feat: Implement LabelEncoder methods and add fit method test

[muzakkirhussain011]

PR: Enhancements to LabelEncoder

Overview

This PR introduces critical enhancements to the LabelEncoder class, enabling the conversion of string labels into numerical identifiers. Due to the limitation of ivy.array not accepting string elements, we've utilized the list data type for encoding operations. This ensures compatibility across all backends and maintains the core functionality of the LabelEncoder.

Details

• String Handling: The LabelEncoder now uses lists to process and encode string objects into numerical identifiers. This change is due to ivy.array's inability to handle string types, which are essential for the LabelEncoder's operation.
• Backend Compatibility: By employing lists, we ensure that the LabelEncoder remains functional across various computational backends, as lists are universally supported.
• Testing Rigor: Tests for the fit method are consistently passing, confirming the method's reliability in identifying and ordering unique labels.
• Method Prerequisites: The tests for transform, fit_transform, and inverse_transform methods are failing because these methods require the LabelEncoder to be fitted with data first. This prerequisite is not currently met within the automated testing setup, leading to failures.
• Manual Verification: Manual testing of the transform and fit_transform methods with direct input has been successful, indicating that the methods function correctly when the encoder is properly fitted.

Attachments

• Screenshot of the fit method test passing.
• Screenshot of the transform method test failing.
• Screenshot of the manual test for transform and fit_transform methods passing.

This PR is a step forward in enhancing our machine learning toolkit's capabilities, ensuring more robust and versatile encoding functionalities and seamless backend integration

Closes #

Checklist

• Did you add a function?
• Did you add the tests?
• Did you run your tests and are your tests passing?
• Did pre-commit not fail on any check?
• Did you follow the steps we provided?

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[muzakkirhussain011]

Hi @Ishticode ,

Could you please review the “Enhancements to LabelEncoder” PR? Your feedback would be invaluable. Screenshots of the tests and manual verifications are attached for reference

Thanks

[Ishticode]

Thank you very much @muzakkirhussain011
We should not really be adding docstring to the frontends as the actual native frameworks explain the function or classes exactly. You can see this as a NOTE on our frontends guide at https://unify.ai/docs/ivy/overview/deep_dive/ivy_frontends.html. Let me know if something is unclear. Thank you very much

[muzakkirhussain011]

Thank you very much @muzakkirhussain011 We should not really be adding docstring to the frontends as the actual native frameworks explain the function or classes exactly. You can see this as a NOTE on our frontends guide at https://unify.ai/docs/ivy/overview/deep_dive/ivy_frontends.html. Let me know if something is unclear. Thank you very much

Hi @Ishticode ,

I acknowledge the frontend documentation standards as highlighted in the guide and will ensure compliance moving forward. Thank you for bringing this to my attention. I’ve removed the docstrings from the frontends as per the guide to maintain consistency with the native frameworks’ documentation.

Best regards

[muzakkirhussain011]

Hi @Ishticode ,

I'm following up on this PR as it has been 5 days since it was opened. Your review would be very helpful for us to proceed. Looking forward to your feedback.

Best Regards

[Ishticode]

Hi. Can we test for newly implemented functions which remove the NotImplementedError. Thansk :)

[muzakkirhussain011]

Hi @Ishticode ,
The tests for transform, fit_transform, and inverse_transform methods are failing because these methods require the LabelEncoder to be fitted with data first. This prerequisite is not currently met within the automated testing setup, leading to failures.

Manual Verification: Manual testing of the transform and fit_transform methods with direct input has been successful, indicating that the methods function correctly when the encoder is properly fitted.

I have attached Screenshot of the fit method test passing.

Screenshot of the transform method test failing.

Screenshot of the manual test for transform and fit_transform methods passing.

In the PR description.

[Ishticode]

Sure, in that case we should still add the test but make a fit call before calling the actual test call. :)

[muzakkirhussain011]

Hi @Ishticode ,
Even though I called the fit method beforehand I'm still getting the not fitted error.

Test for transform method:

@handle_frontend_method(
    class_tree=CLASS_TREE + ".LabelEncoder",
    init_tree="sklearn.preprocessing.LabelEncoder",
    method_name="transform",
    dtype_x=helpers.dtype_and_values(
        available_dtypes=helpers.get_dtypes("float_and_integer"),
        min_num_dims=1,
        max_num_dims=1,
    ),
)
def test_sklearn_label_encoder_transform(
    dtype_x,
    frontend,
    frontend_method_data,
    init_flags,
    method_flags,
    on_device,
    backend_fw,
):
    input_dtype, x = dtype_x
    # Initialize the LabelEncoder and fit it with the input data
    encoder = LabelEncoder()
    encoder.fit(x[0])

    helpers.test_frontend_method(
        init_input_dtypes=input_dtype,
        init_all_as_kwargs_np={},
        method_input_dtypes=input_dtype,
        method_all_as_kwargs_np={
            "y": x[0],
        },
        frontend_method_data=frontend_method_data,
        init_flags=init_flags,
        method_flags=method_flags,
        frontend=frontend,
        on_device=on_device,
        backend_to_test=backend_fw,
    )

Output of the test: