Get target information for tabular dataset

[sooahleex]

Summary

• Set target as dictionary to get input and output column. ex. {"input": "question", "output":["rating", "sentiment"]}

• If target is None, bring all columns

• Set input of target column as media and output of it as annotations

  • For example, if we set target as {"input": "length(m)", "output": ["breed_category", "pet_category"]}
    
    And each item for train and test subset is like below.
    
    Test file did not have column for breed_category and pet_category, so test subset did not have any annotations.

• Set CategoricalDtype for column which have dtype as object but that could be used as label. For this, we should define the threshold.

How to test

• Fix target for unit test and cli test

Checklist

• I have added unit tests to cover my changes.​
• I have added integration tests to cover my changes.​
• I have added the description of my changes into CHANGELOG.​
• I have updated the documentation accordingly

License

• I submit my code changes under the same MIT License that covers the project.
  Feel free to contact the maintainers if that's a concern.
• I have updated the license header for each file (see an example below).

# Copyright (C) 2024 Intel Corporation
#
# SPDX-License-Identifier: MIT

[codecov]

Codecov Report

Attention: Patch coverage is 79.54545% with 9 lines in your changes are missing coverage. Please review.

Project coverage is 80.99%. Comparing base (44cc56a) to head (cc163a6).
Report is 37 commits behind head on develop.

Files	Patch %	Lines
src/datumaro/plugins/data_formats/tabular.py	82.05%	3 Missing and 4 partials ⚠️
src/datumaro/components/media.py	60.00%	1 Missing and 1 partial ⚠️

Additional details and impacted files

@@             Coverage Diff             @@
##           develop    #1471      +/-   ##
===========================================
+ Coverage    80.85%   80.99%   +0.14%     
===========================================
  Files          271      272       +1     
  Lines        30689    31242     +553     
  Branches      6197     6294      +97     
===========================================
+ Hits         24815    25306     +491     
- Misses        4489     4519      +30     
- Partials      1385     1417      +32     

Flag	Coverage Δ
ubuntu-20.04_Python-3.10	80.98% <79.54%> (+0.13%)	⬆️
windows-2022_Python-3.10	80.97% <79.54%> (+0.14%)	⬆️

Flags with carried forward coverage won't be shown. Click here to find out more.

☔ View full report in Codecov by Sentry.
📢 Have feedback on the report? Share it here.

[wonjuleee]

LGTM