Kaggle competition: Titanic - Machine Learning from Disaster
def train_pred(model, train_X, train_y, val_X, val_y, epochs=2, callback=None):
for e in range(epochs):
model.fit(train_X, train_y, batch_size=512, epochs=1, validation_data=(val_X, val_y), callbacks = callback, verbose=0)
 pred_val_y = model.predict([val_X], batch_size=1024, verbose=0)
 best_score = metrics.f1_score(val_y,(pred_val_y > 0.33 ).astype(int))
print("Epoch: ", e, "- Val F1 Score: {:.4f}".format(best_score))
pred_test_y = model.predict([test_X], batch_size=1024, verbose=0)
 print('=' * 60)
 return pred_val_y, pred_test_y, best_score<split>
def get_missing_data_table(dataframe):

 total = dataframe.isnull().sum()
percentage = dataframe.isnull().sum() / dataframe.isnull().count()
missing_data = pd.concat([total, percentage], axis='columns', keys=['TOTAL','PERCENTAGE'])
 return missing_data.sort_index(ascending=True)
 def get_null_observations(dataframe, column):

 return dataframe[pd.isnull(dataframe[column])]
def delete_null_observations(dataframe, column):

 fixed_df = dataframe.drop(get_null_observations(dataframe,column ).index)
 return fixed_df
def transform_dummy_variables(dataframe, columns):

 df = dataframe.copy()
for column in columns:
df[column] = pd.Categorical(df[column])
 df = pd.get_dummies(df, drop_first=False)
 return df
def imput_nan_values(dataframe, column, strateg):
imp = Imputer(strategy=strateg)
 df = dataframe.copy()
df[column] = imp.fit_transform(df[column].values.reshape(-1,1))
return df
print("Everything's ready!" )
train_X, test_X, train_y, word_index = load_and_prec()
embedding_matrix_1 = load_glove(word_index)
 embedding_matrix_3 = load_para(word_index )<compute_test_metric>
df = pd.read_csv('.. /input/train.csv')
 df.head()
embedding_matrix = np.mean([embedding_matrix_1, embedding_matrix_3], axis = 0)
 np.shape(embedding_matrix)
 def threshold_search(y_true, y_proba):
best_threshold = 0
best_score = 0
for threshold in [i * 0.01 for i in range(100)]:
score = f1_score(y_true=y_true, y_pred=y_proba > threshold)
 if score > best_score:
best_threshold = threshold
best_score = score
search_result = {'threshold': best_threshold, 'f1': best_score}
return search_result
DATA_SPLIT_SEED = 2018
clr = CyclicLR(base_lr=0.001, max_lr=0.002,
step_size=300., mode='exp_range',
gamma=0.99994 )<find_best_model_class>
get_missing_data_table(df )
train_meta = np.zeros(train_y.shape)
 test_meta = np.zeros(test_X.shape[0])
 splits = list(StratifiedKFold(n_splits=4, shuffle=True, random_state=DATA_SPLIT_SEED ).split(train_X, train_y))
for idx,(train_idx, valid_idx)in enumerate(splits):
X_train = train_X[train_idx]
y_train = train_y[train_idx]
X_val = train_X[valid_idx]
y_val = train_y[valid_idx]
model = model_lstm_atten(embedding_matrix)
 pred_val_y, pred_test_y, best_score = train_pred(model, X_train, y_train, X_val, y_val, epochs = 8, callback = [clr,])
 train_meta[valid_idx] = pred_val_y.reshape(-1)
 test_meta += pred_test_y.reshape(-1)/ len(splits)
 sub = pd.read_csv('.. /input/sample_submission.csv')
 sub.prediction = test_meta > 0.33
sub.to_csv("submission.csv", index=False )<import_modules>
df = df.drop('Cabin', axis='columns')
 df = delete_null_observations(df, column='Embarked')
 df = df.reset_index(drop=True)
 df['Age'] = df['Age'].fillna(value=1000)
 get_missing_data_table(df )
tqdm.pandas(desc='Progress')
<define_variables>
df['Family Size'] = df['SibSp'] + df['Parch']
df = df.drop('SibSp', axis='columns')
 df = df.drop('Parch', axis='columns')
 df.head(5 )
embed_size = 300
max_features = 120000
maxlen = 70
batch_size = 512
n_epochs = 5
n_splits = 5
SEED = 1029<set_options>
titles = name_row.tolist()
for i in range(len(titles)) :
title = titles[i]
if title != 'Master' and title != 'Miss' and title != 'Mr' and title !='Mrs':
titles[i] = 'Other'
name_row = pd.DataFrame(titles, columns=['Title'])
 df['Title'] = name_row.copy()
df = df.drop('Name', axis='columns')
 df.head(5 )
def seed_everything(seed=1029):
random.seed(seed)
 os.environ['PYTHONHASHSEED'] = str(seed)
 np.random.seed(seed)
 torch.manual_seed(seed)
 torch.cuda.manual_seed(seed)
 torch.backends.cudnn.deterministic = True
seed_everything()<features_selection>
test_df = df.copy()
test_df = pd.DataFrame([df['Age'].tolist() , df['Title'].tolist() ] ).transpose()
test_df.columns = ['Age','Title']
test_df_list = test_df.values
for i in range(len(test_df_list)) :
age = test_df_list[i][0]
title = test_df_list[i][1]
if age == 1000:
if title == 'Master':
test_df_list[i][0] = 5.19
elif title == 'Miss':
test_df_list[i][0] = 21.87
elif title == 'Mr':
test_df_list[i][0] = 32.18
elif title == 'Mrs':
test_df_list[i][0] = 35.48
else:
test_df_list[i][0] = 42.81
df['Age'] = test_df['Age'].copy()
df['Age'] = df['Age'].astype('float64')
 df['Age'].describe()
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>
df = df.drop('Ticket', axis='columns')
 df = df.drop('PassengerId', axis='columns')
 df.head(5 )
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>
df = transform_dummy_variables(df,['Sex','Pclass','Embarked','Title'])
 df.head(5 )
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>
X_train = df.iloc[:,1:].values
y = df.iloc[:,0].values
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
 print('X_train: {0}'.format(X_train[0:5]))
print('y: {0}'.format(y[0:5]))
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>
classifier = XGBClassifier()
classifier.fit(X_train, y )
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>
params = {
'min_child_weight': [1, 5, 10],
'gamma': [0.5, 1, 1.5, 2, 5],
'subsample': [0.6, 0.8, 1.0],
'colsample_bytree': [0.6, 0.8, 1.0],
'max_depth': [3, 4, 5]
}
folds = 4
param_comb = 5
skf = StratifiedKFold(n_splits=folds, shuffle = True, random_state = 1001)
 random_search = RandomizedSearchCV(classifier, param_distributions=params, n_iter=param_comb, scoring='roc_auc', n_jobs=4, cv=skf.split(X_train,y), verbose=3, random_state=1001, iid=True)
 random_search.fit(X_train, y)
 xgboost_classifier = random_search.best_estimator_
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>
classifier = RandomForestClassifier()
classifier.fit(X_train, y )
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>
params = {
'n_estimators': [5, 10, 15],
'criterion': ['gini', 'entropy'],
'max_features': ['auto', 'sqrt', 'log2', None],
'max_depth': [None, 3, 4, 5]
}
folds = 4
param_comb = 5
skf = StratifiedKFold(n_splits=folds, shuffle = True, random_state = 1001)
 random_search = RandomizedSearchCV(classifier, param_distributions=params, n_iter=param_comb, scoring='roc_auc', n_jobs=4, cv=skf.split(X_train,y), verbose=3, random_state=1001, iid=True)
 random_search.fit(X_train, y)
 randomforest_classifier = random_search.best_estimator_
x_train, x_test, y_train, features, test_features, word_index = load_and_prec() 
<save_model>
classifier = SVC(probability=True)
 classifier.fit(X_train, y )
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>
params = {
'C': [0.5, 1, 1.5],
'kernel': ['rbf', 'linear', 'poly', 'sigmoid'],
'gamma': [0.001, 0.0001],
'class_weight': [None, 'balanced']
}
folds = 4
param_comb = 5
skf = StratifiedKFold(n_splits=folds, shuffle = True, random_state = 1001)
 random_search = RandomizedSearchCV(classifier, param_distributions=params, n_iter=param_comb, scoring='roc_auc', n_jobs=4, cv=skf.split(X_train,y), verbose=3, random_state=1001, iid=True)
 random_search.fit(X_train, y)
 svc_classifier = random_search.best_estimator_
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>
classifier = VotingClassifier(estimators=[('xgb', xgboost_classifier),('rf',randomforest_classifier),('svc',svc_classifier)], voting='soft')
 classifier.fit(X_train, y )
seed_everything()
glove_embeddings = load_glove(word_index)
 paragram_embeddings = load_para(word_index)
 embedding_matrix = np.mean([glove_embeddings, paragram_embeddings], axis=0)
 del glove_embeddings, paragram_embeddings
gc.collect()
np.shape(embedding_matrix )<split>
accuracies = cross_val_score(estimator=classifier, X=X_train, y=y, cv=5)
 print('accuracy mean: {0}'.format(accuracies.mean()))
print('accuracy std: {0}'.format(accuracies.std()))
splits = list(StratifiedKFold(n_splits=n_splits, shuffle=True, random_state=SEED ).split(x_train, y_train))
splits[:3]<choose_model_class>
df_test = pd.read_csv('.. /input/test.csv')
 df_test.describe()
class CyclicLR(object):
def __init__(self, optimizer, base_lr=1e-3, max_lr=6e-3,
step_size=2000, mode='triangular', gamma=1.,
scale_fn=None, scale_mode='cycle', last_batch_iteration=-1):
if not isinstance(optimizer, Optimizer):
raise TypeError('{} is not an Optimizer'.format(
 type(optimizer ).__name__))
self.optimizer = optimizer
if isinstance(base_lr, list)or isinstance(base_lr, tuple):
if len(base_lr)!= len(optimizer.param_groups):
raise ValueError("expected {} base_lr, got {}".format(
 len(optimizer.param_groups), len(base_lr)))
 self.base_lrs = list(base_lr)
 else:
self.base_lrs = [base_lr] * len(optimizer.param_groups)
 if isinstance(max_lr, list)or isinstance(max_lr, tuple):
if len(max_lr)!= len(optimizer.param_groups):
raise ValueError("expected {} max_lr, got {}".format(
 len(optimizer.param_groups), len(max_lr)))
 self.max_lrs = list(max_lr)
 else:
self.max_lrs = [max_lr] * len(optimizer.param_groups)
 self.step_size = step_size
if mode not in ['triangular', 'triangular2', 'exp_range'] \
and scale_fn is None:
raise ValueError('mode is invalid and scale_fn is None')
 self.mode = mode
self.gamma = gamma
if scale_fn is None:
if self.mode == 'triangular':
self.scale_fn = self._triangular_scale_fn
self.scale_mode = 'cycle'
elif self.mode == 'triangular2':
self.scale_fn = self._triangular2_scale_fn
self.scale_mode = 'cycle'
elif self.mode == 'exp_range':
self.scale_fn = self._exp_range_scale_fn
self.scale_mode = 'iterations'
else:
self.scale_fn = scale_fn
self.scale_mode = scale_mode
self.batch_step(last_batch_iteration + 1)
 self.last_batch_iteration = last_batch_iteration
def batch_step(self, batch_iteration=None):
if batch_iteration is None:
batch_iteration = self.last_batch_iteration + 1
self.last_batch_iteration = batch_iteration
for param_group, lr in zip(self.optimizer.param_groups, self.get_lr()):
param_group['lr'] = lr
def _triangular_scale_fn(self, x):
return 1.
 def _triangular2_scale_fn(self, x):
return 1 /(2.**(x - 1))
def _exp_range_scale_fn(self, x):
return self.gamma**(x)
 def get_lr(self):
step_size = float(self.step_size)
 cycle = np.floor(1 + self.last_batch_iteration /(2 * step_size))
x = np.abs(self.last_batch_iteration / step_size - 2 * cycle + 1)
 lrs = []
param_lrs = zip(self.optimizer.param_groups, self.base_lrs, self.max_lrs)
 for param_group, base_lr, max_lr in param_lrs:
base_height =(max_lr - base_lr)* np.maximum(0,(1 - x))
if self.scale_mode == 'cycle':
lr = base_lr + base_height * self.scale_fn(cycle)
 else:
lr = base_lr + base_height * self.scale_fn(self.last_batch_iteration)
 lrs.append(lr)
 return lrs 
<choose_model_class>
get_missing_data_table(df_test )
embedding_dim = 300
embedding_path = '.. /save/embedding_matrix.npy'
use_pretrained_embedding = True
hidden_size = 60
gru_len = hidden_size
Routings = 4
Num_capsule = 5
Dim_capsule = 5
dropout_p = 0.25
rate_drop_dense = 0.28
LR = 0.001
T_epsilon = 1e-7
num_classes = 30
class Embed_Layer(nn.Module):
def __init__(self, embedding_matrix=None, vocab_size=None, embedding_dim=300):
super(Embed_Layer, self ).__init__()
self.encoder = nn.Embedding(vocab_size + 1, embedding_dim)
 if use_pretrained_embedding:
self.encoder.weight.data.copy_(t.from_numpy(embedding_matrix))
def forward(self, x, dropout_p=0.25):
return nn.Dropout(p=dropout_p )(self.encoder(x))
class GRU_Layer(nn.Module):
def __init__(self):
super(GRU_Layer, self ).__init__()
self.gru = nn.GRU(input_size=300,
hidden_size=gru_len,
bidirectional=True)

def init_weights(self):
ih =(param.data for name, param in self.named_parameters() if 'weight_ih' in name)
 hh =(param.data for name, param in self.named_parameters() if 'weight_hh' in name)
 b =(param.data for name, param in self.named_parameters() if 'bias' in name)
 for k in ih:
nn.init.xavier_uniform_(k)
 for k in hh:
nn.init.orthogonal_(k)
 for k in b:
nn.init.constant_(k, 0)
 def forward(self, x):
return self.gru(x)
 class Caps_Layer(nn.Module):
def __init__(self, input_dim_capsule=gru_len * 2, num_capsule=Num_capsule, dim_capsule=Dim_capsule, \
routings=Routings, kernel_size=(9, 1), share_weights=True,
activation='default', **kwargs):
super(Caps_Layer, self ).__init__(**kwargs)
 self.num_capsule = num_capsule
self.dim_capsule = dim_capsule
self.routings = routings
self.kernel_size = kernel_size
self.share_weights = share_weights
if activation == 'default':
self.activation = self.squash
else:
self.activation = nn.ReLU(inplace=True)
 if self.share_weights:
self.W = nn.Parameter(
 nn.init.xavier_normal_(t.empty(1, input_dim_capsule, self.num_capsule * self.dim_capsule)))
 else:
self.W = nn.Parameter(
 t.randn(BATCH_SIZE, input_dim_capsule, self.num_capsule * self.dim_capsule))
def forward(self, x):
if self.share_weights:
u_hat_vecs = t.matmul(x, self.W)
 else:
print('add later')
 batch_size = x.size(0)
 input_num_capsule = x.size(1)
 u_hat_vecs = u_hat_vecs.view(( batch_size, input_num_capsule,
self.num_capsule, self.dim_capsule))
u_hat_vecs = u_hat_vecs.permute(0, 2, 1, 3)
 b = t.zeros_like(u_hat_vecs[:, :, :, 0])
 for i in range(self.routings):
b = b.permute(0, 2, 1)
 c = F.softmax(b, dim=2)
 c = c.permute(0, 2, 1)
 b = b.permute(0, 2, 1)
 outputs = self.activation(t.einsum('bij,bijk->bik',(c, u_hat_vecs)))
 if i < self.routings - 1:
b = t.einsum('bik,bijk->bij',(outputs, u_hat_vecs))
return outputs
def squash(self, x, axis=-1):
s_squared_norm =(x ** 2 ).sum(axis, keepdim=True)
 scale = t.sqrt(s_squared_norm + T_epsilon)
 return x / scale
class Capsule_Main(nn.Module):
def __init__(self, embedding_matrix=None, vocab_size=None):
super(Capsule_Main, self ).__init__()
self.embed_layer = Embed_Layer(embedding_matrix, vocab_size)
 self.gru_layer = GRU_Layer()
self.gru_layer.init_weights()
self.caps_layer = Caps_Layer()
self.dense_layer = Dense_Layer()
def forward(self, content):
content1 = self.embed_layer(content)
 content2, _ = self.gru_layer(
 content1)
 content3 = self.caps_layer(content2)
 output = self.dense_layer(content3)
 return output 
<normalization>
df_test = imput_nan_values(df_test,'Fare','median')
 df_test['Age'] = df_test['Age'].fillna(value=1000)
 name_row = df_test['Name'].copy()
name_row = pd.DataFrame(name_row.str.split(', ',1 ).tolist() , columns = ['Last name', 'Name'])
 name_row = name_row['Name'].copy()
name_row = pd.DataFrame(name_row.str.split('.',1 ).tolist() ,columns=["Title","Name"])
 name_row = name_row['Title'].copy()
titles = name_row.tolist()
for i in range(len(titles)) :
title = titles[i]
if title != 'Master' and title != 'Miss' and title != 'Mr' and title !='Mrs':
titles[i] = 'Other'
name_row = pd.DataFrame(titles, columns=['Title'])
 df_test['Title'] = name_row.copy()
test_df = df_test.copy()
test_df = pd.DataFrame([df_test['Age'].tolist() , df_test['Title'].tolist() ] ).transpose()
test_df.columns = ['Age','Title']
test_df_list = test_df.values
for i in range(len(test_df_list)) :
age = test_df_list[i][0]
title = test_df_list[i][1]
if age == 1000:
if title == 'Master':
test_df_list[i][0] = 5.19
elif title == 'Miss':
test_df_list[i][0] = 21.87
elif title == 'Mr':
test_df_list[i][0] = 32.18
elif title == 'Mrs':
test_df_list[i][0] = 35.48
else:
test_df_list[i][0] = 42.81
df_test['Age'] = test_df['Age'].copy()
get_missing_data_table(df_test )
class Attention(nn.Module):
def __init__(self, feature_dim, step_dim, bias=True, **kwargs):
super(Attention, self ).__init__(**kwargs)
 self.supports_masking = True
self.bias = bias
self.feature_dim = feature_dim
self.step_dim = step_dim
self.features_dim = 0
weight = torch.zeros(feature_dim, 1)
 nn.init.xavier_uniform_(weight)
 self.weight = nn.Parameter(weight)
 if bias:
self.b = nn.Parameter(torch.zeros(step_dim))
def forward(self, x, mask=None):
feature_dim = self.feature_dim
step_dim = self.step_dim
eij = torch.mm(
 x.contiguous().view(-1, feature_dim),
self.weight
).view(-1, step_dim)
 if self.bias:
eij = eij + self.b
eij = torch.tanh(eij)
 a = torch.exp(eij)
 if mask is not None:
a = a * mask
a = a / torch.sum(a, 1, keepdim=True)+ 1e-10
weighted_input = x * torch.unsqueeze(a, -1)
 return torch.sum(weighted_input, 1)
 class NeuralNet(nn.Module):
def __init__(self):
super(NeuralNet, self ).__init__()
fc_layer = 16
fc_layer1 = 16
self.embedding = nn.Embedding(max_features, embed_size)
 self.embedding.weight = nn.Parameter(torch.tensor(embedding_matrix, dtype=torch.float32))
self.embedding.weight.requires_grad = False
self.embedding_dropout = nn.Dropout2d(0.1)
 self.lstm = nn.LSTM(embed_size, hidden_size, bidirectional=True, batch_first=True)
 self.gru = nn.GRU(hidden_size * 2, hidden_size, bidirectional=True, batch_first=True)
 self.lstm2 = nn.LSTM(hidden_size * 2, hidden_size, bidirectional=True, batch_first=True)
 self.lstm_attention = Attention(hidden_size * 2, maxlen)
 self.gru_attention = Attention(hidden_size * 2, maxlen)
 self.linear = nn.Linear(hidden_size*8+3, fc_layer1)
 self.relu = nn.ReLU()
self.dropout = nn.Dropout(0.1)
 self.fc = nn.Linear(fc_layer**2,fc_layer)
 self.out = nn.Linear(fc_layer, 1)
 self.lincaps = nn.Linear(Num_capsule * Dim_capsule, 1)
 self.caps_layer = Caps_Layer()
def forward(self, x):
h_embedding = self.embedding(x[0])
 h_embedding = torch.squeeze(
 self.embedding_dropout(torch.unsqueeze(h_embedding, 0)))
 h_lstm, _ = self.lstm(h_embedding)
 h_gru, _ = self.gru(h_lstm)
 content3 = self.caps_layer(h_gru)
 content3 = self.dropout(content3)
 batch_size = content3.size(0)
 content3 = content3.view(batch_size, -1)
 content3 = self.relu(self.lincaps(content3))
h_lstm_atten = self.lstm_attention(h_lstm)
 h_gru_atten = self.gru_attention(h_gru)
 avg_pool = torch.mean(h_gru, 1)
 max_pool, _ = torch.max(h_gru, 1)
 f = torch.tensor(x[1], dtype=torch.float ).cuda()
conc = torch.cat(( h_lstm_atten, h_gru_atten,content3, avg_pool, max_pool,f), 1)
 conc = self.relu(self.linear(conc))
conc = self.dropout(conc)
 out = self.out(conc)
 return out<categorify>
df_test = df_test.drop('Cabin', axis='columns')
 df_test['Family Size'] = df_test['SibSp'] + df_test['Parch']
df_test = df_test.drop('SibSp', axis='columns')
 df_test = df_test.drop('Parch', axis='columns')
 df_test = df_test.drop('Name', axis='columns')
 df_test = df_test.drop('Ticket', axis='columns')
 df_test = df_test.drop('PassengerId', axis='columns')
 df_test['Age'] = df_test['Age'].astype('float64')
 df_test = transform_dummy_variables(df_test,['Sex','Pclass','Embarked','Title'])
 df_test.head(5 )
<define_variables><EOS> 
X_test = df_test.values
sc = StandardScaler()
X_test = sc.fit_transform(X_test)
 pred = classifier.predict(X_test)
 test_dataset = pd.read_csv('.. /input/test.csv')
 ps_id = test_dataset.iloc[:,0].values
d = {'PassengerId':ps_id, 'Survived':pred}
df = pd.DataFrame(data=d)
 df = df.set_index('PassengerId')
 df.to_csv('predictions.csv')
 df.head(15 )