covmetrics 0.1.0

Metrics for conditional coverage estimation

Covmetrics: conditional coverage metrics

This package (PyTorch-based) currently contains different conditional coverage metrics, including our metric ERT (Excess risk of the target coverage).

It accompanies our papers Conditional Coverage Diagnostics for Conformal Prediction. Please cite us if you use this repository for research purposes.

Installation

Work in progress: for now you can use the ERT file to use our metrics, it will be pip installable soon!

Using conditional coverage metrics

For a quick usage, you can evaluate a metric as follows:

from covmetrics import ERT 

ERT_value = ERT().evaluate(x, cover, alpha)

Where the object "x" is a feature vector of shape (n_samples, n_features) (numpy, torch or dataframe), and cover is a vector of shape (n_samples,) with 0's or 1's

The default classifier used to classify the outputs is a LightGBM classifier. You can change this by replacing the model class of the classifier:

from covmetrics import ERT 
from sklearn.linear_model import LogisticRegression

ERT_estimator = ERT(model_cls=LogisticRegression)

We recommend using our k-folds pre-implemented version to evaluate the conditional miscoverage by doing (default value is 5):

ERT_value = ERT_estimator.evaluate(x_test, cover_test, alpha, n_splits = 5)

But you can choose between training the classifier with some data and using it on other doing the following:

ERT_estimator.fit(x_train, cover_train)
ERT_value = ERT_estimator.evaluate(x_test, cover_test, alpha, n_splits=0)

Modifying the loss function

The default loss used to evaluate the classifier provides a lower bound on the $L_1$-ERT. You can change the loss by doing :

ERT_estimator.evaluate(x_test, cover_test, alpha, loss=your_loss)

The package already provides several losses functions to evaluate your models. You can import them as follows:

from covmetrics.losses import (
    brier_score,
    logloss,
    L1_miscoverage,
    brier_score_over,
    L1_miscoverage_over,
    logloss_over,
    brier_score_under,
    logloss_under,
    L1_miscoverage_under
)

If you want to evaluate more losses at the same time, you can use

ERT_value = ERT_estimator.evaluate_multiple_losses(x_test, cover_test, alpha, all_losses = List_of_all_your_losses)

Which returns a dictionnary with all evaluated losses . By default, if all_losses=None, the metrics evaluated are the $L_1$-ERT, $L_2$-ERT and KL-ERT.

Using a custom classifier

You can also use your own classifier with ERT. To do this, define a class with the following methods:

• __init__(self, **model_kwargs): Initialize your model with any required parameters.
• fit(self, X, y, **fit_kwargs): Train the model on your data.
• predict_proba(self, X): Return the predicted probabilities for each class.

Once your class is defined, you can instantiate and evaluate it with ERT as follows:

ERT_estimator = ERT(your_model_class, one_argument=k, another_argument="p")
ERT_value = ERT_estimator.evaluate(x_test, cover_test, alpha, one_fit_argument=m, another_fit_argument="M")

Evaluating Conditional Coverage Rules

You can evaluate conditional coverage rules by providing alpha as an array that matches the type and length of cover. For example, if cover is a PyTorch tensor, alpha should also be a tensor; if cover is a NumPy array, alpha should be a NumPy array.

Each value of alpha must be between 0 and 1 and represents the conditional miscoverage level, meaning:

[ \mathbb{P}(Y \in C(X) \mid X) = 1 - \alpha(X) ]

Example usage:

ERT_estimator = ERT()
tab_alpha = torch.ones(len(cover_test)) * 0.9 # if cover_test is a torch.Tensor
ERT_value = ERT_estimator.evaluate(x_test, cover_test, alpha=tab_alpha)

Other metrics

Other metrics implemented metrics are:

• WSC (Worst slab coverage).
• FSC (Feature-stratified coverage).
• CovGap.
• WeightedCovGap.
• SSC (Size-stratified coverage).
• EOC (Equal opportunity of coverage).
• Pearson's Correlation.
• HSIC's Correlation.

The WSC metric is a vectorized version of the original github : Original code from https://github.com/Shai128/oqr.

from covmetrics import WSC 

WSC_value = WSC().evaluate(x_test, cover_test)

For the CovGap metric, or the WeightedCovGap one, it can be estimated as:

from covmetrics import CovGap 

CovGap_value = CovGap().evaluate(x_test, cover_test, alpha=alpha, weighted=True)

Similar import can be used to use the metrics SSC, FSC, EOC, HSIC and PearsonCorrelation.

The HSIC metric has been built upon the original code from: https://github.com/danielgreenfeld3/XIC.

Contributors

• Sacha Braun
• David Holzmüller