Kaggle competition: PUBG Finish Placement Prediction (Kernels Only)
model.fit(train_data, expected_values)
 print("%.4f" % model.oob_score_ )<load_from_csv>
from lightgbm import LGBMRegressor
from sklearn.model_selection import KFold, StratifiedKFold
from sklearn.metrics import mean_absolute_error
import gc
from sklearn.model_selection import GridSearchCV
passenger_IDs = pd.read_csv("/kaggle/input/titanic/test.csv")[["PassengerId"]].values
preds = model.predict(test_data.values)
 preds<prepare_output>
train = pd.read_csv(".. /input/train.csv")
 test = pd.read_csv(".. /input/test.csv" )
df = {'PassengerId': passenger_IDs.ravel() , 'Survived': preds}
df_predictions = pd.DataFrame(df ).set_index(['PassengerId'])
 df_predictions.head(10 )<save_to_csv>
train = pd.read_csv(".. /input/train.csv")
 test = pd.read_csv(".. /input/test.csv" )
df_predictions.to_csv('/kaggle/working/Predictions.csv' )<load_from_url>
concat = pd.concat([train, test])
 del train
del test
gc.collect()
!wget --quiet https://raw.githubusercontent.com/tensorflow/models/master/official/nlp/bert/tokenization.py<set_options>
concat = reduce_mem_usage(concat )
pd.set_option('display.max_rows', 500)
 pd.set_option('display.max_columns', 500)
 pd.set_option('display.width', 1000)
 SEED = 1337<load_from_csv>
def count_transform(df, cols):
for c in cols:
df[c + "_count"] = df.groupby(c)[c].transform('count')
 return df
df_train = pd.read_csv('.. /input/nlp-getting-started/train.csv', dtype={'id': np.int16, 'target': np.int8})
 df_test = pd.read_csv('.. /input/nlp-getting-started/test.csv', dtype={'id': np.int16})
 print('Training Set Shape = {}'.format(df_train.shape))
print('Training Set Memory Usage = {:.2f} MB'.format(df_train.memory_usage().sum() / 1024**2))
print('Test Set Shape = {}'.format(df_test.shape))
print('Test Set Memory Usage = {:.2f} MB'.format(df_test.memory_usage().sum() / 1024**2))<count_unique_values>
concat = count_transform(concat, ['groupId', 'matchId'] )
print(f'Number of unique values in keyword = {df_train["keyword"].nunique() }(Training)- {df_test["keyword"].nunique() }(Test)')
 print(f'Number of unique values in location = {df_train["location"].nunique() }(Training)- {df_test["location"].nunique() }(Test)' )<feature_engineering>
per_dist_stats = ['assists', 'boosts', 'damageDealt', 'DBNOs',
'headshotKills', 'heals', 'kills',
'teamKills', 'vehicleDestroys', 'weaponsAcquired']
df_train['word_count'] = df_train['text'].apply(lambda x: len(str(x ).split()))
df_test['word_count'] = df_test['text'].apply(lambda x: len(str(x ).split()))
df_train['unique_word_count'] = df_train['text'].apply(lambda x: len(set(str(x ).split())))
 df_test['unique_word_count'] = df_test['text'].apply(lambda x: len(set(str(x ).split())))
 df_train['stop_word_count'] = df_train['text'].apply(lambda x: len([w for w in str(x ).lower().split() if w in STOPWORDS]))
df_test['stop_word_count'] = df_test['text'].apply(lambda x: len([w for w in str(x ).lower().split() if w in STOPWORDS]))
df_train['url_count'] = df_train['text'].apply(lambda x: len([w for w in str(x ).lower().split() if 'http' in w or 'https' in w]))
df_test['url_count'] = df_test['text'].apply(lambda x: len([w for w in str(x ).lower().split() if 'http' in w or 'https' in w]))
df_train['mean_word_length'] = df_train['text'].apply(lambda x: np.mean([len(w)for w in str(x ).split() ]))
df_test['mean_word_length'] = df_test['text'].apply(lambda x: np.mean([len(w)for w in str(x ).split() ]))
df_train['char_count'] = df_train['text'].apply(lambda x: len(str(x)))
 df_test['char_count'] = df_test['text'].apply(lambda x: len(str(x)))
 df_train['punctuation_count'] = df_train['text'].apply(lambda x: len([c for c in str(x)if c in string.punctuation]))
df_test['punctuation_count'] = df_test['text'].apply(lambda x: len([c for c in str(x)if c in string.punctuation]))
df_train['hashtag_count'] = df_train['text'].apply(lambda x: len([c for c in str(x)if c == '
df_test['hashtag_count'] = df_test['text'].apply(lambda x: len([c for c in str(x)if c == '
df_train['mention_count'] = df_train['text'].apply(lambda x: len([c for c in str(x)if c == '@']))
df_test['mention_count'] = df_test['text'].apply(lambda x: len([c for c in str(x)if c == '@']))<categorify>
concat['LogWalk'] = np.log1p(concat['walkDistance'] )
def generate_ngrams(text, n_gram=1):
token = [token for token in text.lower().split(' ')if token != '' if token not in STOPWORDS]
ngrams = zip(*[token[i:] for i in range(n_gram)])
 return [' '.join(ngram)for ngram in ngrams]
N = 100
disaster_unigrams = defaultdict(int)
 nondisaster_unigrams = defaultdict(int)
 for tweet in df_train[DISASTER_TWEETS]['text']:
for word in generate_ngrams(tweet):
disaster_unigrams[word] += 1
for tweet in df_train[~DISASTER_TWEETS]['text']:
for word in generate_ngrams(tweet):
nondisaster_unigrams[word] += 1
df_disaster_unigrams = pd.DataFrame(sorted(disaster_unigrams.items() , key=lambda x: x[1])[::-1])
 df_nondisaster_unigrams = pd.DataFrame(sorted(nondisaster_unigrams.items() , key=lambda x: x[1])[::-1])
 disaster_bigrams = defaultdict(int)
 nondisaster_bigrams = defaultdict(int)
 for tweet in df_train[DISASTER_TWEETS]['text']:
for word in generate_ngrams(tweet, n_gram=2):
disaster_bigrams[word] += 1
for tweet in df_train[~DISASTER_TWEETS]['text']:
for word in generate_ngrams(tweet, n_gram=2):
nondisaster_bigrams[word] += 1
df_disaster_bigrams = pd.DataFrame(sorted(disaster_bigrams.items() , key=lambda x: x[1])[::-1])
 df_nondisaster_bigrams = pd.DataFrame(sorted(nondisaster_bigrams.items() , key=lambda x: x[1])[::-1])
 disaster_trigrams = defaultdict(int)
 nondisaster_trigrams = defaultdict(int)
 for tweet in df_train[DISASTER_TWEETS]['text']:
for word in generate_ngrams(tweet, n_gram=3):
disaster_trigrams[word] += 1
for tweet in df_train[~DISASTER_TWEETS]['text']:
for word in generate_ngrams(tweet, n_gram=3):
nondisaster_trigrams[word] += 1
df_disaster_trigrams = pd.DataFrame(sorted(disaster_trigrams.items() , key=lambda x: x[1])[::-1])
 df_nondisaster_trigrams = pd.DataFrame(sorted(nondisaster_trigrams.items() , key=lambda x: x[1])[::-1] )<load_pretrained>
for stat in per_dist_stats:
concat[stat + '_perLogWalk'] = concat[stat] / concat['LogWalk']
%%time
glove_embeddings = np.load('.. /input/pickled-glove840b300d-for-10sec-loading/glove.840B.300d.pkl', allow_pickle=True)
 fasttext_embeddings = np.load('.. /input/pickled-crawl300d2m-for-kernel-competitions/crawl-300d-2M.pkl', allow_pickle=True )<feature_engineering>
concat['grpSizeMult'] = concat['groupId_count'] /(concat['matchId_count'] / concat['numGroups'] )
def build_vocab(X):
tweets = X.apply(lambda s: s.split() ).values
vocab = {}
for tweet in tweets:
for word in tweet:
try:
vocab[word] += 1
except KeyError:
vocab[word] = 1
return vocab
def check_embeddings_coverage(X, embeddings):
vocab = build_vocab(X)
 covered = {}
oov = {}
n_covered = 0
n_oov = 0
for word in vocab:
try:
covered[word] = embeddings[word]
n_covered += vocab[word]
except:
oov[word] = vocab[word]
n_oov += vocab[word]
vocab_coverage = len(covered)/ len(vocab)
 text_coverage =(n_covered /(n_covered + n_oov))
sorted_oov = sorted(oov.items() , key=operator.itemgetter(1)) [::-1]
return sorted_oov, vocab_coverage, text_coverage
train_glove_oov, train_glove_vocab_coverage, train_glove_text_coverage = check_embeddings_coverage(df_train['text'], glove_embeddings)
 test_glove_oov, test_glove_vocab_coverage, test_glove_text_coverage = check_embeddings_coverage(df_test['text'], glove_embeddings)
 print('GloVe Embeddings cover {:.2%} of vocabulary and {:.2%} of text in Training Set'.format(train_glove_vocab_coverage, train_glove_text_coverage))
print('GloVe Embeddings cover {:.2%} of vocabulary and {:.2%} of text in Test Set'.format(test_glove_vocab_coverage, test_glove_text_coverage))
train_fasttext_oov, train_fasttext_vocab_coverage, train_fasttext_text_coverage = check_embeddings_coverage(df_train['text'], fasttext_embeddings)
 test_fasttext_oov, test_fasttext_vocab_coverage, test_fasttext_text_coverage = check_embeddings_coverage(df_test['text'], fasttext_embeddings)
 print('FastText Embeddings cover {:.2%} of vocabulary and {:.2%} of text in Training Set'.format(train_fasttext_vocab_coverage, train_fasttext_text_coverage))
print('FastText Embeddings cover {:.2%} of vocabulary and {:.2%} of text in Test Set'.format(test_fasttext_vocab_coverage, test_fasttext_text_coverage))<categorify>
match_stats = ['DBNOs',
'assists',
'boosts',
'damageDealt',
'headshotKills',
'heals',
'killPlace',
'killPoints',
'killStreaks',
'kills',
'longestKill',
'revives',
'rideDistance',
'roadKills',
'swimDistance',
'vehicleDestroys',
'walkDistance',
'weaponsAcquired',
'winPoints',
'LogWalk',
'assists_perLogWalk',
'boosts_perLogWalk',
'damageDealt_perLogWalk',
'DBNOs_perLogWalk',
'headshotKills_perLogWalk',
'heals_perLogWalk',
'kills_perLogWalk',
'teamKills_perLogWalk',
'vehicleDestroys_perLogWalk',
'weaponsAcquired_perLogWalk',]
%%time
def clean(tweet):
tweet = re.sub(r"\x89Û_", "", tweet)
 tweet = re.sub(r"\x89ÛÒ", "", tweet)
 tweet = re.sub(r"\x89ÛÓ", "", tweet)
 tweet = re.sub(r"\x89ÛÏWhen", "When", tweet)
 tweet = re.sub(r"\x89ÛÏ", "", tweet)
 tweet = re.sub(r"China\x89Ûªs", "China's", tweet)
 tweet = re.sub(r"let\x89Ûªs", "let's", tweet)
 tweet = re.sub(r"\x89Û÷", "", tweet)
 tweet = re.sub(r"\x89Ûª", "", tweet)
 tweet = re.sub(r"\x89Û\x9d", "", tweet)
 tweet = re.sub(r"å_", "", tweet)
 tweet = re.sub(r"\x89Û¢", "", tweet)
 tweet = re.sub(r"\x89Û¢åÊ", "", tweet)
 tweet = re.sub(r"fromåÊwounds", "from wounds", tweet)
 tweet = re.sub(r"åÊ", "", tweet)
 tweet = re.sub(r"åÈ", "", tweet)
 tweet = re.sub(r"JapÌ_n", "Japan", tweet)
 tweet = re.sub(r"Ì©", "e", tweet)
 tweet = re.sub(r"å¨", "", tweet)
 tweet = re.sub(r"SuruÌ¤", "Suruc", tweet)
 tweet = re.sub(r"åÇ", "", tweet)
 tweet = re.sub(r"å£3million", "3 million", tweet)
 tweet = re.sub(r"åÀ", "", tweet)
 tweet = re.sub(r"he's", "he is", tweet)
 tweet = re.sub(r"there's", "there is", tweet)
 tweet = re.sub(r"We're", "We are", tweet)
 tweet = re.sub(r"That's", "That is", tweet)
 tweet = re.sub(r"won't", "will not", tweet)
 tweet = re.sub(r"they're", "they are", tweet)
 tweet = re.sub(r"Can't", "Cannot", tweet)
 tweet = re.sub(r"wasn't", "was not", tweet)
 tweet = re.sub(r"don\x89Ûªt", "do not", tweet)
 tweet = re.sub(r"aren't", "are not", tweet)
 tweet = re.sub(r"isn't", "is not", tweet)
 tweet = re.sub(r"What's", "What is", tweet)
 tweet = re.sub(r"haven't", "have not", tweet)
 tweet = re.sub(r"hasn't", "has not", tweet)
 tweet = re.sub(r"There's", "There is", tweet)
 tweet = re.sub(r"He's", "He is", tweet)
 tweet = re.sub(r"It's", "It is", tweet)
 tweet = re.sub(r"You're", "You are", tweet)
 tweet = re.sub(r"I'M", "I am", tweet)
 tweet = re.sub(r"shouldn't", "should not", tweet)
 tweet = re.sub(r"wouldn't", "would not", tweet)
 tweet = re.sub(r"i'm", "I am", tweet)
 tweet = re.sub(r"I\x89Ûªm", "I am", tweet)
 tweet = re.sub(r"I'm", "I am", tweet)
 tweet = re.sub(r"Isn't", "is not", tweet)
 tweet = re.sub(r"Here's", "Here is", tweet)
 tweet = re.sub(r"you've", "you have", tweet)
 tweet = re.sub(r"you\x89Ûªve", "you have", tweet)
 tweet = re.sub(r"we're", "we are", tweet)
 tweet = re.sub(r"what's", "what is", tweet)
 tweet = re.sub(r"couldn't", "could not", tweet)
 tweet = re.sub(r"we've", "we have", tweet)
 tweet = re.sub(r"it\x89Ûªs", "it is", tweet)
 tweet = re.sub(r"doesn\x89Ûªt", "does not", tweet)
 tweet = re.sub(r"It\x89Ûªs", "It is", tweet)
 tweet = re.sub(r"Here\x89Ûªs", "Here is", tweet)
 tweet = re.sub(r"who's", "who is", tweet)
 tweet = re.sub(r"I\x89Ûªve", "I have", tweet)
 tweet = re.sub(r"y'all", "you all", tweet)
 tweet = re.sub(r"can\x89Ûªt", "cannot", tweet)
 tweet = re.sub(r"would've", "would have", tweet)
 tweet = re.sub(r"it'll", "it will", tweet)
 tweet = re.sub(r"we'll", "we will", tweet)
 tweet = re.sub(r"wouldn\x89Ûªt", "would not", tweet)
 tweet = re.sub(r"We've", "We have", tweet)
 tweet = re.sub(r"he'll", "he will", tweet)
 tweet = re.sub(r"Y'all", "You all", tweet)
 tweet = re.sub(r"Weren't", "Were not", tweet)
 tweet = re.sub(r"Didn't", "Did not", tweet)
 tweet = re.sub(r"they'll", "they will", tweet)
 tweet = re.sub(r"they'd", "they would", tweet)
 tweet = re.sub(r"DON'T", "DO NOT", tweet)
 tweet = re.sub(r"That\x89Ûªs", "That is", tweet)
 tweet = re.sub(r"they've", "they have", tweet)
 tweet = re.sub(r"i'd", "I would", tweet)
 tweet = re.sub(r"should've", "should have", tweet)
 tweet = re.sub(r"You\x89Ûªre", "You are", tweet)
 tweet = re.sub(r"where's", "where is", tweet)
 tweet = re.sub(r"Don\x89Ûªt", "Do not", tweet)
 tweet = re.sub(r"we'd", "we would", tweet)
 tweet = re.sub(r"i'll", "I will", tweet)
 tweet = re.sub(r"weren't", "were not", tweet)
 tweet = re.sub(r"They're", "They are", tweet)
 tweet = re.sub(r"Can\x89Ûªt", "Cannot", tweet)
 tweet = re.sub(r"you\x89Ûªll", "you will", tweet)
 tweet = re.sub(r"I\x89Ûªd", "I would", tweet)
 tweet = re.sub(r"let's", "let us", tweet)
 tweet = re.sub(r"it's", "it is", tweet)
 tweet = re.sub(r"can't", "cannot", tweet)
 tweet = re.sub(r"don't", "do not", tweet)
 tweet = re.sub(r"you're", "you are", tweet)
 tweet = re.sub(r"i've", "I have", tweet)
 tweet = re.sub(r"that's", "that is", tweet)
 tweet = re.sub(r"i'll", "I will", tweet)
 tweet = re.sub(r"doesn't", "does not", tweet)
 tweet = re.sub(r"i'd", "I would", tweet)
 tweet = re.sub(r"didn't", "did not", tweet)
 tweet = re.sub(r"ain't", "am not", tweet)
 tweet = re.sub(r"you'll", "you will", tweet)
 tweet = re.sub(r"I've", "I have", tweet)
 tweet = re.sub(r"Don't", "do not", tweet)
 tweet = re.sub(r"I'll", "I will", tweet)
 tweet = re.sub(r"I'd", "I would", tweet)
 tweet = re.sub(r"Let's", "Let us", tweet)
 tweet = re.sub(r"you'd", "You would", tweet)
 tweet = re.sub(r"It's", "It is", tweet)
 tweet = re.sub(r"Ain't", "am not", tweet)
 tweet = re.sub(r"Haven't", "Have not", tweet)
 tweet = re.sub(r"Could've", "Could have", tweet)
 tweet = re.sub(r"youve", "you have", tweet)
 tweet = re.sub(r"donå«t", "do not", tweet)
 tweet = re.sub(r"&gt;", ">", tweet)
 tweet = re.sub(r"&lt;", "<", tweet)
 tweet = re.sub(r"&amp;", "&", tweet)
 tweet = re.sub(r"w/e", "whatever", tweet)
 tweet = re.sub(r"w/", "with", tweet)
 tweet = re.sub(r"USAgov", "USA government", tweet)
 tweet = re.sub(r"recentlu", "recently", tweet)
 tweet = re.sub(r"Ph0tos", "Photos", tweet)
 tweet = re.sub(r"amirite", "am I right", tweet)
 tweet = re.sub(r"exp0sed", "exposed", tweet)
 tweet = re.sub(r"<3", "love", tweet)
 tweet = re.sub(r"amageddon", "armageddon", tweet)
 tweet = re.sub(r"Trfc", "Traffic", tweet)
 tweet = re.sub(r"8/5/2015", "2015-08-05", tweet)
 tweet = re.sub(r"WindStorm", "Wind Storm", tweet)
 tweet = re.sub(r"8/6/2015", "2015-08-06", tweet)
 tweet = re.sub(r"10:38PM", "10:38 PM", tweet)
 tweet = re.sub(r"10:30pm", "10:30 PM", tweet)
 tweet = re.sub(r"16yr", "16 year", tweet)
 tweet = re.sub(r"lmao", "laughing my ass off", tweet)
 tweet = re.sub(r"TRAUMATISED", "traumatized", tweet)
 tweet = re.sub(r"IranDeal", "Iran Deal", tweet)
 tweet = re.sub(r"ArianaGrande", "Ariana Grande", tweet)
 tweet = re.sub(r"camilacabello97", "camila cabello", tweet)
 tweet = re.sub(r"RondaRousey", "Ronda Rousey", tweet)
 tweet = re.sub(r"MTVHottest", "MTV Hottest", tweet)
 tweet = re.sub(r"TrapMusic", "Trap Music", tweet)
 tweet = re.sub(r"ProphetMuhammad", "Prophet Muhammad", tweet)
 tweet = re.sub(r"PantherAttack", "Panther Attack", tweet)
 tweet = re.sub(r"StrategicPatience", "Strategic Patience", tweet)
 tweet = re.sub(r"socialnews", "social news", tweet)
 tweet = re.sub(r"NASAHurricane", "NASA Hurricane", tweet)
 tweet = re.sub(r"onlinecommunities", "online communities", tweet)
 tweet = re.sub(r"humanconsumption", "human consumption", tweet)
 tweet = re.sub(r"Typhoon-Devastated", "Typhoon Devastated", tweet)
 tweet = re.sub(r"Meat-Loving", "Meat Loving", tweet)
 tweet = re.sub(r"facialabuse", "facial abuse", tweet)
 tweet = re.sub(r"LakeCounty", "Lake County", tweet)
 tweet = re.sub(r"BeingAuthor", "Being Author", tweet)
 tweet = re.sub(r"withheavenly", "with heavenly", tweet)
 tweet = re.sub(r"thankU", "thank you", tweet)
 tweet = re.sub(r"iTunesMusic", "iTunes Music", tweet)
 tweet = re.sub(r"OffensiveContent", "Offensive Content", tweet)
 tweet = re.sub(r"WorstSummerJob", "Worst Summer Job", tweet)
 tweet = re.sub(r"HarryBeCareful", "Harry Be Careful", tweet)
 tweet = re.sub(r"NASASolarSystem", "NASA Solar System", tweet)
 tweet = re.sub(r"animalrescue", "animal rescue", tweet)
 tweet = re.sub(r"KurtSchlichter", "Kurt Schlichter", tweet)
 tweet = re.sub(r"aRmageddon", "armageddon", tweet)
 tweet = re.sub(r"Throwingknifes", "Throwing knives", tweet)
 tweet = re.sub(r"GodsLove", "God's Love", tweet)
 tweet = re.sub(r"bookboost", "book boost", tweet)
 tweet = re.sub(r"ibooklove", "I book love", tweet)
 tweet = re.sub(r"NestleIndia", "Nestle India", tweet)
 tweet = re.sub(r"realDonaldTrump", "Donald Trump", tweet)
 tweet = re.sub(r"DavidVonderhaar", "David Vonderhaar", tweet)
 tweet = re.sub(r"CecilTheLion", "Cecil The Lion", tweet)
 tweet = re.sub(r"weathernetwork", "weather network", tweet)
 tweet = re.sub(r"withBioterrorism&use", "with Bioterrorism & use", tweet)
 tweet = re.sub(r"Hostage&2", "Hostage & 2", tweet)
 tweet = re.sub(r"GOPDebate", "GOP Debate", tweet)
 tweet = re.sub(r"RickPerry", "Rick Perry", tweet)
 tweet = re.sub(r"frontpage", "front page", tweet)
 tweet = re.sub(r"NewsInTweets", "News In Tweets", tweet)
 tweet = re.sub(r"ViralSpell", "Viral Spell", tweet)
 tweet = re.sub(r"til_now", "until now", tweet)
 tweet = re.sub(r"volcanoinRussia", "volcano in Russia", tweet)
 tweet = re.sub(r"ZippedNews", "Zipped News", tweet)
 tweet = re.sub(r"MicheleBachman", "Michele Bachman", tweet)
 tweet = re.sub(r"53inch", "53 inch", tweet)
 tweet = re.sub(r"KerrickTrial", "Kerrick Trial", tweet)
 tweet = re.sub(r"abstorm", "Alberta Storm", tweet)
 tweet = re.sub(r"Beyhive", "Beyonce hive", tweet)
 tweet = re.sub(r"IDFire", "Idaho Fire", tweet)
 tweet = re.sub(r"DETECTADO", "Detected", tweet)
 tweet = re.sub(r"RockyFire", "Rocky Fire", tweet)
 tweet = re.sub(r"Listen/Buy", "Listen / Buy", tweet)
 tweet = re.sub(r"NickCannon", "Nick Cannon", tweet)
 tweet = re.sub(r"FaroeIslands", "Faroe Islands", tweet)
 tweet = re.sub(r"yycstorm", "Calgary Storm", tweet)
 tweet = re.sub(r"IDPs:", "Internally Displaced People :", tweet)
 tweet = re.sub(r"ArtistsUnited", "Artists United", tweet)
 tweet = re.sub(r"ClaytonBryant", "Clayton Bryant", tweet)
 tweet = re.sub(r"jimmyfallon", "jimmy fallon", tweet)
 tweet = re.sub(r"justinbieber", "justin bieber", tweet)
 tweet = re.sub(r"UTC2015", "UTC 2015", tweet)
 tweet = re.sub(r"Time2015", "Time 2015", tweet)
 tweet = re.sub(r"djicemoon", "dj icemoon", tweet)
 tweet = re.sub(r"LivingSafely", "Living Safely", tweet)
 tweet = re.sub(r"FIFA16", "Fifa 2016", tweet)
 tweet = re.sub(r"thisiswhywecanthavenicethings", "this is why we cannot have nice things", tweet)
 tweet = re.sub(r"bbcnews", "bbc news", tweet)
 tweet = re.sub(r"UndergroundRailraod", "Underground Railraod", tweet)
 tweet = re.sub(r"c4news", "c4 news", tweet)
 tweet = re.sub(r"OBLITERATION", "obliteration", tweet)
 tweet = re.sub(r"MUDSLIDE", "mudslide", tweet)
 tweet = re.sub(r"NoSurrender", "No Surrender", tweet)
 tweet = re.sub(r"NotExplained", "Not Explained", tweet)
 tweet = re.sub(r"greatbritishbakeoff", "great british bake off", tweet)
 tweet = re.sub(r"LondonFire", "London Fire", tweet)
 tweet = re.sub(r"KOTAWeather", "KOTA Weather", tweet)
 tweet = re.sub(r"LuchaUnderground", "Lucha Underground", tweet)
 tweet = re.sub(r"KOIN6News", "KOIN 6 News", tweet)
 tweet = re.sub(r"LiveOnK2", "Live On K2", tweet)
 tweet = re.sub(r"9NewsGoldCoast", "9 News Gold Coast", tweet)
 tweet = re.sub(r"nikeplus", "nike plus", tweet)
 tweet = re.sub(r"david_cameron", "David Cameron", tweet)
 tweet = re.sub(r"peterjukes", "Peter Jukes", tweet)
 tweet = re.sub(r"JamesMelville", "James Melville", tweet)
 tweet = re.sub(r"megynkelly", "Megyn Kelly", tweet)
 tweet = re.sub(r"cnewslive", "C News Live", tweet)
 tweet = re.sub(r"JamaicaObserver", "Jamaica Observer", tweet)
 tweet = re.sub(r"TweetLikeItsSeptember11th2001", "Tweet like it is september 11th 2001", tweet)
 tweet = re.sub(r"cbplawyers", "cbp lawyers", tweet)
 tweet = re.sub(r"fewmoretweets", "few more tweets", tweet)
 tweet = re.sub(r"BlackLivesMatter", "Black Lives Matter", tweet)
 tweet = re.sub(r"cjoyner", "Chris Joyner", tweet)
 tweet = re.sub(r"ENGvAUS", "England vs Australia", tweet)
 tweet = re.sub(r"ScottWalker", "Scott Walker", tweet)
 tweet = re.sub(r"MikeParrActor", "Michael Parr", tweet)
 tweet = re.sub(r"4PlayThursdays", "Foreplay Thursdays", tweet)
 tweet = re.sub(r"TGF2015", "Tontitown Grape Festival", tweet)
 tweet = re.sub(r"realmandyrain", "Mandy Rain", tweet)
 tweet = re.sub(r"GraysonDolan", "Grayson Dolan", tweet)
 tweet = re.sub(r"ApolloBrown", "Apollo Brown", tweet)
 tweet = re.sub(r"saddlebrooke", "Saddlebrooke", tweet)
 tweet = re.sub(r"TontitownGrape", "Tontitown Grape", tweet)
 tweet = re.sub(r"AbbsWinston", "Abbs Winston", tweet)
 tweet = re.sub(r"ShaunKing", "Shaun King", tweet)
 tweet = re.sub(r"MeekMill", "Meek Mill", tweet)
 tweet = re.sub(r"TornadoGiveaway", "Tornado Giveaway", tweet)
 tweet = re.sub(r"GRupdates", "GR updates", tweet)
 tweet = re.sub(r"SouthDowns", "South Downs", tweet)
 tweet = re.sub(r"braininjury", "brain injury", tweet)
 tweet = re.sub(r"auspol", "Australian politics", tweet)
 tweet = re.sub(r"PlannedParenthood", "Planned Parenthood", tweet)
 tweet = re.sub(r"calgaryweather", "Calgary Weather", tweet)
 tweet = re.sub(r"weallheartonedirection", "we all heart one direction", tweet)
 tweet = re.sub(r"edsheeran", "Ed Sheeran", tweet)
 tweet = re.sub(r"TrueHeroes", "True Heroes", tweet)
 tweet = re.sub(r"S3XLEAK", "sex leak", tweet)
 tweet = re.sub(r"ComplexMag", "Complex Magazine", tweet)
 tweet = re.sub(r"TheAdvocateMag", "The Advocate Magazine", tweet)
 tweet = re.sub(r"CityofCalgary", "City of Calgary", tweet)
 tweet = re.sub(r"EbolaOutbreak", "Ebola Outbreak", tweet)
 tweet = re.sub(r"SummerFate", "Summer Fate", tweet)
 tweet = re.sub(r"RAmag", "Royal Academy Magazine", tweet)
 tweet = re.sub(r"offers2go", "offers to go", tweet)
 tweet = re.sub(r"foodscare", "food scare", tweet)
 tweet = re.sub(r"MNPDNashville", "Metropolitan Nashville Police Department", tweet)
 tweet = re.sub(r"TfLBusAlerts", "TfL Bus Alerts", tweet)
 tweet = re.sub(r"GamerGate", "Gamer Gate", tweet)
 tweet = re.sub(r"IHHen", "Humanitarian Relief", tweet)
 tweet = re.sub(r"spinningbot", "spinning bot", tweet)
 tweet = re.sub(r"ModiMinistry", "Modi Ministry", tweet)
 tweet = re.sub(r"TAXIWAYS", "taxi ways", tweet)
 tweet = re.sub(r"Calum5SOS", "Calum Hood", tweet)
 tweet = re.sub(r"po_st", "po.st", tweet)
 tweet = re.sub(r"scoopit", "scoop.it", tweet)
 tweet = re.sub(r"UltimaLucha", "Ultima Lucha", tweet)
 tweet = re.sub(r"JonathanFerrell", "Jonathan Ferrell", tweet)
 tweet = re.sub(r"aria_ahrary", "Aria Ahrary", tweet)
 tweet = re.sub(r"rapidcity", "Rapid City", tweet)
 tweet = re.sub(r"OutBid", "outbid", tweet)
 tweet = re.sub(r"lavenderpoetrycafe", "lavender poetry cafe", tweet)
 tweet = re.sub(r"EudryLantiqua", "Eudry Lantiqua", tweet)
 tweet = re.sub(r"15PM", "15 PM", tweet)
 tweet = re.sub(r"OriginalFunko", "Funko", tweet)
 tweet = re.sub(r"rightwaystan", "Richard Tan", tweet)
 tweet = re.sub(r"CindyNoonan", "Cindy Noonan", tweet)
 tweet = re.sub(r"RT_America", "RT America", tweet)
 tweet = re.sub(r"narendramodi", "Narendra Modi", tweet)
 tweet = re.sub(r"BakeOffFriends", "Bake Off Friends", tweet)
 tweet = re.sub(r"TeamHendrick", "Hendrick Motorsports", tweet)
 tweet = re.sub(r"alexbelloli", "Alex Belloli", tweet)
 tweet = re.sub(r"itsjustinstuart", "Justin Stuart", tweet)
 tweet = re.sub(r"gunsense", "gun sense", tweet)
 tweet = re.sub(r"DebateQuestionsWeWantToHear", "debate questions we want to hear", tweet)
 tweet = re.sub(r"RoyalCarribean", "Royal Carribean", tweet)
 tweet = re.sub(r"samanthaturne19", "Samantha Turner", tweet)
 tweet = re.sub(r"JonVoyage", "Jon Stewart", tweet)
 tweet = re.sub(r"renew911health", "renew 911 health", tweet)
 tweet = re.sub(r"SuryaRay", "Surya Ray", tweet)
 tweet = re.sub(r"pattonoswalt", "Patton Oswalt", tweet)
 tweet = re.sub(r"minhazmerchant", "Minhaz Merchant", tweet)
 tweet = re.sub(r"TLVFaces", "Israel Diaspora Coalition", tweet)
 tweet = re.sub(r"pmarca", "Marc Andreessen", tweet)
 tweet = re.sub(r"pdx911", "Portland Police", tweet)
 tweet = re.sub(r"jamaicaplain", "Jamaica Plain", tweet)
 tweet = re.sub(r"Japton", "Arkansas", tweet)
 tweet = re.sub(r"RouteComplex", "Route Complex", tweet)
 tweet = re.sub(r"INSubcontinent", "Indian Subcontinent", tweet)
 tweet = re.sub(r"NJTurnpike", "New Jersey Turnpike", tweet)
 tweet = re.sub(r"Politifiact", "PolitiFact", tweet)
 tweet = re.sub(r"Hiroshima70", "Hiroshima", tweet)
 tweet = re.sub(r"GMMBC", "Greater Mt Moriah Baptist Church", tweet)
 tweet = re.sub(r"versethe", "verse the", tweet)
 tweet = re.sub(r"TubeStrike", "Tube Strike", tweet)
 tweet = re.sub(r"MissionHills", "Mission Hills", tweet)
 tweet = re.sub(r"ProtectDenaliWolves", "Protect Denali Wolves", tweet)
 tweet = re.sub(r"NANKANA", "Nankana", tweet)
 tweet = re.sub(r"SAHIB", "Sahib", tweet)
 tweet = re.sub(r"PAKPATTAN", "Pakpattan", tweet)
 tweet = re.sub(r"Newz_Sacramento", "News Sacramento", tweet)
 tweet = re.sub(r"gofundme", "go fund me", tweet)
 tweet = re.sub(r"pmharper", "Stephen Harper", tweet)
 tweet = re.sub(r"IvanBerroa", "Ivan Berroa", tweet)
 tweet = re.sub(r"LosDelSonido", "Los Del Sonido", tweet)
 tweet = re.sub(r"bancodeseries", "banco de series", tweet)
 tweet = re.sub(r"timkaine", "Tim Kaine", tweet)
 tweet = re.sub(r"IdentityTheft", "Identity Theft", tweet)
 tweet = re.sub(r"AllLivesMatter", "All Lives Matter", tweet)
 tweet = re.sub(r"mishacollins", "Misha Collins", tweet)
 tweet = re.sub(r"BillNeelyNBC", "Bill Neely", tweet)
 tweet = re.sub(r"BeClearOnCancer", "be clear on cancer", tweet)
 tweet = re.sub(r"Kowing", "Knowing", tweet)
 tweet = re.sub(r"ScreamQueens", "Scream Queens", tweet)
 tweet = re.sub(r"AskCharley", "Ask Charley", tweet)
 tweet = re.sub(r"BlizzHeroes", "Heroes of the Storm", tweet)
 tweet = re.sub(r"BradleyBrad47", "Bradley Brad", tweet)
 tweet = re.sub(r"HannaPH", "Typhoon Hanna", tweet)
 tweet = re.sub(r"meinlcymbals", "MEINL Cymbals", tweet)
 tweet = re.sub(r"Ptbo", "Peterborough", tweet)
 tweet = re.sub(r"cnnbrk", "CNN Breaking News", tweet)
 tweet = re.sub(r"IndianNews", "Indian News", tweet)
 tweet = re.sub(r"savebees", "save bees", tweet)
 tweet = re.sub(r"GreenHarvard", "Green Harvard", tweet)
 tweet = re.sub(r"StandwithPP", "Stand with planned parenthood", tweet)
 tweet = re.sub(r"hermancranston", "Herman Cranston", tweet)
 tweet = re.sub(r"WMUR9", "WMUR-TV", tweet)
 tweet = re.sub(r"RockBottomRadFM", "Rock Bottom Radio", tweet)
 tweet = re.sub(r"ameenshaikh3", "Ameen Shaikh", tweet)
 tweet = re.sub(r"ProSyn", "Project Syndicate", tweet)
 tweet = re.sub(r"Daesh", "ISIS", tweet)
 tweet = re.sub(r"s2g", "swear to god", tweet)
 tweet = re.sub(r"listenlive", "listen live", tweet)
 tweet = re.sub(r"CDCgov", "Centers for Disease Control and Prevention", tweet)
 tweet = re.sub(r"FoxNew", "Fox News", tweet)
 tweet = re.sub(r"CBSBigBrother", "Big Brother", tweet)
 tweet = re.sub(r"JulieDiCaro", "Julie DiCaro", tweet)
 tweet = re.sub(r"theadvocatemag", "The Advocate Magazine", tweet)
 tweet = re.sub(r"RohnertParkDPS", "Rohnert Park Police Department", tweet)
 tweet = re.sub(r"THISIZBWRIGHT", "Bonnie Wright", tweet)
 tweet = re.sub(r"Popularmmos", "Popular MMOs", tweet)
 tweet = re.sub(r"WildHorses", "Wild Horses", tweet)
 tweet = re.sub(r"FantasticFour", "Fantastic Four", tweet)
 tweet = re.sub(r"HORNDALE", "Horndale", tweet)
 tweet = re.sub(r"PINER", "Piner", tweet)
 tweet = re.sub(r"BathAndNorthEastSomerset", "Bath and North East Somerset", tweet)
 tweet = re.sub(r"thatswhatfriendsarefor", "that is what friends are for", tweet)
 tweet = re.sub(r"residualincome", "residual income", tweet)
 tweet = re.sub(r"YahooNewsDigest", "Yahoo News Digest", tweet)
 tweet = re.sub(r"MalaysiaAirlines", "Malaysia Airlines", tweet)
 tweet = re.sub(r"AmazonDeals", "Amazon Deals", tweet)
 tweet = re.sub(r"MissCharleyWebb", "Charley Webb", tweet)
 tweet = re.sub(r"shoalstraffic", "shoals traffic", tweet)
 tweet = re.sub(r"GeorgeFoster72", "George Foster", tweet)
 tweet = re.sub(r"pop2015", "pop 2015", tweet)
 tweet = re.sub(r"_PokemonCards_", "Pokemon Cards", tweet)
 tweet = re.sub(r"DianneG", "Dianne Gallagher", tweet)
 tweet = re.sub(r"KashmirConflict", "Kashmir Conflict", tweet)
 tweet = re.sub(r"BritishBakeOff", "British Bake Off", tweet)
 tweet = re.sub(r"FreeKashmir", "Free Kashmir", tweet)
 tweet = re.sub(r"mattmosley", "Matt Mosley", tweet)
 tweet = re.sub(r"BishopFred", "Bishop Fred", tweet)
 tweet = re.sub(r"EndConflict", "End Conflict", tweet)
 tweet = re.sub(r"EndOccupation", "End Occupation", tweet)
 tweet = re.sub(r"UNHEALED", "unhealed", tweet)
 tweet = re.sub(r"CharlesDagnall", "Charles Dagnall", tweet)
 tweet = re.sub(r"Latestnews", "Latest news", tweet)
 tweet = re.sub(r"KindleCountdown", "Kindle Countdown", tweet)
 tweet = re.sub(r"NoMoreHandouts", "No More Handouts", tweet)
 tweet = re.sub(r"datingtips", "dating tips", tweet)
 tweet = re.sub(r"charlesadler", "Charles Adler", tweet)
 tweet = re.sub(r"twia", "Texas Windstorm Insurance Association", tweet)
 tweet = re.sub(r"txlege", "Texas Legislature", tweet)
 tweet = re.sub(r"WindstormInsurer", "Windstorm Insurer", tweet)
 tweet = re.sub(r"Newss", "News", tweet)
 tweet = re.sub(r"hempoil", "hemp oil", tweet)
 tweet = re.sub(r"CommoditiesAre", "Commodities are", tweet)
 tweet = re.sub(r"tubestrike", "tube strike", tweet)
 tweet = re.sub(r"JoeNBC", "Joe Scarborough", tweet)
 tweet = re.sub(r"LiteraryCakes", "Literary Cakes", tweet)
 tweet = re.sub(r"TI5", "The International 5", tweet)
 tweet = re.sub(r"thehill", "the hill", tweet)
 tweet = re.sub(r"3others", "3 others", tweet)
 tweet = re.sub(r"stighefootball", "Sam Tighe", tweet)
 tweet = re.sub(r"whatstheimportantvideo", "what is the important video", tweet)
 tweet = re.sub(r"ClaudioMeloni", "Claudio Meloni", tweet)
 tweet = re.sub(r"DukeSkywalker", "Duke Skywalker", tweet)
 tweet = re.sub(r"carsonmwr", "Fort Carson", tweet)
 tweet = re.sub(r"offdishduty", "off dish duty", tweet)
 tweet = re.sub(r"andword", "and word", tweet)
 tweet = re.sub(r"rhodeisland", "Rhode Island", tweet)
 tweet = re.sub(r"easternoregon", "Eastern Oregon", tweet)
 tweet = re.sub(r"WAwildfire", "Washington Wildfire", tweet)
 tweet = re.sub(r"fingerrockfire", "Finger Rock Fire", tweet)
 tweet = re.sub(r"57am", "57 am", tweet)
 tweet = re.sub(r"fingerrockfire", "Finger Rock Fire", tweet)
 tweet = re.sub(r"JacobHoggard", "Jacob Hoggard", tweet)
 tweet = re.sub(r"newnewnew", "new new new", tweet)
 tweet = re.sub(r"under50", "under 50", tweet)
 tweet = re.sub(r"getitbeforeitsgone", "get it before it is gone", tweet)
 tweet = re.sub(r"freshoutofthebox", "fresh out of the box", tweet)
 tweet = re.sub(r"amwriting", "am writing", tweet)
 tweet = re.sub(r"Bokoharm", "Boko Haram", tweet)
 tweet = re.sub(r"Nowlike", "Now like", tweet)
 tweet = re.sub(r"seasonfrom", "season from", tweet)
 tweet = re.sub(r"epicente", "epicenter", tweet)
 tweet = re.sub(r"epicenterr", "epicenter", tweet)
 tweet = re.sub(r"sicklife", "sick life", tweet)
 tweet = re.sub(r"yycweather", "Calgary Weather", tweet)
 tweet = re.sub(r"calgarysun", "Calgary Sun", tweet)
 tweet = re.sub(r"approachng", "approaching", tweet)
 tweet = re.sub(r"evng", "evening", tweet)
 tweet = re.sub(r"Sumthng", "something", tweet)
 tweet = re.sub(r"EllenPompeo", "Ellen Pompeo", tweet)
 tweet = re.sub(r"shondarhimes", "Shonda Rhimes", tweet)
 tweet = re.sub(r"ABCNetwork", "ABC Network", tweet)
 tweet = re.sub(r"SushmaSwaraj", "Sushma Swaraj", tweet)
 tweet = re.sub(r"pray4japan", "Pray for Japan", tweet)
 tweet = re.sub(r"hope4japan", "Hope for Japan", tweet)
 tweet = re.sub(r"Illusionimagess", "Illusion images", tweet)
 tweet = re.sub(r"SummerUnderTheStars", "Summer Under The Stars", tweet)
 tweet = re.sub(r"ShallWeDance", "Shall We Dance", tweet)
 tweet = re.sub(r"TCMParty", "TCM Party", tweet)
 tweet = re.sub(r"marijuananews", "marijuana news", tweet)
 tweet = re.sub(r"onbeingwithKristaTippett", "on being with Krista Tippett", tweet)
 tweet = re.sub(r"Beingtweets", "Being tweets", tweet)
 tweet = re.sub(r"newauthors", "new authors", tweet)
 tweet = re.sub(r"remedyyyy", "remedy", tweet)
 tweet = re.sub(r"44PM", "44 PM", tweet)
 tweet = re.sub(r"HeadlinesApp", "Headlines App", tweet)
 tweet = re.sub(r"40PM", "40 PM", tweet)
 tweet = re.sub(r"myswc", "Severe Weather Center", tweet)
 tweet = re.sub(r"ithats", "that is", tweet)
 tweet = re.sub(r"icouldsitinthismomentforever", "I could sit in this moment forever", tweet)
 tweet = re.sub(r"FatLoss", "Fat Loss", tweet)
 tweet = re.sub(r"02PM", "02 PM", tweet)
 tweet = re.sub(r"MetroFmTalk", "Metro Fm Talk", tweet)
 tweet = re.sub(r"Bstrd", "bastard", tweet)
 tweet = re.sub(r"bldy", "bloody", tweet)
 tweet = re.sub(r"MetrofmTalk", "Metro Fm Talk", tweet)
 tweet = re.sub(r"terrorismturn", "terrorism turn", tweet)
 tweet = re.sub(r"BBCNewsAsia", "BBC News Asia", tweet)
 tweet = re.sub(r"BehindTheScenes", "Behind The Scenes", tweet)
 tweet = re.sub(r"GeorgeTakei", "George Takei", tweet)
 tweet = re.sub(r"WomensWeeklyMag", "Womens Weekly Magazine", tweet)
 tweet = re.sub(r"SurvivorsGuidetoEarth", "Survivors Guide to Earth", tweet)
 tweet = re.sub(r"incubusband", "incubus band", tweet)
 tweet = re.sub(r"Babypicturethis", "Baby picture this", tweet)
 tweet = re.sub(r"BombEffects", "Bomb Effects", tweet)
 tweet = re.sub(r"win10", "Windows 10", tweet)
 tweet = re.sub(r"idkidk", "I do not know I do not know", tweet)
 tweet = re.sub(r"TheWalkingDead", "The Walking Dead", tweet)
 tweet = re.sub(r"amyschumer", "Amy Schumer", tweet)
 tweet = re.sub(r"crewlist", "crew list", tweet)
 tweet = re.sub(r"Erdogans", "Erdogan", tweet)
 tweet = re.sub(r"BBCLive", "BBC Live", tweet)
 tweet = re.sub(r"TonyAbbottMHR", "Tony Abbott", tweet)
 tweet = re.sub(r"paulmyerscough", "Paul Myerscough", tweet)
 tweet = re.sub(r"georgegallagher", "George Gallagher", tweet)
 tweet = re.sub(r"JimmieJohnson", "Jimmie Johnson", tweet)
 tweet = re.sub(r"pctool", "pc tool", tweet)
 tweet = re.sub(r"DoingHashtagsRight", "Doing Hashtags Right", tweet)
 tweet = re.sub(r"ThrowbackThursday", "Throwback Thursday", tweet)
 tweet = re.sub(r"SnowBackSunday", "Snowback Sunday", tweet)
 tweet = re.sub(r"LakeEffect", "Lake Effect", tweet)
 tweet = re.sub(r"RTphotographyUK", "Richard Thomas Photography UK", tweet)
 tweet = re.sub(r"BigBang_CBS", "Big Bang CBS", tweet)
 tweet = re.sub(r"writerslife", "writers life", tweet)
 tweet = re.sub(r"NaturalBirth", "Natural Birth", tweet)
 tweet = re.sub(r"UnusualWords", "Unusual Words", tweet)
 tweet = re.sub(r"wizkhalifa", "Wiz Khalifa", tweet)
 tweet = re.sub(r"acreativedc", "a creative DC", tweet)
 tweet = re.sub(r"vscodc", "vsco DC", tweet)
 tweet = re.sub(r"VSCOcam", "vsco camera", tweet)
 tweet = re.sub(r"TheBEACHDC", "The beach DC", tweet)
 tweet = re.sub(r"buildingmuseum", "building museum", tweet)
 tweet = re.sub(r"WorldOil", "World Oil", tweet)
 tweet = re.sub(r"redwedding", "red wedding", tweet)
 tweet = re.sub(r"AmazingRaceCanada", "Amazing Race Canada", tweet)
 tweet = re.sub(r"WakeUpAmerica", "Wake Up America", tweet)
 tweet = re.sub(r"\\Allahuakbar\", "Allahu Akbar", tweet)
 tweet = re.sub(r"bleased", "blessed", tweet)
 tweet = re.sub(r"nigeriantribune", "Nigerian Tribune", tweet)
 tweet = re.sub(r"HIDEO_KOJIMA_EN", "Hideo Kojima", tweet)
 tweet = re.sub(r"FusionFestival", "Fusion Festival", tweet)
 tweet = re.sub(r"50Mixed", "50 Mixed", tweet)
 tweet = re.sub(r"NoAgenda", "No Agenda", tweet)
 tweet = re.sub(r"WhiteGenocide", "White Genocide", tweet)
 tweet = re.sub(r"dirtylying", "dirty lying", tweet)
 tweet = re.sub(r"SyrianRefugees", "Syrian Refugees", tweet)
 tweet = re.sub(r"changetheworld", "change the world", tweet)
 tweet = re.sub(r"Ebolacase", "Ebola case", tweet)
 tweet = re.sub(r"mcgtech", "mcg technologies", tweet)
 tweet = re.sub(r"withweapons", "with weapons", tweet)
 tweet = re.sub(r"advancedwarfare", "advanced warfare", tweet)
 tweet = re.sub(r"letsFootball", "let us Football", tweet)
 tweet = re.sub(r"LateNiteMix", "late night mix", tweet)
 tweet = re.sub(r"PhilCollinsFeed", "Phil Collins", tweet)
 tweet = re.sub(r"RudyHavenstein", "Rudy Havenstein", tweet)
 tweet = re.sub(r"22PM", "22 PM", tweet)
 tweet = re.sub(r"54am", "54 AM", tweet)
 tweet = re.sub(r"38am", "38 AM", tweet)
 tweet = re.sub(r"OldFolkExplainStuff", "Old Folk Explain Stuff", tweet)
 tweet = re.sub(r"BlacklivesMatter", "Black Lives Matter", tweet)
 tweet = re.sub(r"InsaneLimits", "Insane Limits", tweet)
 tweet = re.sub(r"youcantsitwithus", "you cannot sit with us", tweet)
 tweet = re.sub(r"2k15", "2015", tweet)
 tweet = re.sub(r"TheIran", "Iran", tweet)
 tweet = re.sub(r"JimmyFallon", "Jimmy Fallon", tweet)
 tweet = re.sub(r"AlbertBrooks", "Albert Brooks", tweet)
 tweet = re.sub(r"defense_news", "defense news", tweet)
 tweet = re.sub(r"nuclearrcSA", "Nuclear Risk Control Self Assessment", tweet)
 tweet = re.sub(r"Auspol", "Australia Politics", tweet)
 tweet = re.sub(r"NuclearPower", "Nuclear Power", tweet)
 tweet = re.sub(r"WhiteTerrorism", "White Terrorism", tweet)
 tweet = re.sub(r"truthfrequencyradio", "Truth Frequency Radio", tweet)
 tweet = re.sub(r"ErasureIsNotEquality", "Erasure is not equality", tweet)
 tweet = re.sub(r"ProBonoNews", "Pro Bono News", tweet)
 tweet = re.sub(r"JakartaPost", "Jakarta Post", tweet)
 tweet = re.sub(r"toopainful", "too painful", tweet)
 tweet = re.sub(r"melindahaunton", "Melinda Haunton", tweet)
 tweet = re.sub(r"NoNukes", "No Nukes", tweet)
 tweet = re.sub(r"curryspcworld", "Currys PC World", tweet)
 tweet = re.sub(r"ineedcake", "I need cake", tweet)
 tweet = re.sub(r"blackforestgateau", "black forest gateau", tweet)
 tweet = re.sub(r"BBCOne", "BBC One", tweet)
 tweet = re.sub(r"AlexxPage", "Alex Page", tweet)
 tweet = re.sub(r"jonathanserrie", "Jonathan Serrie", tweet)
 tweet = re.sub(r"SocialJerkBlog", "Social Jerk Blog", tweet)
 tweet = re.sub(r"ChelseaVPeretti", "Chelsea Peretti", tweet)
 tweet = re.sub(r"irongiant", "iron giant", tweet)
 tweet = re.sub(r"RonFunches", "Ron Funches", tweet)
 tweet = re.sub(r"TimCook", "Tim Cook", tweet)
 tweet = re.sub(r"sebastianstanisaliveandwell", "Sebastian Stan is alive and well", tweet)
 tweet = re.sub(r"Madsummer", "Mad summer", tweet)
 tweet = re.sub(r"NowYouKnow", "Now you know", tweet)
 tweet = re.sub(r"concertphotography", "concert photography", tweet)
 tweet = re.sub(r"TomLandry", "Tom Landry", tweet)
 tweet = re.sub(r"showgirldayoff", "show girl day off", tweet)
 tweet = re.sub(r"Yougslavia", "Yugoslavia", tweet)
 tweet = re.sub(r"QuantumDataInformatics", "Quantum Data Informatics", tweet)
 tweet = re.sub(r"FromTheDesk", "From The Desk", tweet)
 tweet = re.sub(r"TheaterTrial", "Theater Trial", tweet)
 tweet = re.sub(r"CatoInstitute", "Cato Institute", tweet)
 tweet = re.sub(r"EmekaGift", "Emeka Gift", tweet)
 tweet = re.sub(r"LetsBe_Rational", "Let us be rational", tweet)
 tweet = re.sub(r"Cynicalreality", "Cynical reality", tweet)
 tweet = re.sub(r"FredOlsenCruise", "Fred Olsen Cruise", tweet)
 tweet = re.sub(r"NotSorry", "not sorry", tweet)
 tweet = re.sub(r"UseYourWords", "use your words", tweet)
 tweet = re.sub(r"WordoftheDay", "word of the day", tweet)
 tweet = re.sub(r"Dictionarycom", "Dictionary.com", tweet)
 tweet = re.sub(r"TheBrooklynLife", "The Brooklyn Life", tweet)
 tweet = re.sub(r"jokethey", "joke they", tweet)
 tweet = re.sub(r"nflweek1picks", "NFL week 1 picks", tweet)
 tweet = re.sub(r"uiseful", "useful", tweet)
 tweet = re.sub(r"JusticeDotOrg", "The American Association for Justice", tweet)
 tweet = re.sub(r"autoaccidents", "auto accidents", tweet)
 tweet = re.sub(r"SteveGursten", "Steve Gursten", tweet)
 tweet = re.sub(r"MichiganAutoLaw", "Michigan Auto Law", tweet)
 tweet = re.sub(r"birdgang", "bird gang", tweet)
 tweet = re.sub(r"nflnetwork", "NFL Network", tweet)
 tweet = re.sub(r"NYDNSports", "NY Daily News Sports", tweet)
 tweet = re.sub(r"RVacchianoNYDN", "Ralph Vacchiano NY Daily News", tweet)
 tweet = re.sub(r"EdmontonEsks", "Edmonton Eskimos", tweet)
 tweet = re.sub(r"david_brelsford", "David Brelsford", tweet)
 tweet = re.sub(r"TOI_India", "The Times of India", tweet)
 tweet = re.sub(r"hegot", "he got", tweet)
 tweet = re.sub(r"SkinsOn9", "Skins on 9", tweet)
 tweet = re.sub(r"sothathappened", "so that happened", tweet)
 tweet = re.sub(r"LCOutOfDoors", "LC Out Of Doors", tweet)
 tweet = re.sub(r"NationFirst", "Nation First", tweet)
 tweet = re.sub(r"IndiaToday", "India Today", tweet)
 tweet = re.sub(r"HLPS", "helps", tweet)
 tweet = re.sub(r"HOSTAGESTHROSW", "hostages throw", tweet)
 tweet = re.sub(r"SNCTIONS", "sanctions", tweet)
 tweet = re.sub(r"BidTime", "Bid Time", tweet)
 tweet = re.sub(r"crunchysensible", "crunchy sensible", tweet)
 tweet = re.sub(r"RandomActsOfRomance", "Random acts of romance", tweet)
 tweet = re.sub(r"MomentsAtHill", "Moments at hill", tweet)
 tweet = re.sub(r"eatshit", "eat shit", tweet)
 tweet = re.sub(r"liveleakfun", "live leak fun", tweet)
 tweet = re.sub(r"SahelNews", "Sahel News", tweet)
 tweet = re.sub(r"abc7newsbayarea", "ABC 7 News Bay Area", tweet)
 tweet = re.sub(r"facilitiesmanagement", "facilities management", tweet)
 tweet = re.sub(r"facilitydude", "facility dude", tweet)
 tweet = re.sub(r"CampLogistics", "Camp logistics", tweet)
 tweet = re.sub(r"alaskapublic", "Alaska public", tweet)
 tweet = re.sub(r"MarketResearch", "Market Research", tweet)
 tweet = re.sub(r"AccuracyEsports", "Accuracy Esports", tweet)
 tweet = re.sub(r"TheBodyShopAust", "The Body Shop Australia", tweet)
 tweet = re.sub(r"yychail", "Calgary hail", tweet)
 tweet = re.sub(r"yyctraffic", "Calgary traffic", tweet)
 tweet = re.sub(r"eliotschool", "eliot school", tweet)
 tweet = re.sub(r"TheBrokenCity", "The Broken City", tweet)
 tweet = re.sub(r"OldsFireDept", "Olds Fire Department", tweet)
 tweet = re.sub(r"RiverComplex", "River Complex", tweet)
 tweet = re.sub(r"fieldworksmells", "field work smells", tweet)
 tweet = re.sub(r"IranElection", "Iran Election", tweet)
 tweet = re.sub(r"glowng", "glowing", tweet)
 tweet = re.sub(r"kindlng", "kindling", tweet)
 tweet = re.sub(r"riggd", "rigged", tweet)
 tweet = re.sub(r"slownewsday", "slow news day", tweet)
 tweet = re.sub(r"MyanmarFlood", "Myanmar Flood", tweet)
 tweet = re.sub(r"abc7chicago", "ABC 7 Chicago", tweet)
 tweet = re.sub(r"copolitics", "Colorado Politics", tweet)
 tweet = re.sub(r"AdilGhumro", "Adil Ghumro", tweet)
 tweet = re.sub(r"netbots", "net bots", tweet)
 tweet = re.sub(r"byebyeroad", "bye bye road", tweet)
 tweet = re.sub(r"massiveflooding", "massive flooding", tweet)
 tweet = re.sub(r"EndofUS", "End of United States", tweet)
 tweet = re.sub(r"35PM", "35 PM", tweet)
 tweet = re.sub(r"greektheatrela", "Greek Theatre Los Angeles", tweet)
 tweet = re.sub(r"76mins", "76 minutes", tweet)
 tweet = re.sub(r"publicsafetyfirst", "public safety first", tweet)
 tweet = re.sub(r"livesmatter", "lives matter", tweet)
 tweet = re.sub(r"myhometown", "my hometown", tweet)
 tweet = re.sub(r"tankerfire", "tanker fire", tweet)
 tweet = re.sub(r"MEMORIALDAY", "memorial day", tweet)
 tweet = re.sub(r"MEMORIAL_DAY", "memorial day", tweet)
 tweet = re.sub(r"instaxbooty", "instagram booty", tweet)
 tweet = re.sub(r"Jerusalem_Post", "Jerusalem Post", tweet)
 tweet = re.sub(r"WayneRooney_INA", "Wayne Rooney", tweet)
 tweet = re.sub(r"VirtualReality", "Virtual Reality", tweet)
 tweet = re.sub(r"OculusRift", "Oculus Rift", tweet)
 tweet = re.sub(r"OwenJones84", "Owen Jones", tweet)
 tweet = re.sub(r"jeremycorbyn", "Jeremy Corbyn", tweet)
 tweet = re.sub(r"paulrogers002", "Paul Rogers", tweet)
 tweet = re.sub(r"mortalkombatx", "Mortal Kombat X", tweet)
 tweet = re.sub(r"mortalkombat", "Mortal Kombat", tweet)
 tweet = re.sub(r"FilipeCoelho92", "Filipe Coelho", tweet)
 tweet = re.sub(r"OnlyQuakeNews", "Only Quake News", tweet)
 tweet = re.sub(r"kostumes", "costumes", tweet)
 tweet = re.sub(r"YEEESSSS", "yes", tweet)
 tweet = re.sub(r"ToshikazuKatayama", "Toshikazu Katayama", tweet)
 tweet = re.sub(r"IntlDevelopment", "Intl Development", tweet)
 tweet = re.sub(r"ExtremeWeather", "Extreme Weather", tweet)
 tweet = re.sub(r"WereNotGruberVoters", "We are not gruber voters", tweet)
 tweet = re.sub(r"NewsThousands", "News Thousands", tweet)
 tweet = re.sub(r"EdmundAdamus", "Edmund Adamus", tweet)
 tweet = re.sub(r"EyewitnessWV", "Eye witness WV", tweet)
 tweet = re.sub(r"PhiladelphiaMuseu", "Philadelphia Museum", tweet)
 tweet = re.sub(r"DublinComicCon", "Dublin Comic Con", tweet)
 tweet = re.sub(r"NicholasBrendon", "Nicholas Brendon", tweet)
 tweet = re.sub(r"Alltheway80s", "All the way 80s", tweet)
 tweet = re.sub(r"FromTheField", "From the field", tweet)
 tweet = re.sub(r"NorthIowa", "North Iowa", tweet)
 tweet = re.sub(r"WillowFire", "Willow Fire", tweet)
 tweet = re.sub(r"MadRiverComplex", "Mad River Complex", tweet)
 tweet = re.sub(r"feelingmanly", "feeling manly", tweet)
 tweet = re.sub(r"stillnotoverit", "still not over it", tweet)
 tweet = re.sub(r"FortitudeValley", "Fortitude Valley", tweet)
 tweet = re.sub(r"CoastpowerlineTramTr", "Coast powerline", tweet)
 tweet = re.sub(r"ServicesGold", "Services Gold", tweet)
 tweet = re.sub(r"NewsbrokenEmergency", "News broken emergency", tweet)
 tweet = re.sub(r"Evaucation", "evacuation", tweet)
 tweet = re.sub(r"leaveevacuateexitbe", "leave evacuate exit be", tweet)
 tweet = re.sub(r"P_EOPLE", "PEOPLE", tweet)
 tweet = re.sub(r"Tubestrike", "tube strike", tweet)
 tweet = re.sub(r"CLASS_SICK", "CLASS SICK", tweet)
 tweet = re.sub(r"localplumber", "local plumber", tweet)
 tweet = re.sub(r"awesomejobsiri", "awesome job siri", tweet)
 tweet = re.sub(r"PayForItHow", "Pay for it how", tweet)
 tweet = re.sub(r"ThisIsAfrica", "This is Africa", tweet)
 tweet = re.sub(r"crimeairnetwork", "crime air network", tweet)
 tweet = re.sub(r"KimAcheson", "Kim Acheson", tweet)
 tweet = re.sub(r"cityofcalgary", "City of Calgary", tweet)
 tweet = re.sub(r"prosyndicate", "pro syndicate", tweet)
 tweet = re.sub(r"660NEWS", "660 NEWS", tweet)
 tweet = re.sub(r"BusInsMagazine", "Business Insurance Magazine", tweet)
 tweet = re.sub(r"wfocus", "focus", tweet)
 tweet = re.sub(r"ShastaDam", "Shasta Dam", tweet)
 tweet = re.sub(r"go2MarkFranco", "Mark Franco", tweet)
 tweet = re.sub(r"StephGHinojosa", "Steph Hinojosa", tweet)
 tweet = re.sub(r"Nashgrier", "Nash Grier", tweet)
 tweet = re.sub(r"NashNewVideo", "Nash new video", tweet)
 tweet = re.sub(r"IWouldntGetElectedBecause", "I would not get elected because", tweet)
 tweet = re.sub(r"SHGames", "Sledgehammer Games", tweet)
 tweet = re.sub(r"bedhair", "bed hair", tweet)
 tweet = re.sub(r"JoelHeyman", "Joel Heyman", tweet)
 tweet = re.sub(r"viaYouTube", "via YouTube", tweet)
 tweet = re.sub(r"https?:\/\/t.co\/[A-Za-z0-9]+", "", tweet)
 punctuations = '@
for p in punctuations:
tweet = tweet.replace(p, f' {p} ')
 tweet = tweet.replace('...', '...')
 if '...' not in tweet:
tweet = tweet.replace('.. ', '...')
 tweet = re.sub(r"MH370", "Malaysia Airlines Flight 370", tweet)
 tweet = re.sub(r"mÌ¼sica", "music", tweet)
 tweet = re.sub(r"okwx", "Oklahoma City Weather", tweet)
 tweet = re.sub(r"arwx", "Arkansas Weather", tweet)
 tweet = re.sub(r"gawx", "Georgia Weather", tweet)
 tweet = re.sub(r"scwx", "South Carolina Weather", tweet)
 tweet = re.sub(r"cawx", "California Weather", tweet)
 tweet = re.sub(r"tnwx", "Tennessee Weather", tweet)
 tweet = re.sub(r"azwx", "Arizona Weather", tweet)
 tweet = re.sub(r"alwx", "Alabama Weather", tweet)
 tweet = re.sub(r"wordpressdotcom", "wordpress", tweet)
 tweet = re.sub(r"usNWSgov", "United States National Weather Service", tweet)
 tweet = re.sub(r"Suruc", "Sanliurfa", tweet)
 tweet = re.sub(r"Bestnaijamade", "bestnaijamade", tweet)
 tweet = re.sub(r"SOUDELOR", "Soudelor", tweet)
 return tweet
df_train['text_cleaned'] = df_train['text'].apply(lambda s : clean(s))
df_test['text_cleaned'] = df_test['text'].apply(lambda s : clean(s))
train_glove_oov, train_glove_vocab_coverage, train_glove_text_coverage = check_embeddings_coverage(df_train['text_cleaned'], glove_embeddings)
 test_glove_oov, test_glove_vocab_coverage, test_glove_text_coverage = check_embeddings_coverage(df_test['text_cleaned'], glove_embeddings)
 print('GloVe Embeddings cover {:.2%} of vocabulary and {:.2%} of text in Training Set'.format(train_glove_vocab_coverage, train_glove_text_coverage))
print('GloVe Embeddings cover {:.2%} of vocabulary and {:.2%} of text in Test Set'.format(test_glove_vocab_coverage, test_glove_text_coverage))
train_fasttext_oov, train_fasttext_vocab_coverage, train_fasttext_text_coverage = check_embeddings_coverage(df_train['text_cleaned'], fasttext_embeddings)
 test_fasttext_oov, test_fasttext_vocab_coverage, test_fasttext_text_coverage = check_embeddings_coverage(df_test['text_cleaned'], fasttext_embeddings)
 print('FastText Embeddings cover {:.2%} of vocabulary and {:.2%} of text in Training Set'.format(train_fasttext_vocab_coverage, train_fasttext_text_coverage))
print('FastText Embeddings cover {:.2%} of vocabulary and {:.2%} of text in Test Set'.format(test_fasttext_vocab_coverage, test_fasttext_text_coverage))<drop_column>
for stat in match_stats:
concat['matchRel_' + stat] = concat[stat] / concat.groupby('matchId')[stat].transform('mean' )
del glove_embeddings, fasttext_embeddings, train_glove_oov, test_glove_oov, train_fasttext_oov, test_fasttext_oov
gc.collect()<count_unique_values>
drop_features = ["winPlacePerc", "Id", "groupId", "matchId"]
feats = [c for c in concat.columns if c not in drop_features]
df_mislabeled = df_train.groupby(['text'] ).nunique().sort_values(by='target', ascending=False)
 df_mislabeled = df_mislabeled[df_mislabeled['target'] > 1]['target']
df_mislabeled.index.tolist()<feature_engineering>
aggs = {
'grpSizeMult' : ['mean'],
'groupId_count' : ['mean'],
'matchId_count' : ['mean'],
'winPlacePerc' : ['mean'],
}
for c in feats:
if c not in aggs:
aggs[c] = ['mean', 'min', 'max', 'std']
new_cols = [k + '_' + agg for k in aggs.keys() for agg in aggs[k]]
df_train['target_relabeled'] = df_train['target'].copy()
df_train.loc[df_train['text'] == 'like for the music video I want some real action shit like burning buildings and police chases not some weak ben winston shit', 'target_relabeled'] = 0
df_train.loc[df_train['text'] == 'Hellfire is surrounded by desires so be careful and donÛªt let your desires control you!
df_train.loc[df_train['text'] == 'To fight bioterrorism sir.', 'target_relabeled'] = 0
df_train.loc[df_train['text'] == '.POTUS
df_train.loc[df_train['text'] == 'CLEARED:incident with injury:I-495 inner loop Exit 31 - MD 97/Georgia Ave Silver Spring', 'target_relabeled'] = 1
df_train.loc[df_train['text'] == '
df_train.loc[df_train['text'] == 'In
df_train.loc[df_train['text'] == 'Who is bringing the tornadoes and floods.Who is bringing the climate change.God is after America He is plaguing her
df_train.loc[df_train['text'] == 'RT NotExplained: The only known image of infamous hijacker D.B.Cooper.http://t.co/JlzK2HdeTG', 'target_relabeled'] = 1
df_train.loc[df_train['text'] == "Mmmmmm I'm burning.... I'm burning buildings I'm building.... Oooooohhhh oooh ooh...", 'target_relabeled'] = 0
df_train.loc[df_train['text'] == "wowo--=== 12000 Nigerian refugees repatriated from Cameroon", 'target_relabeled'] = 0
df_train.loc[df_train['text'] == "He came to a land which was engulfed in tribal war and turned it into a land of peace i.e.Madinah.
 df_train.loc[df_train['text'] == "Hellfire! We donÛªt even want to think about it or mention it so letÛªs not do anything that leads to it
df_train.loc[df_train['text'] == "The Prophet(peace be upon him)said 'Save yourself from Hellfire even if it is by giving half a date in charity.'", 'target_relabeled'] = 0
df_train.loc[df_train['text'] == "Caution: breathing may be hazardous to your health.", 'target_relabeled'] = 1
df_train.loc[df_train['text'] == "I Pledge Allegiance To The P.O.P.E.And The Burning Buildings of Epic City.??????", 'target_relabeled'] = 0
df_train.loc[df_train['text'] == "
df_train.loc[df_train['text'] == "that horrible sinking feeling when youÛªve been at home on your phone for a while and you realise its been on 3G this whole time", 'target_relabeled'] = 0<count_unique_values>
groups = concat.groupby('groupId' ).agg(aggs)
 groups.columns = new_cols
K = 2
skf = StratifiedKFold(n_splits=K, random_state=SEED, shuffle=True)
 DISASTER = df_train['target'] == 1
print('Whole Training Set Shape = {}'.format(df_train.shape))
print('Whole Training Set Unique keyword Count = {}'.format(df_train['keyword'].nunique()))
print('Whole Training Set Target Rate(Disaster){}/{}(Not Disaster)'.format(df_train[DISASTER]['target_relabeled'].count() , df_train[~DISASTER]['target_relabeled'].count()))
for fold,(trn_idx, val_idx)in enumerate(skf.split(df_train['text_cleaned'], df_train['target']), 1):
print('
Fold {} Training Set Shape = {} - Validation Set Shape = {}'.format(fold, df_train.loc[trn_idx, 'text_cleaned'].shape, df_train.loc[val_idx, 'text_cleaned'].shape))
print('Fold {} Training Set Unique keyword Count = {} - Validation Set Unique keyword Count = {}'.format(fold, df_train.loc[trn_idx, 'keyword'].nunique() , df_train.loc[val_idx, 'keyword'].nunique()))<prepare_x_and_y>
del concat
gc.collect()
class ClassificationReport(Callback):
def __init__(self, train_data=() , validation_data=()):
super(Callback, self ).__init__()
self.X_train, self.y_train = train_data
self.train_precision_scores = []
self.train_recall_scores = []
self.train_f1_scores = []
self.X_val, self.y_val = validation_data
self.val_precision_scores = []
self.val_recall_scores = []
self.val_f1_scores = []
def on_epoch_end(self, epoch, logs={}):
train_predictions = np.round(self.model.predict(self.X_train, verbose=0))
train_precision = precision_score(self.y_train, train_predictions, average='macro')
 train_recall = recall_score(self.y_train, train_predictions, average='macro')
 train_f1 = f1_score(self.y_train, train_predictions, average='macro')
 self.train_precision_scores.append(train_precision)
 self.train_recall_scores.append(train_recall)
 self.train_f1_scores.append(train_f1)
 val_predictions = np.round(self.model.predict(self.X_val, verbose=0))
val_precision = precision_score(self.y_val, val_predictions, average='macro')
 val_recall = recall_score(self.y_val, val_predictions, average='macro')
 val_f1 = f1_score(self.y_val, val_predictions, average='macro')
 self.val_precision_scores.append(val_precision)
 self.val_recall_scores.append(val_recall)
 self.val_f1_scores.append(val_f1)
 print('
Epoch: {} - Training Precision: {:.6} - Training Recall: {:.6} - Training F1: {:.6}'.format(epoch + 1, train_precision, train_recall, train_f1))
print('Epoch: {} - Validation Precision: {:.6} - Validation Recall: {:.6} - Validation F1: {:.6}'.format(epoch + 1, val_precision, val_recall, val_f1))<choose_model_class>
groups = reduce_mem_usage(groups )
%%time
bert_layer = hub.KerasLayer('https://tfhub.dev/tensorflow/bert_en_uncased_L-12_H-768_A-12/1', trainable=True )<choose_model_class>
class DisasterDetector:
def __init__(self, bert_layer, max_seq_length=128, lr=0.0001, epochs=15, batch_size=32):
self.bert_layer = bert_layer
self.max_seq_length = max_seq_length
vocab_file = self.bert_layer.resolved_object.vocab_file.asset_path.numpy()
do_lower_case = self.bert_layer.resolved_object.do_lower_case.numpy()
self.tokenizer = tokenization.FullTokenizer(vocab_file, do_lower_case)
 self.lr = lr
self.epochs = epochs
self.batch_size = batch_size
self.models = []
self.scores = {}
def encode(self, texts):
all_tokens = []
all_masks = []
all_segments = []
for text in texts:
text = self.tokenizer.tokenize(text)
 text = text[:self.max_seq_length - 2]
input_sequence = ['[CLS]'] + text + ['[SEP]']
pad_len = self.max_seq_length - len(input_sequence)
 tokens = self.tokenizer.convert_tokens_to_ids(input_sequence)
 tokens += [0] * pad_len
pad_masks = [1] * len(input_sequence)+ [0] * pad_len
segment_ids = [0] * self.max_seq_length
all_tokens.append(tokens)
 all_masks.append(pad_masks)
 all_segments.append(segment_ids)
 return np.array(all_tokens), np.array(all_masks), np.array(all_segments)
 def build_model(self):
input_word_ids = Input(shape=(self.max_seq_length,), dtype=tf.int32, name='input_word_ids')
 input_mask = Input(shape=(self.max_seq_length,), dtype=tf.int32, name='input_mask')
 segment_ids = Input(shape=(self.max_seq_length,), dtype=tf.int32, name='segment_ids')
 pooled_output, sequence_output = self.bert_layer([input_word_ids, input_mask, segment_ids])
 clf_output = sequence_output[:, 0, :]
out = Dense(1, activation='sigmoid' )(clf_output)
 model = Model(inputs=[input_word_ids, input_mask, segment_ids], outputs=out)
 optimizer = SGD(learning_rate=self.lr, momentum=0.8)
 model.compile(loss='binary_crossentropy', optimizer=optimizer, metrics=['accuracy'])
 return model
def train(self, X):
for fold,(trn_idx, val_idx)in enumerate(skf.split(X['text_cleaned'], X['keyword'])) :
print('
Fold {}
'.format(fold))
X_trn_encoded = self.encode(X.loc[trn_idx, 'text_cleaned'].str.lower())
 y_trn = X.loc[trn_idx, 'target_relabeled']
X_val_encoded = self.encode(X.loc[val_idx, 'text_cleaned'].str.lower())
 y_val = X.loc[val_idx, 'target_relabeled']
metrics = ClassificationReport(train_data=(X_trn_encoded, y_trn), validation_data=(X_val_encoded, y_val))
model = self.build_model()
model.fit(X_trn_encoded, y_trn, validation_data=(X_val_encoded, y_val), callbacks=[metrics], epochs=self.epochs, batch_size=self.batch_size)
 self.models.append(model)
 self.scores[fold] = {
'train': {
'precision': metrics.train_precision_scores,
'recall': metrics.train_recall_scores,
'f1': metrics.train_f1_scores
},
'validation': {
'precision': metrics.val_precision_scores,
'recall': metrics.val_recall_scores,
'f1': metrics.val_f1_scores
}
}
def plot_learning_curve(self):
fig, axes = plt.subplots(nrows=K, ncols=2, figsize=(20, K * 6), dpi=100)
 for i in range(K):
sns.lineplot(x=np.arange(1, self.epochs + 1), y=clf.models[i].history.history['val_accuracy'], ax=axes[i][0], label='val_accuracy')
 sns.lineplot(x=np.arange(1, self.epochs + 1), y=clf.scores[i]['validation']['precision'], ax=axes[i][0], label='val_precision')
 sns.lineplot(x=np.arange(1, self.epochs + 1), y=clf.scores[i]['validation']['recall'], ax=axes[i][0], label='val_recall')
 sns.lineplot(x=np.arange(1, self.epochs + 1), y=clf.scores[i]['validation']['f1'], ax=axes[i][0], label='val_f1')
 axes[i][0].legend()
axes[i][0].set_title('Fold {} Validation Classification Report'.format(i), fontsize=14)
 sns.lineplot(x=np.arange(1, self.epochs + 1), y=clf.models[0].history.history['loss'], ax=axes[i][1], label='train_loss')
 sns.lineplot(x=np.arange(1, self.epochs + 1), y=clf.models[0].history.history['val_loss'], ax=axes[i][1], label='val_loss')
 axes[i][1].legend()
axes[i][1].set_title('Fold {} Train / Validation Loss'.format(i), fontsize=14)
 for j in range(2):
axes[i][j].set_xlabel('Epoch', size=12)
 axes[i][j].tick_params(axis='x', labelsize=12)
 axes[i][j].tick_params(axis='y', labelsize=12)
 plt.show()
def predict(self, X):
X_test_encoded = self.encode(X['text_cleaned'].str.lower())
 y_pred = np.zeros(( X_test_encoded[0].shape[0], 1))
for model in self.models:
y_pred += model.predict(X_test_encoded)/ len(self.models)
 return y_pred<train_on_grid>
LGBMRegressor()
clf = DisasterDetector(bert_layer, max_seq_length=128, lr=0.0001, epochs=10, batch_size=32)
 clf.train(df_train )<save_to_csv>
def kfold_lightgbm(df, num_folds, stratified = False, debug= False):
train_df = df[df['winPlacePerc_mean'].notnull() ]
test_df = df[df['winPlacePerc_mean'].isnull() ]
print("Starting LightGBM.Train shape: {}, test shape: {}".format(train_df.shape, test_df.shape))
del df
gc.collect()
if stratified:
folds = StratifiedKFold(n_splits= num_folds, shuffle=True, random_state=1001)
 else:
folds = KFold(n_splits= num_folds, shuffle=True, random_state=1001)
 oof_preds = np.zeros(train_df.shape[0])
 sub_preds = np.zeros(test_df.shape[0])
 feature_importance_df = pd.DataFrame()
drop_features = ["winPlacePerc_mean", "Id", "groupId", "matchId"]
feats = [f for f in train_df.columns if f not in drop_features]
for n_fold,(train_idx, valid_idx)in enumerate(folds.split(train_df[feats], train_df['winPlacePerc_mean'])) :
train_x, train_y = train_df[feats].iloc[train_idx], train_df['winPlacePerc_mean'].iloc[train_idx]
valid_x, valid_y = train_df[feats].iloc[valid_idx], train_df['winPlacePerc_mean'].iloc[valid_idx]
reg = LGBMRegressor(num_leaves=100,
learning_rate=.03,
n_estimators=1000,
min_data_in_leaf=100, 
)
 reg.fit(train_x, train_y, eval_set=[(train_x, train_y),(valid_x, valid_y)],
eval_metric= 'mae', verbose= 50, early_stopping_rounds= 200)
 oof_preds[valid_idx] = reg.predict(valid_x)
 sub_preds += reg.predict(test_df[feats])/ folds.n_splits
fold_importance_df = pd.DataFrame()
fold_importance_df["feature"] = feats
fold_importance_df["importance"] = reg.feature_importances_
fold_importance_df["fold"] = n_fold + 1
feature_importance_df = pd.concat([feature_importance_df, fold_importance_df], axis=0)
 print('Fold %2d MAE : %.6f' %(n_fold + 1, mean_absolute_error(valid_y, oof_preds[valid_idx])))
 del reg, train_x, train_y, valid_x, valid_y
gc.collect()
print('Full MAE score %.6f' % mean_absolute_error(train_df['winPlacePerc_mean'], oof_preds))
if not debug:
test_df['winPlacePerc'] = sub_preds
return feature_importance_df, test_df[['winPlacePerc']]
y_pred = clf.predict(df_test)
 model_submission = pd.read_csv("/kaggle/input/nlp-getting-started/sample_submission.csv")
 model_submission['target'] = np.round(y_pred ).astype('int')
 model_submission.to_csv('model_submission.csv', index=False)
 model_submission.describe()<data_type_conversions>
feat_importances, test_df = kfold_lightgbm(groups, num_folds=5, stratified=False, debug=False )
df_leak = pd.read_csv('.. /input/nlp-with-disaster-tweets-test-set-with-labels/socialmedia-disaster-tweets-DFE.csv', encoding ='ISO-8859-1')[['choose_one', 'text']]
df_leak['target'] =(df_leak['choose_one'] == 'Relevant' ).astype(np.int8)
 df_leak['id'] = df_leak.index.astype(np.int16)
 df_leak.drop(columns=['choose_one', 'text'], inplace=True)
 df_test = df_test.merge(df_leak, on=['id'], how='left')
 print('Leaked Data Set Shape = {}'.format(df_leak.shape))
print('Leaked Data Set Memory Usage = {:.2f} MB'.format(df_leak.memory_usage().sum() / 1024**2))
perfect_submission = pd.read_csv("/kaggle/input/nlp-getting-started/sample_submission.csv")
 perfect_submission['target'] = df_test['target'].values
perfect_submission.to_csv('perfect_submission.csv', index=False)
 perfect_submission.describe()<load_pretrained>
display_importances(feat_importances )
TRAIN_FEATURES = ['id', 'keyword', 'location', 'text', 'target', 'text_cleaned', 'target_relabeled']
TEST_FEATURES = ['id', 'keyword', 'location', 'text', 'target', 'text_cleaned']
df_train[TRAIN_FEATURES].to_pickle('train.pkl')
 df_test[TEST_FEATURES].to_pickle('test.pkl')
 print('Training Set Shape = {}'.format(df_train[TRAIN_FEATURES].shape))
print('Training Set Memory Usage = {:.2f} MB'.format(df_train[TRAIN_FEATURES].memory_usage().sum() / 1024**2))
print('Test Set Shape = {}'.format(df_test[TEST_FEATURES].shape))
print('Test Set Memory Usage = {:.2f} MB'.format(df_test[TEST_FEATURES].memory_usage().sum() / 1024**2))<load_from_csv>
feat_gb = feat_importances.groupby('feature' ).mean().sort_values(by="importance", ascending=False )
df_train = pd.read_csv('/kaggle/input/titanic/train.csv')
 df_test = pd.read_csv('/kaggle/input/titanic/test.csv')
 df_train.head()<sort_values>
feat_gb.to_csv("feature_importance.csv" )
df_missing = pd.DataFrame(df_train.isna().sum() + df_test.isna().sum() , columns=['Missing'])
 df_missing = df_missing.drop('Survived')
 df_missing = df_missing.sort_values(by='Missing', ascending=False)
 df_missing = df_missing[df_missing.Missing > 0]
df_missing<drop_column>
test = pd.read_csv(".. /input/test.csv" )
df_train, df_test = [x.drop('Cabin', axis=1)for x in [df_train, df_test]]<categorify>
test = test.merge(test_df, right_index=True, left_on='groupId' )
<categorify><EOS> 
test[['Id', 'winPlacePerc']].to_csv("submission.csv", index=False )