Documentation

This project features a PyTorch implementation of the Deep Recurrent Survival Analysis model that is intended for use on uncensored sequential data in which the event is known to occur at the last time step for each observation More specifically, this library is made up of two small modules.

1. functions.py, which contains utilities for computing conventional survival analysis quantities, given a torch.Tensor of predicted conditional hazard rates.

2. model.py, which contains the DRSA class (a subclass of torch.nn.Module), and is easily extended to handle categorical embeddings, additional layers, or any other arbitrary PyTorch operations.

Installation

$ pip install drsa

Usage

from drsa.functions import event_time_loss, event_rate_loss
from drsa.model import DRSA
import torch
import torch.nn as nn
import torch.optim as optim

# generating random data
batch_size, seq_len, n_features = (64, 25, 10)
def data_gen(batch_size, seq_len, n_features):
    samples = []
    for _ in range(batch_size):
        sample = torch.cat([torch.normal(mean=torch.arange(1., float(seq_len)+1)).unsqueeze(-1) for _ in range(n_features)], dim=-1)
        samples.append(sample.unsqueeze(0))
    return torch.cat(samples, dim=0)
data = data_gen(batch_size, seq_len, n_features)

# generating random embedding for each sequence
n_embeddings = 10
embedding_idx = torch.mul(
    torch.ones(batch_size, seq_len, 1),
    torch.randint(low=0, high=n_embeddings, size=(batch_size, 1, 1)),
)

# concatenating embeddings and features
X = torch.cat([embedding_idx, data], dim=-1)

# instantiating embedding parameters
embedding_size = 5
embeddings = torch.nn.Embedding(n_embeddings, embedding_size)

# instantiating model
model = DRSA(
    n_features=n_features + 1,  # +1 for the embeddings
    hidden_dim=2,
    n_layers=1,
    embeddings=[embeddings],
)

# defining training loop
def training_loop(X, optimizer, alpha, epochs):
    for epoch in range(epochs):
        optimizer.zero_grad()
        preds = model(X)

        # weighted average of survival analysis losses
        evt_loss = event_time_loss(preds)
        evr_loss = event_rate_loss(preds)
        loss = (alpha * evt_loss) + ((1 - alpha) * evr_loss)

        # updating parameters
        loss.backward()
        optimizer.step()
        if epoch % 100 == 0:
            print(f"epoch: {epoch} - loss: {round(loss.item(), 4)}")

# running training loop
optimizer = optim.Adam(model.parameters())
training_loop(X, optimizer, alpha=0.5, epochs=1001)

epoch: 0 - loss: 12.485
epoch: 100 - loss: 10.0184
epoch: 200 - loss: 6.5471
epoch: 300 - loss: 4.6741
epoch: 400 - loss: 3.9786
epoch: 500 - loss: 3.5133
epoch: 600 - loss: 3.1826
epoch: 700 - loss: 2.9421
epoch: 800 - loss: 2.7656
epoch: 900 - loss: 2.6355
epoch: 1000 - loss: 2.5397