def add_jobs_in_avail_which_failed(self):
self.init_connection()
avail_jobs = "{}.avail_jobs".format(self.schema)
running_jobs = "{}.running_jobs".format(self.schema)
select_job = """SELECT * FROM {0} row WHERE EXISTS(SELECT job_id FROM {1} r WHERE r.interrupted = FALSE
AND r.finished = FALSE AND r.job_id = row.job_id)""".format(
avail_jobs, running_jobs)
self.cursor_db.execute(select_job)
all_jobs = self.cursor_db.fetchall()
print("Running jobs are ".format(all_jobs))
self.close_connection()
for job in all_jobs:
date_time = job['job_allocated_time']
duration = get_duration_seconds(job['duration'])
new_date = date_time + timedelta(seconds=duration)
if new_date < datetime.now():
job_id = int(job['job_id'])
print(
"Duration for the Job {} expired so marking it as failed".
format(job_id))
error_message = "exception{}".format(
"InterruptedDueToSomeError")
self.append_error_string_in_running_job(
job_id=job_id, error_message=error_message)
models_done = list(df.columns)
logger.info("Models done {}".format(models_done))
run_jobs = []
dbConnector.cursor_db.execute(select_st.format('masterthesis.avail_jobs'))
for job in dbConnector.cursor_db.fetchall():
run_jobs.append(dict(job))
dbConnector.cursor_db.execute(select_st.format('pymc3.avail_jobs'))
for job in dbConnector.cursor_db.fetchall():
run_jobs.append(dict(job))
dbConnector.close_connection()
for job in run_jobs:
logger.info("learner {} learner_params {}".format(job["learner"], job["learner_params"]))
job_description = run_jobs[0]
rows_list = []
duration = get_duration_seconds('7D')
logger.info("DB config filePath {}".format(config_file_path))
logger.info("Arguments {}".format(arguments))
logger.info("Run optimization {}".format(run_opt))
if dataset_type == 'median':
N_OBJECTS_ARRAY = np.arange(3, 20, step=2)
OPTIMIZE_ON_OBJECTS = [5, 15]
logger.info("N_OBJECTS_ARRAY {}".format(N_OBJECTS_ARRAY))
logger.info("OPTIMIZE_ON_OBJECTS {}".format(OPTIMIZE_ON_OBJECTS))
logger.info("Model {} Dataset {}".format(model, dataset))
seed = int(job_description["seed"])
job_id = int(job_description["job_id"])
fold_id = int(job_description["fold_id"])
dataset_name = job_description["dataset"]
n_inner_folds = int(job_description["inner_folds"])
hash_value = job_description["hash_value"]
random_state = np.random.RandomState(seed=seed + fold_id)
log_path = os.path.join(DIR_PATH, LOGS_FOLDER,
"{}.log".format(hash_value))
optimizer_path = os.path.join(DIR_PATH, OPTIMIZER_FOLDER,
"{}".format(hash_value))
create_dir_recursively(log_path, True)
create_dir_recursively(optimizer_path, True)
setup_logging(log_path=log_path)
configure_numpy_keras(seed=seed)
logger = logging.getLogger('Experiment')
logger.info("DB config filePath {}".format(config_file_path))
logger.info("Arguments {}".format(arguments))
logger.info("Job Description {}".format(
print_dictionary(job_description)))
duration = get_duration_seconds(duration)
dataset_params['random_state'] = random_state
dataset_params['fold_id'] = fold_id
dataset_reader = get_dataset_reader(dataset_name, dataset_params)
X_train, Y_train, X_test, Y_test = dataset_reader.get_single_train_test_split(
)
n_objects = log_test_train_data(X_train, X_test, logger)
del dataset_reader
inner_cv = ShuffleSplit(n_splits=n_inner_folds,
test_size=0.1,
random_state=random_state)
hash_file = os.path.join(DIR_PATH, MODEL_FOLDER,
"{}.h5".format(hash_value))
learner_params['n_objects'], learner_params[
