def main(args):
os.environ["CUDA_VISIBLE_DEVICES"] = ""
# maybe create dir
if not os.path.isdir(SAVE_DIR):
os.makedirs(SAVE_DIR)
# load results if already exist
if os.path.isfile(SAVE_PATH):
results = log_utils.read_pickle(SAVE_PATH)
else:
results = {}
for num_experience in NUM_EXPERIENCE_LIST:
for split_threshold in SPLIT_THRESHOLD_LIST:
for min_conf in CONF_THRESHOLD_LIST:
for run_idx in range(NUM_RUNS):
key = (num_experience, split_threshold, min_conf, run_idx)
# skip if this setting is already in results
if key in results:
continue
accuracy = run(num_experience, split_threshold, min_conf)
results[key] = accuracy
# save results after each run
log_utils.write_pickle(SAVE_PATH, results)
def main(args):
os.environ["CUDA_VISIBLE_DEVICES"] = ""
# maybe create dir
if not os.path.isdir(SAVE_DIR):
os.makedirs(SAVE_DIR)
# load results if already exist
if os.path.isfile(SAVE_PATH):
results = log_utils.read_pickle(SAVE_PATH)
else:
results = {}
for resolution in RESOLUTION_LIST:
for run_idx in range(NUM_RUNS):
key = (resolution, run_idx)
# skip if this setting is already in results
if key in results:
continue
accuracy = run(resolution)
results[key] = accuracy
# save results after each run
log_utils.write_pickle(SAVE_PATH, results)
def main(args):
os.environ["CUDA_VISIBLE_DEVICES"] = ""
NUM_RUNS = 50
NUM_EXPERIENCE = 2000
SPLIT_THRESHOLD_LIST = [50, 100, 200, 500]
SAVE_DIR = "results/homo_g/balanced_mlp"
SAVE_FILE = "continuous_3_evaluation_3.pickle"
SAVE_PATH = os.path.join(SAVE_DIR, SAVE_FILE)
# maybe create dir
if not os.path.isdir(SAVE_DIR):
os.makedirs(SAVE_DIR)
# load results if already exist
if os.path.isfile(SAVE_PATH):
results = log_utils.read_pickle(SAVE_PATH)
else:
results = {}
for t1 in SPLIT_THRESHOLD_LIST:
for t2 in SPLIT_THRESHOLD_LIST:
for run_idx in range(NUM_RUNS):
key = (t1, t2, run_idx)
# skip if this setting is already in results
if key in results:
continue
accuracy = run(NUM_EXPERIENCE, t1, t2)
results[key] = accuracy
# save results after each run
log_utils.write_pickle(SAVE_PATH, results)
def main(args):
os.environ["CUDA_VISIBLE_DEVICES"] = ""
NUM_RUNS = 200
NUM_EXPERIENCE_LIST = [200, 500, 1000]
SPLIT_THRESHOLD_LIST = [50, 100, 200]
SAVE_DIR = "results/homo_g/balanced_mlp"
SAVE_FILE = "experiment_1_thresholds.pickle"
SAVE_PATH = os.path.join(SAVE_DIR, SAVE_FILE)
# maybe create dir
if not os.path.isdir(SAVE_DIR):
os.makedirs(SAVE_DIR)
# load results if already exist
if os.path.isfile(SAVE_PATH):
results = log_utils.read_pickle(SAVE_PATH)
else:
results = {}
for num_experience in NUM_EXPERIENCE_LIST:
for split_threshold in SPLIT_THRESHOLD_LIST:
for run_idx in range(NUM_RUNS):
key = (num_experience, split_threshold, run_idx)
# skip if this setting is already in results
if key in results:
continue
accuracy = run(num_experience, split_threshold)
results[key] = accuracy
# save results after each run
log_utils.write_pickle(SAVE_PATH, results)
