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Test det #204

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d020404
Make more tests use a deterministic rng
cassiersg Jun 24, 2025
79e858e
README
cassiersg Jun 24, 2025
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2 changes: 2 additions & 0 deletions CHANGELOG.rst
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Expand Up @@ -5,6 +5,8 @@ Changelog
Not released
------------

* Make more tests deterministic (#204).

v0.6.4 (2025/06/24)
-------------------

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3 changes: 1 addition & 2 deletions README.rst
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Expand Up @@ -69,8 +69,7 @@ To install from source:

.. code-block::

git clone https://github.com/simple-crypto/SCALib
pip install ./SCALib
pip install scalib --no-binary scalib

See `CONTRIBUTING.rst <CONTRIBUTING.rst>`__ for advanced build configuration.

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18 changes: 10 additions & 8 deletions tests/test_lda.py
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Expand Up @@ -450,16 +450,18 @@ def test_simple_format_check():
pois = [[i * npois + e for e in range(npois)] for i in range(nv)]
ns = npois * nv

rng = get_rng()

# Wrong traces shape
traces = np.random.randint(0, maxt, (n, npois - 1), dtype=np.int16)
x = np.random.randint(0, nc, (n, nv), dtype=np.uint16)
traces = rng.integers(0, maxt, (n, npois - 1), dtype=np.int16)
x = rng.integers(0, nc, (n, nv), dtype=np.uint16)
lda_acc = LdaAcc(nc=nc, pois=pois)
with pytest.raises(ScalibError, match="POI out of bounds."):
lda_acc.fit_u(traces, x)

# Wrong labels shape [too much variables]
traces = np.random.randint(0, maxt, (n, ns), dtype=np.int16)
x = np.random.randint(0, nc, (n, nv + 1), dtype=np.uint16)
traces = rng.integers(0, maxt, (n, ns), dtype=np.int16)
x = rng.integers(0, nc, (n, nv + 1), dtype=np.uint16)
lda_acc = LdaAcc(nc=nc, pois=pois)
expected_error_msg = (
"Number of variables {} does not match previously-fitted classes*".format(
Expand All @@ -473,8 +475,8 @@ def test_simple_format_check():
# Not tested, would imply significant impact on performances

# Validate matrices shape
traces = np.random.randint(0, maxt, (n, ns), dtype=np.int16)
x = np.random.randint(0, nc, (n, nv), dtype=np.uint16)
traces = rng.integers(0, maxt, (n, ns), dtype=np.int16)
x = rng.integers(0, nc, (n, nv), dtype=np.uint16)
lda_acc = LdaAcc(nc=nc, pois=pois)
lda_acc.fit_u(traces, x)
mus = lda_acc.get_mus()
Expand All @@ -496,7 +498,7 @@ def test_simple_format_check():
lda = Lda(lda_acc, p=p)

# Wrong new traces for predictions
new_traces = np.random.randint(0, 256, (20, ns + 1), dtype=np.int16)
new_traces = rng.integers(0, 256, (20, ns + 1), dtype=np.int16)
e_err_msg = "Traces length {} does not match previously-fitted traces*".format(
ns + 1
)
Expand All @@ -505,7 +507,7 @@ def test_simple_format_check():

# Validate probas shape
n_new = 20
new_traces = np.random.randint(0, 256, (n_new, ns), dtype=np.int16)
new_traces = rng.integers(0, 256, (n_new, ns), dtype=np.int16)
pr = lda.predict_proba(new_traces)
assert len(pr) == nv
for prs in pr:
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21 changes: 15 additions & 6 deletions tests/test_postprocessing.py
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@@ -1,9 +1,10 @@
import pytest
import numpy as np
import random
from scalib.postprocessing import rank_accuracy
import os

from utils_test import get_rng

static_probs = [
[0.5, 0.25, 0.25, 0.125],
[0.5, 0.25, 0.125, 0.25],
Expand All @@ -28,9 +29,10 @@ def test_rank_accuracy_hist():
nc = 256
nsubkeys = 4
acc = 0.2
rng = get_rng()

costs = np.zeros((nsubkeys, nc)) + 0.1
secret_key = np.random.randint(0, nc, nsubkeys)
secret_key = rng.integers(0, nc, (nsubkeys,))
costs[np.arange(nsubkeys), secret_key] = 1.0

rmin, r, rmax = rank_accuracy(
Expand All @@ -47,12 +49,13 @@ def test_rank_accuracy_scaled_vs_hist():
nsubkeys = 16
max_error = 0.5
k_probs = np.zeros((nsubkeys, nc))
rng = get_rng()

secret_key = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]

for j in range(nsubkeys):
for i in range(nc):
k_probs[j][i] = 1 / random.randint(2, 20)
k_probs[j][i] = 1 / rng.integers(2, 20, (1,))[0]

rmin, r, rmax = rank_accuracy(-np.log10(k_probs), secret_key, method="hist")
lrmin, lr, lrmax = (np.log2(rmin), np.log2(r), np.log2(rmax))
Expand All @@ -69,8 +72,10 @@ def test_rank_accuracy():
nsubkeys = 4
acc = 0.2

rng = get_rng()

costs = np.zeros((nsubkeys, nc)) + 0.1
secret_key = np.random.randint(0, nc, nsubkeys)
secret_key = rng.integers(0, nc, nsubkeys)
costs[np.arange(nsubkeys), secret_key] = 1.0

rmin, r, rmax = rank_accuracy(-np.log10(costs), secret_key, acc_bit=acc)
Expand All @@ -87,11 +92,13 @@ def test_rank_accuracy_scaled_vs_ntl():
max_error = 0.5
k_probs = np.zeros((nsubkeys, nc))

rng = get_rng()

secret_key = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]

for j in range(nsubkeys):
for i in range(nc):
k_probs[j][i] = 1 / random.randint(2, 20)
k_probs[j][i] = 1 / rng.integers(2, 20, 1)[0]

rmin, r, rmax = rank_accuracy(-np.log10(k_probs), secret_key, method="histbignum")
lrmin, lr, lrmax = (np.log2(rmin), np.log2(r), np.log2(rmax))
Expand All @@ -113,10 +120,12 @@ def test_rank_accuracy_scaled_edge_cases():
k_probs = np.zeros((nsubkeys, nc))
secret_key = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]

rng = get_rng()

for j in range(nsubkeys):
for i in range(nc):
if i < 6:
k_probs[j][i] = 1 / random.randint(2, 5)
k_probs[j][i] = 1 / rng.integers(2, 5, 1)[0]
else:
k_probs[j][i] = 1 / 16

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