def logger(pipe, log_file, node_list, manager, startTime, interval, nodes, services, close_pipe):
f = open("{0}_results.csv".format(log_file), 'w', newline='')
f2 = open("{0}_observations.csv".format(log_file), 'w', newline='')
close_flag = False
logWriter = csv.writer(f, dialect='excel')
logWriter2 = csv.writer(f2, dialect='excel')
logWriter.writerow(["SQL CPU", "Web Worker CPU", "SQL Memory", "Web Worker Memory", "# SQL Containers", "# Web Worker Containers", "Delta Requests", "# Requests", "Iteration", "Minutes", "Seconds"])
logWriter2.writerow(["SQL CPU", "Web Worker CPU", "SQL Memory", "Web Worker Memory", "Minutes", "Seconds"])
#services = {}
#nodes = {}
#getNodeIDs(node_list, nodes)
#getServices(services, manager)
sql_cpu_avg = 0
web_worker_cpu_avg = 0
sql_mem_avg = 0
web_worker_mem_avg = 0
sql_cpu_usages = []
sql_mem_usages = []
web_worker_cpu_usages = []
web_worker_mem_usages = []
for service_name, service in services.items():
get_tasks(service, manager)
sql_cpu_avg, web_worker_cpu_avg, sql_mem_avg, web_worker_mem_avg = get_stats(services, sql_cpu_usages, sql_mem_usages, web_worker_cpu_usages, web_worker_mem_usages, nodes)
diff_time = time.time() - startTime
logWriter2.writerow([sql_cpu_avg, web_worker_cpu_avg, sql_mem_avg, web_worker_mem_avg, diff_time//60, diff_time%60])
while not close_flag:
while not close_pipe.poll():
time.sleep(interval)
sql_cpu_usages = []
sql_mem_usages = []
web_worker_cpu_usages = []
web_worker_mem_usages = []
if pipe.poll():
pipe_tuple = pipe.recv()
if pipe_tuple == "close":
print("Logger shutting down")
close_flag = True
f.close()
f2.close()
else:
if pipe_tuple[11] == True:
#time.sleep(interval)
for service_name, service in services.items():
get_tasks(service, manager)
logWriter.writerow(pipe_tuple[:11])
sql_cpu_avg, web_worker_cpu_avg, sql_mem_avg, web_worker_mem_avg = get_stats(services, sql_cpu_usages, sql_mem_usages, web_worker_cpu_usages, web_worker_mem_usages, nodes)
diff_time = time.time() - startTime
logWriter2.writerow([sql_cpu_avg, web_worker_cpu_avg, sql_mem_avg, web_worker_mem_avg, diff_time//60, diff_time%60])
while not close_flag:
if pipe.poll():
pipe_tuple = pipe.recv()
if pipe_tuple == "close":
print("Logger shutting down")
close_flag = True
f.close()
f2.close()
else:
logWriter.writerow(pipe_tuple[:11])
def compute_features(data, time_interval):
df = pd.DataFrame(data, columns=['timestamp', 'x', 'y', 'z'])
df['timestamp'] = pd.to_datetime(df['timestamp'], unit='s')
x = np.array(df['x'])
y = np.array(df['y'])
z = np.array(df['z'])
timestamp = pd.Series(df['timestamp'])
# Perform flipping x and y axes to ensure standard orientation
# For correct orientation, x-angle should be mostly negative
# So, if median x-angle is positive, flip both x and y axes
# Ref: https://github.com/wadpac/hsmm4acc/blob/524743744068e83f468a4e217dde745048a625fd/UKMovementSensing/prepacc.py
angx = np.arctan2(x, np.sqrt(y * y + z * z)) * 180.0 / math.pi
if np.median(angx) > 0:
x *= -1
y *= -1
ENMO = get_ENMO(x, y, z)
angle_x, angle_y, angle_z = get_tilt_angles(x, y, z)
LIDS = get_LIDS(timestamp, ENMO)
_, ENMO_stats = get_stats(df['timestamp'], ENMO, time_interval)
_, angle_z_stats = get_stats(df['timestamp'], angle_z, time_interval)
timestamp_agg, LIDS_stats = get_stats(df['timestamp'], LIDS, time_interval)
feat = np.hstack((ENMO_stats, angle_z_stats, LIDS_stats))
return feat
def test_get_stats(self):
self.assertIsInstance(get_stats({'a': [10, 2, 0, 0]}), list,
'result is not list')
self.assertEqual(
get_stats({'a': [10, 2, 0, 0]})[0], 0.2, 'wrong compute')
self.assertEqual(
get_stats({'a': [20, 5, 0, 0]})[0], 0.25, 'wrong compute')
self.assertEqual(get_stats({'a': [0, 0, 0, 0]})[0], 0, 'wrong compute')
def stats(self):
""" Query play stats for a given user """
parser = argparse.ArgumentParser(description=get_stats.__doc__,
prog=f'{self.parser.prog} stats')
parser.add_argument('username', help="Username of the user to query")
args = vars(parser.parse_args(argv[2:]))
args['headers'] = self.bot.headers
get_stats(**args)
def main(args):
if args.mode == Mode.STATS:
return stats_mode(args)
if args.mode == Mode.CHECK:
return check_mode(args)
if args.mode == Mode.SCATTER:
return scatter_mode(args)
if args.mode == Mode.FULL:
for f in args.files:
lines = utils.get_log_lines([f])
utils.get_stats(lines)
return
def augment_examples(X,
y,
s=1,
weights_on_scarce=.75,
dataset_name="",
random_state=42,
show_stats=True):
'''
generate randomly transformed example images
:param X: list of 3D image dataset, shape = (r, c, channel)
:param y: labels
:param s: integer scale factor
:parma weights_on_scarse: correction factor on scarce label classes
:param dataset_name: name of dataset being boosted
:param random_state
:return: len(X) * s number of boosted examples
'''
n_channel = X[0].shape[-1]
n_train = len(y)
train_freq = plots.get_label_dist(y)
train_freq_normalized = minmax_normalization(train_freq, eps=1e-8)
n_transform_list = np.floor(
(1 - weights_on_scarce * train_freq_normalized) * s)
X_augmented = []
y_augmented = []
for i, image in enumerate(X):
sys.stdout.write('\r>> Augmenting image %s (%.1f%%)' %
(str(i), float(i + 1) / float(n_train) * 100.0))
sys.stdout.flush()
n_transform = int(n_transform_list[0][y[i]])
for j in range(n_transform):
image = random_transform(image)
image = minmax_normalization(image)
X_augmented.append(image)
y_augmented.append(y[i])
X_augmented = np.array(X_augmented).reshape(len(X_augmented), 32, 32,
n_channel)
X_augmented, y_augmented = shuffle(X_augmented,
y_augmented,
random_state=random_state)
if show_stats:
utils.get_stats(X_augmented, y_augmented, dataset_name)
return X_augmented, y_augmented
def test_stats(self):
_id = utils.insert_url(self.data["url"], self.data["code"])
for i in range(1, 5):
utils.bump_stats(_id)
created_at, last_usage, usage_count = utils.get_stats(
self.data["code"])
self.assertEqual(usage_count, i)
def main(method, model_name, model_version, save_bbox_labels,
frames_train_size, step, ae_model_path):
useKfold = False
if step > 0:
# used for k-fold testing accuracy calculation
useKfold = True
games_stats = []
for k in range(len(utils.test_games)):
games_stats.append([])
acc = []
print("Running with method " + method)
#Evaluate method for all test games
for j, game in enumerate(utils.test_games):
model_path = utils.trained_models_dir + model_name + model_version + '.pth'
if (method == 'net'):
model = utils.load_model_embed(model_path)
elif (method == 'ae'):
model = utils.load_model_embed(ae_model_path, isAE=True)
else:
model = []
offset = 0
while offset <= 512 - frames_train_size:
print(" running for game " + game + " starting from frame " +
str(offset + 1))
names, labels, gt_clusters = run_for_game(game, model, offset)
result_clusters = [[] for y in range(2)]
for m in range(len(labels)):
result_clusters[labels[m]].append(names[m])
two_way_acc = utils.get_stats(gt_clusters, result_clusters, 2)
if useKfold:
games_stats[j].append(two_way_acc)
else:
acc.append(two_way_acc)
if save_bbox_labels:
labels_file_path = 'results/' + game + '_labels_' + method + '.txt'
f_results = open(labels_file_path, 'w')
save_labels(f_results, names, labels)
write_bbox_labels(game, names, labels, method)
f_results.close()
ex.add_artifact(labels_file_path)
if step == 0:
offset = 512
else:
offset = offset + step
if (useKfold):
acc = np.mean(games_stats, axis=1)
print("mean acc: " + str(np.mean(acc)))
print("mean error: " + str(1 - np.mean(acc)))
print("std acc: " + str(np.std(acc)))
print("standard error: " +
str(np.std(acc) / math.sqrt(len(utils.test_games))))
def run_experiments(args):
res = []
for i in range(args.num_trials):
print("Trial {}/{}".format(i + 1, args.num_trials))
acc, _ = main(args)
res.append(acc)
mean, err_bd = get_stats(res, conf_interval=True)
return mean, err_bd
def print_results():
name = {
0: 'No method',
1: 'Mean',
2: 'Interpolation',
3: 'Hotdeck',
4: 'Regression'
}[choice]
print('- - - - - - - - Before imputation - - - - - - - -\n' +
'Missing values:\t' + '{:.2%}'.format(na_fraction(before)) + '\n' +
get_stats(before).to_string() + '\n')
print('- - - - - - - - After imputation of ' + name + ' - - - - -\n' +
'Missing values:\t' + '{:.2%}'.format(na_fraction(after)) + '\n' +
get_stats(after).to_string() + '\n')
print('- - - - - - - - Difference - - - - - - - -\n' +
(get_stats(after) - get_stats(before)).to_string() + '\n')
def augment_examples(X, y, s=1,
weights_on_scarce=.75, dataset_name="",
random_state=42, show_stats=True):
'''
generate randomly transformed example images
:param X: list of 3D image dataset, shape = (r, c, channel)
:param y: labels
:param s: integer scale factor
:parma weights_on_scarse: correction factor on scarce label classes
:param dataset_name: name of dataset being boosted
:param random_state
:return: len(X) * s number of boosted examples
'''
n_channel = X[0].shape[-1]
n_train = len(y)
train_freq = get_label_dist(y)
train_freq_normalized = minmax_normalization(train_freq, eps=1e-8)
n_transform_list = np.floor((1 - weights_on_scarce * train_freq_normalized) * s)
X_augmented = [ ]
y_augmented = [ ]
for i, image in enumerate(X):
sys.stdout.write('\r>> Augmenting image %s (%.1f%%)' % (
str(i), float(i + 1) / float(n_train) * 100.0))
sys.stdout.flush()
n_transform = int(n_transform_list[0][y[i]])
for j in range(n_transform):
image = random_transform(image)
image = minmax_normalization(image)
X_augmented.append(image)
y_augmented.append(y[ i ])
X_augmented = np.array(X_augmented).reshape(len(X_augmented), 32, 32, n_channel )
X_augmented, y_augmented = shuffle(X_augmented, y_augmented, random_state=random_state)
if show_stats:
utils.get_stats(X_augmented, y_augmented, dataset_name)
return X_augmented, y_augmented
def eval_pairs(user_vecs, rel_mat, t, dist_func):
mean, std, max, min = ut.get_stats(user_vecs, dist_func)
pairs = find_pairs_above_threshold(rel_mat, t)
num_pairs_found = 0
dist_sum = 0
for ui, uj in pairs:
dist = ut.get_dist(user_vecs, ui, uj, dist_func)
if dist is not None:
num_pairs_found += 1
norm_dist = np.divide(np.subtract(dist, mean), std)
dist_sum += norm_dist
return dist_sum/num_pairs_found
def run_test(model, test_data, device):
if isinstance(model, VecModel):
print(f'Model weights: {model.w.weight}')
print(f'Model biases: {model.w.bias}')
model.to(device)
predictions = model.predict(test_data, device=device)
stats, class_stats = get_stats(test_data.labels,
predictions,
all_labels=list(model.class_map.keys()))
normal_metrics = {
f'Test {metric}': value
for metric, value in stats.items()
}
class_risk_metrics = {
f'Test class {idx} accuracy':
class_stats[model.idx_to_class_map[idx]]['recall']
for idx in range(2)
}
worst_risk_class, worst_risk_entry = max(list(class_stats.items()),
key=lambda x: 1 - x[1]['recall'])
entry = {
**normal_metrics,
**class_risk_metrics, 'Test risk histogram':
wandb.Image(
class_stats_histogram(class_stats,
lambda x: 1 - x['recall'],
'Risk',
bins=np.arange(11) * 0.1,
range=(0, 1))),
'Test class histogram':
wandb.Image(
class_stats_histogram(class_stats,
lambda x: x['true'],
'Frequency',
cmp_fn=lambda x: -x,
bins=10)),
'Test worst class risk':
1 - worst_risk_entry['recall'],
'Test worst class risk label':
worst_risk_class
}
wandb.log(entry)
pprint(entry)
with open(os.path.join(wandb.run.dir, 'test_result.json'), 'w') as out_f:
out_f.write(json.dumps(class_stats))
def url_stats(code: str):
"""Shows stats for a given code"""
_, exists = utils.code_exists(code)
if not exists:
return jsonify(error="Code Not Found"), 404
else:
created_at, last_usage, usage_count = utils.get_stats(code)
result = {
'created_at': utils.to_iso8601(created_at),
'usage_count': usage_count
}
if last_usage:
result['last_usage'] = utils.to_iso8601(last_usage)
return jsonify(result), 200
def shortcodeStats(shortcode):
"""
Receives a shortcode, check whether it's in the db and if so returns its stats in json
"""
try:
conn = utils.create_connection("test.db")
entry, url = utils.check_entry(shortcode, conn)
if entry:
stats = utils.get_stats(shortcode, conn)
stats = flask.jsonify(stats)
conn.close()
return flask.make_response(stats, 200)
except:
conn.close()
return not_found()
def main(unused_args):
dataset = utils.ThyroidData()
(x_train, y_train), (x_test, y_test) = dataset.load_data()
if FLAGS.train:
tf.gfile.MakeDirs(FLAGS.save_path)
with tf.Session() as sess:
dagmm = DAGMM(sess)
sess.run(tf.global_variables_initializer())
dagmm.fit(x_train)
elif FLAGS.test:
with tf.Session() as sess:
dagmm = DAGMM(sess)
dagmm.load(FLAGS.save_path)
predictions = dagmm.predict(x_test)
precision, recall, f1 = utils.get_stats(predictions=predictions, labels=y_test)
print("Precision: {}".format(precision))
print("Recall: {}".format(recall))
print("F1: {}".format(f1))
def bot():
global DETECTED_LANGUAGE
data = request.form
incoming_msg = request.values.get('Body', '').lower()
phone_number = data.get("From").replace("whatsapp:+", "")
DETECTED_LANGUAGE = detect_language(incoming_msg)
incoming_msg = translate_text(
"en", incoming_msg) if DETECTED_LANGUAGE != "en" else incoming_msg
response = MessagingResponse()
if 'details' in incoming_msg and EVENT:
translate_response = translate_text(
DETECTED_LANGUAGE, str('Here is the link: ')
) if DETECTED_LANGUAGE != "en" else str('Here is the link: ')
message = response.message(translate_response + STATIC_MESSAGE['link'])
message.media(STATIC_MESSAGE['detailed_image'])
return str(response)
elif 'opt in' in incoming_msg or 'optin' in incoming_msg:
send_notification(phone_number, 'Welcome_to_aa')
elif 'news' in incoming_msg:
result = get_news(incoming_msg)
for i in result:
response.message(i)
return str(response)
elif 'stats' in incoming_msg or 'statistics' in incoming_msg:
result = get_stats(incoming_msg)
response.message(result)
return str(response)
elif 'regulations' in incoming_msg:
result = regulations(incoming_msg)
response.message(result)
return str(response)
else:
response_dialogFlow = detect_intent_from_text(str(incoming_msg),
phone_number)
translate_response = translate_text(
DETECTED_LANGUAGE, str(response_dialogFlow.fulfillment_text)
) if DETECTED_LANGUAGE != "en" else str(
response_dialogFlow.fulfillment_text)
response.message(translate_response)
return str(response)
def train(model, num_batch, train_batches, valid_batches, test_batches, opt, num_epochs, hidden_dim, verbose = True):
epoch = 0
step = 0
best_epoch = 0
best_val_ret = 0
best_tst_ret = 0
best_med_ret = 0
best_std_ret = 0
rpt_epoch = 1
test_epoch = 1
while epoch < num_epochs:
batch_X_float, batch_X_embed, batch_label, batch_duration, batch_ret = next(train_batches)
opt.zero_grad()
loss, pred_ret = model(batch_X_float, batch_X_embed, batch_label, batch_ret)
loss.backward()
#clip_grad_norm(model.parameters(), 1)
opt.step()
step += 1
if step >= num_batch:
epoch += 1
step = 0
if epoch % rpt_epoch == 0:
med_diff, avg_diff, max_diff = get_stats(pred_ret, batch_ret)
if verbose:
print('Train: epoch: %d, avg loss: %.3f, median diff: %.3f, mean: %.3f, max: %.3f' % (epoch, loss.data[0], med_diff, avg_diff, max_diff))
valid_X_float, valid_X_embed, valid_label, valid_duration, valid_ret = next(valid_batches)
_, ret = model(valid_X_float, valid_X_embed, valid_label, valid_ret)
valid_avg_ret, valid_med_ret, valid_std_ret = ret_strtgy(ret, valid_ret)
test_X_float, test_X_embed, test_label, test_duration, test_ret = next(test_batches)
_, ret = model(test_X_float, test_X_embed, test_label, test_ret)
test_avg_ret, test_med_ret, test_std_ret = ret_strtgy(ret, test_ret)
if valid_avg_ret > best_val_ret:
best_epoch = epoch
best_val_ret = valid_avg_ret
best_tst_ret = test_avg_ret
best_med_ret = test_med_ret
best_std_ret = test_std_ret
model_name = './models/regmse_' + str(hidden_dim) + 'dim_model.pt'
torch.save(model.state_dict(), model_name)
if epoch % test_epoch == 0 and verbose:
print('Test epoch: %d, avg return: %.3f, median: %.3f, std: %.3f' % (epoch, test_avg_ret, test_med_ret, test_std_ret))
return best_epoch, best_tst_ret, best_med_ret, best_std_ret
def lone_test(functions, N=100, wr=2, times=3):
# creating test arrays
img = np.arange(N**2).reshape((N, N))
# defining function parameters
ws = 2 * wr + 1
# velocity test
for i, function in enumerate(functions):
name = function.__name__
function_stats = get_stats(function, times, return_val=True)
print(f'Statistics for function: {name}(N={N}, ws={ws})')
avg, std, minv, maxv, resp = function_stats(img, wr)
rel_std = 100 * std / np.abs(avg) # %
amp = maxv - minv
print(
f' avg±std:\t{avg:.4g}±{std:.4g} s\t(rel_std: {rel_std:.2f}%)'
)
print(f' amp: {amp:4g} = [{minv:.4g}, {maxv:.4g}] s')
print(f' function run {times} times.', end='\n\n')
def runtime_results(functions, Ns=[5], wss=[3, 5, 7], times=3):
def print_stats(*stats):
if len(stats) == 0:
print('name\tN\tws\tavg\tstd\trel_std\tminv\tmaxv\tamp')
else:
print('\t'.join([str(i) for i in stats]))
print_stats()
for function in functions:
name = function.__name__
function_stats = get_stats(
function,
times,
)
for N in Ns:
img = np.random.rand(N, N)
for ws in wss:
wr = ws // 2 + 1
avg, std, minv, maxv = function_stats(img, wr)
rel_std = 100 * std / np.abs(avg) # %
amp = maxv - minv
print_stats(name, N, ws, avg, std, rel_std, minv, maxv,
amp)
def run(args, log_interval=5000, rerun=False):
# see if we already ran this experiment
code_root = os.path.dirname(os.path.realpath(__file__))
exp_dir = utils.get_path_from_args(
args) if not args.output_dir else args.output_dir
path = "{}/results/{}".format(code_root, exp_dir)
if not os.path.isdir(path):
os.makedirs(path)
if os.path.exists(os.path.join(path, "logs.pkl")) and not rerun:
return utils.load_obj(os.path.join(path, "logs"))
start_time = time.time()
# correctly seed everything
utils.set_seed(args.seed)
# --- initialise everything ---
task_family_train = tasks_sine.RegressionTasksSinusoidal(
"train", args.skew_task_distribution)
task_family_valid = tasks_sine.RegressionTasksSinusoidal(
"valid", args.skew_task_distribution)
# initialise network
model_inner = MamlModel(
task_family_train.num_inputs,
task_family_train.num_outputs,
n_weights=args.num_hidden_layers,
device=args.device,
).to(args.device)
model_outer = copy.deepcopy(model_inner)
if args.detector == "minimax":
task_sampler = TaskSampler(
task_family_train.atoms //
(2 if args.skew_task_distribution else 1)).to(args.device)
elif args.detector == "neyman-pearson":
constrainer = Constrainer(
task_family_train.atoms //
(2 if args.skew_task_distribution else 1)).to(args.device)
# intitialise meta-optimiser
meta_optimiser = optim.Adam(model_outer.weights + model_outer.biases,
args.lr_meta)
# initialise loggers
logger = Logger()
logger.best_valid_model = copy.deepcopy(model_outer)
for i_iter in range(args.n_iter):
# copy weights of network
copy_weights = [w.clone() for w in model_outer.weights]
copy_biases = [b.clone() for b in model_outer.biases]
# get all shared parameters and initialise cumulative gradient
meta_gradient = [
0 for _ in range(
len(copy_weights + copy_biases) +
(2 if args.detector != "bayes" else 0))
]
# sample tasks
if args.detector == "minimax":
task_idxs, task_probs = task_sampler(args.tasks_per_metaupdate)
elif args.detector == "neyman-pearson":
amplitude_idxs = torch.randint(
task_family_train.atoms //
(2 if args.skew_task_distribution else 1),
(args.tasks_per_metaupdate, ),
)
phase_idxs = torch.randint(
task_family_train.atoms //
(2 if args.skew_task_distribution else 1),
(args.tasks_per_metaupdate, ),
)
task_idxs = amplitude_idxs, phase_idxs
else:
task_idxs = None
target_functions = task_family_train.sample_tasks(
args.tasks_per_metaupdate, task_idxs=task_idxs)
for t in range(args.tasks_per_metaupdate):
# reset network weights
model_inner.weights = [w.clone() for w in copy_weights]
model_inner.biases = [b.clone() for b in copy_biases]
# get data for current task
train_inputs = task_family_train.sample_inputs(
args.k_meta_train).to(args.device)
for _ in range(args.num_inner_updates):
# make prediction using the current model
outputs = model_inner(train_inputs)
# get targets
targets = target_functions[t](train_inputs)
# ------------ update on current task ------------
# compute loss for current task
loss_task = F.mse_loss(outputs, targets)
# compute the gradient wrt current model
params = [w for w in model_inner.weights
] + [b for b in model_inner.biases]
grads = torch.autograd.grad(loss_task,
params,
create_graph=True,
retain_graph=True)
# make an update on the inner model using the current model (to build up computation graph)
for i in range(len(model_inner.weights)):
if not args.first_order:
model_inner.weights[i] = (model_inner.weights[i] -
args.lr_inner * grads[i])
else:
model_inner.weights[i] = (
model_inner.weights[i] -
args.lr_inner * grads[i].detach())
for j in range(len(model_inner.biases)):
if not args.first_order:
model_inner.biases[j] = (
model_inner.biases[j] -
args.lr_inner * grads[i + j + 1])
else:
model_inner.biases[j] = (
model_inner.biases[j] -
args.lr_inner * grads[i + j + 1].detach())
# ------------ compute meta-gradient on test loss of current task ------------
# get test data
test_inputs = task_family_train.sample_inputs(args.k_meta_test).to(
args.device)
# get outputs after update
test_outputs = model_inner(test_inputs)
# get the correct targets
test_targets = target_functions[t](test_inputs)
# compute loss (will backprop through inner loop)
if args.detector == "minimax":
importance = task_probs[t]
else:
importance = 1.0 / args.tasks_per_metaupdate
loss_meta_raw = F.mse_loss(test_outputs, test_targets)
loss_meta = loss_meta_raw * importance
if args.detector == "neyman-pearson":
amplitude_idxs, phase_idxs = task_idxs
aux_loss = constrainer(amplitude_idxs[t], phase_idxs[t],
loss_meta_raw)
loss_meta = loss_meta + aux_loss
# compute gradient w.r.t. *outer model*
outer_params = model_outer.weights + model_outer.biases
if args.detector == "minimax":
outer_params += [
task_sampler.tau_amplitude, task_sampler.tau_phase
]
elif args.detector == "neyman-pearson":
outer_params += [
constrainer.tau_amplitude, constrainer.tau_phase
]
task_grads = torch.autograd.grad(
loss_meta,
outer_params,
retain_graph=(args.detector != "bayes"))
for i in range(len(outer_params)):
meta_gradient[i] += task_grads[i].detach()
# ------------ meta update ------------
meta_optimiser.zero_grad()
# print(meta_gradient)
# assign meta-gradient
for i in range(len(model_outer.weights)):
model_outer.weights[i].grad = meta_gradient[i]
meta_gradient[i] = 0
for j in range(len(model_outer.biases)):
model_outer.biases[j].grad = meta_gradient[i + j + 1]
meta_gradient[i + j + 1] = 0
if args.detector == "minimax":
task_sampler.tau_amplitude.grad = -meta_gradient[i + j + 2]
task_sampler.tau_phase.grad = -meta_gradient[i + j + 3]
meta_gradient[i + j + 2] = 0
meta_gradient[i + j + 3] = 0
elif args.detector == "neyman-pearson":
constrainer.tau_amplitude.grad = -meta_gradient[i + j + 2]
constrainer.tau_phase.grad = -meta_gradient[i + j + 3]
meta_gradient[i + j + 2] = 0
meta_gradient[i + j + 3] = 0
# do update step on outer model
meta_optimiser.step()
# ------------ logging ------------
if i_iter % log_interval == 0:
# evaluate on training set
losses = eval(
args,
copy.copy(model_outer),
task_family=task_family_train,
num_updates=args.num_inner_updates,
)
loss_mean, loss_conf = utils.get_stats(np.array(losses))
logger.train_loss.append(loss_mean)
logger.train_conf.append(loss_conf)
# evaluate on valid set
losses = eval(
args,
copy.copy(model_outer),
task_family=task_family_valid,
num_updates=args.num_inner_updates,
)
loss_mean, loss_conf = utils.get_stats(np.array(losses))
logger.valid_loss.append(loss_mean)
logger.valid_conf.append(loss_conf)
# save best model
if logger.valid_loss[-1] == np.min(logger.valid_loss):
print("saving best model at iter", i_iter)
logger.best_valid_model = copy.copy(model_outer)
# save logging results
utils.save_obj(logger, os.path.join(path, "logs"))
# print current results
logger.print_info(i_iter, start_time)
start_time = time.time()
return logger
sumoBinary = 'sumo.exe'
else:
sumoBinary = 'sumo-gui.exe'
# initializations
max_steps = 5400 # seconds = 1 h 30 min each episode
total_episodes = 100
num_experiments = 1
learn = False
traffic_gen = TrafficGenerator(max_steps)
qmodel_filename, stats_filename = utils.get_file_names()
init_experiment, init_epoch = utils.get_init_epoch(stats_filename,
total_episodes)
print('init_experiment={} init_epoch={}'.format(init_experiment,
init_epoch))
stats = utils.get_stats(stats_filename, num_experiments, total_episodes)
for experiment in range(init_experiment, num_experiments):
env = SumoEnv(sumoBinary, max_steps)
tl = TLAgent(env, traffic_gen, max_steps, num_experiments,
total_episodes, qmodel_filename, stats, init_epoch, learn)
init_epoch = 0 # reset init_epoch after first experiment
if learn:
tl.train(experiment)
else:
seeds = np.load('seed.npy')
tl.evaluate_model(experiment, seeds)
stats = copy.deepcopy(tl.stats)
print(stats['rewards'][0:experiment + 1, :])
print(stats['intersection_queue'][0:experiment + 1, :])
path='A1-networks/'
from utils import get_stats, print_stats
from utils import convert_to_table
from os import walk
#compulsory data sets
files=['toy/circle9.net', 'toy/star.net', 'toy/graph3+1+3.net', 'toy/grid-p-6x6.net', 'model/homorand_N1000_K4_0.net', 'model/ER1000k8.net', 'model/SF_1000_g2.7.net', 'model/ws1000.net', 'real/zachary_unwh.net', 'real/airports_UW.net']
files_names=[]
#get all the files in the directory(path)
for i in ['toy/', 'model/', 'real/']:
lstDir=walk(path+i)
for (root, dirs, fil) in lstDir:
for f in fil:
files_names+=[i+f]
print(files_names)
#results=print_stats(files_names,path)
results=get_stats(files_names,path)
f=open('results.txt', 'w')
for i in results:
for j in i:
f.write(str(j)+',')
f.write('\n')
f.close()
convert_to_table('results.txt', 'table.txt')
# The pickled data is a dictionary with 4 key/value pairs: # # - `'features'` is a 4D array containing raw pixel data of the traffic sign images, (num examples, width, height, channels). # - `'labels'` is a 1D array containing the label/class id of the traffic sign. The file `signnames.csv` contains id -> name mappings for each id. # - `'sizes'` is a list containing tuples, (width, height) representing the original width and height the image. # - `'coords'` is a list containing tuples, (x1, y1, x2, y2) representing coordinates of a bounding box around the sign in the image. **THESE COORDINATES ASSUME THE ORIGINAL IMAGE. THE PICKLED DATA CONTAINS RESIZED VERSIONS (32 by 32) OF THESE IMAGES** # # Complete the basic data summary below. Use python, numpy and/or pandas methods to calculate the data summary rather than hard coding the results. For example, the [pandas shape method](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.shape.html) might be useful for calculating some of the summary results. # ### Provide a Basic Summary of the Data Set Using Python, Numpy and/or Pandas # # In[5]: utils.get_stats(train['features'], train['labels'], 'Train') utils.get_stats(valid['features'], valid['labels'], 'Valid') utils.get_stats(test['features'], test['labels'], 'Test') # ### Include an exploratory visualization of the dataset # # Visualize the German Traffic Signs Dataset using the pickled file(s). This is open ended, suggestions include: plotting traffic sign images, plotting the count of each sign, etc. # # The [Matplotlib](http://matplotlib.org/) [examples](http://matplotlib.org/examples/index.html) and [gallery](http://matplotlib.org/gallery.html) pages are a great resource for doing visualizations in Python. # # **NOTE:** It's recommended you start with something simple first. If you wish to do more, come back to it after you've completed the rest of the sections. It can be interesting to look at the distribution of classes in the training, validation and test set. Is the distribution the same? Are there more examples of some classes than others? # In[6]:
import dash_core_components as dcc
from dash.dependencies import Input, Output
import pandas as pd
from datetime import datetime
import utils
import math
# Load the dataset
events_df = pd.read_json('data/regen_event_list_ts.json', lines=True)
heavy_precipitation_events = events_df[events_df["si"] > 0.0]
normal_precipitation_events = events_df[events_df["si"] == 0.0]
ts_events_df = pd.read_json('data/regen_event_list_ts_expanded.json',
lines=True)
heavy_precipitation_events_ts = ts_events_df[ts_events_df["si"] > 0.0]
normal_precipitation_events_ts = ts_events_df[ts_events_df["si"] == 0.0]
stats_table = utils.get_stats(events_df, ts_events_df)
# Get options and range
si_min = 0 # float(min(events_df["si"].min(), ts_events_df["si"].min()))
si_max = math.ceil((max(events_df["si"].max(), ts_events_df["si"].max())))
length_min = int(ts_events_df["length"].min())
length_max = int(ts_events_df["length"].max())
area_min = 0 # float(ts_events_df["area"].min())
area_max = math.ceil(ts_events_df["area"].max())
min_date = events_df.datetime.min().date()
max_date = events_df.datetime.max().date()
# Create the Dash app
external_stylesheets = [
{
"href": "https://fonts.googleapis.com/css2?"
x, cx, y = data
zx, zcx, y = model(x.to(device)), model(cx.to(device)), y.to(device)
loss = contastive_loss(zx, zcx, y)
val_loss.append(loss.item() * x.shape[0])
print('>> Val-loss: %f' % np.mean(val_loss))
print('Computing features for testing set')
embeddings = np.zeros((len(testloader.dataset), args.emb_size))
test_loss = []
with torch.no_grad():
for i, data in enumerate(testloader):
x = data[0]
z = model(x.to(device))
embeddings[i * args.batch_size:(i + 1) * args.batch_size] = z.cpu()
dist_matrix = squareform(pdist(embeddings, metric='euclidean'))
print(dist_matrix.shape)
nearest_neighbors = np.argsort(dist_matrix, axis=1)
for k in [1, 3, 5]:
print('===== k=%d =====' % k)
utils.get_stats(testloader.dataset.image_paths, nearest_neighbors, k=k)
# todo: save to file
# compute feature
if (e + 1) % args.print_every == 0:
log_format = "Epoch {}: loss={:.4f}, val_acc={:.4f}, final_test_acc={:.4f}"
print(log_format.format(e + 1, train_loss, val_acc,
final_test_acc))
print("Best Epoch {}, final test acc {:.4f}".format(
best_epoch, final_test_acc))
return final_test_acc, sum(train_times) / len(train_times)
if __name__ == "__main__":
args = parse_args()
res = []
train_times = []
for i in range(args.num_trials):
print("Trial {}/{}".format(i + 1, args.num_trials))
acc, train_time = main(args)
res.append(acc)
train_times.append(train_time)
mean, err_bd = get_stats(res)
print("mean acc: {:.4f}, error bound: {:.4f}".format(mean, err_bd))
out_dict = {
"hyper-parameters": vars(args),
"result": "{:.4f}(+-{:.4f})".format(mean, err_bd),
"train_time": "{:.4f}".format(sum(train_times) / len(train_times))
}
with open(args.output_path, "w") as f:
json.dump(out_dict, f, sort_keys=True, indent=4)
temp_pos_weight.to(device))
loss_cols = model.head_loss(preds_dict['cols'].reshape(-1),
targets_cols.to(device),
temp_pos_weight.to(device))
loss_rows = model.head_loss(preds_dict['rows'].reshape(-1),
targets_rows.to(device),
temp_pos_weight.to(device))
total_loss = loss_cells + loss_cols + loss_rows
for k, v in preds_dict.items():
preds_dict[k] = v.to(torch.device('cpu'))
val_loss += total_loss.item()
stat_dict = get_stats(preds_dict['cells'], preds_dict['cols'],
preds_dict['rows'], targets_cells,
targets_cols, targets_rows, prediction_thres)
#Iterate over, put tensors to device
loop.set_description(f'Val Epoch [{epoch+1}/{num_epochs}]')
loop.set_postfix_str(
s=f"Total_loss = {round(total_loss.item(),4)}, Cells = {round(loss_cells.item(),4)}, Cols = {round(loss_cols.item(),4)}, Rows = {round(loss_rows.item(),4)}, F1_Cells = {round(stat_dict['cells']['f1'],4)}, F1_Cols = {round(stat_dict['cols']['f1'],4)}, F1_Rows = {round(stat_dict['rows']['f1'],4)}"
)
print(
f"#####AVERAGE: Epoch [{epoch+1}/{num_epochs}] Train Loss: {train_loss/ct_train}, Val Loss: {val_loss/ct_val}####################"
)
if idx % 100 == 0:
Stats['total_loss'].append(total_loss)
Stats['loss_cells'].append(loss_cells)
Stats['loss_cols'].append(loss_cols)
Stats['loss_rows'].append(loss_rows)
def train(self,
train_data,
dev_data,
model,
train_tmp_dir,
device=torch.device('cpu')):
with open(os.path.join(train_tmp_dir, f'model.pkl'), 'wb') as out_f:
pickle.dump(model.save_params(), out_f)
wandb.save(os.path.join(train_tmp_dir, 'model.pkl'))
print(set(dev_data.labels).difference(set(train_data.labels)))
model = model.to(device)
self.best_metrics: Dict[str, Tuple[int, float]] = {}
loss_fn = self.create_loss(model, train_data, dev_data).to(device)
tot_params = [{'params': list(model.parameters())}]
if self.flags.loss_type in ['hcvar', 'cvar']:
tot_params += [{
'params': loss_fn.threshold,
'lr': 10 * self.flags.lr,
'momentum': 0.
}]
if self.flags.optimizer == 'SGD':
optimizer = torch.optim.SGD(tot_params,
lr=self.flags.lr,
momentum=self.flags.momentum)
else:
optimizer = torch.optim.Adam(tot_params, lr=self.flags.lr)
decay_sched = self.create_lr_scheduler(optimizer, loss_fn)
epochs = self.flags.epochs
train_plot = self._make_class_dist_plot(train_data)
wandb.log({'Train class dist': wandb.Image(train_plot)})
for epoch in range(epochs):
start_time = datetime.datetime.now()
model.train()
epoch_loss = self._epoch_closure(train_data, model, epoch, loss_fn,
optimizer, device)
epoch_time = datetime.datetime.now() - start_time
# Decay learning rate
log_entry = {
'epoch': epoch,
'Train loss/sample': epoch_loss,
'Time': epoch_time.total_seconds(),
'Samples/sec': len(train_data) / epoch_time.total_seconds(),
}
if self.flags.lr_decay_type:
current_lr = optimizer.param_groups[0]['lr']
log_entry['Current LR'] = current_lr
if self.flags.lr_decay_type == 'plateau':
decay_sched.step(epoch_loss)
else:
decay_sched.step()
if ((epoch + 1) % self.flags.dev_interval == 0
or epoch == epochs - 1):
model.eval()
dev_preds = model.predict(dev_data, device=device)
global_stats, class_stats = get_stats(
dev_preds,
dev_data.labels,
all_labels=list(model.class_map.keys()))
for metric_name in global_stats:
self._save_best_metric(train_tmp_dir,
model,
epoch,
stats_dict=global_stats,
class_stats=class_stats)
worst_risk_class, worst_risk_entry = max(
list(class_stats.items()),
key=lambda x: 1 - x[1]['recall'])
log_entry = {
**{
f'Dev {metric}': val
for metric, val in global_stats.items()
},
**log_entry,
**{
f'Class {idx} dev risk': 1 - class_stats[model.idx_to_class_map[idx]]['recall']
for idx in range(2)
}, 'Dev risk histogram': wandb.Image(
class_stats_histogram(class_stats,
lambda x: 1 - x['recall'],
'Risk',
range=(0, 1),
bins=np.arange(11) * 0.1)),
'Dev worst class risk': 1 - worst_risk_entry['recall'],
'Dev worst class risk label': worst_risk_class
}
pprint(log_entry)
wandb.log(log_entry)
# At the end save best metrics over the course of the run
wandb.log({
'Dev class histogram':
wandb.Image(
class_stats_histogram(class_stats,
lambda x: x['true'],
'Frequency',
cmp_fn=lambda x: -x,
bins=20))
})
pprint('Best metrics: ')
pprint(self.best_metrics)
def controller(input_pipe, number_of_processes, node_list, req_list, manager,
polling_interval, polls_per_update, log_file, nodes, services):
close_flag = False
# Node list
#node_list = ["192.168.56.102:4000", "192.168.56.103:4000", "192.168.56.101:4000"]
#manager = "192.168.56.102:4000"
#services = {}
# upper and lower cpu usage thresholds where scaling should happen on
cpu_upper_threshold = 50.0
cpu_lower_threshold = 20.0
# create list of processes and pipes
process_list = []
spike_list = []
# pipes that main thread will read from and load threads will write to
par_pipes = []
spike_par_pipes = []
# pipes that main thread will write to and load threads will read from
child_pipes = []
spike_child_pipes = []
sql_cpu_usages = []
sql_mem_usages = []
web_worker_cpu_usages = []
web_worker_mem_usages = []
sql_cpu_avg = 0
sql_mem_avg = 0
web_worker_mem_avg = 0
web_worker_cpu_avg = 0
num_web_workers = 2
num_sql = 1
num_requests = 0
# Storage variables
prev_sql_cpu_avg = 0
prev_sql_mem_avg = 0
prev_web_worker_mem_avg = 0
prev_web_worker_cpu_avg = 0
prev_num_web_workers = 0
prev_num_sql = 0
prev_num_requests = 0
spike_size = 3
# CREATE SPECIFIED NUMBER OF PROCESSES
for i in range(0, number_of_processes):
# Create new pipe
par_pipe, child_pipe = multiprocessing.Pipe()
par_pipes.append(par_pipe)
child_pipes.append(child_pipe)
temp_process = multiprocessing.Process(target=load_process,
args=(req_list, child_pipes[i]))
process_list.append(temp_process)
for i in range(0, spike_size * 2):
par_pipe, child_pipe = multiprocessing.Pipe()
spike_par_pipes.append(par_pipe)
spike_child_pipes.append(child_pipe)
temp_process = multiprocessing.Process(target=load_process,
args=(req_list,
spike_child_pipes[i]))
spike_list.append(temp_process)
# get services, nodes and tasks
#Always start with 2 web worker and 1 sql
scale(services["web-worker"], num_web_workers, manager)
scale(services["mysql"], num_sql, manager)
time.sleep(7)
for service_name, service in services.items():
get_tasks(service, manager)
# get initial stats
# get web-worker stats
sql_cpu_avg, web_worker_cpu_avg, sql_mem_avg, web_worker_mem_avg = get_stats(
services, sql_cpu_usages, sql_mem_usages, web_worker_cpu_usages,
web_worker_mem_usages, nodes)
# initalize estimator
init_x = np.asarray(
(sql_cpu_avg, web_worker_cpu_avg, sql_mem_avg, web_worker_mem_avg))
init_x = init_x.reshape(init_x.size, 1)
estimator = kalmanEstimator(np.identity(4), np.random.random((4, 3)),
init_x)
# APPROACH:
# We need at least 4 measurements to ensure that a solution can be found
# 1st & 2nd containers will remain the same
# ******************************************************************************************************************
# ********************************************* 1st DIFF MEASUREMENT ***********************************************
# store measurements
prev_sql_cpu_avg = sql_cpu_avg
prev_sql_mem_avg = sql_mem_avg
prev_web_worker_cpu_avg = web_worker_cpu_avg
prev_web_worker_mem_avg = web_worker_mem_avg
prev_num_requests = num_requests
prev_num_sql = num_sql
prev_num_web_workers = num_web_workers
# Start generating a load
process_list[0].start()
# Wait a couple seconds
time.sleep(5)
# Send poll request to the process we started
par_pipes[0].send("poll")
while not par_pipes[0].poll():
pass
# If the loop above has been broken then we can read the information from the pipe
num_requests = par_pipes[0].recv()
#print('BOOM {}'.format(num_requests))
# get the stats
sql_cpu_usages = []
sql_mem_usages = []
web_worker_cpu_usages = []
web_worker_mem_usages = []
sql_cpu_avg, web_worker_cpu_avg, sql_mem_avg, web_worker_mem_avg = get_stats(
services, sql_cpu_usages, sql_mem_usages, web_worker_cpu_usages,
web_worker_mem_usages, nodes)
# create some np arrays for the regression
sql_cpu_history = np.asarray(sql_cpu_avg - prev_sql_cpu_avg)
sql_mem_history = np.asarray(sql_mem_avg - prev_sql_mem_avg)
web_worker_cpu_history = np.asarray(web_worker_cpu_avg -
prev_web_worker_cpu_avg)
web_worker_mem_history = np.asarray(web_worker_mem_avg -
prev_web_worker_mem_avg)
request_history = np.asarray(num_requests - prev_num_requests)
web_work_history = np.asarray(num_web_workers - prev_num_web_workers)
sql_history = np.asarray(num_sql - prev_num_sql)
# As before we store the stats
prev_sql_cpu_avg = sql_cpu_avg
prev_sql_mem_avg = sql_mem_avg
prev_web_worker_cpu_avg = web_worker_cpu_avg
prev_web_worker_mem_avg = web_worker_mem_avg
prev_num_requests = num_requests
prev_num_sql = num_sql
prev_num_web_workers = num_web_workers
# Wait a couple more seconds
time.sleep(5)
# ******************************************************************************************************************
# ********************************************* 2nd DIFF MEASUREMENT ***********************************************
# Send poll request to the process we started
par_pipes[0].send("poll")
while not par_pipes[0].poll():
pass
# If the loop above has been broken then we can read the information from the pipe
num_requests = par_pipes[0].recv()
# get the stats
sql_cpu_usages = []
sql_mem_usages = []
web_worker_cpu_usages = []
web_worker_mem_usages = []
sql_cpu_avg, web_worker_cpu_avg, sql_mem_avg, web_worker_mem_avg = get_stats(
services, sql_cpu_usages, sql_mem_usages, web_worker_cpu_usages,
web_worker_mem_usages, nodes)
# Append new values to the histories
sql_cpu_history = np.append(sql_cpu_history,
sql_cpu_avg - prev_sql_cpu_avg)
sql_mem_history = np.append(sql_mem_history,
sql_mem_avg - prev_sql_mem_avg)
web_worker_cpu_history = np.append(
web_worker_cpu_history, web_worker_cpu_avg - prev_web_worker_cpu_avg)
web_worker_mem_history = np.append(
web_worker_mem_history, web_worker_mem_avg - prev_web_worker_mem_avg)
request_history = np.append(request_history,
num_requests - prev_num_requests)
web_work_history = np.append(web_work_history,
num_web_workers - prev_num_web_workers)
sql_history = np.append(sql_history, num_sql - prev_num_sql)
print(web_worker_cpu_avg)
# Store the stats
prev_sql_cpu_avg = sql_cpu_avg
prev_sql_mem_avg = sql_mem_avg
prev_web_worker_cpu_avg = web_worker_cpu_avg
prev_web_worker_mem_avg = web_worker_mem_avg
prev_num_requests = num_requests
prev_num_sql = num_sql
prev_num_web_workers = num_web_workers
print(web_worker_cpu_usages)
# ******************************************************************************************************************
# ********************************************* 3rd DIFF MEASUREMENT ***********************************************
print("Two measurements taken\n")
# Start 2 new containers
num_web_workers = num_web_workers + 1
num_sql = num_sql + 1
scale(services["web-worker"], num_web_workers, manager)
scale(services["mysql"], num_sql, manager)
# We also start another load generator
process_list[1].start()
# as before we sleep and will update
time.sleep(5)
# poll pipes [0] & [1]
for i in range(0, 2):
par_pipes[i].send("poll")
pipes_ready = poll_pipes(par_pipes, 2)
# reset number of requests
num_requests = 0
for i in range(0, 2):
num_requests = num_requests + par_pipes[i].recv()
# update tasks since we scaled
for service_name, service in services.items():
get_tasks(service, manager)
# get the stats
sql_cpu_usages = []
sql_mem_usages = []
web_worker_cpu_usages = []
web_worker_mem_usages = []
sql_cpu_avg, web_worker_cpu_avg, sql_mem_avg, web_worker_mem_avg = get_stats(
services, sql_cpu_usages, sql_mem_usages, web_worker_cpu_usages,
web_worker_mem_usages, nodes)
# Append new values to the histories
sql_cpu_history = np.append(sql_cpu_history,
sql_cpu_avg - prev_sql_cpu_avg)
sql_mem_history = np.append(sql_mem_history,
sql_mem_avg - prev_sql_mem_avg)
web_worker_cpu_history = np.append(
web_worker_cpu_history, web_worker_cpu_avg - prev_web_worker_cpu_avg)
web_worker_mem_history = np.append(
web_worker_mem_history, web_worker_mem_avg - prev_web_worker_mem_avg)
request_history = np.append(request_history,
num_requests - prev_num_requests)
web_work_history = np.append(web_work_history,
num_web_workers - prev_num_web_workers)
sql_history = np.append(sql_history, num_sql - prev_num_sql)
# Store the stats
prev_sql_cpu_avg = sql_cpu_avg
prev_sql_mem_avg = sql_mem_avg
prev_web_worker_cpu_avg = web_worker_cpu_avg
prev_web_worker_mem_avg = web_worker_mem_avg
prev_num_requests = num_requests
prev_num_sql = num_sql
prev_num_web_workers = num_web_workers
# ******************************************************************************************************************
# ********************************************* 4th DIFF MEASUREMENT ***********************************************
print("3 measurements taken\n")
# Now we get the 4th measurement
# Scale down the number of sql containers and scale up web-worker
num_sql = num_sql - 1
num_web_workers = num_web_workers + 1
scale(services["web-worker"], num_web_workers, manager)
scale(services["mysql"], num_sql, manager)
# as before we sleep and will update
time.sleep(5)
for service_name, service in services.items():
get_tasks(service, manager)
for i in range(0, 2):
par_pipes[i].send("poll")
pipes_ready = poll_pipes(par_pipes, 2)
# reset number of requests
num_requests = 0
for i in range(0, 2):
num_requests = num_requests + par_pipes[i].recv()
# get the stats
sql_cpu_usages = []
sql_mem_usages = []
web_worker_cpu_usages = []
web_worker_mem_usages = []
sql_cpu_avg, web_worker_cpu_avg, sql_mem_avg, web_worker_mem_avg = get_stats(
services, sql_cpu_usages, sql_mem_usages, web_worker_cpu_usages,
web_worker_mem_usages, nodes)
# Append new values to the histories
sql_cpu_history = np.append(sql_cpu_history,
sql_cpu_avg - prev_sql_cpu_avg)
sql_mem_history = np.append(sql_mem_history,
sql_mem_avg - prev_sql_mem_avg)
web_worker_cpu_history = np.append(
web_worker_cpu_history, web_worker_cpu_avg - prev_web_worker_cpu_avg)
web_worker_mem_history = np.append(
web_worker_mem_history, web_worker_mem_avg - prev_web_worker_mem_avg)
request_history = np.append(request_history,
num_requests - prev_num_requests)
web_work_history = np.append(web_work_history,
num_web_workers - prev_num_web_workers)
sql_history = np.append(sql_history, num_sql - prev_num_sql)
# Store the stats
prev_sql_cpu_avg = sql_cpu_avg
prev_sql_mem_avg = sql_mem_avg
prev_web_worker_cpu_avg = web_worker_cpu_avg
prev_web_worker_mem_avg = web_worker_mem_avg
prev_num_requests = num_requests
prev_num_sql = num_sql
prev_num_web_workers = num_web_workers
# ******************************************************************************************************************
# ********************************************* REGRESSION *********************************************************
# Use these lines whenever we update the regression
# TODO put this into a function
target_mat = np.vstack([
sql_cpu_history, web_worker_cpu_history, sql_mem_history,
web_worker_mem_history
]).T
design_mat = np.vstack([sql_history, web_work_history, request_history]).T
control_matrix = regularized_lin_regression(design_mat, target_mat, 0.0001)
#print(control_matrix)
estimator.update_B(control_matrix.T)
#print(control_matrix.T)
obs = np.array(
[[sql_cpu_avg, web_worker_cpu_avg, sql_mem_avg, web_worker_mem_avg]]).T
estimator.update(obs, np.identity(4))
#Helper vars
polls_since_update = 0
processes_started = 2
delta_web = 0
delta_sql = 0
delta_requests = 0
scaling_triggered = False
# TODO We have generated an initial estimate
# Begin by starting up the rest of the load generators and then monitoring and adjust
close_flag = False
#print("Experiment Started\n")
output_pipe, log_pipe = multiprocessing.Pipe()
close_pipe, log_close_pipe = multiprocessing.Pipe()
startTime = time.time()
log_process = multiprocessing.Process(
target=logger,
args=(log_pipe, log_file, node_list, manager, startTime,
polling_interval / 4.0, nodes, services, log_close_pipe))
log_process.start()
iteration_count = 0
#old_time = datetime.datetime.now()
output_pipe.send([
estimator.x[0][0], estimator.x[1][0], estimator.x[2][0],
estimator.x[3][0], num_sql, num_web_workers, delta_requests,
num_requests, iteration_count, 0.0, 0.0, True
])
print("Experiment Started")
spike = False
spike_number = 0
while not close_flag:
#old_time = time.time()
if input_pipe.poll():
message = input_pipe.recv()
if message == "Quit":
close_flag = True
print("Shutting down")
for i in range(0, processes_started):
par_pipes[i].send("close")
process_list[i].join()
print("Load process {0}".format(i))
print("Loads spun down")
scale(services["web-worker"], 2, manager)
scale(services["mysql"], 1, manager)
output_pipe.send("close")
close_pipe.send("close")
log_process.join()
print("Logger shut down")
print(estimator.B)
break
if (processes_started < number_of_processes
and (iteration_count % 20 == 0)):
#We haven't started all of the load generators
#So start another
process_list[processes_started].start()
processes_started = processes_started + 1
#Sleep at the start since we need to sleep on first entry
time.sleep(polling_interval)
if scaling_triggered:
for service_name, service in services.items():
get_tasks(service, manager)
output_pipe.send([
estimator.x[0][0], estimator.x[1][0], estimator.x[2][0],
estimator.x[3][0], num_sql, num_web_workers, delta_requests,
num_requests, iteration_count, minutes, seconds,
scaling_triggered
])
scaling_triggered = False
iteration_count = iteration_count + 1
###################################### TEST ABILITY TO REACT TO A SPIKE
if (iteration_count == 120):
for i in range(0, spike_size):
spike_list[i].start()
#processes_started = processes_started + 1
spike = True
spike_number = 1
if (iteration_count == 175):
for i in range(0, spike_size):
spike_par_pipes[i].send("close")
#processes_started = processes_started - 1
spike = False
if (iteration_count == 230):
for i in range(spike_size, 2 * spike_size):
spike_list[i].start()
#processes_started = processes_started + 1
spike = True
spike_number = 2
if (iteration_count == 280):
for i in range(spike_size, 2 * spike_size):
spike_par_pipes[i].send("close")
#processes_started = processes_started - 1
spike = False
for i in range(0, processes_started):
par_pipes[i].send("poll")
if spike:
if spike_number == 1:
for i in range(0, spike_size):
spike_par_pipes[i].send("poll")
for i in range(0, spike_size):
while not spike_par_pipes[i].poll():
pass
if spike_number == 2:
for i in range(spike_size, 2 * spike_size):
spike_par_pipes[i].send("poll")
for i in range(spike_size, 2 * spike_size):
while not spike_par_pipes[i].poll():
pass
pipes_ready = poll_pipes(par_pipes, processes_started)
# reset number of requests
num_requests = 0
for i in range(0, processes_started):
num_requests = num_requests + par_pipes[i].recv()
if spike:
if spike_number == 1:
for i in range(0, spike_size):
num_requests = num_requests + spike_par_pipes[i].recv()
if spike_number == 2:
for i in range(spike_size, 2 * spike_size):
num_requests = num_requests + spike_par_pipes[i].recv()
delta_requests = num_requests - prev_num_requests
#We've slept so poll
sql_cpu_usages = []
sql_mem_usages = []
web_worker_cpu_usages = []
web_worker_mem_usages = []
#Check to see if we need to update the estimator
if polls_since_update == polls_per_update:
sql_cpu_avg, web_worker_cpu_avg, sql_mem_avg, web_worker_mem_avg = get_stats(
services, sql_cpu_usages, sql_mem_usages,
web_worker_cpu_usages, web_worker_mem_usages, nodes)
#need to update the estimator
#Check to see if we have 100 entries in the history list
if sql_cpu_history.size == 100:
#We have 100 entries randomly replace one of them
replacement_index = random.randint(0, 99)
#Use np.put to insert new value at index replacement_index, overwriting previous value
np.put(sql_cpu_history, replacement_index,
sql_cpu_avg - prev_sql_cpu_avg)
np.put(sql_mem_history, replacement_index,
sql_mem_avg - prev_sql_mem_avg)
np.put(web_worker_cpu_history, replacement_index,
web_worker_cpu_avg - prev_web_worker_cpu_avg)
np.put(web_worker_mem_history, replacement_index,
web_worker_mem_avg - prev_web_worker_mem_avg)
np.put(request_history, replacement_index,
num_requests - prev_num_requests)
np.put(web_work_history, replacement_index,
num_web_workers - prev_num_web_workers)
np.put(sql_history, replacement_index, num_sql - prev_num_sql)
else:
#Don't have 100 entries. Append new values
sql_cpu_history = np.append(sql_cpu_history,
sql_cpu_avg - prev_sql_cpu_avg)
sql_mem_history = np.append(sql_mem_history,
sql_mem_avg - prev_sql_mem_avg)
web_worker_cpu_history = np.append(
web_worker_cpu_history,
web_worker_cpu_avg - prev_web_worker_cpu_avg)
web_worker_mem_history = np.append(
web_worker_mem_history,
web_worker_mem_avg - prev_web_worker_mem_avg)
request_history = np.append(request_history,
num_requests - prev_num_requests)
web_work_history = np.append(
web_work_history, num_web_workers - prev_num_web_workers)
sql_history = np.append(sql_history, num_sql - prev_num_sql)
#Do regression
target_mat = np.vstack([
sql_cpu_history, web_worker_cpu_history, sql_mem_history,
web_worker_mem_history
]).T
design_mat = np.vstack(
[sql_history, web_work_history, request_history]).T
control_matrix = regularized_lin_regression(
design_mat, target_mat, 0.0001)
estimator.update_B(control_matrix.T)
#Also need to correct Kalman gain
estimator.update(
np.array([[
sql_cpu_avg, web_worker_cpu_avg, sql_mem_avg,
web_worker_mem_avg
]]).T, 0.002 * np.random.randn(4, 4))
polls_since_update = 0
else:
polls_since_update = polls_since_update + 1
#TODO For Carl: Get Estimate from Estimator, make scaling decision, send values to logger
prev_sql_cpu_avg = sql_cpu_avg
prev_sql_mem_avg = sql_mem_avg
prev_web_worker_cpu_avg = web_worker_cpu_avg
prev_web_worker_mem_avg = web_worker_mem_avg
prev_num_requests = num_requests
prev_num_sql = num_sql
prev_num_web_workers = num_web_workers
estimate = estimator.estimate(np.array([[0, 0, delta_requests]]).T)
#print(estimate)
if (estimate[1] >= cpu_upper_threshold):
#We assume the web worker needs scaling most of the time
while not (estimate[1] < cpu_upper_threshold
or delta_web == search_range or num_web_workers +
(delta_web + 1) > max_containers):
delta_web = delta_web + 1
estimate = estimator.estimate(
np.array([[0, delta_web, delta_requests]]).T)
scaling_triggered = True
if (estimate[0] >= cpu_upper_threshold):
while not (estimate[0] < cpu_upper_threshold
or delta_sql == search_range or num_sql +
(delta_sql + 1) > max_containers):
delta_sql = delta_sql + 1
estimate = estimator.estimate(
np.array([[delta_sql, delta_web, delta_requests]]).T)
scaling_triggered = True
if not scaling_triggered:
#just to prevent two cases triggering
if estimate[1] <= cpu_lower_threshold:
while not (estimate[1] > cpu_lower_threshold
or abs(delta_web) == search_range
or num_web_workers + (delta_web - 1) < 1):
delta_web = delta_web - 1
estimate = estimator.estimate(
np.array([[0, delta_web, delta_requests]]).T)
#We assume the web worker needs scaling most of the time
scaling_triggered = True
if (estimate[0] <= cpu_lower_threshold):
while not (estimate[0] > cpu_lower_threshold
or abs(delta_sql) == search_range or num_sql +
(delta_sql - 1) < 1):
delta_sql = delta_sql - 1
estimate = estimator.estimate(
np.array([[delta_sql, delta_web, delta_requests]]).T)
scaling_triggered = True
#We have made our decision actually update estimator
estimator.predict(np.array([[delta_sql, delta_web, delta_requests]]).T)
if scaling_triggered:
#Actually do the scaling here
num_web_workers = num_web_workers + delta_web
num_sql = num_sql + delta_sql
scale(services["web-worker"], num_web_workers, manager)
scale(services["mysql"], num_sql, manager)
delta_web = 0
delta_sql = 0
#scaling_triggered = 0
#time.sleep(0.05)
#Send the values to the logger
#order will be sql_cpu web_worker_cpu sql_mem web_worker_mem num_sql num_web_workers
#For each value we send actual then predicted
diff_time = time.time() - startTime
minutes, seconds = diff_time // 60, diff_time % 60
if not scaling_triggered:
#diff_time = time.time() - startTime
#minutes, seconds = diff_time // 60, diff_time % 60
output_pipe.send([
estimator.x[0][0], estimator.x[1][0], estimator.x[2][0],
estimator.x[3][0], num_sql, num_web_workers, delta_requests,
num_requests, iteration_count, minutes, seconds,
scaling_triggered
])
if (e + 1) % args.print_every == 0:
log_format = "Epoch {}: loss={:.4f}, val_acc={:.4f}, final_test_acc={:.4f}"
print(log_format.format(e + 1, train_loss, val_acc,
final_test_acc))
print("Best Epoch {}, final test acc {:.4f}".format(
best_epoch, final_test_acc))
return final_test_acc, sum(train_times) / len(train_times)
if __name__ == "__main__":
args = parse_args()
res = []
train_times = []
for i in range(args.num_trials):
print("Trial {}/{}".format(i + 1, args.num_trials))
acc, train_time = main(args)
res.append(acc)
train_times.append(train_time)
mean, err_bd = get_stats(res, conf_interval=False)
print("mean acc: {:.4f}, error bound: {:.4f}".format(mean, err_bd))
out_dict = {
"hyper-parameters": vars(args),
"result": "{:.4f}(+-{:.4f})".format(mean, err_bd),
"train_time": "{:.4f}".format(sum(train_times) / len(train_times))
}
with open(args.output_path, "w") as f:
json.dump(out_dict, f, sort_keys=True, indent=4)
def train_agent(ppo: PPO,
env,
policy_iters,
max_timesteps,
memory,
update_timestep,
env_resets,
log_interval,
lam=0,
n_parallel=500,
var_type='reward'):
running_reward = 0
avg_length = 0
time_step = 0
n_updates = 0
i_episode = 0
prev_performance = np.array(
[-np.inf for _ in range(len(env.model.models))])
memory.clear_memory()
rewards_history = deque(maxlen=6)
best_weights = None
is_done_func = env.model.is_done_func
if var_type == 'reward':
state_dynamics = False
elif var_type == 'state':
state_dynamics = True
else:
raise Exception("Variance must either be 'reward' or 'state'")
for model in env.model.models.values():
model.to(device)
state_mean = torch.FloatTensor(env.state_filter.mean).to(device)
state_stddev = torch.FloatTensor(env.state_filter.stdev).to(device)
action_mean = torch.FloatTensor(env.action_filter.mean).to(device)
action_stddev = torch.FloatTensor(env.action_filter.stdev).to(device)
diff_mean = torch.FloatTensor(env.diff_filter.mean).to(device)
diff_stddev = torch.FloatTensor(env.diff_filter.stdev).to(device)
start_states = torch.FloatTensor(env_resets).to(device)
done_true = [True for _ in range(n_parallel)]
done_false = [False for _ in range(n_parallel)]
while n_updates < policy_iters:
i_episode += n_parallel
state = start_states.clone()
prev_done = done_false
var = 0
t = 0
while t < max_timesteps:
state_f = filter_torch(state, state_mean, state_stddev)
time_step += n_parallel
t += 1
with torch.no_grad():
action = ppo.policy_old.act(state_f, memory)
action = torch.clamp(action, env.action_bounds.lowerbound[0],
env.action_bounds.upperbound[0])
action_f = filter_torch(action, action_mean, action_stddev)
X = torch.cat((state_f, action_f), dim=1)
y = random_env_forward(X, env)
nextstate_f = state_f + filter_torch_invert(
y, diff_mean, diff_stddev)
nextstate = filter_torch_invert(nextstate_f, state_mean,
state_stddev)
if is_done_func:
done = is_done_func(nextstate).cpu().numpy()
done[prev_done] = True
prev_done = done
else:
if t >= max_timesteps:
done = done_true
else:
done = done_false
uncert = get_stats(env, X, state_f, action, diff_mean, diff_stddev,
state_mean, state_stddev, done, state_dynamics)
reward = torch_reward(env.name, nextstate, action, done)
reward = (1 - lam) * reward + lam * uncert
state = nextstate
memory.rewards.append(reward)
memory.is_terminals.append(done)
running_reward += reward
var += uncert**2
# update if it's time
if time_step % update_timestep == 0:
ppo.update(memory)
memory.clear_memory()
time_step = 0
n_updates += 1
if n_updates > 10:
improved, prev_performance = validate_agent_with_ensemble(
ppo, env, start_states, state_mean, state_stddev,
action_mean, action_stddev, diff_mean, diff_stddev,
prev_performance, 0.7, memory, max_timesteps)
if improved:
best_weights = ppo.policy.state_dict()
best_update = n_updates
rewards_history.append(improved)
if len(rewards_history) > 5:
if rewards_history[0] > max(
np.array(rewards_history)[1:]):
print('Policy Stopped Improving after {} updates'.
format(best_update))
ppo.policy.load_state_dict(best_weights)
ppo.policy_old.load_state_dict(best_weights)
return
avg_length += t * n_parallel
if i_episode % log_interval == 0:
avg_length = int(avg_length / log_interval)
running_reward = int((running_reward.sum() / log_interval))
print(
'Episode {} \t Avg length: {} \t Avg reward: {} \t Number of Policy Updates: {}'
.format(i_episode, avg_length, running_reward, n_updates))
running_reward = 0
avg_length = 0
