import os import pickle import numpy as np import pandas as pd from tensorflow.keras.models import load_model from .features.sequence import SequenceFeatureExtractor from .features.cnn_input import CNNInputProcessor from .utils import extract_window_sequences import matplotlib.pyplot as plt import joblib class PRFPredictor: def __init__(self, model_dir=None): """ 初始化PRF预测器 Args: model_dir: 模型目录路径（可选） """ if model_dir is None: from pkg_resources import resource_filename model_dir = resource_filename('FScanpy', 'pretrained') try: # 加载模型 - 使用新的命名约定 self.short_model = self._load_pickle(os.path.join(model_dir, 'short.pkl')) # HistGB模型 self.long_model = self._load_pickle(os.path.join(model_dir, 'long.pkl')) # BiLSTM-CNN模型 # 初始化特征提取器和CNN处理器，使用与训练时相同的序列长度 self.short_seq_length = 33 # HistGB使用的序列长度 self.long_seq_length = 399 # BiLSTM-CNN使用的序列长度 # 初始化特征提取器和CNN输入处理器 self.feature_extractor = SequenceFeatureExtractor(seq_length=self.short_seq_length) self.cnn_processor = CNNInputProcessor(max_length=self.long_seq_length) # 检测模型类型以优化预测性能 self._detect_model_types() except FileNotFoundError as e: raise FileNotFoundError(f"无法找到模型文件: {str(e)}。请确保模型文件 'short.pkl' 和 'long.pkl' 存在于 {model_dir}") except Exception as e: raise Exception(f"加载模型出错: {str(e)}") def _load_pickle(self, path): """安全加载pickle文件""" try: return joblib.load(path) except Exception as e: raise FileNotFoundError(f"无法加载模型文件 {path}: {str(e)}") def _detect_model_types(self): """检测模型类型以优化预测性能""" self.short_is_sklearn = hasattr(self.short_model, 'predict_proba') self.long_is_sklearn = hasattr(self.long_model, 'predict_proba') def _predict_model(self, model, features, is_sklearn, seq_length): """统一的模型预测方法""" try: if is_sklearn: # sklearn模型使用特征向量 if isinstance(features, np.ndarray) and features.ndim > 1: features = features.flatten() features_2d = np.array([features]) pred = model.predict_proba(features_2d) return pred[0][1] else: # 深度学习模型 if seq_length == self.long_seq_length: # 对于长序列，使用CNN处理器 model_input = self.cnn_processor.prepare_sequence(features) else: # 对于短序列，转换为数值编码 base_to_num = {'A': 1, 'T': 2, 'G': 3, 'C': 4, 'N': 0} seq_numeric = [base_to_num.get(base, 0) for base in features.upper()] model_input = np.array(seq_numeric).reshape(1, len(seq_numeric), 1) # 统一的预测调用 try: pred = model.predict(model_input, verbose=0) except TypeError: pred = model.predict(model_input) # 处理预测结果 if isinstance(pred, list): pred = pred[0] if hasattr(pred, 'shape') and len(pred.shape) > 1 and pred.shape[1] > 1: return pred[0][1] else: return pred[0][0] if hasattr(pred[0], '__getitem__') else pred[0] except Exception as e: raise Exception(f"模型预测失败: {str(e)}") def predict_single_position(self, fs_period, full_seq, short_threshold=0.1, ensemble_weight=0.4): ''' 预测单个位置的PRF状态 Args: fs_period: 33bp序列 (short模型使用) full_seq: 完整序列 (long模型使用) short_threshold: short模型的概率阈值 (默认为0.1) ensemble_weight: short模型在集成中的权重 (默认为0.4，long权重为0.6) Returns: dict: 包含预测概率的字典 ''' try: # 验证权重参数 if not (0.0 <= ensemble_weight <= 1.0): raise ValueError("ensemble_weight 必须在 0.0 到 1.0 之间") long_weight = 1.0 - ensemble_weight # 处理序列长度 if len(fs_period) > self.short_seq_length: fs_period = self.feature_extractor.trim_sequence(fs_period, self.short_seq_length) # Short模型预测 (HistGB) try: if self.short_is_sklearn: short_features = self.feature_extractor.extract_features(fs_period) short_prob = self._predict_model(self.short_model, short_features, True, self.short_seq_length) else: short_prob = self._predict_model(self.short_model, fs_period, False, self.short_seq_length) except Exception as e: print(f"Short模型预测时出错: {str(e)}") short_prob = 0.0 # 如果short概率低于阈值，则跳过long模型 if short_prob < short_threshold: return { 'Short_Probability': short_prob, 'Long_Probability': 0.0, 'Ensemble_Probability': 0.0, 'Ensemble_Weights': f'Short:{ensemble_weight:.1f}, Long:{long_weight:.1f}' } # Long模型预测 (BiLSTM-CNN) try: if self.long_is_sklearn: long_features = self.feature_extractor.extract_features(full_seq) long_prob = self._predict_model(self.long_model, long_features, True, self.long_seq_length) else: long_prob = self._predict_model(self.long_model, full_seq, False, self.long_seq_length) except Exception as e: print(f"Long模型预测时出错: {str(e)}") long_prob = 0.0 # 计算集成概率 try: ensemble_prob = ensemble_weight * short_prob + long_weight * long_prob except Exception as e: print(f"计算集成概率时出错: {str(e)}") ensemble_prob = (short_prob + long_prob) / 2 return { 'Short_Probability': short_prob, 'Long_Probability': long_prob, 'Ensemble_Probability': ensemble_prob, 'Ensemble_Weights': f'Short:{ensemble_weight:.1f}, Long:{long_weight:.1f}' } except Exception as e: raise Exception(f"预测过程出错: {str(e)}") def predict_sequence(self, sequence, window_size=3, short_threshold=0.1, ensemble_weight=0.4): """ 预测完整序列中的PRF位点（滑动窗口方法） Args: sequence: 输入DNA序列 window_size: 滑动窗口大小 (默认为3) short_threshold: short模型概率阈值 (默认为0.1) ensemble_weight: short模型在集成中的权重 (默认为0.4) Returns: pd.DataFrame: 包含预测结果的DataFrame """ if window_size < 1: raise ValueError("窗口大小必须大于等于1") if short_threshold < 0: raise ValueError("short模型阈值必须大于等于0") if not (0.0 <= ensemble_weight <= 1.0): raise ValueError("ensemble_weight 必须在 0.0 到 1.0 之间") results = [] long_weight = 1.0 - ensemble_weight try: # 确保序列为字符串并转换为大写 sequence = str(sequence).upper() # 滑动窗口预测 for pos in range(0, len(sequence) - 2, window_size): # 提取窗口序列 fs_period, full_seq = extract_window_sequences(sequence, pos) if fs_period is None or full_seq is None: continue # 预测并记录结果 pred = self.predict_single_position(fs_period, full_seq, short_threshold, ensemble_weight) pred.update({ 'Position': pos, 'Codon': sequence[pos:pos+3], 'Short_Sequence': fs_period, # 更清晰的命名 'Long_Sequence': full_seq # 更清晰的命名 }) results.append(pred) # 创建结果DataFrame results_df = pd.DataFrame(results) return results_df except Exception as e: raise Exception(f"序列预测过程出错: {str(e)}") def plot_sequence_prediction(self, sequence, window_size=3, short_threshold=0.65, long_threshold=0.8, ensemble_weight=0.4, title=None, save_path=None, figsize=(12, 6), dpi=300): """ 绘制序列预测结果的移码概率图 Args: sequence: 输入DNA序列 window_size: 滑动窗口大小 (默认为3) short_threshold: Short模型(HistGB)过滤阈值 (默认为0.65) long_threshold: Long模型(BiLSTM-CNN)过滤阈值 (默认为0.8) ensemble_weight: Short模型在集成中的权重 (默认为0.4) title: 图片标题 (可选) save_path: 保存路径 (可选，如果提供则保存图片) figsize: 图片尺寸 (默认为(12, 6)) dpi: 图片分辨率 (默认为300) Returns: tuple: (pd.DataFrame, matplotlib.figure.Figure) 预测结果和图形对象 """ try: # 验证权重参数 if not (0.0 <= ensemble_weight <= 1.0): raise ValueError("ensemble_weight 必须在 0.0 到 1.0 之间") long_weight = 1.0 - ensemble_weight # 获取预测结果 - 使用新的方法名 results_df = self.predict_sequence(sequence, window_size=window_size, short_threshold=0.1, ensemble_weight=ensemble_weight) if results_df.empty: raise ValueError("预测结果为空，请检查输入序列") # 获取序列长度 seq_length = len(sequence) # 计算显示宽度 prob_width = max(1, seq_length // 300) # 概率标记的宽度 # 创建图形，包含两个子图 fig = plt.figure(figsize=figsize) gs = fig.add_gridspec(2, 1, height_ratios=[0.15, 1], hspace=0.3) # 设置标题 if title: fig.suptitle(title, y=0.95, fontsize=12) else: fig.suptitle(f'序列移码概率预测结果 (权重 {ensemble_weight:.1f}:{long_weight:.1f})', y=0.95, fontsize=12) # 预测概率热图 ax0 = fig.add_subplot(gs[0]) prob_data = np.zeros((1, seq_length)) # 应用双重阈值过滤 for _, row in results_df.iterrows(): pos = int(row['Position']) if (row['Short_Probability'] >= short_threshold and row['Long_Probability'] >= long_threshold): # 为每个满足阈值的位置设置概率值 start = max(0, pos - prob_width//2) end = min(seq_length, pos + prob_width//2 + 1) prob_data[0, start:end] = row['Ensemble_Probability'] im = ax0.imshow(prob_data, cmap='Reds', aspect='auto', vmin=0, vmax=1, interpolation='nearest') ax0.set_xticks([]) ax0.set_yticks([]) ax0.set_title(f'预测概率热图 (Short≥{short_threshold}, Long≥{long_threshold})', pad=5, fontsize=10) # 主图（条形图） ax1 = fig.add_subplot(gs[1]) # 应用过滤阈值 filtered_probs = results_df['Ensemble_Probability'].copy() mask = ((results_df['Short_Probability'] < short_threshold) | (results_df['Long_Probability'] < long_threshold)) filtered_probs[mask] = 0 # 绘制条形图 bars = ax1.bar(results_df['Position'], filtered_probs, alpha=0.7, color='darkred', width=max(1, window_size)) # 设置x轴刻度 step = max(seq_length // 10, 50) x_ticks = np.arange(0, seq_length, step) ax1.set_xticks(x_ticks) ax1.tick_params(axis='x', rotation=45) # 设置标签和标题 ax1.set_xlabel('序列位置 (bp)', fontsize=10) ax1.set_ylabel('移码概率', fontsize=10) ax1.set_title(f'移码概率分布 (集成权重 {ensemble_weight:.1f}:{long_weight:.1f})', fontsize=11) # 设置y轴范围 ax1.set_ylim(0, 1) # 添加网格 ax1.grid(True, alpha=0.3) # 添加阈值和权重说明 info_text = (f'过滤阈值: Short≥{short_threshold}, Long≥{long_threshold}\n' f'集成权重: Short:{ensemble_weight:.1f}, Long:{long_weight:.1f}') ax1.text(0.02, 0.95, info_text, transform=ax1.transAxes, fontsize=9, verticalalignment='top', bbox=dict(boxstyle='round', facecolor='lightgray', alpha=0.8)) # 确保所有子图的x轴范围一致 for ax in [ax0, ax1]: ax.set_xlim(-1, seq_length) # 调整布局 plt.tight_layout() # 如果提供了保存路径，则保存图片 if save_path: plt.savefig(save_path, dpi=dpi, bbox_inches='tight') # 同时保存PDF版本 if save_path.endswith('.png'): pdf_path = save_path.replace('.png', '.pdf') plt.savefig(pdf_path, bbox_inches='tight') print(f"图片已保存至: {save_path}") return results_df, fig except Exception as e: raise Exception(f"绘制序列预测图时出错: {str(e)}") def predict_regions(self, sequences, short_threshold=0.1, ensemble_weight=0.4): ''' 预测区域序列（批量预测已知的399bp序列） Args: sequences: 399bp序列或包含399bp序列的DataFrame/Series/list short_threshold: short模型概率阈值 (默认为0.1) ensemble_weight: short模型在集成中的权重 (默认为0.4) Returns: DataFrame: 包含所有序列预测概率的DataFrame ''' try: # 验证权重参数 if not (0.0 <= ensemble_weight <= 1.0): raise ValueError("ensemble_weight 必须在 0.0 到 1.0 之间") # 统一输入格式 if isinstance(sequences, (pd.DataFrame, pd.Series)): sequences = sequences.tolist() elif isinstance(sequences, str): sequences = [sequences] results = [] for i, seq399 in enumerate(sequences): try: # 从399bp序列中截取中心的33bp (short模型使用) seq33 = self._extract_center_sequence(seq399, target_length=self.short_seq_length) # 使用统一的预测方法 pred_result = self.predict_single_position(seq33, seq399, short_threshold, ensemble_weight) pred_result.update({ 'Short_Sequence': seq33, 'Long_Sequence': seq399 }) results.append(pred_result) except Exception as e: print(f"处理第 {i+1} 个序列时出错: {str(e)}") long_weight = 1.0 - ensemble_weight results.append({ 'Short_Probability': 0.0, 'Long_Probability': 0.0, 'Ensemble_Probability': 0.0, 'Ensemble_Weights': f'Short:{ensemble_weight:.1f}, Long:{long_weight:.1f}', 'Short_Sequence': self._extract_center_sequence(seq399, target_length=self.short_seq_length) if len(seq399) >= self.short_seq_length else seq399, 'Long_Sequence': seq399 }) return pd.DataFrame(results) except Exception as e: raise Exception(f"区域预测过程出错: {str(e)}") def _extract_center_sequence(self, sequence, target_length=33): """从序列中心位置提取指定长度的子序列""" # 确保序列为字符串 sequence = str(sequence).upper() # 如果序列长度小于目标长度，返回原序列 if len(sequence) <= target_length: return sequence # 计算中心位置 center = len(sequence) // 2 half_target = target_length // 2 # 提取中心序列 start = center - half_target end = start + target_length # 边界检查 if start < 0: start = 0 end = target_length elif end > len(sequence): end = len(sequence) start = end - target_length return sequence[start:end] # 兼容性方法（向后兼容，但标记为废弃） def predict_full(self, sequence, window_size=3, short_threshold=0.1, short_weight=0.4, plot=False): """ ⚠️ 已废弃：请使用 predict_sequence() 方法 向后兼容的方法，内部调用新的 predict_sequence() """ import warnings warnings.warn("predict_full() 已废弃，请使用 predict_sequence() 方法", DeprecationWarning, stacklevel=2) # 调用新方法并添加兼容性字段 results_df = self.predict_sequence(sequence, window_size, short_threshold, short_weight) # 添加兼容性字段 if 'Ensemble_Probability' in results_df.columns: results_df['Voting_Probability'] = results_df['Ensemble_Probability'] results_df['Weighted_Probability'] = results_df['Ensemble_Probability'] if 'Ensemble_Weights' in results_df.columns: results_df['Weight_Info'] = results_df['Ensemble_Weights'] if 'Short_Sequence' in results_df.columns: results_df['33bp'] = results_df['Short_Sequence'] if 'Long_Sequence' in results_df.columns: results_df['399bp'] = results_df['Long_Sequence'] if plot: # 如果需要绘图，调用绘图方法 _, fig = self.plot_sequence_prediction(sequence, window_size, 0.65, 0.8, short_weight) return results_df, fig return results_df def predict_region(self, seq, short_threshold=0.1, short_weight=0.4): """ ⚠️ 已废弃：请使用 predict_regions() 方法 向后兼容的方法，内部调用新的 predict_regions() """ import warnings warnings.warn("predict_region() 已废弃，请使用 predict_regions() 方法", DeprecationWarning, stacklevel=2) # 调用新方法并添加兼容性字段 results_df = self.predict_regions(seq, short_threshold, short_weight) # 添加兼容性字段 if 'Ensemble_Probability' in results_df.columns: results_df['Voting_Probability'] = results_df['Ensemble_Probability'] results_df['Weighted_Probability'] = results_df['Ensemble_Probability'] if 'Ensemble_Weights' in results_df.columns: results_df['Weight_Info'] = results_df['Ensemble_Weights'] if 'Short_Sequence' in results_df.columns: results_df['33bp'] = results_df['Short_Sequence'] if 'Long_Sequence' in results_df.columns: results_df['399bp'] = results_df['Long_Sequence'] return results_df