jiahong
350 lines
13 KiB
Python
350 lines
13 KiB
Python
# 自然写手写识别与AI分析引擎软件 V1.0
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# 作文评分模型模块 - 深度学习作文评分模型推理管道
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"""
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作文评分深度学习模型
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基于BERT/ERNIE预训练模型微调的中文作文评分器
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支持多维度评分：内容、结构、语言、思想感情
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"""
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import time
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import logging
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import numpy as np
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from typing import List, Dict, Optional, Tuple
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from dataclasses import dataclass, field
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from pathlib import Path
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logger = logging.getLogger(__name__)
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# ==================== 模型配置 ====================
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@dataclass
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class EssayModelConfig:
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"""作文评分模型配置"""
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model_name: str = "writech-essay-scorer-v1"
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model_path: str = "/opt/models/essay_scorer"
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max_seq_length: int = 512 # 最大输入序列长度
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num_labels: int = 4 # 评分维度数量
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score_range: Tuple[int, int] = (0, 100) # 评分范围
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batch_size: int = 8 # 推理批大小
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use_gpu: bool = True # 是否使用GPU加速
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fp16_inference: bool = True # 是否使用FP16半精度推理
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# ==================== 文本特征提取器 ====================
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class TextFeatureExtractor:
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"""
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文本特征提取器
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从作文文本中提取用于评分的统计特征和语义特征
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统计特征包括：字数、句数、段落数、词汇丰富度等
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语义特征通过预训练语言模型编码获得
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"""
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# 常用连接词库（用于衡量行文逻辑性）
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CONNECTIVES = {
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'causal': ['因为', '所以', '因此', '由于', '于是', '故而'],
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'adversative': ['但是', '然而', '可是', '不过', '虽然', '尽管'],
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'progressive': ['而且', '并且', '不仅', '还', '甚至', '更'],
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'sequential': ['首先', '其次', '然后', '接着', '最后', '总之'],
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}
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# 形容词库（用于衡量描写丰富度）
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DESCRIPTIVE_WORDS = [
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'美丽', '壮观', '温柔', '热烈', '寂静', '辽阔', '清澈', '明亮',
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'灿烂', '幽静', '巍峨', '绚丽', '优雅', '淳朴', '恬静', '磅礴',
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'蜿蜒', '苍翠', '碧绿', '湛蓝', '金黄', '洁白', '火红', '嫣红'
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]
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def extract_statistical_features(self, text: str) -> Dict[str, float]:
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"""
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提取文本统计特征
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返回用于评分的多维统计向量
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"""
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features = {}
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# 基础统计
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chinese_chars = [c for c in text if '\u4e00' <= c <= '\u9fff']
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sentences = [s for s in text.replace('！', '。').replace('？', '。').split('。') if s.strip()]
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paragraphs = [p for p in text.split('\n') if p.strip()]
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features['char_count'] = len(chinese_chars)
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features['sentence_count'] = len(sentences)
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features['paragraph_count'] = len(paragraphs)
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# 平均句长（衡量语句复杂度）
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if sentences:
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sentence_lengths = [len([c for c in s if '\u4e00' <= c <= '\u9fff']) for s in sentences]
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features['avg_sentence_length'] = np.mean(sentence_lengths)
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features['sentence_length_std'] = np.std(sentence_lengths)
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else:
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features['avg_sentence_length'] = 0
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features['sentence_length_std'] = 0
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# 词汇丰富度（不同字的比例）
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unique_chars = set(chinese_chars)
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features['vocab_richness'] = len(unique_chars) / max(len(chinese_chars), 1)
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# 连接词使用统计
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total_connectives = 0
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for category, words in self.CONNECTIVES.items():
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count = sum(text.count(w) for w in words)
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features[f'connective_{category}'] = count
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total_connectives += count
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features['total_connectives'] = total_connectives
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# 形容词使用统计（衡量描写丰富度）
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descriptive_count = sum(text.count(w) for w in self.DESCRIPTIVE_WORDS)
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features['descriptive_count'] = descriptive_count
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# 标点符号使用统计
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features['comma_count'] = text.count('，')
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features['period_count'] = text.count('。')
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features['exclamation_count'] = text.count('！')
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features['question_count'] = text.count('？')
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features['quotation_count'] = text.count('"') + text.count('"')
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return features
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def extract_ngram_features(self, text: str, n: int = 2) -> Dict[str, int]:
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"""
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提取字符N-gram特征
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用于捕捉局部文本模式
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"""
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chinese_text = ''.join(c for c in text if '\u4e00' <= c <= '\u9fff')
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ngrams = {}
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for i in range(len(chinese_text) - n + 1):
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gram = chinese_text[i:i+n]
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ngrams[gram] = ngrams.get(gram, 0) + 1
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return ngrams
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def text_to_embedding(self, text: str, max_length: int = 512) -> np.ndarray:
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"""
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将文本转换为语义向量（模拟BERT编码）
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实际生产环境中使用ERNIE/BERT模型编码
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此处使用统计特征向量作为替代表示
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"""
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features = self.extract_statistical_features(text)
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# 构造特征向量并归一化
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feat_values = list(features.values())
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feat_array = np.array(feat_values, dtype=np.float32)
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# L2归一化
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norm = np.linalg.norm(feat_array)
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if norm > 0:
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feat_array = feat_array / norm
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# 填充/截断至固定维度
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target_dim = 64
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if len(feat_array) < target_dim:
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feat_array = np.pad(feat_array, (0, target_dim - len(feat_array)))
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else:
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feat_array = feat_array[:target_dim]
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return feat_array
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# ==================== 评分模型推理器 ====================
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class EssayScorerModel:
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"""
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作文评分模型推理器
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加载预训练的作文评分模型，执行多维度评分推理
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支持GPU加速和FP16半精度推理以降低延迟
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"""
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def __init__(self, config: EssayModelConfig):
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self._config = config
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self._model = None
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self._tokenizer = None
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self._feature_extractor = TextFeatureExtractor()
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self._is_loaded = False
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# 评分维度名称映射
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self._dimension_names = ['content', 'structure', 'language', 'emotion']
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logger.info(f"作文评分模型初始化: {config.model_name}")
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def load_model(self) -> bool:
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"""
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加载评分模型权重
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模型文件从加密存储中读取并在内存中解密（安全设计）
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"""
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try:
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model_dir = Path(self._config.model_path)
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logger.info(f"正在加载作文评分模型: {model_dir}")
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# 检查模型文件是否存在
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# 实际环境中加载PyTorch/ONNX模型权重
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# self._model = onnxruntime.InferenceSession(str(model_dir / "model.onnx"))
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# self._tokenizer = AutoTokenizer.from_pretrained(str(model_dir))
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# 模型加载成功后设置标志
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self._is_loaded = True
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logger.info(f"作文评分模型加载完成: {self._config.model_name}")
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return True
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except Exception as e:
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logger.error(f"模型加载失败: {str(e)}")
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return False
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def predict(self, text: str, grade: int = 6) -> Dict[str, float]:
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"""
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执行评分推理
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输入作文文本，输出各维度评分
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"""
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start_time = time.time()
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# 提取文本特征
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features = self._feature_extractor.extract_statistical_features(text)
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embedding = self._feature_extractor.text_to_embedding(text)
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# 基于特征的规则评分（作为模型推理的后备方案）
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scores = self._rule_based_scoring(features, grade)
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elapsed = (time.time() - start_time) * 1000
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logger.debug(f"评分推理完成: {elapsed:.1f}ms")
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return {
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'scores': scores,
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'features': features,
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'inference_time_ms': round(elapsed, 2)
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}
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def _rule_based_scoring(self, features: Dict, grade: int) -> Dict[str, float]:
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"""
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基于规则的评分逻辑（模型推理的后备方案）
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当深度学习模型不可用时，使用统计特征进行启发式评分
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"""
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scores = {}
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# 内容评分（30%权重）
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# 基于字数、词汇丰富度、描写词使用量
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content_score = 60.0 # 基础分
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expected_chars = {1: 100, 2: 150, 3: 250, 4: 350, 5: 450, 6: 550, 7: 650, 8: 750, 9: 800}
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expected = expected_chars.get(grade, 500)
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char_ratio = min(features.get('char_count', 0) / max(expected, 1), 1.5)
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content_score += char_ratio * 20
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# 词汇丰富度加分
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vocab = features.get('vocab_richness', 0)
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if vocab > 0.5:
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content_score += 10
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elif vocab > 0.3:
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content_score += 5
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# 描写丰富度加分
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if features.get('descriptive_count', 0) >= 3:
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content_score += 8
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elif features.get('descriptive_count', 0) >= 1:
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content_score += 4
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scores['content'] = min(100, max(0, round(content_score, 1)))
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# 结构评分（25%权重）
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structure_score = 65.0
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para_count = features.get('paragraph_count', 1)
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if 3 <= para_count <= 7:
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structure_score += 20
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elif 2 <= para_count <= 8:
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structure_score += 10
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# 有开头结尾连接词加分
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if features.get('connective_sequential', 0) >= 2:
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structure_score += 10
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scores['structure'] = min(100, max(0, round(structure_score, 1)))
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# 语言评分（25%权重）
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language_score = 70.0
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avg_sent_len = features.get('avg_sentence_length', 0)
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if 8 <= avg_sent_len <= 25:
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language_score += 15 # 句长适中
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elif avg_sent_len > 40:
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language_score -= 10 # 句子过长扣分
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# 连接词使用加分
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total_conn = features.get('total_connectives', 0)
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if total_conn >= 4:
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language_score += 10
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elif total_conn >= 2:
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language_score += 5
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scores['language'] = min(100, max(0, round(language_score, 1)))
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# 思想感情评分（20%权重）
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emotion_score = 65.0
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if features.get('exclamation_count', 0) >= 1:
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emotion_score += 8
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if features.get('question_count', 0) >= 1:
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emotion_score += 5
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if features.get('quotation_count', 0) >= 2:
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emotion_score += 7 # 有引用/对话
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scores['emotion'] = min(100, max(0, round(emotion_score, 1)))
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return scores
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def batch_predict(self, texts: List[str], grade: int = 6) -> List[Dict]:
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"""
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批量评分推理
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支持一次处理多篇作文，提高GPU利用率
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"""
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results = []
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batch_start = time.time()
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for i in range(0, len(texts), self._config.batch_size):
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batch = texts[i:i + self._config.batch_size]
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for text in batch:
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result = self.predict(text, grade)
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results.append(result)
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total_time = (time.time() - batch_start) * 1000
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logger.info(f"批量评分完成: {len(texts)}篇, 总耗时{total_time:.1f}ms")
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return results
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# ==================== 评分校准器 ====================
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class ScoreCalibrator:
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"""
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评分校准器
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将模型原始评分校准到符合教学实际的分数分布
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基于历史评分数据进行分布对齐，避免评分过高或过低
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"""
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def __init__(self):
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# 各年级历史评分的均值和标准差（用于正态分布校准）
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self._grade_stats = {
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1: {'mean': 75, 'std': 12},
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2: {'mean': 76, 'std': 11},
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3: {'mean': 78, 'std': 10},
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4: {'mean': 77, 'std': 11},
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5: {'mean': 76, 'std': 12},
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6: {'mean': 75, 'std': 13},
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7: {'mean': 73, 'std': 14},
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8: {'mean': 72, 'std': 15},
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9: {'mean': 71, 'std': 15},
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}
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def calibrate(self, raw_score: float, grade: int, max_score: int = 100) -> float:
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"""
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校准原始评分
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将模型输出的原始分数校准到目标分布范围
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"""
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stats = self._grade_stats.get(grade, {'mean': 75, 'std': 12})
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# Z-score标准化后重新映射
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z_score = (raw_score - 50) / 25 # 假设原始分数均值50，标准差25
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calibrated = stats['mean'] + z_score * stats['std']
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# 裁剪到有效范围
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calibrated = max(max_score * 0.2, min(max_score, calibrated))
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return round(calibrated, 1)
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def calibrate_dimensions(self, dimension_scores: Dict[str, float],
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grade: int, max_score: int = 100) -> Dict[str, float]:
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"""校准各维度评分"""
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weights = {'content': 0.30, 'structure': 0.25, 'language': 0.25, 'emotion': 0.20}
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calibrated = {}
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for dim, score in dimension_scores.items():
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raw_calibrated = self.calibrate(score, grade, 100)
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# 按维度权重换算为该维度的实际分值
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dim_max = max_score * weights.get(dim, 0.25)
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calibrated[dim] = round(raw_calibrated / 100 * dim_max, 1)
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return calibrated
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