stochtest 0.6.2

Stochtest is a Python library for performing statistical assertions on stochastic outputs.

stochtest

---

Stochtest is a Python library for performing statistical assertions on stochastic outputs (i.e. outputs described by probability distributions) using statistical methods. It enables automated testing for probabilistic code and Monte Carlo simulations.

Installation

pip install stochtest

Usage

1. Asserting Rates & Means

Use stochtest.assert_that(samples) to validate acceptance rates (proportions) or expected values (means).

import stochtest
import numpy as np

# --- Acceptance Rates (Proportions) ---
samples = np.random.normal(1000)

stochtest.assert_that(samples > 0).has_acceptance_rate_greater_than(0.4)
stochtest.assert_that(outcomes).has_acceptance_rate_between(0.45, 0.55)

# --- Expected Values (Means) ---
# Validated using one-sample Student's t-tests
returns = np.random.normal(1, 0.5, 1000)

stochtest.assert_that(returns).has_expected_value_greater_than(0.5)
stochtest.assert_that(returns).has_expected_value_between(0.9, 1.1)

2. Asserting Distributions

Use stochtest.distributions.assert_that(samples) to verify that an entire dataset matches a specific shape or theoretical distribution using a Bootstrapped Kolmogorov-Smirnov (KS) Test.

This asserts that the distributions are sufficiently similar by ensuring the KS distance is consistently within a specific margin with high confidence.

import numpy as np
import scipy.stats
import stochtest.distributions

samples = np.random.normal(0, 1, 1000)

# Assert samples follow a normal distribution (mean=0, std=1)
stochtest.distributions.assert_that(samples).has_normal_distribution(
    loc=0, scale=1, margin=0.05
)

# Assert samples match a custom CDF (e.g., Uniform)
stochtest.distributions.assert_that(samples).has_distribution(
    lambda x: scipy.stats.uniform.cdf(x), 
    margin=0.05
)

License

stochtest is distributed under the terms of the MIT license.