/
ensemble_builder.py
830 lines (743 loc) · 33 KB
/
ensemble_builder.py
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# -*- encoding: utf-8 -*-
import numbers
import multiprocessing
import glob
import os
import re
import time
import traceback
from typing import Optional, Union
import numpy as np
import pynisher
import lockfile
from sklearn.utils.validation import check_random_state
from autosklearn.util.backend import Backend
from autosklearn.constants import BINARY_CLASSIFICATION
from autosklearn.metrics import calculate_score, Scorer
from autosklearn.ensembles.ensemble_selection import EnsembleSelection
from autosklearn.ensembles.abstract_ensemble import AbstractEnsemble
from autosklearn.util.logging_ import get_logger
Y_ENSEMBLE = 0
Y_VALID = 1
Y_TEST = 2
class EnsembleBuilder(multiprocessing.Process):
def __init__(
self,
backend: Backend,
dataset_name: str,
task_type: int,
metric: Scorer,
limit: int,
ensemble_size: int = 10,
max_keep_best: int = 100,
remove_bad_model_files: bool = True,
performance_range_threshold: float = 0,
seed: int = 1,
shared_mode: bool = False,
max_iterations: int = None,
precision: str = "32",
sleep_duration: int = 2,
memory_limit: int = 1000,
read_at_most: int = 5,
random_state: Optional[Union[int, np.random.RandomState]] = None,
):
"""
Constructor
Parameters
----------
backend: util.backend.Backend
backend to write and read files
dataset_name: str
name of dataset
task_type: int
type of ML task
metric: str
name of metric to score predictions
limit: int
time limit in sec
ensemble_size: int
maximal size of ensemble (passed to autosklearn.ensemble.ensemble_selection)
max_keep_best: int/float
if int: consider only the n best prediction
if float: consider only this fraction of the best models
Both wrt to validation predictions
If performance_range_threshold > 0, might return less models
remove_bad_model_files: bool
As new models are created, keep the files the n-best models, and
delete the others, i.e. the ones not used by the ensemble. Currently, this
functionality cannot be used together with shared mode.
performance_range_threshold: float
Keep only models that are better than:
dummy + (best - dummy)*performance_range_threshold
E.g dummy=2, best=4, thresh=0.5 --> only consider models with score > 3
Will at most return max_keep_best models, might return less
seed: int
random seed
if set to -1, read files with any seed (e.g., for shared model mode)
shared_model: bool
auto-sklearn used shared model mode (aka pSMAC)
max_iterations: int
maximal number of iterations to run this script
(default None --> deactivated)
precision: ["16","32","64","128"]
precision of floats to read the predictions
sleep_duration: int
duration of sleeping time between two iterations of this script (in sec)
memory_limit: int
memory limit in mb
read_at_most: int
read at most n new prediction files in each iteration
"""
if remove_bad_model_files and shared_mode:
raise ValueError("Currently, shared_mode can't be used together with "
"keep_just_nbest_models")
super(EnsembleBuilder, self).__init__()
self.backend = backend # communication with filesystem
self.dataset_name = dataset_name
self.task_type = task_type
self.metric = metric
self.time_limit = limit # time limit
self.ensemble_size = ensemble_size
self.performance_range_threshold = performance_range_threshold
if isinstance(max_keep_best, numbers.Integral) and max_keep_best < 1:
raise ValueError("Integer max_keep_best has to be larger 1: %s" %
max_keep_best)
elif not isinstance(max_keep_best, numbers.Integral) \
and (max_keep_best < 0 or max_keep_best > 1):
raise ValueError("Float max_keep_best best has to be >= 0 and <= 1: %s" %
max_keep_best)
self.max_keep_best = max_keep_best
self.keep_just_nbest_models = remove_bad_model_files
self.seed = seed
self.shared_mode = shared_mode # pSMAC?
self.max_iterations = max_iterations
self.precision = precision
self.sleep_duration = sleep_duration
self.memory_limit = memory_limit
self.read_at_most = read_at_most
self.random_state = check_random_state(random_state)
# part of the original training set
# used to build the ensemble
self.dir_ensemble = os.path.join(
self.backend.temporary_directory,
'.auto-sklearn',
'predictions_ensemble',
)
# validation set (public test set) -- y_true not known
self.dir_valid = os.path.join(
self.backend.temporary_directory,
'.auto-sklearn',
'predictions_valid',
)
# test set (private test set) -- y_true not known
self.dir_test = os.path.join(
self.backend.temporary_directory,
'.auto-sklearn',
'predictions_test',
)
self.dir_models = os.path.join(
self.backend.temporary_directory,
'.auto-sklearn',
'models',
)
logger_name = 'EnsembleBuilder(%d):%s' % (self.seed, self.dataset_name)
self.logger = get_logger(logger_name)
if max_keep_best == 1:
self.logger.debug("Behaviour depends on int/float: %s, %s (max_keep_best, type)" %
(max_keep_best, type(max_keep_best)))
self.start_time = 0
self.model_fn_re = re.compile(r'_([0-9]*)_([0-9]*)_([0-9]{1,3}\.[0-9]*)\.npy')
# already read prediction files
# {"file name": {
# "ens_score": float
# "mtime_ens": str,
# "mtime_valid": str,
# "mtime_test": str,
# "seed": int,
# "num_run": int,
# "deleted": bool,
# Y_ENSEMBLE: np.ndarray
# Y_VALID: np.ndarray
# Y_TEST: np.ndarray
# }
# }
self.read_preds = {}
self.last_hash = None # hash of ensemble training data
self.y_true_ensemble = None
self.SAVE2DISC = True
self.validation_performance_ = np.inf
def run(self):
buffer_time = 5 # TODO: Buffer time should also be used in main!?
while True:
time_left = self.time_limit - buffer_time
safe_ensemble_script = pynisher.enforce_limits(
wall_time_in_s=int(time_left),
mem_in_mb=self.memory_limit,
logger=self.logger
)(self.main)
safe_ensemble_script()
if safe_ensemble_script.exit_status is pynisher.MemorylimitException:
# if ensemble script died because of memory error,
# reduce nbest to reduce memory consumption and try it again
if isinstance(self.max_keep_best, numbers.Integral) and \
self.max_keep_best == 1:
self.logger.critical("Memory Exception --"
" Unable to escape from memory exception")
else:
if isinstance(self.max_keep_best, numbers.Integral):
self.max_keep_best = int(self.max_keep_best / 2)
else:
self.max_keep_best = self.max_keep_best / 2
self.logger.warning("Memory Exception -- restart with "
"less max_keep_best: %d" % self.max_keep_best)
# ATTENTION: main will start from scratch;
# all data structures are empty again
continue
break
def main(self):
self.start_time = time.time()
iteration = 0
while True:
# maximal number of iterations
if (self.max_iterations is not None
and 0 < self.max_iterations <= iteration):
self.logger.info("Terminate ensemble building because of max iterations: %d of %d",
self.max_iterations,
iteration)
break
used_time = time.time() - self.start_time
self.logger.debug(
'Starting iteration %d, time left: %f',
iteration,
self.time_limit - used_time,
)
# populates self.read_preds
if not self.read_ensemble_preds():
time.sleep(self.sleep_duration)
continue
# Only the models with the n_best predictions are candidates
# to be in the ensemble
candidate_models = self.get_n_best_preds()
if not candidate_models: # no candidates yet
continue
# populates predictions in self.read_preds
# reduces selected models if file reading failed
n_sel_valid, n_sel_test = self. \
get_valid_test_preds(selected_keys=candidate_models)
candidate_models_set = set(candidate_models)
if candidate_models_set.intersection(n_sel_test):
candidate_models = list(candidate_models_set.intersection(n_sel_test))
elif candidate_models_set.intersection(n_sel_valid):
candidate_models = list(candidate_models_set.intersection(n_sel_valid))
else:
# use candidate_models only defined by ensemble data set
pass
# train ensemble
ensemble = self.fit_ensemble(selected_keys=candidate_models)
if ensemble is not None:
self.predict(set_="valid",
ensemble=ensemble,
selected_keys=n_sel_valid,
n_preds=len(candidate_models),
index_run=iteration)
# TODO if predictions fails, build the model again during the
# next iteration!
self.predict(set_="test",
ensemble=ensemble,
selected_keys=n_sel_test,
n_preds=len(candidate_models),
index_run=iteration)
iteration += 1
else:
time.sleep(self.sleep_duration)
def read_ensemble_preds(self):
"""
reading predictions on ensemble building data set;
populates self.read_preds
"""
self.logger.debug("Read ensemble data set predictions")
if self.y_true_ensemble is None:
try:
self.y_true_ensemble = self.backend.load_targets_ensemble()
except FileNotFoundError:
self.logger.debug(
"Could not find true targets on ensemble data set: %s",
traceback.format_exc(),
)
return False
# no validation predictions so far -- no dir
if not os.path.isdir(self.dir_ensemble):
self.logger.debug("No ensemble dataset prediction directory found")
return False
if self.shared_mode is False:
pred_path = os.path.join(
glob.escape(self.dir_ensemble),
'predictions_ensemble_%s_*_*.npy' % self.seed,
)
# pSMAC
else:
pred_path = os.path.join(
glob.escape(self.dir_ensemble),
'predictions_ensemble_*_*_*.npy',
)
self.y_ens_files = glob.glob(pred_path)
# no validation predictions so far -- no files
if len(self.y_ens_files) == 0:
self.logger.debug("Found no prediction files on ensemble data set:"
" %s" % pred_path)
return False
n_read_files = 0
for y_ens_fn in sorted(self.y_ens_files):
if self.read_at_most and n_read_files >= self.read_at_most:
# limit the number of files that will be read
# to limit memory consumption
break
if not y_ens_fn.endswith(".npy"):
self.logger.info('Error loading file (not .npy): %s', y_ens_fn)
continue
match = self.model_fn_re.search(y_ens_fn)
_seed = int(match.group(1))
_num_run = int(match.group(2))
_budget = float(match.group(3))
if not self.read_preds.get(y_ens_fn):
self.read_preds[y_ens_fn] = {
"ens_score": -1,
"mtime_ens": 0,
"mtime_valid": 0,
"mtime_test": 0,
"seed": _seed,
"num_run": _num_run,
"budget": _budget,
Y_ENSEMBLE: None,
Y_VALID: None,
Y_TEST: None,
# Lazy keys so far:
# 0 - not loaded
# 1 - loaded and in memory
# 2 - loaded but dropped again
"loaded": 0
}
if self.read_preds[y_ens_fn]["mtime_ens"] == os.path.getmtime(y_ens_fn):
# same time stamp; nothing changed;
continue
# actually read the predictions and score them
try:
with open(y_ens_fn, 'rb') as fp:
y_ensemble = self._read_np_fn(fp=fp)
score = calculate_score(solution=self.y_true_ensemble,
# y_ensemble = y_true for ensemble set
prediction=y_ensemble,
task_type=self.task_type,
metric=self.metric,
all_scoring_functions=False)
if self.read_preds[y_ens_fn]["ens_score"] > -1:
self.logger.critical(
'Changing ensemble score for file %s from %f to %f '
'because file modification time changed? %f - %f',
y_ens_fn,
self.read_preds[y_ens_fn]["ens_score"],
score,
self.read_preds[y_ens_fn]["mtime_ens"],
os.path.getmtime(y_ens_fn),
)
self.read_preds[y_ens_fn]["ens_score"] = score
self.read_preds[y_ens_fn][Y_ENSEMBLE] = y_ensemble
self.read_preds[y_ens_fn]["mtime_ens"] = os.path.getmtime(
y_ens_fn
)
self.read_preds[y_ens_fn]["loaded"] = 1
n_read_files += 1
except:
self.logger.warning(
'Error loading %s: %s',
y_ens_fn,
traceback.format_exc(),
)
self.read_preds[y_ens_fn]["ens_score"] = -1
self.logger.debug(
'Done reading %d new prediction files. Loaded %d predictions in '
'total.',
n_read_files,
np.sum([pred["loaded"] > 0 for pred in self.read_preds.values()])
)
return True
def get_n_best_preds(self):
"""
get best n predictions (i.e., keys of self.read_preds)
according to score on "ensemble set"
n: self.ensemble_nbest
Side effect: delete predictions of non-candidate models
"""
# Sort by score - higher is better!
sorted_keys = list(reversed(sorted(
[
(k, v["ens_score"], v["num_run"])
for k, v in self.read_preds.items()
],
key=lambda x: x[1],
)))
# number of models available
num_keys = len(sorted_keys)
# remove all that are at most as good as random
# note: dummy model must have run_id=1 (there is no run_id=0)
dummy_scores = list(filter(lambda x: x[2] == 1, sorted_keys))
# number of dummy models
num_dummy = len(dummy_scores)
dummy_score = dummy_scores[0]
self.logger.debug("Use %f as dummy score" % dummy_score[1])
sorted_keys = filter(lambda x: x[1] > dummy_score[1], sorted_keys)
# remove Dummy Classifier
sorted_keys = list(filter(lambda x: x[2] > 1, sorted_keys))
if not sorted_keys:
# no model left; try to use dummy score (num_run==0)
# log warning when there are other models but not better than dummy model
if num_keys > num_dummy:
self.logger.warning("No models better than random - using Dummy Score!"
"Number of models besides current dummy model: %d. "
"Number of dummy models: %d",
num_keys - 1,
num_dummy)
sorted_keys = [
(k, v["ens_score"], v["num_run"]) for k, v in self.read_preds.items()
if v["seed"] == self.seed and v["num_run"] == 1
]
# reload predictions if scores changed over time and a model is
# considered to be in the top models again!
if not isinstance(self.max_keep_best, numbers.Integral):
# Transform to number of models to keep. Keep at least one
keep_nbest = max(1, min(len(sorted_keys),
int(len(sorted_keys) * self.max_keep_best)))
self.logger.debug(
"Library pruning: keeping only top %f percent of the models (%d out of %d)",
self.max_keep_best * 100, keep_nbest, len(sorted_keys)
)
else:
# Keep only at most max_keep_best
keep_nbest = min(self.max_keep_best, len(sorted_keys))
self.logger.debug("Library pruning: cutting down "
"to %d (out of %d) models" % (keep_nbest, len(sorted_keys)))
for k, _, _ in sorted_keys[:keep_nbest]:
if self.read_preds[k][Y_ENSEMBLE] is None:
self.read_preds[k][Y_ENSEMBLE] = self._read_np_fn(fp=k)
# No need to load valid and test here because they are loaded
# only if the model ends up in the ensemble
self.read_preds[k]['loaded'] = 1
# consider performance_range_threshold
if self.performance_range_threshold > 0:
best_score = sorted_keys[0][1]
min_score = dummy_score[1]
min_score += (best_score - min_score) * self.performance_range_threshold
if sorted_keys[keep_nbest - 1][1] < min_score:
# We can further reduce number of models
# since worst model is worse than thresh
for i in range(0, keep_nbest):
# Look at most at keep_nbest models,
# but always keep at least one model
current_score = sorted_keys[i][1]
if current_score <= min_score:
self.logger.debug("Dynamic library pruning: Further reduce from %d to %d "
"models", keep_nbest, max(1, i))
keep_nbest = max(1, i)
break
ensemble_n_best = keep_nbest
# reduce to keys
sorted_keys = list(map(lambda x: x[0], sorted_keys))
# remove loaded predictions for non-candidate models
for k in sorted_keys[ensemble_n_best:]:
self.read_preds[k][Y_ENSEMBLE] = None
self.read_preds[k][Y_VALID] = None
self.read_preds[k][Y_TEST] = None
if self.read_preds[k]['loaded'] == 1:
self.logger.debug(
'Dropping model %s (%d,%d) with score %f.',
k,
self.read_preds[k]['seed'],
self.read_preds[k]['num_run'],
self.read_preds[k]['ens_score'],
)
self.read_preds[k]['loaded'] = 2
# return best scored keys of self.read_preds
return sorted_keys[:ensemble_n_best]
def get_valid_test_preds(self, selected_keys: list):
"""
get valid and test predictions from disc
and store them in self.read_preds
Parameters
---------
selected_keys: list
list of selected keys of self.read_preds
Return
------
success_keys:
all keys in selected keys for which we could read the valid and test predictions
"""
success_keys_valid = []
success_keys_test = []
for k in selected_keys:
valid_fn = glob.glob(
os.path.join(
glob.escape(self.dir_valid),
'predictions_valid_%d_%d_%s.npy' % (
self.read_preds[k]["seed"],
self.read_preds[k]["num_run"],
self.read_preds[k]["budget"],
)
)
)
test_fn = glob.glob(
os.path.join(
glob.escape(self.dir_test),
'predictions_test_%d_%d_%s.npy' % (
self.read_preds[k]["seed"],
self.read_preds[k]["num_run"],
self.read_preds[k]["budget"]
)
)
)
# TODO don't read valid and test if not changed
if len(valid_fn) == 0:
# self.logger.debug("Not found validation prediction file "
# "(although ensemble predictions available): "
# "%s" % valid_fn)
pass
else:
valid_fn = valid_fn[0]
if self.read_preds[k]["mtime_valid"] == os.path.getmtime(valid_fn) \
and self.read_preds[k][Y_VALID] is not None:
success_keys_valid.append(k)
continue
try:
with open(valid_fn, 'rb') as fp:
y_valid = self._read_np_fn(fp)
self.read_preds[k][Y_VALID] = y_valid
success_keys_valid.append(k)
self.read_preds[k]["mtime_valid"] = os.path.getmtime(valid_fn)
except Exception as e:
self.logger.warning('Error loading %s: %s',
valid_fn, traceback.format_exc())
if len(test_fn) == 0:
# self.logger.debug("Not found test prediction file (although "
# "ensemble predictions available):%s" %
# test_fn)
pass
else:
test_fn = test_fn[0]
if self.read_preds[k]["mtime_test"] == \
os.path.getmtime(test_fn) \
and self.read_preds[k][Y_TEST] is not None:
success_keys_test.append(k)
continue
try:
with open(test_fn, 'rb') as fp:
y_test = self._read_np_fn(fp)
self.read_preds[k][Y_TEST] = y_test
success_keys_test.append(k)
self.read_preds[k]["mtime_test"] = os.path.getmtime(test_fn)
except Exception as e:
self.logger.warning('Error loading %s: %s',
test_fn, traceback.format_exc())
return success_keys_valid, success_keys_test
def fit_ensemble(self, selected_keys: list):
"""
fit ensemble
Parameters
---------
selected_keys: list
list of selected keys of self.read_preds
Returns
-------
ensemble: EnsembleSelection
trained Ensemble
"""
predictions_train = np.array([self.read_preds[k][Y_ENSEMBLE] for k in selected_keys])
include_num_runs = [
(
self.read_preds[k]["seed"],
self.read_preds[k]["num_run"],
self.read_preds[k]["budget"],
)
for k in selected_keys]
# check hash if ensemble training data changed
current_hash = hash(predictions_train.data.tobytes())
if self.last_hash == current_hash:
self.logger.debug(
"No new model predictions selected -- skip ensemble building "
"-- current performance: %f",
self.validation_performance_,
)
# Delete files of non-candidate models
if self.keep_just_nbest_models:
self._delete_non_candidate_models(selected_keys)
return None
self.last_hash = current_hash
ensemble = EnsembleSelection(
ensemble_size=self.ensemble_size,
task_type=self.task_type,
metric=self.metric,
random_state=self.random_state,
)
try:
self.logger.debug(
"Fitting the ensemble on %d models.",
len(predictions_train),
)
start_time = time.time()
ensemble.fit(predictions_train, self.y_true_ensemble,
include_num_runs)
end_time = time.time()
self.logger.debug(
"Fitting the ensemble took %.2f seconds.",
end_time - start_time,
)
self.logger.info(ensemble)
self.validation_performance_ = min(
self.validation_performance_,
ensemble.get_validation_performance(),
)
except ValueError as e:
self.logger.error('Caught ValueError: %s', traceback.format_exc())
time.sleep(self.sleep_duration)
return None
except IndexError as e:
self.logger.error('Caught IndexError: %s' + traceback.format_exc())
time.sleep(self.sleep_duration)
return None
# Delete files of non-candidate models
if self.keep_just_nbest_models:
self._delete_non_candidate_models(selected_keys)
return ensemble
def predict(self, set_: str,
ensemble: AbstractEnsemble,
selected_keys: list,
n_preds: int,
index_run: int):
"""
save preditions on ensemble, validation and test data on disc
Parameters
----------
set_: ["valid","test"]
data split name
ensemble: EnsembleSelection
trained Ensemble
selected_keys: list
list of selected keys of self.read_preds
n_preds: int
number of prediction models used for ensemble building
same number of predictions on valid and test are necessary
index_run: int
n-th time that ensemble predictions are written to disc
Return
------
y: np.ndarray
"""
self.logger.debug("Predicting the %s set with the ensemble!", set_)
# Save the ensemble for later use in the main auto-sklearn module!
if self.SAVE2DISC:
self.backend.save_ensemble(ensemble, index_run, self.seed)
predictions = np.array([
self.read_preds[k][Y_VALID if set_ == 'valid' else Y_TEST]
for k in selected_keys
])
if n_preds == predictions.shape[0]:
y = ensemble.predict(predictions)
if self.task_type == BINARY_CLASSIFICATION:
y = y[:, 1]
if self.SAVE2DISC:
self.backend.save_predictions_as_txt(
predictions=y,
subset=set_,
idx=index_run,
prefix=self.dataset_name,
precision=8,
)
return y
else:
self.logger.info(
"Found inconsistent number of predictions and models (%d vs "
"%d) for subset %s",
predictions.shape[0],
n_preds,
set_,
)
return None
# TODO: ADD saving of predictions on "ensemble data"
def _delete_non_candidate_models(self, candidates):
# Loop through the files currently in the directory
for pred_path in self.y_ens_files:
# Do not delete candidates
if pred_path in candidates:
continue
match = self.model_fn_re.search(pred_path)
_full_name = match.group(0)
_seed = match.group(1)
_num_run = match.group(2)
_budget = match.group(3)
# Do not delete the dummy prediction
if int(_num_run) == 1:
continue
# Besides the prediction, we have to take care of three other files: model,
# validation and test.
model_name = '%s.%s.%s.model' % (_seed, _num_run, _budget)
model_path = os.path.join(self.dir_models, model_name)
pred_valid_name = 'predictions_valid' + _full_name
pred_valid_path = os.path.join(self.dir_valid, pred_valid_name)
pred_test_name = 'predictions_test' + _full_name
pred_test_path = os.path.join(self.dir_test, pred_test_name)
paths = [model_path, pred_path]
if os.path.exists(pred_valid_path):
paths.append(pred_valid_path)
if os.path.exists(pred_test_path):
paths.append(pred_test_path)
# Lets lock all the files "at once" to avoid weird race conditions. Also,
# we either delete all files of a model (model, prediction, validation
# and test), or delete none. This makes it easier to keep track of which
# models have indeed been correctly removed.
locks = [lockfile.LockFile(path) for path in paths]
try:
for lock in locks:
lock.acquire()
except Exception as e:
if isinstance(e, lockfile.AlreadyLocked):
# If the file is already locked, we deal with it later. Not a big deal
self.logger.info(
'Model %s is already locked. Skipping it for now.', model_name)
else:
# Other exceptions, however, should not occur.
# The message bellow is asserted in test_delete_non_candidate_models()
self.logger.error(
'Failed to lock model %s files due to error %s', model_name, e)
for lock in locks:
if lock.i_am_locking():
lock.release()
continue
# Delete files if model is not a candidate AND prediction is old. We check if
# the prediction is old to avoid deleting a model that hasn't been appreciated
# by self.get_n_best_preds() yet.
original_timestamp = self.read_preds[pred_path]['mtime_ens']
current_timestamp = os.path.getmtime(pred_path)
if current_timestamp == original_timestamp:
# The messages logged here are asserted in
# test_delete_non_candidate_models(). Edit with care.
try:
for path in paths:
os.remove(path)
self.logger.info(
"Deleted files of non-candidate model %s", model_name)
except Exception as e:
self.logger.error(
"Failed to delete files of non-candidate model %s due"
" to error %s", model_name, e)
# If we reached this point, all locks were done by this thread. So no need
# to check "lock.i_am_locking()" here.
for lock in locks:
lock.release()
def _read_np_fn(self, fp):
if self.precision is "16":
predictions = np.load(fp, allow_pickle=True).astype(dtype=np.float16)
elif self.precision is "32":
predictions = np.load(fp, allow_pickle=True).astype(dtype=np.float32)
elif self.precision is "64":
predictions = np.load(fp, allow_pickle=True).astype(dtype=np.float64)
else:
predictions = np.load(fp, allow_pickle=True)
return predictions