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Uncertainty Quantification in Deep Learning

This repository contains the code used in the production of results for the thesis Uncertainty Quantification in Machine Learning Models Using Conformal Prediction.

Project Organization

├── LICENSE
├── Makefile           <- Makefile with commands like `make data` or `make train`
├── README.md          <- The top-level README for developers using this project.
├── data
│   ├── processed      <- The final, canonical data sets for modeling.
│   └── raw            <- The original, immutable data dump.
│
│
├── models             <- Trained and saved models,
│
├── .cached             <- cached results and logits from experiments
│
│
├── reports            <- Generated analysis as HTML, PDF, LaTeX, etc.
│   ├── figures        <- Generated graphics and figures to be used in reporting
│   └── tables         <- Generated LaTeX tables.
|
|
├── thesis             <- Detailed thesis write-up.
│   └──  figures        <- PDF document containing the submitted thesis.
|
├── examples            <- Generated analysis as HTML, PDF, LaTeX, etc.
│   └── conformal_predictions.ipynb   <-  Contains examples for using UQ methods
│
├── requirements.txt   <- The requirements file for reproducing the analysis environment, e.g.
│                         generated with `pip freeze > requirements.txt`
│
├── setup.py           <- makes project pip installable (pip install -e .) so src can be imported
├── src                <- Source code for use in this project.
│   ├── __init__.py    <- Makes src a Python module
│   │
│   ├── data           <- Scripts to download or generate data
│   │   ├── make.py
│   │   ├── make_amzn_stock_price_dataset.py
│   │   ├── make_housing_dataset.py
│   │   ├── make_wine_quality_dataset.py
│   │    make_cifar10_dataset.py
│   │
│   ├── train_models  <- Scripts to train models and then use trained models
│   │
│   ├── models  <- Models used for experiments
|   |     ├── catBoost_quantile_regressor.py
|   |     ├── cifar10_conv_model.py
|   |     ├── conformal_classifier.py
|   |     ├── conformal_forcast.py
|   |     ├── CQR.py
|   |     ├── gradient_boosting_quantile_regressor.py
|   |     ├── lstm_model.py
|   |     ├── oracle.py  <- Base class used for active learning
|   |     └── quantile_net.py
|   |
│   ├── models  <- Scripts to run the experiments
|   |     ├── active_learning.py
|   |     ├── conformal_classification.py
|   |     ├── conformal_regression.py
|   |     ├── conformal_time_series.py
|   |     └── monte-carlo-dropout.py
│   │
│   └── visualization  <- Scripts to generate LaTeX tables and figures from experiment results
│
└── tox.ini            <- tox file with settings for running tox; see tox.readthedocs.io

Usage

Requirments

To install requirements:

pip install -r requirements.txt

Download pretrained models

In order run the experiments, you must download the pretrained models by using:

make models

Reproduce tables and figures

To reproduce the tables, type the following command:

make table-<TABLE_NUMBER>

For example:

make table-2.1

or

table-5.1.a

To reproduce figures, use the following command:

make figure-<FIGURE_NUMBER>

The commands above will rerun the experiments. If cached data exsists, it will be used to reproduce the figures. If you wish to reset cached data, simply empty the contents of ./.cached/, and the experiments will run from scratch.

Using the conformal prediction methods

The notebook in examples/conforal_predictions.ipynb contains examples for using the uncertainty quantification methods with pytorch.

The class ConformalClassifier is built to conformalize a neural network clas- sifier, taking a model, calibration dataloader and an error rate α as parameters and outputs prediction sets. Upon initialization, the class calibrates the model using the score function defined in equation 2.5 and computes ˆq. During the forward pass, the class constructs a prediction set from the base model outputs.

import torch
import torchvision
from src.models.conformal_classifier import ConformalClassifier
from src.models.cifar10_conv_model import Cifar10ConvModel
from src.utils.utils import get_CIFAR10_img_transformer
# load datasets
calib_set = torchvision.datasets.CIFAR10(train=True, root='data/processed', download=True, transform=get_CIFAR10_img_transformer())
test_set = torchvision.datasets.CIFAR10(train=False, root='data/processed', download=True, transform=get_CIFAR10_img_transformer())

calib_loader = torch.utils.data.DataLoader(calib_set)
test_loader = torch.utils.data.DataLoader(test_set)
# conformalize
pretrained_model = Cifar10ConvModel()
conformal_model = ConformalClassifier(model=pretrained_model, calib_loader=calib_loader, alpha=0.1)
# prediction
for x, y in test_loader:
    prediction_set = conformal_model(x)

Similar to above, the class CQR is built to conformalize a quantile neural net- work regressor, taking a model, calibration dataloader and an error rate α as parameters and outputs prediction intervals. Upon initialization, the class cali- brates the model using the score function defined in equation 3.7 and computes ˆq. During the forward pass, the class constructs a prediction interval from the base model outputs.

import torch
from src.models.CQR import CQR
from src.models.quantile_net import QuantileNet
from src.data.make_wine_quality_dataset import WineQuality
# load datasets
calib_set = WineQuality(train=True, in_folder='data/raw', out_folder='data/processed')
test_set = WineQuality(train=False, in_folder='data/raw', out_folder='data/processed')

calib_loader = torch.utils.data.DataLoader(calib_set)
test_loader = torch.utils.data.DataLoader(test_set)
# conformalize
pretrained_model = QuantileNet(input_size=11)
conformal_model = CQR(model=pretrained_model,calib_loader=calib_loader, alpha=0.1)
# prediction
for x, y in test_loader:
    prediction_set = conformal_model(x)

The Pinball loss function in equation 3.5 is costume build and can be used by passing a list of desired quantiles as a parameter.

from src.models.quantile_net import QuantileLoss

loss_fn = QuantileLoss([0.05, 0.95])

For active learning, the class Oracle can be used by passing the model, training set, test set, and sampling parameters. Abailable strategies:

  • conformal-score:largest-set
  • least-confidence
  • entropy-sampler
  • random-sampler

See src/models/oracle for more details

import torchvision
from src.models.oracle import Oracle
from src.models.cifar10_conv_model import Cifar10ConvModel
from src.utils.utils import get_CIFAR10_img_transformer
# load datasets
train_set = torchvision.datasets.CIFAR10(
    train=True, root='data/processed', transform=get_CIFAR10_img_transformer())
test_set = torchvision.datasets.CIFAR10(
    train=False, root='data/processed', transform=get_CIFAR10_img_transformer())

model = Cifar10ConvModel()
# number of initial training samples
n_init_training_labels=1000
oracle = Oracle(model=model, train_set=train_set, test_set=test_set, strategy='conformal-score:largest-set', sample_size=1000, n_init_training_labels=n_init_training_labels)
# start active learning for 1 round
oracle.teach(1)
print(oracle.round_accuracies)

Run the tests

pytest .

Contact

If you have any questions or feedback, please feel free to reach out at abdulrahman.ramdan@outlook.com.