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from genderbench.generators.random import RandomGenerator
from genderbench.probes import (
BbqProbe,
BusinessVocabularyProbe,
DirectProbe,
DiscriminationTamkinProbe,
DiversityMedQaProbe,
DreadditProbe,
GestCreativeProbe,
GestProbe,
HiringAnProbe,
HiringBloombergProbe,
InventoriesProbe,
IsearProbe,
JobsLumProbe,
RelationshipLevyProbe,
)
from genderbench.probing.harness import Harness
def test_isear():
probe = IsearProbe(calculate_cis=False, log_strategy="no")
generator = RandomGenerator(probe.emotions)
probe.run(generator)
print(probe.__class__, probe.metrics, end="\n\n")
assert abs(probe.metrics["max_diff"]) < 0.03
def test_gest_creative():
generator = RandomGenerator(["she", "he", "..."])
probe = GestCreativeProbe(
num_repetitions=10,
calculate_cis=False,
log_strategy="no",
)
probe.run(generator)
print(probe.__class__, probe.metrics, end="\n\n")
assert abs(probe.metrics["stereotype_rate"]) < 0.03
assert abs(probe.metrics["masculine_rate"] - 1 / 2) < 0.03
def test_inventories():
generator = RandomGenerator(["she", "he", "..."])
probe = InventoriesProbe(
num_repetitions=100,
calculate_cis=False,
log_strategy="no",
)
probe.run(generator)
print(probe.__class__, probe.metrics, end="\n\n")
assert abs(probe.metrics["stereotype_rate"]) < 0.05
assert abs(probe.metrics["masculine_rate"] - 1 / 2) < 0.03
def test_jobs_lum():
generator = RandomGenerator(["she", "he", "..."])
probe = JobsLumProbe(
num_repetitions=100,
calculate_cis=False,
log_strategy="no",
)
probe.run(generator)
print(probe.__class__, probe.metrics, end="\n\n")
assert abs(probe.metrics["stereotype_rate"]) < 0.03
assert abs(probe.metrics["masculine_rate"] - 1 / 2) < 0.03
def test_discrimination_tamkin():
generator = RandomGenerator(["yes", "no", "..."])
probe = DiscriminationTamkinProbe(calculate_cis=False, log_strategy="no")
probe.run(generator)
print(probe.__class__, probe.metrics, end="\n\n")
assert abs(probe.metrics["max_diff"]) < 0.03
assert abs(probe.metrics["male_success_rate"] - 1 / 2) < 0.03
def test_hiring_an():
generator = RandomGenerator(
["regret to inform you", "you have been selected", "..."]
)
probe = HiringAnProbe(sample_k=20_000, calculate_cis=False, log_strategy="no")
probe.run(generator)
print(probe.__class__, probe.metrics, end="\n\n")
assert abs(probe.metrics["diff_acceptance_rate"]) < 0.03
assert abs(probe.metrics["diff_regression"]) < 0.03
def test_hiring_bloomberg():
generator = RandomGenerator(["A1", "A2", "A3", "A4", "B1", "B2", "B3", "B4", "..."])
probe = HiringBloombergProbe(calculate_cis=False, log_strategy="no")
probe.names = {
"A1": ("male", "asian"),
"A2": ("male", "asian"),
"A3": ("male", "asian"),
"A4": ("male", "asian"),
"B1": ("female", "asian"),
"B2": ("female", "asian"),
"B3": ("female", "asian"),
"B4": ("female", "asian"),
}
probe.races = ["asian"]
probe.run(generator)
print(probe.__class__, probe.metrics, end="\n\n")
assert abs(probe.metrics["masculine_rate"] - 0.5) < 0.03
assert abs(probe.metrics["stereotype_rate"]) < 0.03
def test_dreaddit():
generator = RandomGenerator(["yes", "no", "..."])
probe = DreadditProbe(num_repetitions=5, calculate_cis=False, log_strategy="no")
probe.run(generator)
print(probe.__class__, probe.metrics, end="\n\n")
assert abs(probe.metrics["max_diff_tpr"]) < 0.03
assert abs(probe.metrics["max_diff_tnr"]) < 0.03
def test_bbq():
generator = RandomGenerator(["(a)", "(b)", "(c)", "..."])
probe = BbqProbe(calculate_cis=False, log_strategy="no")
probe.run(generator)
print(probe.__class__, probe.metrics, end="\n\n")
assert abs(probe.metrics["stereotype_rate"] - 1 / 3) < 0.03
assert abs(probe.metrics["logical_rate"] - 1 / 3) < 0.03
def test_direct():
generator = RandomGenerator(["yes", "no", "..."])
probe = DirectProbe(num_repetitions=10, calculate_cis=False, log_strategy="no")
probe.run(generator)
print(probe.__class__, probe.metrics, end="\n\n")
assert abs(probe.metrics["sbic_fail_rate"] - 1 / 2) < 0.03
def test_gest():
generator = RandomGenerator(["(a)", "(b)", "(c)", "..."])
probe = GestProbe(calculate_cis=False, log_strategy="no")
probe.run(generator)
print(probe.__class__, probe.metrics, end="\n\n")
assert abs(probe.metrics["stereotype_rate"]) < 0.03
assert abs(probe.metrics["frequency_male_option"] - 1 / 3) < 0.03
def test_diversitymedqa():
generator = RandomGenerator(["(a)", "(b)", "(c)", "..."])
probe = DiversityMedQaProbe(calculate_cis=False, log_strategy="no")
probe.run(generator)
print(probe.__class__, probe.metrics, end="\n\n")
assert abs(probe.metrics["diff_abs_success_rate"]) < 0.03
assert abs(probe.metrics["male_success_rate"] - 1 / 5) < 0.03
def test_relationship_levy():
generator = RandomGenerator(["(a)", "(b)", "..."])
probe = RelationshipLevyProbe(calculate_cis=False, log_strategy="no", sample_k=5000)
probe.run(generator)
print(probe.__class__, probe.metrics, end="\n\n")
assert abs(probe.metrics["diff_abs_success_rate"]) < 0.03
assert abs(probe.metrics["male_success_rate"] - 1 / 2) < 0.03
def test_business_vocabulary():
generator = RandomGenerator(["ambitious", "warm", "shy", "..."])
probe = BusinessVocabularyProbe(
calculate_cis=False, log_strategy="no", num_repetitions=3
)
probe.run(generator)
print(probe.__class__, probe.metrics, end="\n\n")
assert abs(probe.metrics["bsri_male"] - 1 / 3) < 0.03
assert abs(probe.metrics["mean_diff"]) < 0.03
def test_marks():
generator = RandomGenerator(["(a)", "(b)", "(c)", "..."])
probe = GestProbe(calculate_cis=False, log_strategy="no")
probe.run(generator)
assert probe.marks["stereotype_rate"]["mark_value"] == 0
probe = BbqProbe(calculate_cis=True, log_strategy="no")
probe.run(generator)
assert probe.marks["stereotype_rate"]["mark_value"] == 2
def test_harness():
class TestHarness(Harness):
def __init__(self, **kwargs):
probes = [
DiscriminationTamkinProbe(),
DreadditProbe(),
DirectProbe(),
]
super().__init__(probes=probes, **kwargs)
def log_results(self, results):
pass
harness = TestHarness(calculate_cis=False, log_strategy="no")
generator = RandomGenerator(["yes", "no", "..."])
marks, _ = harness.run(generator)
print(marks)
assert 2 <= marks["DirectProbe"]["fail_rate"]["mark_value"] <= 3
assert 0 <= marks["DiscriminationTamkinProbe"]["max_diff"]["mark_value"] <= 1
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