import abc
import copy
import logging
import os
import pathlib
import signal
import numpy as np
import pandas as pd
from deephyper.core.exceptions import SearchTerminationError
[docs]class Search(abc.ABC):
"""Abstract class which represents a search algorithm.
Args:
problem ([type]): [description]
evaluator ([type]): [description]
random_state ([type], optional): [description]. Defaults to None.
log_dir (str, optional): [description]. Defaults to ".".
verbose (int, optional): [description]. Defaults to 0.
"""
def __init__(
self, problem, evaluator, random_state=None, log_dir=".", verbose=0, **kwargs
):
self._problem = copy.deepcopy(problem)
self._evaluator = evaluator
self._seed = None
if type(random_state) is int:
self._seed = random_state
self._random_state = np.random.RandomState(random_state)
elif isinstance(random_state, np.random.RandomState):
self._random_state = random_state
else:
self._random_state = np.random.RandomState()
# Create logging directory if does not exist
self._log_dir = os.path.abspath(log_dir)
pathlib.Path(log_dir).mkdir(parents=False, exist_ok=True)
self._verbose = verbose
[docs] def terminate(self):
"""Terminate the search.
Raises:
SearchTerminationError: raised when the search is terminated with SIGALARM
"""
logging.info("Search is being stopped!")
raise SearchTerminationError
def _set_timeout(self, timeout=None):
def handler(signum, frame):
self.terminate()
signal.signal(signal.SIGALRM, handler)
if np.isscalar(timeout) and timeout > 0:
signal.alarm(timeout)
[docs] def search(self, max_evals: int = -1, timeout: int = None):
"""Execute the search algorithm.
Args:
max_evals (int, optional): The maximum number of evaluations of the run function to perform before stopping the search. Defaults to ``-1``, will run indefinitely.
timeout (int, optional): The time budget (in seconds) of the search before stopping. Defaults to ``None``, will not impose a time budget.
Returns:
DataFrame: a pandas DataFrame containing the evaluations performed or ``None`` if the search could not evaluate any configuration.
"""
if timeout is not None:
if type(timeout) is not int:
raise ValueError(f"'timeout' shoud be of type'int' but is of type '{type(timeout)}'!")
if timeout <= 0:
raise ValueError(f"'timeout' should be > 0!")
self._set_timeout(timeout)
try:
self._search(max_evals, timeout)
except SearchTerminationError:
if "saved_keys" in dir(self):
self._evaluator.dump_evals(saved_keys=self.saved_keys)
else:
self._evaluator.dump_evals()
try:
path_results = os.path.join(self._log_dir, "results.csv")
df_results = pd.read_csv(path_results)
return df_results
except FileNotFoundError:
return None
@abc.abstractmethod
def _search(self, max_evals, timeout):
"""Search algorithm to be implemented.
Args:
max_evals (int, optional): The maximum number of evaluations of the run function to perform before stopping the search. Defaults to -1, will run indefinitely.
timeout (int, optional): The time budget of the search before stopping.Defaults to None, will not impose a time budget.
"""
