Kaggle competition: ALASKA2 Image Steganalysis
<SOS> metric: WeightedAUC Kaggle data source: alaska2-image-steganalysis<save_to_csv>
!pip install -q efficientnet_pytorch > /dev/null
submission = pd.read_csv('.. /input/tmdb-box-office-prediction/sample_submission.csv')
 submission['revenue'] = np.expm1(test_pred)
 submission.to_csv("submission.csv", index=False)
 plt.show()
submission.head()<load_from_csv>
SEED = 42
def seed_everything(seed):
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
torch.backends.cudnn.benchmark = True
seed_everything(SEED )
sns.set()
train_orig = pd.read_csv('.. /input/train.csv')
 test_orig = pd.read_csv('.. /input/test.csv')
 subm = pd.DataFrame()
subm['id'] = test_orig.id.values<data_type_conversions>
train_orig['bool_belongs_to_collection'] =(train_orig['belongs_to_collection'].notnull() ).astype(int)
 test_orig['bool_belongs_to_collection'] =(test_orig['belongs_to_collection'].notnull() ).astype(int )<feature_engineering>
def get_train_transforms() :
return A.Compose([
A.HorizontalFlip(p=0.5),
A.VerticalFlip(p=0.5),
A.Resize(height=512, width=512, p=1.0),
ToTensorV2(p=1.0),
], p=1.0)
 def get_valid_transforms() :
return A.Compose([
A.Resize(height=512, width=512, p=1.0),
ToTensorV2(p=1.0),
], p=1.0 )
train_orig['split'] = 'train'
test_orig['split'] = 'test'<concatenate>
DATA_ROOT_PATH = '.. /input/alaska2-image-steganalysis'
def onehot(size, target):
vec = torch.zeros(size, dtype=torch.float32)
 vec[target] = 1.
 return vec
class DatasetRetriever(Dataset):
def __init__(self, kinds, image_names, labels, transforms=None):
super().__init__()
self.kinds = kinds
self.image_names = image_names
self.labels = labels
self.transforms = transforms
def __getitem__(self, index: int):
kind, image_name, label = self.kinds[index], self.image_names[index], self.labels[index]
image = cv2.imread(f'{DATA_ROOT_PATH}/{kind}/{image_name}', cv2.IMREAD_COLOR)
 image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB ).astype(np.float32)
 image /= 255.0
if self.transforms:
sample = {'image': image}
sample = self.transforms(**sample)
 image = sample['image']
target = onehot(4, label)
 return image, target
def __len__(self)-> int:
return self.image_names.shape[0]
def get_labels(self):
return list(self.labels )
train_test = pd.concat([train_orig[['popularity','budget','split','bool_belongs_to_collection']], test_orig[['popularity','budget','split','bool_belongs_to_collection']]] )<count_values>
fold_number = 0
train_dataset = DatasetRetriever(
 kinds=dataset[dataset['fold'] != fold_number].kind.values,
image_names=dataset[dataset['fold'] != fold_number].image_name.values,
labels=dataset[dataset['fold'] != fold_number].label.values,
transforms=get_train_transforms() , 
)
 validation_dataset = DatasetRetriever(
 kinds=dataset[dataset['fold'] == fold_number].kind.values,
image_names=dataset[dataset['fold'] == fold_number].image_name.values,
labels=dataset[dataset['fold'] == fold_number].label.values,
transforms=get_valid_transforms() ,
)
print(len(train_orig.columns))
print(len(test_orig.columns))
olang = train_orig.original_language.value_counts() [train_orig.original_language.value_counts() >5].index.tolist()
print(olang)
 print(len(olang))
train_orig_sample = train_orig[train_orig.original_language.isin(olang)].copy()
print(train_orig_sample.original_language.value_counts() )<feature_engineering>
class AverageMeter(object):

 def __init__(self):
self.reset()
def reset(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
def update(self, val, n=1):
self.val = val
self.sum += val * n
self.count += n
self.avg = self.sum / self.count
def alaska_weighted_auc(y_true, y_valid):

 tpr_thresholds = [0.0, 0.4, 1.0]
weights = [2, 1]
fpr, tpr, thresholds = metrics.roc_curve(y_true, y_valid, pos_label=1)
 areas = np.array(tpr_thresholds[1:])- np.array(tpr_thresholds[:-1])
 normalization = np.dot(areas, weights)
 competition_metric = 0
for idx, weight in enumerate(weights):
y_min = tpr_thresholds[idx]
y_max = tpr_thresholds[idx + 1]
mask =(y_min < tpr)&(tpr < y_max)
 x_padding = np.linspace(fpr[mask][-1], 1, 100)
 x = np.concatenate([fpr[mask], x_padding])
 y = np.concatenate([tpr[mask], [y_max] * len(x_padding)])
 y = y - y_min
score = metrics.auc(x, y)
 submetric = score * weight
best_subscore =(y_max - y_min)* weight
competition_metric += submetric
return competition_metric / normalization
class RocAucMeter(object):
def __init__(self):
self.reset()
def reset(self):
self.y_true = np.array([0,1])
 self.y_pred = np.array([0.5,0.5])
 self.score = 0
def update(self, y_true, y_pred):
y_true = y_true.cpu().numpy().argmax(axis=1 ).clip(min=0, max=1 ).astype(int)
 y_pred = 1 - nn.functional.softmax(y_pred, dim=1 ).data.cpu().numpy() [:,0]
self.y_true = np.hstack(( self.y_true, y_true))
self.y_pred = np.hstack(( self.y_pred, y_pred))
self.score = alaska_weighted_auc(self.y_true, self.y_pred)
 @property
def avg(self):
return self.score
train_orig_sample.loc[:,'revenue'] = np.log(train_orig_sample['revenue'].fillna(0)+1 )<categorify>
class LabelSmoothing(nn.Module):
def __init__(self, smoothing = 0.05):
super(LabelSmoothing, self ).__init__()
self.confidence = 1.0 - smoothing
self.smoothing = smoothing
def forward(self, x, target):
if self.training:
x = x.float()
target = target.float()
logprobs = torch.nn.functional.log_softmax(x, dim = -1)
 nll_loss = -logprobs * target
nll_loss = nll_loss.sum(-1)
 smooth_loss = -logprobs.mean(dim=-1)
 loss = self.confidence * nll_loss + self.smoothing * smooth_loss
return loss.mean()
else:
return torch.nn.functional.cross_entropy(x, target )
train_olang = pd.get_dummies(train_orig.original_language)[olang]
train_orig = pd.concat([train_orig,train_olang], axis=1)
<count_values>
warnings.filterwarnings("ignore")
 class Fitter:
def __init__(self, model, device, config):
self.config = config
self.epoch = 0
self.base_dir = './'
self.log_path = f'{self.base_dir}/log.txt'
self.best_summary_loss = 10**5
self.model = model
self.device = device
param_optimizer = list(self.model.named_parameters())
 no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
optimizer_grouped_parameters = [
{'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)], 'weight_decay': 0.001},
{'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay)], 'weight_decay': 0.0}
]
self.optimizer = torch.optim.AdamW(self.model.parameters() , lr=config.lr)
 self.scheduler = config.SchedulerClass(self.optimizer, **config.scheduler_params)
 self.criterion = LabelSmoothing().to(self.device)
 self.log(f'Fitter prepared.Device is {self.device}')
 def fit(self, train_loader, validation_loader):
for e in range(self.config.n_epochs):
if self.config.verbose:
lr = self.optimizer.param_groups[0]['lr']
timestamp = datetime.utcnow().isoformat()
self.log(f'
{timestamp}
LR: {lr}')
 t = time.time()
summary_loss, final_scores = self.train_one_epoch(train_loader)
 self.log(f'[RESULT]: Train.Epoch: {self.epoch}, summary_loss: {summary_loss.avg:.5f}, final_score: {final_scores.avg:.5f}, time: {(time.time() - t):.5f}')
 self.save(f'{self.base_dir}/last-checkpoint.bin')
 t = time.time()
summary_loss, final_scores = self.validation(validation_loader)
 self.log(f'[RESULT]: Val.Epoch: {self.epoch}, summary_loss: {summary_loss.avg:.5f}, final_score: {final_scores.avg:.5f}, time: {(time.time() - t):.5f}')
 if summary_loss.avg < self.best_summary_loss:
self.best_summary_loss = summary_loss.avg
self.model.eval()
self.save(f'{self.base_dir}/best-checkpoint-{str(self.epoch ).zfill(3)}epoch.bin')
 for path in sorted(glob(f'{self.base_dir}/best-checkpoint-*epoch.bin')) [:-3]:
os.remove(path)
 if self.config.validation_scheduler:
self.scheduler.step(metrics=summary_loss.avg)
 self.epoch += 1
def validation(self, val_loader):
self.model.eval()
summary_loss = AverageMeter()
final_scores = RocAucMeter()
t = time.time()
for step,(images, targets)in enumerate(val_loader):
if self.config.verbose:
if step % self.config.verbose_step == 0:
print(
 f'Val Step {step}/{len(val_loader)}, ' + \
f'summary_loss: {summary_loss.avg:.5f}, final_score: {final_scores.avg:.5f}, ' + \
f'time: {(time.time() - t):.5f}', end='\r' 
)
 with torch.no_grad() :
targets = targets.to(self.device ).float()
batch_size = images.shape[0]
images = images.to(self.device ).float()
outputs = self.model(images)
 loss = self.criterion(outputs, targets)
 final_scores.update(targets, outputs)
 summary_loss.update(loss.detach().item() , batch_size)
 return summary_loss, final_scores
def train_one_epoch(self, train_loader):
self.model.train()
summary_loss = AverageMeter()
final_scores = RocAucMeter()
t = time.time()
for step,(images, targets)in enumerate(train_loader):
if self.config.verbose:
if step % self.config.verbose_step == 0:
print(
 f'Train Step {step}/{len(train_loader)}, ' + \
f'summary_loss: {summary_loss.avg:.5f}, final_score: {final_scores.avg:.5f}, ' + \
f'time: {(time.time() - t):.5f}', end='\r' 
)
 targets = targets.to(self.device ).float()
images = images.to(self.device ).float()
batch_size = images.shape[0]
self.optimizer.zero_grad()
outputs = self.model(images)
 loss = self.criterion(outputs, targets)
 loss.backward()
final_scores.update(targets, outputs)
 summary_loss.update(loss.detach().item() , batch_size)
 self.optimizer.step()
if self.config.step_scheduler:
self.scheduler.step()
return summary_loss, final_scores
def save(self, path):
self.model.eval()
torch.save({
'model_state_dict': self.model.state_dict() ,
'optimizer_state_dict': self.optimizer.state_dict() ,
'scheduler_state_dict': self.scheduler.state_dict() ,
'best_summary_loss': self.best_summary_loss,
'epoch': self.epoch,
}, path)
 def load(self, path):
checkpoint = torch.load(path)
 self.model.load_state_dict(checkpoint['model_state_dict'])
 self.optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
 self.scheduler.load_state_dict(checkpoint['scheduler_state_dict'])
 self.best_summary_loss = checkpoint['best_summary_loss']
self.epoch = checkpoint['epoch'] + 1
def log(self, message):
if self.config.verbose:
print(message)
 with open(self.log_path, 'a+')as logger:
logger.write(f'{message}
' )
def extract_id(cell):
return yaml.load(cell)[0]['id'] 
<categorify>
def get_net() :
net = EfficientNet.from_pretrained('efficientnet-b2')
 net._fc = nn.Linear(in_features=1408, out_features=4, bias=True)
 return net
net = get_net().cuda()
test_olang = pd.get_dummies(test_orig.original_language)[olang]
test_orig = pd.concat([test_orig,test_olang], axis=1)
<feature_engineering>
class TrainGlobalConfig:
num_workers = 4
batch_size = 16
n_epochs = 25
lr = 0.001
verbose = True
verbose_step = 1
step_scheduler = False
validation_scheduler = True
SchedulerClass = torch.optim.lr_scheduler.ReduceLROnPlateau
scheduler_params = dict(
 mode='min',
factor=0.5,
patience=1,
verbose=False,
threshold=0.0001,
threshold_mode='abs',
cooldown=0,
min_lr=1e-8,
eps=1e-08 
)
train_orig['cast_crew'] = train_orig.cast + ' ' + train_orig.crew
test_orig['cast_crew'] = test_orig.cast + ' ' + test_orig.crew 
<feature_engineering>
def run_training() :
device = torch.device('cuda:0')
 train_loader = torch.utils.data.DataLoader(
 train_dataset,
sampler=BalanceClassSampler(labels=train_dataset.get_labels() , mode="downsampling"),
batch_size=TrainGlobalConfig.batch_size,
pin_memory=False,
drop_last=True,
num_workers=TrainGlobalConfig.num_workers, 
)
 val_loader = torch.utils.data.DataLoader(
 validation_dataset,
batch_size=TrainGlobalConfig.batch_size,
num_workers=TrainGlobalConfig.num_workers,
shuffle=False,
sampler=SequentialSampler(validation_dataset),
pin_memory=False, 
)
 fitter = Fitter(model=net, device=device, config=TrainGlobalConfig)
 fitter.fit(train_loader, val_loader )
vec = TfidfVectorizer(analyzer='word',max_features=450,token_pattern=r"'name': ' (.*?)'")
 vec.fit(train_orig.cast_crew.fillna(''))
vocab = vec.get_feature_names()
vec = TfidfVectorizer(analyzer='word',vocabulary=vocab)
 train_crew_w = vec.fit_transform(train_orig.cast_crew.fillna(''))
test_crew_w = vec.transform(test_orig.cast_crew.fillna(''))
train_crew_w_cols = vec.get_feature_names()
train_crew_w_cols = ['crew_'+a for a in train_crew_w_cols]
print(train_crew_w.shape)
 print(test_crew_w.shape)
 print(train_crew_w_cols)
 train_crew_w = pd.DataFrame(train_crew_w.toarray() ,columns=train_crew_w_cols)
 test_crew_w = pd.DataFrame(test_crew_w.toarray() ,columns=train_crew_w_cols)
<feature_engineering>
file = open('.. /input/alaska2-public-baseline/log.txt', 'r')
 for line in file.readlines() :
print(line[:-1])
 file.close()
vec = TfidfVectorizer(analyzer='word',max_features=100,token_pattern=r"'name': ' (.*?)'")
 vec.fit(train_orig.production_companies.fillna(''))
vocab = vec.get_feature_names()
vec = TfidfVectorizer(analyzer='word',vocabulary=vocab)
 train_production_companies_w = vec.fit_transform(train_orig.production_companies.fillna(''))
test_production_companies_w = vec.transform(test_orig.production_companies.fillna(''))
train_production_companies_w_cols = vec.get_feature_names()
train_production_companies_w_cols = ['prod_comp_'+a for a in train_production_companies_w_cols]
print(train_production_companies_w.shape)
 print(test_production_companies_w.shape)
 print(train_production_companies_w_cols)
 train_production_companies_w = pd.DataFrame(train_production_companies_w.toarray() ,columns=train_production_companies_w_cols)
 test_production_companies_w = pd.DataFrame(test_production_companies_w.toarray() ,columns=train_production_companies_w_cols)
<feature_engineering>
checkpoint = torch.load('.. /input/alaska2-public-baseline/best-checkpoint-033epoch.bin')
 net.load_state_dict(checkpoint['model_state_dict']);
net.eval() ;
vec = TfidfVectorizer(analyzer='word',max_features=20,token_pattern=r"'name': ' (.*?)'")
 vec.fit(train_orig.production_countries.fillna(''))
vocab = vec.get_feature_names()
vec = TfidfVectorizer(analyzer='word',vocabulary=vocab)
 train_production_countries_w = vec.fit_transform(train_orig.production_countries.fillna(''))
test_production_countries_w = vec.transform(test_orig.production_countries.fillna(''))
train_production_countries_w_cols = vec.get_feature_names()
train_production_countries_w_cols = ['prod_country_'+a for a in train_production_countries_w_cols]
print(train_production_countries_w.shape)
 print(test_production_countries_w.shape)
 print(train_production_countries_w_cols)
 train_production_countries_w = pd.DataFrame(train_production_countries_w.toarray() ,columns=train_production_countries_w_cols)
 test_production_countries_w = pd.DataFrame(test_production_countries_w.toarray() ,columns=train_production_countries_w_cols)
<feature_engineering>
class DatasetSubmissionRetriever(Dataset):
def __init__(self, image_names, transforms=None):
super().__init__()
self.image_names = image_names
self.transforms = transforms
def __getitem__(self, index: int):
image_name = self.image_names[index]
image = cv2.imread(f'{DATA_ROOT_PATH}/Test/{image_name}', cv2.IMREAD_COLOR)
 image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB ).astype(np.float32)
 image /= 255.0
if self.transforms:
sample = {'image': image}
sample = self.transforms(**sample)
 image = sample['image']
return image_name, image
def __len__(self)-> int:
return self.image_names.shape[0]
vec = TfidfVectorizer(analyzer='word',max_features=50,token_pattern=r"'name': ' (.*?)'")
 train_belongs_to_collection_w = vec.fit_transform(train_orig.belongs_to_collection.fillna(''))
test_belongs_to_collection_w = vec.transform(test_orig.belongs_to_collection.fillna(''))
train_belongs_to_collection_w_cols = vec.get_feature_names()
train_belongs_to_collection_w_cols = ['collection_'+a for a in train_belongs_to_collection_w_cols]
print(train_belongs_to_collection_w.shape)
 print(test_belongs_to_collection_w.shape)
 print(train_belongs_to_collection_w_cols)
 train_belongs_to_collection_w = pd.DataFrame(train_belongs_to_collection_w.toarray() ,columns=train_belongs_to_collection_w_cols)
 test_belongs_to_collection_w = pd.DataFrame(test_belongs_to_collection_w.toarray() ,columns=train_belongs_to_collection_w_cols)
<categorify>
dataset = DatasetSubmissionRetriever(
 image_names=np.array([path.split('/')[-1] for path in glob('.. /input/alaska2-image-steganalysis/Test/*.jpg')]),
transforms=get_valid_transforms() , 
)
 data_loader = DataLoader(
 dataset,
batch_size=8,
shuffle=False,
num_workers=2,
drop_last=False,
)
<feature_engineering><EOS> 
submission = pd.DataFrame(result)
 submission.to_csv('submission.csv', index=False)
 submission.head()