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)