spacecutter-torch 0.4.0

Ordinal regression models in PyTorch

spacecutter-torch

spacecutter-torch is a library for implementing ordinal regression models in PyTorch. The library consists of models and loss functions.

Installation

pip install spacecutter-torch

Usage

Models

Define any PyTorch model you want that generates a single, scalar prediction value. This will be our predictor model. This model can then be wrapped with spacecutter.models.OrdinalLogisticModel which will convert the output of the predictor from a single number to an array of ordinal class probabilities. The following example shows how to do this for a two layer neural network predictor for a problem with three ordinal classes.

import torch
from torch import nn

from spacecutter.models import OrdinalLogisticHead


X = torch.tensor([[0.5, 0.1, -0.1],
              [1.0, 0.2, 0.6],
              [-2.0, 0.4, 0.8]]).float()

y = torch.tensor([0, 1, 2]).reshape(-1, 1).long()

num_features = X.shape[1]
num_classes = len(torch.unique(y))

model = nn.Sequential(
    nn.Linear(num_features, num_features),
    nn.ReLU(),
    nn.Linear(num_features, 1),
    OrdinalLogisticHead(num_classes),
)

y_pred = model(X)

print(y_pred)

# tensor([[0.2325, 0.2191, 0.5485],
#         [0.2324, 0.2191, 0.5485],
#         [0.2607, 0.2287, 0.5106]], grad_fn=<CatBackward>)

Training

The following shows how to train the model from the previous section using cumulative link loss:

import torch
from spacecutter.callbacks import AscensionCallback
from spacecutter.losses import CumulativeLinkLoss

def train(model, optimizer, X, y, num_epochs = 10) -> list:
    """
    you can bring your own training loop if you want, but we provide a very simple one here. 
    """
    model.train()
    on_batch_end_callbacks = [AscensionCallback()]
    loss_fn = CumulativeLinkLoss()
    losses = []
    for epoch in range(num_epochs):
        optimizer.zero_grad()
        y_pred = model(X)
        loss = loss_fn(y_pred, y)
        loss.backward()
        optimizer.step()
        losses.append(loss.item())
        with torch.no_grad():
            for callback in on_batch_end_callbacks:
                model.apply(callback)
    return losses

optimizer = torch.optim.Adam(model.parameters(), lr=0.01)
losses = train(model, optimizer, X, y)

Note that we must add the AscensionCallback. This ensures that the ordinal cutpoints stay in ascending order. While ideally this constraint would be factored directly into the model optimization, spacecutter currently hacks an SGD-compatible solution by utilizing a post-backwards-pass callback to clip the cutpoint values.