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name: optional-batch-generators description: "Router for imbalanced-learn balanced mini-batch helpers for Keras and TensorFlow." metadata: disco-role: operating disable-model-invocation: true license: MIT

optional-batch-generators

Use this sub-skill when the task names balanced mini-batches, Keras, or TensorFlow support in imbalanced-learn.

This sub-skill owns the optional generator helpers:

• imblearn.tensorflow.balanced_batch_generator
• imblearn.keras.BalancedBatchGenerator
• imblearn.keras.balanced_batch_generator

It is intentionally narrower than a deep-learning training skill. It covers the balanced data iterator surface, not model architecture design, training loops, or GPU acceleration strategy.

What to do first

1. Check whether tensorflow or keras is actually importable.
2. Decide whether the user wants the TensorFlow iterator, the Keras Sequence, or both.
3. Confirm that the sampler exposes sample_indices_.
4. Keep the smoke tiny: generate a small batch, inspect the shapes, and stop.
5. Treat missing Keras/TensorFlow as an optional-capability skip, not a core package failure.

Typical routing cues

• balanced_batch_generator
• BalancedBatchGenerator
• keras, tensorflow, Sequence, fit
• balanced mini-batches or class-balanced iterators

When to read the bundled references

• references/workflows.md for import and backend decision flow.
• references/api-reference.md for the compact generator catalog.
• references/troubleshooting.md when backend imports or generator construction fail.

Common choices

• Use balanced_batch_generator from imblearn.tensorflow when the workflow expects a simple generator/steps-per-epoch pair.
• Use BalancedBatchGenerator from imblearn.keras when the workflow wants a Keras Sequence-style object that can be passed to fit.
• Use the sampler parameter when the default RandomUnderSampler is not the desired balancing strategy.
• Use keep_sparse=True only when the downstream loop can consume sparse mini- batches.

Native evidence to keep in mind

These repo tests are the most relevant later verification anchors for this sub-skill:

• imblearn/tensorflow/tests/test_generator.py::test_balanced_batch_generator
• imblearn/tensorflow/tests/test_generator.py::test_balanced_batch_generator_function_sparse
• imblearn/keras/tests/test_generator.py::test_balanced_batch_generator_class
• imblearn/keras/tests/test_generator.py::test_balanced_batch_generator_function

Package-specific cautions

• The generator helpers need a sampler with sample_indices_.
• len(generator) uses floor division by batch_size.
• BalancedBatchGenerator depends on the optional backend being importable at instantiation time.
• Keras and TensorFlow support is optional for the package as a whole, even though this sub-skill targets those APIs directly.

Use the script

• scripts/batch_generator_smoke.py for a tiny optional-backend batch smoke.