prefgraph 0.6.0

Revealed preference analysis - rationality scores, efficiency indices, and utility recovery. Rust engine, Python interface.

PrefGraph

Rationality scores for every user. Rust engine, Python interface.

pip install prefgraph

Quick Example

Score how consistently each user's choices align with rational utility maximization:

from prefgraph.datasets import load_demo
from prefgraph.engine import Engine

# 100 synthetic shoppers (prices x quantities), no download needed
users = load_demo()

# Engine scores every user in parallel via Rust/Rayon
engine = Engine(metrics=["garp", "ccei", "mpi", "harp", "hm"])
results = engine.analyze_arrays(users)

for r in results[:3]:
    print(r)

EngineResult: [+] GARP-consistent  ccei=1.0000  hm=15/15  (42us)
EngineResult: [-] 3 violations  ccei=0.8472  mpi=0.0231  hm=12/15  (38us)
EngineResult: [+] GARP-consistent  ccei=1.0000  hm=15/15  (35us)

Every score is a feature you can plug into downstream models. In our benchmarks across 11 e-commerce datasets, revealed preference features rank among the top ten by model importance but add near-zero marginal lift over well-constructed baselines. See the case studies for details.

Your Data

Have a DataFrame? One line:

import prefgraph as rp

# Transaction logs (one row per user x time x item)
results = rp.analyze(df, user_col="user_id", item_col="product",
                     cost_col="price", action_col="quantity", time_col="week")

# Wide format (one row per observation, goods as columns)
results = rp.analyze(df, user_col="user_id",
                     cost_cols=["price_A", "price_B"],
                     action_cols=["qty_A", "qty_B"])

# Menu/click data (which items were shown, which was picked)
results = rp.analyze(df, user_col="user_id",
                     menu_col="shown_items", choice_col="clicked")

Returns a DataFrame with one row per user. Customize with metrics=["garp", "ccei", "mpi", "hm"].

Scores

Score	Field	What it measures	Range
Consistency	is_garp	Are choices rationalizable? (GARP)	bool
Efficiency	ccei	How close to perfectly rational? (Afriat)	0-1
Exploitability	mpi	Value left on the table per choice (Karp cycle)	0-1
Homotheticity	is_harp	Do preferences scale with budget?	bool
Rationalizable fraction	hm_consistent/hm_total	Fraction of rationalizable choices (Houtman-Maks)	0-1
Utility recovery	utility_success	Can latent utility be reconstructed? (Afriat LP)	bool
Per-obs efficiency	vei_mean	Average efficiency across observations (Varian)	0-1

Which API?

	Engine	Function API
Use case	Score thousands of users	Deep-dive one user
Speed	2,000-49,000 users/sec (Rust)	Single-user
Returns	EngineResult (flat scores)	GARPResult, AEIResult, etc. (matrices, cycles, graphs)
Metrics	7 (garp, ccei, mpi, harp, hm, utility, vei) + network	30+ algorithms
Input	list[(prices, quantities)]	BehaviorLog

Engine for batch scoring. Function API when you need violation details, observation/item graphs, or advanced tests:

from prefgraph import BehaviorLog, validate_consistency, compute_integrity_score

# 3 shopping trips, 2 goods
session = BehaviorLog(cost_vectors=prices, action_vectors=quantities)

# GARP: does a consistent utility function exist?
garp = validate_consistency(session)       # GARPResult with violation cycles, matrices

# CCEI: how much must budgets shrink to remove contradictions?
ccei = compute_integrity_score(session)    # AEIResult with binary search details

Budget and Menu Choices

PrefGraph supports two primary choice domains:

Category	Input format	Example domain	Key tests
Budget	(prices T x K, quantities T x K)	E-commerce, grocery, food delivery	GARP, CCEI, MPI, HM, HARP, VEI
Discrete	(menus, choices) or (menus, frequencies)	Surveys, A/B tests, recommendations, LLM eval	SARP, WARP, HM, RUM LP, IIA

Performance

The Rust engine (rpt-core) handles graph algorithms and LP solving via Rayon thread pool. Python handles I/O and the user-facing API.

Configuration	Throughput	10K users	100K users
GARP only	~49,000/s	0.1s	2.0s
GARP + CCEI	~2,400/s	4.2s	39.5s
Comprehensive (GARP, CCEI, MPI, HARP)	~2,000/s	6.8s	67.1s
Menu (SARP + WARP + HM)	~1,900/s	0.3s	5.2s

Memory stays flat via streaming chunks.

Documentation

Full docs at prefgraph.readthedocs.io:

• Loading Data - Parquet, DataFrame, synthetic generators
• Case Studies - LLM consistency, e-commerce benchmarks
• Algorithms - Implementation details
• API Reference - Full method reference

License

MIT