Overview | Installation | Available Models with Quick Start Guides | Benchmark | To DO | Contributing
TabularS3L is a PyTorch Lightning-based library designed to facilitate self- and semi-supervised learning with tabular data. While numerous self- and semi-supervised learning tabular models have been proposed, there is a lack of a comprehensive library that addresses the needs of tabular practitioners. This library aims to fill this gap by providing a unified PyTorch Lightning-based framework for exploring and deploying such models.
We provide a Python package ts3l of TabularS3L for users who want to use semi- and self-supervised learning tabular models.
pip install ts3lTabularS3L employs a two-phase learning approach, where the learning strategies differ between phases. Below is an overview of the models available within TabularS3L, highlighting the learning strategies employed in each phase. The abbreviations 'Self-SL', 'Semi-SL', and 'SL' represent self-supervised learning, semi-supervised learning, and supervised learning, respectively.
According to the original implementation and the paper, the encoder of DAE, VIME, and SubTab is frozen during the second phase of learning. However, you can choose to freeze the encoder or not by setting the freeze_encoder flag in the set_second_phase method.
| Model | First Phase | Second Phase |
|---|---|---|
| DAE (GitHub) | Self-SL | SL |
| VIME (NeurIPS'20) | Self-SL | Semi-SL or SL |
| SubTab (NeurIPS'21) | Self-SL | SL |
| SCARF (ICLR'22) | Self-SL | SL |
| SwitchTab (AAAI'24) | Self-SL | SL |
DAE processes input data that has been partially corrupted, producing clean data and predicting which features are corrupted during the self-supervised learning. The denoising task enables the model to learn the input distribution and generate latent representations that are robust to corruption. These latent representations can be utilized for a variety of downstream tasks.
Quick Start
# Assume that we have X_train, X_valid, X_test, y_train, y_valid, y_test, categorical_cols, and continuous_cols
# Prepare the DAELightning Module
from ts3l.pl_modules import DAELightning
from ts3l.utils.dae_utils import DAEDataset, DAECollateFN
from ts3l.utils import TS3LDataModule
from ts3l.utils.dae_utils import DAEConfig
from pytorch_lightning import Trainer
metric = "accuracy_score"
input_dim = X_train.shape[1]
hidden_dim = 1024
output_dim = 2
encoder_depth=4
head_depth = 2
noise_type = "Swap"
noise_ratio = 0.3
max_epochs = 20
batch_size = 128
X_train, X_unlabeled, y_train, _ = train_test_split(X_train, y_train, train_size = 0.1, random_state=0, stratify=y_train)
config = DAEConfig( task="classification", loss_fn="CrossEntropyLoss", metric=metric, metric_hparams={},
input_dim=input_dim, hidden_dim=hidden_dim,
output_dim=output_dim, encoder_depth=encoder_depth,
head_depth = head_depth,
noise_type = noise_type,
noise_ratio = noise_ratio,
num_categoricals=len(category_cols), num_continuous=len(continuous_cols)
)
pl_dae = DAELightning(config)
### First Phase Learning
train_ds = DAEDataset(X = X_train, unlabeled_data = X_unlabeled, continuous_cols = continuous_cols, category_cols = category_cols)
valid_ds = DAEDataset(X = X_valid, continuous_cols = continuous_cols, category_cols = category_cols)
datamodule = TS3LDataModule(train_ds, valid_ds, batch_size, train_sampler='random', train_collate_fn=DAECollateFN(config), valid_collate_fn=DAECollateFN(config))
trainer = Trainer(
accelerator = 'cpu',
max_epochs = max_epochs,
num_sanity_val_steps = 2,
)
trainer.fit(pl_dae, datamodule)
### Second Phase Learning
pl_dae.set_second_phase()
train_ds = DAEDataset(X = X_train, Y = y_train.values, unlabeled_data=X_unlabeled, continuous_cols=continuous_cols, category_cols=category_cols)
valid_ds = DAEDataset(X = X_valid, Y = y_valid.values, continuous_cols=continuous_cols, category_cols=category_cols)
datamodule = TS3LDataModule(train_ds, valid_ds, batch_size = batch_size, train_sampler="weighted")
trainer = Trainer(
accelerator = 'cpu',
max_epochs = max_epochs,
num_sanity_val_steps = 2,
)
trainer.fit(pl_dae, datamodule)
# Evaluation
from sklearn.metrics import accuracy_score
import torch
from torch.nn import functional as F
from torch.utils.data import DataLoader, SequentialSampler
test_ds = DAEDataset(X_test, category_cols=category_cols, continuous_cols=continuous_cols)
test_dl = DataLoader(test_ds, batch_size, shuffle=False, sampler = SequentialSampler(test_ds))
preds = trainer.predict(pl_dae, test_dl)
preds = F.softmax(torch.concat([out.cpu() for out in preds]).squeeze(),dim=1)
accuracy = accuracy_score(y_test, preds.argmax(1))
print("Accuracy %.2f" % accuracy)VIME enhances tabular data learning through a dual approach. In its first phase, it utilize a pretext task of estimating mask vectors from corrupted tabular data, alongside a reconstruction pretext task for self-supervised learning. The second phase leverages consistency regularization on unlabeled data.
Quick Start
# Assume that we have X_train, X_valid, X_test, y_train, y_valid, y_test, categorical_cols, and continuous_cols
# Prepare the VIMELightning Module
from ts3l.pl_modules import VIMELightning
from ts3l.utils.vime_utils import VIMEDataset
from ts3l.utils import TS3LDataModule
from ts3l.utils.vime_utils import VIMEConfig
from pytorch_lightning import Trainer
metric = "accuracy_score"
input_dim = X_train.shape[1]
hidden_dim = 1024
output_dim = 2
alpha1 = 2.0
alpha2 = 2.0
beta = 1.0
K = 3
p_m = 0.2
batch_size = 128
X_train, X_unlabeled, y_train, _ = train_test_split(X_train, y_train, train_size = 0.1, random_state=0, stratify=y_train)
config = VIMEConfig( task="classification", loss_fn="CrossEntropyLoss", metric=metric, metric_hparams={},
input_dim=input_dim, hidden_dim=hidden_dim,
output_dim=output_dim, alpha1=alpha1, alpha2=alpha2,
beta=beta, K=K, p_m = p_m,
num_categoricals=len(category_cols), num_continuous=len(continuous_cols)
)
pl_vime = VIMELightning(config)
### First Phase Learning
train_ds = VIMEDataset(X = X_train, unlabeled_data = X_unlabeled, config=config, continuous_cols = continuous_cols, category_cols = category_cols)
valid_ds = VIMEDataset(X = X_valid, config=config, continuous_cols = continuous_cols, category_cols = category_cols)
datamodule = TS3LDataModule(train_ds, valid_ds, batch_size, train_sampler='random')
trainer = Trainer(
accelerator = 'cpu',
max_epochs = 20,
num_sanity_val_steps = 2,
)
trainer.fit(pl_vime, datamodule)
### Second Phase Learning
from ts3l.utils.vime_utils import VIMESecondPhaseCollateFN
pl_vime.set_second_phase()
train_ds = VIMEDataset(X_train, y_train.values, config, unlabeled_data=X_unlabeled, continuous_cols=continuous_cols, category_cols=category_cols, is_second_phase=True)
valid_ds = VIMEDataset(X_valid, y_valid.values, config, continuous_cols=continuous_cols, category_cols=category_cols, is_second_phase=True)
datamodule = TS3LDataModule(train_ds, valid_ds, batch_size = batch_size, train_sampler="weighted", train_collate_fn=VIMESecondPhaseCollateFN())
trainer.fit(pl_vime, datamodule)
# Evaluation
from sklearn.metrics import accuracy_score
import torch
from torch.nn import functional as F
from torch.utils.data import DataLoader, SequentialSampler
test_ds = VIMEDataset(X_test, category_cols=category_cols, continuous_cols=continuous_cols, is_second_phase=True)
test_dl = DataLoader(test_ds, batch_size, shuffle=False, sampler = SequentialSampler(test_ds))
preds = trainer.predict(pl_vime, test_dl)
preds = F.softmax(torch.concat([out.cpu() for out in preds]).squeeze(),dim=1)
accuracy = accuracy_score(y_test, preds.argmax(1))
print("Accuracy %.2f" % accuracy)SubTab turns the task of learning from tabular data into as a multi-view representation challenge by dividing input features into multiple subsets during its first phase. During the second phase, collaborative inference is used to derive a joint representation by aggregating latent variables across subsets. This approach improves the model's performance in supervised learning tasks.
Quick Start
# Assume that we have X_train, X_valid, X_test, y_train, y_valid, y_test, categorical_cols, and continuous_cols
# Prepare the SubTabLightning Module
from ts3l.pl_modules import SubTabLightning
from ts3l.utils.subtab_utils import SubTabDataset, SubTabCollateFN
from ts3l.utils import TS3LDataModule
from ts3l.utils.subtab_utils import SubTabConfig
from pytorch_lightning import Trainer
metric = "accuracy_score"
input_dim = X_train.shape[1]
hidden_dim = 1024
output_dim = 2
tau = 1.0
use_cosine_similarity = True
use_contrastive = True
use_distance = True
n_subsets = 4
overlap_ratio = 0.75
mask_ratio = 0.1
noise_type = "Swap"
noise_level = 0.1
batch_size = 128
max_epochs = 3
X_train, X_unlabeled, y_train, _ = train_test_split(X_train, y_train, train_size = 0.1, random_state=0, stratify=y_train)
config = SubTabConfig( task="classification", loss_fn="CrossEntropyLoss", metric=metric, metric_hparams={},
input_dim=input_dim, hidden_dim=hidden_dim,
output_dim=output_dim, tau=tau, use_cosine_similarity= use_cosine_similarity, use_contrastive=use_contrastive, use_distance=use_distance,
n_subsets=n_subsets, overlap_ratio=overlap_ratio, mask_ratio=mask_ratio, noise_type=noise_type, noise_level=noise_level
)
pl_subtab = SubTabLightning(config)
### First Phase Learning
train_ds = SubTabDataset(X_train, unlabeled_data=X_unlabeled)
valid_ds = SubTabDataset(X_valid)
datamodule = TS3LDataModule(train_ds, valid_ds, batch_size, train_sampler='random', train_collate_fn=SubTabCollateFN(config), valid_collate_fn=SubTabCollateFN(config), n_jobs = 4)
trainer = Trainer(
accelerator = 'cpu',
max_epochs = max_epochs,
num_sanity_val_steps = 2,
)
trainer.fit(pl_subtab, datamodule)
### Second Phase Learning
pl_subtab.set_second_phase()
train_ds = SubTabDataset(X_train, y_train.values)
valid_ds = SubTabDataset(X_valid, y_valid.values)
datamodule = TS3LDataModule(train_ds, valid_ds, batch_size = batch_size, train_sampler="weighted", train_collate_fn=SubTabCollateFN(config), valid_collate_fn=SubTabCollateFN(config))
trainer.fit(pl_subtab, datamodule)
# Evaluation
from sklearn.metrics import accuracy_score
import torch
from torch.nn import functional as F
from torch.utils.data import DataLoader, SequentialSampler
test_ds = SubTabDataset(X_test)
test_dl = DataLoader(test_ds, batch_size, shuffle=False, sampler = SequentialSampler(test_ds), num_workers=4, collate_fn=SubTabCollateFN(config))
preds = trainer.predict(pl_subtab, test_dl)
preds = F.softmax(torch.concat([out.cpu() for out in preds]).squeeze(),dim=1)
accuracy = accuracy_score(y_test, preds.argmax(1))
print("Accuracy %.2f" % accuracy)SCARF introduces a contrastive learning framework specifically tailored for tabular data. By corrupting random subsets of features, SCARF creates diverse views for self-supervised learning in its first phase. The subsequent phase transitions to supervised learning, utilizing a pretrained encoder to enhance model accuracy and robustness.
Quick Start
# Assume that we have X_train, X_valid, X_test, y_train, y_valid, y_test, categorical_cols, and continuous_cols
# Prepare the SCARFLightning Module
from ts3l.pl_modules import SCARFLightning
from ts3l.utils.scarf_utils import SCARFDataset
from ts3l.utils import TS3LDataModule
from ts3l.utils.scarf_utils import SCARFConfig
from pytorch_lightning import Trainer
metric = "accuracy_score"
input_dim = X_train.shape[1]
hidden_dim = 1024
output_dim = 2
encoder_depth = 3
head_depth = 1
dropout_rate = 0.04
corruption_rate = 0.6
batch_size = 128
max_epochs = 10
X_train, X_unlabeled, y_train, _ = train_test_split(X_train, y_train, train_size = 0.1, random_state=0, stratify=y_train)
config = SCARFConfig( task="classification", loss_fn="CrossEntropyLoss", metric=metric, metric_hparams={},
input_dim=input_dim, hidden_dim=hidden_dim,
output_dim=output_dim, encoder_depth=encoder_depth, head_depth=head_depth,
dropout_rate=dropout_rate, corruption_rate = corruption_rate
)
pl_scarf = SCARFLightning(config)
### First Phase Learning
train_ds = SCARFDataset(X_train, unlabeled_data=X_unlabeled, config = config)
valid_ds = SCARFDataset(X_valid, config=config)
datamodule = TS3LDataModule(train_ds, valid_ds, batch_size=batch_size, train_sampler="random")
trainer = Trainer(
accelerator = 'cpu',
max_epochs = max_epochs,
num_sanity_val_steps = 2,
)
trainer.fit(pl_scarf, datamodule)
### Second Phase Learning
pl_scarf.set_second_phase()
train_ds = SCARFDataset(X_train, y_train.values, is_second_phase=True)
valid_ds = SCARFDataset(X_valid, y_valid.values, is_second_phase=True)
datamodule = TS3LDataModule(train_ds, valid_ds, batch_size = batch_size, train_sampler="weighted")
trainer.fit(pl_scarf, datamodule)
# Evaluation
from sklearn.metrics import accuracy_score
import torch
from torch.nn import functional as F
from torch.utils.data import DataLoader, SequentialSampler
test_ds = SCARFDataset(X_test, is_second_phase=True)
test_dl = DataLoader(test_ds, batch_size, shuffle=False, sampler = SequentialSampler(test_ds), num_workers=4)
preds = trainer.predict(pl_scarf, test_dl)
preds = F.softmax(torch.concat([out.cpu() for out in preds]).squeeze(),dim=1)
accuracy = accuracy_score(y_test, preds.argmax(1))
print("Accuracy %.2f" % accuracy)SwitchTab introduces a novel self-supervised method specifically designed to decuple mutual and salient features among data pair, resulting in more representative embeddings. Moreover, the pre-trained salient embeddings can be utilized as plug-and-play features to enhance the performance of various traditional classification methods.
Quick Start
# Assume that we have X_train, X_valid, X_test, y_train, y_valid, y_test, categorical_cols, and continuous_cols
# Prepare the SwitchTabLightning Module
from ts3l.pl_modules import SwitchTabLightning
from ts3l.utils.switchtab_utils import SwitchTabDataset, SwitchTabFirstPhaseCollateFN
from ts3l.utils import TS3LDataModule
from ts3l.utils.switchtab_utils import SwitchTabConfig
from pytorch_lightning import Trainer
metric = "accuracy_score"
input_dim = X_train.shape[1]
hidden_dim = 1024
output_dim = 2
encoder_depth = 3
n_head = 2
u_label = -1
batch_size = 128
X_train, X_unlabeled, y_train, _ = train_test_split(X_train, y_train, train_size = 0.1, random_state=0, stratify=y_train)
config = SwitchTabConfig( task="classification", loss_fn="CrossEntropyLoss", metric=metric, metric_hparams={},
input_dim=input_dim, hidden_dim=hidden_dim,
output_dim=output_dim, encoder_depth=encoder_depth,
n_head = n_head,
u_label = u_label
)
pl_switchtab = SwitchTabLightning(config)
### First Phase Learning
train_ds = SwitchTabDataset(X = X_train, unlabeled_data = X_unlabeled, Y = y_train.values, config=config, continuous_cols=continuous_cols, category_cols=category_cols)
valid_ds = SwitchTabDataset(X = X_valid, config=config, Y = y_valid.values, continuous_cols=continuous_cols, category_cols=category_cols)
datamodule = TS3LDataModule(train_ds, valid_ds, batch_size, train_sampler='weighted', train_collate_fn=SwitchTabFirstPhaseCollateFN(), valid_collate_fn=SwitchTabFirstPhaseCollateFN())
trainer = Trainer(
accelerator = 'cpu',
max_epochs = 20,
num_sanity_val_steps = 2,
)
trainer.fit(pl_switchtab, datamodule)
### Second Phase Learning
pl_switchtab.set_second_phase()
train_ds = SwitchTabDataset(X = X_train, Y = y_train.values, continuous_cols=continuous_cols, category_cols=category_cols, is_second_phase=True)
valid_ds = SwitchTabDataset(X = X_valid, Y = y_valid.values, continuous_cols=continuous_cols, category_cols=category_cols, is_second_phase=True)
datamodule = TS3LDataModule(train_ds, valid_ds, batch_size = batch_size, train_sampler="weighted")
trainer = Trainer(
accelerator = 'cpu',
max_epochs = 20,
num_sanity_val_steps = 2,
)
trainer.fit(pl_switchtab, datamodule)
# Evaluation
from sklearn.metrics import accuracy_score
import torch
from torch.nn import functional as F
from torch.utils.data import DataLoader, SequentialSampler
test_ds = SwitchTabDataset(X_test, continuous_cols=continuous_cols, category_cols=category_cols, is_second_phase=True)
test_dl = DataLoader(test_ds, batch_size, shuffle=False, sampler = SequentialSampler(test_ds))
preds = trainer.predict(pl_switchtab, test_dl)
preds = F.softmax(torch.concat([out.cpu() for out in preds]).squeeze(),dim=1)
accuracy = accuracy_score(y_test, preds.argmax(1))
print("Accuracy %.2f" % accuracy)We provide a simple benchmark using TabularS3L against XGBoost. The train-validation-test ratio is 6:2:2 and we tuned each model over 50 trials using Optuna. The results are the average of the random seeds [0,4]. The best results are bold. 'acc', 'b-acc', and 'mse' mean 'Accuracy', 'Balanced Accuracy', and 'Mean Squared Error', respectively.
Use this benchmark for reference only, as only a small number of random seeds were used.
| Model | diabetes (acc) | cmc (b-acc) | abalone (mse) |
|---|---|---|---|
| XGBoost | 0.7325 | 0.4763 | 5.5739 |
| DAE | 0.7208 | 0.4885 | 5.6168 |
| VIME | 0.7182 | 0.5087 | 5.6637 |
| SubTab | 0.7312 | 0.4930 | 7.2418 |
| SCARF | 0.7416 | 0.4710 | 5.8888 |
| SwitchTab | 0.7156 | 0.4886 | 5.9907 |
| Model | diabetes (acc) | cmc (b-acc) | abalone (mse) |
|---|---|---|---|
| XGBoost | 0.7234 | 0.5291 | 4.8377 |
| DAE | 0.7390 | 0.5500 | 4.5758 |
| VIME | 0.7688 | 0.5477 | 4.5804 |
| SubTab | 0.7390 | 0.5432 | 6.3104 |
| SCARF | 0.7442 | 0.5521 | 4.4443 |
| SwitchTab | 0.7585 | 0.5411 | 4.7489 |
- Release nn.Module and Dataset of VIME, SubTab, and SCARF
- Release LightningModules of VIME, SubTab, and SCARF
- Release Denoising AutoEncoder
- Release SwitchTab
- Add example codes
- Release more tabular models
Contributions to this implementation are highly appreciated. Whether it's suggesting improvements, reporting bugs, or proposing new features, feel free to open an issue or submit a pull request.