File size: 9,134 Bytes
811d51e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 | #!/usr/bin/env python3
"""Reproduce Veritiana AI Meter classifier v3.1.0 from training-balanced.jsonl."""
from __future__ import annotations
import argparse, hashlib, json, struct, sys, time
from collections import Counter
from pathlib import Path
import numpy as np
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report, confusion_matrix
from sklearn.model_selection import GroupShuffleSplit
from features import INPUT_SIZE, build_features
TASK_LABELS = ["general_chat","writing","translation","summarization","research","coding","mathematics","document_analysis","high_stakes"]
COMPLEXITY_LABELS = ["low","medium","high"]
def sha256(path: Path) -> str:
return hashlib.sha256(path.read_bytes()).hexdigest()
def _varint(number: int) -> bytes:
number = int(number)
if number < 0: number = (1 << 64) + number
output = bytearray()
while True:
byte = number & 0x7F; number >>= 7
output.append(byte | (0x80 if number else 0))
if not number: return bytes(output)
def _key(field: int, wire: int) -> bytes: return _varint((field << 3) | wire)
def _fv(field: int, value: int) -> bytes: return _key(field,0)+_varint(value)
def _fb(field: int, value: bytes) -> bytes: return _key(field,2)+_varint(len(value))+value
def _fs(field: int, value: str) -> bytes: return _fb(field,value.encode())
def _msg(field: int, value: bytes) -> bytes: return _fb(field,value)
def _packed(field: int, values) -> bytes: return _fb(field,b''.join(_varint(v) for v in values))
def _tensor(name: str, array: np.ndarray) -> bytes:
array=np.ascontiguousarray(array.astype(np.float32))
return _packed(1,array.shape)+_fv(2,1)+_fs(8,name)+_fb(9,array.tobytes(order='C'))
def _dim(value): return _fs(2,value) if isinstance(value,str) else _fv(1,value)
def _shape(dims): return b''.join(_msg(1,_dim(value)) for value in dims)
def _tensor_type(element,dims): return _fv(1,element)+_msg(2,_shape(dims))
def _type_proto(element,dims): return _msg(1,_tensor_type(element,dims))
def _value_info(name,dims): return _fs(1,name)+_msg(2,_type_proto(1,dims))
def _attr_int(name,value): return _fs(1,name)+_fv(3,value)+_fv(20,2)
def _node(inputs,outputs,operation,name='',attributes=None):
value=b''.join(_fs(1,item) for item in inputs)+b''.join(_fs(2,item) for item in outputs)
if name: value += _fs(3,name)
value += _fs(4,operation)
for attribute in attributes or []: value += _msg(5,attribute)
return value
def _graph(nodes,initializers,inputs,outputs,name):
return (b''.join(_msg(1,node) for node in nodes)+_fs(2,name)+
b''.join(_msg(5,item) for item in initializers)+
b''.join(_msg(11,item) for item in inputs)+
b''.join(_msg(12,item) for item in outputs))
def _opset(version): return _fv(2,version)
def _kv(key,value): return _fs(1,key)+_fs(2,value)
def export_onnx(task_model, complexity_model, destination: Path, version: str) -> None:
initializers=[
_tensor('task_weights',task_model.coef_.astype(np.float32).T.copy()),
_tensor('task_bias',task_model.intercept_.astype(np.float32).copy()),
_tensor('complexity_weights',complexity_model.coef_.astype(np.float32).T.copy()),
_tensor('complexity_bias',complexity_model.intercept_.astype(np.float32).copy()),
]
nodes=[
_node(['features','task_weights'],['task_mm'],'MatMul','TaskMatMul'),
_node(['task_mm','task_bias'],['task_logits'],'Add','TaskAdd'),
_node(['task_logits'],['task_probabilities'],'Softmax','TaskSoftmax',[_attr_int('axis',1)]),
_node(['features','complexity_weights'],['complexity_mm'],'MatMul','ComplexityMatMul'),
_node(['complexity_mm','complexity_bias'],['complexity_logits'],'Add','ComplexityAdd'),
_node(['complexity_logits'],['complexity_probabilities'],'Softmax','ComplexitySoftmax',[_attr_int('axis',1)]),
]
graph=_graph(nodes,initializers,[_value_info('features',['batch',1544])],
[_value_info('task_probabilities',['batch',9]),_value_info('complexity_probabilities',['batch',3])],
'VeritianaAIMeterClassifier')
metadata={
'model':'Veritiana Multilingual Intent Classifier','version':version,'input_size':'1544',
'task_labels':json.dumps(TASK_LABELS),'complexity_labels':json.dumps(COMPLEXITY_LABELS),
'training_data':'OASST1 + CoEdIT + MBPP+ + IFEval; filtered, weak-labeled, balanced',
'feature_contract':'AI Meter v1.4.9 compatible'
}
payload=(_fv(1,8)+_fs(2,'veritiana-ai-meter-model-trainer')+_fs(3,version)+
_msg(7,graph)+_msg(8,_opset(13))+b''.join(_msg(14,_kv(k,v)) for k,v in metadata.items()))
destination.write_bytes(payload)
def main() -> int:
parser=argparse.ArgumentParser()
parser.add_argument('dataset',type=Path,help='Prepared training-balanced.jsonl')
parser.add_argument('--output-dir',type=Path,default=Path('output'))
parser.add_argument('--version',default='3.1.0-multisource-balanced')
parser.add_argument('--test-size',type=float,default=0.20)
parser.add_argument('--seed',type=int,default=42)
parser.add_argument('--c',type=float,default=4.0)
args=parser.parse_args()
rows=[json.loads(line) for line in args.dataset.read_text(encoding='utf-8').splitlines() if line.strip()]
if not rows: raise ValueError('Empty dataset')
task_map={label:index for index,label in enumerate(TASK_LABELS)}
complexity_map={label:index for index,label in enumerate(COMPLEXITY_LABELS)}
X=np.vstack([build_features(row['text']) for row in rows]).astype(np.float32)
task_y=np.asarray([task_map[row['task']] for row in rows],dtype=np.int64)
complexity_y=np.asarray([complexity_map[row['complexity']] for row in rows],dtype=np.int64)
groups=[row.get('derived_from') or row.get('normalized_hash') or row.get('id') for row in rows]
splitter=GroupShuffleSplit(n_splits=1,test_size=args.test_size,random_state=args.seed)
train_index,test_index=next(splitter.split(X,task_y,groups=groups))
def fit(target):
model=LogisticRegression(C=args.c,max_iter=3000,solver='lbfgs',class_weight='balanced',random_state=args.seed)
model.fit(X[train_index],target[train_index]); return model
started=time.perf_counter(); task_model=fit(task_y); complexity_model=fit(complexity_y); seconds=time.perf_counter()-started
task_prediction=task_model.predict(X[test_index]); complexity_prediction=complexity_model.predict(X[test_index])
task_report=classification_report(task_y[test_index],task_prediction,labels=list(range(9)),target_names=TASK_LABELS,output_dict=True,zero_division=0)
complexity_report=classification_report(complexity_y[test_index],complexity_prediction,labels=list(range(3)),target_names=COMPLEXITY_LABELS,output_dict=True,zero_division=0)
evaluation={
'summary':{'task_accuracy':task_report['accuracy'],'task_macro_f1':task_report['macro avg']['f1-score'],
'complexity_accuracy':complexity_report['accuracy'],'complexity_macro_f1':complexity_report['macro avg']['f1-score'],
'training_seconds':seconds},
'task_report':task_report,'complexity_report':complexity_report,
'task_confusion_matrix':confusion_matrix(task_y[test_index],task_prediction,labels=list(range(9))).tolist(),
'complexity_confusion_matrix':confusion_matrix(complexity_y[test_index],complexity_prediction,labels=list(range(3))).tolist()
}
args.output_dir.mkdir(parents=True,exist_ok=True)
model_path=args.output_dir/'veritiana-classifier-v2.onnx'; export_onnx(task_model,complexity_model,model_path,args.version)
(args.output_dir/'evaluation.json').write_text(json.dumps(evaluation,ensure_ascii=False,indent=2),encoding='utf-8')
meta={
'model':'Veritiana Multilingual Intent Classifier','version':args.version,'engine':'ONNX Runtime Web',
'architecture':'1544 hashed lexical/character/numeric features; dual multinomial logistic heads',
'input':{'name':'features','dtype':'float32','shape':['batch',1544]},
'outputs':[{'name':'task_probabilities','dtype':'float32','shape':['batch',9]},
{'name':'complexity_probabilities','dtype':'float32','shape':['batch',3]}],
'task_labels':TASK_LABELS,'complexity_labels':COMPLEXITY_LABELS,
'training_data':{'dataset_sha256':sha256(args.dataset),'balanced_rows':len(rows),'train_rows':len(train_index),'test_rows':len(test_index),
'label_method_counts':dict(Counter(row.get('label_method','unknown') for row in rows))},
'validation':evaluation['summary'],'onnx_sha256':sha256(model_path),
'limitations':'Validation uses weak labels and deterministic augmentations; it is not an independent human-ground-truth benchmark.'
}
(args.output_dir/'classifier-meta.json').write_text(json.dumps(meta,ensure_ascii=False,indent=2),encoding='utf-8')
print(json.dumps({'model':str(model_path),'onnx_sha256':sha256(model_path),'evaluation':evaluation['summary']},indent=2))
return 0
if __name__=='__main__': raise SystemExit(main())
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