Cannif: Fixing Metrics Visualization With Nn_ensemble

Introduction

In this article, we will discuss a critical issue encountered while using Cannif, a fantastic frontend for Annif, specifically related to metrics visualization when using the nn_ensemble backend. We'll delve into the problem, the proposed solution, and how it enhances the user experience. A big thank you to the developers and maintainers of Cannif for creating such a valuable utility! Your hard work is greatly appreciated. This article aims to provide a detailed understanding of the problem, its solution, and the broader implications for Cannif users.

Background on Cannif and Annif

Before we dive into the specifics, let's briefly discuss Cannif and Annif. Annif is a versatile tool used for automated subject indexing and text classification. It helps in categorizing documents and information, making it easier to manage and retrieve data. Cannif, on the other hand, serves as a user-friendly frontend for Annif, providing a graphical interface that simplifies interactions with Annif's powerful functionalities. Cannif allows users to easily set up projects, train models, and visualize results, making it an invaluable tool for researchers, librarians, and information professionals.

Cannif's intuitive design and ease of use make it accessible to a wide range of users, even those without extensive technical expertise. Its ability to present complex data in a clear and understandable format is one of its key strengths. However, like any software, Cannif is not without its challenges, and addressing these challenges is crucial for its continued improvement and usability.

The Problem: Metrics Visualization Failure

Issue Description

One particular issue arises when visualizing metrics results for projects that utilize the nn_ensemble backend in Annif. The nn_ensemble backend is designed to combine the outputs of multiple models, often assigning weights to each model's contribution. This approach can lead to more accurate and robust results, but it also introduces complexity in how the results are parsed and displayed.

The core of the problem lies in how Cannif's logic handles parsing source projects for the st.multiselect widget, a component used in the user interface (UI) for selecting and filtering data. The nn_ensemble backend returns source projects as a comma-separated string, which includes weights associated with each source. For example, a typical output might look like this:

sources=sdg-svc:0.5423,sdg-fasttext:0.1073,sdg-omikujiB:0.3504

The existing parsing logic in Cannif attempts to use these weighted strings directly as project IDs. This approach fails because the actual project IDs available in the system are just the names themselves (e.g., sdg-svc), without the weights appended. Consequently, the st.multiselect widget cannot correctly identify and display the source projects, leading to errors in the UI visualization.

Impact on User Experience

This issue can significantly impact the user experience. When metrics visualization fails, users are unable to effectively analyze and interpret the results of their Annif projects. This can hinder the process of fine-tuning models, comparing different approaches, and making informed decisions based on the data. The inability to properly visualize metrics can be particularly frustrating for users who rely on Cannif for critical tasks, such as assessing the performance of classification models or identifying areas for improvement.

Moreover, the error can create confusion and uncertainty for users who are not familiar with the internal workings of Cannif and Annif. Without a clear understanding of the issue, users may struggle to troubleshoot the problem and may incorrectly attribute the error to other factors, such as incorrect project configurations or data issues.

Proposed Solution: Extracting Project IDs

The Logic Behind the Fix

To address this issue, a solution was proposed that involves modifying the source parsing logic within the cannif_streamlit.py file. The key idea is to extract only the project ID (the part before the colon) from each weighted source string. This ensures that the project IDs used for filtering and visualization match the actual project IDs available in the system.

The solution involves the following steps:

1. Splitting the Weighted Source String: The initial step is to split the comma-separated string into individual weighted sources. This is achieved using the split method in Python, with the comma (,) as the delimiter. For example, the string sdg-svc:0.5423,sdg-fasttext:0.1073,sdg-omikujiB:0.3504 would be split into ['sdg-svc:0.5423', 'sdg-fasttext:0.1073', 'sdg-omikujiB:0.3504'].
2. Extracting the Project ID: For each weighted source, the project ID is extracted by splitting the string at the colon (:) and taking the first part. This is accomplished using a list comprehension, which is a concise way to create lists in Python. For instance, 'sdg-svc:0.5423' would be split into ['sdg-svc', '0.5423'], and the first element ('sdg-svc') is retained as the project ID.
3. Using the Extracted List for Multiselect: The extracted list of project IDs is then used to populate the st.multiselect widget. This allows users to select and filter projects based on their actual IDs, resolving the original issue.

Code Snippet

Here’s the code snippet that demonstrates the proposed solution:

# Original source string from Annif: 'sdg-svc:0.5423,sdg-fasttext:0.1073,sdg-omikujiB:0.3504'

# 1. Split the weighted source string
weighted_sources = sources.split(",")

# 2. New logic: Extract only the project ID (the part before the colon)
#    Example: source_list will now be ['sdg-svc', 'sdg-fasttext', 'sdg-omikujiB']
source_list = [s.split(":")[0] for s in weighted_sources]

# 3. Use the extracted list for the multiselect
projects = get_api_projects()
project_ids = [item["project_id"] for item in projects]
new_sources = st.multiselect("Sources", project_ids, source_list)

This code snippet effectively addresses the problem by ensuring that the project IDs used in the st.multiselect widget are the correct, unweighted IDs. This allows users to seamlessly visualize metrics for projects using the nn_ensemble backend.

Benefits of the Solution

The proposed solution offers several key benefits:

• Correct Metrics Visualization: The primary benefit is that it resolves the issue of metrics visualization failure, allowing users to accurately analyze and interpret their project results.
• Improved User Experience: By fixing the visualization problem, the solution significantly enhances the user experience, making Cannif more reliable and user-friendly.
• Simplified Workflow: Users can now seamlessly work with projects using the nn_ensemble backend without encountering visualization errors, streamlining their workflow.
• Enhanced Data Analysis: Accurate metrics visualization empowers users to make more informed decisions based on their data, leading to better model performance and project outcomes.

Implementation Details

The implementation of this solution involves modifying the cannif_streamlit.py file, which is responsible for handling the user interface and data visualization in Cannif. The specific section of the code that needs to be modified is where the source projects are parsed and prepared for the st.multiselect widget.

To implement the solution, the code snippet provided earlier needs to be integrated into the existing codebase. This involves replacing the original parsing logic with the new logic that extracts project IDs. The modified code should then be tested to ensure that it correctly handles the weighted source strings and displays the project IDs in the st.multiselect widget.

Testing and Validation

Before deploying the solution, it is crucial to thoroughly test and validate the changes. This can be done by creating projects that use the nn_ensemble backend and verifying that the metrics visualization works correctly. Additionally, it is important to test the solution with different sets of source projects and weights to ensure that it handles various scenarios effectively.

Testing should also include checking for any unintended side effects or regressions. This involves verifying that the changes do not introduce any new issues or break existing functionalities in Cannif. Comprehensive testing is essential to ensure the stability and reliability of the solution.

Future Improvements

While the proposed solution effectively addresses the current issue, there is always room for improvement. In future releases of Cannif, the parsing logic could be further refined to handle more complex scenarios or edge cases. Additionally, the user interface could be enhanced to provide more informative feedback to users, such as displaying the weights associated with each source project.

Potential Enhancements

Here are some potential enhancements that could be considered for future releases:

• Displaying Source Weights: The user interface could be modified to display the weights associated with each source project. This would provide users with additional context and insights into the contributions of different models within the nn_ensemble backend.
• Handling Edge Cases: The parsing logic could be enhanced to handle edge cases, such as source strings with missing weights or malformed project IDs. This would improve the robustness and reliability of the solution.
• Improving Error Messages: More informative error messages could be provided to users in case of parsing failures. This would help users to quickly identify and resolve any issues.

These enhancements would further improve the user experience and make Cannif an even more valuable tool for working with Annif.

Conclusion

In conclusion, the issue of metrics visualization failure with the nn_ensemble backend in Cannif has been successfully addressed through a simple yet effective solution. By modifying the source parsing logic to extract project IDs, the st.multiselect widget can now correctly display and filter projects, enabling users to seamlessly analyze their results. This fix not only resolves a critical problem but also enhances the overall user experience, making Cannif a more reliable and user-friendly tool.

The continuous improvement of Cannif, as demonstrated by this solution, highlights the dedication of its developers and maintainers to providing a high-quality frontend for Annif. As Cannif evolves, it is expected to incorporate more functionalities and enhancements, further solidifying its position as a valuable asset for researchers, librarians, and information professionals.

For more information on Annif and related topics, you can visit the Annif Official Website.

-Parthasarathi