wehub-resource-sync
88 lines
4.0 KiB
ReStructuredText
88 lines
4.0 KiB
ReStructuredText
How to migrate to versions >= 2.0.0 from pre 1.0.1
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==================================================
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If you previously used older versions of cleanlab,
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this guide helps update your existing code to work with versions >= 2.0.0 in no time!
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Below we outline the major updates and code substitutions to be aware of.
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A detailed API change-log is listed in the `v2.0.0. Release Notes <https://github.com/cleanlab/cleanlab/releases/tag/v2.0.0>`_.
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Function and class name changes
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-------------------------------
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This section covers the most commonly-used functionality from Cleanlab 1.0.
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| **Old:** ``pruning.get_noise_indices(s, psx, prune_method, sorted_index_method, ...)``
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| **New:** :py:func:`filter.find_label_issues <cleanlab.filter.find_label_issues>` ``(labels, pred_probs, filter_by, return_indices_ranked_by, ...)``
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Note: ``inverse_noise_matrix`` is no longer a supported input argument, but ``confident_joint`` remains (you can easily convert between these two).
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----
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| **Old:** ``pruning.order_label_errors(label_errors_bool, psx, labels, sorted_index_method)``
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| **New:** :py:func:`rank.order_label_issues <cleanlab.rank.order_label_issues>` ``(label_issues_mask, labels, pred_probs, rank_by, ...)``
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Note: You can now alternatively use :py:func:`rank.get_label_quality_score() <cleanlab.rank.get_label_quality_score>` to numerically score the labels instead of ranking them.
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----
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| **Old:** ``latent_estimation.num_label_errors(labels, psx, ...)``
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| **New:** :py:func:`count.num_label_issues <cleanlab.count.num_label_issues>` ``(labels, pred_probs, ...)``
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Note: This is the most accurate way to estimate the raw *number* of label errors in a dataset.
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----
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| **Old:** ``classification.LearningWithNoisyLabels(..., prune_method)``
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| **New:** :py:class:`classification.CleanLearning <cleanlab.classification.CleanLearning>` ``(..., find_label_issues_kwargs)``
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Note: :py:class:`CleanLearning <cleanlab.classification.CleanLearning>` can now find label errors for you, neatly organizing them in a ``pandas.DataFrame`` as well as computing the required out-of-sample predicted probabilities. You just specify which classifier, we handle the cross-validation!
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Module name changes
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-------------------
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Reorganized modules:
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- ``cleanlab.pruning`` --> :py:mod:`cleanlab.filter`
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- ``cleanlab.latent_estimation`` --> :py:mod:`cleanlab.count`
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- ``cleanlab.noise_generation`` --> :py:mod:`cleanlab.benchmarking.noise_generation`
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- ``cleanlab.baseline_methods`` --> incorporated into :py:mod:`cleanlab.filter`
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Internal and experimental functionality, marked as such and not guaranteed to be stable between releases:
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- ``cleanlab.models`` --> :py:mod:`cleanlab.experimental`
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- ``cleanlab.coteaching`` --> :py:mod:`cleanlab.experimental.coteaching`
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- ``cleanlab.latent_algebra`` --> :py:mod:`cleanlab.internal.latent_algebra`
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- ``cleanlab.util`` --> :py:mod:`cleanlab.internal.util`
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New modules
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-----------
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- :py:mod:`cleanlab.dataset` : New methods to print summaries of overall types of label issues most common in a dataset.
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- :py:mod:`cleanlab.rank` : Moved all ranking and ordering functions from ``cleanlab.pruning`` to here. This module contains methods to score the label quality of each example and rank your data by the quality of their labels.
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- :py:mod:`cleanlab.internal` and :py:mod:`cleanlab.experimental`: Moved all advanced code and utility methods to this module, including the old ``cleanlab.latent_algebra`` module. Researchers may find useful functions in here.
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Removed modules
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---------------
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- ``cleanlab.polyplex``
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Common argument and variable name changes
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-----------------------------------------
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Here are some common name and terminology changes in Cleanlab 2.0:
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- ``s`` --> ``labels`` (the given labels in the data, which are potentially noisy)
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- ``psx`` --> ``pred_probs`` (predicted probabilities output by trained classifier)
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- ``label_error`` --> ``label_issue`` (a label that is likely to be wrong)
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See the documentation for individual functions for details on how argument names changed.
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