106 lines
3.0 KiB
Python
106 lines
3.0 KiB
Python
import pandas as pd
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import numpy as np
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import os, sys
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sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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# === 配置 ===
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csv_path = "data/trpe_preds.csv"
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props_with_thresholds = {
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"AMES": 0.5,
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"Carcinogens_Lagunin": 0.5,
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"ClinTox": 0.5,
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"DILI": 0.5,
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"hERG": 0.5,
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"Bioavailability_Ma": 0,
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"HIA_Hou": 0,
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}
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# 判定符号：含等号可切换成 <= / >=
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toxic_op_le = "<" # 或 "<="
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adme_op_ge = ">" # 或 ">="
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df = pd.read_csv(csv_path)
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total = len(df)
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toxic_cols = ["AMES","Carcinogens_Lagunin","ClinTox","DILI","hERG"]
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adme_cols = ["Bioavailability_Ma","HIA_Hou"]
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# 统一转数值（非数值转为 NaN）
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for col in toxic_cols + adme_cols:
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if col in df.columns:
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df[col] = pd.to_numeric(df[col], errors="coerce")
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results = []
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os.makedirs("failed_by_rule", exist_ok=True)
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def apply_rule(series, thr, op):
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if op == "<": return series < thr
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if op == "<=": return series <= thr
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if op == ">": return series > thr
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if op == ">=": return series >= thr
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raise ValueError("Unsupported op")
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for col, thr in props_with_thresholds.items():
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if col not in df.columns:
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continue
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# 打印该属性的范围，方便 sanity check
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col_min = float(np.nanmin(df[col].values)) if df[col].notna().any() else np.nan
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col_max = float(np.nanmax(df[col].values)) if df[col].notna().any() else np.nan
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if col in toxic_cols:
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mask = apply_rule(df[col], thr, toxic_op_le)
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rule = f"{col} {toxic_op_le} {thr}"
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else:
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mask = apply_rule(df[col], thr, adme_op_ge)
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rule = f"{col} {adme_op_ge} {thr}"
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passed = int(mask.sum())
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failed = int(total - passed)
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# 导出未通过的分子（含该列原值）
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failed_df = df.loc[~mask, [col]].copy()
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failed_df.to_csv(f"failed_by_rule/{col}_failed.csv", index=False)
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results.append({
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"属性": col,
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"阈值": thr,
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"规则": rule,
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"最小值": col_min,
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"最大值": col_max,
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"通过数": passed,
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"淘汰数": failed,
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"是否全部淘汰": failed == total
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})
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res_df = pd.DataFrame(results).sort_values("淘汰数", ascending=False)
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print(res_df)
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res_df.to_csv("trpe_filter_diagnostics.csv", index=False)
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# === 最终整体过滤（交集） ===
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mask_all = pd.Series(True, index=df.index)
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for col, thr in props_with_thresholds.items():
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if col not in df.columns:
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continue
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if col in toxic_cols:
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mask_all &= df[col] < thr # 毒理类必须 < 阈值
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else:
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mask_all &= df[col] > thr # 吸收类必须 > 阈值
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final_candidates = df[mask_all].copy()
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print("\n=== 最终筛选结果 ===")
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print(f"总数: {total}")
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print(f"符合所有标准的分子数: {len(final_candidates)}")
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if len(final_candidates) > 0:
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print("符合条件的分子索引（行号）:")
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print(final_candidates.index.tolist())
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final_candidates.to_csv("trpe_final_candidates.csv", index=False)
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print("✅ 已保存到 trpe_final_candidates.csv")
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else:
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print("⚠️ 没有分子同时满足所有条件。")
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