TAO VCN Sample Routing Skill

TAO VCN Sample Routing Skill

You are the dispatcher between gap analysis and the augmentation modules in a VCN AOI SDA pipeline. Each augmentation module can only act on labels it knows how to handle:

• k-NN Mining can only mine real-image neighbors for labels that already exist in the source pool CSV. There is no point looking for SHIFT neighbors if the pool has no SHIFT rows.
• AnomalyGen (Cosmos SDG) can only generate synthetic anomalies for the classes its inference pipeline supports: PASS, EXCESS_SOLDER, MISSING, BRIDGE. A weak sample with a label outside this set is unroutable to AnomalyGen.

This skill runs once per SDA iteration immediately after gap analysis. It splits the gap-analysis parquet into one filtered parquet per module so each module operates on its own eligible subset, and it writes a human-readable summary of the per-label routing decisions.

The work is intentionally trivial: read a parquet, do two .isin(...) filters, write two parquets, write one summary. The skill exists to make those decisions auditable — every label must show up in the summary with a yes/no verdict for each module so a downstream reviewer can spot when a label is silently dropped because no module accepted it.

Inputs

1. gaps_parquet — the gap-analysis output (typically <exp_dir>/rca_results/<timestamp>/gaps.parquet from tao-analyze-gaps-visual-changenet). Required columns: filepath, label. Other columns (siamese_score, weakness) are preserved verbatim.
2. source_pool_csv — VCN-format mining source pool CSV with a label column. Empty string or non-existent path is allowed; the mining subset will simply be empty in that case.
3. Output directory — where the two routed parquets, the summary, and the report are written. Default: a timestamped folder under the gap-analysis result directory: <rca_result_dir>/routing_results/<timestamp>/.
4. anomalygen_supported_labels (optional) — override the default AnomalyGen-eligible label set. Default: {"PASS", "EXCESS_SOLDER", "MISSING", "BRIDGE"}. Warning: This must stay in sync with ANOMALYGEN_SUPPORTED_LABELS in mdo-kratos-workflows/pipelines/sda/routing.py and the AnomalyGen integration's actual generator coverage. Adding a new defect class to AnomalyGen means adding it here too.

Method

The whole skill is two .isin(...) masks against the uppercased label column.

Step 1 — Load and uppercase

df = pd.read_parquet(gaps_parquet)
labels_upper = df["label"].astype(str).str.upper()

The match is case-insensitive for both module checks. The original label column is preserved unchanged in the output parquets — only the comparison key is uppercased.

Step 2 — Mining subset

if source_pool_csv and os.path.isfile(source_pool_csv):
    pool_df = pd.read_csv(source_pool_csv)