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name: predictor-inference description: "Use LimiXPredictor for local checkpoint inference on tabular classification, regression, and missing-value imputation." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Predictor Inference

Use this sub-skill when a task needs the direct Python API for LimiX tabular inference with inference.predictor.LimiXPredictor: single-dataset classification, regression, or missing-value imputation (MVI) using a local LimiX checkpoint and local inference configuration.

Immediate routing

• For constructor arguments, predict() returns, config-list caveats, CPU constraints, and DDP handoff, read API reference.
• For copy-adaptable classification, regression, and MVI recipes, read workflows.
• For accepted array/dataframe shapes, targets, categorical/object columns, NaN handling, and size guidance, read data formats.
• For common failures and fixes, read troubleshooting.
• To validate imports, a local config, tiny fixture shapes, and optional full inference without downloading anything, use scripts/predictor_smoke_template.py.
• To generate a deterministic MVI mask and score reconstruction errors on tiny arrays, use scripts/mvi_mask_fixture.py.

Scope

This sub-skill covers direct LimiXPredictor API usage only. The predictor loads the checkpoint in its constructor, so full checkpoint inference requires a local .ckpt file. CUDA/GPU may be required for practical full inference, retrieval configs, flash-attention paths, or DDP; CPU use is limited to non-retrieval configs and automatically disables mixed precision.

Route out of this sub-skill

• Benchmark-style loops over dataset directories, batch result files, and CLI wrappers belong to benchmark-cli.
• Authoring or inspecting inference configuration pipelines, feature transforms, and preprocessing choices belongs to configuration-preprocessing.
• Retrieval hyperparameter search, retrieval parameter tuning, and Optuna-style search belongs to retrieval-optimization.

Operating checklist

1. Confirm the caller has a local LimiX checkpoint path and a local JSON config path or in-memory config list.
2. Pick a config compatible with task and device: no retrieval on CPU; MVI uses mask_prediction=True and an MVI/non-retrieval regression config.
3. Validate input shapes and target type before constructing the predictor.
4. Instantiate LimiXPredictor, then call predict() with exact task type string "Classification" or "Regression".
5. Interpret output by task: classification returns NumPy probabilities, regression returns a torch tensor, and MVI returns a tuple with reconstructed features.