# -*- coding: utf-8 -*-
"""
:mod:`orion.core.worker.producer` -- Produce and register samples to try
========================================================================
.. module:: producer
:platform: Unix
:synopsis: Suggest new parameter sets which optimize the objective.
"""
import copy
import logging
import random
import time
import orion.core
from orion.core.io.database import DuplicateKeyError
from orion.core.utils import format_trials
from orion.core.worker.trials_history import TrialsHistory
log = logging.getLogger(__name__)
[docs]class Producer(object):
"""Produce suggested sets of problem's parameter space to try out.
It uses an `Experiment` object to poll for not yet observed trials which
have been already evaluated and to register new suggestions (points of
the parameter `Space`) to be evaluated.
"""
def __init__(self, experiment, max_idle_time=None):
"""Initialize a producer.
:param experiment: Manager of this experiment, provides convenient
interface for interacting with the database.
"""
log.debug("Creating Producer object.")
self.experiment = experiment
self.space = experiment.space
if self.space is None:
raise RuntimeError("Experiment object provided to Producer has not yet completed"
" initialization.")
self.algorithm = experiment.algorithms
if max_idle_time is None:
max_idle_time = orion.core.config.worker.max_idle_time
self.max_idle_time = max_idle_time
self.strategy = experiment.producer['strategy']
self.naive_algorithm = None
# TODO: Move trials_history into PrimaryAlgo during the refactoring of Algorithm with
# Strategist and Scheduler.
self.trials_history = TrialsHistory()
self.naive_trials_history = None
@property
def pool_size(self):
"""Pool-size of the experiment"""
return self.experiment.pool_size
[docs] def backoff(self):
"""Wait some time and update algorithm."""
waiting_time = min(0, random.gauss(1, 0.2))
log.info('Waiting %d seconds', waiting_time)
time.sleep(waiting_time)
log.info('Updating algorithm.')
self.update()
[docs] def produce(self):
"""Create and register new trials."""
sampled_points = 0
start = time.time()
while sampled_points < self.pool_size and not self.algorithm.is_done:
if time.time() - start > self.max_idle_time:
raise RuntimeError(
"Algorithm could not sample new points in less than {} seconds".format(
self.max_idle_time))
log.debug("### Algorithm suggests new points.")
new_points = self.naive_algorithm.suggest(self.pool_size)
# Sync state of original algo so that state continues evolving.
self.algorithm.set_state(self.naive_algorithm.state_dict)
if new_points is None:
log.info("### Algo opted out.")
self.backoff()
continue
for new_point in new_points:
log.debug("#### Convert point to `Trial` object.")
new_trial = format_trials.tuple_to_trial(new_point, self.space)
try:
new_trial.parents = self.naive_trials_history.children
log.debug("#### Register new trial to database: %s", new_trial)
self.experiment.register_trial(new_trial)
sampled_points += 1
except DuplicateKeyError:
log.debug("#### Duplicate sample.")
self.backoff()
break
[docs] def update(self):
"""Pull all trials to update model with completed ones and naive model with non completed
ones.
"""
trials = self.experiment.fetch_trials()
self._update_algorithm([trial for trial in trials if trial.status == 'completed'])
self._update_naive_algorithm([trial for trial in trials if trial.status != 'completed'])
def _update_algorithm(self, completed_trials):
"""Pull newest completed trials to update local model."""
log.debug("### Fetch completed trials to observe:")
new_completed_trials = []
for trial in completed_trials:
if trial not in self.trials_history:
new_completed_trials.append(trial)
log.debug("### %s", new_completed_trials)
if new_completed_trials:
log.debug("### Convert them to list of points and their results.")
points = list(map(lambda trial: format_trials.trial_to_tuple(trial, self.space),
new_completed_trials))
results = list(map(format_trials.get_trial_results, new_completed_trials))
log.debug("### Observe them.")
self.trials_history.update(new_completed_trials)
self.algorithm.observe(points, results)
self.strategy.observe(points, results)
def _produce_lies(self, incomplete_trials):
"""Add fake objective results to incomplete trials
Then register the trials in the db
"""
log.debug("### Fetch active trials to observe:")
lying_trials = []
log.debug("### %s", incomplete_trials)
for trial in incomplete_trials:
log.debug("### Use defined ParallelStrategy to assign them fake results.")
lying_result = self.strategy.lie(trial)
if lying_result is not None:
lying_trial = copy.deepcopy(trial)
lying_trial.results.append(lying_result)
lying_trials.append(lying_trial)
log.debug("### Register lie to database: %s", lying_trial)
lying_trial.parents = self.trials_history.children
try:
self.experiment.register_lie(lying_trial)
except DuplicateKeyError:
log.debug("#### Duplicate lie. No need to register a duplicate in DB.")
return lying_trials
def _update_naive_algorithm(self, incomplete_trials):
"""Pull all non completed trials to update naive model."""
self.naive_algorithm = copy.deepcopy(self.algorithm)
self.naive_trials_history = copy.deepcopy(self.trials_history)
log.debug("### Create fake trials to observe:")
lying_trials = self._produce_lies(incomplete_trials)
log.debug("### %s", lying_trials)
if lying_trials:
log.debug("### Convert them to list of points and their results.")
points = list(map(lambda trial: format_trials.trial_to_tuple(trial, self.space),
lying_trials))
results = list(map(format_trials.get_trial_results, lying_trials))
log.debug("### Observe them.")
self.naive_trials_history.update(lying_trials)
self.naive_algorithm.observe(points, results)