Upload DataProcessor_pdeeppp.py with huggingface_hub

DataProcessor_pdeeppp.py

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+from transformers.processing_utils import ProcessorMixin
+from transformers.tokenization_utils_base import BatchEncoding
+
+
+class PDeepPPProcessor(ProcessorMixin):
+    def __init__(self, pad_char="X", target_length=33):
+        self.pad_char = pad_char
+        self.target_length = target_length
+    
+    def pad_sequence(self, seq):
+        """确保序列长度为 target_length，不足的部分用 pad_char 在两侧均匀填充"""
+        if len(seq) < self.target_length:
+            total_padding = self.target_length - len(seq)
+            left_padding = total_padding // 2
+            right_padding = total_padding - left_padding
+            seq = self.pad_char * left_padding + seq + self.pad_char * right_padding
+        return seq[:self.target_length]
+    
+    def extract_ptm_sequences(self, sequences):
+        """处理 PTM 数据，确保目标氨基酸（S、T、Y）位于序列中心"""
+        ptm_data = []
+        for seq in sequences:
+            for i in range(len(seq)):
+                if seq[i] in {'S', 'T', 'Y'}:  # 仅提取 S、T、Y 作为中心的片段
+                    start = max(0, i - self.target_length // 2)
+                    end = min(len(seq), start + self.target_length)
+                    padded_seq = self.pad_sequence(seq[start:end])
+                    ptm_data.append(padded_seq)
+        return ptm_data
+    
+    def extract_bps_sequences(self, sequences, overlapping=True, step_size=5):
+        """处理生物活性数据（BPS），关注整个序列，可重叠"""
+        bioactive_data = []
+        for seq in sequences:
+            if len(seq) < self.target_length:
+                # 如果序列长度不足，直接填充到 target_length
+                padded_seq = self.pad_sequence(seq)
+                bioactive_data.append(padded_seq)
+            else:
+                # 如果序列长度足够，按照滑动窗口提取片段
+                for i in range(0, len(seq) - self.target_length + 1, 
+                            step_size if overlapping else self.target_length):
+                    bioactive_data.append(self.pad_sequence(seq[i:i + self.target_length]))
+        return bioactive_data
+    
+    def __call__(
+        self, 
+        sequences, 
+        mode,  # 去除默认值，强制外部传入
+        overlapping=True,
+        step_size=5,
+        **kwargs
+    ):
+        """
+        预处理蛋白质序列，仅处理数据到指定长度。
+
+        Args:
+            sequences: 序列列表或单个序列字符串。
+            mode: 选择处理模式，必须从外部传入，"PTM" 或 "BPS"。
+            overlapping: BPS 模式下是否使用重叠窗口。
+            step_size: BPS 模式下的步长。
+        """
+        # 确保 sequences 是列表
+        if isinstance(sequences, str):
+            sequences = [sequences]
+            
+        # 根据模式提取序列
+        if mode == "PTM":
+            processed_sequences = self.extract_ptm_sequences(sequences)
+        elif mode == "BPS":
+            processed_sequences = self.extract_bps_sequences(
+                sequences, 
+                overlapping=overlapping,
+                step_size=step_size
+            )
+        else:
+            raise ValueError("Invalid mode. Please choose 'PTM' or 'BPS'.")
+        
+        if len(processed_sequences) == 0:
+            raise ValueError("No sequences processed. Check input data and processing logic.")
+
+        # 创建返回字典，仅包含预处理后的序列
+        model_inputs = {
+            "raw_sequences": processed_sequences,  # 预处理后的序列
+        }
+
+        return BatchEncoding(data=model_inputs)  # 返回处理后的数据