def main():
logger.stream_level = 'debug'
sv = Server(num_processes=4)
sv.load_data('../kv_model_v1.0.csv')
sv.tune_hyperparameters(20,
20,
shuffle='train',
split=[0.7, 0.2, 0.1],
eval_set='test')
def main(db_name: str):
# Set up logging
logger.stream_level = 'info'
logger.log_dir = db_name.replace('.csv', '') + '_logs'
logger.file_level = 'debug'
# Split database proportionally based on property value
# Proportions are 70% learn, 20% validate, 10% test
prop_range_from_split(db_name, [0.7, 0.2, 0.1])
# Find the optimal number of input variables
# Train (learn + valid) set used for evaluation
n_desc = len(find_optimal_num_inputs(db_name, 'train', _NUM_PROC)[1])
logger.log('info', 'Optimal number of input variables: {}'.format(n_desc))
# Create server object with base config
sv = Server(model_config=db_name.replace('.csv', '.yml'),
num_processes=_NUM_PROC)
# Load data
sv.load_data(db_name)
# Limit input variables to `n_desc` using Train set
# Outputs to relevant database name
sv.limit_inputs(
n_desc, eval_set='train',
output_filename=db_name.replace('.csv', '.{}.csv'.format(n_desc))
)
# Tune hyperparameters (architecture and ADAM)
# 20 employer bees, 10 search cycles
# Evaluation of solutions based on validation set median absolute error
sv.tune_hyperparameters(20, 10, eval_set='valid', eval_fn='med_abs_error')
# Create an ECNet project (saved and recalled later)
# 5 pools with 75 trials/pool, best ANNs selected from each pool
sv.create_project(db_name.replace('.csv', ''), 5, 75)
# Train project
# Select best candidates based on validation set median absolute error
sv.train(validate=True, selection_set='valid',
selection_fn='med_abs_error')
# Obtain learning, validation, testing set median absolute error, r-squared
err_l = sv.errors('med_abs_error', 'r2', dset='learn')
err_v = sv.errors('med_abs_error', 'r2', dset='valid')
err_t = sv.errors('med_abs_error', 'r2', dset='test')
logger.log('info', 'Learning set performance: {}'.format(err_l))
logger.log('info', 'Validation set performance: {}'.format(err_v))
logger.log('info', 'Testing set performance: {}'.format(err_t))
# Save the project, creating a .prj file and removing un-chosen candidates
sv.save_project(del_candidates=True)
def tune(num_processes,
shuffle=None,
split=[0.7, 0.2, 0.1],
validate=True,
eval_set=None,
eval_fn='rmse'):
logger.stream_level = 'debug'
sv = Server(num_processes=num_processes)
sv.load_data('cn_model_v1.0.csv', random=True, split=split)
sv.tune_hyperparameters(2,
2,
shuffle=shuffle,
split=split,
validate=validate,
eval_set=eval_set,
eval_fn=eval_fn)
