Update torchmetrics requirement from <0.12,>=0.7 to >=0.7,<1.3 in /_requirements

[dependabot]

Updates the requirements on torchmetrics to permit the latest version.

Release notes

Sourced from torchmetrics's releases.

Clustering metrics

Torchmetrics v1.2 is out now! The latest release includes 11 new metrics within a new subdomain: Clustering. In this blog post, we briefly explain what clustering is, why it’s a useful measure and newly added metrics that can be used with code samples.

Clustering - what is it?

Clustering is an unsupervised learning technique. The term unsupervised here refers to the fact that we do not have ground truth targets as we do in classification. The primary goal of clustering is to discover hidden patterns or structures within data without prior knowledge about the meaning or importance of particular features. Thus, clustering is a form of data exploration compared to supervised learning, where the goal is “just” to predict if a data point belongs to one class.

The key goal of clustering algorithms is to split data into clusters/sets where data points from the same cluster are more similar to each other than any other points from the remaining clusters. Some of the most common and widely used clustering algorithms are K-Means, Hierarchical clustering, and Gaussian Mixture Models (GMM).

An objective quality evaluation/measure is required regardless of the clustering algorithm or internal optimization criterion used. In general, we can divide all clustering metrics into two categories: extrinsic metrics and intrinsic metrics.

Extrinsic metrics

Extrinsic metrics are characterized by requirements of some ground truth labeling, even if used for an unsupervised method. This may seem counter-intuitive at first as we, by clustering definition, do not use such ground truth labeling. However, most clustering algorithms are still developed on datasets with labels available, so these metrics use this fact as an advantage.

Intrinsic metrics

In contrast, intrinsic metrics do not need any ground truth information. These metrics estimate inter-cluster consistency (cohesion of all points assigned to a single set) compared to other clusters (separation). This is often done by comparing the distance in the embedding space.

Update to Mean Average Precision

MeanAveragePrecision, the most widely used metric for object detection in computer vision, now supports two new arguments: average and backend.

• The average argument controls averaging over multiple classes. By the core definition, the default way is macro averaging, where the metric is calculated for each class separately and then averaged together. This will continue to be the default in Torchmetrics, but now we also support the setting average="micro". Every object under this setting is essentially considered to be the same class, and the returned value is, therefore, calculated simultaneously over all objects.

• The second argument - backend, is important, as it indicates what computational backend will be used for the internal computations. Since MeanAveragePrecision is not a simple metric to compute, and we value the correctness of our metric, we rely on some third-party library to do the internal computations. By default, we rely on users to have the official pycocotools installed, but with the new argument, we will also be supporting other backends.

[1.2.0] - 2023-09-22

Added

• Added metric to cluster package:
  • MutualInformationScore (#2008)
  • RandScore (#2025)
  • NormalizedMutualInfoScore (#2029)
  • AdjustedRandScore (#2032)
  • CalinskiHarabaszScore (#2036)
  • DunnIndex (#2049)
  • HomogeneityScore (#2053)
  • CompletenessScore (#2053)
  • VMeasureScore (#2053)
  • FowlkesMallowsIndex (#2066)
  • AdjustedMutualInfoScore (#2058)
  • DaviesBouldinScore (#2071)
• Added backend argument to MeanAveragePrecision (#2034)

Full Changelog: Lightning-AI/torchmetrics@v1.1.0...v1.2.0

... (truncated)

Changelog

Sourced from torchmetrics's changelog.

[1.2.0] - 2023-09-22

Added

• Added metric to cluster package:
  • MutualInformationScore (#2008)
  • RandScore (#2025)
  • NormalizedMutualInfoScore (#2029)
  • AdjustedRandScore (#2032)
  • CalinskiHarabaszScore (#2036)
  • DunnIndex (#2049)
  • HomogeneityScore (#2053)
  • CompletenessScore (#2053)
  • VMeasureScore (#2053)
  • FowlkesMallowsIndex (#2066)
  • AdjustedMutualInfoScore (#2058)
  • DaviesBouldinScore (#2071)
• Added backend argument to MeanAveragePrecision (#2034)

[1.1.2] - 2023-09-11

Fixed

• Fixed tie breaking in ndcg metric (#2031)
• Fixed bug in BootStrapper when very few samples were evaluated that could lead to crash (#2052)
• Fixed bug when creating multiple plots that lead to not all plots being shown (#2060)
• Fixed performance issues in RecallAtFixedPrecision for large batch sizes (#2042)
• Fixed bug related to MetricCollection used with custom metrics have prefix/postfix attributes (#2070)

[1.1.1] - 2023-08-29

Added

• Added average argument to MeanAveragePrecision (#2018)

Fixed

• Fixed bug in PearsonCorrCoef is updated on single samples at a time (#2019)
• Fixed support for pixel-wise MSE (#2017)
• Fixed bug in MetricCollection when used with multiple metrics that return dicts with same keys (#2027)
• Fixed bug in detection intersection metrics when class_metrics=True resulting in wrong values (#1924)
• Fixed missing attributes higher_is_better, is_differentiable for some metrics (#2028)

[1.1.0] - 2023-08-22

Added

... (truncated)

Commits

• 0d7d6c7 releasing 1.2.0
• e2150c5 docs: switch to new Lit template (#2097)
• e265e1b docs: fix MD links (#2098)
• 809bb5f precommit: unify formatting with prettier (#2096)
• 05a36de adding sample slash command - rebase (#2092)
• 25bf259 refactor: unify on num instead of nb or n (#2090)
• 6bf705e Add section on wrappers to overview pages (#2085)
• 5699241 ci: minor rename labeler's config file
• 462e4fd Minor fixes before next release (#2086)
• fe5f46f build(deps): update matplotlib requirement from <=3.7.2,>=3.2.0 to >=3.2.0,<3...
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