def predict(args):
day = args[2]
num_lineups = int(args[3])
filename = FILENAME_USED_NN
if len(args) == 5:
filename = "trainedModels/" + args[4]
day_x, playerList = getInputForDay(day, True)
print(day_x.shape)
print(len(playerList))
N, M = day_x.shape
day_X = np.ones((N, M + 1))
day_X[:, 1:] = day_x
model = NeuralNet(NeuralNet.load(filename))
scores, predictedLineups = model.getPrediction(day_X, playerList,
num_lineups)
print()
print("No noise lineup : ")
for i in range(len(scores)):
predictedLineup = predictedLineups[i]
print("player selected:")
for player in predictedLineup:
print(player.toString())
print()
print("Expected total score :" + str(scores[i]))
print("---------------")
def test(train_X, test_X, train_y, test_y, args):
filename = FILENAME_USED_NN
if len(args) == 3:
filename = "trainedModels/" + args[2]
model_loaded = NeuralNet.load(filename)
model = NeuralNet(model_loaded)
print("final train accuracy:", model.score(train_X, train_y))
print("final test accuracy:", model.score(test_X, test_y))
def __loadNNets(self, name, includeOptimisers=False):
nNetA = NeuralNet(in_channels=3).to(self.device)
nNetB = NeuralNet(in_channels=4).to(self.device)
nNetC = NeuralNet(in_channels=4).to(self.device)
if name is not None:
name = name.replace(".pth", "a.pth")
nNetA.load(name)
name = name.replace("a.pth", "b.pth")
nNetB.load(name)
name = name.replace("b.pth", "c.pth")
nNetC.load(name)
else:
nNetA.loadMostRecent("a.pth")
nNetB.loadMostRecent("b.pth")
nNetC.loadMostRecent("c.pth")
nnets = nNetA, nNetB, nNetC
if includeOptimisers:
optimisers = tuple(torch.optim.Adam(
N.parameters(), lr=0.001) for N in nnets)
return nnets, optimisers
else:
return nnets
def run(day):
print("This mETHOD should not be being calleD!")
day_x, day_y, Gamesplayers = getSortedOrderForDay(day)
playersList = [item.playerID for items in Gamesplayers for item in items]
N, M = day_x.shape
day_X = np.ones((N, M + 1))
day_X[:, 1:] = day_x
model = NeuralNet.load(FILENAME_USED_NN)
score, realLineupIndex, predictedLineupIndex = model.scoreDay(
day_X, day_y, True)
realLineup = [playersList[i] for i in realLineupIndex]
predictedLineup = [playersList[i] for i in predictedLineupIndex]
print("test accuracy for day:", score)
print("good players ids:", realLineup)
print("players selected ids :", predictedLineup)
def continue_training(train_X, train_y, test_X, test_y, args):
print("NOT SURE THIS IS WORKING")
epoch = int(args[2])
filename = FILENAME_USED_NN
if len(args) == 4:
filename = "trainedModels/" + args[3]
train_x_flat = np.array([item for items in train_X for item in items])
train_y_flat = np.array([item for items in train_y for item in items])
test_x_flat = np.array([item for items in test_X for item in items])
test_y_flat = np.array([item for items in test_y for item in items])
model_loaded = NeuralNet.load(filename)
model = make_model(model=model_loaded,
learning_r=DEFAULT_LEARNING_RATE,
batch_size_2=DEFAULT_BATCH_SIZE,
epoch=epoch)
print("Continue training on :" + str(len(train_y_flat)) + "games")
model.fit(train_x_flat, train_y_flat, test_x_flat, test_y_flat)
model.save(filename)
