Add evaluation variance benchmark
To do a benchmark for `pubeval`, run `python3 main.py --bench-eval-scores --eval-methods pubeval` Logs will be placed in directory `bench` Use `plot_bench(data_path)` in `plot.py` for plotting
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159
main.py
159
main.py
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@ -3,38 +3,6 @@ import sys
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import os
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import time
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model_storage_path = 'models'
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# Create models folder
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if not os.path.exists(model_storage_path):
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os.makedirs(model_storage_path)
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# Define helper functions
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def log_train_outcome(outcome, trained_eps = 0):
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format_vars = { 'trained_eps': trained_eps,
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'count': len(train_outcome),
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'sum': sum(train_outcome),
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'mean': sum(train_outcome) / len(train_outcome),
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'time': int(time.time())
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}
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with open(os.path.join(config['model_path'], 'logs', "train.log"), 'a+') as f:
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f.write("{time};{trained_eps};{count};{sum};{mean}".format(**format_vars) + "\n")
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def log_eval_outcomes(outcomes, trained_eps = 0):
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for outcome in outcomes:
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scores = outcome[1]
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format_vars = { 'trained_eps': trained_eps,
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'method': outcome[0],
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'count': len(scores),
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'sum': sum(scores),
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'mean': sum(scores) / len(scores),
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'time': int(time.time())
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}
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with open(os.path.join(config['model_path'], 'logs', "eval.log"), 'a+') as f:
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f.write("{time};{method};{trained_eps};{count};{sum};{mean}".format(**format_vars) + "\n")
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# Parse command line arguments
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parser = argparse.ArgumentParser(description="Backgammon games")
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parser.add_argument('--episodes', action='store', dest='episode_count',
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@ -47,13 +15,15 @@ parser.add_argument('--eval-methods', action='store',
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default=['random'], nargs='*',
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help='specifies evaluation methods')
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parser.add_argument('--eval', action='store_true',
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help='whether to evaluate the neural network with a random choice bot')
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help='evaluate the neural network with a random choice bot')
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parser.add_argument('--bench-eval-scores', action='store_true',
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help='benchmark scores of evaluation measures. episode counts and model specified as options are ignored.')
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parser.add_argument('--train', action='store_true',
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help='whether to train the neural network')
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help='train the neural network')
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parser.add_argument('--eval-after-train', action='store_true', dest='eval_after_train',
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help='whether to evaluate after each training session')
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help='evaluate after each training session')
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parser.add_argument('--play', action='store_true',
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help='whether to play with the neural network')
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help='play with the neural network')
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parser.add_argument('--start-episode', action='store', dest='start_episode',
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type=int, default=0,
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help='episode count to start at; purely for display purposes')
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@ -66,27 +36,73 @@ args = parser.parse_args()
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config = {
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'model': args.model,
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'model_path': os.path.join(model_storage_path, args.model),
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'episode_count': args.episode_count,
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'eval_methods': args.eval_methods,
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'train': args.train,
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'play': args.play,
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'eval': args.eval,
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'bench_eval_scores': args.bench_eval_scores,
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'eval_after_train': args.eval_after_train,
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'start_episode': args.start_episode,
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'train_perpetually': args.train_perpetually,
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'model_storage_path': model_storage_path
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'model_storage_path': 'models',
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'bench_storage_path': 'bench'
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}
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# Create models folder
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if not os.path.exists(config['model_storage_path']):
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os.makedirs(config['model_storage_path'])
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model_path = lambda: os.path.join(config['model_storage_path'], config['model'])
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# Make sure directories exist
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model_path = os.path.join(config['model_path'])
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log_path = os.path.join(model_path, 'logs')
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if not os.path.isdir(model_path):
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os.mkdir(model_path)
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log_path = os.path.join(model_path(), 'logs')
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if not os.path.isdir(model_path()):
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os.mkdir(model_path())
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if not os.path.isdir(log_path):
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os.mkdir(log_path)
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# Define helper functions
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def log_train_outcome(outcome, trained_eps = 0, log_path = os.path.join(model_path(), 'logs', "train.log")):
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format_vars = { 'trained_eps': trained_eps,
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'count': len(train_outcome),
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'sum': sum(train_outcome),
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'mean': sum(train_outcome) / len(train_outcome),
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'time': int(time.time())
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}
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with open(log_path, 'a+') as f:
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f.write("{time};{trained_eps};{count};{sum};{mean}".format(**format_vars) + "\n")
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def log_eval_outcomes(outcomes, trained_eps = 0, log_path = os.path.join(model_path(), 'logs', "eval.log")):
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for outcome in outcomes:
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scores = outcome[1]
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format_vars = { 'trained_eps': trained_eps,
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'method': outcome[0],
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'count': len(scores),
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'sum': sum(scores),
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'mean': sum(scores) / len(scores),
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'time': int(time.time())
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}
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with open(log_path, 'a+') as f:
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f.write("{time};{method};{trained_eps};{count};{sum};{mean}".format(**format_vars) + "\n")
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def log_bench_eval_outcomes(outcomes, log_path, index, time, trained_eps = 0):
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for outcome in outcomes:
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scores = outcome[1]
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format_vars = { 'trained_eps': trained_eps,
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'method': outcome[0],
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'count': len(scores),
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'sum': sum(scores),
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'mean': sum(scores) / len(scores),
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'time': time,
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'index': index,
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}
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with open(log_path, 'a+') as f:
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f.write("{method};{count};{index};{time};{sum};{mean}".format(**format_vars) + "\n")
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# Do actions specified by command-line
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if args.list_models:
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def get_eps_trained(folder):
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@ -94,7 +110,7 @@ if args.list_models:
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return int(f.read())
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model_folders = [ f.path
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for f
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in os.scandir(model_storage_path)
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in os.scandir(config['model_storage_path'])
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if f.is_dir() ]
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models = [ (folder, get_eps_trained(folder)) for folder in model_folders ]
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sys.stderr.write("Found {} model(s)\n".format(len(models)))
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@ -106,13 +122,13 @@ if args.list_models:
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if __name__ == "__main__":
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# Set up network
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from network import Network
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network = Network(config, config['model'])
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start_episode = network.episodes_trained
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# Set up variables
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episode_count = config['episode_count']
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if args.train:
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network = Network(config, config['model'])
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start_episode = network.episodes_trained
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while True:
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train_outcome = network.train_model(episodes = episode_count, trained_eps = start_episode)
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start_episode += episode_count
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@ -122,9 +138,58 @@ if __name__ == "__main__":
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log_eval_outcomes(eval_outcomes, trained_eps = start_episode)
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if not config['train_perpetually']:
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break
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elif args.eval:
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outcomes = network.eval()
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network = Network(config, config['model'])
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start_episode = network.episodes_trained
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# Evaluation measures are described in `config`
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outcomes = network.eval(config['episode_count'])
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log_eval_outcomes(outcomes, trained_eps = start_episode)
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# elif args.play:
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# g.play(episodes = episode_count)
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elif args.bench_eval_scores:
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# Make sure benchmark directory exists
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if not os.path.isdir(config['bench_storage_path']):
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os.mkdir(config['bench_storage_path'])
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config = config.copy()
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config['model'] = 'bench'
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network = Network(config, config['model'])
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start_episode = network.episodes_trained
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if start_episode == 0:
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print("Model not trained! Beware of using non-existing models!")
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exit()
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sample_count = 20
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episode_counts = [25, 50, 100, 250, 500, 1000, 2500, 5000,
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10000, 20000]
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def do_eval(sess):
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for eval_method in config['eval_methods']:
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result_path = os.path.join(config['bench_storage_path'],
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eval_method) + "-{}.log".format(int(time.time()))
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for n in episode_counts:
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for i in range(sample_count):
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start_time = time.time()
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# Evaluation measure to be benchmarked are described in `config`
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outcomes = network.eval(episode_count = n,
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tf_session = sess)
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time_diff = time.time() - start_time
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log_bench_eval_outcomes(outcomes,
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time = time_diff,
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index = i,
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trained_eps = start_episode,
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log_path = result_path)
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# CMM: oh no
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import tensorflow as tf
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with tf.Session() as session:
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network.restore_model(session)
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do_eval(session)
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28
network.py
28
network.py
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@ -13,7 +13,7 @@ class Network:
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input_size = 26
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output_size = 1
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# Can't remember the best learning_rate, look this up
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learning_rate = 0.05
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learning_rate = 0.01
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# TODO: Actually compile tensorflow properly
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#os.environ["TF_CPP_MIN_LOG_LEVEL"]="2"
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@ -23,7 +23,7 @@ class Network:
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def __init__(self, config, name):
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self.config = config
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self.checkpoint_path = config['model_path']
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self.checkpoint_path = os.path.join(config['model_storage_path'], config['model'])
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self.name = name
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@ -262,7 +262,7 @@ class Network:
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def eval(self, trained_eps = 0):
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def eval(self, episode_count, trained_eps = 0, tf_session = None):
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def do_eval(sess, method, episodes = 1000, trained_eps = 0):
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start_time = time.time()
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@ -356,13 +356,23 @@ class Network:
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sys.stderr.write("[EVAL ] Evaluation method '{}' is not defined\n".format(method))
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return [0]
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with tf.Session() as session:
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session.run(tf.global_variables_initializer())
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self.restore_model(session)
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outcomes = [ (method, do_eval(session,
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if tf_session == None:
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with tf.Session():
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session.run(tf.global_variables_initializer())
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self.restore_model(session)
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outcomes = [ (method, do_eval(session,
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method,
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episode_count,
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trained_eps = trained_eps))
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for method
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in self.config['eval_methods'] ]
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return outcomes
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else:
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outcomes = [ (method, do_eval(tf_session,
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method,
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self.config['episode_count'],
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episode_count,
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trained_eps = trained_eps))
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for method
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in self.config['eval_methods'] ]
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return outcomes
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return outcomes
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12
plot.py
12
plot.py
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@ -9,9 +9,21 @@ import matplotlib.dates as mdates
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train_headers = ['timestamp', 'eps_train', 'eps_trained_session', 'sum', 'mean']
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eval_headers = ['timestamp', 'method', 'eps_train', 'eval_eps_used', 'sum', 'mean']
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bench_headers = ['method', 'sample_count', 'i', 'time', 'sum', 'mean']
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model_path = 'models'
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def plot_bench(data_path):
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df = pd.read_csv(data_path, sep=";",
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names=bench_headers, index_col=[0,1,2])
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for method_label in df.index.levels[0]:
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cur_df = df.loc[method_label]
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plot = df[['mean']].loc['pubeval'].unstack().T.plot.box()
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plot.set_title("Evaluation variance, {}".format(method_label))
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plot.set_xlabel("Sample count")
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plot.set_ylabel("Mean score")
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plt.show(plot.figure)
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del cur_df, plot
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def dataframes(model_name):
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def df_timestamp_to_datetime(df):
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