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!pip install -U tf-models-official==2.3<import_modules>
|
def get_net() :
net = efficientnet_pytorch.EfficientNet.from_pretrained('efficientnet-b7')
net._fc = nn.Linear(in_features=2560, out_features=10, bias=True)
return net
net = get_net().to(DEVICE )
|
Digit Recognizer
|
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print("TF version: ", tf.__version__ )<load_from_csv>
|
fitter = Fitter(
model=net,
device=DEVICE,
criterion=TrainConfig.criterion,
n_epochs=TrainConfig.n_epochs,
lr=TrainConfig.lr,
sheduler=TrainConfig.scheduler,
scheduler_params=TrainConfig.scheduler_params
)
|
Digit Recognizer
|
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train_df = pd.read_csv(".. /input/nlp-getting-started/train.csv")
train_df.info()
train_df.head(6 )<load_from_csv>
|
fitter.fit(train_loader, valid_loader )
|
Digit Recognizer
|
10,394,678 |
test_df = pd.read_csv(".. /input/nlp-getting-started/test.csv")
test_df.info()
test_df.head(6 )<categorify>
|
checkpoint = torch.load('.. /working/best-checkpoint.bin')
net.load_state_dict(checkpoint['model_state_dict']);
net.eval() ;
|
Digit Recognizer
|
10,394,678 |
for df in [train_df, test_df]:
for col in ['keyword', 'location']:
df[col] = df[col].fillna(f'no_{col}' )<sort_values>
|
df = pd.read_csv('.. /input/digit-recognizer/test.csv')
print(df.shape)
df.head()
|
Digit Recognizer
|
10,394,678 |
mislabeledData = train_df.groupby(['text'] ).nunique().sort_values(by='target', ascending=False)
mislabeledData = mislabeledData[mislabeledData['target'] > 1]['target']
print(f"Total {mislabeledData.shape[0]} mislabled data" )<feature_engineering>
|
X = df.values
|
Digit Recognizer
|
10,394,678 |
train_df['target_relabeled'] = train_df['target'].copy()
train_df.loc[train_df['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
train_df.loc[train_df['text'] == 'Hellfire is surrounded by desires so be careful and donÛªt let your desires control you!
train_df.loc[train_df['text'] == 'To fight bioterrorism sir.', 'target_relabeled'] = 0
train_df.loc[train_df['text'] == '.POTUS
train_df.loc[train_df['text'] == 'CLEARED:incident with injury:I-495 inner loop Exit 31 - MD 97/Georgia Ave Silver Spring', 'target_relabeled'] = 1
train_df.loc[train_df['text'] == '
train_df.loc[train_df['text'] == 'In
train_df.loc[train_df['text'] == 'Who is bringing the tornadoes and floods.Who is bringing the climate change.God is after America He is plaguing her
train_df.loc[train_df['text'] == 'RT NotExplained: The only known image of infamous hijacker D.B.Cooper.http://t.co/JlzK2HdeTG', 'target_relabeled'] = 1
train_df.loc[train_df['text'] == "Mmmmmm I'm burning.... I'm burning buildings I'm building.... Oooooohhhh oooh ooh...", 'target_relabeled'] = 0
train_df.loc[train_df['text'] == "wowo--=== 12000 Nigerian refugees repatriated from Cameroon", 'target_relabeled'] = 0
train_df.loc[train_df['text'] == "He came to a land which was engulfed in tribal war and turned it into a land of peace i.e.Madinah.
train_df.loc[train_df['text'] == "Hellfire! We donÛªt even want to think about it or mention it so letÛªs not do anything that leads to it
train_df.loc[train_df['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
train_df.loc[train_df['text'] == "Caution: breathing may be hazardous to your health.", 'target_relabeled'] = 1
train_df.loc[train_df['text'] == "I Pledge Allegiance To The P.O.P.E.And The Burning Buildings of Epic City.??????", 'target_relabeled'] = 0
train_df.loc[train_df['text'] == "
train_df.loc[train_df['text'] == "that horrible sinking feeling when youÛªve been at home on your phone for a while and you realise its been on 3G this whole time", 'target_relabeled'] = 0
train_df[train_df['text'].isin(mislabeledData.index)]<categorify>
|
class DatasetRetriever(Dataset):
def __init__(self, X, transforms=None):
super().__init__()
self.X = X.reshape(-1, 28, 28 ).astype(np.float32)
self.transforms = transforms
def __getitem__(self, index):
image = self.X[index]
image = np.stack([image] * 3, axis=-1)
image /= 255.
if self.transforms:
image = self.transforms(image=image)['image']
return image
def __len__(self):
return self.X.shape[0]
|
Digit Recognizer
|
10,394,678 |
def clean_special_characters(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"Surṳ", "Suruc", tweet)
tweet = re.sub(r"åÇ", "", tweet)
tweet = re.sub(r"å£3million", "3 million", tweet)
tweet = re.sub(r"åÀ", "", tweet)
return tweet<categorify>
|
test_dataset = DatasetRetriever(
X = X,
transforms=get_valid_transforms() ,
)
|
Digit Recognizer
|
10,394,678 |
def restore_contractions(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)
return tweet<categorify>
|
test_loader = DataLoader(
test_dataset,
batch_size=DataLoaderConfig.batch_size,
shuffle=False,
num_workers=DataLoaderConfig.num_workers
)
|
Digit Recognizer
|
10,394,678 |
def restore_character_entity_references(tweet):
tweet = re.sub(r">", ">", tweet)
tweet = re.sub(r"<", "<", tweet)
tweet = re.sub(r"&", "&", tweet)
return tweet<categorify>
|
result = []
for step, images in enumerate(test_loader):
print(step, end='\r')
y_pred = net(images.to(DEVICE)).detach().cpu().numpy().argmax(axis=1 ).astype(int)
result.extend(y_pred )
|
Digit Recognizer
|
10,394,678 |
def restore_typos_slang_and_informal_abbreviations(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)
return tweet<categorify>
|
sub = pd.read_csv('.. /input/digit-recognizer/sample_submission.csv', index_col=0)
sub.head()
|
Digit Recognizer
|
10,394,678 |
def restore_hashtags_usernames(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)
return tweet<categorify>
|
sub['Label'] = result
|
Digit Recognizer
|
10,394,678 |
def restore_acronyms(tweet):
tweet = re.sub(r"MH370", "Malaysia Airlines Flight 370", tweet)
tweet = re.sub(r"m̼sica", "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)
return tweet<categorify>
|
sub.to_csv('submission.csv', index=True )
|
Digit Recognizer
|
9,946,831 |
def restore_grouping_same_words_without_embeddings(tweet):
tweet = re.sub(r"Bestnaijamade", "bestnaijamade", tweet)
tweet = re.sub(r"SOUDELOR", "Soudelor", tweet)
return tweet<define_variables>
|
%matplotlib inline
np.random.seed(0 )
|
Digit Recognizer
|
9,946,831 |
def remove_urls(tweet):
tweet = re.sub("https?:\/\/t.co\/[A-Za-z0-9]*", '', tweet)
return tweet<drop_column>
|
train = pd.read_csv(r'/kaggle/input/digit-recognizer/train.csv')
test = pd.read_csv(r'/kaggle/input/digit-recognizer/test.csv' )
|
Digit Recognizer
|
9,946,831 |
def remove_emojis(tweet):
emoji_pattern = re.compile("["
u"\U0001F600-\U0001F64F"
u"\U0001F300-\U0001F5FF"
u"\U0001F680-\U0001F6FF"
u"\U0001F1E0-\U0001F1FF"
u"\U00002702-\U000027B0"
u"\U000024C2-\U0001F251"
"]+", flags=re.UNICODE)
tweet = emoji_pattern.sub(r'', tweet)
return tweet<categorify>
|
Y = train['label']
X = train.drop('label',axis=1 )
|
Digit Recognizer
|
9,946,831 |
def remove_punctuations(tweet):
tweet = tweet.translate(str.maketrans('', '', string.punctuation))
return tweet<drop_column>
|
X = X / 255.0
test = test / 255.0
|
Digit Recognizer
|
9,946,831 |
%%time
def clean(tweet):
tweet = clean_special_characters(tweet)
tweet = restore_contractions(tweet)
tweet = restore_character_entity_references(tweet)
tweet = restore_typos_slang_and_informal_abbreviations(tweet)
tweet = restore_hashtags_usernames(tweet)
tweet = restore_acronyms(tweet)
tweet = restore_grouping_same_words_without_embeddings(tweet)
tweet = remove_urls(tweet)
tweet = remove_emojis(tweet)
tweet = remove_punctuations(tweet)
tweet = re.sub("\s+", ' ', tweet)
return tweet
train_df['text_cleaned'] = train_df['text'].apply(lambda s : clean(s))
test_df['text_cleaned'] = test_df['text'].apply(lambda s : clean(s))<concatenate>
|
Y = to_categorical(Y,10 )
|
Digit Recognizer
|
9,946,831 |
concat_df = pd.concat([train_df, test_df], axis = 0 ).reset_index(drop = True)
MAX_SEQ_LEN = len(max(concat_df.text_cleaned, key = len))
print('The maximum length of each sequence is:', MAX_SEQ_LEN )<define_variables>
|
X_train,X_test,Y_train,Y_test = train_test_split(X,Y,test_size=0.2 )
|
Digit Recognizer
|
9,946,831 |
AUTOTUNE = tf.data.experimental.AUTOTUNE
BATCH_SIZE = 32
SEED = 42<count_unique_values>
|
model = keras.models.Sequential([
keras.layers.Conv2D(32,(5,5),input_shape=(28,28,1),activation='relu',padding='same'),
keras.layers.BatchNormalization(axis=1),
keras.layers.MaxPooling2D(2,2),
keras.layers.Conv2D(32,(5,5),activation='relu',padding='same'),
keras.layers.BatchNormalization() ,
keras.layers.MaxPooling2D(2,2),
keras.layers.Conv2D(64,(3,3),activation='relu',padding='same'),
keras.layers.BatchNormalization(axis=1),
keras.layers.Conv2D(64,(3,3),activation='relu',padding='same'),
keras.layers.BatchNormalization() ,
keras.layers.MaxPooling2D(2,2),
keras.layers.Flatten() ,
keras.layers.Dense(256,activation='relu'),
keras.layers.BatchNormalization() ,
keras.layers.Dropout(0.5),
keras.layers.Dense(10,activation='softmax')
] )
|
Digit Recognizer
|
9,946,831 |
K = 2
skf = StratifiedKFold(n_splits=K, random_state=SEED, shuffle=True)
DISASTER = train_df['target_relabeled'] == 1
print('Whole Training Set Shape = {}'.format(train_df.shape[0]))
print('Whole Training Set Unique keyword Count = {}'.format(train_df['keyword'].nunique()))
print('Whole Training Set Target Rate(Disaster/Not Disaster)= {}'.format(train_df[DISASTER]['target_relabeled'].count() /train_df[~DISASTER]['target_relabeled'].count()))
train_ds_list = []
valid_ds_list = []
for fold,(trn_idx, val_idx)in enumerate(skf.split(train_df['text_cleaned'], train_df['target_relabeled'])) :
print('
Fold {} Training Set Shape = {} - Validation Set Shape = {}'.format(fold, train_df.loc[trn_idx, 'text_cleaned'].shape[0], train_df.loc[val_idx, 'text_cleaned'].shape[0]))
print('Fold {} Training Set Unique keyword Count = {} - Validation Set Unique keyword Count = {}'.format(fold, train_df.loc[trn_idx, 'keyword'].nunique() , train_df.loc[val_idx, 'keyword'].nunique()))
print('Fold {} Training Set Target Rate(Disaster/Not Disaster)= {}'.format(fold, train_df.loc[trn_idx, 'target_relabeled'][DISASTER].count() /train_df.loc[trn_idx,'target_relabeled'][~DISASTER].count()))
trn_ds = tf.data.Dataset.from_tensor_slices(( train_df.loc[trn_idx, 'text_cleaned'].values, train_df.loc[trn_idx, 'target_relabeled'].values))
train_ds_list.append(trn_ds)
val_ds = tf.data.Dataset.from_tensor_slices(( train_df.loc[val_idx, 'text_cleaned'].values, train_df.loc[val_idx, 'target_relabeled'].values))
valid_ds_list.append(val_ds )<split>
|
model.compile(optimizer='adam',loss='categorical_crossentropy',metrics=['accuracy'] )
|
Digit Recognizer
|
9,946,831 |
test_ds_raw = tf.data.Dataset.from_tensor_slices(test_df['text_cleaned'].values)
for text in test_ds_raw.take(5):
print(f'Review: {text}' )<train_model>
|
learning_rate_reduction = ReduceLROnPlateau(monitor='val_acc',
patience=3,
verbose=1,
factor=0.5,
min_lr=0.00001 )
|
Digit Recognizer
|
9,946,831 |
BERT_MODEL = 'https://tfhub.dev/tensorflow/bert_en_uncased_L-12_H-768_A-12/3'
PREPROCESS_MODEL = 'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/2'
print(f'BERT model selected : {BERT_MODEL}')
print(f'Preprocess model auto-selected: {PREPROCESS_MODEL}' )<choose_model_class>
|
datagen = ImageDataGenerator(
featurewise_center=False,
samplewise_center=False,
featurewise_std_normalization=False,
samplewise_std_normalization=False,
zca_whitening=False,
rotation_range=10,
zoom_range = 0.1,
width_shift_range=0.1,
height_shift_range=0.1,
horizontal_flip=False,
vertical_flip=False)
datagen.fit(X_train )
|
Digit Recognizer
|
9,946,831 |
def make_bert_preprocess_model(sentence_features, seq_length=128):
input_segments = [
tf.keras.layers.Input(shape=() , dtype=tf.string, name=ft)
for ft in sentence_features]
bert_preprocess = hub.load(PREPROCESS_MODEL)
tokenizer = hub.KerasLayer(bert_preprocess.tokenize, name='tokenizer')
segments = [tokenizer(s)for s in input_segments]
truncated_segments = segments
packer = hub.KerasLayer(bert_preprocess.bert_pack_inputs,
arguments=dict(seq_length=seq_length),
name='packer')
model_inputs = packer(truncated_segments)
return tf.keras.Model(input_segments, model_inputs )<categorify>
|
epochs = 250
batch_size=64
history = model.fit_generator(datagen.flow(X_train,Y_train, batch_size=batch_size),
epochs = epochs, validation_data =(X_test,Y_test),
verbose = 2, steps_per_epoch=X_train.shape[0] // batch_size
,callbacks=[learning_rate_reduction] )
|
Digit Recognizer
|
9,946,831 |
def build_classifier_model() :
inputs = dict(
input_word_ids=tf.keras.layers.Input(shape=(None,), dtype=tf.int32, name='word_ids'),
input_mask=tf.keras.layers.Input(shape=(None,), dtype=tf.int32, name='mask'),
input_type_ids=tf.keras.layers.Input(shape=(None,), dtype=tf.int32, name='type_ids'),
)
encoder = hub.KerasLayer(BERT_MODEL, trainable=True, name='BERT_encoder')
outputs = encoder(inputs)
net = outputs['pooled_output']
net = tf.keras.layers.Dropout(0.1 )(net)
net = tf.keras.layers.Dense(1, activation=None, name='classifier' )(net)
return tf.keras.Model(inputs, net, name='none_em_proc_model' )<categorify>
|
predictions = model.predict(test)
predictions = np.argmax(predictions,axis = 1)
predictions = pd.Series(predictions,name="Label" )
|
Digit Recognizer
|
9,946,831 |
def build_classifier_model_em_proc() :
text_input = tf.keras.layers.Input(shape=() , dtype=tf.string, name='text')
preprocessing_layer = hub.KerasLayer(PREPROCESS_MODEL, name='preprocessing')
encoder_inputs = preprocessing_layer(text_input)
encoder = hub.KerasLayer(BERT_MODEL, trainable=True, name='BERT_encoder')
outputs = encoder(encoder_inputs)
net = outputs['pooled_output']
net = tf.keras.layers.Dropout(0.1 )(net)
net = tf.keras.layers.Dense(1, activation=None, name='classifier' )(net)
return tf.keras.Model(text_input, net, name='em_proc_model' )<define_variables>
|
submission = pd.concat([pd.Series(range(1,28001),name = "ImageId"),predictions],axis = 1)
submission.to_csv("submissions_mnist.csv",index=False)
print("Your file is saved." )
|
Digit Recognizer
|
9,946,831 |
train_ds = train_ds_list[0]
train_ds = train_ds.shuffle(SEED ).batch(BATCH_SIZE)
train_ds = train_ds.cache().prefetch(buffer_size=AUTOTUNE)
valid_ds = valid_ds_list[0]
valid_ds = valid_ds.batch(BATCH_SIZE)
valid_ds = valid_ds.cache().prefetch(buffer_size=AUTOTUNE )<choose_model_class>
|
%matplotlib inline
np.random.seed(0 )
|
Digit Recognizer
|
9,946,831 |
loss = tf.keras.losses.BinaryCrossentropy(from_logits=True)
metrics = tf.metrics.BinaryAccuracy()<choose_model_class>
|
train = pd.read_csv(r'/kaggle/input/digit-recognizer/train.csv')
test = pd.read_csv(r'/kaggle/input/digit-recognizer/test.csv' )
|
Digit Recognizer
|
9,946,831 |
EPOCHS = 10
INIT_LR = 3e-5
steps_per_epoch = tf.data.experimental.cardinality(train_ds ).numpy()
num_train_steps = steps_per_epoch * EPOCHS
num_warmup_steps = int(0.1 * num_train_steps)
optimizer = optimization.create_optimizer(init_lr = INIT_LR,
num_train_steps = num_train_steps,
num_warmup_steps = num_warmup_steps,
optimizer_type = 'adamw' )<choose_model_class>
|
Y = train['label']
X = train.drop('label',axis=1 )
|
Digit Recognizer
|
9,946,831 |
model_em_proc.compile(optimizer=optimizer,
loss=loss,
metrics=metrics )<train_model>
|
X = X / 255.0
test = test / 255.0
|
Digit Recognizer
|
9,946,831 |
print('Training model with embedded preprocess model')
history_em_proc = model_em_proc.fit(x=train_ds,
validation_data=valid_ds,
epochs = EPOCHS )<predict_on_test>
|
Y = to_categorical(Y,10 )
|
Digit Recognizer
|
9,946,831 |
test_ds = test_ds_raw.batch(BATCH_SIZE ).cache().prefetch(buffer_size=AUTOTUNE)
predict_result_em_proc = tf.sigmoid(model_em_proc.predict(test_ds))
print(predict_result_em_proc )<categorify>
|
X_train,X_test,Y_train,Y_test = train_test_split(X,Y,test_size=0.2 )
|
Digit Recognizer
|
9,946,831 |
train_ds = train_ds_list[1]
train_ds = train_ds.shuffle(SEED ).batch(BATCH_SIZE)
train_ds = train_ds.map(lambda x, y:(preprocess_model(x), y))
train_ds = train_ds.cache().prefetch(buffer_size=AUTOTUNE)
valid_ds = valid_ds_list[1]
valid_ds = valid_ds.batch(BATCH_SIZE)
valid_ds = valid_ds.map(lambda x, y:(preprocess_model(x), y))
valid_ds = valid_ds.cache().prefetch(buffer_size=AUTOTUNE )<choose_model_class>
|
model = keras.models.Sequential([
keras.layers.Conv2D(32,(5,5),input_shape=(28,28,1),activation='relu',padding='same'),
keras.layers.BatchNormalization(axis=1),
keras.layers.MaxPooling2D(2,2),
keras.layers.Conv2D(32,(5,5),activation='relu',padding='same'),
keras.layers.BatchNormalization() ,
keras.layers.MaxPooling2D(2,2),
keras.layers.Conv2D(64,(3,3),activation='relu',padding='same'),
keras.layers.BatchNormalization(axis=1),
keras.layers.Conv2D(64,(3,3),activation='relu',padding='same'),
keras.layers.BatchNormalization() ,
keras.layers.MaxPooling2D(2,2),
keras.layers.Flatten() ,
keras.layers.Dense(256,activation='relu'),
keras.layers.BatchNormalization() ,
keras.layers.Dropout(0.5),
keras.layers.Dense(10,activation='softmax')
] )
|
Digit Recognizer
|
9,946,831 |
steps_per_epoch = tf.data.experimental.cardinality(train_ds ).numpy()
num_train_steps = steps_per_epoch * EPOCHS
num_warmup_steps = int(0.1 * num_train_steps)
optimizer = optimization.create_optimizer(init_lr = INIT_LR,
num_train_steps = num_train_steps,
num_warmup_steps = num_warmup_steps,
optimizer_type = 'adamw' )<choose_model_class>
|
model.compile(optimizer='adam',loss='categorical_crossentropy',metrics=['accuracy'] )
|
Digit Recognizer
|
9,946,831 |
model.compile(optimizer=optimizer,
loss=loss,
metrics=metrics )<train_model>
|
learning_rate_reduction = ReduceLROnPlateau(monitor='val_acc',
patience=3,
verbose=1,
factor=0.5,
min_lr=0.00001 )
|
Digit Recognizer
|
9,946,831 |
print('Training model without embedded preprocess model')
history_model = model.fit(x=train_ds,
validation_data=valid_ds,
epochs = EPOCHS )<predict_on_test>
|
datagen = ImageDataGenerator(
featurewise_center=False,
samplewise_center=False,
featurewise_std_normalization=False,
samplewise_std_normalization=False,
zca_whitening=False,
rotation_range=10,
zoom_range = 0.1,
width_shift_range=0.1,
height_shift_range=0.1,
horizontal_flip=False,
vertical_flip=False)
datagen.fit(X_train )
|
Digit Recognizer
|
9,946,831 |
test_ds = test_ds_raw.batch(BATCH_SIZE ).cache().prefetch(buffer_size=AUTOTUNE)
test_ds = test_ds.map(lambda x: preprocess_model(x))
predict_result = tf.sigmoid(model.predict(test_ds))
print(predict_result )<save_to_csv>
|
epochs = 250
batch_size=64
history = model.fit_generator(datagen.flow(X_train,Y_train, batch_size=batch_size),
epochs = epochs, validation_data =(X_test,Y_test),
verbose = 2, steps_per_epoch=X_train.shape[0] // batch_size
,callbacks=[learning_rate_reduction] )
|
Digit Recognizer
|
9,946,831 |
result_1 = predict_result_em_proc.numpy()
result_2 = predict_result.numpy()
result = []
for i in range(len(result_1)) :
result.append(( result_1[i] + result_2[i])/2)
result = np.round(result)
sample_submission = pd.read_csv(".. /input/nlp-getting-started/sample_submission.csv")
ids = sample_submission.id
final_submission = pd.DataFrame(np.c_[ids, result.astype('int')], columns = ['id', 'target'])
final_submission.to_csv('submission.csv', index = False)
final_submission.describe()<set_options>
|
predictions = model.predict(test)
predictions = np.argmax(predictions,axis = 1)
predictions = pd.Series(predictions,name="Label" )
|
Digit Recognizer
|
9,946,831 |
<load_from_csv><EOS>
|
submission = pd.concat([pd.Series(range(1,28001),name = "ImageId"),predictions],axis = 1)
submission.to_csv("submissions_mnist.csv",index=False)
print("Your file is saved." )
|
Digit Recognizer
|
9,895,998 |
<SOS> metric: categorizationaccuracy Kaggle data source: digit-recognizer<count_missing_values>
|
import pandas as pd
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.optim import lr_scheduler
from torch.autograd import Variable
from torch.utils.data import DataLoader, Dataset
from torchvision import transforms
from torchvision.utils import make_grid
import math, random, numbers
from PIL import Image
|
Digit Recognizer
|
9,895,998 |
train.isnull().sum()<remove_duplicates>
|
INPUT_DIR = '.. /input/digit-recognizer'
BATCH_SIZE = 64
N_EPOCHS = 50
|
Digit Recognizer
|
9,895,998 |
train=train.drop_duplicates(subset=['text', 'target'], keep='first')
train.shape<count_values>
|
train_df = pd.read_csv(INPUT_DIR + '/train.csv')
n_train = len(train_df)
n_pixels = len(train_df.columns)- 1
n_class = len(set(train_df['label']))
print('Number of training samples: {0}'.format(n_train))
print('Number of training pixels: {0}'.format(n_pixels))
print('Number of classes: {0}'.format(n_class))
|
Digit Recognizer
|
9,895,998 |
train.target.value_counts()<feature_engineering>
|
test_df = pd.read_csv(INPUT_DIR + '/test.csv')
n_test = len(test_df)
n_pixels = len(test_df.columns)
print('Number of train samples: {0}'.format(n_test))
print('Number of test pixels: {0}'.format(n_pixels))
|
Digit Recognizer
|
9,895,998 |
train['text_length'] = train.text.apply(lambda x: len(x.split()))
test['text_length'] = test.text.apply(lambda x: len(x.split()))<string_transform>
|
class MNIST_data(Dataset):
def __init__(self, file_path,
transform = transforms.Compose([transforms.ToPILImage() , transforms.ToTensor() ,
transforms.Normalize(mean=(0.5,), std=(0.5,)) ])
):
df = pd.read_csv(file_path)
if len(df.columns)== n_pixels:
self.X = df.values.reshape(( -1,28,28)).astype(np.uint8)[:,:,:,None]
self.y = None
else:
self.X = df.iloc[:,1:].values.reshape(( -1,28,28)).astype(np.uint8)[:,:,:,None]
self.y = torch.from_numpy(df.iloc[:,0].values)
self.transform = transform
def __len__(self):
return len(self.X)
def __getitem__(self, idx):
if self.y is not None:
return self.transform(self.X[idx]), self.y[idx]
else:
return self.transform(self.X[idx] )
|
Digit Recognizer
|
9,895,998 |
list_= []
for i in train.text:
list_ += i
list_= ''.join(list_)
allWords=list_.split()
vocabulary= set(allWords )<string_transform>
|
class RandomRotation(object):
def __init__(self, degrees, resample=False, expand=False, center=None):
if isinstance(degrees, numbers.Number):
if degrees < 0:
raise ValueError("If degrees is a single number, it must be positive.")
self.degrees =(-degrees, degrees)
else:
if len(degrees)!= 2:
raise ValueError("If degrees is a sequence, it must be of len 2.")
self.degrees = degrees
self.resample = resample
self.expand = expand
self.center = center
@staticmethod
def get_params(degrees):
angle = np.random.uniform(degrees[0], degrees[1])
return angle
def __call__(self, img):
def rotate(img, angle, resample=False, expand=False, center=None):
return img.rotate(angle, resample, expand, center)
angle = self.get_params(self.degrees)
return rotate(img, angle, self.resample, self.expand, self.center )
|
Digit Recognizer
|
9,895,998 |
def create_corpus(df,target):
corpus=[]
for x in df[df['target']==target]['text'].str.split() :
for i in x:
corpus.append(i)
return corpus<import_modules>
|
class RandomShift(object):
def __init__(self, shift):
self.shift = shift
@staticmethod
def get_params(shift):
hshift, vshift = np.random.uniform(-shift, shift, size=2)
return hshift, vshift
def __call__(self, img):
hshift, vshift = self.get_params(self.shift)
return img.transform(img.size, Image.AFFINE,(1,0,hshift,0,1,vshift), resample=Image.BICUBIC, fill=1 )
|
Digit Recognizer
|
9,895,998 |
string.punctuation<string_transform>
|
train_dataset = MNIST_data(INPUT_DIR + '/train.csv', transform=transforms.Compose(
[transforms.ToPILImage() , RandomRotation(degrees=20), RandomShift(3),
transforms.ToTensor() , transforms.Normalize(mean=(0.5,), std=(0.5,)) ]))
test_dataset = MNIST_data(INPUT_DIR + '/test.csv')
train_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=BATCH_SIZE, shuffle=True)
test_loader = torch.utils.data.DataLoader(dataset=test_dataset, batch_size=BATCH_SIZE, shuffle=False )
|
Digit Recognizer
|
9,895,998 |
stopwords.words('english' )<string_transform>
|
class Net(nn.Module):
def __init__(self):
super(Net, self ).__init__()
self.features = nn.Sequential(
nn.Conv2d(1, 32, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(32),
nn.ReLU(inplace=True),
nn.Conv2d(32, 32, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(32),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=2, stride=2),
nn.Conv2d(32, 64, kernel_size=3, padding=1),
nn.BatchNorm2d(64),
nn.ReLU(inplace=True),
nn.Conv2d(64, 64, kernel_size=3, padding=1),
nn.BatchNorm2d(64),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=2, stride=2)
)
self.classifier = nn.Sequential(
nn.Dropout(p = 0.5),
nn.Linear(64 * 7 * 7, 512),
nn.BatchNorm1d(512),
nn.ReLU(inplace=True),
nn.Dropout(p = 0.5),
nn.Linear(512, 512),
nn.BatchNorm1d(512),
nn.ReLU(inplace=True),
nn.Dropout(p = 0.5),
nn.Linear(512, 10),
)
for m in self.features.children() :
if isinstance(m, nn.Conv2d):
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
m.weight.data.normal_(0, math.sqrt(2./ n))
elif isinstance(m, nn.BatchNorm2d):
m.weight.data.fill_(1)
m.bias.data.zero_()
for m in self.classifier.children() :
if isinstance(m, nn.Linear):
nn.init.xavier_uniform_(m.weight)
elif isinstance(m, nn.BatchNorm1d):
m.weight.data.fill_(1)
m.bias.data.zero_()
def forward(self, x):
x = self.features(x)
x = x.view(x.size(0), -1)
x = self.classifier(x)
return x
|
Digit Recognizer
|
9,895,998 |
text='hey this is me and I am here to help you '
tokens = word_tokenize(text)
tokens=[word for word in tokens if word not in stopwords.words('english')]
' '.join(tokens )<string_transform>
|
model = Net()
optimizer = optim.Adam(model.parameters() , lr=0.003)
criterion = nn.CrossEntropyLoss()
exp_lr_scheduler = lr_scheduler.StepLR(optimizer, step_size=7, gamma=0.1)
if torch.cuda.is_available() :
model = model.cuda()
criterion = criterion.cuda()
|
Digit Recognizer
|
9,895,998 |
pstem = PorterStemmer()
def clean_text(text):
text= text.lower()
text= re.sub('[0-9]', '', text)
text = "".join([char for char in text if char not in string.punctuation])
tokens = word_tokenize(text)
tokens=[pstem.stem(word)for word in tokens]
text = ' '.join(tokens)
return text<feature_engineering>
|
def train(epoch):
model.train()
optimizer.step()
exp_lr_scheduler.step()
for batch_idx,(data, target)in enumerate(train_loader):
data, target = Variable(data), Variable(target)
if torch.cuda.is_available() :
data = data.cuda()
target = target.cuda()
optimizer.zero_grad()
output = model(data)
loss = criterion(output, target)
loss.backward()
optimizer.step()
if(batch_idx + 1)% 100 == 0:
print('Train Epoch: {} [{}/{}({:.0f}%)]\tLoss: {:.6f}'.format(
epoch,(batch_idx + 1)* len(data), len(train_loader.dataset), 100.*(batch_idx + 1)/ len(train_loader), loss.item()))
|
Digit Recognizer
|
9,895,998 |
train["clean"]=train["text"].apply(clean_text)
test["clean"]=test["text"].apply(clean_text )<string_transform>
|
def evaluate(data_loader):
model.eval()
loss = 0
correct = 0
with torch.no_grad() :
for data, target in data_loader:
data, target = Variable(data), Variable(target)
if torch.cuda.is_available() :
data = data.cuda()
target = target.cuda()
output = model(data)
loss += F.cross_entropy(output, target, reduction='sum' ).item()
pred = output.data.max(1, keepdim=True)[1]
correct += pred.eq(target.data.view_as(pred)).cpu().sum()
loss /= len(data_loader.dataset)
print('
Average loss: {:.4f}, Accuracy: {}/{}({:.3f}%)
'.format(
loss, correct, len(data_loader.dataset), 100.* correct / len(data_loader.dataset)) )
|
Digit Recognizer
|
9,895,998 |
list_= []
for i in train.clean:
list_ += i
list_= ''.join(list_)
allWords=list_.split()
vocabulary= set(allWords)
len(vocabulary )<categorify>
|
for epoch in range(N_EPOCHS):
train(epoch)
evaluate(train_loader )
|
Digit Recognizer
|
9,895,998 |
tfidf = TfidfVectorizer(sublinear_tf=True,max_features=60000, min_df=1, norm='l2', ngram_range=(1,2))
features = tfidf.fit_transform(train.clean ).toarray()
features.shape<categorify>
|
def prediciton(data_loader):
model.eval()
test_pred = torch.LongTensor()
with torch.no_grad() :
for i, data in enumerate(data_loader):
data = Variable(data)
if torch.cuda.is_available() :
data = data.cuda()
output = model(data)
pred = output.cpu().data.max(1, keepdim=True)[1]
test_pred = torch.cat(( test_pred, pred), dim=0)
return test_pred
|
Digit Recognizer
|
9,895,998 |
features_test = tfidf.transform(test.clean ).toarray()<prepare_x_and_y>
|
test_pred = prediciton(test_loader )
|
Digit Recognizer
|
9,895,998 |
skf = StratifiedKFold(n_splits=4, random_state=48, shuffle=True)
accuracy=[]
n=1
y=train['target']<choose_model_class>
|
out_df = pd.DataFrame(np.c_[np.arange(1, len(test_dataset)+1)[:,None], test_pred.numpy() ], columns=['ImageId', 'Label'] )
|
Digit Recognizer
|
9,895,998 |
for trn_idx, test_idx in skf.split(features, y):
start_time = time()
X_tr,X_val=features[trn_idx],features[test_idx]
y_tr,y_val=y.iloc[trn_idx],y.iloc[test_idx]
model= LogisticRegression(max_iter=1000,C=3)
model.fit(X_tr,y_tr)
s = model.predict(X_val)
sub[str(n)]= model.predict(features_test)
accuracy.append(accuracy_score(y_val, s))
print(( time() - start_time)/60,accuracy[n-1])
n+=1<compute_test_metric>
|
out_df.to_csv('submission.csv', index=False )
|
Digit Recognizer
|
9,671,849 |
np.mean(accuracy)*100<import_modules>
|
%matplotlib inline
np.random.seed(2)
sns.set(style='white', context='notebook', palette='deep' )
|
Digit Recognizer
|
9,671,849 |
from sklearn.metrics import confusion_matrix, classification_report<predict_on_test>
|
df_train=pd.read_csv('.. /input/digit-recognizer/train.csv')
test=pd.read_csv('.. /input/digit-recognizer/test.csv' )
|
Digit Recognizer
|
9,671,849 |
pred_valid_y = model.predict(X_val)
print(classification_report(y_val, pred_valid_y))<compute_test_metric>
|
X_train = X_train.values.reshape(-1,28,28,1)
test = test.values.reshape(-1,28,28,1 )
|
Digit Recognizer
|
9,671,849 |
print(confusion_matrix(y_val, pred_valid_y))<feature_engineering>
|
X_train = X_train / 255.0
test = test / 255.0
|
Digit Recognizer
|
9,671,849 |
df=sub[['1','2','3','4']].mode(axis=1)
sub['target']=df[0]
sub=sub[['id','target']]
sub['target']=sub['target'].apply(lambda x : int(x))<save_to_csv>
|
Y_train = to_categorical(Y_train, num_classes = 10)
print(Y_train[0] )
|
Digit Recognizer
|
9,671,849 |
sub.to_csv('submission.csv',index=False )<load_from_csv>
|
random_seed=2
X_train, X_val, Y_train, Y_val = train_test_split(X_train, Y_train, test_size = 0.1, random_state=random_seed )
|
Digit Recognizer
|
9,671,849 |
train_df = pd.read_csv("/kaggle/input/nlp-getting-started/train.csv")
test_df = pd.read_csv("/kaggle/input/nlp-getting-started/test.csv" )<remove_duplicates>
|
model = Sequential()
|
Digit Recognizer
|
9,671,849 |
print(len(train_df))
train_df = train_df.drop_duplicates('text', keep='last')
print(len(train_df))<count_values>
|
model = Sequential()
model.add(Conv2D(filters = 32, kernel_size =(5,5),padding = 'Same',
activation ='relu', input_shape =(28,28,1)))
model.add(Conv2D(filters = 32, kernel_size =(5,5),padding = 'Same',
activation ='relu'))
model.add(MaxPool2D(pool_size=(2,2)))
model.add(Dropout(0.25))
model.add(Conv2D(filters = 64, kernel_size =(3,3),padding = 'Same',
activation ='relu'))
model.add(Conv2D(filters = 64, kernel_size =(3,3),padding = 'Same',
activation ='relu'))
model.add(MaxPool2D(pool_size=(2,2), strides=(2,2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(256, activation = "relu"))
model.add(Dense(10, activation = "softmax"))
|
Digit Recognizer
|
9,671,849 |
train_df['target'].value_counts()<choose_model_class>
|
optimizer = RMSprop(lr=0.001, rho=0.9, epsilon=1e-08, decay=0.0 )
|
Digit Recognizer
|
9,671,849 |
wordLemm = WordNetLemmatizer()<string_transform>
|
model.compile(optimizer = optimizer , loss = "categorical_crossentropy", metrics=["accuracy"] )
|
Digit Recognizer
|
9,671,849 |
def preprocess_text(text):
text = re.sub(r"http\S+", "", text)
text = re.sub(URLPATTERN,' URL',text)
for emoji in EMOJIS.keys() :
text = text.replace(emoji, "EMOJI" + EMOJIS[emoji])
text = re.sub(USERPATTERN,' URL',text)
text = re.sub('[^a-zA-z]'," ",text)
text = re.sub(SEQPATTERN,SEQREPLACE,text)
text = text.split()
text = [wordLemm.lemmatize(word)for word in text if not word in stopwords.words('english')and len(word)> 1]
text = ' '.join(text)
return text
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"Surṳ", "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">", ">", tweet)
tweet = re.sub(r"<", "<", tweet)
tweet = re.sub(r"&", "&", 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"m̼sica", "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<feature_engineering>
|
learning_rate_reduction = ReduceLROnPlateau(monitor='val_acc',
patience=3,
verbose=1,
factor=0.5,
min_lr=0.00001)
epochs = 30
batch_size = 86
|
Digit Recognizer
|
9,671,849 |
train_df['text_cleaned'] = train_df['text'].apply(lambda s : clean(s))
test_df['text_cleaned'] = test_df['text'].apply(lambda s : clean(s))<install_modules>
|
datagen = ImageDataGenerator(
featurewise_center=False,
samplewise_center=False,
featurewise_std_normalization=False,
samplewise_std_normalization=False,
zca_whitening=False,
rotation_range=10,
zoom_range = 0.1,
width_shift_range=0.1,
height_shift_range=0.1,
horizontal_flip=False,
vertical_flip=False)
datagen.fit(X_train )
|
Digit Recognizer
|
9,671,849 |
!pip install -U tensorflow_text==2.3<install_modules>
|
history = model.fit_generator(datagen.flow(X_train,Y_train, batch_size=batch_size),
epochs = epochs, validation_data =(X_val,Y_val),
verbose = 2, steps_per_epoch=X_train.shape[0] // batch_size
, callbacks=[learning_rate_reduction] )
|
Digit Recognizer
|
9,671,849 |
!pip install -q tf-models-official==2.3<import_modules>
|
accuracy = model.evaluate(X_train, Y_train)
print(f'Train results - Accuracy: {accuracy[1]*100}%' )
|
Digit Recognizer
|
9,671,849 |
import tensorflow as tf
import tensorflow_hub as hub
import tensorflow_text as text
from official.nlp import optimization<split>
|
accuracy = model.evaluate(X_val, Y_val)
print(f'validation test results - Accuracy: {accuracy[1]*100}%' )
|
Digit Recognizer
|
9,671,849 |
X_train, X_valid, y_train, y_valid = train_test_split(train_df['text'].tolist() ,\
train_df['target'].tolist() ,\
test_size=0.15,\
stratify = train_df['target'].tolist() ,\
random_state=0)
<prepare_x_and_y>
|
predict_val = model.predict(X_val)
y_val_pred=(np.argmax(predict_val,axis=1))
y_true = np.argmax(Y_val,axis = 1 )
|
Digit Recognizer
|
9,671,849 |
batch_size = 26
seed = 42
train_ds = tf.data.Dataset.from_tensor_slices(( train_df['text'].tolist() ,train_df['target'].tolist())).batch(batch_size)
valid_ds = tf.data.Dataset.from_tensor_slices(( X_valid,y_valid)).batch(batch_size)
<define_variables>
|
results = confusion_matrix(y_true,y_val_pred)
print('Confusion Matrix :')
print(results)
print('Accuracy Score :',accuracy_score(y_true,y_val_pred))
print('Report : ')
print(classification_report(y_true,y_val_pred))
|
Digit Recognizer
|
9,671,849 |
bert_model_name = 'bert_en_uncased_L-12_H-768_A-12'
map_name_to_handle = {
'bert_en_uncased_L-12_H-768_A-12':
'https://tfhub.dev/tensorflow/bert_en_uncased_L-12_H-768_A-12/3',
}
map_model_to_preprocess = {
'bert_en_uncased_L-12_H-768_A-12':
'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/2',
}
tfhub_handle_encoder = map_name_to_handle[bert_model_name]
tfhub_handle_preprocess = map_model_to_preprocess[bert_model_name]
print(f'BERT model selected : {tfhub_handle_encoder}')
print(f'Preprocess model auto-selected: {tfhub_handle_preprocess}' )<categorify>
|
val_lr_probs = model.predict_proba(X_val)
val_lr_auc =(roc_auc_score(y_true, val_lr_probs, multi_class="ovr",average="macro")) *100
print("AUC :%.2f%%"%(val_lr_auc))
|
Digit Recognizer
|
9,671,849 |
def build_classifier_model() :
text_input = tf.keras.layers.Input(shape=() , dtype=tf.string, name='text')
preprocessing_layer = hub.KerasLayer(tfhub_handle_preprocess, name='preprocessing')
encoder_inputs = preprocessing_layer(text_input)
encoder = hub.KerasLayer(tfhub_handle_encoder, trainable=True, name='BERT_encoder')
outputs = encoder(encoder_inputs)
net = outputs['pooled_output']
net = tf.keras.layers.Dropout(0.3
)(net)
net = tf.keras.layers.Dense(1, activation= "sigmoid" , name='classifier' )(net)
return tf.keras.Model(text_input, net )<choose_model_class>
|
predictions = model.predict(test)
y_pred= np.argmax(predictions,axis=1 )
|
Digit Recognizer
|
9,671,849 |
<choose_model_class><EOS>
|
submission = pd.concat([pd.Series(range(1,28001),name = "ImageId"),results],axis = 1)
submission.to_csv("submission.csv",index=False )
|
Digit Recognizer
|
9,581,031 |
<SOS> metric: categorizationaccuracy Kaggle data source: digit-recognizer<choose_model_class>
|
%matplotlib inline
np.random.seed(2)
dense_regularizer = L1L2(l2=0.0001)
sns.set(style='white', context='notebook', palette='deep' )
|
Digit Recognizer
|
9,581,031 |
classifier_model.compile(optimizer=optimizer,
loss=loss,
metrics=metrics )<train_model>
|
train = pd.read_csv(".. /input/train.csv")
test = pd.read_csv(".. /input/test.csv" )
|
Digit Recognizer
|
9,581,031 |
print(f'Training model with {tfhub_handle_encoder}')
history = classifier_model.fit(x=train_ds, epochs=epochs,validation_data=valid_ds)
<save_model>
|
X_train = X_train / 255.0
test = test / 255.0
|
Digit Recognizer
|
9,581,031 |
classifier_model.save("./model.h5" )<predict_on_test>
|
Y_train = to_categorical(Y_train, num_classes = 10 )
|
Digit Recognizer
|
9,581,031 |
probs = classifier_model.predict(test_df["text"])
threshold = 0.40
preds = np.where(probs[:,] > threshold, 1, 0 )<load_from_csv>
|
random_seed = 2
|
Digit Recognizer
|
9,581,031 |
submission=pd.read_csv('/kaggle/input/nlp-getting-started/sample_submission.csv' )<prepare_output>
|
X_train, X_val, Y_train, Y_val = train_test_split(X_train, Y_train, test_size = 0.1, random_state=random_seed )
|
Digit Recognizer
|
9,581,031 |
submission["target"]=preds<save_to_csv>
|
def Model_1(x=None):
model = Sequential()
model.add(Conv2D(64,(5, 5), input_shape=(28,28,1), padding='same', kernel_regularizer=dense_regularizer,kernel_initializer="he_normal"))
model.add(BatchNormalization())
model.add(Activation('elu'))
model.add(Conv2D(64,(5, 5), padding='same', kernel_regularizer=dense_regularizer,kernel_initializer="he_normal"))
model.add(BatchNormalization())
model.add(Activation('elu'))
model.add(Conv2D(64,(5, 5), padding='same', kernel_regularizer=dense_regularizer,kernel_initializer="he_normal"))
model.add(BatchNormalization())
model.add(Activation('elu'))
model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))
model.add(Dropout(0.3))
model.add(Conv2D(128,(3, 3), padding='same', kernel_regularizer=dense_regularizer,kernel_initializer="he_normal"))
model.add(BatchNormalization())
model.add(Activation('elu'))
model.add(Conv2D(128,(3, 3), padding='same', kernel_regularizer=dense_regularizer,kernel_initializer="he_normal"))
model.add(BatchNormalization())
model.add(Activation('elu'))
model.add(Conv2D(128,(3, 3), padding='same', kernel_regularizer=dense_regularizer,kernel_initializer="he_normal"))
model.add(BatchNormalization())
model.add(Activation('elu'))
model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))
model.add(Dropout(0.2))
model.add(Conv2D(256,(3, 3), padding='same', kernel_regularizer=dense_regularizer,kernel_initializer="he_normal"))
model.add(BatchNormalization())
model.add(Activation('elu'))
model.add(Conv2D(256,(3, 3), padding='same', kernel_regularizer=dense_regularizer,kernel_initializer="he_normal"))
model.add(BatchNormalization())
model.add(Activation('elu'))
model.add(Conv2D(256,(3, 3), padding='same', kernel_regularizer=dense_regularizer,kernel_initializer="he_normal"))
model.add(BatchNormalization())
model.add(Activation('elu'))
model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))
model.add(Dropout(0.2))
model.add(Conv2D(512,(3, 3), padding='same', kernel_regularizer=dense_regularizer,kernel_initializer="he_normal"))
model.add(BatchNormalization())
model.add(Activation('elu'))
model.add(Conv2D(512,(3, 3), padding='same', kernel_regularizer=dense_regularizer,kernel_initializer="he_normal"))
model.add(BatchNormalization())
model.add(Activation('elu'))
model.add(MaxPooling2D(pool_size=(3, 3), strides=(3, 3)))
model.add(Flatten())
model.add(Dense(10, activation='softmax', kernel_regularizer=dense_regularizer,kernel_initializer="he_normal"))
return model
model = Model_1()
model.summary()
|
Digit Recognizer
|
9,581,031 |
submission.to_csv('submission.csv', index=False, header=True )<load_from_csv>
|
optimizer = RMSprop(lr=0.001, rho=0.9, epsilon=1e-08, decay=0.0 )
|
Digit Recognizer
|
9,581,031 |
df = pd.read_csv('.. /input/nlp-getting-started/train.csv',index_col=0)
df_test = pd.read_csv('.. /input/nlp-getting-started/test.csv',index_col=0)
temp = [(x,y)for x,y in zip(list(df['text']),list(df['target'])) ]
random.shuffle(temp)
tweets = [t[0] for t in temp]
y = [t[1] for t in temp]
y = np.array(y ).astype('float32' )<count_values>
|
model.compile(optimizer = optimizer , loss = "categorical_crossentropy", metrics=["accuracy"] )
|
Digit Recognizer
|
9,581,031 |
print('Observations in training set')
print(df['target'].count())
print()
print('Label proportion in training set')
print(df['target'].value_counts() /(sum(df['target'].value_counts())))
print()
print('Observations in test set')
print(df_test['text'].count() )<import_modules>
|
learning_rate_reduction = ReduceLROnPlateau(monitor='val_accuracy',
patience=3,
verbose=1,
factor=0.5,
min_lr=0.00001 )
|
Digit Recognizer
|
9,581,031 |
import tensorflow as tf
from transformers import RobertaTokenizerFast, TFRobertaForSequenceClassification<load_pretrained>
|
epochs = 100
batch_size = 86
|
Digit Recognizer
|
9,581,031 |
model_name = 'roberta-large'
roberta_tokenizer = RobertaTokenizerFast.from_pretrained(model_name)
roberta_seq = TFRobertaForSequenceClassification.from_pretrained(model_name )<define_variables>
|
Digit Recognizer
|
|
9,581,031 |
for t in tweets:
if '&' in re.sub(r'(&|>|<)','',t):
print(t )<define_variables>
|
datagen = ImageDataGenerator(
featurewise_center=False,
samplewise_center=False,
featurewise_std_normalization=False,
samplewise_std_normalization=False,
zca_whitening=False,
rotation_range=10,
zoom_range = 0.1,
width_shift_range=0.1,
height_shift_range=0.1,
horizontal_flip=False,
vertical_flip=False)
datagen.fit(X_train )
|
Digit Recognizer
|
9,581,031 |
for t in tweets:
if any([x in t for x in [' btw ',' omg ',' lol ',' thx ']]):
print(t )<categorify>
|
history = model.fit_generator(datagen.flow(X_train,Y_train, batch_size=batch_size),
epochs = epochs, validation_data =(X_val,Y_val),
verbose = 1, steps_per_epoch=X_train.shape[0] // batch_size
, callbacks=[learning_rate_reduction] )
|
Digit Recognizer
|
9,581,031 |
def process_tweets(tweets):
r = tweets
r = [re.sub(r'https?://t.co/\w+','',t)for t in r]
r = [re.sub('&','&',t)for t in r]
r = [re.sub('>','gt',t)for t in r]
r = [re.sub('<','lt',t)for t in r]
return r
tweets = process_tweets(tweets )<string_transform>
|
results = model.predict(test)
results = np.argmax(results,axis = 1)
results = pd.Series(results,name="Label" )
|
Digit Recognizer
|
9,581,031 |
temp = roberta_tokenizer(tweets[:5],padding='max_length',max_length=50)
temp.keys()<categorify>
|
submission = pd.concat([pd.Series(range(1,28001),name = "ImageId"),results],axis = 1)
submission.to_csv("cnn_mnist_datagen_MMA.csv",index=False )
|
Digit Recognizer
|
9,309,487 |
print('Original tweet:')
print(tweets[0])
print('Encoded tweet:')
print(temp['input_ids'][0])
print('Decoded tweet:')
print(roberta_tokenizer.decode(temp['input_ids'][0]))<string_transform>
|
train= pd.read_csv('/kaggle/input/digit-recognizer/train.csv')
test= pd.read_csv('/kaggle/input/digit-recognizer/test.csv' )
|
Digit Recognizer
|
9,309,487 |
all_tweets = list(pd.concat([df,df_test],axis=0)['text'])
all_tweets = process_tweets(all_tweets)
max_len = max([len(t)for t in roberta_tokenizer(all_tweets)['input_ids']])
print(max_len )<split>
|
Ytrain= train['label'].astype('float32')
|
Digit Recognizer
|
9,309,487 |
X_train, X_test, y_train, y_test = train_test_split(tweets,y,test_size=0.30)
X_train = roberta_tokenizer(X_train,padding='max_length',max_length=max_len,return_tensors='tf')
X_test = roberta_tokenizer(X_test,padding='max_length',max_length=max_len,return_tensors='tf' )<define_variables>
|
train= train.drop('label',axis=1 )
|
Digit Recognizer
|
9,309,487 |
batch_size = 8
train_dataset = tf.data.Dataset.from_tensor_slices(( dict(X_train),y_train))
train_dataset = train_dataset.batch(batch_size)
test_dataset = tf.data.Dataset.from_tensor_slices(( dict(X_test),y_test))
test_dataset = test_dataset.batch(batch_size )<concatenate>
|
train= train.values.reshape(-1,28,28,1 ).astype('float32')
test= test.values.reshape(-1,28,28,1 ).astype('float32')
train =train / 255.0
test = test / 255.0
|
Digit Recognizer
|
9,309,487 |
temp_x, temp_y = next(iter(test_dataset))
temp = roberta_seq(temp_x,temp_y)
temp<choose_model_class>
|
Ytrain=to_categorical(Ytrain,num_classes=10)
x_train,x_test,y_train,y_test=train_test_split(train,Ytrain,test_size=0.25 )
|
Digit Recognizer
|
9,309,487 |
optimizer = tf.keras.optimizers.Adam(learning_rate=5e-6)
loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
roberta_seq.compile(optimizer=optimizer,loss=loss,metrics=['accuracy'] )<train_model>
|
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPool2D,BatchNormalization
from keras.preprocessing.image import ImageDataGenerator
|
Digit Recognizer
|
9,309,487 |
history = roberta_seq.fit(train_dataset,epochs=3,
validation_data=test_dataset,
callbacks=[callback_chkpt] )<load_pretrained>
|
model = Sequential()
|
Digit Recognizer
|
9,309,487 |
roberta_seq.load_weights(chkpt )<predict_on_test>
|
model.add(Conv2D(32,(3,3),padding='same',activation= 'relu',input_shape=(28,28,1)))
model.add(BatchNormalization())
model.add(Conv2D(32,(3,3),padding='same',activation= 'relu',input_shape=(28,28,1)))
model.add(BatchNormalization())
model.add(MaxPool2D(pool_size=(2,2)))
model.add(Dropout(0.2))
|
Digit Recognizer
|
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