kernel_id int64 24.2k 23.3M | prompt stringlengths 8 1.85M | completetion stringlengths 1 182k | comp_name stringlengths 5 57 |
|---|---|---|---|
7,847,459 | del word_index, embeddings_index, all_embs, embedding_matrix, model, inp, x
time.sleep(10 )<statistical_test> | predictors = data[data.Age > 0]
predictors.drop(['Age'], axis=1, inplace=True)
targets = np.array(data[data.Age > 0].Age)
predictors.shape, targets.shape | Titanic - Machine Learning from Disaster |
7,847,459 | 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.... | predictors = StandardScaler().fit_transform(predictors ) | Titanic - Machine Learning from Disaster |
7,847,459 | model.fit(train_X, train_y, batch_size=512, epochs=3, validation_data=(val_X, val_y))<predict_on_test> | mlp = MLPRegressor(hidden_layer_sizes=(150, 100))
mlp.fit(predictors, targets)
y_pred = mlp.predict(predictors)
mean_squared_error(y_pred, targets), mlp | Titanic - Machine Learning from Disaster |
7,847,459 | pred_paragram_val_y = model.predict([val_X], batch_size=1024, verbose=1)
for thresh in np.arange(0.1, 0.501, 0.01):
thresh = np.round(thresh, 2)
print("F1 score at threshold {0} is {1}".format(thresh, metrics.f1_score(val_y,(pred_paragram_val_y>thresh ).astype(int))))<predict_on_test> | train.loc[train.Age < 0, 'Age'] = mlp.predict(StandardScaler().fit_transform(train[train['Age'] < 0][['Alone', 'Cabin', 'Fare', 'Pclass', 'Sex','Ticket', 'Title']])) | Titanic - Machine Learning from Disaster |
7,847,459 | pred_paragram_test_y = model.predict([test_X], batch_size=1024, verbose=1 )<set_options> | test.loc[test.Age < 0, 'Age'] = mlp.predict(StandardScaler().fit_transform(test[test['Age'] < 0][['Alone', 'Cabin', 'Fare', 'Pclass', 'Sex','Ticket', 'Title']])) | Titanic - Machine Learning from Disaster |
7,847,459 | del word_index, embeddings_index, all_embs, embedding_matrix, model, inp, x
time.sleep(10 )<find_best_params> | test.loc[test.Age < 0, 'Age'] = 0.1
train.loc[train.Age < 0, 'Age'] = 0.1
train.head() | Titanic - Machine Learning from Disaster |
7,847,459 | pred_val_y =(4 * pred_glove_val_y + pred_fasttext_val_y + 3 * pred_paragram_val_y + 2 * pred_cnn_val_y)/ 10.0
thresholds = []
for thresh in np.arange(0.1, 0.501, 0.01):
thresh = np.round(thresh, 2)
res = metrics.f1_score(val_y,(pred_val_y > thresh ).astype(int))
thresholds.append([thresh, res])
print("F1 score at thr... | predictors = train.drop(['PassengerId', 'Survived'], axis=1)
targets = train[['Survived']]
predictors = StandardScaler().fit_transform(predictors ) | Titanic - Machine Learning from Disaster |
7,847,459 | pred_test_y =(4 * pred_glove_test_y + pred_fasttext_test_y + 3 * pred_paragram_test_y + 2 * pred_cnn_test_y)/ 10.0
pred_test_y =(pred_test_y > best_thresh ).astype(int)
out_df = pd.DataFrame({"qid":test_df["qid"].values})
out_df['prediction'] = pred_test_y
out_df.to_csv("submission.csv", index=False )<import_modules> | x_train, x_test, y_train, y_test = train_test_split(predictors, targets, test_size = 0.05, random_state = 0 ) | Titanic - Machine Learning from Disaster |
7,847,459 | import os
import time
import numpy as np
import pandas as pd
from tqdm import tqdm
import math
from sklearn.model_selection import train_test_split
from sklearn import metrics
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pad_sequences
from keras.layers import Add, Concatenate,... | mlp = MLPRegressor(batch_size=50, hidden_layer_sizes=(140))
mlp.fit(x_train, y_train)
y_pred = mlp.predict(x_train ).round()
test_pred = mlp.predict(x_test ).round()
score = accuracy_score(y_train, y_pred)
test_score = accuracy_score(y_test, test_pred)
mlp, score, test_score | Titanic - Machine Learning from Disaster |
7,847,459 | <split><EOS> | ids = test['PassengerId']
predictions = np.abs(mlp.predict(StandardScaler().fit_transform(test.drop(['PassengerId'], axis=1)) ).round() ).astype(int)
output = pd.DataFrame({ 'PassengerId' : ids, 'Survived': predictions })
output.to_csv('submission.csv', index=False ) | Titanic - Machine Learning from Disaster |
6,589,006 | <SOS> metric: categorizationaccuracy Kaggle data source: titanic-machine-learning-from-disaster<statistical_test> | from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split, GridSearchCV, RandomizedSearchCV
from sklearn.metrics import accuracy_score, confusion_matrix, roc_auc_score, roc_curve
from sklearn.preprocessing import StandardScaler, OneHotEncoder, KBinsDiscretizer
from sklearn... | Titanic - Machine Learning from Disaster |
6,589,006 | def getGloVeEmbeddings() :
EMBEDDING_FILE = '.. /input/embeddings/glove.840B.300d/glove.840B.300d.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))
all_embs = np.stack(embeddings_index.values())
emb_mean,emb_std... | warnings.filterwarnings("ignore" ) | Titanic - Machine Learning from Disaster |
6,589,006 | glove_embedding = getGloVeEmbeddings()
fasttext_embedding = getFastTextEmbeddings()
paragram_embedding = getParagramEmbeddings()<set_options> | %matplotlib inline | Titanic - Machine Learning from Disaster |
6,589,006 | class KMaxPooling(Layer):
def __init__(self, k=1, axis=1, **kwargs):
super(KMaxPooling, self ).__init__(**kwargs)
self.input_spec = InputSpec(ndim=3)
self.k = k
assert axis in [1,2], 'expected dimensions(samples, filters, convolved_values),\
cannot fold along samples dimension or axis not in list [1,2]'
self.axis =... | data = pd.read_csv('/kaggle/input/titanic/train.csv')
| Titanic - Machine Learning from Disaster |
6,589,006 | mvcnn = MV_CNN()
mvcnn.fit(train_X, train_y, batch_size=1024, epochs=2, validation_data=(val_X, val_y))<find_best_params> | def test_thresh(lower = 0.1, upper = 0.95, jump = 0.01):
accs = {}
for i in np.arange(lower, upper, jump):
accs[i] = accuracy_score(test[1], predict(test[0], model, transformers, thresh = i, prep = False)['Survived'])
best_thresh = np.round(sorted(accs.items() , key = lambda x: x[1], reverse = True)[0][0], 2)
train_a... | Titanic - Machine Learning from Disaster |
6,589,006 | pred_y = mvcnn.predict([val_X], batch_size=1024, verbose=1)
max_f1 = 0
max_thresh = 0
for thresh in np.arange(0.1, 0.501, 0.01):
thresh = np.round(thresh, 2)
score = metrics.f1_score(val_y,(pred_y > thresh ).astype(int))
print("F1 score at threshold {0} is {1}".format(thresh, score))
if score > max_f1:
max_f1 = score... | def process_data(data, transformers = None):
data = data.copy()
data.set_index('PassengerId', inplace = True)
data['title'] = data.Name.str.split(',' ).str[1].str.strip().str.split().str[0]
low_freq_titles = data.title.value_counts() [lambda x: x < 10].index
data['title'] = data['title'].apply(lambda x: 'Misc' if x in... | Titanic - Machine Learning from Disaster |
6,589,006 | pred_y = mvcnn.predict([test_X], batch_size=1024, verbose=1)
pred_test_y =(pred_y > max_thresh ).astype(int)
out_df = pd.DataFrame({"qid": test_df["qid"].values})
out_df['prediction'] = pred_test_y
out_df.to_csv("submission.csv", index=False )<load_from_csv> | def fit_model(train_data, scale = True):
preped, bins, dummy = process_data(train_data)
scaler = StandardScaler()
X = preped.drop('Survived', axis = 1)
if scale:
X = pd.DataFrame(scaler.fit_transform(X), columns = X.columns, index = X.index)
else:
scaler = None
y = preped['Survived']
X_train, X_test, y_train, y_test... | Titanic - Machine Learning from Disaster |
6,589,006 | 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 )<split> | def predict(test_df, model, transformers, thresh = 0.5, prep = True):
if prep:
temp_df = process_data(test_df, transformers)
idx = temp_df.index
if transformers[2] is not None:
temp_df = pd.DataFrame(transformers[2].transform(temp_df), columns = temp_df.columns)
else:
temp_df = pd.DataFrame(test_df, columns = preped.... | Titanic - Machine Learning from Disaster |
6,589,006 | train_df, val_df = train_test_split(train_df, test_size=0.1, random_state=2018)
embed_size = 300
max_features = 50000
maxlen = 100<prepare_x_and_y> | model, transformers, preped, train, test = fit_model(data, scale = True ) | Titanic - Machine Learning from Disaster |
6,589,006 | train_X = train_df["question_text"].fillna("_na_" ).values
val_X = val_df["question_text"].fillna("_na_" ).values
test_X = test_df["question_text"].fillna("_na_" ).values<string_transform> | test_set = pd.read_csv('/kaggle/input/titanic/test.csv')
predictions = predict(test_set, model, transformers)
predictions.head() | Titanic - Machine Learning from Disaster |
6,589,006 | tokenizer = Tokenizer(num_words=max_features)
tokenizer.fit_on_texts(list(train_X))
train_X = tokenizer.texts_to_sequences(train_X)
val_X = tokenizer.texts_to_sequences(val_X)
test_X = tokenizer.texts_to_sequences(test_X )<string_transform> | model.estimators_ | Titanic - Machine Learning from Disaster |
6,589,006 | train_X = pad_sequences(train_X, maxlen=maxlen)
val_X = pad_sequences(val_X, maxlen=maxlen)
test_X = pad_sequences(test_X, maxlen=maxlen )<prepare_x_and_y> | predictions.to_csv('submission.csv', index = False ) | Titanic - Machine Learning from Disaster |
6,589,006 | train_y = train_df['target'].values
val_y = val_df['target'].values<feature_engineering> | test_labels = pd.read_csv('/kaggle/input/titanic-solutions-for-selfscoring/pub')
accuracy_score(test_labels['survived'], predictions['Survived'] ) | Titanic - Machine Learning from Disaster |
6,556,041 | embeddings_index = {}
f = open('.. /input/embeddings/glove.840B.300d/glove.840B.300d.txt')
for line in tqdm(f):
values = line.split(" ")
word = values[0]
coefs = np.asarray(values[1:], dtype='float32')
embeddings_index[word] = coefs
f.close()
print('Found %s word vectors.' % len(embeddings_index))<define_variables> | gender_submission = pd.read_csv(".. /input/titanic/gender_submission.csv")
test = pd.read_csv(".. /input/titanic/test.csv")
train = pd.read_csv(".. /input/titanic/train.csv" ) | Titanic - Machine Learning from Disaster |
6,556,041 | all_embs = np.stack(embeddings_index.values())
emb_mean,emb_std = all_embs.mean() , all_embs.std()
embed_size = all_embs.shape[1]<feature_engineering> | test2 = pd.read_csv(".. /input/titanic/test.csv" ) | Titanic - Machine Learning from Disaster |
6,556,041 | word_index = tokenizer.word_index
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_matri... | warnings.simplefilter('ignore')
| Titanic - Machine Learning from Disaster |
6,556,041 | class Attention(Layer):
def __init__(self, step_dim,
W_regularizer=None, b_regularizer=None,
W_constraint=None, b_constraint=None,
bias=True, **kwargs):
self.supports_masking = True
self.init = initializers.get('glorot_uniform')
self.W_regularizer = regularizers.get(W_regularizer)
self.b_regularizer = regularizers.ge... | print('Train columns with null values:
', train.isnull().sum())
print("-" * 10)
print('Test columns with null values:
', test.isnull().sum())
print("-" * 10 ) | Titanic - Machine Learning from Disaster |
6,556,041 | model=Sequential()
model.add(Embedding(max_features, embed_size, weights=[embedding_matrix],input_length=maxlen,trainable = False))
model.add(Bidirectional(CuDNNLSTM(128, return_sequences=True)))
model.add(Bidirectional(CuDNNLSTM(64, return_sequences=True)))
model.add(Attention(maxlen))
model.add(Dense(64, activation... | dropping = ['PassengerId', 'Ticket','Cabin','Fare']
train.drop(dropping,axis=1, inplace=True)
test.drop(dropping,axis=1, inplace=True)
train.head() | Titanic - Machine Learning from Disaster |
6,556,041 | model.fit(train_X, train_y, batch_size=512, epochs=3, validation_data=(val_X, val_y))<compute_test_metric> | data = [train,test]
for dataset in data:
dataset['Embarked'].fillna(dataset['Embarked'].mode() [0],inplace=True)
Embarked = np.zeros(len(dataset))
Embarked[dataset['Embarked']== 'C'] = 1
Embarked[dataset['Embarked']== 'Q'] = 2
Embarked[dataset['Embarked']== 'S'] = 3
dataset['Embarked'] = Embarked | Titanic - Machine Learning from Disaster |
6,556,041 | model.evaluate(x=val_X, y=val_y, batch_size=1024, verbose=1 )<predict_on_test> | train['Age'].fillna(train['Age'].median() , inplace = True)
test['Age'].fillna(test['Age'].median() , inplace = True ) | Titanic - Machine Learning from Disaster |
6,556,041 | test_y_prediction = model.predict(test_X, batch_size = 1024, verbose = 1 )<predict_on_test> | print('Train columns with null values:
', train.isnull().sum())
print("-" * 10)
print('Test columns with null values:
', test.isnull().sum())
print("-" * 10 ) | Titanic - Machine Learning from Disaster |
6,556,041 | pred_val_y = model.predict([val_X], batch_size=1024, verbose=1 )<compute_test_metric> | data = [train,test]
for dataset in data:
dataset['Age'] = dataset['Age']
dataset.loc[ dataset['Age'] <= 11, 'Age'] = 0
dataset.loc[(dataset['Age'] > 11)&(dataset['Age'] <= 18), 'Age'] = 1
dataset.loc[(dataset['Age'] > 18)&(dataset['Age'] <= 22), 'Age'] = 2
dataset.loc[(dataset['Age'] > 22)&(dataset['Age'] <= 27), 'Age'... | Titanic - Machine Learning from Disaster |
6,556,041 | for thresh in np.arange(0.1, 0.501, 0.01):
thresh = np.round(thresh, 2)
print("F1 score at threshold {0} is {1}".format(thresh, metrics.f1_score(val_y,(pred_val_y>thresh ).astype(int))))<save_to_csv> | data = [train,test]
titles = {"Mr": 1, "Miss": 2, "Mrs": 3, "Master": 4, "Rare": 5}
for dataset in data:
dataset['Title'] = dataset.Name.str.extract('([A-Za-z]+)\.', expand=False)
dataset['Title'] = dataset['Title'].replace(['Lady', 'Countess','Capt', 'Col','Don', 'Dr',\
'Major', 'Rev', 'Sir', 'Jonkheer', 'Dona'], 'Ra... | Titanic - Machine Learning from Disaster |
6,556,041 | pred_test_y =(test_y_prediction>0.40 ).astype(int)
out_df = pd.DataFrame({"qid":test_df["qid"].values})
out_df['prediction'] = pred_test_y
out_df.to_csv("submission.csv", index=False )<import_modules> | data = [train,test]
for dataset in data:
sex = np.zeros(len(dataset))
sex[dataset['Sex']== 'male'] = 1
sex[dataset['Sex']== 'female'] = 0
dataset['Sex'] = sex | Titanic - Machine Learning from Disaster |
6,556,041 | import os
import time
import numpy as np
import pandas as pd
from tqdm import tqdm
import math
from sklearn.model_selection import train_test_split
from sklearn import metrics
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pad_sequences
from keras.layers import Dense, Input, CuD... | data = [train,test]
for dataset in data:
dataset['FamilySize'] = dataset['SibSp'] + dataset['Parch'] + 1 | Titanic - Machine Learning from Disaster |
6,556,041 | 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 )<split> | data = [train,test]
for dataset in data:
dataset['IsAlone'] = 0
dataset.loc[dataset['FamilySize'] == 1, 'IsAlone'] = 1 | Titanic - Machine Learning from Disaster |
6,556,041 | train_df, val_df = train_test_split(train_df, test_size=0.08, random_state=2018)
embed_size = 300
max_features = 95000
maxlen = 70
train_X = train_df["question_text"].fillna("_
val_X = val_df["question_text"].fillna("_
test_X = test_df["question_text"].fillna("_
tokenizer = Tokenizer(num_words=max_features)
tokenizer... | train = train.drop(['Parch', 'SibSp', 'FamilySize'], axis=1)
test = test.drop(['Parch', 'SibSp', 'FamilySize'], axis=1 ) | Titanic - Machine Learning from Disaster |
6,556,041 | np.random.seed(2018)
trn_idx = np.random.permutation(len(train_X))
val_idx = np.random.permutation(len(val_X))
train_X = train_X[trn_idx]
val_X = val_X[val_idx]
train_y = train_y[trn_idx]
val_y = val_y[val_idx]<train_model> | train_y=train['Survived']
train_ft=train.drop('Survived',axis=1)
kf = StratifiedKFold(n_splits=10)
print(train_ft.head())
print(train_y.head() ) | Titanic - Machine Learning from Disaster |
6,556,041 |
<set_options> | svc = SVC(C = 45, gamma = 0.03)
svc.fit(train_ft, train_y)
acc_SVM = cross_val_score(svc,train_ft,train_y,cv=kf)
print(acc_SVM.mean() ) | Titanic - Machine Learning from Disaster |
6,556,041 | class Attention(Layer):
def __init__(self, step_dim,
W_regularizer=None, b_regularizer=None,
W_constraint=None, b_constraint=None,
bias=True, **kwargs):
self.supports_masking = True
self.init = initializers.get('glorot_uniform')
self.W_regularizer = regularizers.get(W_regularizer)
self.b_regularizer = regularizers.ge... | predictions = svc.predict(test)
print(predictions ) | Titanic - Machine Learning from Disaster |
6,556,041 | <train_model><EOS> | submission = pd.DataFrame({ 'PassengerId': test2['PassengerId'],
'Survived': predictions })
submission.to_csv('submission.csv', index = False ) | Titanic - Machine Learning from Disaster |
6,053,002 | <SOS> metric: categorizationaccuracy Kaggle data source: titanic-machine-learning-from-disaster<predict_on_test> | for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename))
| Titanic - Machine Learning from Disaster |
6,053,002 | pred_glove_val_y = model.predict([val_X], batch_size=1024, verbose=1)
for thresh in np.arange(0.1, 0.501, 0.01):
thresh = np.round(thresh, 2)
print("F1 score at threshold {0} is {1}".format(thresh, metrics.f1_score(val_y,(pred_glove_val_y>thresh ).astype(int))))<predict_on_test> | df = pd.read_csv('.. /input/titanic/train.csv')
df.head() | Titanic - Machine Learning from Disaster |
6,053,002 | pred_glove_test_y = model.predict([test_X], batch_size=1024, verbose=1 )<set_options> | df.columns[df.isna().any() ] | Titanic - Machine Learning from Disaster |
6,053,002 | del word_index, embeddings_index, all_embs, embedding_matrix, model, inp, x
time.sleep(10 )<statistical_test> | df['Age'].fillna(df['Age'].mean() , inplace=True)
df['Embarked'].fillna('S', inplace=True)
df.set_index('PassengerId', inplace=True)
df['Sex'] = df['Sex'].astype('category')
df['Sex'] = df['Sex'].cat.codes
df['Embarked'] = df['Embarked'].astype('category')
df['Embarked'] = df['Embarked'].cat.codes
df.head() | Titanic - Machine Learning from Disaster |
6,053,002 | 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_em... | def convertRanges(data):
_, bins = pd.qcut(data, 5, retbins=True)
for i in range(len(data.values)) :
x = data.values[i]
if(x>=bins[0] and x<bins[1]):
x = 1
elif(x>=bins[1] and x<bins[2]):
x = 2
elif(x>=bins[2] and x<bins[3]):
x = 3
elif(x>=bins[3] and x<bins[4]):
x = 4
elif(x>=bins[4] and x<=bins[5]):
x = 5
data.value... | Titanic - Machine Learning from Disaster |
6,053,002 | model.fit(train_X, train_y, batch_size=512, epochs=3, validation_data=(val_X, val_y))<predict_on_test> | df['Age'] = convertRanges(df['Age'])
df['Fare'] = convertRanges(df['Fare'])
df.head() | Titanic - Machine Learning from Disaster |
6,053,002 | pred_fasttext_val_y = model.predict([val_X], batch_size=1024, verbose=1)
for thresh in np.arange(0.1, 0.501, 0.01):
thresh = np.round(thresh, 2)
print("F1 score at threshold {0} is {1}".format(thresh, metrics.f1_score(val_y,(pred_fasttext_val_y>thresh ).astype(int))))<predict_on_test> | classifiers = [
KNeighborsClassifier(3),
SVC(kernel="linear", C=0.025),
SVC(gamma=2, C=1),
GaussianProcessClassifier(1.0 * RBF(1.0)) ,
DecisionTreeClassifier(max_depth=5),
RandomForestClassifier(max_depth=5, n_estimators=10, max_features=1),
MLPClassifier(alpha=1, max_iter=1000),
AdaBoostClassifier() ,
GaussianNB() ,
Q... | Titanic - Machine Learning from Disaster |
6,053,002 | pred_fasttext_test_y = model.predict([test_X], batch_size=1024, verbose=1 )<set_options> | df = pd.read_csv('.. /input/titanic/test.csv')
df.head() | Titanic - Machine Learning from Disaster |
6,053,002 | del word_index, embeddings_index, all_embs, embedding_matrix, model, inp, x
time.sleep(10 )<statistical_test> | df.columns[df.isna().any() ] | Titanic - Machine Learning from Disaster |
6,053,002 | 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.... | df['Age'].fillna(df['Age'].mean() , inplace=True)
df['Fare'].fillna(df['Fare'].mean() , inplace=True)
df['Age'] = convertRanges(df['Age'])
df['Fare'] = convertRanges(df['Fare'])
df['Sex'] = df['Sex'].astype('category')
df['Sex'] = df['Sex'].cat.codes
df['Embarked'] = df['Embarked'].astype('category')
df['Embarked... | Titanic - Machine Learning from Disaster |
6,053,002 | <predict_on_test><EOS> | X_test = df.iloc[:,1:].values
y_test = model.predict(X_test)
pred = pd.DataFrame({"PassengerId": df.iloc[:,0].values, "Survived": y_test})
pred.to_csv('results.csv', index=False, header=True ) | Titanic - Machine Learning from Disaster |
4,881,309 | <SOS> metric: categorizationaccuracy Kaggle data source: titanic-machine-learning-from-disaster<predict_on_test> | warnings.filterwarnings("ignore" ) | Titanic - Machine Learning from Disaster |
4,881,309 | pred_paragram_test_y = model.predict([test_X], batch_size=1024, verbose=1 )<set_options> | train = pd.read_csv(".. /input/train.csv")
test = pd.read_csv(".. /input/test.csv" ) | Titanic - Machine Learning from Disaster |
4,881,309 | del word_index, embeddings_index, all_embs, embedding_matrix, model, inp, x
time.sleep(10 )<compute_test_metric> | train = pd.read_csv(".. /input/train.csv")
test = pd.read_csv(".. /input/test.csv" ) | Titanic - Machine Learning from Disaster |
4,881,309 | pred_val_y = 0.39*pred_glove_val_y + 0.41*pred_fasttext_val_y + 0.39*pred_paragram_val_y
for thresh in np.arange(0.1, 0.501, 0.01):
thresh = np.round(thresh, 2)
print("F1 score at threshold {0} is {1}".format(thresh, metrics.f1_score(val_y,(pred_val_y>thresh ).astype(int))))<save_to_csv> | combine = pd.concat([train,test],sort = True,ignore_index=True ) | Titanic - Machine Learning from Disaster |
4,881,309 | pred_test_y = 0.35*pred_glove_test_y + 0.42*pred_fasttext_test_y + 0.35*pred_paragram_test_y
pred_test_y =(pred_test_y>0.42 ).astype(int)
out_df = pd.DataFrame({"qid":test_df["qid"].values})
out_df['prediction'] = pred_test_y
out_df.to_csv("submission.csv", index=False )<import_modules> | combine["Surname"] = combine["Name"].str.split(",",expand = True)[0]
combine["Surname"] = combine["Surname"].str.split("-",expand = True)[0]
combine[["Name","Surname"]].head(2 ) | Titanic - Machine Learning from Disaster |
4,881,309 | from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pad_sequences
import os
import numpy as np
import pandas as pd
from tqdm import tqdm
import math
from sklearn.model_selection import train_test_split<load_from_csv> | for name in combine.Surname.values:
combine.loc[combine.Surname==name,"Surname_Counts"]=combine[combine.Surname==name].shape[0]
combine[["Surname","Surname_Counts"]].head(2 ) | Titanic - Machine Learning from Disaster |
4,881,309 | train_df = pd.read_csv(".. /input/train.csv")
train_df, val_df = train_test_split(train_df, test_size=0.1 )<feature_engineering> | combine['Title'] = combine.Name.str.extract('([A-Za-z]+)\.', expand=False ) | Titanic - Machine Learning from Disaster |
4,881,309 | embeddings_index = {}
f = open('.. /input/embeddings/glove.840B.300d/glove.840B.300d.txt')
for line in tqdm(f):
values = line.split(" ")
word = values[0]
coefs = np.asarray(values[1:], dtype='float32')
embeddings_index[word] = coefs
f.close()
print('Found %s word vectors.' % len(embeddings_index))<prepare_x_and_y> | for i,j in combine.groupby(["Sex"]):
j_title_list = set(list(j.Title.values))
print(i,j_title_list ) | Titanic - Machine Learning from Disaster |
4,881,309 | def text_to_array(text):
empyt_emb = np.zeros(300)
text = text[:-1].split() [:100]
embeds = [embeddings_index.get(x, empyt_emb)for x in text]
embeds+= [empyt_emb] *(100 - len(embeds))
return np.array(embeds)
val_vects = np.array([text_to_array(X_text)for X_text in tqdm(val_df["question_text"][:5000])])
val_y = np.ar... | survival_1 = combine[(combine.Gender=="Boy")&(combine.Pclass.isin([1,2])) ]["Survived"].mean()
survival_2 = combine[(combine.Sex=="male")&(combine.Age<16)&(combine.Pclass.isin([1,2])) ]["Survived"].mean()
survival_3 = combine[(((combine.Sex=="male")&(combine.Age<16)) |(combine.Gender=="Boy")) &(combine.Pclass.isin([1,2... | Titanic - Machine Learning from Disaster |
4,881,309 | batch_size = 128
def batch_gen(train_df):
n_batches = math.ceil(len(train_df)/ batch_size)
while True:
train_df = train_df.sample(frac=1.)
for i in range(n_batches):
texts = train_df.iloc[i*batch_size:(i+1)*batch_size, 1]
text_arr = np.array([text_to_array(text)for text in texts])
yield text_arr, np.array(train_df["t... | combine.loc[(combine.Sex=="male")&(combine.Age<16),"Gender"]="Boy" | Titanic - Machine Learning from Disaster |
4,881,309 | from keras.models import Sequential, Input
from keras.models import Model
from keras.layers import concatenate, GlobalAveragePooling1D, GlobalMaxPooling1D, CuDNNLSTM, CuDNNGRU, Dense, Bidirectional, SpatialDropout1D, Conv1D<define_search_model> | combine.drop(["Title","Sex"],axis = 1,inplace=True ) | Titanic - Machine Learning from Disaster |
4,881,309 | dr = 0.25
units = 96
inp = Input(shape =(( 100, 300)))
x1 = SpatialDropout1D(dr )(inp)
x = Bidirectional(CuDNNGRU(units, return_sequences = True))(x1)
x = Conv1D(64, kernel_size = 2, padding = "valid", kernel_initializer = "he_uniform" )(x)
y = Bidirectional(CuDNNLSTM(units, return_sequences = True))(x1)
y = Conv1... | combine_female = combine[combine.Gender == "Woman"]
combine_female.reset_index(inplace = True)
for i,name in enumerate(list(combine_female.Name.values)) :
name = str(name)
name = re.sub("\\s$","",name)
name = re.sub(".*[^\\)]$","",name)
name = re.sub(".*\\s (.*)\\)$","\\1",name)
name = re.sub("^\\(","",name)
comb... | Titanic - Machine Learning from Disaster |
4,881,309 | mg = batch_gen(train_df)
model.fit_generator(mg, epochs=30,
steps_per_epoch=1000,
validation_data=(val_vects, val_y),
verbose=True )<define_variables> | for id in combine_female.PassengerId.values:
id2 = int(id)
name = combine_female[combine_female.PassengerId==id]["Maiden_name"].values
combine.loc[combine.PassengerId==id2,"Maiden"] = name
combine[["Name","Maiden"]].head(2 ) | Titanic - Machine Learning from Disaster |
4,881,309 | batch_size = 256
def batch_gen(test_df):
n_batches = math.ceil(len(test_df)/ batch_size)
for i in range(n_batches):
texts = test_df.iloc[i*batch_size:(i+1)*batch_size, 1]
text_arr = np.array([text_to_array(text)for text in texts])
yield text_arr
test_df = pd.read_csv(".. /input/test.csv")
all_preds = []
for x in tqd... | combine.loc[combine.Maiden=="","Maiden"]=nan | Titanic - Machine Learning from Disaster |
4,881,309 | all_preds_val = []
for x in tqdm(batch_gen(val_df)) :
all_preds_val.extend(model.predict(x ).flatten() )<prepare_x_and_y> | for mai in combine.Maiden.values:
combine.loc[combine.Maiden==mai,"Maiden_Counts"]=combine[combine.Maiden==mai].shape[0]
combine[["Maiden","Maiden_Counts"]].head(2 ) | Titanic - Machine Learning from Disaster |
4,881,309 | val_y = val_df["target"].values<import_modules> | combine["Family_size"] = combine["Parch"]+combine["SibSp"]+1
combine[["Name","Surname_Counts","Maiden_Counts","Family_size"]].head(2 ) | Titanic - Machine Learning from Disaster |
4,881,309 | from sklearn.metrics import f1_score<compute_test_metric> | pclass_gender_list = []
for pclass,gender in zip(combine.Pclass.values,combine.Gender.values):
pclass_gender = "P"+str(pclass)+"-"+str(gender)
pclass_gender_list = pclass_gender_list+[pclass_gender] | Titanic - Machine Learning from Disaster |
4,881,309 | y_val =(np.array(all_preds_val)> 0.1 ).astype(np.int)
f1_score(val_y, y_val )<compute_test_metric> | combine["Pclass_Gender"] = pclass_gender_list
combine[["Pclass","Gender","Pclass_Gender"]].head(2 ) | Titanic - Machine Learning from Disaster |
4,881,309 | f1_score(val_y, y_val )<compute_test_metric> | combine["Ticket"].value_counts() [:5] | Titanic - Machine Learning from Disaster |
4,881,309 | score = 0
thresh =.5
for i in np.arange(0.1, 0.991, 0.01):
y_val =(np.array(all_preds_val)> i ).astype(np.int)
temp_score = f1_score(val_y, y_val)
if(temp_score > score):
score = temp_score
thresh = i
print("CV: {}, Threshold: {}".format(score, thresh))<save_to_csv> | combine["Ticket_new"] = combine["Ticket"].str.extract('([0-9]+)$',expand = False ) | Titanic - Machine Learning from Disaster |
4,881,309 | y_te =(np.array(all_preds)> thresh ).astype(np.int)
submit_df = pd.DataFrame({"qid": test_df["qid"], "prediction": y_te})
submit_df.to_csv("submission.csv", index=False )<import_modules> | combine[combine.Ticket_new.isna() ][["Ticket","Ticket_new"]] | Titanic - Machine Learning from Disaster |
4,881,309 | from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pad_sequences
import os
import numpy as np
import pandas as pd
from tqdm import tqdm
import math
from sklearn.model_selection import train_test_split<define_variables> | combine.loc[combine.Ticket_new.isna() ,"Ticket_new"]="LINE" | Titanic - Machine Learning from Disaster |
4,881,309 | SEQ_LEN = 100<load_from_csv> | for i,tic in enumerate(combine.Ticket_new.values):
combine.loc[i,"Ticket"] = tic
combine.drop(["Ticket_new"],axis = 1,inplace = True ) | Titanic - Machine Learning from Disaster |
4,881,309 | train_df = pd.read_csv(".. /input/train.csv")
train_df, val_df = train_test_split(train_df, test_size=0.07 )<feature_engineering> | combine["Ticket"].value_counts() [:5] | Titanic - Machine Learning from Disaster |
4,881,309 | embeddings_index = {}
f = open('.. /input/embeddings/glove.840B.300d/glove.840B.300d.txt')
for line in tqdm(f):
values = line.split(" ")
word = values[0]
coefs = np.asarray(values[1:], dtype='float32')
embeddings_index[word] = coefs
f.close()
print('Found %s word vectors.' % len(embeddings_index))<string_transform> | tic_list = combine.Ticket.values
for i in tic_list:
combine.loc[combine["Ticket"]==i,"Ticket_Counts"] = combine[combine["Ticket"] == i].shape[0]
combine[["Ticket","Ticket_Counts"]].head(2 ) | Titanic - Machine Learning from Disaster |
4,881,309 | _WORD_SPLIT = re.compile("([.,!?"':; )(])")
_DIGIT_RE = re.compile(br"\d")
STOP_WORDS = "" ' [ ]., ! : ; ?".split(" ")
def basic_tokenizer(sentence):
words = []
for space_separated_fragment in sentence.strip().split() :
words.extend(_WORD_SPLIT.split(space_separated_fragment))
return [w.lower() for w in words if w... | for i,ti in enumerate(combine.Ticket.values):
t = list(ti)[:-2]+list("xx")
d = ''
combine.loc[i,"Ticket2"] = d.join(t ) | Titanic - Machine Learning from Disaster |
4,881,309 | def text_to_array(text):
empyt_emb = np.zeros(300)
text = basic_tokenizer(text[:-1])[:SEQ_LEN]
embeds = [embeddings_index.get(x, empyt_emb)for x in text]
embeds+= [empyt_emb] *(SEQ_LEN - len(embeds))
return np.array(embeds)
val_vects = np.array([text_to_array(X_text)for X_text in tqdm(val_df["question_text"][:3000])]... | tic2_list = combine.Ticket2.values
for i in tic2_list:
combine.loc[combine["Ticket2"]==i,"Ticket2_Counts"] = combine[combine["Ticket2"] == i].shape[0]
combine[["Ticket","Ticket2","Ticket2_Counts"]].head(2 ) | Titanic - Machine Learning from Disaster |
4,881,309 | batch_size = 256
def batch_gen(train_df):
n_batches = math.ceil(len(train_df)/ batch_size)
while True:
train_df = train_df.sample(frac=1.)
for i in range(n_batches):
texts = train_df.iloc[i*batch_size:(i+1)*batch_size, 1]
text_arr = np.array([text_to_array(text)for text in texts])
yield text_arr, np.array(train_df["t... | have_cabin = combine[combine.Cabin.notna() ]
have_cabin.reset_index(inplace = True)
multi_cabin = []
single_cabin = []
for i,j in enumerate(have_cabin.Cabin.values):
passid = have_cabin.loc[i].PassengerId
if len(j)>4:
multi_cabin = multi_cabin+[passid]
else:
single_cabin = single_cabin+[passid]
print(multi_cabin ) | Titanic - Machine Learning from Disaster |
4,881,309 | from keras.models import Sequential,Model
from keras.layers import CuDNNLSTM, Dense, Bidirectional, Input,Dropout
from keras import backend as K
from keras.engine.topology import Layer
from keras import initializers, regularizers, constraints<choose_model_class> | cabin_title = have_cabin[have_cabin.PassengerId.isin(single_cabin)].Cabin.str.extract('^([A-Z])',expand=False)
have_cabin.loc[have_cabin.PassengerId.isin(single_cabin),"Cabin_Title"] = cabin_title
have_cabin[["Cabin","Cabin_Title"]].head(2 ) | Titanic - Machine Learning from Disaster |
4,881,309 | class Attention(Layer):
def __init__(self, step_dim,
W_regularizer=None, b_regularizer=None,
W_constraint=None, b_constraint=None,
bias=True, **kwargs):
self.supports_masking = True
self.init = initializers.get('glorot_uniform')
self.W_regularizer = regularizers.get(W_regularizer)
self.b_regularizer = regularizers.ge... | multi_cabin_title = []
for cabin in have_cabin[have_cabin.PassengerId.isin(multi_cabin)]["Cabin"].values:
cabinA = cabin.split(" ")[0]
cabinA_title = list(cabinA)[0]
cabinB = cabin.split(" ")[1]
cabinB_title = list(cabinB)[0]
if cabinA_title==cabinB_title:
multi_cabin_title = multi_cabin_title+[cabinA_title]
else:
mult... | Titanic - Machine Learning from Disaster |
4,881,309 | inp = Input(shape=(SEQ_LEN,300))
x = Bidirectional(CuDNNLSTM(64, return_sequences=True))(inp)
x = Bidirectional(CuDNNLSTM(64,return_sequences=True))(x)
x = Attention(SEQ_LEN )(x)
x = Dense(256, activation="relu" )(x)
x = Dense(1, activation="sigmoid" )(x)
model = Model(inputs=inp, outputs=x)
model.compile(loss='b... | cabin_title_list = list(have_cabin.Cabin_Title.values)
combine.loc[(combine.Cabin.notna()),"Cabin_Title"] = cabin_title_list
combine[combine.Cabin.notna() ][["Cabin","Cabin_Title"]].head(2 ) | Titanic - Machine Learning from Disaster |
4,881,309 | mg = batch_gen(train_df)
model.fit_generator(mg, epochs=20,
steps_per_epoch=1000,
validation_data=(val_vects, val_y),
verbose=True )<define_variables> | combine.loc[(combine.Fare.notna()),"Fare_PP"] = combine.Fare/combine.Ticket_Counts
combine[["Fare","Fare_PP","Ticket_Counts"]].sample(5 ) | Titanic - Machine Learning from Disaster |
4,881,309 | batch_size = 256
def batch_gen(test_df):
n_batches = math.ceil(len(test_df)/ batch_size)
for i in range(n_batches):
texts = test_df.iloc[i*batch_size:(i+1)*batch_size, 1]
text_arr = np.array([text_to_array(text)for text in texts])
yield text_arr
test_df = pd.read_csv(".. /input/test.csv")
all_preds = []
for x in tqd... | combine[combine.Fare.isna() ] | Titanic - Machine Learning from Disaster |
4,881,309 | y_te =(np.array(all_preds)> 0.35 ).astype(np.int)
submit_df = pd.DataFrame({"qid": test_df["qid"], "prediction": y_te})
submit_df.to_csv("submission.csv", index=False )<import_modules> | combine.loc[(combine.Fare_PP.isna()),"Fare_PP"] = combine[(combine.Embarked=="S")&(combine.Pclass==3)]["Fare_PP"].median()
combine.loc[(combine.Fare.isna()),"Fare"] = combine[(combine.Embarked=="S")&(combine.Pclass==3)]["Fare_PP"].median() | Titanic - Machine Learning from Disaster |
4,881,309 | import os
import time
import numpy as np
import pandas as pd
from tqdm import tqdm
import math
from sklearn.model_selection import train_test_split
from sklearn import metrics
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pad_sequences
from keras.layers import Dense, Input, LST... | multi_ticket = list(combine[(combine.Ticket_Counts>=2)&(combine.Cabin_Title.notna())]["Ticket"].values)
len(set(multi_ticket)) | Titanic - Machine Learning from Disaster |
4,881,309 | 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 )<split> | multi_ticket_cabin = combine[(combine.Cabin_Title.notna())&(combine.Ticket_Counts>=2)][["Ticket","Cabin_Title"]]
same_ticket_differ_cabin = []
for i,j in multi_ticket_cabin.groupby(["Ticket"]):
differ_cabin = set(list(j.Cabin_Title.values))
if len(differ_cabin)>=2:
same_ticket_differ_cabin = same_ticket_differ_cabin+[i... | Titanic - Machine Learning from Disaster |
4,881,309 | train_df, val_df = train_test_split(train_df, test_size=0.1, random_state=2018)
embed_size = 300
max_features = 50000
maxlen = 100
train_X = train_df["question_text"].fillna("_na_" ).values
val_X = val_df["question_text"].fillna("_na_" ).values
test_X = test_df["question_text"].fillna("_na_" ).values
tokenizer = Token... | need_filling = list(combine[(combine.Ticket.isin(multi_ticket)) &(combine.Cabin.isna())].PassengerId.values)
for passid in need_filling:
ticket = list(combine[combine.PassengerId==passid]["Ticket"].values)
cabin_title = combine[combine.Ticket.isin(ticket)]["Cabin_Title"].describe() ["top"]
combine.loc[combine.Passeng... | Titanic - Machine Learning from Disaster |
4,881,309 | inp = Input(shape=(maxlen,))
x = Embedding(max_features, embed_size )(inp)
x = Bidirectional(CuDNNGRU(64, return_sequences=True))(x)
x = GlobalMaxPool1D()(x)
x = Dense(16, activation="relu" )(x)
x = Dropout(0.1 )(x)
x = Dense(1, activation="sigmoid" )(x)
model = Model(inputs=inp, outputs=x)
model.compile(loss='b... | combine.drop(["Cabin","Fare"],axis = 1,inplace = True ) | Titanic - Machine Learning from Disaster |
4,881,309 | model.fit(train_X, train_y, batch_size=512, epochs=2, validation_data=(val_X, val_y))<predict_on_test> | need_check = combine[(combine.PassengerId.isin(maybe_wrong)) &(combine.Ticket_Counts==1)]["PassengerId"].values
need_check | Titanic - Machine Learning from Disaster |
4,881,309 | pred_noemb_val_y = model.predict([val_X], batch_size=1024, verbose=1)
for thresh in np.arange(0.1, 0.501, 0.01):
thresh = np.round(thresh, 2)
print("F1 score at threshold {0} is {1}".format(thresh, metrics.f1_score(val_y,(pred_noemb_val_y>thresh ).astype(int))))<predict_on_test> | def get_from_passid(dataset,passid_list):
return dataset[dataset.PassengerId.isin(passid_list)][['Age', 'Cabin_Title', 'Embarked', 'Name', 'Parch', 'SibSp','Ticket',
'Ticket2', 'Surname','Maiden',"Ticket_Counts", 'Pclass_Gender',
'Family_size',"Survived"]]
def get_from_name(dataset,name_list):
return dataset[(dataset.S... | Titanic - Machine Learning from Disaster |
4,881,309 | pred_noemb_test_y = model.predict([test_X], batch_size=1024, verbose=1 )<set_options> | combine[(combine.Ticket2=="175xx")&(combine.Cabin_Title=="C")&(combine.Embarked=="C")&(combine.Family_size>=2)&(combine.Pclass==1)] | Titanic - Machine Learning from Disaster |
4,881,309 | del model, inp, x
time.sleep(10 )<statistical_test> | combine[(( combine.Age<=3)|(combine.Age>=32))
&(combine.Embarked=="S")&(combine.Family_size>=2)
&(combine.Ticket2=="2506xx")&(combine.Pclass==2)][['Age', 'Cabin_Title', 'Embarked', 'Name', 'Parch', 'SibSp','Ticket',
'Ticket2', 'Surname','Maiden',"Ticket_Counts", 'Pclass_Gender', 'Family_size']] | Titanic - Machine Learning from Disaster |
4,881,309 | EMBEDDING_FILE = '.. /input/embeddings/glove.840B.300d/glove.840B.300d.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))
all_embs = np.stack(embeddings_index.values())
emb_mean,emb_std = all_embs.mean() , all_em... | combine[(combine.Embarked=="S")&(combine.Ticket2=="3470xx")&(combine.Pclass==3)&(combine.Family_size==2)][['Age', 'Cabin_Title', 'Embarked', 'Name', 'Parch', 'SibSp','Ticket', 'Surname', 'Maiden',
'Pclass_Gender', 'Family_size', 'Surname_Counts', 'Ticket_Counts',"Maiden_Counts","Survived"]] | Titanic - Machine Learning from Disaster |
4,881,309 | model.fit(train_X, train_y, batch_size=512, epochs=2, validation_data=(val_X, val_y))<predict_on_test> | combine.loc[combine.PassengerId.isin([137]),"Maiden"] = "Monypeny"
combine.loc[combine.Maiden.isin(["Monypeny"]),"Maiden_Counts"] = 2
combine.loc[combine.PassengerId.isin([274]),"Parch"] = 0
combine.loc[combine.PassengerId.isin([274]),"Family_size"] = 1
combine.loc[combine.PassengerId.isin([418]),"Parch"] = 0
combine.l... | Titanic - Machine Learning from Disaster |
4,881,309 | pred_glove_val_y = model.predict([val_X], batch_size=1024, verbose=1)
for thresh in np.arange(0.1, 0.501, 0.01):
thresh = np.round(thresh, 2)
print("F1 score at threshold {0} is {1}".format(thresh, metrics.f1_score(val_y,(pred_glove_val_y>thresh ).astype(int))))<predict_on_test> | test_error = pd.DataFrame(columns=["Category","Train_data","Test_data","Error_Counts","Error_Probability"])
def get_error_counts(pg,index):
PG_train_size = combine[(combine.Pclass_Gender.isin(pg)) &(combine.Survived.notna())].shape[0]
PG_test_size = combine[(combine.Pclass_Gender.isin(pg)) &(combine.Survived.isna())].... | Titanic - Machine Learning from Disaster |
4,881,309 | pred_glove_test_y = model.predict([test_X], batch_size=1024, verbose=1 )<set_options> | combine.loc[(combine.Pclass_Gender.isin(["P1-Boy","P2-Boy","P1-Woman","P2-Woman"])) ,"Predict"] = 1
combine.loc[(combine.Pclass_Gender=="P2-Man"),"Predict"] = 0 | Titanic - Machine Learning from Disaster |
4,881,309 | del word_index, embeddings_index, all_embs, embedding_matrix, model, inp, x
time.sleep(10 )<statistical_test> | have_family = combine[combine.Family_size>=2] | Titanic - Machine Learning from Disaster |
4,881,309 | 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_em... | have_family[have_family.Ticket2_Counts==1][['Age','Cabin_Title', 'Embarked','Pclass_Gender','Name',
"Surname",'Maiden', 'Parch','SibSp', 'Family_size','Ticket',"Ticket_Counts"]] | Titanic - Machine Learning from Disaster |
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