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name: prediction-and-explanation description: "Run Nitrain prediction workflows and inspect the current OcclusionExplainer surface." disable-model-invocation: true metadata: disco-role: operating license: AGPL 3.0

Prediction and explanation

Use this sub-skill when a user wants to run dataset-level prediction with Predictor, reconstruct slice-based outputs back into ANTs images, or inspect what OcclusionExplainer does in the current release.

What belongs here

• Predictor and its sampler-driven inference path.
• Output post-processing for regression, segmentation, and classification.
• The current OcclusionExplainer API surface and its limitations.

What does not belong here

• Dataset creation and reader selection: use sub-skills/datasets-readers/.
• Transforms, samplers, and loader batching: use sub-skills/preprocessing-and-loading/.
• Architecture discovery and training: use sub-skills/models-training/.

Typical user requests

• "Predict on a dataset"
• "Restore slice predictions into a 3D image"
• "Use a sampler for inference"
• "Check whether the occlusion explainer actually works"

Working pattern

1. Make sure the model was built for the same geometry the predictor expects.
2. Use the same sampler family at inference time that the model was trained with when shape reconstruction matters.
3. Choose the task string carefully because prediction post-processing depends on whether the task is regression, segmentation, or classification.
4. Keep the input dataset tiny when you are only proving the prediction path.

Read these references

• references/api-reference.md for the verified predictor and explainer signatures.
• references/workflows.md for prediction snippets and output-shape rules.
• references/troubleshooting.md for sampler-axis errors, shape mismatch, and the current explainer stub.

Smoke check

After installing dependencies, run the bundled helper scripts/check_install.py:

python scripts/check_install.py --mode predictor

Use this when you want to confirm the prediction path before you hand off to a larger workflow.

Key decisions

• Predictor(model, task, sampler=None, expand_dims=-1) stores the model, task, sampler, and expansion axis.
• Predictor.predict(dataset) loops over dataset records, applies the sampler, runs the model, and then normalizes the outputs by task.
• expand_dims=None disables extra dimension expansion.
• SliceSampler output is rolled back into the original axis position during prediction when the sampler type is recognized.

Common outcomes

• Regression predictions may be returned as ANTs images when the output is multidimensional.
• Segmentation and classification outputs are rounded to uint8 in the current implementation.
• Slice-based predictions should retain the original image orientation once the sampled axis is restored.

Watch for these signals

• If the prediction shape is wrong, the sampler axis or expand_dims setting is usually the first thing to inspect.
• If the output type is not what you expect, check the task string and whether the model output is multidimensional.
• OcclusionExplainer.fit() is a placeholder in this snapshot and currently returns 1.

Before handing off

If the request grows into actual saliency, heatmap, or attribution logic, note that the current explainer surface is incomplete and do not promise more than what the source code provides.