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test_crossplag_detect.py
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test_crossplag_detect.py
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#!/usr/bin/env python3
import pytest, os, jsonlines
from warnings import warn
from crossplag_detect import run_on_file_chunked, run_on_text_chunked
AI_SAMPLE_DIR = 'samples/llm-generated/'
HUMAN_SAMPLE_DIR = 'samples/human-generated/'
MIN_LEN = 150
NUM_JSONL_SAMPLES = 500
ai_files = os.listdir(AI_SAMPLE_DIR)
human_files = os.listdir(HUMAN_SAMPLE_DIR)
CONFIDENCE_THRESHOLD : float = 0.00 # What confidence to treat as error vs warning
def test_training_file(record_property):
res = run_on_file_chunked('ai-generated.txt')
if res is None:
pytest.skip('Unable to classify')
(classification, score) = res
record_property("score", str(score))
assert classification == 'AI', 'The training corpus should always be detected as AI-generated... since it is (score: ' + str(round(score, 8)) + ')'
@pytest.mark.parametrize('f', human_files)
def test_human_samples(f, record_property):
res = run_on_file_chunked(HUMAN_SAMPLE_DIR + f)
if res is None:
pytest.skip('Unable to classify')
(classification, score) = res
record_property("score", str(score))
if score > CONFIDENCE_THRESHOLD:
assert classification == 'Human', f + ' is a human-generated file, misclassified as AI-generated with confidence ' + str(round(score, 8))
else:
if classification != 'Human':
warn("Misclassified " + f + " with score of: " + str(round(score, 8)))
else:
warn("Unable to confidently classify: " + f)
@pytest.mark.parametrize('f', ai_files)
def test_llm_sample(f, record_property):
res = run_on_file_chunked(AI_SAMPLE_DIR + f)
if res is None:
pytest.skip('Unable to classify')
(classification, score) = res
record_property("score", str(score))
if score > CONFIDENCE_THRESHOLD:
assert classification == 'AI', f + ' is an LLM-generated file, misclassified as human-generated with confidence ' + str(round(score, 8))
else:
if classification != 'AI':
warn("Misclassified " + f + " with score of: " + str(round(score, 8)))
else:
warn("Unable to confidently classify: " + f)
HUMAN_JSONL_FILE = 'samples/webtext.test.jsonl'
human_samples = []
with jsonlines.open(HUMAN_JSONL_FILE) as reader:
for obj in reader:
human_samples.append(obj)
@pytest.mark.parametrize('i', human_samples[0:NUM_JSONL_SAMPLES])
def test_human_jsonl(i, record_property):
res = run_on_text_chunked(i.get('text', ''))
if res is None:
pytest.skip('Unable to classify')
(classification, score) = res
record_property("score", str(score))
assert classification == 'Human', HUMAN_JSONL_FILE + ':' + str(i.get('id')) + ' (len: ' + str(i.get('length', -1)) + ') is a human-generated sample, misclassified as AI-generated with confidence ' + str(round(score, 8))
AI_JSONL_FILE = 'samples/xl-1542M.test.jsonl'
ai_samples = []
with jsonlines.open(AI_JSONL_FILE) as reader:
for obj in reader:
ai_samples.append(obj)
@pytest.mark.parametrize('i', ai_samples[0:NUM_JSONL_SAMPLES])
def test_llm_jsonl(i, record_property):
res = run_on_text_chunked(i.get('text', ''))
if res is None:
pytest.skip('Unable to classify')
(classification, score) = res
record_property("score", str(score))
assert classification == 'AI', AI_JSONL_FILE + ':' + str(i.get('id')) + ' (text: ' + i.get('text', "").replace('\n', ' ')[:50] + ') is an LLM-generated sample, misclassified as human-generated with confidence ' + str(round(score, 8))
GPT3_JSONL_FILE = 'samples/GPT-3-175b_samples.jsonl'
gpt3_samples = []
with jsonlines.open(GPT3_JSONL_FILE) as reader:
for o in reader:
for l in o.split('<|endoftext|>'):
if len(l) >= MIN_LEN:
gpt3_samples.append(l)
@pytest.mark.parametrize('i', gpt3_samples[0:NUM_JSONL_SAMPLES])
def test_gpt3_jsonl(i, record_property):
res = run_on_text_chunked(i)
if res is None:
pytest.skip('Unable to classify')
(classification, score) = res
record_property("score", str(score))
assert classification == 'AI', GPT3_JSONL_FILE + ' is an LLM-generated sample, misclassified as human-generated with confidence ' + str(round(score, 8))
NEWS_JSONL_FILE = 'samples/news.jsonl'
news_samples = []
with jsonlines.open(NEWS_JSONL_FILE) as reader:
for obj in reader:
news_samples.append(obj)
@pytest.mark.parametrize('i', news_samples[0:NUM_JSONL_SAMPLES])
def test_humannews_jsonl(i, record_property):
res = run_on_text_chunked(i.get('human', ''))
if res is None:
pytest.skip('Unable to classify')
(classification, score) = res
record_property("score", str(score))
assert classification == 'Human', NEWS_JSONL_FILE + ' is a human-generated sample, misclassified as AI-generated with confidence ' + str(round(score, 8))
@pytest.mark.parametrize('i', news_samples[0:NUM_JSONL_SAMPLES])
def test_chatgptnews_jsonl(i, record_property):
res = run_on_text_chunked(i.get('chatgpt', ''))
if res is None:
pytest.skip('Unable to classify')
(classification, score) = res
record_property("score", str(score))
assert classification == 'AI', NEWS_JSONL_FILE + ' is a AI-generated sample, misclassified as human-generated with confidence ' + str(round(score, 8))
CHEAT_HUMAN_JSONL_FILE = 'samples/ieee-init.jsonl'
ch_samples = []
with jsonlines.open(CHEAT_HUMAN_JSONL_FILE) as reader:
for obj in reader:
if len(obj.get('abstract', '')) >= MIN_LEN:
ch_samples.append(obj)
@pytest.mark.parametrize('i', ch_samples[0:NUM_JSONL_SAMPLES])
def test_cheat_human_jsonl(i, record_property):
res = run_on_text_chunked(i.get('abstract', ''))
if res is None:
pytest.skip('Unable to classify')
(classification, score) = res
record_property("score", str(score))
assert classification == 'Human', CHEAT_HUMAN_JSONL_FILE + ':' + str(i.get('id')) + ' [' + str(len(i.get('abstract', ''))) + '] (title: ' + i.get('title', "").replace('\n', ' ')[:15] + ') is a human-generated sample, misclassified as AI-generated with confidence ' + str(round(score, 8))
CHEAT_GEN_JSONL_FILE = 'samples/ieee-chatgpt-generation.jsonl'
cg_samples = []
with jsonlines.open(CHEAT_GEN_JSONL_FILE) as reader:
for obj in reader:
if len(obj.get('abstract', '')) >= MIN_LEN:
cg_samples.append(obj)
@pytest.mark.parametrize('i', cg_samples[0:NUM_JSONL_SAMPLES])
def test_cheat_generation_jsonl(i, record_property):
res = run_on_text_chunked(i.get('abstract', ''))
if res is None:
pytest.skip('Unable to classify')
(classification, score) = res
record_property("score", str(score))
assert classification == 'AI', CHEAT_GEN_JSONL_FILE + ':' + str(i.get('id')) + ' (title: ' + i.get('title', "").replace('\n', ' ')[:50] + ') is an LLM-generated sample, misclassified as human-generated with confidence ' + str(round(score, 8))
CHEAT_POLISH_JSONL_FILE = 'samples/ieee-chatgpt-polish.jsonl'
cp_samples = []
with jsonlines.open(CHEAT_POLISH_JSONL_FILE) as reader:
for obj in reader:
if len(obj.get('abstract', '')) >= MIN_LEN:
cp_samples.append(obj)
@pytest.mark.parametrize('i', cp_samples[0:NUM_JSONL_SAMPLES])
def test_cheat_polish_jsonl(i, record_property):
res = run_on_text_chunked(i.get('abstract', ''))
if res is None:
pytest.skip('Unable to classify')
(classification, score) = res
record_property("score", str(score))
assert classification == 'AI', CHEAT_POLISH_JSONL_FILE + ':' + str(i.get('id')) + ' (title: ' + i.get('title', "").replace('\n', ' ')[:50] + ') is an LLM-generated sample, misclassified as human-generated with confidence ' + str(round(score, 8))