Building a Tabular Transformer Model with PyTorch: Missing Values & Probability Predictions

To build a transformer model from tabular data with PyTorch, you can utilize the TabTransformer library. This library provides a specialized implementation of the Transformer model designed for tabular data.

For predicting tabular data with missing values, you can employ an imputation technique to fill in the missing values before feeding the data into the model. A common imputation technique is mean imputation, where missing values are replaced with the average of the non-missing values in the same column.

To predict with probability, the softmax function can be used to transform the model's output into a probability distribution. The softmax function maps the model's output to a range between 0 and 1, where each value represents the probability of the input belonging to a particular class.

Here's a sample code demonstrating how to build a transformer model for tabular data with missing values and predict with probability:

import torch
import torch.nn as nn
from tab_transformer import TabTransformer

# Load tabular data with missing values
data = torch.tensor([[1, 2, 3, float('nan')], 
                     [4, 5, 6, 7], 
                     [8, float('nan'), 9, 10]])

# Impute missing values with mean
data_mean = torch.nanmean(data, dim=0)
data = torch.where(torch.isnan(data), data_mean, data)

# Define model architecture
model = TabTransformer(input_dim=data.shape[1], output_dim=2, depth=6, heads=8, 
                       attn_dropout=0.1, ff_dropout=0.1)

# Forward pass
output = model(data)

# Convert output to probability distribution
probs = nn.functional.softmax(output, dim=1)

# Print probabilities
print(probs)

In this example, we first load the tabular data containing missing values and impute the missing values using the mean. Next, we define the architecture of the transformer model using the TabTransformer library. We then perform a forward pass of the model on the input data and convert the output to a probability distribution using the softmax function. Finally, we print the probabilities of each input belonging to each class.