80 lines
2.9 KiB
Python
80 lines
2.9 KiB
Python
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import numpy as np
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from tensorflow.keras.preprocessing.sequence import pad_sequences
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from typing import List, Union
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class CNNInputProcessor:
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"""CNN模型输入数据处理器"""
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def __init__(self, max_length: int = 399):
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self.max_length = max_length
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self.base_to_num = {'A': 0, 'T': 1, 'C': 2, 'G': 3, 'N': 4}
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def trim_sequence(self, seq, target_length):
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"""
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从序列两端等量截取，使其达到目标长度，保持中心位置不变
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参数:
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seq: 原始序列
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target_length: 目标长度
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返回:
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截取后的序列
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"""
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if len(seq) <= target_length:
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return seq
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# 计算需要从每端截取的长度
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excess = len(seq) - target_length
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trim_each_side = excess // 2
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# 从两端等量截取，保持中心位置不变
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return seq[trim_each_side:len(seq)-trim_each_side]
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def prepare_sequence(self, sequence: str) -> np.ndarray:
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"""
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处理单个序列
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Args:
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sequence: DNA序列
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Returns:
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np.ndarray: 处理后的序列数组
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"""
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try:
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# 序列验证和预处理
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if not isinstance(sequence, str):
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sequence = str(sequence)
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sequence = sequence.upper().replace('U', 'T')
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# 如果序列长度不等于目标长度，进行截取
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if len(sequence) > self.max_length:
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sequence = self.trim_sequence(sequence, self.max_length)
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# 使用与训练时相同的编码方式
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self.base_to_num = {'A': 0, 'T': 1, 'C': 2, 'G': 3, 'N': 4} # 与SemiBilstmCnn.py中保持一致
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# 序列转换为数字
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seq_numeric = []
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for base in sequence:
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seq_numeric.append(self.base_to_num.get(base, 4)) # 未知碱基用4表示
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# 填充序列
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if len(seq_numeric) < self.max_length:
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seq_numeric.extend([4] * (self.max_length - len(seq_numeric)))
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# 重塑数据为三维数组 (samples, timesteps, features)
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result = np.array(seq_numeric).reshape(1, self.max_length, 1)
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# 检查结果维度
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if result.ndim != 3:
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print(f"警告: CNN输入维度异常 - {result.ndim}，应为3")
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# 强制修正为正确的维度
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result = result.reshape(1, self.max_length, 1)
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return result
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except Exception as e:
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print(f"CNN序列处理失败: {str(e)}")
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# 出错时返回全零的三维数组
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return np.zeros((1, self.max_length, 1))
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