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name: evaluation-splitting description: "Evaluate fitted LightFM recommenders and create leakage-safe train/test splits." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

LightFM evaluation and splitting

Use this sub-skill when a task is about offline ranking metrics, train/test splits, or debugging leakage in a fitted LightFM recommender.

Read when

• Compute precision_at_k, recall_at_k, auc_score, reciprocal_rank, or LightFM.predict_rank for a fitted model.
• Split an interactions matrix with random_train_test_split and validate that train and test interactions do not overlap.
• Explain train_interactions, check_intersections, preserve_rows, metric output shapes, top-k behavior, rank values, or tie handling.
• Diagnose intersection ValueError, suspiciously high test metrics, empty metric arrays, zero-row users, sparse matrix input problems, or num_threads errors.

Route elsewhere

• Training, tuning, resuming, serializing, direct predict scoring, or loss selection: read model-training.
• Dataset mappings, feature matrix construction, built-in dataset fetchers, cold-start feature design, or interaction schemas: read data-features.
• Repository build, editable install, compiled extension maintenance, native tests, or CI commands: read repo-development.

Fast workflow

1. Start from a fitted lightfm.LightFM model and SciPy sparse train/test matrices with identical (n_users, n_items) shape. If the model was fit with user or item features, pass compatible feature matrices into the metric call as well.
2. Treat non-zero entries in test_interactions as positives to rank. Remove or zero-out negative feedback before ranking metrics; LightFM evaluation treats any non-zero entry as an interaction.
3. Pass train_interactions=train for test metrics so known training positives are excluded from the candidate ranking. Keep check_intersections=True unless performing an intentional diagnostic.
4. Choose preserve_rows=False for aggregate means over users with test positives, or preserve_rows=True when the output must stay aligned to the original user axis.
5. Inspect API details for signatures, return shapes, sparse matrix expectations, intersection checks, and tie semantics.
6. Use workflows for leakage-safe split/evaluate recipes, user-preserving metrics, and predict_rank interpretation.
7. Use troubleshooting when metrics error, look optimistic, contain zero/empty rows, or disagree with expected top-k behavior.

A deterministic, no-network fixture is bundled at scripts/evaluate_lightfm_fixture.py. It trains a tiny CPU model, computes all ranking metrics, validates a random split, and can intentionally demonstrate intersection handling.

Non-negotiables for leakage-safe evaluation

• Do not average test metrics without checking that train/test matrices are disjoint at non-zero coordinates.
• Do not silence intersection checks just to get a score; fix the split unless the task is explicitly evaluating training-set ranking behavior.
• Do not compare metrics across runs unless k, split policy, feature matrices, preserve_rows, and train-interaction filtering are the same.
• Do not interpret random splits as temporal validation. Use a chronological or user-aware holdout when production evaluation requires it, then still validate intersections before scoring.