yyh
/
FScanpy-package
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity
FScanpy-package/FScanpy/predictor.py

499 lines
22 KiB
Python
Raw Permalink Normal View History

优化绘图，增加使用介绍的ipynb
2025-06-11 21:18:52 +08:00
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, 8), dpi=300):
"""
Plot sequence PRF prediction results
Args:
sequence: Input DNA sequence
window_size: Sliding window size (default: 3)
short_threshold: Short model (HistGB) filtering threshold (default: 0.65)
long_threshold: Long model (BiLSTM-CNN) filtering threshold (default: 0.8)
ensemble_weight: Weight of short model in ensemble (default: 0.4)
title: Plot title (optional)
save_path: Save path (optional, saves plot if provided)
figsize: Figure size (default: (12, 8))
dpi: Figure resolution (default: 300)
Returns:
tuple: (pd.DataFrame, matplotlib.figure.Figure) prediction results and figure object
"""
try:
# Validate weight parameter
if not (0.0 <= ensemble_weight <= 1.0):
raise ValueError("ensemble_weight must be between 0.0 and 1.0")
long_weight = 1.0 - ensemble_weight
# Get prediction results
results_df = self.predict_sequence(sequence, window_size=window_size,
short_threshold=0.1, ensemble_weight=ensemble_weight)
if results_df.empty:
raise ValueError("Prediction results are empty, please check input sequence")
# Get sequence length
seq_length = len(sequence)
# Calculate display width
desired_visual_width = max(3, seq_length // 100) # FS site width ~1% of sequence length
prob_width = max(1, desired_visual_width // 3) # Prediction probability width is 1/3 of FS site width
# Create figure with three subplots, set height ratios
fig = plt.figure(figsize=figsize)
# Set title
if title:
fig.suptitle(title, y=0.95, fontsize=10)
else:
fig.suptitle(f'PRF Prediction Results (Weights {ensemble_weight:.1f}:{long_weight:.1f})', y=0.95, fontsize=10)
# Adjust subplot ratios, make top two heatmaps smaller
gs = fig.add_gridspec(3, 1, height_ratios=[0.1, 0.1, 1], hspace=0.2)
# FS site heatmap - using fixed width, no blur effect
ax0 = fig.add_subplot(gs[0])
fs_data = np.zeros((1, seq_length))
# Note: No actual FS site information in sliding window prediction, so keep empty or show predicted sites
# Show high-confidence predictions as potential FS sites
for _, row in results_df.iterrows():
pos = int(row['Position'])
if (row['Short_Probability'] >= short_threshold and
row['Long_Probability'] >= long_threshold and
row['Ensemble_Probability'] >= 0.8): # High confidence threshold
half_width = desired_visual_width // 2
start_pos = max(0, pos - half_width)
end_pos = min(seq_length, pos + half_width + 1)
fs_data[0, start_pos:end_pos] = 1 # Use fixed value, no gradient
ax0.imshow(fs_data, cmap='Reds', aspect='auto', interpolation='nearest')
ax0.set_xticks([])
ax0.set_yticks([])
ax0.set_title('FS site', pad=2, fontsize=8)
# Prediction probability heatmap - using fixed width to display probabilities
ax1 = fig.add_subplot(gs[1])
prob_data = np.zeros((1, seq_length))
# Apply dual threshold filtering
for _, row in results_df.iterrows():
pos = int(row['Position'])
if (row['Short_Probability'] >= short_threshold and
row['Long_Probability'] >= long_threshold):
# Set fixed width for each probability value
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 = ax1.imshow(prob_data, cmap='Reds', aspect='auto', vmin=0, vmax=1, interpolation='nearest')
ax1.set_xticks([])
ax1.set_yticks([])
ax1.set_title('Prediction', pad=2, fontsize=8)
# Main plot (bar chart)
ax2 = fig.add_subplot(gs[2])
# Apply filtering thresholds
filtered_probs = results_df['Ensemble_Probability'].copy()
mask = ((results_df['Short_Probability'] < short_threshold) |
(results_df['Long_Probability'] < long_threshold))
filtered_probs[mask] = 0
# Draw bar chart - use black color and alpha=0.6 to match prediction_sample style
ax2.bar(results_df['Position'], filtered_probs,
alpha=0.6, color='black', width=1.0)
# Set x-axis ticks
step = max(seq_length // 10, 50)
ax2.set_xticks(np.arange(0, seq_length, step))
ax2.tick_params(axis='x', rotation=45)
# Set labels
ax2.set_xlabel('Position')
ax2.set_ylabel('Probability')
# Set y-axis range
ax2.set_ylim(0, 1)
# Add grid
ax2.grid(True, alpha=0.3)
# Ensure all subplots have consistent x-axis range
for ax in [ax0, ax1, ax2]:
ax.set_xlim(-1, seq_length)
# Adjust layout
plt.tight_layout()
# Save plot if save path is provided
if save_path:
plt.savefig(save_path, dpi=dpi, bbox_inches='tight')
# Also save PDF version
if save_path.endswith('.png'):
pdf_path = save_path.replace('.png', '.pdf')
plt.savefig(pdf_path, bbox_inches='tight')
print(f"Plot saved to: {save_path}")
return results_df, fig
except Exception as e:
raise Exception(f"Error plotting sequence prediction: {str(e)}")
def predict_regions(self, sequences, short_threshold=0.1, ensemble_weight=0.4):
'''
Predict region sequences (batch prediction of known 399bp sequences)
Args:
sequences: 399bp sequences or DataFrame/Series/list containing 399bp sequences
short_threshold: Short model probability threshold (default: 0.1)
ensemble_weight: Weight of short model in ensemble (default: 0.4)
Returns:
DataFrame: DataFrame containing prediction probabilities for all sequences
'''
try:
# Validate weight parameter
if not (0.0 <= ensemble_weight <= 1.0):
raise ValueError("ensemble_weight must be between 0.0 and 1.0")
# Unify input format
if isinstance(sequences, (pd.DataFrame, pd.Series)):
sequences = sequences.tolist()
elif isinstance(sequences, str):
sequences = [sequences]
results = []
for i, seq399 in enumerate(sequences):
try:
# Extract central 33bp from 399bp sequence (for short model use)
seq33 = self._extract_center_sequence(seq399, target_length=self.short_seq_length)
# Use unified prediction method
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"Error processing sequence {i+1}: {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"Error in region prediction process: {str(e)}")
def _extract_center_sequence(self, sequence, target_length=33):
"""Extract subsequence of specified length from center position of sequence"""
# Ensure sequence is string
sequence = str(sequence).upper()
# If sequence length is less than target length, return original sequence
if len(sequence) <= target_length:
return sequence
# Calculate center position
center = len(sequence) // 2
half_target = target_length // 2
# Extract center sequence
start = center - half_target
end = start + target_length
# Boundary check
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']
ipynb文件创建，绘图函数完善
2025-05-29 17:58:48 +08:00
return results_df