first add

2025-10-16 17:21:48 +08:00
commit a56e60e9a3
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--- a/example_usage.py
+++ b/example_usage.py
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+"""
+广谱抗菌预测API使用示例
+"""
+
+from typing import List
+from broad_spectrum_api import (
+    ParallelBroadSpectrumPredictor, 
+    PredictionConfig, 
+    MoleculeInput,
+    BroadSpectrumResult,
+    predict_smiles,
+    predict_file
+)
+
+
+def example_single_prediction():
+    """单分子预测示例"""
+    print("=== 单分子预测示例 ===")
+    
+    # 创建预测器
+    predictor = ParallelBroadSpectrumPredictor()
+    
+    # 预测单个分子
+    molecule = MoleculeInput(smiles="CCO", chem_id="ethanol")
+    result = predictor.predict_single(molecule)
+    
+    print(f"化合物: {result.chem_id}")
+    print(f"广谱抗菌: {'是' if result.broad_spectrum else '否'}")
+    print(f"抑制菌株数: {result.ginhib_total}")
+    print(f"抗菌分数: {result.apscore_total:.3f}")
+
+
+def example_batch_prediction():
+    """批量预测示例"""
+    print("\n=== 批量预测示例 ===")
+    
+    # 创建预测器
+    config = PredictionConfig(n_workers=4, batch_size=50)
+    predictor = ParallelBroadSpectrumPredictor(config)
+    
+    # 准备多个分子
+    molecules = [
+        MoleculeInput(smiles="CCO", chem_id="ethanol"),
+        MoleculeInput(smiles="CCN", chem_id="ethylamine"),
+        MoleculeInput(smiles="CC(=O)O", chem_id="acetic_acid"),
+    ]
+    
+    # 批量预测
+    results = predictor.predict_batch(molecules)
+    
+    # 输出结果
+    for result in results:
+        print(f"{result.chem_id}: 广谱={result.broad_spectrum}, 抑制数={result.ginhib_total}")
+
+
+def example_smiles_list_prediction():
+    """SMILES列表预测示例"""
+    print("\n=== SMILES列表预测示例 ===")
+    
+    smiles_list = ["CCO", "CCN", "CC(=O)O"]
+    chem_ids = ["ethanol", "ethylamine", "acetic_acid"]
+    
+    # 使用便捷函数
+    results = predict_smiles(smiles_list, chem_ids)
+    
+    # 统计广谱抗菌化合物
+    broad_spectrum_count = sum(1 for r in results if r.broad_spectrum)
+    print(f"广谱抗菌化合物: {broad_spectrum_count}/{len(results)}")
+
+
+def example_file_prediction():
+    """文件预测示例"""
+    print("\n=== 文件预测示例 ===")
+    
+    # 假设有输入文件 molecules.tsv
+    try:
+        results = predict_file(
+            "molecules.tsv",
+            smiles_column="smiles",
+            id_column="compound_id"
+        )
+        
+        # 保存结果
+        import pandas as pd
+        results_df = pd.DataFrame([r.to_dict() for r in results])
+        results_df.to_csv("broad_spectrum_results.csv", index=False)
+        print(f"预测完成，结果保存到 broad_spectrum_results.csv")
+        
+    except FileNotFoundError:
+        print("输入文件不存在，跳过文件预测示例")
+
+
+def example_custom_config():
+    """自定义配置示例"""
+    print("\n=== 自定义配置示例 ===")
+    
+    # 自定义配置
+    config = PredictionConfig(
+        app_threshold=0.1,  # 更严格的抑制阈值
+        min_nkill=15,       # 更高的广谱标准
+        n_workers=8,        # 更多并行进程
+        batch_size=200      # 更大的批次
+    )
+    
+    predictor = ParallelBroadSpectrumPredictor(config)
+    
+    # 预测
+    molecules = [MoleculeInput(smiles="CCO", chem_id="ethanol")]
+    results = predictor.predict_batch(molecules)
+    
+    print(f"使用自定义配置预测结果: {results[0].to_dict()}")
+
+
+if __name__ == "__main__":
+    # 运行所有示例
+    example_single_prediction()
+    example_batch_prediction()
+    example_smiles_list_prediction()
+    example_file_prediction()
+    example_custom_config()