)for w in('white','black','whites','blacks')))
 df['china'] = df["lower_question_text"].progress_apply(lambda comment: sum(comment.count(w)for w in('china','chinese')))
 df['india'] = df["lower_question_text"].progress_apply(lambda comment: sum(comment.count(w)for w in('india','indian','indians')))
 df['country'] = df["lower_question_text"].progress_apply(lambda comment: sum(comment.count(w)for w in('country','countries')))
 df['quora'] = df["lower_question_text"].progress_apply(lambda comment: sum(comment.count(w)for w in(['quora'])))
 df['america'] = df["lower_question_text"].progress_apply(lambda comment: sum(comment.count(w)for w in('america','american','americans')))
 df['sum_feat'] = df["nb_you"]+df["nb_mother"]+df['sum_expletive']+df['people_person']+df['trump']+df['women']+df['men']+df['race']+df['china']+df['india']+df['country']+df['quora']+df['america']
df['why'] = df["lower_question_text"].progress_apply(lambda comment: fword(comment,'why'))
df['howwhat'] = df["lower_question_text"].progress_apply(lambda comment: fword(comment,'how')+ fword(comment,'what'))
df['doare'] = df["lower_question_text"].progress_apply(lambda comment: fword(comment,'do')+ fword(comment,'are'))
return df
def add_qid_features(train,test):
full=pd.concat([train[['qid','question_text']],test])
 full['qid_base_ten'] = full['qid'].apply(lambda x : int(x, 16))
min_qid = full['qid_base_ten'].min()
max_qid = full['qid_base_ten'].max()
full['qid_base_ten_normalized'] = full['qid_base_ten'].apply(lambda x :(x - min_qid)/(max_qid-min_qid))
train = full[0:len(train)]['qid_base_ten_normalized']
test = full[len(train):]['qid_base_ten_normalized']
return train,test
def create_spacy_feats(x):
x = x.lower()
features = [0]*5
for i, k in enumerate(token_dict_manageable.keys()):
for word in token_dict_manageable[k]:
if word in x:
features[i]+=1
return features
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_df = parallelize_apply(train_df,create_spacy_feats,"question_text",4,token_dict_manageable.keys())
 test_df = parallelize_apply(test_df,create_spacy_feats,"question_text",4,token_dict_manageable.keys())
 train = parallelize_dataframe(train_df, add_features)
 test = parallelize_dataframe(test_df, add_features)
 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: replace_typical_misspell(x))
test_df["question_text"] = test_df["question_text"].apply(lambda x: replace_typical_misspell(x))
train_df["question_text"] = train_df["question_text"].progress_apply(lambda x: x.replace("'",''))
test_df["question_text"] = test_df["question_text"].apply(lambda x: x.replace("'",''))
train_X = train_df["question_text"].fillna("_
test_X = test_df["question_text"].fillna("_

 features = train[['total_length','capitals','words_vs_unique','sum_feat','why','howwhat','doare','GPE', 'PERSON', 'ORG', 'NORP', 'WORK_OF_ART']].fillna(0)
 test_features = test[['total_length','capitals','words_vs_unique','sum_feat','why','howwhat','doare','GPE', 'PERSON', 'ORG', 'NORP', 'WORK_OF_ART']].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)+list(test_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
print(classification_report(y_test,predictions_dtree))<compute_test_metric>
start = time.time()
x_train, x_test, y_train, features, test_features, word_index = load_and_prec()
print(time.time() -start )
print(confusion_matrix(y_test,predictions_dtree))<train_model>
seed_everything()
if debug:
paragram_embeddings = np.random.randn(120000,304)
 glove_embeddings = np.random.randn(120000,304)
 if concat:
embedding_matrix = np.concatenate([glove_embeddings, paragram_embeddings], axis=1)
 print(np.shape(embedding_matrix))
embedding_matrix = embedding_matrix[:,:embed_size]
else:
embedding_matrix = np.mean([glove_embeddings, paragram_embeddings], axis=0)
 else:
glove_embedding_index = load_glove_index()
glove_embeddings = create_glove(word_index,glove_embedding_index)
 del glove_embedding_index
para_embedding_index = load_para_index()
paragram_embeddings = create_para(word_index,para_embedding_index)
 del para_embedding_index
if concat:
embedding_matrix = np.concatenate([glove_embeddings, paragram_embeddings], axis=1)
 print(np.shape(embedding_matrix))
embedding_matrix = embedding_matrix[:,:embed_size]
else:
embedding_matrix = np.mean([glove_embeddings, paragram_embeddings], axis=0 )
rfc = RandomForestClassifier(n_estimators=300)
 rfc.fit(X_train,y_train )<predict_on_test>
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
predictions_rfc = rfc.predict(X_test )<compute_test_metric>
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 )
print(classification_report(y_test,predictions_rfc))<compute_test_metric>
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 = 5000
base_lr, max_lr = 0.0001, 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
def pytorch_model_run_full(x_train,y_train,features,x_test,model_obj, feats = False,clip=True):
seed_everything()
test_preds = np.zeros(( len(x_test)))
 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, dtype=torch.long ).cuda()
y_train_fold = torch.tensor(y_train[:, np.newaxis], dtype=torch.float32 ).cuda()
if feats:
kfold_X_features = features
model = model_obj
model.cuda()
loss_fn = torch.nn.BCEWithLogitsLoss(reduction='sum')
 step_size = 5000
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))
train_loader = torch.utils.data.DataLoader(train, batch_size=batch_size, shuffle=True)
 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)
 elapsed_time = time.time() - start_time
print('Epoch {}/{} \t loss={:.4f} \t time={:.2f}s'.format(
 epoch + 1, n_epochs, avg_loss, elapsed_time))
model.eval()
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[i * batch_size:(i+1)* batch_size] = sigmoid(y_pred.cpu().numpy())[:, 0]
return test_preds
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),disable=True):
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]
print(confusion_matrix(y_test,predictions_rfc))<feature_engineering>
def scoring(y_true, y_proba, verbose=True):
def threshold_search(y_true, y_proba):
precision , recall, thresholds = precision_recall_curve(y_true, y_proba)
 thresholds = np.append(thresholds, 1.001)
 F = 2 /(1/precision + 1/recall)
 best_score = np.max(F)
 best_th = thresholds[np.argmax(F)]
return best_th
rkf = RepeatedStratifiedKFold(n_splits=5, n_repeats=10)
 scores = []
ths = []
for train_index, test_index in rkf.split(y_true, y_true):
y_prob_train, y_prob_test = y_proba[train_index], y_proba[test_index]
y_true_train, y_true_test = y_true[train_index], y_true[test_index]
best_threshold = threshold_search(y_true_train, y_prob_train)
 sc = f1_score(y_true_test,(y_prob_test >= best_threshold ).astype(int))
scores.append(sc)
 ths.append(best_threshold)
 best_th = np.mean(ths)
 score = np.mean(scores)
 if verbose: print(f'Best threshold: {np.round(best_th, 4)}, Score: {np.round(score,5)}')
 return best_th, score
test['Survived']=predictions_rfc
test.head()<define_variables>
def boosted_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):
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 = 5000
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)
 train_preds_fold = np.zeros(( x_train_fold.size(0)))
 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)
 train_preds_fold[index] = sigmoid(y_pred.detach().cpu().numpy())[:, 0]
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))
train_preds_fold =(train_preds_fold > 0.33 ).astype(int)
 print(len(train_preds_fold))
boosting_index = []
for i in range(len(y_train_fold)) :
if train_preds_fold[i] != y_train_fold[i] and train_preds_fold[i] == 1:
boosting_index.append(i)
 boosting_index=np.array(boosting_index)
 boost_x_fold = torch.tensor(x_train_fold.cpu().numpy() [boosting_index.astype(int)], dtype=torch.long ).cuda()
boost_y_fold = torch.tensor(y_train_fold.cpu().numpy() [boosting_index.astype(int)], dtype=torch.float32 ).cuda()
kfold_boost_features = kfold_X_features[boosting_index.astype(int)]
print(boost_x_fold.size())
 boost = MyDataset(torch.utils.data.TensorDataset(boost_x_fold, boost_y_fold))
boost_loader = torch.utils.data.DataLoader(boost, batch_size=batch_size, shuffle=False)
 for epoch in range(2):
start_time = time.time()
model.train()
for i,(x_batch, y_batch,index)in enumerate(boost_loader):
if feats:
f = kfold_boost_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
predictions=test[['PassengerId','Survived']]<save_to_csv>
def ensemble_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(( n_epochs,len(x_train)))
 test_preds = []
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 = 5000
base_lr, max_lr = 0.0001, 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)
 test_preds_fold = np.zeros(( n_epochs,len(x_test)))
 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)))
 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]
train_preds[epoch,valid_idx] = valid_preds_fold
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))
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[epoch,i * batch_size:(i+1)* batch_size] = sigmoid(y_pred.cpu().numpy())[:, 0]
test_preds.append(test_preds_fold)
 avg_losses_f.append(avg_loss)
 avg_val_losses_f.append(avg_val_loss)
 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
predictions.to_csv('output.csv', index=False )<install_modules>
T_epsilon = 1e-7
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.kaiming_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, num_capsule, dim_capsule,
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.kaiming_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
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.kaiming_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)
!pip install bayesian-optimization<set_options>
class BiLSTMNeuralNet_Meta(nn.Module):
def __init__(self,hidden_size,fc_layer,embedding_matrix=embedding_matrix):
super(BiLSTMNeuralNet_Meta, self ).__init__()
gru_len = hidden_size
Routings = 4
Num_capsule = 5
Dim_capsule = 5
dropout_p = 0.25
rate_drop_dense = 0.28
LR = 0.001
fc_layer1 = fc_layer
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.linear = nn.Linear(hidden_size*8+1+features.shape[1], 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(input_dim_capsule=gru_len * 2, num_capsule=Num_capsule, dim_capsule=Dim_capsule,
routings=Routings, kernel_size=(9, 1), share_weights=True)
 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.dropout(conc)
 out = self.out(conc)
 return out
class GRU_Att_NeuralNet_Meta(nn.Module):
def __init__(self,hidden_size,lin_size, embedding_matrix=embedding_matrix):
super(GRU_Att_NeuralNet_Meta, self ).__init__()
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.lstm_attention = Attention(hidden_size*2, maxlen)
 self.gru_attention = Attention(hidden_size*2, maxlen)
 self.linear = nn.Linear(hidden_size*8 +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])
 h_embedding = torch.squeeze(self.embedding_dropout(torch.unsqueeze(h_embedding, 0)))
 h_lstm, _ = self.lstm(h_embedding)
 h_gru, _ = self.gru(h_lstm)
 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, avg_pool, max_pool,f), 1)
 conc = self.relu(self.linear(conc))
conc = self.dropout(conc)
 out = self.out(conc)
 return out
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])
 h_embedding = torch.squeeze(self.embedding_dropout(torch.unsqueeze(h_embedding, 0)))
 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
color = sns.color_palette()
sns.set_style('darkgrid')
 def ignore_warn(*args, **kwargs):
pass
warnings.warn = ignore_warn
np.random.RandomState(seed=1)
<load_from_csv>
class cnn2d_NeuralNet(nn.Module):
def __init__(self,hidden_size,lin_size, embedding_matrix=embedding_matrix):
super(cnn2d_NeuralNet, self ).__init__()
fc_layer = lin_size
fc_layer1 = lin_size
nbr_filers_1cnn = 5
nbr_filers_2cnn = 10
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.lstm_attention = Attention(hidden_size * 2, maxlen)
 self.gru_attention = Attention(hidden_size * 2, maxlen)
 self.CNN2D_1 = nn.Conv2d(
 in_channels = 1,
out_channels = nbr_filers_1cnn,
kernel_size =(12,10),
stride =(2,4),
padding =(3,5)
)
 self.BN_1 = nn.BatchNorm2d(nbr_filers_1cnn)
 self.MaxPool2D_1 = nn.MaxPool2d(kernel_size=(3,3), stride =(3,3))
self.CNN2D_2 = nn.Conv2d(
 in_channels = nbr_filers_1cnn,
out_channels = nbr_filers_2cnn,
kernel_size =(5,5),
stride =(2,2),
padding =(1,1), 
)
 self.BN_2 = nn.BatchNorm2d(nbr_filers_2cnn)
 self.MaxPool2D_2 = nn.MaxPool2d(kernel_size=(5,5))
self.bn = nn.BatchNorm1d(14, momentum=0.5)
 self.linear = nn.Linear(hidden_size*8+10+features.shape[1], 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)
 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)
 x_h_gru = h_gru.unsqueeze(1)
 cnn_1 = self.CNN2D_1(x_h_gru)
 cnn_1 = self.BN_1(cnn_1)
 cnn_1 = self.relu(cnn_1)
 max_pool_1 = self.MaxPool2D_1(cnn_1)
 cnn_2 = self.CNN2D_2(max_pool_1)
 cnn_2 = self.BN_2(cnn_2)
 cnn_2 = self.relu(cnn_2)
 max_pool_2 = self.MaxPool2D_2(cnn_2)
 cnn_out = max_pool_2.reshape(max_pool_2.size(0),-1)
 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,cnn_out, 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
train=pd.read_csv(".. /input/train.csv")
 test=pd.read_csv(".. /input/test.csv")
 train_Id=train.Id
test_Id=test.Id
train=train.drop(['Id'],axis=1)
 test=test.drop(['Id'],axis=1 )<drop_column>
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 )
train = train.drop(train[(train['GrLivArea']>4000)&(train['SalePrice']<300000)].index )<create_dataframe>
test_preds_1 = pytorch_model_run_full(x_train,y_train,features,x_test,GRU_Att_NeuralNet_Meta(80,16, embedding_matrix=embedding_matrix), feats = True)
 test_preds_2 = pytorch_model_run_full(x_train,y_train,features,x_test,cnn2d_NeuralNet(60,14, embedding_matrix=glove_embeddings), feats = True)
 test_preds_3 = pytorch_model_run_full(x_train,y_train,features,x_test,Alex_NeuralNet_Meta(62,28, embedding_matrix=paragram_embeddings), feats = True)
 test_preds_5 = pytorch_model_run_full(x_train,y_train,features,x_test,GRU_Att_NeuralNet_Meta(80,16, embedding_matrix=glove_embeddings), feats = True )
all_data=pd.concat([train,test])
 missing=(all_data.isnull().sum().sort_values(ascending=False)/len(all_data)) [:35]
missing_data = pd.DataFrame({'Missing Ratio' :missing})
 missing_data.head(35 )<categorify>
test_preds = [test_preds_1,test_preds_2,test_preds_3,test_preds_5]
meta_preds = pd.DataFrame(np.array(test_preds ).T)
 weights = np.array([0.25315958, 0.24013115, 0.2043472 , 0.29492363])
 test_preds = np.dot(np.array(meta_preds),weights)
 best_thr = 0.32
<data_type_conversions><EOS> 
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 > best_thr ).astype(int)
 submission.to_csv('submission.csv', index=False )