kopia lustrzana https://github.com/thinkst/zippy
207 wiersze
10 KiB
Python
207 wiersze
10 KiB
Python
#!/usr/bin/env python3
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import pytest, os, jsonlines, csv
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from warnings import warn
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from zippy.zippy import Zippy, EnsembledZippy, PRELUDE_STR, LzmaLlmDetector, BrotliLlmDetector, ZlibLlmDetector, CompressionEngine
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import zippy.zippy
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AI_SAMPLE_DIR = 'samples/llm-generated/'
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HUMAN_SAMPLE_DIR = 'samples/human-generated/'
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MIN_LEN = 150
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NUM_JSONL_SAMPLES = 500
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ai_files = os.listdir(AI_SAMPLE_DIR)
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human_files = os.listdir(HUMAN_SAMPLE_DIR)
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CONFIDENCE_THRESHOLD : float = 0.00 # What confidence to treat as error vs warning
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# Bool on whether to ensemble the models or run a single model
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ENSEMBLE = False
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if not ENSEMBLE:
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# What compression engine to use for the test
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ENGINE = CompressionEngine.LZMA
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if os.environ.get('ZIPPY_PRESET') != None:
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if ENGINE == CompressionEngine.LZMA:
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PRELUDE_RATIO = LzmaLlmDetector(prelude_str=PRELUDE_STR, preset=int(os.environ.get('ZIPPY_PRESET'))).prelude_ratio
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elif ENGINE == CompressionEngine.ZLIB:
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PRELUDE_RATIO = ZlibLlmDetector(prelude_str=PRELUDE_STR, preset=int(os.environ.get('ZIPPY_PRESET'))).prelude_ratio
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elif ENGINE == CompressionEngine.BROTLI:
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PRELUDE_RATIO = BrotliLlmDetector(prelude_str=PRELUDE_STR, preset=int(os.environ.get('ZIPPY_PRESET'))).prelude_ratio
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else:
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if ENGINE == CompressionEngine.LZMA:
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PRELUDE_RATIO = LzmaLlmDetector(prelude_str=PRELUDE_STR).prelude_ratio
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elif ENGINE == CompressionEngine.ZLIB:
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PRELUDE_RATIO = ZlibLlmDetector(prelude_str=PRELUDE_STR).prelude_ratio
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elif ENGINE == CompressionEngine.BROTLI:
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PRELUDE_RATIO = BrotliLlmDetector(prelude_str=PRELUDE_STR).prelude_ratio
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if os.environ.get('ZIPPY_PRESET') != None:
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zippy = Zippy(ENGINE, preset=int(os.environ.get('ZIPPY_PRESET'), normalize=True))
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else:
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zippy = Zippy(ENGINE, normalize=True)
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else:
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zippy = EnsembledZippy()
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PRELUDE_RATIO = None
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def test_training_file(record_property):
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(classification, score) = zippy.run_on_file_chunked('zippy/ai-generated.txt')
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record_property("score", str(score))
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assert classification == 'AI', 'The training corpus should always be detected as AI-generated... since it is'
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@pytest.mark.parametrize('f', human_files)
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def test_human_samples(f, record_property):
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(classification, score) = zippy.run_on_file_chunked(HUMAN_SAMPLE_DIR + f, prelude_ratio=PRELUDE_RATIO)
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record_property("score", str(score))
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if score > CONFIDENCE_THRESHOLD:
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assert classification == 'Human', f + ' is a human-generated file, misclassified as AI-generated with confidence ' + str(round(score, 8))
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else:
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if classification != 'Human':
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warn("Misclassified " + f + " with score of: " + str(round(score, 8)))
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else:
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warn("Unable to confidently classify: " + f)
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@pytest.mark.parametrize('f', ai_files)
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def test_llm_sample(f, record_property):
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(classification, score) = zippy.run_on_file_chunked(AI_SAMPLE_DIR + f, prelude_ratio=PRELUDE_RATIO)
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record_property("score", str(score))
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if score > CONFIDENCE_THRESHOLD:
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assert classification == 'AI', f + ' is an LLM-generated file, misclassified as human-generated with confidence ' + str(round(score, 8))
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else:
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if classification != 'AI':
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warn("Misclassified " + f + " with score of: " + str(round(score, 8)))
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else:
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warn("Unable to confidently classify: " + f)
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HUMAN_JSONL_FILE = 'samples/webtext.test.jsonl'
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human_samples = []
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with jsonlines.open(HUMAN_JSONL_FILE) as reader:
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for obj in reader:
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if obj.get('length', 0) >= MIN_LEN:
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human_samples.append(obj)
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@pytest.mark.parametrize('i', human_samples[0:NUM_JSONL_SAMPLES])
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def test_human_jsonl(i, record_property):
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(classification, score) = zippy.run_on_text_chunked(i.get('text', ''), prelude_ratio=PRELUDE_RATIO)
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record_property("score", str(score))
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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))
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# AI_JSONL_FILE = 'samples/xl-1542M.test.jsonl'
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# ai_samples = []
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# with jsonlines.open(AI_JSONL_FILE) as reader:
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# for obj in reader:
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# if obj.get('length', 0) >= MIN_LEN:
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# ai_samples.append(obj)
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# @pytest.mark.parametrize('i', ai_samples[0:NUM_JSONL_SAMPLES])
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# def test_gpt2_jsonl(i, record_property):
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# (classification, score) = run_on_text_chunked(i.get('text', ''), prelude_ratio=PRELUDE_RATIO)
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# record_property("score", str(score))
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# 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))
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# GPT3_JSONL_FILE = 'samples/GPT-3-175b_samples.jsonl'
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# gpt3_samples = []
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# with jsonlines.open(GPT3_JSONL_FILE) as reader:
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# for o in reader:
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# for l in o.split('<|endoftext|>'):
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# if len(l) >= MIN_LEN:
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# gpt3_samples.append(l)
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# @pytest.mark.parametrize('i', gpt3_samples[0:NUM_JSONL_SAMPLES])
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# def test_gpt3_jsonl(i, record_property):
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# (classification, score) = run_on_text_chunked(i, prelude_ratio=PRELUDE_RATIO)
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# record_property("score", str(score))
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# assert classification == 'AI', GPT3_JSONL_FILE + ' is an LLM-generated sample, misclassified as human-generated with confidence ' + str(round(score, 8))
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NEWS_JSONL_FILE = 'samples/news.jsonl'
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news_samples = []
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with jsonlines.open(NEWS_JSONL_FILE) as reader:
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for obj in reader:
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news_samples.append(obj)
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@pytest.mark.parametrize('i', news_samples[0:NUM_JSONL_SAMPLES])
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def test_humannews_jsonl(i, record_property):
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(classification, score) = zippy.run_on_text_chunked(i.get('human', ''), prelude_ratio=PRELUDE_RATIO)
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record_property("score", str(score))
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assert classification == 'Human', NEWS_JSONL_FILE + ' is a human-generated sample, misclassified as AI-generated with confidence ' + str(round(score, 8))
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@pytest.mark.parametrize('i', news_samples[0:NUM_JSONL_SAMPLES])
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def test_chatgptnews_jsonl(i, record_property):
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(classification, score) = zippy.run_on_text_chunked(i.get('chatgpt', ''), prelude_ratio=PRELUDE_RATIO)
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record_property("score", str(score))
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assert classification == 'AI', NEWS_JSONL_FILE + ' is a AI-generated sample, misclassified as human-generated with confidence ' + str(round(score, 8))
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CHEAT_HUMAN_JSONL_FILE = 'samples/ieee-init.jsonl'
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ch_samples = []
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with jsonlines.open(CHEAT_HUMAN_JSONL_FILE) as reader:
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for obj in reader:
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if len(obj.get('abstract', '')) >= MIN_LEN:
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ch_samples.append(obj)
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@pytest.mark.parametrize('i', ch_samples[0:NUM_JSONL_SAMPLES])
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def test_cheat_human_jsonl(i, record_property):
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(classification, score) = zippy.run_on_text_chunked(i.get('abstract', ''), prelude_ratio=PRELUDE_RATIO)
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record_property("score", str(score))
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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))
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CHEAT_GEN_JSONL_FILE = 'samples/ieee-chatgpt-generation.jsonl'
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cg_samples = []
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with jsonlines.open(CHEAT_GEN_JSONL_FILE) as reader:
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for obj in reader:
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if len(obj.get('abstract', '')) >= MIN_LEN:
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cg_samples.append(obj)
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@pytest.mark.parametrize('i', cg_samples[0:NUM_JSONL_SAMPLES])
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def test_cheat_generation_jsonl(i, record_property):
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(classification, score) = zippy.run_on_text_chunked(i.get('abstract', ''), prelude_ratio=PRELUDE_RATIO)
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record_property("score", str(score))
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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))
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CHEAT_POLISH_JSONL_FILE = 'samples/ieee-chatgpt-polish.jsonl'
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cp_samples = []
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with jsonlines.open(CHEAT_POLISH_JSONL_FILE) as reader:
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for obj in reader:
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if len(obj.get('abstract', '')) >= MIN_LEN:
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cp_samples.append(obj)
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@pytest.mark.parametrize('i', cp_samples[0:NUM_JSONL_SAMPLES])
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def test_cheat_polish_jsonl(i, record_property):
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(classification, score) = zippy.run_on_text_chunked(i.get('abstract', ''), prelude_ratio=PRELUDE_RATIO)
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record_property("score", str(score))
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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))
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CHEAT_VICUNAGEN_JSONL_FILE = 'samples/ieee-vicuna-generation.jsonl'
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vg_samples = []
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with jsonlines.open(CHEAT_VICUNAGEN_JSONL_FILE) as reader:
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for obj in reader:
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if len(obj.get('abstract', '')) >= MIN_LEN:
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vg_samples.append(obj)
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@pytest.mark.parametrize('i', vg_samples[0:NUM_JSONL_SAMPLES])
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def test_vicuna_generation_jsonl(i, record_property):
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(classification, score) = zippy.run_on_text_chunked(i.get('abstract', ''), prelude_ratio=PRELUDE_RATIO)
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record_property("score", str(score))
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assert classification == 'AI', CHEAT_VICUNAGEN_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))
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GPTZERO_EVAL_FILE = 'samples/gptzero_eval.csv'
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ge_samples = []
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with open(GPTZERO_EVAL_FILE) as fp:
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csvr = csv.DictReader(fp)
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for obj in csvr:
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if len(obj.get('Document', '')) >= MIN_LEN:
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ge_samples.append(obj)
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@pytest.mark.parametrize('i', list(filter(lambda x: x.get('Label') == 'Human', ge_samples[0:NUM_JSONL_SAMPLES])))
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def test_gptzero_eval_dataset_human(i, record_property):
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(classification, score) = zippy.run_on_text_chunked(i.get('Document', ''), prelude_ratio=PRELUDE_RATIO)
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record_property("score", str(score))
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assert classification == i.get('Label'), GPTZERO_EVAL_FILE + ':' + str(i.get('Index')) + ' was misclassified with confidence ' + str(round(score, 8))
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@pytest.mark.parametrize('i', list(filter(lambda x: x.get('Label') == 'AI', ge_samples[0:NUM_JSONL_SAMPLES])))
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def test_gptzero_eval_dataset_ai(i, record_property):
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(classification, score) = zippy.run_on_text_chunked(i.get('Document', ''), prelude_ratio=PRELUDE_RATIO)
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record_property("score", str(score))
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assert classification == i.get('Label'), GPTZERO_EVAL_FILE + ':' + str(i.get('Index')) + ' was misclassified with confidence ' + str(round(score, 8))
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