Kaggle competition: Mechanisms of Action (MoA) Prediction
<SOS> metric: MeanColumnwiseLogLoss Kaggle data source: mechanisms-of-action-(moa)-prediction<predict_on_test>
import numpy as np
import pandas as pd
import os
test_data /= 256
test_data = test_data.values.reshape(-1,28,28,1)
 results = cnn.predict(test_data )<concatenate>
!pip install pytorch-tabnet
submission = pd.concat([pd.Series(range(1,28001),name = 'ImageId'),results],axis = 1 )<save_to_csv>
!pip install.. /input/iterative-stratification/iterative_stratification-0.1.6-py3-none-any.whl
submission.to_csv('./submission.csv',index = False )<load_from_csv>
sys.path.append('.. /input/iterativestratification')
 os.environ["CUDA_LAUNCH_BLOCKING"] = "1"
train=pd.read_csv("/kaggle/input/digit-recognizer/train.csv" )<load_from_csv>
def seed_everything(seed_value):
random.seed(seed_value)
 np.random.seed(seed_value)
 torch.manual_seed(seed_value)
 os.environ['PYTHONHASHSEED'] = str(seed_value)
 if torch.cuda.is_available() :
torch.cuda.manual_seed(seed_value)
 torch.cuda.manual_seed_all(seed_value)
 torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
seed_everything(42 )
test=pd.read_csv("/kaggle/input/digit-recognizer/test.csv" )<define_variables>
train_features = pd.read_csv('.. /input/lish-moa/train_features.csv')
 train_targets_scored = pd.read_csv('.. /input/lish-moa/train_targets_scored.csv')
 train_targets_nonscored = pd.read_csv('.. /input/lish-moa/train_targets_nonscored.csv')
 test_features = pd.read_csv('.. /input/lish-moa/test_features.csv')
 sample_submission = pd.read_csv('.. /input/lish-moa/sample_submission.csv' )
label=train["label"].values<drop_column>
GENES = [col for col in train_features.columns if col.startswith('g-')]
CELLS = [col for col in train_features.columns if col.startswith('c-')]
train.drop("label",axis=1,inplace=True )<split>
for col in(GENES + CELLS):
transformer = QuantileTransformer(n_quantiles=100,random_state=0, output_distribution="normal")
 vec_len = len(train_features[col].values)
 vec_len_test = len(test_features[col].values)
 raw_vec = train_features[col].values.reshape(vec_len, 1)
 transformer.fit(raw_vec)
 train_features[col] = transformer.transform(raw_vec ).reshape(1, vec_len)[0]
test_features[col] = transformer.transform(test_features[col].values.reshape(vec_len_test, 1)).reshape(1, vec_len_test)[0]
t=train.values
ttest=test.values<data_type_conversions>
n_comp = 610
data = pd.concat([pd.DataFrame(train_features[GENES]), pd.DataFrame(test_features[GENES])])
 data2 =(PCA(n_components=n_comp, random_state=42 ).fit_transform(data[GENES]))
train2 = data2[:train_features.shape[0]]; test2 = data2[-test_features.shape[0]:]
train2 = pd.DataFrame(train2, columns=[f'pca_G-{i}' for i in range(n_comp)])
 test2 = pd.DataFrame(test2, columns=[f'pca_G-{i}' for i in range(n_comp)])
 train_features = pd.concat(( train_features, train2), axis=1)
 test_features = pd.concat(( test_features, test2), axis=1)
 n_comp = 55
data = pd.concat([pd.DataFrame(train_features[CELLS]), pd.DataFrame(test_features[CELLS])])
 data2 =(PCA(n_components=n_comp, random_state=42 ).fit_transform(data[CELLS]))
train2 = data2[:train_features.shape[0]]; test2 = data2[-test_features.shape[0]:]
train2 = pd.DataFrame(train2, columns=[f'pca_C-{i}' for i in range(n_comp)])
 test2 = pd.DataFrame(test2, columns=[f'pca_C-{i}' for i in range(n_comp)])
 train_features = pd.concat(( train_features, train2), axis=1)
 test_features = pd.concat(( test_features, test2), axis=1 )
t=t.astype('float32')
 ttest=ttest.astype('float32' )<feature_engineering>
var_thresh = VarianceThreshold(0.82)
 data = train_features.append(test_features)
 data_transformed = var_thresh.fit_transform(data.iloc[:, 4:])
 train_features_transformed = data_transformed[ : train_features.shape[0]]
test_features_transformed = data_transformed[-test_features.shape[0] : ]
train_features = pd.DataFrame(train_features[['sig_id','cp_type','cp_time','cp_dose']].values.reshape(-1, 4),\
columns=['sig_id','cp_type','cp_time','cp_dose'])
 train_features = pd.concat([train_features, pd.DataFrame(train_features_transformed)], axis=1)
 test_features = pd.DataFrame(test_features[['sig_id','cp_type','cp_time','cp_dose']].values.reshape(-1, 4),\
columns=['sig_id','cp_type','cp_time','cp_dose'])
 test_features = pd.concat([test_features, pd.DataFrame(test_features_transformed)], axis=1)
 train_features.shape
t/=255
ttest/=255<categorify>
def get_ratio_labels(df):
columns = list(df.columns)
 columns.pop(0)
 ratios = []
toremove = []
for c in columns:
counts = df[c].value_counts()
if len(counts)!= 1:
ratios.append(counts[0]/counts[1])
 else:
toremove.append(c)
 print(f"remove {len(toremove)} columns")
 for t in toremove:
columns.remove(t)
 return columns, np.array(ratios ).astype(np.int32)
 columns, ratios = get_ratio_labels(train_targets_scored)
 columns_nonscored, ratios_nonscored = get_ratio_labels(train_targets_nonscored )
tl = keras.utils.to_categorical(label, 10)
<define_variables>
train = train_features.merge(train_targets_scored, on='sig_id')
 train = train[train['cp_type']!='ctl_vehicle'].reset_index(drop=True)
 test = test_features
target = train[train_targets_scored.columns]
input_shape =(28, 28, 1 )<choose_model_class>
target_cols = target.drop('sig_id', axis=1 ).columns.values.tolist()
model = Sequential()
model.add(Conv2D(32,(3, 3), activation='relu', kernel_initializer='he_uniform', padding='same', input_shape=input_shape))
model.add(MaxPooling2D(( 2, 2)))
 model.add(Dropout(0.2))
model.add(Conv2D(64,(3, 3), activation='relu', kernel_initializer='he_uniform', padding='same'))
model.add(MaxPooling2D(( 2, 2)))
 model.add(Dropout(0.2))
model.add(Conv2D(128,(3, 3), activation='relu', kernel_initializer='he_uniform', padding='same'))
model.add(MaxPooling2D(( 2, 2)))
 model.add(Dropout(0.2))
model.add(Conv2D(256,(3, 3), activation='relu', kernel_initializer='he_uniform', padding='same'))
model.add(MaxPooling2D(( 2, 2)))
 model.add(Dropout(0.2))
model.add(Flatten())
 model.add(Dense(128, activation='relu', kernel_initializer='he_uniform'))
model.add(Dropout(0.5))
model.add(Dense(10, activation='softmax'))<choose_model_class>
def evals(model, X, y, verbose=True):
with torch.no_grad() :
y_preds = model.predict(X)
 y_preds = torch.clamp(y_preds, 0.0,1.0 ).detach().numpy()
score = log_loss_multi(y, y_preds)
 return y_preds, score
def inference_fn(model, X ,verbose=True):
with torch.no_grad() :
y_preds = model.predict(X)
 y_preds = torch.sigmoid(torch.as_tensor(y_preds)).numpy()
return y_preds
model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'] )<split>
p_min = 0.0005
p_max = 0.9995
def log_loss_score(y_true, y_pred):
y_pred = tf.clip_by_value(y_pred,p_min,p_max)
 return -backend.mean(y_true*backend.log(y_pred)+(1-y_true)*backend.log(1-y_pred))
np.random.seed(1234)
(x_train,x_test,y_train,y_test)= train_test_split(t,tl, train_size=0.75, random_state=1 )<train_model>
def log_loss_multi(y_true, y_pred):
M = y_true.shape[1]
results = np.zeros(M)
 for i in range(M):
results[i] = log_loss_score(y_true[:,i], y_pred[:,i])
 return results.mean()
model.fit(x_train, y_train,
batch_size=100,
epochs=400,
verbose=2,
validation_data=(x_test, y_test))<predict_on_test>
def auc_multi(y_true, y_pred):
M = y_true.shape[1]
results = np.zeros(M)
 for i in range(M):
try:
results[i] = roc_auc_score(y_true[:,i], y_pred[:,i])
 except:
pass
return results.mean()
y_pred=model.predict(ttest,verbose=0 )<load_from_csv>
create_dataloaders,
create_explain_matrix)
 class TabModel(BaseEstimator):
def __init__(self, n_d=8, n_a=8, n_steps=3, gamma=1.3, cat_idxs=[], cat_dims=[], cat_emb_dim=1,
n_independent=2, n_shared=2, epsilon=1e-15, momentum=0.02,
lambda_sparse=1e-3, seed=0,
clip_value=1, verbose=1,
optimizer_fn=torch.optim.Adam,
optimizer_params=dict(lr=2e-2),
scheduler_params=None, scheduler_fn=None,
mask_type="sparsemax",
input_dim=None, output_dim=None,
device_name='auto'):

 self.n_d = n_d
self.n_a = n_a
self.n_steps = n_steps
self.gamma = gamma
self.cat_idxs = cat_idxs
self.cat_dims = cat_dims
self.cat_emb_dim = cat_emb_dim
self.n_independent = n_independent
self.n_shared = n_shared
self.epsilon = epsilon
self.momentum = momentum
self.lambda_sparse = lambda_sparse
self.clip_value = clip_value
self.verbose = verbose
self.optimizer_fn = optimizer_fn
self.optimizer_params = optimizer_params
self.device_name = device_name
self.scheduler_params = scheduler_params
self.scheduler_fn = scheduler_fn
self.mask_type = mask_type
self.input_dim = input_dim
self.output_dim = output_dim
self.batch_size = 1024
self.seed = seed
torch.manual_seed(self.seed)
 if device_name == 'auto':
if torch.cuda.is_available() :
device_name = 'cuda'
else:
device_name = 'cpu'
self.device = torch.device(device_name)
 print(f"Device used : {self.device}")
 @abstractmethod
def construct_loaders(self, X_train, y_train, X_valid, y_valid,
weights, batch_size, num_workers, drop_last):

 raise NotImplementedError('users must define construct_loaders to use this base class')
 def init_network(
 self,
input_dim,
output_dim,
n_d,
n_a,
n_steps,
gamma,
cat_idxs,
cat_dims,
cat_emb_dim,
n_independent,
n_shared,
epsilon,
virtual_batch_size,
momentum,
device_name,
mask_type, 
):
self.network = tab_network.TabNet(
 input_dim,
output_dim,
n_d=n_d,
n_a=n_a,
n_steps=n_steps,
gamma=gamma,
cat_idxs=cat_idxs,
cat_dims=cat_dims,
cat_emb_dim=cat_emb_dim,
n_independent=n_independent,
n_shared=n_shared,
epsilon=epsilon,
virtual_batch_size=virtual_batch_size,
momentum=momentum,
device_name=device_name,
mask_type=mask_type ).to(self.device)
 self.reducing_matrix = create_explain_matrix(
 self.network.input_dim,
self.network.cat_emb_dim,
self.network.cat_idxs,
self.network.post_embed_dim)
 def fit(self, X_train, y_train, X_valid=None, y_valid=None, loss_fn=None,
weights=0, max_epochs=100, patience=10, batch_size=1024,
virtual_batch_size=128, num_workers=0, drop_last=False,pretrain=False, optimizer_params=None):

 self.update_fit_params(X_train, y_train, X_valid, y_valid, loss_fn,
weights, max_epochs, patience, batch_size,
virtual_batch_size, num_workers, drop_last)
 train_dataloader, valid_dataloader = self.construct_loaders(X_train,
y_train,
X_valid,
y_valid,
self.updated_weights,
self.batch_size,
self.num_workers,
self.drop_last)
 if not pretrain:
self.init_network(
 input_dim=self.input_dim,
output_dim=self.output_dim,
n_d=self.n_d,
n_a=self.n_a,
n_steps=self.n_steps,
gamma=self.gamma,
cat_idxs=self.cat_idxs,
cat_dims=self.cat_dims,
cat_emb_dim=self.cat_emb_dim,
n_independent=self.n_independent,
n_shared=self.n_shared,
epsilon=self.epsilon,
virtual_batch_size=self.virtual_batch_size,
momentum=self.momentum,
device_name=self.device_name,
mask_type=self.mask_type 
)
 self.optimizer = self.optimizer_fn(self.network.parameters() , **self.optimizer_params)
 else:
self.optimizer = self.optimizer_fn(self.network.parameters() , **optimizer_params)
 self.init_network(
 input_dim=self.input_dim,
output_dim=self.output_dim,
n_d=self.n_d,
n_a=self.n_a,
n_steps=self.n_steps,
gamma=self.gamma,
cat_idxs=self.cat_idxs,
cat_dims=self.cat_dims,
cat_emb_dim=self.cat_emb_dim,
n_independent=self.n_independent,
n_shared=self.n_shared,
epsilon=self.epsilon,
virtual_batch_size=self.virtual_batch_size,
momentum=self.momentum,
device_name=self.device_name,
mask_type=self.mask_type 
)
 self.optimizer = self.optimizer_fn(self.network.parameters() ,
**self.optimizer_params)
 if self.scheduler_fn:
self.scheduler = self.scheduler_fn(self.optimizer, **self.scheduler_params)
 else:
self.scheduler = None
self.losses_train = []
self.losses_valid = []
self.learning_rates = []
self.metrics_train = []
self.metrics_valid = []
if self.verbose > 0:
print("Will train until validation stopping metric",
f"hasn't improved in {self.patience} rounds.")
 msg_epoch = f'| EPOCH | train | valid | total time(s)'
print('---------------------------------------')
 print(msg_epoch)
 total_time = 0
while(self.epoch < self.max_epochs and
self.patience_counter < self.patience):
starting_time = time.time()
self.learning_rates.append(self.optimizer.param_groups[-1]["lr"])
 fit_metrics = self.fit_epoch(train_dataloader, valid_dataloader)
 self.losses_train.append(fit_metrics['train']['loss_avg'])
 self.losses_valid.append(fit_metrics['valid']['total_loss'])
 self.metrics_train.append(fit_metrics['train']['stopping_loss'])
 self.metrics_valid.append(fit_metrics['valid']['stopping_loss'])
 stopping_loss = fit_metrics['valid']['stopping_loss']
if stopping_loss < self.best_cost:
self.best_cost = stopping_loss
self.patience_counter = 0
self.best_network = deepcopy(self.network)
 has_improved = True
else:
self.patience_counter += 1
has_improved=False
self.epoch += 1
total_time += time.time() - starting_time
if self.verbose > 0:
if self.epoch % self.verbose == 0:
separator = "|"
msg_epoch = f"| {self.epoch:<5} | "
msg_epoch += f" {fit_metrics['train']['stopping_loss']:.5f}"
msg_epoch += f' {separator:<2} '
msg_epoch += f" {fit_metrics['valid']['stopping_loss']:.5f}"
msg_epoch += f' {separator:<2} '
msg_epoch += f" {np.round(total_time, 1):<10}"
msg_epoch += f" {has_improved}"
print(msg_epoch)
 if self.verbose > 0:
if self.patience_counter == self.patience:
print(f"Early stopping occured at epoch {self.epoch}")
 print(f"Training done in {total_time:.3f} seconds.")
 print('---------------------------------------')
 self.history = {"train": {"loss": self.losses_train,
"metric": self.metrics_train,
"lr": self.learning_rates},
"valid": {"loss": self.losses_valid,
"metric": self.metrics_valid}}
self.load_best_model()
self._compute_feature_importances(train_dataloader)
 def save_model(self, path):

 saved_params = {}
for key, val in self.get_params().items() :
if isinstance(val, type):
continue
else:
saved_params[key] = val
Path(path ).mkdir(parents=True, exist_ok=True)
 with open(Path(path ).joinpath("model_params.json"), "w", encoding="utf8")as f:
json.dump(saved_params, f)
 torch.save(self.network.state_dict() , Path(path ).joinpath("network.pt"))
shutil.make_archive(path, 'zip', path)
 shutil.rmtree(path)
 print(f"Successfully saved model at {path}.zip")
 return f"{path}.zip"
def load_model(self, filepath):
try:
try:
with zipfile.ZipFile(filepath)as z:
with z.open("model_params.json")as f:
loaded_params = json.load(f)
 with z.open("network.pt")as f:
try:
saved_state_dict = torch.load(f)
 except io.UnsupportedOperation:
saved_state_dict = torch.load(io.BytesIO(f.read()))
except:
with open(os.path.join(filepath, "model_params.json")) as f:
loaded_params = json.load(f)
 saved_state_dict = torch.load(os.path.join(filepath, "network.pt"), map_location="cpu")
 except KeyError:
raise KeyError("Your zip file is missing at least one component")
 if torch.cuda.is_available() :
device_name = 'cuda'
else:
device_name = 'cpu'
loaded_params["device_name"] = device_name
self.__init__(**loaded_params)
 self.init_network(
 input_dim=self.input_dim,
output_dim=self.output_dim,
n_d=self.n_d,
n_a=self.n_a,
n_steps=self.n_steps,
gamma=self.gamma,
cat_idxs=self.cat_idxs,
cat_dims=self.cat_dims,
cat_emb_dim=self.cat_emb_dim,
n_independent=self.n_independent,
n_shared=self.n_shared,
epsilon=self.epsilon,
virtual_batch_size=1024,
momentum=self.momentum,
device_name=self.device_name,
mask_type=self.mask_type 
)
 self.network.load_state_dict(saved_state_dict)
 self.network.eval()
return
def fit_epoch(self, train_dataloader, valid_dataloader):

 train_metrics = self.train_epoch(train_dataloader)
 valid_metrics = self.predict_epoch(valid_dataloader)
 fit_metrics = {'train': train_metrics,
'valid': valid_metrics}
return fit_metrics
@abstractmethod
def train_epoch(self, train_loader):

 raise NotImplementedError('users must define train_epoch to use this base class')
 @abstractmethod
def train_batch(self, data, targets):

 raise NotImplementedError('users must define train_batch to use this base class')
 @abstractmethod
def predict_epoch(self, loader):

 raise NotImplementedError('users must define predict_epoch to use this base class')
 @abstractmethod
def predict_batch(self, data, targets):

 raise NotImplementedError('users must define predict_batch to use this base class')
 def load_best_model(self):
if self.best_network is not None:
self.network = self.best_network
@abstractmethod
def predict(self, X):

 raise NotImplementedError('users must define predict to use this base class')
 def explain(self, X):

 self.network.eval()
dataloader = DataLoader(PredictDataset(X),
batch_size=self.batch_size, shuffle=False)
 for batch_nb, data in enumerate(dataloader):
data = data.to(self.device ).float()
M_explain, masks = self.network.forward_masks(data)
 for key, value in masks.items() :
masks[key] = csc_matrix.dot(value.cpu().detach().numpy() ,
self.reducing_matrix)
 if batch_nb == 0:
res_explain = csc_matrix.dot(M_explain.cpu().detach().numpy() ,
self.reducing_matrix)
 res_masks = masks
else:
res_explain = np.vstack([res_explain,
csc_matrix.dot(M_explain.cpu().detach().numpy() ,
self.reducing_matrix)])
 for key, value in masks.items() :
res_masks[key] = np.vstack([res_masks[key], value])
 return res_explain, res_masks
def _compute_feature_importances(self, loader):
self.network.eval()
feature_importances_ = np.zeros(( self.network.post_embed_dim))
for data, targets in loader:
data = data.to(self.device ).float()
M_explain, masks = self.network.forward_masks(data)
 feature_importances_ += M_explain.sum(dim=0 ).cpu().detach().numpy()
feature_importances_ = csc_matrix.dot(feature_importances_,
self.reducing_matrix)
 self.feature_importances_ = feature_importances_ / np.sum(feature_importances_)
 class TabNetRegressor(TabModel):
def construct_loaders(self, X_train, y_train, X_valid, y_valid, weights,
batch_size, num_workers, drop_last):

 if isinstance(weights, int):
if weights == 1:
raise ValueError("Please provide a list of weights for regression.")
 if isinstance(weights, dict):
raise ValueError("Please provide a list of weights for regression.")
 train_dataloader, valid_dataloader = create_dataloaders(X_train,
y_train,
X_valid,
y_valid,
weights,
batch_size,
num_workers,
drop_last)
 return train_dataloader, valid_dataloader
def update_fit_params(self, X_train, y_train, X_valid, y_valid, loss_fn,
weights, max_epochs, patience,
batch_size, virtual_batch_size, num_workers, drop_last):
if loss_fn is None:
self.loss_fn = torch.nn.functional.mse_loss
else:
self.loss_fn = loss_fn
assert X_train.shape[1] == X_valid.shape[1], "Dimension mismatch X_train X_valid"
self.input_dim = X_train.shape[1]
if len(y_train.shape)== 1:
raise ValueError()
assert y_train.shape[1] == y_valid.shape[1], "Dimension mismatch y_train y_valid"
self.output_dim = y_train.shape[1]
self.updated_weights = weights
self.max_epochs = max_epochs
self.patience = patience
self.batch_size = batch_size
self.virtual_batch_size = virtual_batch_size
self.patience_counter = 0
self.epoch = 0
self.best_cost = np.inf
self.num_workers = num_workers
self.drop_last = drop_last
def train_epoch(self, train_loader):

 self.network.train()
y_preds = []
ys = []
total_loss = 0
for data, targets in train_loader:
batch_outs = self.train_batch(data, targets)
 y_preds.append(batch_outs["y_preds"].cpu().detach().numpy())
 ys.append(batch_outs["y"].cpu().detach().numpy())
 total_loss += batch_outs["loss"]
y_preds = np.vstack(y_preds)
 ys = np.vstack(ys)
 stopping_loss =log_loss_multi(ys, torch.sigmoid(torch.as_tensor(y_preds)).numpy())
 total_loss = total_loss / len(train_loader)
 epoch_metrics = {'loss_avg': total_loss,
'stopping_loss': total_loss,
}
if self.scheduler is not None:
self.scheduler.step()
return epoch_metrics
def train_batch(self, data, targets):

 self.network.train()
data = data.to(self.device ).float()
targets = targets.to(self.device ).float()
self.optimizer.zero_grad()
output, M_loss = self.network(data)
 loss = self.loss_fn(output, targets)
 loss -= self.lambda_sparse*M_loss
loss.backward()
if self.clip_value:
clip_grad_norm_(self.network.parameters() , self.clip_value)
 self.optimizer.step()
loss_value = loss.item()
batch_outs = {'loss': loss_value,
'y_preds': output,
'y': targets}
return batch_outs
def predict_epoch(self, loader):

 y_preds = []
ys = []
self.network.eval()
total_loss = 0
for data, targets in loader:
batch_outs = self.predict_batch(data, targets)
 total_loss += batch_outs["loss"]
y_preds.append(batch_outs["y_preds"].cpu().detach().numpy())
 ys.append(batch_outs["y"].cpu().detach().numpy())
 y_preds = np.vstack(y_preds)
 ys = np.vstack(ys)
 stopping_loss =log_loss_multi(ys, torch.sigmoid(torch.as_tensor(y_preds)).numpy())
 total_loss = total_loss / len(loader)
 epoch_metrics = {'total_loss': total_loss,
'stopping_loss': stopping_loss}
return epoch_metrics
def predict_batch(self, data, targets):

 self.network.eval()
data = data.to(self.device ).float()
targets = targets.to(self.device ).float()
output, M_loss = self.network(data)
 loss = self.loss_fn(output, targets)
 loss -= self.lambda_sparse*M_loss
loss_value = loss.item()
batch_outs = {'loss': loss_value,
'y_preds': output,
'y': targets}
return batch_outs
def predict(self, X):

 self.network.eval()
dataloader = DataLoader(PredictDataset(X),
batch_size=self.batch_size, shuffle=False)
 results = []
for batch_nb, data in enumerate(dataloader):
data = data.to(self.device ).float()
output, M_loss = self.network(data)
 predictions = output.cpu().detach().numpy()
results.append(predictions)
 res = np.vstack(results)
 return res
sample=pd.read_csv('/kaggle/input/digit-recognizer/sample_submission.csv' )<prepare_output>
from iterstrat.ml_stratifiers import MultilabelStratifiedKFold, MultilabelStratifiedShuffleSplit
from sklearn.metrics import roc_auc_score
pred = np.argmax(y_pred, axis = 1 )<concatenate>
feature_cols = [c for c in train.columns if c not in target_cols]
feature_cols = [c for c in feature_cols if c not in ['kfold','sig_id']]
len(feature_cols )
submission = pd.concat([pd.Series(range(1,28001),name = "ImageId"),r],axis = 1 )<save_to_csv>
remove_vehicle= True
submission.to_csv("mnist4.csv",index=False )<import_modules>
def transform_data(train, test, col, normalize=True, removed_vehicle=False):

 mapping = {"cp_type":{"trt_cp": 0, "ctl_vehicle":1},
"cp_time":{48:0, 72:1, 24:2},
"cp_dose":{"D1":0, "D2":1}}
if removed_vehicle:
categories_tr = np.stack([ train[c].apply(lambda x: mapping[c][x] ).values for c in col[1:3]], axis=1)
 categories_test = np.stack([ test[c].apply(lambda x: mapping[c][x] ).values for c in col[1:3]], axis=1)
 else:
categories_tr = np.stack([ train[c].apply(lambda x: mapping[c][x] ).values for c in col[:3]], axis=1)
 categories_test = np.stack([ test[c].apply(lambda x: mapping[c][x] ).values for c in col[:3]], axis=1)
 max_ = 10.
 min_ = -10.
 if removed_vehicle:
numerical_tr = train[col[3:]].values
numerical_test = test[col[3:]].values
else:
numerical_tr = train[col[3:]]
numerical_test = test[col[3:]].values
if normalize:
numerical_tr =(numerical_tr-min_)/(max_ - min_)
 numerical_test =(numerical_test-min_)/(max_ - min_)
 return categories_tr, categories_test, numerical_tr, numerical_test
col_features = list(train_features)[1:]
cat_tr, cat_test, numerical_tr, numerical_test = transform_data(train_features, test_features, col_features, normalize=False, removed_vehicle=remove_vehicle)
 targets_tr = train_targets_scored[columns].values.astype(np.float32)
 targets2_tr = train_targets_nonscored[columns_nonscored].values.astype(np.float32 )
np.random.seed(1)
<load_from_csv>
class LabelSmoothing(nn.Module):

 def __init__(self, smoothing=0.0, n_cls=2):

 super(LabelSmoothing, self ).__init__()
self.confidence = 1.0 - smoothing + smoothing / n_cls
self.smoothing = smoothing / n_cls
def forward(self, x, target):
probs = torch.nn.functional.sigmoid(x,)
 target1 = self.confidence * target +(1-target)* self.smoothing
loss = -(torch.log(probs+1e-15)* target1 +(1-target1)* torch.log(1-probs+1e-15))
return loss.mean()
X_train = pd.read_csv('/kaggle/input/digit-recognizer/train.csv')
 X_test = pd.read_csv('/kaggle/input/digit-recognizer/test.csv')
 print('Shape of the training data: ', X_train.shape)
 print('Shape of the test data: ', X_test.shape )<prepare_x_and_y>
class Config(object):
def __init__(self):
self.num_class = 206
self.verbose=False
self.device = "cuda" if torch.cuda.is_available() else "cpu"
self.SPLITS = 7
self.EPOCHS = 200
self.num_ensembling = 6
self.seed = 0
self.cat_emb_dim= [5, 4]
self.cats_idx = list(range(cat_tr.shape[1]))
self.cat_dims = [len(np.unique(cat_tr[:, i])) for i in range(cat_tr.shape[1])]
self.num_numericals= numerical_tr.shape[1]
self.save_name = "tabnet_raw_step1"
self.strategy = "KFOLD"
cfg = Config()
y_train = X_train['label']
X_train.drop(labels = ['label'], axis=1, inplace=True )<split>
X_test = np.concatenate([cat_test, numerical_test ], axis=1 )
y_train = to_categorical(y_train, num_classes=10)
 X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size = 0.1 )<choose_model_class>
model = Sequential()
model.add(Conv2D(filters = 32, kernel_size =(5,5),padding = 'Same',
activation ='relu', input_shape =(28,28,1)))
 model.add(Conv2D(filters = 64, kernel_size =(5,5),padding = 'Same',
activation ='relu'))
model.add(MaxPool2D(pool_size=(2,2)))
 model.add(Dropout(0.25))
model.add(Conv2D(filters = 128, kernel_size =(3,3),padding = 'Same',
activation ='relu'))
model.add(Conv2D(filters = 256, kernel_size =(3,3),padding = 'Same',
activation ='relu'))
model.add(MaxPool2D(pool_size=(2,2), strides=(2,2)))
 model.add(Dropout(0.25))
model.add(Flatten())
 model.add(Dense(256, activation = "relu"))
model.add(Dropout(0.5))
model.add(Dense(10, activation = "softmax"))
model.summary()<choose_model_class>
warnings.filterwarnings("ignore" )
optimizer = Adam(learning_rate=0.001, epsilon=1e-07)
 model.compile(optimizer = optimizer, loss = 'categorical_crossentropy', metrics=['accuracy'])
 earlyStopping = EarlyStopping(monitor='val_accuracy', patience=10, verbose=0, mode='auto')
 mcp = ModelCheckpoint('.mdl_wts.hdf5', save_best_only=True, monitor='val_accuracy', mode='auto')
 reduce_lr_loss = ReduceLROnPlateau(monitor='val_accuracy', factor=0.1, patience=7, verbose=1, epsilon=1e-4, mode='auto')
<train_model>
if cfg.strategy == "KFOLD":
oof_preds_all = []
oof_targets_all = []
scores_all = []
scores_auc_all= []
preds_test = []
for seed in range(cfg.num_ensembling):
seed_everything(seed)
 print("
mskf = MultilabelStratifiedKFold(n_splits=cfg.SPLITS, random_state=cfg.seed+seed, shuffle=True)
 oof_preds = []
oof_targets = []
scores = []
scores_auc = []
p = []
for j,(train_idx, val_idx)in enumerate(mskf.split(np.zeros(len(cat_tr)) , targets_tr)) :
print("FOLDS : ", j)
 X_train, y_train = torch.as_tensor(np.concatenate([cat_tr[train_idx], numerical_tr[train_idx] ], axis=1)) , torch.as_tensor(targets_tr[train_idx])
 X_val, y_val = torch.as_tensor(np.concatenate([cat_tr[val_idx], numerical_tr[val_idx] ], axis=1)) , torch.as_tensor(targets_tr[val_idx])
 model = TabNetRegressor(n_d=24, n_a=24, n_steps=1, lambda_sparse=0,
cat_dims=cfg.cat_dims, cat_emb_dim=cfg.cat_emb_dim, cat_idxs=cfg.cats_idx,
optimizer_fn=torch.optim.Adam,
optimizer_params=dict(lr=2e-2, weight_decay=1e-5),
mask_type='entmax', device_name=cfg.device,
scheduler_params=dict(milestones=[100, 150], gamma=0.9),
scheduler_fn=torch.optim.lr_scheduler.MultiStepLR)
 print(X_train.shape, y_train.shape)
 print(X_val.shape, y_val.shape)
 model.fit(X_train=X_train, y_train=y_train, X_valid=X_val, y_valid=y_val,max_epochs=cfg.EPOCHS, patience=50,
batch_size=1024, virtual_batch_size=128,
num_workers=0, drop_last=False, loss_fn=LabelSmoothing(0.001)
)
 model.load_best_model()
preds = model.predict(X_val)
 preds = torch.sigmoid(torch.as_tensor(preds)).detach().cpu().numpy()
score = log_loss_multi(y_val, preds)
 name = cfg.save_name + f"_fold{j}_{seed}"
model.save_model(name)
 temp = model.predict(X_test)
 p.append(torch.sigmoid(torch.as_tensor(temp)).detach().cpu().numpy())
 oof_preds.append(preds)
 oof_targets.append(y_val)
 scores.append(score)
 scores_auc.append(auc_multi(y_val,preds))
print(f"validation fold {j} : {score}")
 p = np.stack(p)
 preds_test.append(p)
 oof_preds_all.append(np.concatenate(oof_preds))
oof_targets_all.append(np.concatenate(oof_targets))
scores_all.append(np.array(scores))
scores_auc_all.append(np.array(scores_auc))
preds_test = np.stack(preds_test )
datagen = ImageDataGenerator(
 rotation_range=5,
width_shift_range=0.1,
height_shift_range=0.1,
shear_range=5,
zoom_range=0.1)
 datagen.fit(X_train )<train_model>
if cfg.strategy == "KFOLD":
for i in range(cfg.num_ensembling):
print("CV score fold : ", log_loss_multi(oof_targets_all[i], oof_preds_all[i]))
print("auc mean : ", sum(scores_auc_all[i])/len(scores_auc_all[i]))
history = model.fit_generator(datagen.flow(X_train, y_train, batch_size=64),
epochs = 100,
validation_data =(X_val,y_val),
verbose = 1,
steps_per_epoch=X_train.shape[0]//64,
callbacks = [earlyStopping, mcp, reduce_lr_loss] )<load_pretrained>
if cfg.strategy != "KFOLD":
i = 0
mskf = MultilabelStratifiedShuffleSplit(n_splits=1000, test_size=0.1, random_state=0)
 oof_preds = []
oof_targets = []
scores = []
scores_auc = []
for j,(train_idx, val_idx)in enumerate(mskf.split(np.zeros(len(cat_tr)) , targets_tr)) :
if i == cfg.SPLITS:
break
if not check_targets(targets_tr[train_idx]):
continue
print("FOLDS : ", i, j)
 X_train, y_train = torch.as_tensor(np.concatenate([cat_tr[train_idx], numerical_tr[train_idx] ], axis=1)) , torch.as_tensor(targets_tr[train_idx])
 X_val, y_val = torch.as_tensor(np.concatenate([cat_tr[val_idx], numerical_tr[val_idx] ], axis=1)) , torch.as_tensor(targets_tr[val_idx])
 model = TabNetRegressor(n_d=8, n_a=8, n_steps=3, gamma=1.3, cat_dims=cat_dims, cat_emb_dim=cfg.cat_emb_dim, cat_idxs=cats_idx, optimizer_fn=torch.optim.Adam,
optimizer_params=dict(lr=1e-3, amsgrad=True), mask_type="sparsemax", device_name=cfg.device)
 name = cfg.save_name + f"_{j}"
model.load_model(name)
 preds = model.predict(X_val)
 preds = torch.sigmoid(torch.as_tensor(preds)).detach().cpu().numpy()
score = log_loss_multi(y_val, preds)
 temp = model.predict(X_test)
 p.append(torch.sigmoid(torch.as_tensor(temp)).detach().cpu().numpy())
 oof_preds.append(preds)
 oof_targets.append(y_val)
 scores.append(score)
 scores_auc.append(auc_multi(y_val,preds))
print(f"validation fold {j} : {score}")
 i+=1
p = np.stack(p)
 preds_test.append(p)
 preds_test = np.stack(preds_test)
model.load_weights(filepath = '.mdl_wts.hdf5' )<compute_test_metric>
if cfg.strategy != "KFOLD":
print("auc mean : ", sum(scores_auc)/len(scores_auc))
print("CV score : ", log_loss_multi(np.concatenate(oof_targets), np.concatenate(oof_preds)) )
scores = model.evaluate(X_val, y_val, callbacks = [earlyStopping, mcp, reduce_lr_loss] )<define_variables>
print(model )
img_tensor = X_test[5].reshape(-1, 28, 28, 1 )<categorify>
sample_submission[columns] = preds_test.mean(1 ).mean(0)
 sample_submission.loc[test_features['cp_type']=='ctl_vehicle', sample_submission.columns[1:]] = 0
for layer in model.layers:
if'conv' in layer.name:
filters, biases = layer.get_weights()
print('Layer: ', layer.name, filters.shape)
 f_min, f_max = filters.min() , filters.max()
filters =(filters - f_min)/(f_max - f_min)
 print('Filter size:(', filters.shape[0], ',', filters.shape[1], ')')
 print('Channels in this layer: ', filters.shape[2])
 print('Number of filters: ', filters.shape[3])
 count = 1
plt.figure(figsize =(18, 4))
for i in range(filters.shape[3]):
ax= plt.subplot(4, filters.shape[3]/4, count)
 ax.set_xticks([])
 ax.set_yticks([])
 plt.imshow(filters[:,:,0, i], cmap=plt.cm.binary)
 count+=1
plt.show() 
<choose_model_class>
sample_submission.to_csv("submission.csv", index=False )