class_docstr: Class for storing information about a run used during ensemble building.
  
  Note
  ----
  This is for internal use by the EnsembleBuilder and not for general usage.
class_docstr_tok: ["Class","for","storing","information","about","a","run","used","during","ensemble","building",".","Note","--","--","This","is","for","internal","use","by","the","EnsembleBuilder","and","not","for","general","usage","."]
docstr: Creates a Run from a path pointing to the directory of a run
  
  Parameters
  ----------
  path: Path
      Expects something like /path/to/{seed}_{numrun}_{budget}
docstr_tok: ["Creates","a","Run","from","a","path","pointing","to","the","directory","of","a","run","Parameters","--","--","--","--","--","path",":","Path","Expects","something","like","\/path\/to\/","{","seed","}","_","{","numrun","}","_","{","budget","}"]
code: def __init__(self, path: Path) -> None:
      """Creates a Run from a path pointing to the directory of a run
  
      Parameters
      ----------
      path: Path
          Expects something like /path/to/{seed}_{numrun}_{budget}
      """
      name = path.name
      seed, num_run, budget = name.split("_")
  
      self.dir = path
      self.seed = int(seed)
      self.num_run = int(num_run)
      self.budget = float(budget)
  
      # These are ordered based on preference
      self.losses: dict[str, float] = {}
  
      self._mem_usage: float | None = None
  
      # Items that will be delete when the run is saved back to file
      self._cache: dict[str, np.ndarray] = {}
  
      # The recorded time of ensemble/test predictions modified
      self.recorded_mtimes: dict[str, float] = {}
      self.record_modified_times()
  
imports: from __future__ import annotations
  from typing import Tuple
  import re
  from pathlib import Path
  import numpy
  from autosklearn.util.disk import sizeof
label_desc: Use image node_id 1 to create a new Run object  with example: obj = Run(path)