Kaggle competition: Mechanisms of Action (MoA) Prediction
<SOS> metric: MeanColumnwiseLogLoss Kaggle data source: mechanisms-of-action-(moa)-prediction<load_from_csv>
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nY5bIayd0ItKMsm1Zlpm/j8q1Wpq5eJnmCLdIR/H3221215ppCaG6tA8QEX3WqzM83TXrv7FoYoSf6/7Iw82IkVljnGN3uCPt6s5kWktHgEnbBxX70KR66Yp5R+gwXGbcfG8DlSnq3uOyKeS7vBcG5kSjOGJA5wEGloxFB2eoyfFEam9H5lr5w5xU2xtMaFU2640BIYAILj8DHwszQ7g0VK1XO9NBwaEuz7DSACesQC48V2M681CEvT0/vEnZrfcULod2rby7gEEna22pa1ujcyfjrIQ95jveBB8syJOITPUDqCxiLVLfA8o0iPhFRSjl4EWCFhTWuMl/AQF1pS9ZTQAA', 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', 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'src/utils/splitter.py': 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', 'src/utils/transformers.py': 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'src/utils/cache.py': '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', 'src/utils/environment.py': 'H4sIALjOw18C/41Pu27DMAzc9RWEF8eLP8BAhg4NkK1Dl06GItMxAUVyKCpN/j7yI7BQdAi3I+/BE340CtLQZfQscLG99b/zph10aBcMe/jmiArvBkeB48z9ZPbc/EM9aBtQKdVhD4zXSIyv665a03pwXnLdsp+GNQXMM3blj4/gEDsQD+SCaGvXT+GEvWeEGMidQQYK8PF1rMsq5cufbuLZDNu/M3yr2YuZFzujtB3eyGDWaWNndVAiuyW8XhWFiZ0uJsWynmBNodU3TVafbPIETFlQmDEW1ew14c21TIdSPQG0FUw+vgEAAA=='}
for path, encoded in file_data.items() :
print(path)
 path = Path(path)
 path.parent.mkdir(exist_ok=True)
 path.write_bytes(gzip.decompress(base64.b64decode(encoded)))
 def run(command):
os.system('echo "
os.system('export PYTHONPATH=${PYTHONPATH}:/kaggle/working && ' + command)
 run('python setup.py develop --install-dir /kaggle/working')
 print('b4e58086057ee83c7234b744eb37e39aee01efc3' )
test_df = pd.read_csv(csvs['sample_submission'])
 test_df.head()<define_variables>
logger = LoggerFactory('notebook' ).getLogger('notebook')
 seed_everything()
labels = test_df.columns[1:].tolist()
num_classes = len(labels)
 num_classes<prepare_x_and_y>
(
train_features_df,
stage_1_1_target_cols,
stage_1_2_target_cols,
stage_1_train_features, 
)= preprocess.preprocess_train()
test_features_df, sample_submission_df = preprocess.preprocess_test(train_features_df=train_features_df )
y_train = np.zeros(( len(train_df), num_classes)).astype(int)
 for i, row in enumerate(train_df['labels'].str.split(',')) :
for label in row:
idx = labels.index(label)
 y_train[i, idx] = 1
y_train.shape<load_pretrained>
def get_feature(df):
features_g = list([x for x in df.columns if x.startswith("g-")])
 features_c = list([x for x in df.columns if x.startswith("c-")])
 df["g_sum"] = df[features_g].sum(axis=1)
 df["g_mean"] = df[features_g].mean(axis=1)
 df["g_median"] = df[features_g].median(axis=1)
 df["g_std"] = df[features_g].std(axis=1)
 df["g_kurt"] = df[features_g].kurtosis(axis=1)
 df["g_skew"] = df[features_g].skew(axis=1)
 df["c_sum"] = df[features_c].sum(axis=1)
 df["c_mean"] = df[features_c].mean(axis=1)
 df["c_std"] = df[features_c].std(axis=1)
 df["c_median"] = df[features_c].median(axis=1)
 df["c_kurt"] = df[features_c].kurtosis(axis=1)
 df["c_skew"] = df[features_c].skew(axis=1)
 df["gc_sum"] = df[features_g + features_c].sum(axis=1)
 df["gc_mean"] = df[features_g + features_c].mean(axis=1)
 df["gc_std"] = df[features_g + features_c].std(axis=1)
 df["gc_kurt"] = df[features_g + features_c].kurtosis(axis=1)
 df["gc_skew"] = df[features_g + features_c].skew(axis=1)
 df["gc_median"] = df[features_g + features_c].median(axis=1)
 return df
with open(mels['train_curated'], 'rb')as curated, open(mels['train_noisy'], 'rb')as noisy:
x_train = pickle.load(curated)
 print(len(x_train))
with open(mels['test'], 'rb')as test:
x_test = pickle.load(test)
 len(x_train), len(x_test )<train_model>
n_comp_CELLS = 60
train_features_df = get_feature(train_features_df)
 features_c = list([x for x in train_features_df.columns if x.startswith("c-")])
 pca = PCA(n_components=n_comp_CELLS, random_state=42)
 pca_feat = [f'pca_C-{i}' for i in range(n_comp_CELLS)]
train_features_df[pca_feat] = pca.fit_transform(train_features_df[features_c])
 transformer = normalizer(transformer='quantile', params=dict(n_quantiles=100, random_state=42, output_distribution="normal"), df=train_features_df.loc[:, stage_1_train_features])
 train_features_df[stage_1_train_features] = transformer.transform(train_features_df.loc[:, stage_1_train_features] )
class FATTrainDataset(Dataset):
def __init__(self, mels, labels, transforms):
super().__init__()
self.mels = mels
self.labels = labels
self.transforms = transforms
def __len__(self):
return len(self.mels)
 def __getitem__(self, idx):
image = Image.fromarray(self.mels[idx], mode='RGB')
 time_dim, base_dim = image.size
crop = random.randint(0, time_dim - 1*base_dim)
 image = image.crop([crop, 0, crop + 1*base_dim, base_dim])
 image = self.transforms(image ).div_(255)
 label = self.labels[idx]
label = torch.from_numpy(label ).float()
return image, label<categorify>
test_features_df = get_feature(test_features_df)
 test_features_df[pca_feat] = pca.transform(test_features_df[features_c])
 test_features_df[stage_1_train_features] = transformer.transform(test_features_df.loc[:, stage_1_train_features] )
class FATTestDataset(Dataset):
def __init__(self, fnames, mels, transforms, tta=5):
super().__init__()
self.fnames = fnames
self.mels = mels
self.transforms = transforms
self.tta = tta
def __len__(self):
return len(self.fnames)* self.tta
def __getitem__(self, idx):
new_idx = idx % len(self.fnames)
 image = Image.fromarray(self.mels[new_idx], mode='RGB')
 time_dim, base_dim = image.size
crop = random.randint(0, time_dim - 1*base_dim)
 image = image.crop([crop, 0, crop + 1*base_dim, base_dim])
 image = self.transforms(image ).div_(255)
 fname = self.fnames[new_idx]
return image, fname<set_options>
stage_1_train_features += pca_feat
def set_parameter_requires_grad(model, feature_extracting):
if feature_extracting:
for param in model.parameters() :
param.requires_grad = False<choose_model_class>
from typing import List
import pandas as pd
from src.utils.cache import Cache
from src.utils.splitter import splitter_for_moa
from src.metrics import logloss_for_multilabel
def initialize_model(model_name, num_classes, feature_extract, use_pretrained=False):
model_ft = None
input_size = 0
if model_name == "resnet18":

 model_ft = models.resnet18(pretrained=use_pretrained)
 set_parameter_requires_grad(model_ft, feature_extract)
 num_ftrs = model_ft.fc.in_features
model_ft.fc = nn.Linear(num_ftrs, num_classes)
 input_size = 224
elif model_name == "resnet34":

 model_ft = models.resnet34(pretrained=use_pretrained)
 set_parameter_requires_grad(model_ft, feature_extract)
 num_ftrs = model_ft.fc.in_features
model_ft.fc = nn.Linear(num_ftrs, num_classes)
 input_size = 224
elif model_name == "alexnet":

 model_ft = models.alexnet(pretrained=use_pretrained)
 set_parameter_requires_grad(model_ft, feature_extract)
 num_ftrs = model_ft.classifier[6].in_features
model_ft.classifier[6] = nn.Linear(num_ftrs,num_classes)
 input_size = 224
elif model_name == "vgg":

 model_ft = models.vgg11_bn(pretrained=use_pretrained)
 set_parameter_requires_grad(model_ft, feature_extract)
 num_ftrs = model_ft.classifier[6].in_features
model_ft.classifier[6] = nn.Linear(num_ftrs,num_classes)
 input_size = 224
elif model_name == "squeezenet":

 model_ft = models.squeezenet1_0(pretrained=use_pretrained)
 set_parameter_requires_grad(model_ft, feature_extract)
 model_ft.classifier[1] = nn.Conv2d(512, num_classes, kernel_size=(1,1), stride=(1,1))
model_ft.num_classes = num_classes
input_size = 224
elif model_name == "inception":

 model_ft = models.inception_v3(pretrained=use_pretrained)
 set_parameter_requires_grad(model_ft, feature_extract)
 num_ftrs = model_ft.AuxLogits.fc.in_features
model_ft.AuxLogits.fc = nn.Linear(num_ftrs, num_classes)
 num_ftrs = model_ft.fc.in_features
model_ft.fc = nn.Linear(num_ftrs,num_classes)
 input_size = 299
else:
print("Invalid model name, exiting...")
 exit()
return model_ft, input_size 
<define_search_model>
@Cache('./cache')
 def build_trainer(trainer: MoaBase, df: pd.DataFrame, predictors: List[str], target_cols: List[str], num_seed_blends: int, n_splits: int, params: dict):
folds = splitter_for_moa(df, target_cols, n_splits=n_splits)
 model = trainer(
 target_cols = target_cols,
categorical_cols=[],
ignore_cols=[],
num_seed_blends=num_seed_blends,
metric=logloss_for_multilabel,
params=params,
exp=None 
)
 model.train(df[predictors], df[target_cols], folds)
 return model
stage1_1_tabnet_trainer = build_trainer(
 trainer=Tabnet,
df=train_features_df,
target_cols=stage_1_1_target_cols,
predictors=stage_1_train_features,
num_seed_blends=5,
n_splits=7,
params={}, 
)
 stage1_2_tabnet_trainer = build_trainer(
 trainer=Tabnet,
df=train_features_df,
target_cols=stage_1_2_target_cols,
predictors=stage_1_train_features,
num_seed_blends=5,
n_splits=7,
params={},
)
class ConvBlock(nn.Module):
def __init__(self, in_channels, out_channels):
super().__init__()
self.conv1 = nn.Sequential(
 nn.Conv2d(in_channels, out_channels, 3, 1, 1),
nn.BatchNorm2d(out_channels),
nn.ReLU() , 
)
 self.conv2 = nn.Sequential(
 nn.Conv2d(out_channels, out_channels, 3, 1, 1),
nn.BatchNorm2d(out_channels),
nn.ReLU() , 
)
 self._init_weights()
def _init_weights(self):
for m in self.modules() :
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight)
 if m.bias is not None:
nn.init.zeros_(m.bias)
 elif isinstance(m, nn.BatchNorm2d):
nn.init.constant_(m.weight, 1)
 nn.init.zeros_(m.bias)
 def forward(self, x):
x = self.conv1(x)
 x = self.conv2(x)
 x = F.avg_pool2d(x, 2)
 return x<choose_model_class>
stage1_2_xgb_trainer = build_trainer(
 trainer=XGBTrainer,
df=train_features_df,
target_cols=stage_1_2_target_cols,
predictors=stage_1_train_features,
num_seed_blends=2,
n_splits=5,
params={},
)
class Classifier(nn.Module):
def __init__(self, num_classes):
super().__init__()
self.conv = nn.Sequential(
 ConvBlock(in_channels=3, out_channels=64),
ConvBlock(in_channels=64, out_channels=128),
ConvBlock(in_channels=128, out_channels=256),
ConvBlock(in_channels=256, out_channels=512), 
)
 self.fc = nn.Sequential(
 nn.Dropout(0.2),
nn.Linear(512, 128),
nn.LeakyReLU(0.2),
nn.BatchNorm1d(128),
nn.Dropout(0.1),
nn.Linear(128, num_classes), 
)
 def forward(self, x):
x = self.conv(x)
 x = torch.mean(x, dim=3)
 x, _ = torch.max(x, dim=2)
 x = self.fc(x)
 return x 
<compute_test_metric>
folds = splitter_for_moa(train_features_df, stage_1_2_target_cols, n_splits=4)
 stage1_2_svm_trainer = SVMTrainer(
 target_cols = stage_1_2_target_cols,
categorical_cols=[],
ignore_cols=[],
num_seed_blends=1,
metric=logloss_for_multilabel,
params={
},
exp=None 
)
 stage1_2_svm_trainer.train_predict(
 X_train=train_features_df[stage_1_train_features],
y_train=train_features_df[stage_1_2_target_cols],
X_test=test_features_df[stage_1_train_features],
folds=folds
)
def mixup_data(x, y, alpha=1.0, use_cuda=True):

 if alpha > 0.:
lam = np.random.beta(alpha, alpha)
 else:
lam = 1.
 batch_size = x.size() [0]
if use_cuda:
index = torch.randperm(batch_size ).cuda()
else:
index = torch.randperm(batch_size)
 mixed_x = lam * x +(1 - lam)* x[index,:]
mixed_y = lam * y +(1 - lam)* y[index]
return mixed_x, mixed_y
def mixup_criterion(y_a, y_b, lam):
return lambda criterion, pred: lam * criterion(pred, y_a)+(1 - lam)* criterion(pred, y_b )<compute_train_metric>
stage1_1_nn_pred = stage1_1_nn_trainer.predict(test_features_df)
 stage1_2_nn_pred = stage1_2_nn_trainer.predict(test_features_df)
 stage1_1_tabnet_pred = stage1_1_tabnet_trainer.predict(test_features_df)
 stage1_2_tabnet_pred = stage1_2_tabnet_trainer.predict(test_features_df)
 stage1_2_svm_pred = stage1_2_svm_trainer.result.preds
stage1_2_xgb_pred = stage1_2_xgb_trainer.predict(test_features_df )
def weighted_BCEWithLogits(pos_weight):
def weighted_cross_entropy_with_logits(logits, targets):
return(targets * -logits.sigmoid().log() * pos_weight +(1 - targets)* -(1 - logits.sigmoid() ).log() ).mean()
return weighted_cross_entropy_with_logits<split>
stack_train_df = pd.concat(
 [
pd.DataFrame(stage1_1_nn_trainer.result.oof_preds ).add_prefix("stage1_1_nn_"),
pd.DataFrame(stage1_2_nn_trainer.result.oof_preds ).add_prefix("stage1_2_nn_"),
train_features_df[stage_1_2_target_cols + ["drug_id", "sig_id"]],
],
axis=1, 
)
 stack_test_df = pd.concat(
 [
pd.DataFrame(stage1_1_nn_pred ).add_prefix("stage1_1_nn_"),
pd.DataFrame(stage1_2_nn_pred ).add_prefix("stage1_2_nn_"),
],
axis=1, 
)
 stack_train_predictors = [x for x in stack_train_df.columns if x not in stage_1_2_target_cols + ["drug_id", "sig_id"]]
stack_nn_trainer = train_nn_model(
 df=stack_train_df,
target_cols=stage_1_2_target_cols,
predictors=stack_train_predictors,
model_class=TabularMLP_2,
num_seed_blends=5,
n_splits=7 
)
 stacking_nn_pred = stack_nn_trainer.predict(stack_test_df )
def train_model(x_train, y_train, train_transforms, model_path, num_epochs = 5, batch_size = 32, test_batch_size = 64):
lr = 1e-3
eta_min = 1e-7
t_max = 10
num_classes = y_train.shape[1]
trn_loss = []
val_loss = []
val_lwlrap = []
lr_log = []
x_trn, x_val, y_trn, y_val = train_test_split(x_train, y_train, test_size=0.2, random_state=SEED)
 train_dataset = FATTrainDataset(x_trn, y_trn, train_transforms)
 valid_dataset = FATTrainDataset(x_val, y_val, train_transforms)
 train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
 valid_loader = DataLoader(valid_dataset, batch_size=test_batch_size, shuffle=False)
 model = model_ft.cuda()
if model_path and False:
model.load_state_dict(torch.load(model_path))
criterion = nn.BCEWithLogitsLoss().cuda()
optimizer = Adam(params=model.parameters() , lr=lr, amsgrad=False)
 scheduler = CosineAnnealingLR(optimizer, T_max=t_max, eta_min=eta_min)
 best_epoch = -1
best_lwlrap = 0.
 mb = master_bar(range(num_epochs))
for epoch in mb:
start_time = time.time()
model.train()
avg_loss = 0.
 for x_batch, y_batch in progress_bar(train_loader, parent=mb):
if mixup_on:
x_batch, y_batch = mixup_data(x_batch, y_batch, alpha=1, use_cuda=True)
 if model_type != 'inception':
preds = model(x_batch.cuda())
 loss = criterion(preds, y_batch.cuda())
 else:
outputs, aux_outputs = model(x_batch.cuda())
 loss1 = criterion(outputs, y_batch.cuda())
 loss2 = criterion(aux_outputs, y_batch.cuda())
 loss = loss1 + 0.4*loss2
optimizer.zero_grad()
loss.backward()
optimizer.step()
avg_loss += loss.item() / len(train_loader)
 model.eval()
valid_preds = np.zeros(( len(x_val), num_classes))
avg_val_loss = 0.
 for i,(x_batch, y_batch)in enumerate(valid_loader):
preds = model(x_batch.cuda() ).detach()
loss = criterion(preds, y_batch.cuda())
 preds = torch.sigmoid(preds)
 valid_preds[i * test_batch_size:(i+1)* test_batch_size] = preds.cpu().numpy()
avg_val_loss += loss.item() / len(valid_loader)
 score, weight = calculate_per_class_lwlrap(y_val, valid_preds)
 lwlrap =(score * weight ).sum()
scheduler.step()
for param_group in optimizer.param_groups:
lr_temp = param_group['lr']
lr_log.append(lr_temp)
 if(epoch + 1)% 1 == 0:
elapsed = time.time() - start_time
mb.write(f'Epoch {epoch+1} - avg_train_loss: {avg_loss:.4f} avg_val_loss: {avg_val_loss:.4f} val_lwlrap: {lwlrap:.6f} time: {elapsed:.0f}s')
 trn_loss.append(avg_loss)
 val_loss.append(avg_val_loss)
 val_lwlrap.append(lwlrap)
 if lwlrap > best_lwlrap:
best_epoch = epoch + 1
best_lwlrap = lwlrap
best_train_loss = avg_loss
best_val_loss = avg_val_loss
torch.save(model.state_dict() , model_path)
 return {
'best_epoch': best_epoch,
'best_lwlrap': best_lwlrap,
'best_train_loss':best_train_loss,
'best_val_loss':best_val_loss,
'trn_loss':trn_loss,
'val_loss':val_loss,
'val_lwlrap':val_lwlrap,
'lr_log':lr_log
}<choose_model_class>
stack_train_df = pd.concat(
 [
pd.DataFrame(stage1_1_tabnet_trainer.result.oof_preds ).add_prefix('stage1_1_tabnet_'),
pd.DataFrame(stage1_2_tabnet_trainer.result.oof_preds ).add_prefix('stage1_2_tabnet_'),
train_features_df[stage_1_2_target_cols + ["drug_id", "sig_id"]],
],
axis=1, 
)
 stack_test_df = pd.concat(
 [
pd.DataFrame(stage1_1_tabnet_pred ).add_prefix("stage1_1_tabnet_"),
pd.DataFrame(stage1_2_tabnet_pred ).add_prefix("stage1_2_tabnet_"),
],
axis=1, 
)
 stack_train_predictors = [x for x in stack_train_df.columns if x not in stage_1_2_target_cols + ["drug_id", "sig_id"]]
stack_tabnet_trainer = build_trainer(
 trainer=Tabnet,
df=stack_train_df,
target_cols=stage_1_2_target_cols,
predictors=stack_train_predictors,
num_seed_blends=5,
n_splits=7,
params={}, 
)
 stacking_tabnet_pred = stack_tabnet_trainer.predict(stack_test_df )
num_epochs = 400
batch_size = 64
test_batch_size = 64
lr_method = 'cosine'
aug_flip = 'flip'
mixup_on = True
model_type = 'inception'
model_ft, input_size = initialize_model(model_type, num_classes, False, use_pretrained=False)
 if aug_flip == 'flip':
transforms_dict = {
'train': transforms.Compose([
transforms.RandomResizedCrop(input_size),
transforms.RandomHorizontalFlip(0.5),
transforms.ToTensor() ,
]),
'test': transforms.Compose([
transforms.RandomResizedCrop(input_size),
transforms.RandomHorizontalFlip(0.5),
transforms.ToTensor() ,
]),
}
elif aug_flip == 'noflip':
transforms_dict = {
'train': transforms.Compose([
transforms.RandomResizedCrop(input_size),
transforms.ToTensor() ,
]),
'test': transforms.Compose([
transforms.RandomResizedCrop(input_size),
transforms.ToTensor() ,
]),
}<define_variables>
stage1_2_trainers = {
'nn': stage1_2_nn_trainer,
'tabnet': stage1_2_tabnet_trainer,
'svm': stage1_2_svm_trainer,
'stack_nn': stack_nn_trainer,
'stack_tabnet': stack_tabnet_trainer,
'xgb': stage1_2_xgb_trainer,
}
for name, trainer in stage1_2_trainers.items() :
print(name, logloss_for_multilabel(train_features_df[stage_1_2_target_cols].values, trainer.result.oof_preds))
model_path = '.. /working/weight_best_2s_%(m)s_%(lr)s_%(aug)s.pt'% \
{'m':model_type, 'lr':lr_method, 'aug':aug_flip}
output_path = '.. /working/submission_2s_%(m)s_%(lr)s_%(aug)s.csv'%\
{'m':model_type, 'lr':lr_method, 'aug':aug_flip}
print(model_path )<train_model>
stage3_oofs = [
stack_nn_trainer.result.oof_preds,
stage1_2_svm_trainer.result.oof_preds,
stage1_2_tabnet_trainer.result.oof_preds,
stage1_2_xgb_trainer.result.oof_preds,
]
stage3_preds = [
stacking_nn_pred,
stage1_2_svm_pred,
stage1_2_tabnet_pred,
stage1_2_xgb_pred,
]
<define_variables>
DEVICE = get_device()
class CNNStacking(nn.Module):
def __init__(self, n_features, n_labels):
super(CNNStacking, self ).__init__()
self.sq = nn.Sequential(
 nn.Conv2d(in_channels=1, out_channels=8, kernel_size=(3, 1), bias=False),
nn.ReLU() ,
nn.Conv2d(in_channels=8, out_channels=16, kernel_size=(2, 1), bias=False),
nn.ReLU() ,
nn.Flatten() ,
nn.Linear(in_features=16* n_labels, out_features=4 * n_labels),
nn.ReLU() ,
nn.Linear(in_features=4 * n_labels, out_features=n_labels), 
)
 def forward(self, x):
return self.sq(x)
 class CNNDataset(Dataset):
def __init__(self, X: np.ndarray, y: Optional[pd.DataFrame]):
self.X = X
if y is not None:
self.y = y.values
else:
self.y = y
def __len__(self):
return self.X.shape[0]
def __getitem__(self, idx):
if self.y is None:
return torch.tensor(self.X[idx], dtype=torch.float ).to(DEVICE)
 else:
return(
 torch.tensor(self.X[idx], dtype=torch.float ).to(DEVICE),
torch.tensor(self.y[idx], dtype=torch.float ).to(DEVICE), 
)
 class CNNTrainer(MoaBase):
def __init__(self, params: Optional[dict] = None, **kwargs):
if params is None:
self.params = {}
else:
self.params = params
super().__init__(**kwargs)
 def _get_default_params(self):
return {
'lr': 1e-4,
'batch_size': 256,
'epoch': 20,
}
def _train(self, X: pd.DataFrame, y: pd.DataFrame, predictors: List[str], train_idx: np.ndarray, valid_idx: np.ndarray, seed: int):
X_train, X_valid = X.iloc[train_idx], X.iloc[valid_idx]
y_train, y_valid = y.iloc[train_idx], y.iloc[valid_idx]
target_cols = y_valid.columns.tolist()
_params = self._get_default_params()
_params.update(self.params)
 self.n_predictors = len(predictors)
 self.n_targets = len(target_cols)
 self.n_models = self.n_predictors // self.n_targets
num_epoch = _params['epoch']
batch_size = _params['batch_size']
net = CNNStacking(n_features=self.n_predictors, n_labels=self.n_targets)
 net.to(DEVICE)
 optimizer = RAdam(net.parameters() , lr=_params['lr'])
 valid_criterion = nn.BCEWithLogitsLoss()
criterion = SmoothBCEwLogits(smoothing=0.001)
 scheduler = optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=10, eta_min=0.0001)
 X_train = X_train[predictors].values.reshape(-1, self.n_models, self.n_targets, 1 ).transpose(0, 3, 1, 2)
 X_valid = X_valid[predictors].values.reshape(-1, self.n_models, self.n_targets, 1 ).transpose(0, 3, 1, 2)
 train_dataset = CNNDataset(X_train, y_train)
 train_dataloader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True, drop_last=True)
 valid_dataset = CNNDataset(X_valid, y_valid)
 valid_dataloader = DataLoader(valid_dataset, batch_size=batch_size, shuffle=False)
 bar = trange(num_epoch, desc=f"seed: {seed} train : {X_train.shape[0]} valid:{X_valid.shape[0]}====")
 train_loss = []
valid_loss = []
best_loss = np.inf
best_preds = None
best_loss_epoch = 1
for epoch in bar:
running_loss = []
valid_loss = []
net.train()
for x, y in train_dataloader:
x = x.to(DEVICE)
 y = y.to(DEVICE)
 optimizer.zero_grad()
out = net(x)
 loss = criterion(out, y)
 loss.backward()
running_loss.append(loss.item())
 optimizer.step()
scheduler.step()
preds_valid = []
_valid_loss = []
net.eval()
with torch.no_grad() :
for x, y in valid_dataloader:
x = x.to(DEVICE)
 y = y.to(DEVICE)
 out = net(x)
 loss = valid_criterion(out, y)
 preds_valid.append(out.sigmoid().detach().cpu().numpy())
 _valid_loss.append(loss.item())
 bar.set_postfix(
 running_loss=f"{np.mean(running_loss):.5f}",
valid_loss=f"{np.mean(_valid_loss):.5f}",
best_loss=f"{best_loss:.5f}",
best_loss_epoch=f"{best_loss_epoch}", 
)
 train_loss.append(np.mean(running_loss))
valid_loss.append(np.mean(_valid_loss))
if best_loss > np.mean(_valid_loss):
best_loss = np.mean(_valid_loss)
 best_loss_epoch = epoch + 1
best_preds = np.concatenate(preds_valid)
 best_state = copy.deepcopy(net.state_dict())
 logger.info(f"best loss : {best_loss}")
 model = CNNStacking(n_features=self.n_predictors, n_labels=self.n_targets)
 model.load_state_dict(best_state)
 model.to(DEVICE)
 return best_preds, model
def _predict(self, model: Any, X_valid: pd.DataFrame, predictors: List[str]):
_params = self._get_default_params()
_params.update(self.params)
 X_valid = X_valid[predictors].values.reshape(-1, self.n_models, self.n_targets, 1 ).transpose(0, 3, 1, 2)
 batch_size = _params['batch_size']
valid_dataset = CNNDataset(X_valid, None)
 valid_dataloader = DataLoader(valid_dataset, batch_size=batch_size, shuffle=False)
 tmp_pred = []
model.eval()
with torch.no_grad() :
for x in valid_dataloader:
x = x.to(DEVICE)
 out = model(x)
 tmp_pred.append(out.sigmoid().detach().cpu().numpy())
 return np.concatenate(tmp_pred )
model_path = '.. /input/outputmodels2/weight_best_2s_inception_cosine_flip.pt'<predict_on_test>
cnn_preds = cnn_trainer.predict(make_cnn_inputs(stage3_preds, stage_1_2_target_cols))
start_time = time.time()
test_preds = predict_model(test_df['fname'], x_test, transforms_dict['test'], num_classes )<save_to_csv>
def get_score(weights, train_idx, oofs, labels):
blend = np.zeros_like(oofs[0][train_idx, :])
 for oof, weight in zip(oofs[:-1], weights):
blend += weight * oof[train_idx, :]
blend +=(1 - np.sum(weights)) * oofs[-1][train_idx, :]
return logloss_for_multilabel(labels[train_idx, :], blend)
 def get_best_weights(oofs, labels):
weight_list = []
weights = np.array([1/len(oofs)for x in range(len(oofs)- 1)])
 for n_splits in tqdm([5, 6]):
for i in range(2):
kf = KFold(n_splits=n_splits, random_state=i, shuffle=True)
 for fold,(train_idx, valid_idx)in enumerate(kf.split(X=oofs[0])) :
res = minimize(get_score, weights, args=(train_idx, oofs, labels), method="Nelder-Mead", tol=1e-6)
 logger.info(f"i: {i} fold: {fold} res.x: {res.x}")
 weight_list.append(res.x)
 mean_weight = np.mean(weight_list, axis=0)
 print(f"optimized weight: {mean_weight}")
 return mean_weight
best_weights = get_best_weights(stage3_oofs, train_features_df[stage_1_2_target_cols].values )
test_df[labels] = test_preds.values
test_df.to_csv(output_path, index=False)
 test_df.to_csv('submission.csv', index=False)
 elapsed = time.time() - start_time
print(f'Prediction time: {elapsed:.0f}s' )<define_variables>
best_weights = np.insert(best_weights, len(best_weights), 1 - np.sum(best_weights))
oof_preds = np.stack(stage3_oofs ).transpose(1, 2, 0 ).dot(best_weights)
 print(logloss_for_multilabel(train_features_df[stage_1_2_target_cols].values, oof_preds))
blend_preds = np.stack(stage3_preds ).transpose(1, 2, 0 ).dot(best_weights )
def _one_sample_positive_class_precisions(scores, truth):

 num_classes = scores.shape[0]
pos_class_indices = np.flatnonzero(truth > 0)
 if not len(pos_class_indices):
return pos_class_indices, np.zeros(0)
 retrieved_classes = np.argsort(scores)[::-1]
class_rankings = np.zeros(num_classes, dtype=np.int)
 class_rankings[retrieved_classes] = range(num_classes)
 retrieved_class_true = np.zeros(num_classes, dtype=np.bool)
 retrieved_class_true[class_rankings[pos_class_indices]] = True
retrieved_cumulative_hits = np.cumsum(retrieved_class_true)
 precision_at_hits =(
 retrieved_cumulative_hits[class_rankings[pos_class_indices]] / 
(1 + class_rankings[pos_class_indices].astype(np.float)))
 return pos_class_indices, precision_at_hits
def calculate_per_class_lwlrap(truth, scores):

 assert truth.shape == scores.shape
num_samples, num_classes = scores.shape
precisions_for_samples_by_classes = np.zeros(( num_samples, num_classes))
for sample_num in range(num_samples):
pos_class_indices, precision_at_hits =(
 _one_sample_positive_class_precisions(scores[sample_num, :],
truth[sample_num, :]))
precisions_for_samples_by_classes[sample_num, pos_class_indices] =(
 precision_at_hits)
 labels_per_class = np.sum(truth > 0, axis=0)
 weight_per_class = labels_per_class / float(np.sum(labels_per_class))
per_class_lwlrap =(np.sum(precisions_for_samples_by_classes, axis=0)/
np.maximum(1, labels_per_class))
return per_class_lwlrap, weight_per_class
def lwlrap(scores, truth, **kwargs):
score, weight = calculate_per_class_lwlrap(to_np(truth), to_np(scores))
return torch.Tensor([(score * weight ).sum() ] )<load_from_csv>
final_blend_oofs = oof_preds * 0.7 + cnn_trainer.result.oof_preds * 0.3
test_features_df[stage_1_2_target_cols] = blend_preds * 0.7 + cnn_preds * 0.3
training_curated_df = pd.read_csv(".. /input/train_curated.csv")
 training_noisy_df = pd.read_csv(".. /input/train_noisy.csv")
 training_df = [training_curated_df, training_noisy_df]
testing_df = pd.read_csv('.. /input/sample_submission.csv' )<count_unique_values>
print(logloss_for_multilabel(train_features_df[stage_1_2_target_cols].values, final_blend_oofs))
<count_unique_values><EOS> 
test_features_df.loc[test_features_df['cp_type'] != 'trt_cp', stage_1_2_target_cols] = 0
sub = sample_submission_df.drop(columns=stage_1_2_target_cols ).merge(test_features_df[['sig_id']+stage_1_2_target_cols], on='sig_id', how='left' ).fillna(0)
 sub.to_csv('submission.csv', index=False)
 sub.head()