def run(args):
data_path = args.IN
print "Loading data"
data = read_np(os.path.join(data_path, "a1a1_raw.data.npy"))
w_a = read_np(os.path.join(data_path, "a1a1_raw.w_a.npy"))
w_b = read_np(os.path.join(data_path, "a1a1_raw.w_b.npy"))
perm = read_np(os.path.join(data_path, "a1a1_raw.perm.npy"))
print "Read %d events" % data.shape[0]
print "Processing data"
event = A1A1Event(data, args)
points = EventDatasets(event, w_a, w_b, perm, miniset=args.MINISET, unweighted=args.UNWEIGHTED, smear_polynomial=(args.BETA>0), filtered = True)
num_features = points.train.x.shape[1]
print "Generated %d features" % num_features
print "Initializing model"
with tf.variable_scope("model1") as vs:
model = NeuralNetwork(num_features, num_layers=args.LAYERS, size=args.SIZE, keep_prob=(1-args.DROPOUT), optimizer=args.OPT)
with tf.variable_scope("model1", reuse=True) as vs:
emodel = NeuralNetwork(num_features, num_layers=args.LAYERS, size=args.SIZE, keep_prob=(1-args.DROPOUT), optimizer=args.OPT)
tf.global_variables_initializer().run()
print "Training"
total_train(model, points, emodel=emodel, batch_size=128, epochs=args.EPOCHS)
def run(args):
data_path = args.IN
print "Loading data"
data = read_np(os.path.join(data_path, "rhorho_raw.data.npy"))
w_a = read_np(os.path.join(data_path, "rhorho_raw.w_a.npy"))
w_b = read_np(os.path.join(data_path, "rhorho_raw.w_b.npy"))
perm = read_np(os.path.join(data_path, "rhorho_raw.perm.npy"))
print "Read %d events" % data.shape[0]
print "Processing data"
event = RhoRhoEvent(data, args)
points = EventDatasets(event, w_a, w_b, perm)
num_features = points.train.x.shape[1]
print "Generated %d features" % num_features
model = BoostedTree(treedepth=args.TREEDEPTH)
model.train(points.train)
model.test(points.valid)
