how to build own transformer model from tabular data with pyTorch can the model predict tabular data with missing value can model predict with probability please give me sample code

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

To predict tabular data with missing values, you can use an imputation technique to fill in the missing values before feeding the data into the model. One common imputation technique is mean imputation, where missing values are replaced with the mean of the non-missing values in the same column.

To predict with probability, you can use the softmax function to convert the output of the model into a probability distribution. The softmax function maps the output of the model to a range between 0 and 1, with each value representing the probability of the input belonging to a particular class.

Here is a sample code that demonstrates 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 with missing values and impute the missing values with the mean. We then define the architecture of the transformer model using the TabTransformer library. We 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