ood_detectors

OOD Detectors is a Python package that offers a suite of algorithms designed to identify out-of-distribution samples in datasets. This is crucial for maintaining the reliability and accuracy of machine learning models when faced with unfamiliar data.

Table of Contents

Installation
License

Installation

To install OOD Detectors, run the following command:

pip install ood_detectors

Usage

This package includes several OOD detection algorithms, each tailored to different aspects of OOD detection:

Likelihood Based: SubSDE_DDM, VPSDE_DDM and VESDE_DDM are likelihood-based methods that use different variations stochastic differential equations for DDMS to detect OOD samples.
Residual: This method employs the least significant eigen vector for OOD detection.

All detectors share a common interface:

Initialize the detector with necessary hyperparameters.
Fit the model using fit() with the training data.
Use predict() to obtain OOD scores for new data samples.

Example

import ood_detectors.likelihood as likelihood

ood_detector = likelihood.SubSDE_DDM(feat_dim).to('cuda')
train_loss = ood_detector.fit(train_data, n_epochs, batch_size)
scores = ood_detector.predict(test_data, batch_size)

from ood_detectors import Residual

ood_detector = Residual()
train_loss = ood_detector.fit(train_data)
scores = ood_detector.predict(test_data)

low-level interface

The low-level interface allows you to customize the training process and access the model's internal components.

import ood_detectors.likelihood as likelihood
import ood_detectors.sde as sde_lib 
import ood_detectors.models as models
import ood_detectors.losses as losses
...
sde = sde_lib.subVPSDE(beta_min=beta_min, beta_max=beta_max)

model = models.SimpleMLP(
    channels=feat_dim,
    bottleneck_channels=bottleneck_channels,
    num_res_blocks=num_res_blocks,
    time_embed_dim=time_embed_dim,
    dropout=dropout,
)

optimizer = functools.partial(
                torch.optim.Adam,
                lr=lr,
                betas=(beta1, beta2),
                eps=eps,
                weight_decay=weight_decay,
                )

ood_detector = likelihood.Likelihood(
    sde = sde,
    model = model,
    optimizer = optimizer,
    ).to(device)

update_fn = functools.partial(
    losses.SDE_EMA_Warmup_GradClip, 
    ema_rate=ema_rate,
    warmup=warmup,
    grad_clip=grad_clip,
    continuous=continuous,
    reduce_mean=reduce_mean,
    likelihood_weighting=likelihood_weighting,
    )

train_loss = ood_detector.fit(
    train_data,  
    n_epochs=n_epochs,
    batch_size=batch_size,
    update_fn=update_fn,
    )

Create a custom component

You can create a custom component by doing the same thing as the library does. Good luck!

Evaluate

To assess the performance of the OOD detectors, you can utilize the following metrics:

AUC: Area under the ROC curve
FPR95: False positive rate when the true positive rate is 95%

import ood_detectors.eval_utils as eval_utils
score_id = ood_detector.predict(train_data)
score_ref = ood_detector.predict(reference_data)
print(f"Train AUC: {eval_utils.auc(-score_ref, -score_id):.2%}")
print(f"Train FPR95: {eval_utils.fpr95(-score_ref, -score_id):.2%}")

results = eval_utils.eval_ood(ood_detector, train_data, reference_data, ood_data, batch_size, verbose=False)
plot_utils.plot(results, id_name, ood_names, encoder=embedding, model=ood_detector.name,
                train_loss=train_loss,
                config=conf,
                )

License

ood_detectors is distributed under the terms of the apache-2.0 license.

Metadata

Release files for ood_detectors 0.0.58

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

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Source distribution for ood_detectors 0.0.58
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