重新运行demo，并进行路径管理
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@ -267,9 +267,9 @@ This project is licensed under the MIT License - see the [LICENSE](LICENSE) file
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## 🆘 Support
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- **Issues**: [GitHub Issues](https://github.com/your-org/FScanpy/issues)
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- **Documentation**: [Tutorial](tutorial/tutorial.md)
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- **Examples**: [Demo Notebook](FScanpy_Demo.ipynb)
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- **Usage Example**: [Demo Notebook](FScanpy_Demo.ipynb)
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- **Predict Result Explain**: [Predict Result Explain](tutorial/predict_sample.ipynb)
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## 🏗️ Dependencies
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@ -1,362 +0,0 @@
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#!/usr/bin/env python3
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"""
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FScanpy 序列预测绘图示例
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展示如何使用新的 plot_prf_prediction 函数绘制序列的移码概率预测结果
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包含集成权重参数的使用示例
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"""
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import matplotlib.pyplot as plt
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import os
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from FScanpy import plot_prf_prediction, PRFPredictor
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def example_basic_plotting():
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"""基础绘图示例"""
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print("=" * 50)
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print("基础绘图示例")
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print("=" * 50)
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# 示例序列（可以替换为您的实际序列）
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example_sequence = (
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"ATGCGTACGTTAGCGATCGATCGTAGCTAGCTAGCTAGCTAGCTAGCTAGCTAGC"
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"GATCGATCGTAGCTAGCTAGCTAGCTAGCTAGCTAGCTAGCGATCGATCGTAGCT"
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"AGCTAGCTAGCTAGCTAGCTAGCTAGCGATCGATCGTAGCTAGCTAGCTAGCTAG"
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"CTAGCTAGCTAGCGATCGATCGTAGCTAGCTAGCTAGCTAGCTAGCTAGCTAGC"
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"GATCGATCGTAGCTAGCTAGCTAGCTAGCTAGCTAGCTAGCGATCGATCGTAGCT"
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"AGCTAGCTAGCTAGCTAGCTAGCTAGCGATCGATCGTAGCTAGCTAGCTAGCTAG"
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"CTAGCTAGCTAGCGATCGATCGTAGCTAGCTAGCTAGCTAGCTAGCTAGCTAGC"
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"GATCGATCGTAGCTAGCTAGCTAGCTAGCTAGCTAGCTAGCGATCGATCGTAGCT"
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"AGCTAGCTAGCTAGCTAGCTAGCTAGCGATCGATCGTAGCTAGCTAGCTAGCTAG"
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"CTAGCTAGCTAGCGATCGATCGTAGCTAGCTAGCTAGCTAGCTAGCTAGCTAGC"
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)
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try:
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# 使用默认参数绘图 (0.4:0.6 集成权重比例)
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results, fig = plot_prf_prediction(
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sequence=example_sequence,
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title="示例序列的移码概率预测 (默认集成权重 4:6)"
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)
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print(f"预测完成！共处理 {len(results)} 个位置")
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print(f"满足阈值条件的位点数: {len(results[results['Ensemble_Probability'] > 0])}")
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print(f"使用集成权重比例: Short模型 0.4, Long模型 0.6")
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# 显示图片
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plt.show()
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return results, fig
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except Exception as e:
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print(f"绘图过程中出错: {str(e)}")
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return None, None
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def example_custom_ensemble_weights():
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"""自定义集成权重示例"""
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print("=" * 50)
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print("自定义集成权重绘图示例")
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print("=" * 50)
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# 示例序列
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example_sequence = (
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"ATGCGTACGTTAGCGATCGATCGTAGCTAGCTAGCTAGCTAGCTAGCTAGCTAGC"
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"GATCGATCGTAGCTAGCTAGCTAGCTAGCTAGCTAGCTAGCGATCGATCGTAGCT"
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"AGCTAGCTAGCTAGCTAGCTAGCTAGCGATCGATCGTAGCTAGCTAGCTAGCTAG"
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"CTAGCTAGCTAGCGATCGATCGTAGCTAGCTAGCTAGCTAGCTAGCTAGCTAGC"
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"GATCGATCGTAGCTAGCTAGCTAGCTAGCTAGCTAGCTAGCGATCGATCGTAGCT"
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)
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# 测试不同的集成权重比例
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weight_configs = [
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(0.2, "Long模型主导 (2:8)"),
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(0.5, "等权重组合 (5:5)"),
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(0.7, "Short模型主导 (7:3)")
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]
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for ensemble_weight, description in weight_configs:
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print(f"\n测试集成权重配置: {description}")
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try:
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results, fig = plot_prf_prediction(
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sequence=example_sequence,
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ensemble_weight=ensemble_weight,
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title=f"移码概率预测 - {description}",
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figsize=(14, 7)
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)
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print(f"预测完成！共处理 {len(results)} 个位置")
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print(f"满足阈值条件的位点数: {len(results[results['Ensemble_Probability'] > 0])}")
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# 显示统计信息
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print("预测统计信息:")
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print(f" Short模型平均概率: {results['Short_Probability'].mean():.3f}")
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print(f" Long模型平均概率: {results['Long_Probability'].mean():.3f}")
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print(f" 集成平均概率: {results['Ensemble_Probability'].mean():.3f}")
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print(f" 集成权重比例: Short:{ensemble_weight:.1f}, Long:{1-ensemble_weight:.1f}")
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plt.show()
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except Exception as e:
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print(f"集成权重 {ensemble_weight} 绘图时出错: {str(e)}")
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def example_ensemble_comparison():
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"""集成权重对比示例"""
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print("=" * 50)
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print("集成权重对比绘图示例")
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print("=" * 50)
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# 示例序列
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example_sequence = (
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"ATGCGTACGTTAGCGATCGATCGTAGCTAGCTAGCTAGCTAGCTAGCTAGCTAGC"
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"GATCGATCGTAGCTAGCTAGCTAGCTAGCTAGCTAGCTAGCGATCGATCGTAGCT"
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"AGCTAGCTAGCTAGCTAGCTAGCTAGCGATCGATCGTAGCTAGCTAGCTAGCTAG"
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"CTAGCTAGCTAGCGATCGATCGTAGCTAGCTAGCTAGCTAGCTAGCTAGCTAGC"
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)
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try:
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# 创建预测器实例
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predictor = PRFPredictor()
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# 测试三种不同集成权重
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weights = [0.3, 0.4, 0.6]
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weight_names = ["Long主导 (3:7)", "默认权重 (4:6)", "Short主导 (6:4)"]
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# 创建对比图
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fig, axes = plt.subplots(3, 1, figsize=(15, 12))
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fig.suptitle('不同集成权重配置的预测结果对比', fontsize=16)
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all_results = []
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for i, (weight, name) in enumerate(zip(weights, weight_names)):
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# 获取预测结果
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results = predictor.predict_sequence(
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sequence=example_sequence,
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ensemble_weight=weight
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)
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all_results.append(results)
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# 绘制条形图
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ax = axes[i]
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ax.bar(results['Position'], results['Ensemble_Probability'],
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alpha=0.7, color=f'C{i}', width=2)
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ax.set_title(f'{name} - 平均概率: {results["Ensemble_Probability"].mean():.3f}')
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ax.set_ylabel('概率')
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ax.grid(True, alpha=0.3)
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ax.set_ylim(0, 1)
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if i == len(weights) - 1:
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ax.set_xlabel('序列位置')
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plt.tight_layout()
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plt.show()
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# 打印对比统计
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print("\n集成权重对比统计:")
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for i, (weight, name, results) in enumerate(zip(weights, weight_names, all_results)):
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print(f"{name}:")
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print(f" 平均集成概率: {results['Ensemble_Probability'].mean():.3f}")
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print(f" 最大集成概率: {results['Ensemble_Probability'].max():.3f}")
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print(f" 非零预测数量: {(results['Ensemble_Probability'] > 0).sum()}")
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return all_results, fig
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except Exception as e:
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print(f"集成权重对比时出错: {str(e)}")
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return None, None
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def example_save_plot():
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"""保存图片示例"""
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print("=" * 50)
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print("保存图片示例")
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print("=" * 50)
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# 创建保存目录
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save_dir = "prediction_plots"
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os.makedirs(save_dir, exist_ok=True)
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# 示例序列
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example_sequence = (
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"ATGCGTACGTTAGCGATCGATCGTAGCTAGCTAGCTAGCTAGCTAGCTAGCTAGC"
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"GATCGATCGTAGCTAGCTAGCTAGCTAGCTAGCTAGCTAGCGATCGATCGTAGCT"
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"AGCTAGCTAGCTAGCTAGCTAGCTAGCGATCGATCGTAGCTAGCTAGCTAGCTAG"
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"CTAGCTAGCTAGCGATCGATCGTAGCTAGCTAGCTAGCTAGCTAGCTAGCTAGC"
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)
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try:
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# 保存不同集成权重配置的图片
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weight_configs = [
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(0.3, "long_dominant"),
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(0.5, "equal_weight"),
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(0.7, "short_dominant")
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]
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for ensemble_weight, file_suffix in weight_configs:
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save_path = os.path.join(save_dir, f"prediction_{file_suffix}.png")
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results, fig = plot_prf_prediction(
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sequence=example_sequence,
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short_threshold=0.6,
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long_threshold=0.75,
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ensemble_weight=ensemble_weight,
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title=f"移码概率预测 (集成权重 {ensemble_weight:.1f}:{1-ensemble_weight:.1f})",
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save_path=save_path,
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dpi=300
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)
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print(f"图片已保存至: {save_path}")
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# 不显示图片，直接关闭
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plt.close(fig)
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print("所有集成权重配置的图片都已保存完成")
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return True
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except Exception as e:
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print(f"保存图片过程中出错: {str(e)}")
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return False
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def example_direct_predictor_usage():
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"""直接使用PRFPredictor类的示例"""
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print("=" * 50)
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print("直接使用PRFPredictor类绘图示例")
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print("=" * 50)
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try:
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# 直接创建预测器实例
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predictor = PRFPredictor()
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# 示例序列
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example_sequence = (
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"ATGCGTACGTTAGCGATCGATCGTAGCTAGCTAGCTAGCTAGCTAGCTAGCTAGC"
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"GATCGATCGTAGCTAGCTAGCTAGCTAGCTAGCTAGCTAGCGATCGATCGTAGCT"
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"AGCTAGCTAGCTAGCTAGCTAGCTAGCGATCGATCGTAGCTAGCTAGCTAGCTAG"
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)
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# 使用类方法绘图，展示自定义集成权重
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results, fig = predictor.plot_sequence_prediction(
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sequence=example_sequence,
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short_threshold=0.65,
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long_threshold=0.8,
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ensemble_weight=0.3, # 自定义集成权重
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title="使用PRFPredictor类的绘图结果 (集成权重 3:7)"
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)
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print(f"预测完成！共处理 {len(results)} 个位置")
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print(f"使用集成权重比例: Short:{0.3:.1f}, Long:{0.7:.1f}")
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# 显示详细结果
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print("\n前10个预测结果:")
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columns_to_show = ['Position', 'Short_Probability', 'Long_Probability', 'Ensemble_Probability']
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print(results[columns_to_show].head(10))
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# 显示集成权重信息
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if 'Ensemble_Weights' in results.columns:
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print(f"\n集成权重配置: {results['Ensemble_Weights'].iloc[0]}")
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plt.show()
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return results, fig
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except Exception as e:
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print(f"使用PRFPredictor类时出错: {str(e)}")
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return None, None
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def example_new_api_usage():
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"""新API使用示例"""
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print("=" * 50)
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print("新API方法使用示例")
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print("=" * 50)
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try:
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# 直接创建预测器实例
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predictor = PRFPredictor()
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# 示例序列
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example_sequence = (
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"ATGCGTACGTTAGCGATCGATCGTAGCTAGCTAGCTAGCTAGCTAGCTAGCTAGC"
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"GATCGATCGTAGCTAGCTAGCTAGCTAGCTAGCTAGCTAGCGATCGATCGTAGCT"
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)
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print("1. 使用新的 predict_sequence() 方法:")
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results = predictor.predict_sequence(
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sequence=example_sequence,
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ensemble_weight=0.3
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)
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print(f" 序列预测完成: {len(results)} 个位置")
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print(f" 主要输出字段: {[col for col in results.columns if 'Probability' in col]}")
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print("\n2. 使用新的 predict_regions() 方法:")
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# 模拟一些399bp区域序列
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region_sequences = [example_sequence + "A" * (399 - len(example_sequence))]
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region_results = predictor.predict_regions(
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sequences=region_sequences,
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ensemble_weight=0.4
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)
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print(f" 区域预测完成: {len(region_results)} 个序列")
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print(f" 主要输出字段: {[col for col in region_results.columns if 'Probability' in col or 'Sequence' in col]}")
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# 显示统计
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print("\n3. 结果统计:")
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print(f" 序列预测平均集成概率: {results['Ensemble_Probability'].mean():.3f}")
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print(f" 区域预测平均集成概率: {region_results['Ensemble_Probability'].mean():.3f}")
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return results, region_results
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except Exception as e:
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print(f"新API使用时出错: {str(e)}")
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return None, None
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def main():
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"""主函数"""
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print("FScanpy 序列预测绘图功能演示")
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print("=" * 60)
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print("新功能：规范化的集成权重参数 (ensemble_weight)")
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print("权重范围：0.0 到 1.0 (对应 Short模型的权重，Long模型权重 = 1 - ensemble_weight)")
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print("新命名：Ensemble_Probability 替代 Voting_Probability")
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print("=" * 60)
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examples = [
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("1. 基础绘图示例", example_basic_plotting),
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("2. 自定义集成权重示例", example_custom_ensemble_weights),
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("3. 集成权重对比示例", example_ensemble_comparison),
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("4. 保存图片示例", example_save_plot),
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("5. 直接使用PRFPredictor类示例", example_direct_predictor_usage),
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("6. 新API方法使用示例", example_new_api_usage)
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]
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for name, func in examples:
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print(f"\n{name}")
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try:
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result = func()
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if result is not None and result != False:
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print("✓ 示例执行成功")
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else:
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print("✗ 示例执行失败")
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except Exception as e:
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print(f"✗ 示例执行出错: {str(e)}")
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print("-" * 50)
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print("\n演示完成！")
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print("\n📊 新功能总结:")
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print("1. plot_prf_prediction(): 便捷的绘图函数")
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print("2. PRFPredictor.plot_sequence_prediction(): 类方法绘图")
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print("3. PRFPredictor.predict_sequence(): 序列滑动窗口预测（替代predict_full）")
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print("4. PRFPredictor.predict_regions(): 区域批量预测（替代predict_region）")
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print("5. 支持自定义阈值、标题、保存路径等参数")
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print("6. 新增 ensemble_weight 参数，可调节两个模型的集成权重比例")
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print("\n⚖️ 集成权重示例:")
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print(" - ensemble_weight=0.2: Short模型20%, Long模型80% (Long主导)")
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print(" - ensemble_weight=0.4: Short模型40%, Long模型60% (默认平衡)")
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print(" - ensemble_weight=0.5: Short模型50%, Long模型50% (等权重)")
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print(" - ensemble_weight=0.7: Short模型70%, Long模型30% (Short主导)")
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print("\n📂 输出字段:")
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print(" - Short_Probability: Short模型(HistGB)预测概率")
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print(" - Long_Probability: Long模型(BiLSTM-CNN)预测概率")
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print(" - Ensemble_Probability: 集成预测概率（主要结果）")
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print(" - Ensemble_Weights: 权重配置信息")
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print(" - Short_Sequence: 33bp序列")
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print(" - Long_Sequence: 399bp序列")
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print("7. 自动保存PNG和PDF两种格式")
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if __name__ == "__main__":
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main()
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