434 lines
20 KiB
Python
434 lines
20 KiB
Python
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import os
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import pickle
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import numpy as np
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import pandas as pd
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from tensorflow.keras.models import load_model
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from .features.sequence import SequenceFeatureExtractor
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from .features.cnn_input import CNNInputProcessor
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from .utils import extract_window_sequences
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import matplotlib.pyplot as plt
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import joblib
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class PRFPredictor:
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def __init__(self, model_dir=None):
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"""
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初始化PRF预测器
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Args:
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model_dir: 模型目录路径（可选）
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"""
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if model_dir is None:
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from pkg_resources import resource_filename
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model_dir = resource_filename('FScanpy', 'pretrained')
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try:
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# 加载模型
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self.gb_model = self._load_pickle(os.path.join(model_dir, 'GradientBoosting_all.pkl'))
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self.cnn_model = self._load_pickle(os.path.join(model_dir, 'BiLSTM-CNN_all.pkl'))
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self.voting_model = self._load_pickle(os.path.join(model_dir, 'Voting_all.pkl'))
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# 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
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self.gb_seq_length = 33 # HistGradientBoosting使用的序列长度
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self.cnn_seq_length = 399 # BiLSTM-CNN使用的序列长度
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# 初始化特征提取器和CNN输入处理器
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self.feature_extractor = SequenceFeatureExtractor(seq_length=self.gb_seq_length)
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self.cnn_processor = CNNInputProcessor(max_length=self.cnn_seq_length)
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except FileNotFoundError as e:
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raise FileNotFoundError(f"无法找到模型文件: {str(e)}")
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except Exception as e:
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raise Exception(f"加载模型出错: {str(e)}")
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def _load_pickle(self, path):
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return joblib.load(path)
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def predict_single_position(self, fs_period, full_seq, gb_threshold=0.1):
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'''
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预测单个位置的PRF状态
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Args:
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fs_period: 33bp序列 (将根据gb_seq_length处理)
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full_seq: 完整序列 (将根据cnn_seq_length处理)
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gb_threshold: GB模型的概率阈值 (默认为0.1)
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Returns:
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dict: 包含预测概率的字典
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'''
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try:
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# 处理序列长度
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if len(fs_period) > self.gb_seq_length:
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fs_period = self.feature_extractor.trim_sequence(fs_period, self.gb_seq_length)
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# GB模型预测 - 确保输入是二维数组
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try:
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gb_features = self.feature_extractor.extract_features(fs_period)
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# 检查特征结构并确保是一维数组
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if isinstance(gb_features, np.ndarray):
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# 如果是多维数组，进行扁平化处理
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if gb_features.ndim > 1:
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print(f"警告: 特征是{gb_features.ndim}维数组，进行扁平化处理")
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gb_features = gb_features.flatten()
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# 明确将特征转换为二维数组，正确形状为(1, n_features)
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gb_features_2d = np.array([gb_features])
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# 再次检查维度
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if gb_features_2d.ndim != 2:
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raise ValueError(f"处理后特征仍为{gb_features_2d.ndim}维，需要二维数组")
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gb_prob = self.gb_model.predict_proba(gb_features_2d)[0][1]
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except Exception as e:
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print(f"GB模型预测时出错: {str(e)}")
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# 出错时设置概率为0
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gb_prob = 0.0
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# 如果GB概率低于阈值，则跳过CNN模型
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if gb_prob < gb_threshold:
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return {
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'GB_Probability': gb_prob,
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'CNN_Probability': 0.0,
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'Voting_Probability': 0.0
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}
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# CNN模型预测
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try:
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# 首先检查CNN模型的类型 - 通过尝试识别模型类型
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is_sklearn_model = False
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# 检测模型类型的方法
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if hasattr(self.cnn_model, 'predict_proba'):
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# 这可能是一个scikit-learn模型
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is_sklearn_model = True
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if is_sklearn_model:
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# 如果是sklearn模型 (如HistGradientBoostingClassifier)，使用与GB相同的特征提取
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# 为CNN模型使用相同的特征提取方法，但从399bp序列中提取
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cnn_features = self.feature_extractor.extract_features(full_seq)
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if isinstance(cnn_features, np.ndarray) and cnn_features.ndim > 1:
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cnn_features = cnn_features.flatten()
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# 转为二维数组
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cnn_features_2d = np.array([cnn_features])
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cnn_pred = self.cnn_model.predict_proba(cnn_features_2d)
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cnn_prob = cnn_pred[0][1]
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else:
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# 假设是深度学习模型，需要三维输入
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cnn_input = self.cnn_processor.prepare_sequence(full_seq)
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# 尝试不同的预测方法
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try:
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# 先尝试不带参数
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cnn_pred = self.cnn_model.predict(cnn_input)
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except TypeError:
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try:
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# 再尝试带verbose参数
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cnn_pred = self.cnn_model.predict(cnn_input, verbose=0)
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except Exception:
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# 最后尝试将输入重塑为2D
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reshaped_input = cnn_input.reshape(1, -1)
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cnn_pred = self.cnn_model.predict(reshaped_input)
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# 处理预测结果
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if isinstance(cnn_pred, list):
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cnn_pred = cnn_pred[0]
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# 提取概率值
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if hasattr(cnn_pred, 'shape') and len(cnn_pred.shape) > 1 and cnn_pred.shape[1] > 1:
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cnn_prob = cnn_pred[0][1]
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else:
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cnn_prob = cnn_pred[0][0] if hasattr(cnn_pred[0], '__getitem__') else cnn_pred[0]
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except Exception as e:
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print(f"CNN模型预测时出错: {str(e)}")
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# 出错时设置概率为0
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cnn_prob = 0.0
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# 投票模型预测
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try:
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# 确保投票模型输入是二维数组 (1, n_features)
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voting_input = np.array([[gb_prob, cnn_prob]])
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voting_prob = self.voting_model.predict_proba(voting_input)[0][1]
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except Exception as e:
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print(f"投票模型预测时出错: {str(e)}")
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# 出错时使用两个模型的平均值
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voting_prob = (gb_prob + cnn_prob) / 2
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return {
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'GB_Probability': gb_prob,
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'CNN_Probability': cnn_prob,
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'Voting_Probability': voting_prob
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}
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except Exception as e:
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raise Exception(f"预测过程出错: {str(e)}")
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def predict_full(self, sequence, window_size=3, gb_threshold=0.1, plot=False):
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"""
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预测完整序列中的PRF位点
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Args:
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sequence: 输入DNA序列
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window_size: 滑动窗口大小 (默认为3)
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gb_threshold: GB模型概率阈值 (默认为0.1)
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plot: 是否绘制预测结果图表 (默认为False)
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Returns:
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if plot=False:
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pd.DataFrame: 包含预测结果的DataFrame
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if plot=True:
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tuple: (pd.DataFrame, matplotlib.figure.Figure)
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"""
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if window_size < 1:
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raise ValueError("窗口大小必须大于等于1")
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if gb_threshold < 0:
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raise ValueError("GB阈值必须大于等于0")
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results = []
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try:
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# 确保序列为字符串并转换为大写
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sequence = str(sequence).upper()
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# 滑动窗口预测
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for pos in range(0, len(sequence) - 2, window_size):
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# 提取窗口序列 - 使用与训练时相同的窗口大小
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fs_period, full_seq = extract_window_sequences(sequence, pos)
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if fs_period is None or full_seq is None:
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continue
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# 预测并记录结果
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pred = self.predict_single_position(fs_period, full_seq, gb_threshold)
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pred.update({
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'Position': pos,
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'Codon': sequence[pos:pos+3],
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'33bp': fs_period,
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'399bp': full_seq
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})
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results.append(pred)
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# 创建结果DataFrame
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results_df = pd.DataFrame(results)
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# 如需绘图
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if plot:
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# 创建图形
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fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(15, 10), height_ratios=[2, 1])
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# 绘制折线图
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ax1.plot(results_df['Position'], results_df['GB_Probability'],
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label='GB模型', alpha=0.7, linewidth=1.5)
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ax1.plot(results_df['Position'], results_df['CNN_Probability'],
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label='CNN模型', alpha=0.7, linewidth=1.5)
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ax1.plot(results_df['Position'], results_df['Voting_Probability'],
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label='投票模型', linewidth=2, color='red')
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ax1.set_xlabel('序列位置')
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ax1.set_ylabel('移码概率')
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ax1.set_title('移码预测概率')
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ax1.legend()
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ax1.grid(True, alpha=0.3)
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# 准备热图数据
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positions = results_df['Position'].values
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probabilities = results_df['Voting_Probability'].values
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# 创建热图矩阵
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heatmap_matrix = np.zeros((1, len(positions)))
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heatmap_matrix[0, :] = probabilities
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# 绘制热图
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im = ax2.imshow(heatmap_matrix, aspect='auto', cmap='YlOrRd',
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extent=[min(positions), max(positions), 0, 1])
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# 添加颜色条
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cbar = plt.colorbar(im, ax=ax2)
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cbar.set_label('移码概率')
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# 设置热图轴标签
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ax2.set_xlabel('序列位置')
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ax2.set_title('移码概率热图')
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ax2.set_yticks([])
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# 调整布局
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plt.tight_layout()
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return results_df, fig
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return results_df
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except Exception as e:
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raise Exception(f"序列预测过程出错: {str(e)}")
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def predict_region(self, seq, gb_threshold=0.1):
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'''
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预测区域序列
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Args:
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seq: 399bp序列或包含399bp序列的DataFrame/Series
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gb_threshold: GB模型概率阈值 (默认为0.1)
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Returns:
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DataFrame: 包含所有序列预测概率的DataFrame
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'''
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try:
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# 如果输入是DataFrame或Series，转换为列表
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if isinstance(seq, (pd.DataFrame, pd.Series)):
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seq = seq.tolist()
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# 如果输入是单个字符串，转换为列表
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if isinstance(seq, str):
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seq = [seq]
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results = []
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for i, seq399 in enumerate(seq):
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try:
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# 从399bp序列中截取中心的33bp (GB模型使用)
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seq33 = self._extract_center_sequence(seq399, target_length=self.gb_seq_length)
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# GB模型预测 - 确保输入是二维数组
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try:
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gb_features = self.feature_extractor.extract_features(seq33)
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# 检查特征结构并确保是一维数组
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if isinstance(gb_features, np.ndarray):
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# 如果是多维数组，进行扁平化处理
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if gb_features.ndim > 1:
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print(f"警告: 序列 {i+1} 的特征是{gb_features.ndim}维数组，进行扁平化处理")
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gb_features = gb_features.flatten()
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# 明确将特征转换为二维数组，正确形状为(1, n_features)
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gb_features_2d = np.array([gb_features])
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# 再次检查维度
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if gb_features_2d.ndim != 2:
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raise ValueError(f"处理后特征仍为{gb_features_2d.ndim}维，需要二维数组")
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gb_prob = self.gb_model.predict_proba(gb_features_2d)[0][1]
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except Exception as e:
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print(f"GB模型预测序列 {i+1} 时出错: {str(e)}")
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# 出错时设置概率为0
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gb_prob = 0.0
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# 如果GB概率低于阈值，添加低概率结果
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|
|
if gb_prob < gb_threshold:
|
|||
|
|
results.append({
|
|||
|
|
'GB_Probability': gb_prob,
|
|||
|
|
'CNN_Probability': 0.0,
|
|||
|
|
'Voting_Probability': 0.0,
|
|||
|
|
'33bp': seq33,
|
|||
|
|
'399bp': seq399
|
|||
|
|
})
|
|||
|
|
continue
|
|||
|
|
|
|||
|
|
# CNN模型预测
|
|||
|
|
try:
|
|||
|
|
# 首先检查CNN模型的类型 - 通过尝试识别模型类型
|
|||
|
|
is_sklearn_model = False
|
|||
|
|
|
|||
|
|
# 检测模型类型的方法
|
|||
|
|
if hasattr(self.cnn_model, 'predict_proba'):
|
|||
|
|
# 这可能是一个scikit-learn模型
|
|||
|
|
is_sklearn_model = True
|
|||
|
|
|
|||
|
|
if is_sklearn_model:
|
|||
|
|
# 如果是sklearn模型 (如HistGradientBoostingClassifier)，使用与GB相同的特征提取
|
|||
|
|
# 为CNN模型使用相同的特征提取方法，但从399bp序列中提取
|
|||
|
|
cnn_features = self.feature_extractor.extract_features(seq399)
|
|||
|
|
if isinstance(cnn_features, np.ndarray) and cnn_features.ndim > 1:
|
|||
|
|
cnn_features = cnn_features.flatten()
|
|||
|
|
# 转为二维数组
|
|||
|
|
cnn_features_2d = np.array([cnn_features])
|
|||
|
|
cnn_pred = self.cnn_model.predict_proba(cnn_features_2d)
|
|||
|
|
cnn_prob = cnn_pred[0][1]
|
|||
|
|
else:
|
|||
|
|
# 假设是深度学习模型，需要三维输入
|
|||
|
|
cnn_input = self.cnn_processor.prepare_sequence(seq399)
|
|||
|
|
# 尝试不同的预测方法
|
|||
|
|
try:
|
|||
|
|
# 先尝试不带参数
|
|||
|
|
cnn_pred = self.cnn_model.predict(cnn_input)
|
|||
|
|
except TypeError:
|
|||
|
|
try:
|
|||
|
|
# 再尝试带verbose参数
|
|||
|
|
cnn_pred = self.cnn_model.predict(cnn_input, verbose=0)
|
|||
|
|
except Exception:
|
|||
|
|
# 最后尝试将输入重塑为2D
|
|||
|
|
reshaped_input = cnn_input.reshape(1, -1)
|
|||
|
|
cnn_pred = self.cnn_model.predict(reshaped_input)
|
|||
|
|
|
|||
|
|
# 处理预测结果
|
|||
|
|
if isinstance(cnn_pred, list):
|
|||
|
|
cnn_pred = cnn_pred[0]
|
|||
|
|
|
|||
|
|
# 提取概率值
|
|||
|
|
if hasattr(cnn_pred, 'shape') and len(cnn_pred.shape) > 1 and cnn_pred.shape[1] > 1:
|
|||
|
|
cnn_prob = cnn_pred[0][1]
|
|||
|
|
else:
|
|||
|
|
cnn_prob = cnn_pred[0][0] if hasattr(cnn_pred[0], '__getitem__') else cnn_pred[0]
|
|||
|
|
except Exception as e:
|
|||
|
|
print(f"CNN模型预测序列 {i+1} 时出错: {str(e)}")
|
|||
|
|
# 出错时设置概率为0
|
|||
|
|
cnn_prob = 0.0
|
|||
|
|
|
|||
|
|
# 投票模型预测
|
|||
|
|
try:
|
|||
|
|
# 确保投票模型输入是二维数组 (1, n_features)
|
|||
|
|
voting_input = np.array([[gb_prob, cnn_prob]])
|
|||
|
|
voting_prob = self.voting_model.predict_proba(voting_input)[0][1]
|
|||
|
|
except Exception as e:
|
|||
|
|
print(f"投票模型预测序列 {i+1} 时出错: {str(e)}")
|
|||
|
|
# 出错时使用两个模型的平均值
|
|||
|
|
voting_prob = (gb_prob + cnn_prob) / 2
|
|||
|
|
|
|||
|
|
results.append({
|
|||
|
|
'GB_Probability': gb_prob,
|
|||
|
|
'CNN_Probability': cnn_prob,
|
|||
|
|
'Voting_Probability': voting_prob,
|
|||
|
|
'33bp': seq33,
|
|||
|
|
'399bp': seq399
|
|||
|
|
})
|
|||
|
|
|
|||
|
|
except Exception as e:
|
|||
|
|
print(f"处理第 {i+1} 个序列时出错: {str(e)}")
|
|||
|
|
results.append({
|
|||
|
|
'GB_Probability': 0.0,
|
|||
|
|
'CNN_Probability': 0.0,
|
|||
|
|
'Voting_Probability': 0.0,
|
|||
|
|
'33bp': self._extract_center_sequence(seq399, target_length=self.gb_seq_length) if len(seq399) >= self.gb_seq_length else seq399,
|
|||
|
|
'399bp': 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]
|