Multitargets regression "val_split" issue

[altar31]

Hi,

In the multitargets regression setting, when I try to set a val_split in the trainer.fit() methods, it rises me the following error.

As a direct consequence, it is not possible to use the LR scheduler as shown below

Please find the requirements.txt and the code in order to reproduce this issue. requirements.txt

import random
import pandas as pd
import numpy as np
import pickle
import torch
from pytorch_widedeep.preprocessing import TabPreprocessor
from pytorch_widedeep.models import TabMlp, WideDeep
from pytorch_widedeep.losses_multitarget import MultiTargetRegressionLoss
from pytorch_widedeep import Trainer
from pytorch_widedeep.callbacks import EarlyStopping, ModelCheckpoint
from torch.optim.lr_scheduler import ReduceLROnPlateau
from torch.optim import AdamW

# generate data
data = {
    "X1": [random.uniform(200, 250) for _ in range(1000)],
    "X2": [random.uniform(100, 200) for _ in range(1000)],
    "X3": [random.uniform(0, 15000) for _ in range(1000)],
    "Y1": [random.uniform(100, 2000) for _ in range(1000)],
    "Y2": [random.uniform(40, 100) for _ in range(1000)],
}
df = pd.DataFrame(data)

test_data = {
    "X1": [random.uniform(220, 250) for _ in range(1000)],
    "X2": [random.uniform(156, 200) for _ in range(1000)],
    "X3": [random.uniform(0, 15000) for _ in range(1000)],
}
df_test = pd.DataFrame(test_data)

# set the target
target = df[["Y1", "Y2"]].values.astype(np.float32)
# Tabular preprocessor
tab_preprocessor = TabPreprocessor(continuous_cols=["X1", "X2", "X3"])
X_tab = tab_preprocessor.fit_transform(df).astype(np.float32)
# Model architecture
tab_mlp = TabMlp(
    column_idx=tab_preprocessor.column_idx,
    continuous_cols=tab_preprocessor.continuous_cols,
    mlp_hidden_dims=[64, 32],
)
model = WideDeep(deeptabular=tab_mlp, pred_dim=2)
print("The model architecture:\n", model)

# Multitargets loss
loss = MultiTargetRegressionLoss(weights=[0.5, 0.5], reduction="mean")
# early stopping
early_stopping = EarlyStopping()

# checkpoints
model_checkpoint = ModelCheckpoint(
    # filepath=f"model_check",
    save_best_only=True,
    verbose=1,
)

# optimizer
deep_opt = AdamW(model.deeptabular.parameters(), lr=0.001)

# lr scheduler
deep_sch = ReduceLROnPlateau(
    deep_opt,
    patience=2,
    min_lr=1e-5,
)
# Set the trainer
trainer = Trainer(model, 
    objective="multitarget", 
    custom_loss_function=loss,
    callbacks=[early_stopping, model_checkpoint],
    lr_schedulers={"deeptabular": deep_sch},
    optimizers={"deeptabular": deep_opt},   
)
# Fit the model
trainer.fit(
    X_tab=X_tab,
    target=target,
    n_epochs=10,
    batch_size=16,
    val_split=0.2
)

# save the model
torch.save(model.state_dict(), "multi_regression_torch.pt")
# save preprocessors
with open("multi_regression_.pkl", "wb") as dp:
    pickle.dump(tab_preprocessor, dp)
print("Training completed and model saved.")

# Load the saved preprocessor
with open(f"multi_regression_.pkl", "rb") as tp:
    tab_preprocessor_new = pickle.load(tp)

# Load the trained model for inference on test data
X_test = tab_preprocessor_new.transform(df_test).astype(np.float32)
new_model = WideDeep(deeptabular=tab_mlp, pred_dim=2)
new_model.load_state_dict(torch.load(f"multi_regression_torch.pt"))
new_model.eval()

# Use the trained model for prediction
trainer_new = Trainer(model, objective="multitarget", custom_loss_function=loss)
preds = trainer_new.predict(X_tab=X_test, batch_size=64)
print("Predictions array:\n", preds)

Thanks in advance 😃

[jrzaurin]

Thanks man

As before, try installing the branch and running the code again, should work

[altar31]

Perfect ! Now I can set the va_split.

As a consequence, early stopping and LR scheduler perfectly works too! 👍

Thanks @jrzaurin