Run scripts/01-download_save_data.py with the right configs to download the datasets.
curl -L -o analyses/results/benchmark_total.csv https://figshare.com/ndownloader/files/40081681
Run the scripts and notebooks in the scripts_notebooks folder in the order mentioned to reproduce the experiments.
The model is implemented and added to PyTorch Tabular as one of the many models implemented there.
https://github.com/manujosephv/pytorch_tabular/tree/main/src/pytorch_tabular/models/gandalf
The code is also available in the src folder of this repo, but more to show the implementation. The actual implementation is in the PyTorch Tabular repo.
from pytorch_tabular import TabularModel
from pytorch_tabular.models import GANDALFConfig
from pytorch_tabular.config import (
DataConfig,
OptimizerConfig,
TrainerConfig,
)
data_config = DataConfig(
target=[
"target"
], # target should always be a list. Multi-targets are only supported for regression. Multi-Task Classification is not implemented
continuous_cols=num_col_names,
categorical_cols=cat_col_names,
)
trainer_config = TrainerConfig(
auto_lr_find=True, # Runs the LRFinder to automatically derive a learning rate
batch_size=1024,
max_epochs=100,
)
optimizer_config = OptimizerConfig()
model_config = GANDALFConfig(
task="classification",
gflu_stages=15,
gflu_dropout=0.01,
gflu_feature_init_sparsity=0.3,
learnable_sparsity=True,
learning_rate=1e-3,
)
tabular_model = TabularModel(
data_config=data_config,
model_config=model_config,
optimizer_config=optimizer_config,
trainer_config=trainer_config,
)
tabular_model.fit(train=train, validation=val)
result = tabular_model.evaluate(test)
pred_df = tabular_model.predict(test)
tabular_model.save_model("examples/basic")
loaded_model = TabularModel.load_from_checkpoint("examples/basic")