pyrevealed 0.4.2

Behavioral Signal Analysis for User Understanding - Detect bots, shared accounts, and UI confusion

PyRevealed

Production-ready revealed preference analysis. Test if choices are internally consistent, quantify behavioral consistency, and analyze decision patterns.

Based on: Chambers, C. P., & Echenique, F. (2016). Revealed Preference Theory. Cambridge University Press.

Installation

pip install pyrevealed

For visualization support:

pip install pyrevealed[viz]

Quick Start

from pyrevealed import BehaviorLog, validate_consistency, compute_integrity_score
import numpy as np

# Two purchase observations: prices and quantities
log = BehaviorLog(
    cost_vectors=np.array([[1.0, 2.0], [2.0, 1.0]]),
    action_vectors=np.array([[3.0, 1.0], [1.0, 3.0]])
)

# Check if choices are consistent with utility maximization
is_consistent = validate_consistency(log)  # True

# Get integrity score (0 = inconsistent, 1 = perfectly consistent)
result = compute_integrity_score(log)
print(f"Integrity: {result.efficiency_index:.2f}")  # 1.00

Core Functions

Function	Returns	Score Meaning
validate_consistency(log)	bool	True = rational
compute_integrity_score(log)	AEIResult (0-1)	1 = perfect
compute_confusion_metric(log)	MPIResult (0-1)	0 = no cycles
fit_latent_values(log)	UtilityRecoveryResult	Utility values
compute_minimal_outlier_fraction(log)	HoutmanMaksResult (0-1)	0 = all consistent

Quick interpretation: Integrity >= 0.95 is excellent, >= 0.90 is good, < 0.70 indicates problems.

Menu-Based Choice

For discrete choices without prices (surveys, recommendations, voting):

from pyrevealed import MenuChoiceLog, validate_menu_sarp, fit_menu_preferences

log = MenuChoiceLog(
    menus=[frozenset({0, 1, 2}), frozenset({1, 2}), frozenset({0, 2})],
    choices=[0, 1, 0],
    item_labels=["Pizza", "Burger", "Salad"]
)

result = validate_menu_sarp(log)
print(f"Consistent: {result.is_consistent}")

prefs = fit_menu_preferences(log)
print(f"Preference order: {prefs.preference_order}")

Documentation

Full Documentation

Tutorials

1. Budget-Based Analysis - GARP, CCEI, MPI, Bronars power
2. Menu-Based Choice - WARP, SARP, attention models
3. Welfare Analysis - CV, EV, deadweight loss
4. Demand Analysis - Slutsky matrix, integrability
5. Stochastic & Production - Logit, IIA, firm behavior
6. E-Commerce at Scale - 1.85M Amazon transactions

Features

• Consistency Testing: GARP, WARP, SARP axiom verification
• Behavioral Metrics: Afriat Efficiency Index, Money Pump Index
• Utility Recovery: Reconstruct utility functions from choices
• ML Integration: sklearn-compatible PreferenceEncoder
• Multiple Data Types: Budgets, menus, stochastic choice, production
• Production Ready: Fast parallel processing, validated against R's revealedPrefs

License

MIT