Merge branch 'rework-1' into 'fuck_git'

Rework 1

See merge request Pownie/backgammon!2
This commit is contained in:
Christoffer Müller Madsen 2018-03-27 10:16:37 +00:00
commit d4e699bc49
4 changed files with 172 additions and 49 deletions

3
.gitignore vendored
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@ -169,3 +169,6 @@ venv.bak/
README.*
!README.org
models/
.DS_Store
bench/

157
main.py
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@ -3,38 +3,6 @@ import sys
import os
import time
model_storage_path = 'models'
# Create models folder
if not os.path.exists(model_storage_path):
os.makedirs(model_storage_path)
# Define helper functions
def log_train_outcome(outcome, trained_eps = 0):
format_vars = { 'trained_eps': trained_eps,
'count': len(train_outcome),
'sum': sum(train_outcome),
'mean': sum(train_outcome) / len(train_outcome),
'time': int(time.time())
}
with open(os.path.join(config['model_path'], 'logs', "train.log"), 'a+') as f:
f.write("{time};{trained_eps};{count};{sum};{mean}".format(**format_vars) + "\n")
def log_eval_outcomes(outcomes, trained_eps = 0):
for outcome in outcomes:
scores = outcome[1]
format_vars = { 'trained_eps': trained_eps,
'method': outcome[0],
'count': len(scores),
'sum': sum(scores),
'mean': sum(scores) / len(scores),
'time': int(time.time())
}
with open(os.path.join(config['model_path'], 'logs', "eval.log"), 'a+') as f:
f.write("{time};{method};{trained_eps};{count};{sum};{mean}".format(**format_vars) + "\n")
# Parse command line arguments
parser = argparse.ArgumentParser(description="Backgammon games")
parser.add_argument('--episodes', action='store', dest='episode_count',
@ -47,13 +15,15 @@ parser.add_argument('--eval-methods', action='store',
default=['random'], nargs='*',
help='specifies evaluation methods')
parser.add_argument('--eval', action='store_true',
help='whether to evaluate the neural network with a random choice bot')
help='evaluate the neural network with a random choice bot')
parser.add_argument('--bench-eval-scores', action='store_true',
help='benchmark scores of evaluation measures. episode counts and model specified as options are ignored.')
parser.add_argument('--train', action='store_true',
help='whether to train the neural network')
help='train the neural network')
parser.add_argument('--eval-after-train', action='store_true', dest='eval_after_train',
help='whether to evaluate after each training session')
help='evaluate after each training session')
parser.add_argument('--play', action='store_true',
help='whether to play with the neural network')
help='play with the neural network')
parser.add_argument('--start-episode', action='store', dest='start_episode',
type=int, default=0,
help='episode count to start at; purely for display purposes')
@ -66,27 +36,73 @@ args = parser.parse_args()
config = {
'model': args.model,
'model_path': os.path.join(model_storage_path, args.model),
'episode_count': args.episode_count,
'eval_methods': args.eval_methods,
'train': args.train,
'play': args.play,
'eval': args.eval,
'bench_eval_scores': args.bench_eval_scores,
'eval_after_train': args.eval_after_train,
'start_episode': args.start_episode,
'train_perpetually': args.train_perpetually,
'model_storage_path': model_storage_path
'model_storage_path': 'models',
'bench_storage_path': 'bench'
}
# Create models folder
if not os.path.exists(config['model_storage_path']):
os.makedirs(config['model_storage_path'])
model_path = lambda: os.path.join(config['model_storage_path'], config['model'])
# Make sure directories exist
model_path = os.path.join(config['model_path'])
log_path = os.path.join(model_path, 'logs')
if not os.path.isdir(model_path):
os.mkdir(model_path)
log_path = os.path.join(model_path(), 'logs')
if not os.path.isdir(model_path()):
os.mkdir(model_path())
if not os.path.isdir(log_path):
os.mkdir(log_path)
# Define helper functions
def log_train_outcome(outcome, trained_eps = 0, log_path = os.path.join(model_path(), 'logs', "train.log")):
format_vars = { 'trained_eps': trained_eps,
'count': len(train_outcome),
'sum': sum(train_outcome),
'mean': sum(train_outcome) / len(train_outcome),
'time': int(time.time())
}
with open(log_path, 'a+') as f:
f.write("{time};{trained_eps};{count};{sum};{mean}".format(**format_vars) + "\n")
def log_eval_outcomes(outcomes, trained_eps = 0, log_path = os.path.join(model_path(), 'logs', "eval.log")):
for outcome in outcomes:
scores = outcome[1]
format_vars = { 'trained_eps': trained_eps,
'method': outcome[0],
'count': len(scores),
'sum': sum(scores),
'mean': sum(scores) / len(scores),
'time': int(time.time())
}
with open(log_path, 'a+') as f:
f.write("{time};{method};{trained_eps};{count};{sum};{mean}".format(**format_vars) + "\n")
def log_bench_eval_outcomes(outcomes, log_path, index, time, trained_eps = 0):
for outcome in outcomes:
scores = outcome[1]
format_vars = { 'trained_eps': trained_eps,
'method': outcome[0],
'count': len(scores),
'sum': sum(scores),
'mean': sum(scores) / len(scores),
'time': time,
'index': index,
}
with open(log_path, 'a+') as f:
f.write("{method};{count};{index};{time};{sum};{mean}".format(**format_vars) + "\n")
# Do actions specified by command-line
if args.list_models:
def get_eps_trained(folder):
@ -94,7 +110,7 @@ if args.list_models:
return int(f.read())
model_folders = [ f.path
for f
in os.scandir(model_storage_path)
in os.scandir(config['model_storage_path'])
if f.is_dir() ]
models = [ (folder, get_eps_trained(folder)) for folder in model_folders ]
sys.stderr.write("Found {} model(s)\n".format(len(models)))
@ -106,13 +122,13 @@ if args.list_models:
if __name__ == "__main__":
# Set up network
from network import Network
network = Network(config, config['model'])
start_episode = network.episodes_trained
# Set up variables
episode_count = config['episode_count']
if args.train:
network = Network(config, config['model'])
start_episode = network.episodes_trained
while True:
train_outcome = network.train_model(episodes = episode_count, trained_eps = start_episode)
start_episode += episode_count
@ -122,9 +138,58 @@ if __name__ == "__main__":
log_eval_outcomes(eval_outcomes, trained_eps = start_episode)
if not config['train_perpetually']:
break
elif args.eval:
outcomes = network.eval()
network = Network(config, config['model'])
start_episode = network.episodes_trained
# Evaluation measures are described in `config`
outcomes = network.eval(config['episode_count'])
log_eval_outcomes(outcomes, trained_eps = start_episode)
# elif args.play:
# g.play(episodes = episode_count)
elif args.bench_eval_scores:
# Make sure benchmark directory exists
if not os.path.isdir(config['bench_storage_path']):
os.mkdir(config['bench_storage_path'])
config = config.copy()
config['model'] = 'bench'
network = Network(config, config['model'])
start_episode = network.episodes_trained
if start_episode == 0:
print("Model not trained! Beware of using non-existing models!")
exit()
sample_count = 20
episode_counts = [25, 50, 100, 250, 500, 1000, 2500, 5000,
10000, 20000]
def do_eval(sess):
for eval_method in config['eval_methods']:
result_path = os.path.join(config['bench_storage_path'],
eval_method) + "-{}.log".format(int(time.time()))
for n in episode_counts:
for i in range(sample_count):
start_time = time.time()
# Evaluation measure to be benchmarked are described in `config`
outcomes = network.eval(episode_count = n,
tf_session = sess)
time_diff = time.time() - start_time
log_bench_eval_outcomes(outcomes,
time = time_diff,
index = i,
trained_eps = start_episode,
log_path = result_path)
# CMM: oh no
import tensorflow as tf
with tf.Session() as session:
network.restore_model(session)
do_eval(session)

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@ -22,7 +22,7 @@ class Network:
def __init__(self, config, name):
self.config = config
self.checkpoint_path = config['model_path']
self.checkpoint_path = os.path.join(config['model_storage_path'], config['model'])
self.name = name
@ -392,6 +392,24 @@ class Network:
writer.close()
return outcomes
# take turn, which finds the best state and picks it, based on the current network
# save current state
# run training operation (session.run(self.training_op, {x:x, value_next, value_next})), (something which does the backprop, based on the state after having taken a turn, found before, and the state we saved in the beginning and from now we'll save it at the end of the turn
# save the current state again, so we can continue running backprop based on the "previous" turn.
# NOTE: We need to make a method so that we can take a single turn or at least just pick the next best move, so we know how to evaluate according to TD-learning. Right now, our game just continues in a while loop without nothing to stop it!
def eval(self, episode_count, trained_eps = 0, tf_session = None):
def do_eval(sess, method, episodes = 1000, trained_eps = 0):
start_time = time.time()
writer.close()
return outcomes
# take turn, which finds the best state and picks it, based on the current network
@ -403,3 +421,23 @@ class Network:
# save the current state again, so we can continue running backprop based on the "previous" turn.
if tf_session == None:
with tf.Session():
session.run(tf.global_variables_initializer())
self.restore_model(session)
outcomes = [ (method, do_eval(session,
method,
episode_count,
trained_eps = trained_eps))
for method
in self.config['eval_methods'] ]
return outcomes
else:
outcomes = [ (method, do_eval(tf_session,
method,
episode_count,
trained_eps = trained_eps))
for method
in self.config['eval_methods'] ]
return outcomes

17
plot.py
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@ -9,9 +9,26 @@ import matplotlib.dates as mdates
train_headers = ['timestamp', 'eps_train', 'eps_trained_session', 'sum', 'mean']
eval_headers = ['timestamp', 'method', 'eps_train', 'eval_eps_used', 'sum', 'mean']
bench_headers = ['method', 'sample_count', 'i', 'time', 'sum', 'mean']
model_path = 'models'
def plot_bench(data_path):
df = pd.read_csv(data_path, sep=";",
names=bench_headers, index_col=[0,1,2])
for method_label in df.index.levels[0]:
df_prime = df[['mean']].loc[method_label].unstack().T
plot = df_prime.plot.box()
plot.set_title("Evaluation variance, {}".format(method_label))
plot.set_xlabel("Sample count")
plot.set_ylabel("Mean score")
plt.show(plot.figure)
# for later use:
variances = df_prime.var()
print(variances)
del df_prime, plot, variances
def dataframes(model_name):
def df_timestamp_to_datetime(df):