"""Monitor and influence the optimization procedure via callbacks.
Callbacks are callables which are invoked after each iteration of the optimizer
and are passed the results "so far". Callbacks can monitor progress, or stop
the optimization early by returning `True`.
"""
try:
from collections.abc import Callable
except ImportError:
from collections import Callable
from time import time
import numpy as np
from deephyper.skopt.utils import dump
[docs]def check_callback(callback):
"""
Check if callback is a callable or a list of callables.
"""
if callback is not None:
if isinstance(callback, Callable):
return [callback]
elif isinstance(callback, list) and all(
[isinstance(c, Callable) for c in callback]
):
return callback
else:
raise ValueError(
"callback should be either a callable or " "a list of callables."
)
else:
return []
[docs]class VerboseCallback(object):
"""
Callback to control the verbosity.
Parameters
----------
n_init : int, optional
Number of points provided by the user which are yet to be
evaluated. This is equal to `len(x0)` when `y0` is None
n_random : int, optional
Number of points randomly chosen.
n_total : int
Total number of func calls.
Attributes
----------
iter_no : int
Number of iterations of the optimization routine.
"""
def __init__(self, n_total, n_init=0, n_random=0):
self.n_init = n_init
self.n_random = n_random
self.n_total = n_total
self.iter_no = 1
self._start_time = time()
self._print_info(start=True)
def _print_info(self, start=True):
iter_no = self.iter_no
if start:
status = "started"
eval_status = "Evaluating function"
search_status = "Searching for the next optimal point."
else:
status = "ended"
eval_status = "Evaluation done"
search_status = "Search finished for the next optimal point."
if iter_no <= self.n_init:
print(
"Iteration No: %d %s. %s at provided point."
% (iter_no, status, eval_status)
)
elif self.n_init < iter_no <= (self.n_random + self.n_init):
print(
"Iteration No: %d %s. %s at random point."
% (iter_no, status, eval_status)
)
else:
print("Iteration No: %d %s. %s" % (iter_no, status, search_status))
[docs] def __call__(self, res):
"""
Parameters
----------
res : `OptimizeResult`, scipy object
The optimization as a OptimizeResult object.
"""
time_taken = time() - self._start_time
self._print_info(start=False)
curr_y = res.func_vals[-1]
curr_min = res.fun
print("Time taken: %0.4f" % time_taken)
print("Function value obtained: %0.4f" % curr_y)
print("Current minimum: %0.4f" % curr_min)
self.iter_no += 1
if self.iter_no <= self.n_total:
self._print_info(start=True)
self._start_time = time()
[docs]class TimerCallback(object):
"""
Log the elapsed time between each iteration of the minimization loop.
The time for each iteration is stored in the `iter_time` attribute which
you can inspect after the minimization has completed.
Attributes
----------
iter_time : list, shape (n_iter,)
`iter_time[i-1]` gives the time taken to complete iteration `i`
"""
def __init__(self):
self._time = time()
self.iter_time = []
[docs] def __call__(self, res):
"""
Parameters
----------
res : `OptimizeResult`, scipy object
The optimization as a OptimizeResult object.
"""
elapsed_time = time() - self._time
self.iter_time.append(elapsed_time)
self._time = time()
[docs]class EarlyStopper(object):
"""Decide to continue or not given the results so far.
The optimization procedure will be stopped if the callback returns True.
"""
[docs] def __call__(self, result):
"""
Parameters
----------
result : `OptimizeResult`, scipy object
The optimization as a OptimizeResult object.
"""
return self._criterion(result)
def _criterion(self, result):
"""Compute the decision to stop or not.
Classes inheriting from `EarlyStop` should use this method to
implement their decision logic.
Parameters
----------
result : `OptimizeResult`, scipy object
The optimization as a OptimizeResult object.
Returns
-------
decision : boolean or None
Return True/False if the criterion can make a decision or `None` if
there is not enough data yet to make a decision.
"""
raise NotImplementedError(
"The _criterion method should be implemented"
" by subclasses of EarlyStopper."
)
[docs]class DeltaXStopper(EarlyStopper):
"""Stop the optimization when ``|x1 - x2| < delta``
If the last two positions at which the objective has been evaluated
are less than `delta` apart stop the optimization procedure.
"""
def __init__(self, delta):
super(EarlyStopper, self).__init__()
self.delta = delta
def _criterion(self, result):
if len(result.x_iters) >= 2:
return (
result.space.distance(result.x_iters[-2], result.x_iters[-1])
< self.delta
)
else:
return None
[docs]class DeltaYStopper(EarlyStopper):
"""Stop the optimization if the `n_best` minima are within `delta`
Stop the optimizer if the absolute difference between the `n_best`
objective values is less than `delta`.
"""
def __init__(self, delta, n_best=5):
super(EarlyStopper, self).__init__()
self.delta = delta
self.n_best = n_best
def _criterion(self, result):
if len(result.func_vals) >= self.n_best:
func_vals = np.sort(result.func_vals)
worst = func_vals[self.n_best - 1]
best = func_vals[0]
# worst is always larger, so no need for abs()
return worst - best < self.delta
else:
return None
[docs]class HollowIterationsStopper(EarlyStopper):
"""
Stop if the improvement over the last n iterations is below a threshold.
"""
def __init__(self, n_iterations, threshold=0):
super(HollowIterationsStopper, self).__init__()
self.n_iterations = n_iterations
self.threshold = abs(threshold)
def _criterion(self, result):
if len(result.func_vals) <= self.n_iterations:
return False
cummin = np.minimum.accumulate(result.func_vals)
return cummin[-self.n_iterations - 1] - cummin[-1] <= self.threshold
[docs]class DeadlineStopper(EarlyStopper):
"""
Stop the optimization before running out of a fixed budget of time.
Attributes
----------
iter_time : list, shape (n_iter,)
`iter_time[i-1]` gives the time taken to complete iteration `i`
Parameters
----------
total_time : float
fixed budget of time (seconds) that the optimization must
finish within.
"""
def __init__(self, total_time):
super(DeadlineStopper, self).__init__()
self._time = time()
self.iter_time = []
self.total_time = total_time
def _criterion(self, result):
elapsed_time = time() - self._time
self.iter_time.append(elapsed_time)
self._time = time()
if result.x_iters:
time_remaining = self.total_time - np.sum(self.iter_time)
return time_remaining <= np.max(self.iter_time)
else:
return None
[docs]class ThresholdStopper(EarlyStopper):
"""
Stop the optimization when the objective value is lower
than the given threshold.
"""
def __init__(self, threshold: float) -> None:
super(EarlyStopper, self).__init__()
self.threshold = threshold
def _criterion(self, result) -> bool:
return np.any([val <= self.threshold for val in result.func_vals])
[docs]class CheckpointSaver(object):
"""
Save current state after each iteration with :class:`deephyper.skopt.dump`.
Examples
--------
>>> import deephyper.skopt
>>> def obj_fun(x):
... return x[0]**2
>>> checkpoint_callback = deephyper.skopt.callbacks.CheckpointSaver("./result.pkl")
>>> deephyper.skopt.gp_minimize(obj_fun, [(-2, 2)], n_calls=10,
... callback=[checkpoint_callback]) # doctest: +SKIP
Parameters
----------
checkpoint_path : string
location where checkpoint will be saved to;
dump_options : string
options to pass on to `deephyper.skopt.dump`, like `compress=9`
"""
def __init__(self, checkpoint_path, **dump_options):
self.checkpoint_path = checkpoint_path
self.dump_options = dump_options
[docs] def __call__(self, res):
"""
Parameters
----------
res : `OptimizeResult`, scipy object
The optimization as a OptimizeResult object.
"""
dump(res, self.checkpoint_path, **self.dump_options)
