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Secure Source of Randomness #464

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Sep 20, 2024
Merged

Secure Source of Randomness #464

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e6b1d45
Secure Source of Randomness
pixeebot[bot] Sep 11, 2024
cfab3f6
[pre-commit.ci] auto fixes from pre-commit.com hooks
pre-commit-ci[bot] Sep 11, 2024
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18 changes: 9 additions & 9 deletions integration_test/test_xgboost_experiment.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3,7 +3,7 @@
# SPDX-License-Identifier: MIT
import copy
import os
import random
import secrets

import pytest

Expand Down Expand Up @@ -33,14 +33,14 @@ class TestXGBoostExperiment:

def _generate_randomized_search_space(self):
search_space = copy.deepcopy(SEARCH_SPACES)
if random.randint(0, 1) == 1: # add bounds and type to eta randomly
if secrets.SystemRandom().randint(0, 1) == 1: # add bounds and type to eta randomly
search_space[0]["type"] = "double"
search_space[0]["bounds"] = {"min": 0.1, "max": 0.5}
if random.randint(0, 1) == 1: # add bounds and type to min_child_weight randomly
if secrets.SystemRandom().randint(0, 1) == 1: # add bounds and type to min_child_weight randomly
search_space[1]["type"] = "double"
search_space[1]["bounds"] = {"min": 0.0, "max": 0.3}
random_subset_size = random.randint(1, len(search_space))
search_space = random.sample(search_space, random_subset_size)
random_subset_size = secrets.SystemRandom().randint(1, len(search_space))
search_space = secrets.SystemRandom().sample(search_space, random_subset_size)
if not any(p["name"] in ["eta", "min_child_weight"] for p in search_space):
search_space.append(SEARCH_SPACES[0])
return search_space
Expand All @@ -49,9 +49,9 @@ def _form_random_experiment_config(self, task):
experiment_params = _form_random_run_params(task)
is_classification = bool(task in ("binary", "multiclass"))
if is_classification:
metric_to_optimize = random.choice(CLASSIFICATION_METRIC_CHOICES)
metric_to_optimize = secrets.choice(CLASSIFICATION_METRIC_CHOICES)
else:
metric_to_optimize = random.choice(REGRESSION_METRIC_CHOICES)
metric_to_optimize = secrets.choice(REGRESSION_METRIC_CHOICES)
search_space = self._generate_randomized_search_space()

for param in search_space:
Expand All @@ -65,9 +65,9 @@ def _form_random_experiment_config(self, task):
"parameters": search_space,
"metrics": [{"name": metric_to_optimize, "strategy": "optimize", "objective": "maximize"}],
"parallel_bandwidth": 1,
"budget": random.randint(1, 3),
"budget": secrets.SystemRandom().randint(1, 3),
}
if random.randint(0, 1) == 0:
if secrets.SystemRandom().randint(0, 1) == 0:
del experiment_config["metrics"]

experiment_params["experiment_config"] = experiment_config
Expand Down
50 changes: 25 additions & 25 deletions integration_test/test_xgboost_run.py
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Original file line number Diff line number Diff line change
Expand Up @@ -6,7 +6,7 @@
import math
import os
import platform
import random
import secrets
from inspect import signature

import pytest
Expand All @@ -32,20 +32,20 @@


POSSIBLE_PARAMETERS = {
"eta": 10 ** random.uniform(-4, 1),
"gamma": random.uniform(0, 4),
"max_depth": random.randint(1, 5),
"min_child_weight": random.uniform(0, 3),
"lambda": random.uniform(1, 3),
"alpha": 10 ** random.uniform(-4, 0),
"tree_method": random.choice(["hist", "exact", "approx", "auto"]),
"eta": 10 ** secrets.SystemRandom().uniform(-4, 1),
"gamma": secrets.SystemRandom().uniform(0, 4),
"max_depth": secrets.SystemRandom().randint(1, 5),
"min_child_weight": secrets.SystemRandom().uniform(0, 3),
"lambda": secrets.SystemRandom().uniform(1, 3),
"alpha": 10 ** secrets.SystemRandom().uniform(-4, 0),
"tree_method": secrets.choice(["hist", "exact", "approx", "auto"]),
}


def _create_random_dataset(task="binary"):
if task == "binary":
n_samples = random.randint(180, 300)
n_features = random.randint(5, 25)
n_samples = secrets.SystemRandom().randint(180, 300)
n_features = secrets.SystemRandom().randint(5, 25)
n_classes = 2

return datasets.make_classification(
Expand All @@ -54,10 +54,10 @@ def _create_random_dataset(task="binary"):
n_classes=n_classes,
)
elif task == "multiclass":
n_samples = random.randint(180, 300)
n_classes = random.randint(3, 8)
n_informative = random.randint(2 * n_classes, 20)
n_features = random.randint(n_informative + 2, 40)
n_samples = secrets.SystemRandom().randint(180, 300)
n_classes = secrets.SystemRandom().randint(3, 8)
n_informative = secrets.SystemRandom().randint(2 * n_classes, 20)
n_features = secrets.SystemRandom().randint(n_informative + 2, 40)

return datasets.make_classification(
n_samples=n_samples,
Expand All @@ -66,9 +66,9 @@ def _create_random_dataset(task="binary"):
n_classes=n_classes,
)
else:
n_samples = random.randint(200, 500)
n_features = random.randint(50, 100)
n_informative = random.randint(10, n_features - 2)
n_samples = secrets.SystemRandom().randint(200, 500)
n_features = secrets.SystemRandom().randint(50, 100)
n_informative = secrets.SystemRandom().randint(10, n_features - 2)

return datasets.make_regression(
n_samples=n_samples,
Expand All @@ -81,17 +81,17 @@ def _create_random_dataset(task="binary"):
def _create_random_metric_objective(task="binary"):
if task == "binary":
return {
"objective": random.choice(["binary:logistic", "binary:hinge", "binary:logitraw"]),
"objective": secrets.choice(["binary:logistic", "binary:hinge", "binary:logitraw"]),
"eval_metric": ["logloss", "aucpr", "error"],
}
elif task == "multiclass":
return {
"objective": random.choice(["multi:softmax", "multi:softprob"]),
"objective": secrets.choice(["multi:softmax", "multi:softprob"]),
"eval_metric": ["mlogloss", "merror"],
}
else:
return {
"objective": random.choice(["reg:squarederror", "reg:pseudohubererror"]),
"objective": secrets.choice(["reg:squarederror", "reg:pseudohubererror"]),
"eval_metric": ["rmse", "mae", "mape"],
}

Expand All @@ -103,8 +103,8 @@ def _form_random_run_params(task):
D_test = xgb.DMatrix(X_test, label=Y_test)

possible_params = POSSIBLE_PARAMETERS
random_subset_size = random.randint(1, len(possible_params))
subset_keys = random.sample(possible_params.keys(), random_subset_size)
random_subset_size = secrets.SystemRandom().randint(1, len(possible_params))
subset_keys = secrets.SystemRandom().sample(possible_params.keys(), random_subset_size)
subset_params = {k: possible_params[k] for k in subset_keys}
subset_params.update(_create_random_metric_objective(task))
if task == "multiclass":
Expand All @@ -115,9 +115,9 @@ def _form_random_run_params(task):
return dict(
params=subset_params,
dtrain=D_train,
evals=[(D_test, f"test{n}") for n in range(random.randint(1, 3))],
num_boost_round=random.randint(3, 15),
verbose_eval=random.choice([True, False]),
evals=[(D_test, f"test{n}") for n in range(secrets.SystemRandom().randint(1, 3))],
num_boost_round=secrets.SystemRandom().randint(3, 15),
verbose_eval=secrets.choice([True, False]),
run_options=run_options,
)

Expand Down
4 changes: 2 additions & 2 deletions sigopt/ratelimit.py
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Original file line number Diff line number Diff line change
@@ -1,7 +1,7 @@
# Copyright © 2022 Intel Corporation
#
# SPDX-License-Identifier: MIT
import random
import secrets
import threading
import time

Expand All @@ -18,7 +18,7 @@ def increment_and_check(self):
multiples_over = self.count // self.limit
if multiples_over:
quadratic_backoff = multiples_over**2
jitter = random.random() * 2
jitter = secrets.SystemRandom().random() * 2
time.sleep(quadratic_backoff + jitter)

def clear(self):
Expand Down