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iterative_interactions.py
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iterative_interactions.py
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import csv
from keras.optimizers import Adagrad, RMSprop
import axelrod as axl
from knn_strategy import KNN
from generate_data import yield_data
from train import train_model, test_model, load_model, save_model
def process_data(filename, outfilename):
with open(outfilename, 'w') as outputfile:
writer = csv.writer(outputfile)
for line in yield_data(filename):
writer.writerow(line)
def interactions_generator(player, opponents=None, repetitions=100, noise=0,
turns=200):
if not opponents:
opponents = [s() for s in axl.all_strategies if axl.obey_axelrod(s())
and not s().classifier['long_run_time']]
for opponent in opponents:
match = axl.Match((player, opponent), turns, noise=noise)
for rep in range(repetitions):
match.play()
yield (player.history, opponent.history)
def write_interactions(interactions_gen, filename):
with open(filename, 'w') as handle:
writer = csv.writer(handle)
for h1, h2 in interactions_gen:
row = [0, 0, '', '', ''.join(h1), ''.join(h2)]
writer.writerow(row)
def create_interactions(s=""):
g = interactions_generator(KNN(), repetitions=20)
filename = "/ssd/raw_train_extra.csv{}".format(s)
write_interactions(g, filename)
outfilename = "/ssd/train_extra.csv{}".format(s)
process_data(filename, outfilename)
g = interactions_generator(KNN(), repetitions=4)
filename = "/ssd/raw_test_extra.csv{}".format(s)
write_interactions(g, filename)
outfilename = "/ssd/test_extra.csv{}".format(s)
process_data(filename, outfilename)
if __name__ == "__main__":
# try:
# s = sys.argv[1]
# except IndexError:
# s = ""
# create_interactions(s)
epochs = 1
for i in range(0, 10):
# Generate new data
create_interactions(str(i))
# Load
model = load_model(name="model")
model.compile(loss='mse',
optimizer=RMSprop())
# Train
train_filename = "/ssd/train_extra.csv{}".format(i)
model, losses = train_model(model, epochs, train_filename, nb_epoch=2)
# Test
print("MSE", losses[-1])
test_filename = "/ssd/test_extra.csv{}".format(i)
m = test_model(model, test_filename)
# Save model
save_model(model, name="model")
# if m > 0.93:
# break