kernel_id int64 24.2k 23.3M | prompt stringlengths 8 1.85M | completetion stringlengths 1 182k | comp_name stringlengths 5 57 |
|---|---|---|---|
14,242,857 | class Mish(nn.Module):
def __init__(self):
super().__init__()
def forward(self, x):
return x *(torch.tanh(F.softplus(x)))
def gem(x, p=3, eps=1e-6):
return F.avg_pool2d(x.clamp(min=eps ).pow(p),(x.size(-2), x.size(-1)) ).pow(1./p)
class GeM(nn.Module):
def __init__(self, p=3, eps=1e-6):
super(GeM,self ).__init__()
se... | def fill_na(df):
df['Age'].fillna(titanic_df['Age'].mean() , inplace=True)
df['Cabin'].fillna('N',inplace=True)
df['Embarked'].fillna('N',inplace=True)
df['Fare'].fillna(0, inplace=True)
return df
def drop_feature(df):
df.drop(['PassengerId','Name','Ticket'],axis=1,inplace=True)
return df
def format_feature(df):
d... | Titanic - Machine Learning from Disaster |
14,242,857 | class JaneyMultiheadAttentionClassifier(nn.Module):
def __init__(self,num_classes,out_features,ninp,nhead,dropout,nlayers=1,attention_dropout=0.1):
super(JaneyMultiheadAttentionClassifier, self ).__init__()
encoder_layers = nn.TransformerEncoderLayer(ninp, nhead, ninp*4, attention_dropout)
encoder_layers.activation=Mi... | titanic_df = pd.read_csv("/kaggle/input/titanic/train.csv")
y_titanic_df = titanic_df['Survived']
X_titanic_df = titanic_df.drop('Survived', axis=1)
transform_features(X_titanic_df ) | Titanic - Machine Learning from Disaster |
14,242,857 | df=df.set_index('image_id')
df["tile_idx"]=np.nan
df=df.astype(object)
iuniq=index_df.image_id.unique()
for i in tqdm(iuniq):
idxs=index_df.loc[index_df.image_id==i].idxs.values
df.at[i,"tile_idx"]=idxs
df = df.reset_index()
del dataset
del loader
del index_df
del y
gc.collect()<define_variables> | X_train, X_test, y_train, y_test = train_test_split(X_titanic_df, y_titanic_df,
test_size=0.2, random_state=1 ) | Titanic - Machine Learning from Disaster |
14,242,857 | Shujun_PRED_LIST=[]
for pred_list_index in range(len(ensemble_shujun_list)) :
Shujun_PRED_LIST.append([])
for phase_index, ensemble_recipe in enumerate(ensemble_shujun_list):
print("shujun_ensemble_phase",phase_index)
MODELS=[]
top=3
num_classes=[6,4,4,]
if ensemble_recipe['tileImageSize'] == 288:
print("top_fold is ... | from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score | Titanic - Machine Learning from Disaster |
14,242,857 | label_list = []
for total_list_index, list_item in enumerate(total_list.tolist()):
predictions = list(list_item)
final_value = np.mean(predictions)
label=classification_threshold_item(final_value)
if debug:
print("final_value",final_value)
print("!!label!!",label)
label_list.append(label)
predictions_output=np.as... | dt_ = DecisionTreeClassifier(random_state=1)
dt_.fit(X_train, y_train)
rf_ = RandomForestClassifier(random_state=1)
rf_.fit(X_train, y_train)
lr_ = LogisticRegression(random_state=1)
lr_.fit(X_train, y_train)
| Titanic - Machine Learning from Disaster |
14,242,857 |
<define_variables> | dt_pred = dt_.predict(X_test)
rf_pred = rf_.predict(X_test)
lr_pred = lr_.predict(X_test ) | Titanic - Machine Learning from Disaster |
14,242,857 | DEBUG = False<define_variables> | print('Accuarcy of decision tree: ', accuracy_score(y_test,dt_pred))
print('Accuracy of random forest: ', accuracy_score(y_test,rf_pred))
print('Accuracy of logistic regression: ', accuracy_score(y_test, lr_pred)) | Titanic - Machine Learning from Disaster |
14,242,857 | sys.path = [
'.. /input/efficientnet-pytorch/EfficientNet-PyTorch/EfficientNet-PyTorch-master',
] + sys.path<import_modules> | def KFold_test(clf, folds=5):
kfold = KFold(n_splits=folds)
scores = []
for iter_count,(train_index, test_index)in enumerate(kfold.split(X_titanic_df)) :
X_train, X_test = X_titanic_df.values[train_index], X_titanic_df.values[test_index]
y_train, y_test = y_titanic_df.values[train_index], y_titanic_df.values[test_inde... | Titanic - Machine Learning from Disaster |
14,242,857 | import skimage.io
import numpy as np
import pandas as pd
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader, Dataset
from efficientnet_pytorch import model as enet
import matplotlib.pyplot as plt
from tqdm import tqdm_notebook as tqdm
<load_from_csv> | parameters={'max_depth':[2,3,5,10],
'min_samples_split':[2,3,5],
'min_samples_leaf':[1,4,5,8]}
grid_dt = GridSearchCV(dt_,param_grid=parameters,scoring='accuracy',cv=5, refit=True)
grid_dt.fit(X_train,y_train)
print('best parameter: ', grid_dt.best_params_)
print('best accuracy: ', grid_dt.best_score_)
best_dt=grid... | Titanic - Machine Learning from Disaster |
14,242,857 | data_dir = '.. /input/prostate-cancer-grade-assessment'
df_train = pd.read_csv(os.path.join(data_dir, 'train.csv'))
df_test = pd.read_csv(os.path.join(data_dir, 'test.csv'))
df_sub = pd.read_csv(os.path.join(data_dir, 'sample_submission.csv'))
model_dir = '.. /input/panda-public-models'
image_folder = os.path.join(data... | titanic_test = pd.read_csv('.. /input/titanic/test.csv')
transform_features(titanic_test)
y_pred = grid_dt.predict(titanic_test)
test_df = pd.read_csv('.. /input/titanic/test.csv')
output = pd.DataFrame({'PassengerId': test_df.PassengerId, 'Survived': y_pred})
output.to_csv('submission.csv', index=False ) | Titanic - Machine Learning from Disaster |
14,206,631 | class enetv2(nn.Module):
def __init__(self, backbone, out_dim):
super(enetv2, self ).__init__()
self.enet = enet.EfficientNet.from_name(backbone)
self.myfc = nn.Linear(self.enet._fc.in_features, out_dim)
self.enet._fc = nn.Identity()
def extract(self, x):
return self.enet(x)
def forward(self, x):
x = self.extract(x)... | train_data = pd.read_csv("/kaggle/input/titanic/train.csv", index_col="PassengerId")
train_data.head() | Titanic - Machine Learning from Disaster |
14,206,631 | def get_tiles(img, mode=0):
result = []
h, w, c = img.shape
pad_h =(tile_size - h % tile_size)% tile_size +(( tile_size * mode)// 2)
pad_w =(tile_size - w % tile_size)% tile_size +(( tile_size * mode)// 2)
img2 = np.pad(img,[[pad_h // 2, pad_h - pad_h // 2], [pad_w // 2,pad_w - pad_w//2], [0,0]], constant_values=255)... | test_data = pd.read_csv("/kaggle/input/titanic/test.csv", index_col="PassengerId")
test_data.head() | Titanic - Machine Learning from Disaster |
14,206,631 | dataset = PANDADataset(df, image_size, n_tiles, 0)
loader = DataLoader(dataset, batch_size=batch_size, num_workers=num_workers, shuffle=False)
dataset2 = PANDADataset(df, image_size, n_tiles, 2)
loader2 = DataLoader(dataset2, batch_size=batch_size, num_workers=num_workers, shuffle=False )<save_to_csv> | combined = pd.concat([train_data, test_data])
combined.head() | Titanic - Machine Learning from Disaster |
14,206,631 | LOGITS = []
LOGITS2 = []
with torch.no_grad() :
for data in tqdm(loader):
data = data.to(device)
logits = models[0](data)
LOGITS.append(logits)
for data in tqdm(loader2):
data = data.to(device)
logits = models[0](data)
LOGITS2.append(logits)
LOGITS =(torch.cat(LOGITS ).sigmoid().cpu() + torch.cat(LOGITS2 ).sigmoi... | combined.iloc[891:] | Titanic - Machine Learning from Disaster |
14,206,631 | DEBUG = False<define_variables> | combined.Ticket = combined.Ticket.str.replace(".","" ).str.replace("/", "" ) | Titanic - Machine Learning from Disaster |
14,206,631 | sys.path = [
'.. /input/efficientnet-pytorch/EfficientNet-PyTorch/EfficientNet-PyTorch-master',
] + sys.path<define_variables> | combined.Ticket[combined.Ticket.str.contains("LINE")] | Titanic - Machine Learning from Disaster |
14,206,631 | sys.path<import_modules> | combined.Ticket = combined.Ticket.str.replace("SOTON","STON" ) | Titanic - Machine Learning from Disaster |
14,206,631 | import skimage.io
import numpy as np
import pandas as pd
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader, Dataset
from efficientnet_pytorch import model as enet
import albumentations
from albumentations.pytorch import ToTensor
import matplotlib.pyplot as plt
fr... | def ticket_type(row):
t = row.Ticket.split(" ")[0].lower()
return t[:2] if not t.isdigit() else "U"
combined["ticket_type"] = combined.apply(ticket_type, axis=1)
combined["ticket_type"].head() | Titanic - Machine Learning from Disaster |
14,206,631 | data_dir = '.. /input/prostate-cancer-grade-assessment'
df_train = pd.read_csv(os.path.join(data_dir, 'train.csv'))
df_test = pd.read_csv(os.path.join(data_dir, 'test.csv'))
df_sub = pd.read_csv(os.path.join(data_dir, 'sample_submission.csv'))
model_dir = '.. /input/effnet-model-assemble'
image_folder = os.path.join(da... | Titanic - Machine Learning from Disaster | |
14,206,631 | class enetv2(nn.Module):
def __init__(self, backbone, out_dim):
super(enetv2, self ).__init__()
self.enet = enet.EfficientNet.from_name(backbone)
self.myfc = nn.Linear(self.enet._fc.in_features, out_dim)
self.enet._fc = nn.Identity()
def extract(self, x):
return self.enet(x)
def forward(self, x):
x = self.extract(x)... | combined.isnull().sum() | Titanic - Machine Learning from Disaster |
14,206,631 | model_dir_1 = '.. /input/effnet-model-assemble/'
backbone_1 = 'efficientnet-b1'
class enetv2_1(nn.Module):
def __init__(self, backbone_1, out_dim):
super(enetv2_1, self ).__init__()
self.enet = enet.EfficientNet.from_name(backbone_1)
self.myfc = nn.Linear(self.enet._fc.in_features, out_dim)
self.enet._fc = nn.Identit... | combined[combined.Embarked.isnull() ] | Titanic - Machine Learning from Disaster |
14,206,631 | test_transform = albumentations.Compose([
albumentations.Transpose(p=0.5),
albumentations.VerticalFlip(p=0.5),
albumentations.HorizontalFlip(p=0.5),
])
transforms_val = albumentations.Compose([] )<categorify> | combined.Embarked.mode() | Titanic - Machine Learning from Disaster |
14,206,631 | def get_tiles(img, mode=0):
result = []
h, w, c = img.shape
pad_h =(tile_size - h % tile_size)% tile_size +(( tile_size * mode)// 2)
pad_w =(tile_size - w % tile_size)% tile_size +(( tile_size * mode)// 2)
img2 = np.pad(img,[[pad_h // 2, pad_h - pad_h // 2], [pad_w // 2,pad_w - pad_w//2], [0,0]], constant_values=255)... | combined["Embarked"].fillna("S", inplace=True ) | Titanic - Machine Learning from Disaster |
14,206,631 | TTA_num = 16
LOGITS_bags_0= []
LOGITS_bags_1= []
LOGITS_bags_b0=[]<define_variables> | def process_age_type(row):
age = row.Age
if age <=5:
return "infant"
elif age <=18:
return "adolescent"
elif age <= 24:
return "adult"
elif age <= 44:
return "middle_aged"
elif age <= 64:
return "old"
else:
return "senior"
combined["Age_type"] = combined.apply(process_age_type, axis=1 ) | Titanic - Machine Learning from Disaster |
14,206,631 | for i in range(2*TTA_num):
LOGITS_bags_0.append([])
LOGITS_bags_1.append([])
LOGITS_bags_b0.append([] )<create_dataframe> | combined.isnull().sum() | Titanic - Machine Learning from Disaster |
14,206,631 | LOGITS_total=[]
LOGITS_total_b0=[]
with torch.no_grad() :
for i in range(2*TTA_num):
if i%2 == 0:
dataset=(PANDADataset(df, image_size, n_tiles, 0, transform=test_transform))
loader=(DataLoader(dataset, batch_size=batch_size, num_workers=num_workers, shuffle=False))
else:
dataset=(PANDADataset(df, image_size, n_tiles, ... | combined.loc[889] | Titanic - Machine Learning from Disaster |
14,206,631 | LOGITS=sum(LOGITS_total)/(2*TTA_num*2)
LOGITS_b0=sum(LOGITS_total_b0)/(2*TTA_num )<save_to_csv> | test_data[test_data.Fare.isnull() ] | Titanic - Machine Learning from Disaster |
14,206,631 | LOGITS_final =(LOGITS + LOGITS_b0)/2
PREDS = LOGITS_final.sum(1 ).round().numpy()
df['isup_grade'] = PREDS.astype(int)
df[['image_id', 'isup_grade']].to_csv('submission.csv', index=False)
print(df.head())
print()
print(df.isup_grade.value_counts() )<define_variables> | combined.Fare.fillna(combined[combined.Pclass == 3]["Fare"].mode() [0], inplace=True ) | Titanic - Machine Learning from Disaster |
14,206,631 | sys.path = [
'.. /input/efficientnet-pytorch/EfficientNet-PyTorch/EfficientNet-PyTorch-master',
] + sys.path
<load_from_csv> | def process_deck(row):
cabin = row.Cabin
if pd.isnull(cabin):
return "U"
else:
return cabin[0]
combined["Deck"] = combined.apply(process_deck, axis=1)
combined["Deck"].head() | Titanic - Machine Learning from Disaster |
14,206,631 | data_dir = '.. /input/prostate-cancer-grade-assessment'
df_train = pd.read_csv(os.path.join(data_dir, 'train.csv'))
df_test = pd.read_csv(os.path.join(data_dir, 'test.csv'))
df_sub = pd.read_csv(os.path.join(data_dir, 'sample_submission.csv'))
model_dir = '.. /input/panda-base-model'
image_folder = os.path.join(data_di... | combined.groupby(["Pclass", "Deck"] ).mean() ["Fare"] | Titanic - Machine Learning from Disaster |
14,206,631 | class enetv2(nn.Module):
def __init__(self, backbone, out_dim):
super(enetv2, self ).__init__()
self.enet = enet.EfficientNet.from_name(backbone)
self.myfc = nn.Linear(self.enet._fc.in_features, out_dim)
self.enet._fc = nn.Identity()
def extract(self, x):
return self.enet(x)
def forward(self, x):
x = self.extract(x)... | def process_unknown_decks(row):
deck = row.Deck
if deck == "U":
fare = row.Fare
if fare == 0:
return "U"
if row.Pclass == 1:
if fare <= 35.075:
return "A"
elif fare <= 53.1:
return "D"
elif fare <= 55.5:
return "E"
elif fare <= 82.27:
return "B"
else:
return "C"
elif row.Pclass == 2:
if fare <= 11.5:
return "E"
elif fa... | Titanic - Machine Learning from Disaster |
14,206,631 | def get_tiles(img, n_tiles, mode=0):
result = []
h, w, c = img.shape
pad_h =(tile_size - h % tile_size)% tile_size
pad_w =(tile_size - w % tile_size)% tile_size
mode_pad_h =(tile_size * mode)// 4
mode_pad_w =(tile_size * mode)// 4
img2 = np.pad(img,[[pad_h // 2 + mode_pad_h, pad_h - pad_h // 2 + tile_size - mode_pad_h]... | def get_title(row):
return row.Name.split(",")[1].strip().split(".")[0]
combined["Title"] = combined.apply(get_title, axis=1)
combined.Title.unique() | Titanic - Machine Learning from Disaster |
14,206,631 | model_loaders = []
for model_dict, model in zip(model_files, models):
loaders = []
for mode in [0, 1, 2, 3]:
for rotate in [False, True]:
dataset = PANDADataset(df, image_size, n_tiles=model_dict['n_tiles'], tile_mode=mode, rotate=rotate)
loader = DataLoader(dataset, batch_size=batch_size, num_workers=num_workers, shu... | title_mappings = {
"Capt": "Officer",
"Col": "Officer",
"Major": "Officer",
"Jonkheer": "Royalty",
"Don": "Royalty",
"Sir" : "Royalty",
"Dr": "Officer",
"Rev": "Officer",
"the Countess":"Royalty",
"Mme": "Mrs",
"Mlle": "Miss",
"Ms": "Mrs",
"Mr" : "Mr",
"Mrs" : "Mrs",
"Miss" : "Miss",
"Master" : "Royalty",
"Lady" : "Roy... | Titanic - Machine Learning from Disaster |
14,206,631 | LOGITSS = []
with torch.no_grad() :
for model, loaders in model_loaders:
for loader in loaders:
LOGITS = []
for data in tqdm(loader):
data = data.to(device)
logits = model(data)
LOGITS.append(logits)
LOGITS = torch.cat(LOGITS ).sigmoid().cpu().numpy().sum(1)
LOGITSS.append(LOGITS)
LOGITS = np.array(LOGITSS ).mean(... | grouped_train = combined.groupby(['Sex','Pclass','Title'])
grouped_median_train = grouped_train.median()
grouped_median_train = grouped_median_train.reset_index() [['Sex', 'Pclass', 'Title', 'Age']]
grouped_median_train.head() | Titanic - Machine Learning from Disaster |
14,206,631 | DEBUG = False<define_variables> | def fill_age(row):
condition =(
(grouped_median_train['Sex'] == row['Sex'])&
(grouped_median_train['Title'] == row['Title'])&
(grouped_median_train['Pclass'] == row['Pclass'])
)
return grouped_median_train[condition]['Age'].values[0]
combined["Age"] = combined.apply(lambda row: fill_age(row)if np.isnan(row['Age'])el... | Titanic - Machine Learning from Disaster |
14,206,631 | sys.path = [
'.. /input/efficientnet-pytorch/EfficientNet-PyTorch/EfficientNet-PyTorch-master',
] + sys.path<import_modules> | combined.Sex = combined.Sex.map({"male":0, "female":1} ) | Titanic - Machine Learning from Disaster |
14,206,631 | import skimage.io
import numpy as np
import pandas as pd
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader, Dataset
from efficientnet_pytorch import model as enet
import matplotlib.pyplot as plt
from tqdm import tqdm_notebook as tqdm
<load_from_csv> | combined["Family_size"] = combined["Parch"] + combined["SibSp"] + 1
combined["Family_size"].head() | Titanic - Machine Learning from Disaster |
14,206,631 | data_dir = '.. /input/prostate-cancer-grade-assessment'
df_train = pd.read_csv(os.path.join(data_dir, 'train.csv'))
df_test = pd.read_csv(os.path.join(data_dir, 'test.csv'))
df_sub = pd.read_csv(os.path.join(data_dir, 'sample_submission.csv'))
model_dir = '.. /input/panda-public-models'
image_folder = os.path.join(data... | oh_features = ["Embarked", "Deck", "Title"]
combined = pd.get_dummies(combined, columns=oh_features, prefix=oh_features)
combined.head() | Titanic - Machine Learning from Disaster |
14,206,631 | class enetv2(nn.Module):
def __init__(self, backbone, out_dim):
super(enetv2, self ).__init__()
self.enet = enet.EfficientNet.from_name(backbone)
self.myfc = nn.Linear(self.enet._fc.in_features, out_dim)
self.enet._fc = nn.Identity()
def extract(self, x):
return self.enet(x)
def forward(self, x):
x = self.extract(x)... | label_features = ["ticket_type", "Age_type"]
le = LabelEncoder()
for feature in label_features:
combined[feature] = le.fit_transform(combined[feature] ) | Titanic - Machine Learning from Disaster |
14,206,631 | def get_tiles(img, mode=0):
result = []
h, w, c = img.shape
pad_h =(tile_size - h % tile_size)% tile_size +(( tile_size * mode)// 2)
pad_w =(tile_size - w % tile_size)% tile_size +(( tile_size * mode)// 2)
img2 = np.pad(img,[[pad_h // 2, pad_h - pad_h // 2], [pad_w // 2,pad_w - pad_w//2], [0,0]], constant_values=255)... | from numpy import mean
from numpy import std
from sklearn.model_selection import StratifiedKFold
from sklearn.model_selection import cross_val_score
from xgboost import XGBClassifier
| Titanic - Machine Learning from Disaster |
14,206,631 | transforms_train = albumentations.Compose([
albumentations.Transpose(p=0.5),
albumentations.VerticalFlip(p=0.5),
albumentations.HorizontalFlip(p=0.5),
])
transforms_val = albumentations.Compose([])
transforms_val1 = albumentations.Compose([
albumentations.Transpose(p=1)
])
transforms_val2 = albumentations.Compose([... | combined.drop(["Name", "Ticket","Cabin"], axis=1, inplace=True ) | Titanic - Machine Learning from Disaster |
14,206,631 | dataset = PANDADataset(df, image_size, n_tiles, 0, False, False, transforms_val)
loader = DataLoader(dataset, batch_size=batch_size, num_workers=num_workers, shuffle=False)
dataset2 = PANDADataset(df, image_size, n_tiles, 2, False, False, transforms_val)
loader2 = DataLoader(dataset2, batch_size=batch_size, num_work... | y = combined.iloc[:891].Survived | Titanic - Machine Learning from Disaster |
14,206,631 | dataset = PANDADataset(df, image_size, n_tiles, 0, False, False, transforms_val1)
loader = DataLoader(dataset, batch_size=batch_size, num_workers=num_workers, shuffle=False)
dataset2 = PANDADataset(df, image_size, n_tiles, 2, False, False, transforms_val1)
loader2 = DataLoader(dataset2, batch_size=batch_size, num_wo... | combined.drop(["Survived"], axis=1,inplace=True)
X = combined.iloc[:891]
test_data = combined.iloc[891:] | Titanic - Machine Learning from Disaster |
14,206,631 | dataset = PANDADataset(df, image_size, n_tiles, 0, False, False, transforms_val2)
loader = DataLoader(dataset, batch_size=batch_size, num_workers=num_workers, shuffle=False)
dataset2 = PANDADataset(df, image_size, n_tiles, 2, False, False, transforms_val2)
loader2 = DataLoader(dataset2, batch_size=batch_size, num_wo... | y = y.astype("uint8" ) | Titanic - Machine Learning from Disaster |
14,206,631 | dataset = PANDADataset(df, image_size, n_tiles, 0, False, False, transforms_val3)
loader = DataLoader(dataset, batch_size=batch_size, num_workers=num_workers, shuffle=False)
dataset2 = PANDADataset(df, image_size, n_tiles, 2, False, False, transforms_val3)
loader2 = DataLoader(dataset2, batch_size=batch_size, num_wo... | cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=1)
model = XGBClassifier(learning_rate=0.01,
n_estimators=1150,
max_depth=5,
objective= 'binary:logistic',
)
scores = cross_val_score(model, X, y, scoring='accuracy', cv=cv)
mean(scores)*100 | Titanic - Machine Learning from Disaster |
14,206,631 | dataset = PANDADataset(df, image_size, n_tiles, 0, False, False, transforms_val4)
loader = DataLoader(dataset, batch_size=batch_size, num_workers=num_workers, shuffle=False)
dataset2 = PANDADataset(df, image_size, n_tiles, 2, False, False, transforms_val4)
loader2 = DataLoader(dataset2, batch_size=batch_size, num_wo... | model.fit(X,y)
predictions = model.predict(test_data ) | Titanic - Machine Learning from Disaster |
14,206,631 | !pip install.. /input/kaggle-efficientnet-repo/efficientnet-1.0.0-py3-none-any.whl
gc.enable()
sz = 256
N = 48
def tile(img):
result = []
shape = img.shape
pad0,pad1 =(sz - shape[0]%sz)%sz,(sz - shape[1]%sz)%sz
img = np.pad(img,[[pad0//2,pad0-pad0//2],[pad1//2,pad1-pad1//2],[0,0]],constant_values=255)
img = img.reshap... | import lightgbm as lgb
from lightgbm import LGBMClassifier | Titanic - Machine Learning from Disaster |
14,206,631 | model.compile(optimizer = tf.keras.optimizers.Adam(lr= 1e-05), loss= tf.nn.sigmoid_cross_entropy_with_logits)
model.load_weights('.. /input/pandaenetb042x256x256x3/efficientnet-b0-48-full-epochs60.h5')
if os.path.exists(PRED_PATH):
predictions10 = []
for index, row in tqdm(df.iterrows() , total = df.shape[0]):
image_... | params= {
"learning_rate":0.03,
"num_leaves" : 63,
"boosting_type":'gbdt',
"objective":'binary',
"metric": 'binary_logloss,auc',
"feature_fraction": 0.85,
"bagging_freq" : 10,
"bagging_fraction" : 0.85,
"n_estimators":750,
"max_bin":300,
"subsample_for_bin":50000,
"min_data_in_leaf": 55,
"min_sum_hessian_in_leaf" :5.0,... | Titanic - Machine Learning from Disaster |
14,206,631 | class ConvNet(tf.keras.Model):
def __init__(self, engine, input_shape, weights):
super(ConvNet, self ).__init__()
self.engine = engine(
include_top=False, input_shape=input_shape, weights=weights)
self.avg_pool2d = tf.keras.layers.GlobalAveragePooling2D()
self.dropout = tf.keras.layers.Dropout(0.5)
self.dense_1 = tf... | model = LGBMClassifier(learning_rate = 0.03,
num_leaves=63,
boosting_type='gbdt',
objective = 'binary',
metric= 'binary_logloss,auc',
n_estimators= 750,
max_bin = 300,
subsample_for_bin = 50000,
min_data_in_leaf= 55,
min_sum_hessian_in_leaf = 7.5,
feature_fraction = 0.85,
bagging_freq = 10,
bagging_fraction = 0.85,
) | Titanic - Machine Learning from Disaster |
14,206,631 | model.compile(optimizer = tf.keras.optimizers.Adam(lr= 1e-05), loss= tf.nn.sigmoid_cross_entropy_with_logits)
model.load_weights('.. /input/pandaenetb042x256x256x3/efficientnet-b1-48-full-epochs60.h5')
if os.path.exists(PRED_PATH):
predictions20 = []
for index, row in tqdm(df.iterrows() , total = df.shape[0]):
image_... | model.fit(X,y)
y_pred = model.predict(X)
print(roc_auc_score(y_pred,y)*100 ) | Titanic - Machine Learning from Disaster |
14,206,631 | class ConvNet(tf.keras.Model):
def __init__(self, engine, input_shape, weights):
super(ConvNet, self ).__init__()
self.engine = engine(
include_top=False, input_shape=input_shape, weights=weights)
self.avg_pool2d = tf.keras.layers.GlobalAveragePooling2D()
self.dropout = tf.keras.layers.Dropout(0.5)
self.dense_1 = tf... | predictions = model.predict(test_data ) | Titanic - Machine Learning from Disaster |
14,206,631 | model.compile(optimizer = tf.keras.optimizers.Adam(lr= 1e-05), loss= tf.nn.sigmoid_cross_entropy_with_logits)
model.load_weights('.. /input/pandaenetb042x256x256x3/efficientnet-b2-48-full-epochs60.h5')
if os.path.exists(PRED_PATH):
predictions30 = []
for index, row in tqdm(df.iterrows() , total = df.shape[0]):
image_... | output = pd.DataFrame({'PassengerId': test_data.index, 'Survived': predictions})
output.to_csv('my_submission34.csv', index=False ) | Titanic - Machine Learning from Disaster |
14,206,631 | <choose_model_class><EOS> | Titanic - Machine Learning from Disaster | |
14,198,732 | <SOS> metric: categorizationaccuracy Kaggle data source: titanic-machine-learning-from-disaster<categorify> | import numpy as np
import pandas as pd | Titanic - Machine Learning from Disaster |
14,198,732 | sz = 256
N = 42
def tile(img):
result = []
shape = img.shape
pad0,pad1 =(sz - shape[0]%sz)%sz,(sz - shape[1]%sz)%sz
img = np.pad(img,[[pad0//2,pad0-pad0//2],[pad1//2,pad1-pad1//2],[0,0]],constant_values=255)
img = img.reshape(img.shape[0]//sz,sz,img.shape[1]//sz,sz,3)
img = img.transpose(0,2,1,3,4 ).reshape(-1,sz,sz,... | train_data = pd.read_csv('.. /input/titanic/train.csv')
test_data = pd.read_csv('.. /input/titanic/test.csv')
test_ids = test_data['PassengerId']
data = pd.concat([train_data, test_data], axis=0)
data.head() | Titanic - Machine Learning from Disaster |
14,198,732 | model.compile(optimizer = tf.keras.optimizers.Adam(lr= 1e-05), loss= tf.nn.sigmoid_cross_entropy_with_logits)
model.load_weights('.. /input/pandaenetb042x256x256x3/efficientnet-b0-fold0-epochs40.h5')
if os.path.exists(PRED_PATH):
predictions50 = []
for index, row in tqdm(df.iterrows() , total = df.shape[0]):
image_id... | data = data.drop(['Cabin', 'Ticket'], axis=1 ) | Titanic - Machine Learning from Disaster |
14,198,732 | model.compile(optimizer = tf.keras.optimizers.Adam(lr= 1e-05), loss= tf.nn.sigmoid_cross_entropy_with_logits)
model.load_weights('.. /input/pandaenetb042x256x256x3/efficientnet-b0-fold4-epochs60.h5')
if os.path.exists(PRED_PATH):
predictions60 = []
for index, row in tqdm(df.iterrows() , total = df.shape[0]):
image_id... | data['Title'] = [value.split(', ')[1].split('.')[0] for value in data['Name'].values]
data.loc[(data['Title'] == 'Lady')|(data['Title'] == 'Mme')|(data['Title'] == 'Ms')|(data['Title'] == 'the Countess')|(data['Title'] == 'Mlle'), 'Title'] = 'Miss'
data.loc[(data['Title'] != 'Mr')&(data['Title'] != 'Mrs')&(data['Title'... | Titanic - Machine Learning from Disaster |
14,198,732 | model.compile(optimizer = tf.keras.optimizers.Adam(lr= 1e-05), loss= tf.nn.sigmoid_cross_entropy_with_logits)
model.load_weights('.. /input/pandaenetb042x256x256x3/efficientnet-b0-fold2-epochs40.h5')
if os.path.exists(PRED_PATH):
predictions70 = []
for index, row in tqdm(df.iterrows() , total = df.shape[0]):
image_id... | data['Alone'] = np.zeros(data.shape[0], dtype=np.int)
data.loc[(data['SibSp'] == 0)&(data['Parch'] == 0), 'Alone'] = 1
data['FamilyCount'] = data['SibSp'] + data['Parch']
data.head() | Titanic - Machine Learning from Disaster |
14,198,732 | PREDS =(1/5)*PREDS +(1/5)*PREDS1 +(1/5)*PREDS2 +(1/5)*PREDS3 +(1/5)*PREDS4
FINAL = np.round(( 6/10)*PREDS +
(2/60)*np.array(predictions10)+(2/60)*np.array(predictions12)+
(2/60)*np.array(predictions20)+(2/60)*np.array(predictions22)+
(2/60)*np.array(predictions30)+(2/60)*np.array(predictions32)+
(0.5/10)*np.array(p... | np.random.seed(seed=157)
mean_ages_title = data[['Title', 'Age']].loc[data['Age'].notna() ].groupby('Title' ).mean()
age_nan_titles = data['Title'].loc[data['Age'].isnull() ].unique()
for i in range(len(age_nan_titles)) :
if age_nan_titles[i] == 'Mr' or age_nan_titles[i] == 'Mrs' or age_nan_titles[i] == 'Miss':
data.l... | Titanic - Machine Learning from Disaster |
14,198,732 | DEBUG = False<define_variables> | _, age_categories = pd.cut(data['Age'], 5, retbins=True)
age_categories = [np.floor(value)for value in age_categories]
data['AgeRange'] = np.zeros(len(data), dtype=np.int)
data.loc[data['Age'] <= age_categories[1], 'AgeRange'] = 0
data.loc[(data['Age'] > age_categories[1])&(data['Age'] <= age_categories[2]), 'AgeRang... | Titanic - Machine Learning from Disaster |
14,198,732 | sys.path = [
'.. /input/efficientnet-pytorch/EfficientNet-PyTorch/EfficientNet-PyTorch-master',
] + sys.path<import_modules> | _, fare_categories = pd.qcut(data['Fare'], 4, retbins=True)
data['FareRange'] = np.zeros(len(data), dtype=np.int)
data.loc[data['Fare'] <= fare_categories[1], 'FareRange'] = 0
data.loc[(data['Fare'] > fare_categories[1])&(data['Fare'] <= fare_categories[2]), 'FareRange'] = 1
data.loc[(data['Fare'] > fare_categories[2... | Titanic - Machine Learning from Disaster |
14,198,732 | import skimage.io
import numpy as np
import pandas as pd
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader, Dataset
from efficientnet_pytorch import model as enet
import matplotlib.pyplot as plt
from tqdm import tqdm_notebook as tqdm
<load_from_csv> | data = pd.get_dummies(data=data, columns=['Sex', 'Embarked', 'Pclass', 'AgeRange', 'FareRange', 'Title'], dtype=np.int ) | Titanic - Machine Learning from Disaster |
14,198,732 | data_dir = '.. /input/prostate-cancer-grade-assessment'
df_train = pd.read_csv(os.path.join(data_dir, 'train.csv'))
df_test = pd.read_csv(os.path.join(data_dir, 'test.csv'))
df_sub = pd.read_csv(os.path.join(data_dir, 'sample_submission.csv'))
model_dir = '.. /input/panda-enet-b1-model/'
image_folder = os.path.join(dat... | data = data.drop(['Name', 'Age', 'Fare', 'SibSp', 'Parch'], axis=1 ) | Titanic - Machine Learning from Disaster |
14,198,732 | class enetv2(nn.Module):
def __init__(self, backbone, out_dim):
super(enetv2, self ).__init__()
self.enet = enet.EfficientNet.from_name(backbone)
self.myfc = nn.Linear(self.enet._fc.in_features, out_dim)
self.enet._fc = nn.Identity()
def extract(self, x):
return self.enet(x)
def forward(self, x):
x = self.extract(x)... | X_train = data.loc[data['PassengerId'].isin(test_ids)== False]
y_train = X_train['Survived']
X_test = data.loc[data['PassengerId'].isin(test_ids)]
X_train = X_train.drop(['PassengerId', 'Survived'], axis=1)
X_test = X_test.drop(['PassengerId', 'Survived'], axis=1)
X_train, X_val, y_train, y_val = train_test_split(X_t... | Titanic - Machine Learning from Disaster |
14,198,732 | def get_tiles(img, mode=0):
result = []
h, w, c = img.shape
pad_h =(tile_size - h % tile_size)% tile_size +(( tile_size * mode)// 2)
pad_w =(tile_size - w % tile_size)% tile_size +(( tile_size * mode)// 2)
img2 = np.pad(img,[[pad_h // 2, pad_h - pad_h // 2], [pad_w // 2,pad_w - pad_w//2], [0,0]], constant_values=255)... | scaler = StandardScaler()
column_names = X_train.columns
X_train = pd.DataFrame(scaler.fit_transform(X_train), columns=column_names)
column_names = X_val.columns
X_val = pd.DataFrame(scaler.fit_transform(X_val), columns=column_names)
column_names = X_test.columns
X_test = pd.DataFrame(scaler.fit_transform(X_test), co... | Titanic - Machine Learning from Disaster |
14,198,732 | dataset = PANDADataset(df, image_size, n_tiles,0)
loader = DataLoader(dataset, batch_size=batch_size, num_workers=num_workers, shuffle=False)
dataset2 = PANDADataset(df, image_size, n_tiles, 2)
loader2 = DataLoader(dataset2, batch_size=batch_size, num_workers=num_workers, shuffle=False )<save_to_csv> | init_tanh = glorot_uniform(seed=157)
model = Sequential()
model.add(Dense(units=X_train.shape[1], kernel_initializer=init_tanh, kernel_constraint=maxnorm(3)))
model.add(BatchNormalization())
model.add(Activation(tanh))
model.add(Dense(units=X_train.shape[1] * 2, kernel_initializer=init_tanh, kernel_constraint=maxnor... | Titanic - Machine Learning from Disaster |
14,198,732 | LOGITS = []
LOGITS2 = []
with torch.no_grad() :
for data in tqdm(loader):
data = data.to(device)
logits = models[0](data)
LOGITS.append(logits)
for data in tqdm(loader2):
data = data.to(device)
logits = models[0](data)
LOGITS2.append(logits)
LOGITS =(torch.cat(LOGITS ).sigmoid().cpu() + torch.cat(LOGITS2 ).sigmoi... | os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = ""
os.environ['PYTHONHASHSEED'] = str(157)
random.seed(157)
np.random.seed(157)
tf.compat.v1.set_random_seed(157)
session_conf = tf.compat.v1.ConfigProto(intra_op_parallelism_threads=1,
inter_op_parallelism_threads=1)
sess = tf.com... | Titanic - Machine Learning from Disaster |
14,198,732 | DEBUG = False<define_variables> | opt = Adam(learning_rate=0.0009, amsgrad=True)
model.compile(optimizer=opt,
loss='binary_crossentropy',
metrics=['accuracy'])
checkpoint = ModelCheckpoint('neural_network_checkpoint_training.h5', monitor='val_loss', verbose=1, save_best_only=True, mode='min')
tensorboard = TensorBoard(log_dir='./logs',
histogram_fre... | Titanic - Machine Learning from Disaster |
14,198,732 | sys.path = [
'.. /input/efficientnet-pytorch/EfficientNet-PyTorch/EfficientNet-PyTorch-master',
] + sys.path<import_modules> | clf = load_model('neural_network_checkpoint_training.h5')
threshold = 0.5
test_prediction = clf.predict(X_test.values ).flatten()
test_prediction[test_prediction <= threshold] = 0
test_prediction[test_prediction > threshold] = 1
test_prediction = pd.DataFrame(test_prediction, columns=['Survived'], dtype=np.int)
predi... | Titanic - Machine Learning from Disaster |
14,109,563 | import skimage.io
import numpy as np
import pandas as pd
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader, Dataset
from efficientnet_pytorch import model as enet
import matplotlib.pyplot as plt
from tqdm import tqdm_notebook as tqdm
<load_from_csv> | train_data = pd.read_csv(".. /input/titanic/train.csv")
train_data.head() | Titanic - Machine Learning from Disaster |
14,109,563 | data_dir = '.. /input/prostate-cancer-grade-assessment'
df_train = pd.read_csv(os.path.join(data_dir, 'train.csv'))
df_test = pd.read_csv(os.path.join(data_dir, 'test.csv'))
df_sub = pd.read_csv(os.path.join(data_dir, 'sample_submission.csv'))
model_dir = '.. /input/panda-enet-b1-model/'
image_folder = os.path.join(dat... | test_data = pd.read_csv(".. /input/titanic/test.csv")
test_data.head() | Titanic - Machine Learning from Disaster |
14,109,563 | class enetv2(nn.Module):
def __init__(self, backbone, out_dim):
super(enetv2, self ).__init__()
self.enet = enet.EfficientNet.from_name(backbone)
self.myfc = nn.Linear(self.enet._fc.in_features, out_dim)
self.enet._fc = nn.Identity()
def extract(self, x):
return self.enet(x)
def forward(self, x):
x = self.extract(x)... | data_dt.isna().sum() | Titanic - Machine Learning from Disaster |
14,109,563 | def get_tiles(img, mode=0):
result = []
h, w, c = img.shape
pad_h =(tile_size - h % tile_size)% tile_size +(( tile_size * mode)// 2)
pad_w =(tile_size - w % tile_size)% tile_size +(( tile_size * mode)// 2)
img2 = np.pad(img,[[pad_h // 2, pad_h - pad_h // 2], [pad_w // 2,pad_w - pad_w//2], [0,0]], constant_values=255)... | data_dt.drop(['Cabin'], axis=1, inplace=True)
data_dt.head() | Titanic - Machine Learning from Disaster |
14,109,563 | dataset = PANDADataset(df, image_size, n_tiles,0)
loader = DataLoader(dataset, batch_size=batch_size, num_workers=num_workers, shuffle=False)
dataset2 = PANDADataset(df, image_size, n_tiles, 2)
loader2 = DataLoader(dataset2, batch_size=batch_size, num_workers=num_workers, shuffle=False )<save_to_csv> | data_dt_proc = data_dt.copy()
print("Mediana de idade: {}".format(data_dt_proc.Age.median()))
data_dt_proc.Age.fillna(data_dt_proc.Age.median() , inplace=True ) | Titanic - Machine Learning from Disaster |
14,109,563 | LOGITS = []
LOGITS2 = []
with torch.no_grad() :
for data in tqdm(loader):
data = data.to(device)
logits = models[0](data)
LOGITS.append(logits)
for data in tqdm(loader2):
data = data.to(device)
logits = models[0](data)
LOGITS2.append(logits)
LOGITS =(torch.cat(LOGITS ).sigmoid().cpu() + torch.cat(LOGITS2 ).sigmoi... | data_dt_proc.Fare.fillna(data_dt_proc.Fare.median() , inplace=True ) | Titanic - Machine Learning from Disaster |
14,109,563 | sys.path = [
'.. /input/efficientnet-pytorch/EfficientNet-PyTorch/EfficientNet-PyTorch-master',
] + sys.path
<import_modules> | data_dt_proc.isna().sum() | Titanic - Machine Learning from Disaster |
14,109,563 | import skimage.io
import numpy as np
import pandas as pd
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader, Dataset
from efficientnet_pytorch import model as enet
import matplotlib.pyplot as plt
from tqdm import tqdm_notebook as tqdm<load_from_csv> | data_dt_proc.drop(['Ticket', 'PassengerId'], axis=1, inplace=True ) | Titanic - Machine Learning from Disaster |
14,109,563 | data_dir = '.. /input/prostate-cancer-grade-assessment'
df_train = pd.read_csv(os.path.join(data_dir, 'train.csv'))
df_test = pd.read_csv(os.path.join(data_dir, 'test.csv'))
df_sub = pd.read_csv(os.path.join(data_dir, 'sample_submission.csv'))
model_dir = '.. /input/panda-enet-b1-model/'
image_folder = os.path.join(dat... | data_dt_proc['Title'] = data_dt_proc['Name'].str.split(", ", expand=True)[1].str.split(".", expand=True)[0]
data_dt_proc.head() | Titanic - Machine Learning from Disaster |
14,109,563 | class enetv2(nn.Module):
def __init__(self, backbone, out_dim):
super(enetv2, self ).__init__()
self.enet = enet.EfficientNet.from_name(backbone)
self.myfc = nn.Linear(self.enet._fc.in_features, out_dim)
self.enet._fc = nn.Identity()
def extract(self, x):
return self.enet(x)
def forward(self, x):
x = self.extract(x)... | data_dt_proc['Title'].value_counts()
| Titanic - Machine Learning from Disaster |
14,109,563 | def get_tiles(img, mode=0):
result = []
h, w, c = img.shape
pad_h =(tile_size - h % tile_size)% tile_size +(( tile_size * mode)// 2)
pad_w =(tile_size - w % tile_size)% tile_size +(( tile_size * mode)// 2)
img2 = np.pad(img,[[pad_h // 2, pad_h - pad_h // 2], [pad_w // 2,pad_w - pad_w//2], [0,0]], constant_values=255)... | print(len(data_dt_proc['Title'].value_counts())) | Titanic - Machine Learning from Disaster |
14,109,563 | dataset = PANDADataset(df, image_size, n_tiles,0)
loader = DataLoader(dataset, batch_size=batch_size, num_workers=num_workers, shuffle=False)
dataset2 = PANDADataset(df, image_size, n_tiles, 2)
loader2 = DataLoader(dataset2, batch_size=batch_size, num_workers=num_workers, shuffle=False )<save_to_csv> | Title_Dictionary = {
"Capt": "Officer",
"Col": "Officer",
"Major": "Officer",
"Jonkheer": "Royalty",
"Don": "Royalty",
"Sir" : "Royalty",
"Dr": "Officer",
"Rev": "Officer",
"the Countess":"Royalty",
"Mme": "Mrs",
"Mlle": "Miss",
"Ms": "Mrs",
"Mr" : "Mr",
"Mrs" : "Mrs",
"Miss" : "Miss",
"Master" : "Master",
"Lady" : "Ro... | Titanic - Machine Learning from Disaster |
14,109,563 | LOGITS = []
LOGITS2 = []
with torch.no_grad() :
for data in tqdm(loader):
data = data.to(device)
logits = models[0](data)
LOGITS.append(logits)
for data in tqdm(loader2):
data = data.to(device)
logits = models[0](data)
LOGITS2.append(logits)
LOGITS =(torch.cat(LOGITS ).sigmoid().cpu() + torch.cat(LOGITS2 ).sigmoi... | data_dt_proc['FamilySize'] = data_dt_proc['Parch'] + data_dt_proc['SibSp'] + 1
data_dt_proc.head() | Titanic - Machine Learning from Disaster |
14,109,563 | DEBUG = False<define_variables> | grouped = data_dt_proc.groupby(['Sex', 'Pclass', 'Title'])
grouped.first() | Titanic - Machine Learning from Disaster |
14,109,563 | sys.path = ['.. /input/efficientnet-pytorch/EfficientNet-PyTorch/EfficientNet-PyTorch-master', ] + sys.path<import_modules> | def fill_age(row, grouped_median):
condition =(
(grouped_median['Sex'] == row['Sex'])&
(grouped_median['Pclass'] == row['Pclass'])&
(grouped_median['Title'] == row['Title'])
)
return grouped_median[condition]['Age'].values[0]
data_dt['Title'] = data_dt_proc['Title']
print("Idade com mediana: {}".format(data_dt_proc[... | Titanic - Machine Learning from Disaster |
14,109,563 | import numpy as np
import pandas as pd
import skimage.io
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader, Dataset
from efficientnet_pytorch import model as enet
import matplotlib.pyplot as plt
from tqdm import tqdm_notebook as tqdm<load_from_csv> | data_dt[(data_dt['Title'] == "Master")& data_dt['Age'].isna() ] | Titanic - Machine Learning from Disaster |
14,109,563 | data_dir = '.. /input/prostate-cancer-grade-assessment'
df_train = pd.read_csv(os.path.join(data_dir, 'train.csv'))
df_test = pd.read_csv(os.path.join(data_dir, 'test.csv'))
df_sub = pd.read_csv(os.path.join(data_dir, 'sample_submission.csv'))
model_dir = '.. /input/panda-public-models'
image_folder = os.path.join(data... | data_dt[(data_dt['Title'] == "Master")] | Titanic - Machine Learning from Disaster |
14,109,563 | class enetv2(nn.Module):
def __init__(self, backbone, out_dim):
super(enetv2, self ).__init__()
self.enet = enet.EfficientNet.from_name(backbone)
self.myfc = nn.Linear(self.enet._fc.in_features, out_dim)
self.enet._fc = nn.Identity()
def extract(self, x):
return self.enet(x)
def forward(self, x):
x = self.extract(x)... | data_dt.drop(['Title'], axis=1, inplace=True)
data_dt_proc.drop(['Name'], axis=1, inplace=True)
| Titanic - Machine Learning from Disaster |
14,109,563 | def get_tiles(img, mode=0):
result = []
h, w, c = img.shape
pad_h =(tile_size - h % tile_size)% tile_size +(( tile_size * mode)// 2)
pad_w =(tile_size - w % tile_size)% tile_size +(( tile_size * mode)// 2)
img2 = np.pad(img,[[pad_h // 2, pad_h - pad_h // 2], [pad_w // 2,pad_w - pad_w//2], [0,0]], constant_values=255)... | data_dt_proc_pre_dummies = data_dt_proc.copy()
data_dt_proc = pd.get_dummies(data_dt_proc)
data_dt_proc.head() | Titanic - Machine Learning from Disaster |
14,109,563 | dataset = PANDADataset(df, image_size, n_tiles, 0)
loader = DataLoader(dataset, batch_size=batch_size, num_workers=num_workers, shuffle=False)
dataset2 = PANDADataset(df, image_size, n_tiles, 2)
loader2 = DataLoader(dataset2, batch_size=batch_size, num_workers=num_workers, shuffle=False )<save_to_csv> | data_dt_proc['Pclass_1'] = pd.get_dummies(data_dt_proc['Pclass'])[1]
data_dt_proc['Pclass_2'] = pd.get_dummies(data_dt_proc['Pclass'])[2]
data_dt_proc['Pclass_3'] = pd.get_dummies(data_dt_proc['Pclass'])[3]
data_dt_proc.head() | Titanic - Machine Learning from Disaster |
14,109,563 | LOGITS = []
LOGITS2 = []
with torch.no_grad() :
for data in tqdm(loader):
data = data.to(device)
logits = models[0](data)
LOGITS.append(logits)
for data in tqdm(loader2):
data = data.to(device)
logits = models[0](data)
LOGITS2.append(logits)
LOGITS =(torch.cat(LOGITS ).sigmoid().cpu() + torch.cat(LOGITS2 ).sigmoi... | x = np.concatenate([np.ones(25), np.ones(10), np.zeros(25), np.zeros(40)])
y = np.concatenate([np.ones(25), np.zeros(10), np.ones(25), np.zeros(40)])
print("Acurácia {:.2f}".format(accuracy_score(y,x)))
print("Precisão {:.2f}".format(precision_score(y,x)))
print("Recall {:.2f}".format(recall_score(y,x)))
print("F1... | Titanic - Machine Learning from Disaster |
14,109,563 | DEBUG = False<define_variables> | %matplotlib inline
plt.figure(figsize=(10,10))
sns.countplot(x='Survived',data=train_data ) | Titanic - Machine Learning from Disaster |
14,109,563 | sys.path = ['.. /input/efficientnet-pytorch/EfficientNet-PyTorch/EfficientNet-PyTorch-master', ] + sys.path<import_modules> | print("Classificador One Rule:")
women = train_data[train_data.Sex == 'female']["Survived"]
rate_women = sum(women)/len(women)*100
print("% de mulheres que sobrevivem: {:.2f}".format(rate_women)) | Titanic - Machine Learning from Disaster |
14,109,563 | import numpy as np
import pandas as pd
import skimage.io
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader, Dataset
from efficientnet_pytorch import model as enet
import matplotlib.pyplot as plt
from tqdm import tqdm_notebook as tqdm<load_from_csv> | data_dt_proc_pre_dummies['Sex'] = pd.factorize(data_dt_proc_pre_dummies['Sex'])[0]
data_dt_proc_pre_dummies['Embarked'] = pd.factorize(data_dt_proc_pre_dummies['Embarked'])[0]
data_dt_proc_pre_dummies['Title'] = pd.factorize(data_dt_proc_pre_dummies['Title'])[0] | Titanic - Machine Learning from Disaster |
14,109,563 | data_dir = '.. /input/prostate-cancer-grade-assessment'
df_train = pd.read_csv(os.path.join(data_dir, 'train.csv'))
df_test = pd.read_csv(os.path.join(data_dir, 'test.csv'))
df_sub = pd.read_csv(os.path.join(data_dir, 'sample_submission.csv'))
model_dir = '.. /input/panda-public-models'
image_folder = os.path.join(data... | y = train_data['Survived']
data_dt_proc.drop(['Survived'], axis=1, inplace=True)
features_all = ["Pclass", "Age", "Pclass_1","Pclass_2","Pclass_3", "Fare", "Sex_female","Sex_male", "SibSp", "Parch", 'FamilySize', 'Embarked_C', 'Embarked_Q', 'Embarked_S',
'Title_Master', 'Title_Miss', 'Title_Mr', 'Title_Mrs', 'Title_Of... | Titanic - Machine Learning from Disaster |
14,109,563 | class enetv2(nn.Module):
def __init__(self, backbone, out_dim):
super(enetv2, self ).__init__()
self.enet = enet.EfficientNet.from_name(backbone)
self.myfc = nn.Linear(self.enet._fc.in_features, out_dim)
self.enet._fc = nn.Identity()
def extract(self, x):
return self.enet(x)
def forward(self, x):
x = self.extract(x)... | def warn(*args, **kwargs):
pass
warnings.warn = warn
def run_all_classifiers(x, y, x_test, y_test):
classifiers = {
'knn': KNeighborsClassifier(1),
'svm': SVC(probability=True),
'decision_tree' : DecisionTreeClassifier() ,
'random_forest' : RandomForestClassifier(n_estimators=50, max_depth=5, random_state=1),
'ada_boos... | Titanic - Machine Learning from Disaster |
14,109,563 | def get_tiles(img, mode=0):
result = []
h, w, c = img.shape
pad_h =(tile_size - h % tile_size)% tile_size +(( tile_size * mode)// 2)
pad_w =(tile_size - w % tile_size)% tile_size +(( tile_size * mode)// 2)
img2 = np.pad(img,[[pad_h // 2, pad_h - pad_h // 2], [pad_w // 2,pad_w - pad_w//2], [0,0]], constant_values=255)... | print("Resultado com todas as características: ")
all_results_all, best_acc_all = run_all_classifiers(train_data_all, y, test_data_all, y_test ) | Titanic - Machine Learning from Disaster |
14,109,563 | dataset = PANDADataset(df, image_size, n_tiles, 0)
loader = DataLoader(dataset, batch_size=batch_size, num_workers=num_workers, shuffle=False)
dataset2 = PANDADataset(df, image_size, n_tiles, 2)
loader2 = DataLoader(dataset2, batch_size=batch_size, num_workers=num_workers, shuffle=False )<save_to_csv> | print("Resultado com as características selecionadas apenas via correlação: ")
all_results_selected, best_acc_selected = run_all_classifiers(train_data_selected, y, test_data_selected, y_test ) | Titanic - Machine Learning from Disaster |
14,109,563 | LOGITS = []
LOGITS2 = []
with torch.no_grad() :
for data in tqdm(loader):
data = data.to(device)
logits = models[0](data)
LOGITS.append(logits)
for data in tqdm(loader2):
data = data.to(device)
logits = models[0](data)
LOGITS2.append(logits)
LOGITS =(torch.cat(LOGITS ).sigmoid().cpu() + torch.cat(LOGITS2 ).sigmoi... | print("Resultado com as características selecionadas manualmente: ")
all_results_proc, best_acc_proc = run_all_classifiers(train_data_proc, y, test_data_proc, y_test ) | Titanic - Machine Learning from Disaster |
14,109,563 | poisson = pd.read_csv('/kaggle/input/m5-first-public-notebook-under-0-50/submission.csv')
tweedie = pd.read_csv('/kaggle/input/m5-accuracy-tweedie-is-back/submission.csv' )<save_to_csv> | output = pd.DataFrame({'PassengerId': test_data.PassengerId, 'Survived': all_results_proc[best_acc_proc['classifier']] })
output.to_csv('my_submission.csv', index=False)
print("Arquivo de submissão gerado com sucesso!!" ) | Titanic - Machine Learning from Disaster |
14,109,563 | poisson = poisson.sort_values(by = 'id' ).reset_index(drop = True)
tweedie = tweedie.sort_values(by = 'id' ).reset_index(drop = True)
sub = poisson.copy()
for i in sub.columns :
if i != 'id' :
sub[i] = 0.35*poisson[i] + 0.65*tweedie[i]
sub.to_csv('submission.csv', index = False )<import_modules> | y_gender = pd.read_csv(".. /input/titanic/gender_submission.csv")
y_gender = y_gender['Survived']
acc_gender = round(accuracy_score(y_gender, y_test)* 100, 2)
print("Acurácia com Genero: {}".format(acc_gender))
print("Diferença com proc de {:.2f}%".format(best_acc_proc['accuracy'] - acc_gender))
print("Diferença com ... | Titanic - Machine Learning from Disaster |
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