def _fit(self, X_train, y_train, n_epochs=50, eval_set=()):
seed_torch()
x_train = torch.tensor(X_train, dtype=torch.float32).to(self.device)
y = torch.tensor(y_train[:, np.newaxis],
dtype=torch.float32).to(self.device)
train = torch.utils.data.TensorDataset(x_train, y)
train_loader = torch.utils.data.DataLoader(train,
batch_size=self.train_batch,
shuffle=True)
if len(eval_set) == 2:
x_val = torch.tensor(eval_set[0],
dtype=torch.float32).to(self.device)
y_val = torch.tensor(eval_set[1][:, np.newaxis],
dtype=torch.float32).to(self.device)
valid = torch.utils.data.TensorDataset(x_val, y_val)
valid_loader = torch.utils.data.DataLoader(
valid, batch_size=self.val_batch, shuffle=False)
model = self.model(**self.kwargs)
model.to(self.device)
optimizer = optim.Adam(model.parameters())
if self.anneal:
scheduler = optim.lr_scheduler.CosineAnnealingLR(optimizer,
T_max=n_epochs)
best_score = -np.inf
for epoch in range(n_epochs):
with timer(f"Epoch {epoch+1}/{n_epochs}", self.logger):
model.train()
avg_loss = 0.
for (x_batch, y_batch) in train_loader:
y_pred = model(x_batch)
loss = self.loss_fn(y_pred, y_batch)
optimizer.zero_grad()
loss.backward()
optimizer.step()
avg_loss += loss.item() / len(train_loader)
valid_preds, avg_val_loss = self._val(valid_loader, model)
search_result = threshold_search(eval_set[1], valid_preds)
val_mcc, val_threshold = search_result["mcc"], search_result[
"threshold"]
self.logger.info(
f"loss: {avg_loss:.4f} val_loss: {avg_val_loss:.4f}")
self.logger.info(f"val_mcc: {val_mcc} best_t: {val_threshold}")
if self.anneal:
scheduler.step()
if val_mcc > best_score:
torch.save(model.state_dict(),
self.path / f"best{self.fold}.pt")
self.logger.info(f"Save model on epoch {epoch+1}")
best_score = val_mcc
model.load_state_dict(torch.load(self.path / f"best{self.fold}.pt"))
valid_preds, avg_val_loss = self._val(valid_loader, model)
self.logger.info(f"Validation loss: {avg_val_loss}")
return valid_preds
for path in args.features:
path = Path(path)
assert path.exists()
with open(path, "rb") as f:
feats = pickle.load(f)
if isinstance(feats, list):
feats = np.concatenate(feats)
features.append(feats)
test = np.concatenate(features, axis=2)
with open(f"trainer/{args.tag}/scaler.pkl", "rb") as f:
scaler = pickle.load(f)
logger.info(f"scaler size: {len(scaler)}")
if len(scaler) > 0:
for i in range(test.shape[1]):
with timer(f"scaling dim{i+1}", logger):
test[:, i, :] = scaler[i].transform(test[:, i, :])
test_tensor = torch.tensor(test, dtype=torch.float32).to(args.device)
dataset = torch.utils.data.TensorDataset(test_tensor)
loader = torch.utils.data.DataLoader(dataset,
batch_size=batch_size,
shuffle=False)
with open(f"trainer/{args.tag}/trainer.pkl", "rb") as f:
trainer = pickle.load(f)
bin_path = Path(f"bin/{args.tag}")
test_preds = np.zeros(test.shape[0])
for path in bin_path.iterdir():
with timer(f"use binary {path}", logger):
name="robust-denoising", tag=f"robust-denoising/{args.n_dims}")
meta_train = pd.read_csv("../input/metadata_train.csv")
meta_test = pd.read_csv("../input/metadata_test.csv")
train_path = Path("../input/train.parquet")
test_path = Path("../input/test.parquet")
n_line = int(meta_train.shape[0] // 3)
nchunk_train = 2
step = (n_line // nchunk_train) * 3
current_head = meta_train.signal_id[0]
logger.info(f"step: {step}")
logger.info(f"initial head: {current_head}")
X = []
for i in range(nchunk_train):
with timer(f"chunk{i+1}", logger):
X_temp = robust_denoised_data(train_path, current_head, step,
args.n_dims)
X.append(X_temp)
current_head += step
logger.info(f"current head: {current_head}")
X = np.concatenate(X)
logger.info(f"X_shape: {X.shape}")
with open(outdir / "train.pkl", "wb") as f:
pickle.dump(X, f)
n_line = int(meta_test.shape[0] // 3)
nchunk_test = 7
step = (n_line // 6) * 3
current_head = meta_test.signal_id[0]
logger.info(f"step: {step}")
for i in range(test.shape[1]):
test[:, i, :] = scaler[i].transform(test[:, i, :])
test_tensor = torch.tensor(test, dtype=torch.float32).to(args.device)
dataset = torch.utils.data.TensorDataset(test_tensor)
loader = torch.utils.data.DataLoader(dataset,
batch_size=batch_size,
shuffle=False)
with open(f"trainer/{args.tag}/trainer.pkl", "rb") as f:
trainer = pickle.load(f)
bin_path = Path(f"bin/{args.tag}")
test_preds = np.zeros(test.shape[0])
for path in bin_path.iterdir():
with timer(f"use binary {path}", logger):
model = LSTMAttentionNet(**trainer.kwargs)
model.to(args.device)
model.load_state_dict(torch.load(path))
model.eval()
temp = np.zeros(test.shape[0])
for i, (x_batch, ) in enumerate(loader):
with torch.no_grad():
y_pred = model(x_batch).detach()
temp[i * batch_size:(i + 1) * batch_size] = sigmoid(
y_pred.cpu().numpy())[:, 0]
test_preds += temp / trainer.n_splits
prob_path = Path(f"probability/{args.tag}")
prob_path.mkdir(exist_ok=True, parents=True)
with open(prob_path / "probability.pkl", "wb") as f:
