ablevlabs 0.1.0

AbleVLabs tools. Includes lana, a weighted record-comparison engine that weighs two records attribute by attribute and tells you which wins.

ablevlabs

Python tools by AbleVLabs. The first tool in the package is lana.

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lana — Logical Attribute Node Aligner

Point lana at two records and it weighs them attribute by attribute, then tells you which one wins. It works on anything with comparable fields — job candidates, products, ML model runs, vehicles, portfolios, game characters.

Unlike a diff tool (which tells you what changed between two versions of the same thing), lana is a matchup tool: it relates two distinct records and renders a weighted verdict based on the fields you decide matter most.

Install

pip install ablevlabs

Optional, for the DataFrame export:

pip install "ablevlabs[pandas]"

Quick start

from ablevlabs import lana

laptop_a = {"name": "Aero 14",  "price": 1200, "ram_gb": 16, "battery_hrs": 10}
laptop_b = {"name": "Vortex 15", "price": 1500, "ram_gb": 32, "battery_hrs": 8}

result = lana.compare(
    laptop_a, laptop_b,
    priority={"ram_gb": 5, "battery_hrs": 2, "price": 2},  # 1-5: how much each matters
    lower_better=["price"],                                # cheaper wins
)

lana.show(result)

  Aero 14  vs  Vortex 15
  --------------------------------------------------
  battery_hrs           10 <-- 8          (priority 2)
  name             Aero 14 !=  Vortex 15
  price               1200 <-- 1500       (priority 2)
  ram_gb                16 --> 32         (priority 5)
  --------------------------------------------------
  OVERALL: Vortex 15 wins   (Aero 14 44.4%  /  Vortex 15 55.6%)

The Aero wins two fields (battery, price) — but they're lower priority. The Vortex wins only RAM, but RAM is rated 5, and that one high-priority win carries the verdict. That is the whole point of lana.

How the verdict works

Each field you list in priority is rated 1–5 (1 = barely matters, 5 = matters most). Whichever record wins a field takes its full priority points; a tie splits them. The record with more total points wins overall. The per-field margins are shown as delta, but they do not secretly sway the verdict — winning a field is winning a field.

compare() options

Argument	What it does
a, b	The two records (dicts). Required.
name_a, name_b	Labels for the output. If omitted, lana reads a name/model/id field, else uses A/B.
priority	{field: 1-5} — how much each numeric field counts toward the winner.
lower_better	List of fields where a smaller number wins (price, latency, weight).
aliases	{variant: canonical} to reconcile differing field names between records.

Getting the result out

compare() returns a plain dict. From there:

lana.show(result)         # readable scorecard (terminal)
lana.to_json(result)      # JSON string
lana.to_csv(result)       # CSV string (Excel / Sheets)
lana.to_markdown(result)  # Markdown table (docs / GitHub)
lana.to_df(result)        # pandas DataFrame (needs pandas)

lana.save(result, "out.json")   # format chosen from the extension
lana.save(result, "out.csv")
lana.save(result, "out.md")

Nested fields

If a field holds a dict, lana compares inside it — shared keys get a winner, and keys unique to one side are flagged:

a = {"stats": {"hp": 100, "atk": 20}}
b = {"stats": {"hp": 80,  "atk": 30, "def": 5}}
lana.show(lana.compare(a, b, "A", "B"))

Guardrails

• priority values must be 1–5; anything else raises a clear ValueError.
• Misspelled priority/lower_better field names emit a warning instead of silently doing nothing.
• Booleans are treated as text, not numbers (so True/False aren't scored as 1/0).

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License

MIT © AbleVLabs