use category as xaxis type for per-class perf charts

[imanjra]

What changes are proposed in this pull request?

use category as xaxis type for per-class perf charts to avoid binning by plotly.js

How is this patch tested? If it is not, please explain why.

Using the model evaluation panel with numerical class labels

Release Notes

Is this a user-facing change that should be mentioned in the release notes?

• No. You can skip the rest of this section.
• Yes. Give a description of this change to be included in the release
  notes for FiftyOne users.

use category as xaxis type for per-class perf charts

What areas of FiftyOne does this PR affect?

• App: FiftyOne application changes
• Build: Build and test infrastructure changes
• Core: Core fiftyone Python library changes
• Documentation: FiftyOne documentation changes
• Other

Summary by CodeRabbit

• New Features
  • Enhanced chart visuals by configuring the evaluation plots and confusion matrix to interpret x-axis values as categorical, providing clearer, more intuitive displays.

[coderabbitai]

Walkthrough

This pull request updates the layout configuration for the EvaluationPlot component within the Evaluation function. When classMode is set to "chart", the x-axis is now explicitly defined as categorical. The same layout setting is also applied to the confusion matrix plot, ensuring that both visualizations correctly interpret the x-axis data as categorical. No other functional or logical changes have been made.

Changes

File	Change Summary
app/.../Evaluation.tsx	Added layout configuration to EvaluationPlot for categorical x-axis in both chart and confusion matrix views.

Possibly related PRs

• fix PlotlyView layout issue #4565: Focuses on layout configuration in the PlotlyView component, aligning with the layout changes in this PR.
• bugfix: y-axis autorange reversed on all evaluation chart #5193: Involves modifications to the EvaluationPlot component layout, directly related to the adjustments made here.
• fix confusion matrix in model evaluation panel #5186: Enhances the display properties of the confusion matrix within Evaluation.tsx, paralleling the layout updates in this PR.

Suggested reviewers

• ritch
• brimoor

Poem

I'm a coding rabbit, hopping in delight,
Tweaking layouts to set each plot just right.
The x-axis now prances in a categorical beat,
Making metrics and matrices visually neat.
With a joyful hop and a clever code tweak,
I celebrate improvements that make our charts unique!

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📥 Commits

Reviewing files that changed from the base of the PR and between 931fee1 and 109802e.

📒 Files selected for processing (1)

• app/packages/core/src/plugins/SchemaIO/components/NativeModelEvaluationView/Evaluation.tsx (1 hunks)

🧰 Additional context used

📓 Path-based instructions (1)

`**/*.{ts,tsx}`: Review the Typescript and React code for co...

**/*.{ts,tsx}: Review the Typescript and React code for conformity with best practices in React, Recoil, Graphql, and Typescript. Highlight any deviations.

• app/packages/core/src/plugins/SchemaIO/components/NativeModelEvaluationView/Evaluation.tsx

⏰ Context from checks skipped due to timeout of 90000ms (6)

• GitHub Check: test / test-python (ubuntu-latest-m, 3.10)
• GitHub Check: test / test-app
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🔇 Additional comments (3)

app/packages/core/src/plugins/SchemaIO/components/NativeModelEvaluationView/Evaluation.tsx (3)

1135-1137: LGTM! The categorical x-axis type will improve data visualization.

The addition of type: "category" for the x-axis ensures that the plotly.js library treats class labels as categorical data rather than attempting numerical binning. This change aligns perfectly with the PR objective and will enhance the clarity of per-class performance charts.

---

1278-1287: LGTM! Consistent use of categorical axes in confusion matrix.

The confusion matrix plot also uses categorical type for both x and y axes, which maintains consistency in how class labels are displayed across different visualizations.

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1330-1339: LGTM! Consistent configuration in comparison view.

The same categorical axis configuration is correctly applied to the comparison confusion matrix plot, ensuring consistent behavior across both the main and comparison views.

---

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