Deep Kernel Learning Classification

%%capture
%pip install git+https://github.com/lightning-uq-box/lightning-uq-box.git

Imports

import os
import tempfile
from collections import defaultdict
from functools import partial

import torch
from lightning import Trainer
from lightning.pytorch import seed_everything
from lightning.pytorch.loggers import CSVLogger

from lightning_uq_box.datamodules import TwoMoonsDataModule
from lightning_uq_box.models import MLP
from lightning_uq_box.uq_methods import DKLClassification
from lightning_uq_box.viz_utils import (
    plot_predictions_classification,
    plot_training_metrics,
    plot_two_moons_data,
)

seed_everything(2)

Seed set to 2

2

# temporary directory for saving
my_temp_dir = tempfile.mkdtemp()

dm = TwoMoonsDataModule(batch_size=100)

# define data
X_train, Y_train, X_test, Y_test, test_grid_points = (
    dm.X_train,
    dm.Y_train,
    dm.X_test,
    dm.Y_test,
    dm.test_grid_points,
)

fig = plot_two_moons_data(X_train, Y_train, X_test, Y_test)

Feature Extractor

feature_extractor = MLP(
    n_inputs=2, n_outputs=13, n_hidden=[50], activation_fn=torch.nn.ELU()
)

Deep Kernel Learning Model

dkl_model = DKLClassification(
    feature_extractor,
    gp_kernel="RBF",
    num_classes=2,
    optimizer=partial(torch.optim.Adam, lr=1e-2),
    n_inducing_points=20,
)

Trainer

logger = CSVLogger(my_temp_dir)
trainer = Trainer(
    accelerator="cpu",
    max_epochs=100,  # number of epochs we want to train
    logger=logger,  # log training metrics for later evaluation
    log_every_n_steps=1,
    enable_checkpointing=False,
    enable_progress_bar=False,
    default_root_dir=my_temp_dir,
)

GPU available: False, used: False
TPU available: False, using: 0 TPU cores
💡 Tip: For seamless cloud logging and experiment tracking, try installing [litlogger](https://pypi.org/project/litlogger/) to enable LitLogger, which logs metrics and artifacts automatically to the Lightning Experiments platform.

trainer.fit(dkl_model, dm)

  | Name              | Type              | Params | Mode  | FLOPs
------------------------------------------------------------------------
0 | feature_extractor | MLP               | 813    | train | 0
1 | train_metrics     | MetricCollection  | 0      | train | 0
2 | val_metrics       | MetricCollection  | 0      | train | 0
3 | test_metrics      | MetricCollection  | 0      | train | 0
4 | gp_layer          | DKLGPLayer        | 5.8 K  | train | 0
5 | scale_to_bounds   | ScaleToBounds     | 0      | train | 0
6 | likelihood        | SoftmaxLikelihood | 26     | train | 0
7 | elbo_fn           | VariationalELBO   | 5.8 K  | train | 0
------------------------------------------------------------------------
6.6 K     Trainable params
0         Non-trainable params
6.6 K     Total params
0.026     Total estimated model params size (MB)
31        Modules in train mode
0         Modules in eval mode
0         Total Flops
/home/docs/checkouts/readthedocs.org/user_builds/lightning-uq-box/envs/latest/lib/python3.12/site-packages/lightning/pytorch/utilities/_pytree.py:21: `isinstance(treespec, LeafSpec)` is deprecated, use `isinstance(treespec, TreeSpec) and treespec.is_leaf()` instead.
`Trainer.fit` stopped: `max_epochs=100` reached.

Training Metrics

fig = plot_training_metrics(
    os.path.join(my_temp_dir, "lightning_logs"), ["train_loss", "trainAcc"]
)

Prediction

# save predictions
trainer.test(dkl_model, dm.test_dataloader())

────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
       Test metric             DataLoader 0
────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
         testAcc            0.9950000047683716
     testCalibration       0.004979097284376621
 testEmpirical Coverage     0.9950000047683716
        test_loss           0.04922037571668625
────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────

[{'test_loss': 0.04922037571668625,
  'testAcc': 0.9950000047683716,
  'testCalibration': 0.004979097284376621,
  'testEmpirical Coverage': 0.9950000047683716}]

Evaluate Predictions

# due to the GP we need to predict in batches
batch_size = 200
batches = test_grid_points.chunk(
    (test_grid_points.size(0) + batch_size - 1) // batch_size
)


preds = defaultdict(list)

for batch in batches:
    for key, value in dkl_model.predict_step(batch).items():
        if key != "out":
            preds[key].append(value)

preds = {key: torch.cat(value, dim=0) for key, value in preds.items()}

fig = plot_predictions_classification(
    X_test,
    Y_test,
    preds["pred"].argmax(-1),
    test_grid_points,
    preds["pred_uct"].cpu().numpy(),
)