Kaggle competition: COVID19 Global Forecasting (Week 4)
<SOS> metric: MCRMSLE Kaggle data source: covid19-global-forecasting-(week-4)<define_variables>
import numpy as np
import pandas as pd
import datetime as datetime
from sklearn import preprocessing
embed_size = 300
max_features = 120000
maxlen = 70
batch_size = 512
n_epochs = 5
n_splits = 5
SEED = 1029<set_options>
register_matplotlib_converters()
def seed_everything(seed=1029):
random.seed(seed)
 os.environ['PYTHONHASHSEED'] = str(seed)
 np.random.seed(seed)
 torch.manual_seed(seed)
 torch.cuda.manual_seed(seed)
 torch.backends.cudnn.deterministic = True
seed_everything()<load_pretrained>
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import PolynomialFeatures
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestRegressor
from xgboost import XGBRegressor
def load_glove(word_index):
EMBEDDING_FILE = '.. /input/embeddings/glove.840B.300d/glove.840B.300d.txt'
def get_coefs(word,*arr): return word, np.asarray(arr, dtype='float32')[:300]
embeddings_index = dict(get_coefs(*o.split(" ")) for o in open(EMBEDDING_FILE))
all_embs = np.stack(embeddings_index.values())
 emb_mean,emb_std = -0.005838499,0.48782197
embed_size = all_embs.shape[1]
nb_words = min(max_features, len(word_index))
embedding_matrix = np.random.normal(emb_mean, emb_std,(nb_words, embed_size))
for word, i in word_index.items() :
if i >= max_features: continue
embedding_vector = embeddings_index.get(word)
 if embedding_vector is not None: embedding_matrix[i] = embedding_vector
return embedding_matrix
def load_fasttext(word_index):
EMBEDDING_FILE = '.. /input/embeddings/wiki-news-300d-1M/wiki-news-300d-1M.vec'
def get_coefs(word,*arr): return word, np.asarray(arr, dtype='float32')
 embeddings_index = dict(get_coefs(*o.split(" ")) for o in open(EMBEDDING_FILE)if len(o)>100)
 all_embs = np.stack(embeddings_index.values())
 emb_mean,emb_std = all_embs.mean() , all_embs.std()
embed_size = all_embs.shape[1]
nb_words = min(max_features, len(word_index))
embedding_matrix = np.random.normal(emb_mean, emb_std,(nb_words, embed_size))
for word, i in word_index.items() :
if i >= max_features: continue
embedding_vector = embeddings_index.get(word)
 if embedding_vector is not None: embedding_matrix[i] = embedding_vector
return embedding_matrix
def load_para(word_index):
EMBEDDING_FILE = '.. /input/embeddings/paragram_300_sl999/paragram_300_sl999.txt'
def get_coefs(word,*arr): return word, np.asarray(arr, dtype='float32')
 embeddings_index = dict(get_coefs(*o.split(" ")) for o in open(EMBEDDING_FILE, encoding="utf8", errors='ignore')if len(o)>100)
 all_embs = np.stack(embeddings_index.values())
 emb_mean,emb_std = -0.0053247833,0.49346462
embed_size = all_embs.shape[1]
nb_words = min(max_features, len(word_index))
embedding_matrix = np.random.normal(emb_mean, emb_std,(nb_words, embed_size))
for word, i in word_index.items() :
if i >= max_features: continue
embedding_vector = embeddings_index.get(word)
 if embedding_vector is not None: embedding_matrix[i] = embedding_vector
return embedding_matrix<load_from_csv>
training_set = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
 training_set.shape
df_train = pd.read_csv(".. /input/train.csv")
 df_test = pd.read_csv(".. /input/test.csv")
 df = pd.concat([df_train ,df_test],sort=True )<feature_engineering>
test_set = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/test.csv')
 test_set.shape
def build_vocab(texts):
sentences = texts.apply(lambda x: x.split() ).values
vocab = {}
for sentence in sentences:
for word in sentence:
try:
vocab[word] += 1
except KeyError:
vocab[word] = 1
return vocab
vocab = build_vocab(df['question_text'] )<define_variables>
N_train=len(training_set[training_set['Country_Region']==my_country])
 N_train
sin = len(df_train[df_train["target"]==0])
 insin = len(df_train[df_train["target"]==1])
 persin =(sin/(sin+insin)) *100
perinsin =(insin/(sin+insin)) *100
print("
print("<feature_engineering>
N_test=len(test_set[test_set['Country_Region']==my_country])
 N_test
def build_vocab(texts):
sentences = texts.apply(lambda x: x.split() ).values
vocab = {}
for sentence in sentences:
for word in sentence:
try:
vocab[word] += 1
except KeyError:
vocab[word] = 1
return vocab
def known_contractions(embed):
known = []
for contract in contraction_mapping:
if contract in embed:
known.append(contract)
 return known
def clean_contractions(text, mapping):
specials = ["’", "‘", "´", "`"]
for s in specials:
text = text.replace(s, "'")
 text = ' '.join([mapping[t] if t in mapping else t for t in text.split(" ")])
 return text
def correct_spelling(x, dic):
for word in dic.keys() :
x = x.replace(word, dic[word])
 return x
def unknown_punct(embed, punct):
unknown = ''
for p in punct:
if p not in embed:
unknown += p
unknown += ' '
return unknown
def clean_numbers(x):
x = re.sub('[0-9]{5,}', '
x = re.sub('[0-9]{4}', '
x = re.sub('[0-9]{3}', '
x = re.sub('[0-9]{2}', '
return x
def clean_special_chars(text, punct, mapping):
for p in mapping:
text = text.replace(p, mapping[p])
 for p in punct:
text = text.replace(p, f' {p} ')
 specials = {'\u200b': ' ', '…': '...', '\ufeff': '', 'करना': '', 'है': ''}
for s in specials:
text = text.replace(s, specials[s])
 return text
def add_lower(embedding, vocab):
count = 0
for word in vocab:
if word in embedding and word.lower() not in embedding:
embedding[word.lower() ] = embedding[word]
count += 1
print(f"Added {count} words to embedding" )<define_variables>
df_test = test_set.copy()
df_test['DsType']='test'
df_test['ConfirmedCases']=0
df_test['Fatalities']=0
df_test.info()
puncts = [',', '.', '"', ':', ')', '(', '-', '!', '?', '|', ';', "'", '$', '&', '/', '[', ']', '>', '%', '=', '
'·', '_', '{', '}', '©', '^', '®', '`', '<', '→', '°', '€', '™', '›', '♥', '←', '×', '§', '″', '′', 'Â', '█', '½', 'à', '…',
'“', '★', '”', '–', '●', 'â', '►', '−', '¢', '²', '¬', '░', '¶', '↑', '±', '¿', '▾', '═', '¦', '║', '―', '¥', '▓', '—', '‹', '─',
'▒', '：', '¼', '⊕', '▼', '▪', '†', '■', '’', '▀', '¨', '▄', '♫', '☆', 'é', '¯', '♦', '¤', '▲', 'è', '¸', '¾', 'Ã', '⋅', '‘', '∞',
'∙', '）', '↓', '、', '│', '（', '»', '，', '♪', '╩', '╚', '³', '・', '╦', '╣', '╔', '╗', '▬', '❤', 'ï', 'Ø', '¹', '≤', '‡', '√', ]
def clean_text(x):
x = str(x)
 for punct in puncts:
x = x.replace(punct, f' {punct} ')
 return x
def clean_numbers(x):
x = re.sub('[0-9]{5,}', '
x = re.sub('[0-9]{4}', '
x = re.sub('[0-9]{3}', '
x = re.sub('[0-9]{2}', '
return x
mispell_dict = {"ain't": "is not", "aren't": "are not","can't": "cannot", "'cause": "because", "could've": "could have", "couldn't": "could not", "didn't": "did not", "doesn't": "does not", "don't": "do not", "hadn't": "had not", "hasn't": "has not", "haven't": "have not", "he'd": "he would","he'll": "he will", "he's": "he is", "how'd": "how did", "how'd'y": "how do you", "how'll": "how will", "how's": "how is", "I'd": "I would", "I'd've": "I would have", "I'll": "I will", "I'll've": "I will have","I'm": "I am", "I've": "I have", "i'd": "i would", "i'd've": "i would have", "i'll": "i will", "i'll've": "i will have","i'm": "i am", "i've": "i have", "isn't": "is not", "it'd": "it would", "it'd've": "it would have", "it'll": "it will", "it'll've": "it will have","it's": "it is", "let's": "let us", "ma'am": "madam", "mayn't": "may not", "might've": "might have","mightn't": "might not","mightn't've": "might not have", "must've": "must have", "mustn't": "must not", "mustn't've": "must not have", "needn't": "need not", "needn't've": "need not have","o'clock": "of the clock", "oughtn't": "ought not", "oughtn't've": "ought not have", "shan't": "shall not", "sha'n't": "shall not", "shan't've": "shall not have", "she'd": "she would", "she'd've": "she would have", "she'll": "she will", "she'll've": "she will have", "she's": "she is", "should've": "should have", "shouldn't": "should not", "shouldn't've": "should not have", "so've": "so have","so's": "so as", "this's": "this is","that'd": "that would", "that'd've": "that would have", "that's": "that is", "there'd": "there would", "there'd've": "there would have", "there's": "there is", "here's": "here is","they'd": "they would", "they'd've": "they would have", "they'll": "they will", "they'll've": "they will have", "they're": "they are", "they've": "they have", "to've": "to have", "wasn't": "was not", "we'd": "we would", "we'd've": "we would have", "we'll": "we will", "we'll've": "we will have", "we're": "we are", "we've": "we have", "weren't": "were not", "what'll": "what will", "what'll've": "what will have", "what're": "what are", "what's": "what is", "what've": "what have", "when's": "when is", "when've": "when have", "where'd": "where did", "where's": "where is", "where've": "where have", "who'll": "who will", "who'll've": "who will have", "who's": "who is", "who've": "who have", "why's": "why is", "why've": "why have", "will've": "will have", "won't": "will not", "won't've": "will not have", "would've": "would have", "wouldn't": "would not", "wouldn't've": "would not have", "y'all": "you all", "y'all'd": "you all would","y'all'd've": "you all would have","y'all're": "you all are","y'all've": "you all have","you'd": "you would", "you'd've": "you would have", "you'll": "you will", "you'll've": "you will have", "you're": "you are", "you've": "you have", 'colour': 'color', 'centre': 'center', 'favourite': 'favorite', 'travelling': 'traveling', 'counselling': 'counseling', 'theatre': 'theater', 'cancelled': 'canceled', 'labour': 'labor', 'organisation': 'organization', 'wwii': 'world war 2', 'citicise': 'criticize', 'youtu ': 'youtube ', 'Qoura': 'Quora', 'sallary': 'salary', 'Whta': 'What', 'narcisist': 'narcissist', 'howdo': 'how do', 'whatare': 'what are', 'howcan': 'how can', 'howmuch': 'how much', 'howmany': 'how many', 'whydo': 'why do', 'doI': 'do I', 'theBest': 'the best', 'howdoes': 'how does', 'mastrubation': 'masturbation', 'mastrubate': 'masturbate', "mastrubating": 'masturbating', 'pennis': 'penis', 'Etherium': 'Ethereum', 'narcissit': 'narcissist', 'bigdata': 'big data', '2k17': '2017', '2k18': '2018', 'qouta': 'quota', 'exboyfriend': 'ex boyfriend', 'airhostess': 'air hostess', "whst": 'what', 'watsapp': 'whatsapp', 'demonitisation': 'demonetization', 'demonitization': 'demonetization', 'demonetisation': 'demonetization'}
def _get_mispell(mispell_dict):
mispell_re = re.compile('(%s)' % '|'.join(mispell_dict.keys()))
return mispell_dict, mispell_re
mispellings, mispellings_re = _get_mispell(mispell_dict)
 def replace_typical_misspell(text):
def replace(match):
return mispellings[match.group(0)]
return mispellings_re.sub(replace, text )<feature_engineering>
df_union=pd.concat([df_train,df_test],sort=False ).copy()
df_union.fillna('ND', inplace = True )
def stem_text(text):
lemma = nltk.wordnet.WordNetLemmatizer()
class FasterStemmer(object):
def __init__(self):
self.words = {}
def stem(self, x):
if x in self.words:
return self.words[x]
t = lemma.lemmatize(x)
 self.words[x] = t
return t
faster_stemmer = FasterStemmer()
text = text.split()
stemmed_words = [faster_stemmer.stem(word)for word in text]
text = " ".join(stemmed_words)
 del faster_stemmer
gc.collect
return text<feature_engineering>
df_union['Month']=df_union['Date'].apply(lambda s : int(s.replace('-','')[4:6]))
df_union['Day']=df_union['Date'].apply(lambda s : int(s.replace('-','')[6:9]))
df_union['Date']=df_union['Date'].apply(lambda s : datetime.datetime.strptime(s, '%Y-%m-%d'))
df_union['DateOrd']=df_union['Date'].apply(lambda s : s.toordinal())
 df_union['Province_Norm']=df_union['Country_Region']+'-'+df_union['Province_State']
df_union.tail()
def add_features(df):
df['question_text'] = df['question_text'].progress_apply(lambda x:str(x))
df['total_length'] = df['question_text'].progress_apply(len)
 df['capitals'] = df['question_text'].progress_apply(lambda comment: sum(1 for c in comment if c.isupper()))
df['caps_vs_length'] = df.progress_apply(lambda row: float(row['capitals'])/float(row['total_length']),
axis=1)
 df['num_words'] = df.question_text.str.count('\S+')
 df['num_unique_words'] = df['question_text'].progress_apply(lambda comment: len(set(w for w in comment.split())))
 df['words_vs_unique'] = df['num_unique_words'] / df['num_words']
return df
def load_and_prec() :
train_df = pd.read_csv(".. /input/train.csv")
 test_df = pd.read_csv(".. /input/test.csv")
 print("Train shape : ",train_df.shape)
 print("Test shape : ",test_df.shape)
 train_df["question_text"] = train_df["question_text"].apply(lambda x: x.lower())
 test_df["question_text"] = test_df["question_text"].apply(lambda x: x.lower())
 train_df["question_text"] = train_df["question_text"].progress_apply(lambda x: clean_text(x))
test_df["question_text"] = test_df["question_text"].apply(lambda x: clean_text(x))
train_df["question_text"] = train_df["question_text"].progress_apply(lambda x: clean_numbers(x))
test_df["question_text"] = test_df["question_text"].apply(lambda x: clean_numbers(x))
train_df["question_text"] = train_df["question_text"].progress_apply(lambda x: replace_typical_misspell(x))
test_df["question_text"] = test_df["question_text"].apply(lambda x: replace_typical_misspell(x))
train_X = train_df["question_text"].fillna("_
test_X = test_df["question_text"].fillna("_
train = add_features(train_df)
 test = add_features(test_df)
 features = train[['caps_vs_length', 'words_vs_unique']].fillna(0)
 test_features = test[['caps_vs_length', 'words_vs_unique']].fillna(0)
 ss = StandardScaler()
ss.fit(np.vstack(( features, test_features)))
 features = ss.transform(features)
 test_features = ss.transform(test_features)
 tokenizer = Tokenizer(num_words=max_features)
 tokenizer.fit_on_texts(list(train_X))
train_X = tokenizer.texts_to_sequences(train_X)
 test_X = tokenizer.texts_to_sequences(test_X)
 train_X = pad_sequences(train_X, maxlen=maxlen)
 test_X = pad_sequences(test_X, maxlen=maxlen)
 train_y = train_df['target'].values
np.random.seed(SEED)
 trn_idx = np.random.permutation(len(train_X))
train_X = train_X[trn_idx]
train_y = train_y[trn_idx]
features = features[trn_idx]
return train_X, test_X, train_y, features, test_features, tokenizer.word_index 
<train_model>
le1 = preprocessing.LabelEncoder()
le1.fit(df_union['Country_Region'])
 df_union['Country'] = le1.transform(df_union['Country_Region'] )
x_train, x_test, y_train, features, test_features, word_index = load_and_prec() 
<save_model>
le2 = preprocessing.LabelEncoder()
le2.fit(df_union['Province_Norm'])
 df_union['Province'] = le2.transform(df_union['Province_Norm'])
 df_union.drop('Province_Norm',axis=1,inplace=True )
np.save("x_train",x_train)
 np.save("x_test",x_test)
 np.save("y_train",y_train)
 np.save("features",features)
 np.save("test_features",test_features)
 np.save("word_index.npy",word_index )<load_pretrained>
t_max_tr = max(df_union['Date'][df_union['DsType']=='train'])
 t_min_te = min(df_union['Date'][df_union['DsType']=='test'])
 Noverlap=(t_max_tr-t_min_te ).days+1
Noverlap
x_train = np.load("x_train.npy")
 x_test = np.load("x_test.npy")
 y_train = np.load("y_train.npy")
 features = np.load("features.npy")
 test_features = np.load("test_features.npy")
 word_index = np.load("word_index.npy" ).item()<normalization>
def cut_neg(y):
m = 0
for i in range(0,len(y)) :
if y[i]<m : y[i]=m
return y
seed_everything()
glove_embeddings = load_glove(word_index)
 paragram_embeddings = load_para(word_index)
 fasttext_embeddings = load_fasttext(word_index)
 embedding_matrix = np.mean([glove_embeddings, paragram_embeddings, fasttext_embeddings], axis=0)
 del glove_embeddings, paragram_embeddings, fasttext_embeddings
gc.collect()
np.shape(embedding_matrix )<split>
def polynomial_trend(deg,X_train,y_true,X_test):
pf = PolynomialFeatures(degree=deg)
 pr = pf.fit_transform(X_train)
 lrm = LinearRegression()
lrm.fit(pr, y_true)
 y_valid = lrm.predict(pf.fit_transform(X_train))
y_pred = lrm.predict(pf.fit_transform(X_test))
y_valid = cut_neg(y_valid)
 y_pred = cut_neg(y_pred)
 return y_valid,y_pred
splits = list(StratifiedKFold(n_splits=n_splits, shuffle=True, random_state=SEED ).split(x_train, y_train))
splits[:3]<choose_model_class>
Nlim = 5
delta_max = 0.1
def calc_trends(X_train,y_true,X_test,deg):
pct_hist = 100*sum(1 for x in y_true if x > 0)/N_train
y=np.array(y_true ).flatten()
y_last = y[len(y)-1]
y_check = y[len(y)-1-Nlim]
if(y_check==0):
den = 1
else :
den = y_check
diff = np.abs(y_last-y_check)/den
lim = np.mean(y_true[len(y_true)-Nlim:len(y_true)])
 if(diff<=delta_max):
y_valid = np.full([len(y_true)],lim)
 y_pred = np.full([len(X_test)],lim)
 msg1 = 'cost'
msg2 = 'cost'
if(diff>delta_max):
y_valid,y_pred = polynomial_trend(deg,X_train,y_true,X_test)
 msg1 = 'poly'
msg2 = 'poly'
return y_valid, y_pred, msg1, msg2
class CyclicLR(object):
def __init__(self, optimizer, base_lr=1e-3, max_lr=6e-3,
step_size=2000, mode='triangular', gamma=1.,
scale_fn=None, scale_mode='cycle', last_batch_iteration=-1):
if not isinstance(optimizer, Optimizer):
raise TypeError('{} is not an Optimizer'.format(
 type(optimizer ).__name__))
self.optimizer = optimizer
if isinstance(base_lr, list)or isinstance(base_lr, tuple):
if len(base_lr)!= len(optimizer.param_groups):
raise ValueError("expected {} base_lr, got {}".format(
 len(optimizer.param_groups), len(base_lr)))
 self.base_lrs = list(base_lr)
 else:
self.base_lrs = [base_lr] * len(optimizer.param_groups)
 if isinstance(max_lr, list)or isinstance(max_lr, tuple):
if len(max_lr)!= len(optimizer.param_groups):
raise ValueError("expected {} max_lr, got {}".format(
 len(optimizer.param_groups), len(max_lr)))
 self.max_lrs = list(max_lr)
 else:
self.max_lrs = [max_lr] * len(optimizer.param_groups)
 self.step_size = step_size
if mode not in ['triangular', 'triangular2', 'exp_range'] \
and scale_fn is None:
raise ValueError('mode is invalid and scale_fn is None')
 self.mode = mode
self.gamma = gamma
if scale_fn is None:
if self.mode == 'triangular':
self.scale_fn = self._triangular_scale_fn
self.scale_mode = 'cycle'
elif self.mode == 'triangular2':
self.scale_fn = self._triangular2_scale_fn
self.scale_mode = 'cycle'
elif self.mode == 'exp_range':
self.scale_fn = self._exp_range_scale_fn
self.scale_mode = 'iterations'
else:
self.scale_fn = scale_fn
self.scale_mode = scale_mode
self.batch_step(last_batch_iteration + 1)
 self.last_batch_iteration = last_batch_iteration
def batch_step(self, batch_iteration=None):
if batch_iteration is None:
batch_iteration = self.last_batch_iteration + 1
self.last_batch_iteration = batch_iteration
for param_group, lr in zip(self.optimizer.param_groups, self.get_lr()):
param_group['lr'] = lr
def _triangular_scale_fn(self, x):
return 1.
 def _triangular2_scale_fn(self, x):
return 1 /(2.**(x - 1))
def _exp_range_scale_fn(self, x):
return self.gamma**(x)
 def get_lr(self):
step_size = float(self.step_size)
 cycle = np.floor(1 + self.last_batch_iteration /(2 * step_size))
x = np.abs(self.last_batch_iteration / step_size - 2 * cycle + 1)
 lrs = []
param_lrs = zip(self.optimizer.param_groups, self.base_lrs, self.max_lrs)
 for param_group, base_lr, max_lr in param_lrs:
base_height =(max_lr - base_lr)* np.maximum(0,(1 - x))
if self.scale_mode == 'cycle':
lr = base_lr + base_height * self.scale_fn(cycle)
 else:
lr = base_lr + base_height * self.scale_fn(self.last_batch_iteration)
 lrs.append(lr)
 return lrs<init_hyperparams>
df_train = df_union[df_union['DsType']=='train'].drop('DsType',axis=1)
 df_test = df_union[df_union['DsType']=='test'].drop('DsType',axis=1)
 df_train['ConfirmedCasesValid'] = 0
df_train['FatalitiesValid'] = 0
df_train['ConfirmedCasesTrend'] = 0
df_train['FatalitiesTrend'] = 0
df_train['ConfirmedCasesResid'] = 0
df_train['FatalitiesResid'] = 0
df_test['ConfirmedCases'] = 0
df_test['Fatalities'] = 0
df_test['ConfirmedCasesTrend'] = 0
df_test['FatalitiesTrend'] = 0
df_test['ConfirmedCasesResid'] = 0
df_test['FatalitiesResid'] = 0
embedding_dim = 300
embedding_path = '.. /save/embedding_matrix.npy'
use_pretrained_embedding = True
hidden_size = 40
gru_len = hidden_size
Routings = 4
Num_capsule = 5
Dim_capsule = 5
dropout_p = 0.25
rate_drop_dense = 0.28
LR = 0.001
T_epsilon = 1e-7
num_classes = 30
class FocalLoss(nn.Module):
def __init__(self, alpha=1, gamma=2, logits=True, reduction='elementwise_mean'):
super(FocalLoss, self ).__init__()
self.alpha = alpha
self.gamma = gamma
self.logits = logits
self.reduction = reduction
def forward(self, inputs, targets):
if self.logits:
BCE_loss = F.binary_cross_entropy_with_logits(inputs, targets, reduction='none')
 else:
BCE_loss = F.binary_cross_entropy(inputs, targets, reduction='none')
 pt = torch.exp(-BCE_loss)
 F_loss = self.alpha *(1-pt)**self.gamma * BCE_loss
if self.reduction is None:
return F_loss
else:
return torch.mean(F_loss)
 class Embed_Layer(nn.Module):
def __init__(self, embedding_matrix=None, vocab_size=None, embedding_dim=300):
super(Embed_Layer, self ).__init__()
self.encoder = nn.Embedding(vocab_size + 1, embedding_dim)
 if use_pretrained_embedding:
self.encoder.weight.data.copy_(t.from_numpy(embedding_matrix))
def forward(self, x, dropout_p=0.25):
return nn.Dropout(p=dropout_p )(self.encoder(x))
class GRU_Layer(nn.Module):
def __init__(self):
super(GRU_Layer, self ).__init__()
self.gru = nn.GRU(input_size=300,
hidden_size=gru_len,
bidirectional=True)

def init_weights(self):
ih =(param.data for name, param in self.named_parameters() if 'weight_ih' in name)
 hh =(param.data for name, param in self.named_parameters() if 'weight_hh' in name)
 b =(param.data for name, param in self.named_parameters() if 'bias' in name)
 for k in ih:
nn.init.xavier_uniform_(k)
 for k in hh:
nn.init.orthogonal_(k)
 for k in b:
nn.init.constant_(k, 0)
 def forward(self, x):
return self.gru(x)
 class Caps_Layer(nn.Module):
def __init__(self, input_dim_capsule=gru_len * 2, num_capsule=Num_capsule, dim_capsule=Dim_capsule, \
routings=Routings, kernel_size=(9, 1), share_weights=True,
activation='default', **kwargs):
super(Caps_Layer, self ).__init__(**kwargs)
 self.num_capsule = num_capsule
self.dim_capsule = dim_capsule
self.routings = routings
self.kernel_size = kernel_size
self.share_weights = share_weights
if activation == 'default':
self.activation = self.squash
else:
self.activation = nn.ReLU(inplace=True)
 if self.share_weights:
self.W = nn.Parameter(
 nn.init.xavier_normal_(t.empty(1, input_dim_capsule, self.num_capsule * self.dim_capsule)))
 else:
self.W = nn.Parameter(
 t.randn(BATCH_SIZE, input_dim_capsule, self.num_capsule * self.dim_capsule))
def forward(self, x):
if self.share_weights:
u_hat_vecs = t.matmul(x, self.W)
 else:
print('add later')
 batch_size = x.size(0)
 input_num_capsule = x.size(1)
 u_hat_vecs = u_hat_vecs.view(( batch_size, input_num_capsule,
self.num_capsule, self.dim_capsule))
u_hat_vecs = u_hat_vecs.permute(0, 2, 1, 3)
 b = t.zeros_like(u_hat_vecs[:, :, :, 0])
 for i in range(self.routings):
b = b.permute(0, 2, 1)
 c = F.softmax(b, dim=2)
 c = c.permute(0, 2, 1)
 b = b.permute(0, 2, 1)
 outputs = self.activation(t.einsum('bij,bijk->bik',(c, u_hat_vecs)))
 if i < self.routings - 1:
b = t.einsum('bik,bijk->bij',(outputs, u_hat_vecs))
return outputs
def squash(self, x, axis=-1):
s_squared_norm =(x ** 2 ).sum(axis, keepdim=True)
 scale = t.sqrt(s_squared_norm + T_epsilon)
 return x / scale
class Capsule_Main(nn.Module):
def __init__(self, embedding_matrix=None, vocab_size=None):
super(Capsule_Main, self ).__init__()
self.embed_layer = Embed_Layer(embedding_matrix, vocab_size)
 self.gru_layer = GRU_Layer()
self.gru_layer.init_weights()
self.caps_layer = Caps_Layer()
self.dense_layer = Dense_Layer()
def forward(self, content):
content1 = self.embed_layer(content)
 content2, _ = self.gru_layer(
 content1)
 content3 = self.caps_layer(content2)
 output = self.dense_layer(content3)
 return output 
<normalization>
y1_train = df_train['ConfirmedCases'].astype(float)
 y2_train = df_train['Fatalities'].astype(float )
class Attention(nn.Module):
def __init__(self, feature_dim, step_dim, bias=True, **kwargs):
super(Attention, self ).__init__(**kwargs)
 self.supports_masking = True
self.bias = bias
self.feature_dim = feature_dim
self.step_dim = step_dim
self.features_dim = 0
weight = torch.zeros(feature_dim, 1)
 nn.init.xavier_uniform_(weight)
 self.weight = nn.Parameter(weight)
 if bias:
self.b = nn.Parameter(torch.zeros(step_dim))
def forward(self, x, mask=None):
feature_dim = self.feature_dim
step_dim = self.step_dim
eij = torch.mm(
 x.contiguous().view(-1, feature_dim),
self.weight
).view(-1, step_dim)
 if self.bias:
eij = eij + self.b
eij = torch.tanh(eij)
 a = torch.exp(eij)
 if mask is not None:
a = a * mask
a = a / torch.sum(a, 1, keepdim=True)+ 1e-10
weighted_input = x * torch.unsqueeze(a, -1)
 return torch.sum(weighted_input, 1)
 class NeuralNet(nn.Module):
def __init__(self):
super(NeuralNet, self ).__init__()
fc_layer = 16
fc_layer1 = 16
self.embedding = nn.Embedding(max_features, embed_size)
 self.embedding.weight = nn.Parameter(torch.tensor(embedding_matrix, dtype=torch.float32))
self.embedding.weight.requires_grad = False
self.embedding_dropout = nn.Dropout2d(0.1)
 self.lstm = nn.LSTM(embed_size, hidden_size, bidirectional=True, batch_first=True)
 self.gru = nn.GRU(hidden_size * 2, hidden_size, bidirectional=True, batch_first=True)
 self.lstm2 = nn.LSTM(hidden_size * 2, hidden_size, bidirectional=True, batch_first=True)
 self.lstm3 = nn.LSTM(hidden_size * 2, hidden_size, bidirectional=True, batch_first=True)
 self.lstm_attention = Attention(hidden_size * 2, maxlen)
 self.gru_attention = Attention(hidden_size * 2, maxlen)
 self.bn = nn.BatchNorm1d(16, momentum=0.1)
 self.linear = nn.Linear(hidden_size*8+3, fc_layer1)
 self.relu = nn.ReLU()
self.dropout = nn.Dropout(0.1)
 self.fc = nn.Linear(fc_layer**2,fc_layer)
 self.out = nn.Linear(fc_layer, 1)
 self.lincaps = nn.Linear(Num_capsule * Dim_capsule, 1)
 self.caps_layer = Caps_Layer()
def forward(self, x):
h_embedding = self.embedding(x[0])
 h_embedding = torch.squeeze(
 self.embedding_dropout(torch.unsqueeze(h_embedding, 0)))
 h_lstm, _ = self.lstm(h_embedding)
 h_lstm, _ = self.lstm2(h_lstm)
 h_gru, _ = self.gru(h_lstm)
 content3 = self.caps_layer(h_gru)
 content3 = self.dropout(content3)
 batch_size = content3.size(0)
 content3 = content3.view(batch_size, -1)
 content3 = self.relu(self.lincaps(content3))
h_lstm_atten = self.lstm_attention(h_lstm)
 h_gru_atten = self.gru_attention(h_gru)
 avg_pool = torch.mean(h_gru, 1)
 max_pool, _ = torch.max(h_gru, 1)
 f = torch.tensor(x[1], dtype=torch.float ).cuda()
conc = torch.cat(( h_lstm_atten, h_gru_atten,content3, avg_pool, max_pool,f), 1)
 conc = self.relu(self.linear(conc))
conc = self.bn(conc)
 conc = self.dropout(conc)
 out = self.out(conc)
 return out<categorify>
def isPos(x):
if(x>0):
return 1
else :
return 0
class MyDataset(Dataset):
def __init__(self,dataset):
self.dataset = dataset
def __getitem__(self, index):
data, target = self.dataset[index]
return data, target, index
def __len__(self):
return len(self.dataset )<define_variables>
df_train['ConfirmedCasesPos']=df_train['ConfirmedCases'].apply(isPos)
 df_train['FatalitiesPos']=df_train['Fatalities'].apply(isPos)
 df_test['ConfirmedCasesPos']=df_test['ConfirmedCases'].apply(isPos)
 df_test['FatalitiesPos']=df_test['Fatalities'].apply(isPos )
def sigmoid(x):
return 1 /(1 + np.exp(-x))
train_preds = np.zeros(( len(x_train)))
 test_preds = np.zeros(( len(df_test)))
 seed_everything()
x_test_cuda = torch.tensor(x_test, dtype=torch.long ).cuda()
test = torch.utils.data.TensorDataset(x_test_cuda)
 test_loader = torch.utils.data.DataLoader(test, batch_size=batch_size, shuffle=False)
 avg_losses_f = []
avg_val_losses_f = []<data_type_conversions>
sel_cols = ['Country_Region','Province_State','ConfirmedCasesPos','FatalitiesPos']
df_tr_cnt = df_train[sel_cols].groupby(by=['Country_Region','Province_State'] ).sum()
df_te_cnt = df_test[sel_cols].groupby(by=['Country_Region','Province_State'] ).sum()
for i,(train_idx, valid_idx)in enumerate(splits):
x_train = np.array(x_train)
 y_train = np.array(y_train)
 features = np.array(features)
 x_train_fold = torch.tensor(x_train[train_idx.astype(int)], dtype=torch.long ).cuda()
y_train_fold = torch.tensor(y_train[train_idx.astype(int), np.newaxis], dtype=torch.float32 ).cuda()
kfold_X_features = features[train_idx.astype(int)]
kfold_X_valid_features = features[valid_idx.astype(int)]
x_val_fold = torch.tensor(x_train[valid_idx.astype(int)], dtype=torch.long ).cuda()
y_val_fold = torch.tensor(y_train[valid_idx.astype(int), np.newaxis], dtype=torch.float32 ).cuda()
model = NeuralNet()
model.cuda()
gamma = 1.1 + i/16
loss_fn = FocalLoss(gamma=gamma)
 step_size = 300
base_lr, max_lr = 0.001, 0.003
optimizer = torch.optim.Adam(filter(lambda p: p.requires_grad, model.parameters()),
lr=max_lr)
 scheduler = CyclicLR(optimizer, base_lr=base_lr, max_lr=max_lr,
step_size=step_size, mode='exp_range',
gamma=0.99994)
 train = torch.utils.data.TensorDataset(x_train_fold, y_train_fold)
 valid = torch.utils.data.TensorDataset(x_val_fold, y_val_fold)
 train = MyDataset(train)
 valid = MyDataset(valid)
 train_loader = torch.utils.data.DataLoader(train, batch_size=batch_size, shuffle=True)
 valid_loader = torch.utils.data.DataLoader(valid, batch_size=batch_size, shuffle=False)
 print(f'Fold {i + 1}')
 for epoch in range(n_epochs):
start_time = time.time()
model.train()
avg_loss = 0.
 for i,(x_batch, y_batch, index)in enumerate(train_loader):
f = kfold_X_features[index]
y_pred = model([x_batch,f])
 if scheduler:
scheduler.batch_step()
loss = loss_fn(y_pred, y_batch)
 optimizer.zero_grad()
loss.backward()
optimizer.step()
avg_loss += loss.item() / len(train_loader)
 model.eval()
valid_preds_fold = np.zeros(( x_val_fold.size(0)))
 test_preds_fold = np.zeros(( len(df_test)))
 avg_val_loss = 0.
 for i,(x_batch, y_batch, index)in enumerate(valid_loader):
f = kfold_X_valid_features[index]
y_pred = model([x_batch,f] ).detach()
avg_val_loss += loss_fn(y_pred, y_batch ).item() / len(valid_loader)
 valid_preds_fold[i * batch_size:(i+1)* batch_size] = sigmoid(y_pred.cpu().numpy())[:, 0]
elapsed_time = time.time() - start_time
print('Epoch {}/{} \t loss={:.4f} \t val_loss={:.4f} \t time={:.2f}s'.format(
 epoch + 1, n_epochs, avg_loss, avg_val_loss, elapsed_time))
avg_losses_f.append(avg_loss)
 avg_val_losses_f.append(avg_val_loss)
 for i,(x_batch,)in enumerate(test_loader):
f = test_features[i * batch_size:(i+1)* batch_size]
y_pred = model([x_batch,f] ).detach()
test_preds_fold[i * batch_size:(i+1)* batch_size] = sigmoid(y_pred.cpu().numpy())[:, 0]
train_preds[valid_idx] = valid_preds_fold
test_preds += test_preds_fold / len(splits)
 print('All \t loss={:.4f} \t val_loss={:.4f} \t '.format(np.average(avg_losses_f),np.average(avg_val_losses_f)))
<compute_test_metric>
sel_cols = ['Date','Country_Region','Province_State','ConfirmedCases','Fatalities']
df_tr_last = df_train[df_train['Date']==max(df_train['Date'])][sel_cols]
df_te_last = df_test[df_test['Date']==max(df_test['Date'])][sel_cols]
def bestThresshold(y_train,train_preds):
tmp = [0,0,0]
delta = 0
for tmp[0] in tqdm(np.arange(0.1, 0.501, 0.01)) :
tmp[1] = f1_score(y_train, np.array(train_preds)>tmp[0])
 if tmp[1] > tmp[2]:
delta = tmp[0]
tmp[2] = tmp[1]
print('best threshold is {:.4f} with F1 score: {:.4f}'.format(delta, tmp[2]))
return delta
delta = bestThresshold(y_train,train_preds )<save_to_csv>
df_tr_last = df_tr_last.merge(df_tr_cnt,on=['Country_Region','Province_State'])
 df_te_last = df_te_last.merge(df_te_cnt,on=['Country_Region','Province_State'] )
submission = df_test[['qid']].copy()
submission['prediction'] =(test_preds > delta ).astype(int)
 submission.to_csv('submission.csv', index=False )<load_from_csv>
df_last = df_tr_last.merge(df_te_last,on=['Country_Region','Province_State'])
 df_last['DeltaConfirmedCases']=100*(df_last['ConfirmedCases_y']-df_last['ConfirmedCases_x'])/df_last['ConfirmedCases_x']
df_last['DeltaFatalities']=100*(df_last['Fatalities_y']-df_last['Fatalities_x'])/df_last['Fatalities_x']
df_last['DeltaConfirmedCases']=df_last['DeltaConfirmedCases'].apply(np.abs)
 df_last['DeltaFatalities']=df_last['DeltaFatalities'].apply(np.abs)
 df_last.fillna(0,inplace=True)
 df_last = df_last.replace([np.inf, -np.inf], 0 )
train_df = pd.read_csv(".. /input/train.csv")
 X_train = train_df["question_text"].fillna("dieter" ).values
test_df = pd.read_csv(".. /input/test.csv")
 X_test = test_df["question_text"].fillna("dieter" ).values
y = train_df["target"]<import_modules>
n_head = 5
d_lim1 = 120
d_lim2 = 200
sel_var ='ConfirmedCases'
sel_cols = ['Country_Region','Province_State',sel_var+'_x','Delta'+sel_var,sel_var+'Pos_x']
condition =(df_last['Delta'+sel_var]>d_lim1)&(df_last['Delta'+sel_var]<d_lim2)
 df_last[condition][sel_cols].sort_values(by=['Delta'+sel_var], ascending=True ).head(n_head )
tqdm.pandas() 
<define_variables>
n_head = 5
d_lim1 = 0
d_lim2 = 10
sel_var = 'Fatalities'
sel_cols = ['Country_Region','Province_State',sel_var+'_x','Delta'+sel_var,sel_var+'Pos_x']
condition =(df_last['Delta'+sel_var]>d_lim1)&(df_last['Delta'+sel_var]<d_lim2)
 df_last[condition][sel_cols].sort_values(by=['Delta'+sel_var], ascending=True ).head(n_head )
puncts = [',', '.', '"', ':', ')', '(', '-', '!', '?', '|', ';', "'", '$', '&', '/', '[', ']', '>', '%', '=', '
'·', '_', '{', '}', '©', '^', '®', '`', '<', '→', '°', '€', '™', '›', '♥', '←', '×', '§', '″', '′', 'Â', '█', '½', 'à', '…',
'“', '★', '”', '–', '●', 'â', '►', '−', '¢', '²', '¬', '░', '¶', '↑', '±', '¿', '▾', '═', '¦', '║', '―', '¥', '▓', '—', '‹', '─',
'▒', '：', '¼', '⊕', '▼', '▪', '†', '■', '’', '▀', '¨', '▄', '♫', '☆', 'é', '¯', '♦', '¤', '▲', 'è', '¸', '¾', 'Ã', '⋅', '‘', '∞',
'∙', '）', '↓', '、', '│', '（', '»', '，', '♪', '╩', '╚', '³', '・', '╦', '╣', '╔', '╗', '▬', '❤', 'ï', 'Ø', '¹', '≤', '‡', '√', ]<define_variables>
def plotExample() :
train_cond =(( df_train['Country_Region']==my_country)&(df_train['Province_State']==my_province))
test_cond =(( df_test['Country_Region']==my_country)&(df_test['Province_State']==my_province))
x_train_plt = df_train[train_cond]['Date']
y_train_plt = df_train[train_cond][my_variable]
y_valid_plt = df_train[train_cond][my_variable+'Valid']
y_trend_plt = df_train[train_cond][my_variable+'Trend']
y_resid_plt = df_train[train_cond][my_variable+'Resid']
x_test_plt = df_test[test_cond]['Date']
y_test_plt = df_test[test_cond][my_variable]
plt.rcParams["figure.figsize"] =(12,6)
 fig, ax = plt.subplots()
ax.plot(x_train_plt,y_train_plt,'o',color='orange', label='y_true')
 ax.plot(x_train_plt,y_valid_plt,'x',color='gray' , label ='y_valid')
 ax.plot(x_train_plt,y_trend_plt,'.',color='lightblue', label='y_trend')
 ax.plot(x_train_plt,y_resid_plt,'-',color='green', label='y_resid')
 ax.plot(x_test_plt,y_test_plt,'*',color='red', label='y_test')
 ax.set_xticks([])
 ax.legend()
mispell_dict = {"aren't" : "are not",
"can't" : "cannot",
"couldn't" : "could not",
"didn't" : "did not",
"doesn't" : "does not",
"don't" : "do not",
"hadn't" : "had not",
"hasn't" : "has not",
"haven't" : "have not",
"he'd" : "he would",
"he'll" : "he will",
"he's" : "he is",
"i'd" : "I would",
"i'd" : "I had",
"i'll" : "I will",
"i'm" : "I am",
"isn't" : "is not",
"it's" : "it is",
"it'll":"it will",
"i've" : "I have",
"let's" : "let us",
"mightn't" : "might not",
"mustn't" : "must not",
"shan't" : "shall not",
"she'd" : "she would",
"she'll" : "she will",
"she's" : "she is",
"shouldn't" : "should not",
"that's" : "that is",
"there's" : "there is",
"they'd" : "they would",
"they'll" : "they will",
"they're" : "they are",
"they've" : "they have",
"we'd" : "we would",
"we're" : "we are",
"weren't" : "were not",
"we've" : "we have",
"what'll" : "what will",
"what're" : "what are",
"what's" : "what is",
"what've" : "what have",
"where's" : "where is",
"who'd" : "who would",
"who'll" : "who will",
"who're" : "who are",
"who's" : "who is",
"who've" : "who have",
"won't" : "will not",
"wouldn't" : "would not",
"you'd" : "you would",
"you'll" : "you will",
"you're" : "you are",
"you've" : "you have",
"'re": " are",
"wasn't": "was not",
"we'll":" will",
"didn't": "did not",
"tryin'":"trying"} 
<categorify>
sel_cols = ['Country_Region','Province_State','ConfirmedCases_x','ConfirmedCases_y','DeltaConfirmedCases','ConfirmedCasesPos_x']
df_last[sel_cols][(( df_last['Country_Region']==my_country)&(df_last['Province_State']==my_province)) ]
def clean_text(x):
x = str(x)
 for punct in puncts:
x = x.replace(punct, f' {punct} ')
 return x
def clean_numbers(x):
x = str(x)
 x = re.sub(r'[0-9]{5,}', '
x = re.sub(r'[0-9]{4}', '
x = re.sub(r'[0-9]{3}', '
x = re.sub(r'[0-9]{2}', '
return x
def _get_mispell(mispell_dict):
mispell_re = re.compile('(%s)' % '|'.join(mispell_dict.keys()))
return mispell_dict, mispell_re
mispellings, mispellings_re = _get_mispell(mispell_dict)
 def replace_typical_misspell(text):
def replace(match):
return mispellings[match.group(0)]
text = mispellings_re.sub(replace, text)
 return text<categorify>
sel_cols = ['Country_Region','Province_State','ConfirmedCases_x','ConfirmedCases_y','DeltaConfirmedCases','ConfirmedCasesPos_x']
df_last[sel_cols][(( df_last['Country_Region']==my_country)&(df_last['Province_State']==my_province)) ]
X_train["question_text"] = X_train["question_text"].map(clean_numbers)
 X_test["question_text"] = X_test["question_text"].map(clean_numbers)
 X_train["question_text"] = X_train["question_text"].map(replace_typical_misspell)
 X_test["question_text"] = X_test["question_text"].map(replace_typical_misspell)
<string_transform>
sel_cols = ['Country_Region','Province_State','ConfirmedCases_x','ConfirmedCases_y','DeltaConfirmedCases','ConfirmedCasesPos_x']
df_last[sel_cols][(( df_last['Country_Region']==my_country)&(df_last['Province_State']==my_province)) ]
SEQ_LEN = 100
maxlen = 100
max_features = 50000
tokenizer = text.Tokenizer(num_words=max_features)
 tokenizer.fit_on_texts(list(X_train)+ list(X_test))
X_train = tokenizer.texts_to_sequences(X_train)
 X_test = tokenizer.texts_to_sequences(X_test)
 x_train = sequence.pad_sequences(X_train, maxlen=maxlen)
 x_test = sequence.pad_sequences(X_test, maxlen=maxlen )<statistical_test>
sel_cols = ['Country_Region','Province_State','ConfirmedCases_x','ConfirmedCases_y','DeltaConfirmedCases','ConfirmedCasesPos_x']
df_last[sel_cols][(( df_last['Country_Region']==my_country)&(df_last['Province_State']==my_province)) ]
EMBEDDING_FILE_GLOVE = '.. /input/embeddings/glove.840B.300d/glove.840B.300d.txt'
def get_coefs(word,*arr): return word, np.asarray(arr, dtype='float32')
 embeddings_index_glove = dict(get_coefs(*o.split(" ")) for o in open(EMBEDDING_FILE_GLOVE))
all_embs_glove = np.stack(embeddings_index_glove.values())
 emb_mean_glove,emb_std_glove = -0.005838499,0.48782197
embed_size_glove = all_embs_glove.shape[1]
word_index = tokenizer.word_index
nb_words = min(max_features, len(word_index))
embedding_matrix_glove = np.random.normal(emb_mean_glove, emb_std_glove,(nb_words, embed_size_glove))
for word, i in word_index.items() :
if i >= max_features: continue
embedding_vector_glove = embeddings_index_glove.get(word)
 if embedding_vector_glove is not None: embedding_matrix_glove[i] = embedding_vector_glove<statistical_test>
y1_valid = cut_neg(df_train['ConfirmedCasesValid'].copy())
 y2_valid = cut_neg(df_train['FatalitiesValid'].copy())
 y1_pred = cut_neg(df_test['ConfirmedCases'].copy())
 y2_pred = cut_neg(df_test['Fatalities'].copy() )
def squash(x, axis=-1):
s_squared_norm = K.sum(K.square(x), axis, keepdims=True)
 scale = K.sqrt(s_squared_norm + K.epsilon())
 return x / scale
class Capsule(Layer):
def __init__(self, num_capsule, dim_capsule, routings=3, kernel_size=(9, 1), share_weights=True,
activation='default', **kwargs):
super(Capsule, self ).__init__(**kwargs)
 self.num_capsule = num_capsule
self.dim_capsule = dim_capsule
self.routings = routings
self.kernel_size = kernel_size
self.share_weights = share_weights
if activation == 'default':
self.activation = squash
else:
self.activation = Activation(activation)
 def build(self, input_shape):
super(Capsule, self ).build(input_shape)
 input_dim_capsule = input_shape[-1]
if self.share_weights:
self.W = self.add_weight(name='capsule_kernel',
shape=(1, input_dim_capsule,
self.num_capsule * self.dim_capsule),
initializer='glorot_uniform',
trainable=True)
 else:
input_num_capsule = input_shape[-2]
self.W = self.add_weight(name='capsule_kernel',
shape=(input_num_capsule,
input_dim_capsule,
self.num_capsule * self.dim_capsule),
initializer='glorot_uniform',
trainable=True)
 def call(self, u_vecs):
if self.share_weights:
u_hat_vecs = K.conv1d(u_vecs, self.W)
 else:
u_hat_vecs = K.local_conv1d(u_vecs, self.W, [1], [1])
 batch_size = K.shape(u_vecs)[0]
input_num_capsule = K.shape(u_vecs)[1]
u_hat_vecs = K.reshape(u_hat_vecs,(batch_size, input_num_capsule,
self.num_capsule, self.dim_capsule))
u_hat_vecs = K.permute_dimensions(u_hat_vecs,(0, 2, 1, 3))
b = K.zeros_like(u_hat_vecs[:, :, :, 0])
 for i in range(self.routings):
b = K.permute_dimensions(b,(0, 2, 1))
c = K.softmax(b)
 c = K.permute_dimensions(c,(0, 2, 1))
b = K.permute_dimensions(b,(0, 2, 1))
outputs = self.activation(tf.keras.backend.batch_dot(c, u_hat_vecs, [2, 2]))
if i < self.routings - 1:
b = tf.keras.backend.batch_dot(outputs, u_hat_vecs, [2, 3])
 return outputs
def compute_output_shape(self, input_shape):
return(None, self.num_capsule, self.dim_capsule )<choose_model_class>
mse =(mean_squared_log_error(y1_train, y1_valid)+mean_squared_log_error(y2_train, y2_valid)) /2
mse
def capsule() :
K.clear_session()
inp = Input(shape=(maxlen,))
x = Embedding(max_features, embed_size_glove, weights=[embedding_matrix_glove], trainable=False )(inp)
 x = SpatialDropout1D(rate=0.2 )(x)
 x = Bidirectional(CuDNNLSTM(160, return_sequences=True,
kernel_initializer=glorot_normal(seed=12300), recurrent_initializer=orthogonal(gain=1.0, seed=10000)) )(x)
 x = Capsule(num_capsule=15, dim_capsule=12, routings=8, share_weights=True )(x)
 x = Flatten()(x)
 x = Dense(100, activation=None, kernel_initializer=glorot_normal(seed=12300))(x)
 x = Dropout(0.2 )(x)
 x = BatchNormalization()(x)
 x = Dense(1, activation="sigmoid" )(x)
 model = Model(inputs=inp, outputs=x)
 model.compile(loss='binary_crossentropy', optimizer=Adam() ,)
 return model
model_glove = capsule()<define_variables>
submission = pd.DataFrame({'ForecastId': df_test['ForecastId'],'ConfirmedCases': y1_pred,'Fatalities': y2_pred})
 submission.head()
batch_size = 128
epochs = 3<split>
submission.to_csv('submission.csv',index = False )