def fit_and_predict(config, experiment_name, test_set, model_name):
"""Runs:
- train
- model_selection_df
- predict
- analyze
"""
run_name = "fit_and_predict:{}:{}:{}".format(
experiment_name, test_set, model_name)
config = C.Config.load(config)
print(utils.colored("Running {} end-to-end.".format(run_name)))
timer = utils.TimerHolder()
timer.start(run_name)
logger.debug("Running model={} with experiment_name={} at {}"
.format(model_name, experiment_name,
timer.get_start(run_name)))
driver = hcnn.driver.Driver(config,
model_name=model_name,
experiment_name=experiment_name,
load_features=True)
result = driver.fit_and_predict_one(test_set)
print("{} - {} complted in duration {}".format(
run_name, utils.result_colored(result), timer.end(run_name)))
return result
def extract_features(master_config):
"""Extract CQTs from all files collected in collect."""
config = C.Config.load(master_config)
print(utils.colored("Extracting CQTs from note audio."))
driver = hcnn.driver.Driver(config, load_features=False)
result = driver.extract_features()
print("Extraction {}".format(utils.result_colored(result)))
return result
def integration_test(config):
"""AKA "model" test.
This is equivalent to running
python manage.py -c data/integrationtest_config.yaml run_all_experiments
"""
# Load integrationtest config
experiment_name = "integrationtest"
print(utils.colored("Extracting features from tinydata set."))
print(utils.colored(
"Running integration test on tinydata set : {}."
.format(config)))
# Begin by cleaning the feature data
result = clean(config, force=True)
if result:
result = run_all_experiments(config, experiment_root=experiment_name)
print("IntegrationTest Result: {}".format(utils.result_colored(result)))
return result
