nep框架重构 01.select修复
This commit is contained in:
123
src/workflow.py
123
src/workflow.py
@@ -210,7 +210,7 @@ class Workflow:
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self.logger.info(f"=== Step: 01.select ({method}) ===")
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os.makedirs(step_dir, exist_ok=True)
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# [新增] 提前建立 output 目录，用于存放 select.csv 和 select.png
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# Output 目录
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output_dir = os.path.join(iter_path, "05.output")
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os.makedirs(output_dir, exist_ok=True)
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@@ -249,22 +249,36 @@ class Workflow:
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params = step_conf.get('params', [0.01, 60, 120])
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kit_path = self.machine.config['paths'].get('gpumdkit', 'gpumdkit.sh')
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# === 分支 A: 按距离筛选 (Loop) ===
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# === 分支 A: 按距离筛选 (二分法 Binary Search) ===
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if method == "distance":
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threshold = params[0]
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target_min, target_max = params[1], params[2]
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step_size = 0.001
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# 定义二分查找的初始区间
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# lower_bound = 0.0 (极其宽松，选所有)
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# upper_bound = 0.2 (通常足够大，甚至可以设大一点，视体系而定)
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# 我们取 param[0] 作为初始猜测，但搜索范围设宽一点
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lower_bound = 0.0
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upper_bound = 0.2
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max_attempts = 15
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success = False
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# [新增] 用于记录筛选历史
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selection_log = [] # List of dicts
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selection_log = []
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self.logger.info(f"Targeting {target_min}-{target_max} structures. Initial thr={threshold}")
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# 用于记录“最佳失败结果” (如果最终没收敛，用这个)
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best_result = None
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min_dist_to_range = float('inf') # 距离目标区间的差距
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self.logger.info(f"Targeting {target_min}-{target_max} structures using Binary Search.")
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for attempt in range(max_attempts):
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current_attempt = attempt + 1
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self.logger.info(f"--- Attempt {current_attempt}: Threshold {threshold:.4f} ---")
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# 二分取值
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threshold = (lower_bound + upper_bound) / 2.0
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self.logger.info(
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f"--- Attempt {current_attempt}: Threshold {threshold:.6f} (Range: {lower_bound:.4f}-{upper_bound:.4f}) ---")
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input_str = f"203\ndump.xyz train.xyz nep.txt\n1\n{threshold}"
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@@ -272,7 +286,7 @@ class Workflow:
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self.logger.error("gpumdkit execution failed.")
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break
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# 检查 selected.xyz 数量
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# 检查数量
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count = 0
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if os.path.exists(os.path.join(step_dir, "selected.xyz")):
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try:
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@@ -281,76 +295,101 @@ class Workflow:
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count = int(count_out.decode().strip())
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except:
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count = 0
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self.logger.info(f"Selected count: {count}")
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# [新增] 记录本次结果
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# 记录日志
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status = "Fail"
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if target_min <= count <= target_max:
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status = "Success"
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selection_log.append({
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"attempt": current_attempt,
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"threshold": threshold,
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"count": count,
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"result": "Success" if target_min <= count <= target_max else "Fail"
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"result": status
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})
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# 判断逻辑
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# 记录最佳结果 (距离 target_min 或 target_max 最近的)
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dist = 0
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if count < target_min:
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dist = target_min - count
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elif count > target_max:
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dist = count - target_max
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if dist < min_dist_to_range:
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min_dist_to_range = dist
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# 备份当前的 selected.xyz 为 best_selected.xyz
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shutil.copy(os.path.join(step_dir, "selected.xyz"),
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os.path.join(step_dir, "best_selected.xyz"))
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best_result = {"threshold": threshold, "count": count}
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# 逻辑判断
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if target_min <= count <= target_max:
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self.logger.info(f"Success! {count} structures selected.")
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self.logger.info(f"Success! Count {count} is within range.")
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success = True
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break
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elif count < target_min:
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self.logger.info("Too few. Decreasing threshold (Loosening).")
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threshold -= step_size
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if threshold < 0: threshold = 0.0001
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else:
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self.logger.info("Too many. Increasing threshold (Tightening).")
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threshold += step_size
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# [新增] 循环结束后，写入 CSV 日志
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# 二分调整逻辑:
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# 假设: 阈值越小(Loose) -> 选的越多; 阈值越大(Tight) -> 选的越少
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if count < target_min:
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# 选少了 -> 需要更宽松 -> 降低阈值 -> 往 [lower, current] 搜
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self.logger.info("Too few. Need Looser (Lower) threshold.")
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upper_bound = threshold
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else:
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# 选多了 -> 需要更严格 -> 提高阈值 -> 往 [current, upper] 搜
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self.logger.info("Too many. Need Tighter (Higher) threshold.")
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lower_bound = threshold
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# 极小区间保护：如果上下界太接近，直接退出，避免死循环
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if (upper_bound - lower_bound) < 1e-6:
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self.logger.warning("Search interval too small. Stopping.")
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break
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# 循环结束后的处理
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if not success and best_result:
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self.logger.warning(
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f"Could not strictly satisfy range. Using best result: {best_result['count']} (Thr={best_result['threshold']:.6f})")
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# 恢复最佳文件
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shutil.move(os.path.join(step_dir, "best_selected.xyz"),
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os.path.join(step_dir, "selected.xyz"))
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# 清理临时文件
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if os.path.exists(os.path.join(step_dir, "best_selected.xyz")):
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os.remove(os.path.join(step_dir, "best_selected.xyz"))
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# 写CSV日志
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csv_path = os.path.join(output_dir, "select_log.csv")
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try:
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with open(csv_path, 'w') as f:
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f.write("Attempt,Threshold,Count,Result\n")
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for entry in selection_log:
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f.write(
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f"{entry['attempt']},{entry['threshold']:.4f},{entry['count']},{entry['result']}\n")
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self.logger.info(f"Selection log saved to {csv_path}")
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except Exception as e:
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self.logger.error(f"Failed to write select_log.csv: {e}")
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f"{entry['attempt']},{entry['threshold']:.6f},{entry['count']},{entry['result']}\n")
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except:
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pass
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# [新增] 复制 select.png (如果生成了的话)
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# 归档图片
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src_png = os.path.join(step_dir, "select.png")
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if os.path.exists(src_png):
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shutil.copy(src_png, os.path.join(output_dir, "select.png"))
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self.logger.info("Archived select.png to output.")
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if success:
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self.tracker.mark_done(task_id_select)
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else:
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self.logger.warning("Failed to reach target count. Using last result.")
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self.tracker.mark_done(task_id_select)
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self.tracker.mark_done(task_id_select)
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# === 分支 B: 按个数/随机筛选 ===
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# === 分支 B: 随机筛选 (保持不变) ===
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elif method == "random":
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min_n, max_n = params[1], params[2]
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input_str = f"203\ndump.xyz train.xyz nep.txt\n2\n{min_n} {max_n}"
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self.logger.info(f"Random selection: {min_n}-{max_n}")
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if run_cmd_with_log(kit_path, step_dir, "select_exec.log", input_str=input_str):
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# 随机筛选没有迭代过程，但也可以记录一下
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with open(os.path.join(output_dir, "select_log.csv"), 'w') as f:
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f.write("Method,Min,Max,Result\n")
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f.write(f"Random,{min_n},{max_n},Executed\n")
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# 同样尝试复制图片
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src_png = os.path.join(step_dir, "select.png")
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if os.path.exists(src_png):
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shutil.copy(src_png, os.path.join(output_dir, "select.png"))
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if os.path.exists(src_png): shutil.copy(src_png, os.path.join(output_dir, "select.png"))
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self.tracker.mark_done(task_id_select)
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else:
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self.logger.error("Random selection failed.")
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return
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else:
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self.logger.info("Skipping Select (Already Done).")
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# ==========================
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