PyRevealed

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

pip install pyrevealed

Quick Example

Score how consistently each user's choices align with rational utility maximization. Paste and run:

from pyrevealed.datasets import load_demo
from pyrevealed.engine import Engine

users = load_demo()  # 100 synthetic consumers, no download needed
engine = Engine(metrics=["garp", "ccei", "mpi", "harp", "hm"])
results = engine.analyze_arrays(users)

for r in results[:5]:
    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. Use them for fraud detection, user segmentation, A/B testing, churn prediction, or personalization.

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
Noise 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	10,000+ users/sec (Rust)	Single-user
Returns	`EngineResult` (flat scores)	`GARPResult`, `AEIResult`, etc. (matrices, cycles, graphs)
Metrics	7 (garp, ccei, mpi, harp, hm, utility, vei)	30+ algorithms
Input	`list[(prices, quantities)]`	`BehaviorLog`

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

from pyrevealed import BehaviorLog, validate_consistency, compute_integrity_score
session = BehaviorLog(cost_vectors=prices, action_vectors=quantities)
garp = validate_consistency(session)       # GARPResult with violation cycles, matrices
ccei = compute_integrity_score(session)    # AEIResult with binary search details

4 Choice Categories

                Test (bool)     Score (0-1)     Recover (vector)  Structure (bool)
Budget          GARP, WARP      CCEI, MPI, HM   Utility, CV/EV    HARP, Separability
Discrete        SARP, RUM LP    HM (menu)        Ordinal utility   Congruence
Production      Prod GARP       Prod CCEI        Tech efficiency   Cost minimization
Intertemporal   Exp discount    —                Discount delta    Quasi-hyperbolic

Category	Input format	Example domain
Budget	`(prices T×K, quantities T×K)`	E-commerce, grocery, food delivery
Discrete	`(menus, choices)` or `(menus, frequencies)`	Surveys, A/B tests, recommendations
Production	`(input_p, input_q, output_p, output_q)`	Supply chain, manufacturing
Intertemporal	`(amounts, dates, chosen)`	Subscriptions, savings, loyalty

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.

Users	Metrics	Time	Throughput
1,000	5	0.1s	10,000/s
10,000	5	2s	5,000/s
100,000	5	20s	5,000/s

18-100x faster than pure Python. Memory stays bounded via streaming chunks.

Documentation

Full docs — tutorials, theory, API reference, application examples.

License

MIT

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