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### pyhegp --- Homomorphic encryption of genotypes and phenotypes
### Copyright © 2025 Arun Isaac <arunisaac@systemreboot.net>
###
### This file is part of pyhegp.
###
### pyhegp is free software: you can redistribute it and/or modify it
### under the terms of the GNU General Public License as published by
### the Free Software Foundation, either version 3 of the License, or
### (at your option) any later version.
###
### pyhegp is distributed in the hope that it will be useful, but
### WITHOUT ANY WARRANTY; without even the implied warranty of
### MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
### General Public License for more details.
###
### You should have received a copy of the GNU General Public License
### along with pyhegp. If not, see <https://www.gnu.org/licenses/>.
import math
from hypothesis import given, settings, strategies as st
from hypothesis.extra.numpy import arrays, array_shapes
import numpy as np
from pytest import approx
from pyhegp.pyhegp import Stats, hegp_encrypt, hegp_decrypt, random_key, pool_stats, standardize, unstandardize
from pyhegp.utils import negate
@given(st.lists(st.lists(arrays("float64",
st.shared(array_shapes(min_dims=1, max_dims=1),
key="pool-vector-length"),
elements=st.floats(min_value=-100, max_value=100)),
min_size=2),
min_size=1))
def test_pool_stats(pools):
combined_pool = sum(pools, [])
pooled_stats = pool_stats([Stats(len(pool),
np.mean(pool, axis=0),
np.std(pool, axis=0, ddof=1))
for pool in pools])
assert (pooled_stats.n == len(combined_pool)
and pooled_stats.mean == approx(np.mean(combined_pool, axis=0),
rel=1e-6)
and pooled_stats.std == approx(np.std(combined_pool, axis=0, ddof=1),
rel=1e-6))
def no_column_zero_standard_deviation(matrix):
return not np.any(np.isclose(np.std(matrix, axis=0), 0))
@given(st.one_of(
arrays("int32",
array_shapes(min_dims=2, max_dims=2, min_side=2),
elements=st.integers(min_value=0, max_value=2)),
# The array above is the only realistic input, but we test more
# kinds of inputs for good measure.
arrays("int32",
array_shapes(min_dims=2, max_dims=2, min_side=2),
elements=st.integers(min_value=0, max_value=100)),
arrays("float64",
array_shapes(min_dims=2, max_dims=2, min_side=2),
elements=st.floats(min_value=0, max_value=100))))
def test_hegp_encryption_decryption_are_inverses(plaintext):
rng = np.random.default_rng()
key = random_key(rng, len(plaintext))
assert hegp_decrypt(hegp_encrypt(plaintext, key), key) == approx(plaintext)
@given(arrays("float64",
array_shapes(min_dims=2, max_dims=2),
elements=st.floats(min_value=0, max_value=100))
# Reject matrices with zero standard deviation columns since
# they trigger a division by zero.
.filter(no_column_zero_standard_deviation))
def test_standardize_unstandardize_are_inverses(matrix):
mean = np.mean(matrix, axis=0)
standard_deviation = np.std(matrix, axis=0)
assert unstandardize(standardize(matrix, mean, standard_deviation),
mean, standard_deviation) == approx(matrix)
def square_matrices(order, elements=None):
def generate(draw):
n = draw(order)
return draw(arrays("float64", (n, n), elements=elements))
return st.composite(generate)
def is_singular(matrix):
# We want to avoid nearly singular matrices as well. Hence, we set
# a looser absolute tolerance.
return math.isclose(np.linalg.det(matrix), 0, abs_tol=1e-6)
@given(square_matrices(st.shared(st.integers(min_value=2, max_value=7),
key="n"),
elements=st.floats(min_value=0, max_value=10))()
.filter(negate(is_singular)),
arrays("float64",
st.shared(st.integers(min_value=2, max_value=7),
key="n"),
elements=st.floats(min_value=0, max_value=10)))
def test_conservation_of_solutions(genotype, phenotype):
rng = np.random.default_rng()
key = random_key(rng, len(genotype))
assert (approx(np.linalg.solve(genotype, phenotype),
abs=1e-6, rel=1e-6)
== np.linalg.solve(hegp_encrypt(genotype, key),
hegp_encrypt(phenotype, key)))
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