def dry_run(args, opt_file_lookup, run_uuid, fp, random=np_random):
"""Write to buffer description of commands for running all experiments.
This function is almost pure by writing to a buffer, but it could be switched to a generator.
Parameters
----------
args : dict(CmdArgs, [int, str])
Arguments of options to pass to the experiments being launched. The keys corresponds to the same arguments
passed to this program.
opt_file_lookup : dict(str, str)
Mapping from method name to filename containing wrapper class for the method.
run_uuid : uuid.UUID
UUID for this launcher run. Needed to generate different experiments UUIDs on each call. This function is
deterministic provided the same `run_uuid`.
fp : writable buffer
File handle to write out sequence of commands to execute (broken into jobs on each line) to execute all the
experiments (possibly each job in parallel).
random : RandomState
Random stream to use for reproducibility.
"""
assert args[CmdArgs.n_jobs] > 0, "Must have non-zero jobs for dry run"
# Taking in file pointer since then we can test without actual file. Could also build generator that returns lines
# to write.
manual_setup_info = XRSerializer.init_db_manual(args[CmdArgs.db_root],
db=args[CmdArgs.db],
keys=EXP_VARS)
warnings.warn(manual_setup_info, UserWarning)
# Get the commands
dry_run_commands = {}
G = gen_commands(args, opt_file_lookup, run_uuid)
for (_, _, _, optimizer, _), full_cmd in G:
cmd_str = shell_join(full_cmd)
dry_run_commands.setdefault(optimizer, []).append(cmd_str)
# Make sure we never have any empty jobs, which is a waste
n_commands = sum(len(v) for v in dry_run_commands.values())
n_jobs = min(args[CmdArgs.n_jobs], n_commands)
# Would prob also work with pyrandom, but only tested np random so far
subcommands = strat_split(list(dry_run_commands.values()),
n_jobs,
random=random)
# Make sure have same commands overall, delete once we trust strat_split
assert sorted(np.concatenate(subcommands)) == sorted(
sum(list(dry_run_commands.values()), []))
job_suffix = run_uuid.hex[:UUID_JOB_CHARS]
# Include comments as reproducibility lines
args_str = serializable_dict(args)
fp.write("# running: %s\n" % str(args_str))
fp.write("# cmd: %s\n" % cmd.cmd_str())
for ii, ii_str in range_str(n_jobs):
assert len(subcommands[ii]) > 0
fp.write("job_%s_%s %s\n" %
(job_suffix, ii_str, " && ".join(subcommands[ii])))
def experiment_main(opt_class, args=None): # pragma: main
"""This is in effect the `main` routine for this experiment. However, it is called from the optimizer wrapper file
so the class can be passed in. The optimizers are assumed to be outside the package, so the optimizer class can't
be named from inside the main function without using hacky stuff like `eval`.
"""
if args is None:
description = "Run a study with one benchmark function and an optimizer"
args = cmd.parse_args(cmd.experiment_parser(description))
args[CmdArgs.opt_rev] = opt_class.get_version()
run_uuid = uuid.UUID(args[CmdArgs.uuid])
logging.captureWarnings(True)
# Setup logging to both a file and stdout (if verbose is set to True)
logger.setLevel(logging.INFO) # Note this is the module-wide logger
logfile = XRSerializer.logging_path(args[CmdArgs.db_root],
args[CmdArgs.db], run_uuid)
logger_file_handler = logging.FileHandler(logfile, mode="w")
logger.addHandler(logger_file_handler)
if args[CmdArgs.verbose]:
logger.addHandler(logging.StreamHandler())
warnings_logger = logging.getLogger("py.warnings")
warnings_logger.addHandler(logger_file_handler)
if args[CmdArgs.verbose]:
warnings_logger.addHandler(logging.StreamHandler())
logger.info("running: %s" % str(cmd.serializable_dict(args)))
logger.info("cmd: %s" % cmd.cmd_str())
assert (args[CmdArgs.metric]
in METRICS_LOOKUP[get_problem_type(args[CmdArgs.data])]
), "reg/clf metrics can only be used on compatible dataset"
# Setup random streams for computing the signature, must use same seed
# across all runs to ensure signature is consistent. This seed is random:
_setup_seeds(
"7e9f2cabb0dd4f44bc10cf18e440b427") # pragma: allowlist secret
signature = get_objective_signature(args[CmdArgs.classifier],
args[CmdArgs.data],
args[CmdArgs.metric],
data_root=args[CmdArgs.data_root])
logger.info("computed signature: %s" % str(signature))
opt_kwargs = load_optimizer_kwargs(args[CmdArgs.optimizer],
args[CmdArgs.optimizer_root])
# Setup the call back for intermediate logging
if cc.BASELINE not in XRSerializer.get_derived_keys(args[CmdArgs.db_root],
db=args[CmdArgs.db]):
warnings.warn("Baselines not found. Will not log intermediate scores.")
callback = None
else:
test_case_str = SklearnModel.test_case_str(args[CmdArgs.classifier],
args[CmdArgs.data],
args[CmdArgs.metric])
optimizer_str = str_join_safe(
ARG_DELIM, (args[CmdArgs.optimizer], args[CmdArgs.opt_rev],
args[CmdArgs.rev]))
baseline_ds, baselines_meta = XRSerializer.load_derived(
args[CmdArgs.db_root], db=args[CmdArgs.db], key=cc.BASELINE)
# Check the objective function signatures match in the baseline file
sig_errs, _ = analyze_signature_pair({test_case_str: signature[1]},
baselines_meta["signature"])
logger.info("Signature errors:\n%s" % sig_errs.to_string())
print(json.dumps({"exp sig errors": sig_errs.T.to_dict()}))
def log_mean_score_json(evals, iters):
assert evals.shape == (len(OBJECTIVE_NAMES), )
assert not np.any(np.isnan(evals))
log_msg = {
cc.TEST_CASE: test_case_str,
cc.METHOD: optimizer_str,
cc.TRIAL: args[CmdArgs.uuid],
cc.ITER: iters,
}
for idx, obj in enumerate(OBJECTIVE_NAMES):
assert OBJECTIVE_NAMES[idx] == obj
# Extract relevant rescaling info
slice_ = {cc.TEST_CASE: test_case_str, OBJECTIVE: obj}
best_opt = baseline_ds[cc.PERF_BEST].sel(
slice_, drop=True).values.item()
base_clip_val = baseline_ds[cc.PERF_CLIP].sel(
slice_, drop=True).values.item()
# Perform the same rescaling as found in experiment_analysis.compute_aggregates()
score = linear_rescale(evals[idx],
best_opt,
base_clip_val,
0.0,
1.0,
enforce_bounds=False)
# Also, clip the score from below at -1 to limit max influence of single run on final average
score = np.clip(score, -1.0, 1.0)
score = score.item() # Make easiest for logging in JSON
assert isinstance(score, float)
# Note: This is not the raw score but the rescaled one!
log_msg[obj] = score
log_msg = json.dumps(log_msg)
print(log_msg, flush=True)
# One second safety delay to protect against subprocess stdout getting lost
sleep(1)
callback = log_mean_score_json
# Now set the seeds for the actual experiment
_setup_seeds(args[CmdArgs.uuid])
# Now do the experiment
logger.info("starting sklearn study %s %s %s %s %d %d" % (
args[CmdArgs.optimizer],
args[CmdArgs.classifier],
args[CmdArgs.data],
args[CmdArgs.metric],
args[CmdArgs.n_calls],
args[CmdArgs.n_suggest],
))
logger.info("with data root: %s" % args[CmdArgs.data_root])
function_evals, timing, suggest_log = run_sklearn_study(
opt_class,
opt_kwargs,
args[CmdArgs.classifier],
args[CmdArgs.data],
args[CmdArgs.metric],
args[CmdArgs.n_calls],
args[CmdArgs.n_suggest],
data_root=args[CmdArgs.data_root],
callback=callback,
)
# Curate results into clean dataframes
eval_ds = build_eval_ds(function_evals, OBJECTIVE_NAMES)
time_ds = build_timing_ds(*timing)
suggest_ds = build_suggest_ds(suggest_log)
# setup meta:
meta = {"args": cmd.serializable_dict(args), "signature": signature}
logger.info("saving meta data: %s" % str(meta))
# Now the final IO to export the results
logger.info("saving results")
XRSerializer.save(eval_ds,
meta,
args[CmdArgs.db_root],
db=args[CmdArgs.db],
key=cc.EVAL,
uuid_=run_uuid)
logger.info("saving timing")
XRSerializer.save(time_ds,
meta,
args[CmdArgs.db_root],
db=args[CmdArgs.db],
key=cc.TIME,
uuid_=run_uuid)
logger.info("saving suggest log")
XRSerializer.save(suggest_ds,
meta,
args[CmdArgs.db_root],
db=args[CmdArgs.db],
key=cc.SUGGEST_LOG,
uuid_=run_uuid)
logger.info("done")
def experiment_main(opt_class, args=None): # pragma: main
"""This is in effect the `main` routine for this experiment. However, it is called from the optimizer wrapper file
so the class can be passed in. The optimizers are assumed to be outside the package, so the optimizer classs can't
be named from inside the main function without using hacky stuff like `eval`.
"""
if args is None:
description = "Run a study with one benchmark function and an optimizer"
args = cmd.parse_args(cmd.experiment_parser(description))
args[CmdArgs.opt_rev] = opt_class.get_version()
run_uuid = uuid.UUID(args[CmdArgs.uuid])
logging.captureWarnings(True)
# Setup logging to both a file and stdout (if verbose is set to True)
logger.setLevel(logging.INFO) # Note this is the module-wide logger
logfile = XRSerializer.logging_path(args[CmdArgs.db_root],
args[CmdArgs.db], run_uuid)
logger_file_handler = logging.FileHandler(logfile, mode="w")
logger.addHandler(logger_file_handler)
if args[CmdArgs.verbose]:
logger.addHandler(logging.StreamHandler())
warnings_logger = logging.getLogger("py.warnings")
warnings_logger.addHandler(logger_file_handler)
if args[CmdArgs.verbose]:
warnings_logger.addHandler(logging.StreamHandler())
logger.info("running: %s" % str(cmd.serializable_dict(args)))
logger.info("cmd: %s" % cmd.cmd_str())
assert (args[CmdArgs.metric]
in METRICS_LOOKUP[get_problem_type(args[CmdArgs.data])]
), "reg/clf metrics can only be used on compatible dataset"
# Setup random streams for computing the signature, must use same seed
# across all runs to ensure signature is consistent. This seed is random:
_setup_seeds(
"7e9f2cabb0dd4f44bc10cf18e440b427") # pragma: allowlist secret
signature = get_objective_signature(args[CmdArgs.classifier],
args[CmdArgs.data],
args[CmdArgs.metric],
data_root=args[CmdArgs.data_root])
logger.info("computed signature: %s" % str(signature))
opt_kwargs = load_optimizer_kwargs(args[CmdArgs.optimizer],
args[CmdArgs.optimizer_root])
# Now set the seeds for the actual experiment
_setup_seeds(args[CmdArgs.uuid])
# Now do the experiment
logger.info("starting sklearn study %s %s %s %s %d %d" % (
args[CmdArgs.optimizer],
args[CmdArgs.classifier],
args[CmdArgs.data],
args[CmdArgs.metric],
args[CmdArgs.n_calls],
args[CmdArgs.n_suggest],
))
logger.info("with data root: %s" % args[CmdArgs.data_root])
function_evals, timing = run_sklearn_study(
opt_class,
opt_kwargs,
args[CmdArgs.classifier],
args[CmdArgs.data],
args[CmdArgs.metric],
args[CmdArgs.n_calls],
args[CmdArgs.n_suggest],
data_root=args[CmdArgs.data_root],
)
# Curate results into clean dataframes
eval_ds = build_eval_ds(function_evals)
time_ds = build_timing_ds(*timing)
# setup meta:
meta = {"args": cmd.serializable_dict(args), "signature": signature}
logger.info("saving meta data: %s" % str(meta))
# Now the final IO to export the results
logger.info("saving results")
XRSerializer.save(eval_ds,
meta,
args[CmdArgs.db_root],
db=args[CmdArgs.db],
key=cc.EVAL,
uuid_=run_uuid)
logger.info("saving timing")
XRSerializer.save(time_ds,
meta,
args[CmdArgs.db_root],
db=args[CmdArgs.db],
key=cc.TIME,
uuid_=run_uuid)
logger.info("done")