Class Parity Label Analysis Tutorial#

Problem Statement#

For machine learning tasks, a discrepancy in label frequencies between train and test datasets can result in poor model performance.

To help with this, DataEval has a tool that compares the label distributions of two datasets.

When to use#

The Parity class and similar should be used when you would like to determine if two datasets have statistically independent labels.

What you will need#

  1. A labeled training image dataset

  2. A labeled test image dataset to evaluate the label distribution of

Setting up#

Let’s import the required libraries needed to set up a minimal working example

from dataeval._internal.datasets import MNIST
from dataeval.metrics.bias import label_parity

Load the data#

We will use the MNIST dataset from torchvision for this tutorial on class label statistics

train_ds = MNIST("./data", train=True, download=True, size=2000)
test_ds = MNIST("./data", train=False, download=True, size=500)

# Take a subset of 2000 training images and 500 test images
train_labels = train_ds.targets
test_labels = test_ds.targets

Files already downloaded and verified
Files already downloaded and verified

Evaluate label statistical independence#

Now, let’s look at how to use DataEval’s label statistics analyzer. Start by initializing a Parity object. Compute the chi-squared value of hypothesis that test_ds has the same class distribution as train_ds by specifying the two datasets to be compared, as well as the number of unique classes (for MNIST, there are 10 unique classes). It also returns the p-value of the test.

results = label_parity(train_labels, test_labels)
print(f"The chi-squared value for the two label distributions is {results.score}, with p-value {results.p_value}")

The chi-squared value for the two label distributions is 0.0, with p-value 1.0
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