def test_with_apc(self):
odx = ODX(0, 1)
odx.load_gtfs()
day = dt.datetime.strptime("01/30/18 00:00", "%m/%d/%y %H:%M")
self.megas = odx.preprocess_gtfs(day)
builder = NetworkBuilder(700)
net = builder.build(self.megas, 1)
fileDir = os.path.realpath(__file__).split('/version_1_0')[0]
path = os.path.join(fileDir, 'Data/breeze_test.pick')
breeze_load = Breeze_Loader()
df = breeze_load.load_breeze(path)
self.assertTrue(isinstance(df, pd.DataFrame), msg="Loader works well")
df = breeze_load.get_marta_only(df)
self.assertTrue(df.shape[0] -
df.Dev_Operator.str.contains('MARTA').sum() == 0,
msg=' contains non Marta Data')
bus, rail = breeze_load.split_frame(df)
rail[~(rail.Dev_Operator.str.contains("Rail"))].to_csv('bad_data.csv')
self.assertEqual(rail.shape[0] -
rail.Dev_Operator.str.contains("Rail").sum(),
0,
msg='Contains non rail data')
self.assertEqual(bus.shape[0] -
bus.Dev_Operator.str.contains("Bus").sum(),
0,
msg='Contains non bus data')
path = os.path.join(fileDir, 'Data/RailStopsMap.csv')
loader = RailMappingLoader()
map_df = loader.load_rail_mappings(path)
map_df = loader.clean_rail_mappings(map_df)
map_df = loader.fit_2_network(map_df, net)
path = os.path.join(fileDir, 'Data/apc_test.pick')
apc_load = APC_Loader(net)
apc_df = apc_load.load_apc(path)
apc_df = apc_load.join_megas(apc_df)
#load.match_2_apc(bus, apc_df).to_csv('apc_match_test.csv')
bus_dict = apc_load.build_search_dict(apc_df)
bus_df = breeze_load.apc_match(bus, bus_dict)
bus_df.head(n=2000).to_csv('apc_breeze_test.csv')
rail_df = breeze_load.match_rail_stops(rail, map_df)
rail_df.head(n=100).to_csv('rail_breeze_test.csv')
data = pd.concat([bus_df, rail_df])
data.to_csv('Data_set_11_13.csv')
print(data.columns)
def test_single(self):
odx = ODX(0, 1)
odx.load_gtfs()
day = dt.datetime.strptime("01/30/18 00:00", "%m/%d/%y %H:%M")
self.megas = odx.preprocess_gtfs(day)
builder = NetworkBuilder(700)
net = builder.build(self.megas, 1)
fileDir = os.path.realpath(__file__).split('/version_1_0')[0]
path = os.path.join(fileDir, 'Data/apc_test.pick')
load = apc_loader.APC_Loader(net)
df = load.load_apc(path)
self.assertTrue(isinstance(load.load_apc(path), pd.DataFrame),
msg="Loader works well")
self.assertTrue(type(load.get_route_tree('19')) != int,
msg='Network stored as int works incorrectly')
self.assertTrue(type(load.get_route_tree(19)) == int,
msg='Test works incorrectly')
self.assertTrue(load.join_megas(df, True) == 0,
msg='Test for bad matches')
_ = load.join_megas(df)
print(_)
_.to_csv('apc_test_w_ms.csv')
print(load.build_bus_tree(df))
def build(self, simulator_info, simulator="", seed=None):
"""
Builds all NetworkPool instances required to conduct the specified
experiments.
:param simulator_info: Information about the used simulator as returned
from PyNNLess.get_simulator_info() -- contains the maximum number of
neurons and the supported software concurrency.
:param seed: seed to be used to spawn the seeds for the data generation.
"""
# Spawn more random seeds
old_state = utils.initialize_seed(seed)
try:
data_seed = np.random.randint(1 << 30)
build_seed = np.random.randint(1 << 30)
finally:
utils.finalize_seed(old_state)
# Add a dummy experiment if there are no experiments specified
if len(self["experiments"]) == 0:
self["experiments"] = [ExperimentDescriptor(name="eval")]
# "Count sources" flag
cs = simulator_info["sources_are_neurons"]
# Create all NetworkPool instances
pools = []
for i, experiment in enumerate(self["experiments"]):
# Gather the input and topology parameters for this experiment
input_params_list, topology_params_list = \
self.build_parameters(experiment)
# Generate an experiment name
# TODO: Make sure the name is unique
if experiment["name"] == "":
experiment["name"] = "experiment_" + str(i)
# Generate new pools for this experiment
min_pool = len(pools)
pidx = simulator_info["concurrency"]
# Assemble a name for this repetition
pools = pools + [NetworkPool(name=experiment["name"] + "." + str(c))
for c in xrange(simulator_info["concurrency"])]
# Metadata to store along with the networks
meta_data = {
"experiment_idx": i,
"experiment_name": experiment["name"],
"experiment_size": (experiment["repeat"] *
(len(input_params_list) * len(topology_params_list))),
"keys": experiment.get_keys(),
"output_params": self["output"],
"simulator": simulator
}
# Repeat the experiment as many times as specified in the "repeat"
# parameter
local_build_seed = build_seed
net_idx = 0
for j in xrange(experiment["repeat"]):
# Create a random permutation of the topology parameters list
perm = range(0, len(topology_params_list))
random.shuffle(perm)
for k in xrange(len(topology_params_list)):
# Print the current network number
net_idx = net_idx + 1
if (net_idx % 100 == 0):
n_nets = len(topology_params_list) * experiment["repeat"]
print("Generating network " + str(net_idx) + "/" +
str(n_nets))
# Create a build instance coupled with the topology
# parameters
topology_params = topology_params_list[perm[k]]
builder = NetworkBuilder(
data_params=topology_params["data"],
seed=data_seed)
# Build a network instance and add it to the network pool
net = builder.build(
topology_params=topology_params["topology"],
input_params=input_params_list,
meta_data=meta_data,
seed=local_build_seed)
# Search for a pool to which the network should be added.
# Use the pool with the fewest neurons which still has
# space for this experiment.
target_pool_idx = -1
for l in xrange(min_pool, len(pools)):
if ((target_pool_idx == -1 or pools[l].neuron_count(cs)
< pools[target_pool_idx].neuron_count(cs)) and
pools[l].neuron_count(cs)
+ net.neuron_count(cs)
<= simulator_info["max_neuron_count"]):
# If uniform parameter are required (Spikey), check
# whether the target network parameters are the same
# as the current network parameters
if pools[l].neuron_count(cs) > 0:
if self._check_shared_parameters_equal(
simulator_info["shared_parameters"],
pools[l]["topology_params"][0]["params"],
topology_params["topology"]["params"]):
target_pool_idx = l
else:
target_pool_idx = l
# No free pool has been found, add a new one
if target_pool_idx == -1:
pool_name = experiment["name"] + "." + str(pidx)
pools.append(NetworkPool(name=pool_name))
pidx = pidx + 1
target_pool_idx = len(pools) - 1
# Add the network to the pool
pools[target_pool_idx].add_network(net)
# Advance the build_seed -- the input and topology
# parameters should still vary between trials,
# but reproducibly
local_build_seed = local_build_seed * 2
# Return non-empty pool instances
return filter(lambda x: x.neuron_count(cs) > 0, pools)
test_dataset = CIFAR10(root='datasets',
train=False,
transform=get_transforms(resize_shape=64),
download=True)
test_dataloader = get_dataloader(dataset=test_dataset,
bs=test_batch_size,
num_workers=test_dataloader_num_workers)
num_classes = len(train_dataset.classes)
optimizer = optim.Adam
loss_fn = CrossEntropyLoss()
for ARCH in ARCH_NAMES:
print("Training:: {}".format(ARCH))
network = NetworkBuilder(num_classes=num_classes,
arch=ARCH,
optimizer=optimizer,
loss_fn=loss_fn)
network.train_network(train_epochs=train_epochs,
device=device,
dataloader=train_dataloader,
lr=learning_rate)
model_save_path = 'models/cifar_{}.pth'.format(ARCH)
network.save_network_params(save_path=model_save_path)
softmax_save_path = 'softmax_outputs/outputs_softmax_{}.pt'.format(
ARCH)
network.test_network(device=device,
dataloader=test_dataloader,
model_path=model_save_path,
save_softmax_outputs=True,
softmax_save_path=softmax_save_path)
if __name__ == "__main__":
## print arrays in full
np.set_printoptions(threshold='nan')
print "*** Select the training set: ***"
print "1. Train HTM on USPS train100 training set"
print "2. Train HTM on USPS train1000 training set"
print "3. Train HTM on USPS full training set (over 7000 elements)"
print "4. Load HTM from file"
print "5. Quit"
choice = int(raw_input())
if choice == 1 or choice == 2 or choice == 3:
builder = NetworkBuilder(config.usps_net)
htm = builder.build()
htm.start()
t0 = time.time()
print
print "*** Training HTM **"
seq_count = {}
if choice == 1: directory = "train100"
elif choice == 2: directory = "train1000"
else: directory = "train"
sequences = usps.get_training_sequences(directory, uSeqCount=seq_count)
