Added the ability to ensemble different compression models

Signed-off-by: Jacob Torrey <jacob@thinkst.com>

test_zippy_detect.py

@@ -2,7 +2,7 @@
 
 import pytest, os, jsonlines, csv
 from warnings import warn
-from zippy import run_on_file_chunked, run_on_text_chunked, PRELUDE_STR, LzmaLlmDetector, CompressionEngine, ZlibLlmDetector, ENGINE
+from zippy import Zippy, EnsembledZippy, PRELUDE_STR, LzmaLlmDetector, BrotliLlmDetector, ZlibLlmDetector, CompressionEngine
 import zippy
 
 AI_SAMPLE_DIR = 'samples/llm-generated/'
@@ -16,19 +16,34 @@ human_files = os.listdir(HUMAN_SAMPLE_DIR)
 
 CONFIDENCE_THRESHOLD : float = 0.00 # What confidence to treat as error vs warning
 
-if ENGINE == CompressionEngine.LZMA:
+# Bool on whether to ensemble the models or run a single model
-    PRELUDE_RATIO = LzmaLlmDetector(prelude_str=PRELUDE_STR).prelude_ratio
+ENSEMBLE = True
-elif ENGINE == CompressionEngine.ZLIB:
+
-    PRELUDE_RATIO = ZlibLlmDetector(prelude_str=PRELUDE_STR).prelude_ratio
+if not ENSEMBLE:
+    # What compression engine to use for the test
+    ENGINE = CompressionEngine.LZMA
+
+    if ENGINE == CompressionEngine.LZMA:
+        PRELUDE_RATIO = LzmaLlmDetector(prelude_str=PRELUDE_STR).prelude_ratio
+    elif ENGINE == CompressionEngine.ZLIB:
+        PRELUDE_RATIO = ZlibLlmDetector(prelude_str=PRELUDE_STR).prelude_ratio
+    elif ENGINE == CompressionEngine.BROTLI:
+        PRELUDE_RATIO = BrotliLlmDetector(prelude_str=PRELUDE_STR).prelude_ratio
+
+    zippy = Zippy(ENGINE)
+
+else:
+    zippy = EnsembledZippy()
+    PRELUDE_RATIO = None
 
 def test_training_file(record_property):
-    (classification, score) = run_on_file_chunked('ai-generated.txt')
+    (classification, score) = zippy.run_on_file_chunked('ai-generated.txt')
     record_property("score", str(score))
     assert classification == 'AI', 'The training corpus should always be detected as AI-generated... since it is'
 
 @pytest.mark.parametrize('f', human_files)
 def test_human_samples(f, record_property):
-    (classification, score) = run_on_file_chunked(HUMAN_SAMPLE_DIR + f, prelude_ratio=PRELUDE_RATIO)
+    (classification, score) = zippy.run_on_file_chunked(HUMAN_SAMPLE_DIR + f, prelude_ratio=PRELUDE_RATIO)
     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))
@@ -40,7 +55,7 @@ def test_human_samples(f, record_property):
 
 @pytest.mark.parametrize('f', ai_files)
 def test_llm_sample(f, record_property):
-   (classification, score) = run_on_file_chunked(AI_SAMPLE_DIR + f, prelude_ratio=PRELUDE_RATIO)
+   (classification, score) = zippy.run_on_file_chunked(AI_SAMPLE_DIR + f, prelude_ratio=PRELUDE_RATIO)
    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))
@@ -59,7 +74,7 @@ with jsonlines.open(HUMAN_JSONL_FILE) as reader:
 
 @pytest.mark.parametrize('i', human_samples[0:NUM_JSONL_SAMPLES])
 def test_human_jsonl(i, record_property):
-    (classification, score) = run_on_text_chunked(i.get('text', ''), prelude_ratio=PRELUDE_RATIO)
+    (classification, score) = zippy.run_on_text_chunked(i.get('text', ''), prelude_ratio=PRELUDE_RATIO)
     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))
 
@@ -98,13 +113,13 @@ with jsonlines.open(NEWS_JSONL_FILE) as reader:
 
 @pytest.mark.parametrize('i', news_samples[0:NUM_JSONL_SAMPLES])
 def test_humannews_jsonl(i, record_property):
-    (classification, score) = run_on_text_chunked(i.get('human', ''), prelude_ratio=PRELUDE_RATIO)
+    (classification, score) = zippy.run_on_text_chunked(i.get('human', ''), prelude_ratio=PRELUDE_RATIO)
     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):
-    (classification, score) = run_on_text_chunked(i.get('chatgpt', ''), prelude_ratio=PRELUDE_RATIO)
+    (classification, score) = zippy.run_on_text_chunked(i.get('chatgpt', ''), prelude_ratio=PRELUDE_RATIO)
     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))
 
@@ -117,7 +132,7 @@ with jsonlines.open(CHEAT_HUMAN_JSONL_FILE) as reader:
 
 @pytest.mark.parametrize('i', ch_samples[0:NUM_JSONL_SAMPLES])
 def test_cheat_human_jsonl(i, record_property):
-    (classification, score) = run_on_text_chunked(i.get('abstract', ''), prelude_ratio=PRELUDE_RATIO)
+    (classification, score) = zippy.run_on_text_chunked(i.get('abstract', ''), prelude_ratio=PRELUDE_RATIO)
     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))
 
@@ -130,7 +145,7 @@ with jsonlines.open(CHEAT_GEN_JSONL_FILE) as reader:
 
 @pytest.mark.parametrize('i', cg_samples[0:NUM_JSONL_SAMPLES])
 def test_cheat_generation_jsonl(i, record_property):
-    (classification, score) = run_on_text_chunked(i.get('abstract', ''), prelude_ratio=PRELUDE_RATIO)
+    (classification, score) = zippy.run_on_text_chunked(i.get('abstract', ''), prelude_ratio=PRELUDE_RATIO)
     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))
 
@@ -143,7 +158,7 @@ with jsonlines.open(CHEAT_POLISH_JSONL_FILE) as reader:
 
 @pytest.mark.parametrize('i', cp_samples[0:NUM_JSONL_SAMPLES])
 def test_cheat_polish_jsonl(i, record_property):
-    (classification, score) = run_on_text_chunked(i.get('abstract', ''), prelude_ratio=PRELUDE_RATIO)
+    (classification, score) = zippy.run_on_text_chunked(i.get('abstract', ''), prelude_ratio=PRELUDE_RATIO)
     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))
 
@@ -156,7 +171,7 @@ with jsonlines.open(CHEAT_VICUNAGEN_JSONL_FILE) as reader:
 
 @pytest.mark.parametrize('i', vg_samples[0:NUM_JSONL_SAMPLES])
 def test_vicuna_generation_jsonl(i, record_property):
-    (classification, score) = run_on_text_chunked(i.get('abstract', ''), prelude_ratio=PRELUDE_RATIO)
+    (classification, score) = zippy.run_on_text_chunked(i.get('abstract', ''), prelude_ratio=PRELUDE_RATIO)
     record_property("score", str(score))
     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))
 
@@ -170,12 +185,12 @@ with open(GPTZERO_EVAL_FILE) as fp:
 
 @pytest.mark.parametrize('i', list(filter(lambda x: x.get('Label') == 'Human', ge_samples[0:NUM_JSONL_SAMPLES])))
 def test_gptzero_eval_dataset_human(i, record_property):
-    (classification, score) = run_on_text_chunked(i.get('Document', ''), prelude_ratio=PRELUDE_RATIO)
+    (classification, score) = zippy.run_on_text_chunked(i.get('Document', ''), prelude_ratio=PRELUDE_RATIO)
     record_property("score", str(score))
     assert classification == i.get('Label'), GPTZERO_EVAL_FILE + ':' + str(i.get('Index')) + ' was misclassified with confidence ' + str(round(score, 8))
 
 @pytest.mark.parametrize('i', list(filter(lambda x: x.get('Label') == 'AI', ge_samples[0:NUM_JSONL_SAMPLES])))
 def test_gptzero_eval_dataset_ai(i, record_property):
-    (classification, score) = run_on_text_chunked(i.get('Document', ''), prelude_ratio=PRELUDE_RATIO)
+    (classification, score) = zippy.run_on_text_chunked(i.get('Document', ''), prelude_ratio=PRELUDE_RATIO)
     record_property("score", str(score))
     assert classification == i.get('Label'), GPTZERO_EVAL_FILE + ':' + str(i.get('Index')) + ' was misclassified with confidence ' + str(round(score, 8))

zippy.py

@@ -6,9 +6,12 @@
 
 import lzma, argparse, os, itertools
 from zlib import compressobj, Z_FINISH
-import re, sys
+from brotli import compress as brotli_compress, MODE_TEXT
+from numpy import array_split
+import re, sys, statistics
 from abc import ABC, abstractmethod
 from enum import Enum
+from math import ceil
 from typing import List, Optional, Tuple, TypeAlias
 from multiprocessing import Pool, cpu_count
 
@@ -17,8 +20,7 @@ Score : TypeAlias = tuple[str, float]
 class CompressionEngine(Enum):
     LZMA = 1
     ZLIB = 2
-
+    BROTLI = 3
-ENGINE : CompressionEngine = CompressionEngine.ZLIB
 
 def clean_text(s : str) -> str:
     '''
@@ -49,19 +51,21 @@ class AIDetector(ABC):
     def score_text(self, sample : str) -> Optional[Score]:
         pass
 
-class ZlibLlmDetector(AIDetector):
+class BrotliLlmDetector(AIDetector):
-    '''Class providing functionality to attempt to detect LLM/generative AI generated text using the zlib compression algorithm'''
+    '''Class providing functionality to attempt to detect LLM/generative AI generated text using the brotli compression algorithm'''
-    def __init__(self, prelude_file : Optional[str] = None, prelude_str : Optional[str] = None, prelude_ratio : Optional[float] = None):
+    def __init__(self, prelude_file : Optional[str] = None, prelude_str : Optional[str] = None, prelude_ratio : Optional[float] = None, preset : int = 8):
-        self.PRESET = 9
+        self.PRESET = preset
-        self.WBITS = -15
+        self.WIN_SIZE = 24
+        self.BLOCK_SIZE = 0
         self.prelude_ratio = 0.0
         if prelude_ratio != None:
             self.prelude_ratio = prelude_ratio
         
         if prelude_file != None:
             with open(prelude_file) as fp:
-                self.prelude_str = fp.read()
+                self.prelude_str = clean_text(fp.read())
-                self.prelude_ratio = self._compress(self.prelude_str)
+            self.prelude_ratio = self._compress(self.prelude_str)
+            return
     
         if prelude_str != None:
             self.prelude_str = prelude_str
@@ -69,11 +73,7 @@ class ZlibLlmDetector(AIDetector):
 
     def _compress(self, s : str) -> float:
         orig_len = len(s.encode())
-        c = compressobj(level=self.PRESET, wbits=self.WBITS, memLevel=9)
+        c_len = len(brotli_compress(s.encode(), mode=MODE_TEXT, quality=self.PRESET, lgwin=self.WIN_SIZE, lgblock=self.BLOCK_SIZE))
-        bytes = c.compress(s.encode())
-        bytes += c.flush(Z_FINISH)
-        c_len = len(bytes)
-        #c_len = len(compress(s.encode(), level=self.PRESET, wbits=self.WBITS))
         return c_len / orig_len
     
     def score_text(self, sample: str) -> Score | None:
@@ -84,7 +84,54 @@ class ZlibLlmDetector(AIDetector):
         if self.prelude_ratio == 0.0:
             return None
         sample_score = self._compress(self.prelude_str + sample)
-        #print(str((self.prelude_ratio, sample_score)))
+        #print('Brotli: ' + str((self.prelude_ratio, sample_score)))
+        delta = self.prelude_ratio - sample_score
+        determination = 'AI'
+        if delta < 0:
+            determination = 'Human'
+
+        return (determination, abs(delta * 100))
+
+class ZlibLlmDetector(AIDetector):
+    '''Class providing functionality to attempt to detect LLM/generative AI generated text using the zlib compression algorithm'''
+    def __init__(self, prelude_file : Optional[str] = None, prelude_str : Optional[str] = None, prelude_ratio : Optional[float] = None, preset : int = 9):
+        self.PRESET = preset
+        self.WBITS = -15
+        self.prelude_ratio = 0.0
+        if prelude_ratio != None:
+            self.prelude_ratio = prelude_ratio
+        
+        if prelude_file != None:
+            with open(prelude_file) as fp:
+                self.prelude_str = clean_text(fp.read())
+            lines = self.prelude_str.split('\n')
+            self.prelude_chunks = array_split(lines, ceil(len(self.prelude_str) / 2**abs(self.WBITS)))
+            self.prelude_ratio = statistics.mean(map(lambda x: self._compress('\n'.join(list(x))), self.prelude_chunks))   
+            return
+         
+        if prelude_str != None:
+            self.prelude_str = prelude_str
+            lines = self.prelude_str.split('\n')
+            self.prelude_chunks = array_split(lines, ceil(len(self.prelude_str) / 2**abs(self.WBITS)))
+            self.prelude_ratio = statistics.mean(map(lambda x: self._compress('\n'.join(list(x))), self.prelude_chunks))
+
+    def _compress(self, s : str) -> float:
+        orig_len = len(s.encode())
+        c = compressobj(level=self.PRESET, wbits=self.WBITS, memLevel=9)
+        bytes = c.compress(s.encode())
+        bytes += c.flush(Z_FINISH)
+        c_len = len(bytes)
+        return c_len / orig_len
+    
+    def score_text(self, sample: str) -> Score | None:
+        '''
+        Returns a tuple of a string (AI or Human) and a float confidence (higher is more confident) that the sample was generated 
+        by either an AI or human. Returns None if it cannot make a determination
+        '''
+        if self.prelude_ratio == 0.0:
+            return None
+        sample_score = statistics.mean(map(lambda x: self._compress('\n'.join(x) + sample), self.prelude_chunks))
+        #print('ZLIB: ' + str((self.prelude_ratio, sample_score)))
         delta = self.prelude_ratio - sample_score
         determination = 'AI'
         if delta < 0:
@@ -95,64 +142,34 @@ class ZlibLlmDetector(AIDetector):
 
 class LzmaLlmDetector(AIDetector):
     '''Class providing functionality to attempt to detect LLM/generative AI generated text using the LZMA compression algorithm'''
-    def __init__(self, prelude_file : Optional[str] = None, prelude_str : Optional[str] = None, prelude_ratio : Optional[float] = None) -> None:
+    def __init__(self, prelude_file : Optional[str] = None, prelude_str : Optional[str] = None, prelude_ratio : Optional[float] = None, preset : int = 3) -> None:
         '''Initializes a compression with the passed prelude file, and optionally the number of digits to round to compare prelude vs. sample compression'''
-        self.PRESET : int = 2
+        self.PRESET : int = preset
-        self.comp = lzma.LZMACompressor(preset=self.PRESET)
         self.c_buf : List[bytes] = []
         self.in_bytes : int = 0
         self.prelude_ratio : float = 0.0
         if prelude_ratio != None:
             self.prelude_ratio = prelude_ratio
-        self.SHORT_SAMPLE_THRESHOLD : int = 350 # What sample length is considered "short"
 
         if prelude_file != None:
             # Read it once to get the default compression ratio for the prelude
             with open(prelude_file, 'r') as fp:
-                self._compress_str(fp.read())
+                self.prelude_str = fp.read()
-            self.prelude_ratio = self._finalize()
+            self.prelude_ratio = self._compress(self.prelude_str)
+            return
             #print(prelude_file + ' ratio: ' + str(self.prelude_ratio))
-            # Redo this to prime the compressor
-            self.comp = lzma.LZMACompressor(preset=self.PRESET)
-            with open(prelude_file, 'r') as fp:
-                self._compress_str(fp.read())
 
         if prelude_str != None:
             if self.prelude_ratio == 0.0:
-                self._compress_str(prelude_str)
+                self.prelude_ratio = self._compress(prelude_str)
-                self.prelude_ratio = self._finalize()
+
-                self.comp = lzma.LZMACompressor(preset=self.PRESET)
+    def _compress(self, s : str) -> float:
-            self._compress_str(prelude_str)
+        orig_len = len(s.encode())
-            
+        c = lzma.LZMACompressor(preset=self.PRESET)
-    def _compress_str(self, s : str) -> None:
+        bytes = c.compress(s.encode())
-        '''
+        bytes += c.flush()
-        Internal helper function to compress a string
+        c_len = len(bytes)
-        '''
+        return c_len / orig_len
-        strb : bytes = s.encode('ascii', errors='ignore')
-        self.c_buf.append(self.comp.compress(strb))
-        self.in_bytes += len(strb)
-    
-    def _finalize(self) -> float:
-        '''
-        Finalizes an LZMA compression cycle and returns the percentage compression ratio
-        
-        post: _ >= 0
-        '''
-        self.c_buf.append(self.comp.flush())
-        compressed_size : int = len(b''.join(self.c_buf))
-        if self.in_bytes == 0:
-            return 0.0
-        score = compressed_size / self.in_bytes
-        self.in_bytes = 0
-        self.c_buf = []
-        return score
-    
-    def get_compression_ratio(self, s : str) -> Tuple[float, float]:
-        '''
-        Returns a tuple of floats with the compression ratio of the prelude (0 if no prelude) and passed string
-        '''
-        self._compress_str(s)
-        return (self.prelude_ratio, self._finalize())
 
     def score_text(self, sample : str) -> Optional[Score]:
         '''
@@ -161,104 +178,188 @@ class LzmaLlmDetector(AIDetector):
         '''
         if self.prelude_ratio == 0.0:
             return None
-        (prelude_score, sample_score) = self.get_compression_ratio(sample)
+        #print('LZMA: ' + str((self.prelude_ratio, sample_score)))
-        print(str((self.prelude_ratio, sample_score)))
+        delta = self.prelude_ratio - self._compress(self.prelude_str + sample)
-        delta = prelude_score - sample_score
         determination = 'AI'
         if delta < 0:
             determination = 'Human'
 
         return (determination, abs(delta * 100))
         
-def run_on_file(filename : str) -> Optional[Score]:
+class Zippy:
-    '''Given a filename (and an optional number of decimal places to round to) returns the score for the contents of that file'''
+    '''
-    with open(filename, 'r') as fp:
+    Class to wrap the functionality of Zippy
-        if ENGINE == CompressionEngine.LZMA:
+    '''
-            l = LzmaLlmDetector(prelude_file=PRELUDE_FILE)
+    def __init__(self, engine : CompressionEngine = CompressionEngine.LZMA, preset : Optional[int] = None) -> None:
-        elif ENGINE == CompressionEngine.ZLIB:
+        self.ENGINE = engine
-            l = ZlibLlmDetector(prelude_file=PRELUDE_FILE)
+        self.PRESET = preset
-        txt = fp.read()
+        if engine == CompressionEngine.LZMA:
-        #print('Calculating score for input of length ' + str(len(txt)))
+            if self.PRESET:
-        return l.score_text(txt)
+                self.detector = LzmaLlmDetector(prelude_file=PRELUDE_FILE, preset=self.PRESET)
+            else:
+                self.detector = LzmaLlmDetector(prelude_file=PRELUDE_FILE)
+        elif engine == CompressionEngine.BROTLI:
+            if self.PRESET:
+                self.detector = BrotliLlmDetector(prelude_file=PRELUDE_FILE, preset=self.PRESET)
+            else:
+                self.detector = BrotliLlmDetector(prelude_file=PRELUDE_FILE)
+        elif engine == CompressionEngine.ZLIB:
+            if self.PRESET:
+                self.detector = ZlibLlmDetector(prelude_file=PRELUDE_FILE, preset=self.PRESET)
+            else:
+                self.detector = ZlibLlmDetector(prelude_file=PRELUDE_FILE)
 
-def _score_chunk(c : str, prelude_file : Optional[str] = None, prelude_ratio : Optional[float] = None) -> Score:
+    def run_on_file(self, filename : str) -> Optional[Score]:
-        if prelude_file != None:
+        '''Given a filename (and an optional number of decimal places to round to) returns the score for the contents of that file'''
-            if ENGINE == CompressionEngine.LZMA:
+        with open(filename, 'r') as fp:
-                l = LzmaLlmDetector(prelude_file=prelude_file)
+            txt = fp.read()
-            if ENGINE == CompressionEngine.ZLIB:
+            #print('Calculating score for input of length ' + str(len(txt)))
-                l = ZlibLlmDetector(prelude_file=prelude_file)
+        return self.detector.score_text(txt)
+
+    def _score_chunk(self, c : str, prelude_file : Optional[str] = None, prelude_ratio : Optional[float] = None) -> Score:
+        if prelude_file is None and prelude_ratio != None:
+            self.detector.prelude_str = PRELUDE_STR
+            self.detector.prelude_ratio = prelude_ratio
+
+        return self.detector.score_text(c)
+
+    def run_on_file_chunked(self, filename : str, chunk_size : int = 1500, prelude_ratio : Optional[float] = None) -> Optional[Score]:
+        '''
+        Given a filename (and an optional chunk size and number of decimal places to round to) returns the score for the contents of that file.
+        This function chunks the file into at most chunk_size parts to score separately, then returns an average. This prevents a very large input
+        being skewed because its compression ratio starts to overwhelm the prelude file.
+        '''
+        with open(filename, 'r') as fp:
+            contents = fp.read()
+        return self.run_on_text_chunked(contents, chunk_size, prelude_ratio=prelude_ratio)
+
+    def run_on_text_chunked(self, s : str, chunk_size : int = 1500, prelude_file : Optional[str] = None, prelude_ratio : Optional[float] = None) -> Optional[Score]:
+        '''
+        Given a string (and an optional chunk size and number of decimal places to round to) returns the score for the passed string.
+        This function chunks the input into at most chunk_size parts to score separately, then returns an average. This prevents a very large input
+        being skewed because its compression ratio starts to overwhelm the prelude file.
+        '''
+        contents = clean_text(s)
+
+        start = 0
+        end = 0
+        chunks = []
+        while start + chunk_size < len(contents) and end != -1:
+            end = contents.rfind(' ', start, start + chunk_size + 1)
+            chunks.append(contents[start:end])
+            start = end + 1
+        chunks.append(contents[start:])
+        scores = []
+        if len(chunks) > 2:
+            with Pool(cpu_count()) as pool:
+                for r in pool.starmap(self._score_chunk, zip(chunks, itertools.repeat(prelude_file), itertools.repeat(prelude_ratio))):
+                    scores.append(r)
         else:
-            if ENGINE == CompressionEngine.LZMA:
+            for c in chunks:
-                l = LzmaLlmDetector(prelude_str=PRELUDE_STR, prelude_ratio=prelude_ratio)
+                scores.append(self._score_chunk(c, prelude_file=prelude_file, prelude_ratio=prelude_ratio))
-            if ENGINE == CompressionEngine.ZLIB:
+        ssum : float = 0.0
-                l = ZlibLlmDetector(prelude_str=PRELUDE_STR, prelude_ratio=prelude_ratio)
+        for i, s in enumerate(scores):
-        return l.score_text(c)
+            if s[0] == 'AI':
-
+                ssum -= s[1] * (len(chunks[i]) / len(contents))
-def run_on_file_chunked(filename : str, chunk_size : int = 1500, prelude_ratio : Optional[float] = None) -> Optional[Score]:
+            else:
-    '''
+                ssum += s[1] * (len(chunks[i]) / len(contents))
-    Given a filename (and an optional chunk size and number of decimal places to round to) returns the score for the contents of that file.
+        sa : float = ssum
-    This function chunks the file into at most chunk_size parts to score separately, then returns an average. This prevents a very large input
+        if sa < 0:
-    being skewed because its compression ratio starts to overwhelm the prelude file.
+            return ('AI', abs(sa))
-    '''
-    with open(filename, 'r') as fp:
-        contents = fp.read()
-    return run_on_text_chunked(contents, chunk_size, prelude_ratio=prelude_ratio)
-
-def run_on_text_chunked(s : str, chunk_size : int = 1500, prelude_file : Optional[str] = None, prelude_ratio : Optional[float] = None) -> Optional[Score]:
-    '''
-    Given a string (and an optional chunk size and number of decimal places to round to) returns the score for the passed string.
-    This function chunks the input into at most chunk_size parts to score separately, then returns an average. This prevents a very large input
-    being skewed because its compression ratio starts to overwhelm the prelude file.
-    '''
-    contents = clean_text(s)
-
-    start = 0
-    end = 0
-    chunks = []
-    while start + chunk_size < len(contents) and end != -1:
-        end = contents.rfind(' ', start, start + chunk_size + 1)
-        chunks.append(contents[start:end])
-        start = end + 1
-    chunks.append(contents[start:])
-    scores = []
-    if len(chunks) > 2:
-        with Pool(cpu_count()) as pool:
-            for r in pool.starmap(_score_chunk, zip(chunks, itertools.repeat(prelude_file), itertools.repeat(prelude_ratio))):
-                scores.append(r)
-    else:
-        for c in chunks:
-            scores.append(_score_chunk(c, prelude_file=prelude_file, prelude_ratio=prelude_ratio))
-    ssum : float = 0.0
-    for i, s in enumerate(scores):
-        if s[0] == 'AI':
-            ssum -= s[1] * (len(chunks[i]) / len(contents))
         else:
-            ssum += s[1] * (len(chunks[i]) / len(contents))
+            return ('Human', abs(sa))
-    sa : float = ssum# / len(scores)
-    if sa < 0:
-        return ('AI', abs(sa))
-    else:
-        return ('Human', abs(sa))
 
+class EnsembledZippy:
+    '''
+    Class to wrap the functionality of Zippy into an ensemble
+    '''
+    def __init__(self) -> None:
+        self.ENGINES = [CompressionEngine.LZMA, CompressionEngine.BROTLI, CompressionEngine.ZLIB]
+        self.WEIGHTS = [.33, .33, .33]
+        self.component_classifiers : list[AIDetector] = []
+        for i, e in enumerate(self.ENGINES):
+            self.component_classifiers.append(Zippy(e))
+        
+    def _combine_scores(self, scores : list[Score]) -> Score:
+        ssum : float = 0.0
+        for i, s in enumerate(scores):
+            if s[0] == 'AI':
+                ssum -= s[1] * self.WEIGHTS[i]
+            else:
+                ssum += s[1] * self.WEIGHTS[i]
+        sa : float = ssum
+        if sa < 0:
+            return ('AI', abs(sa))
+        else:
+            return ('Human', abs(sa))
+
+    def run_on_file(self, filename : str) -> Optional[Score]:
+        '''Given a filename (and an optional number of decimal places to round to) returns the score for the contents of that file'''
+        with open(filename, 'r') as fp:
+            txt = fp.read()
+        scores = []
+        for c in self.component_classifiers:
+            scores.append(c.score_text(txt))
+        return self._combine_scores(scores)
+
+    def _score_chunk(self, c : str, prelude_file : Optional[str] = None, prelude_ratio : Optional[float] = None) -> Score:
+        scores = []
+        for c in self.component_classifiers:
+            scores.append(c.score_text(c))
+        return self._combine_scores(scores)
+
+    def run_on_file_chunked(self, filename : str, chunk_size : int = 1500, prelude_ratio : Optional[float] = None) -> Optional[Score]:
+        '''
+        Given a filename (and an optional chunk size and number of decimal places to round to) returns the score for the contents of that file.
+        This function chunks the file into at most chunk_size parts to score separately, then returns an average. This prevents a very large input
+        being skewed because its compression ratio starts to overwhelm the prelude file.
+        '''
+        with open(filename, 'r') as fp:
+            contents = fp.read()
+        return self.run_on_text_chunked(contents, chunk_size)
+
+    def run_on_text_chunked(self, s : str, chunk_size : int = 1500, prelude_file : Optional[str] = None, prelude_ratio : Optional[float] = None) -> Optional[Score]:
+        '''
+        Given a string (and an optional chunk size and number of decimal places to round to) returns the score for the passed string.
+        This function chunks the input into at most chunk_size parts to score separately, then returns an average. This prevents a very large input
+        being skewed because its compression ratio starts to overwhelm the prelude file.
+        '''
+        scores = []
+        for c in self.component_classifiers:
+            scores.append(c.run_on_text_chunked(s, chunk_size=chunk_size))
+        return self._combine_scores(scores)
 
 if __name__ == '__main__':
     parser = argparse.ArgumentParser()
-    parser.add_argument("-e", choices=['zlib', 'lzma'], help='Which compression engine to use: lzma or zlib', default='lzma', required=False)
+    parser.add_argument("-e", choices=['zlib', 'lzma', 'brotli', 'ensemble'], help='Which compression engine to use: lzma, zlib, brotli, or an ensemble of all engines', default='lzma', required=False)
     group = parser.add_mutually_exclusive_group()
     group.add_argument("-s", help='Read from stdin until EOF is reached instead of from a file', required=False, action='store_true')
     group.add_argument("sample_files", nargs='*', help='Text file(s) containing the sample to classify', default="")
     args = parser.parse_args()
+    engine = 'lzma'
     if args.e:
         if args.e == 'lzma':
-            ENGINE = CompressionEngine.LZMA
+            engine = CompressionEngine.LZMA
         elif args.e == 'zlib':
-            ENGINE = CompressionEngine.ZLIB
+            engine = CompressionEngine.ZLIB
+        elif args.e == 'brotli':
+            engine = CompressionEngine.BROTLI
+        elif args.e == 'ensemble':
+            engine = None
     if args.s:
-        print(str(run_on_text_chunked(''.join(list(sys.stdin)))))
+        if engine:
+            z = Zippy(engine)
+        else:
+            z = EnsembledZippy()
+        print(str(z.run_on_text_chunked(''.join(list(sys.stdin)))))
     elif len(args.sample_files) == 0:
         print("Please call with either a list of text files to analyze, or the -s flag to classify stdin.\nCall with the -h flag for additional help.")
     else:
+        if engine:
+            z = Zippy(engine)
+        else:
+            z = EnsembledZippy()
         for f in args.sample_files:
             print(f)
             if os.path.isfile(f):
-                print(str(run_on_file_chunked(f)))
+                print(str(z.run_on_file_chunked(f)))