tao-route-visual-changenet-samples

Imported from nvidia/skills (skills/tao-route-visual-changenet-samples/SKILL.md) via skills.sh. Install upstream with npx skills add nvidia/skills --skill tao-route-visual-changenet-samples. Copyright stays with the author (Apache-2.0).

TAO VCN Sample Routing Skill

Standalone install? If this session was not initialized by the TAO skill bank plugin, run the tao-setup skill first (host preflight, credentials, cross-skill discovery).

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)
    pool_labels = {str(l).upper() for l in pool_df["label"].unique()}
    mn_mask = labels_upper.isin(pool_labels)
    mn_df = df[mn_mask]
else:
    pool_missing = True
    pool_labels = set()
    mn_df = df.iloc[0:0]   # empty, but with the same schema
mn_df.to_parquet(mining_gaps_parquet, index=False)

If the pool CSV is missing or empty, the mining subset is an empty DataFrame with the same columns as the input so downstream readers don't crash on schema mismatch. Flag this case in the summary.

Step 3 — AnomalyGen subset

ANOMALYGEN_SUPPORTED = {"PASS", "EXCESS_SOLDER", "MISSING", "BRIDGE"}
ag_mask = labels_upper.isin(ANOMALYGEN_SUPPORTED)
ag_df = df[ag_mask]
ag_df.to_parquet(anomalygen_gaps_parquet, index=False)

Rows whose label is in the AnomalyGen-supported set are written verbatim to anomalygen_gaps.parquet. The schema matches the input parquet exactly — downstream AnomalyGen (Cosmos SDG) needs no other changes.

Step 4 — Per-label routing breakdown

For every distinct label in the input gaps parquet (uppercased), record:

• count — how many rows have this label
• mining — yes if the label is in pool_labels, otherwise no
• anomalygen — yes if the label is in ANOMALYGEN_SUPPORTED, otherwise no

A label can route to both modules (e.g. PASS rows route to AnomalyGen, and if the source pool also contains PASS rows they route to Mining too). A label can also route to none — flag those, since they are silently dropped and may signal a configuration mismatch.

Write the breakdown to routing_summary.txt. The format mirrors the reference component exactly:

Weak-sample routing summary
Total weak samples: <N>
Mining subset:      <N_mn> -> <mining_gaps_parquet>
AnomalyGen subset:  <N_ag> -> <anomalygen_gaps_parquet>

[If pool missing:]
No source pool CSV at '<path>'; mining subset is empty.

Per-label breakdown (count, mining, anomalygen):
  PASS: 50 (mining=yes, anomalygen=yes)
  MISSING: 32 (mining=no, anomalygen=yes)
  SHIFT: 14 (mining=yes, anomalygen=no)
  EXCESS_SOLDER: 9 (mining=yes, anomalygen=yes)
  ...

Step 5 — Sanity checks

After both subsets are written, verify:

• The sum of subset sizes is not required to equal len(df) — overlap is allowed (a label can route to both modules). What matters is that every input row appears in at least one subset, OR appears in the "none" list with an explicit reason.
• If len(mn_df) == 0 and len(ag_df) == 0, something is wrong — flag prominently in the report.
• If an entire label group routes to no module, the Recommended Actions section must call this out so the user can either seed the source pool with that label or extend AnomalyGen's supported set.

Reference Python Recipe

This is the exact computation, lifted from mdo-kratos-workflows/pipelines/sda/routing.py. Run as a single Python script via Bash; it produces every artifact except the report.

import os
import pandas as pd

ANOMALYGEN_SUPPORTED = {"PASS", "EXCESS_SOLDER", "MISSING", "BRIDGE"}

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

# Mining subset
pool_missing = False
if source_pool_csv and os.path.isfile(source_pool_csv):
    pool_df = pd.read_csv(source_pool_csv)
    pool_labels = {str(l).upper() for l in pool_df["label"].unique()}
    mn_mask = labels_upper.isin(pool_labels)
    mn_df = df[mn_mask]
else:
    pool_missing = True
    pool_labels = set()
    mn_df = df.iloc[0:0]
os.makedirs(os.path.dirname(mining_gaps_parquet) or ".", exist_ok=True)
mn_df.to_parquet(mining_gaps_parquet, index=False)

# AnomalyGen subset
ag_mask = labels_upper.isin(ANOMALYGEN_SUPPORTED)
ag_df = df[ag_mask]
os.makedirs(os.path.dirname(anomalygen_gaps_parquet) or ".", exist_ok=True)
ag_df.to_parquet(anomalygen_gaps_parquet, index=False)

# Per-label breakdown
summary_lines = [
    "Weak-sample routing summary",
    f"Total weak samples: {len(df)}",
    f"Mining subset:      {len(mn_df)} -> {mining_gaps_parquet}",
    f"AnomalyGen subset:  {len(ag_df)} -> {anomalygen_gaps_parquet}",
    "",
]
if pool_missing:
    summary_lines.append(f"No source pool CSV at {source_pool_csv!r}; mining subset is empty.")
    summary_lines.append("")
summary_lines.append("Per-label breakdown (count, mining, anomalygen):")
label_counts = labels_upper.value_counts()
for label, count in label_counts.items():
    in_mn = (not pool_missing) and label in pool_labels
    in_ag = label in ANOMALYGEN_SUPPORTED
    summary_lines.append(
        f"  {label}: {count} "
        f"(mining={'yes' if in_mn else 'no'}, "
        f"anomalygen={'yes' if in_ag else 'no'})"
    )
summary_text = "\n".join(summary_lines) + "\n"

os.makedirs(logs_dir, exist_ok=True)
with open(os.path.join(logs_dir, "routing_summary.txt"), "w", encoding="utf-8") as f:
    f.write(summary_text)
print(summary_text.strip())

Outputs

Write everything into a timestamped folder. Any runtime packaging hook may add routing_config/ and session-capture artifacts after Routing_Report.md is written.

<output_dir>/routing_results/YYYY-MM-DD_HHMMSS/
├── Routing_Report.md           # Full routing report
├── mining_gaps.parquet         # Subset routed to k-NN Mining
├── anomalygen_gaps.parquet     # Subset routed to AnomalyGen (Cosmos SDG)
├── routing_summary.txt         # Plain-text per-label breakdown
├── routing_config/             # Auto-copied by hook
└── session log/artifacts       # Optional, runtime-dependent packaging capture

At the start of the run, get the real timestamp by running date +%Y-%m-%d_%H%M%S in Bash. If the user specifies a custom output path, use it directly but maintain the internal layout.

Report Structure

Keep the report short (400–800 words). Routing is a deterministic decision; the value is making the decisions auditable, not narrative.

# VCN Routing Report: <Iteration / Experiment Name>

## 1. Verdict
- Total weak samples in: <N>
- Mining subset:     <N_mn> rows  →  `mining_gaps.parquet`
- AnomalyGen subset: <N_ag> rows  →  `anomalygen_gaps.parquet`
- Source pool present? <yes/no — and the path>
- One-line headline: "<X> labels routed, <Y> labels dropped (no module accepted)"

## 2. Inputs
| Input | Path | Notes |
|-------|------|-------|
| gaps_parquet     | … | rows=<N>, columns=<col list> |
| source_pool_csv  | … | rows=<M> or "not provided" / "missing" |

## 3. Per-Label Routing Decisions
| Label | Count in gaps | In source pool? | Mining? | AnomalyGen? | Routed To |
|-------|----------------|------------------|----------|--------------|-----------|

(One row per distinct label in `gaps_parquet`, uppercased. `Routed To` is one of:
`mining only`, `anomalygen only`, `mining+anomalygen`, `neither (DROPPED)`.
Use `neither (DROPPED)` whenever no module accepted the label. Sort by count descending.)

## 4. Module-Level Summaries
### 4.1 k-NN Mining
- Pool labels (from source_pool_csv): <list, or "pool missing">
- Labels accepted from input: <list>
- Total rows routed: <N_mn>
- Per-label row counts: <breakdown>

### 4.2 AnomalyGen (Cosmos SDG)
- Eligible labels (configured): PASS, EXCESS_SOLDER, MISSING, BRIDGE
- Labels accepted from input: <list>
- Total rows routed: <N_ag>
- Per-label row counts: <breakdown>

## 5. Dropped Labels (routed to NEITHER module)
| Label | Count | Why dropped | Suggested fix |
|-------|-------|-------------|----------------|

(Empty table is OK and means no labels were dropped. If non-empty, every row needs a
"why" — typically one of: "not in source pool AND not in AnomalyGen supported set",
"source pool missing entirely AND label not in AnomalyGen set", "label name doesn't
match any module's expected canonicalization".)

## 6. Recommended Actions
1. **If any labels are dropped**: seed the source pool with that label, OR extend
   `ANOMALYGEN_SUPPORTED_LABELS` (and the AnomalyGen generator coverage).
2. **If source pool is missing**: provide `source_pool_csv` to enable the Mining branch.
   Without it, half of the augmentation pipeline is dark.
3. **If AnomalyGen subset is empty**: gap analysis only surfaced labels AnomalyGen cannot
   generate; rely on Mining for this iteration, or extend the AnomalyGen integration.
4. **If both subsets are empty**: stop the SDA iteration. Nothing downstream can run.

Execution Order

1. Run date +%Y-%m-%d_%H%M%S to get the timestamp; create <output_dir>/routing_results/<timestamp>/.
2. Run the Python recipe (Steps 1–4) to produce mining_gaps.parquet, anomalygen_gaps.parquet, and routing_summary.txt. Print summary stats to stdout so the script-check hook can verify it ran.
3. Build the per-label decision table by reading both parquets and computing the routed-to verdict per label.
4. Write Routing_Report.md last — writing it triggers the packaging hook, which copies session logs and skill config alongside.