def run_exp(config_file, plot_timechops=True, run_exp=True, n_jobs=1):
if plot_timechops:
visualize_timechop(config_file)
config, sql_engine, proj_folder = setup_experiment(config_file)
if run_exp:
if n_jobs> 1:
experiment = MultiCoreExperiment(
config=config,
db_engine=sql_engine,
n_processes=n_jobs,
n_db_processes=n_jobs,
project_path=proj_folder,
replace=False,
cleanup=True
)
else:
experiment = SingleThreadedExperiment(
config=config,
db_engine=sql_engine,
project_path=proj_folder,
cleanup=True
)
st = time.time()
experiment.run()
en = time.time()
print('Took {} seconds to run the experiement'.format(en-st))
def test_experiment_tracker(test_engine, project_path):
experiment = MultiCoreExperiment(
config=sample_config(),
db_engine=test_engine,
project_path=project_path,
n_processes=4,
)
experiment_run = Session(bind=test_engine).query(ExperimentRun).get(experiment.run_id)
assert experiment_run.current_status == ExperimentRunStatus.started
assert experiment_run.experiment_hash == experiment.experiment_hash
assert experiment_run.experiment_class_path == 'triage.experiments.multicore.MultiCoreExperiment'
assert experiment_run.platform
assert experiment_run.os_user
assert experiment_run.installed_libraries
assert experiment_run.matrices_skipped == 0
assert experiment_run.matrices_errored == 0
assert experiment_run.matrices_made == 0
assert experiment_run.models_skipped == 0
assert experiment_run.models_errored == 0
assert experiment_run.models_made == 0
experiment.run()
experiment_run = Session(bind=test_engine).query(ExperimentRun).get(experiment.run_id)
assert experiment_run.start_method == "run"
assert experiment_run.matrices_made == len(experiment.matrix_build_tasks)
assert experiment_run.matrices_skipped == 0
assert experiment_run.matrices_errored == 0
assert experiment_run.models_skipped == 0
assert experiment_run.models_errored == 0
assert experiment_run.models_made == len(list(task['train_kwargs']['model_hash'] for batch in experiment._all_train_test_batches() for task in batch.tasks))
assert isinstance(experiment_run.matrix_building_started, datetime.datetime)
assert isinstance(experiment_run.model_building_started, datetime.datetime)
assert isinstance(experiment_run.last_updated_time, datetime.datetime)
assert not experiment_run.stacktrace
assert experiment_run.current_status == ExperimentRunStatus.completed
def run(config_filename, verbose, replace, predictions, validate_only):
# configure logging
log_filename = 'logs/modeling_{}'.format(
str(datetime.datetime.now()).replace(' ', '_').replace(':', ''))
if verbose:
logging_level = logging.DEBUG
else:
logging_level = logging.INFO
logging.basicConfig(
format='%(asctime)s %(process)d %(levelname)s: %(message)s',
level=logging_level,
handlers=[logging.FileHandler(log_filename),
logging.StreamHandler()])
# config_filename = 'experiment_config'
features_directory = 'features'
# load main experiment config
with open('config/{}.yaml'.format(config_filename)) as f:
experiment_config = yaml.load(f)
# load feature configs and update experiment config with their contents
all_feature_aggregations = []
for filename in os.listdir('config/{}/'.format(features_directory)):
with open('config/{}/{}'.format(features_directory, filename)) as f:
feature_aggregations = yaml.load(f)
for aggregation in feature_aggregations:
all_feature_aggregations.append(aggregation)
experiment_config['feature_aggregations'] = all_feature_aggregations
with open('config/db_default_profile.json') as f:
DB_CONFIG = json.load(f)
db_engine = create_engine(
f"postgresql://{DB_CONFIG['user']}:{DB_CONFIG['pass']}@{DB_CONFIG['host']}/{DB_CONFIG['db']}"
)
experiment = MultiCoreExperiment(
config=experiment_config,
db_engine=db_engine,
project_path=PROJECT_PATH,
replace=replace,
n_db_processes=4,
n_processes=40,
save_predictions=predictions,
)
experiment.validate()
if not validate_only:
experiment.run()
def test_experiment_tracker(test_engine, project_path):
with mock.patch("triage.util.conf.open",
side_effect=open_side_effect) as mock_file:
experiment = MultiCoreExperiment(
config=sample_config(),
db_engine=test_engine,
project_path=project_path,
n_processes=4,
)
experiment_run = Session(bind=test_engine).query(TriageRun).get(
experiment.run_id)
assert experiment_run.current_status == TriageRunStatus.started
assert experiment_run.run_hash == experiment.experiment_hash
assert experiment_run.run_type == "experiment"
assert (experiment_run.experiment_class_path ==
"triage.experiments.multicore.MultiCoreExperiment")
assert experiment_run.platform
assert experiment_run.os_user
assert experiment_run.installed_libraries
assert experiment_run.matrices_skipped == 0
assert experiment_run.matrices_errored == 0
assert experiment_run.matrices_made == 0
assert experiment_run.models_skipped == 0
assert experiment_run.models_errored == 0
assert experiment_run.models_made == 0
experiment.run()
experiment_run = Session(bind=test_engine).query(TriageRun).get(
experiment.run_id)
assert experiment_run.start_method == "run"
assert experiment_run.matrices_made == len(experiment.matrix_build_tasks)
assert experiment_run.matrices_skipped == 0
assert experiment_run.matrices_errored == 0
assert experiment_run.models_skipped == 0
assert experiment_run.models_errored == 0
assert experiment_run.models_made == len(
list(task["train_kwargs"]["model_hash"]
for batch in experiment._all_train_test_batches()
for task in batch.tasks))
assert isinstance(experiment_run.matrix_building_started,
datetime.datetime)
assert isinstance(experiment_run.model_building_started, datetime.datetime)
assert isinstance(experiment_run.last_updated_time, datetime.datetime)
assert not experiment_run.stacktrace
assert experiment_run.current_status == TriageRunStatus.completed
def run_experiment(experiment_file, output_path, replace):
start_time = datetime.datetime.now()
logging.info(
f"Reading the file experiment configuration from {experiment_file}")
# Load the experiment configuration file
s3 = s3fs.S3FileSystem()
with s3.open(experiment_file, 'rb') as f:
experiment_config = yaml.load(f.read())
host = os.environ['POSTGRES_HOST']
user = os.environ['POSTGRES_USER']
db = os.environ['POSTGRES_DB']
password = os.environ['POSTGRES_PASSWORD']
port = os.environ['POSTGRES_PORT']
db_url = f"postgresql://{user}:{password}@{host}:{port}/{db}"
logging.info(
f"Using the database: postgresql://{user}:XXXXX@{host}:{port}/{db}")
try:
n_processes = int(os.environ.get('NUMBER_OF_PROCESSES', 12))
except ValueError:
n_processes = 12
try:
n_db_processes = int(os.environ.get('NUMBER_OF_DB_PROCESSES', 6))
except ValueError:
n_db_processes = 6
logging.info(f"The experiment will use {n_processes} cores in the host")
logging.info(
f"The output (matrices and models) of this experiment will be stored in {output_path}"
)
logging.info(
f"The experiment will utilize any preexisting matrix or model: {not replace}"
)
logging.info(f"Creating experiment object")
experiment = MultiCoreExperiment(
n_processes=n_processes,
n_db_processes=n_db_processes,
config=experiment_config,
db_engine=triage.create_engine(db_url),
project_path=output_path,
#matrix_storage_class=HDFMatrixStore,
replace=replace,
cleanup=True,
cleanup_timeout=2)
logging.info(
f"Experiment created: all the file permissions, and db connections are OK"
)
logging.info(f"Validating the experiment")
experiment.validate()
logging.info("""
The experiment configuration doesn't contain any obvious errors.
Any error that occurs possibly is related to number of columns or collision in
the column names, both due to PostgreSQL limitations.
""")
logging.debug(f"Experiment configuration: {experiment.config}")
experiment_name = os.path.splitext(os.path.split(experiment_file)[1])[0]
logging.info(f"Running the experiment: {experiment_name}")
experiment.run()
end_time = datetime.datetime.now()
logging.info(
f"Experiment {experiment_file} completed in {end_time - start_time} seconds"
)
logging.info("Done!")
db_engine = create_engine(db_url)
# loading config file
with open('donors-choose-config.yaml', 'r') as fin:
config = yaml.load(fin)
# generating temporal config plot
chopper = Timechop(**config['temporal_config'])
# We aren't interested in seeing the entire feature_start_time represented
# in our timechop plot. That would hide the interesting information. So we
# set it to equal label_start_time for the plot.
chopper.feature_start_time = chopper.label_start_time
visualize_chops(chopper, save_target='triage_output/timechop.png')
# creating experiment object
experiment = MultiCoreExperiment(
config=config,
db_engine=db_engine,
project_path='s3://dsapp-education-migrated/donors-choose',
n_processes=32,
n_db_processes=4,
replace=False)
experiment.run()