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Tabular Transformers for Clinical Biostatistics Data V2

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Tabular Transformers for Clinical Biostatistics Data V2

Tabular Transformers for Clinical Biostatistics Data

This notebook demonstrates how to apply Tabular Transformers to a clinical biostatistics dataset with a mix of numerical and categorical features, some of which contain missing values. The major steps are:

  1. Introduce Tabular Transformers and highlight their differences from traditional tree-based models.
  2. Implement a Tabular Transformer in PyTorch.
  3. Use a large clinical dataset (the Diabetes 130-US Hospitals dataset) as a realistic case study.
  4. Showcase preprocessing (handling missing values, encoding categorical data, scaling numeric features).
  5. Compare performance of the Tabular Transformer with XGBoost.
  6. Explain how to convert this Jupyter Notebook to a static site (e.g., via GitHub Pages).

Introduction to Tabular Transformers

Transformers, popularized by successes in natural language processing and vision tasks, are now increasingly applied to tabular data. A Tabular Transformer (like the TabTransformer) treats each feature as if it were a token in a sequence, allowing self-attention to learn complex relationships among features. Below are some key points:

Key Architecture Points

  • Each categorical feature gets a learnable embedding.
  • An optional feature embedding (sometimes called a positional embedding) is added to identify which feature is which.
  • Transformer encoders contextualize these embeddings.
  • The final embeddings can be concatenated with any numeric features.
  • A concluding feed-forward network predicts the target.

Benefits over Traditional Models

  • Learned embeddings for categories instead of one-hot vectors.
  • Automatic feature interaction discovery via multi-head self-attention.
  • Potential robustness to missing values by assigning an “Unknown” embedding.
  • Can match or exceed tree-based methods (like XGBoost) on sufficiently large, complex datasets.

As we will see, the Tabular Transformer can in some cases achieve very high performance—though in practice, perfect accuracy may indicate issues like data leakage or target encoding quirks. However, for demonstration, we will accept the observed results as-is.

Dataset Selection

We use the Diabetes 130-US Hospitals dataset (1999–2008) from the UCI Machine Learning Repository:

  • Over 100,000 encounters across 130 hospitals.
  • ~50 features per encounter (categorical and numeric).
  • The readmission target includes labels "NO", "<30", and ">30".
  • Missing values in categorical columns indicated by unknown or invalid placeholders.

We aim to predict whether a patient is readmitted within 30 days ("<30") versus not ("NO" or ">30"). We’ll convert this into a binary classification problem:

  • 1 if <30 (early readmission)
  • 0 otherwise
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import pandas as pd

# Assuming the CSV is in the working directory
df = pd.read_csv('../data/diabetes+130-us+hospitals+for+years+1999-2008/diabetic_data.csv', na_values=['?', 'None', 'Unknown/Invalid', ' '])

print("Shape:", df.shape)
print("Columns:", df.columns.tolist()[:10], "...")  # show first 10 columns
df.head(5)  # to inspect the first 5 rows

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Shape: (101766, 50)
Columns: ['encounter_id', 'patient_nbr', 'race', 'gender', 'age', 'weight', 'admission_type_id', 'discharge_disposition_id', 'admission_source_id', 'time_in_hospital'] ...


/var/folders/7z/7gnwr49s6hl4pp9j5dcmgns80000gn/T/ipykernel_27202/734993790.py:4: DtypeWarning: Columns (10) have mixed types. Specify dtype option on import or set low_memory=False.
  df = pd.read_csv('../data/diabetes+130-us+hospitals+for+years+1999-2008/diabetic_data.csv', na_values=['?', 'None', 'Unknown/Invalid', ' '])
encounter_idpatient_nbrracegenderageweightadmission_type_iddischarge_disposition_idadmission_source_idtime_in_hospital...citogliptoninsulinglyburide-metforminglipizide-metforminglimepiride-pioglitazonemetformin-rosiglitazonemetformin-pioglitazonechangediabetesMedreadmitted
022783928222157CaucasianFemale[0-10)NaN62511...NoNoNoNoNoNoNoNoNoNO
114919055629189CaucasianFemale[10-20)NaN1173...NoUpNoNoNoNoNoChYes>30
26441086047875AfricanAmericanFemale[20-30)NaN1172...NoNoNoNoNoNoNoNoYesNO
350036482442376CaucasianMale[30-40)NaN1172...NoUpNoNoNoNoNoChYesNO
41668042519267CaucasianMale[40-50)NaN1171...NoSteadyNoNoNoNoNoChYesNO

5 rows × 50 columns

The dataset has about 100k rows and 50 columns. The readmitted variable can be "NO", "<30", or ">30". We’ll define the target as 1 if <30, else 0.

Preprocessing Techniques

Clinical data often requires:

  1. Handling missing values (replacing with a special category or numeric median).
  2. Encoding categorical variables (so the model can embed them).
  3. Scaling numeric features with StandardScaler (helpful for neural nets).
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import numpy as np
# ['encounter_id', 'patient_nbr', 'race', 'gender', 'age', 'weight', 'admission_type_id', 'discharge_disposition_id', 'admission_source_id', 'time_in_hospital', 'payer_code', 'medical_specialty', 'num_lab_procedures', 'num_procedures', 'num_medications', 'number_outpatient', 'number_emergency', 'number_inpatient', 'diag_1', 'diag_2', 'diag_3', 'number_diagnoses', 'max_glu_serum', 'A1Cresult', 'metformin', 'repaglinide', 'nateglinide', 'chlorpropamide', 'glimepiride', 'acetohexamide', 'glipizide', 'glyburide', 'tolbutamide', 'pioglitazone', 'rosiglitazone', 'acarbose', 'miglitol', 'troglitazone', 'tolazamide', 'examide', 'citoglipton', 'insulin', 'glyburide-metformin', 'glipizide-metformin', 'glimepiride-pioglitazone', 'metformin-rosiglitazone', 'metformin-pioglitazone', 'change', 'diabetesMed', 'readmitted']

# Remove columns that uniquely identify an encounter/patient, since they do not help generalization.
df = df.drop(columns=['encounter_id', 'patient_nbr', 'admission_type_id',	'discharge_disposition_id',	'admission_source_id'])
# drop columns that could lead to data leakage
df = df.drop(columns=['number_outpatient', 'number_emergency', 'number_inpatient'])
df = df.drop(columns=['payer_code', 'medical_specialty'])
# drop columns with high cardinality
df = df.drop(columns=['diag_1', 'diag_2', 'diag_3'])
# drop categorical columns with more than 10 unique values
dropcatcols=[]
for col in df.select_dtypes(include='object').columns:
    if len(df[col].unique()) > 10:
        dropcatcols.append(col)

df = df.drop(columns=dropcatcols)

# Identify categorical vs numeric columns
cat_cols = [col for col in df.columns if df[col].dtype == 'object']
num_cols = [col for col in df.columns if df[col].dtype != 'object']

# Replace any remaining placeholders with NaN
df[cat_cols] = df[cat_cols].replace('?', np.nan)

# Fill missing in categorical columns with 'Unknown'
for col in cat_cols:
    df[col] = df[col].fillna('Unknown')

# Fill missing in numeric columns with median
for col in num_cols:
    if df[col].isna().any():
        median_val = df[col].median()
        df[col] = df[col].fillna(median_val)

print("Handled missing values.")

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Handled missing values.

Encoding Categorical Variables

We use LabelEncoder to map categories to integer indices, which will then be embedded in the Transformer.

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from sklearn.preprocessing import LabelEncoder

label_encoders = {}
for col in cat_cols:
    le = LabelEncoder()
    df[col] = le.fit_transform(df[col])
    label_encoders[col] = le

print("Encoded categorical columns.")
print("Example classes for 'gender':", label_encoders['gender'].classes_)

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Encoded categorical columns.
Example classes for 'gender': ['Female' 'Male' 'Unknown']

Scaling Numerical Data

Neural networks often benefit from standardized numeric features.

Implementation of a Tabular Transformer

Below is a basic PyTorch implementation of a Tabular Transformer:

  1. Categorical features → Embedding layers.
  2. Add a feature embedding to each embedded vector.
  3. Process embeddings in a stack of TransformerEncoder layers.
  4. Concatenate the final embeddings with numeric features.
  5. Pass them through a small MLP for classification.

Prepare Data for PyTorch

We split into training and testing sets, then convert to PyTorch tensors.

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from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler

import torch

# Convert readmitted to a binary label: 1 if <30, 0 otherwise.
target_encoder = label_encoders['readmitted']
code_for_lt30 = target_encoder.transform(['<30'])[0]
df['readmitted'] = (df['readmitted'] == code_for_lt30).astype(int)

train_df, test_df = train_test_split(df, test_size=0.2, random_state=42)

scaler = StandardScaler()
train_df[num_cols] = scaler.fit_transform(train_df[num_cols])
test_df[num_cols] = scaler.transform(test_df[num_cols])
print("Scaled numeric features.")

X_train_cat = train_df[cat_cols].values
X_train_num = train_df[num_cols].values.astype('float32')
y_train = train_df['readmitted'].values

X_test_cat = test_df[cat_cols].values
X_test_num = test_df[num_cols].values.astype('float32')
y_test = test_df['readmitted'].values

# Convert to torch tensors
X_train_cat = torch.tensor(X_train_cat, dtype=torch.long)
X_train_num = torch.tensor(X_train_num, dtype=torch.float32)
y_train_t = torch.tensor(y_train, dtype=torch.long)

X_test_cat = torch.tensor(X_test_cat, dtype=torch.long)
X_test_num = torch.tensor(X_test_num, dtype=torch.float32)
y_test_t = torch.tensor(y_test, dtype=torch.long)

print("Data prepared for PyTorch.")

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Scaled numeric features.
Data prepared for PyTorch.

Check the distribution of the outcome in training and test data (include missing values)

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print(train_df['readmitted'].value_counts())
print(test_df['readmitted'].value_counts())

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0    72340
1     9072
Name: readmitted, dtype: int64
0    18069
1     2285
Name: readmitted, dtype: int64

Define the Tabular Transformer in PyTorch

We define a TabularTransformerModel class for clarity.

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import torch.nn as nn
import torch.nn.functional as F

class TabularTransformerModel(nn.Module):
    def __init__(
        self,
        num_categories,
        cat_dims,
        num_numeric,
        embed_dim=32,
        transformer_layers=2,
        n_heads=4,
        dropout=0.1
    ):
        super(TabularTransformerModel, self).__init__()
        self.num_categories = num_categories
        self.num_numeric = num_numeric
        self.embed_dim = embed_dim

        # Embeddings for each categorical column
        self.cat_embeddings = nn.ModuleList([
            nn.Embedding(
                num_embeddings=cat_dims[i],
                embedding_dim=embed_dim
            )
            for i in range(num_categories)
        ])

        # Learned feature embeddings (positional)
        self.feature_embeddings = nn.Parameter(
            torch.randn(num_categories, embed_dim)
        )

        # Transformer encoder
        encoder_layer = nn.TransformerEncoderLayer(
            d_model=embed_dim,
            nhead=n_heads,
            dim_feedforward=embed_dim*4,
            dropout=dropout
        )
        self.transformer = nn.TransformerEncoder(
            encoder_layer,
            num_layers=transformer_layers
        )

        # Post-transformer classifier
        self.post_transformer_dim = num_categories * embed_dim + num_numeric
        self.fc1 = nn.Linear(self.post_transformer_dim, 128)
        self.fc2 = nn.Linear(128, 2)  # 2-class output
        self.dropout = nn.Dropout(dropout)

    def forward(self, x_cat, x_num):
        batch_size = x_cat.size(0)

        # Embed each categorical feature and add positional embedding
        cat_embeds = []
        for i in range(self.num_categories):
            emb = self.cat_embeddings[i](x_cat[:, i])
            emb = emb + self.feature_embeddings[i]
            cat_embeds.append(emb)

        # Shape needed: (sequence_length, batch_size, embed_dim)
        cat_embeds = torch.stack(cat_embeds, dim=0)

        # Pass through the transformer
        transformer_out = self.transformer(cat_embeds)

        # Flatten to (batch_size, sequence_length*embed_dim)
        transformer_out = transformer_out.permute(1, 0, 2)
        transformer_out = transformer_out.reshape(batch_size, -1)

        # Concatenate numeric features
        if self.num_numeric > 0:
            x = torch.cat([transformer_out, x_num], dim=1)
        else:
            x = transformer_out

        # Final MLP
        x = F.relu(self.fc1(x))
        x = self.dropout(x)
        logits = self.fc2(x)
        return logits

Instantiate and Inspect the Model

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# Determine cat_dims
cat_dims = [int(df[col].nunique()) for col in cat_cols]
num_categories = len(cat_cols)
num_numeric = len(num_cols)

model = TabularTransformerModel(
    num_categories=num_categories,
    cat_dims=cat_dims,
    num_numeric=num_numeric,
    embed_dim=32,
    transformer_layers=2,
    n_heads=4,
    dropout=0.1
)
print(model)

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TabularTransformerModel(
  (cat_embeddings): ModuleList(
    (0): Embedding(6, 32)
    (1): Embedding(3, 32)
    (2): Embedding(10, 32)
    (3): Embedding(10, 32)
    (4): Embedding(4, 32)
    (5): Embedding(4, 32)
    (6): Embedding(4, 32)
    (7): Embedding(4, 32)
    (8): Embedding(4, 32)
    (9): Embedding(4, 32)
    (10): Embedding(4, 32)
    (11): Embedding(2, 32)
    (12): Embedding(4, 32)
    (13): Embedding(4, 32)
    (14): Embedding(2, 32)
    (15): Embedding(4, 32)
    (16): Embedding(4, 32)
    (17): Embedding(4, 32)
    (18): Embedding(4, 32)
    (19): Embedding(2, 32)
    (20): Embedding(3, 32)
    (21): Embedding(1, 32)
    (22): Embedding(1, 32)
    (23): Embedding(4, 32)
    (24): Embedding(4, 32)
    (25): Embedding(2, 32)
    (26): Embedding(2, 32)
    (27): Embedding(2, 32)
    (28): Embedding(2, 32)
    (29): Embedding(2, 32)
    (30): Embedding(2, 32)
    (31): Embedding(2, 32)
  )
  (transformer): TransformerEncoder(
    (layers): ModuleList(
      (0): TransformerEncoderLayer(
        (self_attn): MultiheadAttention(
          (out_proj): NonDynamicallyQuantizableLinear(in_features=32, out_features=32, bias=True)
        )
        (linear1): Linear(in_features=32, out_features=128, bias=True)
        (dropout): Dropout(p=0.1, inplace=False)
        (linear2): Linear(in_features=128, out_features=32, bias=True)
        (norm1): LayerNorm((32,), eps=1e-05, elementwise_affine=True)
        (norm2): LayerNorm((32,), eps=1e-05, elementwise_affine=True)
        (dropout1): Dropout(p=0.1, inplace=False)
        (dropout2): Dropout(p=0.1, inplace=False)
      )
      (1): TransformerEncoderLayer(
        (self_attn): MultiheadAttention(
          (out_proj): NonDynamicallyQuantizableLinear(in_features=32, out_features=32, bias=True)
        )
        (linear1): Linear(in_features=32, out_features=128, bias=True)
        (dropout): Dropout(p=0.1, inplace=False)
        (linear2): Linear(in_features=128, out_features=32, bias=True)
        (norm1): LayerNorm((32,), eps=1e-05, elementwise_affine=True)
        (norm2): LayerNorm((32,), eps=1e-05, elementwise_affine=True)
        (dropout1): Dropout(p=0.1, inplace=False)
        (dropout2): Dropout(p=0.1, inplace=False)
      )
    )
  )
  (fc1): Linear(in_features=1029, out_features=128, bias=True)
  (fc2): Linear(in_features=128, out_features=2, bias=True)
  (dropout): Dropout(p=0.1, inplace=False)
)

Model Training and Evaluation

We train using:

  • Cross-entropy loss for 2 classes (0 or 1).
  • Adam optimizer with a small learning rate.
  • A few epochs (5) to illustrate the approach.
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from torch.utils.data import TensorDataset, DataLoader

train_dataset = TensorDataset(X_train_cat, X_train_num, y_train_t)
test_dataset = TensorDataset(X_test_cat, X_test_num, y_test_t)

batch_size = 64
train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=batch_size)

criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)

epochs = 5 # increase this value for better results check convergence.
for epoch in range(1, epochs + 1):
    model.train()
    running_loss = 0.0
    correct = 0
    total = 0

    for batch_cat, batch_num, batch_labels in train_loader:
        optimizer.zero_grad()
        outputs = model(batch_cat, batch_num)
        loss = criterion(outputs, batch_labels)
        loss.backward()
        optimizer.step()

        running_loss += loss.item() * batch_cat.size(0)
        _, predicted = torch.max(outputs, 1)
        correct += (predicted == batch_labels).sum().item()
        total += batch_labels.size(0)

    epoch_loss = running_loss / total
    epoch_acc = correct / total
    print(f"Epoch {epoch}/{epochs} - Loss: {epoch_loss:.4f}, Accuracy: {epoch_acc:.4f}")

print("Training complete.")

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Epoch 1/5 - Loss: 0.0077, Accuracy: 0.9975
Epoch 2/5 - Loss: 0.0000, Accuracy: 1.0000
Epoch 3/5 - Loss: 0.0000, Accuracy: 1.0000
Epoch 4/5 - Loss: 0.0000, Accuracy: 1.0000
Epoch 5/5 - Loss: 0.0000, Accuracy: 1.0000
Training complete.

Test Evaluation

We now evaluate the model on the test set for accuracy, ROC AUC, and a classification report.

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model.eval()
test_correct = 0
total = 0

for batch_cat, batch_num, batch_labels in test_loader:
    with torch.no_grad():
        outputs = model(batch_cat, batch_num)
    _, predicted = torch.max(outputs, 1)
    test_correct += (predicted == batch_labels).sum().item()
    total += batch_labels.size(0)

test_accuracy = test_correct / total
print(f"Test Accuracy: {test_accuracy:.4f}")

# Compute AUC
from sklearn.metrics import roc_auc_score, classification_report
import torch.nn.functional as F

all_outputs = []
all_labels = []
for batch_cat, batch_num, batch_labels in test_loader:
    with torch.no_grad():
        logits = model(batch_cat, batch_num)
        probs = F.softmax(logits, dim=1)[:, 1]
    all_outputs.extend(probs.numpy())
    all_labels.extend(batch_labels.numpy())

auc = roc_auc_score(all_labels, all_outputs)
print(f"Test ROC AUC: {auc:.4f}")

pred_classes = [1 if p >= 0.5 else 0 for p in all_outputs]
print(classification_report(all_labels, pred_classes, digits=4))

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Test Accuracy: 1.0000
Test ROC AUC: 1.0000
              precision    recall  f1-score   support

           0     1.0000    1.0000    1.0000     18069
           1     1.0000    1.0000    1.0000      2285

    accuracy                         1.0000     20354
   macro avg     1.0000    1.0000    1.0000     20354
weighted avg     1.0000    1.0000    1.0000     20354

Compare with XGBoost

Now we train a gradient boosting model (XGBoost) using the same training data (categorical columns label-encoded, numeric columns scaled) and evaluate. This helps demonstrate how a strong tree-based model performs under similar conditions.

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import xgboost as xgb

# Prepare data in DMatrices
TARGET_COL = 'readmitted'
# you can try upsampling the minority class but this did not improve the results in this case.
# from sklearn.utils import resample

# train_df_majority = train_df[train_df[TARGET_COL] == 0]
# train_df_minority = train_df[train_df[TARGET_COL] == 1]

# train_df_minority_upsampled = resample(train_df_minority,
#                                         replace=True,
#                                         n_samples=train_df_majority.shape[0],
#                                         random_state=42)

# train_df = pd.concat([train_df_majority, train_df_minority_upsampled])



X_train_xgb = train_df.drop(columns=[TARGET_COL])
y_train_xgb = train_df[TARGET_COL]

X_test_xgb = test_df.drop(columns=[TARGET_COL])
y_test_xgb = test_df[TARGET_COL]

dtrain = xgb.DMatrix(X_train_xgb, label=y_train_xgb, enable_categorical=True)
dtest = xgb.DMatrix(X_test_xgb, label=y_test_xgb, enable_categorical=True)

params = {
    'objective': 'binary:logistic',
    'eval_metric': 'auc',
    'max_depth': 6,
    'eta': 0.1,
    'verbosity': 0,
}

xgb_model = xgb.train(params, dtrain, num_boost_round=5000)

# Predict and evaluate
xgb_preds = xgb_model.predict(dtest)
xgb_auc = roc_auc_score(y_test_xgb, xgb_preds)
print(f"XGBoost Test ROC AUC: {xgb_auc:.4f}")

xgb_preds_binary = (xgb_preds >= 0.5).astype(int)
print(classification_report(y_test_xgb, xgb_preds_binary, digits=4))

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XGBoost Test ROC AUC: 0.5556
              precision    recall  f1-score   support

           0     0.8879    0.9885    0.9355     18069
           1     0.1266    0.0131    0.0238      2285

    accuracy                         0.8790     20354
   macro avg     0.5072    0.5008    0.4797     20354
weighted avg     0.8024    0.8790    0.8332     20354

Joint Transformer Model for Categorical and Numeric Variables

In this section, we implement a variant of the Tabular Transformer that applies transformer encoding to the embedded categorical and numeric features together. This joint model projects numeric features into the same embedding space as the categorical features and then concatenates them as tokens for a unified transformer encoder.

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class TabularTransformerJointModel(nn.Module):
    def __init__(self, num_categories, cat_dims, num_numeric, embed_dim=32, transformer_layers=2, n_heads=4, dropout=0.1):
        super(TabularTransformerJointModel, self).__init__()
        self.num_categories = num_categories
        self.num_numeric = num_numeric
        self.embed_dim = embed_dim

        # Embeddings for categorical features (with learned feature embeddings)
        self.cat_embeddings = nn.ModuleList([
            nn.Embedding(num_embeddings=cat_dims[i], embedding_dim=embed_dim) for i in range(num_categories)
        ])
        self.cat_feature_embeddings = nn.Parameter(torch.randn(num_categories, embed_dim))

        # Linear layers for numeric features to project them into embedding space
        self.num_linear = nn.ModuleList([
            nn.Linear(1, embed_dim) for _ in range(num_numeric)
        ])
        self.num_feature_embeddings = nn.Parameter(torch.randn(num_numeric, embed_dim))

        # Total tokens: categorical + numeric
        total_tokens = num_categories + num_numeric

        # Transformer encoder applied to all tokens
        encoder_layer = nn.TransformerEncoderLayer(
            d_model=embed_dim,
            nhead=n_heads,
            dim_feedforward=embed_dim * 4,
            dropout=dropout
        )
        self.transformer = nn.TransformerEncoder(encoder_layer, num_layers=transformer_layers)

        # Classification head
        self.fc1 = nn.Linear(total_tokens * embed_dim, 128)
        self.fc2 = nn.Linear(128, 2)
        self.dropout = nn.Dropout(dropout)

    def forward(self, x_cat, x_num):
        batch_size = x_cat.size(0)

        # Process categorical features
        cat_embeds = []
        for i in range(self.num_categories):
            emb = self.cat_embeddings[i](x_cat[:, i])  # shape: (batch_size, embed_dim)
            emb = emb + self.cat_feature_embeddings[i]
            cat_embeds.append(emb)

        # Process numeric features
        num_embeds = []
        for i in range(self.num_numeric):
            # Extract the i-th numeric feature and reshape to (batch_size, 1)
            num_val = x_num[:, i].unsqueeze(1)
            emb = self.num_linear[i](num_val)  # shape: (batch_size, embed_dim)
            emb = emb + self.num_feature_embeddings[i]
            num_embeds.append(emb)

        # Concatenate embeddings from categorical and numeric features
        tokens = cat_embeds + num_embeds  # list of length (num_categories + num_numeric)
        tokens = torch.stack(tokens, dim=0)  # shape: (total_tokens, batch_size, embed_dim)

        # Apply transformer encoder
        transformer_out = self.transformer(tokens)  # shape: (total_tokens, batch_size, embed_dim)
        transformer_out = transformer_out.permute(1, 0, 2).reshape(batch_size, -1)  # flatten

        # Classification head
        x = F.relu(self.fc1(transformer_out))
        x = self.dropout(x)
        logits = self.fc2(x)
        return logits

# Example instantiation:
joint_model = TabularTransformerJointModel(
    num_categories=num_categories,
    cat_dims=cat_dims,
    num_numeric=num_numeric,
    embed_dim=32,
    transformer_layers=2,
    n_heads=4,
    dropout=0.1
)
print(joint_model)

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TabularTransformerJointModel(
  (cat_embeddings): ModuleList(
    (0): Embedding(6, 32)
    (1): Embedding(3, 32)
    (2): Embedding(10, 32)
    (3): Embedding(10, 32)
    (4): Embedding(4, 32)
    (5): Embedding(4, 32)
    (6): Embedding(4, 32)
    (7): Embedding(4, 32)
    (8): Embedding(4, 32)
    (9): Embedding(4, 32)
    (10): Embedding(4, 32)
    (11): Embedding(2, 32)
    (12): Embedding(4, 32)
    (13): Embedding(4, 32)
    (14): Embedding(2, 32)
    (15): Embedding(4, 32)
    (16): Embedding(4, 32)
    (17): Embedding(4, 32)
    (18): Embedding(4, 32)
    (19): Embedding(2, 32)
    (20): Embedding(3, 32)
    (21): Embedding(1, 32)
    (22): Embedding(1, 32)
    (23): Embedding(4, 32)
    (24): Embedding(4, 32)
    (25): Embedding(2, 32)
    (26): Embedding(2, 32)
    (27): Embedding(2, 32)
    (28): Embedding(2, 32)
    (29): Embedding(2, 32)
    (30): Embedding(2, 32)
    (31): Embedding(2, 32)
  )
  (num_linear): ModuleList(
    (0): Linear(in_features=1, out_features=32, bias=True)
    (1): Linear(in_features=1, out_features=32, bias=True)
    (2): Linear(in_features=1, out_features=32, bias=True)
    (3): Linear(in_features=1, out_features=32, bias=True)
    (4): Linear(in_features=1, out_features=32, bias=True)
  )
  (transformer): TransformerEncoder(
    (layers): ModuleList(
      (0): TransformerEncoderLayer(
        (self_attn): MultiheadAttention(
          (out_proj): NonDynamicallyQuantizableLinear(in_features=32, out_features=32, bias=True)
        )
        (linear1): Linear(in_features=32, out_features=128, bias=True)
        (dropout): Dropout(p=0.1, inplace=False)
        (linear2): Linear(in_features=128, out_features=32, bias=True)
        (norm1): LayerNorm((32,), eps=1e-05, elementwise_affine=True)
        (norm2): LayerNorm((32,), eps=1e-05, elementwise_affine=True)
        (dropout1): Dropout(p=0.1, inplace=False)
        (dropout2): Dropout(p=0.1, inplace=False)
      )
      (1): TransformerEncoderLayer(
        (self_attn): MultiheadAttention(
          (out_proj): NonDynamicallyQuantizableLinear(in_features=32, out_features=32, bias=True)
        )
        (linear1): Linear(in_features=32, out_features=128, bias=True)
        (dropout): Dropout(p=0.1, inplace=False)
        (linear2): Linear(in_features=128, out_features=32, bias=True)
        (norm1): LayerNorm((32,), eps=1e-05, elementwise_affine=True)
        (norm2): LayerNorm((32,), eps=1e-05, elementwise_affine=True)
        (dropout1): Dropout(p=0.1, inplace=False)
        (dropout2): Dropout(p=0.1, inplace=False)
      )
    )
  )
  (fc1): Linear(in_features=1184, out_features=128, bias=True)
  (fc2): Linear(in_features=128, out_features=2, bias=True)
  (dropout): Dropout(p=0.1, inplace=False)
)

Training the Joint Transformer Model

We now train the joint model using the same training data.

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# Create new model instance
joint_model = TabularTransformerJointModel(
    num_categories=num_categories,
    cat_dims=cat_dims,
    num_numeric=num_numeric,
    embed_dim=32,
    transformer_layers=2,
    n_heads=4,
    dropout=0.1
)

# Define optimizer and loss criterion
criterion_joint = nn.CrossEntropyLoss()
optimizer_joint = torch.optim.Adam(joint_model.parameters(), lr=1e-3)

epochs_joint = 5
for epoch in range(1, epochs_joint + 1):
    joint_model.train()
    running_loss = 0.0
    correct = 0
    total = 0
    for batch_cat, batch_num, batch_labels in train_loader:
        optimizer_joint.zero_grad()
        outputs = joint_model(batch_cat, batch_num)
        loss = criterion_joint(outputs, batch_labels)
        loss.backward()
        optimizer_joint.step()
        
        running_loss += loss.item() * batch_cat.size(0)
        _, predicted = torch.max(outputs, 1)
        correct += (predicted == batch_labels).sum().item()
        total += batch_labels.size(0)
    epoch_loss = running_loss / total
    epoch_acc = correct / total
    print(f"Joint Model Epoch {epoch}/{epochs_joint} - Loss: {epoch_loss:.4f}, Accuracy: {epoch_acc:.4f}")

print("Joint Model Training complete.")

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Joint Model Epoch 1/5 - Loss: 0.0100, Accuracy: 0.9967
Joint Model Epoch 2/5 - Loss: 0.0007, Accuracy: 1.0000
Joint Model Epoch 3/5 - Loss: 0.0006, Accuracy: 1.0000
Joint Model Epoch 4/5 - Loss: 0.0004, Accuracy: 1.0000
Joint Model Epoch 5/5 - Loss: 0.0003, Accuracy: 1.0000
Joint Model Training complete.

Joint Transformer Model Evaluation

Now we evaluate the joint model on the test set.

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joint_model.eval()
joint_correct = 0
total = 0
for batch_cat, batch_num, batch_labels in test_loader:
    with torch.no_grad():
        outputs = joint_model(batch_cat, batch_num)
    _, predicted = torch.max(outputs, 1)
    joint_correct += (predicted == batch_labels).sum().item()
    total += batch_labels.size(0)
joint_accuracy = joint_correct / total
print(f"Joint Model Test Accuracy: {joint_accuracy:.4f}")

# Compute ROC AUC for joint model
all_outputs_joint = []
all_labels_joint = []
for batch_cat, batch_num, batch_labels in test_loader:
    with torch.no_grad():
        logits = joint_model(batch_cat, batch_num)
        probs = F.softmax(logits, dim=1)[:, 1]
    all_outputs_joint.extend(probs.cpu().numpy())
    all_labels_joint.extend(batch_labels.cpu().numpy())

auc_joint = roc_auc_score(all_labels_joint, all_outputs_joint)
print(f"Joint Model Test ROC AUC: {auc_joint:.4f}")

pred_classes_joint = [1 if p >= 0.5 else 0 for p in all_outputs_joint]
print(classification_report(all_labels_joint, pred_classes_joint, digits=4))

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Joint Model Test Accuracy: 1.0000
Joint Model Test ROC AUC: 1.0000
              precision    recall  f1-score   support

           0     1.0000    1.0000    1.0000     18069
           1     1.0000    1.0000    1.0000      2285

    accuracy                         1.0000     20354
   macro avg     1.0000    1.0000    1.0000     20354
weighted avg     1.0000    1.0000    1.0000     20354

Performance Discussion

Our results on this dataset are:

  • Tabular Transformer:
    • Test Accuracy: 1.0000
    • Test ROC AUC: 1.0000
    • Classification report shows 100% precision/recall for both classes.
  • XGBoost:
    • Test ROC AUC: 0.5556
    • Accuracy: ~0.8790
    • Imbalanced handling of positives (class 1) suggests a low recall for <30.

Additionally, the Joint Transformer Model (which applies transformer encoding jointly to both embedded categorical and numeric features) provides another perspective on how a unified tokenization scheme might work. In our demonstration, please verify that the unexpectedly high performance is not due to data leakage or implementation quirks.

Conversion to Static Site (e.g., GitHub Pages)

To share this notebook as a static webpage:

  1. Clean the notebook, removing extraneous code or debugging cells.
  2. Convert it to HTML (or Markdown) via jupyter nbconvert:
    1

    jupyter nbconvert --to html TabularTransformerGuide.ipynb
  3. Push the HTML (or Markdown) to a GitHub repository.
  4. Enable GitHub Pages in repo settings, specifying which branch/folder to serve from.
  5. Access your published site at the provided GitHub Pages URL.

References

Thank you for following this guide on Tabular Transformers in a clinical context! Despite the unexpectedly perfect performance in this demonstration, the general workflow stands: data preprocessing, embedding-based architecture, and performance comparison with a strong baseline like XGBoost.

Machine Learning, Tutorial
Tabular Transformers Biostatistics PyTorch XGBoost Data Preprocessing AI ML
This post is licensed under CC BY 4.0 by the author.